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 .
Priority
The instant application is the National Stage of International Application No. PCT/JP2022/028345, filed on 07/21/2022. Thus, the effective filing date of Claims 1-8 and 10-11 are 07/21/2022.
Information Disclosure Statement
The information disclosure statements (“IDS”) filed on 10/17/2024 and 09/30/2025 was reviewed and the listed references were noted.
Drawings
The 14 page drawings have been considered and placed on record in the file.
Status of Claims
Claims 1-8 and 10-11 are currently pending. Claim 9 is canceled.
Specification
The title of the invention is not descriptive. A new title is required that is clearly indicative of the invention to which the claims are directed.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
Determining the scope and contents of the prior art.
Ascertaining the differences between the prior art and the claims at issue.
Resolving the level of ordinary skill in the pertinent art.
Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1-4 and 7-11 are rejected under 35 U.S.C. 103 as being unpatentable over Tan et. al. (“Human identification from at-a-distance images by simultaneously exploiting iris and periocular features” with publishing date of November 11-15, 2012) in view of Ryota et. al. (JP2019023785 published on 02/14/2019).
Consider Claim 1, “(Tan Figure 1 (See image below); “Feature Extraction” of the iris and periocular area.) “Tan Figure 1 (See image below); “Matching”) “weights. (Tan; 3. Experimental Results, Score Combination; “Score combination In order to consolidate matching scores from two different matching distances, score normalization is necessary as a priori. The min-max normalization scheme was used for the matching scores computed from the iris and periocular images. The normalized iris and periocular scores,
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, are then combined using weighted sum method” (emphasis added))
Tan does not explicitly disclose “An information processing device comprising: a storage medium configured to store instructions; and a processor configured to execute the instructions to:” or “…identify a weight of similarity for each of the multiple regions calculated based on the feature vector of each of the multiple regions and a feature vector relating to each of corresponding regions that are pre- stored for the target;”. However, in an analogous field of endeavor, Ryota teaches “An information processing device comprising: a storage medium configured to store instructions;” (Ryota, [0005]; “…a non-transitory computer readable medium storing a program”) “and a processor configured to execute the instructions to:” (Ryota, [0016]; “The control unit 10 includes a CPU ( Central Processing Unit ), an interface, and the like. The CPU operates according to a program 110 stored in the storage unit…”). Furthermore, Ryota teaches ““identify a weight of similarity for each of the multiple regions calculated based on the feature vector of each of the multiple regions and a feature vector relating to each of corresponding regions that are pre- stored for the target;” (Ryota; [0041]; “The coefficient w 1 on the face is a value indicating the weighting of information (feature amount, first probability, etc.) obtained from the face area 202 with respect to the integrated information F. The coefficient w 2 related to the body is a value indicating the weighting of information (feature amount, first probability, etc.) obtained from the body region 203 with respect to the integrated information F.”).
Accordingly, before the effective filing date of the instant application, it would have been obvious to one of ordinary skill in the art to combine Tan with the teachings of Ryota to further utilize different weights for each feature vector. One of ordinary skill in the art would be motivated to combine Tan and Ryota to indicate greater importance on one feature vector over the other to increase the accuracy in determining the similarity between an acquired and pre-stored image (Ryota, [0005]). Accordingly, the combination of Tan and Ryota discloses the invention of Claim 1.
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Consider Claim 2, the combination of Tan and Ryota “The information processing device according to claim 1, wherein the processor is further configured to execute the instructions to: detect landmark information indicating a position relating to the eye of the target included in the acquired image;” (Tan; Figure2(a) (See image below); 2.1 Iris Segmentation; “The iris boundary can be localized in a similar manner by searching the optimal iris center (xi, yi)…”) “cut out the multiple regions” (Tan; Figure 1 (See image above); Iris and Periocular Segmentation; Figure 2 - 3) based on the landmark information” (Tan; 2.1 Iris Segmentation - 2.2 Periocular region segmentation; “The candidate center with radius
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which produces the largest voting score is then employed as the optimal pupil center. The iris boundary can be localized in a similar manner by searching the optimal iris center
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and radius
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around a small window centered at
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….segmenting the periocular region with respect to the segmented iris information
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. Firstly, the input image is normalized (upscaling/downscaling) based on a scale factor,
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, where
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is the normalized iris radius. Note that the
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is shifted due to the normalization process and the resultant center can be computed as
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. The periocular region ( ) defined as the rectangular region of size W x H centered at
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.”) “wherein the processor is configured to execute the instructions to: in extracting, extract the feature vector of each of the multiple regions that are cut out by.” (Tan Figure 1 (See image above); “Feature Extraction” of the iris and periocular area.). The proposed combination as well as the motivation for combining the Tan and Ryota references presented in the rejection of claim 1, apply to claim 2 and are incorporated herein by reference. Thus, the method recited in claim 2 is met by Tan and Ryota.
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Consider Claim 3, the combination of Tan and Ryota teaches “The information processing device according to claim 2, wherein the processor is configured to execute the instructions to: in the detecting, detect means detects the landmark information included in the acquired image;” (Tan; 1.Introduction; “In this paper, we performed rigorous experiments to evaluate the automated iris recognition performance using a combination of simultaneously acquired/segmented iris and periocular features. The iris and periocular images are automatically segmented from the face images…”) “and in identifying, calculate the weight of similarity based on the landmark information.” (Ryota; [0041]; “The coefficient w 1 on the face is a value indicating the weighting of information (feature amount, first probability, etc.) obtained from the face area 202 with respect to the integrated information F. The coefficient w 2 related to the body is a value indicating the weighting of information (feature amount, first probability, etc.) obtained from the body region 203 with respect to the integrated information F.”). The proposed combination as well as the motivation for combining the Tan and Ryota references presented in the rejection of claim 1, apply to claim 3 and are incorporated herein by reference. Thus, the method recited in claim 3 is met by Tan and Ryota.
Consider Claim 4, the combination of Tan and Ryota teaches “The information processing device according to claim 3, wherein the processor is configured to execute the instructions to: in identifying, calculate the weight of similarity based on a parameter calculated by using the landmark information.” (Ryota; [0042]; “The information integrator 104 increases the weight of one of the information obtained from the face region 202 and the information obtained from the body region 203 that has a larger amount of information. To be more specific, the information integrator 104 sets the coefficient having a larger amount of information out of the coefficient w1 related to the face and the coefficient w2 related to the body to a value larger than the coefficient having a smaller amount of information based on, for example, the ratio of the area of the face region 202 to the area of the body region 203, the similarity of the clothes of the person 200, the photographing time, and a combination thereof.”). The proposed combination as well as the motivation for combining the Tan and Ryota references presented in the rejection of claim 1, apply to claim 4 and are incorporated herein by reference. Thus, the method recited in claim 4 is met by Tan and Ryota.
Consider Claim 7, the combination of Tan and Ryota “The information processing device according to claim 1, wherein the processor is configured to execute the instructions to: in extracting, extract a feature vector of a first region including at least a region of an iris of the eye and not including a region around the eye, and a feature vector of a second region including both the region of the iris of the eye and the region around the eye;” (Tan; Figure 1 (See image above); “Iris Segmentation” and “Periocular Segmentation”) “and in calculating, calculate, using the weights identified with respect to similarity between the feature vectors obtained from each of features of the first region and the second region, and the feature vectors pre- stored for the first region and the second region,” (Tan; 3. Experimental Results, Score Combination; “The min-max normalization scheme was used for the matching scores computed from the iris and periocular images. The normalized iris and periocular scores,
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, are then combined using weighted sum method…” “the similarity between the feature vectors of the target included in the acquired image and the pre-stored feature vectors of the target. (Tan; Figure 1 (See image above); Score 1, Score 2, and Matching). The proposed combination as well as the motivation for combining the Tan and Ryota references presented in the rejection of claim 1, apply to claim 7 and are incorporated herein by reference. Thus, the method recited in claim 7 is met by Tan and Ryota.
Consider Claim 8, the combination of Tan and Ryota teaches “The information processing device according to claim 3, wherein the processor is configured to execute the instructions to: in identifying, calculate the weight based on the landmark information” (Ryota; [0041]; “The coefficient w 1 on the face is a value indicating the weighting of information (feature amount, first probability, etc.) obtained from the face area 202 with respect to the integrated information F. The coefficient w 2 related to the body is a value indicating the weighting of information (feature amount, first probability, etc.) obtained from the body region 203 with respect to the integrated information F.”) “and a model obtained by machine learning.” (Ryota; [0026]; “For example, the face region information extraction unit 102 extracts the face region 202 on the basis of an image related to a face of a person learned in advance by machine learning such as Deep Leaning .”). The proposed combination as well as the motivation for combining the Tan and Ryota references presented in the rejection of claim 1, apply to claim 8 and are incorporated herein by reference. Thus, the method recited in claim 8 is met by Tan and Ryota.
Claim 10 recites a method with steps corresponding to the components of the device recited in Claim 1. Therefore, the recited steps of this claim are mapped to the proposed combination in the same manner as the corresponding elements in its corresponding system claim. Additionally, the rationale and motivation to combine the Tan and Ryota references, presented in rejection of Claim 1, apply to this claim.
Claim 11 recites a computer-readable storage medium storing a program with instructions corresponding to the components recited in Claim 1. Therefore, the recited programming instructions of this claim are mapped to the proposed combination in the same manner as the corresponding steps in its corresponding method claim. Additionally, the rationale and motivation to combine the Tan and Ryota references, presented in rejection of Claim 1, apply to this claim. Finally, the combination of Tan and Ryota discloses a non-transitory computer readable storage medium (Ryota, [0005]; “…a non-transitory computer readable medium storing a program”).
.
Claims 5-6 are rejected under 35 U.S.C. 103 as being unpatentable over Tan et. al. (“Human identification from at-a-distance images by simultaneously exploiting iris and periocular features” with publishing date of November 11-15, 2012) in view of Ryota et. al. (JP2019023785 published on 02/14/2019) and in further view of Hamza (US 8098901 B2 filed on 02/15/2007).
Consider Claim 5, the combination of Tan and Ryota does not explicitly disclose “The information processing device according to claim 3, wherein the processor is configured to execute the instructions to: in identifying, calculate the weight of similarity for each of the multiple regions” (Ryota; [0041]; “The coefficient w 1 on the face is a value indicating the weighting of information (feature amount, first probability, etc.) obtained from the face area 202 with respect to the integrated information F. The coefficient w 2 related to the body is a value indicating the weighting of information (feature amount, first probability, etc.) obtained from the body region 203 with respect to the integrated information F.”).
The combination of Tan and Ryota does not explicitly disclose “based on an open/closed degree of the eye calculated based on the landmark information.”. However, in an analogous field of endeavor, Hamza teaches “…based on an open/closed degree of the eye calculated based on the landmark information.” (Hamza; Col. 4-5, Line 64-13; “Once the iris inner border at the pupil is estimated, one may move outward from the pupil with some margin that represents the least possible width of an iris….An offset 90 of FIG. 9 may vary from zero to some value depending on the visibility of the pupil within the eye image during image acquisition. For instance, one offset may vary dependent on a scoring and/or a validation of a pupil profile being captured. Relative to a closed or highly obscured eye, an offset may be at a minimum or zero. For an open eye with no obscuration and having a high score and/or validation of a pupil profile, the offset may be large. The offset may vary depending on the areas or angular segments of the eye that are visible. Offset may vary according to the border type. For example, the iris/sclera border may warrant significant offset, and the offset for the iris/eyelash-lid may be low, minimus or zero.” (emphasis added)). Accordingly, before the effective filing date of the instant application, it would have been obvious to one of ordinary skill in the art to combine Tan and Ryota with the teachings of Hamza to further determine weight of similarity based on the open or closed degree of an eye. One of ordinary skill in the art would be motivated to combine Tan, Ryota, and Hamza to indicate a greater emphasis on opened eye images to derive a clearer segment for comparison and calculate similarity with more accuracy. Accordingly, the combination of Tan, Ryota, and Hamza discloses the invention of Claim 5.
Consider Claim 6, the combination of Tan, Ryota, and Hamza teaches “The information processing device according to claim 3, wherein the processor is configured to execute the instructions to: in identifying, calculate the weight with respect to similarity for each of the multiple regions based on pixel information for an iris of the eye calculated based on the landmark information.” (Hamza; Col. 9, Line 29-38; “ One may make use of Gabor filters to encode the iris map image to its minimum possible number of bits so that metrics can be used to give one range of values when comparing templates with capture maps. Similarly, any similarity metrics may be used to measure the information similarity among templates. One metric in particular that may be used is the weighted hamming distance (WHD). The WHD may give more weight to the pixels associated with valid edges and less weight to the pixels that are associated with non-valid pixels.” (emphasis added)). The proposed combination as well as the motivation for combining the Tan, Ryota, and Hamza references presented in the rejection of claim 5, apply to claim 6 and are incorporated herein by reference. Thus, the method recited in claim 6 is met by Tan, Ryota, and Hamza.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Annie Pham whose telephone number is (571)272-1673. The examiner can be normally be reached Mon-Fri 9:00a – 5:00p.
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/ANNIE H PHAM/Examiner, Art Unit 2662
/Siamak Harandi/Primary Examiner, Art Unit 2662
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