DETAILED ACTION
Notice of Pre-AIA or AIA Status
1. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Continued Examination Under 37 CFR 1.114
2. A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on January 15, 2026 has been entered.
Response to Amendment
3. The amendment filed January 15, 2026 has been entered. Claims 1-20 remain pending in the application.
Response to Arguments
4. Applicant's arguments filed January have been fully considered but they are not persuasive.
5. Applicant argues that Chupeau et al. (U.S. Patent Application Publication No. 2024/0249462 A1), hereinafter referred to as Chupeau, and Criminisi et al. (“Extracting Layers and Analyzing their Specular Properties using Epipolar-Plane-Image Analysis”), hereinafter referred to as Criminisi, fail to teach “selecting the portion of the pixels from one or more of a plurality of epipolar plane image (EPI) lines based on curvature values of the one or more of the plurality of EPI lines being nonzero”.
Examiner replies Criminisi does teach the above cited limitation cited above. Criminisi in Section 5.1.1. teaches in paragraph 3-4 that the EPI traces are curved for reflected points in a scene and can be detected through the disparity deviation or curvature value of the EPI trace or line. Thus, detecting the curved EPI traces or lines for the reflected or non-diffuse points teaches a nonzero curvature value. The pixels or points are also selected by this detection.
Criminisi in Section 5.1.2 also teaches the disparity value is the curvature in the EPI and can be calculated through equation 12. The values calculated can be non-zero which teaches curvature values of one or more EPI lines being nonzero. Fig. 18 and Section 5.2.1 teaches detecting pixels or surface points with curved EPI lines as seen in Fig. 18C. This teaches a curvature value of an EPI line being nonzero. Thus, Criminisi teaches selecting a portion of pixels based on curvatures values of the EPI lines being nonzero.
6. Applicant argues that Chupeau, Criminisi, and Thoreau et al. (European Patent Application No. 3,171)1,598 A1), hereinafter referred to as Thoreau, fail to teach “a quantity of the selected portion of the pixels in the one or more non-diffuse objects of the scene being determined based on a function of at least one of (i) a pixel processing rate of a client device that corresponds to a capacity of the client device to process pixel data per unit of time or (ii) a transmission rate to the client device”.
Examiner replies that after another review, Criminisi paragraph 120 teaches a quantity of the selected portion of the pixels is determined based on a function of a transmission rate to the client device. Paragraph 120 teaches “To optimize the size of the stream, only a subset of the points of the scene may be encoded, for instance the subset of points that may be seen according to a rendering space of view”. The stream being optimized is the bitstream from the encoder to the decoder as seen in Figure 2, marker 22. Thus, this bitstream size optimization teaches a function of a transmission rate that affects the quantity of pixels selected for the encoding. The size of the bitstream teaches a transmission rate affecting the selection of the portion of pixels because the size of the bitstream affects the quantity of data which can be transmitted to a client device.
7. Conclusion: Thus, all independent claims and their dependents remain rejected.
Claim Rejections - 35 USC § 103
8. 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.
9. The text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action.
10. Claim(s) 1-3, 5-7, 9-10, 13-15, and 17-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Chupeau et al. (U.S. Patent Application Publication No. 2024/0249462 A1), hereinafter referred to as Chupeau, in view of Criminisi et al. (“Extracting Layers and Analyzing their Specular Properties using Epipolar-Plane-Image Analysis”), hereinafter referred to as Criminisi.
11. Regarding claim 1, Chupeau teaches a method of media processing, comprising:
encoding, by processing circuitry of a server device (Paragraph 22 teaches the method is run with a processor; Paragraph 107 teaches the method can be implemented with a server), first media data of a basic view of a scene and second media data of an alternate view of the scene into a bitstream (Paragraph 70 and Figure 2 teach the media data of views 20 input into an encoder 21 which becomes a bitstream. The views include multiple views, one that can be the basic view and the other the alternate view),
the second media data lacking reflectance information for one or more non-diffuse objects of the scene in the alternate view (Paragraph 130 and Figure 8 teach two views, 811 and 835, taken of the same scene. One view can be considered the basic view and the other the alternate view and has non-diffuse objects like an oven door and reflective vase. The views can comprise information on points or objects in the 3D scene that are not visible from the other view. The other view can be considered the alternate view that lacks information; Paragraph 10-19 teach information on parts in the scene include reflectance information. Thus, the alternate view and its media data can lack reflectance information on parts in the scene);
a quantity of the selected portion of the pixels in the one or more non-diffuse objects of the scene being determined based on a function of at least one of (i) a pixel processing rate of a client device that corresponds to a capacity of the client device to process pixel data per unit of time or (ii) a transmission rate to the client device (Paragraph 120 teaches “To optimize the size of the stream, only a subset of the points of the scene may be encoded, for instance the subset of points that may be seen according to a rendering space of view”. The stream being optimized is the bitstream from the encoder to the decoder as seen in Figure 2, marker 22. This bitstream size optimization teaches a function of a transmission rate that affects the quantity of pixels selected for the encoding. The size of the bitstream teaches a transmission rate affecting the selection of the portion of pixels because the size of the bitstream affects the quantity of data which can be transmitted to a client device);
generating non-diffuse data that indicates the reflectance information of the portion of the pixels in the one or more non-diffuse objects of the scene, the non-diffuse data including a first number of data pieces that indicate the reflectance information, the first number being smaller than a second number of pixels in the one or more non-diffuse objects (Paragraphs 10-17 teaches generating the reflectance information which is the non-diffuse data for patches in the scene. The patches are the data pieces. Each reflectance patch has reflectance information in the form of a Bidirectional Reflectance Distribution model; Paragraph 21 teaches the reflectance patches correspond to the non-diffuse parts in the scene which are the non-diffuse objects; Paragraph 111 teaches the patches are a clustering of projected pixels. Thus, if patches comprise of multiple pixels, there will be less patches or data pieces than there are pixels);
and transmitting the bitstream and the non-diffuse data to the client device (Paragraph 70 and Figure 2 teach the bitstream and non-diffuse data 22 is transmitted to a decoder 23; Paragraph 80 teaches the decoder can be a device like in a head-mounted device which is a client device).
However, Chupeau is not relied upon for the below claim language: selecting a portion of pixels in the one or more non-diffuse objects of the scene, the selecting the portion of the pixels in the one or more non-diffuse objects including selecting the portion of the pixels from one or more of a plurality of epipolar plane image (EPI) lines based on curvature values of the one or more of the plurality of EPI lines being nonzero, the plurality of EPI lines being associated with the pixels of the one or more non-diffuse objects of the scene.
Criminisi teaches selecting a portion of pixels in the one or more non-diffuse objects of the scene, the selecting the portion of the pixels in the one or more non-diffuse objects including selecting the portion of the pixels from one or more of a plurality of epipolar plane image (EPI) lines based on curvature values of the one or more of the plurality of EPI lines being nonzero (Section 5 Paragraph 3 teaches detecting specular reflections through EPI; Section 5.1.1. teaches in paragraph 3-4 that the EPI traces are curved for reflected points in a scene and can be detected through the disparity deviation or curve value of the EPI trace or line. Thus, detecting the curved EPI traces or lines detects reflecting or non-diffuse objects. The points are selected by the detection; Section 5.1.2 teaches the disparity value is the curvature in the EPI and can be calculated through equation 12. The values calculated can be non-zero which teaches curvature values of one or more EPI lines being nonzero; Fig. 18 and Section 5.2.1 teaches detecting pixels or surface points with curved EPI lines as seen in Fig. 18C. This teaches a curvature value of an EPI line being nonzero; Section 5.2.1.3 teaches that specular points or non-diffuse objects can be detected through the curvature values and photometric constraints),
the plurality of EPI lines being associated with the pixels of the one or more non-diffuse objects of the scene (Section 5, Paragraph 3 teaches characterizing specular reflections of shiny objects within the EPI framework. Thus, the lines in the EPI are associated with the pixels of a reflective or non-diffuse object; Section 5.1.1.1 Paragraph teaches the EPI lines are associated with points or pixels in the image. It also teaches that Figure 15 shows an EPI curve for a specular surface. A specular surface can be considered a non-diffuse object. Thus, the EPI curve or line is associated with pixels for a non-diffuse object).
Chupeau and Criminisi are considered analogous to the claimed invention as because both are in the same field of analyzing the reflections of an object from multiple images. Thus, it would have been obvious to a person holding ordinary skill in the art before the effective filing date to modify the method of generating non-diffuse data taught by Chupeau with the selection of pixels from EPI lines taught by Criminisi in order to improve 3D scene reconstruction and the textures of regions by using EPI (Chupeau Introduction).
12. Regarding claim 2, Chupeau in view of Criminisi teaches the limitations of claim 1. Chupeau further teaches the method wherein the generating the non-diffuse data further comprises: generating the non-diffuse data that includes the reflectance information of the portion of the pixels in the one or more non-diffuse objects (Paragraphs 10-19 teach creating patches which are a portion of pixels and then generating the Bidirectional Reflectance Distribution Function (BRDF) model for the patch. The BRDF is the generated reflectance information; Paragraph 146 teaches the reflectance atlas which has reflectance patches corresponding to the reflecting parts of the 3D scene which are the non-diffuse objects; Paragraph 111 teaches patches are a clustering of pixels; Paragraph 130 teaches the scene can have one or more non-diffuse objects like the vase 82 and oven door 81).
13. Regarding claim 3, Chupeau in view of Criminisi teaches the limitations of claim 1. Chupeau further teaches the method wherein the selecting the portion of the pixels in the one or more non-diffuse objects further comprises: selecting the portion of the pixels according to the at least one of the pixel processing rate of the client device or the transmission rate to the client device (Paragraph 120 teaches “To optimize the size of the stream, only a subset of the points of the scene may be encoded, for instance the subset of points that may be seen according to a rendering space of view”. The stream being optimized is the bitstream from the encoder to the decoder as seen in Figure 2, marker 22. This bitstream size optimization teaches a function of a transmission rate that affects the quantity of pixels selected for the encoding. The size of the bitstream teaches a transmission rate affecting the selection of the portion of pixels because the size of the bitstream affects the quantity of data which can be transmitted to a client device).
14. Regarding claim 5, Chupeau in view of Criminisi teaches the limitations of claim 1. However, Chupeau fails to teach the method wherein the selecting the portion of the pixels in the one or more non-diffuse objects further comprises: associating the pixels in the one or more non-diffuse objects with the plurality of EPI lines; determining the respective curvature values of the plurality of EPI lines; and selecting the portion of the pixels according to the respective curvature values of the plurality of EPI lines.
Criminisi teaches the method wherein the selecting the portion of the pixels in the one or more non-diffuse objects further comprises: associating the pixels in the one or more non-diffuse objects with the plurality of EPI lines (Section 5, Paragraph 3 teaches characterizing specular reflections of shiny objects within the EPI framework. Thus, the lines in the EPI are associated with the pixels of a reflective or non-diffuse object; Section 5.1.1.1 Paragraph teaches the EPI lines are associated with points or pixels in the image. It also teaches that Figure 15 shows an EPI curve for a specular surface. A specular surface can be considered a non-diffuse object. Thus, the EPI curve or line is associated with pixels for a non-diffuse object);
determining the respective curvature values of the plurality of EPI lines; and selecting the portion of the pixels according to the respective curvature values of the plurality of EPI lines (Section 5 Paragraph 3 teaches detecting specular reflections through EPI; Section 5.1.1. teaches in paragraph 3-4 that the EPI traces are curved for reflected points in a scene and can be detected through the disparity deviation or curve value of the EPI trace or line. Thus, detecting the curved EPI traces or lines detects reflecting or non-diffuse objects. The points are selected by the detection; Section 5.1.2 teaches the disparity value is the curvature in the EPI and can be calculated through equation 12; Fig. 18 and Section 5.2.1 teaches detecting pixels or surface points with curved EPI lines as seen in Fig. 18C; Section 5.2.1.3 teaches that specular points or non-diffuse objects can be detected through the curvature values and photometric constraints).
Chupeau and Criminisi are considered analogous to the claimed invention as because both are in the same field of analyzing the reflections of an object from multiple images. Thus, it would have been obvious to a person holding ordinary skill in the art before the effective filing date to modify the method of generating non-diffuse data taught by Chupeau with the selection of pixels from EPI lines taught by Criminisi in order to improve 3D scene reconstruction and the textures of regions by using EPI (Chupeau Introduction).
15. Regarding claim 6, Chupeau in view of Criminisi teaches the limitations of claim 1. Chupeau further teaches the method further comprising: selecting an encoding scheme for coding the non-diffuse data according to the at least one of the pixel processing rate of the client device or the transmission rate to the client device (Paragraph 120 teaches “To optimize the size of the stream, only a subset of the points of the scene may be encoded, for instance the subset of points that may be seen according to a rendering space of view”. It also teaches that the data of the points also contain data like the texture and geometry. The stream being optimized is the bitstream from the encoder to the decoder as seen in Figure 2, marker 22. The size of the bitstream teaches a transmission rate affecting the encoding scheme selection because the size of the bitstream affects the quantity of data which can be transmitted to a client device. Since the Applicant does not define “encoding scheme” in the claims, the encoding scheme can be broadly interpreted to be the subset of points and their texture and geometry that are chosen to be selected for encoding like the ones seen according to a rendering space of view).
16. Regarding claim 7, Chupeau in view of Criminisi teaches the limitations of claim 1. Chupeau further teaches the method wherein the non-diffuse data comprises the data pieces for the reflectance information respectively corresponding to the one or more non-diffuse objects (Paragraphs 10-19 teach creating patches which are a portion of pixels and then generating the Bidirectional Reflectance Distribution Function (BRDF) model for the patch. The BRDF is the generated reflectance information; Paragraph 146 teaches creating reflectance patches, or data pieces, corresponding to the reflecting parts of the 3D scene. The reflecting parts of the 3D scene are the non-diffuse objects; Paragraph 130 teaches the scene can have one or more non-diffuse objects like the vase 82 and oven door 81).
17. Regarding claim 9, Chupeau in view of Criminisi teaches the limitations of claim 7. Chupeau further teaches the method wherein one of the data pieces for the reflectance information corresponding to a non-diffuse object in the one or more non-diffuse objects comprises a set of parameters of a reflectance model of the non-diffuse object (Paragraphs 10-19 teach creating patches which are a portion of pixels and then generating the parameters for the Bidirectional Reflectance Distribution Function (BRDF) model for the patch. The BRDF is the generated reflectance information; Paragraph 146 teaches the reflectance patches have BRDF information for patches in the scene; Paragraph 130 teaches the scene can have one or more non-diffuse objects like the vase 82 and oven door 81).
18. Regarding claim 10, Chupeau in view of Criminisi teaches the limitations of claim 1. Chupeau further teaches the method wherein the generating the non-diffuse data further comprises: receiving a position for a specific view of the scene; generating the specific view associated with the position (Paragraph 86 teaches rendering a synthesized view from a new point of view. Synthesizing a view from a new point of view inherently includes receiving the position which is the new point of view); and generating the non-diffuse data that indicates the reflectance information for the one or more non-diffuse objects in the specific view (Paragraph 86 teaches modifying the position and appearance of the reflected content and generating information for rendering of a complex light effect; Paragraph 121 teaches rendering with ray-tracing and using the light reflection properties from the patches to synthesize a view with realistic light effects. This teaches generating non-diffuse data with reflectance information for objects in the view).
19. Regarding claim 13, claim 13 is the server device claim (Chupeau Paragraphs 98-107 and Figure 3 teach the device 30, which runs the method, can be a server; Paragraphs 87-88 teach the device 30 has processing circuitry to encode the data) of method claim 1 and is accordingly rejected using substantially similar rationale as to that which is set for with respect to claim 1.
20. Regarding claim 14, Chupeau in view of Criminisi teaches the limitations of claim 13. Claim 14 is similar in scope to claim 2. Therefore, similar rationale as applied in the rejection of claim 2 applies herein.
21. Regarding claim 15, Chupeau in view of Criminisi teaches the limitations of claim 13. Claim 15 is similar in scope to claim 3. Therefore, similar rationale as applied in the rejection of claim 3 applies herein.
22. Regarding claim 17, Chupeau in view of Criminisi teaches the limitations of claim 13. Claim 17 is similar in scope to claim 6. Therefore, similar rationale as applied in the rejection of claim 6 applies herein.
23. Regarding claim 18, Chupeau in view of Criminisi teaches the limitations of claim 13. Claim 18 is similar in scope to claim 7. Therefore, similar rationale as applied in the rejection of claim 7 applies herein.
24. Regarding claim 19, Chupeau in view of Criminisi teaches the limitations of claim 13. Claim 19 is similar in scope to claim 10. Therefore, similar rationale as applied in the rejection of claim 10 applies herein.
25. Regarding claim 20, claim 20 is the non-transitory computer readable storage medium claim (Chupeau Paragraph 173 teaches a processor can run instructions from memory) of method claim 1 and is accordingly rejected using substantially similar rationale as to that which is set for with respect to claim 1.
26. Claim(s) 4, 8, 11, and 16 is/are rejected under 35 U.S.C. 103 as being unpatentable over Chupeau et al. (U.S. Patent Application Publication No. 2024/0249462 A1), hereinafter referred to as Chupeau, in view of Criminisi et al. (“Extracting Layers and Analyzing their Specular Properties using Epipolar-Plane-Image Analysis”), hereinafter referred to as Criminisi, as applied to claim 1, 7, 10, and 13 above, and further in view of Lee et al. (U.S. Patent Application Publication No. 2023/0011027 A1), hereinafter referred to as Lee.
27. Regarding claim 4, Chupeau in view of Criminisi teaches the limitations of claim 1. However, Chupeau and Criminisi fail to teach the method wherein the selecting the portion of the pixels in the one or more non-diffuse objects further comprises: selecting the portion of the pixels based on differences of first textures of the pixels in the basic view and second textures of the pixels in the alternate view.
Lee teaches the method wherein the selecting the portion of the pixels in the one or more non-diffuse objects further comprises: selecting the portion of the pixels based on differences of first textures of the pixels in the basic view and second textures of the pixels in the alternate view (Paragraphs 55-57 teach taking a difference between pixels in an additional image and reference image. The reference image is the basic view and the additional image is the alternate view. After taking a difference, the additional image is pruned and then patches are generated based off the pruning or differences in textures; Paragraphs 119-122 teach packing an additional texture image into a patch based on pruning. Pruning is done by comparing the difference of textures of pixels in the views V3_T2).
Chupeau and Criminisi are considered analogous to the claimed invention because both are in the same field of analyzing the reflections of an object from multiple images. Lee is considered analogous to the claimed invention because both are in the same field of encoding non-diffuse data. Thus, it would have been obvious to a person holding ordinary skill in the art before the effective filing date to modify the method of encoding non-diffuse data taught by Chupeau in view of Criminisi with the selecting the portion of pixels based on texture differences taught by Lee in order to remove redundancies and reduce the amount of data to be encoded/decoded while minimizing the degradation of quality (Lee Paragraph 95 and 136).
28. Regarding claim 8, Chupeau in view of Criminisi teaches the limitations of claim 7. However, Chupeau and Criminisi fail to teach the method wherein one of the data pieces for the reflectance information corresponding to a non-diffuse object in the one or more non-diffuse objects comprises a texture difference of the non-diffuse object in the basic view and the alternate view.
Lee teaches the method wherein one of the data pieces for the reflectance information corresponding to a non-diffuse object in the one or more non-diffuse objects comprises a texture difference of the non-diffuse object in the basic view and the alternate view (Paragraphs 55-57 teach taking a difference between pixels in an additional image and reference image. The reference image is the basic view and the additional image is the alternate view. After taking a difference, the additional image is pruned and then patches, or data pieces, are generated based off the pruning or differences in textures; Paragraphs 119-122 teach packing an additional texture image into a patch based on pruning. Pruning is done by comparing the difference of textures of pixels in the views V3_T2. The pruning is information that comprises of texture differences).
Chupeau and Criminisi are considered analogous to the claimed invention because both are in the same field of analyzing the reflections of an object from multiple images. Lee is considered analogous to the claimed invention because both are in the same field of encoding non-diffuse data. Thus, it would have been obvious to a person holding ordinary skill in the art before the effective filing date to modify the method of encoding non-diffuse data taught by Chupeau in view of Criminisi with determining texture differences taught by Lee in order to remove redundancies and reduce the amount of data to be encoded/decoded while minimizing the degradation of quality (Lee Paragraph 95 and 136).
29. Regarding claim 11, Chupeau teaches the limitations of claim 10. However, Chupeau and Criminisi fail to teach the method wherein the non-diffuse data comprises a difference of a first texture of a non-diffuse object of the one or more non-diffuse objects in the specific view and a second texture of the non-diffuse object in at least one of the basic view or the alternate view.
Lee teaches the method wherein the non-diffuse data comprises a difference of a first texture of a non-diffuse object of the one or more non-diffuse objects in the specific view and a second texture of the non-diffuse object in at least one of the basic view or the alternate view (Paragraph 9 teaches the non-diffuse data or metadata includes information about a difference between the texture in a first and second texture image. The first texture image is the basic view and second texture image is the alternate view; Paragraphs 55-57 teach taking a difference between pixels in an additional image and reference image. The reference image is the basic view and the additional image is the alternate view. After taking a difference, the additional image is pruned and then patches, or data pieces, are generated based off the pruning or differences in textures).
Chupeau and Criminisi are considered analogous to the claimed invention because both are in the same field of analyzing the reflections of an object from multiple images. Lee is considered analogous to the claimed invention because both are in the same field of encoding non-diffuse data. Thus, it would have been obvious to a person holding ordinary skill in the art before the effective filing date to modify the method of encoding non-diffuse data taught by Chupeau in view of Criminisi with determining texture differences taught by Lee in order to remove redundancies and reduce the amount of data to be encoded/decoded while minimizing the degradation of quality (Lee Paragraph 95 and 136).
30. Regarding claim 16, Chupeau in view of Criminisi teaches the limitations of claim 13. Claim 16 is similar in scope to claim 4. Therefore, similar rationale as applied in the rejection of claim 4 applies herein.
31. Claim(s) 12 is/are rejected under 35 U.S.C. 103 as being unpatentable over Chupeau et al. (U.S. Patent Application Publication No. 2024/0249462 A1), hereinafter referred to as Chupeau, in view of Criminisi et al. (“Extracting Layers and Analyzing their Specular Properties using Epipolar-Plane-Image Analysis”), hereinafter referred to as Criminisi, as applied to claim 10 above, and further in view of Martin Brualla et al. (U.S. 2022/0130111 A1), hereinafter referred to as Martin Brualla.
Regarding claim 12, Chupeau in view of Criminisi teaches the limitations of claim 10. However, Chupeau and Criminisi fail to teach the method wherein the generating the specific view further comprises: computing a texture of a non-diffuse object of the one or more non-diffuse objects in the specific view as a weighted average of a first texture of the non-diffuse object in the basic view and a second texture of the non-diffuse object in the alternate view.
Martin Brualla teaches the method wherein the generating the specific view further comprises: computing a texture of a non-diffuse object of the one or more non-diffuse objects in the specific view as a weighted average of a first texture of the non-diffuse object in the basic view and a second texture of the non-diffuse object in the alternate view (Paragraph 8 teaches taking a weighted average of the textures in multiple images to compute a neural texture; Paragraph 93 teaches input images from a number of views which can comprise of a basic and alternate view; Paragraph 100 and Equation 5 teach getting the texture of the objects in the image; Paragraph 103-104 and Equation 6 teach taking a weighted average of the textures in the different views to compute the texture of the non-diffuse object in the view).
Chupeau, Criminisi, and Martin Brualla are considered analogous to the claimed invention because both are in the same field of generating texture of an object based on multiple views. Thus, it would have been obvious to a person holding ordinary skill in the art before the effective filing date to modify the method of encoding non-diffuse data taught by Chupeau with the weighted average of textures taught by Martin Brualla in order to generate a neural texture that can be used for an accurate rendering of unseen views of the object (Martin Brualla Paragraph 21).
Conclusion
32. The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
- Roimela et al. (U.S. Patent Application Publication No. 2021/0383590 A1) teaches encoding reflection information through patches and synthesizing a new view.
- Boyce (U.S. Patent Application Publication No. 2022/0159298 A1) teaches immersive video coding which includes encoding non-diffuse data.
- Liang et al. (U.S. Patent No. 8,988,317 B1) teaches encoding light information through epipolar plane images.
33. Any inquiry concerning this communication or earlier communications from the examiner should be directed to CHRISTINE Y AHN whose telephone number is (571)272-0672. The examiner can normally be reached M-F 9-5pm.
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/CHRISTINE YERA AHN/Examiner, Art Unit 2615
/ALICIA M HARRINGTON/Supervisory Patent Examiner, Art Unit 2615
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