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 .
Preliminary Amendment
The Preliminary Amendment submitted on 01/03/2025 has been entered and made of record.
Status of Claims
This communication is in response to the Application Filed on 01/03/2025
Claims 9–16 are pending in this application.
Drawings
The drawings are objected to under 37 CFR 1.83(a). The drawings must show every feature of the invention specified in the claims. Therefore, the “wherein the generation unit is configured to generate the labeled validation data from sensor data acquired by a sensor, and wherein the labeled validation data relate to different alignments of the sensor” must be shown or the feature(s) canceled from the claim(s). No new matter should be entered.
The limitation seems to represent Fig. 3 embodiment which seems to be present in the specification and claims but absent in the drawings. Corrected drawing sheets in compliance with 37 CFR 1.121(d) are required in reply to the Office action to avoid abandonment of the application. Any amended replacement drawing sheet should include all of the figures appearing on the immediate prior version of the sheet, even if only one figure is being amended. The figure or figure number of an amended drawing should not be labeled as “amended.” If a drawing figure is to be canceled, the appropriate figure must be removed from the replacement sheet, and where necessary, the remaining figures must be renumbered and appropriate changes made to the brief description of the several views of the drawings for consistency. Additional replacement sheets may be necessary to show the renumbering of the remaining figures. Each drawing sheet submitted after the filing date of an application must be labeled in the top margin as either “Replacement Sheet” or “New Sheet” pursuant to 37 CFR 1.121(d). If the changes are not accepted by the examiner, the applicant will be notified and informed of any required corrective action in the next Office action. The objection to the drawings will not be held in abeyance.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 01/03/2025 and 02/19/2025 are in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
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.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier.
Such claim limitation(s) is:
“a first provision unit” in claim(s) 13
“a second provision unit” in claim(s) 13
“an ascertainment unit” in claim(s) 13
“a provision unit” in claim(s) 13
“a generation unit” in claim(s) 13, 14 and 16
“a validation unit” in claim(s) 13 And 15
Because this claim limitation(s) is being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it is being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
Claim(s) 13: “a first provision unit” corresponds to “wherein the system comprises a first provision unit designed to provide image data showing the technical component", Applicant Specification [Pg. 10, ln. 24–26].
Claim(s) 13: “a second provision unit” corresponds to “a second provision unit designed a machine learning algorithm which is trained to ascertain a quality state of the technical component on the basis of image data showing the technical component ", Applicant Specification [Pg. 10, ln. 26–29].
Claim(s) 13: “an ascertainment unit”’ corresponds to “an ascertainment unit designed to ascertain the quality state of the technical component on the basis of the provided image data and the provided machine learning algorithm”, Applicant Specification [Pg. 11, ln. 2–4].
Claim(s) 13: “a provision unit”’ corresponds to [Fig. 2] – element [11]. “the system 10 comprises a provision unit 11 designed to provide a machine learning algorithm which is trained to recognize objects in image data”, Applicant Specification [Pg. 16, ln. 14–16].
Claim(s) 13, 14 and 16: “a generation unit”’ corresponds to [Fig. 2] – element [12]. “a generation unit 12 designed to generate labeled validation data for validating the machine learning algorithm”, Applicant Specification [Pg. 16, ln. 16–18].
Claim(s) 13 and 15: “a validation unit”’ corresponds to [Fig. 2] – element [13]. “validation unit 13 designed to validate the machine learning algorithm on the basis of the generated validation data”, Applicant Specification [Pg. 16, ln. 19–21].
If applicant does not intend to have this limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claim 13 rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 13 is rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, because of the limitation “provision unit” on Pg. 7, the first 2 lines. It is indefinite whether the provision unit is the “second provision unit” or a separate provision unit. For the purpose of examination the Examiner is interpreting the “provision unit” as the “second provision unit”.
Claim Objections
Claim 13 is objected to because of the following informalities:
Independent Claim 13, there seems to be a typo on Pg. 6, line 5 for the limitation “a first provision unit configured o provide image data showing the technical component”. It seems that “o” is supposed to be “to”. Appropriate correction is required.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or non-obviousness.
Claim(s) 9, 10, 13 and 14 are rejected under 35 U.S.C. 103 as being unpatentable over Milne et al. (US 20230196096 A1, hereafter, "Milne") in view of Bu et al. (US 20240290027 A1, hereafter, "Bu").
Regarding claim 9, Milne teaches A method for ascertaining a quality state of a technical component, during a manufacturing process, using a machine learning algorithm which is trained to ascertain a quality state of the technical component on the basis of image data showing the technical component (See Milne, ¶ [0046], In addition to training, module 116 may implement/run the trained AVI neural network(s), e.g., by applying images newly acquired by visual inspection system 102 (or another visual inspection system) to the neural network(s), possibly after certain pre-processing is performed on the images as discussed below. In various embodiments, the AVI neural network(s) trained and/or run by module 116 may classify entire images (e.g., defect vs. no defect, or presence or absence of a particular type of defect, etc.). ¶ [0039], As used herein, “defect detection” may refer to the classification of container images as exhibiting or not exhibiting defects (or particular defect categories), and/or may refer to the detection of particular objects or features (e.g., particles or cracks) that are relevant to whether a container and/or its contents should be considered defective, depending on the embodiment. Note: Examiner is interpreting the object as the technical component as an object and the quality state as whether it is defective or not), the method comprising the following steps:
providing image data showing the technical component (See Milne, ¶ [0042], Computer system 104 may generally be configured to control/automate the operation of visual inspection system 102, and to receive and process images captured/generated by visual inspection system 102);
providing a machine learning algorithm which is trained to ascertain a quality state of the technical component based on image data showing the technical component (See Milne, ¶ [0046], In various embodiments, the AVI neural network(s) trained and/or run by module 116 may classify entire images (e.g., defect vs. no defect, or presence or absence of a particular type of defect, etc.)), wherein the machine learning algorithm has been validated by a method for validating a machine learning algorithm (See Milne, ¶ [0047], Module 116 may run the trained AVI neural network(s) for purposes of validation, qualification, and/or inspection during commercial production. In one embodiment, for example, module 116 is used only to train and validate the AVI neural network(s), and the trained neural network(s) is/are then transported to another computer system for qualification and inspection during commercial production (e.g., using another module similar to module 116)); and
ascertaining the quality state of the technical component based on the provided image data and the provided machine learning algorithm (See Milne, ¶ [0046], In various embodiments, the AVI neural network(s) trained and/or run by module 116 may classify entire images (e.g., defect vs. no defect, or presence or absence of a particular type of defect, etc.)),
wherein the method for validating a machine learning algorithm includes (See Milne, ¶ [0047], In one embodiment, for example, module 116 is used only to train and validate the AVI neural network(s), and the trained neural network(s) is/are then transported to another computer system for qualification and inspection during commercial production (e.g., using another module similar to module 116)):
providing the machine learning algorithm which is trained to recognize objects in image data (See Milne, ¶ [0046], In various embodiments, the AVI neural network(s) trained and/or run by module 116 may classify entire images (e.g., defect vs. no defect, or presence or absence of a particular type of defect, etc.), detect objects in images (e.g., detect the position of foreign objects that are not bubbles within container images), or some combination thereof (e.g., one neural network classifying images, and another performing object detection)),
[generating labeled validation data for validating the machine learning algorithm, wherein the validation data each contain at least one disturbance variable, and
validating the machine learning algorithm based on the generated validation data, and wherein machine learning algorithm which is robust against the at least one disturbance variable has been selected based on validation results in order to ensure a desired process reliability in ascertaining a quality state of a technical component during a manufacturing process, wherein the at least one disturbance variable includes vibrations, humidity, and dust].
However, Milne fail(s) to teach generating labeled validation data for validating the machine learning algorithm, wherein the validation data each contain at least one disturbance variable, and validating the machine learning algorithm based on the generated validation data, and wherein machine learning algorithm which is robust against the at least one disturbance variable has been selected based on validation results in order to ensure a desired process reliability in ascertaining a quality state of a technical component during a manufacturing process, wherein the at least one disturbance variable includes vibrations, humidity, and dust.
Bu, working in the same field of endeavor, teaches: generating labeled validation data for validating the machine learning algorithm, wherein the validation data each contain at least one disturbance variable (See Bu, ¶ [0021], In some examples, the synthetic images may include foreground objects of randomized dimensions, textures, and positions rendered on randomly transformed backgrounds. In some examples, techniques to ensure photorealism may include simulation of non-uniform illumination, out-of-focus effects of camera lens, motion blurring, shadows cast by objects, and the addition of different kinds of noise.¶ [0069], The synthetic images and annotations may form a dataset for training the ML model, ..., The synthetic images may be used for each of the training data, test data, and validation data. Note: Examiner is interpreting the motion blurring as the disturbance variable), and
validating the machine learning algorithm based on the generated validation data (See Bu, ¶ [0069], The synthetic images and annotations may form a dataset for training the ML model, ..., The synthetic images may be used for each of the training data, test data, and validation data), and wherein machine learning algorithm which is robust against the at least one disturbance variable has been selected based on validation results in order to ensure a desired process reliability in ascertaining a quality state of a technical component during a manufacturing process (See Bu, ¶ [0021], In some examples, the synthetic images may include foreground objects of randomized dimensions, textures, and positions rendered on randomly transformed backgrounds. In some examples, techniques to ensure photorealism may include simulation of non-uniform illumination, out-of-focus effects of camera lens, motion blurring, shadows cast by objects, and the addition of different kinds of noise. ¶ [0074], The synthetic images may simulate ill-conditioned images with poor lighting, out-of-focus blurring, directional motion blurring, and various types of noise. Note: Examiner is interpreting the using ML model as selecting the model since the claim does not specify if the model is selected from a plurality of models. Examiner is interpreting motion blurring as the vibration disturbance since vibration would cause motion blurring), wherein the at least one disturbance variable includes vibrations, humidity, and dust (See Bu, ¶ [0074], The synthetic images may simulate ill-conditioned images with poor lighting, out-of-focus blurring, directional motion blurring, and various types of noise. Note: Examiner is interpreting the using ML model as selecting the model since the claim does not specify if the model is selected from a plurality of models. Examiner is interpreting motion blurring as the vibration disturbance since vibration would cause motion blurring).
Thus, it would have been obvious to one of ordinary skills in the art before the effective filing date of the claimed invention to modify Milne’s reference to generating labeled validation data for validating the machine learning algorithm, wherein the validation data each contain at least one disturbance variable, and validating the machine learning algorithm based on the generated validation data, and wherein machine learning algorithm which is robust against the at least one disturbance variable has been selected based on validation results in order to ensure a desired process reliability in ascertaining a quality state of a technical component during a manufacturing process, wherein the at least one disturbance variable includes vibrations, humidity, and dust based on the method of Bu’s reference. The suggestion/motivation would have been to keep the machine learning model robust in various conditions (See Bu, ¶ [0048]).
Further, one skilled in the art could have combined the elements as described above by known method with no change in their respective functions, and the combination would have yielded nothing more than predictable results.
Therefore, it would have been obvious to combine Bu with Milne to obtain the invention as specified in claim 9.
Regarding claim 10, Milne teaches the method according to claim 9, wherein the step of generating labeled validation data includes generating labeled validation data by using a generative adversarial network (See Milne, ¶ [0116], Thus, for example, the generator of a cycle GAN can transform a large number of non-defect, real-world container images (which as noted above are generally easier to obtain than defect images) to images that exhibit a particular class of defects. These transformed images can be added to image library 140 to expand the training and/or validation data).
Regarding claim 13, claim 13 is rejected the same as claim 9 and the arguments similar to that presented above for claim 9 are equally applicable to the claim 13, and all of the other limitations similar to claim 9 are not repeated herein, but incorporated by reference. Furthermore, Milne teaches a system for ascertaining a quality state of a technical component (See Milne, [FIG. 1], 100).
Regarding claim 14, claim 14 is rejected the same as claim 10 and the arguments similar to that presented above for claim 10 are equally applicable to the claim 14, and all of the other limitations similar to claim 10 are not repeated herein, but incorporated by reference.
Claim(s) 12 and 16 are rejected under 35 U.S.C. 103 as being unpatentable over Milne et al. (US 20230196096 A1, hereafter, "Milne") in view of Bu et al. (US 20240290027 A1, hereafter, "Bu") further in view of Cheng et al. (US 12429878 B1, hereafter, "Cheng").
Regarding claim 12, Milne in view of Bu teaches [The method according to claim 9, [wherein the labeled validation data are generated from sensor data acquired by a sensor, and wherein the labeled validation data relate to different alignments of the sensor].
However, Milne and Bu fail(s) to teach wherein the labeled validation data are generated from sensor data acquired by a sensor, and wherein the labeled validation data relate to different alignments of the sensor.
Cheng, working in the same field of endeavor, teaches: wherein the labeled validation data are generated from sensor data acquired by a sensor, and wherein the labeled validation data relate to different alignments of the sensor (See Cheng, [Col. 27, ln. 59–67 and Col. 28, ln. 1], The training data can include a plurality of training sequences (e.g., 143 sequences) divided between multiple datasets (e.g., a training dataset, a validation dataset, or testing dataset). Each training sequence can include a plurality of three-dimensional images (e.g., multi-modal sensor data). In some implementations, each training sequence can include images captured through one or more (e.g., five, etc.) different viewpoints (e.g., through cameras aimed at different camera angles, etc.)).
Thus, it would have been obvious to one of ordinary skills in the art before the effective filing date of the claimed invention to modify Milne’s reference to wherein the labeled validation data are generated from sensor data acquired by a sensor, and wherein the labeled validation data relate to different alignments of the sensor based on the method of Cheng’s reference. The suggestion/motivation would have been to improve the accuracy of object detection and recognition (See Cheng, [Col. 1, ln. 20–29]).
Further, one skilled in the art could have combined the elements as described above by known method with no change in their respective functions, and the combination would have yielded nothing more than predictable results.
Therefore, it would have been obvious to combine Cheng with Milne and Bu to obtain the invention as specified in claim 12.
Regarding claim 16, claim 16 is rejected the same as claim 12 and the arguments similar to that presented above for claim 12 are equally applicable to the claim 16, and all of the other limitations similar to claim 12 are not repeated herein, but incorporated by reference.
Allowable Subject Matter
Claim(s) 11 and 15 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
Claim(s) 11 and 15 contain subject matter that is not disclosed or made obvious in the cited art.
In regard to claim 11, when considering claim 11 as a whole, prior art of record fails to disclose or render obvious, alone or in combination:
“The method according to claim 9, wherein the step of validating the machine learning algorithm further includes the following steps:
for each generated validation data, respectively ascertaining a robustness value based on ground-truth information regarding the validation data, a magnitude of a corresponding one of the at least one disturbance variable, and output values of the machine learning algorithm for the validation data;
ascertaining a robustness value for the machine learning algorithm from the robustness values for all generated validation data; and
comparing the robustness value for the machine learning algorithm to a threshold value for the machine learning algorithm”.
In regard to claim 15, when considering claim 15 as a whole, prior art of record fails to disclose or render obvious, alone or in combination:
“The system according to claim 13, wherein the validation unit includes:
a first ascertainment unit configured to, for each of the generated validation data, respectively ascertain a robustness value based on ground-truth information regarding the generated validation data, a magnitude of a corresponding one of the at least one disturbance variable, and output values of the machine learning algorithm for the generated validation data;
a second ascertainment unit configured to ascertain a robustness value for the machine learning algorithm from the robustness values for all of the generated validation data; and
a comparison unit configured to compare the robustness value for the machine learning algorithm to a threshold value for the machine learning algorithm”.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Ziyadinov et al. (See NPL attached, “Noise Immunity and Robustness Study of Image Recognition Using a Convolutional Neural Network”) teaches the problem surrounding convolutional neural network robustness and noise immunity is currently of great interest. In this paper, we propose a technique that involves robustness estimation and stability improvement. We also examined the noise immunity of convolutional neural networks and estimated the influence of uncertainty in the training and testing datasets on recognition probability. For this purpose, we estimated the recognition accuracies of multiple datasets with different uncertainties; we analyzed these data and provided the dependence of recognition accuracy on the training dataset uncertainty.
Chung et al. (US 10726535 B2) teaches systems and methods relating to image processing and artificial intelligence. Given a small number of defect images, a multitude of other defect images can be generated to serve as training data sets for training artificially intelligent systems to recognize and detect similar defects. Given original images showing defects, a clean image of the background of the original images is created. The defect image is then isolated from each of the original images. The characteristics of each defect image are determined and characteristics of similar defects are also determined, either from other images or from subject matter experts. Based on these characteristics of similar defects, multiple other defect images are then generated. The generated defect images are combined with the clean image to result in defect images with a suitable background. Each of the resulting images can be used in training systems in recognizing and detecting defects.
Tetelman et al. (US 20230206055 A1) teaches a method is provided. The method includes generating a set of candidate training data based on a training data generator. The method also includes training a first machine learning model based on the set of candidate training data. The first machine learning model generates a set of inferences during the training based on the set of candidate training data. The method further includes determining a set of importance factors based on the set of inferences and a second machine learning model. The method further includes updating the training data generator based on one or more distributions of properties determined based on the set of importance factors.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to DION J SATCHER whose telephone number is (703)756-5849. The examiner can normally be reached Monday - Thursday 5:30 am - 2:30 pm, Friday 5:30 am - 9:30 am PST.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Henok Shiferaw can be reached at (571) 272-4637. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/DION J SATCHER/Patent Examiner, Art Unit 2676
/Henok Shiferaw/Supervisory Patent Examiner, Art Unit 2676
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