DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Rejections - 35 USC § 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) 1-6, 8, 10-22 are rejected under 35 U.S.C. 103 as being unpatentable over Walser et al. (U.S. PGPub No. 2023/0056526 A1) in view of Weiss et al. (U.S. PGPub No. 2018/0300865 A1).
As to claims 1, 12 and 17, Walser discloses and shows in figures 1 and 12, a cable processing device, method and non-volatile computer program product comprising:
a first receiving device (22, where the examiner is interpreting the structure of Walser as a structural equivalent for performing the same function) adapted to receive a first cable end (area 14 which gets stripped as disclosed, shown as 10 in figure 12) of a cable and fix the first cable end in a predetermined position (more clearly shown in figure 12, the cable 10 gets fixed via the rollers and other components clearly shown in figure 12) ([0076], ll. 1-8);
an image recording device (25) which is adapted to capture at least one image of the first cable end ([0075], ll. 27-30); and
an evaluation device (30, 32 and 50) which is adapted to apply a trained algorithm (AI module 32) to the at least one image, and to generate and output a control signal based on a result output by the trained algorithm (neural net AI module) ([0043], ll. 1-9; [0077], ll. 1-29; whereas disclosed the evaluation device uses the AI module to help in image analysis for “control-specific parameters” that are used to control first receiving device 22); and
wherein the trained algorithm is adapted to identify a predetermined feature (i.e. parameters assigned to each pixel by the ai module) in the at least one image, respectively, and to output a positive result when the predetermined feature is identifiable in the image (e.g. a positive result is the area not in black (disclosed black is used by the AI to represent no cable end) that is the specifics of each cable area as explicitly shown in figures 2-5) ([0081], ll. 9-32).
Walser does not explicitly disclose wherein the result output by the trained algorithm comprises a result vector where the number of elements of the vector corresponds to the number of features that were identified by the trained algorithm.
However, Weiss does disclose ([0040]; [0111]; [0113]) the use of a machine learning based training algorithm that aggregates a series of features into result vector. Further that the features identified are a function of a trained algorithm, which as explicitly disclosed is trained to vary weight/priority of particular features that are output into the result vector. The examiner notes that Weiss is image analysis of circuit boards for defects, however Walser also considers defect analysis. As such, obviously the basic machine learning principles of defect analysis with image based feature vector analysis of Weiss could likewise obviously be applied to cable analysis in the same manner.
It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to modify Weiss wherein the result output by the trained algorithm comprises a result vector where the number of elements of the vector corresponds to the number of features that were identified by the trained algorithm in order to provide the advantage of expected results and increased efficiency in using a common AI based machine learning method one can execute real-time anomaly detection, defect propagation and trend detection of the sample under test imaged [0134].
The subject matter of claims 1, 11 and 17 relate in that the technical features of apparatus claim 1 are in each case suitable for implementing the method/CRM of claims 11 and 17 therefore the method/CRM are inherent, in view of the above apparatus rejection.
As to claim 2, Walser does not explicitly disclose a processing device, comprising: a second receiving device adapted to receive and fix a second cable end of the cable in a predetermined position, wherein the image recording device is further adapted to capture at least one image of the second cable end.
However, Walser does disclose and show in figure 13 and in ([0102], ll. 1-9; [0103], ll. 11-14) the use of a second receiving device (60, 70 or 80). Each designed to be imaged by image sensor device 25. As such Walser does not explicitly disclose the limitation of doing so with “a second cable end”. However the examiner takes Office Notice that obviously as soon as a cable is cut during creation it has two ends. Further generally cables have two ends designed to interconnect two items each with specific and generally similar end configurations. As such obviously the system of Walser could not simply judge one end but both ends of a cable under test.
It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to modify Walser with a processing device, comprising: a second receiving device adapted to receive and fix a second cable end of the cable in a predetermined position, wherein the image recording device is further adapted to capture at least one image of the second cable end in order to provide the advantages of expected results and increased versatility, as obviously all cables have generally two ends and in connecting two things together could benefit obviously from ensuring both ends are created within the desired specification by the measurement system of Walser enabling a more robust understanding of the cables being created as a final product.
As to claim 3, Walser discloses a cable processing device, wherein the image recording device comprises a first camera (25), which is configured to capture images in a top view of the first cable end (where the examiner notes that the cable is a cylinder which does not have a top or bottom, but rather a radius/circumference, as such any image of a cable can be considered a top view, as such the images of Walser are considered “top view” images) ([0076], ll. 30-33).
As to claims 4, 13 and 18, Walser discloses a cable processing device, wherein the image recording device comprises a first camera (25), which is configured to capture images in a top view of the first cable end (where the examiner notes that the cable is a cylinder which does not have a top or bottom, but rather a radius/circumference, as such any image of a cable can be considered a top view, as such the images of Walser are considered “top view” images) ([0076], ll. 30-33)
wherein the image recording device is adapted to capture images in a perspective view (i.e. front face view shown in figure 8) of the first cable end ([0086], ll. 1-8; [0088], ll. 1-8).
Walser fails to disclose where the image of the perspective view is taken via a second camera.
However, it would have been obvious to one of ordinary skill in the art at the time the invention was made to use a second camera to take the perspective view, since it has been held that mere duplication of the essential working parts of a device involves only routine skill in the art. St. Regis Paper Co. v. Bemis Co., 193 USPQ 8.
It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to modify Walser where the image of the perspective view is taken via a second camera in order to provide the advantage of increased efficiency as obviously adding a second camera to take the second perspective view increases how rapidly one can image a sample under test in contrast to moving the camera between two locations which seems to be the implication disclosed by Walser.
As to claim 5, Walser discloses a cable processing device, wherein the trained algorithm comprises a convolutional neural network ([0014]; where the convolution in the reference Liang-Chieh Chen is a convolution neural network, the examiner has provided the noted reference hereinwith for evidence that the method used in Walser based on Liang-Chieh Chen is indeed a common CNN).
As to claim 6, Walser discloses and shows in figures 2-5, a cable processing device, wherein the predetermined feature comprises a marker (where the examiner is interpreting cable end types, cables types, sealing elements and terminals are all markers hatched as explicitly disclosed in the figures to define if the cable is correctly formed) at the first cable end and the trained algorithm is trained to identify the marker at the first cable end, wherein the evaluation device is adapted to output a positive control signal (e.g. a mere indication that the desired part is present (12d or foil 17a”), or that the parts measured as shown in the figures are within tolerances) when the marker is at exactly the first cable end (clearly the noted figures show ends) in the least one image, and to output an error signal if the marker is not identified at exactly the first cable end in the least one image (i.e. a control-specific parameter to fix the stripping knife to properly deal with the foil 17a”) ([0077], ll. 14-27; [0082], [0083], ll. 16-21).
As to claim 8, Walser discloses a cable processing device, wherein the predetermined feature comprises a contact locking device (where the coaxial cable plug connector terminal 16’ is being interpreted as a locking device, as it locks the cable onto a desired male end as known in the art), and the trained algorithm is trained to identify a presence of the contact locking device at the first cable end, wherein the evaluation device is adapted to output a positive control signal (e.g. a control-specific parameter that requires no variation in the tool installing the noted ends) when the contact locking device is in the at least on image, and to output an error signal (i.e. when the connector is out of tolerance) if the contact locking device is not identified in the at least on image ([0082], [0098]; inherently a measurement of the presence is required to create figure 5).
As to claim 10, Walser discloses ae cable processing device, wherein the predetermined feature identifies a state of a locking device (i.e. whether the electrical conductors 14”’ are locked together or not (I.e. twisted)) of the locking device at the first cable end image ([0087]).
As to claim 11, Walser discloses a cable processing device, wherein at least one of: wherein the evaluation device is adapted to output a positive control signal when the state of the locking device in the at least on image is identified as a locked state, and to output an error signal when the state is not identified as a locked state, or wherein the evaluation device is adapted to output an error signal when the state of the locking device is identified as a locked state in the at least on image and to output a positive control signal when the state is not identified as a locked state ([0087]; where the examiner is interpreting the signal that finds the conductors twisted (i.e. locked) as an error signal and when they aren’t twisted as desired in Walser as a positive control signal).
As to claims 14 and 19, Walser discloses a method, wherein the predetermined feature comprises a mark at the at least one cable end and the trained algorithm is trained to identify the mark at the at least one cable end, wherein, a positive control signal is output, when the mark is identified at exactly the at least one cable end in the at least one image, and an error signal is output, when the mark is not identified at exactly the at least one cable end in the at least one image (([0077], ll. 14-27; [0082]; [0083], ll. 16-21, please see rejection of claims 6, and 7 above for identification of particular features of the noted claim element relative to the prior art).
As to claims 15 and 20, Walser discloses a method, wherein said predetermined feature comprises a locking device and the trained algorithm is trained to identify a presence of the locking device at the at least one cable end, wherein a positive control signal is output when the locking device is identified in the at least one image, and an error signal is output when the locking device is not identified in the at least one image ([0082]; [0098], please see rejection of claims 8 and 9 above for identification of particular features of the noted claim element relative to the prior art).
As to claim 16, Walser discloses a method, wherein the predetermined feature identifies a state of a locking device and the trained algorithm is trained to identify the state of the locking device at the at least one cable end, and at least one of: wherein a positive control signal is output when the state of the locking device is identified in the at least one image as a locked state, and an error signal is output when the state is not identified as a locked state, or wherein an error signal is output when the state of the locking device is identified as a locked state in the at least one image, and a positive control signal is output when the state is not identified as a locked state ([0087]; where the examiner is interpreting the signal that finds the conductors twisted (i.e. locked) as an error signal and when they aren’t twisted as desired in Walser as a positive control signal, again please see the rejection of claims 10 and 11 above for identification of particular features of the noted claim element relative to the prior art).
As to claims 21 and 22, Walser does not explicitly disclose a cable processing device, wherein each of the elements in the vector corresponds to a different predetermined feature in the at least one image, wherein each of the elements store a value calculated by the trained algorithm that is within a predefined range of values or, wherein the positive result is output when the value corresponding to the predetermined feature exceeds a threshold, or the value corresponding to the predetermined feature is greater than the other values of all the other elements in the vector.
However, Weiss does disclose and show in figure 1 and in ([0010]; [0012]; [0085]; [0113]) the use of detection of a set of features (explicitly shown in figure 1 under area s120/s122 as different geometric components within the image). Where each elements store a value calculated by the trained algorithm (e.g. the label, but further obviously the vectors inherently have a value associated with each). As noted in the machine learning section this trained algorithm uses trained “predefined values” to make the determinations. Further Weiss discloses the use thresholding and providing outputs when the measured data (i.e. feature) is either greater than (i.e. distance between the new vector and the nearest cluster) or less than (i.e. if nearest vector deviates from nearest cluster of vectors by more than a preset low threshold) the predetermined threshold. The examiner notes that the same algorithmic analysis can be used in the same obvious manner for any structure that has varying geometric structures to determine within the image of said structure if it is within spec/tolerance. In other words obviously the same algorithm that analyzes chips on a board can obviously analyze parts of a cable as measured in Walser.0
It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to modify Walser with a cable processing device, wherein each of the elements in the vector corresponds to a different predetermined feature in the at least one image, wherein each of the elements store a value calculated by the trained algorithm that is within a predefined range of values or, wherein the positive result is output when the value corresponding to the predetermined feature exceeds a threshold, or the value corresponding to the predetermined feature is greater than the other values of all the other elements in the vector in order to provide the advantage of efficiency, in using a common thresholding based trained algorithm, one can rapidly determine whether or not defects are present avoiding human are and processing image data in a more rapid manner.
Response to Arguments
Applicant’s arguments with respect to claim(s) 1 and 21-22 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Examiner’s Note
The examiner notes that the evaluation device of claim 1 is currently not being interpreted under 112(f) due to the basic nature of a common function tied to the device in AI based processing. However, if the claim is amended to more narrowly calculate a variable of the cable under test it is suggested to ensure that the instant specification has sufficient written description to support the 112(f) algorithmic requirement for the evaluation device.
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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MICHAEL P LAPAGE whose telephone number is (571)270-3833. The examiner can normally be reached Monday-Friday 8-5:30.
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/Michael P LaPage/Primary Examiner, Art Unit 2877