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 .
Status of Claims
Claims 1-11, 13, and 15-16 are now pending.
Claim Interpretation
Previous interpretation of claims under 112(f) has been withdrawn in view of amendments made by the applicant.
Response to Arguments
Applicant's amendments as filed on 07/06/2026 have been considered and entered, however, applicant’s arguments as filed have been fully considered but they are not persuasive.
Applicant argues that previously made 101 rejections have been overcome in view of the amendments currently made such as “the authenticating comprising unlocking at least one feature of a computing device associated with the object”. Applicant argues that this limitation doesn’t involve mental step and instead provides practical application since this limitation involves partly covered face scenario and being able to correctly perform facial recognition without any partially obscuring parts during authentication process as described in paragraphs 6, 10, and 43 of disclosure.
In reply, examiner disagrees and asserts that firstly, practical application aspects of the invention such as details of digitally performing facial recognition in a sophisticated authentication process avoiding partial coverings are not positively recited in the claim(s) as seemed to be argued by the applicant. Moreover, claims are silent as to how the facial recognition avoiding partial covering is being performed? How authenticating means unlocking and performing facial recognition? The details of paragraphs 6, 10, 43 and how facial recognition constitutes a practical application amounting to higher accuracy are absent from recited claims.
Thus, claims are still very broad and cover the aspect of abstract idea under mental process utilizing generic computer components as highlighted in the detailed 101 rejection(s) repeated below.
Applicant further argues that cited references fail to teach the newly amended features of claim 1 such as: “each image showing a different part of the object under illumination with patterned light”. Applicant argues that Rein instead shows the same area of object with different with or without structured illumination not patterned light illumination.
In reply, examiner disagrees and asserts that Rein teaches having structured patterned light illumination and nowhere it teaches having illumination without structured illumination as wrongfully seemed to be argued by applicant. For example, Rein teaches at least one illumination source configured for projecting at least one illumination pattern comprising a plurality of illumination features on at least one area comprising at least one object and an optical sensor having at least one light sensitive area, wherein the optical sensor is configured for determining at least one first image comprising at least one two dimensional image of the area, and determining at least one second image comprising a plurality of reflection features generated by the area in response to illumination by the illumination features, wherein, first image and the second image may be determined at different time points with different illuminating patterns for distinguishing different parts between the two such as second image may comprise generating a three-dimensional image by determining at least one image region of the second image corresponding to the geometrical feature in the first image by identifying the pixels of the second image corresponding to the pixels of the first image inside the border and/or limit of the geometrical feature and for determining a plurality of elements at different pixel depth levels between the first and second images such that material property of the object including information about shape and/or size of the object can be carefully determined based on analyzing different parts of the object between the 2D first image and 3D second image comprising different geometrical features and pixel depth levels between the two images which are determined at different time points highlighting different parts of the object, embodiments 1-2, 20, page 40-41, 43 and page 12, first paragraph, page 16, 2nd paragraph, page 24, last two paragraphs.
In response to applicant's argument that the references fail to show certain features of the invention, it is noted that the features upon which applicant relies (i.e., “determining a material score for each of the at least two images…each material score being independent of the other” and “images of an object illumination under the same illumination conditions are evaluated” and “material of the object can be determined with increased reliability”) are not recited in the rejected claim(s). Although the claims are interpreted in light of the specification, limitations from the specification are not read into the claims. See In re Van Geuns, 988 F.2d 1181, 26 USPQ2d 1057 (Fed. Cir. 1993).
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefore, subject to the conditions and requirements of this title.
Claims 1-11, 13 and 15-16 are directed toward a system, a method, a computer program product, and computer readable medium, which falls within one of the four statutory categories of invention but do not meet the three-prong test for patentability.
Regarding independent claim 1, under step 1, the claim is directed to a system which is one of the categories of eligible inventions.
Under step 2A Prong 1, the claim recites a system with steps reciting optimizing human activity, specifically, acquiring two images showing part of object is just a data gathering step and steps of determining/assigning a value to each image based on presence/absence of certain features/pixels, then performing evaluation of images and then performing extra solution activity of producing and outputting result of said evaluation is just an abstract idea, comprising:
“A system for determining a material of an object, said system comprising at least one processor and at least one memory, wherein the at least one processor is programmed to: provide at least two images, each image showing a different part of the object under illumination with patterned light, determine a material score for each of the at least two images, said material score being indicative of a presence of a predefined material in the respective image, evaluate the material scores determined for each of the at least two images, determine the material of the object based on the evaluation, output the determined material of the object, and authenticate the object using the determined material of the object, the authenticating comprising unlocking at least one feature of a computing device associated with the object”.
It is further noted that steps of acquiring different image data highlighting different areas are just data gathering or an extra solution activity in the field of endeavor. And steps of determining/assigning a value to each image based on presence/absence of certain features/pixels, then performing evaluation of images and then performing extra solution activity of producing and outputting result of said evaluation with identification of certain object based on determining and unlocking features based on said broad authentication/identification process is just an abstract idea and can all be considered mental processes and can be fully accomplished mentally by manipulating the acquired image while having a pen and paper.
Under step 2A Prong 2, the claim does not recite any additional elements which integrate the judicial exception into a practical application. There are no additional elements recited such as use of a particular machine or components other than using generic computer components such as processor and a memory.
Moreover, all of the limitations in the recited method can be accomplished without reliance on any specific machine and could be accomplished by human minds and interactions between humans as they are not specifically tailored for execution in any specific machine other than using generic computer components. The claim as a whole does not present a practical application of the abstract idea.
Under Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more than the abstract idea judicial exception because, as discussed in step 2A, there is merely a recitation of using generic computer components such as processor performing the process, which are all well-understood routine practices and are purely generic. This finding is consistent with the level of detail of the specification, and the well-understood and conventional nature of these elements is broadly covered under court decisions (i) and (iv) from MPEP 2106.05(d)(II) related to receiving and retrieving information in a generic computer environment. The claim is not patent eligible.
Regarding claim 2, it merely adds to claim 1 and further details “wherein the at least two images are partial images, each showing a different part of a single region of interest contained in a region-of- interest-image showing a region of interest of the object”. This again only provides further extra solution activity of acquiring images and setting region of interest area, which is a mental process on a generic device, that could be performed unaided by a human, recited as performed by a generic computing device, and thus, as in the claim do not constitute practical application or significantly more than the abstract idea.
Regarding claim 3, it merely adds to claim 1 and further details “a data-driven model configured for determining the material score for each of the at least two images or the material score determination unit comprises a mechanistic model configured for determining the material score for each of the at least two images”. This again only provides further extra solution activity of acquiring images and assigning value using a generic computer components including utilizing generic neural networks performed on a generic device, that could be performed unaided by a human, recited as performed by a generic computing device, and thus, as in the claim do not constitute practical application or significantly more than the abstract idea.
Regarding claim 4, it merely adds to claim 1 and further details “the material scores determined for each of the at least two images by forming an average material score of the determined material scores”. This again only provides a mental process of calculating averages of the two scores of the two images which is trivial process executed on a generic device, that could be performed unaided by a human, recited as performed by a generic computing device, and thus, as in the claim do not constitute practical application or significantly more than the abstract idea.
Regarding claim 5, it merely adds to claim 4 and further details “the material scores determined for each of the at least two images by giving a weight to each of the material scores and by forming a weight average material score of the determined material scores based on the weights assigned to each of the material scores”. This again only provides a mental process of calculating averages of the two scores of the two images which are trivial executed on a generic device, that could be performed unaided by a human, recited as performed by a generic computing device, and thus, as in the claim do not constitute practical application or significantly more than the abstract idea.
Regarding claim 6, it merely adds to claim 4 and further details “determining the material of the object based on the comparison of the average material score or the weight average material score with the predefined threshold value”. This again only provides human activity of comparing the calculated averages of the two scores of the two images with the predefined threshold which is a mental process executed on a generic device, that could be performed unaided by a human, recited as performed by a generic computing device, and thus, as in the claim do not constitute practical application or significantly more than the abstract idea.
Regarding claim 7, it merely adds to claim 1 and further details “comparing the material scores determined for the at least two images to a reference, said reference comprising at least one reference material score determined from a reference image showing a known reference material”. This again only provides human activity of comparing the calculated averages of the two scores of the two images with the predefined threshold which is a mental process executed on a generic device, that could be performed unaided by a human, recited as performed by a generic computing device, and thus, as in the claim do not constitute practical application or significantly more than the abstract idea.
Regarding claim 8, it merely adds to claim 7 and further details “evaluating the material scores by comparing each of the material scores determined for the at least two images in an element-wise manner to the reference”. This again only provides human activity of comparing the calculated averages of the two scores of the two images with the predefined threshold which is a mental process executed on a generic device, that could be performed unaided by a human, recited as performed by a generic computing device, and thus, as in the claim do not constitute practical application or significantly more than the abstract idea.
Regarding claim 9, it merely adds to claim 7 and further details “a neural network that is trained for receiving the material scores of the at least two images and the reference as input and for outputting based on the input a prediction of the material of the object”. This again only provides a mental process of acquiring images and assigning values and outputting predicted results using a generic computer components including utilizing generic neural networks which is a mental process performed on a generic device, that could be performed unaided by a human, recited as performed by a generic computing device, and thus, as in the claim do not constitute practical application or significantly more than the abstract idea.
Regarding claim 10, it merely adds to claim 7 and further details “determining the material of the object based on the element-wise difference formation of the material scores and the reference or wherein the material determination unit is configured for determining the material of the object by comparing the prediction of the material of the object provided by the trained neural network to a predefined use case threshold value”. This again only provides a mental process of acquiring images and assigning values and performing comparison analysis with outputting predicted results using a generic computer components including utilizing generic neural networks which is a mental process performed on a generic device, that could be performed unaided by a human, recited as performed by a generic computing device, and thus, as in the claim do not constitute practical application or significantly more than the abstract idea.
Regarding claim 11, it merely adds to claim 1 and further details “wherein each of the at least two images is provided together with a position information indicative of a relative position on the object and wherein a neural network that is trained for receiving the material score of a respective image together with the position information of this image as input and for outputting based on the input a prediction of the material of the object”. This again only provides a mental process of acquiring images and assigning values and performing comparison analysis with outputting predicted results using a generic computer components including utilizing generic neural networks which is a mental process performed on a generic device, that could be performed unaided by a human, recited as performed by a generic computing device, and thus, as in the claim do not constitute practical application or significantly more than the abstract idea.
Regarding independent claim 13, under step 1, the claim is directed to a system, which is one of the categories of eligible inventions.
Under step 2A Prong 1, the claim recites a system with steps reciting optimizing human activity, specifically, acquiring two images showing part of object is just a data gathering step and steps of determining/assigning a value to each image based on presence/absence of certain features/pixels, then performing evaluation of images and then performing extra solution activity of producing and outputting result of said evaluation is just an abstract idea, comprising:
“A method for determining a material of an object, said method comprising the steps of providing at least two images, each image showing a different part of the object under illumination with patterned light, determining a material score for each of the at least two images, said material score being indicative of a presence of a predefined material in the respective image, evaluating the material scores determined for each of the at least two images, determining the material of the object based on the evaluation, outputting the determined material of the object; and authenticating the object using the determined material of the object, the authenticating comprising unlocking at least one feature of a computing device associated with the object”.
It is further noted that steps of acquiring different image data highlighting different areas are just data gathering or an extra solution activity in the field of endeavor. And steps of determining/assigning a value to each image based on presence/absence of certain features/pixels, then performing evaluation of images and then performing extra solution activity of producing and outputting result of said evaluation with identification of certain object based on determining and unlocking features based on said broad authentication/identification process is just an abstract idea and can all be considered mental processes and can be fully accomplished mentally by manipulating the acquired image while having a pen and paper.
Under step 2A Prong 2, the claim does not recite any additional elements which integrate the judicial exception into a practical application. There are no additional elements recited such as use of a particular machine or components other than using generic computer components such as system.
Moreover, all of the limitations in the recited method can be accomplished without reliance on any specific machine and could be accomplished by human minds and interactions between humans as they are not specifically tailored for execution in any specific machine other than using generic computer components. The claim as a whole does not present a practical application of the abstract idea.
Under Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more than the abstract idea judicial exception because, as discussed in step 2A, there is merely a recitation of using generic computer components performing the process, which are all well-understood routine practices and are purely generic. This finding is consistent with the level of detail of the specification, and the well-understood and conventional nature of these elements is broadly covered under court decisions (i) and (iv) from MPEP 2106.05(d)(II) related to receiving and retrieving information in a generic computer environment. The claim is not patent eligible.
Regarding claim 15, it merely adds to claim 14 and further details non-transitory computer readable data medium storing the instruction for executing the method of claim 13. This again only provides further extra solution activity of executing process using generic computer components including a generic computer instructions performed on a generic device, that could be performed unaided by a human, recited as performed by a generic computing device, and thus, as in the claim, do not constitute practical application or significantly more than the abstract idea.
Regarding claim 16, it merely adds to claim 3 and further details “wherein the data-driven model is a neural network trained for determining the material score for each of the at least two images using the at least two images as input”. This again only provides further extra solution activity of acquiring images and assigning value using a generic computer components including utilizing generic neural networks performed on a generic device, that could be performed unaided by a human, recited as performed by a generic computing device, and thus, as in the claim do not constitute practical application or significantly more than the abstract idea.
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 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 1-11, 13, and 15-16 are rejected under 35 U.S.C. 103 as being unpatentable over Rein et al., WO 2021/152070 in view of Suzuki et al., US 2025/0037365.
Regarding claim 1, Rein discloses a system for determining a material of an object (detector for object recognition, embodiment 1, line 28, page 40), said system comprising at least one processor and at least one memory, wherein the at least one processor is programmed to (evaluation device can incorporate at least one of a processor, a graphics processor, a CPU and one or more memory blocks such as ROM, RAM, EEPROM, or flash memory, page 14, lines 28-41):
provide at least two images, each image showing a different part of the object under illumination with patterned light (at least one illumination source configured for projecting at least one illumination pattern comprising a plurality of illumination features on at least one area comprising at least one object and an optical sensor having at least one light sensitive area, wherein the optical sensor is configured for determining at least one first image comprising at least one two dimensional image of the area, and determining at least one second image comprising a plurality of reflection features generated by the area in response to illumination by the illumination features, wherein, first image and the second image may be determined at different time points with different illuminating patterns for distinguishing different parts between the two such as second image may comprise generating a three-dimensional image by determining at least one image region of the second image corresponding to the geometrical feature in the first image by identifying the pixels of the second image corresponding to the pixels of the first image inside the border and/or limit of the geometrical feature and for determining a plurality of elements at different pixel depth levels between the first and second images such that material property of the object including information about shape and/or size of the object can be carefully determined based on analyzing different parts of the object between the 2D first image and 3D second image comprising different geometrical features and pixel depth levels between the two images which are determined at different time points highlighting different parts of the object, embodiments 1-2, 20, page 40-41, 43 and page 12, first paragraph, page 16, 2nd paragraph, page 24, last two paragraphs).
determine a material score for each of the at least two images, said material score being indicative of a presence of a predefined material in the respective image (determining beam profile information for each of the reflection features by analysis of their beam profiles, wherein evaluation of the first image and second image comprises identifying at least one pre-defined or pre-determined geometrical feature which are located inside an image region the geometrical feature and/or for identifying the reflection features which are located outside the image region of the geometrical feature, and determining at least one depth level from the beam profile information of the reflection features located inside and/or outside of the image region of the geometrical feature, and wherein the evaluation device is configured for determining at least one depth level from the beam profile information of the reflection features located inside and/or outside of the image region of the geometrical feature, embodiment 1, line 36, page 40 – line 5, page 41),
evaluate the material scores determined for each of the at least two images (wherein the evaluation device is configured for evaluating the first image and the second image, wherein material dependent filter may be applied for returning a value correlating with measure of soft/hard transitions of a spot as a material feature based on evaluation of the material for the two images, embodiment 1, pages 32-33, 40),
determine the material of the object based on the evaluation (wherein the evaluation device is configured for determining at least one material property of the object from the beam profile information of the reflection features located inside and/or outside of the image region of the geometrical feature, wherein the evaluation device is configured for determining at least one position and/or orientation of the object by considering the depth level and/or the material property and pre-determined or predefined information about shape and/or size of the object, embodiment 1, lines 6-11, page 41),
determining material of the object (evaluation device may be configured for determining the material property m by evaluating the material feature φ .sub.2m. As used herein, the term “material dependent” image filter refers to an image having a material dependent output. The output of the material dependent image filter is denoted herein “material feature φ .sub.2m” or “material dependent feature φ .sub.2m”. The material feature may be or may comprise at least one information about the at least one material property of the object, page 29, lines 10-15),
and authenticate the object using the determined material of the object (detector identifies and distinguishes (authenticates) whether the object belongs to human skin or non-human objects (cloth/fabrics) based on the predefined criteria involving determined material of the object, lines 23-25, page 28, page 27, first paragraph, and table as shown in page 30), the authenticating comprising unlocking at least one feature of a computing device associated with the object (identification/distinction (authentication) of different materials (skin, food, fabric) comprises applying/unlocking specific features and parameters such as using material dependent filter or particular predefined beam profile where output light intensity of the illumination source and criterion may be corrected for deviations, such as by using a corrected peak intensity of beam profile divided by the output light intensity of the illumination source, page 27, last paragraph – page 29, third paragraph, table of page 30).
Rein fails to explicitly disclose to output determined material of object.
Suzuki teaches to output determined material of object (material determination unit 143 outputs information indicating the determined material of the mapping target object to the generation unit 16 and the mass estimation unit 144, paragraph 51).
Rein and Suzuki are combinable because they both are in the same field of endeavor dealing with determining material of the object by analyzing/processing plurality of images.
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the application to combine the teachings of Rein with the teachings of Suzuki for the benefit of providing a generation method capable of generating a general-purpose digital twin that can be used in a plurality of applications as taught by Suzuki at paragraph 7.
Regarding claim 2, Combination of Rein with Suzuki further teaches wherein the at least two images are partial images, each showing a different part of a region of interest contained in a single region-of-interest-image (Rein, a single camera comprising the optical sensor 120 may record with a temporal shift a two-dimensional image and an image of a projected pattern) showing a region of interest of the object (Rein, (at least one illumination source configured for projecting at least one illumination pattern comprising a plurality of illumination features on at least one area comprising at least one object and an optical sensor having at least one light sensitive area, wherein the optical sensor is configured for determining at least one first image comprising at least one two dimensional image of the area, and determining at least one second image comprising a plurality of reflection features generated by the area in response to illumination by the illumination features, wherein, first image and the second image may be determined at different time points with different illuminating patterns for distinguishing different parts between the two such as second image may comprise generating a three-dimensional image by determining at least one image region of the second image corresponding to the geometrical feature in the first image by identifying the pixels of the second image corresponding to the pixels of the first image inside the border and/or limit of the geometrical feature and for determining a plurality of elements at different pixel depth levels between the first and second images such that material property of the object including information about shape and/or size of the object can be carefully determined based on analyzing different parts of the object between the 2D first image and 3D second image comprising different geometrical features and pixel depth levels between the two images which are determined at different time points highlighting different parts of the object, embodiments 1-2, 20, page 40-41, 43 and page 12, first paragraph, page 16, 2nd paragraph, page 24, last two paragraphs).
Rein and Suzuki are combinable because they both are in the same field of endeavor dealing with determining material of the object by analyzing/processing plurality of images.
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the application to combine the teachings of Rein with the teachings of Suzuki for the benefit of providing a generation method capable of generating a general-purpose digital twin that can be used in a plurality of applications as taught by Suzuki at paragraph 7.
Regarding claim 3, Combination of Rein with Suzuki further teaches wherein the at least one processor is further programmed to execute a data-driven model configured for determining the material score for each of the at least two images or a mechanistic model configured for determining the material score for each of the at least two images (Rein, analysis of the beam profile may comprise at least one of a histogram analysis step, a calculation of a difference measure, application of a neural network, application of a machine learning algorithm. The evaluation device may be configured for symmetrizing and/or for normalizing and/or for filtering the beam profile, in particular to remove noise or asymmetries from recording under larger angles, recording edges or the like, page 17, lines 30-43 and description given pages 34-35).
Rein and Suzuki are combinable because they both are in the same field of endeavor dealing with determining material of the object by analyzing/processing plurality of images.
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the application to combine the teachings of Rein with the teachings of Suzuki for the benefit of providing a generation method capable of generating a general-purpose digital twin that can be used in a plurality of applications as taught by Suzuki at paragraph 7.
Regarding claim 4, Combination of Rein with Suzuki further teaches wherein the at least one processor is further programmed to evaluate the material scores determined for each of the at least two images by forming an average material score of the determined material scores (Rein, the analysis of the beam profile of one of the reflection features may comprise determining at least one first area and at least one second area of the beam profile comprising first and second images, wherein, evaluation device may be configured for integrating the first area and the second area to have a combined signal/score, in particular a quotient Q, by one or more of dividing the integrated first area and the integrated second area, dividing multiples of the integrated first area and the integrated second area, dividing linear combinations of the integrated first area and the integrated second area (i.e., averaging) and determining at least two areas of the beam profile and/or to segment the beam profile in at least two segments comprising different areas of the beam profile, wherein overlapping of the areas may be possible as long as the areas are not congruent such as segmenting the light spot into at least two areas of the beam profile and/or to segment the beam profile in at least two segments comprising different areas of the beam profile. The evaluation device may be configured for deter- mining for at least two of the areas an integral of the beam profile over the respective area, page 18, last paragraph and page 20, 3rd paragraph and Suzuki, the estimation unit 14 performs material estimation for each of two or more object images including the same object, and obtains an average of two or more estimation results, thereby acquiring a more accurate material estimation result, paragraphs 41, 50).
Rein and Suzuki are combinable because they both are in the same field of endeavor dealing with determining material of the object by analyzing/processing plurality of images.
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the application to combine the teachings of Rein with the teachings of Suzuki for the benefit of providing a generation method capable of generating a general-purpose digital twin that can be used in a plurality of applications as taught by Suzuki at paragraph 7.
Regarding claim 5, Combination of Rein with Suzuki further teaches wherein the at least one processor is further programmed to evaluate the material scores determined for each of the at least two images by giving a weight to each of the material scores (Rein, page 53, last paragraph, embodiment 3, page 41, page 56, lines 6-10, The evaluation device 124 is configured for determining the material evaluating scores based on positions and depth level from the beam profile information of the reflection features located inside and outside of the image region of the geometrical features comprising first and second images, wherein, evaluation device compares at least two of the determined integrals and the reflection beam profile (first area and second area integrated as reflection beam profile) and the predetermined or predefined beam profile, specifically, the evaluation device 124 may be configured for determining at least one first area and at least one second area of the reflection beam profile. The first area of the beam profile and the second area of the reflection beam profile may be one or both of adjacent or overlapping regions. The evaluation device 124 may be adapted to integrate the first area and the second area. The evaluation device 124 may be configured for using at least one predetermined relationship between the quotient Q and the longitudinal coordinate and comparing with threshold quotient) and by forming a weight average material score of the determined material scores based on the weights assigned to each of the material scores (Rein, the analysis of the beam profile of one of the reflection features may comprise determining at least one first area and at least one second area of the beam profile comprising first and second images, wherein, evaluation device may be configured for integrating the first area and the second area to have a combined signal/score, in particular a quotient Q, by one or more of dividing the integrated first area and the integrated second area, dividing multiples of the integrated first area and the integrated second area, dividing linear combinations of the integrated first area and the integrated second area (i.e., averaging) and determining at least two areas of the beam profile and/or to segment the beam profile in at least two segments comprising different areas of the beam profile, wherein overlapping of the areas may be possible as long as the areas are not congruent such as segmenting the light spot into at least two areas of the beam profile and/or to segment the beam profile in at least two segments comprising different areas of the beam profile. The evaluation device may be configured for deter- mining for at least two of the areas an integral of the beam profile over the respective area, page 18, last paragraph and page 20, 3rd paragraph, and Suzuki, the estimation unit 14 performs material estimation for each of two or more object images including the same object, and obtains an average of two or more estimation results, thereby acquiring a more accurate material estimation result, paragraphs 41, 50).
Rein and Suzuki are combinable because they both are in the same field of endeavor dealing with determining material of the object by analyzing/processing plurality of images.
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the application to combine the teachings of Rein with the teachings of Suzuki for the benefit of providing a generation method capable of generating a general-purpose digital twin that can be used in a plurality of applications as taught by Suzuki at paragraph 7.
Regarding claim 6, Combination of Rein with Suzuki further teaches wherein the at least one processor is further programmed to determine the material of the object based on the comparison of the average material score or the weight average material score with the predefined threshold value (Rein, material of the object is determined by analyzing whether the object is or comprises biological tissue. The properties of biological tissue may be used to distinguish skin from other materials, by analyzing the back scattering (averaging) beam profile. The surface may be determined as biological tissue in case the reflection beam profile fulfills at least one predetermined or predefined criterion. The at least one predetermined or predefined criterion may be at least one property and/or value suitable to distinguish biological tissue, in particular human skin, from other materials (fabric). Specifically, the evaluation device may be adapted for comparing the beam profile with at least one predetermined and/or prerecorded and/or predefined beam profile. The comparison may comprise overlaying (averaging) the reflection beam profile and the predetermined or predefined beam profile such that their centers of intensity match, page 27, last paragraph).
Rein and Suzuki are combinable because they both are in the same field of endeavor dealing with determining material of the object by analyzing/processing plurality of images.
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the application to combine the teachings of Rein with the teachings of Suzuki for the benefit of providing a generation method capable of generating a general-purpose digital twin that can be used in a plurality of applications as taught by Suzuki at paragraph 7.
Regarding claim 7, Combination of Rein with Suzuki further teaches wherein the at least one processor is further programmed to compare the material scores determined for the at least two images to a reference, said reference comprising at least one reference material score determined from a reference image showing a known reference material (Rein, evaluation device compares the overlaying the reflection beam profile and the predetermined or predefined beam profile such that their centers of intensity match, page 27, last paragraph, wherein the analysis of the beam profile of one of the reflection features may comprise determining at least one first area and at least one second area of the beam profile. The evaluation device 124 may be configured for integrating the first area and the second area. The evaluation device 124 may be configured for comparing at least two of the determined integrals. Specifically, the evaluation device 124 may be configured for determining at least one first area and at least one second area of the reflection beam profile. The first area of the beam profile and the second area of the reflection beam profile may be one or both of adjacent or overlapping regions. The evaluation device 124 may be adapted to integrate the first area and the second area. The evaluation device 124 may be configured for using at least one predetermined relationship between the quotient Q and the longitudinal coordinate, page 53, last paragraph - page 54, first paragraph).
Rein and Suzuki are combinable because they both are in the same field of endeavor dealing with determining material of the object by analyzing/processing plurality of images.
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the application to combine the teachings of Rein with the teachings of Suzuki for the benefit of providing a generation method capable of generating a general-purpose digital twin that can be used in a plurality of applications as taught by Suzuki at paragraph 7.
Regarding claim 8, Combination of Rein with Suzuki further teaches wherein the at least one processor is further programmed to evaluate the material scores by comparing each of the material scores determined for the at least two images in an element-wise manner to the reference (Rein, page 53, last paragraph, embodiment 3, page 41, page 56, lines 6-10, The evaluation device 124 is configured for determining the at least one depth level from the beam profile information of the reflection features located inside and/or outside of the image region of the geometrical feature. The area comprising the object may comprise a plurality of elements at different depth levels, wherein, evaluation device compares at least two of the determined integrals and the reflection beam profile (first area and second area integrated as reflection beam profile) and the predetermined or predefined beam profile).
Rein and Suzuki are combinable because they both are in the same field of endeavor dealing with determining material of the object by analyzing/processing plurality of images.
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the application to combine the teachings of Rein with the teachings of Suzuki for the benefit of providing a generation method capable of generating a general-purpose digital twin that can be used in a plurality of applications as taught by Suzuki at paragraph 7.
Regarding claim 9, Combination of Rein with Suzuki further teaches wherein at least one processor is further programmed to execute a neural network that is trained (Rein, analysis of the beam profile may comprise an application of a neural network, page 17, lines 30-35) for receiving the material scores of the at least two images and the reference as input and for outputting based on the input a prediction of the material of the object (Rein, properties of biological tissue may be used as input to predict and output that the skin wither belongs to human or non-human object by determining if input of biological tissue fulfills at least one predetermined or predefined criterion by comparing with beam profile, basically, the evaluation device may be adapted for comparing the beam profile with at least one predetermined and/or prerecorded and/or predefined beam profile and based on comparison, outputting and predicting in case the quotient Q is below and/or equal the quotient threshold, that the surface of object is a biological tissue or not as also shown in table of page 30, see page 27, last paragraph – page 28, first paragraph).
Rein and Suzuki are combinable because they both are in the same field of endeavor dealing with determining material of the object by analyzing/processing plurality of images.
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the application to combine the teachings of Rein with the teachings of Suzuki for the benefit of providing a generation method capable of generating a general-purpose digital twin that can be used in a plurality of applications as taught by Suzuki at paragraph 7.
Regarding claim 10, Combination of Rein with Suzuki further teaches wherein the at least one processor is further programmed to determine the material of the object based on the element-wise difference formation of the material scores and the reference (Rein, page 53, last paragraph, embodiment 3, page 41, page 56, lines 6-10, The evaluation device 124 is configured for determining the at least one depth level from the beam profile information of the reflection features located inside and/or outside of the image region of the geometrical feature. The area comprising the object may comprise a plurality of elements at different depth levels, wherein, evaluation device compares at least two of the determined integrals and the reflection beam profile (first area and second area integrated as reflection beam profile) and the predetermined or predefined beam profile) or determine the material of the object by comparing the prediction of the material of the object provided by the trained neural network to a predefined use case threshold value (Rein, analysis of the beam profile may comprise an application of a neural network, page 17, lines 30-35, wherein, properties of biological tissue may be used as input to predict and output that the skin wither belongs to human or non-human object by determining if input of biological tissue fulfills at least one predetermined or predefined criterion by comparing with beam profile, basically, the evaluation device may be adapted for comparing the beam profile with at least one predetermined and/or prerecorded and/or predefined beam profile and based on comparison, outputting and predicting in case the quotient Q is below and/or equal the quotient threshold, that the surface of object is a biological tissue or not as also shown in table of page 30, see page 27, last paragraph – page 28, first paragraph).
Rein and Suzuki are combinable because they both are in the same field of endeavor dealing with determining material of the object by analyzing/processing plurality of images.
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the application to combine the teachings of Rein with the teachings of Suzuki for the benefit of providing a generation method capable of generating a general-purpose digital twin that can be used in a plurality of applications as taught by Suzuki at paragraph 7.
Regarding claim 11, Combination of Rein with Suzuki further teaches wherein each of the at least two images is provided together with a position information indicative of a relative position on the object (Rein, determining an image position of the identified geometrical feature in the first image. The evaluation device may be configured for determining the pixels of the first image inside the border and/or limit and their image position in the first image. The evaluation device may be configured for determining at least one image region of the second image corresponding to the geometrical feature in the first image by identifying the pixels of the second image corresponding to the pixels of the first image inside the border and/or limit of the geometrical feature. The evaluation device is configured for determining the position and/or the orientation of the object by considering the depth level and pre-determined or predefined information about shape and/or size of the object. For example, the information about shape and/or size may be entered by a user via the user interface of the detector, pages 24-25), and wherein at least one processor is further programmed to execute a neural network that is trained for (Rein, analysis of the beam profile may comprise an application of a neural network, page 17, lines 30-35) receiving the material score of a respective image together with the position information of this image as input and for outputting based on the input a prediction of the material of the object (Rein, properties of biological tissue may be used as input to predict and output that the skin wither belongs to human or non-human object by determining if input of biological tissue fulfills at least one predetermined or predefined criterion by comparing with beam profile, basically, the evaluation device may be adapted for comparing the beam profile with at least one predetermined and/or prerecorded and/or predefined beam profile and based on comparison, outputting and predicting in case the quotient Q is below and/or equal the quotient threshold, that the surface of object is a biological tissue or not as also shown in table of page 30, see page 27, last paragraph – page 28, first paragraph).
Rein and Suzuki are combinable because they both are in the same field of endeavor dealing with determining material of the object by analyzing/processing plurality of images.
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the application to combine the teachings of Rein with the teachings of Suzuki for the benefit of providing a generation method capable of generating a general-purpose digital twin that can be used in a plurality of applications as taught by Suzuki at paragraph 7.
Regarding claim 13, is a method version of claim 1 reciting similar features and thus is rejected on the same rationale as presented for claim 1.
Regarding claim 15, which recites a non-transitory computer readable data medium version for executing the method of claim 13, see rationale as applied above. Note that non-transitory computer readable data medium is taught by Suzuki in paragraphs 79-80.
Regarding claim 16, Combination of Rein with Suzuki further teaches wherein the data-driven model is a neural network trained for determining the material score for each of the at least two images using the at least two images as input (Rein, analysis of the beam profile may comprise at least one of a histogram analysis step, a calculation of a difference measure, application of a neural network, application of a machine learning algorithm. The evaluation device may be configured for symmetrizing and/or for normalizing and/or for filtering the beam profile, in particular to remove noise or asymmetries from recording under larger angles, recording edges or the like, page 17, lines 30-43 and description given pages 34-35).
Rein and Suzuki are combinable because they both are in the same field of endeavor dealing with determining material of the object by analyzing/processing plurality of images.
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the application to combine the teachings of Rein with the teachings of Suzuki for the benefit of providing a generation method capable of generating a general-purpose digital twin that can be used in a plurality of applications as taught by Suzuki at paragraph 7.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Knapp et al., US 2024/0402342
Schindler et al., US 2024/0011891
Eberspach et al., US 2023/0213610
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/PAWAN DHINGRA/Examiner, Art Unit 2683
/ABDERRAHIM MEROUAN/ Supervisory Patent Examiner, Art Unit 2683
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