ETAILED 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 12/18/2024, 02/27/2025 and 12/18/2024 was considered by the examiner. Initialed copy(ies) of the IDS(s) are included.
Claim Objections
Claims 1-2, 6 and 8-9 are objected to because of the following informalities:
In claim 1, line 6, the Examiner suggests deleting the phrase “in such a manner that”, remove the commas after the word “is” in lines 9 and 10 and rewrite the claim to improve clarity to avoid clarity issues and prevent a rejection under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph.
In claim 2, lines 5-6, the Examiner suggests deleting the phrase “in such a manner that” and rewrite the claim to avoid clarity issues and prevent a rejection under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph.
Regarding claim 3, the Examiner suggests amending the claim as shown below to improve clarity:
“…..image is equal to or higher than a second threshold higher than the first threshold;
determine that the detection accuracy has been decreased in the latest captured image.”
Regarding claim 4, the Examiner suggests amending the claim as shown below to improve clarity:
“….in a case where the detection accuracy in the past captured image is continuously equal to or higher than the second threshold;
determine that the detection accuracy has been decreased in the latest captured image.”
In claim 6, lines 4-5, the Examiner suggests deleting the phrase “in such a manner that” and rewrite the claim to avoid clarity issues and prevent a rejection under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph.
In claim 8, lines 3-4, the Examiner suggests deleting the phrase “in such a manner that”, remove the commas after the word “is” in lines 8 and 9 and rewrite the claim to avoid clarity issues and prevent a rejection under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph.
Appropriate correction is required.
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 abstract without significantly more.
Claims 1-7
Independent claim 1
Step 1 Analysis: Claim 1 is directed to a device/method, which falls within one of the four statutory categories.
Step 2A Prong 1 Analysis: Claim 1, in part recites “perform a concealment processing in such a manner that the lower a detection accuracy for the detected object or the detected specific part of the acquired captured image is, the wider a concealment range in the acquired captured image is, on the concealment range a concealment being performed”.
The step of “perform a concealment processing in such a manner that the lower a detection accuracy for the detected object or the detected specific part of the acquired captured image is, the wider a concealment range in the acquired captured image is, on the concealment range a concealment being performed” of the claim encompass for e.g. using a mask to over a person face or an electronic mask for the case when the image is a digital image. Also, if the face being masked can’t be clearly decern, then it would be obvious to expand the mask. This step falls under mental observations and these, mental observations or evaluations fall within the “mental processed” grouping of abstract ideas.
Step 2A Prong 2 Analysis: The claim does include any additional elements that amount to an integration of the judicial exceptions into a practical application. In particular, the claim in part recites the additional elements –
a controller comprising a processor;
from an acquired captured image, detect an object having a specific part that needs to be concealed to protect personal information or the specific part;
The “a controller comprising a processor” are recited as carry out the steps of acquiring and performing. The “a controller comprising a processor” is recited at a high level of generality and amounts to no more a processor used to apply the exception and is a part of a generic computer.
The claim element of “from an acquired captured image, detect an object having a specific part that needs to be concealed to protect personal information or the specific part” impart additional element, the additional elements merely constitute pre-solution activating involving receiving the input image.
Step 2B Analysis: The claim does not include additional elements that are
sufficient to amount to significantly more than the judicial exception. For the reasons
given in Step 2A Prong 2. Thus, the claim is not patent eligible.
Dependent claims 2-7
Claim 2 recites in part “the processor is further configured to: perform the concealment processing in such a manner that the lower the detection accuracy is, the wider the concealment range is, in a case where the detection accuracy has been decreased in a latest captured image in time series” do not overcome the rejection of the parent claims 1 as stated above because the additional elements does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
Claim 3 recites in part “the processor is further configured to: in a case where the detection accuracy in the latest captured image is lower than a predetermined first threshold, when the detection accuracy in a past captured image previous to the latest captured image is equal to or higher than a second threshold higher than the first threshold, determine that the detection accuracy has been decreased in the latest captured image” do not overcome the rejection of the parent claim 2 as stated above because the additional elements does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
Claim 4 recites in part “the processor is further configured to: in a case where the detection accuracy in the past captured image is continuously equal to or higher than the second threshold, determine that the detection accuracy has been decreased in the latest captured image” do not overcome the rejection of the parent claim 3 as stated above because the additional elements does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
Claim 5 recites in part “the processor is further configured to: in a case where it is determined that the detection accuracy has been decreased in the latest captured image, when the detection accuracy is equal to or higher than a third threshold that is set according to the detection accuracy and is lower than the first threshold, perform the concealment processing” do not overcome the rejection of the parent claims 3 as stated above because the additional elements does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
Claim 6 recites in part “wherein the processor is further configured to: perform the concealment processing in such a manner that the concealment range is expanded according to a motion vector of the detected object or specific part.” do not overcome the rejection of the parent claims 1 as stated above because the additional elements does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
Claim 7 recites in part “wherein the processor is further configured to: perform the concealment processing in such a manner that the concealment range is expanded while imparting directivity in accordance with directivity of the detection accuracy with respect to an image” do not overcome the rejection of the parent claims 1 as stated above because the additional elements does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
Claims 8-9
Independent claim 8
Step 1 Analysis: Claim 8 is directed to a device/method, which falls within one of the four statutory categories.
Step 2A Prong 1 Analysis: Claim 8, in part recites “performing a concealment processing in such a manner that the lower a detection accuracy for the detected object or the detected specific part of the acquired captured image is, the wider a concealment range in the acquired captured image is, on the concealment range a concealment being performed”.
The step of “performing a concealment processing in such a manner that the lower a detection accuracy for the detected object or the detected specific part of the acquired captured image is, the wider a concealment range in the acquired captured image is, on the concealment range a concealment being performed” of the claim encompass for e.g. using a mask to over a person face or an electronic mask for the case when the image is a digital image and adjusting the mask if the face being masked can’t be clearly decern. This step falls under mental observations and these, mental observations or evaluations fall within the “mental processed” grouping of abstract ideas.
Step 2A Prong 2 Analysis: The claim does include any additional elements that amount to an integration of the judicial exceptions into a practical application. In particular, the claim in part recites the additional elements –
from an acquired captured image, detecting an object having a specific part that needs to be concealed to protect personal information or the specific part;
The claim element of “at least one sensor to collect data regarding a person
The claim element of “from an acquired captured image, detect an object having a specific part that needs to be concealed to protect personal information or the specific part” impart additional element, the additional elements merely constitute pre-solution activating involving receiving the input image.
Step 2B Analysis: The claim does not include additional elements that are
sufficient to amount to significantly more than the judicial exception. For the reasons
given in Step 2A Prong 2. Thus, the claim is not patent eligible.
dependent claim 9
Claim 9 recites in part “performing the concealment processing in such a manner that the lower the detection accuracy is, the wider the concealment range is, in a case where the detection accuracy has been decreased in a latest captured image in time series” do not overcome the rejection of the parent claims 1 as stated above because the additional elements does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
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 and 7-8 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by
Narayanan (NPL titled: The De-Identification Camera).
Regarding independent claim 1, Narayanan teaches an image processing device (De-Identification Camera – see abstract) comprising: a controller comprising a processor (TI’s OMAP4430 application processor, which is a 1GHz Dual Core ARM Cortex A9 processor – see section III, subsection B, step 2, [p][001]) configured to: from an acquired captured image (video feed – see Fig 1), detect an object having a specific part that needs to be concealed to protect personal information or the specific part (Person Detection: Once we have foreground objects in the scene we apply HOG based person detector. The detector is only applied on the region with foreground objects in it and the image is downsized to 0.4 of its original size to increase detector performance - see section III, subsection B, step 1, sub-step 2); perform a concealment processing in such a manner that the lower a detection accuracy for the detected object or the detected specific part of the acquired captured image is, the wider a concealment range in the acquired captured image is, on the concealment range a concealment being performed (the detector returns a bounding box which fits the person with-in, we also increase the size of this bounding box to avoid the case when the detected bounding box fails to enclose complete human body - see section III, subsection B, step 1, sub-step 2 and De-Identification Transformation: After the segmentation of the video tube, we apply the de-identification transformation to bounding box region of the image.
We use two types of de-identification transformation to obscure the person’s identity: 1) Gaussian Blur of the pixels inside the bounding box, and 2)binarizing the intensity values of the bounding box image - see section III, subsection B, step 1, sub-step 5).
Regarding claim 7, Narayanan teaches the image processing device according to claim 1, wherein the processor is further configured to: perform the concealment processing in such a manner that the concealment range is expanded while imparting directivity in accordance with directivity of the detection accuracy with respect to an image (the bounding box is passed to the tracking module and is only called periodically based on the movement in the scene that is being monitored. We call the detector more often if scene has more noise which can adversely affect the performance of the tracking module. For our experiments we call the detector every 5 frames - see section III, subsection B, step 1, sub-step 2).
Regarding independent claim 8, Narayanan teaches an image processing method (method for using De-Identification Camera – see abstract) comprising: from an acquired captured image (video feed – see Fig 1), detecting an object having a specific part that needs to be concealed to protect personal information or the specific part (Person Detection: Once we have foreground objects in the scene we apply HOG based person detector. The detector is only applied on the region with foreground objects in it and the image is downsized to 0.4 of its original size to increase detector performance - see section III, subsection B, step 1, sub-step 2); and performing a concealment processing in such a manner that the lower a detection accuracy for the detected object or the detected specific part of the acquired captured image is, the wider a concealment range in the acquired captured image is, on the concealment range a concealment being performed (The detector returns a bounding box which fits the person with-in, we also increase the size of this bounding box to avoid the case when the detected bounding box fails to enclose complete human body - see section III, subsection B, step 1, sub-step 2 and De-Identification Transformation: After the segmentation of the video tube, we apply the de-identification transformation to bounding box region of the image. We use two types of de-identification transformation to obscure the person’s identity: 1) Gaussian Blur of the pixels inside the bounding box, and 2) binarizing the intensity values of the bounding box image - see section III, subsection B, step 1, sub-step 5).
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 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 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.
Claims 2 and 9 are rejected under 35 U.S.C. 103 as being unpatentable over Narayanan (NPL titled: The De-Identification Camera) in view of IGARASHI et al (Pub No.: 20220284595).
Regarding claim 2, Narayanan teaches the image processing device according to claim 1, the processor is further configured to: perform the concealment processing in such a manner that the lower the detection accuracy is, the wider the concealment range is (the detector returns a bounding box which fits the person with-in, we also increase the size of this bounding box to avoid the case when the detected bounding box fails to enclose complete human body - see section III, subsection B, step 1, sub-step 2), however, Narayanan does not teach in a case where the detection accuracy has been decreased in a latest captured image in time series.
IGARASHI teaches in a case where the detection accuracy has been decreased in a latest captured image in time series (On the other hand, among two Bboxes determined to be matched by the matching unit 15, when one Bbox detected from the current frame image is the low accuracy Bbox – see [p][0043]).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of Narayanan an image processing device comprising: a controller comprising a processor with the teachings of IGARASHI in a case where the detection accuracy has been decreased in a latest captured image in time series.
Wherein having Narayanan teaches in a case where the detection accuracy has been decreased in a latest captured image in time series.
The motivation behind the modification would have been to perform for de-identifying videos in real time, transforming the video on the camera itself ensures protection from various attacks and refine a low-accuracy Bbox when the reliability of the low-accuracy Bbox is equal to or less than a predetermined value (see [p][0042])
since both Narayanan and IGARASHI object detection. Wherein Narayanan de-identify videos in real time, transforming the video on the camera itself ensures protection from various attacks while IGARASHI refine a low-accuracy Bbox when the reliability of the low-accuracy Bbox is equal to or less than a predetermined value (Narayanan (NPL titled: The De-Identification Camera), see Abstract and IGARASHI et al (Pub No.: 20220284595), [p][0042]).
Claim 9 is rejected for the same reasons as set forth in the rejection of the claim 2, as claim 2 is device claim for the method claimed in claim 9.
Claim 6 is rejected under 35 U.S.C. 103 as being unpatentable over Narayanan (NPL titled: The De-Identification Camera) in view of YUEHONG et al (English translation of CN 102855635 A).
Regarding claim 6, Narayanan teaches the image processing device according to claim 1, wherein the processor is further configured to: perform the concealment processing in such a manner that the concealment range is expanded (the detector returns a bounding box which fits the person with-in, we also increase the size of this bounding box to avoid the case when the detected bounding box fails to enclose complete human body - see section III, subsection B, step 1, sub-step 2 and De-Identification Transformation: After the segmentation of the video tube, we apply the de-identification transformation to bounding box region of the image. We use two types of de-identification transformation to obscure the person’s identity: 1) Gaussian Blur of the pixels inside the bounding box, and 2) binarizing the intensity values of the bounding box image - see section III, subsection B, step 1, sub-step 5); however does not explicitly teach according to a motion vector of the detected object or specific part. YUEHONG teaches according to a motion vector of the detected object or specific part (determine based on the motion vector of each human body parts in the present frame activities cycle of described each part – see page 2, [p][013]).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of Narayanan an image processing device comprising: a controller comprising a processor with the teachings of YUEHONG according to a motion vector of the detected object or specific part.
Wherein having YUEHONG teaches according to a motion vector of the detected object or specific part.
The motivation behind the modification would have been de-identify videos in real time, transforming the video on the camera itself ensures protection from various attacks by identifying human action in the video since both Charette and YUEHONG are directed to object detection. Wherein Narayanan de-identify videos in real time, transforming the video on the camera itself ensures protection from various attacks while YUEHONG for identifying human action in the video (Narayanan (NPL titled: The De-Identification Camera), see Abstract and YUEHONG et al (English translation of CN 102855635 A) page 2, [p][005]).
Allowable Subject Matter
Claims 3-5 would be allowable if rewritten to overcome the rejection(s) under 35 U.S.C. 101, set forth in this Office action and to include all of the limitations of the base claim and any intervening claims.
None of the cited references disclose the processor is further configured to: in a case where the detection accuracy in the latest captured image is lower than a predetermined first threshold, when the detection accuracy in a past captured image previous to the latest captured image is equal to or higher than a second threshold higher than the first threshold, determine that the detection accuracy has been decreased in the latest captured image.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
GONG et al (Pub No.: 20200218888 ) - A computer implemented method and system for training a machine to identify a target within video data, the method comprising the steps of providing a training data set including identified labelled targets within video data having the same target within different video views. Generating, using a learning model, a bounding box action policy for determining required adjustments to a bounding box around a target in the video data by: generating a bounding box around a labelled target within a first view of the video data. Converting the target bounded by the bounding box to a quantitative representation. Determining a matching level between the quantitative representation and a quantitative representation of a further labelled target within the video data from a second view different to the first view. Looping the following steps one or more times, the looped steps comprising: using the bounding box action policy to determine an action to change the bounding box. Applying the determined action to change the bounding box to a new bounding box. Converting the target bounded by the new bounding box to a new quantitative representation. Determining a further matching level between the new quantitative representation and the quantitative representation of the labelled target within the video data from the second view. Applying a reward to the learning model to adjust the bounding box action policy based on an improvement in the matching level.
TAKAHASHI et al (Pub No.: 20210365731 ) - An information processing method includes the following executed by a computer: acquiring a first image and object data of an object appearing in the first image, extracting a portion of the first image that corresponds to a difference between the object data and an object detection result obtained by inputting the first image to a trained model, the trained model receiving an image as input to output an object detection result, acquiring a second image that includes a portion corresponding to the same object data as object data corresponding to the extracted portion of the first image, reflecting an image based on the extracted portion of the first image in the portion of the acquired second image that corresponds to the same object data, and generating training data for the trained model.
OKADA et al (Pub No.: 20220301315 ) - An object to reduce a possibility that a detection target appearing in a captured image is not detected. In order to achieve such an object, an image processing device includes: a first detection unit that is configured to detect a predetermined object and a predetermined part of the predetermined object from an acquired captured image; a setting unit that sets a detection range in the captured image based on a detection result of the first detection unit in a case where only the predetermined object out of the predetermined object and the predetermined part has been detected by the first detection unit; and a second detection unit that attempts to detect the predetermined part in the detection range.
Mariappan et al (Pub No.: 20170161549 ) – A method for detecting a presence of at least one face in at least one image is comprises creating an image patch map based on a plurality of face patches identified for at least one window in the in at least one image, estimating a bounding box, and searching within the bounding box to detect presence of the at least one face in the at least one image. The present disclosure discloses use of any classifier which works on top of any feature representation to identify face patches and then using a masking system to identify bounding boxes.
Kusakabe (US Patent No.: 10540546 ) - an image processing apparatus for reducing an influence of a false detection from processing for detecting a predetermined object, and a control method thereof are provided. An image processing apparatus, comprising: a detecting unit for detecting a region of a predetermined object from an image; a determining unit for determining whether the detection by the detecting unit is a false detection; a processing unit for performing processing relating to the predetermined object on the region detected by the detecting unit; and a controlling unit for controlling, based on a determination result by the determining unit, execution of the processing by the processing unit on the region detected by the detecting unit.
Yoshida (US Patent No.: 11917301)- An image capturing apparatus includes: an image capturing unit configured to control an imaging sensor; a first obtaining unit configured to obtain a first detection result that is a result of detection of a subject from image data captured by the image capturing unit; a second obtaining unit configured to transmit the image data to an external image processing apparatus and obtain a second detection result that is a result of detection of a subject from the image data by the image processing apparatus; and a control unit configured to perform shooting processing using settings of shooting parameters determined based on at least one of the first detection result and the second detection result, wherein the control unit is configured to perform shooting processing using settings of the shooting parameters determined based on the first detection result until the second detection result is obtained from the image processing apparatus.
Newton et al (NPL titled: Preserving Privacy by De-Identifying Face Images) - In the context of sharing video surveillance data, a significant threat to privacy is face
recognition software, which can automatically identify known people, such as from a database of drivers’ license photos, and thereby track people regardless of suspicion. This paper introduces an algorithm to protect the privacy of individuals in video surveillance data by de-identifying faces such that many facial characteristics remain but the face cannot be reliably recognized. A trivial solution to de-identifying faces involves blacking out each face. This thwarts any possible face recognition,
but because all facial details are obscured, the result is of limited use. Many ad hoc attempts, such as covering eyes, fail to thwart face recognition because of the robustness of face recognition methods. This paper presents a new privacy-enabling algorithm, named k-Same, that guarantees face recognition software cannot reliably recognize de-identified faces, even though many facial details are preserved. The algorithm determines similarity between faces based on a distance metric and creates new faces by averaging image components, which may be the original image pixels (k-Same-Pixel) or eigenvectors (k-Same-Eigen). Results are presented on a standard collection of real face images with varying k.
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Any inquiry concerning this communication or earlier communications from the examiner should be directed to ANDRAE S ALLISON whose telephone number is (571)270-1052. The examiner can normally be reached on Monday-Friday 9am-5pm EST.
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/ANDRAE S ALLISON/Primary Examiner, Art Unit 2673 September 29, 2026
