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 .
Drawings
The drawings are objected to as failing to comply with 37 CFR 1.84(p)(5) because they include the following reference character(s) not mentioned in the description: 221-225 in Fig. 2B. Corrected drawing sheets in compliance with 37 CFR 1.121(d), or amendment to the specification to add the reference character(s) in the description in compliance with 37 CFR 1.121(b) are required in reply to the Office action to avoid abandonment of the application. Any amended replacement drawing sheet should include all of the figures appearing on the immediate prior version of the sheet, even if only one figure is being amended. Each drawing sheet submitted after the filing date of an application must be labeled in the top margin as either “Replacement Sheet” or “New Sheet” pursuant to 37 CFR 1.121(d). If the changes are not accepted by the examiner, the applicant will be notified and informed of any required corrective action in the next Office action. The objection to the drawings will not be held in abeyance.
The drawings are objected to as failing to comply with 37 CFR 1.84(p)(5) because they do not include the following reference sign(s) mentioned in the description: “300” on page 17 line 19. Corrected drawing sheets in compliance with 37 CFR 1.121(d) are required in reply to the Office action to avoid abandonment of the application. Any amended replacement drawing sheet should include all of the figures appearing on the immediate prior version of the sheet, even if only one figure is being amended. Each drawing sheet submitted after the filing date of an application must be labeled in the top margin as either “Replacement Sheet” or “New Sheet” pursuant to 37 CFR 1.121(d). If the changes are not accepted by the examiner, the applicant will be notified and informed of any required corrective action in the next Office action. The objection to the drawings will not be held in abeyance.
The drawings are objected to under 37 CFR 1.83(a). The drawings must show every feature of the invention specified in the claims. Therefore, the “palette in the Hue, Saturation, Lightness format” in claim 3 must be shown or the feature(s) canceled from the claim(s). No new matter should be entered.
Corrected drawing sheets in compliance with 37 CFR 1.121(d) are required in reply to the Office action to avoid abandonment of the application. Any amended replacement drawing sheet should include all of the figures appearing on the immediate prior version of the sheet, even if only one figure is being amended. The figure or figure number of an amended drawing should not be labeled as “amended.” If a drawing figure is to be canceled, the appropriate figure must be removed from the replacement sheet, and where necessary, the remaining figures must be renumbered and appropriate changes made to the brief description of the several views of the drawings for consistency. Additional replacement sheets may be necessary to show the renumbering of the remaining figures. Each drawing sheet submitted after the filing date of an application must be labeled in the top margin as either “Replacement Sheet” or “New Sheet” pursuant to 37 CFR 1.121(d). If the changes are not accepted by the examiner, the applicant will be notified and informed of any required corrective action in the next Office action. The objection to the drawings will not be held in abeyance.
Specification
The disclosure is objected to because of the following informalities:
On page 3 line 6-7, “which method includes creating physically based rendering palette” should read “in which the method includes creating a physically based rendering palette”.
On page 5 line 9, “which system includes” should read “in which the system includes”.
On page 7 line 14, “which method includes obtaining” should read “in which the method includes obtaining”.
On page 10 line 18, “metlaness” should read “metalness”.
On page 10 line 27, “which image is based on” should read “in which the image is based on”.
On page 13 line 20, “Not also that” should read “Note also that”.
On page 14 line 15, “one entry for teach material” should read “one entry for each material”.
On page 17 line 23, “which frustrum adds a dimension” should read “in which the frustrum adds a dimension”.
On page 18 line 22-23, “which physical system includes” should read “in which the physical system includes”.
On page 21 line 15, “which diffuse hue” should read “in which the diffuse hue”.
On page 33 line 7, “which `computer executable code” should read “in which the computer-executable code”.
Appropriate correction is required.
The use of the term “OpenGL”, which is a trade name or a mark used in commerce, has been noted in this application. The term should be accompanied by the generic terminology; furthermore the term should be capitalized wherever it appears or, where appropriate, include a proper symbol indicating use in commerce such as ™, SM , or ® following the term.
Although the use of trade names and marks used in commerce (i.e., trademarks, service marks, certification marks, and collective marks) are permissible in patent applications, the proprietary nature of the marks should be respected and every effort made to prevent their use in any manner which might adversely affect their validity as commercial marks.
Claim Objections
Claims 1-7, 10-12, and 15-20 are objected to because of the following informalities:
“the minimum finish value” and “the smallest finish value” in claims 1 and 15 should be changed to the same term for clarity.
“the maximum finish value” and “the greatest finish value” in claims 1 and 15 should be changed to the same term for clarity.
On page 1 line 19-20 (claim 1), “the diffuse color of the basic palette” should read “the diffuse color of the corresponding basic palette”.
On page 2 line 20 (claim 5), “where specular hue” should read “where the specular hue”.
On page 4 line 16 (claim 10), “the diffuse color of the basic palette” should read “the diffuse color of the corresponding basic palette”.
On page 5 line 3 (claim 11), “where specular hue” should read “where the specular hue”.
On page 6 line 5 (claim 15), “which `computer executable” should read “in which the computer-executable”.
On page 6 line 24-25 (claim 15), “the diffuse color of the basic palette” should read “the diffuse color of the corresponding basic palette”.
Claims 2-7 are objected to because of their dependency on claim 1.
Claim 12 is objected to because of its dependency on claim 11.
Claims 16-20 are objected to because of their dependency on claim 15.
Appropriate correction is required.
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitation(s) is/are: “a color determination module”, “a metalness module”, “a roughness module”, and “an image generation module” in claim 8; and “a transformation module” in claim 9.
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof. The corresponding structure can be found on page 12 lines 18-28 in the specification, which discloses a processing unit, as well as on Figures 4-7.
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 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.
Claims 1-2 and 4 are rejected under 35 U.S.C. 103 as being unpatentable over Fang et al. (Make-it-Real: Unleashing Large Multimodal Model’s Ability for Painting 3D Objects with Realistic Materials), Bavitha et al. (Color Identification), Gadelmawla et al. (Roughness Parameters), and TurboSquid 3D Resources (Diffuse/Specular vs BaseColor), hereinafter Fang, Bavitha, Gadelmawla, and Turbo respectively.
Regarding claim 1, Fang teaches a computer-implemented method (Paragraph 2 on Page 16 – “We commence by elaborating on the generation of SVBRDF Maps, incorporating illustrative figures to detail the process involving operations in computer graphics”; Note: it is implied that a computer implements the method since they involve computer graphics operations) comprising:
obtaining a model (3D mesh/object [Wingdings font/0xE0] see quote below) for a computer-aided-design environment, the model comprising a plurality of materials and not comprising a PBR palette (Fig. 1 Caption on Page 1, Paragraph 3 on Page 5, Paragraph 3 on Page 7 – “Make-it-Real can refine any diffuse-map-only 3D object from both CAD design and generative models…Our goal is to utilize the Make-it-Real pipeline for the identification and synthesis of Spatially Varing Bidirectional Reflectance Distribution (SVBRDF) material maps, leveraging the existing diffuse map and prior knowledge to query the most appropriate material attribute maps SV = {N, R, M, H, S}, to use as references before further refinement…Following the successful segmentation of regions for multi-views of 3D meshes, our subsequent objective focuses on the allocation of appropriate materials to these regions”; Note: the 3D meshes are models for CAD, and they comprise diffuse RGB, which is not PBR, and materials);
obtaining a basic palette (diffuse RGB maps [Wingdings font/0xE0] see quote below) corresponding to the model, the basic palette comprising a plurality of basic palette entries (diffuse RGB maps of each segmented region [Wingdings font/0xE0] see quote) (Paragraph 3 on Page 5, Paragraph 2 on Page 9, Paragraph 2 on Page 18 – “To accurately identify and segment different material regions on 3D meshes with diffuse RGB, we adopt an innovative segmentation strategy based on multi-view 2D image rendering… To precisely estimate the spatially varying BRDFs per pixel, we draw on the common practice among artists who typically use the diffuse map as a guide to create maps for other material properties when generating various texture maps. This process involves using the original object’s diffuse map as a reference, and the matched material sphere’s diffuse map as a key. We finds the nearest neighbor pixel index in the key diffuse for each pixel’s RGB value in the query diffuse pixel by pixel, which we employ the K-d tree algorithm to accelerate the querying process… “diffuse” typically denotes the primary color of a material, a concept analogous to “base color” in Physically-Based Rendering (PBR) paradigms, both representing the inherent color of the material under uniformly scattered illumination”; Note: the diffuse maps make up a basic palette. The diffuse maps of each segmented region are basic palette entries);
finding from the plurality of basic palette entries, a finish value (Fig. 4, Paragraph 2 on Page 6, Paragraph 3 on Page 13 – “Each material is represented by seven maps: diffuse, diffuse normal, height, roughness, metallic and specular…To examine the individual contributions of various material maps to the rendering process, we incrementally introduced each material map—specifically, roughness, metalness, and displacement maps—into our model, shown in Fig. 9”; Note: Fig. 4 shows that a roughness value is found, which is equivalent to a finish value; see screenshot of Fig. 4 below);
creating from the basic palette, a physically based rendering (“PBR”) palette (PBR/BRDF maps [Wingdings font/0xE0] see quote below) for the model (Paragraph 4 on Page 8, Paragraph 2 on Page 9, Paragraph 2 on Page 14 – “referenced by the original diffuse map of an object, an estimation of BRDF values in the pixel space is performed by querying the matched realistic material maps…We get SVBRDF values at the corresponding pixel location by querying the key material map…we present a novel framework leveraging MLLMs prior of the world to build a material library and proposing an automatic pipeline to refine and synthesize new PBR maps for initial 3D models, achieving highly photorealistic PBR textures maps synthesis”; Note: starting from a diffuse maps, which make up a basic palette, additional values are found to create PBR maps, which make up a PBR palette), the PBR palette comprising a plurality of PBR palette entries, each PBR entry corresponding to a one of the basic palette entries of the basic palette (Paragraph 2 on Page 9 – “This process involves using the original object’s diffuse map as a reference, and the matched material sphere’s diffuse map as a key. We finds the nearest neighbor pixel index in the key diffuse for each pixel’s RGB value in the query diffuse pixel by pixel, which we employ the K-d tree algorithm to accelerate the querying process. We get SVBRDF values at the corresponding pixel location by querying the key material map. Notably, this procedure applies histogram equalization to both the query diffuse and key diffuse, normalizing them to a similar color space. This method enables the generation of a series of spatially variant BRDF maps, which maintain high consistency with the texture of the diffuse color”; Note: each PBR map corresponds to a diffuse map, which is implied because the diffuse map is used as a reference for each PBR map. The PBR map and BRDF map refer to the same thing), by, for each PBR palette entry:
setting the color value of the PBR palette to a color selected from the specular color of the corresponding basic palette entry and the diffuse color of the basic palette entry (Fig. 4, Paragraph 2 on Page 6, Paragraph 3 on Page 13 – “Each material is represented by seven maps: diffuse, diffuse normal, height, roughness, metallic and specular…”; Note: Specular and diffuse values are set, which together make up color. Fig. 4 also shows how the diffuse map is used to generate the material; see screenshot of Fig. 4 below);
setting a metalness parameter (Fig. 4, Paragraph 2 on Page 6, Paragraph 3 on Page 13 – “Each material is represented by seven maps: diffuse, diffuse normal, height, roughness, metallic and specular…To examine the individual contributions of various material maps to the rendering process, we incrementally introduced each material map—specifically, roughness, metalness, and displacement maps—into our model, shown in Fig. 9”; Note: Fig. 4 shows how a metalness parameter is set and used to generate a rendering of the model; see screenshot of Fig. 4 below);
setting a roughness parameter (Fig. 4, Paragraph 2 on Page 6, Paragraph 3 on Page 13 – “Each material is represented by seven maps: diffuse, diffuse normal, height, roughness, metallic and specular…To examine the individual contributions of various material maps to the rendering process, we incrementally introduced each material map—specifically, roughness, metalness, and displacement maps—into our model, shown in Fig. 9”; Note: Fig. 4 shows how a roughness parameter is set and used to generate a rendering of the model; see screenshot of Fig. 4 below);
and rendering an image from the PBR palette (Fig. 5, Paragraph 3 on Page 9, Paragraph 3 on Page 18 – “we successfully develop a pipeline for generating the material maps from 3D objects with constrained diffuse maps. This process finally project the generated material textures back onto the 3D meshes, thereby achieving a natural and authentic appearance, along with the physical properties of the materials available for photo-realistic rendering in different environment…This approach allows for the preservation of material visual quality in scenarios where the diffuse map is missing or of inferior quality, by leveraging the base color map as an effective substitute, thereby ensuring consistency and realism within the PBR workflow. Furthermore, the flexible application of various map types and 2D-3D alignment techniques enhances the detail and realism of rendered objects, meeting the demands of diverse rendering scenarios”; Note: the model is rendered using the “generated material textures”, which refers to the PBR palette. Fig. 5 shows the rendered image of the model; see screenshot of Fig. 5 below).
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Screenshot of Fig. 4 (taken from Fang)
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Screenshot of Fig. 5 (taken from Fang)
Fang does not teach obtaining an H-S palette corresponding to the model, the H-S palette comprising a plurality of H-S entries, each H-S entry corresponding to a one of the basic palette entries, and comprising a corresponding hue parameter and a corresponding saturation parameter; “the H-S palette” from the limitation: “creating from the basic palette and the H-S palette, a physically based rendering (“PBR”) palette for the model”. However, Bavitha teaches obtaining an H-S palette (HSV [Wingdings font/0xE0] see quote below) corresponding to the model, the H-S palette comprising a plurality of H-S entries, each H-S entry corresponding to a one of the basic palette entries, and comprising a corresponding hue parameter and a corresponding saturation parameter (Paragraph 2 on Page 34, Paragraph 1-2 on Page 35 – “The HSV or Hue, Saturation and Value of a given object is the color space associated with the object in OpenCV where Hue represents the color, Saturation represents the greyness and Value represents the brightness…The syntax to define HSV range in OpenCV is as follows: hsvcolorspace = cv.cvtColor(image, cv.COLOR_BGR2HSV) lower_hsvcolorspace = np.array([Hue range, Saturation range, Value range]) upper_hsvcolorspace = np.array([Hue range, Saturation range, Value range]) Where hsvcolorspace is the conversion of the given image in RGB format to HSV format, lower_hsvcolorspace is the lower threshold for a range of some color, upper_hsvcolorspace is the upper threshold for a range of some color”; Note: colors of a RGB color palette, which is equivalent to a basic palette, are converted to an HSV palette. Because the colors are converted, it is implied that each basic entry corresponds to an HSV entry); and the H-S palette (Paragraph 2 on Page 34, Paragraph 1-2 on Page 35 – “The HSV or Hue, Saturation and Value of a given object is the color space associated with the object in OpenCV where Hue represents the color, Saturation represents the greyness and Value represents the brightness…The syntax to define HSV range in OpenCV is as follows:…”; Note: HSV is the H-S palette). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Fang to incorporate the teachings of Bavitha to obtain and use an H-S palette because “The HSV or Hue, Saturation and Value of a given object is the color space associated with the object in OpenCV where Hue represents the color, Saturation represents the greyness and Value represents the brightness and it is used to solve the problems related to computer vision because of its better performance when compared to RGB or Red, Blue and Green color space and the Hue range in HSV is [0,179], the Saturation range in HSV is [0,255] and the Value range in HSV is [0,255] and to perform object detection, finding the range of HSV is necessary” (Bavitha: Paragraph 2 on Page 34, Paragraph 1 on Page 35). In other words, an HSV palette is useful for representing certain color details, which can help with tasks such as object detection and modeling.
Fang modified by Bavitha still does not teach finding from the plurality of basic palette entries, the minimum finish value and the maximum finish value; nor setting a roughness parameter based on the smallest finish value of the basic palette and the greatest finish value of the basic palette. However, Gadelmawla teaches finding from the plurality of basic palette entries, the minimum finish value and the maximum finish value (Paragraph 2 in 1st Col. of Page 1, Paragraph 1 in 2nd Col. of Page 1, Paragraph 5-6 in 2nd Col. of Page 4 – “The 2D roughness parameters then calculated for each section separately, and the average of each parameter is taken for all sections. This research presents all roughness parameters and their calculation methods. Amplitude parameters are the most important parameters to characterise surface topography. They are used to measure the vertical characteristics of the surface deviations… Rp is defined as the maximum height of the profile above the mean line within the assessment length… Rv is defined as the maximum depth of the profile below the mean line within the assessment length”; Note: the maximum height of the profile and the maximum depth of the profile are the equivalent to the maximum finish value and minimum finish value respectively); and setting a roughness parameter based on the smallest finish value of the basic palette and the greatest finish value of the basic palette (Paragraph 2 in 1st Col. of Page 1, Paragraph 1 in 2nd Col. of Page 1, Paragraph 5-6 in 2nd Col. of Page 4 – “The 2D roughness parameters then calculated for each section separately, and the average of each parameter is taken for all sections. This research presents all roughness parameters and their calculation methods. Amplitude parameters are the most important parameters to characterise surface topography. They are used to measure the vertical characteristics of the surface deviations… Rp is defined as the maximum height of the profile above the mean line within the assessment length… Rv is defined as the maximum depth of the profile below the mean line within the assessment length”; Note: the maximum height of the profile and the maximum depth of the profile are the equivalent to the greatest finish value and smallest finish value respectively. They are used to set a roughness parameter). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Fang to incorporate the teachings of Gadelmawla to use the max and min finish values for setting roughness because “Amplitude parameters are the most important parameters to characterise surface topography. They are used to measure the vertical characteristics of the surface deviations” (Gadelmawla: Paragraph 1 in 2nd Col. of Page 1).
Finally, Fang modified by Bavitha and Gadelmawla still does not teach setting a metalness parameter by assessing the specular hue and the diffuse hue from the corresponding H-S palette. However, Turbo teaches setting a metalness parameter by assessing the specular hue and the diffuse hue from the corresponding H-S palette (Page 6, 8-10 – “In a Metallic workflow, the BaseColor map technically contains both the Diffuse and Specular map information, while the Metallic map determines how much of the BaseColor map is interpreted as Diffuse output or as Specular output. The more metallic (white) an area on the map is the more it will have reflected color…A Metallic value of 0 makes a basic non-reflective material. The BaseColor will contribute only Diffuse color. Specular values will default to a mid grey tone…A Metallic value of 1 makes a completely reflective material. The BaseColor will contribute only Specular color values. The Diffuse color will default to pure black…Setting your Metallic value between 0 and 1 will blend the BaseColor a percentage. Think of it like pouring paint from one container to another. Diffuse contribution will darken toward black (emptying cup) and the default Specular grey will get more of the BaseColor added (filling cup) the higher the Metallic value”; Note: the diffuse and specular values determine the metalness parameter. The diffuse and specular hues from the HS palette were previously taught by Fang and Bavitha earlier in this rejection of claim 1). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Fang to incorporate the teachings of Turbo to use specular and diffuse values for setting metalness because the specular and diffuse values affect how metallic an object looks, and thus, they can be used toward determining the metalness of the object. For instance, using specular values adds reflectivity (Turbo: Page 6, 8-10); the image on page 8 of Turbo, shown below, demonstrates how the diffuse and specular values affect the metallic value.
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Screenshot of Image on Page 8 (taken from Turbo)
Regarding claim 2, Fang in view of Bavitha, Gadelmawla, and Turbo teaches the method of claim 1. Fang does not teach wherein the H-S palette comprises a palette in the Hue, Saturation, Value format. However, Bavitha teaches wherein the H-S palette comprises a palette in the Hue, Saturation, Value format (Paragraph 2 on Page 34, Paragraph 1-2 on Page 35 – “The HSV or Hue, Saturation and Value of a given object is the color space associated with the object in OpenCV where Hue represents the color, Saturation represents the greyness and Value represents the brightness”). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Fang to incorporate the teachings of Bavitha to have the H-S palette comprise the hue, saturation, and value format because “The HSV or Hue, Saturation and Value of a given object is the color space associated with the object in OpenCV where Hue represents the color, Saturation represents the greyness and Value represents the brightness and it is used to solve the problems related to computer vision because of its better performance when compared to RGB or Red, Blue and Green color space and the Hue range in HSV is [0,179], the Saturation range in HSV is [0,255] and the Value range in HSV is [0,255] and to perform object detection, finding the range of HSV is necessary” (Bavitha: Paragraph 2 on Page 34, Paragraph 1 on Page 35). In other words, an HSV palette is useful for representing certain color details, which can help with tasks such as object detection and modeling.
Regarding claim 4, Fang in view of Bavitha, Gadelmawla, and Turbo teaches the method of claim 1. Fang does not teach wherein obtaining an H-S palette corresponding to the model comprises converting the basic palette to the H-S palette. However, Bavitha teaches converting the basic palette to the H-S palette (Paragraph 2 on Page 34, Paragraph 1-2 on Page 35 – “The HSV or Hue, Saturation and Value of a given object is the color space associated with the object in OpenCV where Hue represents the color, Saturation represents the greyness and Value represents the brightness…The syntax to define HSV range in OpenCV is as follows: hsvcolorspace = cv.cvtColor(image, cv.COLOR_BGR2HSV) lower_hsvcolorspace = np.array([Hue range, Saturation range, Value range]) upper_hsvcolorspace = np.array([Hue range, Saturation range, Value range]) Where hsvcolorspace is the conversion of the given image in RGB format to HSV format, lower_hsvcolorspace is the lower threshold for a range of some color, upper_hsvcolorspace is the upper threshold for a range of some color”; Note: colors of a RGB color palette, which is equivalent to a basic palette, are converted to an HSV palette). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Fang to incorporate the teachings of Bavitha to convert the basic palette to the H-S palette because “The HSV or Hue, Saturation and Value of a given object is the color space associated with the object in OpenCV where Hue represents the color, Saturation represents the greyness and Value represents the brightness and it is used to solve the problems related to computer vision because of its better performance when compared to RGB or Red, Blue and Green color space and the Hue range in HSV is [0,179], the Saturation range in HSV is [0,255] and the Value range in HSV is [0,255] and to perform object detection, finding the range of HSV is necessary” (Bavitha: Paragraph 2 on Page 34, Paragraph 1 on Page 35). In other words, an H-S palette is more useful than a basic RGB palette when performing certain tasks, making it beneficial to do the conversion from RGB to HSV.
Claim 3 is rejected under 35 U.S.C. 103 as being unpatentable over Fang in view of Bavitha, Gadelmawla, Turbo, and Admesy (Using Color Wheels and Color Spaces to Describe Light), hereinafter Admesy.
Regarding claim 3, Fang in view of Bavitha, Gadelmawla, and Turbo teaches the method of claim 1. Fang does not teach wherein the H-S palette comprises a palette in the Hue, Saturation, Lightness format. However, Admesy teaches wherein the H-S palette comprises a palette in the Hue, Saturation, Lightness format (Paragraph 2 on Page 3, Paragraph 2 on Page 4 – “An example of a spherical color model is the L*C*h* color space. It is based on a circular color scale, quite like a color wheel, with polar coordinates which define the chroma [saturation] and hue…Hue [h] is determined as an angle starting from red at 0° towards yellow at 90°. Green and blue are positioned at 180° and 270°, respectively. The lightness axis L is perpendicular to the color circle and has a value between 0 and 100: the greater the value of L, the lighter the color”; Note: there is a hue, chroma/saturation, and lightness format). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Fang to incorporate the teachings of Admesy to have the H-S palette comprise the hue, saturation, and lightness format because hue, saturation, and lightness format (HSL) is a common format for color palettes, and having the lightness measurement makes it easy to represent shades of a color and to represent white.
Claims 8-10 are rejected under 35 U.S.C. 103 as being unpatentable over Fang in view of Bavitha.
Regarding claim 8, Fang teaches a system (Paragraph 2 on Page 16 – “We commence by elaborating on the generation of SVBRDF Maps, incorporating illustrative figures to detail the process involving operations in computer graphics”; Note: it is implied there is a computer system that implements the method since the method involves computer graphics operations) comprising:
a memory to store (Paragraph 3 on Page 5, Paragraph 2 on Page 16 – “Our goal is to utilize the Make-it-Real pipeline for the identification and synthesis of Spatially Varing Bidirectional Reflectance Distribution (SVBRDF) material maps, leveraging the existing diffuse map and prior knowledge to query the most appropriate material attribute maps SV = {N, R, M, H, S}, to use as references before further refinement… We commence by elaborating on the generation of SVBRDF Maps, incorporating illustrative figures to detail the process involving operations in computer graphics”; Note: it is implied that there is a memory to perform the process since it utilizes prior knowledge and maps. The process would not be able to work without a memory):
a CAD model to be rendered (3D mesh/object [Wingdings font/0xE0] see quote below), the CAD model having a plurality of materials (Fig. 1 Caption on Page 1, Paragraph 3 on Page 5, Paragraph 3 on Page 7 – “Make-it-Real can refine any diffuse-map-only 3D object from both CAD design and generative models…Our goal is to utilize the Make-it-Real pipeline for the identification and synthesis of Spatially Varing Bidirectional Reflectance Distribution (SVBRDF) material maps, leveraging the existing diffuse map and prior knowledge to query the most appropriate material attribute maps SV = {N, R, M, H, S}, to use as references before further refinement…Following the successful segmentation of regions for multi-views of 3D meshes, our subsequent objective focuses on the allocation of appropriate materials to these regions”; Note: the 3D meshes are models for CAD, and it is implied that they have materials because the method is used to identify and later render those materials);
and a basic palette (diffuse RGB maps [Wingdings font/0xE0] see quote below) corresponding to the CAD model, the basic palette having a plurality of basic palette entries (diffuse RGB maps of each segmented region [Wingdings font/0xE0] see quote), each basic palette entry of the plurality of basic palette entries corresponding to a corresponding material from the plurality of materials (Paragraph 3 on Page 5, Paragraph 2 on Page 9, Paragraph 2 on Page 18 – “To accurately identify and segment different material regions on 3D meshes with diffuse RGB, we adopt an innovative segmentation strategy based on multi-view 2D image rendering… To precisely estimate the spatially varying BRDFs per pixel, we draw on the common practice among artists who typically use the diffuse map as a guide to create maps for other material properties when generating various texture maps. This process involves using the original object’s diffuse map as a reference, and the matched material sphere’s diffuse map as a key. We finds the nearest neighbor pixel index in the key diffuse for each pixel’s RGB value in the query diffuse pixel by pixel, which we employ the K-d tree algorithm to accelerate the querying process… “diffuse” typically denotes the primary color of a material, a concept analogous to “base color” in Physically-Based Rendering (PBR) paradigms, both representing the inherent color of the material under uniformly scattered illumination”; Note: the diffuse maps make up a basic palette. The diffuse maps of each segmented region are basic palette entries. Each segmented region corresponds to a different material);
a set of modules configured to generate a set of PBR palette entries in a PBR palette (PBR/BRDF maps [Wingdings font/0xE0] see quote below) corresponding to the CAD model (Paragraph 4 on Page 8, Paragraph 2 on Page 9, Paragraph 2 on Page 14 – “referenced by the original diffuse map of an object, an estimation of BRDF values in the pixel space is performed by querying the matched realistic material maps…We get SVBRDF values at the corresponding pixel location by querying the key material map…we present a novel framework leveraging MLLMs prior of the world to build a material library and proposing an automatic pipeline to refine and synthesize new PBR maps for initial 3D models, achieving highly photorealistic PBR textures maps synthesis”; Note: starting from a diffuse maps, which make up a basic palette, additional values are found to create PBR maps, which make up a PBR palette), each PBR palette entry (PBR/BRDF map) of the plurality of PBR palette entries corresponding to a corresponding material from the plurality of materials (Paragraph 2 on Page 9 – “This process involves using the original object’s diffuse map as a reference, and the matched material sphere’s diffuse map as a key. We finds the nearest neighbor pixel index in the key diffuse for each pixel’s RGB value in the query diffuse pixel by pixel, which we employ the K-d tree algorithm to accelerate the querying process. We get SVBRDF values at the corresponding pixel location by querying the key material map. Notably, this procedure applies histogram equalization to both the query diffuse and key diffuse, normalizing them to a similar color space. This method enables the generation of a series of spatially variant BRDF maps, which maintain high consistency with the texture of the diffuse color”; Note: each PBR map corresponds to a diffuse map, which is implied because the diffuse map is used as a reference for each PBR map. The diffuse map corresponds to a segmented region with a specific material, as expressed earlier. The PBR map and BRDF map refer to the same thing), the set of modules comprising:
a color determination module configured to determine, for each PBR palette entry, a color parameter (Fig. 4, Paragraph 2 on Page 6, Paragraph 3 on Page 13 – “Each material is represented by seven maps: diffuse, diffuse normal, height, roughness, metallic and specular…”; Note: Specular and diffuse values are set, which together make up color. The diffuse and specular maps are equivalent to the color determination module. Fig. 4 also shows how the diffuse map is used to generate the material; see screenshot of Fig. 4 above);
a metalness module configured to determine, for each PBR palette entry, a metalness parameter (Fig. 4, Paragraph 2 on Page 6, Paragraph 3 on Page 13 – “Each material is represented by seven maps: diffuse, diffuse normal, height, roughness, metallic and specular…To examine the individual contributions of various material maps to the rendering process, we incrementally introduced each material map—specifically, roughness, metalness, and displacement maps—into our model, shown in Fig. 9”; Note: Fig. 4 shows how a metalness parameter is set and used to generate a rendering of the model; see screenshot of Fig. 4 above. The metalness map is equivalent to the metalness module);
a roughness module configured to determine, for each PBR palette entry, a roughness parameter (Fig. 4, Paragraph 2 on Page 6, Paragraph 3 on Page 13 – “Each material is represented by seven maps: diffuse, diffuse normal, height, roughness, metallic and specular…To examine the individual contributions of various material maps to the rendering process, we incrementally introduced each material map—specifically, roughness, metalness, and displacement maps—into our model, shown in Fig. 9”; Note: Fig. 4 shows how a roughness parameter is set and used to generate a rendering of the model; see screenshot of Fig. 4 above. The roughness map is equivalent to the roughness module);
and an image generation module configured to generate an image of the CAD model from the PBR palette (Fig. 5, Paragraph 3 on Page 9, Paragraph 3 on Page 18 – “we successfully develop a pipeline for generating the material maps from 3D objects with constrained diffuse maps. This process finally project the generated material textures back onto the 3D meshes, thereby achieving a natural and authentic appearance, along with the physical properties of the materials available for photo-realistic rendering in different environment…This approach allows for the preservation of material visual quality in scenarios where the diffuse map is missing or of inferior quality, by leveraging the base color map as an effective substitute, thereby ensuring consistency and realism within the PBR workflow. Furthermore, the flexible application of various map types and 2D-3D alignment techniques enhances the detail and realism of rendered objects, meeting the demands of diverse rendering scenarios”; Note: the model is rendered using the “generated material textures”, which refers to the PBR palette. Fig. 5 shows the rendered image of the model; see screenshot of Fig. 5 above. The algorithm used for the rendering is equivalent to the image generation module).
Fang does not teach an H-S palette corresponding to the CAD model, the H-S palette having a plurality of H-S palette entries, each H-S palette entry of the plurality of H-S palette entries corresponding to a corresponding material from the plurality of materials. However, Bavitha teaches an H-S palette corresponding to the CAD model, the H-S palette having a plurality of H-S palette entries, each H-S palette entry of the plurality of H-S palette entries corresponding to a corresponding material from the plurality of materials (Paragraph 2 on Page 34, Paragraph 1-2 on Page 35 – “The HSV or Hue, Saturation and Value of a given object is the color space associated with the object in OpenCV where Hue represents the color, Saturation represents the greyness and Value represents the brightness…The syntax to define HSV range in OpenCV is as follows: hsvcolorspace = cv.cvtColor(image, cv.COLOR_BGR2HSV) lower_hsvcolorspace = np.array([Hue range, Saturation range, Value range]) upper_hsvcolorspace = np.array([Hue range, Saturation range, Value range]) Where hsvcolorspace is the conversion of the given image in RGB format to HSV format, lower_hsvcolorspace is the lower threshold for a range of some color, upper_hsvcolorspace is the upper threshold for a range of some color”; Note: colors of a RGB color palette, which is equivalent to a basic palette, are converted to an HSV palette. Because the colors are converted from a basic palette, it is implied that the material in the basic entry corresponds to an HSV entry. Basic palette and entries were previously taught by Fang earlier in the rejection). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Fang to incorporate the teachings of Bavitha to obtain and use an H-S palette because “The HSV or Hue, Saturation and Value of a given object is the color space associated with the object in OpenCV where Hue represents the color, Saturation represents the greyness and Value represents the brightness and it is used to solve the problems related to computer vision because of its better performance when compared to RGB or Red, Blue and Green color space and the Hue range in HSV is [0,179], the Saturation range in HSV is [0,255] and the Value range in HSV is [0,255] and to perform object detection, finding the range of HSV is necessary” (Bavitha: Paragraph 2 on Page 34, Paragraph 1 on Page 35). In other words, an HSV palette is useful for representing certain color details, which can help with tasks such as object detection and modeling.
Regarding claim 9, Fang in view of Bavitha teaches the system of claim 8. Fang does not teach a transformation module configured to generate the H-S palette from the basic palette. However, Bavitha teaches a transformation module configured to generate the H-S palette from the basic palette (Paragraph 2 on Page 34, Paragraph 1-2 on Page 35 – “The HSV or Hue, Saturation and Value of a given object is the color space associated with the object in OpenCV where Hue represents the color, Saturation represents the greyness and Value represents the brightness…The syntax to define HSV range in OpenCV is as follows: hsvcolorspace = cv.cvtColor(image, cv.COLOR_BGR2HSV) lower_hsvcolorspace = np.array([Hue range, Saturation range, Value range]) upper_hsvcolorspace = np.array([Hue range, Saturation range, Value range]) Where hsvcolorspace is the conversion of the given image in RGB format to HSV format, lower_hsvcolorspace is the lower threshold for a range of some color, upper_hsvcolorspace is the upper threshold for a range of some color”; Note: colors of a RGB color palette, which is equivalent to a basic palette, are converted to an HSV palette. The algorithm for the conversation corresponds to the transformation module). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Fang to incorporate the teachings of Bavitha to convert the basic palette to the H-S palette because “The HSV or Hue, Saturation and Value of a given object is the color space associated with the object in OpenCV where Hue represents the color, Saturation represents the greyness and Value represents the brightness and it is used to solve the problems related to computer vision because of its better performance when compared to RGB or Red, Blue and Green color space and the Hue range in HSV is [0,179], the Saturation range in HSV is [0,255] and the Value range in HSV is [0,255] and to perform object detection, finding the range of HSV is necessary” (Bavitha: Paragraph 2 on Page 34, Paragraph 1 on Page 35). In other words, an H-S palette is more useful than a basic RGB palette when performing certain tasks, making it beneficial to do the conversion from RGB to HSV.
Regarding claim 10, Fang in view of Bavitha teaches the system of claim 8. Fang further teaches wherein the color determination module is configured to determine, for each PBR palette entry, a color parameter by: setting the color value of the PBR palette to a color selected from the specular color of the corresponding basic palette entry and the diffuse color of the basic palette entry (Fig. 4, Paragraph 2 on Page 6, Paragraph 3 on Page 13 – “Each material is represented by seven maps: diffuse, diffuse normal, height, roughness, metallic and specular…”; Note: Specular and diffuse values are set, which together make up color. The specular and diffuse maps are equivalent to the color determination module. Fig. 4 also shows how the diffuse map is used to generate the material; see screenshot of Fig. 4 above).
Claims 15-16 and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Fang in view of Bavitha, Gadelmawla, Turbo, and Huynh-Thu et al. (US 20190266788 A1), hereinafter Huynh-Thu.
Regarding claim 15, Fang teaches a computer performing a method (Paragraph 2 on Page 16 – “We commence by elaborating on the generation of SVBRDF Maps, incorporating illustrative figures to detail the process involving operations in computer graphics”; Note: it is implied that a computer implements the method since they involve computer graphics operations) comprising:
obtaining a model (3D mesh/object [Wingdings font/0xE0] see quote below) for a computer-aided-design environment, the model comprising a plurality of materials (Fig. 1 Caption on Page 1, Paragraph 3 on Page 5, Paragraph 3 on Page 7 – “Make-it-Real can refine any diffuse-map-only 3D object from both CAD design and generative models…Our goal is to utilize the Make-it-Real pipeline for the identification and synthesis of Spatially Varing Bidirectional Reflectance Distribution (SVBRDF) material maps, leveraging the existing diffuse map and prior knowledge to query the most appropriate material attribute maps SV = {N, R, M, H, S}, to use as references before further refinement…Following the successful segmentation of regions for multi-views of 3D meshes, our subsequent objective focuses on the allocation of appropriate materials to these regions”; Note: the 3D meshes are models for CAD, and it is implied that they have materials because the method is used to identify and later render those materials);
obtaining a basic palette (diffuse RGB maps [Wingdings font/0xE0] see quote below) corresponding to the model, the basic palette comprising a plurality of basic palette entries (diffuse RGB maps of each segmented region [Wingdings font/0xE0] see quote) (Paragraph 3 on Page 5, Paragraph 2 on Page 9, Paragraph 2 on Page 18 – “To accurately identify and segment different material regions on 3D meshes with diffuse RGB, we adopt an innovative segmentation strategy based on multi-view 2D image rendering… To precisely estimate the spatially varying BRDFs per pixel, we draw on the common practice among artists who typically use the diffuse map as a guide to create maps for other material properties when generating various texture maps. This process involves using the original object’s diffuse map as a reference, and the matched material sphere’s diffuse map as a key. We finds the nearest neighbor pixel index in the key diffuse for each pixel’s RGB value in the query diffuse pixel by pixel, which we employ the K-d tree algorithm to accelerate the querying process… “diffuse” typically denotes the primary color of a material, a concept analogous to “base color” in Physically-Based Rendering (PBR) paradigms, both representing the inherent color of the material under uniformly scattered illumination”; Note: the diffuse maps make up a basic palette. The diffuse maps of each segmented region are basic palette entries);
finding from the plurality of basic palette entries, a finish value (Fig. 4, Paragraph 2 on Page 6, Paragraph 3 on Page 13 – “Each material is represented by seven maps: diffuse, diffuse normal, height, roughness, metallic and specular…To examine the individual contributions of various material maps to the rendering process, we incrementally introduced each material map—specifically, roughness, metalness, and displacement maps—into our model, shown in Fig. 9”; Note: Fig. 4 shows that a roughness value is found, which is equivalent to a finish value; see screenshot of Fig. 4 above);
creating from the basic palette, a physically based rendering (“PBR”) palette (PBR/BRDF maps [Wingdings font/0xE0] see quote below) for the model (Paragraph 4 on Page 8, Paragraph 2 on Page 9, Paragraph 2 on Page 14 – “referenced by the original diffuse map of an object, an estimation of BRDF values in the pixel space is performed by querying the matched realistic material maps…We get SVBRDF values at the corresponding pixel location by querying the key material map…we present a novel framework leveraging MLLMs prior of the world to build a material library and proposing an automatic pipeline to refine and synthesize new PBR maps for initial 3D models, achieving highly photorealistic PBR textures maps synthesis”; Note: starting from a diffuse maps, which make up a basic palette, additional values are found to create PBR maps, which make up a PBR palette), the PBR palette comprising a plurality of PBR palette entries, each PBR entry corresponding to a one of the basic palette entries of the basic palette (Paragraph 2 on Page 9 – “This process involves using the original object’s diffuse map as a reference, and the matched material sphere’s diffuse map as a key. We finds the nearest neighbor pixel index in the key diffuse for each pixel’s RGB value in the query diffuse pixel by pixel, which we employ the K-d tree algorithm to accelerate the querying process. We get SVBRDF values at the corresponding pixel location by querying the key material map. Notably, this procedure applies histogram equalization to both the query diffuse and key diffuse, normalizing them to a similar color space. This method enables the generation of a series of spatially variant BRDF maps, which maintain high consistency with the texture of the diffuse color”; Note: each PBR map corresponds to a diffuse map, which is implied because the diffuse map is used as a reference for each PBR map. The PBR map and BRDF map refer to the same thing), by, for each PBR palette entry:
setting the color value of the PBR palette to a color selected from the specular color of the corresponding basic palette entry and the diffuse color of the basic palette entry (Fig. 4, Paragraph 2 on Page 6, Paragraph 3 on Page 13 – “Each material is represented by seven maps: diffuse, diffuse normal, height, roughness, metallic and specular…”; Note: Specular and diffuse values are set, which together make up color. Fig. 4 also shows how the diffuse map is used to generate the material; see screenshot of Fig. 4 above);
setting a metalness parameter (Fig. 4, Paragraph 2 on Page 6, Paragraph 3 on Page 13 – “Each material is represented by seven maps: diffuse, diffuse normal, height, roughness, metallic and specular…To examine the individual contributions of various material maps to the rendering process, we incrementally introduced each material map—specifically, roughness, metalness, and displacement maps—into our model, shown in Fig. 9”; Note: Fig. 4 shows how a metalness parameter is set and used to generate a rendering of the model; see screenshot of Fig. 4 above);
setting a roughness parameter (Fig. 4, Paragraph 2 on Page 6, Paragraph 3 on Page 13 – “Each material is represented by seven maps: diffuse, diffuse normal, height, roughness, metallic and specular…To examine the individual contributions of various material maps to the rendering process, we incrementally introduced each material map—specifically, roughness, metalness, and displacement maps—into our model, shown in Fig. 9”; Note: Fig. 4 shows how a roughness parameter is set and used to generate a rendering of the model; see screenshot of Fig. 4 above);
and rendering an image from the PBR palette (Fig. 5, Paragraph 3 on Page 9, Paragraph 3 on Page 18 – “we successfully develop a pipeline for generating the material maps from 3D objects with constrained diffuse maps. This process finally project the generated material textures back onto the 3D meshes, thereby achieving a natural and authentic appearance, along with the physical properties of the materials available for photo-realistic rendering in different environment…This approach allows for the preservation of material visual quality in scenarios where the diffuse map is missing or of inferior quality, by leveraging the base color map as an effective substitute, thereby ensuring consistency and realism within the PBR workflow. Furthermore, the flexible application of various map types and 2D-3D alignment techniques enhances the detail and realism of rendered objects, meeting the demands of diverse rendering scenarios”; Note: the model is rendered using the “generated material textures”, which refers to the PBR palette. Fig. 5 shows the rendered image of the model; see screenshot of Fig. 5 above).
Fang does not teach a non-transitory computer readable medium having computer-executable code therein, which the computer executable code, when executed by a computer, causes the computer to perform a method. However, Huynh-Thu teaches a non-transitory computer readable medium having computer-executable code therein, which the computer executable code, when executed by a computer, causes the computer to perform a method (Paragraph 0068-0069 – “The software 133 is typically stored in the HDD 110 or the memory 106. The software is loaded into the computer system 100 from a computer readable medium, and executed by the computer system 100…the software can also be loaded into the computer system 100 from other computer readable media. Computer readable storage media refers to any non-transitory tangible storage medium that provides recorded instructions and/or data to the computer system 100 for execution and/or processing”). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Fang to incorporate the teachings of Huynh-Thu to have a non-transitory computer readable medium for the benefit of a persistent and reliable storage, which allows the code to run smoothly and to be used again in the future.
Fang modified by Huynh-Thu does not teach obtaining an H-S palette corresponding to the model, the H-S palette comprising a plurality of H-S entries, each H-S entry corresponding to a one of the basic palette entries, and comprising a corresponding hue parameter and a corresponding saturation parameter; “the H-S palette” from the limitation: “creating from the basic palette and the H-S palette, a physically based rendering (“PBR”) palette for the model”. However, Bavitha teaches obtaining an H-S palette (HSV [Wingdings font/0xE0] see quote below) corresponding to the model, the H-S palette comprising a plurality of H-S entries, each H-S entry corresponding to a one of the basic palette entries, and comprising a corresponding hue parameter and a corresponding saturation parameter (Paragraph 2 on Page 34, Paragraph 1-2 on Page 35 – “The HSV or Hue, Saturation and Value of a given object is the color space associated with the object in OpenCV where Hue represents the color, Saturation represents the greyness and Value represents the brightness…The syntax to define HSV range in OpenCV is as follows: hsvcolorspace = cv.cvtColor(image, cv.COLOR_BGR2HSV) lower_hsvcolorspace = np.array([Hue range, Saturation range, Value range]) upper_hsvcolorspace = np.array([Hue range, Saturation range, Value range]) Where hsvcolorspace is the conversion of the given image in RGB format to HSV format, lower_hsvcolorspace is the lower threshold for a range of some color, upper_hsvcolorspace is the upper threshold for a range of some color”; Note: colors of a RGB color palette, which is equivalent to a basic palette, are converted to an HSV palette. Because the colors are converted, it is implied that each basic entry corresponds to an HSV entry); and the H-S palette (Paragraph 2 on Page 34, Paragraph 1-2 on Page 35 – “The HSV or Hue, Saturation and Value of a given object is the color space associated with the object in OpenCV where Hue represents the color, Saturation represents the greyness and Value represents the brightness…The syntax to define HSV range in OpenCV is as follows:…”; Note: HSV is the H-S palette). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Fang to incorporate the teachings of Bavitha to obtain and use an H-S palette because “The HSV or Hue, Saturation and Value of a given object is the color space associated with the object in OpenCV where Hue represents the color, Saturation represents the greyness and Value represents the brightness and it is used to solve the problems related to computer vision because of its better performance when compared to RGB or Red, Blue and Green color space and the Hue range in HSV is [0,179], the Saturation range in HSV is [0,255] and the Value range in HSV is [0,255] and to perform object detection, finding the range of HSV is necessary” (Bavitha: Paragraph 2 on Page 34, Paragraph 1 on Page 35). In other words, an HSV palette is useful for representing certain color details, which can help with tasks such as object detection and modeling.
Furthermore, Fang modified by Huynh-Thu and Bavitha still does not teach finding from the plurality of basic palette entries, the minimum finish value and the maximum finish value; nor setting a roughness parameter based on the smallest finish value of the basic palette and the greatest finish value of the basic palette. However, Gadelmawla teaches finding from the plurality of basic palette entries, the minimum finish value and the maximum finish value (Paragraph 2 in 1st Col. of Page 1, Paragraph 1 in 2nd Col. of Page 1, Paragraph 5-6 in 2nd Col. of Page 4 – “The 2D roughness parameters then calculated for each section separately, and the average of each parameter is taken for all sections. This research presents all roughness parameters and their calculation methods. Amplitude parameters are the most important parameters to characterise surface topography. They are used to measure the vertical characteristics of the surface deviations… Rp is defined as the maximum height of the profile above the mean line within the assessment length… Rv is defined as the maximum depth of the profile below the mean line within the assessment length”; Note: the maximum height of the profile and the maximum depth of the profile are the equivalent to the maximum finish value and minimum finish value respectively); and setting a roughness parameter based on the smallest finish value of the basic palette and the greatest finish value of the basic palette (Paragraph 2 in 1st Col. of Page 1, Paragraph 1 in 2nd Col. of Page 1, Paragraph 5-6 in 2nd Col. of Page 4 – “The 2D roughness parameters then calculated for each section separately, and the average of each parameter is taken for all sections. This research presents all roughness parameters and their calculation methods. Amplitude parameters are the most important parameters to characterise surface topography. They are used to measure the vertical characteristics of the surface deviations… Rp is defined as the maximum height of the profile above the mean line within the assessment length… Rv is defined as the maximum depth of the profile below the mean line within the assessment length”; Note: the maximum height of the profile and the maximum depth of the profile are the equivalent to the greatest finish value and smallest finish value respectively. They are used to set a roughness parameter). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Fang to incorporate the teachings of Gadelmawla to use the max and min finish values for setting roughness because “Amplitude parameters are the most important parameters to characterise surface topography. They are used to measure the vertical characteristics of the surface deviations” (Gadelmawla: Paragraph 1 in 2nd Col. of Page 1).
Finally, Fang modified by Huynh-Thu, Bavitha, and Gadelmawla still does not teach setting a metalness parameter by assessing the specular hue and the diffuse hue from the corresponding H-S palette. However, Turbo teaches setting a metalness parameter by assessing the specular hue and the diffuse hue from the corresponding H-S palette (Page 6, 8-10 – “In a Metallic workflow, the BaseColor map technically contains both the Diffuse and Specular map information, while the Metallic map determines how much of the BaseColor map is interpreted as Diffuse output or as Specular output. The more metallic (white) an area on the map is the more it will have reflected color…A Metallic value of 0 makes a basic non-reflective material. The BaseColor will contribute only Diffuse color. Specular values will default to a mid grey tone…A Metallic value of 1 makes a completely reflective material. The BaseColor will contribute only Specular color values. The Diffuse color will default to pure black…Setting your Metallic value between 0 and 1 will blend the BaseColor a percentage. Think of it like pouring paint from one container to another. Diffuse contribution will darken toward black (emptying cup) and the default Specular grey will get more of the BaseColor added (filling cup) the higher the Metallic value”; Note: the diffuse and specular values determine the metalness parameter. The diffuse and specular hues from the HS palette were previously taught by Fang and Bavitha earlier in this rejection of claim 1). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Fang to incorporate the teachings of Turbo to use specular and diffuse values for setting metalness because the specular and diffuse values affect how metallic an object looks, and thus, they can be used toward determining the metalness of the object. For instance, using specular values adds reflectivity (Turbo: Page 6, 8-10); the image on page 8 of Turbo, shown above, demonstrates how the diffuse and specular values affect the metallic value.
Regarding claim 16, Fang in view of Bavitha, Gadelmawla, Turbo, and Huynh-Thu teaches the non-transitory computer readable medium of claim 15. Fang does not teach wherein the H-S palette comprises a palette in the Hue, Saturation, Value format. However, Bavitha teaches wherein the H-S palette comprises a palette in the Hue, Saturation, Value format (Paragraph 2 on Page 34, Paragraph 1-2 on Page 35 – “The HSV or Hue, Saturation and Value of a given object is the color space associated with the object in OpenCV where Hue represents the color, Saturation represents the greyness and Value represents the brightness”). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Fang to incorporate the teachings of Bavitha to have the H-S palette comprise the hue, saturation, and value format because “The HSV or Hue, Saturation and Value of a given object is the color space associated with the object in OpenCV where Hue represents the color, Saturation represents the greyness and Value represents the brightness and it is used to solve the problems related to computer vision because of its better performance when compared to RGB or Red, Blue and Green color space and the Hue range in HSV is [0,179], the Saturation range in HSV is [0,255] and the Value range in HSV is [0,255] and to perform object detection, finding the range of HSV is necessary” (Bavitha: Paragraph 2 on Page 34, Paragraph 1 on Page 35). In other words, an HSV palette is useful for representing certain color details, which can help with tasks such as object detection and modeling.
Regarding claim 18, Fang in view of Bavitha, Gadelmawla, Turbo, and Huynh-Thu teaches the non-transitory computer readable medium of claim 15. Fang does not teach wherein obtaining an H-S palette corresponding to the model comprises converting the basic palette to the H-S palette. However, Bavitha teaches converting the basic palette to the H-S palette (Paragraph 2 on Page 34, Paragraph 1-2 on Page 35 – “The HSV or Hue, Saturation and Value of a given object is the color space associated with the object in OpenCV where Hue represents the color, Saturation represents the greyness and Value represents the brightness…The syntax to define HSV range in OpenCV is as follows: hsvcolorspace = cv.cvtColor(image, cv.COLOR_BGR2HSV) lower_hsvcolorspace = np.array([Hue range, Saturation range, Value range]) upper_hsvcolorspace = np.array([Hue range, Saturation range, Value range]) Where hsvcolorspace is the conversion of the given image in RGB format to HSV format, lower_hsvcolorspace is the lower threshold for a range of some color, upper_hsvcolorspace is the upper threshold for a range of some color”; Note: colors of a RGB color palette, which is equivalent to a basic palette, are converted to an HSV palette). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Fang to incorporate the teachings of Bavitha to convert the basic palette to the H-S palette because “The HSV or Hue, Saturation and Value of a given object is the color space associated with the object in OpenCV where Hue represents the color, Saturation represents the greyness and Value represents the brightness and it is used to solve the problems related to computer vision because of its better performance when compared to RGB or Red, Blue and Green color space and the Hue range in HSV is [0,179], the Saturation range in HSV is [0,255] and the Value range in HSV is [0,255] and to perform object detection, finding the range of HSV is necessary” (Bavitha: Paragraph 2 on Page 34, Paragraph 1 on Page 35). In other words, an H-S palette is more useful than a basic RGB palette when performing certain tasks, making it beneficial to do the conversion from RGB to HSV.
Allowable Subject Matter
Claims 5-7, 11-14, 17, and 19-20 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.
Claim 5 would be allowable for disclosing wherein specular hue is quantitatively specified in degrees on a color wheel and diffuse hue is quantitatively specified in degrees on said color wheel, and wherein setting a metalness parameter by assessing the specular hue and the diffuse hue from the corresponding H-S palette comprises: for each entry in which the specular hue is not near the diffuse hue, setting the metalness parameter to a value of zero; and for each entry in which the specular hue is near the diffuse hue, and for which a ratio of diffuse saturation to specular saturation is not less than a predetermined diffuse/saturation threshold, setting the metalness parameter to a value of one; and for each entry in which the specular hue is near the diffuse hue, and for which a ratio of diffuse saturation to specular saturation is less than the predetermined diffuse/saturation threshold, setting the metalness parameter to a value of the ratio of diffuse saturation to specular saturation, wherein specular hue is near the diffuse hue if the specular hue is within a pre-determined number of degrees of the diffuse hue on the color wheel, and is otherwise not near the diffuse hue.
Regarding claim 5, Fang in view of Bavitha, Gadelmawla, and Turbo teaches the method of claim 1. Admesy teaches wherein specular hue is quantitatively specified in degrees on a color wheel and diffuse hue is quantitatively specified in degrees on said color wheel (Paragraph 2 on Page 3, Paragraph 2 on Page 4 – “An example of a spherical color model is the L*C*h* color space. It is based on a circular color scale, quite like a color wheel, with polar coordinates which define the chroma [saturation] and hue…Hue [h] is determined as an angle starting from red at 0° towards yellow at 90°. Green and blue are positioned at 180° and 270°, respectively”; Note: hue is specified in degrees on a color wheel. Specular and diffuse values were previously taught by Fang in the rejection of claim 1). Durmus et al. (EVALUATION OF HUE SHIFT FORMULAE IN CIELAB AND CAM02) teaches determining hue nearness (Paragraph 3 on Page 2 – “Hue angle ([Symbol font/0x44]h) and hue ([Symbol font/0x44]H) differences can be calculated in two of most widely used colour spaces”; Note: hue difference is a measure of how near one hue is to another). Turbo teaches setting a metalness parameter by assessing the specular hue and the diffuse hue from the corresponding H-S palette (Page 6, 8-10 – “In a Metallic workflow, the BaseColor map technically contains both the Diffuse and Specular map information, while the Metallic map determines how much of the BaseColor map is interpreted as Diffuse output or as Specular output. The more metallic (white) an area on the map is the more it will have reflected color…A Metallic value of 0 makes a basic non-reflective material. The BaseColor will contribute only Diffuse color. Specular values will default to a mid grey tone…A Metallic value of 1 makes a completely reflective material. The BaseColor will contribute only Specular color values. The Diffuse color will default to pure black…Setting your Metallic value between 0 and 1 will blend the BaseColor a percentage. Think of it like pouring paint from one container to another. Diffuse contribution will darken toward black (emptying cup) and the default Specular grey will get more of the BaseColor added (filling cup) the higher the Metallic value”; Note: the diffuse and specular values determine the metalness parameter. The diffuse and specular hues from the HS palette were previously taught by Fang and Bavitha earlier in this rejection of claim 1); setting the metalness parameter to a value of one (Page 9 – “A Metallic value of 1 makes a completely reflective material. The BaseColor will contribute only Specular color values. The Diffuse color will default to pure black”); and setting the metalness parameter to a value of zero (Page 8 – “A Metallic value of 0 makes a basic non-reflective material. The BaseColor will contribute only Diffuse color. Specular values will default to a mid grey tone”).
However, none of the prior art teaches the claim limitation of “for which a ratio of diffuse saturation to specular saturation is not less than a predetermined diffuse/saturation threshold, setting the metalness parameter to a value of one; and for each entry in which the specular hue is near the diffuse hue, and for which a ratio of diffuse saturation to specular saturation is less than the predetermined diffuse/saturation threshold, setting the metalness parameter to a value of the ratio of diffuse saturation to specular saturation, wherein specular hue is near the diffuse hue if the specular hue is within a pre-determined number of degrees of the diffuse hue on the color wheel, and is otherwise not near the diffuse hue”. Based on the configuration, it would be improper hindsight to modify Fang to have those features. Therefore, the combination of features is considered allowable.
Claim 6 would be allowable for disclosing wherein the pre-determined number of degrees comprises a Diffuse and Specular Nearness Parameter, and the method includes receiving specification of the Diffuse and Specular Nearness Parameter from a user interface.
Regarding claim 6, none of the prior art teaches the method of claim 5. Therefore, the combination of features is considered allowable.
Claim 7 would be allowable for disclosing wherein setting the metalness parameter by assessing the specular hue and the diffuse hue from the corresponding H-S palette comprises: categorizing the PBR entry into one of a set of categories, and setting the metalness to a metalness value corresponding to the category, wherein the categories are selected from: (a) a category for situations in which the specular hue is not near the diffuse hue, and the corresponding metalness value is zero; (b) a category for situations in which the specular hue is near the diffuse hue and the ratio of diffuse saturation to specular saturation is less than a threshold, and the corresponding metalness value is the ratio divided by the threshold; and (c) a category for situations in which the specular hue is not near the diffuse hue and the ratio of diffuse saturation to specular saturation is not less than the threshold, and the corresponding metalness value is one.
Regarding claim 7, Fang in view of Bavitha, Gadelmawla, and Turbo teaches the method of claim 1. Turbo teaches setting a metalness parameter by assessing the specular hue and the diffuse hue from the corresponding H-S palette and setting the metalness to a metalness value corresponding to a category (Page 6, 8-10 – “In a Metallic workflow, the BaseColor map technically contains both the Diffuse and Specular map information, while the Metallic map determines how much of the BaseColor map is interpreted as Diffuse output or as Specular output. The more metallic (white) an area on the map is the more it will have reflected color…A Metallic value of 0 makes a basic non-reflective material. The BaseColor will contribute only Diffuse color. Specular values will default to a mid grey tone…A Metallic value of 1 makes a completely reflective material. The BaseColor will contribute only Specular color values. The Diffuse color will default to pure black…Setting your Metallic value between 0 and 1 will blend the BaseColor a percentage. Think of it like pouring paint from one container to another. Diffuse contribution will darken toward black (emptying cup) and the default Specular grey will get more of the BaseColor added (filling cup) the higher the Metallic value”; Note: the diffuse and specular values determine the metalness parameter. The diffuse and specular hues from the HS palette were previously taught by Fang and Bavitha earlier in this rejection of claim 1); wherein the categories are selected from: a category for situations in which the corresponding metalness value is zero (Page 8 – “A Metallic value of 0 makes a basic non-reflective material. The BaseColor will contribute only Diffuse color. Specular values will default to a mid grey tone”); and a category for situations in which the corresponding metalness value is one (Page 9 – “A Metallic value of 1 makes a completely reflective material. The BaseColor will contribute only Specular color values. The Diffuse color will default to pure black”).
However, none of the prior art teaches the claim limitation of categorizing the PBR entry into one of a set of categories and having a category for situations in which the specular hue is near the diffuse hue and the ratio of diffuse saturation to specular saturation is less than a threshold, and the corresponding metalness value is the ratio divided by the threshold. Based on the configuration, it would be improper hindsight to modify Fang to have those features. Therefore, the combination of features is considered allowable. Therefore, the combination of features is considered allowable.
Claim 11 would be allowable for disclosing wherein specular hue is quantitatively specified in degrees on a color wheel and diffuse hue is quantitatively specified in degrees on said color wheel, and wherein setting a metalness parameter by assessing the specular hue and the diffuse hue from the corresponding H-S palette comprises: for each entry in which the specular hue is not near the diffuse hue, setting the metalness parameter to a value of zero; and for each entry in which the specular hue is near the diffuse hue, and for which a ratio of diffuse saturation to specular saturation is not less than a predetermined diffuse/saturation threshold, setting the metalness parameter to a value of one; and for each entry in which the specular hue is near the diffuse hue, and for which a ratio of diffuse saturation to specular saturation is less than the predetermined diffuse/saturation threshold, setting the metalness parameter to a value of the ratio of diffuse saturation to specular saturation, wherein specular hue is near the diffuse hue if the specular hue is within a pre-determined number of degrees of the diffuse hue on the color wheel, and is otherwise not near the diffuse hue.
Regarding claim 11, Fang in view of Bavitha teaches the system of claim 8. Admesy teaches wherein specular hue is quantitatively specified in degrees on a color wheel and diffuse hue is quantitatively specified in degrees on said color wheel (Paragraph 2 on Page 3, Paragraph 2 on Page 4 – “An example of a spherical color model is the L*C*h* color space. It is based on a circular color scale, quite like a color wheel, with polar coordinates which define the chroma [saturation] and hue…Hue [h] is determined as an angle starting from red at 0° towards yellow at 90°. Green and blue are positioned at 180° and 270°, respectively”; Note: hue is specified in degrees on a color wheel. Specular and diffuse values were previously taught by Fang in the rejection of claim 1). Durmus et al. (EVALUATION OF HUE SHIFT FORMULAE IN CIELAB AND CAM02) teaches determining hue nearness (Paragraph 3 on Page 2 – “Hue angle ([Symbol font/0x44]h) and hue ([Symbol font/0x44]H) differences can be calculated in two of most widely used colour spaces”; Note: hue difference is a measure of how near one hue is to another). Turbo teaches setting a metalness parameter by assessing the specular hue and the diffuse hue from the corresponding H-S palette (Page 6, 8-10 – “In a Metallic workflow, the BaseColor map technically contains both the Diffuse and Specular map information, while the Metallic map determines how much of the BaseColor map is interpreted as Diffuse output or as Specular output. The more metallic (white) an area on the map is the more it will have reflected color…A Metallic value of 0 makes a basic non-reflective material. The BaseColor will contribute only Diffuse color. Specular values will default to a mid grey tone…A Metallic value of 1 makes a completely reflective material. The BaseColor will contribute only Specular color values. The Diffuse color will default to pure black…Setting your Metallic value between 0 and 1 will blend the BaseColor a percentage. Think of it like pouring paint from one container to another. Diffuse contribution will darken toward black (emptying cup) and the default Specular grey will get more of the BaseColor added (filling cup) the higher the Metallic value”; Note: the diffuse and specular values determine the metalness parameter. The diffuse and specular hues from the HS palette were previously taught by Fang and Bavitha earlier in this rejection of claim 1); setting the metalness parameter to a value of one (Page 9 – “A Metallic value of 1 makes a completely reflective material. The BaseColor will contribute only Specular color values. The Diffuse color will default to pure black”); and setting the metalness parameter to a value of zero (Page 8 – “A Metallic value of 0 makes a basic non-reflective material. The BaseColor will contribute only Diffuse color. Specular values will default to a mid grey tone”).
However, none of the prior art teaches the claim limitation of “for which a ratio of diffuse saturation to specular saturation is not less than a predetermined diffuse/saturation threshold, setting the metalness parameter to a value of one; and for each entry in which the specular hue is near the diffuse hue, and for which a ratio of diffuse saturation to specular saturation is less than the predetermined diffuse/saturation threshold, setting the metalness parameter to a value of the ratio of diffuse saturation to specular saturation, wherein specular hue is near the diffuse hue if the specular hue is within a pre-determined number of degrees of the diffuse hue on the color wheel, and is otherwise not near the diffuse hue”. Based on the configuration, it would be improper hindsight to modify Fang to have those features. Therefore, the combination of features is considered allowable.
Claim 12 would be allowable for disclosing wherein the pre-determined number of degrees comprises a Diffuse and Specular Nearness Parameter, and the method includes receiving specification of the Diffuse and Specular Nearness Parameter from a user interface.
Regarding claim 12, none of the prior art teaches the system of claim 11. Therefore, the combination of features is considered allowable.
Claim 13 would be allowable for disclosing wherein the metalness module is configured to determine, for each PBR palette entry, a metalness parameter by: categorizing the PBR entry into one of a set of categories, and setting the metalness to a metalness value corresponding to the category, wherein the categories are selected from: (a) a category for situations in which the specular hue is not near the diffuse hue, and the corresponding metalness value is zero; (b) a category for situations in which the specular hue is near the diffuse hue and the ratio of diffuse saturation to specular saturation is less than a threshold, and the corresponding metalness value is the ratio divided by the threshold; and (c) a category for situations in which the specular hue is not near the diffuse hue and the ratio of diffuse saturation to specular saturation is not less than the threshold, and the corresponding metalness value is one.
Regarding claim 13, Fang in view of Bavitha teaches the system of claim 8. Turbo teaches setting the metalness to a metalness value corresponding to a category (Page 6, 8-10 – “In a Metallic workflow, the BaseColor map technically contains both the Diffuse and Specular map information, while the Metallic map determines how much of the BaseColor map is interpreted as Diffuse output or as Specular output. The more metallic (white) an area on the map is the more it will have reflected color…A Metallic value of 0 makes a basic non-reflective material. The BaseColor will contribute only Diffuse color. Specular values will default to a mid grey tone…A Metallic value of 1 makes a completely reflective material. The BaseColor will contribute only Specular color values. The Diffuse color will default to pure black…Setting your Metallic value between 0 and 1 will blend the BaseColor a percentage. Think of it like pouring paint from one container to another. Diffuse contribution will darken toward black (emptying cup) and the default Specular grey will get more of the BaseColor added (filling cup) the higher the Metallic value”; Note: the diffuse and specular values determine the metalness parameter. The diffuse and specular hues from the HS palette were previously taught by Fang and Bavitha earlier in this rejection of claim 1); wherein the categories are selected from: a category for situations in which the corresponding metalness value is zero (Page 8 – “A Metallic value of 0 makes a basic non-reflective material. The BaseColor will contribute only Diffuse color. Specular values will default to a mid grey tone”); and a category for situations in which the corresponding metalness value is one (Page 9 – “A Metallic value of 1 makes a completely reflective material. The BaseColor will contribute only Specular color values. The Diffuse color will default to pure black”).
However, none of the prior art teaches the claim limitation of categorizing the PBR entry into one of a set of categories and having a category for situations in which the specular hue is near the diffuse hue and the ratio of diffuse saturation to specular saturation is less than a threshold, and the corresponding metalness value is the ratio divided by the threshold. Based on the configuration, it would be improper hindsight to modify Fang to have those features. Therefore, the combination of features is considered allowable. Therefore, the combination of features is considered allowable.
Claim 14 would be allowable for disclosing wherein the metalness module is configured to determine, for each PBR palette entry, a metalness parameter by: receiving specification of a Diffuse/Specular Threshold Parameter from a user interface; and comparing a ratio of diffuse hue to specular hue to the Diffuse/Specular Threshold Parameter.
Regarding claim 14, Fang in view of Bavitha teaches the system of claim 8. Hobart and William Smith Colleges (Introduction to Lighting) teaches receiving specification of a Diffuse/Specular Parameter from a user interface (Image on Page 3 – see below).
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Screenshot of Image on Page 3 (taken from Hobart and William Smith Colleges)
However, none of the prior art teaches the claim limitation of receiving specification of a Diffuse/Specular Threshold Parameter from a user interface; and comparing a ratio of diffuse hue to specular hue to the Diffuse/Specular Threshold Parameter. Hobart and William Smith Colleges teaches receiving specification of a Diffuse/Specular Parameter from a user interface (see image above), but it does not teach receiving a Diffuse/Specular Threshold Parameter. Based on the configuration, it would be improper hindsight to modify Fang to have those features. Therefore, the combination of features is considered allowable.
Claim 17 would be allowable for disclosing wherein specular hue is quantitatively specified in degrees on a color wheel and diffuse hue is quantitatively specified in degrees on said color wheel, and wherein setting a metalness parameter by assessing the specular hue and the diffuse hue from the corresponding H-S palette comprises: for each entry in which the specular hue is not near the diffuse hue, setting the metalness parameter to a value of zero; and for each entry in which the specular hue is near the diffuse hue, and for which a ratio of diffuse saturation to specular saturation is not less than a predetermined diffuse/saturation threshold, setting the metalness parameter to a value of one; and for each entry in which the specular hue is near the diffuse hue, and for which a ratio of diffuse saturation to specular saturation is less than the predetermined diffuse/saturation threshold, setting the metalness parameter to a value of the ratio of diffuse saturation to specular saturation.
Regarding claim 17, Fang in view of Bavitha, Gadelmawla, Turbo, and Huynh-Thu teaches the non-transitory computer readable medium of claim 15. Admesy teaches wherein specular hue is quantitatively specified in degrees on a color wheel and diffuse hue is quantitatively specified in degrees on said color wheel (Paragraph 2 on Page 3, Paragraph 2 on Page 4 – “An example of a spherical color model is the L*C*h* color space. It is based on a circular color scale, quite like a color wheel, with polar coordinates which define the chroma [saturation] and hue…Hue [h] is determined as an angle starting from red at 0° towards yellow at 90°. Green and blue are positioned at 180° and 270°, respectively”; Note: hue is specified in degrees on a color wheel. Specular and diffuse values were previously taught by Fang in the rejection of claim 1). Durmus et al. (EVALUATION OF HUE SHIFT FORMULAE IN CIELAB AND CAM02) teaches determining hue nearness (Paragraph 3 on Page 2 – “Hue angle ([Symbol font/0x44]h) and hue ([Symbol font/0x44]H) differences can be calculated in two of most widely used colour spaces”; Note: hue difference is a measure of how near one hue is to another). Turbo teaches setting a metalness parameter by assessing the specular hue and the diffuse hue from the corresponding H-S palette (Page 6, 8-10 – “In a Metallic workflow, the BaseColor map technically contains both the Diffuse and Specular map information, while the Metallic map determines how much of the BaseColor map is interpreted as Diffuse output or as Specular output. The more metallic (white) an area on the map is the more it will have reflected color…A Metallic value of 0 makes a basic non-reflective material. The BaseColor will contribute only Diffuse color. Specular values will default to a mid grey tone…A Metallic value of 1 makes a completely reflective material. The BaseColor will contribute only Specular color values. The Diffuse color will default to pure black…Setting your Metallic value between 0 and 1 will blend the BaseColor a percentage. Think of it like pouring paint from one container to another. Diffuse contribution will darken toward black (emptying cup) and the default Specular grey will get more of the BaseColor added (filling cup) the higher the Metallic value”; Note: the diffuse and specular values determine the metalness parameter. The diffuse and specular hues from the HS palette were previously taught by Fang and Bavitha earlier in this rejection of claim 1); setting the metalness parameter to a value of one (Page 9 – “A Metallic value of 1 makes a completely reflective material. The BaseColor will contribute only Specular color values. The Diffuse color will default to pure black”); and setting the metalness parameter to a value of zero (Page 8 – “A Metallic value of 0 makes a basic non-reflective material. The BaseColor will contribute only Diffuse color. Specular values will default to a mid grey tone”).
However, none of the prior art teaches the claim limitation of “for which a ratio of diffuse saturation to specular saturation is not less than a predetermined diffuse/saturation threshold, setting the metalness parameter to a value of one; and for each entry in which the specular hue is near the diffuse hue, and for which a ratio of diffuse saturation to specular saturation is less than the predetermined diffuse/saturation threshold, setting the metalness parameter to a value of the ratio of diffuse saturation to specular saturation”. Based on the configuration, it would be improper hindsight to modify Fang to have those features. Therefore, the combination of features is considered allowable.
Claim 19 would be allowable for disclosing wherein setting a metalness parameter by assessing the specular hue and the diffuse hue from the corresponding H-S palette comprises: categorizing the PBR entry into one of a set of categories, and setting the metalness to a metalness value corresponding to the category, wherein the categories are selected from: (a) a category for situations in which the specular hue is not near the diffuse hue, and the corresponding metalness value is zero; (b) a category for situations in which the specular hue is near the diffuse hue and the ratio of diffuse saturation to specular saturation is less than a threshold, and the corresponding metalness value is the ratio divided by the threshold; and (c) a category for situations in which the specular hue is not near the diffuse hue and the ratio of diffuse saturation to specular saturation is not less than the threshold, and the corresponding metalness value is one.
Regarding claim 19, Fang in view of Bavitha, Gadelmawla, Turbo, and Huynh-Thu teaches the non-transitory computer readable medium of claim 15. Turbo teaches wherein setting a metalness parameter by assessing the specular hue and the diffuse hue from the corresponding H-S palette comprises: setting the metalness to a metalness value corresponding to a category (Page 6, 8-10 – “In a Metallic workflow, the BaseColor map technically contains both the Diffuse and Specular map information, while the Metallic map determines how much of the BaseColor map is interpreted as Diffuse output or as Specular output. The more metallic (white) an area on the map is the more it will have reflected color…A Metallic value of 0 makes a basic non-reflective material. The BaseColor will contribute only Diffuse color. Specular values will default to a mid grey tone…A Metallic value of 1 makes a completely reflective material. The BaseColor will contribute only Specular color values. The Diffuse color will default to pure black…Setting your Metallic value between 0 and 1 will blend the BaseColor a percentage. Think of it like pouring paint from one container to another. Diffuse contribution will darken toward black (emptying cup) and the default Specular grey will get more of the BaseColor added (filling cup) the higher the Metallic value”; Note: the diffuse and specular values determine the metalness parameter. The diffuse and specular hues from the HS palette were previously taught by Fang and Bavitha earlier in this rejection of claim 1); wherein the categories are selected from: a category for situations in which the corresponding metalness value is zero (Page 8 – “A Metallic value of 0 makes a basic non-reflective material. The BaseColor will contribute only Diffuse color. Specular values will default to a mid grey tone”); and a category for situations in which the corresponding metalness value is one (Page 9 – “A Metallic value of 1 makes a completely reflective material. The BaseColor will contribute only Specular color values. The Diffuse color will default to pure black”).
However, none of the prior art teaches the claim limitation of categorizing the PBR entry into one of a set of categories and having a category for situations in which the specular hue is near the diffuse hue and the ratio of diffuse saturation to specular saturation is less than a threshold, and the corresponding metalness value is the ratio divided by the threshold. Based on the configuration, it would be improper hindsight to modify Fang to have those features. Therefore, the combination of features is considered allowable. Therefore, the combination of features is considered allowable.
Claim 20 would be allowable for disclosing wherein assessing the specular hue and the diffuse hue from the corresponding H-S palette comprises: receiving specification of a Diffuse/Specular Threshold Parameter from a user interface; and comparing a ratio of diffuse hue to specular hue to the Diffuse/Specular Threshold Parameter.
Regarding claim 20, Fang in view of Bavitha, Gadelmawla, Turbo, and Huynh-Thu teaches the non-transitory computer readable medium of claim 15. Hobart and William Smith Colleges (Introduction to Lighting) teaches receiving specification of a Diffuse/Specular Parameter from a user interface (Image on Page 3 – see above). However, none of the prior art teaches the claim limitation of receiving specification of a Diffuse/Specular Threshold Parameter from a user interface; and comparing a ratio of diffuse hue to specular hue to the Diffuse/Specular Threshold Parameter. Hobart and William Smith Colleges teaches receiving specification of a Diffuse/Specular Parameter from a user interface (see image above), but it does not teach receiving a Diffuse/Specular Threshold Parameter. Based on the configuration, it would be improper hindsight to modify Fang to have those features. Therefore, the combination of features is considered allowable.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Hickman et al. (US 8525846 B1) teaches a method of shading and rendering 3D object models based on material properties, including diffuse and specular maps.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MICHELLE HAU MA whose telephone number is (571)272-2187. The examiner can normally be reached M-Th 7-5:30.
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/MICHELLE HAU MA/ Examiner, Art Unit 2617
/KING Y POON/Supervisory Patent Examiner, Art Unit 2617