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 .
Response to Arguments
The reply filed on 2/17/2026 has been entered. Applicant’s arguments with respect to claims 1-20 have been considered but are moot in view of new ground(s) of rejection caused by the amendments. Claims 1-20 are pending in this application and have been considered below.
Priority
Receipt is acknowledged of certified copies of papers required by 37 CFR 1.55.
Information Disclosure Statement
The IDS dated 11/20/2023 that have been previously considered remain placed in the application file.
1st 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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1, 2, 3, 4, 6, 7, 8, 9, 10, 11, 12, 13, 15, 16, 17, 18, and 19 are rejected under 35 U.S.C. 103 as obvious over US Patent Publication 2023 0342903 A1, (Hasegawa) in view of US Patent Publication 2020 0226048 A1, (Kimura et al.) and US Patent Publication 2021 0040713 A1, (Yamanaka et al.).
Claim 1
Regarding Claim 1, Hasegawa teaches to compute a first degree of abnormality being a degree of abnormality of the work at each image photographing time, (“the abnormality determination unit determines the presence or absence of an abnormality with respect to each region of the input image based on the abnormality degree calculated based on the distribution peak,” par. 97) based on a processing result of each of the plurality of images (“the sensor device 205 is a sensor for acquiring an image that indicates the appearance of an abnormality detection target object that serves as the target object to be subjected to abnormality detection. The number, type, and arrangement of the sensor devices 205 may be appropriately selected depending on the abnormality detection target object,” par. 48,49).
Hasegawa does not explicitly teach all of a processing apparatus comprising: at least one memory configured to store one or more instructions; and at least one processor configured to execute the one or more instructions to: analyze a plurality of images in time series to detect a switching timing of a work; classify the plurality of images into each work based on the detected switching timing of the work; for each of the plurality of images, compute, for each work, a second degree of abnormality being a degree of abnormality of each work, based on a plurality of the first degrees of abnormality computed in association with the plurality of images classified into each work.
However, Yamanaka et al. teach analyze a plurality of images in time series to detect a switching timing of a work; ("by capturing the images of the work vehicle 1 in a time sequence, a time study of the work performed by the work vehicle 1 can be performed easily and automatically by the computer," par. 50) classify the plurality of images into each work based on the detected switching timing of the work; ("the classification of the work is determined from the images of the work vehicle 1 and the work time period," par. 50) for each of the plurality of images, ("The plurality of cameras 101 may capture images of a plurality of the work vehicles," par. 63) compute, for each work, ("images in a time sequence," par. 50) and the plurality of images classified into each work ("the classification of the work is determined from the images of the work," par. 50).
Therefore, taking the teachings of Hasegawa and Yamanaka et al. as a whole, it would have been obvious to a person having ordinary skill in the art before the time of the effective filing date of the claimed invention of the instant application to modify the abnormality analysis of images as taught by Hasegawa to use the analysis images in a time series as taught by Yamanaka et al. The suggestion/motivation for doing so would have been that, “by capturing the images of the work vehicle 1 in a time sequence, a time study of the work performed by the work vehicle 1 can be performed easily and automatically by the computer” as noted by the Yamanaka et al. disclosure in paragraph [0050], which also motivates combination because the combination would predictably have a greater accuracy as there is a reasonable expectation that abnormalities could be detected more accurately or reliably by analyzing changes in state over time rather than relying on a single static image; and/or because doing so merely combines prior art elements according to known methods to yield predictable results.
Additionally, Kimura et al. teach a processing apparatus comprising: at least one memory configured to store one or more instructions; and at least one processor configured to execute the one or more instructions to: (“In the case where a plurality of processors are used, each processor may implement one of the units, or two or more of the units. Additionally, each storage described above (the storage 121, the storage 221) may be any storage medium that is generally used,” par. 54,55) compute a second degree of abnormality being a degree of abnormality of the first work, based on a plurality of the first degrees of abnormality computed in association with the plurality of images classified into each work (“calculator 202 calculates a degree of abnormality indicating a degree of change in the statistical information of the output data (second output data) acquired by the acquisition unit 201, with respect to the statistical information of a plurality of pieces of output data (first output data) that are obtained on the basis of the reference information. As described above, the reference information is the output data itself, or the statistical information of a plurality of pieces of output data,” par. 39, wherein the output data is the first degrees of abnormality).
Therefore, taking the teachings of Hasegawa, Yamanaka et al., and Kimura et al. as a whole, it would have been obvious to a person having ordinary skill in the art before the time of the effective filing date of the claimed invention of the instant application to modify the abnormality analysis of images as taught by Hasegawa to use the analysis images in a time series as taught by Yamanaka et al. and degree of abnormality calculation as taught by Kimura et al. The suggestion/motivation for doing so would have been that, “This enables the cause of determination of the abnormality to be more easily grasped, thereby providing a reference for considering a method of correcting the learning model” as noted by the Kimura et al. disclosure in paragraph [0089], which also motivates combination because the combination would predictably have a additional utility as there is a reasonable expectation that the accuracy and interpretability of the diagnostic model would be improved, leading to more reliable, automated detection of abnormalities over time; and/or because doing so merely combines prior art elements according to known methods to yield predictable results.
The rejection system of claim 1 above applies mutatis mutandis to the corresponding limitations of method claim 10 and non-transitory storage medium claim 16 while noting that the rejection above cites to both non-transitory storage medium and method disclosures. Claims 10 and 16 are mapped below for clarity of the record and to specify any new limitations not included in claim 1.
Claim 2
Regarding Claim 2, Hasegawa, Yamanaka et al., and Kimura et al. teach the processing apparatus according to claim 1 as noted above.
Hasegawa does not explicitly teach to compute the second degree of abnormality by statistically processing the plurality of the first degrees of abnormality.
However, Kimura et al. teach to compute the second degree of abnormality by statistically processing a plurality of the first degrees of abnormality (“calculator 202 calculates a degree of abnormality indicating a degree of change in the statistical information of the output data (second output data) acquired by the acquisition unit 201, with respect to the statistical information of a plurality of pieces of output data (first output data) that are obtained on the basis of the reference information. As described above, the reference information is the output data itself, or the statistical information of a plurality of pieces of output data,” par. 39, wherein the output data is the first degrees of abnormality).
Hasegawa, Yamanaka et al., and Kimura et al. are combined as per claim 1.
Claim 3
Regarding Claim 3, Hasegawa, Yamanaka et al., and Kimura et al. teach the processing apparatus according to claim 1 as noted above.
Hasegawa does not explicitly teach to perform warning processing, when the second degree of abnormality exceeds a warning value.
However, Kimura et al. teach to perform warning processing (“output controller 204 may output that there is occurrence of an abnormality, to an output device such as a display device (such as a liquid crystal display), an audio output device (such as a speaker), or a warning indicator light that is provided in the monitoring apparatus 200 or connected to the monitoring apparatus,” par. 48), when the second degree of abnormality exceeds a warning value (“the determination unit 203 compares the calculated degree of abnormality and a threshold that is determined in advance, and determines there is occurrence of an abnormality in the learning model,” par. 47).
Hasegawa, Yamanaka et al., and Kimura et al. are combined as per claim 1.
Claim 4
Regarding Claim 4, Hasegawa, Yamanaka et al., and Kimura et al. teach the processing method according to claim 3 as noted above.
Hasegawa does not explicitly teach to determine which one of the plurality of the warning values the second degree of abnormality has exceeded, and perform the warning processing by a method associated with a determined result.
However, Kimura et al. teach to determine which one of the plurality of the warning values the second degree of abnormality has exceeded, (“The determination unit 20 determines whether an abnormality is caused in the learning model, on the basis of the degree of abnormality calculated by the calculator 202,” par. 47) and perform the warning processing by a method associated with a determined result (“output controller 204 may output that there is occurrence of an abnormality, to an output device such as a display device (such as a liquid crystal display), an audio output device (such as a speaker), or a warning indicator light that is provided in the monitoring apparatus 200 or connected to the monitoring apparatus,” par. 48).
Hasegawa, Yamanaka et al., and Kimura et al. are combined as per claim 1.
Claim 6
Regarding Claim 6, Hasegawa, Yamanaka et al., and Kimura et al. teach the processing apparatus according to claim 1 as noted above.
Hasegawa does not explicitly teach to compute a trend value of a degree of abnormality of a first work, based on the second degree of abnormality of the first work being performed a plurality of times at each time.
However, Kimura et al. teach to compute a trend value of a degree of abnormality of the first work, based on the second degree of abnormality of a first work being performed a plurality of times at each time (“A monitoring system ... detects an abnormality in a learning model by storing output data of a learning model corresponding to input data that is input in a reference period or statistical information of the output data (statistics), and by comparing statistical information of output data of the learning model corresponding to later input data and the statistical information that is obtained from stored data. This allows a change in a trend of the input data to be grasped,” par. 18).
Hasegawa, Yamanaka et al., and Kimura et al. are combined as per claim 1.
Claim 7
Regarding Claim 6, Hasegawa, Yamanaka et al., and Kimura et al. teach the processing apparatus according to claim 1 as noted above.
Hasegawa does not explicitly teach to compute the trend value by statistically processing a plurality of the second degrees of abnormality.
However, Kimura et al. teach to compute the trend value by statistically processing (“The generator 206-2 generates the distribution as illustrated in FIG. 7 by referring to the information stored in the storage,” par. 86) a plurality of the second degrees of abnormality (“In the case where the degree of abnormality exceeds the threshold (step S304: Yes), the extractor 205-2 extracts output data that is the cause of the abnormality,” par. 80).
Hasegawa, Yamanaka et al., and Kimura et al. are combined as per claim 1.
Claim 8
Regarding Claim 8, Hasegawa, Yamanaka et al., and Kimura et al. teaches the processing apparatus according to claim 6 as noted above.
Hasegawa does not explicitly teach to output the trend value of each of a plurality of works to be arranged.
However, Kimura et al. teach to output the trend value of each of a plurality of works to be arranged (“the display screen 701 is a graph indicating a transition of the degree of abnormality calculated by the calculator 202 up to a display time point. Such a graph allows grasping of period when there is an increase in the abnormal data, or in other words, a period when the accuracy of the learning model is reduced. Furthermore, a value of the degree of abnormality is displayed, and thus, the level of abnormality may be grasped,” par. 84).
Hasegawa, Yamanaka et al., and Kimura et al. are combined as per claim 1.
Claim 9
Regarding Claim 8, Hasegawa, Yamanaka et al., and Kimura et al. teaches the processing apparatus according to claim 6 as noted above.
Hasegawa does not explicitly teach to output warning information when the trend value exceeds a reference value.
However, Kimura et al. teach to output warning information (“the output controller 204 outputs information indicating occurrence of an abnormality,” par. 48) when the trend value exceeds a reference value (“the determination unit 203 compares the calculated degree of abnormality and a threshold that is determined in advance, and determines there is occurrence of an abnormality in the learning model,” par. 47).
Hasegawa, Yamanaka et al., and Kimura et al. are combined as per claim 1.
Claim 10
Regarding Claim 10, Hasegawa teaches to computing a first degree of abnormality being a degree of abnormality of the work at each image photographing time, (“the abnormality determination unit determines the presence or absence of an abnormality with respect to each region of the input image based on the abnormality degree calculated based on the distribution peak,” par. 97) based on a processing result of each of the plurality of images (“the sensor device 205 is a sensor for acquiring an image that indicates the appearance of an abnormality detection target object that serves as the target object to be subjected to abnormality detection. The number, type, and arrangement of the sensor devices 205 may be appropriately selected depending on the abnormality detection target object,” par. 48,49).
Hasegawa does not explicitly teach all of a processing method comprising, by one or more computers: analyzing a plurality of images in time series to detect a switching timing of a work; classifying the plurality of images into each work based on the detected switching timing of the work; for each of the plurality of images, computing, for each work, a second degree of abnormality being a degree of abnormality of each work, based on a plurality of the first degrees of abnormality computed in association with the plurality of images classified into each work.
However, Yamanaka et al. teach analyzing a plurality of images in time series to detect a switching timing of a work; ("by capturing the images of the work vehicle 1 in a time sequence, a time study of the work performed by the work vehicle 1 can be performed easily and automatically by the computer," par. 50) classifying the plurality of images into each work based on the detected switching timing of the work; ("the classification of the work is determined from the images of the work vehicle 1 and the work time period," par. 50) for each of the plurality of images, ("The plurality of cameras 101 may capture images of a plurality of the work vehicles," par. 63) computing, for each work, ("images in a time sequence," par. 50) and the plurality of images classified into each work ("the classification of the work is determined from the images of the work," par. 50).
Additionally, Kimura et al. teach a processing method comprising, by one or more computers: (“Each unit described above may alternatively be implemented by a processor such as a dedicated IC, or in other words, by hardware,” par. 54) computing a second degree of abnormality being a degree of abnormality of the first work, based on a plurality of the first degrees of abnormality computed in association with each of the plurality of images (“calculator 202 calculates a degree of abnormality indicating a degree of change in the statistical information of the output data (second output data) acquired by the acquisition unit 201, with respect to the statistical information of a plurality of pieces of output data (first output data) that are obtained on the basis of the reference information. As described above, the reference information is the output data itself, or the statistical information of a plurality of pieces of output data,” par. 39, wherein the output data is the first degrees of abnormality).
Hasegawa, Yamanaka et al., and Kimura et al. are combined as per claim 1.
Claim 11
Regarding Claim 11, Hasegawa, Yamanaka et al., and Kimura et al. teach the processing apparatus according to claim 10 as noted above.
Hasegawa does not explicitly teach: computes the second degree of abnormality by statistically processing the plurality of the first degrees of abnormality.
However, Kimura et al. teach: computes the second degree of abnormality by statistically processing the plurality of the first degrees of abnormality “calculator 202 calculates a degree of abnormality indicating a degree of change in the statistical information of the output data (second output data) acquired by the acquisition unit 201, with respect to the statistical information of a plurality of pieces of output data (first output data) that are obtained on the basis of the reference information. As described above, the reference information is the output data itself, or the statistical information of a plurality of pieces of output data,” par. 39, wherein the output data is the first degrees of abnormality.
Hasegawa, Yamanaka et al., and Kimura et al. are combined as per claim 1.
Claim 12
Regarding Claim 12, Hasegawa, Yamanaka et al., and Kimura et al. teach the processing apparatus according to claim 10 as noted above.
Hasegawa does not explicitly teach performing warning processing, when the second degree of abnormality exceeds a warning value.
However, Kimura et al. teach performing warning processing (“output controller 204 may output that there is occurrence of an abnormality, to an output device such as a display device (such as a liquid crystal display), an audio output device (such as a speaker), or a warning indicator light that is provided in the monitoring apparatus 200 or connected to the monitoring apparatus,” par. 48), when the second degree of abnormality exceeds a warning value (“the determination unit 203 compares the calculated degree of abnormality and a threshold that is determined in advance, and determines there is occurrence of an abnormality in the learning model,” par. 47).
Hasegawa, Yamanaka et al., and Kimura et al. are combined as per claim 1.
Claim 13
Regarding Claim 13, Hasegawa, Yamanaka et al., and Kimura et al. teach the processing method according to claim 12 as noted above.
Hasegawa does not explicitly teach: determines which one of the plurality of the warning values the second degree of abnormality has exceeded, and performs the warning processing by a method associated with a determined result.
However, Kimura et al. teach: determines which one of the plurality of the warning values the second degree of abnormality has exceeded, (“The determination unit 20 determines whether an abnormality is caused in the learning model, on the basis of the degree of abnormality calculated by the calculator 202,” par. 47) and performs the warning processing by a method associated with a determined result (“output controller 204 may output that there is occurrence of an abnormality, to an output device such as a display device (such as a liquid crystal display), an audio output device (such as a speaker), or a warning indicator light that is provided in the monitoring apparatus 200 or connected to the monitoring apparatus,” par. 48).
Hasegawa, Yamanaka et al., and Kimura et al. are combined as per claim 1.
Claim 15
Regarding Claim 15, Hasegawa, Yamanaka et al., and Kimura et al. teach the processing apparatus according to claim 10 as noted above.
Hasegawa does not explicitly teach: computes a trend value of a degree of abnormality of a first work, based on the second degree of abnormality of the first work being performed a plurality of times at each time.
However, Kimura et al. teach: computes a trend value of a degree of abnormality of a first work, based on the second degree of abnormality of the first work being performed a plurality of times at each time (“A monitoring system ... detects an abnormality in a learning model by storing output data of a learning model corresponding to input data that is input in a reference period or statistical information of the output data (statistics), and by comparing statistical information of output data of the learning model corresponding to later input data and the statistical information that is obtained from stored data. This allows a change in a trend of the input data to be grasped,” par. 18).
Hasegawa, Yamanaka et al., and Kimura et al. are combined as per claim 1.
Claim 16
Regarding Claim 16, Hasegawa teaches to compute a first degree of abnormality being a degree of abnormality of the work at each image photographing time, (“the abnormality determination unit determines the presence or absence of an abnormality with respect to each region of the input image based on the abnormality degree calculated based on the distribution peak,” par. 97) based on a processing result of each of the plurality of images (“the sensor device 205 is a sensor for acquiring an image that indicates the appearance of an abnormality detection target object that serves as the target object to be subjected to abnormality detection. The number, type, and arrangement of the sensor devices 205 may be appropriately selected depending on the abnormality detection target object,” par. 48,49).
Hasegawa does not explicitly teach all of a non-transitory storage medium storing a program causing a computer to: analyze a plurality of images in time series to detect a switching timing of a work; classify the plurality of images into each work based on the detected switching timing of the work; for each of the plurality of images, compute, for each work, a second degree of abnormality being a degree of abnormality of each work, based on a plurality of the first degrees of abnormality computed in association with the plurality of images classified into each work.
However, Yamanaka et al. teach analyze a plurality of images in time series to detect a switching timing of a work; ("by capturing the images of the work vehicle 1 in a time sequence, a time study of the work performed by the work vehicle 1 can be performed easily and automatically by the computer," par. 50) classify the plurality of images into each work based on the detected switching timing of the work; ("the classification of the work is determined from the images of the work vehicle 1 and the work time period," par. 50) for each of the plurality of images, ("The plurality of cameras 101 may capture images of a plurality of the work vehicles," par. 63) compute, for each work, ("images in a time sequence," par. 50) and the plurality of images classified into each work ("the classification of the work is determined from the images of the work," par. 50).
Additionally, Kimura et al. teach a non-transitory storage medium storing a program causing a computer to: (“each unit described above may be implemented by execution of a program by a processor such as a CPU, or in other words, by software,” par. 49) compute a second degree of abnormality being a degree of abnormality of the first work, based on a plurality of the first degrees of abnormality computed in association with each of the plurality of images (“calculator 202 calculates a degree of abnormality indicating a degree of change in the statistical information of the output data (second output data) acquired by the acquisition unit 201, with respect to the statistical information of a plurality of pieces of output data (first output data) that are obtained on the basis of the reference information. As described above, the reference information is the output data itself, or the statistical information of a plurality of pieces of output data,” par. 39, wherein the output data is the first degrees of abnormality).
Hasegawa, Yamanaka et al., and Kimura et al. are combined as per claim 1.
Claim 17
Regarding Claim 17, Hasegawa, Yamanaka et al., and Kimura et al. teach the processing apparatus according to claim 16 as noted above.
Hasegawa does not explicitly teach all of to compute the second degree of abnormality by statistically processing the plurality of the first degrees of abnormality.
However, Kimura et al. teach to compute the second degree of abnormality by statistically processing the plurality of the first degrees of abnormality “calculator 202 calculates a degree of abnormality indicating a degree of change in the statistical information of the output data (second output data) acquired by the acquisition unit 201, with respect to the statistical information of a plurality of pieces of output data (first output data) that are obtained on the basis of the reference information. As described above, the reference information is the output data itself, or the statistical information of a plurality of pieces of output data,” par. 39, wherein the output data is the first degrees of abnormality.
Hasegawa, Yamanaka et al., and Kimura et al. are combined as per claim 1.
Claim 18
Regarding Claim 18, Hasegawa, Yamanaka et al., and Kimura et al. teach the processing apparatus according to claim 16 as noted above.
Hasegawa does not explicitly teach to perform warning processing, when the second degree of abnormality exceeds a warning value.
However, Kimura et al. teach to perform warning processing (“output controller 204 may output that there is occurrence of an abnormality, to an output device such as a display device (such as a liquid crystal display), an audio output device (such as a speaker), or a warning indicator light that is provided in the monitoring apparatus 200 or connected to the monitoring apparatus,” par. 48), when the second degree of abnormality exceeds a warning value (“the determination unit 203 compares the calculated degree of abnormality and a threshold that is determined in advance, and determines there is occurrence of an abnormality in the learning model,” par. 47).
Hasegawa, Yamanaka et al., and Kimura et al. are combined as per claim 1.
Claim 19
Regarding Claim 19, Hasegawa, Yamanaka et al., and Kimura et al. teach the processing method according to claim 18 as noted above.
Hasegawa does not explicitly teach to determine which one of the plurality of the warning values the second degree of abnormality has exceeded, and perform the warning processing by a method associated with a determined result.
However, Kimura et al. teach to determine which one of the plurality of the warning values the second degree of abnormality has exceeded, (“The determination unit 20 determines whether an abnormality is caused in the learning model, on the basis of the degree of abnormality calculated by the calculator 202,” par. 47) and perform the warning processing by a method associated with a determined result (“output controller 204 may output that there is occurrence of an abnormality, to an output device such as a display device (such as a liquid crystal display), an audio output device (such as a speaker), or a warning indicator light that is provided in the monitoring apparatus 200 or connected to the monitoring apparatus,” par. 48).
Hasegawa, Yamanaka et al., and Kimura et al. are combined as per claim 1.
2nd Claim Rejections - 35 USC § 103
Claims 5, 14, and 20 are rejected under 35 U.S.C. 103 as obvious over US Patent Publication 2024 12032467 B2, (Kimura et al.), US Patent Publication 2023 0342903 A1, (Hasegawa), and US Patent Publication 2021 0040713 A1, (Yamanaka et al.) in further view of US Patent Publication 2007 0291991 A1, (Otsu et al.).
Claim 5
Regarding Claim 5, Hasegawa teaches to analyze each image (“the gradient distribution generation unit (for example, the gradient distribution generation unit 234 illustrated in FIG. 2) analyzes the gradient,” par. 82).
Kimura et al. teach to perform the warning processing in response to completion of a first work (“output controller 204 may output that there is occurrence of an abnormality, to an output device such as a display device (such as a liquid crystal display), an audio output device (such as a speaker), or a warning indicator light that is provided in the monitoring apparatus 200 or connected to the monitoring apparatus,” par. 48).
Hasegawa, Yamanaka et al., and Kimura et al. do not explicitly teach to determine a work being performed when each image is photographed.
However, Otsu et al. teach to determine a work being performed when each image is photographed (“the present invention detects the position of an object by integrating CHLAC data within a predetermined area,” par. 36 wherein the position of an object could identify the work).
It would have been obvious to a person having ordinary skill in the art before the time of the effective filing date of the claimed invention of the instant application to modify the abnormality analysis of images as taught by Hasegawa, the analysis images in a time series as taught by Yamanaka et al., and degree of abnormality calculation as taught by Kimura et al. to use the classification of objects within the image as taught by Otsu et al. The suggestion/motivation for doing so would have been that, “Even a plurality of types of objects can be classified according to the position to further improve the accuracy. The classification may be previously performed or may be automatically updated simultaneously with the abnormality determination.” as noted by the Otsu et al. disclosure in paragraph [0018], which also motivates combination because the combination would predictably have a higher accuracy as there is a reasonable expectation that implementing object classification within the known image analysis frameworks would yield more refined and accurate abnormality detection results; and/or because doing so merely combines prior art elements according to known methods to yield predictable results.
The rejection system of claim 5 above applies mutatis mutandis to the corresponding limitations of method claim 14 and non-transitory storage medium claim 20 while noting that the rejection above cites to both non-transitory storage medium and method disclosures. Claims 14 and 20 are mapped below for clarity of the record and to specify any new limitations not included in claim 5.
Claim 14
Regarding Claim 14, Hasegawa teaches: analyzes each image (“the gradient distribution generation unit (for example, the gradient distribution generation unit 234 illustrated in FIG. 2) analyzes the gradient,” par. 82).
Kimura et al. teach: performs the warning processing in response to completion of a first work (“output controller 204 may output that there is occurrence of an abnormality, to an output device such as a display device (such as a liquid crystal display), an audio output device (such as a speaker), or a warning indicator light that is provided in the monitoring apparatus 200 or connected to the monitoring apparatus,” par. 48).
Hasegawa, Yamanaka et al., and Kimura et al. do not explicitly teach to determine a work being performed when each image is photographed.
However, Otsu et al. teach: determines a work being performed when each image is photographed (“the present invention detects the position of an object by integrating CHLAC data within a predetermined area,” par. 36 wherein the position of an object could classify a work).
Hasegawa, Yamanaka et al., Kimura et al., and Otsu et al. are combined as per claim 5.
Claim 20
Regarding Claim 20, Hasegawa teaches to analyze each image (“the gradient distribution generation unit (for example, the gradient distribution generation unit 234 illustrated in FIG. 2) analyzes the gradient,” par. 82).
Kimura et al. teach to perform the warning processing in response to completion of a first work (“output controller 204 may output that there is occurrence of an abnormality, to an output device such as a display device (such as a liquid crystal display), an audio output device (such as a speaker), or a warning indicator light that is provided in the monitoring apparatus 200 or connected to the monitoring apparatus,” par. 48).
Hasegawa, Yamanaka et al., and Kimura et al. do not explicitly teach to determine a work being performed when each image is photographed.
However, Otsu et al. teach to determine a work being performed when each image is photographed (“the present invention detects the position of an object by integrating CHLAC data within a predetermined area,” par. 36 wherein the position of an object could identify the work).
Hasegawa, Yamanaka et al., Kimura et al., and Otsu et al. are combined as per claim 5.
Conclusion
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Karsten F. Lantz whose telephone number is (571)272-4564. The examiner can normally be reached Monday-Friday 8:00-4:00.
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/Karsten F. Lantz/Examiner, Art Unit 2664
Date: 4/30/2026
/JENNIFER MEHMOOD/Supervisory Patent Examiner, Art Unit 2664
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