DETAILED ACTIONNotice 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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 1/25/2022 has been entered.
Examiner's Note
The instant application has a lengthy prosecution history and the examiner encourages the applicant to have a telephonic interview with the examiner prior to filing a response to the instant office action. Also, prior to the interview the examiner encourages the applicant to present multiple possible claim amendments, so as to enable the examiner to identify claim amendments that will advance prosecution in a meaningful manner.
Acknowledgment
Claims 1-3, 9-11, and 16, amended on 1/26/2026, are acknowledged by the examiner.
Response to Arguments
Applicant’s arguments with respect to claims 1, 9, 16 and their dependent claims have been considered but they are moot in view of the new grounds of rejection necessitated by amendments initiated by the applicant. Examiner addresses the main arguments of the Applicant as below.
Regarding the drawing objection, the Applicant’s argument is persuasive. As a result, the drawing objection is withdrawn.
Regarding the U.S.C. 103 rejection, the Applicant amended the claim then argued that, “Gousev et al.'s disclosures of iris authentication and radial changes of the iris are neither a disclosure nor a suggestion of, among other things, releasing a state in which security of an electronic device is configured, based on authenticating the user by determining that the first facial data corresponds to the second facial data and determining that the radial movement is detected in the part of the face.” [Paragraph 2 on page 9 of the Remarks]. Examiner respectfully disagrees with the Applicant’s argument for few reasons.
First of all, iris is a part of the facial data. In addition, Gousev discloses how his invention uses intensity of light to detect the change of the iris shape and size for an iris authentication, ((FIG. 21 is a block diagram illustrating a technique to incrementally increase the intensity of a visible light source and change the shape of the iris, until a condition based on the value r1 is satisfied, to facilitate iris authentication in accordance with an embodiment of the disclosure) [Gousev: col. 5, line 6-10; Figs. 21]; (According to various embodiments of the present disclosure, pupil size is controlled using visible light, to provide iris shape normalization and improve the accuracy and efficiency of iris authentication. While implementations can vary, iris authentication essentially compares an iris region ( or a representation thereof) of an unknown user against those of known users. As mentioned previously, iris authentication can be extremely effective, as the uniqueness of a single human iris is estimated to be on the order of one (1) in one million (1,000,000)) [Gousev: col. 42, line 2-11; Figs. 21]). Gousev also discloses a comparison between two radius in order to determine the radial movement, ((FIG. 22 is a block diagram illustrating a technique to incrementally increase the intensity of a visible light source and change the shape of the iris, until a condition based on the ratio r1/r2 is satisfied, to facilitate iris authentication in accordance with an embodiment of the disclosure) [Gousev: col. 5, line 11-16; Figs. 22]; (Comparing an image of the iris under the dark lighting condition against an image of the same iris under the brighter lighting condition can result in a mismatch, simply due to the change in the shape of the iris. This is because as the shape of the iris changes radially (i.e., as the inner circular boundary 2008 expands and contracts), the shape and location of the fine features within the iris change radially, as well) [Gousev: col. 42, line 54-61]). Gousev further teaches that his biometric authentication technique can be combined with one or more other authentication techniques, provides a markedly greater level of security (A biometric authentication technique, used either alone or in combination with one or more other authentication techniques, provides a markedly greater level of security) [Gousev: col. 1, line 38-41; Please see more details in Figs. 1, 13-16].
Gousev discloses limitations of the independent claim as follow:
receiving (receive sensor data) [Gousev: col. 60, line 17] a user input (The sensor system of the mobile device 105 recognizes the face of the user 130, a hand gesture, other objects in the scene, and/or the like) [Gousev: col. 7, line 30-41; Figs. 12, 15-16; 32A-32B – Note: The hand gesture, for example, is an user’s input] in a state in which security of the electronic device is configured (A biometric authentication technique, used either alone or in combination with one or more other authentication techniques, provides a markedly greater level of security) [Gousev: col. 1, line 38-41; Figs. 1, 13-16]; displaying a designated screen on the display (Mobile device 1200 may also include a display) [Gousev: col. 30, line 48; Figs. 12, 15-16] based on receiving of the user input ((The sensor system of the mobile device 105, however, may remain active and may be capable, for example, of recognizing the face of the user 130, a hand gesture, other objects in the scene, and/or the like. Upon recognizing a certain reference occurrence has taken place-in this case, the specific facial features of the user 130 are within the sensor system's field of view 110-the sensor system can send an event to the mobile device's general-purpose microprocessor indicating that the facial features of the user 130 have been recognized and/ or causing the mobile device's general purpose microprocessor to exit the low-power mode and become fully active) [Gousev: col. 7, line 30-41; Figs. 12, 15-16; 32A-32B]; (when the face of the user 130 enters into the field of view 110 of the mobile device 105), it may be detected by the sensor array unit 212) [Gousev: col. 11, line 6-8; Fig. 1]); obtaining (computer-vision computations and operations may obtain) [Gousev: col. 28, line 29-30] image data (In each loop iteration, a new image of the user's eye is captured) [Gousev: col. 43, line 17-18; Fig. 21] including a face of an user using the image sensor (an image is captured that includes the face of a user to be authenticated) [Gousev: col. 53, line 23-24; Figs. 32A-32B]; generating event data (data in support of iris-related operations is generated to accelerate iris-related operations, such as iris detection and/or iris authentication) [Gousev: col. 33, line 6-8] using the DVS while obtaining the image data (a smart sensor array can comprise a dynamic vision sensor (DVS) in which, for each pixel in the smart sensor array, a pixel value is asynchronously output when the value changes from a previous value by a threshold amount) [Gousev: col. 9, line 44-48; Please see more details in Figs. 12-15]);
determining whether a radial movement is detected (Comparing an image of the iris under the dark lighting condition against an image of the same iris under the brighter lighting condition can result in a mismatch, simply due to the change in the shape of the iris. This is because as the shape of the iris changes radially (i.e., as the inner circular boundary 2008 expands and contracts), the shape and location of the fine features within the iris change radially, as well) [Gousev: col. 42, line 54-61] in a part of the face based on event data detected (In each loop iteration, a new image of the user's eye is captured. The radius r1 of the inner circular boundary separating the pupil region and the iris region is determined. The radius r2 of the outer circular boundary separating the iris region and the sclera region is also determined. Next, a reference radius rref may be determined. In one embodiment, the reference radius rref is determined based on r2 , e.g., by using a lookup table. Here, the reference radius rref represents a threshold to which the inner radius r1 is meant to reach, by incremental adjustment. Having the inner radius r1 reach the reference threshold rref corresponds to achieving a satisfactory iris shape. Accordingly, as shown in the figure, a decision step compares the inner circular radius r1 to the reference radius rref. If the inner circular radius r1 is greater than (not less than or equal to) the reference radius r refi the system increases the intensity of a light source by an increment value Linc) [Gousev: col. 43, line 17-33 – Note: User’s eyes are parts of the face]; identifying (In a step 1708, the system isolates an iris or a portion of an iris within the image. For example, a face detection operation may generate one or more iris locations. In a step 1710, image data of the isolated iris or portion of iris is sent to a second processor for iris recognition/processing.) [Gousev: col. 36, line 29-34; Boxes 1708 and 1710 in Fig. 17, Box 3426 in Fig. 34B]; (In a step 3424, a first plurality of sectors of the iris region within the image are identified, the first plurality of sectors corresponding to a first size or size range. In a step 3426, a second plurality of sectors of the iris region within the image are identified, the second plurality of sectors corresponding to a second size or size range smaller than the first size or size range) [Gousev: col. 56, line 6-12; Boxes 4324 and 3426 in Fig. 34B]) first facial data corresponding to the face in the image data (the microprocessor 216 can send an a facial-detection event to the main processor 220, indicating that a face detection has occurred) [Gousev: col. 11, line 12-14] and determining whether the first facial data corresponds to second facial data stored in the electronic device ((detecting a match of sector identifier(s) between the selected one or more sectors and at least one registered iris data record, comparing computer vision (CV) features of the selected one or more sectors of the iris region to CV features of the at least one registered iris data record. In this context, CV features useful for comparing to determine a match between the selected one or more sectors of the iris region to the at least one registered iris data record can include SURF and/or SIFT-type CV features” [Gousev: col. 55, line 30-57; Fig. 34B]; (Once the CV computation hardware 242 computes and provides the requested LBP features, (LBP2l , ... , LBP2m), the cascade classifier hardware 244 performs another summation of a dot product of each of the LBP features with one or more weights, (w2l , w2m), to generate a second weighted scalar sum value. The second weighted scalar sum value is then compared to a second threshold. If the scalar sum is less than the second threshold, there is a low likelihood of a reference object being present in the portion of the image represented by the signals stored in the hardware scanning window array 238, and the cascade classifier sends a signal to the other components in the vision sensor array to continue scanning and move to a next portion of the image. If the second weighted scalar sum value is greater than the second threshold, the process continues to a third stage as described above. At the end of a final stage, for example an Nth stage in an N-stage cascade classifier, if the Nth weighted scalar sum value is greater than the Nth threshold, then a reference object is detected in the portion of the image stored in the hardware scanning window array 238. The cascade classifier hardware 244 can then indicate to the microprocessor 216 that the reference object has been detected, and may further optionally indicate the location of the portion of the image in which the reference object, or portion of reference object, was detected. In general, the cascade classifier hardware 244 can be configured to send an indication to the microprocessor 216 that the reference object was detected along with data associated with the reference object, such as the all or some of the CV features computed in the process of detecting the reference object, the location within the image of those CV features, or any other data associated with the computations or operations performed by the CV computation hardware 242 and/or the cascade classifier hardware 244) [Gousev: col. 16, line 7-42; Figs. 2A-2B]; and releasing the state in which the security is configured (As noted elsewhere herein, an event can be an indication that one or more reference occurrences have occurred. Put more generally, events can include data related to a reference occurrence. Depending on desired functionality, the data included in an event can be indicative of a detected reference object, location information related to the reference object, number of reference objects, movement associated with detected reference object, and the like. This data may be conveyed in any of a variety of ways. For example, in the case of object detection, an event can be a simply binary output where "0" means the reference object has not been detected, and "1" means the reference object has been detected) [Gousev: col. 11, line 17-29; Fig. 34B], based on detecting the radial movement using the DVS (Comparing an image of the iris under the dark lighting condition against an image of the same iris under the brighter lighting condition can result in a mismatch, simply due to the change in the shape of the iris. This is because as the shape of the iris changes radially (i.e., as the inner circular boundary 2008 expands and contracts), the shape and location of the fine features within the iris change radially, as well) [Gousev: col. 42, line 54-61]; (FIG. 21 is a block diagram illustrating a technique to incrementally increase the intensity of a visible light source and change the shape of the iris, until a condition based on the value r1 is satisfied, to facilitate iris authentication in accordance with an embodiment of the disclosure. FIG. 22 is a block diagram illustrating a technique to incrementally increase the intensity of a visible light source and change the shape of the iris, until a condition based on the ratio r1/r2 is satisfied, to facilitate iris authentication in accordance with an embodiment of the disclosure) [Gousev: col. 5, line 6-16; Please see more details in Figs. 20A-35]) and based on authenticating the user ((According to various embodiments of the present disclosure, pupil size is controlled using visible light, to provide iris shape normalization and improve the accuracy and efficiency of iris authentication. While implementations can vary, iris authentication essentially compares an iris region ( or a representation thereof) of an unknown user against those of known users. As mentioned previously, iris authentication can be extremely effective, as the uniqueness of a single human iris is estimated to be on the order of one (1) in one million (1,000,000)) [Gousev: col. 42, line 2-11; Figs. 21]; (Comparing an image of the iris under the dark lighting condition against an image of the same iris under the brighter lighting condition can result in a mismatch, simply due to the change in the shape of the iris. This is because as the shape of the iris changes radially (i.e., as the inner circular boundary 2008 expands and contracts), the shape and location of the fine features within the iris change radially, as well) [Gousev: col. 42, line 54-61]) by determining that the first facial data corresponds to the second facial data ((detecting a match of sector identifier(s) between the selected one or more sectors and at least one registered iris data record, comparing computer vision (CV) features of the selected one or more sectors of the iris region to CV features of the at least one registered iris data record. In this context, CV features useful for comparing to determine a match between the selected one or more sectors of the iris region to the at least one registered iris data record can include SURF and/or SIFT-type CV features) [Gousev: col. 55, line 30-57; Please see more details in Fig. 34B]; (FIG. 14 is a block diagram illustrating a one-sensor approach for iris scanning, employing a visual sensor system to perform low-power face detection to trigger iris-related operations, as well as perform preparatory tasks in support of iris-related operations. FIG. 15 depicts an example of a visual image and a bounding box resulting from a successful face detection, according to an embodiment of the disclosure) [Gousev: col. 4, line 37-45; Please see more details in Figs. 14-17, 32A]; (In some such implementations, a histogram of all LBP labels computed for a sample window of the image stored in the scanning window array 238 can be compared to a reference histogram to detect the presence of a face in the sample window stored in the scanning window array 238. In some implementations, dedicated hardware may be implemented to detect, for example, a face using histograms. Such an implementation may include such dedicated hardware in the place of, or in addition to, cascade classifier hardware 244) [Gousev: col. 17, line 66 – col. 18, line 7] and determining that the radial movement is detected in the part of the face ((Comparing an image of the iris under the dark lighting condition against an image of the same iris under the brighter lighting condition can result in a mismatch, simply due to the change in the shape of the iris. This is because as the shape of the iris changes radially (i.e., as the inner circular boundary 2008 expands and contracts), the shape and location of the fine features within the iris change radially, as well) [Gousev: col. 42, line 54-61];( As noted elsewhere herein, an event can be an indication that one or more reference occurrences have occurred. Put more generally, events can include data related to a reference occurrence. Depending on desired functionality, the data included in an event can be indicative of a detected reference object, location information related to the reference object, number of reference objects, movement associated with detected reference object, and the like. This data may be conveyed in any of a variety of ways. For example, in the case of object detection, an event can be a simply binary output where "0" means the reference object has not been detected, and "1" means the reference object has been detected) [Gousev: col. 11, line 17-29; Fig. 34B]; ; (FIG. 21 is a block diagram illustrating a technique to incrementally increase the intensity of a visible light source and change the shape of the iris, until a condition based on the value r1 is satisfied, to facilitate iris authentication in accordance with an embodiment of the disclosure. FIG. 22 is a block diagram illustrating a technique to incrementally increase the intensity of a visible light source and change the shape of the iris, until a condition based on the ratio r1/r2 is satisfied, to facilitate iris authentication in accordance with an embodiment of the disclosure) [Gousev: col. 5, line 6-16; Please see more details in Figs. 20A-35]).
Gousev discloses all features of the independent claims. Accordingly, the Examiner respectfully maintains the rejections and applicability of the arts used.
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 of this title, 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.
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied for establishing a background for determining obviousness under pre-AIA 35 U.S.C. 103(a) 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.
This application currently names joint inventors. In considering patentability of the claims under pre-AIA 35 U.S.C. 103(a), the examiner presumes that the subject matter of the various claims was commonly owned at the time any inventions covered therein were made absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and invention dates of each claim that was not commonly owned at the time a later invention was made in order for the examiner to consider the applicability of pre-AIA 35 U.S.C. 103(c) and potential pre-AIA 35 U.S.C. 102(e), (f) or (g) prior art under pre-AIA 35 U.S.C. 103(a).
Claims 1-3, 5-11 and 13-17 are rejected under 35 U.S.C. 103 as being unpatentable over Gousev (US Patent 10,984,235 B2), (“Gousev”), in view of Niwa et al. (US Patent Application Publication 2023/0039270 A1), (“Niwa”).
Regarding claim 1, Gousev meets the claim limitations as follow.
An electronic device (a mobile device) [Gousev: col. 4, line 2; Fig. 1] comprising: a display (a display) [Gousev: col. 10, line 66];an image sensor (one or more image sensors) [Gousev: col. 47, line 3-4]; a dynamic vision sensor (DVS) (a dynamic vision sensor (DVS)) [Gousev: col. 9, line 45]; at least one processor (one or more microprocessors) [Gousev: col. 38, line 55], comprising processing circuitry (include circuitry) [Gousev: col. 12, line 36], electrically connected to (connection to other computing devices such as network input/output devices) [Gousev: col. 58, line 65-66] the display (Mobile device 1200 may also include a display) [Gousev: col. 30, line 48; Fig. 12], the image sensor, and the DVS ((The electronic sensor can be communicatively coupled through either a wired or wireless connection with a main processor 220 of an electronic device ( such as an application processor of a mobile phone), which can provide queries to the sensor system 210 and receive events and/or other triggers from the sensor system 210) [Gousev: col. 8, line 40-46; Figs. 12-16]; (a smart sensor array can comprise a dynamic vision sensor (DVS) in which, for each pixel in the smart sensor array, a pixel value is asynchronously output when the value changes from a previous value by a threshold amount) [Gousev: col. 9, line 44-48; Figs. 12-15]); and
memory storing instructions that, when executed by the at least one processor individually or collectively, cause the electronic device to (In various implementations, memory 3560 can include non-transitory computer-readable medium storing instructions therein for execution by one or more processing units, comprising instructions to perform any of the functionality described herein) [Gousev: col. 58, line 42-46]: receive (receive sensor data) [Gousev: col. 60, line 17] a user input (The sensor system of the mobile device 105 recognizes the face of the user 130, a hand gesture, other objects in the scene, and/or the like) [Gousev: col. 7, line 30-41; Figs. 12, 15-16; 32A-32B – Note: The hand gesture, for example, is an user’s input] in a state in which security of the electronic device is configured (A biometric authentication technique, used either alone or in combination with one or more other authentication techniques, provides a markedly greater level of security) [Gousev: col. 1, line 38-41; Please see more details in Figs. 1, 13-16]; display a designated screen on the display (Mobile device 1200 may also include a display) [Gousev: col. 30, line 48; Figs. 12, 15-16] based on receiving of the user input ((The sensor system of the mobile device 105, however, may remain active and may be capable, for example, of recognizing the face of the user 130, a hand gesture, other objects in the scene, and/or the like. Upon recognizing a certain reference occurrence has taken place-in this case, the specific facial features of the user 130 are within the sensor system's field of view 110-the sensor system can send an event to the mobile device's general-purpose microprocessor indicating that the facial features of the user 130 have been recognized and/ or causing the mobile device's general purpose microprocessor to exit the low-power mode and become fully active) [Gousev: col. 7, line 30-41; Figs. 12, 15-16; 32A-32B]; (when the face of the user 130 enters into the field of view 110 of the mobile device 105), it may be detected by the sensor array unit 212) [Gousev: col. 11, line 6-8; Fig. 1]); obtain (computer-vision computations and operations may obtain) [Gousev: col. 28, line 29-30] image data (In each loop iteration, a new image of the user's eye is captured) [Gousev: col. 43, line 17-18; Fig. 21] including a face of a user using the image sensor (an image is captured that includes the face of a user to be authenticated) [Gousev: col. 53, line 23-24; Figs. 32A-32B]; generate event data (data in support of iris-related operations is generated to accelerate iris-related operations, such as iris detection and/or iris authentication) [Gousev: col. 33, line 6-8] using the DVS while obtaining the image data (a smart sensor array can comprise a dynamic vision sensor (DVS) in which, for each pixel in the smart sensor array, a pixel value is asynchronously output when the value changes from a previous value by a threshold amount) [Gousev: col. 9, line 44-48; Please see more details in Figs. 12-15]);
determine whether a radial movement is detected (Comparing an image of the iris under the dark lighting condition against an image of the same iris under the brighter lighting condition can result in a mismatch, simply due to the change in the shape of the iris. This is because as the shape of the iris changes radially (i.e., as the inner circular boundary 2008 expands and contracts), the shape and location of the fine features within the iris change radially, as well) [Gousev: col. 42, line 54-61] in a part of the face based on event data detected (In each loop iteration, a new image of the user's eye is captured. The radius r1 of the inner circular boundary separating the pupil region and the iris region is determined. The radius r2 of the outer circular boundary separating the iris region and the sclera region is also determined. Next, a reference radius rref may be determined. In one embodiment, the reference radius rref is determined based on r2 , e.g., by using a lookup table. Here, the reference radius rref represents a threshold to which the inner radius r1 is meant to reach, by incremental adjustment. Having the inner radius r1 reach the reference threshold rref corresponds to achieving a satisfactory iris shape. Accordingly, as shown in the figure, a decision step compares the inner circular radius r1 to the reference radius rref. If the inner circular radius r1 is greater than (not less than or equal to) the reference radius r refi the system increases the intensity of a light source by an increment value Linc) [Gousev: col. 43, line 17-33 – Note: User’s eyes are parts of the face]; identify (In a step 1708, the system isolates an iris or a portion of an iris within the image. For example, a face detection operation may generate one or more iris locations. In a step 1710, image data of the isolated iris or portion of iris is sent to a second processor for iris recognition/processing.) [Gousev: col. 36, line 29-34; Boxes 1708 and 1710 in Fig. 17, Box 3426 in Fig. 34B]; (In a step 3424, a first plurality of sectors of the iris region within the image are identified, the first plurality of sectors corresponding to a first size or size range. In a step 3426, a second plurality of sectors of the iris region within the image are identified, the second plurality of sectors corresponding to a second size or size range smaller than the first size or size range) [Gousev: col. 56, line 6-12; Boxes 4324 and 3426 in Fig. 34B]) first facial data corresponding to the face in the obtained image data (the microprocessor 216 can send an a facial-detection event to the main processor 220, indicating that a face detection has occurred) [Gousev: col. 11, line 12-14] and determine whether the first facial data corresponds to second facial data stored in the electronic device ((detecting a match of sector identifier(s) between the selected one or more sectors and at least one registered iris data record, comparing computer vision (CV) features of the selected one or more sectors of the iris region to CV features of the at least one registered iris data record. In this context, CV features useful for comparing to determine a match between the selected one or more sectors of the iris region to the at least one registered iris data record can include SURF and/or SIFT-type CV features” [Gousev: col. 55, line 30-57; Fig. 34B]; (Once the CV computation hardware 242 computes and provides the requested LBP features, (LBP2l , ... , LBP2m), the cascade classifier hardware 244 performs another summation of a dot product of each of the LBP features with one or more weights, (w2l , w2m), to generate a second weighted scalar sum value. The second weighted scalar sum value is then compared to a second threshold. If the scalar sum is less than the second threshold, there is a low likelihood of a reference object being present in the portion of the image represented by the signals stored in the hardware scanning window array 238, and the cascade classifier sends a signal to the other components in the vision sensor array to continue scanning and move to a next portion of the image. If the second weighted scalar sum value is greater than the second threshold, the process continues to a third stage as described above. At the end of a final stage, for example an Nth stage in an N-stage cascade classifier, if the Nth weighted scalar sum value is greater than the Nth threshold, then a reference object is detected in the portion of the image stored in the hardware scanning window array 238. The cascade classifier hardware 244 can then indicate to the microprocessor 216 that the reference object has been detected, and may further optionally indicate the location of the portion of the image in which the reference object, or portion of reference object, was detected. In general, the cascade classifier hardware 244 can be configured to send an indication to the microprocessor 216 that the reference object was detected along with data associated with the reference object, such as the all or some of the CV features computed in the process of detecting the reference object, the location within the image of those CV features, or any other data associated with the computations or operations performed by the CV computation hardware 242 and/or the cascade classifier hardware 244) [Gousev: col. 16, line 7-42; Please see more details in Figs. 2A-2B]; and release the state in which the security is configured (As noted elsewhere herein, an event can be an indication that one or more reference occurrences have occurred. Put more generally, events can include data related to a reference occurrence. Depending on desired functionality, the data included in an event can be indicative of a detected reference object, location information related to the reference object, number of reference objects, movement associated with detected reference object, and the like. This data may be conveyed in any of a variety of ways. For example, in the case of object detection, an event can be a simply binary output where "0" means the reference object has not been detected, and "1" means the reference object has been detected) [Gousev: col. 11, line 17-29; Fig. 34B]; (In one embodiment, iris-related operations comprise iris authentication. Iris authentication may involve comparing an iris to a plurality of registered iris data records. In one embodiment, data in support of iris-related operations is generated to accelerate iris-related operations, such as iris detection and/or iris authentication, to be performed by a second processing unit (such as main processor 1320). Just as an example, generating data in support of iris-related operations may comprise generating data indicating the location of landmarks within an image to demarcate the eye(s) of a user, indicating or facilitating detection of the eye(s) of the user, detecting of one or more eyes or irises of the user, as well as authentication of one or more irises of the 15
user, as discussed below with respect to FIG. 16) [Gousev: col. 33, line 2-16]), based on authenticating the user ((According to various embodiments of the present disclosure, pupil size is controlled using visible light, to provide iris shape normalization and improve the accuracy and efficiency of iris authentication. While implementations can vary, iris authentication essentially compares an iris region ( or a representation thereof) of an unknown user against those of known users. As mentioned previously, iris authentication can be extremely effective, as the uniqueness of a single human iris is estimated to be on the order of one (1) in one million (1,000,000)) [Gousev: col. 42, line 2-11; Figs. 21]; (Comparing an image of the iris under the dark lighting condition against an image of the same iris under the brighter lighting condition can result in a mismatch, simply due to the change in the shape of the iris. This is because as the shape of the iris changes radially (i.e., as the inner circular boundary 2008 expands and contracts), the shape and location of the fine features within the iris change radially, as well) [Gousev: col. 42, line 54-61]) by determining that the first facial data corresponds to the second facial data ((detecting a match of sector identifier(s) between the selected one or more sectors and at least one registered iris data record, comparing computer vision (CV) features of the selected one or more sectors of the iris region to CV features of the at least one registered iris data record. In this context, CV features useful for comparing to determine a match between the selected one or more sectors of the iris region to the at least one registered iris data record can include SURF and/or SIFT-type CV features) [Gousev: col. 55, line 30-57; Please see more details in Fig. 34B]; (FIG. 14 is a block diagram illustrating a one-sensor approach for iris scanning, employing a visual sensor system to perform low-power face detection to trigger iris-related operations, as well as perform preparatory tasks in support of iris-related operations. FIG. 15 depicts an example of a visual image and a bounding box resulting from a successful face detection, according to an embodiment of the disclosure) [Gousev: col. 4, line 37-45; Please see more details in Figs. 14-17, 32A]; (In some such implementations, a histogram of all LBP labels computed for a sample window of the image stored in the scanning window array 238 can be compared to a reference histogram to detect the presence of a face in the sample window stored in the scanning window array 238. In some implementations, dedicated hardware may be implemented to detect, for example, a face using histograms. Such an implementation may include such dedicated hardware in the place of, or in addition to, cascade classifier hardware 244) [Gousev: col. 17, line 66 – col. 18, line 7] and determining that the radial movement is detected in the part of the face ((Comparing an image of the iris under the dark lighting condition against an image of the same iris under the brighter lighting condition can result in a mismatch, simply due to the change in the shape of the iris. This is because as the shape of the iris changes radially (i.e., as the inner circular boundary 2008 expands and contracts), the shape and location of the fine features within the iris change radially, as well) [Gousev: col. 42, line 54-61];( As noted elsewhere herein, an event can be an indication that one or more reference occurrences have occurred. Put more generally, events can include data related to a reference occurrence. Depending on desired functionality, the data included in an event can be indicative of a detected reference object, location information related to the reference object, number of reference objects, movement associated with detected reference object, and the like. This data may be conveyed in any of a variety of ways. For example, in the case of object detection, an event can be a simply binary output where "0" means the reference object has not been detected, and "1" means the reference object has been detected) [Gousev: col. 11, line 17-29; Fig. 34B]; ; (FIG. 21 is a block diagram illustrating a technique to incrementally increase the intensity of a visible light source and change the shape of the iris, until a condition based on the value r1 is satisfied, to facilitate iris authentication in accordance with an embodiment of the disclosure. FIG. 22 is a block diagram illustrating a technique to incrementally increase the intensity of a visible light source and change the shape of the iris, until a condition based on the ratio r1/r2 is satisfied, to facilitate iris authentication in accordance with an embodiment of the disclosure) [Gousev: col. 5, line 6-16; Please see more details in Figs. 20A-35]).
Gousev does not explicitly disclose the following claim limitations (Emphasis added).
display a designated screen on the display.
However, in the same field of endeavor Niwa further discloses the deficient claim limitations as follows:
display a designated screen on the display (Furthermore, the audio and image output unit 12052 may control the display unit 12062 to display an icon or the like indicating a pedestrian at a desired position) [Niwa: para. 0315; Figs. 3, 36-37].
It would have been obvious to one with an ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Gousev with Niwa to program the system to implement of Niwa’s method.
Therefore, the combination of Gousev with Niwa will enable the system to improve image quality [Niwa: para. 0137] and measurement accuracy [Niwa: para. 0006].
Regarding claims 2 and 10, Gousev meets the claim limitations as set forth in claims 1 and 9.Gousev further meets the claim limitations as follow.
wherein the memory stores instructions that, when executed by the at least one processor, comprising processing circuitry, individually or collectively cause the electronic device to (In various implementations, memory 3560 can include non-transitory computer-readable medium storing instructions therein for execution by one or more processing units, comprising instructions to perform any of the functionality described herein) [Gousev: col. 58, line 42-46]:
identify a first area corresponding to the face in the obtained image data (a higher-power operation may detect one or more faces, or one or more people, in a scene viewed by the sensor system 210. The sensor system 210 may then provide a parameter indicating a number of people or faces in a scene, or an indicator of a level of occupancy of an area) [Gousev: col. 27, line 23-28; Figs. 15-17]; (In a step 1708, the system isolates an iris or a portion of an iris within the image. For example, a face detection operation may generate one or more iris locations. In a step 1710, image data of the isolated iris or portion of iris is sent to a second processor for iris recognition/processing.) [Gousev: col. 36, line 29-34; Boxes 1708 and 1710 in Fig. 17, Box 3426 in Fig. 34B]; (In a step 3424, a first plurality of sectors of the iris region within the image are identified, the first plurality of sectors corresponding to a first size or size range. In a step 3426, a second plurality of sectors of the iris region within the image are identified, the second plurality of sectors corresponding to a second size or size range smaller than the first size or size range) [Gousev: col. 56, line 6-12; Boxes 4324 and 3426 in Fig. 34B]); identify a second area corresponding to an eye of the user in the first area in the image data ((captured of the eye) [Gousev: col. 43, line 55-56; Figs. 24-26; 32A]; (identify the location of the user's eyes as the relevant regions of interest (ROI)) [Gousev: col. 32, line 53-54]; (FIG. 20A shows an image of a user's eye comprising a pupil region, an iris region, and a sclera region) [Gousev: col. 5, line 1-2]; Figs. 15-16, 20A] – Note: Fig. 20A displays an eye extracted from the face in Figs 15-16); and identify a third area, in the event data, corresponding to the second area ((An iris scan involves capturing an image of the user's iris with sufficient level of detail to include iris features) [Gousev: col. 1, line 59-61; Fig. 27]; (FIG. 27 illustrates a manner by which a plurality of sectors may be defined for an iris region within a captured image of an eye, according to an embodiment of the disclosure) [Gousev: col. 5, line 35-39; Fig. 27]; (each sector defined over the iris region covers an equally sized area, as is the case in of the example shown in FIG. 26A) [Gousev: col. 51, line 11-12; Figs. 20A, 26A-31B] – Note: Fig. 26A-31B display an eye extracted from Figs 15-16).
Regarding claim 3, Gousev meets the claim limitations as set forth in claim 2. Gousev further meets the claim limitations as follow.
wherein the memory stores instructions that, when executed by the at least one processor, comprising processing circuitry, individually or collectively cause the electronic device to (In various implementations, memory 3560 can include non-transitory computer-readable medium storing instructions therein for execution by one or more processing units, comprising instructions to perform any of the functionality described herein) [Gousev: col. 58, line 42-46]:
identify (identified) [Gousev: col. 56, line 10] the first facial data in the first area in the image data ((In a step 3424, a first plurality of sectors of the iris region within the image are identified, the first plurality of sectors corresponding to a first size or size range. In a step 3426, a second plurality of sectors of the iris region within the image are identified, the second plurality of sectors corresponding to a second size or size range smaller than the first size or size range) [Gousev: col. 56, line 6-12; Boxes 4324 and 3426 in Fig. 34B]; (One example of processing the one or more computed CV features to detect the presence of at least one face in the scene is described with reference, for example, to FIG. 2B) [Gousev: col. 37, line 5-7; Fig. 2B]); anddetect (detecting) [Gousev: col. 7, line 11] the radial movement in the third area (Comparing an image of the iris under the dark lighting condition against an image of the same iris under the brighter lighting condition can result in a mismatch, simply due to the change in the shape of the iris. This is because as the shape of the iris changes radially (i.e., as the inner circular boundary 2008 expands and contracts), the shape and location of the fine features within the iris change radially, as well) [Gousev: col. 42, line 54-61].
Regarding claims 5 and 13, Gousev meets the claim limitations as set forth in claims 1 and 9. Gousev further meets the claim limitations as follow.
wherein the memory stores instructions that, when executed by the at least one processor, comprising processing circuitry, individually or collectively cause the electronic device to (In various implementations, memory 3560 can include non-transitory computer-readable medium storing instructions therein for execution by one or more processing units, comprising instructions to perform any of the functionality described herein) [Gousev: col. 58, line 42-46] detect (detecting) [Gousev: col. 7, line 11], as the radial movement ((Comparing an image of the iris under the dark lighting condition against an image of the same iris under the brighter lighting condition can result in a mismatch, simply due to the change in the shape of the iris. This is because as the shape of the iris changes radially (i.e., as the inner circular boundary 2008 expands and contracts), the shape and location of the fine features within the iris change radially, as well) [Gousev: col. 42, line 54-61]; (FIG. 21 is a block diagram illustrating a technique to incrementally increase the intensity of a visible light source and change the shape of the iris, until a condition based on the value r1 is satisfied, to facilitate iris authentication in accordance with an embodiment of the disclosure. FIG. 22 is a block diagram illustrating a technique to incrementally increase the intensity of a visible light source and change the shape of the iris, until a condition based on the ratio r1/r2 is satisfied, to facilitate iris authentication in accordance with an embodiment of the disclosure) [Gousev: col. 5, line 6-16; Please see more details in Figs. 20A-35]), radial movement generated by reduction of an iris size of the user (Comparing an image of the iris under the dark lighting condition against an image of the same iris under the brighter lighting condition can result in a mismatch, simply due to the change in the shape of the iris. This is because as the shape of the iris changes radially (i.e., as the inner circular boundary 2008 expands and contracts), the shape and location of the fine features within the iris change radially, as well) [Gousev: col. 42, line 54-61]; (In each loop iteration, a new image of the user's eye is captured. The radius r1 of the inner circular boundary separating the pupil region and the iris region is determined. The radius r2 of the outer circular boundary separating the iris region and the sclera region is also determined. Next, a reference radius rref may be determined. In one embodiment, the reference radius rref is determined based on r2 , e.g., by using a lookup table. Here, the reference radius rref represents a threshold to which the inner radius r1 is meant to reach, by incremental adjustment. Having the inner radius r1 reach the reference threshold rref corresponds to achieving a satisfactory iris shape. Accordingly, as shown in the figure, a decision step compares the inner circular radius r1 to the reference radius rref. If the inner circular radius r1 is greater than (not less than or equal to) the reference radius r refi the system increases the intensity of a light source by an increment value Linc) [Gousev: col. 43, line 17-33 – Note: User’s eyes are parts of the face] ; (a smart sensor array can comprise a dynamic vision sensor (DVS) in which, for each pixel in the smart sensor array, a pixel value is asynchronously output when the value changes from a previous value by a threshold amount) [Gousev: col. 9, line 44-48; Figs. 12-15]) while the designated screen is displayed on the display (Mobile device 1200 may also include a display) [Gousev: col. 30, line 48; Figs. 12, 15-16].
Gousev does not explicitly disclose the following claim limitations (Emphasis added).
the designated screen is displayed on the display.
However, in the same field of endeavor Niwa further discloses the deficient claim limitations as follows:
the designated screen is displayed on the display (Furthermore, the audio and image output unit 12052 may control the display unit 12062 to display an icon or the like indicating a pedestrian at a desired position) [Niwa: para. 0315; Figs. 3, 36-37].
It would have been obvious to one with an ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Gousev with Niwa to program the system to implement of Niwa’s method.
Therefore, the combination of Gousev with Niwa will enable the system to improve image quality [Niwa: para. 0137] and measurement accuracy [Niwa: para. 0006].
Regarding claims 6 and 14, Gousev meets the claim limitations as set forth in claims 1 and 9. Gousev further meets the claim limitations as follow.
wherein the memory stores instructions that, when executed by the at least one processor, comprising processing circuitry, individually or collectively cause the electronic device to (In various implementations, memory 3560 can include non-transitory computer-readable medium storing instructions therein for execution by one or more processing units, comprising instructions to perform any of the functionality described herein) [Gousev: col. 58, line 42-46]:
based on receiving (receive sensor data) [Gousev: col. 60, line 17] of the user input (The sensor system of the mobile device 105 recognizes the face of the user 130, a hand gesture, other objects in the scene, and/or the like) [Gousev: col. 7, line 30-41; Figs. 12, 15-16; 32A-32B – Note: The hand gesture, for example, is an user’s input], determine (determine the appropriate intensity level of a visible light source based) [Gousev: col. 5, line 27-28; Fig. 25], using the illuminance sensor (the IR sensor 1206 may be used to capture images of the surroundings illuminated by the IR light source 1204. Images captured by the IR sensor 1206 may be used for iris-related tasks such as iris detection, iris authentication, etc.) [Gousev: col. 31, line 19-23], whether the illuminance around the electronic device (determine the appropriate intensity level of a visible light source based) [Gousev: col. 5, line 27-28; Fig. 25] is less than a designated illuminance (outputs a correction to shape vector depending upon whether the difference of its two input pixel intensities is greater or less than a threshold) [Gousev: col. 41, line 1-3];based on the illuminance around the electronic device being less than the designated illuminance (depending upon whether the difference of its two input pixel intensities is , display a first screen corresponding to the designated screen on the display (outputs a correction to shape vector depending upon whether the difference of its two input pixel intensities is ; and detect (detecting) [Gousev: col. 7, line 11] the radial movement using the DVS (Comparing an image of the iris under the dark lighting condition against an image of the same iris under the brighter lighting condition can result in a mismatch, simply due to the change in the shape of the iris. This is because as the shape of the iris changes radially (i.e., as the inner circular boundary 2008 expands and contracts), the shape and location of the fine features within the iris change radially, as well) [Gousev: col. 42, line 54-61] using the DVS (In each loop iteration, a new image of the user's eye is captured. The radius r1 of the inner circular boundary separating the pupil region and the iris region is determined. The radius r2 of the outer circular boundary separating the iris region and the sclera region is also determined. Next, a reference radius rref may be determined. In one embodiment, the reference radius rref is determined based on r2 , e.g., by using a lookup table. Here, the reference radius rref represents a threshold to which the inner radius r1 is meant to reach, by incremental adjustment. Having the inner radius r1 reach the reference threshold rref corresponds to achieving a satisfactory iris shape. Accordingly, as shown in the figure, a decision step compares the inner circular radius r1 to the reference radius rref. If the inner circular radius r1 is greater than (not less than or equal to) the reference radius r refi the system increases the intensity of a light source by an increment value Linc) [Gousev: col. 43, line 17-33 – Note: User’s eyes are parts of the face] ; (a smart sensor array can comprise a dynamic vision sensor (DVS) in which, for each pixel in the smart sensor array, a pixel value is asynchronously output when the value changes from a previous value by a threshold amount) [Gousev: col. 9, line 44-48; Figs. 12-15]) while the designated screen is displayed on the display (Mobile device 1200 may also include a display) [Gousev: col. 30, line 48; Figs. 12, 15-16].
Gousev does not explicitly disclose the following claim limitations (Emphasis added).
display a first screen corresponding to the designated screen on the display.
However, in the same field of endeavor Niwa further discloses the deficient claim limitations as follows:
display a first screen corresponding to the designated screen on the display (Furthermore, the audio and image output unit 12052 may control the display unit 12062 to display an icon or the like indicating a pedestrian at a desired position) [Niwa: para. 0315; Figs. 3, 36-37].
It would have been obvious to one with an ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Gousev with Niwa to program the system to implement of Niwa’s method.
Therefore, the combination of Gousev with Niwa will enable the system to improve image quality [Niwa: para. 0137] and measurement accuracy [Niwa: para. 0006].
Regarding claims 7 and 15, Gousev meets the claim limitations as set forth in claims 6 and 14. Gousev further meets the claim limitations as follow.
wherein the memory stores instructions that, when executed by the at least one processor, comprising processing circuitry, individually or collectively cause the electronic device to (In various implementations, memory 3560 can include non-transitory computer-readable medium storing instructions therein for execution by one or more processing units, comprising instructions to perform any of the functionality described herein) [Gousev: col. 58, line 42-46]:
based on the illuminance around the electronic device being equal to or greater than the designated illuminance (depending upon whether the difference of its two input pixel intensities is greater , display, on the display (outputs a correction to shape vector depending upon whether the difference of its two input pixel intensities is greater or less than a threshold) [Gousev: col. 41, line 1-3], a second screen different from the first screen ((The sensor system 210 then determines a change in the differences based on the first set and the second set. The sensor system 210 detects a reference occurrence if the change in the differences exceeds a reference motion threshold. In one aspect, the sensor system 210 may detect a motion event if a first effective pixel indicates a positive change in sensed light relative to a second 20 effective pixel, and subsequently the first effective pixel indicates a negative change in sensed light relative to a second effective pixel) [Gousev: col. 24, line 13-22]; (outputs a correction to shape vector depending upon whether the difference of its two input pixel intensities is greater or less than a threshold) [Gousev: col. 41, line 1-3]) and which includes an object moving in a designated pattern ((The sensor system 210 then determines a change in the differences based on the first set and the second set. The sensor system 210 detects a reference occurrence if the change in the differences exceeds a reference motion threshold. In one aspect, the sensor system 210 may detect a motion event if a first effective pixel indicates a positive change in sensed light relative to a second 20 effective pixel, and subsequently the first effective pixel indicates a negative change in sensed light relative to a second effective pixel) [Gousev: col. 24, line 13-22]; (the data included in an event can be indicative of a detected reference object, location information related to the reference object, number of reference objects, movement associated with detected reference object, and the like. This data may be conveyed in any of a variety of ways.) [Gousev: col. 11, line 20-25]); and determine (to compare against a threshold to determine) [Gousev: col. 45, line 10-11] using the DVS ((In each loop iteration, a new image of the user's eye is captured. The radius r1 of the inner circular boundary separating the pupil region and the iris region is determined. The radius r2 of the outer circular boundary separating the iris region and the sclera region is also determined. Next, a reference radius rref may be determined. In one embodiment, the reference radius rref is determined based on r2 , e.g., by using a lookup table. Here, the reference radius rref represents a threshold to which the inner radius r1 is meant to reach, by incremental adjustment. Having the inner radius r1 reach the reference threshold rref corresponds to achieving a satisfactory iris shape. Accordingly, as shown in the figure, a decision step compares the inner circular radius r1 to the reference radius rref. If the inner circular radius r1 is greater than (not less than or equal to) the reference radius r refi the system increases the intensity of a light source by an increment value Linc) [Gousev: col. 43, line 17-33 – Note: User’s eyes are parts of the face] ; (a smart sensor array can comprise a dynamic vision sensor (DVS) in which, for each pixel in the smart sensor array, a pixel value is asynchronously output when the value changes from a previous value by a threshold amount) [Gousev: col. 9, line 44-48; Figs. 12-15]), whether movement of an iris of the user corresponds to the designated pattern (Comparing an image of the iris under the dark lighting condition against an image of the same iris under the brighter lighting condition can result in a mismatch, simply due to the change in the shape of the iris. This is because as the shape of the iris changes radially (i.e., as the inner circular boundary 2008 expands and contracts), the shape and location of the fine features within the iris change radially, as well) [Gousev: col. 42, line 54-61].
Gousev does not explicitly disclose the following claim limitations (Emphasis added).
display a first screen corresponding to the designated screen on the display.
However, in the same field of endeavor Niwa further discloses the deficient claim limitations as follows:
display a first screen corresponding to the designated screen on the display (Furthermore, the audio and image output unit 12052 may control the display unit 12062 to display an icon or the like indicating a pedestrian at a desired position) [Niwa: para. 0315; Figs. 3, 36-37].
It would have been obvious to one with an ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Gousev with Niwa to program the system to implement of Niwa’s method.
Therefore, the combination of Gousev with Niwa will enable the system to improve image quality [Niwa: para. 0137] and measurement accuracy [Niwa: para. 0006].
Regarding claim 8, Gousev meets the claim limitations as set forth in claim 1. Gousev further meets the claim limitations as follow.
wherein the memory stores instructions that, when executed by the at least one processor, comprising processing circuitry, individually or collectively cause the electronic device to (In various implementations, memory 3560 can include non-transitory computer-readable medium storing instructions therein for execution by one or more processing units, comprising instructions to perform any of the functionality described herein) [Gousev: col. 58, line 42-46] maintain the state in which the security is configured (a second size measurement indicative of a size of the outer circular boundary is determined from the initial image) [Gousev: col. 47, line 15-17], based on at least one of the radial movement not being detected or the first facial data not corresponding to the second facial data (since the outer circular boundary does not change as a result of exposure to light, while the inner circular boundary does react to light exposure) [Gousev: col. 47, line 21-23].
Regarding claim 9, Gousev meets the claim limitations as follow.
An operation method (a method) [Gousev: col. 3, line 13] of an electronic device (a mobile device) [Gousev: col. 4, line 2; Fig. 1], the operation method comprising (a method) [Gousev: col. 3, line 13]:receiving (receive sensor data) [Gousev: col. 60, line 17] a user input (The sensor system of the mobile device 105 recognizes the face of the user 130, a hand gesture, other objects in the scene, and/or the like) [Gousev: col. 7, line 30-41; Figs. 12, 15-16; 32A-32B – Note: The hand gesture, for example, is an user’s input] in a state in which security of the electronic device is configured (A biometric authentication technique, used either alone or in combination with one or more other authentication techniques, provides a markedly greater level of security) [Gousev: col. 1, line 38-41; Figs. 1, 13-16]; displaying a designated screen on the display (Mobile device 1200 may also include a display) [Gousev: col. 30, line 48; Figs. 12, 15-16] based on receiving of the user input ((The sensor system of the mobile device 105, however, may remain active and may be capable, for example, of recognizing the face of the user 130, a hand gesture, other objects in the scene, and/or the like. Upon recognizing a certain reference occurrence has taken place-in this case, the specific facial features of the user 130 are within the sensor system's field of view 110-the sensor system can send an event to the mobile device's general-purpose microprocessor indicating that the facial features of the user 130 have been recognized and/ or causing the mobile device's general purpose microprocessor to exit the low-power mode and become fully active) [Gousev: col. 7, line 30-41; Figs. 12, 15-16; 32A-32B]; (when the face of the user 130 enters into the field of view 110 of the mobile device 105), it may be detected by the sensor array unit 212) [Gousev: col. 11, line 6-8; Fig. 1]); obtaining (computer-vision computations and operations may obtain) [Gousev: col. 28, line 29-30] image data (In each loop iteration, a new image of the user's eye is captured) [Gousev: col. 43, line 17-18; Fig. 21] including a face of an user using the image sensor (an image is captured that includes the face of a user to be authenticated) [Gousev: col. 53, line 23-24; Figs. 32A-32B]; generating event data (data in support of iris-related operations is generated to accelerate iris-related operations, such as iris detection and/or iris authentication) [Gousev: col. 33, line 6-8] using the DVS while obtaining the image data (a smart sensor array can comprise a dynamic vision sensor (DVS) in which, for each pixel in the smart sensor array, a pixel value is asynchronously output when the value changes from a previous value by a threshold amount) [Gousev: col. 9, line 44-48; Please see more details in Figs. 12-15]);
determining whether a radial movement is detected (Comparing an image of the iris under the dark lighting condition against an image of the same iris under the brighter lighting condition can result in a mismatch, simply due to the change in the shape of the iris. This is because as the shape of the iris changes radially (i.e., as the inner circular boundary 2008 expands and contracts), the shape and location of the fine features within the iris change radially, as well) [Gousev: col. 42, line 54-61] in a part of the face based on event data detected (In each loop iteration, a new image of the user's eye is captured. The radius r1 of the inner circular boundary separating the pupil region and the iris region is determined. The radius r2 of the outer circular boundary separating the iris region and the sclera region is also determined. Next, a reference radius rref may be determined. In one embodiment, the reference radius rref is determined based on r2 , e.g., by using a lookup table. Here, the reference radius rref represents a threshold to which the inner radius r1 is meant to reach, by incremental adjustment. Having the inner radius r1 reach the reference threshold rref corresponds to achieving a satisfactory iris shape. Accordingly, as shown in the figure, a decision step compares the inner circular radius r1 to the reference radius rref. If the inner circular radius r1 is greater than (not less than or equal to) the reference radius r refi the system increases the intensity of a light source by an increment value Linc) [Gousev: col. 43, line 17-33 – Note: User’s eyes are parts of the face]; identifying (In a step 1708, the system isolates an iris or a portion of an iris within the image. For example, a face detection operation may generate one or more iris locations. In a step 1710, image data of the isolated iris or portion of iris is sent to a second processor for iris recognition/processing.) [Gousev: col. 36, line 29-34; Boxes 1708 and 1710 in Fig. 17, Box 3426 in Fig. 34B]; (In a step 3424, a first plurality of sectors of the iris region within the image are identified, the first plurality of sectors corresponding to a first size or size range. In a step 3426, a second plurality of sectors of the iris region within the image are identified, the second plurality of sectors corresponding to a second size or size range smaller than the first size or size range) [Gousev: col. 56, line 6-12; Boxes 4324 and 3426 in Fig. 34B]) first facial data corresponding to the face in the image data (the microprocessor 216 can send an a facial-detection event to the main processor 220, indicating that a face detection has occurred) [Gousev: col. 11, line 12-14] and determining whether the first facial data corresponds to second facial data stored in the electronic device ((detecting a match of sector identifier(s) between the selected one or more sectors and at least one registered iris data record, comparing computer vision (CV) features of the selected one or more sectors of the iris region to CV features of the at least one registered iris data record. In this context, CV features useful for comparing to determine a match between the selected one or more sectors of the iris region to the at least one registered iris data record can include SURF and/or SIFT-type CV features” [Gousev: col. 55, line 30-57; Fig. 34B]; (Once the CV computation hardware 242 computes and provides the requested LBP features, (LBP2l , ... , LBP2m), the cascade classifier hardware 244 performs another summation of a dot product of each of the LBP features with one or more weights, (w2l , w2m), to generate a second weighted scalar sum value. The second weighted scalar sum value is then compared to a second threshold. If the scalar sum is less than the second threshold, there is a low likelihood of a reference object being present in the portion of the image represented by the signals stored in the hardware scanning window array 238, and the cascade classifier sends a signal to the other components in the vision sensor array to continue scanning and move to a next portion of the image. If the second weighted scalar sum value is greater than the second threshold, the process continues to a third stage as described above. At the end of a final stage, for example an Nth stage in an N-stage cascade classifier, if the Nth weighted scalar sum value is greater than the Nth threshold, then a reference object is detected in the portion of the image stored in the hardware scanning window array 238. The cascade classifier hardware 244 can then indicate to the microprocessor 216 that the reference object has been detected, and may further optionally indicate the location of the portion of the image in which the reference object, or portion of reference object, was detected. In general, the cascade classifier hardware 244 can be configured to send an indication to the microprocessor 216 that the reference object was detected along with data associated with the reference object, such as the all or some of the CV features computed in the process of detecting the reference object, the location within the image of those CV features, or any other data associated with the computations or operations performed by the CV computation hardware 242 and/or the cascade classifier hardware 244) [Gousev: col. 16, line 7-42; Figs. 2A-2B]; and releasing the state in which the security is configured (As noted elsewhere herein, an event can be an indication that one or more reference occurrences have occurred. Put more generally, events can include data related to a reference occurrence. Depending on desired functionality, the data included in an event can be indicative of a detected reference object, location information related to the reference object, number of reference objects, movement associated with detected reference object, and the like. This data may be conveyed in any of a variety of ways. For example, in the case of object detection, an event can be a simply binary output where "0" means the reference object has not been detected, and "1" means the reference object has been detected) [Gousev: col. 11, line 17-29; Fig. 34B], based on detecting the radial movement using the DVS (Comparing an image of the iris under the dark lighting condition against an image of the same iris under the brighter lighting condition can result in a mismatch, simply due to the change in the shape of the iris. This is because as the shape of the iris changes radially (i.e., as the inner circular boundary 2008 expands and contracts), the shape and location of the fine features within the iris change radially, as well) [Gousev: col. 42, line 54-61]; (FIG. 21 is a block diagram illustrating a technique to incrementally increase the intensity of a visible light source and change the shape of the iris, until a condition based on the value r1 is satisfied, to facilitate iris authentication in accordance with an embodiment of the disclosure. FIG. 22 is a block diagram illustrating a technique to incrementally increase the intensity of a visible light source and change the shape of the iris, until a condition based on the ratio r1/r2 is satisfied, to facilitate iris authentication in accordance with an embodiment of the disclosure) [Gousev: col. 5, line 6-16; Please see more details in Figs. 20A-35]) and based on authenticating the user ((According to various embodiments of the present disclosure, pupil size is controlled using visible light, to provide iris shape normalization and improve the accuracy and efficiency of iris authentication. While implementations can vary, iris authentication essentially compares an iris region ( or a representation thereof) of an unknown user against those of known users. As mentioned previously, iris authentication can be extremely effective, as the uniqueness of a single human iris is estimated to be on the order of one (1) in one million (1,000,000)) [Gousev: col. 42, line 2-11; Figs. 21]; (Comparing an image of the iris under the dark lighting condition against an image of the same iris under the brighter lighting condition can result in a mismatch, simply due to the change in the shape of the iris. This is because as the shape of the iris changes radially (i.e., as the inner circular boundary 2008 expands and contracts), the shape and location of the fine features within the iris change radially, as well) [Gousev: col. 42, line 54-61]) by determining that the first facial data corresponds to the second facial data ((detecting a match of sector identifier(s) between the selected one or more sectors and at least one registered iris data record, comparing computer vision (CV) features of the selected one or more sectors of the iris region to CV features of the at least one registered iris data record. In this context, CV features useful for comparing to determine a match between the selected one or more sectors of the iris region to the at least one registered iris data record can include SURF and/or SIFT-type CV features) [Gousev: col. 55, line 30-57; Please see more details in Fig. 34B]; (FIG. 14 is a block diagram illustrating a one-sensor approach for iris scanning, employing a visual sensor system to perform low-power face detection to trigger iris-related operations, as well as perform preparatory tasks in support of iris-related operations. FIG. 15 depicts an example of a visual image and a bounding box resulting from a successful face detection, according to an embodiment of the disclosure) [Gousev: col. 4, line 37-45; Please see more details in Figs. 14-17, 32A]; (In some such implementations, a histogram of all LBP labels computed for a sample window of the image stored in the scanning window array 238 can be compared to a reference histogram to detect the presence of a face in the sample window stored in the scanning window array 238. In some implementations, dedicated hardware may be implemented to detect, for example, a face using histograms. Such an implementation may include such dedicated hardware in the place of, or in addition to, cascade classifier hardware 244) [Gousev: col. 17, line 66 – col. 18, line 7] and determining that the radial movement is detected in the part of the face ((Comparing an image of the iris under the dark lighting condition against an image of the same iris under the brighter lighting condition can result in a mismatch, simply due to the change in the shape of the iris. This is because as the shape of the iris changes radially (i.e., as the inner circular boundary 2008 expands and contracts), the shape and location of the fine features within the iris change radially, as well) [Gousev: col. 42, line 54-61];( As noted elsewhere herein, an event can be an indication that one or more reference occurrences have occurred. Put more generally, events can include data related to a reference occurrence. Depending on desired functionality, the data included in an event can be indicative of a detected reference object, location information related to the reference object, number of reference objects, movement associated with detected reference object, and the like. This data may be conveyed in any of a variety of ways. For example, in the case of object detection, an event can be a simply binary output where "0" means the reference object has not been detected, and "1" means the reference object has been detected) [Gousev: col. 11, line 17-29; Fig. 34B]; ; (FIG. 21 is a block diagram illustrating a technique to incrementally increase the intensity of a visible light source and change the shape of the iris, until a condition based on the value r1 is satisfied, to facilitate iris authentication in accordance with an embodiment of the disclosure. FIG. 22 is a block diagram illustrating a technique to incrementally increase the intensity of a visible light source and change the shape of the iris, until a condition based on the ratio r1/r2 is satisfied, to facilitate iris authentication in accordance with an embodiment of the disclosure) [Gousev: col. 5, line 6-16; Please see more details in Figs. 20A-35]).
Gousev does not explicitly disclose the following claim limitations (Emphasis added).
displaying a designated screen on the display.
However, in the same field of endeavor Niwa further discloses the deficient claim limitations as follows:
displaying a designated screen on the display (Furthermore, the audio and image output unit 12052 may control the display unit 12062 to display an icon or the like indicating a pedestrian at a desired position) [Niwa: para. 0315; Figs. 3, 36-37].
It would have been obvious to one with an ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Gousev with Niwa to program the system to implement of Niwa’s method.
Therefore, the combination of Gousev with Niwa will enable the system to improve image quality [Niwa: para. 0137] and measurement accuracy [Niwa: para. 0006].
Regarding claim 11, Gousev meets the claim limitations as set forth in claim 10. Gousev further meets the claim limitations as follow.
the detecting (detecting) [Gousev: col. 7, line 11] the radial movement (Comparing an image of the iris under the dark lighting condition against an image of the same iris under the brighter lighting condition can result in a mismatch, simply due to the change in the shape of the iris. This is because as the shape of the iris changes radially (i.e., as the inner circular boundary 2008 expands and contracts), the shape and location of the fine features within the iris change radially, as well) [Gousev: col. 42, line 54-61] comprises detecting the radial movement in the third area (In each loop iteration, a new image of the user's eye is captured. The radius r1 of the inner circular boundary separating the pupil region and the iris region is determined. The radius r2 of the outer circular boundary separating the iris region and the sclera region is also determined. Next, a reference radius rref may be determined. In one embodiment, the reference radius rref is determined based on r2 , e.g., by using a lookup table. Here, the reference radius rref represents a threshold to which the inner radius r1 is meant to reach, by incremental adjustment. Having the inner radius r1 reach the reference threshold rref corresponds to achieving a satisfactory iris shape. Accordingly, as shown in the figure, a decision step compares the inner circular radius r1 to the reference radius rref. If the inner circular radius r1 is greater than (not less than or equal to) the reference radius r refi the system increases the intensity of a light source by an increment value Linc) [Gousev: col. 43, line 17-33 – Note: User’s eyes are parts of the face] ; (a smart sensor array can comprise a dynamic vision sensor (DVS) in which, for each pixel in the smart sensor array, a pixel value is asynchronously output when the value changes from a previous value by a threshold amount) [Gousev: col. 9, line 44-48; Figs. 12-15]), and
the identifying (identified) [Gousev: col. 56, line 10] the first facial data ((In a step 3424, a first plurality of sectors of the iris region within the image are identified, the first plurality of sectors corresponding to a first size or size range. In a step 3426, a second plurality of sectors of the iris region within the image are identified, the second plurality of sectors corresponding to a second size or size range smaller than the first size or size range) [Gousev: col. 56, line 6-12; Boxes 4324 and 3426 in Fig. 34B]; (One example of processing the one or more computed CV features to detect the presence of at least one face in the scene is described with reference, for example, to FIG. 2B) [Gousev: col. 37, line 5-7; Fig. 2B]) comprises detecting the first facial data in the first area in the image data ((In a step 3424, a first plurality of sectors of the iris region within the image are identified, the first plurality of sectors corresponding to a first size or size range. In a step 3426, a second plurality of sectors of the iris region within the image are identified, the second plurality of sectors corresponding to a second size or size range smaller than the first size or size range) [Gousev: col. 56, line 6-12; Boxes 4324 and 3426 in Fig. 34B]; (Once the CV computation hardware 242 computes and provides the requested LBP features, (LBP2l , ... , LBP2m), the cascade classifier hardware 244 performs another summation of a dot product of each of the LBP features with one or more weights, (w2l , w2m), to generate a second weighted scalar sum value. The second weighted scalar sum value is then compared to a second threshold. If the scalar sum is less than the second threshold, there is a low likelihood of a reference object being present in the portion of the image represented by the signals stored in the hardware scanning window array 238, and the cascade classifier sends a signal to the other components in the vision sensor array to continue scanning and move to a next portion of the image. If the second weighted scalar sum value is greater than the second threshold, the process continues to a third stage as described above. At the end of a final stage, for example an Nth stage in an N-stage cascade classifier, if the Nth weighted scalar sum value is greater than the Nth threshold, then a reference object is detected in the portion of the image stored in the hardware scanning window array 238. The cascade classifier hardware 244 can then indicate to the microprocessor 216 that the reference object has been detected, and may further optionally indicate the location of the portion of the image in which the reference object, or portion of reference object, was detected. In general, the cascade classifier hardware 244 can be configured to send an indication to the microprocessor 216 that the reference object was detected along with data associated with the reference object, such as the all or some of the CV features computed in the process of detecting the reference object, the location within the image of those CV features, or any other data associated with the computations or operations performed by the CV computation hardware 242 and/or the cascade classifier hardware 244) [Gousev: col. 16, line 7-42; Figs. 2A-2B]).
Regarding claim 16, Gousev meets the claim limitations as follow.
A non-transitory computer-readable storage medium storing instructions that, when executed by at least one processor of an electronic device, cause the electronic device to perform operations comprising (In various implementations, memory 3560 can include non-transitory computer-readable medium storing instructions therein for execution by one or more processing units, comprising instructions to perform any of the functionality described herein) [Gousev: col. 58, line 42-46]:receiving (receive sensor data) [Gousev: col. 60, line 17] a user input (The sensor system of the mobile device 105 recognizes the face of the user 130, a hand gesture, other objects in the scene, and/or the like) [Gousev: col. 7, line 30-41; Figs. 12, 15-16; 32A-32B – Note: The hand gesture, for example, is an user’s input] in a state in which security of the electronic device is configured (A biometric authentication technique, used either alone or in combination with one or more other authentication techniques, provides a markedly greater level of security) [Gousev: col. 1, line 38-41; Figs. 1, 13-16]; displaying a designated screen on the display (Mobile device 1200 may also include a display) [Gousev: col. 30, line 48; Figs. 12, 15-16] based on receiving of the user input ((The sensor system of the mobile device 105, however, may remain active and may be capable, for example, of recognizing the face of the user 130, a hand gesture, other objects in the scene, and/or the like. Upon recognizing a certain reference occurrence has taken place-in this case, the specific facial features of the user 130 are within the sensor system's field of view 110-the sensor system can send an event to the mobile device's general-purpose microprocessor indicating that the facial features of the user 130 have been recognized and/ or causing the mobile device's general purpose microprocessor to exit the low-power mode and become fully active) [Gousev: col. 7, line 30-41; Figs. 12, 15-16; 32A-32B]; (when the face of the user 130 enters into the field of view 110 of the mobile device 105), it may be detected by the sensor array unit 212) [Gousev: col. 11, line 6-8; Fig. 1]); obtaining (computer-vision computations and operations may obtain) [Gousev: col. 28, line 29-30] image data (In each loop iteration, a new image of the user's eye is captured) [Gousev: col. 43, line 17-18; Fig. 21] including a face of an user using the image sensor (an image is captured that includes the face of a user to be authenticated) [Gousev: col. 53, line 23-24; Figs. 32A-32B]; generating event data (data in support of iris-related operations is generated to accelerate iris-related operations, such as iris detection and/or iris authentication) [Gousev: col. 33, line 6-8] using the DVS while obtaining the image data (a smart sensor array can comprise a dynamic vision sensor (DVS) in which, for each pixel in the smart sensor array, a pixel value is asynchronously output when the value changes from a previous value by a threshold amount) [Gousev: col. 9, line 44-48; Please see more details in Figs. 12-15]);
determining whether a radial movement is detected (Comparing an image of the iris under the dark lighting condition against an image of the same iris under the brighter lighting condition can result in a mismatch, simply due to the change in the shape of the iris. This is because as the shape of the iris changes radially (i.e., as the inner circular boundary 2008 expands and contracts), the shape and location of the fine features within the iris change radially, as well) [Gousev: col. 42, line 54-61] in a part of the face based on event data detected (In each loop iteration, a new image of the user's eye is captured. The radius r1 of the inner circular boundary separating the pupil region and the iris region is determined. The radius r2 of the outer circular boundary separating the iris region and the sclera region is also determined. Next, a reference radius rref may be determined. In one embodiment, the reference radius rref is determined based on r2 , e.g., by using a lookup table. Here, the reference radius rref represents a threshold to which the inner radius r1 is meant to reach, by incremental adjustment. Having the inner radius r1 reach the reference threshold rref corresponds to achieving a satisfactory iris shape. Accordingly, as shown in the figure, a decision step compares the inner circular radius r1 to the reference radius rref. If the inner circular radius r1 is greater than (not less than or equal to) the reference radius r refi the system increases the intensity of a light source by an increment value Linc) [Gousev: col. 43, line 17-33 – Note: User’s eyes are parts of the face]; identifying (In a step 1708, the system isolates an iris or a portion of an iris within the image. For example, a face detection operation may generate one or more iris locations. In a step 1710, image data of the isolated iris or portion of iris is sent to a second processor for iris recognition/processing.) [Gousev: col. 36, line 29-34; Boxes 1708 and 1710 in Fig. 17, Box 3426 in Fig. 34B]; (In a step 3424, a first plurality of sectors of the iris region within the image are identified, the first plurality of sectors corresponding to a first size or size range. In a step 3426, a second plurality of sectors of the iris region within the image are identified, the second plurality of sectors corresponding to a second size or size range smaller than the first size or size range) [Gousev: col. 56, line 6-12; Boxes 4324 and 3426 in Fig. 34B]) first facial data corresponding to the face in the image data (the microprocessor 216 can send an a facial-detection event to the main processor 220, indicating that a face detection has occurred) [Gousev: col. 11, line 12-14] and determine whether the first facial data corresponds to second facial data stored in the electronic device ((detecting a match of sector identifier(s) between the selected one or more sectors and at least one registered iris data record, comparing computer vision (CV) features of the selected one or more sectors of the iris region to CV features of the at least one registered iris data record. In this context, CV features useful for comparing to determine a match between the selected one or more sectors of the iris region to the at least one registered iris data record can include SURF and/or SIFT-type CV features” [Gousev: col. 55, line 30-57; Fig. 34B]; (Once the CV computation hardware 242 computes and provides the requested LBP features, (LBP2l , ... , LBP2m), the cascade classifier hardware 244 performs another summation of a dot product of each of the LBP features with one or more weights, (w2l , w2m), to generate a second weighted scalar sum value. The second weighted scalar sum value is then compared to a second threshold. If the scalar sum is less than the second threshold, there is a low likelihood of a reference object being present in the portion of the image represented by the signals stored in the hardware scanning window array 238, and the cascade classifier sends a signal to the other components in the vision sensor array to continue scanning and move to a next portion of the image. If the second weighted scalar sum value is greater than the second threshold, the process continues to a third stage as described above. At the end of a final stage, for example an Nth stage in an N-stage cascade classifier, if the Nth weighted scalar sum value is greater than the Nth threshold, then a reference object is detected in the portion of the image stored in the hardware scanning window array 238. The cascade classifier hardware 244 can then indicate to the microprocessor 216 that the reference object has been detected, and may further optionally indicate the location of the portion of the image in which the reference object, or portion of reference object, was detected. In general, the cascade classifier hardware 244 can be configured to send an indication to the microprocessor 216 that the reference object was detected along with data associated with the reference object, such as the all or some of the CV features computed in the process of detecting the reference object, the location within the image of those CV features, or any other data associated with the computations or operations performed by the CV computation hardware 242 and/or the cascade classifier hardware 244) [Gousev: col. 16, line 7-42; Figs. 2A-2B]; and releasing the state in which the security is configured (As noted elsewhere herein, an event can be an indication that one or more reference occurrences have occurred. Put more generally, events can include data related to a reference occurrence. Depending on desired functionality, the data included in an event can be indicative of a detected reference object, location information related to the reference object, number of reference objects, movement associated with detected reference object, and the like. This data may be conveyed in any of a variety of ways. For example, in the case of object detection, an event can be a simply binary output where "0" means the reference object has not been detected, and "1" means the reference object has been detected) [Gousev: col. 11, line 17-29; Fig. 34B], based on detecting the radial movement using the DVS (Comparing an image of the iris under the dark lighting condition against an image of the same iris under the brighter lighting condition can result in a mismatch, simply due to the change in the shape of the iris. This is because as the shape of the iris changes radially (i.e., as the inner circular boundary 2008 expands and contracts), the shape and location of the fine features within the iris change radially, as well) [Gousev: col. 42, line 54-61]; (FIG. 21 is a block diagram illustrating a technique to incrementally increase the intensity of a visible light source and change the shape of the iris, until a condition based on the value r1 is satisfied, to facilitate iris authentication in accordance with an embodiment of the disclosure. FIG. 22 is a block diagram illustrating a technique to incrementally increase the intensity of a visible light source and change the shape of the iris, until a condition based on the ratio r1/r2 is satisfied, to facilitate iris authentication in accordance with an embodiment of the disclosure) [Gousev: col. 5, line 6-16; Please see more details in Figs. 20A-35]) and based on authenticating the user ((According to various embodiments of the present disclosure, pupil size is controlled using visible light, to provide iris shape normalization and improve the accuracy and efficiency of iris authentication. While implementations can vary, iris authentication essentially compares an iris region ( or a representation thereof) of an unknown user against those of known users. As mentioned previously, iris authentication can be extremely effective, as the uniqueness of a single human iris is estimated to be on the order of one (1) in one million (1,000,000)) [Gousev: col. 42, line 2-11; Figs. 21]; (Comparing an image of the iris under the dark lighting condition against an image of the same iris under the brighter lighting condition can result in a mismatch, simply due to the change in the shape of the iris. This is because as the shape of the iris changes radially (i.e., as the inner circular boundary 2008 expands and contracts), the shape and location of the fine features within the iris change radially, as well) [Gousev: col. 42, line 54-61]) by determining that the first facial data corresponds to the second facial data ((detecting a match of sector identifier(s) between the selected one or more sectors and at least one registered iris data record, comparing computer vision (CV) features of the selected one or more sectors of the iris region to CV features of the at least one registered iris data record. In this context, CV features useful for comparing to determine a match between the selected one or more sectors of the iris region to the at least one registered iris data record can include SURF and/or SIFT-type CV features) [Gousev: col. 55, line 30-57; Please see more details in Fig. 34B]; (FIG. 14 is a block diagram illustrating a one-sensor approach for iris scanning, employing a visual sensor system to perform low-power face detection to trigger iris-related operations, as well as perform preparatory tasks in support of iris-related operations. FIG. 15 depicts an example of a visual image and a bounding box resulting from a successful face detection, according to an embodiment of the disclosure) [Gousev: col. 4, line 37-45; Please see more details in Figs. 14-17, 32A]; (In some such implementations, a histogram of all LBP labels computed for a sample window of the image stored in the scanning window array 238 can be compared to a reference histogram to detect the presence of a face in the sample window stored in the scanning window array 238. In some implementations, dedicated hardware may be implemented to detect, for example, a face using histograms. Such an implementation may include such dedicated hardware in the place of, or in addition to, cascade classifier hardware 244) [Gousev: col. 17, line 66 – col. 18, line 7] and determining that the radial movement is detected in the part of the face ((Comparing an image of the iris under the dark lighting condition against an image of the same iris under the brighter lighting condition can result in a mismatch, simply due to the change in the shape of the iris. This is because as the shape of the iris changes radially (i.e., as the inner circular boundary 2008 expands and contracts), the shape and location of the fine features within the iris change radially, as well) [Gousev: col. 42, line 54-61];( As noted elsewhere herein, an event can be an indication that one or more reference occurrences have occurred. Put more generally, events can include data related to a reference occurrence. Depending on desired functionality, the data included in an event can be indicative of a detected reference object, location information related to the reference object, number of reference objects, movement associated with detected reference object, and the like. This data may be conveyed in any of a variety of ways. For example, in the case of object detection, an event can be a simply binary output where "0" means the reference object has not been detected, and "1" means the reference object has been detected) [Gousev: col. 11, line 17-29; Fig. 34B]; ; (FIG. 21 is a block diagram illustrating a technique to incrementally increase the intensity of a visible light source and change the shape of the iris, until a condition based on the value r1 is satisfied, to facilitate iris authentication in accordance with an embodiment of the disclosure. FIG. 22 is a block diagram illustrating a technique to incrementally increase the intensity of a visible light source and change the shape of the iris, until a condition based on the ratio r1/r2 is satisfied, to facilitate iris authentication in accordance with an embodiment of the disclosure) [Gousev: col. 5, line 6-16; Please see more details in Figs. 20A-35]).
Gousev does not explicitly disclose the following claim limitations (Emphasis added).
displaying a designated screen on the display.
However, in the same field of endeavor Niwa further discloses the deficient claim limitations as follows:
displaying a designated screen on the display (Furthermore, the audio and image output unit 12052 may control the display unit 12062 to display an icon or the like indicating a pedestrian at a desired position) [Niwa: para. 0315; Figs. 3, 36-37].
It would have been obvious to one with an ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Gousev with Niwa to program the system to implement of Niwa’s method.
Therefore, the combination of Gousev with Niwa will enable the system to improve image quality [Niwa: para. 0137] and measurement accuracy [Niwa: para. 0006].
Claims 4 and 12 are rejected under 35 U.S.C. 103 as being unpatentable over Gousev (US Patent 10,984,235 B2), (“Gousev”), in view of Niwa et al. (US Patent Application Publication 2023/0039270 A1), (“Niwa”), in view of Satoh et al. (US Patent US 11,741,700 B2), (“Satoh”)
Regarding claims 4 and 12, Gousev and Niwa meet the claim limitations as set forth in claims 3 and 11. Gousev further meets the claim limitations as follow.
wherein the memory stores instructions that, when executed by the at least one processor, comprising processing circuitry, individually or collectively cause the electronic device to (In various implementations, memory 3560 can include non-transitory computer-readable medium storing instructions therein for execution by one or more processing units, comprising instructions to perform any of the functionality described herein) [Gousev: col. 58, line 42-46]:
obtain (computer-vision computations and operations may obtain) [Gousev: col. 28, line 29-30] motion data corresponding to a movement of the electronic device using the motion sensor while obtaining the event data ((The sensor system 210 then determines a change in the differences based on the first set and the second set. The sensor system 210 detects a reference occurrence if the change in the differences exceeds a reference motion threshold. In one aspect, the sensor system 210 may detect a motion event if a first effective pixel indicates a positive change in sensed light relative to a second 20 effective pixel, and subsequently the first effective pixel indicates a negative change in sensed light relative to a second effective pixel) [Gousev: col. 24, line 13-22]; (the data included in an event can be indicative of a detected reference object, location information related to the reference object, number of reference objects, movement associated with detected reference object, and the like. This data may be conveyed in any of a variety of ways.) [Gousev: col. 11, line 20-25]; (Choi J., et al., "A 3.4[mu]W CMOS Image Sensor with Embedded Feature-extraction Algorithm for Motion-Triggered Object-of interest Imaging," Solid-State Circuits Conference Digest of Technical Papers (ISSCC), 2013 IEEE International, IEEE, Feb. 17, 2013 (Feb. 17, 2013)) [Gousev: Reference Cited); perform shake correction of the event data, based on the motion data () [Gousev: col. 7, line 11]; and identify (identifying the location of the one or more detected irises within the image) [Gousev: col. 33, line 43-44] the third area ((An iris scan involves capturing an image of the user's iris with sufficient level of detail to include iris features) [Gousev: col. 1, line 59-61; Fig. 27]; (FIG. 27 illustrates a manner by which a plurality of sectors may be defined for an iris region within a captured image of an eye, according to an embodiment of the disclosure) [Gousev: col. 5, line 35-39; Fig. 27]; (each sector defined over the iris region covers an equally sized area, as is the case in of the example shown in FIG. 26A) [Gousev: col. 51, line 11-12; Figs. 20A, 26A-31B] – Note: Fig. 26A-31B display an eye extracted from Figs 15-16) in the shake-corrected event data.
Gousev and Niwa do not explicitly disclose the following claim limitations (Emphasis added).
perform shake correction of the event data, based on the motion data; and obtain the third area in the shake-corrected event data.
However, in the same field of endeavor Satoh further discloses the deficient claim limitations as follows:
perform shake correction of the event data, based on the motion data (performs moving image compression or camera shake correction using motion detection) [Satoh: col. 45, line 3-5]; and obtain the third area in the shake-corrected event data (performs moving image compression or camera shake correction using motion detection, and the detection result of motion detection in this another imaging apparatus is to be input to the output controller 15b as motion information) [Satoh: col. 45, line 3-9].
It would have been obvious to one with an ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Gousev and Niwa with Satoh to program the system to implement of Sato’s method.
Therefore, the combination of Gousev and Niwa with Satoh will enable the system to improve image quality [Niwa: para. 0137] and improve the recognition accuracy [Satoh: col. 1, line 40-47].
Regarding claim 17, Gousev meets the claim limitations as set forth in claim 16. Gousev further meets the claim limitations as follow.
wherein the event data has a smaller data size than the image data ((outputs a correction to shape vector depending upon whether the difference of its two input pixel intensities is
Reference Notice
Additional prior arts, included in the Notice of Reference Cited, made of record and not relied upon is considered pertinent to applicant's disclosure.
Contact Information
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Philip Dang whose telephone number is (408) 918-7529. The examiner can normally be reached on Monday-Thursday between 8:30 am - 5:00 pm (PST).
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Sath Perungavoor can be reached on 571-272-7455. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/Philip P. Dang/ Primary Examiner, Art Unit 2488
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