DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
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.
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.
Claim(s) 1-3, 6, 9, 11-13, 16-18 are rejected under 35 U.S.C. 103 as being unpatentable over Prabhu (US Pat. Pub. No. 20240244022 “Prabhu”) in view of Dunstan et al. (US Pat. Pub. No. 20120223940 “Dunstan”) and Akriti et al. (US Pat. Pub. No. 20250156481 “Akriti”).
Regarding claim 16 Prabhu teaches A system comprising: a processor; and a memory coupled to the processor, with instructions stored thereon that, when executed by the processor, cause the processor to perform operations (“[0272] Computing device 900 includes a processor 902, memory 904, a storage device 906, a high-speed interface 908 connecting to memory 904 and high-speed expansion ports 910, and a low speed interface 912 connecting to low speed bus 914 and storage device 906. Each of the components 902, 904, 906, 908, 910, and 912, are interconnected using various busses, and can be mounted on a common motherboard or in other manners as appropriate. The processor 902 can process instructions for execution within the computing device 900, including instructions stored in the memory 904 or on the storage device 906 to display graphical information for a GUI on an external input/output device, such as display 916 coupled to high speed interface 908.[0275]….. The computing device 900 can be implemented in a number of different forms, as shown in the figure. For example, it can be implemented as a standard server 920, or multiple times in a group of such servers”) comprising:
receiving a three-dimensional (3D) avatar designed by a user in a virtual environment (“[0223] Respective users of user devices 610 and 620 may use the user devices 610, 620 to virtually enter into a metaverse environment 640. The metaverse environment can include, for example, a virtual reality environment where an avatar 610a, 620a for each user of user devices 610, 620 can be created to interact responsive to input from the user devices 610, 620, respectively”);
generating two-dimensional (2D) images of the 3D avatar (“[0072]….. . A scene of a metaverse environment can include a snapshot of a metaverse environment at any particular point in time or period of time. A snapshot of a metaverse environment at a particular point in time may include, e.g., a display capture of each of the avatar's falling within a displayed portion of the metaverse at the particular point in time. A snapshot of a metaverse environment of a particular period of time can include, e.g., a capture of one or more images, video, audio, textual messages. [0227] In some implementations, a metaverse scene be a two-dimensional image depicting a scene of a metaverse environment at the time the impact was captured” As the avatar is in metaverse environment so the avatar is a 3d avatar) but is silent about generating from different angles that surround the 3D avatar;
Dunstan teaches generating two-dimensional (2D) images of 3D avatar from different angles that surround the 3D avatar (ABSTRACT “The 2D images are generated based on the request, each 2D image representing the 3D avatar from a different viewing angle in the virtual world”);
Prabhu and Dunstan are analogous art as both of them are related to image generation.
Therefore it would have been obvious for an ordinary skilled person in the art before the effective filing date of claimed invention to have modified Prabhu by generating two-dimensional (2D) images of 3D avatar from different angles that surround the 3D avatar as taught by Dunstan.
The motivation for the above is to get a better view of the avatar.
Prabhu modified by Dunstan teaches providing the 2D images as input to a trained machine-learning model (Prabhu “[0231] The metaverse scene capture engine 631 can provide the captured metaverse scene as an input to the input engine 632. The input engine is configured to obtain the metaverse scene and generate an input 632a to the machine learning model 633 based on the received metaverse scene”);
generating, with the machine-learning model, an embedding of the 2D images
(“[0172]……. Then, the input engine 332 adjust the weight or rank of features in the input feature vector 332a to emphasize or deemphasize the consideration of such features by the machine learning model 333 during the inferences stage based on the determined trend or topic, with the inference stage being the stage of execution of system 300 where the machine learning model 333 processes the input vector 332a to generation output data 333a”) but is silent about a concatenated embedding of the 2D images;
Akriti teaches generating, with machine-learning model, a concatenated embedding of a 2D image (“[0030]….. These scores are then used to create a weighted combination of the input embeddings. [0041]…..For instance, the embedding for the image 202 is a vector representation of the image 202. In some sense, the embeddings serves a compact, mathematical representation of the image that captures the salient features of the image 202. The image embeddings may then be used for further operations, such as comparisons to other embeddings in the embedding space (e.g., vector space). [0042] The embeddings generator 302 may generate the image embeddings using various techniques or methods, such as through the use of a generative AI model, convolutional neural networks (CNNs), and/or or deep learning models that generate an embedding from an image”);
Akriti and Prabhu modified by Dunstan are analogous art as both of them are related to image processing.
Therefore it would have been obvious for an ordinary skilled person in the art before the effective filing date of claimed invention to have modified Prabhu modified by Dunstan by generating, with machine-learning model, a concatenated embedding of the 2D images by applying Akriti’s teaching of generating, with machine-learning model, a concatenated embedding of a 2D image to multiple 2D images of Prabhu modified by Dunstan.
The motivation for the above is to create single embedding for easier analysis.
Prabhu modified by Dunstan and Akriti teaches analyzing, with the machine-learning model based on the concatenated embedding, at least one attribute associated with the 3D avatar, wherein the at least one attribute is selected from a group of a shape of the 3D avatar, an outfit on the 3D avatar, and combinations thereof (Prabhu “[0234] In some implementations, the labeled input content data can include a labeled set of features that have been extracted from input content. Features of such input content to be numerically represented during feature extraction can include pixels of the captured metaverse scene, words or phrases uttered in the metaverse scene, or a combination thereof. An example of offensive content in a metaverse environment may include, for example, a metaverse scene showing an avatar 620a “mooning” another avatar, a metaverse scene showing an avatar 620a giving a “middle finger” to another avatar, a metaverse scene showing an avatar 620a raising its fist to another avatar, a metaverse scene having audio of an avatar uttering a curse word, a metaverse scene having audio of an avatar saying “I hope you die,” a metaverse scene having audio of an avatar saying “you are a loser,” or the like”); and
outputting, with the machine-learning model, a determination that the at least one attribute of the 3D avatar is abusive (Prabhu “[0236] At runtime, during the inference stage, the trained machine learning model 633 can obtain the input vector 632a representing an actual metaverse scene, process the input vector 632a through each layer of the machine learning model 633, and generate, based on the process of input vector 632a, output data 633a indicating likelihood that input vector 632a representing a scene in a metaverse environment is indicative of offensive behavior”).
Claim 1 is directed to a method claim and its steps are similar in scope and functions performed by the elements of device claim 16 and therefore claim 1 is also rejected with the same rationale as specified in the rejection of claim 16.
Claim 11 is directed to a non-transitory computer-readable medium (Prabhu “[0274] The storage device 906 is capable of providing mass storage for the computing device 900. In one implementation, the storage device 906 can be or contain a computer-readable medium, such as a floppy disk device, a hard disk device, an optical disk device, or a tape device”) and its elements are similar in scope and functions performed by the elements of device claim 16 and therefore claim 11 is also rejected with the same rationale as specified in the rejection of claim 16.
Regarding claims 2, 12 and 17 Prabhu modified by Dunstan and Akriti teaches detecting text on one or more of the 2D images of the 3D avatar; extracting the text from the one or more of the 2D images; and classifying the extracted text to determine if the extracted text is abusive, wherein the determination that the at least one attribute is abusive is based on classifying the extracted text as abusive (Prabhu “ [0124]……In some implementations, the mitigation engine 139 can perform, for example, natural language processing on the content 152 to identify and extract the likely offensive content. For example, in some implementations, the mitigation engine 139 can process the content 152 and remove all non-adjective words include, for example, entity names, location names, articles, and the like. Then, the remaining term(s), which are likely to correspond to the offensive content, can be stored in the offensive content database 140. In other implementations, a human user can review the flagged content 152 and determine which part of the content is likely offensive. [01232]….. . In some implementations, for example, the machine learning model 633 can be trained to classify data representing features of a scene in a metaverse environment from a user device such as user device 610 or 620 into one or more classes corresponding to (i) an offensive metaverse scene or (ii) a non-offensive metaverse scene. In some implementations, the machine learning model 633 can include one or more neural networks”).
Regarding claims 3, 13 and 18 Prabhu modified by Dunstan and Akriti teaches ,responsive to outputting the determination, providing a notification to the user that the 3D avatar includes the at least one attribute that is abusive and one or more of an identification of a category of abuse, a severity of abusiveness of the at least one attribute, a reason that the at least one attribute is abusive, and combinations thereof (Prabhu “[0041] In some implementations, performing, using one or more computers, a remedial operation to mitigate exposure of other users to the message content from a user of the first user device can include generating, using one or more computers, notification data for transmission to the first user device that, when processed by the first user device, (i) notifies the user of the first device that one or more forms of mental instability have been detected”).
Regarding claim 6 Prabhu modified by Dunstan and Akriti teaches wherein the machine-learning model includes a Convolutional Neural Network (CNN) that generates the concatenated embedding of the 2D images by: extracting, with a pooling layer, a respective view embedding for each of the 2D images (Akriti [0042] The embeddings generator 302 may generate the image embeddings using various techniques or methods, such as through the use of a generative AI model, convolutional neural networks (CNNs), and/or or deep learning models that generate an embedding from an image”. Here pooling layer is an integral part of CNN) ; and concatenating the view embeddings to form the concatenated embedding (Akriti (“[0030]….. These scores are then used to create a weighted combination of the input embeddings. [0041]…..For instance, the embedding for the image 202 is a vector representation of the image 202. In some sense, the embeddings serves a compact, mathematical representation of the image that captures the salient features of the image 202. The image embeddings may then be used for further operations, such as comparisons to other embeddings in the embedding space (e.g., vector space).”).
Regarding claim 9 Prabhu modified by Dunstan and Akriti teaches wherein the machine-learning model is trained using training data and the method further comprises updating the training data associated with the machine-learning model to include training images of attributes that are determined to be abusive (Prabhu “[0125] In some implementations, the performance of a remedial operation by the mitigation engine 139 can include the mitigation engine using the content 152 of the post 150 to train or retrain a machine learning model to detect offensive content. In some implementations, training or retraining such a machine learning model can include actually causing the content 152 to be labeled as offensive content and then be processed through the machine learning model”).
Claim(s) 4-5, 14-15 and 19-20 are rejected under 35 U.S.C. 103 as being unpatentable over Prabhu modified by Dunstan and Akriti as applied to claim 3 above, and further in view of Zavesky et al. (US Pat. Pub. No. 20220284735 “Zavesky”).
Regarding Claims 4, 14 and 19 Prabhu modified by Dunstan and Akriti teaches receiving, from the user, an updated 3D avatar; generating updated 2D images of the updated 3D avatar from different angles that surround the updated 3D avatar (“Dunstan “[0057]….. At step 920, the application 150 receives a request from a client, the request specifying customizations to a second avatar in the virtual world. At step 930, the application 150 determines whether the customizations correspond to the cached sprite strip. For example, the application 150 may determine whether the customizations match those customizations from which the cached sprite strip is generated. If so, the application 150 sends the cached sprite strip to the client for display, without having to generate a second sprite strip that is identical to the cached sprite strip. Otherwise, the application 150 generates the sprite strip and sends the sprite strip to the client (e.g., by performing the steps 620 and 630 of FIG. 6)”);
providing the updated 2D images as input to the machine-learning model; outputting, with the machine-learning model, a determination that the at least one attribute is acceptable (Prabhu “[0111] The evaluation engine 138 can also analyze the output data 134a to determine whether the post 150 content 152 does not include offensive content use the first counter (e.g., a likely offensive counter), the second counter (e.g., a likely not offensive counter), or both. In some implementations, the evaluation engine 138 may make such determinations based on a current count of the second counter (e.g., a likely not offensive counter) alone. For example, in such implementations, if the second counter (e.g., a likely not offensive counter) satisfies a predetermined threshold number of occurrences, then the evaluation engine 138 can generate output data 138a indicating that the post 150 content 152 likely does not include offensive content. [0226]…… In some implementations, periodic metaverse scene captures can occur so fast, for example, every 30 millisecond's, or less, that the periodic metaverse scene captures essentially perform continuous monitoring of the metaverse environment”) but is silent about providing the user with an option to use the updated 3D avatar in the virtual environment.
Zavesky teaches providing user with an option to use updated 3D avatar in a virtual environment (“[0024] In one example, the processing system may alternatively or additionally notify a receiver (or sender) of a potential remediation and ask for consent/authorization to override or to select a non-default remediation option for the action, e.g., altering the visual representation of the action to appear differently”);
Zavesky and Prabhu modified by Dunstan and Akriti are analogous art as they are related to avatar processing.
Therefore it would have been obvious for an ordinary skilled person in the art before the effective filing date of claimed invention to have modified Prabhu modified by Dunstan and Akriti by providing user with an option to use updated 3D avatar in a virtual environment as taught by Zavesky.
The motivation for the above is to provide controllability to the user.
Regarding Claims 5, 15 and 20 Prabhu modified by Dunstan and Akriti and Zavesky teaches wherein the machine-learning model is trained using training data and the method further comprises: responsive to the updated 3D avatar being used in the virtual environment, receiving an abuse report from a player in the virtual environment that describes the updated 3D avatar as having an abusive attribute; determining that the updated 3D avatar has the abusive attribute (Zavesky “[0020] In one example, the processing system learns new actions for detection and classification through participant-labeled examples. For example, an offensive action (e.g., one that is actually offensive to a participant, one that is questionable and which the participant believes may be offensive to others, etc.) may be signaled as such by an input from the participant. For instance, the participant may provide an input to the processing system via a keyboard or mouse, via a voice command, using a gesture captured via a wearable computing device, and so on”);
and updating the training data associated with the machine-learning model to include the updated 2D images and a label with the category of abuse (Zavesky “[0017] The types of remediation may be selected by default or may be user-specified. For instance, a participant may flag an action as offensive. [0021] For a negative/objectionable flagged action, the processing system may label the action, and create and activate an action detection model (e.g., a MLM) for detecting subsequent occurrences of the action….. Alternatively, or in addition, learning of an event detection model for a new action can include developing a “lite” version on a local client (e.g., a given participant's communication system) and then comprehensive tuning of the event detection model may be performed with respect to inputs from a plurality of participants regarding various segments of visual content”)
Claim(s) 10 is rejected under 35 U.S.C. 103 as being unpatentable over Prabhu modified by Dunstan and Akriti as applied to claim 1 above, and further in view of Amirghodsi et al. (US Pat. Pub. No. 20210142463 “Amirghodsi”).
Regarding claim 10 Even though Prabhu modified by Dunstan and Akriti teaches wherein generating the 2D images of the 3D avatar using a virtual camera system (“[0020] Embodiments of the invention generally provide techniques for generating two-dimensional images for a three-dimensional avatar in a virtual world. One embodiment provides a computer game that includes a virtual world server application (or application) and multiple virtual world client applications (or clients)” Virtual camera system is a virtual world server application) but is silent about capturing high-resolution thumbnails;
Amirghodsi teaches capturing high-resolution thumbnails using a virtual camera system (“[0050] In some embodiments, the patch matching system 102 further stores the nearest neighbor field 204 within the database 202. Thus, the patch matching system 102 can access the nearest neighbor field 204 and apply the nearest neighbor field 204 to other digital images such as the digital image 208 (e.g., a high-resolution version of the thumbnail digital image 206). [0109]……” Alternatively or additionally, the components of the patch matching system 102 may be implemented in any application that allows creation and delivery of marketing content to users, including, but not limited to, applications in ADOBE CREATIVE CLOUD, such as ADOBE PHOTOSHOP, ADOBE LIGHTROOM, and ADOBE CAMERA RAW. “ADOBE,” “ADOBE PHOTOSHOP,” “ADOBE LIGHTROOM,” and “ADOBE CAMERA RAW” are trademarks of Adobe Inc. in the United States and/or other countries.);
Amirghodsi and Prabhu modified by Dunstan and Akriti are analogous art as they are related to image processing.
Therefore it would have been obvious for an ordinary skilled person in the art before the effective filing date of claimed invention to have modified Prabhu modified by Dunstan and Akriti by capturing high-resolution thumbnails using a virtual camera system as taught by Amirghodsi.
The motivation for the above is to have miniature size of image for better memory management.
Allowable Subject Matter
Claims 7-8 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
Regarding claim 7 , wherein the machine-learning model is trained using synthetic training data for text and the synthetic training data is generated by: identifying a category of abuse for text where the determination that the attribute is abusive is associated with a confidence score that falls below a threshold confidence value (Prabhu “[0184] In some implementations, the evaluation engine 334 can be configured to define different levels of mental stability or instability using one or more thresholds. As a simple example, assume that the output data 333a generated by the machine learning model 333, based on processing an input vector 332a that represents input content data collected from a user device 310, is a probability represented by a numerical value between 0 and 1. In such implementations, the evaluation engine can establish a first classification of greater than 0 and less than 0.33 corresponding to a mentally stable individual and a second superset classification of greater than 0.33”);
generating abusive text that is associated with the identified category; applying a label to the training 2D images that includes the category of abuse; and training the machine-learning model to minimize a difference between a predicted category of abuse for the training 2D images and the label for the training 2D images (Prabhu “[0232] The machine learning model 633 can process the input 632a through the machine learning model 633 to generate output data 633a. In the example of FIG. 6, the machine learning model 633 is trained to predict a likelihood that data representing features of a scene in a metaverse environment is indicative of offensive behavior. The machine learning model 633 can include any type of machine learning model that can be trained to generate output data capable of being used to classify data representing features of a scene in a metaverse environment into one of multiple different classifications. In some implementations, for example, the machine learning model 633 can be trained to classify data representing features of a scene in a metaverse environment”);
However the combination of the available prior art fails to expressly teach “generating a training 3D avatar; projecting the abusive text onto the training 3D avatar; generating training 2D images from the training 3D avatar with the projected abusive text”.
Claim 8 is also objected by virtue of dependency.
Response to Arguments
Applicant’s arguments, see remarks Page 8, filed 7/23/2026, with respect to objections to claims 4, 14 and 19 have been fully considered and are persuasive. The objection has been withdrawn.
Applicant’s arguments, see remarks Pages 9-10, filed 7/23/2026, with respect to rejection of claim 1 under 35 USC 103 have been fully considered and are not persuasive. The rejection has been maintained.
Application argues, see remarks Page 9, “The cited portions of Prabhu, Dunstan, and Akriti, taken individually or together, fail to teach or suggest "receiving a three-dimensional (3D) avatar designed by a user for use in a virtual environment" or "generating, with the machine-learning model, a concatenated embedding of the 2D images" as recited by amended claim 1”.
Examiner replies, Prabhu [0223] and Fig.6 teaches, receiving a three-dimensional (3D) avatar designed by a user for use in a virtual environment See Prabhu, “[0223] Respective users of user devices 610 and 620 may use the user devices 610, 620 to virtually enter into a metaverse environment 640. The metaverse environment can include, for example, a virtual reality environment where an avatar 610a, 620a for each user of user devices 610, 620 can be created to interact responsive to input from the user devices 610, 620, respectively”. Here user of device 610 provides an input to create an avatar 610a for use in the virtual environment. Similarly user of device 620 provides an input to create an avatar 620a for use in the virtual environment. .
Examiner also wants to note, Prabhu as modified by Dunstan and Akriti teaches the limitation "generating, with the machine-learning model, a concatenated embedding of the 2D images" as recited by amended claim 1.
Prabhu as modified by Dunstan teaches generation of multiple two dimensional images. See Prabhu [0072]. Now Akriti teaches, generating, with machine-learning model, a concatenated embedding of a 2D image. See Akriti “[0041] The embedding generator 302 of the description generator 110 receives the image 202 to generate an image embedding for the image 202. The image embedding generated by the embedding generator 302 is a numerical representations of the image 202 that captures the features and information of the image 202 in a vector and/or matrix format. For instance, the embedding for the image 202 is a vector representation of the image 202. In some sense, the embeddings serves a compact, mathematical representation of the image that captures the salient features of the image 202.” Emeddings are compact representation of an image. Examiner proposed to modify Prabhu as modified by Dunstan to have included generating, with machine-learning model, a concatenated embedding of the 2D images by applying the teaching of generating concatenated embedding from an image taught by Akriti to multiple images of Prabhu as modified by Dunstan. So Akriti’s teaching of generating concatenated embedding is applied to multiple 2D images to get a concatenated embedding of multiple 2D images.
Applicant argues, see remarks Page 9, “The examiner states on pages 11-12 of the office action with reference to claim 4 that Prabhu, Dunstan, and Akriti are silent regarding "providing the user with an option to use the updated 3D avatar in a virtual environment." As a result, Prabhu, Dunstan, and Akriti also fail to teach or suggest "receiving a three-dimensional (3D) avatar designed by a user for use in a virtual environment."……..altering the visual representation of the action to appear differently, rather than simply blocking the action from the video content." First, "video content" is not the same as a "virtual environment" as recited in amended claim 1. Second, the cited portions of Zavesky are silent regarding "a three-dimensional (3D) avatar designed by a user" as recited by amended claim 1.”
Examiner replies, the argued limitation of claim 1 doesn’t have any relation with limitation of claim 4. Claim 4 recites “option of use of the updated 3D model” and amended portion of claim 1 recites about avatar designed by a user. Prabhu [0223] teaches the amended limitation of claim 1, receiving a three-dimensional (3D) avatar designed by a user in a virtual environment as described above.
Applicant argues, see remarks page 10, “The cited portions of the references include additional deficiencies. The examiner states on page 5 of the office action that Akriti discloses "generating, with the machine-learning model, a concatenated embedding of the 2D images" in paragraphs [0030], [0041], and [0042]. At most, paragraph [0041] of Akriti discloses that…..….Generating "a concatenated embedding of the 2D images" advantageously provides a single concatenated embedding of the 2D images that is able to identify at least one abusive attribute that may not be identifiable from a single 2D image, but is determined from the concatenated embedding. Because Akriti only uses a single image to generate an embedding, Akriti individually or taken together with Prabhu and Dunstan, fails to solve the technical problem that is addressed by claim 1.”
Examiner replies, Akriti teaches, generating a concatenated embedding from an image. The teaching is applied to multiple images of Prabhu as modified by Dunstan’s multiple 2D images to generate a concatenated embedding. In response to applicant's argument that Akriti teaches an embedding on a single image, the test for obviousness is not whether the features of a secondary reference may be bodily incorporated into the structure of the primary reference; nor is it that the claimed invention must be expressly suggested in any one or all of the references. Rather, the test is what the combined teachings of the references would have suggested to those of ordinary skill in the art. See In re Keller, 642 F.2d 413, 208 USPQ 871 (CCPA 1981). Here Akriti generates a concatenated embedding from an image. An ordinary skilled person can use the teaching to apply to multiple 2D images.
In response to applicant’s argument for dependent claims and independent claim 11 and 16, see remarks Page 10-11, Examiner refers applicant to the reply given above for claim 1.as applicant didn’t provide any additional arguments for these claims.
Conclusion
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to SAPTARSHI MAZUMDER whose telephone number is (571)270-3454. The examiner can normally be reached 8 am-4 pm PST.
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/SAPTARSHI MAZUMDER/ Primary Examiner, Art Unit 2612
