DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Objections
Claim 1 is objected to because of the following informalities: Line 1, “the evolution” should be “an evolution”. Appropriate correction is required.
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
Regarding claim 7, the limitation “implementing means” is interpreted as “a processing unit” according to paragraph 62 of the published present application. The limitation “predicting means” is interpreted as “a processing unit” according to paragraph 62 of the published present application.
Regarding claim 7, the limitation “training means” is interpreted as “a processing unit” according to paragraph 62 of the published present application.
Regarding claim 8, the limitation “generating means” is interpreted as “a processing unit” according to paragraph 62 of the published present application.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-9 are rejected under 35 U.S.C. 103 as being unpatentable over Nair et al. (U.S. Publication No. 20220050015) in view of Govrin et al. (U.S. Publication No. 20230213928).
Regarding claim 1, Nair teaches a method for predicting the evolution of a defect of a bearing comprising: predicting an evolution of the identified defect of the bearing (Abstract) from a type of the identified defect (Paragraph 97, “The life module 340 is configured to predict a defect propagation based on location of the defect and type of the defect”) and from operating parameters of the bearing and from a model of the bearing (Abstract, “The bearing model is based on one of condition data associated with operation of the bearing, historical condition data of the bearing, bearing specification and technical specification of a technical system including the bearing. The method further includes predicting a defect in the bearing based on the bearing model and predicting the remaining life of the bearing based on the predicted defect”).
Nair is silent about identifying a defect of the bearing and extracting geometrical parameters of the identified defect by a trained deep learning algorithm from a picture of the bearing, predicting an evolution of the identified defect of the bearing from the extracted geometrical parameters of the identified defect
Govrin teaches identifying a defect of the bearing and extracting geometrical parameters of the identified defect (Paragraph 41, “According to some embodiments, the system is configured to monitor a mode of failure of a bearing, and further including: identifying at least one segment including boundaries of a perimeter of a surface defect within the received signals, such that identifying the at least one change in the received signals includes identifying a change or rate of change of the shape and/or propagation of the at least one segment, and wherein the mode of failure includes a critical defect size, and wherein generating at least one model of a trend in the identified change includes modeling a trend in the growth of the surface defect in specific mode of operation of the bearing”) by a trained deep learning algorithm (Paragraph 98, “According to some embodiments, identifying a previously unknown failure mode may include applying the received signals and/or the identified segment to a machine learning algorithm 324 configured to determine a mode of failure of the machine or the component thereof. According to some embodiments, the machine learning algorithm 324 may be trained to identify a potential failure mode of the identified segment”) from a picture of the bearing (Paragraph 27, “According to some embodiments, the at least one signal includes at least one image, a portion of an image, a set of images, or a video”), predicting an evolution of the identified defect of the bearing from the extracted geometrical parameters of the identified defect (Paragraph 41, “According to some embodiments, the system is configured to monitor a mode of failure of a bearing, and further including: identifying at least one segment including boundaries of a perimeter of a surface defect within the received signals, such that identifying the at least one change in the received signals includes identifying a change or rate of change of the shape and/or propagation of the at least one segment, and wherein the mode of failure includes a critical defect size, and wherein generating at least one model of a trend in the identified change includes modeling a trend in the growth of the surface defect in specific mode of operation of the bearing”).
It would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention to use Govrin’s system includes optical sensors and machine learning to predicte evolution of a defect of a bearing because it would increase accuracy and efficiency.
Regarding claim 2, the combination of Nair and Govrin teaches all the features of claim 1 as outlined above, Govrin further teaches generating a recommendation based on the predicated evolution of the identified defect (Paragraph 162).
It would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention to use Govrin’s system includes optical sensors and machine learning to predicte evolution of a defect of a bearing because it would increase accuracy and efficiency.
Regarding claim 3, the combination of Nair and Govrin teaches all the features of claim 1 as outlined above, Govrin further teaches wherein the picture of the bearing is a picture of the bearing mounted in a machine (Paragraphs 27 and 101).
It would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention to use Govrin’s system includes optical sensors and machine learning to predicte evolution of a defect of a bearing because it would increase accuracy and efficiency.
Regarding claim 4, the combination of Nair and Govrin teaches all the features of claim 1 as outlined above, Govrin further teaches wherein the defect comprises a spall, and wherein the extracted geometrical parameters comprise a size of the spall, a perimeter of the spall and a location of the spall on the picture (Paragraphs 154-155).
It would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention to use Govrin’s system includes optical sensors and machine learning to predicte evolution of a defect of a bearing because it would increase accuracy and efficiency.
Regarding claim 5, the combination of Nair and Govrin teaches all the features of claim 1 as outlined above, Govrin further teaches wherein the method further includes training the deep learning algorithm to identify the defect of the bearing and to extract the geometrical parameters of the identified defect from pictures stored in a reference data base (Paragraphs 41, 77 and 98).
It would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention to use Govrin’s system includes optical sensors and machine learning to predicte evolution of a defect of a bearing because it would increase accuracy and efficiency.
The combination of Nair and Govrin is silent about wherein the deep learning algorithm comprises a neuronal network.
However, neuronal network is well known in the art.
It would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention to use neuronal network, since it has been held to be within the general skill of a worker in the art to be aware that known work in one field of endeavor may prompt variations of it for use in either the same field or a different one based on design incentives or other market forces if the variations would have been predictable to one of ordinary skill in the art. KSR International Co. v Teleflex Inc., 550 U.S. 398, 82 USPQ2d 1385 (2007).
Regarding claims 6-8, the claims are commensurate in scope with the above claims 1-2 and 5, and are rejected for the same reasons as set forth above.
Regarding claim 9, the combination of Nair and Govrin teaches all the features of claim 6 as outlined above, Govrin further teaches a system for predicting the evolution of a defect of a bearing comprising a device for predicting according to claim 6 and a mobile device (Fig.1, 112 and 74) configured to take the picture of the bearing while the bearing is mounted in a machine (Paragraphs 27 and 101) and configured to communicate with the device for predicting (Paragraphs 41, 70 and 77).
It would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention to use neuronal network, since it has been held to be within the general skill of a worker in the art to be aware that known work in one field of endeavor may prompt variations of it for use in either the same field or a different one based on design incentives or other market forces if the variations would have been predictable to one of ordinary skill in the art. KSR International Co. v Teleflex Inc., 550 U.S. 398, 82 USPQ2d 1385 (2007).
The combination of Nair and Govrin is silent about communicate wirelessly.
However, wireless communication is well known in the art.
It would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention to use wireless communication, since it has been held to be within the general skill of a worker in the art to be aware that known work in one field of endeavor may prompt variations of it for use in either the same field or a different one based on design incentives or other market forces if the variations would have been predictable to one of ordinary skill in the art. KSR International Co. v Teleflex Inc., 550 U.S. 398, 82 USPQ2d 1385 (2007).
Conclusion
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/XIN Y ZHONG/ Primary Examiner, Art Unit 2855