DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 12/17/2024 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Response to Preliminary Amendment
The preliminary amendments filed 12/17/2024 have been acknowledged.
Claims 1-7 and 9 have been amended.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-9 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea (Mental Process) without significantly more. All of the claims are system claims (Claims 1-7), method claims (Claim 8 ) or manufacture claim (Claim 9 ) under (Step 1), but under Step 2A all of these claims recite abstract ideas and specifically mental processes—concepts performed in the human mind including observation, evaluation, judgement and opinion; furthermore, these mental processes are more particularly:
Recited in claims 1, 8 and 9
“acquiring authentication history information indicating a history of authentication processing of collating/verifying a target image with a registered image; detect an abnormality in the authentication processing, based on the authentication history information; classify a cause of the abnormality by using the authentication history information in response to the abnormality being detected give notice of the classified cause of the abnormality.” In claim 1.
“An information processing method that is executed by at least one computer, the information processing method comprising :acquiring authentication history information indicating a history of authentication processing of collating/verifying a target image with a registered image; detecting an abnormality in the authentication processing, based on the authentication history information; classifying a cause of the abnormality by using the authentication history information in response to the abnormality being detected; and giving notice of the classified cause of the abnormality.” In claim 8.
“A non-transitory recording medium on which a computer program that allows at least one computer to execute an information processing method is recorded, the information processing method including: acquiring authentication history information indicating a history of authentication processing of collating/verifying a target image with a registered image; detecting an abnormality in the authentication processing, based on the authentication history information; classifying a cause of the abnormality by using the authentication history information in response to the abnormality being detected; and giving notice of the classified cause of the abnormality.” In claim 9.
It is noted that the above analysis is according to the 2019 Revised Patent Subject Matter Eligibility Guidance published in the Federal Register (84 FR 50) on January 7, 2019 and MPEP 2106.04(a)(2)(III).
Consider also that “If a claim recites a limitation that can practically be performed in the human mind, with or without the use of a physical aid such as pen and paper, the limitation falls within the mental processes grouping, and the claim recites an abstract idea” as per MPEP 2106.04(a)(2)(III)(B). See also footnotes 14 and 15 of the Federal Register Notice. As detailed above, the steps of: Acquiring authentication history information indicating a history of processing of verifying a target image with a registered image, detecting an abnormality in the authentication process based on the authentication history information, classifying a cause of the abnormality using the authentication history information in response to the abnormality being detected and giving notice of the classified cause of the abnormality. may be practically performed in the human mind with the use of a physical aid such as a pen and paper (marking the label on the package with a pen). The human mind naturally collates visual information, can detect abnormalities in a process when given the history of the process and can classify a cause of an abnormality based on the history of a process.
Under Step 2B, this judicial exception is not integrated into a practical application because each of claims 1-9 do not recite additional elements that integrate the exception into a practical application. These claims are recited at a high level of generality and merely equate to “apply it” or otherwise merely uses a generic computer as a tool to perform an abstract which are not indicative of integration into a practical application as per MPEP 2106.05(f). See also MPEP 2106.04(a)(2)(III) with respect to Mental Processes: “Nor do the courts distinguish between claims that recite mental processes performed by humans and claims that recite mental processes performed on a computer”. See also MPEP 2106.04(a)(2)(III)(C)(3) Using a computer as tool to perform a mental process and MPEP 2106.04(a)(2)(III)(D) as well as the case law cited therein.
In other words, the additional elements and/or are recited at a high level of generality that does not amount to significantly more and/ such that they could practically be performed in the human mind.
For all of the above reasons, taken alone or in combination, claims 1-9 recite a non-statutory mental process and will be rejected under this premise.
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1-3, 5, 7-9 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Nechyba et al (Nechyba hereinafter US 8254647 B1)
As per claim 1
Nechyba teaches An information processing system comprising: at least one memory that is configured to store instructions (Figure 2); and at least one processor that is configured to execute the instructions to: acquire authentication history information indicating a history of authentication processing of collating/verifying a target image with a registered image (Figure 7, Paragraph 17 “computing device 10 may store the captured templates 32 in template repository 24 for later use in the authentication phase. More specifically, mobile computing device 10 may compare the templates stored in template repository 24 with the captured templates 32 of a user that is trying to authenticate (referred to as an "authentication user)"” Paragraph 50 “user 20 is engaged in the enrollment phase, facial recognition module 68 may store captured templates 32 based on captured image 28 in template repository 24. If user 20 is engaged in the authentication phase, facial recognition module 68 may compare captured templates 32 with one or more templates from template repository 24 to determine whether captured templates 32 match one or more of the templates stored in template repository 24” Paragraph (51) “Responsive to denying user 20 access to the resources of mobile computing device 10 and/or alerting user 20 to issues with captured image 28, either due to captured templates 32 being of low quality or because captured templates 32 fail to match one or more of the images of template repository 24,” When denying a user Nechyba’s system necessarily acquires authentication history information indicating a history of verifying the target with the registered images within the repository. ) detect an abnormality in the authentication processing (Figure 7, Paragraph (3) “Computing device 10 may determine that the captured image 28 is of low quality based on one or more of a geometric consistency score, a facial landmark detection confidence score, and a facial detection confidence score. Each of the three scores is discussed in further detail below.” Being of “low quality” can be deemed as an abnormality. Paragraph (24) “As an example, a low facial detection confidence score may indicate that the lighting is bad, and computing device 10 may update GUI 22 to suggest taking a picture in better lighting conditions. As another example, a low geometric consistency score may indicate significant yaw, and mobile computing device 10 may update GUI 22 to suggest that user 20 center his or her face with respect to the camera 26.” Indications of poor lighting and incorrect orientation are detections of abnormalities. ) Paragraph (25) “ Computing device 10 may attempt to detect pitch, yaw, and/or roll, and may attempt to fix other issues, such as poor lighting or centering….In the example of FIG. 1, the face of user 20 may not be aligned straight on with camera 26.” Paragraph (26) “when the head of user 20 is not properly aligned, problems may occur during the enrollment and/or authentication phases of facial recognition performed by mobile computing device 10.” Paragraph (45) “ some examples, facial recognition module 68 may classify captured image 28 as either good or poor quality. In an example, if any of the face detection confidence score, one or more facial landmark detection confidence scores, and the geometric consistency score fall below a certain threshold, then the image quality score may indicate that the captured image 28 is of poor quality. “ The classification for low image quality is an abnormality. Defects in lighting as well as rotational orientation are also classified abnormalities that lead to the broader abnormality of a poor/low image quality.) based on the authentication history information (Paragraph (17) “More specifically, mobile computing device 10 may compare the templates stored in template repository 24 with the captured templates 32 of a user that is trying to authenticate “ Paragraph (24) “Computing device 10 may determine that captured image 28 is of low quality based on a low geometric consistency score value that computing device 10 determines….Message box control 30 may include suggestions on how to improve the quality of future images captured by camera 26 of mobile computing device 10 via GUI 22 based on low confidence scores and/or failures to match features of the templates…in some examples, computing device 10 may attempt to capture an additional image some pre-determined number of times before alerting the user that captured templates 32 is of low quality.”) classify a cause of the abnormality by using the authentication history information in response to the abnormality being detected (Paragraph (24) “Message box control 30 may include suggestions on how to improve the quality of future images captured by camera 26 of mobile computing device 10 via GUI 22 based on low confidence scores and/or failures to match features of the templates. As an example, a low facial detection confidence score may indicate that the lighting is bad, and computing device 10 may update GUI 22 to suggest taking a picture in better lighting conditions” Paragraph (28) “ Mobile computing device 10 may further alert user 20 of the determined rotational issues that caused the captured templates 32 to be determined as having low quality…The mobile device may further attempt to acquire one or more additional images after user 20 has been informed of the quality issues affecting the previously captured templates 32.” Paragraph (30) “Based in part on the image quality score, mobile computing device 10 may classify a quality of captured image 28. As an example, mobile computing device 10 may classify captured image 28 as good quality or poor quality based on the image quality score.” Paragraph (51) “Responsive to denying user 20 access to the resources of mobile computing device 10 and/or alerting user 20 to issues with captured image 28, either due to captured templates 32 being of low quality or because captured templates 32 fail to match one or more of the images of template repository 24…” The classification for low image quality is an abnormality. Defects in lighting as well as rotational orientation are also classified abnormalities that lead to the broader abnormality of a poor/low image quality) give notice of the classified cause of the abnormality. (Figure 1)
As per claim 2
Nechyba teaches all claim limitations previously rejected in claim 1’s 102 rejection. See claim 1’s 102 rejection.
Nechyba teaches The information processing system according to claim 1, wherein the at least one processor that is configured to execute the instructions to perform first processing of determining whether or not quality of at least one of the target image and the registered image is greater than or equal to predetermined quality (Figure 7, Paragraph (11) “The mobile computing device may reject captured images that have a quality score lower than a configurable threshold value.” Paragraph (45) “ an example, if any of the face detection confidence score, one or more facial landmark detection confidence scores, and the geometric consistency score fall below a certain threshold, then the image quality score may indicate that the captured image 28 is of poor quality.” The scores are based in comparison to captured images and images in the repository. If “quality” is equal to or greater than a threshold, that implies satisfactory quality for authentication, therefore the inverse is true. Furthermore , Nechyba states in Paragraph (50) “If user 20 is engaged in the authentication phase, facial recognition module 68 may compare captured templates 32 with one or more templates from template repository 24 to determine whether captured templates 32 match one or more of the templates stored in template repository 24.” To “match” an image is to be of good quality.) and classify the cause of the abnormality, based on a result of the first processing. (Figure 1, Figure 7, Paragraph (25) “Captured image 28 may be of low quality for facial recognition due to a number of reasons. As some examples, captured image 28 may suffer from alignment issues, such as pitch, yaw, and/or roll. Computing device 10 may attempt to detect pitch, yaw, and/or roll, and may attempt to fix other issues, such as poor lighting or centering.” Paragraph (31) “ Mobile computing device 10 may take a variety of actions responsive to determining a quality of captured image 28. As an example, whether mobile computing device 10 determines that captured image 28 is of high or low quality…Mobile computing device 10 may further output recommended actions to increase the quality of future captured images. Some suggestions may include telling the user to hold mobile computing device 10 in alignment with the face of user 20, and instructing user 20 not to tilt his or her head to one side or the other or up and down.” Paragraph (49) “In some examples, facial recognition module 68 may determine that captured image 28 suffers from pitch, and may reclassify captured image 28 as having poor quality “ Whenever the system suggests an action to the user to help authentication, the system has determined (classified) an issue (abnormality) with centering and or lighting. )
As per claim 3
Nechyba teaches all claim limitations previously rejected in claim 1’s 102 rejection. See claim 1’s 102 rejection.
Nechyba teaches the information processing system according to claim 1 wherein the at least one processor that is configured to execute the instructions to perform second processing of collating/verifying a matching image that is the registration image matching the target image in the authentication processing (Figure 7) with a non-matching image that is the registered image not matching the target image (Figure 3A-3I, Figure 6A-6, Figure 7, Paragraph (59) “To determine whether images 4A-4B are of suitable quality for facial authentication, facial recognition module 68 may analyze captured images 4A and 4B and produce templates based on captured images 4A and 4B. Facial recognition module 68 may base the determination of whether images 4A or 4B or templates associated with both of images 4A-4B are of low quality based on the values of one or more confidence scores, which confidence score module 70 may determine.”)
As per claim 5
Nechyba teaches all claim limitations previously rejected in claim 1’s 102 rejection. See claim 1’s 102 rejection.
Nechyba teaches fourth processing of acquiring a plurality of matching scores each indicating a matching degree between the target image and respective one of a plurality of registered images, (Figure 7, Paragraph (74) “Process 250 may further include facial recognition module 68 of mobile computing device 10 generating an image quality score based at least in part on a combination of the facial detection confidence score, the facial landmark detection confidence score, and the geometric consistency score (260).”) classify the cause of the abnormality, based on the plurality of matching scores. (Paragraph (25) “Captured image 28 may be of low quality for facial recognition due to a number of reasons. As some examples, captured image 28 may suffer from alignment issues, such as pitch, yaw, and/or roll. Computing device 10 may attempt to detect pitch, yaw, and/or roll, and may attempt to fix other issues, such as poor lighting or centering. Paragraph (28) “Mobile computing device 10 may further alert user 20 of the determined rotational issues that caused the captured templates 32 to be determined as having low quality…mobile computing device 10 may also update GUI 22 to instruct user 20 to look directly at the camera in a case where the geometric alignment determined as part of the geometric consistency score indicates that the head of user 20 is rotated to the left or the right. In another example, mobile computing device 10 may alert user 20 that his or her head is pitched too far up or down with GUI 22 to in a case where a geometric consistency, facial landmark consistency, and/or facial detection scores indicate that the head of user 20 is pitched up or down. “Paragraph (75) “when the captured image is classified as a poor quality image, output devices 18 of mobile computing device 10 may output an indication that the image was classified as a poor quality image. In an example, mobile computing device 10 may output recommended actions to increase the quality of the image, when the captured image is classified as a poor quality image.” “Recommended actions” are based on the “number of reasons” That computing device 10 classifies as issues in its “an attempt to fix” which include “poor lighting or centering” Furthermore the “abnormality” can be seen as the “low quality image” itself rather than what causes the image to fail authentication.)
As per claim 7
Nechyba teaches all claim limitations previously rejected in claim 1’s 102 rejection. See claim 1’s 102 rejection.
Nechyba teaches at least one processor that is configured to execute the instructions to change notification destination depending on the cause of the abnormality (Paragraph (15) “In various examples, mobile computing device 10 may cause one or more of output devices 16 to update GUI 22 to include different user interface controls, text, images, or other graphical contents. Displaying or updating GUI 22 may generally refer to the process of causing one or more of output devices 16 to change the contents of GUI 22, which may be displayed to the user.” Paragraph (24) “Based on the determination that a captured image 28 is of low quality, mobile computing device 10 may, for example, alert user 20 by updating GUI 22 with an indication that the captured image is of low quality via a message box control 30… an example, a low facial detection confidence score may indicate that the lighting is bad, and computing device 10 may update GUI 22 to suggest taking a picture in better lighting conditions…a low geometric consistency score may indicate significant yaw, and mobile computing device 10 may update GUI 22 to suggest that user 20 center his or her face with respect to the camera 26” Paragraph (28) “Mobile computing device 10 may further alert user 20 of the determined rotational issues that caused the captured templates 32 to be determined as having low quality. As an example, mobile computing device 10 may also update GUI 22 to instruct user 20 to look directly at the camera in a case where the geometric alignment determined as part of the geometric consistency score indicates that the head of user 20 is rotated to the left or the right. In another example, mobile computing device 10 may alert user 20 that his or her head is pitched too far up or down with GUI 22 to in a case where a geometric consistency, facial landmark consistency, and/or facial detection scores indicate that the head of user 20 is pitched up or down. “ Paragraph (31) “in some examples, mobile computing device 10 may reject an image classified as a poor quality image and alert that user 20 via message box 30 that captured image 28 has been rejected” Paragraph (46) “Facial recognition module 68 may also update GUI 22 to indicate a problem with captured templates 32 to user 20 of FIG. 1.” Paragraph (47) “if facial recognition module 68 determines that captured image 28 exhibit pitch, yaw, and/or roll, mobile computing device 10 may update GUI 22 of FIG. 1 with information that captured image 28 suffers from the one or more of yaw, pitch or roll.” Paragraph (49) “ In an example, GUI 22 may be updated to alert user 20 that facial recognition module 68 could not detect the face of user 20, his or her facial features, and/or that the distance between the features of user 20 was not consistent with that of a normal person's facial features” Paragraph (65) “ Based on the determination that image 4A exhibits significant yaw, facial recognition module 4A may update GUI 22 to alert user 20 that image 4A exhibits significant yaw.” In all these examples each update consists of a new/altered destination on the GUI. The processor writes new pixel data to a specific area of memory in display updates. Graphics hardware then reads that memory and sends it to the screen. Therefore, the specific UI element and pixel region changes on the screen depending on what error the updated pertains to. This logical destination change constitutes a change of notification destination due to the issue (abnormality) being detected)
As per claim 8
Claim 8 is the method claim that parallels claim 1 and will be rejected under the same premise. See claim 1
As per claim 9
Claim 9 is the non-transitory recording medium claim that parallels claim 1 and will be rejected under the same premise. See claim 1.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 4 and 6 are rejected under 35 U.S.C. 103 as being unpatentable over Nechyba et al (Nechyba hereinafter US 8254647 B1) in view of Li et al (Li hereinafter US 10776646 B2)
As per claim 4
Nechyba teaches all claim limitations previously rejected in claim 1’s 102 rejection. See claim 1’s 102 rejection.
Nechyba does not teach third processing of collating/verifying again the target image with the registered image by using a second threshold that is greater than the first threshold used in the authentication processing
Li teaches third processing of collating/verifying again the target image with the registered image by using a second threshold that is greater than the first threshold used in the authentication processing ( Paragraph (6) “In the embodiments, facial recognition is a biometric recognition technology for identification based on human facial feature information, also commonly referred to as portrait recognition, face recognition.” Paragraph (50) “the identifying the target object in step 120 or step 230 may include: comparing the acquired face image with face images in a face image library; selecting a first group of face images from the face image library, the first group of face images being face images whose similarities with the acquired face image are greater than a first threshold…wherein the specified eye images are associated with the face images in the first group of face images; and identifying the target object successfully if the similarity between an iris feature of an eye image in the eye image library and the iris feature of the acquired eye image is greater than a second threshold.” Paragraph (56) “selecting a first group of eye images from the specified eye images, the first group of eye images being eye images having iris features whose similarities with the iris feature of the acquired eye image are greater than a second threshold in the specified eye images…comparing the eye-print feature of the acquired eye image with eye-print features of the eye images in the first group of eye images; and identifying the target object successfully if the similarity between an eye-print feature of an eye image in the first group of eye images and the eye-print feature of the acquired eye image is greater than a third threshold.”)
In regards to “classify the cause of the abnormality, based on a result of the third processing.” As presented in previous claims, Nechyba classifies the abnormality as a lighting issue, an issue with pitch/yaw/roll as well as classifying the image abnormality as poor/low quality. A person of ordinary skill in the art would find it obvious to transition this determination within the newly modified Nechyba/Li system.
Accordingly, a person of ordinary skill in the art, at the time this invention was effectively filed would have found it obvious to modify Nechyba’s system with Li’s concept of using a threshold greater than the preceding threshold to verify the target image with the registered image. A person of ordinary skill in the art is aware both Nechyba and Li are in the same realm of endeavor, authentication of identity through facial recognition. A person of ordinary skill in the art would be motivated to do so because installing a two threshold system acts as a high security checkpoint to reduce false authentication rates. Nechyba states that “ facial recognition authentication fails in two basic ways: 1) you are the authorized user, but the device fails to authenticate you (false negative) or 2) you are not the authorized user, but the device grants you access as if you are an authorized user (false positive).” (Paragraph 10). Nechyba geometric consistency score, angle line segment, and pitch thresholding all exist to mitigate false positive authentication. A person of ordinary skill in the art would have found it obvious to instill Li’s concept of a multi threshold system either between each of Nechyba’s scores in series and or after each failure of authentication due to low image quality to boost security precision and lower false positive authentication.
As per claim 6
Nechyba teaches all claim limitations previously rejected in claim 1’s 102 rejection. See claim 1’s 102 rejection.
Nechyba teaches The information processing system according to claim 1, wherein the at least one processor that is configured to execute the instructions to perform: first processing of determining whether or not quality of at least one of the target image and the registered image is greater than or equal to predetermined quality (Figure 1, Figure 7 , Paragraph (50) “If user 20 is engaged in the authentication phase, facial recognition module 68 may compare captured templates 32 with one or more templates from template repository 24 to determine whether captured templates 32 match one or more of the templates stored in template repository 24.” To “match” an image is to be of good quality. Furthermore, Paragraph (72) “Process 250 may begin when mobile computing device 10 captures an image, e.g. captured image 28. Based on captured image 28, computing device 10 may produce one or more templates, e.g. captured templates 32, using camera 26. (152). Process 250 may further include confidence score module 70 generating a facial detection confidence score based at least in part on a likelihood that a representation of at least a portion of a face is included in captured image 28 (254). In some examples, the facial detection confidence score may indicate a lower amount of confidence as an absolute value of a yaw angle of the user's face included in the captured image increases.” )second processing of collating/verifying a matching image that is the registration image matching the target image, with a non-matching image that is the registered image not matching the target image (Figures 3A-3I, Figures 4A-4B, Paragraph (59) “To determine whether images 4A-4B are of suitable quality for facial authentication, facial recognition module 68 may analyze captured images 4A and 4B and produce templates based on captured images 4A and 4B. Facial recognition module 68 may base the determination of whether images 4A or 4B or templates associated with both of images 4A-4B are of low quality based on the values of one or more confidence scores, which confidence score module 70 may determine.”)
Li teaches third processing of collating/verifying again the target image with the registered image by using a second threshold that is greater than the first threshold used in the authentication processing ( Paragraph (6) “In the embodiments, facial recognition is a biometric recognition technology for identification based on human facial feature information, also commonly referred to as portrait recognition, face recognition.” Paragraph (50) “the identifying the target object in step 120 or step 230 may include: comparing the acquired face image with face images in a face image library; selecting a first group of face images from the face image library, the first group of face images being face images whose similarities with the acquired face image are greater than a first threshold…wherein the specified eye images are associated with the face images in the first group of face images; and identifying the target object successfully if the similarity between an iris feature of an eye image in the eye image library and the iris feature of the acquired eye image is greater than a second threshold.” Paragraph (56) “selecting a first group of eye images from the specified eye images, the first group of eye images being eye images having iris features whose similarities with the iris feature of the acquired eye image are greater than a second threshold in the specified eye images…comparing the eye-print feature of the acquired eye image with eye-print features of the eye images in the first group of eye images; and identifying the target object successfully if the similarity between an eye-print feature of an eye image in the first group of eye images and the eye-print feature of the acquired eye image is greater than a third threshold.”)
In regards to classify the cause of the abnormality, based on results of the first processing, the second processing, and the third processing.
As presented in previous claims, Nechyba classifies the abnormality as a lighting issue, an issue with pitch/yaw/roll as well as classifying the image abnormality as poor/low quality. A person of ordinary skill in the art would find it obvious to transition this determination within the newly modified Nechyba/Li system based on the series of processing steps. In Nechyba’s methodology, they classify a quality of the image based in part on the image quality score (Figure 7), a person of ordinary skill in the art would have found it obvious to include Li’s system of multiple thresholding to influence the classification of the image quality score, the abnormality of the classification of a low quality image as well as the subsequent root cause of the low quality image.
Conclusion
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/SHANE WRENSFORD CODRINGTON/Examiner, Art Unit 2667
/MATTHEW C BELLA/Supervisory Patent Examiner, Art Unit 2667
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