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 .
Style
In this action unitalicized bold is used for claim language, while italicized bold is used for emphasis.
Applicant Reply
“The claims may be amended by canceling particular claims, by presenting new claims, or by rewriting particular claims as indicated in 37 CFR 1.121(c). The requirements of 37 CFR 1.111(b) must be complied with by pointing out the specific distinctions believed to render the claims patentable over the references in presenting arguments in support of new claims and amendments. . . . The prompt development of a clear issue requires that the replies of the applicant meet the objections to and rejections of the claims. Applicant should also specifically point out the support for any amendments made to the disclosure. See MPEP § 2163.06. . . . An amendment which does not comply with the provisions of 37 CFR 1.121(b), (c), (d), and (h) may be held not fully responsive. See MPEP § 714.” MPEP § 714.02. Generic statements or listing of numerous paragraphs do not “specifically point out the support for” claim amendments. “With respect to newly added or amended claims, applicant should show support in the original disclosure for the new or amended claims. See, e.g., Hyatt v. Dudas, 492 F.3d 1365, 1370, n.4, 83 USPQ2d 1373, 1376, n.4 (Fed. Cir. 2007) (citing MPEP § 2163.04 which provides that a ‘simple statement such as ‘applicant has not pointed out where the new (or amended) claim is supported, nor does there appear to be a written description of the claim limitation ‘___’ in the application as filed’ may be sufficient where the claim is a new or amended claim, the support for the limitation is not apparent, and applicant has not pointed out where the limitation is supported.’)” MPEP § 2163(II)(A).
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 1-10 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention.
Claim 1 recites “recording at least one first calibration data set of structure-borne noise data from the running of the trained artificial intelligence using the first assessment data sets[.]” The language “recording . . . structure-borne noise data from running of the trained artificial intelligence” implies that structural vibrations and model outputs are the same thing. It is not clear where the Specification describes operations that provide support for this technique.
All dependent claims are rejected as containing the limitations of the claims from which they depend.
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-10 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor, or for pre-AIA the applicant regards as the invention.
Claim 1 recites “recording training data sets of structure-borne noise data, the training data sets each being recorded under one or more conditions while a force acts on a component . . . the at least one first calibration data set being recorded under one or more conditions different from the one or more conditions used during the recording of the training data sets . . . assessing at least one of a second condition of the one or more conditions[.]” It is not clear whether “a second condition of the one or more conditions” refers back to “the one or more conditions used during the recording of the training data sets” or to “one or more conditions different from the one or more conditions used during the recording of the training data sets[.]” The former seems more likely, but the latter is also plausible. Since there are two reasonable but inconsistent interpretations of the claim language, claim 1 is indefinite.
Claim 1 recites “training an artificial intelligence . . . running the artificial intelligence trained on the training data set . . . running of the trained artificial intelligence . . . retraining the artificial intelligence trained on the training data sets using the at least one first calibration set[.]” It is not clear whether “the trained artificial intelligence” refers to the same claim element as “the artificial intelligence trained on the training data set.” It is suggested that unique terms be used for different claim elements.
Claim 1 recites “recording at least one first calibration data set of structure-borne noise data from the running of the trained artificial intelligence using the first assessment data sets[.]” It is not clear whether “recording . . . structure-borne noise data from the running of the trained artificial intelligence” refers to physical recording of noise (i.e. using a microphone to pick up vibrations) or if this language refers to recording the output of a model. The language implies both. Since there are two similarly reasonable but inconsistent interpretations of the claim language, the claims are indefinite.
Claim 1 recites “running the artificial intelligence trained on the training data sats using the_ first assessment data sets as an input to determine whether the first assessment data sets are associated with a defective first component from the production process[.]” The remarks and some language in the Specification are consistent with limiting the scope of “a defective first component from the production process” to a component being produced. Further, Applicant’s Remarks indicate that scope of a “defective first component from the production process” should be limited to a component that is undergoing manufacture. Rem. 2-3. But the plain meaning of a “defective first component from the production process” reads on any component used in the process. For instance, components of robots used on an assembly line include components that are “from the production process.” This leaves two inconsistent but similarly reasonable interpretations of claim 1. Therefore, the claim is indefinite.
Claim 1 recites “wherein the at least one second condition and the second component represent a deviation in the production process from the first condition and the defective first component.” It is not clear how to interpret a “second component” that “represent[s] a deviation from . . . the first component.” A second component may be different from a first component. Similarly, a second component may have different attributes from a first component. But relating two components based on one “represent[ing] a deviation from” the other so unconventional, in English, as to render the claimed relationship unclear. Further, the claim language could be read as requiring “the second component represents a deviation in the production process from . . . and the defective first component.” This implies two components that have undergone different production processes, without actually requiring that to be the case. This makes it unclear whether or not the first and second components are limited such that they have undergone different production processes.
Claim 3 recites “recording second assessment data sets from the retrained artificial intelligence based on the at least one first calibration data set[.]”This could be understood as recording the output of “the retrained artificial intelligence” in response to the calibration data set being input to the model. But that seems inconsistent with the language “recording second assessment data sets[.]” Since it is not clear whether the second assessment data sets are model outputs or measured data, the claim scope is indefinite.
Claim 3 recites “running the retrained artificial intelligence using the at least one first calibration data set as the input[.]” The “first calibration set” was used to train and thereby “establish” “the retrained artificial intelligence.” See Claim 1. Therefore, “running” the “artificial intelligence” (model) appears to refer to using the model to predict its own training data. This is a rather useless task in the world of machine learning because testing a model on its own training data effectively asks the model for answers that the model already has (instead of determining whether the model has generalized the solution or using the model to make online predictions during use of the trained model.) Further, nothing in the Specification indicates any reason for performing this extremely unconventional operation. The combination of ambiguity in the language, use of the generic term “running,” and the futility of testing a model on its own training data with no description of any advantage of this unconventional operation in the Specification tend to support the conclusion that this is not meant, despite the plain meaning of the language. Alternatively, this could refer to validation or testing of the model with a subset of the first calibration data, but that is not what the claim says. Yet another possibility is that this limitation refers to further training of the second trained artificial intelligence, (beyond that required in claim 1.) Since there are multiple possible interpretations, none of which are clearly more likely than the other(s), the claim is indefinite. Similarly, claim 3 recite “recording second assessment data sets from the retrained artificial intelligence based on the at least one first calibration set; and retraining the retrained artificial intelligence using the second assessment data sets[.]” This also appears to use a model’s output to retrain itself. While unsupervised techniques using the output of a model exist, it requires more than just feeding the output of a model back to itself. But the claims appear to recite merely feeding the output of a model back to itself. This is confusing, because this operation, alone, would not automatically update the model. Since the claimed operations do not appear to have the claimed result of a “twice retrained artificial intelligence being configured to assess whether a defective second component . . . is associates with the second assessment data sets,” at least differently than the “retrained artificial intelligence,” the claims can more reasonably be read such that the “recording” refers to actual data recording. It is further submitted that, if a technique of unsupervised learning is meant, it may be helpful to put this technique in the claims.
Claim 3 recites “recording second assessment data sets from the retrained artificial intelligence based on the at least one first calibration set[.]” It is not clear how “recording” is “based on the at least one first calibration set.” Specifically, it is not clear whether this language requires inputting the “at least one first calibration set” into the model, or if the “first calibration set” is somehow applied to the recorded second assessment data sets. Since there are at least two inconsistent but similarly reasonable ways of interpreting the claim the claim language is indefinite.
Claim 6 recites “wherein the retrained artificial intelligence is trained using the second calibration data sets.” The convention in this claim set is to rename a model after training on a given training data set. See e.g. claim 1 ll. 6-7, 16-17 and claim 7 ll. 4-5. The language of claim 6 does not follow this pattern, thereby differentiating itself from the language of the other claims. This indicates that the “second calibration data sets” are used in the creation of the “retrained artificial intelligence.” Note that this interpretation would require a change in the structure of all instances of “retrained artificial intelligence” recited in claims from which claim 6 depends. Alternatively, this claim could be read as modifying the “retrained artificial intelligence” recited in claim 1 to create another, unnamed, “artificial intelligence.”
All dependent claims are rejected as containing the limitations of the claims from which they depend.
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 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.
Claim 1, 3-4, 6, 9-10 are rejected under 35 U.S.C. 103 as being unpatentable over Lee (Deep convolutional neural network with new training method and transfer learning for structural fault classification of vehicle instrument panel structure, 2020) Dohr (US 2023/0288896; Foreign application filed 2020; PCT filed 2021; different assignee) and Sun (Deep Transfer Learning Based on Sparse Autoencoder for Remaining Useful Life Prediction of Tool in Manufacturing, 2019.)
1. A computer-implemented method for assessing structure-borne noise, the method comprising the following steps: (Lee teaches “Structural defect have been detected by attaching sensors to all possible defect locations. A new method is proposed to enable the identification of structural defect locations with minimal data collection points using a deep convolutional neural network.” Lee Abstract.) - recording training data sets of structure-borne noise data, the training data sets each being recorded under one or more conditions while a force acts on a component, and the structure-borne noise data depending on the force; - training an artificial intelligence using the training data sets; (“In this study, vibration data were generated by applying white Gaussian random force to a finite element (FE) model of a vehicle’s instrument panel structure, and used for the training and test data of SFC-DCNN. Vibration data were taken from a maximum of 10 measurement points in 50 seconds and augmented by sampling the data in the time domain to have a sufficient number of data.” Lee P. 4489-4490.) - recording first assessment data sets of structure-borne noise data during a production process; (“ . . . vibration data were generated by applying white Gaussian random force to a finite element (FE) model of a vehicle’s instrument panel structure, and used for the training and test data of SFC-DCNN.” Lee PP. 4489-4490.
Lee does not expressly teach recording data sets “during a production process.”
Dohr (US 2023/0288896) teaches “The object is achieved by a method for producing a plurality of transmission components of a transmission component type, wherein the transmission components are manufactured in a manufacturing machine, wherein at least one vibration sensor is mounted on the manufacturing machine, wherein vibrations during manufacture of the transmission components in the manufacturing machine are detected by way of the vibration sensor, wherein the detected vibrations are in each case evaluated by a computer unit, for example an industrial PC or a virtual machine, wherein the transmission components are in each case classified by the computer unit into one of at least two classes of the transmission component type depending on the detected vibrations.” Dohr ¶9. “According to the present disclosure, a quality check is carried out during production of a transmission component directly during the manufacture of the transmission component.” Dohr ¶10.
It would have been obvious to one of ordinary skill in the art before the effective filing date to combine the teaching of Dohr with respect to this limitation because it is useful to detect issues effecting manufacturing in real time (i.e. so you can avoid making more of them.) See also Dohr ¶11.
- running the artificial intelligence trained on the training data sets using the first assessment data sets as an input to determine whether the first assessment data sets are associated with a defective first component from the production process; (“SFC-DCNN was trained and tested[.]” Lee P. 4490.) and - recording at least one first calibration data set of structure-borne noise data, from the running of the trained artificial intelligence using the first assessment data sets as the input, the at least one first calibration data set being recorded under one or more conditions different from the one or more conditions used during the recording of the training sets; - retraining the artificial intelligence trained on the training data sets using the at least one first calibration data set, the retrained artificial intelligence being adapted for assessing a least one of a second condition of the one or more conditions and a second component of the production process based on the at least on calibration data wherein the at least one second condition and the second component represent a deviation in the production process from the first condition and the defective first component. (“Transfer learning for the initial value was used to increase the accuracy of classifying a more complicated structural defect problem by using the extracted features from a less complicated structural defect problem.” Lee P. 4489. “The first training method is training the whole SFC-DCNN architecture based on the He initial value. The second training method is transfer learning by using the optimized parameters from Sec. 3 as the initial value of in this study. It is common to pretrain a network on a very large dataset, and then to use the optimized parameters of the network as either an initialization or a fixed feature extractor for a case that does not have sufficient training data. Transfer learning that was used in this study is pretraining the network on a relatively easy task of classifying boundary conditions and then the optimized parameters used as an initial value for a more complicated task. The major benefit of this process is that structural characteristics can be extracted well with an easily distinguishable task.” Lee P. 4496. The teaching of using one model as an “initial value” for “transfer learning” “for a case that does not have sufficient training data” would be understood by one of ordinary skill as a teaching of further training of a pretrained model on data in a different domain (i.e. a calibration data set.”
But the previously cited art does not expressly teach “recording” of calibration data. Note also that the support for this limitation in paragraph 18 of the Specification describes operations that read on transfer learning.
Sun teaches “An SAE [Sparse Auto Encoder] network is first trained by run-to-failure data with RUL information of a cutting tool in an off-line process. The trained network is then transferred to a new tool under operation for on-line RUL [Remaining Useful Life] prediction. The prediction result with high accuracy shows advantage of the DTL method for RUL prediction.” Sun 2416. “In this kind of method, multivariable sensors, like vibration sensor, acoustic emission sensor, and torque sensor, are added to machine tool for sensing operation states of machine.” Sun 2416. Further, the algorithm on page 2421 of Sun teaches training and SAE before steps described as “1) Copy the trained SAE to a new deep SAE network. 2) Input data of the new tool to the new SAE network, performing feature transfer learning and weight update to update the new SAE.” Sun p. 2421. “Monitoring data of tool2 are input to this network for feature extraction. Features learned for tool2 by the DTL network after weight transfer, features transfer learning, and weight update is shown in Fig. 7.” Sun p. 2422. In other words, the data from the new tool is input to a model trained on another tool, thereby forming a new model trained on data from both tools in a form of transfer learning.
It would have been obvious to one of ordinary skill in the art before the effective filing date to combine the teaching of Sun because collecting new data of a under a different condition (from the original training data) and using the new to update the model is a data efficient way of training models when there is minimal data available for a specific use case.
3. The method of claim 1, wherein after retraining the artificial intelligence, the method comprising: running the retrained artificial intelligence using the at least one first calibration data set as the input; recording second assessment data sets from the retrained artificial intelligence based on the at least one first calibration set; and retraining the retrained artificial intelligence using the second assessment data sets, the twice retrained artificial intelligence being configured to assess whether a defective second component from the production process is associated with the second assessment data sets. (There is no clear way of interpreting this claim language. “First, where the degree of uncertainty is not great, and where the claim is subject to more than one interpretation and at least one interpretation would render the claim unpatentable over the prior art, an appropriate course of action would be for the examiner to enter two rejections: (A) a rejection based on indefiniteness under 35 U.S.C. 112(b) or pre-AIA 35 U.S.C. 112, second paragraph; and (B) a rejection over the prior art based on the interpretation of the claims which renders the prior art applicable. See, e.g., Ex parte Ionescu, 222 USPQ 537 (Bd. App. 1984). When making a rejection over prior art in these circumstances, it is important for the examiner to point out how the claim is being interpreted. Second, where there is a great deal of confusion and uncertainty as to the proper interpretation of the limitations of a claim, it would not be proper to reject such a claim on the basis of prior art. As stated in In re Steele, 305 F.2d 859, 134 USPQ 292 (CCPA 1962), a rejection under 35 U.S.C. 103 should not be based on considerable speculation about the meaning of terms employed in a claim or assumptions that must be made as to the scope of the claims.” MPEP § 2173.06(II). See rejections under §112b, above. In an attempt at compact prosecution, art is cited for the best guess at the scope of this claim. The claims recite “running” of the “retrained artificial intelligence using” the same data used to train the model. This reads on both training of the model using the same dataset (see rejection of claim 1) and on testing the model (see rejection of claim 1.) The claimed “recording second data assessment data sets from the retrained artificial intelligence” and separately retraining the same model to determine defects based on the same data recorded from itself, may be meant to recite the continuous process of evaluating data by the retrained artificial intelligence, which was trained based on the first calibration data set. That aspect is taught in the art cited in the rejection of claim 1. Note also that training using outputs of a model is a repetitious process. See e.g. Lee P. 4496.)
4. The method of claim 3, further comprising using the twice retrained artificial intelligence for assessing the first and/or second assessment data sets as being associated with defect-free components or as being associated with at least one of the defective first and second component, respectively. (See Lee P. 4494 Tables 5 and 6.)
6. The method of claim 3, further comprising using a first type of component for recording the training data sets, the first and second assessment data sets, and the at least one first calibration data set, and, after the assessment of the second assessment data sets, (There is no antecedent basis for “the second assessment data sets” or for “the assessment of the second assessment data sets.” Without any specific distinction between the “first and second assessment data sets” this language reads on subsets of the testing data of Lee. Lee teaches “The response is acceleration from 10 measurement points that are assigned in IP structure shown in Fig. 3. Acceleration data from the instrument panel structure’s FE model were stacked in sequence at each measurement point. Input data were augmented by cutting the total 50 seconds of data into 0.2 second lengths. The time step is 0.001 seconds, and 1000 input data samples were prepared. 80% of the data were used for training, and 20% was used for testing.” Lee P. 4492. “The overall procedure is the same as in the case study in Sec. 3. Classification with SFC-DCNN was conducted with 800 training sets and 200 test sample sets.” Lee P. 4496.) the method further comprises using a second type of component for recording second calibration data sets, wherein, upon recording of the training data sets, the first and second assessment data sets and the at least one first calibration data set are not used for a second type of component, and wherein the retrained artificial intelligence is trained using the second calibration data sets. (Note that Lee teaches multiple experiments for sensing multiple structural flaws test data an data used for transfer learning (calibration data.) “SFC-DCNN was trained and tested with three cases of structural defects and one case of multi-channel acoustic noise data for classifying gear fault from public dataset [4]. The first structural defect case is for classifying the boundary conditions of an instrument panel structure, the second and third case is for classifying the conditions for partial weld elimination and cracks respectively.” Lee P. 4490.)
9. The method of claim 1, wherein the components are part of motor vehicles. (See rejection of claim 1.)
10. A system comprising a digital electronic storage medium and a digital electronic processing unit, wherein instructions are stored in the storage medium, the processing unit is configured to read out and execute the instructions, and the processing unit is configured to perform the method of claim 1 when executing the instructions. (See rejection of claim 1. One of ordinary skill in the art would understand the number of operations required to train the convolutional neural network of figure 1 table 1, as a teaching of a model implemented in software using a processor and memory. See also Lee P. 4496 (“Classification with SFC-DCNN was conducted with 800 training sets and 200 test sample sets. The batch number is 50, the learning rate is 0.001, and 1000 epochs were used in this study.”)
Claims 2 is rejected under 35 U.S.C. 103 as being unpatentable over Lee, Dohr, Sun and Sohail (A Comprehensive Introduction to Clustering Methods, 2018.)
2. The method of claim 1, further comprising: using the trained artificial intelligence to apply a quality score to each training data set and classify the training data sets into clusters based on the applied quality score of the training data sets, wherein only training data sets with a quality score above a limit are used for training the artificial intelligence. (The previously cited art does not teach classifying training data sets into clusters.
Sohail teaches “In terms of unsupervised learning methods, some of the most well researched and common methods can be grouped under clustering. The basic idea is simple. If you can figure out how to define distances between data points, then data points that are closer together may exhibit some kind of group characteristic we could exploit for modeling or extract new understanding from. Some examples include patients with similar blood test results that have the same disease, consumers with a similar purchase history that are part of the same socioeconomic class or occupation, and flowers with similar colors and petal lengths that are part of the same species. Obviously, some of these problems can be solved as classification problems, but this is only possible if the labels are available. Clustering, as with other unsupervised methods, operate without a label of interest.” Sohail PP. 1-2. See also Sohail P. 10-11 showing points that are closer being grouped in the same group. That the data exhibits an observable clustering pattern, indicates a level of “quality score” in the training data.
It would have been obvious to one of ordinary skill in the art before the effective filing date to combine the teaching of Sohail because clustering is one way of preparing unsupervised data for use in training machine learning models and avoid the task of labeling data.)
Claim 5 is rejected under 35 U.S.C. 103 as being unpatentable over Lee, Dohr, Sun, and Wang (Detecting anomalies in time series data from a manufacturing system using recurrent neural networks, 2020)
5. The method of claim 4, wherein only structure-borne noise data from defect- free components is used to record the at least one first calibration data set. (The previously cited art does not expressly state that only data from defect-free components is used to record the calibration set.
Wang teaches “The model takes time series data as an input and reconstructs the input data. Time series data with an anomaly would causes patterns in the reconstruction errors that are inconsistent with error patterns of anomaly-free data.” Wang Abstract. “In general, anomalous data show patterns that are different from anomaly-free data (i.e., data collected during routine functioning of the system). For example, as shown in Fig. 1, in assembling a crank, the rotational angle of the driving equipment and the corresponding torque applied to rotate the crank can be recorded as a time series.” Wang P. 823 col. 2. “But these methods need a large number of labeled anomalies (the anomalies must be identified a priori) to train the model; such detailed information is usually not available [9]. . . . A second group of studies uses semi-supervised learning or unsupervised learning to solve the problems caused by the lack of labeled anomalies [9]. The basic idea of these methods is to use time series data collected under routine operation to establish a time series model (including estimates of parameters). . . . Since the model was trained using data having no anomalies, the reconstruction errors corresponding to data with anomalies would be large [21].” Wang P. 824 col. 1. “The proposed model was trained using sensor data from routine operation of a diesel engine assembly line (anomaly-free data set).” Wang P. 829 col. 1.
It would have been obvious to one of ordinary skill in the art before the effective filing date to combine the teaching of Wang because defect free data is easier to acquire than data associated with a given defect.)
Claims 7-8 are rejected under 35 U.S.C. 103 as being unpatentable over Lee, Dohr, Sun, and Wang2 (Transfer learning for enhanced machine fault diagnosis in manufacturing)
7. The method of claim 6, further comprising: using the second type of component for recording third assessment data sets of structure-borne noise data; retraining the twice retrained artificial intelligence with the second calibration data; (The Specification describes a third data set used to analyze a different component. See Spec. ¶¶18-19 (“For example, when the first type of component is used for a first motor vehicle model for a particular purpose, and the second type of component is used for the same purpose in a second motor vehicle model, the training with the second calibration data sets can be sufficient for assessing the structure-borne noise data of the second type of component. Advantageously, a completely new training of the artificial intelligence or another artificial intelligence can then be omitted. [0019] According to some embodiments, third assessment data sets of structure-borne noise data can be recorded using a second type of component. The third assessment data sets can be assessed after training the artificial intelligence with the second calibration data sets. For example, the assessment can comprise the third assessment data sets being assessed as belonging to defective or defect-free components.”) Without any limitation on the type of data in the calibration data set, this limitation reads on merely further training of the twice retrained artificial intelligence with more “calibration data” from another “type” of component. Sun teaches “An SAE [Sparse Auto Encoder] network is first trained by run-to-failure data with RUL information of a cutting tool in an off-line process. The trained network is then transferred to a new tool under operation for on-line RUL [Remaining Useful Life] prediction. The prediction result with high accuracy shows advantage of the DTL method for RUL prediction.” Sun 2416. “In this kind of method, multivariable sensors, like vibration sensor, acoustic emission sensor, and torque sensor, are added to machine tool for sensing operation states of machine.” Sun 2416. Further, the algorithm on page 2421 of Sun teaches training and SAE before steps described as “1) Copy the trained SAE to a new deep SAE network. 2) Input data of the new tool to the new SAE network, performing feature transfer learning and weight update to update the new SAE.” Sun p. 2421. “Monitoring data of tool2 are input to this network for feature extraction. Features learned for tool2 by the DTL network after weight transfer, features transfer learning, and weight update is shown in Fig. 7.” Sun p. 2422. In other words, the data from the new tool is input to a model trained on another tool, thereby forming a new model trained on data from both tools in a form of transfer learning.
The previously cited art does not expressly teach transfer learning between two types of components.
Wang2 teaches “One limiting factor for successful DL applications is the availability of sufficient amount of data of relevance to the specific application. A solution is presented in this paper for cross domain data learning and effective network training, enabled by the generalization of the DL’s feature learning capability, which is independent of the specific application domains. The developed method is experimentally verified by transferring a DL model trained by non-manufacturing data to manufacturing machine condition monitoring, and transferring model among different working conditions and machines.” Wang2 Abstract. “Transfer Learning (TL) [7] may provide a promising solution to the problems by (1) adapting a network that is well trained using generic data to manufacturing domain problems, and (2) further transferring the adapted network across multiple scenarios (such as different types of machines or fault severity levels) within the manufacturing domain to solve a specific, manufacturing related problem.” Wang2 p. 413. “The paper presents a CNN-based TL technique, using vibration analysis for rolling bearing fault diagnosis as a case study.” Wang2 P. 413. “Specifically, by adapting a pretrained CNN structure using non-manufacturing data (i.e., model transfer) and transferring the adapted network structure to different fault severity levels and bearing types (i.e., feature transfer), a new approach to solving the two existing problems for fault diagnosis of rolling bearings is demonstrated.” Wang2 pp. 413-414. “Subsequent to the model transfer, feature transfer has been performed to generalize the adapted VGG19 to different operating conditions, fault types, severities, and machine types. This is achieved by further modifying the FC layers to extract common latent features that are representative of different data under different scenarios (e.g. from different bearings).” Wang2 P. 415. See also Wang2 Fig. 1.
It would have been obvious to one of ordinary skill in the art before the effective filing date to combine the teaching of Wang2 because 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 are predictable to one of ordinary skill in the art. The scope and content of the prior art, whether in the same field of endeavor as that of the applicant’s invention or a different field of endeavor, included a similar or analogous device (method, or product) (Wang teaches use of transfer learning to create models for one set of industrial machinery based on previously created models trained on a different type of machine (with another type of bearing.) This is analogous to the claimed use of transfer learning for using a model trained on a different type of component.) There were design incentives or market forces which would have prompted adaptation of the known device (method, or product) (as explained in Wang, there was design incentive: the lack of data specific to a given type of device or machine part. The differences between the claimed invention and the prior art were encompassed in known variations or in a principle known in the prior art (the difference between using this technique on a manufactured part and using this technique on a part of a machine in the factory are encompassed in the concept explained in Wang2. See Wang2 Abstract and Wang2 p. 413. One of ordinary skill in the art, in view of the identified design incentives or other market forces, could have implemented the claimed variation of the prior art, and the claimed variation would have been predictable to one of ordinary skill in the art. See MPEP § 2143(I)(F).) and running the thrice retrained artificial intelligence using the third assessment data sets as the input, the thrice retrained artificial intelligence assessing whether the third assessment data sets are associated with defect-free or defective third components. (As best understood, this claim recites yet another component used for collecting data and training a model in the same way as claim 6. See rejection of claim 6 showing Lee to teach at least three different types of data used to train three different models.)
8. The method of claim 7, wherein the assessment of the first, second, and/or third assessment data sets comprises assessing, a movement of the respective component relative to another component, a strength of the respective component, and/or the presence of an imbalance. (See Lee P. 4494 teaching the sensing of cracks and missing welds.)
Response to Arguments
Applicant's arguments filed 08/04/2026 have been fully considered but they are not persuasive.
Rejections under § 101:
No specific arguments are submitted.
Rejections under § 112b:
No specific arguments are submitted.
Rejections under § 103:
There is a great deal of uncertainty as to the proper interpretation of the limitations of the independent claim. In the interest of compact prosecution, a rejection over the closest prior art was written to apprise Applicant of his/her best path forward. Note however, the MPEP guides examiners to omit a rejection where there is a great deal of uncertainty as to the claim scope. “Second, where there is a great deal of confusion and uncertainty as to the proper interpretation of the limitations of a claim, it would not be proper to reject such a claim on the basis of prior art. As stated in In re Steele, 305 F.2d 859, 134 USPQ 292 (CCPA 1962), a rejection under 35 U.S.C. 103 should not be based on considerable speculation about the meaning of terms employed in a claim or assumptions that must be made as to the scope of the claims.” MPEP § 2173.06. It is submitted that the most productive path toward allowance is to clarify the claim scope. If Applicant’s representative believes Examiner is missing the inventive concept and would be able to articulate the concept via telephone or video call, an interview may be productive.
Applicant states that the tool in Sun is not being produced and suggests that Sun does not teach a production process. Rem. 3. Sun teaches a tool used in a production process. Further, Dohr is cited for the teaching of using vibrations to determine defects in a component undergoing production.
Conclusion
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PAUL M. KNIGHT
/PAUL M KNIGHT/
Primary Examiner, Art Unit 2148
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