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 Objections
Claims 1, 2, 5, and 22 are objected to because of the following informalities:
Claims 1 and 22 include the limitation “for actuating the at least door component” in line 3. The limitation is viewed as having a typographical error and will be viewed as “for actuating the at least one door component.”
Claim 2 recites the limitation "and monitors the track of the door (16)" in line 2. There is insufficient antecedent basis for this limitation in the claim. The limitation will be viewed as “and monitors a track of the door (16).”
Claim 5 uses the term “door leafs” for plural of door leaf. It is grammatically incorrect. The term will be read as “door leaves” as leaves is the proper plural for leaf.
Appropriate correction is required.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION. —The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 2, 5, 8, 13, and 17 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
The term “an integral part” in claim 2 is a relative term which renders the claim indefinite. The term “an integral part” is not defined by the claim, the specification does not provide a standard for ascertaining the requisite degree, and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention. It is unclear and indefinite what would make the camera an integral part of the safety functionality of the door. For the purposes of examination, the limitation will be read as “characterized in that the camera (40) monitors the safety and the functionality of a door (16).”
Claim 5 includes the limitation “the recording includes the door leafs (20) at least partially, in particular entirely.” It is unclear and indefinite if the claim is limiting the recording of the door leaves to the entirety of the door leaves or that the recording just partially includes the door leaves. For the purposes of examination, the limitation will be viewed as “the recording includes the door ”
Claim 8 includes the limitation “characterized in that the maintenance state includes the clearance.” It is unclear and indefinite what clearance is being included in the maintenance state. For the purposes of examination, the limitation will be viewed as “characterized in that the maintenance state includes a of a door (16).”
Claim 13 includes the limitation “in particular wherein the presence of an irregularity, like an obstacle in the track, is determined if the movement of the object is non-uniform and/or if vibrations are present.” It is unclear and indefinite if the irregularity is limited to an obstacle on the track or what other irregularities are like an obstacle on the track. For the purposes of examination, the limitation will be viewed as “in particular wherein the presence of an irregularity”
Claim 17 includes the limitation “in particular wherein the further information include weather data, like wind speed, wind direction, humidity and/or ambient temperature.” It is unclear and indefinite if the weather data includes the listed data. For the purposes of examination, the limitation will be viewed as “in particular wherein the further information wherein the weather data includes ”
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-9, 14-17, and 22 are rejected under 35 U.S.C. 103 as being unpatentable over Dreyer (US 20200013021 A1) as modified by Wegner (US 20220268084 A1).
With respect to claim 1,
Dreyer teaches,
Method for monitoring an automatic door system (14) using a monitoring unit (12), (Para. [0048] teaches “The supervise unit 4 comprise one or more sensors 6-1, 6-2, . . ., 6-m and a status management unit 12.”)
wherein the door system (14) comprises at least one door component (18), in particular a movable door leaf (20), (Para. [0010] teaches “the door system comprising at least a door leaf”)
at least one drive unit (38) for actuating the at least door component (18), (Para. [0002] teaches “A door operator typically comprises a control unit and a drive unit. The control unit controls the drive unit to move a door leaf between a closed and an open position.”)
a camera (40) (Para. [0050] teaches “According to some aspects the one or more sensors 6-1, 6-2, . . . , 6-m is one of a sound sensor, a movement sensor, voltage sensor, current sensor, resistance sensor, temperature sensor, a light sensor, a pressure sensor, a humidity sensor, a time sensor, a global positioning system (GPS), infrared sensor, a camera, a ccd-camera, a time of flight sensor and/or ultrasonic sensor.”)
and a control unit (36) for controlling the drive unit (38), (Para. [0002] teaches “The control unit controls the drive unit to move a door leaf between a closed and an open position. The control unit controls the trajectory of the door including the speed of the door, the opening angle of the door and time that the door should stay opened.”)
wherein the method comprises the following steps: capturing at least one recording by the camera (40), wherein the recording includes at least parts of the door component (18), (Para. [0049] teaches “The sensors 6-1, 6-2, . . . , 6-m are adapted to observe and provide sensor data of the operation of the door operator 1 and/or the operation of the door leaf 4. Put in another way, the sensors 6-1, 6-2, . . . , 6-m are arranged to create sensor data corresponding to the functionality of the operation of the door. By providing sensor data is meant that the sensor 6-1, 6-2, . . . , 6-m provide/create/measure/obtains/observe it's surrounding and components and create data of it that could be transferred.” Para. [0050] teaches “According to some aspects the one or more sensors 6-1, 6-2, . . . , 6-m is one of a sound sensor, a movement sensor, voltage sensor, current sensor, resistance sensor, temperature sensor, a light sensor, a pressure sensor, a humidity sensor, a time sensor, a global positioning system (GPS), infrared sensor, a camera, a ccd-camera, a time-of-flight sensor and/or ultrasonic sensor.”)
transmitting the recording to the monitoring unit (12), (Para. [0057] teaches “The status management unit 12 is configured to receive the sensor data from the one or more sensors 6-1, 6-2, . . . , 6-m.”)
determining a state of the door system (14) based on the transmitted recording by the monitoring unit (12), (Para. [0057] teaches “The status management unit 12 is configured to determine a status pattern of the operation of the door operator 1 and/or the door leaf 5 out of a plurality of status patterns of the operation of the door operator 1 and/or the door leaf 5 at least based on the received sensor data.”)
and determining a remaining useful life and/or a need for servicing the door system (14) based on the determined state of the door system (14). (Para. [0022] teaches “An advantage with the electronic device is that with help from the status pattern, the electronic device can determine the status of a certain door system and also predict e.g. when in time a certain service is needed in order to reduce the downtime of the operation of the door system.”
Dreyer does not explicitly teach,
and a control unit (36) for controlling the drive unit (38), based on the recordings generated by the camera (40).
Wegner teaches,
and a control unit (36) for controlling the drive unit (38), based on the recordings generated by the camera (40), (Abstract teaches “providing the sensor unit in the form of a radar sensor and/or a camera with an image processing unit; recording a distance of the person from the door system using the sensor unit; recording an approach speed with the sensor unit, at which the person approaches the door system; transmitting the distance and approach speed; determining a probable approach time of the person with the control unit; determining the movement parameters for moving the door leaf by the control unit and actuating the door leaf by the door actuator based on the movement parameters determined by the control unit.” )
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Dreyer with a control unit (36) for controlling the drive unit (38), based on the recordings generated by the camera (40), such as that of Wegner.
One of ordinary skill would have been motivated to modify Dreyer, because using a camera to control the drive unit would prevent the door closing on someone who is passing through the door frame preventing injuries and damage to the door. Furthermore, Dreyer teaches a sensor 8 which detects objects and persons approaching the door as seen in Para. [0045].
With respect to claim 2,
Dreyer does not explicitly teach,
Method according to claim 1, characterized in that the camera (40) is an integral part of the safety functionality of the door (16) and monitors the track of the door (16), particularly of the door leaf (20) and/or the camera (40) is a camera based opening sensor.
Wegner teaches,
the camera (40) is a camera based opening sensor, (Abstract teaches “providing the sensor unit in the form of a radar sensor and/or a camera with an image processing unit; recording a distance of the person from the door system using the sensor unit; recording an approach speed with the sensor unit, at which the person approaches the door system; transmitting the distance and approach speed; determining a probable approach time of the person with the control unit; determining the movement parameters for moving the door leaf by the control unit and actuating the door leaf by the door actuator based on the movement parameters determined by the control unit.” )
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Dreyer with the camera (40) is a camera based opening sensor such as that of Wegner.
One of ordinary skill would have been motivated to modify Dreyer, because using the camera to control the drive unit would prevent the door closing on someone who is passing through the door frame preventing injuries and damage to the door. Furthermore, Dreyer teaches a sensor 8 which detects objects and persons approaching the door as seen in Para. [0045]. Using the same sensor to perform both functions would decrease the costs of the device and streamline the design.
With respect to claim 3,
Dreyer further teaches,
Method according to claim 1, characterized in that, if a remaining useful life below a predetermined threshold and/or a need for servicing the door system (14) has been determined by the monitoring unit (12), or service of the door system (14) is initiated, for example by informing a service technician. (Para. [0005] teaches “call for service based on the actual status of the door operator and/or door leaf.” Para. [0012] teaches “A service event information can indicate a certain service that is applicable for a certain status pattern. The no service event information can give a feedback that the door is operating as normal and no additional service is needed for a certain status pattern. A breakdown information can indicate that a service is needed urgently for a certain status pattern”)
With respect to claim 4,
Dreyer further teaches,
Method according to claim 1, characterized in that, if a remaining useful life below a predetermined threshold and/or a need for servicing the door system (14) has been determined by the monitoring unit (12), or service of the door system (14) is initiated. (Para. [0005] teaches “call for service based on the actual status of the door operator and/or door leaf.” Para. [0012] teaches “A service event information can indicate a certain service that is applicable for a certain status pattern. The no service event information can give a feedback that the door is operating as normal and no additional service is needed for a certain status pattern. A breakdown information can indicate that a service is needed urgently for a certain status pattern”)
With respect to claim 5,
Dreyer further teaches,
Method according to claim 1, characterized in that the at least one door component (18) is a movable door leaf (20) and/or a protective wing (22), in particular wherein the door system (14) comprises two or more movable door leafs (20) and the recording includes the door leafs (20) at least partially, in particular entirely. (Para. [0049] teaches “The sensors 6-1, 6-2, . . . , 6-m are adapted to observe and provide sensor data of the operation of the door operator 1 and/or the operation of the door leaf” Fig. 1a shows two door leaves 5.)
With respect to claim 6,
Dreyer further teaches,
Method according to claim 1, characterized in that the state of the door system (14) is a state of wear, a maintenance state and/or the presence of an irregularity. (Para. [0058] teaches “According to some aspects, the determined status patterns is an indication of that something is wrong in the door operator 1 and/or the door leaf 5.” (i.e. irregularity)
With respect to claim 7,
Dreyer further teaches,
Method according to claim 6, characterized in that the state of wear includes damages of the door component (18) and/or parts of the door component (18), in particular damages, detachment, misplacement and/or changes to seals (26), brushes (28), profiles (30), window panes (32), protection devices (24) and/or panels (34) of the door system. (Para. [0058] teaches “According to some aspects, the determined status patterns is an indication of that something has broken in the door operator 1 and/or the door leaf 5.”)
With respect to claim 8,
Dreyer further teaches
Method according to claim 6, characterized in that the maintenance state includes the clearance, the presence of a protective wing (22), the position of the protective wing (22), the presence of a protection device (24), the position of the protection device (24), the presence of a protective profile, and/or the position of the protective profile. (Para. [0041] teaches a supervise unit 4 which is viewed as a protection device.))
With respect to claim 9,
Dreyer further teaches,
Method according to claim 6, characterized in that an irregularity is the absence of the door component (18) in the recording, vibrations during movement of the door (16) and/or of the door component (18), an emergency stop, side pressure induced door leaf bending and/or an obstacle (43) in a track of the door (16) or at the ground. (Para. [0049] teaches “Sensor data is a digital version of the things that the sensors 6-1, 6-2, . . . , 6-m has observed and could be data comprising information of sound waves, temperature, vibration, number of cycles, current, voltage, inertia, light, light waves, pictures, acceleration, friction and many other things, encoder etc. of the components and areas that the sensor 6-1, 6-2, . . . , 6-m are sensing.” (i.e. vibrations could be a part of the monitored pattern.))
With respect to claim 14,
Dreyer further teaches,
Method according to claim 1, characterized in that the door systems (14) comprises at least one sensor (42) transmitting at least one measurement value to the monitoring unit (12), wherein the state, the remaining useful life and/or the need for servicing the door system (14) is also determined based on the at least one measurement value. (Para. [0049] teaches “The sensors 6-1, 6-2, . . ., 6-m are adapted to observe and provide sensor data of the operation of the door operator 1 and/or the operation of the door leaf 4. Put in another way, the sensors 6-1, 6-2, . . ., 6-m are arranged to create sensor data corresponding to the functionality of the operation of the door. By providing sensor data is meant that the sensor 6-1, 6-2, . . ., 6-m provide/create/measure/obtains/observe it's surrounding and components and create data of it that could be transferred. According to an aspect, the one or more sensors 6-1, 6-2, . . ., 6-m are configured to provide sensor data associated with the operation of the door operator 1 and/or the at least one door leaf 5.” Para. [0012] teaches “A service event information can indicate a certain service that is applicable for a certain status pattern.” Para. [0057] teaches “The status management unit 12 is configured to receive the sensor data from the one or more sensors 6-1, 6-2, . . . , 6-m. The status management unit 12 is configured to determine a status pattern of the operation of the door operator 1 and/or the door leaf 5 out of a plurality of status patterns of the operation of the door operator 1 and/or the door leaf 5 at least based on the received sensor data.”))
With respect to claim 15,
Dreyer further teaches,
Method according to claim 14, characterized in that the sensor (42) is a vibration sensor, a acceleration sensor for determining the transversal acceleration on the door leaf (20) and/or a microphone for airborne noise and/or structure-borne noise. (Para. [0049] teaches “Sensor data is a digital version of the things that the sensors 6-1, 6-2, . . . , 6-m has observed and could be data comprising information of sound waves, temperature, vibration, number of cycles, current, voltage, inertia, light, light waves, pictures, acceleration, friction and many other things, encoder etc. of the components and areas that the sensor 6-1, 6-2, . . . , 6-m are sensing.”)
With respect to claim 16,
Dreyer further teaches,
Method according to claim 1, characterized in that the drive unit (38) and/or the control unit (36) measures at least one further measurement value and transmits the at least one further measurement value to the monitoring unit (12), wherein the at least one further measurement value is used for determining the state, the remaining useful life and/or the need for servicing the door system (14), in particular wherein the at least one further measurement values is one or more value of the following group: motor current; temperature of the motor and/or of a battery of the drive unit (38); health of the battery of the drive unit (38); voltage patterns on the motor operator of the drive unit (38); power consumption for performing a full closing cycle; change in mass inertia of the door (16); usage patterns and kinematics resulting from usage patterns; exceptional usages, in particular emergency stops; and signals of a communication bus. (Para. [0049] teaches “Sensor data is a digital version of the things that the sensors 6-1, 6-2, . . . , 6-m has observed and could be data comprising information of sound waves, temperature, vibration, number of cycles, current, voltage, inertia, light, light waves, pictures, acceleration, friction and many other things, encoder etc. of the components and areas that the sensor 6-1, 6-2, . . . , 6-m are sensing.” Para. [0057] teaches “The status management unit 12 is configured to receive the sensor data from the one or more sensors 6-1, 6-2, . . . , 6-m. The status management unit 12 is configured to determine a status pattern of the operation of the door operator 1 and/or the door leaf 5 out of a plurality of status patterns of the operation of the door operator 1 and/or the door leaf 5 at least based on the received sensor data.”)
With respect to claim 17,
Dreyer further teaches,
Method according to claim 1, characterized in that the monitoring unit (12) receives further information, wherein the information is used for determining the state, the remaining useful life and/or the need for servicing the door system (14), in particular wherein the further information include weather data, like wind speed, wind direction, humidity and/or ambient temperature; information about services performed at the door system (14); and/or information from a system of the building the door system (14) in installed in, for example a temperature of an air conditioning system of the building. (Para. [0073] teaches “The door system identification information is in an example comprising information relating to a environmental information of the door system 100a, 100b, 100c, 100d such as temperature information, average temperature information, average temperature information over long and short cycles, humidity information, and atmospheric pressure information, wind speed information, light condition information.”)
With respect to claim 22,
Dreyer teaches,
System comprising a monitoring unit (12) and an automatic door system (14) (Para. [0048] teaches “The supervise unit 4 comprise one or more sensors 6-1, 6-2, . . ., 6-m and a status management unit 12.”)
having at least one door component (18), in particular a movable door leaf (20), (Para. [0010] teaches “the door system comprising at least a door leaf”)
at least one drive unit (38) for actuating the at least door component (18), (Para. [0002] teaches “A door operator typically comprises a control unit and a drive unit. The control unit controls the drive unit to move a door leaf between a closed and an open position.”)
a camera (40) having a field of view including at least parts of the door component (18), (Para. [0050] teaches “According to some aspects the one or more sensors 6-1, 6-2, . . . , 6-m is one of a sound sensor, a movement sensor, voltage sensor, current sensor, resistance sensor, temperature sensor, a light sensor, a pressure sensor, a humidity sensor, a time sensor, a global positioning system (GPS), infrared sensor, a camera, a ccd-camera, a time of flight sensor and/or ultrasonic sensor.” Para. [0049] teaches “The sensors 6-1, 6-2, . . . , 6-m are adapted to observe and provide sensor data of the operation of the door operator 1 and/or the operation of the door leaf 4.”)
and a control unit (36) for controlling 31 the drive unit (38) (Para. [0002] teaches “The control unit controls the drive unit to move a door leaf between a closed and an open position. The control unit controls the trajectory of the door including the speed of the door, the opening angle of the door and time that the door should stay opened.”)
wherein the system (10) is configured to carry out a method according to claim 1, (see the rejection of claim 1 above)
in particular wherein the monitoring unit (12) is part of the door system (14), for example the control unit. (Fig. 1a)
Dreyer does not explicitly teach,
and a control unit (36) for controlling the drive unit (38), based on the recordings generated by the camera (40).
Wegner teaches,
and a control unit (36) for controlling the drive unit (38), based on the recordings generated by the camera (40), (Abstract teaches “providing the sensor unit in the form of a radar sensor and/or a camera with an image processing unit; recording a distance of the person from the door system using the sensor unit; recording an approach speed with the sensor unit, at which the person approaches the door system; transmitting the distance and approach speed; determining a probable approach time of the person with the control unit; determining the movement parameters for moving the door leaf by the control unit and actuating the door leaf by the door actuator based on the movement parameters determined by the control unit.” )
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Dreyer with a control unit (36) for controlling the drive unit (38), based on the recordings generated by the camera (40), such as that of Wegner.
One of ordinary skill would have been motivated to modify Dreyer, because using a camera to control the drive unit would prevent the door closing on someone who is passing through the door frame preventing injuries and damage to the door. Furthermore, Dreyer teaches a sensor 8 which detects objects and persons approaching the door as seen in Para. [0045].
Claims 10-13 and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Dreyer (US 20200013021 A1) as modified by Wegner (US 20220268084 A1) as applied to claim 1 above, and further in view of Wagner (US 20220403690 A1).
With respect to claim 10,
The combination of Dreyer and Wegner does not explicitly teach,
Method according to claim 1, characterized in that the recording is a recording of a single image, a series of images and/or a video sequence.
Wagner teaches,
characterized in that the recording is a recording of a single image, a series of images and/or a video sequence. (Para. [0207] teaches “The measurement information can thereby represent a comparison or a comparative result between a plurality of image recordings, in particular at least two image recordings, which have been recorded at different times, in particular recorded after a determined time interval.”)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the combination of Dreyer and Wegner characterized in that the recording is a recording of a single image, a series of images and/or a video sequence such as that of Wagner.
One of ordinary skill would have been motivated to modify the combination of Dreyer and Wegner, because Dreyer teaches a camera as one of the sensors as seen in Para. [0050]. A camera will record either video or images.
With respect to claim 11,
The combination of Dreyer and Wegner does not explicitly teach,
characterized in that the state of the door system (14) is determined by a comparison of the recording with a reference recording, in particular wherein the reference recording has been captured immediately after the first installation of the door system (14), after a major service of the door system (14), recorded at a previous point in time, is generated by the manufacturer and/or has been generated on the basis of many different recordings that have been captured with a certain time difference.
Wagner teaches,
characterized in that the state of the door system (14) is determined by a comparison of the recording with a reference recording, in particular wherein the reference recording has been captured immediately after the first installation of the door system (14), after a major service of the door system (14), recorded at a previous point in time, is generated by the manufacturer and/or has been generated on the basis of many different recordings that have been captured with a certain time difference. (Para. [0207] teaches “According to one embodiment of the present disclosure, it is conceivable that the at least one measuring device and/or control device has at least one optical sensor, in particular a camera and/or an infrared sensor. The measurement information can thereby represent a comparison or a comparative result between a plurality of image recordings, in particular at least two image recordings, which have been recorded at different times, in particular recorded after a determined time interval.” (i.e. recorded at a previous point in time.)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the combination of Dreyer and Wegner characterized in that the state of the door system (14) is determined by a comparison of the recording with a reference recording, in particular wherein the reference recording has been captured immediately after the first installation of the door system (14), after a major service of the door system (14), recorded at a previous point in time, is generated by the manufacturer and/or has been generated on the basis of many different recordings that have been captured with a certain time difference such as that of Wagner.
One of ordinary skill would have been motivated to modify the combination of Dreyer and Wegner, because Dreyer teaches a camera as one of the sensors as seen in Para. [0050]. A camera will record either video or images. Comparing recordings over time would help the system identify differences over time and report any wear or abnormalities as seen in Para. [0207] of Wagner.
With respect to claim 12,
The combination of Dreyer and Wegner does not explicitly teach,
Method according to claim 1, characterized in that the state of the door system (14) is determined by comparing multiple images of the recording with one another, in particular by comparing a plurality of images of a video sequence.
Wagner teaches,
characterized in that the state of the door system (14) is determined by comparing multiple images of the recording with one another, in particular by comparing a plurality of images of a video sequence. (Para. [0207] teaches “According to one embodiment of the present disclosure, it is conceivable that the at least one measuring device and/or control device has at least one optical sensor, in particular a camera and/or an infrared sensor. The measurement information can thereby represent a comparison or a comparative result between a plurality of image recordings, in particular at least two image recordings, which have been recorded at different times, in particular recorded after a determined time interval.” (i.e. plurality of images is viewed as a video sequence.)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the combination of Dreyer and Wegner characterized in that the state of the door system (14) is determined by comparing multiple images of the recording with one another, in particular by comparing a plurality of images of a video sequence such as that of Wagner.
One of ordinary skill would have been motivated to modify the combination of Dreyer and Wegner, because Dreyer teaches a camera as one of the sensors as seen in Para. [0050]. A camera will record either video or images. Comparing recordings over time would help the system identify differences over time and report any wear or abnormalities as seen in Para. [0207] of Wagner.
With respect to claim 13,
The combination of Dreyer and Wegner does not explicitly teach,
Method according to claim 1, characterized in that the state of the door system (14) is determined by the following steps: recognizing an object in the images of the recording and its coordinate within each image; determining the change of coordinates of the object over the course of at least two images, and optionally determining a vibration, movement and/or strain of the object by comparing the coordinates of the object of each image, in particular wherein the presence of an irregularity, like an obstacle in the track, is determined if the movement of the object is non-uniform and/or if vibrations are present.
Wagner teaches,
characterized in that the state of the door system (14) is determined by the following steps: recognizing an object in the images of the recording and its coordinate within each image; determining the change of coordinates of the object over the course of at least two images (Para. [0207] teaches “According to one embodiment of the present disclosure, it is conceivable that the at least one measuring device and/or control device has at least one optical sensor, in particular a camera and/or an infrared sensor. The measurement information can thereby represent a comparison or a comparative result between a plurality of image recordings, in particular at least two image recordings, which have been recorded at different times, in particular recorded after a determined time interval. Such measurement information can in particular enable detection of wear on larger parts, in particular on parts, for example on a gear wheel of a gear unit, on a rotor of the motor and/or on a toothed belt, in particular of sliding door systems and/or revolving door systems. Such detection is possible because such parts are generally dimensioned to be larger.” (i.e. the parts would have to be recognized in the image in order to determine wear of the specific part. If the part moves such as the gear, rotor, or motor the position of the part will be different in the two images.)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the combination of Dreyer and Wegner characterized in that the state of the door system (14) is determined by comparing multiple images of the recording with one another, in particular by comparing a plurality of images of a video sequence such as that of Wagner.
One of ordinary skill would have been motivated to modify the combination of Dreyer and Wegner, because Dreyer teaches a camera as one of the sensors as seen in Para. [0050]. A camera will record either video or images. Comparing recordings over time would help the system identify differences over time and report any wear or abnormalities. Furthermore, Dreyer teaches comparing differences in status patterns as detected by the sensor as seen in Para. [0094].
With respect to claim 19,
the combination of Dreyer and Wegner does not explicitly teach,
Method according to claim 1, characterized in that the determination of the state of the door system (14), of the remaining useful life and/or of a need for servicing is performed by a deterministic algorithm, a machine learning algorithm, a support vector machine and/or a trained artificial neural network of the monitoring unit (12).
Wagner teaches,
characterized in that the determination of the state of the door system (14), of the remaining useful life and/or of a need for servicing is performed by a deterministic algorithm, a machine learning algorithm, a support vector machine and/or a trained artificial neural network of the monitoring unit (12). (Para. [0093] teaches “According to the disclosure, it is therefore conceivable that, in particular in the status determination step, characteristic wear and/or damage features are advantageously extracted from the operating and/or sensor data by machine learning algorithms with the optional assistance of statistical and/or stochastic methods (for example by means of a correlation between operating parameters and anomalies).”
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the combination of Dreyer and Wegner characterized in that the determination of the state of the door system (14), of the remaining useful life and/or of a need for servicing is performed by a deterministic algorithm, a machine learning algorithm, a support vector machine and/or a trained artificial neural network of the monitoring unit (12) such as that of Wagner.
One of ordinary skill would have been motivated to modify the combination of Dreyer and Wegner, because as seen in para. [0010] of Wagner “According to the disclosure, an advantageous status monitoring can be hereby achieved which enables improved planning for maintenance work. According to the disclosure, it is in particular therefore possible to achieve efficient, predictive maintenance for a door system. Therefore, it is in particular possible to determine by means of the artificial intelligence system whether there is wear of the door system and/or of a subsystem of the door system and/or to what extent the wear has progressed.” Therefore, using artificial intelligence would help increase the efficiency of the system.
Claims 18 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Dreyer (US 20200013021 A1) as modified by Wegner (US 20220268084 A1) as applied to claim 1 above, and further in view of Youn (KR 102235728 B1).
With respect to claim 18,
The combination of Dreyer and Wegner does not explicitly teach,
Method according to claim 1, characterized in that the monitoring unit (12) determines the cause for the state of the door system (14), in particular the deterioration of the state, in particular wherein the cause may be severe and/or enduring weather conditions, vandalism, misuse, emergency stops, side pressure induced door leaf bending and/or accidents.
Youn teaches,
characterized in that the monitoring unit (12) determines the cause for the state of the door system (14), in particular the deterioration of the state, in particular wherein the cause may be severe and/or enduring weather conditions, vandalism, misuse, emergency stops, side pressure induced door leaf bending and/or accidents. (Abstract teaches “a data collection module that collects on-line data on the current state of the door and off-line data on the past state of the door; A preprocessing module preprocessing each of the on-line data and the off-line data; And performing learning by applying the pre-processed off-line data to an off-line learning model for diagnosing and predicting a fault, cause, or remaining life of the door, and the pre-processed on the learned off-line learning model.”)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the combination of Dreyer and Wegner characterized in that the monitoring unit (12) determines the cause for the state of the door system (14), in particular the deterioration of the state, in particular wherein the cause may be severe and/or enduring weather conditions, vandalism, misuse, emergency stops, side pressure induced door leaf bending and/or accidents such as that of Youn.
One of ordinary skill would have been motivated to modify the combination of Dreyer and Wegner, because determining the cause could allow the system to prevent future accidents.
With respect to claim 20,
the combination of Dreyer and Wegner does not explicitly teach,
Method according to claim 1, characterized in that the determination of the cause for the state of the door (16) is performed by a deterministic algorithm, a machine learning algorithm, a support vector machine and/or a trained artificial neural network of the monitoring unit (12).
Wagner teaches,
characterized in that the determination of the state of the door system (14), of the remaining useful life and/or of a need for servicing is performed by a deterministic algorithm, a machine learning algorithm, a support vector machine and/or a trained artificial neural network of the monitoring unit (12). (Para. [0046] teaches “The prediction model unit (312) analyzes the analysis results of the diagnosis model unit (311) through a prediction learning model, for example, a deep learning-based artificial intelligence algorithm, to predict the cause of the failure defect and the remaining lifespan of the electric door. In this case, the prediction model unit (312) utilizes the analysis results received from the diagnosis model unit (311), and predicts the cause of failure defects and remaining lifespan of the electric door by combining or modifying a Deep Neural Network (DNN), Recurrent Neural Network (RNN), Convolutional Neural Network (CNN), Logistic regression, Bayesian Neural Network, Support Vector Machine (SVM), and Support Vector Regression (SVR).”
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the combination of Dreyer and Wegner characterized in that the determination of the state of the door system (14), of the remaining useful life and/or of a need for servicing is performed by a deterministic algorithm, a machine learning algorithm, a support vector machine and/or a trained artificial neural network of the monitoring unit (12) such as that of Youn.
One of ordinary skill would have been motivated to modify the combination of Dreyer and Wegner, because using a machine learning algorithm would help increase the efficiency of the system as they can handle large amounts of data quickly and accurately.
Claim 21 is rejected under 35 U.S.C. 103 as being unpatentable over Dreyer (US 20200013021 A1), Wegner (US 20220268084 A1), and Wagner (US 20220403690 A1) as applied to claim 19 above, and further in view of Zadeh (US 20180204111 A1).
With respect to claim 21,
Dreyer does not explicitly teach,
Method according to claim 19, characterized in that the artificial neural network is trained using training data, wherein the training data comprises, for various training situations, input data of the same type and structure as the data which is fed to the artificial neural network during regular operation of the door system (14), and information about the expected correct output of the artificial neural network for the training situations;
the training comprises the following training steps: feed forward of the input data through the artificial neural network;
determining an answer output by the artificial neural network based on the input data,
determining an error between the answer output of the artificial neural network and the expected correct output of the artificial neural network;
and changing the weights of the artificial neural network by back-propagating the error through the artificial neural network,
in particular wherein the input data includes recordings generated by the camera (40), the at least one measurement value of the at least one sensor (42), of the control unit (36) and/or of the drive unit (38), and/or further information receivable by the monitoring unit (12);
wherein the information about the expected correct output includes the state of the door system (14), the remaining useful life and/or the need for servicing the door system (14) in the training situations;
and wherein the answer output includes the state of the door system (14), the remaining useful life and/or the need for servicing determined based on the input data.
Wagner teaches,
characterized in that the artificial neural network is trained using training data, wherein the training data comprises, for various training situations, input data of the same type and structure as the data which is fed to the artificial neural network during regular operation of the door system (14), and information about the expected correct output of the artificial neural network for the training situations; (Para. [0050] teaches “it is conceivable that the training data comprises training measurement information relating to the door system and/or relating to a door device comprising the door system and/or that the training data comprises training measurement information relating to a further door system and/or a further door device comprising the further door system. The training measurement information is determined in particular by measuring one or a plurality of measurement variable by means of corresponding measuring devices at the door system and/or the door device and/or at one or a plurality of further door systems and/or one or a plurality of further door devices. Additionally or alternatively, the training measurement information is determined in particular by determining one or a plurality of actuation variables by means of corresponding control devices at the door system and/or the door device and/or at one or a plurality of further door systems and/or one or a plurality of further door devices. It is conceivable that the training measurement information is determined partially or completely in a separate test phase of the door system and/or of the door device and/or of the one or of the plurality of further door systems and/or of the one or of the plurality of further door devices” Para. [0143] teaches “The regular and/or correct data are in particular part of the training data which is used for training the artificial intelligence system.”
determining an answer output by the artificial neural network based on the input data, (Para. [0092] teaches “a maintenance indication relating to the door system and/or relating to one or a plurality of the subsystems of the door system is output and/or maintenance of the door system and/or of one or of a plurality of the subsystems of the door system is carried out.” Para. [0176] teaches “It is conceivable that the artificial intelligence system of the status determination device automatically learns an initial good status of the door system and/or of the door device. It is preferably conceivable that the artificial intelligence system outputs a warning fully automatically and without a connection to a cloud when previously defined trigger signals are exceeded.”)
in particular wherein the input data includes recordings generated by the camera (40), the at least one measurement value of the at least one sensor (42), of the control unit (36) and/or of the drive unit (38), and/or further information receivable by the monitoring unit (12); (Para. [0154] teaches “It is conceivable to use different types of data to classify regular data and anomaly data. For this purpose, one item or a plurality of items of the following data is in particular conceivable: [0155] position profiles, [0156] speed profiles, [0157] acceleration profiles, [0158] motor actuation profiles, in particular a pulse width modulation, [0159] current profiles, in particular of a motor and/or overall current, [0160] voltage profiles, in particular of a power supply voltage and/or motor voltage, [0161] temperature profiles, in particular of a temperature of an environment, of the motor, of the power supply and/or of one or of a plurality of electric parts, [0162] vibration profiles, preferably noises and/or acoustics, in particular structure-borne sound profiles.”
wherein the information about the expected correct output includes the state of the door system (14), the remaining useful life and/or the need for servicing the door system (14) in the training situations; (Para. [0147] teaches “The training data, in particular the regular and/or correct data, can for example comprise data for one or a plurality of the following properties of a door system and/or of a door device: [0148] different door weights, [0149] different door dimensions and/or measurements, in particular different widths and/or heights, [0150] setting of the operating parameters, [0151] other particularities” (i.e. state of the door))
and wherein the answer output includes the state of the door system (14), the remaining useful life and/or the need for servicing determined based on the input data. (Para. [0092] teaches “a maintenance indication relating to the door system and/or relating to one or a plurality of the subsystems of the door system is output and/or maintenance of the door system and/or of one or of a plurality of the subsystems of the door system is carried out.” Para. [0176] teaches “It is conceivable that the artificial intelligence system of the status determination device automatically learns an initial good status of the door system and/or of the door device. It is preferably conceivable that the artificial intelligence system outputs a warning fully automatically and without a connection to a cloud when previously defined trigger signals are exceeded.”)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the combination of Dreyer, Wegner, and Wagner characterized in that the artificial neural network is trained using training data, wherein the training data comprises, for various training situations, input data of the same type and structure as the data which is fed to the artificial neural network during regular operation of the door system (14), and information about the expected correct output of the artificial neural network for the training situations; determining an answer output by the artificial neural network based on the input data, in particular wherein the input data includes recordings generated by the camera (40), the at least one measurement value of the at least one sensor (42), of the control unit (36) and/or of the drive unit (38), and/or further information receivable by the monitoring unit (12); wherein the information about the expected correct output includes the state of the door system (14), the remaining useful life and/or the need for servicing the door system (14) in the training situations; and wherein the answer output includes the state of the door system (14), the remaining useful life and/or the need for servicing determined based on the input data such as that of Wagner.
One of ordinary skill would have been motivated to modify the combination of Dreyer, Wegner, and Wagner, because artificial intelligence needs to be trained in order to function and therefore, it would be obvious to train the model using training data.
The combination of Dreyer, Wegner, and Wagner does not explicitly teach,
the training comprises the following training steps: feed forward of the input data through the artificial neural network;
determining an error between the answer output of the artificial neural network and the expected correct output of the artificial neural network;
and changing the weights of the artificial neural network by back-propagating the error through the artificial neural network,
Zadeh teaches,
the training comprises the following training steps: feed forward of the input data through the artificial neural network; (Para. [1352] teaches “In one embodiment, a multi-layer feed-forward neural network is used. In one embodiment, the training is done by back propagation, using the total squared error between the actual responses and desired responses for the nodes in the output layer. In one embodiment, the decision surfaces consisting of intersecting hyperplanes are implemented using a 3-layer network. FIG. 115 is an example of a system described above.”)
determining an error between the answer output of the artificial neural network and the expected correct output of the artificial neural network; and changing the weights of the artificial neural network by back-propagating the error through the artificial neural network, (Para. [2122] teaches “In one embodiment, for training networks, we use backpropagation method, to go backward to get the weights set, from the last layer, based on desired response and error backward.”
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the combination of Dreyer, Wegner, and Wagner where the training comprises the following training steps: feed forward of the input data through the artificial neural network; determining an error between the answer output of the artificial neural network and the expected correct output of the artificial neural network; and changing the weights of the artificial neural network by back-propagating the error through the artificial neural network, such as that of Wagner.
One of ordinary skill would have been motivated to modify the combination of Dreyer, Wegner, and Wagner because backpropagation algorithms minimize errors in AI models and feed forward networks are highly structured and increase efficiency. Therefore, one would be motivated to modify the combination in order to increase efficiency and reduce errors.
Conclusion
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/JOSHUA L FORRISTALL/Examiner, Art Unit 2857
/ANDREW SCHECHTER/Supervisory Patent Examiner, Art Unit 2857
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