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 .
This action is in response to the Amendment filed on 02/05/2026.
Claims 1-20 are pending. Claims 1, 8, 15 have been amended.
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, 4, 5, 8, 11, 12, 15, 18, 19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Gausebeck et al. (US 20190026958 A1, hereinafter Gausebeck), in view of Rumiński et al. (“Semantic model for distributed augmented reality services”,. 2017, ACM, hereinafter Rumiński), further in view of Hu et al. (US 20180253886 A1, hereinafter Hu).
Regarding Claim 8, Gausebeck teaches a system comprising (Gausebeck, Fig. 1, Element 100 System): a memory (Gausebeck, Fig. 1, Element 122 memory); and at least one hardware processor coupled to the memory (Gausebeck, Fig. 1, Element 124, processor coupled to the memory 124) and comprising instructions that causes the system to perform operations comprising (Gausebeck, Paragraph [0061], “Such components, when executed by the one or more machines, e.g. computer(s), computing device(s), virtual machine(s), etc. can cause the machine( s) to perform the operations described”): causing display of a presentation of an object at a display of a client device (Gausebeck, Paragraph [0226], “the AR data object generation component 3014 can be configured to extract object image data of an object included in a 2D image” [0068], “deriving 3D data from 2D images can learn using multiple levels of representations that correspond to different levels of abstraction [0058], “Terms such as "user equipment," "user equipment device," "mobile device," "user device," "client device," "handset," or terms representing similar terminology can refer to a device utilized by a subscriber or user to receive data”), the presentation of the object comprising image data that includes a display of the object at a position within the image data (Gausebeck, Paragraph [0220], “at least one of the one or more cameras 1404 of the user device 3002 capture image data corresponding to the current perspective of the user, the 3D-from-2D processing module 1406 can derive depth data from the image data that corresponds to actual 3D positions (e.g., depth/distance) of the user relative to physical structures of the house ( e.g., walls, ceilings, kitchen cabinets, appliances, openings, doors, windows, etc.)”); accessing a repository that includes a set of semantic labels associated with the object (Gausebeck, Fig. 9, Element 928 Semantic Labeling Component; Fig. 33, Element 3310 Indexed Semantic Label Data Paragraph [0038], “the auxiliary data comprises one or more semantic labels for one or more object depicted in the 2D image” [0161], “the semantic label/segmentation information associated with a 2D image… and/or stored in memory (e.g., memory 122 or an external memory) for additional applications” [0137], “The auxiliary data component 806 can include various computer-executable components that facilitate processing the native auxiliary data 802 and/or received 2D image data 102 to generate structured auxiliary data 930 and/or pre-processed 2D image data 932. In the embodiment shown, these components include… semantic labeling component 928”); [[ based on the presentation of the object, the set of semantic labels included within a semantic texture map that comprises a set of texels that comprise semantic labels and two-dimensional (2D) texture coordinates;]] retrieving Augmented-Reality (AR) content (Gausenbeck, Paragraph [0031], “Other applications include employing the predicted depth data to facilitate augmented reality applications, live object tracking, live navigation of an environment, user face based biometric authentication applications, and the like” [0023], “FIG. 30 presents an example system that facilitates using one or more 3D-from-2D techniques to in association with an augmented reality (AR) application in accordance with various aspects and embodiments described herein.” [0024], “FIG. 31 presents an example computer-implemented method for using one or more 3D-from-2D techniques to in association with an AR application in accordance with various aspects and embodiments described herein”) [[ based on user profile data associated with a user of the client device and the presentation display of the object ]]; and causing display of a presentation of the AR content at the position of the object within the display of image data at the client device (Gausenbeck, Paragraph [0051]-[0052], “determine a position for integrating a … graphical data object on or within a representation of the object or environment viewed on or through the display based on the current perspective and the 3D data”, “an integration component configured to integrate the graphical data object on or within the representation of the object or environment based on the position”; “the computer executable components can further comprise an occlusion mapping component configured to determine a relative position of the graphical data object to another object included in the representation of the object or environment based on the current perspective and the 3D data”) [[ based on the set of semantic labels that correspond with the object ]].
But Gausenbeck does not explicitly disclose [[ retrieving Augmented-Reality (AR) content ]] based on user profile data associated with a user of the client device and the presentation display of the object…[[ causing display of a presentation of the AR content ]] based on the set of semantic labels that correspond with the object.
However, Rumiński teaches retrieving Augmented-Reality (AR) content based on user profile data associated with a user of the client device and the presentation display of the object (Rumiński, Page 5, “Augmented Reality presentations are built on the basis of a user’s context, which includes such elements as indoor and outdoor location, time and date, user’s preferences and device type”) causing display of a presentation of the AR content based on the set of semantic labels that correspond with the object (Rumiński, Page 3, “Augmented Reality (AR) is a field in computer science which concerns computer vision-based techniques that enable superimposing interactive computer-generated content – such as 2D and 3D multimedia objects – in real time on a view of real objects” “Semantic Augmented Reality Ontology (SARO)” “SARO consists of five independent ontologies that describe the context and its relations to AR services that provide components of AR presentations”).
Gausebeck and Rumiński are analogous since both are directed to Augmented Reality in which computer-generated content is presented relative to real-world views/objects. Gausebeck provides the technical mechanism for determining a position in the displayed representation and integrating AR content "based on the position" and maintains a database including indexed semantic label data" with semantic labels associated with objects/features. Rumiński teaches provides a semantic/contextual approach where AR presentations are composed based on user context, profile, preferences and semantically described data sources Therefore, it would have been obvious to one of ordinary skill in the art to incorporate semantic-label-driven and user-context-driven AR content selection taught by Rumiński into modified invention of Gausebeck such that the AR content integrated at the object position in Gausebeck is selected/presented based on semantic labels corresponding to the object and based on user profile/context which can improve relevance and contextual appropriateness of AR overlay.
The combination does not explicitly disclose but Cowburn teaches semantic labels associated with object based on the presentation of the object (Hu, Abstract, "applies color values to the first UV map based on correspondence between a first color image and the first UV map mapped to the first region of the 3D model"; Paragraph [0019], "the plurality of color images may be captured from a plurality of viewing angles. The captured plurality of color images may be stored as texture data 112 in the server 104. The stored plurality of color images may be further utilized to texture the 3D model"), the set of semantic labels included within a semantic texture map (Hu, Abstract, “Apparatus and method for texturing a 3D model by UV map inpainting, generates a first UV map from the 3D model"; Paragraph [0021], "the first UV map, may represent the 3D model on the 2D surface plane. The letters "U" and "V" of the UV map may denote the axes of the 2D surface plane") that comprises a set of texels (Hu, Paragraph [0066], "The 2D layout of the first UV map 308 may be a 2D mesh that comprises a plurality of points of a plurality of polygons, such as a plurality of triangles", [0021], "a plurality of points of a plurality of polygons, such as a plurality of triangles, of the t riangular mesh structure. The plurality of points of the plurality of triangles may correspond to a plurality of vertices of triangles"; [0080], "The generated UV map that may be a 2D triangle mesh structure comprising a plurality of triangles. Color values may be applied to the plurality of triangles of the UV map based on the plurality of color images") that comprise semantic labels and two-dimensional (2D) texture coordinates (Hu, Paragraph [0012], " The apparatus may further comprise a circuitry configured to generate a first UV map from the 3D model. The first UV map may be a two-dimensional {2D) layout of at least a first region of the 3D model"; [0021]-[0022], "the apparatus 102 may be configured to determine coordinates for the plurality of vertices of the first UV map while unwrapping the 3D model 110A"; "The apparatus 102 may be further configured to estimate a correspondence between the first UV map and a first color image of the plurality of color images stored as the texture data 112. The correspondence may be estimated based on 3D to 2D projection of the 3D coordinates of the 3D model 11 0A to the 2D surface plane. The 3D coordinates of the 3D model 110A may be mapped to the 2D surface of the first UV map based on 2D coordinates of the first color image").
Hu and Gausebeck are analogous since both are directed to three-dimensional
(3D) object modeling and representation technologies in which per-surface data derived
from captured image presentations of real-world objects is stored and utilized to render
or augment visual representations of those objects. Gausebeck provides the technical
mechanism for deriving semantic labels associated with objects based on their 20
image presentations using neural networks, and maintaining a repository of semantic
label data indexed to objects for use in AR content rendering. Hu teaches a structured
texture map architecture - specifically, a UV map comprising a triangular mesh of
texels defined by two-dimensional (u,v) texture coordinates - in which per-surface data
values derived from captured color image presentations of a 3D object are stored at
addressable texel positions within the 2D UV layout. Therefore, it would have been
obvious to one of ordinary skill in the art to incorporate the UV texture map storage
architecture taught by Hu into the modified invention of Gausebeck, such that the
semantic labels derived from object image presentations are stored within a UV texture map comprising texels with 2D texture coordinates indexed texels is the artrecognized
standard data structure for storing and accessing per-surface object information in 3D graphics and AR rendering pipelines which will providing a predictable improvement in the organization, accessibility, and rendering efficiency of the semantic label data.
Regarding Claim 11, the combination of Gausebeck, Rumiński and Hu teaches the invention in Claim 8.
The combination further teaches wherein the presentation of the object image data comprises metadata (Gausebeck, Paragraph [0159], “alter a 2D image based on camera/image parameters associated with the image (e.g ., received as metadata with the image), and the operations method further comprise: retrieving the AR content (Gausebeck, Paragraph [0023], “FIG. 30 presents an example system that facilitates using one or more 3D-from-2D techniques to in association with an augmented reality (AR) application in accordance with various aspects and embodiments described herein.”; [0159], “alter ( e.g., edit, modify, etc.) one or more characteristics of the 2D image based on the variances. In some implementations, the one or more characteristics can include visual characteristics and the pre-processing component 926 can alter the one or more visual characteristics”).
Gausebeck does not explicitly disclose but Rumiński teaches based on the user profile data associated with the user of the client device, and the metadata of the presentation of the object image data (Rumiński, Page 3, 5, “executes semantic queries to retrieve annotations of POIs relevant to the user’s prole, which are in the user’s surroundings.” “for visualization is context, which incorporates aspects such as user indoor/outdoor location, preferences, date and time, device type and others”).
Gausebeck and Rumiński are analogous since both are directed to Augmented Reality in which computer-generated content is presented relative to real-world views/objects. Gausebeck provides the technical mechanism for determining a position in the displayed representation and integrating AR content "based on the position" and maintains a database including indexed semantic label data" with semantic labels associated with objects/features. Rumiński teaches provides a semantic/contextual approach where AR presentations are composed based on user context, profile, preferences and semantically described data sources Therefore, it would have been obvious to incorporate of selecting/retrieving AR content based on user profile and context (including time/date) taught by Rumiński into modified invention of Gausebeck 's AR system that already processes image metadata (camera/image parameters received as meta data) such that system will be able to improve relevance/personalization and context-appropriateness of retrieved AR overlays (consistent with PA2's contextual selection/composition motivation.
Regarding Claim 12, the combination of Gausebeck, Rumiński and Hu teaches the invention in Claim 11.
The combination further teaches wherein the metadata includes temporal data (Rumiński, Page 3, 4, “The system uses metadata to select the most appropriate information.” “enabling selection of diverse information for visualization is context, which incorporates aspects such as user indoor/outdoor location, preferences, date and time <read on temporal data>, device type and others”).
Gausebeck and Rumiński are analogous since both are directed to Augmented Reality in which computer-generated content is presented relative to real-world views/objects. Gausebeck provides the technical mechanism for determining a position in the displayed representation and integrating AR content "based on the position" and maintains a database including indexed semantic label data" with semantic labels associated with objects/features. Rumiński teaches provides different kind of metadata which include date/time information in the augmented reality environment during the process. Therefore, it would have been obvious to incorporate of date/time metadata content taught by Rumiński into modified invention of Gausebeck such that system will be able to leverage more image-associated metadata for processing which enhance the system functionality and increase the flexibility of the image process in the augmented reality environment.
Regarding Claim 1, it recites limitations similar in scope to the limitations of Claim 8 but as a method and the combination of Gausebeck, Rumiński and Hu teaches all the limitations as of Claim 8. Therefore is rejected under the same rationale.
Regarding Claim 4, it recites limitations similar in scope to the limitations of Claim 11 and therefore is rejected under the same rationale.
Regarding Claim 5, it recites limitations similar in scope to the limitations of Claim 12 and therefore is rejected under the same rationale.
Regarding Claim 15, it recites limitations similar in scope to the limitations of claim 8 and the combination of Gausebeck, Rumiński and Hu teaches all the limitations as of Claim 8. And Gausebeck discloses these features can be implemented on a computer-readable storage medium (Gausebeck, Paragraph [0030], “By way of introduction, the subject disclosure is directed to systems, methods, apparatuses and computer readable media that provide techniques for deriving 3D data from 2D images using one or more machine learning models and employing the 3D data for 3D modeling applications and other applications” [0178], “The devices and/or systems described in FIGS. 14-25 can include machine-executable components embodied within machine(s), e.g. embodied in one or more computer readable mediums (or media) associated with one or more machines… The at least one memory can further store the computer-executable instructions/components that when executed by the at least one processor facilitate performance of operations defined by the computer-executable instructions/components”).
Regarding Claim 18, it recites limitations similar in scope to the limitations of Claim 11 and therefore is rejected under the same rationale.
Regarding Claim 19, it recites limitations similar in scope to the limitations of Claim 12 and therefore is rejected under the same rationale.
Claim(s) 2, 3, 9, 10 ,16, 17 is/are rejected under 35 U.S.C. 103 as being unpatentable over Gausebeck et al. (US 20190026958 A1, hereinafter Gausebeck), in view of Rumiński et al. (“Semantic model for distributed augmented reality services”,. 2017, ACM, hereinafter Rumiński), further in view of Hu et al. (US 20180253886 A1, hereinafter Hu) as applied to Claim 1, 8, 15 above respectively and further in view of Sturm et al (“A unified GLTF-X3D extension to bring physically-based rendering to the web”, 20160724, ACM, hereinafter Sturm).
Regarding Claim 9, the combination of Gausebeck, Ruminski and Hu teaches the
invention in Claim 8.
The combination further teaches wherein the set of semantic labels include a
semantic label (Gausebeck, "the auxiliary data comprises one or more semantic
labels for one or more object depicted in the 2D image" ) [[that corresponds with
a material parameter]], and "wherein the causing display of the presentation of
the AR content is based on the material parameter" (Gausebeck, "FIG. 30
presents an example system that facilitates using one or more 3D-from-2D
techniques in association with an augmented reality (AR) application in
accordance with various aspects and embodiments described herein." ).
But, the combination of Gausebeck, Ruminski and Hu does not explicitly disclose that
the semantic label corresponds with a material parameter or that the AR presentation
behavior is based on a specific material parameter.
However, Sturm teaches the set of semantic labels include a semantic label that
corresponds with a material parameter, and wherein the causing display of the
presentation of the AR content is based on the material parameter (Sturm, Page 120,
"The base of the specular-glossiness material model consists of the following
three parameters Diffuse Specular Glossiness" "The base of the metal-roughness
material model consists of the following three parameters BaseColor Metallic
Roughness").
Sturm and Gausebeck are analogous because both are concerned with
rendering realistic graphical/virtual content in an interactive environment: Gausebeck
provides a way of determining where and how to integrate and display graphical/AR
content in association with real-world image data, and of associating semantic labels
with objects. Sturm provides a way of parameterizing material appearance of rendered
objects via "baseColor, metallic and roughness. Therefore, it would have been obvious
to one of ordinary skill in the art, before the effective filing date of the claimed invention,
to incorporate the material parameter scheme taught by Sturm into the modified
invention of Gausebeck such that at least one of the semantic labels associated with an
object corresponds with a material parameter, and the AR system causes display of the
AR presentation based on that material parameter when rendering the AR content for
that object. The motivation would be to obtain physically plausible, consistent material
appearance for AR content across devices and environments by using standardized
PBR material parameters as part of the semantic/auxiliary data attached to real-world
objects and used during AR rendering, as discussed by Sturm in defining the metalroughness material model for realistic Web-based rendering.
Regarding Claim 10, the combination of Gausebeck, Ruminski and Sturm
teaches the invention in Claim 9.
The combination further teaches wherein the material parameter includes one or more of: a roughness value; a metallic value; a specular value; and a base color value (Sturm, Page 119, "The base of the specular-glossiness material model consists of the following three parameters Diffuse Specular Glossiness"; in that Sturm discloses that physically based material models are defined by parameters including diffuse/specular/glossiness and baseColor/metallic/roughness, namely).
As explained in rejection of claim 9, the obviousness for combining of different
material parameters of Sturm into Gausebeck is provided above.
Regarding Claim 2, it recites limitations similar in scope to the limitations of Claim 9 and therefore is rejected under the same rationale.
Regarding Claim 3, it recites limitations similar in scope to the limitations of Claim 10 and therefore is rejected under the same rationale.
Regarding Claim 16, it recites limitations similar in scope to the limitations of Claim 9 and therefore is rejected under the same rationale.
Regarding Claim 17, it recites limitations similar in scope to the limitations of Claim 10 and therefore is rejected under the same rationale.
Claim(s) 6, 7, 13, 14, 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Gausebeck et al. (US 20190026958 A1, hereinafter Gausebeck), in view of Rumiński et al. (“Semantic model for distributed augmented reality services”,. 2017, ACM, hereinafter Rumiński), further in view of Hu et al. (US 20180253886 A1, hereinafter Hu) as applied to Claim 1, 8, 15 above respectively and further in view of Li et al. (“Location Recognition using Prioritized Feature Matching”, 2010, Cornell University, hereinafter Li).
Regarding Claim 13, the combination of Gausebeck, Ruminski and Hu teaches the
invention in Clam 8.
The combination further teaches that the image data comprises a set of image
features (Gausebeck, Paragraph [0209], “receives or captures 2D images of an object or environment. At 2704, the device employs one or more 3D-from-2D neural network
models to derive 3D data for the 2D images (e.g., using 3D-from-2D processing module 1406)” [0040], “provided that comprises receiving, by a system operatively coupled to a processor, related 2D images captured of an object or environment, wherein the 2D images are related based on providing different perspectives of the object or environment; which inherently operates over image features such as "image pixels," "features," and "correspondences" used to generate the derived 3D data; see, e.g., "feature correspondences" and "3D model and alignment data" used for registration and navigation).
But, the combination of does not explicitly disclose identifying the location based
on the set of image features.
However Li teaches identifying the location based on the set of image features
(Li, Page 1, Abstract, "we present a fast, simple location recognition and image
localization method that leverages feature correspondence and geometry
estimated from large Internet photo collections" "we devise an adaptive,
prioritized algorithm for matching a representative set of SIFT features covering
a large scene to a query image for efficient localization" ; Page 4, "given a new
query image from the same scene, our goal is to find correspondences between
these scene features and the query image, then determine the camera pose";
Page 6, "given a set of SIFT descriptors in a query image ... and a set of SIFT
descriptors representing the points in our model. .. we consider two basic
matching strategies" and that "the ultimate goal of our system is to produce an
accurate pose estimate of a query image, given a relevant image database").
Li and Gausebeck are analogous because both are concerned with determining where a camera is located in a real-world environment from image data so that virtual or graphical content can be geometrically registered with that environment. Gausebeck provides an AR system that consumes derived 3D data and pose information to integrate AR content at correct positions in an image or live view of an environment. Li !provide a concrete, feature-based location-recognition and pose-estimation pipeline that can be used to identify a location directly from image features. Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to incorporate the feature-based location recognition taught by Li into the modified AR system of Gausebeck such that the image data comprises 1a set of image features and the system identifies the location based on that set of image features, and then uses the identified location to select and render the appropriate AR presentation at that location. The motivation would be to obtain robust, scalable, and accurate location and pose estimation for AR content placement by using well-known local feature descriptors (such as SIFT) and prioritized feature matching.
Regarding Claim 14, the combination of Gausebeck, Ruminski and Li teaches
the invention in !Claim 13.
The combination further teaches identifying the location based on the set of
image features (Li, Page 1, Abstract: "we present a fast, simple location recognition and image localization method that leverages feature correspondence and geometry estimated from large Internet photo collections" and "we devise an adaptive, prioritized algorithm for matching a representative set of SIFT features covering a large scene to a query image for efficient localization"; Page 4, "given a new query image from the same scene, our goal is to find correspondences between these scene features and the query image, then determine the camera pose"; Page 6, "given a set of SIFT descriptors in a query image ... and a set of SIFT descriptors representing the points in our model... we consider two basic matching strategies" and "the ultimate goal of our system is to produce an accurate pose estimate of a query image, given a relevant image database"); identifying the location based on the object (Li, Page 4, "For each point p 2 P, we know the set of images in which p was successfully detected and matched during the feature matching process (and deemed to be a geometrically consistent detection during SfM)"; Page 2,
"the number of times a given scene point is viewed is also r elated to the "popularity" of nearby viewpoints-some parts of a scene may be photographed much more often than others"; Page 9, "If the matching algorithm terminates successfully, then the set of matches M links 2D features in the query image directly to 3D points in the model. These matches are fed into our pose estimation routine. We use the 6-point DLT approach to solve for the projection matrix of the query camera, followed by a local bundle adjustment to refine the pose.).
As explained in rejection of claim 13, the obviousness for combining of feature based
location recognition of Li into Gausebeck is provided above.
Regarding Claim 6, it recites limitations similar in scope to the limitations of Claim 13 and therefore is rejected under the same rationale.
Regarding Claim 7, it recites limitations similar in scope to the limitations of Claim 14 and therefore is rejected under the same rationale.
Regarding Claim 20, the combination of Gausebeck, Ruminski and Hu teaches the
invention in Clam 15.
The combination does not explicitly disclose but Li teaches wherein the image
data comprises a set of image features, and the method further comprises: identifying the location based on the set of image features
(Li, Page 1, Abstract, "we present a fast, simple location recognition and image localization method that leverages feature correspondence and geometry estimated from large Internet photo collections" and "we devise an adaptive, prioritized algorithm for matching a representative set of SIFT features covering a large scene to a query image for efficient localization"; Page 4, "given a new query image from the same scene, our goal is to find correspondences between these scene features and the query image, then determine the camera pose"; Page 6, "given a set of SIFT descriptors in a query image ... and a set of SIFT descriptors representing the points in our model. .. we c9nsider two basic matching strategies" and "the ultimate goal of our system is to produce an accurate pose estimate of a query image, given a relevant image database").
Li and Gausebeck are analogous because both address determining where a
camera is located in an environment from image data so that further processing (such
as generating al3o model or integrating AR content) can be performed. Gausebeck
provides an AR system that consumes derived 30 data and pose information to
integrate AR content at correct positions in an image or live view of an environment. Li
provides a complementary, feature-based way to identify the location and camera pose
directly from image features. Therefore, it would have been obvious to one of ordinary
skill in the art, before the effective filing date of the claimed invention, to incorporate the
feature-based Iodation recognition taught by Li into the modified AR system of Gausebeck suet, that the image data comprises a set of image features and the system identifies the location based on that set of image features, and then uses the identified location to select and render the appropriate AR presentation at that location. The motivation would be to obtain robust, scalable, and accurate location and pose estimation for AR content placement by using well-known local feature descriptors (such as SIFT) and prioritized feature matching.
Response to Arguments
The rejection of Claims 1-20 under Nonstatutory Double Patenting are withdrawn in view of Applicant filed eTerminal/Terminal Disclaimer on 2/5/2026.
Applicant’s arguments with respect to claims 1, 8, 15, filed on 2/5/2026, with respect to rejection under 35 USC § 103 have been considered but are moot in view of the new ground(s) of rejection. it has now been taught by the combination of Gausebeck, Ruminski and Hu.
In regard to Claims 2-7, 9-14, 16-20, they directly/indirectly depends on independent Claim 1, 8, 15 respectively. Applicant does not argue anything other than the independent claim 1, 8, 15. The limitations in those claims in conjunction with combination previously established as explained.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
US 20130127889 A1 System and Method for Adding Vector Textures to Vector Graphics Images
US 20190026958 A1 EMPLOYING THREE-DIMENSIONAL (3D) DATA PREDICTED FROM TWO-DIMENSIONAL (2D) IMAGES USING NEURAL NETWORKS FOR 3D MODELING APPLICATIONS AND OTHER APPLICATIONS
US 20190147296 A1 CREATING AN IMAGE UTILIZING A MAP REPRESENTING DIFFERENT CLASSES OF PIXELS
US 20190355103 A1 GUIDED HALLUCINATION FOR MISSING IMAGE CONTENT USING A NEURAL NETWORK
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to YUJANG TSWEI whose telephone number is (571)272-6669. The examiner can normally be reached 8:30am-5:30pm EST.
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/YuJang Tswei/Primary Examiner, Art Unit 2614
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