DETAILED ACTION
A. This action is in response to the following communications: Transmittal of New Application filed 08/26/2024.
B. Claims 1-16 remains pending.
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claim(s) 1-16 is/are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Ghosh, Arnab et al. (US Pub. 2024/0404225 A1), herein referred to as “Ghosh”. Provisional application No. 63/504,984, filed on May 30, 2023.
As for claims 1 and 9, Ghosh teaches. A computer-implemented system and corresponding method of claim 9 for generating one or more characters associated with one or more digital interactive platforms to optimize a user experience in the one or more digital interactive platforms, the computer-implemented system comprising: one or more hardware processors and
a memory coupled to the one or more hardware processors, wherein the memory comprises a set of program instructions in form of a plurality of subsystems, configured to be executed by the one or more hardware processors, wherein the plurality of subsystems comprises(par. 51-73 Example hardware subsystems are discussed which include image processing system, augmentation system, media overlay, augmentation creation system, communication system, user management system, collection management system, map system, game system, external resource system, software development kit or SDK, interaction client, advertisement system and artificial intelligence and machine learning systems );
a character creating subsystem configured to pre-create the one or more characters for one or more users (par. 59 The augmentation creation system 214 supports augmented reality developer platforms and includes an application for content creators (e.g., artists and developers) to create and publish augmentations (e.g., augmented reality experiences) of the interaction client 104. The augmentation creation system 214 provides a library of built-in features and tools to content creators including, for example custom shaders, tracking technology, and templates ) based on first one or more inputs from one or more user devices of the one or more users, by at least one of: diffusion models, a cycle generative adversarial network (GAN) model, and a pix2pix model (par. 73 An artificial intelligence and machine learning system 230 provides a variety of services to different subsystems within the interaction system 100. For example, the artificial intelligence and machine learning system 230 operates with the image processing system 202 and the camera system 204 to analyze images and extract information such as objects, text, or faces. This information can then be used by the image processing system 202 to enhance, filter, or manipulate images. The artificial intelligence and machine learning system 230 may be used by the augmentation system 206 to generate augmented content and augmented reality experiences, such as adding virtual objects or animations to real-world images. Par. 117 pix2pix/ conditional generative adversarial networks; par. 136 Generative adversarial networks; par. 128-129 diffusion; par. 162 characters created by prompt using machine learning model; fig. 6 depicts character creation selection by input/prompt);
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wherein the one or more characters being assigned with one or more face expressions based on the first one or more inputs from the one or more users (par. 115 altering users facial expression) ;
a character selecting subsystem configured to select at least one character with the one or more face expressions during playing of one or more video contents associated with the one or more digital interactive platforms based on at least one of: historical data associated with the one or more digital interactive platforms and the first one or more inputs from the one or more user devices of the one or more users (par.99-109 image input into the system through various means, such as camera input from clients device; takes images from camera sensor and transforms them through machine learning model to change the style of the face to a different face/character as demonstrated in figure 6; par. 130 The machine learning network is trained to generate a modified media content item from an original image of a user, and/or to generate a remodified media content item from an already modified face portion of an image. The machine learning algorithm is trained using historical information that includes historical media content items and resulting modified media content items. The machine learning algorithm is trained by applying historical prompts (as described further herein);
an input obtaining subsystem configured to obtain second one or more inputs from the one or more user devices of the one or more users upon at least one of: one or more actions performed by the one or more users during the one or more digital interactive platforms, and a current state of the one or more digital interactive platforms (par. 109 The first machine learning model takes the user's self-image as input and applies a transformation which generates a stylized version of the original self-image (referred to herein as the first modified self-image or first modified media content item). The transformation involves one or more changes in style, color, texture, and/or the like. In some cases, the first machine learning model applies modification on a portion or the whole face of the user. In some cases, the first machine learning model applied modification on more than the face of the user, such as the background and body); and
a character determining subsystem configured to determine corresponding one or more characters providing the one or more face expressions to be played as one or more animated media contents with the one or more video contents, based on the second one or more inputs from the one or more user devices of the one or more users, to optimize the user experience in the one or more digital interactive platforms, by a machine learning model (par. 53 The augmentation system 206 provides functions related to the generation and publishing of augmentations (e.g., media overlays) for images captured in real-time by cameras of the user system 102 or retrieved from memory of the user system 102. For example, the augmentation system 206 operatively selects, presents, and displays media overlays (e.g., an image filter or an image lens) to the interaction client 104 for the augmentation of real-time images received via the camera system 204 or stored images retrieved from memory 902 of a user system 102. These augmentations are selected by the augmentation system 206 and presented to a user of an interaction client 104, based on a number of inputs and data; par. 90 The system can capture an image or video stream on a client device (e.g., the user system 102) and perform complex image manipulations locally on the user system 102 while maintaining a suitable user experience, computation time, and power consumption. The complex image manipulations may include size and shape changes, emotion transfers (e.g., changing a face from a frown to a smile), state transfers (e.g., aging a subject, reducing apparent age, changing gender), style transfers, graphical element application, and any other suitable image or video manipulation implemented by a convolutional neural network that has been configured to execute efficiently on the user system 102.).
As for claims 2 and 10, Ghosh teaches. The computer-implemented system as claimed in claim 1 and corresponding method of claim 9, wherein the first one or more inputs comprises first information associated with at least one of: a selection of the one or more characters in the one or more digital interactive platforms, and assigning of the one or more face expressions to each of the one or more characters in the one or more digital interactive platforms (fig. 6 par. 162 and par. 90 facial expression manipulation of camera sensor input this input as modified by stylizFed media content items).
As for claims 3 and 11, Ghosh teaches. The computer-implemented system as claimed in claim 1 and corresponding method of claim 9, wherein the second one or more inputs comprises second information associated with one or more real-time face expressions provided by the one or more users, on at least one of: the one or more actions performed by the one or more users during the one or more digital interactive platforms, and the current state of the one or more digital interactive platforms (par. 170 The interaction client applies the content augmentation upon opening of the camera system. The interaction system actively applies the content augmentation to the real-time video feed by displaying, on the user interface, the user and digital items that are overlaying, modifying, or otherwise augmenting the real-time video feed. The first user selects another user interface element to capture a video or picture of the real-time video feed modified with the digital items to send to the second user).
As for claims 4 and 12, Ghosh teaches. The computer-implemented system as claimed in claim 1 and corresponding method of claim 9, wherein the one or more pre-created characters with the one or more face expressions, is pre-stored in a cloud database comprising at least one of: a content delivery network, and an object storage service (par. 286 cloud computing and other network hardware implementations discussed among other examples).
As for claims 5 and 13, Ghosh teaches. The computer-implemented system as claimed in claim 1 and corresponding method of claim 9, wherein in determining, by the machine learning model, the corresponding one or more characters providing the one or more face expressions to be played with the one or more video contents, the character determining subsystem is configured to:
obtain the second one or more inputs comprising the second information associated with the one or more real-time face expressions provided by the one or more users, on at least one of: the one or more actions performed by the one or more users during the one or more digital interactive platforms, and the current state of the one or more digital interactive platforms;
compare the one or more real-time face expressions provided by the one or more users, with the one or more face expressions assigned to the one or more pre-created characters; and
determine the corresponding one or more characters providing the one or more face expressions to be played as the one or more animated media contents with the one or more video contents based on the comparison of the one or more real-time face expressions provided by the one or more users, with the one or more face expressions assigned to the one or more pre-created characters, by the machine learning model (par. 84 augmentation data includes augmented reality content items, overlays, image transformations, AR images, and similar terms refer to modifications that may be applied to image data (e.g., videos or images). This includes real-time modifications, which modify an image as it is captured using device sensors (e.g., one or multiple cameras) of the user system 102 and then displayed on a screen of the user system 102 with the modifications. This also includes modifications to stored content, such as video clips in a collection or group that may be modified; par. 86 Real-time video processing can be performed with any kind of video data (e.g., video streams, video files, etc.) saved in a memory of a computerized system of any kind. For example, a user can load video files and save them in a memory of a device or can generate a video stream using sensors of the device. Additionally, any objects can be processed using a computer animation model, such as a human's face and parts of a human body, animals, or non-living things such as chairs, cars, or other objects.; par. 91 he system operating within the interaction client 104 determines the presence of a face within the image or video stream and provides modification icons associated with a computer animation model to transform image data, or the computer animation model can be present as associated with an interface described herein. The system may implement a complex convolutional neural network on a portion of the image or video stream to generate and apply the selected modification. That is, the user may capture the image or video stream and be presented with a modified result in real-time or near real-time once a modification icon has been selected. Further, the modification may be persistent while the video stream is being captured, and the selected modification icon remains toggled. Machine-taught neural networks may be used to enable such modifications; par. 268, 277 comparison to determine different between captured image and avatar created to determine a difference.
As for claims 6 and 14, Ghosh teaches. The computer-implemented system as claimed in claim 1 and corresponding method of claim 9, wherein the one or more face expressions provided by the one or more characters, is dynamically updated based on the one or more real-time face expressions provided by the one or more users (par. 53 The augmentation system 206 provides functions related to the generation and publishing of augmentations (e.g., media overlays) for images captured in real-time by cameras of the user system 102 or retrieved from memory of the user system 102. For example, the augmentation system 206 operatively selects, presents, and displays media overlays (e.g., an image filter or an image lens) to the interaction client 104 for the augmentation of real-time images received via the camera system 204 or stored images retrieved from memory 902 of a user system 102. These augmentations are selected by the augmentation system 206 and presented to a user of an interaction client 104, based on a number of inputs and data; par. 91 he system operating within the interaction client 104 determines the presence of a face within the image or video stream and provides modification icons associated with a computer animation model to transform image data, or the computer animation model can be present as associated with an interface described herein. The system may implement a complex convolutional neural network on a portion of the image or video stream to generate and apply the selected modification. That is, the user may capture the image or video stream and be presented with a modified result in real-time or near real-time once a modification icon has been selected. Further, the modification may be persistent while the video stream is being captured, and the selected modification icon remains toggled. Machine-taught neural networks may be used to enable such modifications.).
As for claims 7 and 15, Ghosh teaches. The computer-implemented system as claimed in claim 1 and corresponding method of claim 9, wherein the one or more animated media contents comprises at least one of: the determined one or more characters providing the one or more face expressions to be played with the one or more video contents, and second one or more video contents, to optimize the user experience in the one or more digital interactive platforms, and
wherein the second one or more video contents is played upon the one or more user actions performed by the one or more users in the one or more video contents (par. 91-93 he system operating within the interaction client 104 determines the presence of a face within the image or video stream and provides modification icons associated with a computer animation model to transform image data, or the computer animation model can be present as associated with an interface described herein. The system may implement a complex convolutional neural network on a portion of the image or video stream to generate and apply the selected modification. That is, the user may capture the image or video stream and be presented with a modified result in real-time or near real-time once a modification icon has been selected. Further, the modification may be persistent while the video stream is being captured, and the selected modification icon remains toggled. Machine-taught neural networks may be used to enable such modifications. A collection may also constitute a “live story,” which is a collection of content from multiple users that is created manually, automatically, or using a combination of manual and automatic techniques. For example, a “live story” may constitute a curated stream of user-submitted content from various locations and events. Users whose client devices have location services enabled and are at a common location event at a particular time may, for example, be presented with an option, via a user interface of the interaction client 104, to contribute content to a particular live story. The live story may be identified to the user by the interaction client 104, based on his or her location. The end result is a “live story” told from a community perspective.).
As for claims 8 and 16, Ghosh teaches. The computer-implemented system as claimed in claim 1 and corresponding method of claim 9, wherein the character creating subsystem is further configured to create second one or more characters with the one or more face expressions as the one or more animated media contents based on transformers when the one or more users personalizes the one or more video contents associated with the one or more digital interactive platforms (par. 86 Real-time video processing can be performed with any kind of video data (e.g., video streams, video files, etc.) saved in a memory of a computerized system of any kind. For example, a user can load video files and save them in a memory of a device or can generate a video stream using sensors of the device. Additionally, any objects can be processed using a computer animation model, such as a human's face and parts of a human body, animals, or non-living things such as chairs, cars, or other objects.; par. 91 he system operating within the interaction client 104 determines the presence of a face within the image or video stream and provides modification icons associated with a computer animation model to transform image data, or the computer animation model can be present as associated with an interface described herein. The system may implement a complex convolutional neural network on a portion of the image or video stream to generate and apply the selected modification. That is, the user may capture the image or video stream and be presented with a modified result in real-time or near real-time once a modification icon has been selected. Further, the modification may be persistent while the video stream is being captured, and the selected modification icon remains toggled. Machine-taught neural networks may be used to enable such modifications; par. 109 The first machine learning model takes the user's self-image as input and applies a transformation which generates a stylized version of the original self-image (referred to herein as the first modified self-image or first modified media content item). The transformation involves one or more changes in style, color, texture, and/or the like. In some cases, the first machine learning model applies modification on a portion or the whole face of the user. In some cases, the first machine learning model applied modification on more than the face of the user, such as the background and body; par. 227,229 transformer models/ transformers).
(Note :) It is noted that any citation to specific, pages, columns, lines, or figures in the prior art references and any interpretation of the references should not be considered to be limiting in any way. A reference is relevant for all it contains and may be relied upon for all that it would have reasonably suggested to one having ordinary skill in the art. In re Heck, 699 F.2d 1331, 1332-33, 216 USPQ 1038, 1039 (Fed. Cir. 1983) (quoting In re Lemelson, 397 F.2d 1006,1009, 158 USPQ 275, 277 (CCPA 1968)).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
SYSTEM AND METHOD FOR COLLABORATIVE SHOPPING, BUSINESS AND ENTERTAINMENT
Document ID
US 20100030578 A1
Date Published
2010-02-04
Abstract
The methods and systems described herein relate to online methods of collaboration in community environments. The methods and systems are related to an online apparel modeling system that allows users to have three-dimensional models of their physical profile created. Users may purchase various goods and/or services and collaborate with other users in the online environment.
Systems And Methods For Creating Animations Using Human Faces
Document ID
US 9626788 B2
Date Published
2017-04-18
Abstract
Systems and methods in accordance with embodiments of the invention enable collaborative creation, transmission, sharing, non-linear exploration, and modification of animated video messages. One embodiment includes a video camera, a processor, a network interface, and storage containing an animated message application, and a 3D character model. In addition, the animated message application configures the processor to: capture a video sequence using the video camera; detect a human face within a sequence of video frames; track changes in human facial expression of a human face detected within a sequence of video frames; map tracked changes in human facial expression to motion data, where the motion data is generated to animate the 3D character model; apply motion data to animate the 3D character model; render an animation of the 3D character model into a file as encoded video; and transmit the encoded video to a remote device via the network interface.
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Inquires
Any inquiry concerning this communication should be directed to NICHOLAS AUGUSTINE at telephone number (571)270-1056.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
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/NICHOLAS AUGUSTINE/Primary Examiner, Art Unit 2178 September 10, 2026
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