DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 1-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Schaefer (US 2021/0166124) in view of Sunkavalli (US 2019/0347526).
Regarding Claims 1 and 10, Schaefer teaches information processing device and method for a time-of-flight system [#10, #16 of Fig 1; 0047], comprising circuitry configured to: obtain time-of-flight data of at least one time-of-flight measurement of light reflected from a scene that is illuminated with infrared light [0052]; and input the time-of-flight data into a neural network [0060]. Schaefer does not explicitly teach – but Sunkavalli does teach wherein the neural network is trained to estimate, for each pixel or a subset of pixels of the time-of-flight data, a spatially varying bi- directional reflection distribution function [0023-26]. It would have been obvious to modify the system and method of Schaefer to use a neural network to train to estimate a multivariate distribution function in order to decode the feature map into spatially-varying bidirectional reflectance distribution function properties, namely surface normals, diffuse texture, and roughness and uses the classification results as weights to combine the predictions from different material types to obtain the final material properties.
Regarding Claim 19, Schaefer teaches a time-of-flight system [#10, #16 of Fig 1; 0047], comprising: an illumination device including a light source configured to illuminate a scene with infrared light for at least one time-of-flight measurement [#10, #16 of Fig 1; 0047]; an imaging device, including an image sensor [0047], configured to image light reflected from the scene on the image sensor and to generate time-of-flight data of the at least one time-of-flight measurement in accordance with the light imaged on the image sensor [Fig 1; 0046-48]; and an information processing device including circuitry configured to: obtain the time-of-flight data of the at least one time-of-flight measurement of the light reflected from the scene that is illuminated with infrared light [0052]; and input the time-of-flight data into a neural network [0060]. Schaefer does not explicitly teach – but Sunkavalli does teach wherein the neural network is trained to estimate, for each pixel or a subset of pixels of the time-of-flight data, a spatially varying bi-directional reflection distribution function [0023-26]. It would have been obvious to modify the system and method of Schaefer to use a neural network to train to estimate a multivariate distribution function in order to decode the feature map into spatially-varying bidirectional reflectance distribution function properties, namely surface normals, diffuse texture, and roughness and uses the classification results as weights to combine the predictions from different material types to obtain the final material properties.
Regarding Claims 2-3, and 11-12, Schaefer also teaches wherein the time-of-flight data include correlation data [0050-51]… and/or amplitude data [0051].
Regarding Claims 4-5, and 13-14, Schaefer also teaches wherein the time-of-flight data include intensity data [0045-47]… or depth data [0045-47].
Regarding Claims 6 and 15, Schaefer does not explicitly teach – but Sunkavalli does teach wherein the at least one time-of- flight measurement is a single time-of-flight measurement [0024]. It would have been obvious to modify the system and method of Schaefer to include single time-of-flight measurements in order to extract material properties from a single image captured from readily available devices with flash illumination.
Regarding Claims 7 and 16, Schaefer also teaches wherein the at least one time-of- flight measurement includes a first time-of-flight measurement at a first viewpoint and a second time-of-flight measurement at a second viewpoint being different than the first viewpoint [0045-47; 00505-52; 0059-61] – as this would be required for any BRDF measurements for cataloguing. Sunkavalli additionally teaches this limitation in [0023-26].
Regarding Claims 8 and 17, Schaefer also teaches wherein the spatially varying bi- directional reflection distribution function is represented by parameters of a material model [0097]. Sunkavalli additionally teaches this limitation in [0066].
Regarding Claims 9 and 18, Schaefer also teaches wherein the spatially varying bi- directional reflection distribution function is represented by a set of sampling points [0013]. Sunkavalli additionally teaches this limitation in [0051].
Regarding Claim 20, Schaefer does not explicitly teach – but Sunkavalli does wherein the light source is configured to illuminate the scene with flooded light or with spotted light [0024]. It would have been obvious to modify the system of Schaefer to include single time-of-flight measurements in order to extract material properties from a single image captured from readily available devices with flash illumination.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JAMES R HULKA whose telephone number is (571)270-7553. The examiner can normally be reached M-R: 9am-6pm, F: 10am-2pm.
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JAMES R. HULKA
Primary Examiner
Art Unit 3645
/JAMES R HULKA/Primary Examiner, Art Unit 3645
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