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 .
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, 2, 4-6, 8, and 9 is/are rejected under 35 U.S.C. 103 as being unpatentable over Bunkasem et al., US PGPUB No. 20210166477 A1, hereinafter Bunkasem, in view of Monaghan, US Patent No. 11593921 B1, hereinafter Monaghan, and further in view of Sharma et al., US PGPUB No. 20240242483 A1, hereinafter Sharma.
Regarding claim 1, Bunkasem discloses a method comprising:
outputting, by a processing device, a user interface that previews an object model (Bunkasem; outputting a UI that previews an object model [¶ 0085 and ¶ 0090-0092] by a processing device [¶ 0040 and ¶ 0042]);
receiving, by the processing device, a material selection that designates a material part of the object model (Bunkasem; receiving a material selection that designates a material part of the object model [¶ 0090-0091] by the processing device [as addressed above]; moreover, material selection also corresponds to defining data of an object [¶ 0017-0018 and ¶ 0073-0074]);
obtaining, by the processing device, a plurality of sample images showing different views of the object model and the material part designated by the material selection (Bunkasem; obtaining a plurality of sample images showing different views of the object model and the material part designated by the material selection [¶ 0017-0018] by the processing device [as addressed above]; moreover, images of different viewpoints [¶ 0083-0085], as illustrated within Figs. 4A-C);
inputting, by the processing device, the sample images and the material selection into a material selector model that creates a similarity point cloud enabling material similarity querying across multiple material parts of the object model (Bunkasem; inputting the sample images and the material selection into a material selector model [¶ 0024-0025] that creates a similarity point cloud enabling material similarity querying across multiple material parts of the object model [¶ 0018 and ¶ 0021-0024] by the processing device [as addressed above]; additionally, inputting sample images corresponds to learning input data [¶ 0024-0026 and ¶ 0095-0096]); and
presenting, by the processing device via the user interface, an indication of different material parts of the object model that have a similar material as the material part designated by the material selection (Bunkasem; presenting an implicit indication of different material parts (given the model and its configuration of polygons and depth data) of the object model that have a similar material as the material part designated by the material selection [¶ 0085 and ¶ 0087-0088] by the processing device [as addressed above] via the user interface [¶ 0090-0091], as illustrated within Fig. 4C and Fig. 6A).
Bunkasem fails to explicitly disclose querying across multiple material parts; and
an indication of different material parts.
However, Monaghan teaches present an indication of different material parts of the three dimensional object that have similar material as a material part designated by the material selection (Monaghan; present an indication of different material parts of the 3D object that have similar material as a material part designated by the material selection [Col. 5, line 3-50]; moreover, identifying spectral properties of each detected surface, feature, or article [Col. 5, lines 51-59]).
Bunkasem and Monaghan are considered to be analogous art because both pertain to generating and/or managing data in relation with providing media data, wherein one or more computerized units are utilized in order to produce a modeling/simulated environment.
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing of the claimed invention was made to modify Bunkasem, to incorporate present an indication of different material parts of the three dimensional object that have similar material as a material part designated by the material selection (as taught by Monaghan), in order to provide improved realism for three-dimensional objects (Monaghan; [Col. 1, lines 9-30]).
Bunkasem as modified by Monaghan fails to explicitly disclose querying across multiple material parts.
However, Sharma teaches an indication of different material parts of the three dimensional object that have similar material as a material part designated by the material selection (Sharma; an indication of different material parts of the three dimensional object that have similar material as a material part designated by the material selection [¶ 0043 and ¶ 0049-0050]); and
creating a similarity point cloud enabling material similarity querying across multiple material parts of the object model (Sharma; creating a similarity point cloud enabling material similarity querying across multiple material parts of the object model [¶ 0024-30025 and ¶ 0027-0029]; wherein, identifying a plurality of pixels (corresponding to a point cloud) and querying material information [¶ 0036-0038]; additionally, material selection prediction [¶ 0043, ¶ 0045-0046, and ¶ 0049]).
Bunkasem in view of Monaghan and Sharma are considered to be analogous art because both pertain to generating and/or managing data in relation with providing media data, wherein one or more computerized units are utilized in order to produce a modeling/simulated environment.
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing of the claimed invention was made to modify Bunkasem as modified by Monaghan, to incorporate an indication of different material parts of the three dimensional object that have similar material as a material part designated by the material selection; and creating a similarity point cloud enabling material similarity querying across multiple material parts of the object model (as taught by Sharma), in order to provide improved computer vision for materials (Sharma; [¶ 0012-0015]).
Regarding claim 2, Bunkasem in view of Monaghan and Sharma further discloses the method of claim 1, wherein the inputting (Bunkasem; inputting [as addressed within the parent claim(s)]) includes:
applying machine learning of the material selector model by deriving a similarity map corresponding to each sample image indicating material similarities across the different material parts of the object model and the material part designated by the material selection (Bunkasem; applying ML of the material selector model [¶ 0024-0026] by deriving a similarity object (i.e. 3D model) corresponding to each sample image indicating material similarities across the different material parts of the object model and the material part designated by the material selection [¶ 0073-0074 and ¶ 0085]).
Monaghan further teaches applying machine learning of the material selector model by deriving a similarity mapping corresponding to each sample image indicating material similarities across the different material parts of the object model and the material part designated by the material selection (Monaghan; applying ML of the material selector model by deriving a similarity mapping (in relation with classification) corresponding to each sample image indicating material similarities across the different material parts of the object model and the material part designated by the material selection [Col. 4, lines 25-35 and Col. 5, lines 4-37]; wherein, color mappings are used to indicate materials [Col. 3, lines 44-63 and Col. 5, line 62 to Col. 6, line 15]).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing of the claimed invention was made to modify Bunkasem, to incorporate applying machine learning of the material selector model by deriving a similarity mapping corresponding to each sample image indicating material similarities across the different material parts of the object model and the material part designated by the material selection (as taught by Monaghan), in order to provide improved realism for three-dimensional objects (Monaghan; [Col. 1, lines 9-30]).
Sharma further teaches applying machine learning of the material selector model by deriving a similarity map corresponding to each sample image indicating material similarities across the different material parts of the object model and the material part designated by the material selection (Sharma; applying ML of the material selector model by deriving a similarity map (corresponding to visually indications) corresponding to each sample image indicating material similarities across the different material parts of the object model and the material part designated by the material selection [¶ 0043 and ¶ 0045-0047]; moreover, distances between embeddings [¶ 0048] and visually indicated materials [¶ 0049-0050]).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing of the claimed invention was made to modify Bunkasem as modified by Monaghan and Sharma, to incorporate applying machine learning of the material selector model by deriving a similarity map corresponding to each sample image indicating material similarities across the different material parts of the object model and the material part designated by the material selection (as taught by Sharma), in order to provide improved computer vision for materials (Sharma; [¶ 0012-0015]).
Regarding claim 4, Bunkasem in view of Monaghan and Sharma further discloses the method of claim 1, the presenting (Bunkasem; the presenting [as addressed above]).
Sharma further teaches outputting a rendered image of the indication at a display device that modifies the user interface by previewing the different material parts of the object model that have the similar material as the material part designated by the material selection (Sharma; outputting a rendered image of the indication at a display device that modifies the user interface by previewing the different material parts of the object model that have the similar material as the material part designated by the material selection [¶ 0045 and ¶ 0048-0050], as illustrated within Fig. 4).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing of the claimed invention was made to modify Bunkasem as modified by Monaghan and Sharma, to incorporate outputting a rendered image of the indication at a display device that modifies the user interface by previewing the different material parts of the object model that have the similar material as the material part designated by the material selection (as taught by Sharma), in order to provide improved computer vision for materials (Sharma; [¶ 0012-0015]).
Regarding claim 5, Bunkasem in view of Monaghan and Sharma further discloses the method of claim 4, the presenting (Bunkasem; the presenting [as addressed above]).
Sharma further teaches obtaining, from the similarity point cloud, coordinates of the different material parts of the object model that have the similar material as the material part designated by the material selection (Sharma; obtaining coordinates of the different material parts of the object model that have the similar material as the material part designated by the material selection [¶ 0045-0047 and ¶ 0049 from the similarity point cloud [as addressed within the parent claim(s)], as illustrated within Fig. 4; wherein, pixels implicitly correspond to coordinates given that they are locations within in image and/or scene [¶ 0053]); and
presenting the rendered image in the user interface as the indication of the different material parts of the object model that have the similar material as the material part designated by the material selection (Sharma; presenting the rendered image in the UI as the indication of the different material parts of the object model that have the similar material as the material part designated by the material selection [¶ 0043, ¶ 0045-0047, and ¶ 0049-0050], as illustrated within Fig. 4).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing of the claimed invention was made to modify Bunkasem as modified by Monaghan and Sharma, to incorporate outputting a rendered image of the indication at a display device that modifies the user interface by previewing the different material parts of the object model that have the similar material as the material part designated by the material selection (as taught by Sharma), in order to provide improved computer vision for materials (Sharma; [¶ 0012-0015]).
Regarding claim 6, Bunkasem in view of Monaghan and Sharma further discloses the method of claim 1, wherein the material selection comprises at least one of a prompt selection describing a specific material that designates the material part of the object model, or a user interface selection that designates the material part of the object model (Sharma; a UI selection that designates the material part of the object model [¶ 0025 and ¶ 0051]; moreover, user device and application [¶ 0022]; and moreover, input image can be used in designating materials [¶ 0027-0028 and ¶ 0043-0046]).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing of the claimed invention was made to modify Bunkasem as modified by Monaghan and Sharma, to incorporate a user interface selection that designates the material part of the object model (as taught by Sharma), in order to provide improved computer vision for materials (Sharma; [¶ 0012-0015]).
Regarding claim 8, Bunkasem in view of Monaghan and Sharma further discloses the method of claim 1, further comprising:
performing, by the processing device, a lookup of the different material parts of the object model by querying the similarity point cloud and determining the different material parts of the object model that have the similar material as the material part designated by the material selection (Bunkasem; performing a lookup of the different material parts of the object model by querying the similarity point cloud and determining the different material parts of the object model that have the similar material as the material part designated by the material selection [¶ 0057 and ¶ 0073-0074], by the processing device [as addressed within the parent claim(s)]).
Monaghan further teaches performing, by the processing device, a nearest-neighbor lookup of the different material parts of the object model by querying the similarity point cloud and determining the different material parts of the object model that have the similar material as the material part designated by the material selection (Monaghan; performing a nearest-neighbor lookup (corresponding to the consideration of adjacent or nearby information) of the different material parts of the object model by querying the similarity point cloud and determining the different material parts of the object model that have the similar material as the material part designated by the material selection [Col. 5, lines 3-26 and Col. 5, line 37 to Col. 6, line 6], by the processing device [Col. 11, line 64 to Col. 12, line 14]; moreover, scanning [Col. 6, lines 16-25] and determining a material or material property of a surface [Col. 6, line 61 to Col. 7, line 19]).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing of the claimed invention was made to modify Bunkasem as modified by Monaghan and Sharma, to incorporate performing, by the processing device, a nearest-neighbor lookup of the different material parts of the object model by querying the similarity point cloud and determining the different material parts of the object model that have the similar material as the material part designated by the material selection (as taught by Monaghan), in order to provide improved realism for three-dimensional objects (Monaghan; [Col. 1, lines 9-30]).
Regarding claim 9, Bunkasem in view of Monaghan and Sharma further discloses the method of claim 1, wherein the inputting (Bunkasem; inputting [as addressed within the parent claim(s)]) includes:
constructing, by the processing device, a video by inserting each of the sample images into a sequence of frames (Bunkasem; constructing a video by inserting each of the sample images into a sequence of frames [¶ 0018-0019], as illustrated within Figs. 1A-B, by the processing device [as addressed within the parent claim(s)]; moreover, video files captured from a scene [¶ 0017 and ¶ 0033]);
causing, by the processing device, the material selector model to receive the sample images by receiving the video (Bunkasem; causing the material selector model to receive the sample images by receiving the video [¶ 0017-0019], as illustrated within Figs. 1A-B, by the processing device [as addressed within the parent claim(s)]); and
evaluating each subsequent sample image of a subsequent frame in the video sequence based on a previous evaluation of a previous sample image of a previous frame in the sequence (Bunkasem; evaluating each subsequent sample image of a subsequent frame in the video sequence based on a previous evaluation of a previous sample image of a previous frame in the sequence [¶ 0022-0023]; additionally, using learned data [¶ 0024 and ¶ 0026]).
Claim(s) 3 is/are rejected under 35 U.S.C. 103 as being unpatentable over Bunkasem in view of Monaghan and Sharma as applied to claim(s) 2 above, and further in view of Schillen et al., US PGPUB No. 20250095335 A1, hereinafter Schillen.
Regarding claim 3, Bunkasem in view of Monaghan and Sharma further discloses the method of claim 2, wherein the similarity map corresponding to each sample image includes a heat map image that uses pixel color and intensity variation to indicate different degrees of similarity between the material part designated by the material selection and the different material parts of the object model (Monaghan; the similarity map [as addressed within the parent claim(s)] corresponding to each sample image includes a heat map image that uses pixel color and intensity variation/ranges to indicate different degrees of similarity between the material part designated by the material selection and the different material parts of the object model [Col. 3, line 44 to Col. 4, line 9 and Col. 5, line 62 to Col. 6, line 15]; wherein, rules can be applied to the primitives that affect color [Col. 4, lines 10-15 and Col. 4, line 37 to Col. 5, line 26]; additionally, different changes or adjustments to data points can be applied [Col. 5, lines 27-36]).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing of the claimed invention was made to modify Bunkasem in view of Monaghan and Sharma, to incorporate the similarity map corresponding to each sample image includes a heat map image that uses pixel color and intensity variation to indicate different degrees of similarity between the material part designated by the material selection and the different material parts of the object model (as taught by Monaghan), in order to provide improved realism for three-dimensional objects (Monaghan; [Col. 1, lines 9-30]).
Bunkasem as modified by Monaghan and Sharma fails to explicitly disclose a heat map.
However, Schillen teaches a heat map image that uses pixel color and intensity variation to indicate different degrees of similarity between the material part (Schillen; a heat map image that uses pixel color and intensity variation to indicate different degrees of similarity between the material part [¶ 0207-0208]; moreover, heat map signifying material distribution [¶ 0159-0160]).
Bunkasem in view of Monaghan and Sharma and Schillen are considered to be analogous art because they pertain to generating and/or managing data in relation with providing media data, wherein one or more computerized units are utilized in order to produce a modeling/simulated environment.
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing of the claimed invention was made to modify Bunkasem as modified by Monaghan and Sharma, to incorporate a heat map image that uses pixel color and intensity variation to indicate different degrees of similarity between the material part (as taught by Schillen), in order to provide improved computer vision for materials (Schillen; [¶ 0002-0003 and ¶ 0005]).
Claim(s) 7 is/are rejected under 35 U.S.C. 103 as being unpatentable over Bunkasem in view of Monaghan and Sharma as applied to claim(s) 6 above, and further in view of Huynh et al., US PGPUB No. 20250252676 A1, hereinafter Huynh.
Regarding claim 7, Bunkasem in view of Monaghan and Sharma further discloses the method of claim 6, further comprising:
a user interface selection received at the user interface that designates the material part of the object model (Monaghan; a UI selection received at the UI that designates the material part of the object model [Col. 5, lines 27-36 and lines 54-61]).
Sharma further teaches processing, by the processing device, a user interface selection received at the user interface that designates the material part of the object model into selection describing the specific material that designates the material part of the object model (Sharma; processing a UI selection received at the UI that designates the material part of the object model describing the specific material that designates the material part of the object model [¶ 0025 and ¶ 0051] by the processing device [¶ 0058-0060]; moreover, user device and material selection [¶ 0021-0022 and ¶ 0051]; still further, image based inputs [¶ 0043-0046]).
Bunkasem in view of Monaghan and Sharma fails to disclose converting into a prompt.
However, Huynh teaches converting a user interface selection received at the user interface that designates the input into the prompt selection (Huynh; converting a user interface selection received at the user interface that designates the input into the prompt selection [¶ 0081]; moreover, prompts correspond to several different forms of inputs [¶ 0025]; wherein, AI models are associated with prompts [¶ 0031-0032]; additionally, image production using prompts [¶ 0024]).
Bunkasem in view of Monaghan and Sharma and Huynh are considered to be known methods ready for improvement to yield predictable results.
One of ordinary skill in the art would have recognized user input and user-interface would be improved by using a speech-to-text function and prompt thereby increasing the intuitive manner of the interface and/or interaction.
It would have been obvious to one of ordinary skill in the art to use the known technique of prompting (e.g. speech-to-text) to improve the known user-interface to achieve the predictable result of improving user engagement and thereby improving user experience.
Claim(s) 10 and 13 is/are rejected under 35 U.S.C. 103 as being unpatentable over Dave et al., US PGPUB No. 20220335682 A1, hereinafter Dave, and further in view of Bunkasem.
Regarding claim 10, Dave discloses a method comprising:
receiving, by a processing device, a plurality of training sample images of three dimensional objects and material selections designating material parts of the three dimensional objects (Dave; receiving a plurality of training sample images of 3D objects and material selections designating material parts of the 3D objects [¶ 0055-0058] by a processing device [¶ 0018 and ¶ 0022]);
inputting, by the processing device, the training sample images and the material selections as training inputs to a material selector model that generates training similarity maps indicating material similarity across the three dimensional objects (Dave; inputting the training sample images and the material selections as training inputs to a material selector model that generates training similarity maps indicating material similarity across the 3D objects [¶ 0029 and ¶ 0038-0039] by the processing device [¶ 0018 and ¶ 0022]; moreover, training engine [¶ 0055-0057]).
adjusting, by the processing device, parameters of the material selector model based on comparisons between the example similarity data and ground truth material segmentations that reduce differences between the example similarity data and the ground truth material segmentations (Dave; adjusting parameters of the material selector model based on comparisons between the example similarity data and ground truth material segmentations that reduce differences between the example similarity data and the ground truth material segmentations [¶ 0058-0059] by the processing device [¶ 0022]); and
configuring the trained material selector model to output material parts based on a similarity data created based on similarity maps generated from a plurality of sample images of a three dimensional object and a material selection (Dave; configuring the trained material selector model to output material parts based on a similarity data created based on similarity maps generated from a plurality of sample images of a 3D object and a material selection [¶ 0038-0040 and ¶ 0044-0045]; additionally, predicting material maps [¶ 0055 and ¶ 0057-0059]).
However, Bunkasem teaches generating, by the processing device, example similarity point clouds created based on the training similarity maps output from the material selector model (Bunkasem; generating example similarity point clouds created based on the training similarity maps output from the material selector model [¶ 0024 and ¶ 0031-0032] by the processing device [¶ 0040 and ¶ 0042]).
Dave and Bunkasem are considered to be analogous art because both pertain to generating and/or managing data in relation with providing media data, wherein one or more computerized units are utilized in order to produce a modeling/simulated environment.
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing of the claimed invention was made to modify Dave, to incorporate generating, by the processing device, example similarity point clouds created based on the training similarity maps output from the material selector model (as taught by Bunkasem), in order to provide improved learning in relation with computer vision (Bunkasem; [¶ 0002-0004 and ¶ 0014-0015]).
Regarding claim 13, Dave in view of Bunkasem further discloses the method of claim 10, wherein the adjusting includes:
computing a loss function that measures discrepancies between the training similarity maps and the ground truth material segmentations (Dave; computing a loss function that measures discrepancies between the training similarity maps and the ground truth material segmentations [¶ 0064-0065])
backpropagating the loss through the material selector model (Bunkasem; implicitly backpropagating the loss (given learning with optimization techniques) through the material selector model [¶ 0038-0039 and ¶ 0044-0045]); and
updating model using an optimization algorithm (Bunkasem; updating model using an optimization algorithm [¶ 0042 and ¶ 0044-0046]; moreover, using optimized material maps [¶ 0016-0017 and ¶ 0029]).
Bunkasem further teaches backpropagating the loss through the material selector model (Bunkasem; backpropagating the loss [¶ 0030-0031] through the material selector model [¶ 0050 and ¶ 0073-0074]; moreover, generating and/or training an ML model [¶ 0024-0026]; wherein, backpropagation corresponds to feedback [¶ 0030 and ¶ 0037]); and
updating model weights using a gradient descent optimization algorithm (Bunkasem; updating model weights using a gradient descent optimization algorithm [¶ 0031]; moreover, an ANN involves weights and layers within its functioning [¶ 0028-0029]).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing of the claimed invention was made to modify Dave as modified by Bunkasem, to incorporate backpropagating the loss through the material selector model; and updating model weights using a gradient descent optimization algorithm (as taught by Bunkasem), in order to provide improved learning in relation with computer vision (Bunkasem; [¶ 0002-0004 and ¶ 0014-0015]).
Claim(s) 14 is/are rejected under 35 U.S.C. 103 as being unpatentable over Dave in view of Bunkasem as applied to claim(s) 10 above, and further in view of Sharma.
Regarding claim 14, Dave in view of Bunkasem further discloses the method of claim 10, further comprising:
by the processing device, a rendered image of the material parts identified differently from other material parts captured (outputting a rendered image of the material parts identified differently from other material parts captured [¶ 0043-0044 and ¶ 0053-0054] by the processing device [as addressed within the parent claim(s)]).
Bunkasem further teaches outputting, by the processing device, a rendered image of the material parts identified differently from other material parts captured by the similarity point cloud (Bunkasem; outputting a rendered image [¶ 0090-0091] of the material parts identified implicitly differently from other material parts captured (given capture from one or more axes) by the similarity point cloud [¶ 0083-0085] by the processing device [as addressed within the parent claim(s)])
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing of the claimed invention was made to modify Dave as modified by Bunkasem, to incorporate outputting, by the processing device, a rendered image of the material parts identified differently from other material parts captured by the similarity point cloud (as taught by Bunkasem), in order to provide improved learning in relation with computer vision (Bunkasem; [¶ 0002-0004 and ¶ 0014-0015])
Dave as modified by Bunkasem fails to explicitly disclose a rendered image of the material parts identified differently from other material parts captured.
However, Sharma teaches outputting, by the processing device, a rendered image of the material parts identified differently from other material parts captured by the similarity point cloud (Sharma; outputting a rendered image of the material parts identified differently from other material parts captured by the similarity point cloud [¶ 0043, ¶ 0045 and ¶ 0049-0050] by the processing device [¶ 0060 and ¶ 0065]; wherein, a point cloud corresponds to a selected pixel or group of pixels [¶ 0024 and ¶ 0034-0035]; moreover, populating an environment with objects comprising the same and/or different materials and produce per-pixel material information in relation with one or more target pixels [¶ 0016 and ¶ 0018]; additionally, one or more plurality of pixels can be identified which can be accessed using loss [¶ 0036 and ¶ 0038-0040]).
Dave in view of Bunkasem and Sharma are considered to be analogous art because they pertain to generating and/or managing data in relation with providing media data, wherein one or more computerized units are utilized in order to produce a modeling/simulated environment.
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing of the claimed invention was made to modify Dave as modified by Bunkasem, to incorporate outputting, by the processing device, a rendered image of the material parts identified differently from other material parts captured by the similarity point cloud (as taught by Sharma), in order to provide improved computer vision for materials (Sharma; [¶ 0012-0015]).
Claim(s) 15-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Bunkasem, and further in view of Monaghan.
Regarding claim 15, Bunkasem discloses a system (Bunkasem; a system [¶ 0039-0040 and ¶ 0042], as illustrated within Fig. 2) comprising:
a memory component (Bunkasem; the system [as addressed above] comprises a memory component [¶ 0040 and ¶ 0042]); and
one or more processing devices coupled to the memory component (Bunkasem; the system [as addressed above] comprises one or more processing devices coupled to the memory component [¶ 0040 and ¶ 0042]), the processing devices operable to:
receive a plurality of sample images of a three dimensional object and a material selection designating a material part of the three dimensional object (Bunkasem; the processing devices [as addressed above] operable to receive a plurality of sample images of a 3D object and a material selection designating a material part of the 3D object [¶ 0017-0018], as illustrated within Fig. 1A-B; moreover, capturing imaging data and material data of objects [¶ 0014 and ¶ 0073-0074]);
process the sample images and the material selection using a material selector model that generates similarity maps indicating material similarity across the three dimensional object used in creating a similarity point cloud (Bunkasem; the processing devices [as addressed above] operable to process the sample images and the material selection using a material selector model [¶ 0024-0025] that generates similarity maps indicating material similarity across the 3D object used in creating a similarity point cloud [¶ 0018 and ¶ 0021-0022]);
output a material part based on the similarity point cloud (Bunkasem; the processing devices [as addressed above] operable to output a material part based on the similarity point cloud [¶ 0057, ¶ 0073-0074, and ¶ 0085], as illustrated within Fig. 4C); and
present, via a user interface, an indication of different material parts of the three dimensional object that have a similar material as a material part designated by the material selection (Bunkasem; present an implicit indication of different material parts (given the model and its configuration of polygons and depth data) of the 3D object that have a similar material as a material part designated by the material selection [¶ 0085 and ¶ 0087-0088] via a user interface [¶ 0090-0091]).
Bunkasem fails to explicitly disclose an indication of different material parts.
However, Monaghan teaches present an indication of different material parts of the three dimensional object that have similar material as a material part designated by the material selection (Monaghan; present an indication of different material parts of the 3D object that have similar material as a material part designated by the material selection [¶ Col. 5, line 3-50]; moreover, identifying spectral properties of each detected surface, feature, or article [Col. 5, lines 51-59]).
Bunkasem and Monaghan are considered to be analogous art because both pertain to generating and/or managing data in relation with providing media data, wherein one or more computerized units are utilized in order to produce a modeling/simulated environment.
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing of the claimed invention was made to modify Bunkasem, to incorporate present an indication of different material parts of the three dimensional object that have similar material as a material part designated by the material selection (as taught by Monaghan), in order to provide improved realism for three-dimensional objects (Monaghan; [Col. 1, line 9-30]).
Regarding claim 16, Bunkasem in view of Monaghan further discloses the system of claim 15, wherein the material selector model is configured to process the sample images as a video sequence (Bunkasem; the material selector model is configured to process the sample images as a video sequence [¶ 0018-0019 and ¶ 0021]), and the processing devices are further operable to:
construct a video by inserting each of the sample images into a sequence of frames (Bunkasem; the processing devices [as addressed within the parent claim(s)] are further operable to construct a video by inserting each of the sample images into a sequence of frames [¶ 0018-0019], as illustrated within Figs. 1A-B; moreover, video files captured from a scene [¶ 0017 and ¶ 0033]);
cause the material selector model to receive the sample images by receiving the video (Bunkasem; the processing devices [as addressed within the parent claim(s)] are further operable to cause the material selector model to receive the sample images by receiving the video [¶ 0017-0019], as illustrated within Figs. 1A-B); and
evaluate each subsequent sample image of a subsequent frame in the video sequence based on a previous evaluation of a previous sample image of a previous frame in the sequence (Bunkasem; the processing devices [as addressed within the parent claim(s)] are further operable to evaluate each subsequent sample image of a subsequent frame in the video sequence based on a previous evaluation of a previous sample image of a previous frame in the sequence [¶ 0022-0023]; additionally, using learned data [¶ 0024 and ¶ 0026]).
Regarding claim 17, Bunkasem in view of Monaghan further discloses the system of claim 15, wherein the processing devices are further operable to:
convert a user interface selection received at the user interface that designates the material part of the three dimensional object into a prompt selection describing a specific material that designates the material part of the three dimensional object.
Regarding claim 18, Bunkasem in view of Monaghan further discloses the system of claim 15, wherein the processing devices (Bunkasem; the processing devices [as addressed within the parent claim(s)]) are further operable to:
output a rendered image of the indication at a display device that modifies the user interface by previewing the different material parts of the three dimensional object that have the similar material as the material part designated by the material selection (Bunkasem; the processing devices [as addressed above] are further operable to output a rendered image of the indication at a display device that modifies the UI by previewing the different material parts of the 3D object that have the similar material as the material part designated by the material selection [¶ 0085 and ¶ ¶ 0090-0091]).
Regarding claim 19, Bunkasem in view of Monaghan further discloses the system of claim 15, wherein the material selector model is configured to generate the sample images of the three dimensional object by generating different views of an object model describing the three dimensional object including at least one of Neural Radiance Fields object model, three dimensional Gaussian object model, and mesh object model (Bunkasem; the material selector model [as addressed within the parent claim(s)] is configured to generate the sample images of the 3D object by generating different views of an object model describing the 3D object including (at least one of) mesh object model [¶ 0018 and ¶ 0022], as illustrated within Figs. 1B-C; moreover, 3D model using of polygons [¶ 0085], as illustrated within Fig. 4C).
Regarding claim 20, Bunkasem in view of Monaghan further discloses the system of claim 15, wherein the processing devices (Bunkasem; the processing devices [as addressed within the parent claim(s)]) are further operable to:
receive an editing input for modifying material properties of the material parts (Bunkasem; the processing devices [as addressed above] are further operable to receive an editing input for modifying material properties of the material parts [¶ 0091-0093]; moreover, modifying a 3D model [¶ 0058-0059]; additionally, synthetically generated using 3D model [¶ 0094]);
apply the editing input to modify the material properties of the material parts in the three dimensional object (Bunkasem; the processing devices [as addressed above] are further operable to apply the editing input to modify the material properties of the material parts in the 3D object [¶ 0091-0093]; moreover, modifying one or more aspects of visual data [0058]); and
generate an edited version of the three dimensional object with the modified material properties (Bunkasem; the processing devices [as addressed above] are further operable to generate an edited version of the 3D object with the modified material properties [¶ 0058 and ¶ 0088]).
Allowable Subject Matter
Claims 11 and 12 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.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
(regarding material determination)
Dal Mutto et al. (US PGPUB No. 20180189611 A1);
Short, Jr. et al. (US Patent No. 12451217 B1);
Sung (US Patent No. 11455789 B1);
Deschaintre et al. (US PGPUB No. 20240020916 A1); and
Kremar et al. (US Patent No. 12482283 B1 & 12602899 B1).
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Refer to PTO-892, Notice of Reference Cited for a listing of analogous art.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Charles Lloyd Beard whose telephone number is (571)272-5735. The examiner can normally be reached Monday - Friday, 8:00 AM - 5: 00 PM, alternate Fridays EST.
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CHARLES LLOYD. BEARD
Primary Examiner
Art Unit 2611
/CHARLES L BEARD/ Primary Examiner, Art Unit 2611
Prosecution Insights