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 Status
Claims 1-15 are pending for examination in the Application No. 18/926,634 filed October 25th, 2024. Accordingly, claims 1-15 are currently pending for examination in the application.
Priority
Acknowledgment is made of applicant’s status as a continuation (CON) of Patent Application No. 17/630,228 filed on January 26th, 2022, now abandoned, which claims priority under 35 U.S.C. § 371 as an U.S. National Stage Filing of International Application No. PCT/JP2019/030364, filed on August 1st, 2019.
Information Disclosure Statement
The information disclosure statement(s) (IDS(s)) submitted on October 25th, 2024, November 8th, 2024, and November 12th, 2024, is/are in compliance with the provisions of 37 CFR 1.97. Accordingly, the IDS(s) is/are being considered and attached by the examiner.
Claim Objections
Claims 1, 14, and 15 are objected to because of the following informalities:
In claims 1, 14, and 15, the term ‘Fourier’ should be capitalized in the phrase “fourier transforming” recited in the claims.
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, as explained in MPEP § 2181, subsection I (note that the list of generic placeholders below is not exhaustive, and other generic placeholders may invoke 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 Generic Placeholder (A Term That Is Simply A Substitute for "Means")
With respect to the first prong of this analysis, a claim element that does not include the term "means" or "step" triggers a rebuttable presumption that 35 U.S.C. 112(f) does not apply. When the claim limitation does not use the term "means," examiners should determine whether the presumption that 35 U.S.C. 112(f) does not apply is overcome. The presumption may be overcome if the claim limitation uses a generic placeholder (a term that is simply a substitute for the term "means"). The following is a list of non-structural generic placeholders that may invoke 35 U.S.C. 112(f): "mechanism for," "module for," "device for," "unit for," "component for," "element for," "member for," "apparatus for," "machine for," or "system for." Welker Bearing Co., v. PHD, Inc., 550 F.3d 1090, 1096, 89 USPQ2d 1289, 1293-94 (Fed. Cir. 2008); Mass. Inst. of Tech. v. Abacus Software, 462 F.3d 1344, 1354, 80 USPQ2d 1225, 1228 (Fed. Cir. 2006); Personalized Media, 161 F.3d at 704, 48 USPQ2d at 1886–87; Mas-Hamilton Group v. LaGard, Inc., 156 F.3d 1206, 1214-1215, 48 USPQ2d 1010, 1017 (Fed. Cir. 1998). Note that there is no fixed list of generic placeholders that always result in 35 U.S.C. 112(f) interpretation, and likewise there is no fixed list of words that always avoid 35 U.S.C. 112(f) interpretation. Every case will turn on its own unique set of facts.
Such claim limitation(s) is/are:
"a measuring apparatus configured to capture… " in claims 12 and 13 described in paragraph [0020] (e.g., “The measuring apparatus 12 captures…”) and implemented on hardware disclosed in paragraphs [0063-0065] (e.g., “the processes in each of the components can also be implemented by having a CPU (Central Processing Unit) execute a computer program…” and/or “The authenticating image extraction apparatus 11 described in any of the above example embodiments is implemented as the processor 501…”).
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.
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1-3, 7, 9, and 11-15 are rejected under 35 U.S.C. 103 as being unpatentable over Darlow et al. (Darlow 2015A; “Study on internal to surface fingerprint correlation using optical coherence tomography and internal fingerprint extraction,” 2015; provided by Applicant's IDS filed November 8th, 2024) in view of Darlow et al. (Darlow 2015B; “Internal fingerprint zone detection in optical coherence tomography fingertip scans,” 2015; provided by Applicant's IDS filed November 8th, 2024), and further in view of Lamare et al. (Lamare; US 2017/0083742 A1), and further more in view of Sun et al. (Sun; “3D Automatic Segmentation Method for Retinal Optical Coherence Tomography Volume Data Using Boundary Surface Enhancement,” 2015; provided by Applicant's IDS filed November 8th, 2024).
Regarding claim 1, Darlow 2015A discloses a processing apparatus comprising:
one or more processors;
a memory storing executable instructions that, when executed by the one or more processors, causes the one or more processors to perform operations comprising:
determining a searching range for depth that is limited to a range in the depth direction from a depth which is a (between) first and second depths at which striped pattern sharpness is a maximum (pg. 3, recites
[pg.3] “To do so, fuzzy c-means clustering is applied to en face 1-D intensity signals (known as A-lines, exemplified in Fig. 2) for internal fingerprint zone detection. This information is used to define en face localized regions for each A-line over which to average pixel intensities for the 2-D internal fingerprint. Following this, the internal fingerprint is enhanced...
1. Relative distance to the stratum corneum: Black arrows in Fig. 2. The stratum corneum is detected as detailed in Fig. 3(a). The distances from this estimate—the dashed (green) line in Fig. 2—to the extracted data points are normalized on a B-scan basis by estimating the average (median) distance between the two strongest peaks in all A-lines in the respective B-scan.
2. Relative intensity: Height of the (green) dots in Fig. 2. The second strongest local maximum usually (but not always) corresponds with the papillary junction. The median (on a B-scan basis) of these maxima is used to normalize this feature. The median is used as it is robust regarding outliers…
PC describes the center of the papillary junction only and is thus inefficient at capturing the internal fingerprint undulations. PC is fine-tuned into papillary junction upper edge coordinates (denoted as P). P describes the internal fingerprint zone entirely. Fine-tuning involves processing small image regions. This region is exemplified as the region between the (red and blue) lines in Figs. 3(c) and 3(d). These image regions contain the papillary junction upper edge.”
, where the “internal fingerprint zone” is a searching range for depth that is limited to a range in the depth direction (e.g., “papillary junction”) which is a value between first and second depths at which striped pattern sharpness (e.g., “en face 1-D intensity signals”) is a maximum (i.e., the ”strongest local maximum usually…corresponds with the papillary junction”));
extracting, from three-dimensional luminance data indicating an authentication target, three-dimensional luminance data within the searching range for depth (pg. 3—see citation immediately above—, where the “en face 1-D intensity signals (known as A-lines…)” are three-dimensional (3D) luminance data indicating an authentication target (e.g., a fingerprint) within a searching range for depth (e.g., the “papillary junction” or “internal fingerprint zone”));
determining a range of a plurality of regions on a plane perpendicular to a depth direction of the authentication target (Fig. 2 and lines 3-16 in col. 1 of pg. 3, recites
[lines 3-16 in col. 1 of pg. 3] “There is an intensity depth dependency roll-off problem inherent in OCT scans of a curved object. Regions further from the scanner have lower intensity, and the natural finger curvature makes internal fingerprint zone detection complex. This research endeavors to detect the internal fingerprint zone in unprocessed, touchless OCT fingertip scans.
To do so, fuzzy c-means clustering is applied to en face 1-D intensity signals (known as A-lines, exemplified in Fig. 2) for internal fingerprint zone detection. This information is used to define en face localized regions for each A-line over which to average pixel intensities for the 2-D internal fingerprint. Following this, the internal fingerprint is enhanced.”
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, where the “en face localized regions for each A-line” are a plurality of regions perpendicular to a depth direction of the authentication target; wherein the standard deviations of depths regarding each region (i.e., the standard deviation ranges of each local maxima as depicted in Fig. 2 above) are a range of the plurality of regions);
calculating, from the extracted three-dimensional luminance data within the searching range for depth, depth dependence of striped pattern sharpness in a plurality of regions on a plane perpendicular to a depth direction of the authentication target (Fig. 2 and lines 3-16 in col. 1 of pg. 3—see citations in the limitation immediately above—, where Fig. (1a) further recite(s):
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, where OCT scans are 3D luminance data (see Fig. 1(a)) indicating an authentication target (e.g., fingerprints) and the “1-D intensity signals” or “A-lines” are depth dependence of striped pattern sharpness (see the “Pixel depth” vs. “Intensity” graph in Fig. 2 above));
calculating a depth at which the striped pattern sharpness is the greatest in the depth dependence of striped pattern sharpness (lines 24-28 in col. 1 of pg. 3, recite
[lines 24-28 in col. 1 of pg. 3] “Clustering requires input data and descriptive features. A number (n) of intensity local maxima in each A-line are extracted as data. Examples of this data are the (green) dots in Fig. 2. Refer to this graph for visual descriptors of the following features:…”
, where an “intensity local maxima” is a greatest in the depth dependence of striped pattern sharpness);
correcting the calculated depth on the basis of depths of other regions positioned respectively around the plurality of regions (3rd to 5th paras. of col. 2 on pg. 3, recites
[3rd to 5th paras. of col. 2 on pg. 3] “The cluster best describing the internal fingerprint zone is determined by comparing each cluster to the estimate used for relative distance normalization. The data contained in the chosen cluster are denoted as C.
Owing to the imposed requirement of strong cluster membership, it is entirely possible that A-lines may contain no data within C. Interpolating missing data is thus required.
The inpaint_nan23 interpolation algorithm is used to calculate missing values in C. A median filter is applied to the coordinates to reduce anomalies. The resultant coordinates are denoted as PC. The process followed is exhibited in Fig. 4.”
, where the “interpolation algorithm” and/or “median filter” is/are correcting the calculated depths of other regions on the basis of depths of other regions position respectively around the plurality of regions, which is consistent with para. [0028] of the instant Specification (i.e., “…Examples of means for correcting the depth of the region deviated from the depths of the surrounding regions include: image processing such as a median filter…”));
selecting a depth (5th para. of col.2 on pg. 3—see citation in preceding limitation above—and 6th para. of col.2 and 2nd para. of col. 1 on pg. 3, recites
[6th para. of col.2 on pg. 3] “PC describes the center of the papillary junction only and is thus inefficient at capturing the internal fingerprint undulations. PC is fine-tuned into papillary junction upper-edge coordinates (denoted as P). P describes the internal fingerprint zone entirely. Fine-tuning involves processing small image regions. This region is exemplified as the region between the (red and blue) lines in Figs. 3(c) and 3(d). These image regions contain the papillary junction upper edge.”
[2nd para. of col. 1 on pg. 3] “Fine-tuning is discussed in Sec. 3.4. Cluster analysis was used to determine a rough estimation of the center of the papillary junction. Column-wise intensity local maxima were used as data points. The manner in which this was accomplished is outlined in Sec. 3.2. Image enhancement was used to fine-tune this estimation to coordinates detailing the top-edge of the papillary junction.”
, where the “resultant coordinates” (“PC”) is selecting a depth at which the striped pattern sharpness is at an extreme (i.e., “intensity local maxima”)); and
extracting an image with a luminance on the basis of the selected depth (subsection 2.2 on pg. 5 and Fig. 7 recite
[2.2. Localized Internal Fingerprint Extraction] “A 2-D en face coordinate mean-map and standard deviation-map is calculated from P. These are P convolved with averaging and standard deviation filters, respectively. They are localized to an individual XY pixel and are used to provide statistical evaluations of the papillary junction undulations…
The internal fingerprint is extracted by averaging the pixels in the above-mentioned region. It is enhanced following the procedure outlined in Fig. 7: speckle noise is reduced using OBNLM, contrast is normalized on a local basis, and the intensity values are saturated. The experimental setup, designed to test the correlation between internal and surface fingerprints, and the extraction algorithm are detailed in the following section.”
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, where the internal fingerprint image (see Fig. 7 above) is an image with a luminance extracted on the basis of the selected depth (i.e., “P”, which is the papillary junction coordinates recited previously in the 6th para. of col.2 on pg. 3—see citation in limitation “selecting a depth…” above).
Where Darlow 2015A does not specifically disclose
determining a searching range for depth that is limited to a range in the depth direction from a depth which is a median value of first and second depths at which striped pattern sharpness is a maximum;
Darlow 2015B teaches in the same field of endeavor of 2D fingerprint extraction based on optical coherence tomography (OCT) imaging
determining a searching range for depth that is limited to a range in the depth direction from a depth which is a median value of first and second depths at which striped pattern sharpness is a maximum (Fig. 3, last para. in col. 1 of pg. 4, and “1. Relative distance…” in col. 2 of pg. 4, recite
[last para. in col. 1 of pg. 4] “In terms of the data points themselves, an analysis of A-lines was made. An A-line corresponds to a single column in a B-scan. In this way, a column-wise perspective was taken: A-line intensity changes can be seen to yield patterns in the data. Figure 3 exemplifies the analysis performed and shows the correspondence of intensity local maxima to interesting regions in a B-scan. The signals are individually smoothed before the strongest local maxima are extracted as data points…”
[“1. Relative distance…” in col. 2 of pg. 4] “…Each normalization constant is defined as the approximate pixel distance between the stratum corneum and the papillary junction over a single B-scan. This is estimated as the median of all measured distances between the two largest column-wise extrema, and is used to normalize each data-point’s distance to the stratum corneum.”
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, where the “papillary junction” is a searching range for a depth that is limited to a range in the depth direction (e.g., the region labeled as “Searching Range” in Fig. 3 above, where the “Row index” is a depth direction) from a depth (i.e., the lower boundary of the “papillary junction” region labeled as PJLB in Fig. 3 above) which is a median value between first and second depths (e.g., maximum depths labeled D1 and D2 , in Fig. 3 above) at which striped pattern sharpness is maximum (i.e., “a median…between the two largest column-wise extrema”)).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the presently filed invention to modify the system of Darlow 2015A to incorporate determining a search range for depth that is limited to a range in the depth direction from a depth which is a median value of first and second depths at which striped pattern sharpness is a maximum to more accurately extract an internal fingerprint by accurately detecting the papillary junction as taught by Darlow 2015B (para. between cols. 1 and 2 of pg. 1, and col. 2 on pg. 2, recite
[para. between cols. 1 and 2 of pg. 1] “The acquisition and use of an internal fingerprint can mitigate these disadvantages. Between the epidermis and dermis is an intermediary layer of skin known as the papillary junction. …The emergence of this structure in the papillary junction gives rise to an internal fingerprint. The direct correlation between the surface and the papillary junction results in identical topographical features (ridges and valleys). Thus, the papillary junction and surface fingerprint have the same structure”
[col. 2 on pg. 2] “The internal fingerprints acquired in previous research were not tested or optimized in terms of fingerprint quality. …These approaches captured the papillary junction undulation to a degree sufficient for internal fingerprint representation, but failed to define the region in which pertinent fingerprint information resides. This is inefficient. The research presented here provides an improved and accurate means of extracting an internal fingerprint by detection of the papillary junction. The approach taken is detailed in the next section.”
).
Where Darlow 2015A in view of Darlow 2015B does not specifically disclose
calculating spatial frequency of a striped pattern by Fourier transforming a tomographic image at the first or second depths;
Lamare teaches in the same field of endeavor of optical coherence tomography (OCT) imaging capturing an authentication target
calculating spatial frequency of a striped pattern by Fourier transforming a tomographic image at the first or second depths (para(s). [0002-0004], [0092], and [0094-0096], particularly [0095] recite(s):
[0095] “By Fourier transform 103 a time-domain reflectogram 104 is obtained, which allows (step 105 ) the position of the peak of interest (air/finger interface, epidermis/dermis interface, etc.) to be estimated by direct measurement of the time of flight, using the envelope of each A-scan.”
, where calculating the “time-domain reflectogram” is calculating spatial frequency (i.e., “spatial domain” as recited in para. [0096]) of a striped pattern (e.g., “air/finger interface, epidermis/dermis interface, etc.”) by Fourier transforming (i.e., “Fourier transform”) a tomographic image (e.g., OCT “phase images” as seen in para. [0002]) at first or second depths (i.e., “the peak of interest (air/finger interface, epidermis/dermis interface, etc.)”)).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the presently filed invention to modify the system of Darlow 2015A in view of Darlow 2015B to incorporate calculating spatial frequency of a striped pattern by Fourier transforming a tomographic image at the first or second depths to improve detection of the striped pattern (i.e., fingerprint) from 3D luminance data as taught by Lamare above.
Where Darlow 2015A, as modified by Darlow 2015B and Lamare, does not specifically disclose
selecting a depth closest to the corrected depth and at which…sharpness is at an extreme;
Sun teaches in the same field of endeavor of optical coherence tomography (OCT) imaging
selecting a depth closest to the corrected depth and at which…sharpness is at an extreme (subheading “Step 3” on pgs. 9-10 and lines 1-9 of pg. 7, recite
[Step 3: Correct the error points] “The depth positions with large errors are corrected using surface smoothness constraints, which require that the difference between the z coordinates of adjacent pixels is small. In step 2, the boundary surface preliminary positions (z coordinates) make up a depth information matrix, A. Given a weighted matrix, W1, for a depth element, p, in A, the absolute value of the difference between p and the weighted average of its adjacent entries is called the error distance (ED) of the element (associated with W1). For a threshold T, if the error distance of the element p is larger than the threshold, then p is considered to be an error point (associated with the threshold T). Given a weighted matrix W2, if an element p in A is an error point, the weighted average of its adjacent entries with W2 is taken as its correcting value (associated with W2). The matrix A can be smoothed through iteration as follows:
Choose a number of iterations, N. For the ith iteration, pick a threshold, Ti. For each entry in A, calculate its error distance. If an entry is an error point, then it is replaced by its corresponding correcting value. If there exists at least one error point and the iteration i is not equal to N, then advance to the next iteration until either there are no error points or N is reached.
The final depth information in A constitutes the desired boundary surface.”
[lines 1-9 of pg. 7] “…The basic idea of this method is to use the characteristics of the boundary of interest to design a 3D operator and apply it to the original volume data so that the pixel value on the desired boundary in the new volume data is likely to be the maximum value in its A-scan. This approach makes the new volume data a better indicator of the desired boundaries...”
, where the “final depth information in A constitutes the desired boundary surface” is selecting a depth (i.e., a “correcting value”) closest to the corrected depth (i.e., the “adjacent entries” comprise of corrected depths because the “adjacent entries” used to determine a “correcting value” for an error point include previously corrected entries from previous iterations of the iterative depth correction process of Sun) at which the sharpness (i.e., “pixel value”, which is pixel intensity) is at an extreme (i.e., the “desired boundary in the new volume data [is] likely to be the maximum value in its A-scan” as recited in lines 1-9 of pg. 7 above is the “final depth information in A constitut[ing] the desired boundary surface” recited in subheading “Step 3”)).
Since Darlow 2015A discloses detecting the papillary junction layer in a fingertip OCT scan to extract an internal fingerprint image (Darlow 2015A; last para. in col. 1 of pg. 1, recites
[last para. in col. 1 of pg. 1] “There is a layer of skin, known as the papillary junction, that has the same topography as the surface. Thus, the surface and internal fingerprint have the same structure.9 Owing to the relative reflectivity of the papillary junction to the epidermis, the upper edge of the papillary junction contains the most pertinent internal fingerprint information.”
), it would have been obvious to one of ordinary skill in the art before the effective filing date of the presently filed invention to modify the system of Darlow 2015A, as modified by Darlow 2015B and Lamare, to incorporate selecting a depth closest to the corrected depth in order to correct and eliminate depth error points of boundary surfaces in OCT images to make it easier to detect layer boundary surfaces in OCT images as taught by Sun (lines 6-13 of pg. 5, recite
[lines 6-13 of pg. 5] “…Then, preliminary discrete boundary points are detected from the A-Scans of the volume data. Finally, surface smoothness constraints and a dynamic threshold are applied to obtain a smoothed boundary surface by correcting a small number of error points. Our methods can extract retinal boundary surfaces sequentially within a decreasing region of volume data. The key idea is to use pixel position information, gradient information and intensity information simultaneously to enhance the boundary surface to be detected so that preliminary discrete boundary points can be detected more correctly and error points can be eliminated more easily. ”
).
Regarding claim 2, Darlow 2015A, as modified by Darlow 2015B, Lamare, and Sun, discloses the processing apparatus according to Claim 1, wherein Sun further teaches the operations further comprise:
calculating, for each region, a difference amount between roughly adjusted depth information indicating the corrected depth and finely adjusted depth information indicating the selected depth (subheading “Step 3” on pgs. 9-10—see citation in claim 1 above—, where the “error distance (ED)” is the claimed difference amount between roughly adjusted depth information (i.e., the “depth element p”) and finely adjusted depth information (i.e., the “weighted average of its [p] adjacent entries”)),
in a case in which the difference amount is no less than a threshold value, re-correcting the corrected depth on the basis of depths of other regions positioned respectively around the plurality of regions;
re-selecting, as the re-corrected depth, a depth closest to the re-corrected depth and at which the striped pattern sharpness is at an extreme (subheading “Step 3” on pgs. 9-10—see citation in claim 1 above—, where
“For a threshold T, if the error distance of the element p is larger than the threshold, then p is considered to be an error point (associated with the threshold T).”
, is synonymous to performing the claimed re-correction and re-selection when the difference amount (i.e., the “error distance”) is no less than a threshold value);
calculating, for each region, a difference amount between roughly re-adjusted depth information indicating the re-corrected depth and finely re-adjusted depth information indicating the re-selected depth (subheading “Step 3” on pgs. 9-10—see citation in claim 1 above—, specifically
“Choose a number of iterations, N. For the ith iteration, pick a threshold, Ti. For each entry in A, calculate its error distance. If an entry is an error point, then it is replaced by its corresponding correcting value. If there exists at least one error point and the iteration i is not equal to N, then advance to the next iteration until either there are no error points or N is reached.”
, where calculating an “error distance” for a “number of iterations” until there “are no error points” comprises of calculating for each region a difference amount (i.e., the “error distance”) between the roughly re-adjusted depth information and finely re-adjusted depth information (i.e., the method repeats for any depth elements p in depth information matrix A that are still defined as error points, including previously re-adjusted depth elements p from previous iterations if the previously re-adjusted depth elements p has an error distance greater than a threshold T)); and
extracting the image with the luminance on the basis of the re-selected depth in a case in which the difference amount is less than the threshold value (subheading “Step 3” on pgs. 9-10—see citation in claim 1 above—, specifically the citation in the preceding citation above where the correction process continues until “there are no error points” is extracting an image with the luminance on the basis of the re-selected depth (i.e., “final depth information in A constitutes the desired boundary surface”) in a case in which the difference amount is less than the threshold value (i.e., less than the threshold “T” or “Ti”, which indicates no error points)).
Regarding claim 3, Darlow 2015A, as modified by Darlow 2015B, Lamare, and Sun, discloses the processing apparatus according to Claim 1, wherein Darlow 2015A further discloses the operations further comprise restricting calculation of the depth dependence of the striped pattern sharpness to a specified depth (section 3 on pg. 5, recites
[3 Experimental Setup] “…The depth and resolution of A-lines obtained were set at 3 mm and 512 pixels, respectively...”
).
Regarding claim 7, Darlow 2015A, as modified by Darlow 2015B, Lamare, and Sun, discloses the processing apparatus according to Claim 1, wherein Darlow 2015A further discloses the striped pattern sharpness indicates uniformity with respect to widths of light and dark stripes in the regions (2nd para. in col. 1 of pg. 6, recites
[2nd para. in col. 1 of pg. 6] “…Regarding fingerprint quality evaluation, the NIST fingerprint score (NFIQ) and the orientation certainty level (OCL)28 were calculated for all fingerprints. NFIQ is a category-based score that ranges from 1 (best) to 5 (worst) and is dependent on information level (i.e., number and quality of minutiae points), while lower OCL scores indicate better energy concentration along the dominant ridge-valley orientation.”
, where the quality evaluation “orientation certainty level (OCL)” of the fingerprint image is an indication that the striped pattern sharpness indicates uniformity with respect to widths of light and dark stripes in the regions, which is consistent with para. [0024] of the instant Specification (i.e., “…OCL (Orientation Certainty Level)…indicating that there are a plurality of stripes of the same shape consisting of light and dark portions in an image…”)).
Regarding claim 9, Darlow 2015A, as modified by Darlow 2015B, Lamare, and Sun, discloses the processing apparatus according to Claim 1, wherein Darlow 2015A further discloses the operations further comprise using a median filter (5th para. of col.2 on pg. 3—see citation in claim 1 limitation “correcting the calculated depth…” above).
Regarding claim 11, Darlow 2015A, as modified by Darlow 2015B, Lamare, and Sun, discloses the processing apparatus according to Claim 1, wherein Lamare further teaches the operations further comprise using a filter for spatial frequency (paras. [0030-0031], recite
[0030] “Once the position of the peak of interest has been determined, spatial filtering may be carried out on the signal, in particular passband filtering of the interferogram in the spatial domain, the filtering consisting at least in retaining the interferometric signal contained in a window centred on the peak of interest and of a predefined width that is especially of the order of magnitude of the axial resolution of the OCT acquiring system. …”
[0031] “In the case where the sample is a fingerprint, the reference used to measure the phase data is preferably the average envelope of the surface of the finger. This average envelope corresponds to the surface enveloping the finger without its valleys, as shown in FIG. 9. A 3 D surface may be coded as a topographical image S(x,y) in which each (x,y) is associated with a depth value or preferably here a time of flight or phase value. The average envelope, called Em(x,y), is then obtained by applying an averaging filter and especially a 2D passband filter to the topographical image S(x,y). Since the valleys have higher spatial frequencies, the latter are advantageously removed during the filtering operation.”
, where the “spatial filtering” is using a filter (e.g., “averaging filter” and/or “passband filter”) for spatial frequency (i.e., “spatial frequencies”)).
Regarding claim 12, Darlow 2015A, as modified by Darlow 2015B, Lamare, and Sun, discloses a system comprising:
a measuring apparatus configured to capture three-dimensional luminance data indicating a recognition target (Darlow 2015A; fingertip OCT “scanner” in lines 3-16 in col. 1 of pg. 3, and Fig. 1(a)—see citation in claim 1 limitation “determining a range of…” above—, where a fingertip is a recognition target); and
the processing apparatus according to Claim 1 (see rejection for claim 1 above),
wherein the operations further comprise acquiring a tomographic image having a striped pattern inside the recognition target (Darlow 2015A; abstract, recites
[abstract] “…Techniques for obtaining the internal fingerprint from OCT scans have since been developed. This research presents an internal fingerprint extraction algorithm designed to extract high-quality internal fingerprints from touchless OCT fingertip scans…”
, where the “internal fingerprint” image from “OCT scans” is acquiring a tomographic image having a striped pattern (i.e., fingerprint) inside the recognition target).
Regarding claim 13, Darlow 2015A, as modified by Darlow 2015B, Lamare, and Sun, discloses a biometric authentication system comprising:
a measuring apparatus configured to capture three-dimensional luminance data indicating a living body as a recognition target (Darlow 2015A; fingertip OCT “scanner” in lines 3-16 in col. 1 of pg. 3, and Fig. 1(a)—see citation in claim 1 limitation “calculating, from three-dimensional luminance data indicating,…” above—, where a fingertip is a living body);
the processing apparatus according to Claim 1 (see rejection for claim 1 above);
wherein the operations further comprise:
comparing a tomographic image having a striped pattern inside the recognition target with image data associated with individual information; and identifying an individual through comparison between the tomographic image and the image data (Darlow 2015A; 2nd para. of col. 1 of pg. 10, recites
[2nd para. of col. 1 of pg. 10] “…In addition, this research endeavors to provide an advanced technique for fingerprint extraction from OCT fingertip scans. That said, the results shown in Fig. 12 give the most useful internal fingerprint performance indication. Future research will certainly entail error rate assessments using a large database of OCT scans and will provide a traditional biometric system evaluation.”
, where the method of Darlow 2015A is recited to be implemented in a “traditional biometric system evaluation” is a processing apparatus configured to identify an individual through comparison of the claimed tomographic image and the image data).
Regarding claim 14, the claim is the method performed by the system of claim 1. Therefore, claim 15 recites similar limitations to claim 1 and is rejected for similar rationale and reasoning (see the analysis for claim 1 above).
Regarding claim 15, the claim is the memory (i.e., a non-transitory computer readable medium is a form of memory) of the system of claim 1. Therefore, claim 16 recites similar limitations to claim 1 and is rejected for similar rationale and reasoning (see the analysis for claim 1 above).
Claims 8 and 4-6 are rejected under 35 U.S.C. 103 as being unpatentable over Darlow 2015A, as modified by Darlow 2015B, Lamare, and Sun, as applied to claim 1 above, and further in view of Sharma et al. (Sharma; "Fingerprints liveness detection using local quality features," 2018; provided by Applicant's IDS filed November 8th, 2024).
Regarding claim 8, Darlow 2015A, as modified by Darlow 2015B, Lamare, and Sun, discloses the processing apparatus according to Claim 1, wherein Sharma teaches in the same field of endeavor of fingerprint-based authentication the striped pattern sharpness is a combination of the striped pattern sharpnesses that indicates unidirectionality of the striped pattern in the regions (subheading “Gabor quality” in col. 2 of pg. 1399, recites
[Gabor quality] “Gabor filter is used to measure the quality of live and fake fingerprint images [44, 45]. Gabor filter bank with different orientations is utilized to compute the Gabor quality at the local level. Gabor filter bank is applied to each pixel of the block. The strength of the Gabor response for a fingerprint block with the regular ridge–valley pattern will be high for one or few filters having orientations similar to block orientation. On the contrary, for a block containing unclear ridge–valley structure, the Gabor responses for all orientations will be low and constant. Finally, the standard deviation of the Gabor filter bank responses is computed. This indicates the Gabor quality (G) of the block…”
), unity of spatial frequency in the regions (subheading “Frequency domain analysis” in col. 1 of pg. 1399, recites
[Frequency domain analysis] “FDA [30, 44] of a local block is computed by extracting 1D signature of ridge–valley structure. DFT of this 1D signature is computed to obtain the frequency of the sinusoidal ridge–valley structure. Live fingerprints have a uniform frequency of sinusoidal ridge–valley structure, while it varies in fake fingerprints...”
), uniformity of luminance in each of light and dark portions in the regions (subheading “Ridge valley clarity” in col. 1 of pg. 1398, recites
[Ridge valley clarity] “Separation between two consecutive ridges and valleys in a local block of the live fingerprint image is almost constant. On the other hand, this separation can vary in fake fingerprints due to the varying widths of ridges and valleys in a block. Average ridge and valley widths of a block are computed to measure ridge–valley clarity…”
) and uniformity with respect to widths of light and dark stripes in the regions (subheading “Ridge width smoothness/valley width smoothness” in col. 2 of pg. 1396, recites
[Ridge width smoothness/valley width smoothness] “It indicates ridge width smoothness and valley width smoothness in a fingerprint block. Figure 2 shows that live fingerprints have almost constant ridge and valley width, while the fake fingerprints have varying ridge and valley widths due to the elasticity of fabrication materials and non-uniform pressure at the time of fake fingerprint fabrication…”
), respectively.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the presently filed invention to modify the system of Darlow 2015A, as modified by Darlow 2015B, Lamare, and Sun, to have the striped pattern sharpness indicate unidirectionality of the striped pattern in the regions, unity of spatial frequency in the regions, uniformity of luminance in each of light and dark portions in the regions, and uniformity with respect to widths of light and dark stripes in the regions to extract image quality features for a biometric authentication system useful in detecting and/or distinguishing between live and fake fingerprints as taught by Sharma (subsection 3.1 on pg. 1396, recites
[3.1 Feature extraction] “Image quality features based on ridge–valley properties of fingerprints are useful for detection of fake fingerprints. The elasticity of the materials used to fabricate fake fingerprints introduces non-uniformity in the ridge–valley structure of the captured image. As ridges and valleys are core part of a fingerprint image, measuring the characteristic of ridge–valley structure for live and fake fingerprints is crucial in fingerprint liveness detection. Characteristics of the ridge–valley structure of live and fake fingerprints are shown in Fig. 2. Based on these minute observations, we propose RWS, VWS, Rab, Vab, and RVC features. In addition to these features, FDA, OCL, and G features are also considered for liveness detection in this work. All features are extracted at the local level by dividing a fingerprint image into a number of blocks and rotating each block...”
).
Regarding claim 4, Darlow 2015A, as modified by Darlow 2015B, Lamare, and Sun, discloses the processing apparatus according to Claim 1, wherein Sharma teaches in the same field of endeavor of fingerprint-based authentication the striped pattern sharpness indicates unidirectionality of the striped pattern in the regions (subheading “Gabor quality” in col. 2 of pg. 1399—see citation for rejection of similar limitation in claim 8 above).
Claim 4 recites similar limitations to claim 8 and is rejected for similar rationale and reasoning (see the analysis for claim 8 above).
Regarding claim 5, Darlow 2015A, as modified by Darlow 2015B, Lamare, and Sun, the processing apparatus according to Claim 1, wherein Sharma teaches in the same field of endeavor of fingerprint-based authentication the striped pattern sharpness indicates unity of spatial frequency in the regions (subheading “Frequency domain analysis” in col. 1 of pg. 1399—see citation for rejection of similar limitation in claim 8 above).
Claim 5 recites similar limitations to claim 8 and is rejected for similar rationale and reasoning (see the analysis for claim 8 above).
Regarding claim 6, Darlow 2015A, as modified by Darlow 2015B, Lamare, and Sun, discloses the processing apparatus according to Claim 1, wherein Sharma teaches in the same field of endeavor of fingerprint-based authentication the striped pattern sharpness indicates uniformity of luminance in each of light and dark portions in the regions (subheading “Ridge valley clarity” in col. 1 of pg. 1398—see citation for rejection of similar limitation in claim 8 above).
Claim 6 recites similar limitations to claim 8 and is rejected for similar rationale and reasoning (see the analysis for claim 8 above).
Claim 10 is rejected under 35 U.S.C. 103 as being unpatentable over Darlow 2015A, as modified by Darlow 2015B, Lamare, and Sun, as applied to claim 1 above, and further in view of Das et al. (Das; "A Comparative Study of Different Noise Filtering Techniques in Digital Images," 2015; provided by Applicant's IDS filed November 8th, 2024).
Regarding claim 10, Darlow 2015A, as modified by Darlow 2015B, Lamare, and Sun, discloses the processing apparatus according to Claim 1, wherein Das teaches in the same field of endeavor of reducing noise in images the operations further comprise using a bilateral filter (subsection 1.6.2 on pg. 187, recites
[1.6.2 Edge-preserving Filtering with the Bilateral Filter] “The bilateral filter is also defined as a weighted average of nearby pixels, in a manner very similar to Gaussian convolution. The difference is that the bilateral filter takes into account the difference in value with the neighbours to preserve edges while smoothing. The key idea of the bilateral filter is that for a pixel to influence another pixel, it should not only occupy a nearby location but also have a similar value…”
).
Since Darlow 2015A discloses OCT images are subjective to speckle noise (lines 5-8 of col.1 of pg. 1, recite
[lines 5-8 of col.1 of pg. 1] “…OCT is subject to signal-degrading speckle noise that originates from reflective elements of roughly the same size as the imaging wavelength…”
), it would have been obvious to one of ordinary skill in the art before the effective filing date of the presently filed invention to modify the system of Darlow 2015A, as modified by Darlow 2015B, Lamare, and Sun, incorporate employing a bilateral filter to preserve edges by correcting pixels based on neighboring pixels because it is a common noise filtering technique used to reduce noise (e.g., speckle or salt and pepper noise) in images as taught by Das (abstract, recites
[abstract] “Although various solutions are available for de-noising them, a detail study of the research is required in order to design a filter which will fulfil the desire aspects along with handling most of the image filtering issues. In this paper we want to present some of the most commonly used noise filtering techniques namely: Median filter, Gaussian filter, Kuan filter, Morphological filter, Homomorphic Filter, Bilateral Filter and wavelet filter…Bilateral Filter is a non-linear edge preserving and noise reducing smoothing filter for images…Salt and pepper noise, Speckle noise and Gaussian noise are introduced to clean images and filtered using the filtering techniques mentioned above…”
).
Conclusion
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/J.Z.Y./Examiner, Art Unit 2666
/MING Y HON/Primary Examiner, Art Unit 2666
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