DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on/after Mar. 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 § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claim 24 is rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter.
Claim 24 recites: “A computer program product comprising a non-volatile computer-readable medium on which program instructions are stored …” The instant specification discloses: “The present disclosure further provides a computer program product comprising program instructions which, when executed by a processor, cause the processor to carry out the above method. The computer program product may comprise a non-volatile computer-readable medium on which the program instructions are stored. The non-volatile medium may include a hard disk, a solid-state drive, a memory card or any other type of computer-readable medium as it is well known in the art” (¶ [0124]).
The broadest reasonable interpretation of a claim drawn to a ‘computer-readable medium’ typically covers forms of non-transitory tangible media and transitory propagating signals per se in view of the ordinary and customary meaning of computer-readable storage medium, particularly when the specification is silent.
Applicant is advised to amend the claim to exclude such transitory embodiments by adding ‘non-transitory’ to “computer-readable medium …”, as “non-transitory computer-readable storage medium …” which would render the claim statutory.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 USC 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.
Claims 1-2, 4-6, 8, and 20-24 are rejected under 35 U.S.C. 103 as being unpatentable over Steenhoek et al. (U.S. PG-PUB 2020/0089991, 'STEENHOEK') in view of Rump et al. (U.S. Patent 8,872,811; 'RUMP').
Regarding claim 1, STEENHOEK discloses a computer-implemented method for visualizing the appearances of at least two materials, the method comprising:
obtaining a first set of appearance attributes, the appearance attributes of the first set being associated with a target material, the first set comprising measured appearance attributes of the target material which have been determined based on measurements of a target object comprising the target material (STEENHOEK; ¶ 0068-69; “The color measuring device 82 can be a colorimeter, a spectrophotometer, or a gonio-spectrophotometer. … The appearance measuring device 83 can comprise an imaging device for capturing … appearance image(s) of the target coating … for generating appearance data from the appearance images.”);
determining a recipe of a candidate material composed of … constituent materials such that the candidate material has expected appearance attributes that match the appearance attributes of the target material (STEENHOEK; ¶ 0101-102; “FIG. 2 shows a flow chart 200 of [a] method for displaying … image(s) to select … matching formula(s) [‘determining a recipe’] to match color and appearance of a target coating of an article. The method comprises the following steps: in a first step 210, retrieving … preliminary matching formula(s) from a multitude of repair formulas from a first [DB], wherein the first [DB] comprise interrelated repair formulas, color characteristics, and appearance characteristics …The preliminary matching formulas may be selected in the first step 210 so that these matching formulas are close to the target coating or color of the article to be repaired. … the preliminary matching formulas are a group of candidate coatings similar to the actual color of the article.”);
obtaining a second set of appearance attributes, the appearance attributes of the second set being associated with a candidate material the second set comprising appearance attributes that have been calculated from predetermined appearance attributes associated with … reference materials that comprise the constituent materials of the candidate material (STEENHOEK; FIG. 2; ¶ 0104; “In the third step 230, individual matching images are generated based on the color and appearance characteristics and further based on the article or [3-D] mapping of the geometry of an article selected from the second [DB].”);
obtaining a geometric model of … virtual object(s), the geometric model defining a [3-D] macroscopic surface geometry of the virtual object (STEENHOEK; ¶ 0070; “The 3D-scanner may have … visual detecting device(s). In case the 3D-scanner has multiple visual detecting devices, these may be arranged spaced apart from each other so as to capture the geometry of a surface from different points of view. The 3D-scanner … captures the geometry and the contour of an object. Based on the captured geometry, a 3D CAD (computer-aided design) model may be generated. The 3D CAD model may then be used to generate an individual matching image with the preliminary matching formula. … the appearance of the preliminary matching formula with the specific geometry of an object can be analyzed. … the repair formulas in accordance with the preliminary matching formula is applied to the specific 3D-geometry of an object and the visual appearance of the repair formula on this specific object is simulated.”); and
visualizing, using a display device (STEENHOEK; FIG. 1, ‘Display Device 85’; ¶ 0066), a scene comprising the … virtual object(s) (STEENHOEK; FIG. 2; ¶ 0108; “In the fourth step 240, the individual matching images or realistic matching images are displayed on a display device. Each of the matching images can be displayed as an image representing a single viewing angle or a realistic matching image representing multiple viewing angles, such as a curved view.”).
PNG
media_image1.png
678
406
media_image1.png
Greyscale
STEENHOEK does not explicitly disclose using the first and second sets of appearance attributes and the geometric model, a first portion of the … virtual object(s) being visualized using the first set of appearance attributes, and a second portion of the … virtual object(s) being visualized based on the second set of appearance attributes, which RUMP discloses (RUMP; FIG. 1; Col. 4, Lines 62-67 ~ Col. 5, Lines 1-4; “A … large surface area of a reference material MR is measured using a complex reflectance scanner (11) to provide a set of reflectance values [‘first … set of appearance attributes’] for each pixel of the scanned surface area under … illumination directions … and … viewing directions … The result is a first set of data indicative of the values of measured appearance attributes of the MR. This first set of data is also referred to herein as the densely populated original BTF database B (12) for the MR.” Col. 5, Lines 11-23; “A measuring spot of a source material MS is measured using a simple reflectance scanner or color measuring device (13) to provide sparse reflectance values D (14) [‘second … set of appearance attributes’]. The result is a second set of data indicative of a value of … measured appearance attribute(s) of the MS. The appearance attribute of the MS includes … the appearance attributes measured on the MR. The second set of data is also referred to herein as the "sparsely populated" data set. "Sparse" means less than at least 50% the amount in the densely populated BTF database B for the MR.” Col. 6, Lines 4-12; “The third set of data [is] processed to form an image representative of the synthesized appearance of the simulated material. … the image may be [3-D]. FIG. 1 shows the third set of data (e.g., the edited BTF B') (16) can be fed as input data to a conventional rendering engine (17) and visualized on a display (18). Rendering and displaying a BTF are known in the art …”).
Before the effective filing date of the claimed invention, it would have been obvious to a person having ordinary skill in the art to modify the method for visualizing the appearances of … materials of STEENHOEK to include the using the first and second sets of appearance attributes and the geometric model, a first portion of the … virtual object(s) being visualized using the first set of appearance attributes, and a second portion of the … virtual object(s) being visualized based on the second set of appearance attributes of RUMP. The motivation for this modification is to visualize materials or surfaces on a monitor by using computer graphic techniques. A digital representation of a real material or surface is rendered, mapped onto a target object of arbitrary shape, and the simulated appearance of the target object is then visualized under user selected illumination conditions and viewing directions (RUMP; Col. 1, Lines 13-18).
STEENHOEK-RUMP disclose that
each of the first and second sets of appearance attributes comprises texture attributes in the form of image data (STEENHOEK; ¶ 0105; “… appearance characteristics include … texture, metallic, pearlescent effect, gloss, distinctness of image, flake appearances such as texture, sparkle, glint and glitter as well as the enhancement of depth perception in the coatings imparted by the flakes, specially produced by metallic flakes …”),
the image data in the first set are calculated based on texture attributes associated with … the reference material(s) (STEENHOEK; ¶ 0105; “… flake appearance characteristics can be obtained by measurements, calculations and modeling, or a combination of measurements and calculations … A coating formula and its color or appearance characteristics can be retrieved based on related vehicle identification information. The same formula and related vehicle identification information, on the other hand, can also be retrieved based on color or appearance characteristics. Color or appearance characteristics, or both color and appearance characteristics can also be retrieved based on vehicle identification information.”), or
wherein the image data in the second set are calculated based on texture attributes associated with the target material and/or with a trial object that comprises a previously determined, different candidate material (RUMP; Col. 3, Lines 51-58; “B denotes a (densely populated) [Bidirectional Texture Function] measured for a reference material also referred to as target material. For each pixel of the measured area of the reference material and for a large number of illumination and viewing directions B holds a set of reflectance values which can be e.g. RGB color values or spectral values. In the following sets of reflectance values will be referred to shortly as reflectance values or color values.” Col. 4, Lines 17-22; “B' denotes a (densely populated) BTF database derived from B by modifying the original BTF B with sparse reflectance values D. Modifying a BTF is also referred to as editing a BTF. The modified BTF B' represents a simulated material [‘different candidate material’] that combines appearance properties of both the reference material and the source material.”).
PNG
media_image2.png
364
559
media_image2.png
Greyscale
Regarding claim 23, STEENHOEK-RUMP disclose a device for visualizing the appearances of … materials, comprising a display device (FIG. 1, element 85), … processor(s) (FIG. 1, ‘Computing Device 80’) and at least one memory (FIG. 1, ‘Databases 95/105’), the at least one memory (FIG. 6, ‘Shared Memory 610’; ¶ 0125) comprising program instructions (FIG. 1; ¶ 0057, ¶ 0063; “The computing device 80 and/or the host computer 86 are instructed by instructions of a computer program product to carry out those steps and/or functions. The computer program product [is] accessible to the computing device 80 and/or the host computer 86 and instructs these devices to perform a computing process …”) configured to cause the … processor(s) to carry out a method comprising: …
based on predetermined appearance attributes associated with … reference materials … (RUMP; Col. 4, Lines 39-41; “’Reference material’ means a material which has been extensively measured to densely populate a data set of … appearance attributes.”) …
while at least some of the appearance attributes in the first set are measured appearance attributes of the target material which have been determined based on measurements of the target object … (RUMP; Col. 3, Lines 51-56; “B denotes a (densely populated) BTF measured for a … target material. For each pixel of the measured area of the reference material and for … illumination and viewing directions B holds a set of reflectance values which can be e.g. RGB color values or spectral values.”) or wherein …
the candidate material while at least some of the appearance attributes in the second set are appearance attributes that have been calculated from the predetermined appearance attributes associated with the reference materials (RUMP; Col. 4, Lines 17-22; “B' denotes a (densely populated) BTF database derived from B by modifying the original BTF B with sparse reflectance values D. Modifying a BTF is also referred to as editing a BTF. The modified BTF B' represents a simulated material [‘candidate material’] that combines appearance properties of both the reference material and the source material.” Col. 4, Lines 49-58; “"Physically plausible" means that the simulated material is typically capable of being created in the physical, real world. The term "Physically plausible" is used in contrast to that of "physically feasible." A "physically feasible" appearance is one that one can imagine constructing physically, but the real-world construction of such materials may not be possible. The present invention enables creation of physically plausible simulated materials because the first and second data sets are physically measured from tangible source and reference materials.”).
Regarding claim 24, STEENHOEK-RUMP disclose a computer program product comprising a non-volatile computer-readable medium on which program instructions are stored, wherein the program instructions which, when executed by … processor(s), cause the … processor(s) to carry out a method comprising (STEENHOEK; ¶ 0054; “The term “memory” relates to a computer readable storage device or media and may include volatile and nonvolatile storage in read-only memory (ROM), random-access memory (RAM), and keep-alive memory (KAM), for example. KAM is a persistent or non-volatile memory that may be used to store various operating variables while the processor is powered down. The computer-readable storage device or media may be implemented using any of a number of known memory devices such as PROMs (programmable read-only memory), EPROMs (electrically PROM), EEPROMs (electrically erasable PROM), flash memory, or any other electric, magnetic, optical, or combination memory devices capable of storing data, some of which represent executable instructions, used by the processor in executing the steps described herein.”): … ([The remaining limitations are repeated verbatim from those recited in claim 23.]).
Regarding claim 2, STEENHOEK-RUMP disclose the method of claim 1, wherein the image data in the first set are calculated image data that are based on image data associated with the reference materials (RUMP; Col. 3, Lines 51-58; “B denotes a (densely populated) BTF measured for a reference material also referred to as target material. For each pixel of the measured area of the reference material and for a large number of illumination and viewing directions B holds a set of reflectance values which can be e.g. RGB color values or spectral values. In the following sets of reflectance values will be referred to shortly as reflectance values or color values.”).
Regarding claim 4, STEENHOEK-RUMP disclose the method of claim 1, wherein calculating the image data in the first set comprises modifying pixel values of images associated with the candidate material to match at least one statistical property of the first set to a known statistical property of the target material (RUMP; Col. 11, Lines 55-67 ~ Col. 12, Lines 1-21; “Automotive Paint: Car paint materials typically are homogeneous up to the distortion by the sparkling due to certain pigments or flakes incorporated in the paints. Since car paints are near-homogeneous materials, the whole measured surface area of the reference material can be included into the editing process by setting P to the set of all pixels. The BRDF of the surface is represented using the Cook-Torrance BRDF model … and an angular-dependent color table. The residual contains the local effects caused by the flakes, e.g. sparkling, and is represented by a specially encoded BTF. This whole BTF is then edited in three steps: (1) new parameters for the BRDF model are calculated by matching the gray-scale reflectance with the given sparse reflectance values D; (2) the entries of color table “C” are recomputed to match the colors to the sparse measured reflectance values D; and (3) the colors in all pixels of the flake BTF are changed to match the edit performed on C and the angular distribution of the images is changed according to the edit. Since no spatial information is given in D, no sensible changes to the distribution of the flake effects within one image of the flake BTF are possible. … new values for the diffuse and specular coefficients as well as the roughness parameters of the Cook-Torrance model are determined using optimization. For the second step, the original color table is first warped in angular domain based on the average surface roughness change [‘match … statistical property of the first set to a known statistical property of the target material’]. Since the bi-angular color change of metallic paints depends on the alignment of the flake particles, and since the roughness parameter reflects the degree of misalignment, this operation corrects for the difference in flake alignment between the source and target paint.”).
Regarding claim 5, STEENHOEK-RUMP disclose the method of claim 1, wherein the image data in the second set are based on texture attributes associated with the target material and/or with the trial object (STEENHOEK; ¶ 0105; “The color and appearance characteristics can be obtained through measurements of a test coating resulted from the corresponding formula or through mathematical calculation and modeling. … the color characteristics can comprise L, a, b, or L*, a*, b*, or XYZ values … and can be obtained by using a colorimeter, a spectrophotometer, or a gonio-spectrophotometer. Examples of appearance characteristics include, but not limited to, texture, metallic, pearlescent effect, gloss, distinctness of image, flake appearances such as texture, sparkle, glint and glitter as well as the enhancement of depth perception in the coatings imparted by the flakes, specially produced by metallic flakes, such as aluminum flakes. … flake appearance characteristics can be obtained by measurements, calculations and modeling, or a combination of measurements and calculations”).
Regarding claim 6, STEENHOEK-RUMP disclose the method of claim 5, wherein the image data in the second set are based on measured image data associated with the target material (STEENHOEK; ¶ 0067; “… a section of the surface of the article can be marked or selected by a user. This marked section typically corresponds to the section to be repaired. The marked section is rendered with the preliminary matching formulas (one at a time) while the unmarked section that is adjacent to the marked section is rendered with the target coating [which] is the actually measured color of the article. … based on this approach, the preliminary matching formula and the target coating are rendered on a true 3D-model of the article so that the color and appearance of the article is shown more realistic and a possible mismatch between the preliminary matching formula and the target coating can be detected before repairing the marked section. … the unrepaired portions (unmarked sections) of the article are rendered according to a model generated from a 3-angle color measurement of an unrepaired portion of the article, and the repaired portion (marked section) of the article are rendered according to a model generated using 3-angle color data for the selected preliminary matching formula.”).
Regarding claim 8, STEENHOEK-RUMP disclose the method of claim 5, wherein calculating the image data in the second set comprises modifying pixel values of images associated with the target material and/or with the trial object to match at least one statistical property of the second set to a known statistical property of the candidate material (RUMP; Col. 11, Lines 55-67 ~ Col. 12, Lines 1-21; [See the rejection of claim 4 above.]).
PNG
media_image3.png
420
619
media_image3.png
Greyscale
Regarding claim 20, STEENHOEK-RUMP disclose the computer-implemented method of claim 1, wherein the texture attributes in each of the first and second sets of appearance attributes comprise … sets of image data, each set of image data associated with a different combination of illumination and viewing directions (STEENHOEK; FIG. 7; ¶ 129; “Color and appearance of a coating can vary in relation to illumination. … when a coating 51 is illuminated by an illumination source 52, such as a light bulb or sun light, at a given angle …, a number of viewing angles [‘viewing directions’] can be used, such as, 1) near aspecular angles 54, that are the viewing angles from about 15° to about 25° from the reflection 53 of the illumination; 2) mid aspecular angles 55, that are the viewing angles about 45° from the reflection 53 of the illumination; and 3) far aspecular angles 56, that are the viewing angles from about 75° to about 110° from the reflection 53 of the illumination. … color appears to be slightly brighter at near aspecular angles and slightly darker at far aspecular angles.”).
Regarding claim 21, STEENHOEK-RUMP disclose the computer-implemented method of claim 1, comprising:
carrying out measurements on the target object to determine … sets of measured image data for the target material, using an appearance capture device (STEENHOEK; ¶ 0055; FIG. 1, ‘Appearance Measuring Device 83’; ¶ 0069-70; “The 3D-scanner may have … visual detecting device(s). [If] the 3D-scanner has multiple visual detecting devices, these may be arranged spaced apart from each other so as to capture the geometry of a surface from different points of view. The 3D-scanner is configured to capture the geometry and the contour of an object. Based on the captured geometry, a 3D CAD (computer-aided design) model may be generated. The 3D CAD model may then be used to generate an individual matching image with the preliminary matching formula. … the appearance of the preliminary matching formula with the specific geometry of an object can be analyzed. … the repair formulas in accordance with the preliminary matching formula is applied to the specific 3D-geometry of an object and the visual appearance of the repair formula on this specific object is simulated.”), each set of measured image data associated with a different combination of illumination and viewing directions (STEENHOEK; FIG. 7; ¶ 0129).
Regarding claim 22, STEENHOEK-RUMP disclose the computer-implemented method of claim 1, further comprising:
determining measured appearance attributes of a trial object that comprises the candidate material by carrying out measurements on the trial object, using an appearance capture device (RUMP; ¶ Col. 3, Lines 40-56; “BTF denotes a Bidirectional Texture Function or [DB]. The BTF is a [6-D] function containing a color reflectance value (e.g. but not necessarily RGB) for every point (pixel) on a surface area of the material (2 spatial dimensions, i.e. x-y coordinates) as well as for a large number of viewing and illumination directions (2*2 spatial direction dimensions, i.e. 2 elevation and 2 azimuth angles). Due to its discrete nature the BTF is a collection of data or [DB] rather than a continuous function in the strict mathematical sense. … B denotes a (densely populated) BTF measured for a reference material also referred to as target material. For each pixel of the measured area of the reference material and for a large number of illumination and viewing directions B holds a set of reflectance values which can be e.g. RGB color values or spectral values.”), and further comprising at least one of the following steps:
visualizing at least a portion of the … virtual object(s) using the measured appearance attributes of the trial object (STEENHOEK; ¶ 0105; “The color and appearance characteristics can be obtained through measurements of a test coating resulted from the corresponding formula or through mathematical calculation and modeling. … the color characteristics can comprise L, a, b, or L*, a*, b*, or XYZ values known to those skilled in the art and can be obtained by using a colorimeter, a spectrophotometer, or a gonio-spectrophotometer. Examples of appearance characteristics include, but not limited to, texture, metallic, pearlescent effect, gloss, distinctness of image, flake appearances such as texture, sparkle, glint and glitter as well as the enhancement of depth perception in the coatings imparted by the flakes, specially produced by metallic flakes, such as aluminum flakes. … flake appearance characteristics can be obtained by measurements, calculations and modeling, or a combination of measurements and calculations”); and/or
determining an amended recipe, using the measured appearance attributes of the trial object and the calculated appearance attributes of the candidate material (STEENHOEK; ¶ 0067; “… a section of the surface of the article can be marked or selected by a user. This marked section typically corresponds to the section to be repaired. The marked section is rendered with the preliminary matching formulas [‘amended recipe’] (one at a time) while the unmarked section that is adjacent to the marked section is rendered with the target coating. The target coating [‘calculated appearance attributes of the candidate material’] is the actually measured color of the article [‘measured appearance attributes of the trial object’]. … based on this approach, the preliminary matching formula and the target coating are rendered on a true 3D-model of the article so that the color and appearance of the article is shown more realistic and a possible mismatch between the preliminary matching formula and the target coating can be detected before repairing the marked section. … the unrepaired portions (unmarked sections) of the article are rendered according to a model generated from a 3-angle color measurement of an unrepaired portion of the article, and the repaired portion (marked section) of the article are rendered according to a model generated using 3-angle color data for the selected preliminary matching formula.”).
Claims 3 and 7 are rejected under 35 U.S.C. 103 as being unpatentable over STEENHOEK in view of RUMP as applied to claims 1 and 5 above, respectively, and further in view of Torfs et al. (U.S. PG-PUB 2002/0118357, 'TORFS').
Regarding claim 3 and claim 7, STEENHOEK-RUMP disclose the method of claim 1 and the method of claim 5; however, STEENHOEK-RUMP do not explicitly disclose that the image data in the first/second set are based on measured image data associated with the trial object, which TORFS discloses (TORFS; ¶ 0016-19; “Combining the method … for characterizing the appearance of an object … and predicting the appearance of an object … provides a sound basis for appearance matching. … the reflectance value of a reference object … or a number of desired reflectance values at a range of wave lengths associated and/or viewing/illumination angles is determined. This [is] established from actual measurements of an object (that is, a real reference object) or a theoretical appearance that is desired (a "virtual" object). … a sample object is produced that is selected to have reflectance value(s) that approximate the predetermined value(s). This method comprises the steps of: A. measuring … the reflectance values of a reference object whose appearance is to be matched at … predetermined viewing (h) and illumination (i) angles; B. measuring the reflectance value … of a test object manufactured from a pre-selected material having a pre-selected amount and type of colorant(s) and/or other additive(s) using a pre-selected method of manufacture …”).
Before the effective filing date of the claimed invention, it would have been obvious to a person having ordinary skill in the art to modify the method of claim 1 and the method of claim 5 of STEENHOEK-RUMP to include the disclosure that the image data in the first/second set are based on measured image data associated with the trial object of TORFS. The motivation for this modification is provide a method for characterizing, by means of a measurement, the appearance of an object, more particularly, to a method for characterizing the contribution of the surface to the appearance of an object and for predicting the object's surface appearance (TORFS; ¶ [0002]).
Claims 25-26 are rejected under 35 U.S.C. 103 as being unpatentable over STEENHOEK in view of RUMP as applied to claims 4 and 8 above, respectively, and further in view of Imber et al. (U.S. PG-PUB 2016/0042556, 'IMBER').
Regarding claim 25 and claim 26, STEENHOEK-RUMP disclose the method of claim 4 and the method of claim 8; however, STEENHOEK-RUMP do not explicitly disclose that the known statistical property of the target/candidate material is a global texture attribute, which IMBER discloses (IMBER; ¶ 0051; “Accurate estimation of ρ(x) uses an estimate of the scene radiance R(x) [analogous to ‘global sparkle parameter’] which is estimated concurrently. This is solved in two stages. First, the texture T(x) is segmented into regions of similar appearance, and the attached shadow processing logic 213 obtains a coarse albedo estimate [also analogous to ‘global sparkle parameter’] … based upon the initial segmentation by neglecting the visibility term V (x, ω) … and dealing directly with a global irradiance estimate. In a second stage, the cast shadow processing logic 215 uses the albedo estimate … to initialize a full radiance estimate taking visibility into account.” {The Examiner notes that the instant specification discloses “… Examples of texture attributes include global texture attributes such as a global coarseness parameter or a global sparkle parameter” at ¶ [0157].}).
Before the effective filing date of the claimed invention, it would have been obvious to a person having ordinary skill in the art to modify the method of claim 4 and the method of claim 8 of STEENHOEK-RUMP to include the disclosure that the known statistical property of the target/candidate material is a global texture attribute of IMBER. The motivation for this modification is to control the lighting of a scene at the time when cameras capture different views of the scene to relight the textures for a novel viewpoint. Diffuse lighting can be used in the initial video capture to avoid creating excess shaded areas and specularities that will damage the plausibility of the scenes rendered using extracted textures. The effects of changes in lighting may be reproduced by estimating the material properties of the textures, for example the intrinsic color (albedo) and fine detail (surface normals), for subsequent relighting using conventional computer graphics techniques. This may be addressed using an active lighting (or “light-stage”) arrangement, in which images of the scene are captured under a variety of calibrated lighting conditions, with material properties of the textures (such as the intrinsic color, or “albedo”, and the fine detail of the surfaces) being fitted to the images (IMBER; ¶ [0005]).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JONATHAN M COFINO whose telephone number is (303) 297-4268. The examiner can normally be reached Monday-Friday 10A-4P MT.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Kent Chang can be reached at 571-272-7667. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/JONATHAN M COFINO/ Examiner, Art Unit 2614
/YuJang Tswei/ Primary Examiner, Art Unit 2614