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 Amendment
Applicant has submitted the following:
Claims 7-22 are pending examination;
Claims 7 and 20 are newly amended;
Claims 21 and 22 are newly added; and
Claims 1-6 remain cancelled.
Response to Arguments
Applicant's arguments filed 07/15/2025 have been fully considered but they are not persuasive.
Applicant amended claim 20 to remove the recitation of “the weighing conveyor” in order to overcome the rejection under 35 USC 112(b). The rejection under 35 USC 112(b) for claim 20 is withdrawn.
Applicant argues that newly amended independent claim 7 is not taught by the prior art.
Examiner respectfully disagrees. As cited in previous Office Action, Yasutomi in view of Memsense and Brandorff teach the previously recited limitations of claim 7 and the newly added limitations:
“determine a correlation (Brandorff: Fig. 8, curves A and B) between a) a weight waveform, representing the weight of the test object generated by the weighing mechanism, and the shape thereof (Brandorff: Fig. 8, curve A), and b) a frequency and an amplitude of the detected acceleration (Brandorff: Fig. 8, curve B) and angular velocity (Yasutomi: VI.D “To distinguish the event of passage through a joint and other events, the angular velocities obtained with the experimental setup were analyzed”) detected by the motion sensor of the test object; and
determine the proportion of how much factors attributed to the transfer of the test object from the upstream section to the weighing conveyor (Yasutomi: Fig. 13, relation between speed and tilt angle; Brandorff: col 8 lines 57-60, “To reduce the effects of such vibrational noise, an accelerometer (not shown) is preferably mounted inside weighing apparatus 100 to measure the amount of vibration transmitted to the weighing apparatus.”, Equation 8) affect the weighing accuracy (Brandorff: Fig. 5; col 5 lines 23-28 “The two-line least-squares fit analysis uses the load cell signals as the package approaches and leaves the tip point to reconstruct what the signal at the tip point would have looked like in the absence of noise. As a result, the sensitivity to noise is reduced and the accuracy improved.”; Fig. 9, “raw load cell signal” and “adjusted load cell signal”). Therefore, newly amended claim 7 remains rejected under 35 USC 103.
Applicant argues that newly amended independent claim 20 is not taught by the prior art. Specifically, Applicant argues that the prior art does not teach the following:
the processor is further configured to determine the inspection accuracy of the goods inspection device based on the data from the environmental diagnostic sensor that measures a parameter of the ambient environment around the test object, and includes the temperature sensor, the humidity sensor, the air pressure sensor, the wind velocity sensor, the microphone, or the magnetism detector, thereby
determining the inspection accuracy based on data from the temperature sensor, the humidity sensor, the air pressure sensor, the wind velocity sensor, the microphone, or the magnetism detector, and
the three axes of the motion sensor attached to the test object are different from the three axes of the motion sensor when the motion sensor is attached to the inspection object, and when the motion sensor is attached to the inspection object, the processor comprises an axis corrector configured to identify from the acquired data the direction of the axes of the motion sensor attached to the inspection object, and correct the acquired data acquired from the motion sensor based on the identified directions of the three axes of the motion sensor attached to the inspection object, and tilt detection data of the acquired data from the motion sensor attached to the test object so as to correct the diagnosis of the inspection device based on the acquired data from the motion sensor.
Examiner respectfully disagrees.
Regarding Feature i), previously cited Yasutomi teaches determine the inspection accuracy (VIII.A “To classify the event as a step, thresholds were defined empirically. However, instead of the threshold, machine learning algorithms for pattern recognition such as Support Vector Machines (SVM) may be used for the detection accuracy, currently of about 90%, to 100% which makes the inspection system more robust.”) of the goods inspection device based on the data from the environmental diagnostic sensor that measures a parameter of the ambient environment around the test object (VIII.B “Those magnetic fields, however, may be used as key features for the localization of the inspection device along the belt line, which may contribute to the identification of the joints.”).
Regarding Feature ii), previously cited Yasutomi teaches determining the inspection accuracy (VIII.A “To classify the event as a step, thresholds were defined empirically. However, instead of the threshold, machine learning algorithms for pattern recognition such as Support Vector Machines (SVM) may be used for the detection accuracy, currently of about 90%, to 100% which makes the inspection system more robust.”) based on data from the temperature sensor, the humidity sensor, the air pressure sensor, the wind velocity sensor, the microphone, or the magnetism detector (VIII.B “Those magnetic fields, however, may be used as key features for the localization of the inspection device along the belt line, which may contribute to the identification of the joints.”). The inspection accuracy, being based on measurements of localization of the object by environmental sensor (e.g. magnetic sensor), is based on data from the magnetism detector.
Regarding Feature iii), previously cited Yasutomi teaches the “axis corrector” that corrects for the differences in orientation between the test object and inspection object (VI.C “A common approach to estimate the progressing direction would be to use sensor fusion algorithms to keep track of the heading of the inspection device at all times. Those algorithms involve the integration of the angular velocity from the gyroscope and the correction of its errors with the earth's magnetism from the magnetometer”),
Therefore, independent claims 7 and 20 are rejected under 35 USC 103.
Dependent claims remain rejected under 35 USC 103. Newly added claims 21 and 22 recite similar limitations of the newly amended limitations of claim 20, and are rejected for similar reasons (see detailed action for claim rejections under 35 USC 103).
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.
Claim(s) 7-12, 16-17, and 21-22 is/are rejected under 35 U.S.C. 103 as being unpatentable over Yasutomi (A. Y. Yasutomi, H. Enoki, S. Yamaguchi and K. Tamura, "Inspection System for Automatic Measurement of Level Differences in Belt Conveyors Using Inertial Measurement Unit," 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems ( IROS), Madrid, Spain, 2018, pp. 6155-6161, previously cited) in view of MEMSENSE (MEMSENSE, MS-IMU 3030 Product Specification & User Guide, Feb. 2, 2018, previously cited, hereafter “Memsense”, previously cited), and Brandorff (US 5561274 A, previously cited)
Regarding claim 7, Yasutomi teaches An inspection device that inspects an inspection object (Fig. 2, Inspection Device) carried on a conveyor (Fig. 3, Experimental setup) comprising:
a goods inspection device (Fig. 3) having a communication interface (Table I: Communication: Wireless (2.4 GHz band 16 ch) and Micro-USB; IV.A: “wireless communication capacity”; “wireless communication allows remote monitoring and control.”) configured to acquire data of the acceleration and angular velocity of a test object when the test object is carried on the (Inertial measurement units (IMU), Table I: Accelerometers, Gyroscopes, N of Axes: 3’; VI.B. “To detect events in the belt conveyor such as the passage through the joints of the belt lines, it was searched for positive and negative peaks in the angular velocities obtained in the X and Y directions” and “a classification of the joint was necessary to choose the correct equation. To do so, it was integrated the angular velocity in the Z axis from 0.2 seconds before the first peak until 0.8 seconds after, as in (10), to estimate the Z-axis rotation angle during the passage through the joint.”). Accelerometers with 3 axes are the motion sensors to detect acceleration with respect to respective direction of three-dimensional axes. Gyroscopes with 3 axes are the motion sensors to detect angular velocity with respect to respective direction of three-dimensional axes, and
a processor configured to receive the acquired data from the communication interface, wherein the processor is also configured to diagnose the (PC, VII.A: The inspection device was passed 10 times through the system and the data obtained were processed after the experiments with a software which included the inspection method described in Section VI.), wherein the test object includes:
a motion sensor configured to detect acceleration and angular velocity of the test object in three directions of three-dimensional axes thereof (Inertial measurement units (IMU), Table I: Accelerometers, Gyroscopes, N of Axes: 3’; VI.B. “To detect events in the belt conveyor such as the passage through the joints of the belt lines, it was searched for positive and negative peaks in the angular velocities obtained in the X and Y directions” and “a classification of the joint was necessary to choose the correct equation. To do so, it was integrated the angular velocity in the Z axis from 0.2 seconds before the first peak until 0.8 seconds after, as in (10), to estimate the Z-axis rotation angle during the passage through the joint.”). Accelerometers with 3 axes are the motion sensors to detect acceleration with respect to respective direction of three-dimensional axes. Gyroscopes with 3 axes are the motion sensors to detect angular velocity with respect to respective direction of three-dimensional axes;
a support member configured to support the motion sensor (IV.B: “The inspection device developed consisted on the IMU inserted inside a packaging which enabled the IMU conveyance (Fig. 2). The packaging was composed by a jig and a container. The jig enclosed the IMU circuit and the battery, and had openings that enabled access to a connector of the IMU for battery charging and USB communication to a PC.”). The packaging with jig and container is the support member. Figure 2 of Yasutomi is reproduced below having a bottom surface (Fig. 2, wherein the “container” has a bottom surface) (Figs. 1, 4a, and 13; IV.B: “The container accommodated the jig with the IMU and had a diameter slightly smaller than the width of the belt conveyor. It was made of a plastic material which would grip to the belt while sliding through the walls of the belt conveyor.”). One of ordinary skill in the art would recognize that the container has a bottom surface, even if not directly seen in the figure, as it is made to grip the belt or carrying surface, shown in contact below the bottom surface in Fig. 4a. of the (belts of Figs. 3, 4 and 11) the inspection object (Fig. 2, Inspection Device) to be inspected to the goods inspection device(Fig. 3, Inspection System); and
an external interface configured to output data including the acceleration and the angular velocity of the test object to the communication interface of the goods inspection device (Fig. 2, connector; IV.A: “wireless communication capacity”; “wireless communication allows remote monitoring and control.”; IV.B: “The jig enclosed the IMU circuit and the battery, and had openings that enabled access to a connector of the IMU for battery charging and USB communication to a PC.”; VII.A: “The inspection device was passed 10 times through the system and the data obtained were processed after the experiments with a software which included the inspection method described in Section VI.”). The connector and wireless communication capacity are the external interface unit. One of ordinary skill in the art would recognize that the communication, either by USB connector or wireless communication, for monitoring, data acquirement, and processing would be outputting data including acceleration and angular velocity.
wherein the processor is further configured to determine the extent to which the flatness and level of the upstream section (VI.A “Since the angular velocity of the IMU would present values different to zero even when static, an algorithm was necessary to filter this zero level offset. A common approach would be to take the average of the data while the IMU is static and subtract it from the measured data”), and the shaking of the test object upon transfer from the upstream section to the (shown in Figs. 7 and 10 for Joints, indicated by fluctuations following exponential curve fit) (Inertial measurement units (IMU), Table I: Accelerometers, Gyroscopes, N of Axes: 3, Communication’; VI.B. “To detect events in the belt conveyor such as the passage through the joints of the belt lines, it was searched for positive and negative peaks in the angular velocities obtained in the X and Y directions”; Fig. 10; VI.D “To distinguish the event of passage through a joint and other events, the angular velocities obtained with the experimental setup were analyzed”), and
the processor is further configured to
determine (Fig. 10), and b) a frequency and an amplitude of the detected acceleration (III. “acquire the acceleration which may contribute to event detections (e.g. belt start/stop, object entanglement).”) and angular velocity detected by the motion sensor of the test object (Fig. 10), and
determine the proportion of how much factors attributed to the transfer of the test object from the upstream section to the (Equations 7-9; Fig. 13, relation between speed and tilt angle) affect the (VIII.A “the peaks and the time constants of exponential curves were fitted before the peaks as features to detect and classify events. To classify the event as a step, thresholds were defined empirically. However, instead of the threshold, machine learning algorithms for pattern recognition such as Support Vector Machines (SVM) may be used for the event classification. This approach could increase the event detection accuracy, currently of about 90%, to 100% which makes the inspection system more robust.”).
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Yasutomi does not teach a goods inspection device that measures a weight of the inspection object having a weighing conveyor usable as a weighing platform, wherein the weighing conveyor is configured to carry the inspection object from an upstream section, a weighing mechanism placed under the weighing conveyor,
a support member configured to support the motion sensor having a top surface, and the top surface of the support member includes an identifier including a single arrow mark identifying a normal orientation of the test object on the conveyor in the conveying direction and configured to be aligned with the conveying direction of the carrying surface of the conveyor; and
wherein the processor is further configured to determine the extent to which the flatness and level of the upstream section, and the shaking of the test object upon transfer from the upstream section to the weighing conveyor affect the weighing accuracy of the weighing mechanism, based on the data of the acceleration and angular velocity of the test object acquired from the test object via the communication interface, when the test object travels from the upstream section to the weighing conveyor, and
the processor is further configured to
determine a correlation between a) a weight waveform, and the shape thereof, and b) a frequency and an amplitude of the detected acceleration and angular velocity detected by the motion sensor of the test object , and
determine the proportion of how much factors attributed to the transfer of the test object from the upstream section to the weighing conveyor affect the weighing accuracy.
Memsense teaches an analogous motion sensing device, comprising a support member configured to support the motion sensor (Figs. 1 and 3; 3.1 “The MS-IMU3030 is contained in a 6061-T6 aluminum housing anodized to MIL-A-8625 standards.”) having a top surface (Figs. 3 and 4, 3.2 “As an example, with the IMU pictured in Figure 2, if the Z axis is pointed straight UP away from the earth”), and the top surface of the support member includes an identifier including a single arrow mark identifying a normal orientation of the test object on the conveyor in the conveying direction and configured to be aligned with the conveying direction of the carrying surface of the conveyor(Fig. 4, reproduced with annotation below). One of ordinary skill in the art would recognize that the top surface has an identifier including an arrow mark (shown in the annotated rectangle). As the identifier is aligned with the measuring axes, one of ordinary skill in the art would recognize that the identifier is configured to be aligned with the conveying direction.
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Even if Memsense does not teach a single arrow mark identifying a normal orientation of the test object on the conveyor in the conveying direction, it would
have been obvious to one having ordinary skill in the art at the time the invention was made to omit arrows not identifying a normal orientation of the object, since it has been held that omission of an element and its function in a combination where the remaining elements perform the same functions as before involves only routine skill in the art. In re Karlson, 136 USPQ 184.
It would be obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the device of Yasutomi to include the top surface with identifier including an arrow mark of Memsense because it would yield predictable and advantageous results of further containing the motion sensors, thereby advantageously protecting it from damage, and by enabling alignment with the conveying system, thereby giving more consistent and accurate measurements by aligning the axes of measurement with the expected axes of motion (Yasutomi: “Measurements were made 10 times for each step, and in every measurement the inspection device was placed in the upstream belt line with its local Y axis toward the progressing direction, which enabled the calculation of the tilt angles with only the angular velocities in the X axis”).
Yasutomi in view of Memsense does not teach a goods inspection device that measures a weight of the inspection object having a weighing conveyor usable as a weighing platform, wherein the weighing conveyor is configured to carry the inspection object from an upstream section, a weighing mechanism placed under the weighing conveyor, and
wherein the processor is further configured to determine the extent to which the flatness and level of the upstream section, and the shaking of the test object upon transfer from the upstream section to the weighing conveyor affect the weighing accuracy of the weighing mechanism, based on the data of the acceleration and angular velocity of the test object acquired from the test object via the communication interface, when the test object travels from the upstream section to the weighing conveyor, and
the processor is further configured to determine a correlation between a) a weight waveform, and the shape thereof, and b) a frequency and an amplitude of the detected acceleration and angular velocity detected by the motion sensor of the test object , and
determine the proportion of how much factors attributed to the transfer of the test object from the upstream section to the weighing conveyor affect the weighing accuracy.
Brandorff teaches an analogous device for measuring objects on a conveyor system, comprising:
a goods inspection device (weighing apparatus 100) that measures a weight of the inspection object (package 112) having a weighing conveyor (conveyor 110) usable as a weighing platform (col 2 lines 61-64, “The downstream end of inclined conveyor 110 is supported by load sensor 138, which generates signals representative of the weight of objects supported by inclined conveyor 110.”), a weighing mechanism placed under the weighing conveyor (Fig. 1, load sensor 138), and
wherein the processor (col 3 lines 35-40, “Load sensor 138, which supports the downstream end of inclined conveyor 110, may include load cell 140, which generates electrical signals representing the weight supported by load sensor 138. These electrical signals are transmitted to a signal processor (not shown) which converts the signals into weight measurements.”) is further configured to determine the extent to which the flatness and level of the upstream section (col 7 line 64 – col 8 line 2, “an inclinometer (not shown) is preferably mounted to the weighing apparatus to measure the angle of inclination of the mounting surface (i.e., the deviation of load cell orientation from vertical). The measured angle of inclination may then be used to compensate the load cell signals”; Equation 7), and the shaking of the test object upon transfer from the upstream section to the weighing conveyor (col 1 lines 30-32, “The apparatus comprises a conveyor for moving the object from an upstream end toward a downstream end of the conveyor.”; col 8 lines 57-60, “To reduce the effects of such vibrational noise, an accelerometer (not shown) is preferably mounted inside weighing apparatus 100 to measure the amount of vibration transmitted to the weighing apparatus.”; Equation 8) affect the weighing accuracy of the weighing mechanism (Fig. 9, corrected load cell data) when the test object travels from the upstream section to the weighing conveyor (col 2 line 61 – col 3 line 9, “The downstream end of inclined conveyor 110 is supported by load sensor 138, which generates signals representative of the weight of objects supported by inclined conveyor 110. As package 112 passes over the downstream end of inclined conveyor 110, package 112 tips from inclined conveyor 110 onto receiving conveyor 120. When package 112 is at this tip point, the entire weight of package 112 is supported by the downstream end of inclined conveyor 110. Signals generated by load sensor 138 when the package approaches, tips over, and leaves the tip point are used to determine the weight of package 112. Package 112 tips onto receiving conveyor 120 and proceeds downstream. Receiving conveyor 120 is negatively inclined, having its downstream end lower than its upstream end. Package 112 forms no part of the present invention.”). One of ordinary skill in the extent the flatness, level and shaking have on the accuracy of the weighing is apparent, and therefore determined, in Fig. 9, as shown as the comparison between the measured and corrected data. and
the processor is further configured to determine a correlation (Fig. 8, curves A and B) between a) a weight waveform, and the shape thereof (Fig. 8, curve A), and b) a frequency and an amplitude of the detected acceleration detected by the motion sensor of the test object (Fig. 8, curve B), and
determine the proportion of how much factors attributed to the transfer of the test object from the upstream section to the weighing conveyor (col 8 lines 57-60, “To reduce the effects of such vibrational noise, an accelerometer (not shown) is preferably mounted inside weighing apparatus 100 to measure the amount of vibration transmitted to the weighing apparatus.”, Equation 8) affect the weighing accuracy (Fig. 5; col 5 lines 23-28 “The two-line least-squares fit analysis uses the load cell signals as the package approaches and leaves the tip point to reconstruct what the signal at the tip point would have looked like in the absence of noise. As a result, the sensitivity to noise is reduced and the accuracy improved.”; Fig. 9, “raw load cell signal” and “adjusted load cell signal”). The comparison of the raw load cell to the adjusted load cell, wherein fit analysis is applied, resulting in is the determination of the proportion of factors attributed to the transfer of the test object (e.g. acceleration, angle) on the weighing accuracy.
It would be obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the inspection device Yasutomi in view of Memsense to include the weighing conveyor of Brandorff because it would yield predictable and advantageous results of weighing the inspection object as it transits the conveying system. The use of weighing platforms in conveying devices is well understood in the art and yields predictable results. The inclusion of flatness and shaking data in determining the accuracy of the weighing would yield advantageous results of increasing the accuracy of measuring the weight while the object travels on the conveyor (Brandorff: col 5 lines 20-28, “If the weight were to be determined by simply measuring the load cell signal at the instant the package tips (as with a peak detector), the accuracy would be very dependent on the noise present at that instant. The two-line least-squares fit analysis uses the load cell signals as the package approaches and leaves the tip point to reconstruct what the signal at the tip point would have looked like in the absence of noise. As a result, the sensitivity to noise is reduced and the accuracy improved.”). As Brandorff uses accelerometers and similar sensors, It would be obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to substitute the sensors of Yasutomi in view of Memsense in measuring the acceleration and angular velocity to determine the flatness, level, and shaking. Similarly, the utilization of the weighing waveform and acceleration information of Brandorff in determining the proportion of how much factors attributed to the transfer affect accuracy of Yasutomi in view of Memsense would be obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, because it is using well known techniques to yield predictable results.
Regarding claim 8, Yasutomi in view of Memsense and Brandorff teaches The inspection device according to claim 7, wherein the processor is further configured to acquire the data stored in a data storer in the test object via a medium (Yasutomi: Table I: Communication: Wireless (2.4 GHz band 16 ch) and Micro-USB; Memory size: 32 MB). One of ordinary skill in the art would recognize that the Wireless communication via radio waves and the micro-USB are each medium.
Regarding claim 9, Yasutomi in view of Memsense and Brandorff teaches The inspection device according to claim 7, wherein the processor is further configured to acquire the data from a communicator of the test object via radio transmission (Yasutomi: Table I: Communication: Wireless (2.4 GHz band 16 ch); III.A. “Large battery capacity allows usage during long maintenance periods and wireless communication allows remote monitoring and control.”). One of ordinary skill in the art would recognize that wireless communication in the 2.4 GHz band would be radio transmissions.
Regarding claim 10, Yasutomi in view of Memsense and Brandorff teaches The inspection device according to claim 7, further comprising upstream and downstream conveyors configured to carry the inspection object (Yasutomi: Fig. 3, belt lines), wherein the processor is further configured to determine whether or not a deviation in the data of the acceleration and angular velocity of the test object falls within a predetermined range (Yasutomi: Figs. 10 and 11, Joints 1-3; III. “acquire the acceleration which may contribute to event detections (e.g. belt start/stop, object entanglement).” ; VI.B: “To detect events in the belt conveyor such as the passage through the joints of the belt lines, it was searched for positive and negative peaks in the angular velocities obtained in the X and Y directions. The minimum height for peak detection was set to ±15°/s and the minimum distance between peaks was set to 0.1 seconds. The former threshold was chosen to be slightly higher than the angular velocity during regular transport (<5°/s) and the latter to detect at least 3 peaks within an event (minimum length 0.3 s) in order to avoid missing it.”; VIII.A. “To classify the event as a step, thresholds were defined empirically. However, instead of the threshold, machine learning algorithms for pattern recognition such as Support Vector Machines (SVM) may be used for the event classification.”) for a period of time when the test object is transferred between the weighing conveyor and a section of the each of upstream and downstream conveyors (Yasutomi: Fig. 9; VI.D: “An exponential curve y = Aet/τ was then fitted to an interval between the peak and 0.2 s before the peak, and it was identified that the time constants τ of those curves were within τmin = 14.3*10^-3 to τmax = 50*10^-3 seconds. This interval was used as the main feature to classify the signal as joint of the belt lines, and with that, it was possible to identify the joints with more than 90% accuracy.”; Figs. 3, 4a, 10, and 11; Brandorff: Fig. 1, conveyors 110 and 120 ). Yasutomi teaches a plurality of conveyor belts, arranged in upstream and downstream configuration, wherein each transfers the object from a section of an upstream conveyor to a section of a downstream conveyor. Brandorff teaches an upstream and downstream end of conveyor and receiving conveyor. Therefore, Yasutomi in view of Memsense and Brandorff teaches determining the deviation for the time periods for each of the transfers between conveyors.
Regarding claim 11, Yasutomi in view of Memsense and Brandorff teaches The inspection device according to claim 7, wherein the processor is further configured to determine whether or not a deviation in the output data of the acceleration and angular velocity of the test object falls within a predetermined range (Yasutomi: Fig. 10, Joints 1-3; III. “acquire the acceleration which may contribute to event detections (e.g. belt start/stop, object entanglement).” ; VI.B: “To detect events in the belt conveyor such as the passage through the joints of the belt lines, it was searched for positive and negative peaks in the angular velocities obtained in the X and Y directions. The minimum height for peak detection was set to ±15°/s and the minimum distance between peaks was set to 0.1 seconds. The former threshold was chosen to be slightly higher than the angular velocity during regular transport (<5°/s) and the latter to detect at least 3 peaks within an event (minimum length 0.3 s) in order to avoid missing it.”; VIII.A. “To classify the event as a step, thresholds were defined empirically. However, instead of the threshold, machine learning algorithms for pattern recognition such as Support Vector Machines (SVM) may be used for the event classification.”) in an inspection region (Yasutomi: Fig. 4a gap; Joints 1-3) obtained when a masterwork with the motion sensor installed therein (Yasutomi: IX. Even though this system was used for a considerably small however complex belt conveyors, it is possible to extend its usage to a larger and more complex belt conveyor, as long as an inspection device at the size of the object transported is developed or the inspection device developed is incorporated in the transported object.) is carried as the test object falls within a predefined range (Yasutomi: VI.F: The angle was compared to a predetermined threshold γt = 10° and if the angle calculated was greater than that, the joint was considered as orthogonal.; Equation 10). The transported object with inspection device incorporated therein is the masterwork.
Regarding claim 12, Yasutomi in view of Memsense and Brandorff teaches The inspection device according to claim 7, further comprising a data storer configured to store diagnosis results (Yasutomi: PC, VI.G: Fig. 9 shows the flow chart of the method developed for the inspection, which considers all the data processing explained in Section VI. This method together with the inspection device, comprises the inspection system. VII.A: The inspection device was passed 10 times through the system and the data obtained were processed after the experiments with a software which included the inspection method described in Section VI.) and wherein the processor is further configured to perform predictive maintenance function for monitoring transition of diagnosis results stored in the storage unit and presuming performance degradation or deterioration (Yasutomi: I: “When installing or conducting maintenance of those belt conveyors, it is necessary to inspect the steps (i.e. differences in level) at the joints of the belt lines. Steps higher than normal cause excessive oscillations of the materials transported, which in the case of fragile materials or liquid containers, may lead to damage, spillage, particle degradation or cross contamination; In this paper, it is presented a cost-effective system to promptly inspect all the steps at the joints of the belt lines at once.”; VIII.A: “In this study, the peaks and the time constants of exponential curves were fitted before the peaks as features to detect and classify events. To classify the event as a step, thresholds were defined empirically. However, instead of the threshold, machine learning algorithms for pattern recognition such as Support Vector Machines (SVM) may be used for the event classification.”; IX. “With this system, it is possible to measure all the steps of a complex belt conveyor system automatically and at once. By knowing the height of the steps, it is possible to conduct the maintenance of only the points where the steps are high, reducing the maintenance workload and time.”). One of ordinary skill in the art would recognize the PC of the inspection system must contain storage to perform the method, acquire the data from the test object, perform the calculations, and output the results and graphs. One of ordinary skill in the art would recognize that the PC performing the method, through repeated runs through the system, to inspect the steps of the belt system, wherein thresholds determine if steps are excessive, and therefore have degraded or deteriorated performance, and those thresholds may be determined by machine learning algorithms, would be a function of predicting maintenance of the belt system and would presume deterioration of the system.
Regarding claim 16, Yasutomi in view of Memsense and Brandorff teaches The inspection device according to claim 7, wherein the carrying surface of the weighing conveyor has two portions separated by a gap in the conveying direction of the carrying surface of the two portions (Yasutomi: Figs. 4a and 4b, “gap”), and wherein the test object further comprises a processor configured to receive data from the external interface (Yasutomi: IV.B: The jig enclosed the IMU circuit and the battery, and had openings that enabled access to a connector of the IMU for battery charging and USB communication to a PC.; wireless communication; I: This system involves the acquirement of data of the belt conveyor with an IMU, and the processing of this data with original algorithms for zero offset filtering, sensor progressing direction detection, step event detection and step height calculation.) and to determine whether the gap between the two conveyor portions is properly adjusted (Yasutomi: V.A. “The experimental setup built to develop the inspection system is shown in Fig. 3. The setup allowed the transport of the device from one belt line to another (Fig. 4), which enabled the collection of data to find the tilt angles and relate it to the steps. In the setup, the step and the gap were set by using re-configurable supports for the belt lines, which were consisted of a XYZ stage, a θ joint and a spacer at one end (Fig. 5a), and a XYZ stage, a θ joint and a slider at the other end (Fig. 5b).”; V.B. “Experiments were carried out with steps of 0 to 3 mm and gaps of 3 mm. Steps and gaps out of those ranges would cause the tumbling of the inspection device, and for that reason, were not included in the conditions. The belt speed was kept at 0.15 m/s during the experiments and both straight and orthogonal configurations were used. Measurements were made 10 times for each step, and in every measurement the inspection device was placed in the upstream belt line with its local Y axis toward the progressing direction, which enabled the calculation of the tilt angles with only the angular velocities in the X axis.”; Figs. 1, 4a and 13; II. “The tilts, rotations and contacts during the transition between belt lines are the effects of high steps at the joint of the belt lines, which must be avoided to prevent the problems introduced in Section I.”). One of ordinary skill in the art would recognize that improperly adjusted gaps would have similar measured effect on such measured values as tilt as improperly adjusted step. As shown in Figs. 1 and 13, steps and gaps produce tilt of the conveyed object at the joint, and it is the tilt of the conveyed object that determines if the belts require maintenance. Therefore, in determining that a joint requires adjustment from the measured values, the test object would also determine that a gap is or is not properly adjusted.
Regarding claim 17, Yasutomi in view of Memsense and Brandorff teaches The inspection device according to claim 7, wherein the carrying surface of the weighing conveyor has two portions separated by a gap in the conveying direction of the carrying surface of the two portions (Yasutomi: Figs. 4a and 4b, “gap”), and the motion sensor is positioned closer to a bottom surface than a top surface of the test object (Yasutomi: Fig. 2, Sensor) and is configured to detect the impact of the test object on the carrying surface (Yasutomi: II. “When there is a considerably high step at the joint of the belt lines, the object transported tilts and contacts abruptly the downstream belt line during the passage. Depending on the conveyor speed and the step height, the object can further tilt at the same direction after the contact.”; VI.D. “In these signals, it was observed that the angular velocity increased exponentially due to the initial tilting of the inspection device, and then had a sharp drop which is due to the contact to the downstream belt line.”). One of ordinary skill in the art would recognize that the sensor in Fig. 2 is positioned closer to the bottom surface (“container”) than the top, and that the determination of the contact with the downstream belt is the detection of the impact of the test object on the carrying surface.,
or closer to the top surface of the test object than the bottom surface of the test object and is configured to detect shaking of the test object on the carrying surface (Yasutomi: I. “When installing or conducting maintenance of those belt conveyors, it is necessary to inspect the steps (i.e. differences in level) at the joints of the belt lines. Steps higher than normal cause excessive oscillations of the materials transported, which in the case of fragile materials or liquid containers, may lead to damage, spillage, particle degradation or cross contamination”; VII.B “Other peaks were also found; however, they were not considered as an step event since their time constant were not in the interval that characterizes it as a peak of a step.”; Figs. 7 and 10; VII.C. “The other 10% were false positives caused by the start and stop operations of the belt conveyor, which may be easily left out when checking the steps that require maintenance in a real application.”). One of ordinary skill in the art would recognize that oscillations detected from tilts not directly associated with steps would be the detection of shaking of the test object on the carrying surface.
Even if Yasutomi in view of Memsense and Brandorff did not teach the motion sensor is positioned closer to a bottom surface than a top surface of the test object or closer to the top surface of the test object than the bottom surface of the test object, it would have been obvious to one having ordinary skill in the art at the time the invention was made to select the optimum relative height of the motion sensor for detection, since it has been held that where the general conditions of a claim are disclosed in the prior art, discovering the optimum or workable ranges involves only routine skill in the art. In re Aller, 105 USPQ 233.
Regarding claim 18, Yasutomi in view of Memsense and Brandorff teaches The inspection device according to claim 7, wherein the processor is further configured to determine whether the weight of the inspection object is within a predefined range (Brandorff: col 4 lines 40-54, “Load sensor signals are continuously sampled, for example, every 2 milliseconds. Each sampled signal corresponds to a value representing the tare weight of inclined conveyor 110 plus the force exerted by package 112 onto load sensor 138. After sampling n consecutive values with magnitudes greater than a specified threshold, the sampled signals are stored. In a preferred embodiment, n is 20 and the specified threshold is one pound greater than the scale tare weight. Thus, storage of sampled signals preferably begins as soon as 20 consecutive signals greater than 1 pound above the scale tare weight are sampled. This is represented as time t.sub.1 in FIG. 5. The sampled signals are stored until 20 consecutive signals less than the specified threshold are sampled, represented as time t.sub.6.”), and to determine vibration and fluctuation of the test object with respect to each axis of the motion sensor (Brandorff: col 8 lines 57-64, “To reduce the effects of such vibrational noise, an accelerometer (not shown) is preferably mounted inside weighing apparatus 100 to measure the amount of vibration transmitted to the weighing apparatus. Referring now to curve A of FIG. 8, there is depicted the load cell signal generated in response to a dropped package. Curve B of FIG. 8 depicts the acceleration signal generated by an accelerometer mounted within the weighing apparatus.”; Yasutomi: Table I Sensors; Fig. 10), and determine a correlation of the vibration and fluctuation with the frequency, amplitude, and waveform shape of a weight waveform (Brandorff: col 4 line 63 – col 5 line 2, “A least-squares fit is performed on the raw signal data between times t.sub.2 and t.sub.3 to generate a first line 1.sub.1. Similarly, a least-squares fit is performed on the raw signal data between times t.sub.4 and t.sub.5 to generate a second line 1.sub.2. The point where lines 1.sub.1 and 1.sub.2 intersect is then selected to represent the weight of package 112 (after subtracting the scale tare weight).”; Fig. 8, curve A (load cell signal, a measurement of weight) and curve B (acceleration signal from accelerometer); col 8 lines 53-56, “Digital filtering cannot completely eliminate these vibrational noise effects, since some of the noise frequencies coincide with frequencies in which there is valid package information.”; Fig. 9, Equation 8; Yasutomi: Fig. 10.). One of ordinary skill in the art would recognize that, in measuring the vibration and fluctuation through the accelerometer and the weight waveform through the load cell, in determining least-squares fit lines and comparing measured acceleration and measured weight over time, and in formulating an equation relating acceleration and weight, Brandorff teaches determining a correlation between the vibration and fluctuation measured of an object on the conveyor and the weight waveform, which comprises frequencies, amplitudes, and wave shapes. Yasutomi teaches measuring the vibration (via accelerations and angular velocities) in each axis of the motion sensor.
Regarding claim 19, Yasutomi in view of Memsense and Brandorff teaches The inspection device according to claim 7, wherein the processor is further configured to detect and correct a gravitational acceleration component of acceleration data received from the motion sensor (Memsense: 3.2 “As an example, with the IMU pictured in Figure 2, if the Z axis is pointed straight UP away from the earth, it will produce 0 g for the X and Y axes and a positive 1 g for the Z axis.”; Yasutomi: III. “acquire the acceleration which may contribute to event detections (e.g. belt start/stop, object entanglement)”; VI.A “Since the angular velocity of the IMU would present values different to zero even when static, an algorithm was necessary to filter this zero level offset.”; Brandorff: col 4 lines 18-22, “the tare weight of inclined conveyor 110 is subtracted from the load sensor signals to generate net load signals representative of the force exerted by package 112 onto load sensor 138.”) when the motion sensor is attached to the inspection object rather than the test object (Yasutomi: IX. “Even though this system was used for a considerably small however complex belt conveyors, it is possible to extend its usage to a larger and more complex belt conveyor, as long as an inspection device at the size of the object transported is developed or the inspection device developed is incorporated in the transported object.”). One of ordinary skill in the art would recognize that the acceleration measurements of Yasutomi are for acquiring the acceleration that contribute to event detections (and not for the consistent gravitational component as detected by Memsense), and that Yasutomi and Brandorff teach correcting for offsets in the data (Yasutomi’s offset and Brandorff’s subtraction of tare weight), that Yasutomi in view of Memsense and Brandorff teaches correcting for the known gravitational acceleration component of the acceleration data.
Regarding claim 21, Yasutomi in view of Memsense and Brandorff teaches The inspection device according to claim 7, wherein the test object includes an environmental diagnostic sensor supported by the test object and configured to measure a parameter of the ambient environment around the test object including a temperature sensor, a humidity sensor, an air pressure sensor, a wind velocity sensor, a microphone, or a magnetism detector (Yasutomi: Table I: Magnetometer),
the processor is further configured to determine the inspection accuracy (Yasutomi: VIII.A “To classify the event as a step, thresholds were defined empirically. However, instead of the threshold, machine learning algorithms for pattern recognition such as Support Vector Machines (SVM) may be used for the detection accuracy, currently of about 90%, to 100% which makes the inspection system more robust.”) of the goods inspection device (Fig. 2) based on the data from the environmental diagnostic sensor that measures the parameter of the ambient environment around the test object, including the temperature sensor, the humidity sensor, the air pressure sensor, the wind velocity sensor, the microphone, or the magnetism detector (Yasutomi: VIII.B “Those magnetic fields, however, may be used as key features for the localization of the inspection device along the belt line, which may contribute to the identification of the joints.”).
Regarding claim 22, Yasutomi in view of Memsense and Brandorff teaches The inspection device according to claim 7, wherein the three axes of the motion sensor attached to the test object are different from the three axes of the motion sensor when the motion sensor is attached to the inspection object (VI. C “Thus, the objects rotate in its Z axis during the transfer from one belt to another, changing therefore its progressing direction related to its local X and Y axes. Having this considered, an algorithm to estimate the progressing direction was required, since the tilt angle is calculated from the angular velocity perpendicular to the progressing direction.”; Equation 6; VII.B “The inspection device was passed 10 times through the system and the data obtained were processed after the experiments with a software which included the inspection method described in Section VI.”). The three axes of motion, being uniquely measured for each time it passed through the system, and the direction of these axes being updated (“estimate the progressing direction”), is the three axes of the motion sensor attached to the test object being different to those of the inspection object.
Further, it would be obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include two different axes of motion for two objects moving through space, wherein their motion is relative to a reference axis of motion (“progression direction”), because the use of and conversion between measured axes is well known in the art and yields predictable results., and
when the motion sensor is attached to the inspection object, the processor comprises an axis corrector configured to identify from the acquired data the direction of the axes of the motion sensor attached to the inspection object (VI.C “A common approach to estimate the progressing direction would be to use sensor fusion algorithms to keep track of the heading of the inspection device at all times. Those algorithms involve the integration of the angular velocity from the gyroscope and the correction of its errors with the earth's magnetism from the magnetometer”), and
correct the acquired data acquired from the motion sensor based on the identified directions of the three axes of the motion sensor attached to the inspection object (Equations 6 and 10), and
tilt detection data of the acquired data from the motion sensor attached to the test object (VI.E tilt angle α, Equation 7) so as to correct the diagnosis of the inspection device based on the acquired data from the motion sensor (Equations 8 and 9; Figs. 8 and 12).
Claim(s) 13-15 is/are rejected under 35 U.S.C. 103 as being unpatentable over Yasutomi in view of Memsense and Brandorff as applied to claim 7 above, and further in view of Vossough et al. (US 20180186623 A1, previously cited).
Regarding claim 13, Yasutomi in view of Memsense and Brandorff teaches The inspection device according to claim 7, wherein the test object further includes an environmental sensor (Yasutomi: Table I: Magnetometer; VIII.B: “Those magnetic fields, however, may be used as key features for the localization of the inspection device along the belt line, which may contribute to the identification of the joints.”), wherein the support member supports the environmental sensor (Yasutomi: IV.B: “The inspection device developed consisted on the IMU inserted inside a packaging which enabled the IMU conveyance (Fig. 2). The packaging was composed by a jig and a container. The jig enclosed the IMU circuit and the battery, and had openings that enabled access to a connector of the IMU for battery charging and USB communication to a PC.”), and wherein the external interface is configured to output data generated by the environmental sensor outside of the test object (Yasutomi: I: “In this paper, it is presented a cost-effective system to promptly inspect all the steps at the joints of the belt lines at once. This system involves the acquirement of data of the belt conveyor with an IMU, and the processing of this data with original algorithms for zero offset filtering, sensor progressing direction detection, step event detection and step height calculation.”; Wireless communication).
Yasutomi in view of Memsense, and Brandorff does not teach the test object, comprising an environmental sensor including a microphone
Vossough teaches an analogous sensing device, comprising an environmental sensor including a microphone ([0030] lines 18-22,“This sensing platform (FIG. 3) is applicable to a variety of different MEMS sensors such MEMS microphone (acoustics), pressure sensing, accelerometers, gyroscopes and combined Inertial Measurement Units (IMU).).
It would be obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the environmental sensor of Yasutomi in view of Memsense and Brandorff to include the microphone of Vossough because it would yield predictable and advantageous results. The inclusion of multiple and distinct sensors in such devices including MEMS and IMUs is well known in the art and yields predictable results. The inclusion of a microphone as one of the sensors would yield advantageous results including detecting acoustic vibrations that might affect the acceleration and/or angular velocity measurements of the object, thereby increasing the accuracy of detection and/or determining when the measurements might be unreliable.
Regarding claim 14, Yasutomi in view of Memsense, Brandorff and Vossough teaches The inspection device according to claim 13, wherein the test object further includes a processor configured to receive data from the external interface (Yasutomi: IV.B: “The jig enclosed the IMU circuit and the battery, and had openings that enabled access to a connector of the IMU for battery charging and USB communication to a PC.”; wireless communication; I: “This system involves the acquirement of data of the belt conveyor with an IMU, and the processing of this data with original algorithms for zero offset filtering, sensor progressing direction detection, step event detection and step height calculation.”) and to determine the inspection accuracy of the inspection device (Yasutomi: Fig. 12, Steps calculated by the inspection system; IX. “This system successfully detected and measured the steps of a complex belt conveyor with an accuracy of ±0.3 mm.”) based on the data from the motion sensor (Yasutomi: I: “This system involves the acquirement of data of the belt conveyor with an IMU, and the processing of this data with original algorithms for zero offset filtering, sensor progressing direction detection, step event detection and step height calculation.”; VII.B.“ Fig. 10 shows the angular velocities obtained for the 3 axes in one of the measurements. It also shows the Z-axis rotation angle calculated. The angular velocities shown in X and Y axes were plotted with the local coordinate axis already rotated to maximize the peaks in X or Y for each joint. As it can be seen, the algorithm successfully recognizes the event of passage through the 3 joints, which in the figure is characterized by an exponential curve fitted to some peaks found in axis X and Y.”) and the environmental sensor (Yasutomi: Measuring the offset for all sensor temperatures using the Soak or Ramp methods were considered, but the lack of repeatability of the temperature sensor of the IMU would cause errors in the offset filtering.”) received from the external interface (Yasutomi: Fig. 2, connector; IV.A: “wireless communication capacity”; “wireless communication allows remote monitoring and control.”; IV.B: “The jig enclosed the IMU circuit and the battery, and had openings that enabled access to a connector of the IMU for battery charging and USB communication to a PC.”; VII.A: “The inspection device was passed 10 times through the system and the data obtained were processed after the experiments with a software which included the inspection method described in Section VI.”).
Regarding claim 15, Yasutomi in view of Memsense, Brandorff and Vossough teaches The inspection device according to claim 13, wherein the test object further includes a processor configured to receive data from the external interface (Yasutomi: IV.B: “The jig enclosed the IMU circuit and the battery, and had openings that enabled access to a connector of the IMU for battery charging and USB communication to a PC.”; wireless communication; I: “This system involves the acquirement of data of the belt conveyor with an IMU, and the processing of this data with original algorithms for zero offset filtering, sensor progressing direction detection, step event detection and step height calculation.”) and to determine characteristics of the inspection device (Yasutomi: Fig. 12, Steps calculated by the inspection system; IX. “This system successfully detected and measured the steps of a complex belt conveyor with an accuracy of ±0.3 mm.”) based on the data from the motion sensor (Yasutomi: I: “This system involves the acquirement of data of the belt conveyor with an IMU, and the processing of this data with original algorithms for zero offset filtering, sensor progressing direction detection, step event detection and step height calculation.”; VII.B.“ Fig. 10 shows the angular velocities obtained for the 3 axes in one of the measurements. It also shows the Z-axis rotation angle calculated. The angular velocities shown in X and Y axes were plotted with the local coordinate axis already rotated to maximize the peaks in X or Y for each joint. As it can be seen, the algorithm successfully recognizes the event of passage through the 3 joints, which in the figure is characterized by an exponential curve fitted to some peaks found in axis X and Y.”) and the environmental sensor (Yasutomi: Measuring the offset for all sensor temperatures using the Soak or Ramp methods were considered, but the lack of repeatability of the temperature sensor of the IMU would cause errors in the offset filtering.”) received from the external interface (Yasutomi: Fig. 2, connector; IV.A: “wireless communication capacity”; “wireless communication allows remote monitoring and control.”; IV.B: “The jig enclosed the IMU circuit and the battery, and had openings that enabled access to a connector of the IMU for battery charging and USB communication to a PC.”; VII.A: “The inspection device was passed 10 times through the system and the data obtained were processed after the experiments with a software which included the inspection method described in Section VI.”).
Claim(s) 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Yasutomi in view of Memsense, Brandorff, and Takahashi et al. (US 20190041537 A1, previously cited).
Regarding claim 20, Yasutomi teaches An inspection device that inspects an inspection object (Fig. 2, Inspection Device) carried on a conveyor (Fig. 3, Experimental setup) comprising:
a goods inspection device (Fig. 3),
a communication interface (Table I: Communication: Wireless (2.4 GHz band 16 ch) and Micro-USB; IV.A: “wireless communication capacity”; “wireless communication allows remote monitoring and control.”) configured to acquire data of the acceleration and angular velocity of a test object when the test object is carried on the conveyor (Inertial measurement units (IMU), Table I: Accelerometers, Gyroscopes, N of Axes: 3’; VI.B. “To detect events in the belt conveyor such as the passage through the joints of the belt lines, it was searched for positive and negative peaks in the angular velocities obtained in the X and Y directions” and “a classification of the joint was necessary to choose the correct equation. To do so, it was integrated the angular velocity in the Z axis from 0.2 seconds before the first peak until 0.8 seconds after, as in (10), to estimate the Z-axis rotation angle during the passage through the joint.”). Accelerometers with 3 axes are the motion sensors to detect acceleration with respect to respective direction of three-dimensional axes. Gyroscopes with 3 axes are the motion sensors to detect angular velocity with respect to respective direction of three-dimensional axes, and
a processor configured to receive the acquired data from the communication interface (PC, VII.A: The inspection device was passed 10 times through the system and the data obtained were processed after the experiments with a software which included the inspection method described in Section VI.),
wherein the processor is also configured to diagnose the conveyor based on the acquired data (IX. “With this system, it is possible to measure all the steps of a complex belt conveyor system automatically and at once.”), wherein the test object includes:
a motion sensor supported by the test object and configured to detect acceleration and angular velocity of the test object in three directions of three-dimensional axes thereof (Inertial measurement units (IMU), Table I: Accelerometers, Gyroscopes, N of Axes: 3’; VI.B. “To detect events in the belt conveyor such as the passage through the joints of the belt lines, it was searched for positive and negative peaks in the angular velocities obtained in the X and Y directions” and “a classification of the joint was necessary to choose the correct equation. To do so, it was integrated the angular velocity in the Z axis from 0.2 seconds before the first peak until 0.8 seconds after, as in (10), to estimate the Z-axis rotation angle during the passage through the joint.”). Accelerometers with 3 axes are the motion sensors to detect acceleration with respect to respective direction of three-dimensional axes. Gyroscopes with 3 axes are the motion sensors to detect angular velocity with respect to respective direction of three-dimensional axes,
an environmental diagnostic sensor supported by the test object that measures a parameter of the ambient environment around the test object,
including a temperature sensor, a humidity sensor, an air pressure sensor, a wind velocity sensor, a microphone, or a magnetism detector (Table I: Magnetometer; VIII.B: “Those magnetic fields, however, may be used as key features for the localization of the inspection device along the belt line, which may contribute to the identification of the joints.”),
a support member configured to support the motion sensor (IV.B: “The inspection device developed consisted on the IMU inserted inside a packaging which enabled the IMU conveyance (Fig. 2). The packaging was composed by a jig and a container. The jig enclosed the IMU circuit and the battery, and had openings that enabled access to a connector of the IMU for battery charging and USB communication to a PC.”). The packaging with jig and container is the support member. Figure 2 of Yasutomi is reproduced below, having a bottom surface (Fig. 2, wherein the “container” has a bottom surface) (Figs. 1, 4a, and 13; IV.B: “The container accommodated the jig with the IMU and had a diameter slightly smaller than the width of the belt conveyor. It was made of a plastic material which would grip to the belt while sliding through the walls of the belt conveyor.”). One of ordinary skill in the art would recognize that the container has a bottom surface, even if not directly seen in the figure, as it is made to grip the belt or carrying surface, shown in contact below the bottom surface in Fig. 4a. of the conveyor configured to convey in a conveying direction (belts of Figs. 3, 4 and 11) the inspection object (Fig. 2, Inspection Device) to be inspected to the goods inspection device (Fig. 3, Inspection System); and
an external interface configured to output data including the acceleration and the angular velocity of the test object to the communication interface of the goods inspection device (Fig. 2, connector; IV.A: “wireless communication capacity”; “wireless communication allows remote monitoring and control.”; IV.B: “The jig enclosed the IMU circuit and the battery, and had openings that enabled access to a connector of the IMU for battery charging and USB communication to a PC.”; VII.A: “The inspection device was passed 10 times through the system and the data obtained were processed after the experiments with a software which included the inspection method described in Section VI.”). The connector and wireless communication capacity are the external interface unit. One of ordinary skill in the art would recognize that the communication, either by USB connector or wireless communication, for monitoring, data acquirement, and processing would be outputting data including acceleration and angular velocity.,
wherein the processor is further configured to determine whether or not a deviation in the output data of the inspection device (Figs. 10 and 11, Joints 1-3; III. “acquire the acceleration which may contribute to event detections (e.g. belt start/stop, object entanglement).” ; VI.B: “To detect events in the belt conveyor such as the passage through the joints of the belt lines, it was searched for positive and negative peaks in the angular velocities obtained in the X and Y directions. The minimum height for peak detection was set to ±15°/s and the minimum distance between peaks was set to 0.1 seconds. The former threshold was chosen to be slightly higher than the angular velocity during regular transport (<5°/s) and the latter to detect at least 3 peaks within an event (minimum length 0.3 s) in order to avoid missing it.”; VIII.A. “To classify the event as a step, thresholds were defined empirically. However, instead of the threshold, machine learning algorithms for pattern recognition such as Support Vector Machines (SVM) may be used for the event classification.”)); and
wherein the processor is further configured to determine the inspection accuracy (VIII.A “To classify the event as a step, thresholds were defined empirically. However, instead of the threshold, machine learning algorithms for pattern recognition such as Support Vector Machines (SVM) may be used for the detection accuracy, currently of about 90%, to 100% which makes the inspection system more robust.”) of the goods inspection device (Fig. 2) based on the data from the environmental diagnostic sensor that measures the parameter of the ambient environment around the test object, and includes the temperature sensor, the humidity sensor, the air pressure sensor, the wind velocity sensor, the microphone, or the magnetism detector (Table I, Magnetometer), thereby determining the inspection accuracy based on data from the temperature sensor, the humidity sensor, the air pressure sensor, the wind velocity sensor, the microphone, or the magnetism detector (VIII.B “Those magnetic fields, however, may be used as key features for the localization of the inspection device along the belt line, which may contribute to the identification of the joints.”),
the three axes of the motion sensor attached to the test object are different from the three axes of the motion sensor when the motion sensor is attached to the inspection object (VI. C “Thus, the objects rotate in its Z axis during the transfer from one belt to another, changing therefore its progressing direction related to its local X and Y axes. Having this considered, an algorithm to estimate the progressing direction was required, since the tilt angle is calculated from the angular velocity perpendicular to the progressing direction.”; Equation 6; VII.B “The inspection device was passed 10 times through the system and the data obtained were processed after the experiments with a software which included the inspection method described in Section VI.”). The three axes of motion, being uniquely measured for each time it passed through the system, and the direction of these axes being updated (“estimate the progressing direction”), is the three axes of the motion sensor attached to the test object being different to those of the inspection object.
Further, it would be obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include two different axes of motion for two objects moving through space, wherein their motion is relative to a reference axis of motion (“progression direction”), because the use of and conversion between measured axes is well known in the art and yields predictable results., and
when the motion sensor is attached to the inspection object, the processor comprises an axis corrector configured to identify from the acquired data the direction of the axes of the motion sensor attached to the inspection object (VI.C “A common approach to estimate the progressing direction would be to use sensor fusion algorithms to keep track of the heading of the inspection device at all times. Those algorithms involve the integration of the angular velocity from the gyroscope and the correction of its errors with the earth's magnetism from the magnetometer”), and
correct the acquired data acquired from the motion sensor based on the identified directions of the three axes of the motion sensor attached to the inspection object (Equations 6 and 10), and
tilt detection data of the acquired data from the motion sensor attached to the test object (VI.E tilt angle α, Equation 7) so as to correct the diagnosis of the inspection device based on the acquired data from the motion sensor (Equations 8 and 9; Figs. 8 and 12).
Yasutomi does not teach a goods inspection device that detects foreign matter in the inspection object, including
a metal detector configured to detect foreign matter in the inspection object by
generating an alternating magnetic field with a predetermined frequency,
magnetizing a metal included in the inspection object,
detecting residual magnetization of the magnetized metal with a magnetic sensor, and
outputting with the magnetic sensor a signal whose amplitude and phase change in response to a change in the magnetic field attributed to the inspection object passing through the alternating magnetic field,
or
an x-ray inspection device configured to detect foreign matter in the inspection object having
an X-ray source configured and positioned to emit X-rays toward the inspection object on the conveyor, and
an X-ray detector configured and positioned to receive X-rays emitted by the X-ray source and transmitted through the inspection object, and
output an electric signal in accordance with the amount of X-rays detected,
a processor configured to receive the acquired data from the communication interface and to receive the electric signal from the X-ray detector or the signal from the magnetic detector
the top surface of the support member includes an identifier including a single arrow mark identifying a normal orientation of the test object on the conveyor in the conveying direction and configured to be aligned with the conveying direction of the carrying surface of the conveyor
Memsense teaches an analogous motion sensing device, comprising a support member configured to support the motion sensor (Figs. 1 and 3; 3.1 “The MS-IMU3030 is contained in a 6061-T6 aluminum housing anodized to MIL-A-8625 standards.”) having a top surface (Figs. 3 and 4, 3.2 “As an example, with the IMU pictured in Figure 2, if the Z axis is pointed straight UP away from the earth”), and the top surface of the support member includes an identifier including a single arrow mark identifying a normal orientation of the test object on the conveyor in the conveying direction and configured to be aligned with the conveying direction of the carrying surface of the conveyor(Fig. 4, reproduced with annotation below). One of ordinary skill in the art would recognize that the top surface has an identifier including an arrow mark (shown in the annotated rectangle). As the identifier is aligned with the measuring axes, one of ordinary skill in the art would recognize that the identifier is configured to be aligned with the conveying direction.
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Even if Memsense does not teach a single arrow mark identifying a normal orientation of the test object on the conveyor in the conveying direction, it would
have been obvious to one having ordinary skill in the art at the time the invention was made to omit arrows not identifying a normal orientation of the object, since it has been held that omission of an element and its function in a combination where the remaining elements perform the same functions as before involves only routine skill in the art. In re Karlson, 136 USPQ 184.
It would be obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the device of Yasutomi to include the top surface with identifier including an arrow mark of Memsense because it would yield predictable and advantageous results of further containing the motion sensors, thereby advantageously protecting it from damage, and by enabling alignment with the conveying system, thereby giving more consistent and accurate measurements by aligning the axes of measurement with the expected axes of motion (Yasutomi: “Measurements were made 10 times for each step, and in every measurement the inspection device was placed in the upstream belt line with its local Y axis toward the progressing direction, which enabled the calculation of the tilt angles with only the angular velocities in the X axis”).
Yasutomi in view of Memsense does not teach a goods inspection device that detects foreign matter in the inspection object, including
a metal detector configured to detect foreign matter in the inspection object by
generating an alternating magnetic field with a predetermined frequency,
magnetizing a metal included in the inspection object,
detecting residual magnetization of the magnetized metal with a magnetic sensor, and
outputting with the magnetic sensor a signal whose amplitude and phase change in response to a change in the magnetic field attributed to the inspection object passing through the alternating magnetic field,
or
an x-ray inspection device configured to detect foreign matter in the inspection object having
an X-ray source configured and positioned to emit X-rays toward the inspection object on the conveyor, and
an X-ray detector configured and positioned to receive X-rays emitted by the X-ray source and transmitted through the inspection object, and
output an electric signal in accordance with the amount of X-rays detected,
a processor configured to receive the acquired data from the communication interface and to receive the electric signal from the X-ray detector or the signal from the magnetic detector; and
the weighing conveyor
Brandorff teaches an analogous device for measuring objects on a conveyor system, comprising: the weighing conveyor (weighing apparatus 100, conveyor 110)
It would be obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the inspection device Yasutomi in view of Memsense to include the weighing conveyor of Brandorff because it would yield predictable and advantageous results of weighing the inspection object as it transits the conveying system. The use of weighing platforms in conveying devices is well understood in the art and yields predictable results.
Yasutomi in view of Memsense and Brandorff does not teach a goods inspection device that detects foreign matter in the inspection object, including
a metal detector configured to detect foreign matter in the inspection object by
generating an alternating magnetic field with a predetermined frequency,
magnetizing a metal included in the inspection object,
detecting residual magnetization of the magnetized metal with a magnetic sensor, and
outputting with the magnetic sensor a signal whose amplitude and phase change in response to a change in the magnetic field attributed to the inspection object passing through the alternating magnetic field,
or
an x-ray inspection device configured to detect foreign matter in the inspection object having
an X-ray source configured and positioned to emit X-rays toward the inspection object on the conveyor, and
an X-ray detector configured and positioned to receive X-rays emitted by the X-ray source and transmitted through the inspection object, and
output an electric signal in accordance with the amount of X-rays detected,
a processor configured to receive the acquired data from the communication interface and to receive the electric signal from the X-ray detector or the signal from the magnetic detector
Takahashi teaches an analogous inspection device (Abstract, paragraph [0003] with conveyor system, comprising:
a goods inspection device that detects foreign matter in the inspection object (Fig. 1, metal detection apparatus 20), including
a metal detector configured to detect foreign matter in the inspection object ([0062] lines 1-6, “the array direction of the pair of receiving coils 43b and 43c (the longitudinal direction of the core 43a) is set to be orthogonal to the array direction of the magnetic sensors 43 (the longitudinal direction of the head 40) so as to dispose a number of magnetic sensors in the limited long side dimension of the head 40 and thus increase the detection resolution of foreign metal”) by
generating an alternating magnetic field with a predetermined frequency ([0078] lines 1-12, “as the magnetic field for inspection, an alternate current magnetic field from which the sensitivity to both ferrous metal and nonferrous metal can be obtained is used; however, in a case in which the head 40 is scanned in a reciprocating manner for a single time of the output of the inspection control signal as described above, it is possible to broaden the kind of detectable foreign substances by switching inspection conditions such as the kind (alternate current, direct current, and the like), intensity, magnetic field frequency, and the like of the magnetic field for inspection when the head is scanned forwards and backwards respectively.”),
magnetizing a metal included in the inspection object ([0073] “when an article including contained metal is placed on the placing table 21, and an inspection control signal is output by, for example, the operation of the operation panel 24, the head 40 is scanned by the scanning controller 55, when the location of the contained foreign metal comes to a location A near the receiving coil 43b on one end side of the core 43a of the magnetic sensor 43 as illustrated in FIG. 4, the influence of the contained foreign metal on the magnetic fluxes interlinked with the receiving coil 43b increases (the interlinked magnetic fluxes increase in a case in which the contained foreign metal is an ferrous metal and the interlinked magnetic fluxes decrease in a case in which the contained metal is a nonferrous metal), and the influence on the magnetic fluxes interlinked with the receiving coil 43c decreases, and thus, in the output of the differential amplifier 61, a signal with an amplitude of a predetermined amount ΔE corresponding to a change in the magnetic field received by one receiving coil 43b appears.”). The influence of the foreign metal on the interlinked magnetic fluxes is magnetizing the metal,
detecting residual magnetization of the magnetized metal with a magnetic sensor ([0079] lines 1-8, “the contained foreign metal is detected by scanning the magnetic sensors 43 to the article placed on the placing table 21, and thus, compared with an article and a magnetic sensor-fixed metal detection apparatus like a metal detection apparatus of the related art or a method in which an article is manually carried and passed over the magnetic sensors 43, more reliably metal detection can be carried out”), and
outputting with the magnetic sensor a signal whose amplitude and phase change in response to a change in the magnetic field attributed to the inspection object passing through the alternating magnetic field (Figs. 2 and 5; [0067] lines 4-14, “a differential amplifier 61 that receives the parallel connection output of the respective magnetic sensors 43 with a differential input terminal, a detector circuit 62 that synchronized-detects the output of the differential amplifier 61 with an output signal of the signal generator 60, determination means 63 for determining the presence or absence of contained foreign metal by comparing the output of the detector circuit 62 and a predetermined threshold, and notification means 64 for notifying the determination result by the determination means 63”; [0072] “In a case in which there is no foreign metal contained in an article which is an inspection object, magnetic fluxes being generated by the transmission coil 42 almost equally intersect the two receiving coils 43b and 43c, and thus signals having almost the same amplitude and phase, which are determined at the connection point between the coils as a criterion, are output from the output terminals of the receiving coils 43b and 43c, the input and output of the differential amplifier 61 becomes almost zero, and the detection output also becomes almost zero.”),
or
an x-ray inspection device configured to detect foreign matter in the inspection object having
an X-ray source configured and positioned to emit X-rays toward the inspection object on the conveyor, and
an X-ray detector configured and positioned to receive X-rays emitted by the X-ray source and transmitted through the inspection object, and
output an electric signal in accordance with the amount of X-rays detected,
a processor configured to receive the acquired data (Fig. 2, detector circuit 62, determination means 63, notification means 64) from the communication interface and to receive the electric signal from the X-ray detector or the signal from the magnetic detector ([0066] Wires of the transmission coil 42 and the plurality of magnetic sensors 43, 43, . . . , 43 of the head 40 are connected to, for example, a circuit board (not illustrated) in the space 33 through flexible cables not illustrated.) The processor (detector circuit) receives signals from the magnetic detector (magnetic sensors) from the communication interface (cables).
It would be obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the inspection device of Yasutomi in view of Memsense and Brandorff to include the metal detector configured to detect foreign matter in the inspection object of Takahashi because the inspection of objects for metal is well-known in the art and would yield predictable results. The detection of foreign metal in said object would predictably and advantageously detect the inspection object as it interacts with the inspection device, and would thereby increase the accuracy of detection of said or other objects, and their properties, on the conveyor. Further, it would be obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to utilize the diagnostic sensor of Yasutomi (magnetometer) to substitute the diagnostic sensor of Takahashi (article detection means) because both sensors are used to locate the object within the system to improve the accuracy of the detections (Yasutomi: Table I: Magnetometer; VIII.B: “Those magnetic fields, however, may be used as key features for the localization of the inspection device along the belt line, which may contribute to the identification of the joints.”) and would thereby yield predictable results.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). 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.
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/B.B.G./Examiner, Art Unit 2863
/Catherine T. Rastovski/Supervisory Primary Examiner, Art Unit 2863
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