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 .
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.
Claim 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a Judicial Exception in the form of an Abstract Idea, without significantly more:
Beginning with independent claim 1, a process claim, which recites:
A method comprising: receiving a new image of an item based on a time-stamped user interaction with the item; applying object detection to the new image to identify the item; determining a respective similarity of the image to each old image of the item in a library of old images; and determining that each respective similarity is below a similarity threshold and, in response, adding the new image to the library.
The claim recites abstract ideas:
“receiving” is mere data gathering and output recited at a high level of generality, and thus are insignificant extra-solution activity.
A process that encompass a human performing the steps mentally with or without a physical aid in the form of the “determining” steps, with the “receiving” step and “applying” step being pre-solution acts of processing information which could be performed visually and/or mentally; and
A method of organizing human behavior in the form of a social activity of following rules or instructions informing a person to perform the “receiving” step and “applying” step and “determining” steps.
These two abstract ideas will be considered together for analysis as a single abstract idea per MPEP 2106:
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This judicial exception is not integrated into a practical application because there are no recited additional elements that amount to a practical application, such as but no limited to the following as noted in MPEP 2106:
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The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception for the same reason: There are not additional elements other than the abstract idea.
The independent claim 8, a system claim, which recites:
A system comprising: a fixed-position camera having a field of view; an item scanner disposed within the field of view; and a computing system in electronic communication with the camera and the scanner, the computing system comprising a non-transitory, computer-readable memory and a processor configured to execute instructions stored in the memory to cause the computing system to perform operations comprising: receiving a video stream from the camera; isolating a frame of the video stream based on a scan of an item by the scanner; applying object detection to the frame to identify a new item image in the frame; and determining a respective similarity of the new item image to each old image of the item in a library of old images; and adding the new item image to the library only if each respective similarity is below a similarity threshold.
The claim recites abstract ideas:
“An system, comprising a processor, a memory, and a computer program that is stored in the memory and capable of being executed the processor to perform the “receiving” step, “isolating” step, “applying” step, “determining” step and “adding” step” are considered being performed by a generic computer. In addition, the limitation does it does not provide any details about how “obtaining” step and “outputting” steps are performed. Therefore, If the apparatus, processor and memory are removed from the claim, the method can be easily performed by a human being without the need of any of a computer component. The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception.
“receiving” is mere data gathering and output recited at a high level of generality, and thus are insignificant extra-solution activity.
A process that encompass a human performing the steps mentally with or without a physical aid in the form of the “adding” steps, with the “receiving” step, “isolating” step, “applying” step and “determining” step are being pre-solution acts of processing information which could be performed visually and/or mentally; and
A method of organizing human behavior in the form of a social activity of following rules or instructions informing a person to perform the “receiving” step, “isolating” step, “applying” step, “determining” step and “adding” step.
These two abstract ideas will be considered together for analysis as a single abstract idea per MPEP 2106:
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This judicial exception is not integrated into a practical application because there are no recited additional elements that amount to a practical application, such as but no limited to the following as noted in MPEP 2106:
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The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception for the same reason: There are not additional elements other than the abstract idea.
The independent claim 15, a system claim, which recites:
A system comprising: a camera; and a computing system in electronic communication with the camera, the computing system comprising a non-transitory, computer-readable memory and a processor configured to execute instructions stored in the memory to cause the computing system to perform operations comprising: receiving a video stream from the camera; isolating a frame of the video stream based on a scan of an item by a scanner disposed in a field of view of the camera; applying object detection to the frame to identify a new item image in the frame; calculating a quality score for the new item image; and adding the new item image to a library of images of the item only if the quality score exceeds a quality threshold
The claim recites abstract ideas:
“An system, comprising a processor, a memory, and a computer program that is stored in the memory and capable of being executed the processor to perform the “receiving” step, “isolating” step, “applying” step, “calculating” step and “adding” step” are considered being performed by a generic computer. In addition, the limitation does it does not provide any details about how “obtaining” step and “outputting” steps are performed. Therefore, If the apparatus, processor and memory are removed from the claim, the method can be easily performed by a human being without the need of any of a computer component. The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception.
“receiving” is mere data gathering and output recited at a high level of generality, and thus are insignificant extra-solution activity.
“calculating” explicitly recites performing mathematical calculations, the limitation falls within the “mathematical concepts” grouping of abstract ideas.
A process that encompass a human performing the steps mentally with or without a physical aid in the form of the “adding” steps, with the “receiving” step, “isolating” step, “applying” step and “calculating” step are being pre-solution acts of processing information which could be performed visually and/or mentally; and
A method of organizing human behavior in the form of a social activity of following rules or instructions informing a person to perform the “receiving” step, “isolating” step, “applying” step, “calculating” step and “adding” step.
These two abstract ideas will be considered together for analysis as a single abstract idea per MPEP 2106:
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This judicial exception is not integrated into a practical application because there are no recited additional elements that amount to a practical application, such as but no limited to the following as noted in MPEP 2106:
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The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception for the same reason: There are not additional elements other than the abstract idea.
Independent claims 1, 8 and 15 are merely a generic computer implementation of the abstract ideas and likewise do not amount to significantly more. See MPEP 2106:
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Likewise, the following dependent claims have been analyzed and do not recite elements that recite a practical application or significantly more and remain rejected under 35 USC 101: Claims 2-7, 9-14 and 16-20.
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.
Claims 1-5, 8, 10, 11 and 14 are rejected under 35 U.S.C. 102 (a)(1) as being anticipated by Ryan’513 (US 2023/0040513).
With respect to claim 8, Ryan’513 teaches a system (abstract) comprising:
a fixed-position camera having a field of view [Each camera captures images of every entity that passes by its field of view and creates a dataset of images for processing and the cameras may capture images of a queuing area or may be located within local infrastructure, such as a check-in desk, kiosk desk, a self-service bag drop machine, or an Automatic Tag Reading machine (paragraphs 61, 94 and 158)];
an item scanner disposed within the field of view [ Each camera captures images of every entity that passes by its field of view and creates a dataset of images for processing (paragraphs 61 and 94)]; and
a computing system in electronic communication with the camera and the scanner, the computing system comprising a non-transitory, computer-readable memory and a processor configured to execute instructions stored in the memory to cause the computing system to perform operations [the processes described may be performed in real-time using a centralized processor and receiving data at the centralized processor, the device may comprise a computer processor running one or more server processes for communicating with client devices and the server processes comprise computer readable program instructions for carrying out the operations (paragraphs 61, 87, 94, 192 and 195)] comprising:
receiving a video stream from the camera [a first camera (Fig.3, item 301) is orientated to capture videos or a sequence of images and data relating to one or more entities within the observable environment (paragraphs 96 and 141)];
isolating a frame of the video stream based on a scan of an item by the scanner [Each camera captures images of every entity that passes by its field of view and creates a dataset of images for processing. Each image is timestamped and associated with location information so that the exact location of each entity can be tracked by the system (paragraphs 61, 94, 96, 170 and Fig.17, steps 1410 and 1412)];
applying object detection to the frame to identify a new item image in the frame [The machine learning models are trained to identify various characteristics associated with an image, including one or more passengers and/or objects and once one or more of the models have been trained using the training data ... use one or more trained models to identify entities, such as passengers or articles of baggage, within each image by extracting, mapping and comparing unique features associated with the entity (paragraphs 103, 104, 141, 167, 170, Fig.11, step 1101, Fig.15A, steps 1501, 1502 and 1506, and Fig.17, steps 1410, 1411 and 1412)]; and
determining a respective similarity of the new item image to each old image of the item in a library of old images [a search database is queried in order to find similar characteristic feature vectors and corresponding metadata in the search database, for example by using a machine learning model to compare between the characteristic feature vector for the new image and each of the characteristic feature vectors in the search database ... be used for uniquely identifying any entity by comparing the similarity of a number of similar images taken over time, from different angles, or in various locations; the unique ID may be associated with an identifier associated with each entity, such as passenger related information or a bag tag number (paragraphs 103, 104 and 141)]; and
adding the new item image to the library only if each respective similarity is below a similarity threshold [the system determines whether a passenger is a new passenger by comparing the distance score between the query image and the nearest neighbor to a predetermined threshold value. If the distance is above the predefined threshold (i.e. if the semantic similarity is below a threshold), the identified passenger
is considered to be new and a new unique ID is assigned (paragraphs 103, 104, 141, 151, 160 and Fig.11)].
With respect to claim 10, which further limits claim 8, Ryan’513 teaches wherein the operations further comprise: receiving, after adding the new image to the library, an image captured by a CCTV camera substantially in real time, the CCTV camera disposed at a facility [the system determines whether a passenger is a new passenger by comparing the distance score between the query image and the nearest neighbor to a predetermined threshold value. If the distance is above the predefined threshold (i.e. if the semantic similarity is below a threshold), the identified passenger is considered to be new and a new unique ID is assigned (61, 141, 151, 158, 160 and Fig.11)];
processing the new image to identify an item in the new image (Fig.11, Fig.15A and Fig.5B); and
determining that the identified item in the new image is prohibited from entering or being removed from the facility and, in response, causing an alert to be output at the facility [if the system identifies an object to be the same shape as a prohibited object, such as a weapon, then an anomaly alert may be sent automatically (paragraph 61, 70, 129 and 141, and Fig.11)].
With respect to claim 11, which further limits claim 8, Ryan’513 teaches wherein the camera comprises a closed-circuit television camera [Each camera captures images of every entity that passes by its field of view and creates a dataset of images for processing and the system is integrated with camera data deriving from, for example, CCTV feeds.) (paragraphs 94 and 158)]
With respect to claim 14, which further limits claim 8, Ryan’513 teaches wherein: applying object detection to the frame is to identify a plurality of new item images of a plurality of items [a first camera (Fig.3, item 301) is orientated to capture videos or a sequence of images and data relating to one or more entities within the observable environment (paragraph 96), As shown in FIG. 17, the pre-processing step 1410 comprises: in a first step 1411, obtaining image data from one or more cameras; in a second step 1412, analyzing each frame within the image data (paragraph 170) and as shown in Fig.15A, in a first step 1501, image data is obtained from a plurality of cameras. In a second step 1502, fine boundaries of the detected object are identified for each camera data set ... In a sixth step 1506, the patterns are matched between the different camera data sets in order to identify a time difference between the bag being detected by the different cameras (paragraph 167)]; and
the operations further comprise:
determining, for each new item image of each item, a respective similarity of the new item image to each old image of the item in a library of old images [In a first step 1101, a new image is obtained of the entity ... As shown in Fig. 11, in a third step 1103, a search database is queried in order to find similar characteristic feature vectors and corresponding metadata in the search database, for example by using a machine learning model to compare between the characteristic feature vector for the new image and each of the characteristic feature vectors in the search database ... be used for uniquely identifying any entity by comparing the similarity of a number of similar images taken over time, from different angles, or in various locations; the unique ID may be associated with an identifier associated with each entity, such as passenger related information or a bag tag number (paragraph 141)] ; and
adding the new item image to the library only if each respective similarity is below a similarity threshold [as shown in Fig.11, a new image is obtained of the entity; In a third step 1103, a search database is queried in order to find similar characteristic feature vectors and corresponding metadata in the search database, for example by using a machine learning model to compare between the characteristic feature vector for the new image and each of the characteristic feature vectors in the search database ... be used for uniquely identifying any entity by comparing the similarity of a number of similar images taken over time, from different angles, or in various locations; the unique ID may be associated with an identifier associated with each entity, such as passenger related information or a bag tag number (paragraph 141) and the system determines whether a passenger is a new passenger by comparing the distance score between the query image and the nearest neighbor to a predetermined threshold value. If the distance is above the predefined threshold (i.e. if the semantic similarity is below a threshold), the identified passenger is considered to be new and a new unique ID is assigned (paragraph 151). In an initial phase, the machine learning model is trained using a training database of training data once enough raw data has been captured ... newly collected data is added to the training data in order to adjust the models (paragraph 160)].
With respect to claims 1, 3 and 5, they are method claims and they are rejected for the same manner as described in the rejected claims 8, 11 and 10.
With respect to claim 2, which further limits claim 1, Ryan’513 teaches wherein determining the respective similarity comprises: generating a new embeddings vector representative of the new image [An new image is captured of one or more entities (Fig.11), for example a person and their accompanying belongings when they first enter an observable environment (paragraph 60) and Each image is processed and analyzed by a machine learning algorithm during an edge process 131 in order to identify one or more embedding vectors 132 associated with each identified entity (paragraph 65)]; and
comparing the new embeddings vector to a respective embeddings vector representative of each old image [When seeking to identify, or re-identify, an entity from a newly obtained image, the system generates a list of images that are most similar to the query image (also known as a list of nearest neighbors). This is achieved by searching the query, or search, database for embedding vectors that are closest, in the Euclidean distance sense, to the query image embedding (paragraph 142)].
With respect to claim 4, which further limits claim 1, Ryan’513 teaches comprising: receiving a video stream [As shown in FIG. 3, a first camera 301 is orientated to capture videos or a sequence of images and data relating to one or more entities within the observable environment 300 (paragraph 96)]; and
isolating a frame of the video stream based on the time of the time-stamped user interaction with the item [Each camera captures images of every entity that passes by its field of view and creates a dataset of images for processing. Each image is timestamped and associated with location information so that the exact location of each entity can be tracked by the system (paragraph 94) and as shown in FIG. 17, the pre-processing step 1410 comprises: in a first step 1411, obtaining image data from one or more cameras; in a second step 1412, analyzing each frame within the image data (paragraph 170)];
wherein the isolated frame is the new image [a new image is obtained of the entity (Fig.11, step 1101), and as shown in FIG. 17, the pre-processing step 1410 comprises: in a first step 1411, obtaining image data from one or more cameras; in a second step 1412, analyzing each frame within the image data (paragraph 170)].
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.
Claim 9 is rejected under 35 U.S.C. 103 as being unpatentable over Ryan’513 (US 2023/0040513), and further in view of Kim’882(US 2021/0064882).
With respect to claim 9, which further limits claim 8, Ryan’513 teaches wherein isolating the frame of the video stream based on the scan of the item by the scanner comprises: determining a time stamp of the scan [Each camera captures images of every entity that passes by its field of view and creates a dataset of images for processing. Each image is timestamped and associated with location information so that the exact location of each entity can be tracked by the system (paragraphs 61, 94 and 96)].
Ryan’513 does not teach determining a time stamp of the scan; and determining that the frame matches the time stamp and, in response, isolating the frame.
Kim’882 teaches determining that the frame matches the time stamp and, in response, isolating the frame [displaying a playlist including a frame thumbnail matched with a search condition and a timestamp in a video search result, a user can immediately check the search result and the processor extracts a frame from a video, selects a tag corresponding to an object recognized in the extracted frame, and stores the tag in the frame (paragraphs 56 and 320).]
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Ryan’513 according to the teaching of Kim’882 to determine the frames which are matched with a desired timestamp because this will update a video database for a new video.
Claims 6, 12 and 15-19 are rejected under 35 U.S.C. 103 as being unpatentable over Ryan’513 (US 2023/0040513), and further in view of Kim’345 (US 2021/0019345).
With respect to claim 12, which further limits claim 8, Ryan’513 does not teach wherein the operations further comprise: calculating a quality score for the new item image; and wherein adding the new item image to the library is further only if the quality score exceeds a quality threshold.
Kim’345 teaches wherein the operations further comprise: calculating a quality score for the new item image [The image analysis unit calculates an image quality score of the object of interest based on the quality analysis result of the image of the object of interest (paragraph 138)]; and
wherein adding the new item image to the library is further only if the quality score exceeds a quality threshold [the server compares the image quality score with the second threshold (S270); the image in which the object of interest is detected, having the image quality score that is equal to or higher than the second threshold is selected as the image of interest (S270). When the image quality score of the detected patch is equal to or higher than the second threshold, the detected patch is stored in the retrieval database (paragraph 184 and Fig.6)].
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Ryan’513 according to the teaching of Kim’345 to achieve fast retrieval using only the image selected based on the image quality of the object because this will allow the objects in the new images to be identified more effectively.
With respect to claim 15, Ryan’513 teaches a system comprising:
a camera (Fig.15A, items 1501); and
a computing system in electronic communication with the camera, the computing system comprising a non-transitory, computer-readable memory and a processor configured to execute instructions stored in the memory to cause the computing system to perform operations comprising [the processes described may be performed in real-time using a centralized processor and receiving data at the centralized processor, the device may comprise a computer processor running one or more server processes for communicating with client devices and the server processes comprise computer readable program instructions for carrying out the operations (paragraphs 61, 87, 94, 192 and 195)]:
receiving a video stream from the camera [a first camera (Fig.3, item 301) is orientated to capture videos or a sequence of images and data relating to one or more entities within the observable environment (paragraphs 96 and 141)];
isolating a frame of the video stream based on a scan of an item by a scanner disposed in a field of view of the camera [Each camera captures images of every entity that passes by its field of view and creates a dataset of images for processing. Each image is timestamped and associated with location information so that the exact location of each entity can be tracked by the system (paragraphs 61, 94, 96, 170 and Fig.17, steps 1410 and 1412)];
applying object detection to the frame to identify a new item image in the frame [The machine learning models are trained to identify various characteristics associated with an image, including one or more passengers and/or objects and once one or more of the models have been trained using the training data ... use one or more trained models to identify entities, such as passengers or articles of baggage, within each image by extracting, mapping and comparing unique features associated with the entity (paragraphs 103, 104, 141, 167, 170, Fig.11, step 1101, Fig.15A, steps 1501, 1502 and 1506, and Fig.17, steps 1410, 1411 and 1412)];
Ryan’513 does not teach calculating a quality score for the new item image; and adding the new item image to a library of images of the item only if the quality score exceeds a quality threshold.
Kim’345 teaches calculating a quality score for the new item image [The image analysis unit calculates an image quality score of the object of interest based on the quality analysis result of the image of the object of interest (paragraph 138)]; and
adding the new item image to a library of images of the item only if the quality score exceeds a quality threshold [the server compares the image quality score with the second threshold (S270); the image in which the object of interest is detected, having the image quality score that is equal to or higher than the second threshold is selected as the image of interest (S270). When the image quality score of the detected patch is equal to or higher than the second threshold, the detected patch is stored in the retrieval database (paragraph 184 and Fig.6)].
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Ryan’513 according to the teaching of Kim’345 to achieve fast retrieval using only the image selected based on the image quality of the object because this will allow the objects in the new images to be identified more effectively.
With respect to claim 16, which further limits claim 15, Ryan’513 teaches wherein the operations further comprise: determining a respective similarity of the new item image to each old image of the item in the library of old images [as shown in Fig.11, in a third step 1103, a search database is queried in order to find similar characteristic feature vectors and corresponding metadata in the search database, for example by using a machine learning model to compare between the characteristic feature vector for the new image and each of the characteristic feature vectors in the search database ... be used for uniquely identifying any entity by comparing the similarity of a number of similar images taken over time, from different angles, or in various locations; the unique ID may be associated with an identifier associated with each entity, such as passenger related information or a bag tag number (paragraph 141)]; and
adding the new item image to the library only if each respective similarity is below a similarity threshold [As shown in Fig.11, in a first step 1101, a new image is obtained of the entity; In a third step 1103, a search database is queried in order to find similar characteristic feature vectors and corresponding metadata in the search database, for example by using a machine learning model to compare between the characteristic feature vector for the new image and each of the characteristic feature vectors in the search database ... be used for uniquely identifying any entity by comparing the similarity of a number of similar images taken over time, from different angles, or in various locations; the unique ID may be associated with an identifier associated with each entity, such as passenger related information or a bag tag number (paragraph 141). The system determines whether a passenger is a new passenger by comparing the distance score between the query image and the nearest neighbor to a predetermined threshold value. If the distance is above the predefined threshold (i.e. if the semantic similarity is below a threshold), the identified passenger
is considered to be new and a new unique ID is assigned (paragraph 151)].
With respect to claim 17, which further limits claim 15, Ryan’513 teaches wherein the operations further comprise: receiving, after adding the new image to the library, an image captured by a CCTV camera substantially in real time, the CCTV camera disposed at a facility [As shown in Fig.11, in a first step 1101, a new image is obtained of the entity. The cameras may capture images of a queuing area or may be located within local infrastructure, such as a check-in desk, kiosk desk, a self-service bag drop machine, or an Automatic Tag Reading machine (paragraph 61). The system is integrated with camera data deriving from, for example, CCTV feeds (paragraph 158)
processing the new image to identify an item in the new image [As shown in Fig.11, in a first step 1101, a new image is obtained of the entity and As show in Fig,.15A a first step 1501, image data is obtained from a plurality of cameras. In a second step 1502, fine boundaries of the detected object are identified for each camera data set ... In a sixth step 1506, the patterns are matched between the different camera data sets in order to identify a time difference between the bag being detected by the different cameras (paragraph 167)]; and
determining that the identified item in the new image is prohibited from entering or being removed from the facility and, in response, causing an alert to be output at the facility [if the system identifies an object to be the same shape as a prohibited object, such as a weapon, then an anomaly alert may be sent automatically (paragraph 129) and If an anomaly is detected, an alert is generated that may be sent to various external systems, such as security checkpoints, electronic check-in kiosks, electronic boarding gates, or automatic border control gates (paragraph 70)].
With respect to claim 18, which further limits claim 15, Ryan’513 teaches wherein the operations further comprise: training an object detection model according to the library of images [Once one or more of the models have been trained using the training data ... use one or more trained models to identify entities, such as passengers or articles of baggage, within each image by extracting, mapping and comparing unique features associated with the entity (paragraph 104 ) and in an initial phase, the machine learning model is trained using a training database of training data once enough raw data has been captured ... newly collected data is added to the training data in order to adjust the models (paragraph 160).
With respect to claim 19, which further limits claim 15, Ryan’513 teaches further comprising the item scanner [For example, the cameras may capture images of a queuing area or may be located within local infrastructure, such as a check-in desk, kiosk desk, a self-service bag drop machine, or an Automatic Tag Reading machine [item scanner] (paragraph 61)].
With respect to claim 6, it is a method claim and it is rejected for the same manner as described in the rejected claim 12.
Claim 13 is rejected under 35 U.S.C. 103 as being unpatentable over Ryan’513 (US 2023/0040513), and further in view of Kim’345 (US 2021/0019345), Zheng’638 (US 2024/0046638).
With respect to claim 13, which further limits claim 12, the combination of Ryan’513 and Kim’345 does not teach wherein calculating the quality score comprises one or more of: calculating a variance of Laplacian (VOL) value; calculating a CLAHE optimization value; or calculating a CLIP-IQA value.
Zheng’638 teaches wherein calculating the quality score comprises one or more of: calculating a variance of Laplacian (VOL) value [the video product retrieval system adopts data augmentation approaches and labels each augmented box with a quality score based on certain empirical metrics, such as intersection over union ratio with respect to the ground truth, and variance of Laplacian as an estimate of its blurriness.) (Abstract and paragraph 214)];
calculating a CLAHE optimization value; or calculating a CLIP-IQA value [the video product retrieval system adopts data augmentation approaches and labels each augmented box with a quality score based on certain empirical metrics, such as intersection over union ratio with respect to the ground truth, and variance of Laplacian as an estimate of its blurriness.) (Abstract and paragraph 214)].
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Ryan’513 according to the teaching of Kim’345 to use the quality scores to fuse the final features because this will allow the objects in the new images to be identified more effectively.
With respect to claim 7, it is a method claim and it is rejected for the same manner as described in the rejected claim 13.
Claim 20 is rejected under 35 U.S.C. 103 as being unpatentable over Ryan’513 (US 2023/0040513), Kim’345 (US 2021/0019345), and further in view of Huang’193 (US 2014/0241703).
With respect to claim 20, which further limits claim 15, the combination of Ryan’513 and Kim’345 does not teach wherein the camera is a low image quality camera.
Huang’193 teaches wherein the camera is a low image quality camera [An analog camera used for the DVR system may be classified based on image quality. For example, there are a low image quality camera having a video band of about 6 MHz and about 720 horizontal pixels (hereinafter, a video of Such image quality is referred to as a "low image quality video") (paragraph 3)].
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify the combination of Ryan’513 and Kim’345 according to the teaching of Huang’193 to include the camera having a low image quality camera because this will reduce the cost of the system.
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Any inquiry concerning this communication or earlier communications from the examiner should be directed to HUO LONG CHEN whose telephone number is (571)270-3759. The examiner can normally be reached on M-F 9am - 5pm. If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Tieu, Benny can be reached on (571) 272-7490. The fax phone number for the organization where this application or proceeding is assigned is (571) 273-8300. Information regarding the status of an application may be obtained from the Patent Application Information Retrieval (PAIR) system. Status information for published applications may be obtained from either Private PAIR or Public PAIR. Status information for unpublished applications is available through Private PAIR only. For more information about the PAIR system, see http://pair-direct.uspto.gov. Should you have questions on access to the Private PAIR system, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative or access to the automated information system, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/HUO LONG CHEN/Primary Examiner, Art Unit 2682
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