DETAILED ACTION
Acknowledgement
This final office action is in response to the amendment filed on 01/28/2026.
Status of Claims
Claim 5 has been cancelled.
Claims 1, 6-9, 11-12, and 19-20 have been amended.
Claims 21-23 have been added
Claims 1-4 and 6-23 are now pending.
Response to Arguments
Applicant's arguments filed on 01/28/2026 regarding the 35 U.S.C. 101, 102, and 103 rejections of the claims have been fully considered. The Applicant argues the following.
(1) As per the 101 rejection, the Applicant argues, in summary, that (i) claim 1 as amended, does not recite the organization of human relationships, contractual interactions, or commercial arrangements. Rather, the claim recites a computer-implemented pipeline in which a server performs analysis of video sensor data to automatically derive structed project parameters and generate a machine-readable project profile. (ii) the Applicant's claimed subject matter integrates any alleged abstract idea into a practical application and therefore is patent eligible, and (iii) claim 1 includes additional elements that amount to significantly more than the alleged abstract idea.
The Examiner respectfully disagrees with all arguments. The Examiner submits that although claim 1 recites a computer-implement method involving a server and computing devices, the claim describes receiving a request for a project to be performed via video submission from a human person with a computing device, obtaining project data and parameters from the video, generating a project profile, matching the project to a project-seeker (i.e. human person), and generating a notification of the project at a computing device of the matched project-seeker (i.e. human person). These steps clearly reflect a process of managing interactions between people (e.g. a project submitter and a project-seeker) such as in a commercial interaction. MPEP 2106.04(a) states that a claim recites a judicial exception when the judicial exception is “set forth” or “described” in the claim. Therefore, claim 1 is directed to the abstract grouping of Certain Methods of Organizing Human Activity. The dependent claims 2-4, 6-18, and 21-23 were also assessed under Step 2A(1), which further describes the computer-implemented method of claim 1 steps of processing of project data to determine project parameters and matching of project-seekers including extracting parameters from video data, speech to text conversion, and image analysis, which can practically be performed in the human mind via pen and paper through observation and evaluation (i.e. mental processes).
The Examiner also submits that the additional elements recited in all the claims and listed in Steps 2A(2) and 2B do not integrate the abstract idea into a practical application nor provide significantly more because the claims do not improve the functioning of a computer nor improve upon another technology. The additional elements are viewed as mere instructions to apply or implement the abstract idea on a computer. Applying an abstract idea on a computer does not integrate a judicial exception into a practical application or provide an inventive concept (see MPEP 2106.05(f)). The computer, server, software, speech-to-text, machine learning, automated image analysis technologies are used to perform the abstract idea of facilitating the matching of projects to project-seekers by processing and analyzing video data to define project parameters and match/notify project seekers. The technologies themselves are not improved beyond their original capabilities with the implementation of the Applicant’s invention. These technologies also reflect automation of a once manual process. The Applicant argues that the present systems and methods combines an improved way to request a bid, through video, thereby improving the process for request for proposal. In the present systems and methods, the bid submission process is improved, for example, more information can be gleaned from a video than from a phone call. This improvement reflects an improvement in requesting a bid or proposal (i.e. an abstract idea) and not an improvement in the functioning of a computer or an improvement in a specific technology. Per MPEP 2106.05(a)(II), an improvement in the abstract idea itself is not an improvement technology. Also per MPEP 2106.05(a)(I), mere automation of manual processes or increasing the speed of a process where these purported improvements come solely from the capabilities of a general-purpose computer are not sufficient to show an improvement in computer-functionality. Although the Applicant argues that the claimed subject matter reduces the amount of computing resources such as network communication and network bandwidth to complete the post-transfer instructions, this improvement is not apparent in the claims nor recited in the Applicant’s specification. Per MPEP 2106.05 (a), if it is asserted that the invention improves upon conventional functioning of a computer, or upon conventional technology or technological processes, a technical explanation as to how to implement the invention should be present in the specification. That is, the disclosure must provide sufficient details such that one of ordinary skill in the art would recognize the claimed invention as providing an improvement. An indication that the claimed invention provides an improvement can include a discussion in the specification that identifies a technical problem and explains the details of an unconventional technical solution expressed in the claim, or identifies technical improvements realized by the claim over the prior art. The Examiner submits that the Applicant’s specification and claims fail to provide detailed support for an improvement in technology or a technical solution to a technical problem. Therefore, the 35 U.S.C. 101 rejection is maintained.
(2) As per the 102 and 103 rejections, the Applicant argues the Roth fails to teach any form of software-based analysis of video or any conversion of the video into project parameters, as recited in amended claims 1, 19, and 20.
The Examiner finds the Applicant’s arguments persuasive. Therefore, the previous 102 and 103 rejections have been withdrawn. However, upon further search and consideration, a new ground of 103 rejection is made. See details below.
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 .
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 1-4 and 6-23 are rejected under 35 U.S.C. 101 because the claimed invention, “Video-Based Configuration of a Profile for a Project to be Performed at a Physical Location”, is directed to an abstract idea, specifically Certain Methods of Organizing Human Activity and Mental Processes, without significantly more. The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements individually or in combination provide mere instructions to implement the abstract idea on a computer.
Step 1: Claims 1-4 and 6-23 are directed to a statutory category, namely a process (claims 1-4, 6-18, and 21-23), a machine (claim 19), and a manufacture (claim 20).
Step 2A (1): Independent claims 1, 19, and 20 are directed to an abstract idea of Certain Methods of Organizing Human Activity, based on the following claim limitations: “receiving,…, video data defining a video purporting to include at least one scene of a work environment for a project to be performed at a physical location; obtaining project definition data defining one or more parameters of the project…; generating a project profile based on at least a portion of the video data and at least a portion of the project definition data; matching the project profile to at least one project-seeker account; and generating a notification of the project associated with the project profile… associated with the matched project-seeker account”. These claim limitations describe processing of project data in order to match projects to project-seekers (i.e. humans), thus facilitating a commercial interaction. Dependent claims 2-4, 6-18, and 21-23 further describe the processing of project data to determine project parameters and matching of project-seekers. The processing of project data includes extracting parameters from video data, speech to text conversion, and image analysis, which can practically be performed in the human mind via pen and paper through observation and evaluation. Therefore, these limitations, under the broadest reasonable interpretation, fall within the abstract groupings of Certain Methods of Organizing Human Activity which encompasses managing personal behavior or relationships or interactions between people including commercial interactions and Mental Processes which include concepts performed in the human mind such as observations, evaluations, judgments, and opinions. Certain Methods of Organizing Human Activity can encompass the activity of a single person (e.g. a person following a set of instructions), activity that involve multiple people (e.g. a commercial interaction), and certain activity between a person and a computer (e.g. a method of anonymous loan shopping). Mental Processes include claims directed to collecting information, analyzing it, and displaying certain results of the collection and analysis even if they are claimed as being performed on a computer. The courts have found claims requiring a generic computer or nominally reciting a generic computer may still recite a mental process even though the claim limitations are not performed entirely in the human mind. Therefore, claims 1-4 and 6-23 are directed to an abstract idea and are not patent eligible.
Step 2A (2): This judicial exception is not integrated into a practical application. In particular, claims 1, 4, 9, 10, 12, 13, 14, 16, 17, 18, 19, 20, and 21 recite additional elements of “a computer-implemented method performed at a server (claim 1); remote computing device; wherein one or more of the parameters of the project are determined through software analysis of the video data a computing device (claims 1, 19, and 20); receiving at the server and from the computing device…application video (claim 4); a speech-to-text module that performs speech recognition (claim 9); a machine learning module trained (claims 10 and 13); performing automated image analysis of one or more frames of the video (claims 12-14 and 21); receiving, at the server, an indication of selection of the selectable option to output the video on the computing device; and in response to receiving the indication of selection of the selectable option to output the video on the computing device, serving at least a portion of the video to the computing device for output thereon (claim 16); generating a notification at the remote computing device… (claim 17); receiving, at the server and from the remote computing device, first video data…generating a notification at the remote computing device (claim 18); the server a server comprising: a communications system; a memory; and a processor coupled to the communications system and the memory, the memory having stored thereon processor-executable instructions which, when executed, configure the processor to cause the server to (claim 19); and a non-transitory computer-readable storage medium comprising computer-executable instructions which, when executed, configure a server to (claim 20) ”. These additional elements do not integrate the abstract idea into a practical application because the claims do not recite (a) an improvement to another technology or technical field and (b) an improvement to the functioning of the computer itself and (c) implementing the abstract idea with or by use of a particular machine, (d) effecting a particular transformation or reduction of an article, or (e) applying the judicial exception in some other meaningful way beyond generally linking the use of an abstract idea to a particular technological environment. These additional elements evaluated individually and in combination are viewed as computing and display devices that are used to perform the processing of project data in order to match projects to project-seekers. Limitations that recite mere instructions to implement an abstract idea on a computer or merely uses a computer as a tool to perform an abstract idea are not indicative of integration into a practical application (see MPEP 2106.05(f)). Therefore, claims 1-4 and 6-23 do not include individual or a combination of additional elements that integrate the judicial exception into a practical application and thus are not patent eligible.
Step 2B: The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. Claims 1, 4, 9, 10, 12, 13, 14, 16, 17, 18, 19, 20, and 21 recite additional elements of “a computer-implemented method performed at a server (claim 1); remote computing device; wherein one or more of the parameters of the project are determined through software analysis of the video data a computing device (claims 1, 19, and 20); receiving at the server and from the computing device…application video (claim 4); a speech-to-text module that performs speech recognition (claim 9); a machine learning module trained (claims 10 and 13); performing automated image analysis of one or more frames of the video (claims 12-14 and 21); receiving, at the server, an indication of selection of the selectable option to output the video on the computing device; and in response to receiving the indication of selection of the selectable option to output the video on the computing device, serving at least a portion of the video to the computing device for output thereon (claim 16); generating a notification at the remote computing device… (claim 17); receiving, at the server and from the remote computing device, first video data…generating a notification at the remote computing device (claim 18); the server a server comprising: a communications system; a memory; and a processor coupled to the communications system and the memory, the memory having stored thereon processor-executable instructions which, when executed, configure the processor to cause the server to (claim 19); and a non-transitory computer-readable storage medium comprising computer-executable instructions which, when executed, configure a server to (claim 20)”. These additional elements evaluated individually and in combination are viewed as mere instructions to apply or implement the abstract idea on a computer. Applying an abstract idea on a computer does not integrate a judicial exception into a practical application or provide an inventive concept (see MPEP 2106.05(f)). Therefore, claims 1-4 and 6-23 do not include individual or a combination of additional elements that are sufficient to amount to significantly more than the judicial exception and thus are not patent eligible.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1-4, 6-7, 9-17, 19-20, and 22 are rejected under 35 U.S.C. 103 as being unpatentable over Roth et al. (US 2023/0081319 A1) in view of Nvs et al. (US 2024/0428204 A1).
As per claim 1, 19, and 20 (Currently Amended), Roth teaches a computer-implemented method performed at a server, the method comprising (Roth e.g. There is disclosed, in an embodiment, video-based requests for proposals and video-based bids used to create a contract through a set of terms of service, to provide a contract that dictates the release schedule of escrow funds (Abstract). FIG. 1 is a flowchart of example application program (app) implemented construction project video-based bidding, escrow and milestone payment process 100, according to various embodiments of the present systems and methods [0020].); Roth teaches a server comprising: a communications system; a memory; and a processor coupled to the communications system and the memory, the memory having stored thereon processor-executable instructions which, when executed, configure the processor to cause the server to (Roth e.g. FIG. 8 is a block diagram of example construction project video-based bidding, escrow and milestone payment system 800, according to some embodiments. System 800 includes server 805, client (property owner) user devices 810 a-n, service provider devices 815a-n and, in some embodiments site camera(s) 820, all linked via network 825, which may be one or more wireless (cellular) (data) communications networks [0048]. Server 805, portions of client (property owner) user devices 810 a-n portions of service provider devices 815a-n and, in some embodiments at least portions of site camera( s) 820 may be implemented, at least in part, by a computer system. One such computer system is illustrated in FIG. 9 [0058].); Roth teaches a non-transitory computer-readable storage medium comprising computer-executable instructions which, when executed, configure a server to (Roth e.g. As illustrated, computer system 900 includes one or more processors 910 (and/or one or more processors having one or more processor cores, a processor with an integrated graphics processor, and/or the like) coupled to a system memory 920 via bus 930 [0059]. System memory 920 may be configured to store program instructions and/or data accessible by processor 910. In other embodiments, program instructions and/or data may be received, sent, or stored upon different types of computer-accessible media or on similar media separate from system memory 920 or computer system 900 [0061]. A computer-accessible medium may include any tangible and/or non-transitory storage media or memory media such as electronic, magnetic, or optical media [0062].):
Roth teaches receiving/receive, at the server and from a remote computing device, video data defining a video purporting to include at least one scene of a work environment for a project to be performed at a physical location; obtaining/obtain project definition data defining one or more parameters of the project,… (Roth e.g. In an embodiment, there is provided an application program (app) implemented process, wherein recording or uploading a video describing a project, such as a construction project, and showing a site of the project, from a client user is enabled, such as via the app [0004]. At 105, embodiments of a construction project video-based bidding, escrow and milestone payment process app practicing the present systems and methods asks a client user of the app, such as the property owner to describe their construction needs through a video, by enabling recording and/or uploading one or more videos describing the project and showing the site of the project (Fig. 1 and [0021]).)
Roth teaches generating/generate a project profile based on at least a portion of the video data and at least a portion of the project definition data; (Roth e.g. The app may send the video describing the project and showing the site of the project to one or more service provider users who can carry out the project [0004]. At 110, the construction project video-based bidding, escrow and milestone payment process app then sends the video describing the project and showing the site of the project to service provider app users, such as contractors, and the like, who can accomplish what the property owner is seeking [0022]. Fig. 5 shows an example interface 500 with a project profile.)
Roth teaches matching/match the project profile to at least one project-seeker account; and (Roth e.g. The construction project video-based bidding, escrow and milestone payment process app may, at 115, enable the contractor, or the like, to record or upload a video bid for completion of the project [0022]. At 120, the construction project video-based bidding, escrow and milestone payment process app enables the property owner to accept one video bid for completion of the project by that contractor (See FIG. 2 described below) [0023]. Server 805, or the like, may host, develop and use databases, in which tables may include, by way of example, self-referential tables of service providers (contractors), so as to provide a self-referential database. Further, such self-referential databases and tables may be developed using, and/or may be used by, machine learning to provide a depth to selection of one or more service providers who can carry out the project that results in significantly improved outcomes. Further, self-referential databases and tables may be developed using, and/or may be used by, data fusion, machine learning, and/or the like to provide a greater depth to the identification of identification of one or more service provider users who can carry out the project [0050].)
Roth teaches generating/generate a notification of the project associated with the project profile on a computing device associated with the matched project-seeker account. (Roth e.g. The app may send the video describing the project and showing the site of the project to one or more service provider users who can carry out the project [0004]. In accordance with the foregoing, an email and/or a (push) notification through the construction project video-based bidding, escrow and milestone payment process app, may be issued informing the service provider of the accepted bid [0026].)
Roth does not explicitly teach, however, Nvs teaches wherein one or more of the parameters of the project are determined through software analysis of the video data; (Nvs e.g. This disclosure generally relates to artificial intelligence (AI) based/machine learning (ML) techniques and, in particular, to training and use of AI/ML systems to: determine damage to physical structures and estimate the effort required to repair the damage by analyzing videos of the physical structures [0001]. Methods and systems for training AI/ML systems and use of such systems for performing analysis of video and, optionally, of any associated audio, so that damage to physical structure can be determined accurately and efficiently, and the cost of repairing can be predicted, are disclosed [0003]. Some embodiments can thus automate the process of estimating vehicle damage and repair costs from a video of the vehicle, such as those taken at the site of the accident [0017]. The models may be trained to perform deep learning based computer vision and speech analytics, to detect automatically any damaged part(s) and the damage to such parts, and to assess the severity of the damage [0024]. FIG. 4 is a block diagram of a systems 400 for assessing damage to a structure using video of the structure, according to some embodiments. In the system 400, the damage assessment includes two parallel analyses, one of visual content provided in video content that includes several image frames, and may include one or more images, of the structure and the other of the associated audio content [0027]. In the system 400, each analysis includes using multi-step machine learning and/or deep learning models (AI/ML models, in general) for detecting parts of the structure and determining the damage to such part(s), if any [0028].)
The Examiner submits that before the effective filing date, it would have been obvious to one of ordinary skill in the art to combine Roth’s Construction Project Video-Based Bidding System with Nvs’s AI/ML Video Analysis System in order to automatically process video data to determine parameters of the project so that damage to physical structure can be determined accurately and efficiently, and the cost of repairing can be predicted (Nvs e.g. [0003]).
As per claim 2 (Original), Roth in view of Nvs teach the computer-implemented method of claim 1, Roth teaches wherein matching the project profile to the project-seeker account includes matching the project profile based on at least some data extracted from the video data. (Roth e.g. Server 805, or the like, may host, develop and use databases, in which tables may include, by way of example, self-referential tables of service providers (contractors), so as to provide a self-referential database. Further, such self-referential databases and tables may be developed using, and/or may be used by, machine learning to provide a depth to selection of one or more service providers who can carry out the project that results in significantly improved outcomes. Further, self-referential databases and tables may be developed using, and/or may be used by, data fusion, machine learning, and/or the like to provide a greater depth to the identification of identification of one or more service provider users who can carry out the project [0050].)
As per claim 3 (Original), Roth in view of Nvs teach the computer-implemented method of claim 1, Roth teaches wherein the notification of the project includes a selectable option to initiate an application for the project (Roth e.g. The app may enable each of the service provider users to record or upload a video bid for completion of the project [0004]. FIG. 2 is an example simplified diagrammatic screen shot from the construction project video-based bidding, escrow and milestone payment process app, showing interface 200 for playing a video submission of a proposal by contractor 205 for acceptance by a property owner, according to some embodiments [0034].).
As per claim 4 (Original), Roth in view of Nvs teach the computer-implemented method of claim 3, Roth teaches further comprising: receiving, at the server and from the computing device associated with the matched project-seeker account, application video data defining an application video purporting to include parameters of the application; and generating the application based on at least a portion of the application video data (Roth e.g. The construction project video-based bidding, escrow and milestone payment process app may, at 115, enable the contractor, or the like, to record or upload a video bid for completion of the project [0022]. The property owner may view the contractor's video submission 210 and review the displayed bid amount 215 and bid description 220. Button 225 may be used to accept the bid from contractor 205 [0034]. This results in the above described creation of the contract between the property owner and the selected contactor, at 125, wherein the contract comprises the video, from 105, describing the project and showing the site of a project, the video bid for completion of the project from the selected contractor, from 115, and the mutually accepted set of terms of service of the app [0023]. At 130, as a part of creating the contract between the property owner and the contractor, a set of milestones for completion by the contractor to complete the project are created and descriptions of payment release milestones that are to be met,...[0023].)
As per claim 6 (Currently Amended), Roth in view of Nvs teach the computer-implemented method of claim 1, Roth teaches wherein obtaining one or more of the parameters of the project based on the video data includes obtaining input from the remote computing device and verifying at least a portion of the obtained input based on the video data (Roth e.g. FIG. 7 is a simplified diagrammatic flow illustration 700 of screen shots of the construction project video-based bidding, escrow and milestone payment process app showing example partially completed timeline 702 and (retrieved) review process 704, according to some embodiments [0041]. Review process 704, may enable the owner to approve the project (overall), or a milestone, or to request changes. Therein, review screen 708 may be presented as a part of enabling acceptance completion of the milestone indicated, or the project overall, as illustrated, by the property owner. The property owner may approve the milestone, or the project overall, as illustrated, by selections of approve button 710 or may request changes by selection of request changes button 712 [0043]. Selection of approve button 710 results in presentation of screen 714, wherein the property owner may upload (716) a video to send to the contractor, and/or the property owner by select continue 718 and record video 720 to send to the contactor in screen 722. Whereupon, the property owner is presented with confirmation screen 732 confirming that they have approved the milestone, or project overall, as illustrated, and that the funds have been released to the contactor [0044].).
As per claim 7 (Currently Amended), Roth in view of Nvs teach the computer-implemented method of claim 1, wherein obtaining one or more of the parameters of the project based on the video data includes obtaining one or more of the parameters based on metadata included in the video data (Roth e.g. Consistent with the foregoing descriptions, each client user device 8l0a-n comprises a processor and a memory configured to store construction project video-based bidding, escrow and milestone payment process app instructions that are configured for execution by the client user device process to enable recording or uploading, via the app on the client user device, a video describing a project and showing a site of the project (i.e. location), from the client user and sending of the video to one or more service provider users (such as may be identified by server 805) who can carry out the project [0049].).
As per claim 9 (Currently Amended), Roth in view of Nvs teach the computer-implemented method of claim 1, Roth does not explicitly teach, however, Nvs teaches wherein obtaining one or more of the parameters of the project based on the video data includes passing the video data to a speech-to-text module that performs speech recognition to obtain text data and where one or more of the parameters are obtained from the text data. (Nvs e.g. This disclosure generally relates to artificial intelligence (AI) based/machine learning (ML) techniques and, in particular, to training and use of AI/ML systems to: determine damage to physical structures and estimate the effort required to repair the damage by analyzing videos of the physical structures [0001]. Some embodiments of a technique described herein feature artificial intelligence/machine learning enabled computer vision and analysis, along with optional speech analysis, so that different parts and/or components of a physical structure are recognized from a video of the structure [0017]. FIG. 6 is a flow chart of a process 600 for assessing damage to a structure using audio associated with a visual content in a video of the structure, according to some embodiments [0051]. In step 602, the noise suppressed audio content 604 is received from the pre-processing stage (e.g., the audio content 412 provided by the pre-processing module 408 shown in FIG. 4). In step 602, the audio content 604 is converted to text using a speech-to-text engine [0052].)
The Examiner submits that before the effective filing date, it would have been obvious to one of ordinary skill in the art to combine Roth’s Construction Project Video-Based Bidding System with Nvs’s AI/ML Video Analysis System in order to automatically process video data to determine parameters of the project so that damage to physical structure can be determined accurately and efficiently, and the cost of repairing can be predicted (Nvs e.g. [0003]).
As per claim 10 (Original), Roth in view of Nvs teaches the computer-implemented method of claim 9, Roth does not explicitly teach, however, Nvs teaches wherein obtaining one or more of the parameters of the project based on the video data includes passing at least a portion of the text data to a machine learning module trained to identify one or more of the parameters. (Nvs e.g. Some embodiments of a technique described herein feature artificial intelligence/machine learning enabled computer vision and analysis, along with optional speech analysis, so that different parts and/or components of a physical structure are recognized from a video of the structure [0017]. FIG. 6 is a flow chart of a process 600 for assessing damage to a structure using audio associated with a visual content in a video of the structure, according to some embodiments [0051]. FIG. 6 is a flow chart of a process 600 for assessing damage to a structure using audio associated with a visual content in a video of the structure, according to some embodiments [0051]. In step 606, the generated text is contextually parsed using computation linguistic techniques and stored for every specified time window (e.g., 5, 10, 12 seconds, etc.) of the corresponding extracted video content (e.g., the frames 410 shown in FIG. 4). Based on the contextual content parsing, various keywords and/or phrases in the speech that may be relevant to the detection of parts and/or damage are identified in the step 606 [0052]. In step 608, the identified keywords and/or phrases are matched with a parts dictionary, to detect vehicle parts (structure parts, in general) such as door, headlight, etc., that are described in the speech. In step 610, the identified keywords and/or phrases are matched with a damage-types dictionary, to detect the different types of damages, such as dents, scratches, missing parts, etc., that are described in the speech [0053].)
The Examiner submits that before the effective filing date, it would have been obvious to one of ordinary skill in the art to combine Roth’s Construction Project Video-Based Bidding System with Nvs’s AI/ML Video Analysis System in order to automatically process video data to determine parameters of the project so that damage to physical structure can be determined accurately and efficiently, and the cost of repairing can be predicted (Nvs e.g. [0003]).
As per claim 11 (Currently Amended), Roth in view of Nvs teach the computer-implemented method of claim 1, Roth teaches wherein the one or more of the parameters of the project obtained from the video data includes one or more of: a category parameter defining a category of the project; a time parameter defining one or more timing preferences for the project; a material parameter defining a material to be used in performing the project; a tool parameter defining a tool to be used in performing the project; a size parameter for the project; and a location parameter defining a location at which the project is to be performed (Roth e.g. In an embodiment, there is provided an application program (app) implemented process, wherein recording or uploading a video describing a project, such as a construction project, and showing a site of the project, from a client user is enabled, such as via the app. The app may send the video describing the project and showing the site of the project to one or more service provider users who can carry out the project [0004]. FIG. 1 is a flowchart of example application program (app) implemented construction project video-based bidding, escrow and milestone payment process 100, according to various embodiments of the present systems and methods [0020]. At 105, embodiments of a construction project video-based bidding, escrow and milestone payment process app practicing the present systems and methods asks a client user of the app, such as the property owner to describe their construction needs through a video, by enabling recording and/or uploading one or more videos describing the project and showing the site of the project [0021].).
As per claim 12 (Currently Amended), Roth in view of Nvs teach the computer-implemented method of claim 1, Roth does not explicitly teach, however, Nvs teaches wherein obtaining the one or more parameters of the project based on the video data includes performing an automated image analysis of one or more frames of the video. (Nvs e.g. Some embodiments of a technique described herein feature artificial intelligence/machine learning enabled computer vision and analysis, along with optional speech analysis, so that different parts and/or components of a physical structure are recognized from a video of the structure. The video includes a visual component and an optional audio component. Any damaged parts and/or components of the structure may be identified, and the severity of the damage can be assessed automatically [0017]. FIG. 4 is a block diagram of a systems 400 for assessing damage to a structure using video of the structure, according to some embodiments. In the system 400, the damage assessment includes two parallel analyses, one of visual content provided in video content that includes several image frames, and may include one or more images, of the structure and the other of the associated audio content [0027]. The video 402 uploaded by the user (as described above with reference to FIG. 3 ) includes image frames 404 and audio content 406 [0029]. After the pre-processing, an AI/ML model is used by the visual assessment module 414. The visual assessment module 414 may include one or more processors that are configured using the AI/ML model (e.g., a deep learning model), where the model is trained to detect damaged structure parts from the selected video frames 410, and to assess the scope of damage to such part(s) [0032].)
The Examiner submits that before the effective filing date, it would have been obvious to one of ordinary skill in the art to combine Roth’s Construction Project Video-Based Bidding System with Nvs’s AI/ML Video Analysis System in order to automatically process video data to determine parameters of the project so that damage to physical structure can be determined accurately and efficiently, and the cost of repairing can be predicted (Nvs e.g. [0003]).
As per claim 13 (Original), Roth in view of Nvs teach the computer-implemented method of claim 12, Roth does not explicitly teach, however, Nvs teaches wherein performing the automated image analysis of one or more frames of the video includes passing at least a portion of the video data to a machine learning module trained to identify the one or more project parameters (Nvs e.g. Some embodiments of a technique described herein feature artificial intelligence/machine learning enabled computer vision and analysis, along with optional speech analysis, so that different parts and/or components of a physical structure are recognized from a video of the structure. The video includes a visual component and an optional audio component. Any damaged parts and/or components of the structure may be identified, and the severity of the damage can be assessed automatically [0017]. The models may be trained to perform deep learning based computer vision and speech analytics, to detect automatically any damaged part(s) and the damage to such parts, and to assess the severity of the damage [0024].)
The Examiner submits that before the effective filing date, it would have been obvious to one of ordinary skill in the art to combine Roth’s Construction Project Video-Based Bidding System with Nvs’s AI/ML Video Analysis System in order to automatically process video data to determine parameters of the project so that damage to physical structure can be determined accurately and efficiently, and the cost of repairing can be predicted (Nvs e.g. [0003]).
As per claim 14 (Original), Roth in view of Nvs teach the computer-implemented method of claim 13, Roth does not explicitly teach, however, Nvs teaches wherein performing the automated image analysis of one or more frames of the video includes determining one or more size parameters as a parameter of the project based on the one or more frames of the video. (Nvs e.g. FIG. 2A illustrates the time line of an exemplary artificial intelligence (AI)/machine learning (ML) based process 200 for assessing damage to a vehicle and for processing vehicle insurance claims [0018]. In step 208, a video of the vehicle (structure, in general) taken by the inspector or the user/owner of the vehicle is received. In step 210, an AI/ML system analyzes the video received from the inspector/customer and determines whether the vehicle is a total loss or is repairable [0018]. In step 212, the AI/ML system analyzes the information about the parts detected to be damaged and the types of damages, and may estimate damage to any internal parts. Using this information, the AI/ML system then generates a list of repairs and replacements that are likely needed and predicts the required time and/or cost for the repairs and/or replacements (i.e. size of project). The damage analysis and the repair/replacement estimates generated by the AI/ML system are presented in an estimation platform [0019].)
The Examiner submits that before the effective filing date, it would have been obvious to one of ordinary skill in the art to combine Roth’s Construction Project Video-Based Bidding System with Nvs’s AI/ML Video Analysis System in order to automatically process video data to determine parameters of the project so that damage to physical structure can be determined accurately and efficiently, and the cost of repairing can be predicted (Nvs e.g. [0003]).
As per claim 15 (Original), Roth in view of Nvs teach the computer-implemented method of claim 14, Roth does not explicitly teach, however, Nvs teaches wherein obtaining the one or more parameters of the project based on the video data includes identifying a quantum of a material to be used in performing the project based on the one or more size parameters. (Nvs e.g. FIG. 2A illustrates the time line of an exemplary artificial intelligence (AI)/machine learning (ML) based process 200 for assessing damage to a vehicle and for processing vehicle insurance claims [0018]. In step 208, a video of the vehicle (structure, in general) taken by the inspector or the user/owner of the vehicle is received. In step 210, an AI/ML system analyzes the video received from the inspector/customer and determines whether the vehicle is a total loss or is repairable [0018]. In step 212, the AI/ML system analyzes the information about the parts detected to be damaged and the types of damages, and may estimate damage to any internal parts. Using this information, the AI/ML system then generates a list of repairs and replacements that are likely needed and predicts the required time and/or cost for the repairs and/or replacements. The damage analysis and the repair/replacement estimates generated by the AI/ML system are presented in an estimation platform [0019].)
The Examiner submits that before the effective filing date, it would have been obvious to one of ordinary skill in the art to combine Roth’s Construction Project Video-Based Bidding System with Nvs’s AI/ML Video Analysis System in order to automatically process video data to determine parameters of the project so that damage to physical structure can be determined accurately and efficiently, and the cost of repairing can be predicted (Nvs e.g. [0003]).
As per claim 16 (Original), Roth in view of Nvs teach the computer-implemented method of claim 1, Roth teaches wherein the notification of the project includes a selectable option to output the video on the computing device and wherein the method further includes: receiving, at the server, an indication of selection of the selectable option to output the video on the computing device; and in response to receiving the indication of selection of the selectable option to output the video on the computing device, serving at least a portion of the video to the computing device for output thereon. (Roth e.g. The app may send the video describing the project and showing the site of the project to one or more service provider users who can carry out the project [0004]. FIG. 2 is an example simplified diagrammatic screen shot from the construction project video-based bidding, escrow and milestone payment process app, showing interface 200 for playing a video submission of a proposal by contractor 205 for acceptance by a property owner, according to some embodiments. The property owner may view the contractor's video submission 210 and review the displayed bid amount 215 and bid description 220. Button 225 may be used to accept the bid from contractor 205 [0034]. FIG. 7 is a simplified diagrammatic flow illustration 700 of screen shots of the construction project video-based bidding, escrow and milestone payment process app showing example partially completed timeline 702 and (retrieved) review process 704, according to some embodiments [0041]. Review process 704, may enable the owner to approve the project ( overall), or a milestone, or to request changes. Therein, review screen 708 may be presented as a part of enabling acceptance completion of the milestone indicated, or the project overall, as illustrated, by the property owner [0043]. The property owner may approve the milestone, or the project overall, as illustrated, by selections of approve button 710 or may request changes by selection of request changes button 712 [0043]. Selection of approve button 710 results in presentation of screen 714, wherein the property owner may upload (716) a video to send to the contractor, and/or the property owner by select continue 718 and record video 720 to send to the contactor in screen 722. Whereupon, the property owner is presented with confirmation screen 732 confirming that they have approved the milestone, or project overall, as illustrated, and that the funds have been released to the contactor [0044].)
As per claim 17 (Original), Roth in view of Nvs teach the computer-implemented method of claim 1, Roth teaches further comprising: comparing one or more of the parameters of the project to a representation of related parameters of one or more other projects of a same category; and selectively generating a notification at the remote computing device based on a result of the comparing (Roth e.g. Server 805, or the like, may host, develop and use databases, in which tables may include, by way of example, self-referential tables of service providers (contractors), so as to provide a self-referential database. Therein, all service provider types providing a particular service can be stored in a single table and the table rows can contain information defining the table columns, resulting in (eventually, deeply) nested hierarchies of service providers. Further, such self-referential databases and tables may be developed using, and/or may be used by, machine learning to provide a depth to selection of one or more service providers who can carry out the project that results in significantly improved outcomes. That is machine learning may not only enable implementation of self-referencing databases and tables to create deeply nested hierarchies of service provider who can carry out the project, but also provide a mechanism to explore such nested hierarchies of service provider outcomes for each specific project. Server 805, or the like, may use a database tool for data fusion and cross-correlation for identification of one or more service provider users who can carry out the project." Further, self-referential databases and tables may be developed using, and/or may be used by, data fusion, machine learning, and/or the like to provide a greater depth to the identification of identification of one or more service provider users who can carry out the project 0050]. FIG. 1 is a flowchart of example application program (app) implemented construction project video-based bidding, escrow and milestone payment process 100, according to various embodiments of the present systems and methods [0020]. At 105, embodiments of a construction project video-based bidding, escrow and milestone payment process app practicing the present systems and methods asks a client user of the app, such as the property owner to describe their construction needs through a video, by enabling recording and/or uploading one or more videos describing the project and showing the site of the project [0021]. At 110, the construction project video-based bidding, escrow and milestone payment process app then sends the video describing the project and showing the site of the project to service provider app users, such as contractors, and the like, who can accomplish what the property owner is seeking [0022].)
As per claim 22 (New), Roth in view of Nvs teach the method of claim 1, Roth does not explicitly teach, however, Nvs teaches wherein obtaining the one or more parameters of the project based on the video data comprises determining a category parameter defining a type of job for the project. (Nvs e.g. One benefit of the technique described herein is that the part detection and identification (also referred to as part classification) and/or damage detection are performed using not just one image but using several relevant frames 506. Some of these frames can yield a more accurate determination than other frames. As such, in some embodiments, a confidence score is provided for each type of estimation, i.e., part detection and damage detection. A final inference of a damaged part and the type/severity of the damage may include a weighted average of the respective inferences derived from several frames [0049]. FIG. 6 is a flow chart of a process 600 for assessing damage to a structure using audio associated with a visual content in a video of the structure, according to some embodiments [0051]. In step 608, the identified keywords and/or phrases are matched with a parts dictionary, to detect vehicle parts (structure parts, in general) such as door, headlight, etc., that are described in the speech. In step 610, the identified keywords and/or phrases are matched with a damage-types dictionary, to detect the different types of damages, such as dents, scratches, missing parts, etc., that are described in the speech [0053].)
Claim 8 is rejected under 35 U.S.C. 103 as being unpatentable over Roth et al. (US 2023/0081319 A1) in view of Nvs et al. (US 2024/0428204 A1) and in further view of Tsai et al. (US 2012/0078626 A1).
As per claim 8 (Currently Amended), Roth in view of Nvs teach the computer-implemented method of claim 7, wherein obtaining one or more of the parameters of the project based on the video data includes obtaining location data representing a geospatial location at which the project is to be performed based on geospatial information defined in the metadata included in the video data
Roth teaches wherein obtaining one or more of the parameters of the project based on the video data includes obtaining location data representing a physical location at which the project is to be performed (Roth e.g. In an embodiment, there is provided an application program (app) implemented process, wherein recording or uploading a video describing a project, such as a construction project, and showing a site of the project (i.e. physical location), from a client user is enabled, such as via the app [0004].).
Roth nor Nvs explicitly teach, however, Tsai teaches obtaining location data representing a geospatial location at which the project is to be performed based on geospatial information defined in the metadata included in the video data (Tsai e.g. The present disclosure relates to methods and systems for receipt, processing, and delivery of multimedia content, as well as enrichment of multimedia content for enhanced search and delivery [0041]. In the context of the present disclosure, multimedia content can include any type of content containing, for example, one or more of images, video, audio, or a combination thereof [0042]. Referring now to FIGS. 6 and 7A-7M, block diagrams of systems and data useable for processing and storing enhanced multimedia content [0088]. The video processing module 608 is configured to process the video portion(s) of multimedia content to identify one or more objects of interest appearing in the video [0096]. Example objects of interest include a location at which a scene takes place, a particular person or object appearing in video content, conditions apparent in multimedia content (e.g., lighting, weather, mood, etc.) [0091]. The video conversion module 610 converts the received multimedia content from a format in which it is received from a content provider into a format useable with the metadata generated by the audio processing module 606 and the video processing module 608 [0098]. The metadata from the audio processing module 606 and video processing module 608 is passed to a database 626, which collects metadata and other information derived from the multimedia content. In the embodiment shown, the database receives click through events 628, a full text search database 630, video 632, video metadata 634, and position metadata 636 based on processing of content [0100]. Video metadata 634 includes any of the data describing the video that can be tracked. For example, the video metadata 632 can include information about objects of interest defined as associated with multimedia content [0103]. The position metadata 636 defines the position or location of one or more objects in the video content. The position metadata 636 can take any of a number of forms. In certain embodiments, the position metadata 636 corresponds to GPS metadata associated with one or more pieces of content. In other embodiments, the position metadata 636 can be captured from some other type of position sensor, such as a location sensor or radio frequency identification (RFID) tag [0104]. FIG. 7F illustrates example location information 636 that can be captured and associated with a person or object within the multimedia content itself. the location information 636 can include identification of the object and associated content, and can relate to absolute position information (e.g., latitude and longitude), and can also include relative positional information, such as a degree of inclination or direction of orientation of a camera relative to the object [0114].)
The Examiner submits that before the effective filing date, it would have been obvious to one of ordinary skill in the art to combine Roth in view of Nvs’s Construction Project Video-Based Bidding System/AI/ML Video Analysis System with Tsai’s system for capturing and processing video metadata in order to provide an easy way to identify objects appearing in the content such that a user can individually search for and identify those objects (Tsai e.g. [0005]).
Claim 18 is rejected under 35 U.S.C. 103 as being unpatentable over Roth et al. (US 2023/0081319 A1) in view of Nvs et al. (US 2024/0428204 A1) in further view of Singh et al. (US 2023/0245686 A1).
As per claim 18 (Original), Roth in view of Nvs teach the computer-implemented method of claim 1, Roth teaches wherein the video data is second video data and wherein the method further includes: receiving, at the server and from the remote computing device, first video data defining a first video purporting to include at least one scene of the work environment for the project to be performed at the physical location (Roth e.g. In an embodiment, there is provided an application program (app) implemented process, wherein recording or uploading a video describing a project, such as a construction project, and showing a site of the project, from a client user is enabled, such as via the app [0004].); Roth nor Nvs explicitly teach, however, Singh teaches determining, based on the first video data and a threshold length parameter, that the first video is too long; and in response to determining that the first video is too long, generating a notification at the remote computing device.(Singh e.g. According to various embodiments, techniques and mechanisms described herein provide for systems, devices, methods, and machine readable media for generating composite or aggregate videos [0004]. According to various embodiments, a designated one of the video creation request messages includes a quality parameter. Whether a designated one of the input videos satisfies the quality parameter may be determined. A feedback message may be transmitted in response to the designated input video [0006]. In some implementations, whether a designated one of the input videos has a length exceeding a minimum length threshold and not exceeding a maximum length threshold may be determined. The designated input video may be rejected when it is determined that the length does not exceed the minimum length threshold or exceeds the maximum length threshold [0007]. FIG. 3 illustrates an example of a method 300 for distributed video configuration, performed in accordance with one or more embodiments. According to various embodiments, the method 300 may be used to configure and coordinate distributed video creation. The method 300 may be performed at a computing device such as the computing device 700 shown in FIG. 7 [0045]. A request to configure distributed video creation is received at 302. According to various embodiments, the request may be generated based on user input. For instance, a user of a distributed video creation system may request to create a new distributed video [0047]. One or more configuration parameters are determined at 304. In some implementations, a configuration parameter may be determined based on user input. Alternatively, or additionally, one or more configuration parameters may be determined automatically or dynamically. For example, an organization or individual may specify one or more default configuration parameters applicable to various distributed videos [0048]. Possible configuration parameters may include, but are not limited to: a number of input videos to include in an aggregate video, a number of valid input videos to collect, a length in time of the distributed video,...[0049]. An input video is received from a remote computing device at 310 [0057]. A determination is made at 312 as to whether the received input video is valid. In some implementations, the determination may be made by evaluating the input video against one or more quality criteria. For example, the input video may be compared against a minimum and/or maximum length to determine whether the input video is of sufficient but not excessive length [0058]. If it is determined that the received input video is not valid, then it is rejected at 314. In some implementations, rejecting an input video may involve sending a rejection message to the remote computing device that generated the input video. The rejection message may identify one or more reasons that the input video was rejected. In such a situation, the recipient may elect to create and submit a replacement input video [0059].)
The Examiner submits that before the effective filing date, it would have been obvious to one of ordinary skill in the art to modify Roth in view of Nvs’s Construction Project Video-Based Bidding System/AI/ML Video Analysis System to include a video threshold length parameter as taught by Singh in order to ensure and/or meet quality and configuration criteria (Singh e.g. [0037]).
Claims 21 and 23 are rejected under 35 U.S.C. 103 as being unpatentable over Roth et al. (US 2023/0081319 A1) in view of Nvs et al. (US 2024/0428204 A1) in further view of Jakka et al. (US 2021/0279852 A1).
As per claim 21 (New) Roth in view of Nvs teach the method of claim 1, wherein obtaining one or more of the parameters of the project based on the video data comprises:
Roth does not explicitly teach, however, Nvs teaches performing automated image analysis on a plurality of frames of the video; (Nvs e.g. FIG. 4 is a block diagram of a systems 400 for assessing damage to a structure using video of the structure, according to some embodiments. In the system 400, the damage assessment includes two parallel analyses, one of visual content provided in video content that includes several image frames, and may include one or more images, of the structure and the other of the associated audio content [0027]. The video 402 uploaded by the user (as described above with reference to FIG. 3 ) includes image frames 404 and audio content 406 [0029]. After the pre-processing, an AI/ML model is used by the visual assessment module 414. The visual assessment module 414 may include one or more processors that are configured using the AI/ML model (e.g., a deep learning model), where the model is trained to detect damaged structure parts from the selected video frames 410, and to assess the scope of damage to such part(s) [0032].)
Roth does not explicitly teach, however, Nvs teaches identifying, within the frames, a region corresponding to a physical work environment or an object within the work environment based on a category of the project; and (Nvs e.g. A method and system are provided for assessing damage to a structure (Abstract). The method includes detecting one or more external parts of the structure from a video of the structure using a first machine learning (ML) module trained to identify in one or more frames of a video of a structure an external part of the structure (Abstract). The method also includes using a second ML module, trained to detect and classify damaged regions of a structure from one or more frames of the video: (i) identifying one or more damaged regions of the structure, and (ii) classifying the one or more damaged regions based on damage types (Abstract). FIG. 6 is a flow chart of a process 600 for assessing damage to a structure using audio associated with a visual content in a video of the structure, according to some embodiments [0051]. In step 608, the identified keywords and/or phrases are matched with a parts dictionary, to detect vehicle parts (structure parts, in general) such as door, headlight, etc., that are described in the speech. In step 610, the identified keywords and/or phrases are matched with a damage-types dictionary, to detect the different types of damages, such as dents, scratches, missing parts, etc., that are described in the speech [0053].)
The Examiner submits that before the effective filing date, it would have been obvious to one of ordinary skill in the art to combine Roth’s Construction Project Video-Based Bidding System with Nvs’s AI/ML Video Analysis System in order to automatically process video data to determine parameters of the project so that damage to physical structure can be determined accurately and efficiently, and the cost of repairing can be predicted (Nvs e.g. [0003]).
Roth nor Nvs explicitly teach, however, Jakka teaches determining, from depth data or LiDAR data associated with the video, one or more physical size parameters of the identified region, the size parameters including at least one of a surface area, a linear dimension, or a radial dimension. (Jakka e.g. Disclosed is a method for using a virtual representation of an indoor environment to identify contents that have been damaged (e.g., by flooding).The virtual representation may include 2-dimensional representations of the physical scene (e.g., images or video) or a 3-dimensional representation of the physical scene (e.g., 3D digital model) (Abstract). FIG. 1 illustrates a system configured for facilitating AI-based cost estimates for services, in accordance with one or more implementations [0067]. The receiving scanned data component 108 may be configured to receive, at one or more hardware processors, data from a scan of a location, the scan performed by one or more of a camera, a computer vision device, an inertial measurement unit, a depth sensor, or other scanners. In some implementations, scanning includes data generated by video or image acquisition devices, voice recording devices, a user interface, or any combination thereof [0069]. In some implementations, a workflow may include a user launching an app or another messaging channel (SMS, MMS, Facebook Messenger, web browser, etc.) and scanning a location (e.g., a home or another location) where camera(s) data or sensor(s) data may be collected. The app may use the camera or IMU, and optionally a depth sensor, to collect and fuse data to detect surfaces to be painted, objects to be moved, etc. and estimate their surface area data, or move related data, in addition to answers to specific questions. An AI algorithm (or neural network etc.) specifically trained to identify key elements may be used (e.g., walls, ceiling, floor, furniture, wall hangings, appliances, or other objects). Other relevant characteristics may be detected including identification of light switches/electrical outlets that would need to be covered or replaced, furniture that would need to be moved, carpet/flooring that would need to be covered, or other relevant characteristics [0077]. FIG. 5 illustrates an exemplary system 500 wherein detected objects may create an inventory, size, or weight information for objects that are detected, as well as create a list of questions that the AI algorithm may need to provide a more accurate data to a service provider or user (for example, questions on the pictures sent by the user, or follow up questions based on past responses) [0097].)
The Examiner submits that before the effective filing date, it would have been obvious to one of ordinary skill in the art to modify Roth in view of Nvs’s Construction Project Video-Based Bidding System/AI/ML Video Analysis System to include determining from depth data associated with the video physical size parameters of the identified region as taught by Jakka in order to provide upfront, accurate cost/price estimates (Jakka e.g. [0061]).
As per claim 23 (New), Roth in view of Nvs teach the method of claim 22, Roth nor Nvs explicitly teach, however Jakka teaches wherein the category parameter indicates a trade or service category selected from plumbing, electrical, heating ventilation and air- conditioning (HVAC), landscaping, roofing, painting, and carpentry (Jakka e.g. The present disclosure involves using computer vision, cameras, and optional depth sensors on the smartphone, or inertial measurement unit (IMU) data (e.g., data collected from an accelerometer, a gyroscope, a magnetometer, or other sensors) in addition to text data: questions asked by a human agent or an AI algorithm based on sent images, videos, and previous answers, as well as answers by the consumer on a mobile device (e.g., smartphone, tablet, or other mobile device) to come up with an estimate of how much it will cost to perform a moving job, a paint job, or other services [0076]. In some implementations, a workflow may include a user launching an app or another messaging channel (SMS, MMS, Facebook Messenger, web browser, etc.) and scanning a location (e.g., a home or another location) where camera(s) data or sensor(s) data may be collected. The app may use the camera or IMU, and optionally a depth sensor, to collect and fuse data to detect surfaces to be painted, objects to be moved, etc. and estimate their surface area data, or move related data, in addition to answers to specific questions. An AI algorithm (or neural network etc.) specifically trained to identify key elements may be used (e.g., walls, ceiling, floor, furniture, wall hangings, appliances, or other objects). Other relevant characteristics may be detected including identification of light switches/electrical outlets that would need to be covered or replaced, furniture that would need to be moved, carpet/flooring that would need to be covered, or other relevant characteristics [0077].).
The Examiner submits that before the effective filing date, it would have been obvious to one of ordinary skill in the art to modify Roth in view of Nvs’s Construction Project Video-Based Bidding System/AI/ML Video Analysis System to include the category parameter indicates a trade or service category as taught by Jakka in order to provide upfront, accurate cost/price estimates (Jakka e.g. [0061]).
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). 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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Ayanna Minor whose telephone number is (571)272-3605. The examiner can normally be reached M-F 9am-5 pm.
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/A.M./Examiner, Art Unit 3624
/Jerry O'Connor/Supervisory Patent Examiner,Group Art Unit 3624
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