DETAILED ACTION
This Office Action is in response to the remarks entered on 12/08/2025. Claims 1, 8, 15, 17-20 are amended. Claims 2, 9 and 16 are cancelled. Claims 1, 3-8, 10-15, 17-20 are pending.
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 .
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.
Claims 1, 3-8, 10-15, 17-20 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1, 3, 8, 10, 15, and 17 of U.S. Patent No. 12,322,036 in view of Gonsalves et al (US Pub. 2021/0295148- hereinafter Gonsalves), El Fahli et al (“Visual Effects as a Filmmaking Tool”- hereinafter El Fahli) and further in view of Anandraj et al (NPL: “The Impact of AI Revolution in Transforming Film Making Industry for the Digital Age”- hereinafter Anandraj).
Although the claims at issue are not identical, they are not patentably distinct from each other because the main purpose of these claims is the same, as they are directed towards the same inventive concept.
Instant Application
Patent No. 12,322,036
Claim 1
A computer-implemented method for training one or more artificial intelligence (AI) models using both synthetic and real-world filmmaking data, the method comprising:
receiving, via one or more processors, first metadata related to professional filmmaking techniques, including camera settings, shot composition, and lighting setups;
integrating Lidar data with the received first metadata to provide a three-dimensional understanding of space and object relationships;
generating synthetic data based on the integrated metadata and Lidar data to simulate professional filmmaking techniques;
receiving, via one or more processors, feedback generated during actual film production operations and updating parameters of at least one of the AI models based on the feedback; and
training the one or more AI models using a combination of the synthetic data and real-world filmmaking data including raw daily footage annotated with second metadata,
wherein the training includes incorporating the synthetic data and the first metadata into training datasets together with the real-world filmmaking data.
Claim 1
A computer-implemented method for enhancing artificial intelligence (AI) model training in filmmaking through a use of Lidar data, the method comprising:
correlating two-dimensional video data with three-dimensional spatial data obtained from Lidar to simulate professional camera techniques;
receiving detailed metadata related to professional filmmaking techniques, including camera settings, shot composition, and lighting setups;
processing the received metadata alongside the Lidar data to provide one or more AI models with a granular understanding of spatial relationships and the physics of camera movement; and
training the AI models using the processed metadata and Lidar data to accurately simulate professional filmmaking techniques, thereby enhancing realism and quality of generated video content.
The main purpose of these claims is the same, as they are directed towards the same inventive concept. Both inventions are directed to the integration of real world filmmaking production data with the use of Lidar technologies and AI models to enhance video content generation. Although the claimed limitations are not identical (verbatim), they are not patentably distinct from each other because the main purpose of these claims is the same, as they are directed towards the same inventive concept
However, Patent cited above fails to teach the limitations in italic above, however, Gonsalves and El Fahli teaches this (see Gonsalves at [0019, 0021-0022] and El Fahli at p.26).
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify the teachings of Affleck with the above combination of Gonsalves and El Fahli in order to acquire important data in a virtual effects set as they are becoming more and more an indispensable part of filmmaking due to the increasing use of them in this new era of filmmaking.
Claim 3
Claim 3
Claim 4
Claim 1
Patent cited above fails to teach the limitation at this claim, however, Gonsalves teaches them at Fig. 7 and [0041] and El Fahli at p. 26.
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify the teachings of Affleck with the above combination of Gonsalves and El Fahli in order to acquire important data in a virtual effects set as they are becoming more and more an indispensable part of filmmaking due to the increasing use of them in this new era of filmmaking.
Claim 5
Claim 1
Patent cited above fails to teach the limitation at this claim, however, Anandraj teaches it at pp. 83-84.
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify the teachings of Affleck with the above combination of Gonsalves, El Fahli and Anandraj in order to examine the impact of AI technologies on the efficiency, creativity, quality and audience recommendations of film making in the digital age.
Claim 6
Claim 1
Patent cited above fails to teach the limitation at this claim, however, Gonsalves teaches them at [0039] and El Fahli at p. 26.
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify the teachings of Affleck with the above combination of Gonsalves and El Fahli in order to acquire important data in a virtual effects set as they are becoming more and more an indispensable part of filmmaking due to the increasing use of them in this new era of filmmaking.
Claim 7
Claim 1
Patent cited above fails to teach the limitation at this claim, however, Anandraj teaches it at pp. 83-84.
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify the teachings of Affleck with the above combination of Gonsalves, El Fahli and Anandraj in order to examine the impact of AI technologies on the efficiency, creativity, quality and audience recommendations of film making in the digital age.
Claim 8
Claim 8
8. A computing system for training artificial intelligence (AI) models using both synthetic and real-world filmmaking data, the system comprising:
one or more processors; and
one or more memories having stored thereon computer-executable instructions that, when executed, cause the system to:
receive and process first metadata related to professional filmmaking techniques;
integrate Lidar data with the processed first metadata;
generate synthetic data based on the integrated first metadata and Lidar data;
receiving, via one or more processors, feedback generated during actual film production operations and updating parameters of at least one of the AI models based on the feedback; and
train the AI models using a combination of the synthetic data and real-world filmmaking data including raw daily footage annotated with second metadata by incorporating the synthetic data and the first metadata into training datasets together with the real-world filmmaking data.
8. A computing system for enhancing artificial intelligence (AI) model training in filmmaking through use of Lidar data, the system comprising:
one or more processors; and
one or more memories having stored thereon computer-executable instructions that, when executed, cause the system to:
receive and process metadata related to professional filmmaking techniques;
correlate two-dimensional video data with three-dimensional spatial data obtained from Lidar; and
train one or more AI models using the processed metadata and Lidar data to accurately simulate professional filmmaking techniques.
The main purpose of these claims is the same, as they are directed towards the same inventive concept. Both inventions are directed to the integration of real world filmmaking production data with the use of Lidar technologies and AI models to enhance video content generation. Although the claimed limitations are not identical (verbatim), they are not patentably distinct from each other because the main purpose of these claims is the same, as they are directed towards the same inventive concept
However, Patent cited above fails to teach the limitation “incorporate feedback from actual film production use to adjust model parameters”, however, Gonsalves and El Fahli teaches this (see Gonsalves at [0019, 0021-0022] and El Fahli at p.26).
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify the teachings of Affleck with the above combination of Gonsalves and El Fahli in order to acquire important data in a virtual effects set as they are becoming more and more an indispensable part of filmmaking due to the increasing use of them in this new era of filmmaking.
Claim 10
Claim 10
Claim 11
Claim 8
Patent cited above fails to teach the limitation at this claim, however, Gonsalves teaches them at Fig. 7 and [0041] and El Fahli at p. 26.
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify the teachings of Affleck with the above combination of Gonsalves and El Fahli in order to acquire important data in a virtual effects set as they are becoming more and more an indispensable part of filmmaking due to the increasing use of them in this new era of filmmaking.
Claim 12
Claim 8
Patent cited above fails to teach the limitation at this claim, however, Anandraj teaches it at pp. 83-84.
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify the teachings of Affleck with the above combination of Gonsalves, El Fahli and Anandraj in order to examine the impact of AI technologies on the efficiency, creativity, quality and audience recommendations of film making in the digital age.
Claim 13
Claim 8
Patent cited above fails to teach the limitation at this claim, however, Gonsalves teaches them at [0039] and El Fahli at p. 26.
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify the teachings of Affleck with the above combination of Gonsalves and El Fahli in order to acquire important data in a virtual effects set as they are becoming more and more an indispensable part of filmmaking due to the increasing use of them in this new era of filmmaking.
Claim 14
Claim 8
Patent cited above fails to teach the limitation at this claim, however, Anandraj teaches it at pp. 83-84.
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify the teachings of Affleck with the above combination of Gonsalves, El Fahli and Anandraj in order to examine the impact of AI technologies on the efficiency, creativity, quality and audience recommendations of film making in the digital age.
Claim 15
15. A computer-readable medium having stored thereon instructions that when executed by a processor cause a system to perform:
receiving detailed metadata related to professional filmmaking techniques;
integrating Lidar data with the received metadata; generating synthetic data based on the integrated metadata and Lidar data;
receiving, via one or more processors, feedback generated during actual film production operations and updating parameters of at least one of one or more artificial intelligence (AI) models based on the feedback; and
training the AI models using a combination of the synthetic data and real-world filmmaking data including raw daily footage annotated with second metadata, wherein the training includes incorporating the synthetic data and the first metadata into training datasets togethers with the real-world filmmaking data.
Claim 15
15. A non-transitory computer-readable medium having stored thereon instructions that when executed by a processor cause a system to perform:
correlating two-dimensional video data with three-dimensional spatial data obtained from Lidar;
receiving detailed metadata related to professional filmmaking techniques;
processing the received metadata alongside the Lidar data; and
training one or more artificial intelligence (AI) models using the processed metadata and Lidar data to accurately simulate professional filmmaking techniques, thereby enhancing the realism and quality of generated video content.
The main purpose of these claims is the same, as they are directed towards the same inventive concept. Both inventions are directed to the integration of real world filmmaking production data with the use of Lidar technologies and AI models to enhance video content generation. Although the claimed limitations are not identical (verbatim), they are not patentably distinct from each other because the main purpose of these claims is the same, as they are directed towards the same inventive concept
However, Patent cited above fails to teach the limitation “incorporating feedback from actual film production use to adjust model parameters to align with filmmaking practices and technologies”, however, Gonsalves and El Fahli teaches this (see Gonsalves at [0019, 0021-0022] and El Fahli at p.26).
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify the teachings of Affleck with the above combination of Gonsalves and El Fahli in order to acquire important data in a virtual effects set as they are becoming more and more an indispensable part of filmmaking due to the increasing use of them in this new era of filmmaking.
Claim 17
Claim 17
Claim 18
Claim 15
Patent cited above fails to teach the limitation at this claim, however, Gonsalves teaches them at Fig. 7 and [0041] and El Fahli at p. 26.
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify the teachings of Affleck with the above combination of Gonsalves and El Fahli in order to acquire important data in a virtual effects set as they are becoming more and more an indispensable part of filmmaking due to the increasing use of them in this new era of filmmaking.
Claim 19
Claim 15
Patent cited above fails to teach the limitation at this claim, however, Gonsalves teaches them at [0039] and El Fahli at p. 26.
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify the teachings of Affleck with the above combination of Gonsalves and El Fahli in order to acquire important data in a virtual effects set as they are becoming more and more an indispensable part of filmmaking due to the increasing use of them in this new era of filmmaking.
Claim 20
Claim 15
Patent cited above fails to teach the limitation at this claim, however, Anandraj teaches it at pp. 83-84.
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify the teachings of Affleck with the above combination of Gonsalves, El Fahli and Anandraj in order to examine the impact of AI technologies on the efficiency, creativity, quality and audience recommendations of film making in the digital age.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied 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, 3-4, 6, 8, 10-11, 13, 15, 17-19 are rejected under 35 U.S.C. 103 as being unpatentable over Gonsalves et al (US Pub. 2021/0295148- hereinafter Gonsalves) in view of El Fahli et al (“Visual Effects as a Filmmaking Tool”- hereinafter El Fahli).
Referring to Claim 1, Gonsalves teaches a computer-implemented method for training one or more artificial intelligence (AI) models using both synthetic and real-world filmmaking data, the method comprising:
receiving, via one or more processors, first metadata related to professional filmmaking techniques (see Gonsalves at [0007]: “a system comprises a memory for storing computer-readable instructions; and a processor connected to the memory”. Further at [0018]: “The terms “media content creation” and “media editing” are used interchangeably. Media content refers herein to time-based media, such as film, video, and audio, and also to musical scores”. See Gonsalves at [0019]: “The pre-training of adaptive AI-based media editing applications may use training data sets sourced from editors who are viewed as expert media content creators in one or more of video content, audio content, and musical score layout”. See Gonsalves at [0021]: “Deep learning system 102 receives training data from one or more training data sets that have been created independently of the end-users of the media editing applications. Such data sets may include expert training data extracted from the editing of media compositions by media editors who are acknowledged to be experts with their respective content creation applications”. Therefore, this expert training data extracted from the editing of media compositions by media editors who are acknowledged to be experts with their respective content creation applications is interpreted as the claimed “first metadata related to professional filmmaking techniques”);
receiving, via one or more processors, feedback generated during actual film production operations and updating parameters of at least one of the AI models based on the feedback (see Gonsalves at [0019]: “The pre-training of adaptive AI-based media editing applications may use training data sets sourced from editors who are viewed as expert media content creators in one or more of video content, audio content, and musical score layout. The input vectors of the training data include the raw media as received by the expert editors, together with additional metadata. In general, it is desirable to provide the machine learning system with all the raw material that is available to the expert editor when editing and creating media content”. Further, see Gonsalves at Fig. 5, [0012]: “FIG. 5 is a diagrammatic illustration of a user interface enabling a user to adjust and monitor machine learning parameters of a user-adaptive machine learning-based content creation application” and [0031]: “Users choose the function of the application to which the user-adaptive machine learning is to be applied in box 502. Interactive controls for each hyperparameter enable the user to adjust their values manually, for example with mouse or touch-controlled slider 504 or to allow the system to determine the value automatically 506”. Further at [0043]: “For video compositions, it may be desirable to include video data as well as audio data in the training, since the audio editing decisions for audio tracks of a video composition may be affected by whether a particular audio source is shown on the screen. For example, an editor may choose the volume of an actor's voice to be louder when the actor is on-screen”); and
training the one or more AI models using a combination of the synthetic data and real-world filmmaking data including raw daily footage annotated with second metadata, wherein the training includes incorporating the synthetic data and the first metadata into training datasets together with the real-world filmmaking data (See Gonsalves at [0019]: “The pre-training of adaptive AI-based media editing applications may use training data sets sourced from editors who are viewed as expert media content creators in one or more of video content, audio content, and musical score layout. The input vectors of the training data include the raw media as received by the expert editors, together with additional metadata. In general, it is desirable to provide the machine learning system with all the raw material that is available to the expert editor when editing and creating media content”. Further at [0021]: “Deep learning system 102 receives training data from one or more training data sets that have been created independently of the end-users of the media editing applications. Such data sets may include expert training data extracted from the editing of media compositions by media editors who are acknowledged to be experts with their respective content creation applications”. See [0022]: “Training data from a particular source, e.g., expert, or community, is specific to the media editing application, and to the function of that application which is being performed by the deep learning system”. See [0045]: “The training data inputs for automatic dialog editing include, for each instance, the script, the audio from the dailies, and optionally the corresponding video. Each input vector corresponds to a particular scene, and comprises the scene's script, audio samples that are representative of the corresponding spoken dialog, and optionally samples of the co-temporal video”. Therefore, the training data for the deep learning systems by Gonsalves being the dataset been created independently of the end users in addition to the raw media from the dailies received with additional metadata is interpreted as the combination of synthetic and real world data annotated with second metadata).
However, Gonsalves fails to teach:
first metadata related to professional filmmaking techniques, including camera settings, shot composition, and lighting setups;
integrating Lidar data with the received first metadata to provide a three-dimensional understanding of space and object relationships;
generating synthetic data based on the integrated first metadata and Lidar data to simulate professional filmmaking techniques.
El Fahli teaches, in an analogous system,
metadata related to professional filmmaking techniques, including camera settings, shot composition, and lighting setups (see El Fahli at p.26: “This data can be divided into three types, camera information, set information, and LIDAR5. First, camera information, in which the VFX data-wrangler notes down all the camera settings used on-set, in every shot that the VFX team will be working on in post-production, this camera information can include camera angles, types of lenses used, focus range, camera movements, and other data, this step is very important for the camera tracking. Second, set information, which refers to all the set elements that are either outside the frame or distant in the footage, because according to Dinur (2017), “you only see what the lens sees”. Elements outside the frame include specifically the lighting, its source, its direction, its intensity, and other lighting data. This also includes other environmental factors such as the weather for example, that will help keep the touch of reality in the VFX work. Elements that are distant in the footage include set elements and props, that the VFX data-wrangler takes close pictures of them to help later in texturing”. Therefore, the camera angles, types of lenses used, focus range, camera movements correspond to the claims “camera settings”, the set information, which refers to all the set elements that are either outside the frame or distant in the footage correspond to the claimed “shot composition”, and the lighting, its source, its direction, its intensity, and other lighting data correspond to the claimed “lighting setups”);
integrating Lidar data with the received first metadata to provide a three-dimensional understanding of space and object relationships (see El Fahli at p.26: “Third, LIDAR, which basically means “a laser light that can be used to scan real environments and obtain data to create CGI models that mimic the real world (Sawicki, 2007), and it is a 3D scanning of the set that provides the VFX team with more detailed and accurate design of the set and environment, this LIDAR 3D scanning can also be done to actors when the VFX team needs to create a digital double of the actor”. Therefore, the use of Lidar data along with the previous mentioned camera settings, composition and lights correspond to the claimed “integrating Lidar data”. Further, since Lidar data is used by El Fahli to create a 3D scanning of the set corresponds to the claimed “three-dimensional understanding”);
generating synthetic data based on the integrated first metadata and Lidar data to simulate professional filmmaking techniques (see El Fahli at p.26: “Third, LIDAR, which basically means “a laser light that can be used to scan real environments and obtain data to create CGI models that mimic the real world (Sawicki, 2007), and it is a 3D scanning of the set that provides the VFX team with more detailed and accurate design of the set and environment, this LIDAR 3D scanning can also be done to actors when the VFX team needs to create a digital double of the actor”. Therefore, the use of Lidar data to create a digital double of an actor corresponds to the claimed synthetic data to simulate professional filmmaking techniques).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Gonsalves with the above teachings of El Fahli by training a model for enhancing content generation, as taught by Gonsalves, and using training data from camera, composition lighting and Lidar data to simulate a 3D environment for filmmaking purposes, as taught by El Fahli. The modification would have been obvious because one of ordinary skill in the art would be motivated to acquire important data in a virtual effects set as they are becoming more and more an indispensable part of filmmaking due to the increasing use of them in this new era of filmmaking (as suggested by El Fahli at Abstract and p. 26).
Referring to Claim 3, the combination of Gonsalves and El Fahli teaches the method of claim 1, further comprising implementing continuous learning mechanisms that dynamically adjust the AI models based on structured feedback mechanisms, enabling iterative improvements in video content generation (see Gonsalves at Abstract: “Adaptive automatic editing may assist in the creation of video and audio compositions, as well as in the generation of musical scores”. See [0028]: “The randomization method of combining training data produces more accurate results since the model is entirely retrained by the latest available data set combination”).
Referring to Claim 4, the combination of Gonsalves and El Fahli teaches the method of claim 1, further comprising simulating dynamic scene changes and systematically altering key filmmaking variables in the metadata to teach the AI models the impact of each filmmaking element on a video output (see Gonsalves at Fig. 7 and [0041]: “FIG. 7 illustrates the integration of automatic color correction options 702 into user interface 700 of a non-linear video editing application. The deep learning-based option for the currently selected function may be invoked by a user control, such a light bulb button 704. The user is able to apply automatic color correction to an individual clip, for example by selecting one of the clips listed in a displayed clip list and selecting “apply,” or invoke automatic color correction for the entire sequence of clips by selecting “apply to all.””. Furthermore, El Fahli teaches at p.26 “Third, LIDAR, which basically means “a laser light that can be used to scan real environments and obtain data to create CGI models that mimic the real world (Sawicki, 2007), and it is a 3D scanning of the set that provides the VFX team with more detailed and accurate design of the set and environment, this LIDAR 3D scanning can also be done to actors when the VFX team needs to create a digital double of the actor”. Therefore, the use of Lidar data is used by El Fahli to create a 3D scanning of the set corresponds to the claimed “three-dimensional understanding” and CGI models, and even digital doubles of an actor, which correspond to the claimed “simulating dynamic scene changes”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Gonsalves with the above teachings of El Fahli by training a model for enhancing content generation, as taught by Gonsalves, and using training data from camera, composition lighting and Lidar data to simulate a 3D environment for filmmaking purposes, as taught by El Fahli. The modification would have been obvious because one of ordinary skill in the art would be motivated to acquire important data in a virtual effects set as they are becoming more and more an indispensable part of filmmaking due to the increasing use of them in this new era of filmmaking (as suggested by El Fahli at Abstract and p. 26).
Referring to Claim 6, the combination of Gonsalves and El Fahli teaches the method of claim 1, further comprising using one or more convolutional neural networks (CNNs) to analyze visual patterns in the synthetic and real-world filmmaking data, to train the AI models to generate output including a cinematographic technique or visual styles (see Gonsalves at Abstract: “Adaptive automatic editing may assist in the creation of video and audio compositions” and [0039]: “The size of the input images may depend on whether a fully connected neural network or a convolutional neural network is deployed, with the latter having a convolution layer following the input layer, which greatly reduces the size of the hidden layers of the neural network. Thus, while 256×256×3 represents a practical input image size for fully connected networks, much larger images may be handled when training a convolutional network”. In addition, see El Fahli at p.26: “Third, LIDAR, which basically means “a laser light that can be used to scan real environments and obtain data to create CGI models that mimic the real world (Sawicki, 2007), and it is a 3D scanning of the set that provides the VFX team with more detailed and accurate design of the set and environment, this LIDAR 3D scanning can also be done to actors when the VFX team needs to create a digital double of the actor” and at p. 31: “We know that visual effects and cinematography are deeply interwinding, but we should also acknowledge that cinematography can exist without visual effects, whereas visual effects can’t exist without cinematography. Cinematography is very important to visual effects, it is a fundamental element in visual effects work, and we can see this clearly in two aspects, the significance of lighting in VFX workflow, and the cinematography in a 3D virtual space”. Therefore, combining Gonsalves’s models to generate media content with El Fahli’s use of Lidar to simulate and create CGI and actor doubles in visual effects would lead to the claimed “generate output including a cinematographic technique or visual styles”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Gonsalves with the above teachings of El Fahli by training a model for enhancing content generation, as taught by Gonsalves, and using training data from camera, composition lighting and Lidar data to simulate a 3D environment, as taught by El Fahli. The modification would have been obvious because one of ordinary skill in the art would be motivated to acquire important data in a virtual effects set as they are becoming more and more an indispensable part of filmmaking due to the increasing use of them in this new era of filmmaking (as suggested by El Fahli at Abstract and p. 26).
Referring to independent Claim 8 and Claim 15, they are rejected on the same basis as independent claim 1 since they are analogous claims in a broader version of Claim 1.
Referring to dependent Claim 10 and Claim 17, they are rejected on the same basis as dependent claim 3 since they are analogous claims.
Referring to dependent Claim 11 and Claim 18, they are rejected on the same basis as dependent claim 4 since they are analogous claims.
Referring to dependent Claim 13 and Claim 19, they are rejected on the same basis as dependent claim 6 since they are analogous claims.
Claims 5, 7, 12, 14 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Gonsalves et al (US Pub. 2021/0295148- hereinafter Gonsalves) in view of El Fahli et al (“Visual Effects as a Filmmaking Tool”- hereinafter El Fahli) and further in view of Anandraj et al (NPL: “The Impact of AI Revolution in Transforming Film Making Industry for the Digital Age”- hereinafter Anandraj).
Referring to Claim 5, the combination of Gonsalves and El Fahli teaches the method of claim 1, however, fails to teach further comprising employing a quality control process to evaluate generated video content against predefined criteria for technical and creative filmmaking standards.
Anandraj teaches, in an analogous system, employing a quality control process to evaluate generated video content against predefined criteria for technical and creative filmmaking standards (see Anandraj at pp. 83-84 section 4. Objectives of the Study: 3rd bullet point “To examine the impact of AI technologies on the efficiency, creativity, quality and audience recommendations of film making in the digital age”. Further at p. 88 section 6.3 Results of Integrating AI in Film Industry: “The results achieved in respect of efficiency, creativity, quality and audience engagement are outlined below along with the individual films in Fig 3 to 6”. Further at p. 89 left column Quality: “Enhanced Visuals and Effects: AI technologies improve the quality of visual effects and image restoration, ensuring that films achieve high standards of visual excellence” and right column: “These examples demonstrate how AI technologies contribute to the efficiency, creativity, and quality of filmmaking in cartoon and animated movies, showcasing their transformative impact on the industry”. Therefore, the combination of Gonsalves’s use of models to generate videos and El Fahli’s use of metadata and Lidar to simulate and create CGI with Anandraj’s examination of the use of AI in filmmaking would suggest to evaluate a generated video content against predefined criteria according to filmmaking standards).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the combination of Gonsalves and El Fahli with the above teachings of Anandraj by training a model for enhancing content generation using training data from camera, composition lighting and Lidar data to simulate a 3D environment, as taught by Gonsalves and El Fahli, and, further evaluating the quality of the use of AI in the film industry according to its standards, as taught by Anandraj. The modification would have been obvious because one of ordinary skill in the art would be motivated to examine the impact of AI technologies on the efficiency, creativity, quality and audience recommendations of film making in the digital age (as suggested by Anandraj at Abstract and pp. 83-84).
Referring to Claim 7, the combination of Gonsalves and El Fahli teaches the method of claim 1, further comprising integrating the trained AI models with user interfaces that allow users to specify video characteristics using metadata language, facilitating the generation of customized video content (see Gonsalves at Fig. 5, [0012]: “FIG. 5 is a diagrammatic illustration of a user interface enabling a user to adjust and monitor machine learning parameters of a user-adaptive machine learning-based content creation application” and at Fig. 7 and [0041]: “FIG. 7 illustrates the integration of automatic color correction options 702 into user interface 700 of a non-linear video editing application. The deep learning-based option for the currently selected function may be invoked by a user control, such a light bulb button 704. The user is able to apply automatic color correction to an individual clip, for example by selecting one of the clips listed in a displayed clip list and selecting “apply,” or invoke automatic color correction for the entire sequence of clips by selecting “apply to all.””. This is interpreted as customized video content using models. Further, El Fahli teaches the use of models to create CGI and even digital doubles in filmmaking, which corresponds to the generation of video content in filmmaking).
However, the combination of Gonsalves and El Fahli fails to teach facilitating the generation of customized video content that adheres to specific filmmaking preferences and requirements.
Anandraj teaches, in an analogous system, facilitating the generation of customized video content that adheres to specific filmmaking preferences and requirements (see Anandraj at pp. 83-84 section 4. Objectives of the Study: 3rd bullet point “To examine the impact of AI technologies on the efficiency, creativity, quality and audience recommendations of film making in the digital age”. Further at p. 88 section 6.3 Results of Integrating AI in Film Industry: “The results achieved in respect of efficiency, creativity, quality and audience engagement are outlined below along with the individual films in Fig 3 to 6”. Further at p. 89 left column Quality: “Enhanced Visuals and Effects: AI technologies improve the quality of visual effects and image restoration, ensuring that films achieve high standards of visual excellence” and right column: “These examples demonstrate how AI technologies contribute to the efficiency, creativity, and quality of filmmaking in cartoon and animated movies, showcasing their transformative impact on the industry”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the combination of Gonsalves and El Fahli with the above teachings of Anandraj by training a model for enhancing content generation using training data from camera, composition lighting and Lidar data to simulate a 3D environment, as taught by Gonsalves and El Fahli, and, further evaluating the quality of the use of AI in the film industry according to its preferences and requirements, as taught by Anandraj. The modification would have been obvious because one of ordinary skill in the art would be motivated to examine the impact of AI technologies on the efficiency, creativity, quality and audience recommendations of film making in the digital age (as suggested by Anandraj at Abstract and pp. 83-84).
Referring to dependent Claim 12, it is rejected on the same basis as dependent claim 5 since they are analogous claims.
Referring to dependent Claim 14 and Claim 20, they are rejected on the same basis as dependent claim 7 since they are analogous claims.
Response to Arguments
Applicant's arguments filed 12/08/2025 have been fully considered.
In reference to Applicant’s arguments:
- Double patenting rejections.
Examiner’s response:
Rejections under non-statutory double patenting rejection are maintained under obviousness analysis, as explained above at the beginning of this office action.
In reference to Applicant’s arguments:
- Claim rejections under 35 USC 112(b).
Examiner’s response:
Rejections are withdrawn in view of amendments.
In reference to Applicant’s arguments:
- Claim rejections under 35 USC 101.
Examiner’s response:
Rejections are withdrawn in view of amendments and applicant’s arguments.
In reference to Applicant’s arguments:
- Claim rejections under 35 USC 103.
Examiner’s response:
In regards to:
“1. Independent claims: missing elements and improper alleged mapping”:
“Integrating LiDAR data with the received metadata to provide a three-dimensional understanding of space and object relationships”, Examiner respectfully disagrees. Applicant seems to address each reference individually by stating what Gonsalves teaches and what El Fahli teaches. In response to applicant's arguments against the references individually, one cannot show nonobviousness by attacking references individually where the rejections are based on combinations of references. See In re Keller, 642 F.2d 413, 208 USPQ 871 (CCPA 1981); In re Merck & Co., 800 F.2d 1091, 231 USPQ 375 (Fed. Cir. 1986).
“Generating synthetic data based on the integrated metadata and LiDAR data to simulate professional filmmaking techniques”, Examiner respectfully disagrees. Examiner understands that Gonsalves doesn’t teach the generation of synthetic data, however, El Fahli does. Gonsalves teaches the training of a deep learning system for media/video content creators. El Fahli teaches generating CGI models (which under BRI, is equivalent to a synthetic example) that mimic real world using Lidar and metadata related to VFX (which are filmmaking techniques). The combination (emphasis added) of Gonsalves and El Fahli would have been obvious to include these types of data into the training of a deep neural network for the purposes of improving filmmaking in this new era.
“Incorporating feedback from actual film production use to adjust model parameters to align with filmmaking practices and technologies”, Examiner respectfully disagrees. This limitation was amended and now recites “receiving, via one or more processors, feedback generated during actual film production operations and updating parameters of at least one of the AI models based on the feedback”. Based on the BRI of the amended claim limitation, Examiner understands that it is directed to updating or adjusting a model for the purposes of filmmaking practices using feedback from films, and Examiner understands that Gonsalves teaches it, as it can be seen for example at [0019]: “The pre-training of adaptive AI-based media editing applications may use training data sets sourced from editors who are viewed as expert media content creators in one or more of video content, audio content, and musical score layout. The input vectors of the training data include the raw media as received by the expert editors, together with additional metadata. In general, it is desirable to provide the machine learning system with all the raw material that is available to the expert editor when editing and creating media content”. Examiner reasonably interprets the raw material available to the expert editors as feedback from actual fil production use. Furthermore, Gonsalves at [0012]: “FIG. 5 is a diagrammatic illustration of a user interface enabling a user to adjust and monitor machine learning parameters of a user-adaptive machine learning-based content creation application” and [0031]: “Users choose the function of the application to which the user-adaptive machine learning is to be applied in box 502. Interactive controls for each hyperparameter enable the user to adjust their values manually, for example with mouse or touch-controlled slider 504 or to allow the system to determine the value automatically 506”. Therefore, user-adaptive machine learning model by the experts is reasonably interpreted as updating parameters of the AI model.
“Training Al models using a combination of the synthetic data and real-world filmmaking data to enhance their video content generation capabilities”, Examiner respectfully disagrees. This limitation was amended and now recites “training the one or more AI models using a combination of the synthetic data and real-world filmmaking data including raw daily footage annotated with second metadata”. Based on the amended claim limitation, Examiner understands that there is no generation of video content found in the amended limitation. Nevertheless, Gonsalves is directed to generating edited media composition including video clips, which is reasonably interpreted as video content generation under BRI.
“2. Insufficient motivation to combine”, Examiner respectfully disagrees. First, Gonsalves discloses at Abstract “Training data input vectors for the model comprise representative portions of a composition's raw media, and corresponding output vectors include values of parameters that define editing functions applied to the raw media to generate an edited media composition. A user interface enabling a user to adjust and monitor machine learning parameters is provided. Adaptive automatic editing may assist in the creation of video and audio compositions, as well as in the generation of musical scores” and at [0047]: “Adaptive training may be used to improve accuracy in the content domains in which the work of a given editor is concentrated. Local training data may be obtained from randomly selected full-resolution images in a final composition created by a local user, with the corresponding synthetic input obtained by downscaling these images. The neural network model is then trained to upscale from the synthetically generated downscaled images to the original image. In production mode, low-resolution imagery is input, and the system automatically generates high-resolution output”. El Fahli discloses at Abstract “Visual effects are becoming more and more an indispensable part of filmmaking due to the increasing use of them in this new era of filmmaking”, and “we are going to make use of invisible visual effects to prove the endless contributions of visual effects to filmmaking, and also break the causal relationship between cinematography and visual effects to understand why cinematography is important to visual effects, as well as revealing how they can both affect and change each other’s workflow”, and at p. 26 “LIDAR, which basically means “a laser light that can be used to scan real environments and obtain data to create CGI models that mimic the real world” (Sawicki, 2007), and it is a 3D scanning of the set that provides the VFX team with more detailed and accurate design of the set and environment, this LIDAR 3D scanning can also be done to actors when the VFX team needs to create a digital double of the actor”. After careful reconsideration of the claim limitations and the combination (emphasis added) of Gonsalves and El Fahli, Examiner understands that one of ordinary skill in the art would be motivated to train a model for enhancing content generation, as taught by Gonsalves, and using training data from camera, composition lighting and Lidar data to simulate a 3D environment for filmmaking purposes, as taught by El Fahli, in order to acquire important data in a virtual effects set as they are becoming more and more an indispensable part of filmmaking due to the increasing use of them in this new era of filmmaking.
For these reasons above, rejections are still maintained.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
US 20170085863 (this art is pertinent as it discloses the generation of images using CGI after conversion of LIDAR data).
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
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/LUIS A SITIRICHE/Primary Examiner, Art Unit 2126