IP Library Granted Patent US 12,593,003
Granted Patent B1
US 12,593,003 · App. 18/959,291 · Granted Mar 31, 2026

AI-based filmmaking tools for consumer use

Inventor: Benjamin Geza Affleck-Boldt (West Hollywood, CA)
Assignee: InterPositive, LLC
H04N5/262G11B27/031
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Quick Facts
Patent No.
US 12,593,003
App. No.
18/959,291
Granted
Mar 31, 2026
Kind
B1
Abstract

A method delivers AI-based filmmaking tools by capturing scenes in various formats and applying processing techniques to affect visual outcomes. A system includes processors and memory to capture scenes, use distinctive film stocks, and apply film processing techniques for AI-based filmmaking tools. A non-transitory computer-readable medium has instructions for capturing scenes in different formats, using film stocks for distinctive looks, and applying processing techniques for AI-based filmmaking tools.

Claims (40)

1 . A computer-implemented method for delivering artificial intelligence (AI)-based filmmaking tools to consumers, the method comprising:

receiving a digital control image of a scene using standard digital video as a baseline for comparison;

receiving a digital test image of the scene, the digital test image corresponding to one or more different analog film stocks having distinctive looks in respective film formats;

comparing, via one or more processors, the digital control image of the scene to the digital test image of the scene to document visual effects;

causing footage to be shot with the intention of processing it with specific techniques to affect a visual outcome of the footage;

applying film processing techniques to determine an impact of the techniques on color and texture; and

leveraging integration techniques for existing video large language models and simulation-driven learning environments by adjusting the existing video large language models and simulation-driven learning environments using the visual effects.

2 . The method of claim 1 , wherein capturing the scene using different film formats includes formats such as 35 mm, 16 mm, 8 mm, and Super 8 mm.

3 . The method of claim 1 , wherein using notable film stocks includes Kodak Portra for color rendition and Ilford Delta for black and white photography.

4 . The method of claim 1 , wherein causing footage to be shot with the intention of processing it includes pushing a film one stop.

5 . The method of claim 1 , wherein applying film processing techniques includes bleach bypass.

6 . The method of claim 1 , wherein leveraging integration techniques includes integration with existing large language models for video processing.

7 . The method of claim 1 , further comprising training a video large language model using the scene, processed footage, and applied film processing techniques as training data, wherein the training includes adjusting the model to recognize and replicate the visual effects associated with different film formats, stocks, and processing techniques.

8 . A computing system for delivering AI-based filmmaking tools to consumers, the system comprising:

one or more processors; and

one or more memories having stored thereon instructions that when executed by the one or more processors, cause the system to:

receive a digital control image of a scene using standard digital video as a baseline for comparison;

receive a digital test image of the scene, the digital test image corresponding to one or more different analog film stocks having distinctive looks in respective film formats;

compare, via one or more processors, the digital control image of the scene to the digital test image of the scene to document visual effects;

cause footage to be shot with the intention of processing it with specific techniques to affect a visual outcome of the footage;

apply film processing techniques to determine an impact of the techniques on color and texture; and

apply integration techniques for existing video large language models and simulation-driven learning environments by adjusting the existing video large language models and simulation-driven learning environments using the visual effects.

9 . The system of claim 8 , wherein the instructions further cause the system to capture scenes using film formats such as 35 mm, 16 mm, 8 mm, and Super 8 mm.

10 . The system of claim 8 , wherein the instructions further cause the system to use notable film stocks including Kodak Portra for color rendition and Ilford Delta for black and white photography.

11 . The system of claim 8 , wherein the instructions further cause the system to cause footage to be shot by pushing a film one stop.

12 . The system of claim 8 , wherein the instructions further cause the system to apply film processing techniques including bleach bypass.

13 . The system of claim 8 , wherein the instructions further cause the system to apply integration techniques with existing large language models for video processing.

14 . A non-transitory computer-readable medium having stored thereon instructions that when executed by one or more processors of a system, cause the system to perform a method for delivering AI-based filmmaking tools to consumers, the method comprising:

receiving a digital control image of a scene using standard digital video as a baseline for comparison;

receiving a digital test image of the scene, the digital test image corresponding to one or more different analog film stocks having distinctive looks in respective film formats;

compare, via one or more processors, the digital control image of the scene to the digital test image of the scene to document visual effects;

causing footage to be shot with the intention of processing it with specific techniques to affect a visual outcome of the footage;

applying film processing techniques to determine an impact of the techniques on color and texture; and

leveraging integration techniques for existing video large language models and simulation-driven learning environments by adjusting the existing video large language models and simulation-driven learning environments using the visual effects.

15 . The computer-readable medium of claim 14 , wherein the instructions further cause the system to capture scenes using film formats such as 35 mm, 16 mm, 8 mm, and Super 8 mm.

16 . The computer-readable medium of claim 14 , wherein the instructions further cause the system to use notable film stocks including Kodak Portra for color rendition and Ilford Delta for black and white photography.

17 . The computer-readable medium of claim 14 , wherein the instructions further cause the system to cause footage to be shot by pushing a film one stop.

18 . The computer-readable medium of claim 14 , wherein the instructions further cause the system to apply film processing techniques including bleach bypass.

19 . The computer-readable medium of claim 14 , wherein the instructions further cause the system to apply integration techniques with existing large language models for video processing.

20 . The computer-readable medium of claim 14 , wherein the instructions further cause the system to train a video large language model using the scene, processed footage, and applied film processing techniques as training data, wherein the training includes adjusting the model to recognize and replicate the visual effects associated with different film formats, stocks, and processing techniques.

Assignments (3)
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Apr 16, 2026
From: INTERPOSITIVE, LLC
To: NETFLIX, INC.
Reel/Frame 075423/0760 →
CHANGE OF NAME Recorded Nov 24, 2025
From: FIN BONE, LLC
To: INTERPOSITIVE, LLC
Reel/Frame 073322/0142 →
NUNC PRO TUNC ASSIGNMENT Recorded Jul 2, 2025
From: AFFLECK-BOLDT, BENJAMIN GEZA
To: FIN BONE, LLC
Reel/Frame 071595/0698 →
Continuity (1)
Provisional Application 63657756 · Jun 7, 2024
References Cited (87)
US 9681064B1 · Hodges et al. · 2017 [cited by applicant]
US 10645356B1 · Suhy · 2020 [cited by examiner]
US 11222667B2 · Ratias · 2022 [cited by applicant]
US 11288864B2 · George et al. · 2022 [cited by applicant]
US 11398255B1 · Mann · 2022 [cited by examiner]
US 11450053B1 · Georgis · 2022 [cited by examiner]
US 11461963B2 · Manivasagam · 2022 [cited by examiner]
US 11570378B2 · Newman · 2023 [cited by applicant]
US 11830159B1 · Mann · 2023 [cited by examiner]
US 11928799B2 · Chopra et al. · 2024 [cited by applicant]
US 12051205B1 · Deutsch · 2024 [cited by examiner]
US 12133030B2 · Zink et al. · 2024 [cited by applicant]
US 12236517B2 · Bradley · 2025 [cited by examiner]
US 12322036B1 · Affleck-Boldt · 2025 [cited by applicant]
US 20080018667A1 · Cheng et al. · 2008 [cited by applicant]
US 20120102023A1 · Osman et al. · 2012 [cited by applicant]
US 20150286644A1 · Turner et al. · 2015 [cited by applicant]
US 20160071544A1 · Waterston · 2016 [cited by examiner]
US 20160205379A1 · Kurihara · 2016 [cited by applicant]
US 20180124382A1 · Smith et al. · 2018 [cited by applicant]
US 20180136332A1 · Barfield, Jr. et al. · 2018 [cited by applicant]
US 20200082431A1 · Rajasekharan et al. · 2020 [cited by applicant]
US 20200111447A1 · Yaacob et al. · 2020 [cited by applicant]
US 20200364877A1 · Bradski · 2020 [cited by examiner]
US 20210241473A1 · Schmidt et al. · 2021 [cited by applicant]
US 20210241474A1 · Schmidt et al. · 2021 [cited by applicant]
US 20210295148A1 · Gonsalves et al. · 2021 [cited by applicant]
US 20220197306A1 · Cella et al. · 2022 [cited by applicant]
US 20230124190A1 · Chui et al. · 2023 [cited by applicant]
US 20230281913A1 · Rematas et al. · 2023 [cited by applicant]
US 20230342481A1 · Nikoghossian et al. · 2023 [cited by applicant]
US 20240005960A1 · Murarka · 2024 [cited by examiner]
US 20240015259A1 · Yu et al. · 2024 [cited by applicant]
US 20240105231A1 · Ratias · 2024 [cited by examiner]
US 20240112394A1 · Neal et al. · 2024 [cited by applicant]
US 20240134926A1 · Tunnicliffe et al. · 2024 [cited by applicant]
US 20240177412A1 · Ranganath · 2024 [cited by examiner]
US 20240290119A1 · Fashandi · 2024 [cited by examiner]
US 20240320918A1 · Amador et al. · 2024 [cited by applicant]
US 20240346731A1 · Graham et al. · 2024 [cited by applicant]
US 20240362897A1 · Klinghoffer et al. · 2024 [cited by applicant]
US 20240394511A1 · Thevenin et al. · 2024 [cited by applicant]
US 20240412542A1 · O'Neill · 2024 [cited by examiner]
US 20240419923A1 · Chollampatt Muhammed Ashraf et al. · 2024 [cited by applicant]
US 20250014606A1 · Wong et al. · 2025 [cited by applicant]
US 20250032945A1 · Mechlowicz · 2025 [cited by examiner]
US 20250148671A1 · Sainz-Nieto · 2025 [cited by applicant]
US 20250190761A1 · Filip et al. · 2025 [cited by applicant]
US 20250204987A1 · Mohareri et al. · 2025 [cited by applicant]
GB 2623644A · 2024 [cited by applicant]
Lin et al. “VideoDirectorGPT: Consistent Multi-Scene Video Generation via LLM-Guided Planning” 2024. [cited by applicant]
Hong et al., “CogVideo: Large-scale pretraining for text-to-video generation via transformers” 2022. [cited by applicant]
Anandraj et al., The impact of AI revolution in transforming film making industry for the digital age, ILIS Journal of Librarianship and Informatics, 6(2):82-91 (Dec. 2023). [cited by applicant]
Azzarelli et al., Reviewing intelligent cinematography: AI research for camera-based video production, arxiv.org, Cornell University Library (May 8, 2024). [cited by applicant]
Bao et al., Vidu: a highly consistent, dynamic and skilled text-to-video generator with diffusion models, arxiv.org, Cornell University Library, 17 pages (May 7, 2024). [cited by applicant]
Chen et al., LiDAR-video driving dataset: learning driving policies effectively, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5870-5878 (Jun. 18, 2018). [cited by applicant]
De Lima et al., Video-based interactive storytelling using real-time video compositing techniques, Multimed Tools Appl., 77:2333-57 (2018). [cited by applicant]
De Lima et al., Virtual cinematography director for interactive storytelling, Advances in Computer Entertainment Technology, pp. 263070 (Oct. 2009). [cited by applicant]
El Megdani, Visual effects as a filmmaking tool, A research paper submitted as a partial fulfillment of the requirement for getting a bachelor degree in the English department, University Sidi Mohamed Ben Abdellah, 57 p… [cited by applicant]
Ignatov et al., DSLR-quality photos on mobile devices with deep convolutional networks, 2017 IEEE International Conference on Computer Vision, pp. 3297-3305 (Oct. 22, 2017). [cited by applicant]
International Application No. PCT/US2025/032580, International Search Report and Written Opinion, mailed Oct. 9, 2025. [cited by applicant]
International Application No. PCT/US2025/032581, International Search Report and Written Opinion, mailed Oct. 1, 2025. [cited by applicant]
International Application No. PCT/US2025/032582, International Search Report and Written Opinion, mailed Sep. 29, 2025. [cited by applicant]
International Application No. PCT/US2025/032584, International Search Report and Written Opinion, mailed Oct. 9, 2025. [cited by applicant]
International Application No. PCT/US2025/032586, International Search Report and Written Opinion, mailed Sep. 15, 2025. [cited by applicant]
International Application No. PCT/US2025/032594, International Search Report and Written Opinion, mailed Oct. 6, 2025. [cited by applicant]
International Application No. PCT/US2025/032597, International Search Report and Written Opinion, mailed Oct. 6, 2025. [cited by applicant]
International Application No. PCT/US2025/032601, International Search Report and Written Opinion, mailed Oct. 16, 2025. [cited by applicant]
International Application No. PCT/US2025/032603, International Search Report and Written Opinion, mailed Oct. 7, 2025. [cited by applicant]
International Application No. PCT/US2025/032607, International Search Report and Written Opinion, mailed Oct. 13, 2025. [cited by applicant]
International Application No. PCT/US2025/032619, International Search Report and Written Opinion, mailed Sep. 10, 2025. [cited by applicant]
Jiang et al., Cinematographic camera diffusion model, Computer Graphics, 43(2):e15055 (2024). [cited by applicant]
Liu et al., Sora: A review on background, technology, limitations, and opportunities of large vision models, arxiv.org, Cornell University Laboratory, 38 pages (Apr. 17, 2024). [cited by applicant]
Maharaj, Dehancer: the cinematic film look made easy?, accessed online: <https://www.theotivity.com/review/dehancer-the-cinematic-film-look-made-easy-an-honest-review/> (Feb. 24, 2024). [cited by applicant]
Momot, Artificial intelligence in filmmaking process: Future scenarios, Bachelor's thesis, School of Business, JAMK University of Applied Sciences, 46 pages, May 2022. [cited by applicant]
Mumuni et al., A survey of synthetic data augmentation methods in computer vision, arxiv.org, Cornell University Library, 33 pages (Mar. 18, 2024). [cited by applicant]
Nassar, Futuristic scenarios: Utilization of AI technological settings to foster the filmmaking visual creation & mass production, International Design Journal, 14(2):207-12 (Mar. 2024). [cited by applicant]
Paul et al., CamTuner: Reinforcement-learning based system for camera parameter tuning to enhance analytics, arxiv.org, Cornell University Library, 15 pages (Jul. 8, 2021). [cited by applicant]
Pierre et al., CNNs for style transfer of digital to film photography, arxiv.org, 15 pages (Nov. 24, 2024). [cited by applicant]
Wu et al., Automatic camera trajectory control with enhanced immersion for virtual cinematography, IEEE Transactions on Multimedia, 14(8), 16 pages (Aug. 2015). [cited by applicant]
Wu et al., The secret of immersion: actor driven camera movement generation for auto-cinematography, arxiv.org, Cornell University Library (May 23, 2023). [cited by applicant]
Yu et al., Enabling automatic cinematography with reinforcement learning, 2022 IEEE 5th International Conference on Multimedia Information Processing and Retrieval (MIPR), pp. 103-108 (Aug. 2022). [cited by applicant]
Zhu et al., MovieFactory: Automatic movie creation from text using large generative models for language images, arxiv.org, Cornell University Library, 7 pages (Jun. 12, 2023). [cited by applicant]
Jiang et al., “Example-driven Virtual Cinematography by Learning Camera Behaviors”, ACM Trans. Graph. 39:4 (2020). [cited by applicant]
Roush et al., LLM as an Art Director (LaDi): Using LLM's to improve Text-to-Media Generators, 2023 (Year: 2023). [cited by applicant]
International Application No. PCT/US2025/032611, International Search Report and Written Opinion, mailed Nov. 14, 2025. [cited by applicant]
Galvane, Automatic Cinematography and Editing in Virtual Environments, Dissertation, Universite Grenoble Alpes (2015). [cited by applicant]