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 .
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
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Claims 1-20 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1 and 3-20 of U.S. Patent No. 12,430,974. Although the claims at issue are not identical, they are not patentably distinct from each other because they are claiming the same invention with little additional change to the claim language.
Instant claims 1 and 2 are rejected as being obvious to U.S. Patent No. 12,430,974 claim 1.
Instant claims 3-14 each are respectfully rejected as being obvious to U.S. Patent No. 12,430,974 claims 3-14, respectfully.
Instant claim 15 is rejected as being obvious to U.S. Patent No. 12,430,974 claim 15.
Instant claims 16-19 are each are respectfully rejected as being obvious to U.S. Patent No. 12,430,974 claims 16-19, respectfully.
Instant claim 20 is rejected as being obvious to U.S. Patent No. 12,430,974 claim 20.
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.
Claims 1, 3, 5, 7-15 and 18-20 are rejected under 35 U.S.C. 103 as being unpatentable over Ryhorchuk (US Patent No. 20210287469 A1), in view of Shain (US Patent No. 20230045699 A1).
In re claim 1, Ryhorchuk teaches A method comprising:
detecting, using a camera, a person within a particular distance of a secure area (Para [0277]: “The proposed technology is currently able to detect and recognize people 3 meters from the sensor. This can be concerning if an authorized individual is walking in front of the entrance but not planning to go in. For example in a hallway, a person can pass several entrances before going through one. Only intended doors must be unlocked for security reasons.”) by determining a face size of the person (Para [0223]: “It should also be noted that the subject invention contemplates not only a system that captures and stores images as images, but a system in which the images are processed to extract key aspects of the images, and only that information, which may be thought of as metadata, is stored. Such metadata may be based on aspects of the images such as depth information about the face, the size and shape of and distances between key landmarks (eyes, nose, mouth, etc.) or other descriptive and/or distinctive aspects of the image.”), and comparing the face size to a threshold (Para [0007]: “An increasingly prevalent form of biometric verification is facial recognition. Facial recognition generally uses one or more digital cameras or sensors to capture one or more images, which are used to generate a digital file containing data about a person's face. Image processing software uses this data to perform analysis to detect facial features and to determine attributes such as distances between different facial features, description of those facial features and the shape of the head. Algorithms running on one or more processors then uses this data to compare the captured face to one or more faces that have been previously analyzed to estimate the probability that they are the same person.”);
identifying, based on comparing the face size to the threshold, that the face size traversed the threshold;
in response to identifying that the face size traversed the threshold, capturing, using the camera (Para [0241]: “FIG. 25 is a high level flow chart illustrating the steps involved in an exemplary efficient facial recognition process. In step 2502, RGB camera xxx captures an image of the entire field of view of RGB camera 502. In step 2504, the captured image is analyzed—not (initially) for purposes of determining who is there, but simply to determine if the captured image includes a person, and if so, where in the captured image the person is. This is a computationally simpler task, and can be completed relatively quickly. This step can consist of searching for a face, or it can consist of searching for a shape likely to be human body. In step 2506 it is determined whether or not a person appears to be present. If no face or body is detected then the process loops until a face is detected. When a person is detected, the region of interest, or the portion of the field of view that contains the image of interest (the face) is defined in step 2508. This region can be of virtually any appropriate size. In the currently preferred embodiment, it can be as small as 120 pixels by 120 pixels. In step 2510, a subsequent RGB image is captured, and the image region defined in step 2406 is analyzed to determine if the person has moved, and if so, in step 2512 the amount of movement is estimated. This process can be computationally intensive, so the load on the processor is reduced by limiting the analysis to the previously defined region, or a region slightly larger to accommodate potential motion. If the person is determined to have moved in 2514, then in step 2516 the defined region of interest is adjusted accordingly…” and SEE FIG. 32), a set of images of at least a facial feature of the person;
preprocessing the set of images to identify the facial feature in at least one image of the set of images;
identifying, using processing circuitry, a gaze status of the person based on the identified facial feature in the at least one image;
determining, using a trained machine learning model, an intent of the person to access the secure area based on using the gaze status and a second gaze status of a second image of the set of images as input to the trained machine learning model (Para [0280]: “In step 3302 an image of the potential entrant is captured, and in step 3304, the image is processed. (In an actual implementation, a series of images will be captured and analyzed, but for simplicity a single image capture is described.) In step 3306 the trajectory of the potential entrant is calculated; in step 3308 the gaze of the potential entrant is analyzed. In addition to these steps, the direction in which the potential entrant is facing may be analyzed, and other indicators of intent may be detected. In step 3310 these inputs are used to produce a probability that the person intends to enter the access point.” para [0287]: “The subject facial recognition system, like most or all neural network-based systems, requires training in order to develop the algorithms used to perform as intended. This requires that a large number of images of faces be presented and analyzed. In the currently preferred embodiment, much of this training is to be performed while developing the system and prior to deployment in specific installations, thereby significantly reducing the ramp-up period in an actual deployment.” and para [0278]: “The proposed module includes facial and eye analysis to detect if the person is specifically gazing in the direction of the door. If this condition is met, the unlock mechanism will be activated. If gaze towards the general direction of the sensor is not detected, no action is taken. The system combines the “in the flow” target by not asking the user to do anything specific and the intent detection to unlock only requested entrances.”).
Ryhorchuk fails to teach based on determining the intent, granting access to the secure area.
However, Shain teaches based on determining the intent, granting access to the secure area (Para [0025]: “For example, operations of system 100 may be used to provide seamless experience for the user (the intent is determined before the user reaches the access point and quicker access may be granted). Other advantages may include that users don't need to “teach” the system to recognize their intent, the learning is done automatically.”).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Ryhorchuk to incorporate the teachings of Shain to provide based on determining the intent, granting access to the secure area with the System and method for provisioning a facial recognition-based system for controlling access to a building of Ryhorchuk. Doing so provides seamless experience for the user because the intent is determined before the user reaches the access point and quicker access may be granted and users don't need to “teach” the system to recognize their intent, the learning is done automatically, as recognized by Shain (Para [0025]).
Non-transitory machine readable medium claim 15 and system claim 20 are rejected for the same reasons as method claim 1 for having similar limitations and being similar in scope.
In re claim 3, Ryhorchuk and Shain teach all of the limitations of claim 1 stated above where Ryhorchuk further teaches wherein granting access to the secure area includes opening a door to the secure area (SEE FIG. 32 and para [0278]: “The proposed module includes facial and eye analysis to detect if the person is specifically gazing in the direction of the door. If this condition is met, the unlock mechanism will be activated. If gaze towards the general direction of the sensor is not detected, no action is taken. The system combines the “in the flow” target by not asking the user to do anything specific and the intent detection to unlock only requested entrances.”).
In re claim 5, Ryhorchuk and Shain teach all of the limitations of claim 1 stated above where Ryhorchuk further teaches wherein the camera is an infrared camera (Para [0348]: “The infrared camera or cameras are preferably focused on a narrower field of view 4528, because they are preferably optimized for facial recognition, which will generally be improved by allocating the available pixels on a given sensor to a smaller viewing angle in order to improve resolution.”).
In re claim 7, Ryhorchuk and Shain teach all of the limitations of claim 1 stated above where Ryhorchuk further teaches wherein the facial feature is one or both eyes of the person (Para [0223]: “It should also be noted that the subject invention contemplates not only a system that captures and stores images as images, but a system in which the images are processed to extract key aspects of the images, and only that information, which may be thought of as metadata, is stored. Such metadata may be based on aspects of the images such as depth information about the face, the size and shape of and distances between key landmarks (eyes, nose, mouth, etc.) or other descriptive and/or distinctive aspects of the image.”).
In re claim 8, Ryhorchuk and Shain teach all of the limitations of claim 1 stated above where Shain further teaches wherein the trained machine learning model is a recurrent neural network (Para [0044]: “For example, machine learning system 240 may implement any type of machine learning technique to determine the user intent as described herein… In some embodiments, the machine learning models may include… artificial neural networks (e.g., fully connected neural networks, deep convolutional neural networks, or recurrent neural networks)...”).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the combination of Ryhorchuk and Shain to further incorporate the teachings of Shain to provide wherein the trained machine learning model is a recurrent neural network with the System and method for provisioning a facial recognition-based system for controlling access to a building of Ryhorchuk as modified by Shain. Doing so enables determination of the user intent, as recognized by Shain (Para [0044]).
Machine readable medium claim 18 is rejected for the same reasons as method claim 8 for having similar limitations and being similar in scope.
In re claim 9, Ryhorchuk and Shain teach all of the limitations of claim 1 stated above where Shain further teaches wherein the trained machine learning model is trained using a plurality of camera positions relative to an access device including the processing circuitry (Para [0028]: “In some embodiments, sensors 102 may include optical sensors configured to generate one or more image data. The image data, in some embodiments, may be used to determine intent of the user. In some embodiments, system 100 may use the image data obtained by the sensors to train the intent models to determine/detect intent of the user. For example, the image data may be used for features or information extraction from data sets received from the optical sensors using a machine learning system (as explained herein below)… In some embodiments, sensor data obtained from sensors 102 may be processed (e.g., using processors 510 described herein with reference to FIG. 5) to extract image information. In some embodiments, the processors may be included in the sensors. In some embodiments, the sensor data obtained by sensors 102 may include images, videos, multi-dimensional depth images, thermal images, infrared light measurements, light reflection time measurements, radio wave measurements, range, angle, and/or other sensor data. In some embodiments, a plurality of sensor data from a plurality of sensors of sensors 102 may be combined to extract the information. For example, images from different locations and angles, multi-dimensional depth images, thermal images, ranges, angles, and/or other image data obtained from sensors 102 may be combined to provide information about the user and/or the controlled area. In some embodiments, computer vision techniques may be used to extract information about the user or the controlled area from the optical sensors. In some embodiments, computer vision may be used for people or object detection, recognition, or identification.”).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the combination of Ryhorchuk and Shain to further incorporate the teachings of Shain to provide wherein the trained machine learning model is trained using a plurality of camera positions relative to an access device including the processing circuitry with the System and method for provisioning a facial recognition-based system for controlling access to a building of Ryhorchuk as modified by Shain. Doing so enables system 100 to use the image data obtained by the sensors to train the intent models to determine/detect intent of the user, as recognized by Shain (Para [0028]).
In re claim 10, Ryhorchuk and Shain teach all of the limitations of claim 1 stated above where Ryhorchuk further teaches wherein the gaze status is identified as being in a direction towards the secure area (Para [0278]: “The proposed module includes facial and eye analysis to detect if the person is specifically gazing in the direction of the door. If this condition is met, the unlock mechanism will be activated.”).
In re claim 11, Ryhorchuk and Shain teach all of the limitations of claim 1 stated above where Shain further teaches wherein determining the intent is based on a detected trajectory of the person, the detected trajectory obtained from the set of images (Para [0028]: “In some embodiments, sensors 102 may include optical sensors configured to generate one or more image data. The image data, in some embodiments, may be used to determine intent of the user.” and para [0029]: “In some embodiments, information generated by sensors 102 may include behavioral characteristics of the user. The behavioral characteristics of the user may include user movement characteristics (e.g., gait, coordination, walking speed, number of steps taken, pace, manner, and pattern of walking, or other movement characteristics). In some embodiments, the behavioral characteristics may include motion, position, or orientation of one or more body parts of the user (e.g., gesture, facial expression, eye movement, head position, etc.).”).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the combination of Ryhorchuk and Shain to further incorporate the teachings of Shain to provide wherein determining the intent is based on a detected trajectory of the person, the detected trajectory obtained from the set of images with the System and method for provisioning a facial recognition-based system for controlling access to a building of Ryhorchuk as modified by Shain. Doing so enables the image data of behavioral characteristics to be used to determine intent of the user, as recognized by Shain (Paras [0028]-[0029]).
In re claim 12, Ryhorchuk and Shain teach all of the limitations of claim 1 stated above where Ryhorchuk further teaches further comprising, before granting access to the secure area, determining whether the person is authorized to access the secure area, and based on determining the person is authorized to access the secure area, granting access to the secure area (SEE FIG. 32).
Machine readable medium claim 19 is rejected for the same reasons as method claim 12 for having similar limitations and being similar in scope.
In re claim 13, Ryhorchuk and Shain teach all of the limitations of claim 12 stated above where Ryhorchuk further teaches further comprising, selecting an order of operations for determining the intent or authorizing the person based on a distance of the person to the secure area (Para [0277]: “The proposed technology is currently able to detect and recognize people 3 meters from the sensor.”).
In re claim 14, Ryhorchuk and Shain teach all of the limitations of claim 12 stated above where Ryhorchuk further teaches wherein determining whether the person is authorized to access the secure area includes using the set of images to authenticate the person (Para [0007]: “An increasingly prevalent form of biometric verification is facial recognition. Facial recognition generally uses one or more digital cameras or sensors to capture one or more images, which are used to generate a digital file containing data about a person's face. Image processing software uses this data to perform analysis to detect facial features and to determine attributes such as distances between different facial features, description of those facial features and the shape of the head. Algorithms running on one or more processors then uses this data to compare the captured face to one or more faces that have been previously analyzed to estimate the probability that they are the same person.”).
Claim 2 is rejected under 35 U.S.C. 103 as being unpatentable over Ryhorchuk (US Patent No. 20210287469 A1), in view of Shain (US Patent No. 20230045699 A1) and further in view of Sugimoto (US Patent No. 7764321 B2).
In re claim 2, Ryhorchuk and Shain teach all of the limitations of claim 1 stated above but fails to teach wherein detecting the person within the particular distance includes comparing the face size to a second threshold different from the threshold.
However, Sugimoto teaches wherein detecting the person within the particular distance includes comparing the face size to a second threshold different from the threshold (Col 11, lines 39-58: “In the third embodiment, a method in which the subject distance is determined by varying a threshold value, for determining whether the difference between the subject distance "A" calculated from the size of a face and subject distance "B" measured by the distance measuring sensor 81 is within an allowable range, according to the size of a face will be described. The external view, functional block, major process sequence, and focusing process of the digital camera 1 in the third embodiment are identical to those in the first embodiment explained using FIGS. 1 to 5. Therefore, they will not be elaborated upon further here, and only a subject distance detection means will be described.” “FIG. 10 is a flowchart illustrating the flow of a subject distance detection process according to the third embodiment. First, the CPU 75 moves the focus lens 20a to a predetermined position through the focus lens drive section 51, and causes the face detection section 65 to detect a face of a person from the obtained image data, thereby the size of the face is calculated (step S61). Then, a subject distance "A" is obtained from the calculated face size (step S62).”).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the combination of Ryhorchuk and Shain to further incorporate the teachings of Sugimoto to provide wherein detecting the person within the particular distance includes comparing the face size to a second threshold different from the threshold with the System and method for provisioning a facial recognition-based system for controlling access to a building of Ryhorchuk as modified by Shain. Doing so enables accurately measuring a subject distance, as recognized by Sugimoto (Col 1, lines 56-57).
Claims 4 and 16 are rejected under 35 U.S.C. 103 as being unpatentable over Ryhorchuk (US Patent No. 20210287469 A1), in view of Shain (US Patent No. 20230045699 A1) and further in view of Riviere (US Patent No. 11989971 B2).
In re claim 4, Ryhorchuk and Shain teach all of the limitations of claim 1 stated above but fails to teach wherein capturing the set of images includes using two camera lenses that are cross polarized.
However, Riviere teaches wherein capturing the set of images includes using two camera lenses that are cross polarized (Abstract: “Techniques are disclosed for capturing facial appearance properties. In some examples, a facial capture system includes light source(s) that produce linearly polarized light, at least one camera that is cross-polarized with respect to the polarization of light produced by the light source(s)… Images captured by the cross-polarized camera(s) are used to determine facial appearance properties other than specular intensity, such as diffuse albedo…”).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the combination of Ryhorchuk and Shain to further incorporate the teachings of Riviere to provide wherein capturing the set of images includes using two camera lenses that are cross polarized with the System and method for provisioning a facial recognition-based system for controlling access to a building of Ryhorchuk as modified by Shain. Doing so enables determination of facial appearance properties other than specular intensity, such as diffuse albedo, as recognized by Riviere (Abstract).
Non-transitory machine readable medium claim 16 is rejected for the same reasons as method claim 4 and method claim 5 for having similar limitations and being similar in scope.
Claims 6 and 17 are rejected under 35 U.S.C. 103 as being unpatentable over Ryhorchuk (US Patent No. 20210287469 A1), in view of Shain (US Patent No. 20230045699 A1) and further in view of Zhang (US Patent No. 11681364 B1).
In re claim 6, Ryhorchuk and Shain teach all of the limitations of claim 1 stated above but fails to teach wherein the set of images includes at least one second of video.
However, Zhang teaches wherein the set of images includes at least one second of video (Col 2, lines 48-56: “Gaze behaviors may be represented by, or categorized into, gaze patterns. The user interface system may include one or more models trained to recognize various gaze patterns, and predict whether a person's gaze behavior as exhibited in image data corresponds to one of the learned gaze patterns. Gaze patterns may be categorized based on duration. “Atomic-level gaze patterns” may be brief in duration (e.g., lasting one or several image frames up to several seconds).” and col 20, lines 56-58: “The device 110 may include still image and/or video capture components such as a camera or cameras 118 to capture one or more images.”).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the combination of Ryhorchuk and Shain to further incorporate the teachings of Zhang to provide wherein the set of images includes at least one second of video with the System and method for provisioning a facial recognition-based system for controlling access to a building of Ryhorchuk as modified by Shain. Doing so enables prediction of whether a person's gaze behavior is exhibited in image data corresponding to one of the learned gaze patterns, as recognized by Zhang (Col 2, lines 48-56).
Non-transitory machine readable medium claim 17 is rejected for the same reasons as method claim 6 for having similar limitations and being similar in scope.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
US 20220343690 A1 teaches A presentation attack detection system includes RGB and thermal imaging cameras for acquiring images of an individual and a processor operable to determine a metric for whether the individual is a real human based on the thermal imaging and RGB image data. Related methods are also described.
US Patent No. 11144754 B2 teaches apparatuses, systems, and techniques are described to determine locations of objects using images including digital representations of those objects. In at least one embodiment, a gaze of one or more occupants of a vehicle is determined independently of a location of one or more sensors used to detect those occupants.
US Patent No. 11594072 B1 teaches methods for generating a heatmap including a plurality of sampling points having a plurality of characteristic values associated with the detected non-visible light, identifying one or more macroblocks each includes a subset of the plurality of sampling points, calculating a number of occurrences of the local pattern value within each subset of the plurality of the sampling points for each of the one or more macroblocks, generating a first array including a plurality of weighted values by calculating the plurality of weighted values based on the numbers of occurrences of the local pattern value and corresponding sizes of the one or more macroblocks, assigning a unique index to each of the plurality of weighted values, generating a second array of the unique index by ranking the plurality of weighted values, and generating a third array including a plurality of ranking distances.
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/JAMES E MUNION/Examiner, Art Unit 2688 09/04/2026
