The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
DETAILED ACTION
Status of the Claims
This is in response to the amendment filed on 1/2/26. Claims 1-20 are pending in the application.
The text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action.
Specification
Objections to the Specification have been withdrawn.
Claim Rejections - 35 USC § 101
Rejections under 35 USC § 101 to original Claim 16 has been withdrawn.
Claim Rejections - 35 USC § 102
Claims 1, 8 and 15-16 are rejected under 35 U.S.C. 102(a)(1) and 102(a)(2) as being anticipated by Wang et al. (Wang; CN 109977854 A).
Regarding Claim 1, Wang discloses a method for detecting an entrapment situation inside an elevator car (Abstract), the method comprising the steps of:
receiving an inoperable notification indicating an inoperable condition of the elevator car (Summary video analysis classification unit analyzes and classifies the pre-processed data… abnormal behavior of the passengers who occur in the elevator, such as…infant is trapped in the elevator, Claim 1);
obtaining from at least one imaging device arranged inside the elevator car real-time image data of an interior of the elevator car in response to receiving the inoperable notification (Summary video analysis classification unit analyzes and classifies the pre-processed data, and obtains whether an abnormal behavior occurs in the elevator at the current time in real time; Claim 1);
detecting one or more human objects inside the elevator car by performing a detection procedure based on the obtained real-time image data (Summary abnormal behavior of the passengers who occur in the elevator, such as the old man fainting in the elevator, the infant is trapped in the elevator, the woman is harassed) and at least one previously generated reference image (Summary step 102 Video training step: When data training is performed, 30 positive example packages and 30 negative example packages are randomly selected for modeling training; modeling training obtains the anomaly coefficient of each video, thereby obtaining the example score and negative of the positive example video segment. The example score of the example video segment, and then the corresponding loss value according to the loss function, and the network learning model is established according to the size of the loss value; The feature of the video clip is extracted by the model) ,
wherein generating of the at least one reference image comprises:
obtaining from the at least one imaging device random image data of the interior of the elevator car (Detailed Description 102 Video training step: During the data training process, 30 positive example packages and 30 negative example packages are randomly selected for modeling training, Claim 2), wherein the random image data comprises data from a plurality of images (Summary step 101 Training preparation steps…obtain the abnormal video stream and the normal video stream segmentation, wherein each video partition is a video segment of 5 seconds/segment, and each segment of the video is evenly divided into 32 segments as a package; Claim 2) captured in a plurality of random reference scenarios (Summary step 102 Video training step: When data training is performed, 30 positive example packages and 30 negative example packages are randomly selected for modeling training); and
processing the obtained random image data to generate the at least one reference image (Summary step 102 Video training step: When data training is performed, 30 positive example packages and 30 negative example packages are randomly selected for modeling training; modeling training obtains the anomaly coefficient of each video, thereby obtaining the example score and negative of the positive example video segment. The example score of the example video segment, and then the corresponding loss value according to the loss function, and the network learning model is established according to the size of the loss value; The feature of the video clip is extracted by the model; Claim 2 ); and
generating, to an elevator control system and/or to a service center, a signal indicating a detection of the entrapment situation in response to the detecting the one or more human objects (Claim 1 alarm playing unit determines, according to the result of receiving the video analysis classification unit, a warning video or a comfort video that needs to be matched in the abnormal behavior in the elevator).
Regarding Claim 8, Wang discloses an elevator computing unit for detecting an entrapment situation inside an elevator car (Abstract), the elevator computing unit comprising:
a processing unit comprising at least one processor ; and a memory unit comprising at least one memory including computer program code, wherein the at least one memory and the computer program code are configured to, with the at least one processor (Summary An abnormal behavior detection and analysis system in an elevator monitoring environment, comprising a camera, a monitoring video acquisition unit, a video analysis classification unit, a video result processing unit, an alarm playing unit, and an in-air elevator video playing unit), cause the elevator computing unit to:
receive an inoperable notification indicating an inoperable condition of the elevator car (Summary video analysis classification unit analyzes and classifies the pre-processed data; Claim 1);
obtain from at least one imaging device arranged inside the elevator car real-time image data of the interior of the elevator car in response to receiving the inoperable notification (Summary video analysis classification unit analyzes and classifies the pre-processed data, and obtains whether an abnormal behavior occurs in the elevator at the current time in real time; Claim 1); and
detect one or more human objects inside the elevator car by performing a detection procedure based on the obtained real-time image data (Summary abnormal behavior of the passengers who occur in the elevator, such as the old man fainting in the elevator, the infant is trapped in the elevator, the woman is harassed) and at least one previously generated reference image (Summary step 102 Video training step: When data training is performed, 30 positive example packages and 30 negative example packages are randomly selected for modeling training; modeling training obtains the anomaly coefficient of each video, thereby obtaining the example score and negative of the positive example video segment. The example score of the example video segment, and then the corresponding loss value according to the loss function, and the network learning model is established according to the size of the loss value; The feature of the video clip is extracted by the model),
wherein to generate the at least one reference image the elevator computing unit is configured to:
obtain from the at least one imaging device random image data of the interior of the elevator car (Detailed Description 102 Video training step: During the data training process, 30 positive example packages and 30 negative example packages are randomly selected for modeling training, Claim 2), wherein the random image data comprises data from a plurality of images (Summary step 101 Training preparation steps…obtain the abnormal video stream and the normal video stream segmentation, wherein each video partition is a video segment of 5 seconds/segment, and each segment of the video is evenly divided into 32 segments as a package; Claim 2) captured in a plurality of random reference scenarios (Summary step 102 Video training step: When data training is performed, 30 positive example packages and 30 negative example packages are randomly selected for modeling training); and
process the obtained random image data to generate the at least one reference image (Summary step 102 Video training step: When data training is performed, 30 positive example packages and 30 negative example packages are randomly selected for modeling training; modeling training obtains the anomaly coefficient of each video, thereby obtaining the example score and negative of the positive example video segment. The example score of the example video segment, and then the corresponding loss value according to the loss function, and the network learning model is established according to the size of the loss value; The feature of the video clip is extracted by the model; Claim 2); and
generate, to an elevator control system and/or to a service center, a signal indicating a detection of the entrapment situation in response to the detecting the one or more human objects (Claim 1 alarm playing unit determines, according to the result of receiving the video analysis classification unit, a warning video or a comfort video that needs to be matched in the abnormal behavior in the elevator).
Regarding Claim 15, Wang discloses a detection system for detecting an entrapment situation inside an elevator car (Abstract), the detection system comprising: at least one imaging device arranged inside the elevator car (Summary An abnormal behavior detection and analysis system in an elevator monitoring environment, comprising a camera, a monitoring video acquisition unit, a video analysis classification unit, a video result processing unit, an alarm playing unit, and an in-air elevator video playing unit); and the elevator computing unit according to claim 8.
Regarding Claim 16, Wang discloses computer executable instructions which, whenAn abnormal behavior detection and analysis system in an elevator monitoring environment, comprising a camera, a monitoring video acquisition unit, a video analysis classification unit, a video result processing unit, an alarm playing unit, and an in-air elevator video playing unit), cause the to be performed.
Claim Rejections - 35 USC § 103
Claims 2, 6, 9, 13 and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Wang.
Regarding Claims 2 and 9, Wang discloses the detection procedure comprises: an object detection phase comprising detecting one or more abnormal behavior of the passengers who occur in the elevator, such as the old man fainting in the elevator, the infant is trapped in the elevator, the woman is harassed), but doesn’t specify a separate object detection.
However, as the system obtains can discern different scenarios of humans in distress, it has to discern between inanimate objects and humans, to train a model, it would eventually obtain a video of objects not human.
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to modify Wang with using scenarios with objects in order to create a robust sample set improving accurate detection of presence in the elevator.
Regarding Claims 6, 13 and 18, Wang discloses the plurality of reference scenarios comprises at least multiple Video training step: When data training is performed, 30 positive example packages and 30 negative example packages are randomly selected for modeling training; modeling training obtains the anomaly coefficient of each video, thereby obtaining the example score and negative of the positive example video segment. The example score of the example video segment, and then the corresponding loss value according to the loss function, and the network learning model is established according to the size of the loss value; The feature of the video clip is extracted by the model). But doesn’t specify empty elevator scenarios. However, as the system obtains randomly selected video clips, to train a model, it would eventually obtain a video of an empty elevator.
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to modify Wang with using scenarios with empty elevators in order to create a robust sample set improving accurate detection of presence in the elevator.
Claims 7 and 14 are rejected under 35 U.S.C. 103 as being unpatentable over Wang in view of Lienard et al. (Lienard; US 20070058780 A1).
Regarding Claims 7 and 14, Wang discloses the processing of the obtained random image data, but doesn’t teach performing a median operation on pixel values of the plurality of images of the random image data to generate the at least one reference image.
Lienard teaches performing a median operation on pixel values of the plurality of images of the random image data to generate the at least one reference image ([0034] processing comprises a spatial filtering operation of the pixels of class 1, for example by using a non-linear filter, for example a median filter or a bilateral filter. Such filters are particularly adapted to preserve the contours).
Lienard applies a known technique (median operations) to a known device (imaging systems) ready for improvement to yield predictable results, by adding a median operation applicable from one system onto another conventional system for image processing.
The adaptation would yield predictable results, providing improved processing of images to reduce noise, as suggested by Lienard ([0006]).
Allowable Subject Matter
Claim 3-5, 10-12, 17 and 19-20 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
Response to Arguments
Applicant's arguments filed 1/2/26 have been fully considered but they are not persuasive for the following reasons:
Arguments:
Applicant argues that Claim 1 recites that generating of the at least one reference image comprises obtaining from the at least one imaging device random image data of the interior of the elevator car, wherein the random image data comprises data from a plurality of images captured in a plurality of random reference scenarios and processing the obtained random image data to generate the at least one reference image.
Wang discloses a system for determining an abnormal situation in an elevator car such as people trapped in the elevator. Wang discloses analyzing video of an elevator and modeling training. The analysis of video includes a monitoring video acquisition unit that preprocesses the video by cropping, amplifying, segmentation and forming pre-processed data, a video analysis classification unit which classifies the preprocessed data, decides whether abnormal behavior occurs, a video processing unit that determines an abnormal behavior category and an alarm playing unit and video playback unit that determines a video to play and plays the relevant video. In this entire process, there is no disclosure of using a reference image.
Wang also discloses modeling training. The Office Action refers to Step 102 as disclosing generating a reference image as recited in claim 1. Step 102 is a video training step beginning with selecting one video segment having a highest score among 60 video segments. As disclosed by Wang, "only the sample with the largest score is used for training." This video for training purposes is not a reference image. The step does not refer to generating a reference image but rather calculating a loss value and refers to convolution layers, pooling layers and convolution kernels. Moreover, even assuming the modeling training generates a reference image, the modeling training uses a single video segment, not data from a plurality of images captured in a plurality of random reference scenarios.
It is respectfully submitted that a video segment (as taught by Wang Claim 2) is made of a plurality of images, and therefore reads on the claimed reference image. The video segment selected for training, reads on the reference image. Although a video is disclosed, a video is made up of a number of images.
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 extension fee 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 date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MARK S RUSHING whose telephone number is (571)270-5876. The examiner can normally be reached on 10-6pm.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Davetta Goins can be reached at 571-272-2957. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/MARK S RUSHING/Primary Examiner, Art Unit 2689
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