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 .
Specification
The lengthy specification has not been checked to the extent necessary to
determine the presence of all possible minor errors. Applicant's cooperation is
requested in correcting any errors of which applicant may become aware in the
specification.
Examiner Notes
Examiner cites particular columns, paragraphs, figures and line numbers in the references as applied to the claims below for the convenience of the applicant. Although the specified citations are representative of the teachings in the art and are applied to the specific limitations within the individual claim, other passages and figures may apply as well. It is respectfully requested that, in preparing responses, the applicant fully consider the references in their entirety as potentially teaching all or part of the claimed invention, as well as the context of the passage as taught by the prior art or disclosed by the examiner. The entire reference is considered to provide disclosure relating to the claimed invention. The claims & only the claims form the metes & bounds of the invention. Office personnel are to give the claims their broadest reasonable interpretation in light of the supporting disclosure. Unclaimed limitations appearing in the specification are not read into the claim. Prior art was referenced using terminology familiar to one of ordinary skill in the art. Such an approach is broad in concept and can be either explicit or implicit in meaning. Examiner's Notes are provided with the cited references to assist the applicant to better understand how the examiner interprets the applied prior art. Such comments are entirely consistent with the intent & spirit of compact prosecution.
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 1-20 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 applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claims 1, 10 and 16 recite “wherein the identified different training data is determined to be more suitable for the use case for which the model is to be used for representing the infrastructure;”. The term more suitable is a relative term which renders the claim indefinite. The term “more suitable” is not defined by the claim, the specification does not provide a standard for ascertaining the requisite degree, and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention.
Therefore, the claims have an indefinite scope. Since dependent claims are dependent on the independent claims and included all the limitations of the independent claims, the dependent claims recite the indefinite scope in the independent claims.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. These claims are directed to an abstract idea without significantly more.
As to claim 1,
Step 1: Claim 1 is directed to a method. Therefore, the claim is eligible under Step 1 for being directed to processes.
Step 2A Prong One
Claim 1 recites
obtaining at least one virtual representation of an infrastructure, wherein the virtual representation comprises at least one model useable to represent the infrastructure; (input and data description)
identifying training data from at least one of a plurality of data sources, wherein the identified training data is determined to be suitable for a use case for which the model is to be used for representing the infrastructure; (mental process)
training the model based on the identified training data; (mere instructions to apply an exception)
monitoring at least one of a performance and an accuracy of the model; (mental process)
identifying different training data from at least one of the plurality of data sources, responsive to the monitoring, wherein the identified different training data is determined to be more suitable for the use case for which the model is to be used for representing the infrastructure; (mental process)
and retraining the model based on the identified different training data; (mere instructions to apply an exception)
wherein the steps are performed by at least one processor and at least one memory storing executable computer program instructions. (generic computer function)
The claimed concept is a method of evaluating training data and model which is directed to “Mental Process” grouping. These limitations can be performed in a human mind or using pen and paper.
Recitations of “training the model based on the identified training data” and “retraining the model based on the identified different training data” amounts to mere instructions to apply an exception in accordance with MPEP 2106.05(f) (1) and (3). For example, the claim recites only the idea of a solution or outcome i.e., the claim fails to recite details of how a solution to a problem is accomplished. Therefore, claim 1 is an abstract idea.
Therefore, claim 1 is an abstract idea.
Step 2A Prong Two
The obtaining at least one virtual representation of an infrastructure step is recited at a high level of generality (i.e., as a general means of obtaining input for use in the evaluation step) and amounts to mere data inputting, which is a form of insignificant extra-solution activity.
The claim recites additional elements such as “wherein the steps are performed by at least one processor and at least one memory storing executable computer program instructions”. Each of the additional limitations is no more than mere instructions to apply the exception using a generic computer component. Simply implementing the abstract idea on a generic computer is not a practical application of the abstract idea. See applicant’s specification page 19-20 and Fig. 7 for generic computer description.
The judicial exception is not integrated into a practical application.
Step 2B:
The same analysis of Step 2A Prong Two applies here in 2B. That is, simply using a conventional machine to collect data. The present claim does not recite any limitation that would integrate a judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B. See MPEP 2106.05(d).
The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception. In particular, the claim limitations do not recite a combination of additional elements that tie or “integrate the invention into a practical application”.
Thus, claim 1 is not patent eligible. Same conclusion for dependent claims of claim 1. See below.
2. The method of claim 1, further comprising applying data anonymization to one or more of the identified training data and the identified different training data prior to training and retraining the model, respectively. (mental process)
3. The method of claim 1, wherein the plurality of data sources comprises an operational data source, a test data source, and a synthetic data source. (data description)
4. The method of claim 1, wherein identifying training data further comprises computing respective suitability scores for a plurality of datasets associated with the infrastructure based on the use case for which the model is to be used for representing the infrastructure and identifying the training data based on the computed suitability scores. (mental process)
5. The method of claim 4, wherein identifying different training data further comprises recomputing respective suitability scores for the plurality of datasets associated with the infrastructure based on the monitoring being indicative of at least one of a performance and an accuracy being below a given threshold. (mental process)
6. The method of claim 5, further comprising adjusting data anonymization applied to one or more of the identified training data and the identified different training data based on one or more of the suitability scores. (mental process)
7. The method of claim 1, wherein the use case corresponds to at least one attribute associated with the infrastructure. (data description)
8. The method of claim 1, wherein the model comprises an artificial intelligence-driven model. (data description)
9. The method of claim 1, wherein the virtual representation comprises at least one digital twin. (data description)
Same conclusion for independent claims 10, 16 and dependent claims. The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception. In particular, the claim limitations do not recite a combination of additional elements that tie or “integrate the invention into a practical application”.
Thus, claims 1-20 are not patent eligible.
Claim Rejections - 35 USC § 102
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 the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claim(s) 1, 3-5, 7-10, 12-14, 16, 18-19 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Elkhomri (US 2023/0144359 A1).
Claim 1. A method, comprising: Elkhomri discloses obtaining at least one virtual representation of an infrastructure, wherein the virtual representation comprises at least one model useable to represent the infrastructure;
Elkhomri: [0018] “A digital twin may be an integrated multi-physics, multi-scale, probabilistic simulation of an as-built system, enabled by digital thread, that uses the best available models, sensor information, and input data to emulate activities and/or performance over the life of a physical system (e.g., a well system). …”
Examiner considers the well system as infrastructure.
Elkhomri discloses identifying training data from at least one of a plurality of data sources, wherein the identified training data is determined to be suitable for a use case for which the model is to be used for representing the infrastructure;
Elkhomri: [0024] “Keeping with FIG. 1, in some embodiments, the digital twin manager (160) may include hardware and/or software with functionality for determining an updated well dynamics behavior (e.g., mass flow, pressure, and temperature) using a ML algorithm (162) based on the modeled well dynamics behavior data (e.g., physics-based well dynamics behavior data (164)) obtained from the physics-based model server (130) and obtained well dynamics behavior data (163) from the well system (e.g., well mass flow data A (111), … well temperature data B (123)). In some embodiments, the digital twin manager (160) transmits a command (e.g., command (194), command (195)) to a well site (e.g., well site A (110), well site B (120), respectively) to perform one or more well operations to control well activities (e.g., to control well subsystem A (114), …and other well activities data stored in a database (e.g., database A (115), database B (125)) for the well system. For example, a digital twin manager (160) may implement a ML model (165) to update well mass flow stored in a database (166) based on the obtained well dynamics behavior data from the physics-based model server (130) (e.g., physics-based well dynamics behavior data (164)) and well dynamics behavior data (163) from one or more wells to tune a particular monitoring criterion (161) for the well system. Thus, different inputs (e.g., types of data or different data sources) may provide the initial setup of a particular monitoring criterion, where the data inputs may be customized by a data preprocessing module (168) according to different physics-based models to better arrange a monitoring criterion.”
Elkhomri discloses training the model based on the identified training data;
Elkhomri: [0026] “In some embodiments, for example, the digital twin manager (160) applies one or more ML algorithms (162) (e.g., an artificial neural network) to train a ML model (165) to determine well dynamics behavior at a well (e.g., well site A (110), well site B (120)). Likewise, the digital twin manager (160) includes a model validation module (167) to validate the ML model (165). In some embodiments, a digital twin manager (160) may generate augmented or synthetic data to produce a large amount of interpreted data for training a particular model. …”
Elkhomri discloses monitoring at least one of a performance and an accuracy of the model;
Elkhomri: [0041] “… The k-fold inner cross-validation and the k-fold outer cross-validation may have different values of the “k” parameter. In some example embodiments, the nested inner/outer cross-validation defines one or more physics constrained machine learning algorithms and their corresponding models in a grid and evaluates one or more performance metrics of interest (e.g., area under curve (AUC), accuracy, geometric mean, f1 score, mean absolute error, mean squared error, sensitivity, specificity, etc.) to find the optimal parameters of the physics constrained machine learning model.”
Elkhomri discloses identifying different training data from at least one of the plurality of data sources, responsive to the monitoring, wherein the identified different training data is determined to be more suitable for the use case for which the model is to be used for representing the infrastructure;
Elkhomri: [0040] “Furthermore, the physics constrained machine learning algorithms (200) may include an activation function in a ReLU layer (e.g., hidden layer F (286)) to calculate the misfit function based on the difference between the updated well dynamics behavior data (250) and a ground truth (e.g., field well dynamics behavior data (210) obtained from the well system). In some embodiments, a physics constrained machine learning algorithm (200) may use a simple data split technique to separate the input dynamics behavior data (e.g., physics-based well dynamics behavior data (205) and field well dynamics behavior data (210)) used for the training, validation, and testing of the physics constrained machine learning models. An example, the data split technique may consider 70% of the obtained well dynamics behavior data for model training (e.g., tuning of the model parameters), 15% of the obtained well dynamics behavior data for validation (e.g., performance validation for each different set of model parameters), and 15% of the obtained well dynamics behavior data for testing the final trained model. …”
Elkhomri discloses retraining the model based on the identified different training data;
Elkhomri: [0034] “…Therefore, the updated well dynamics behavior data (250) may also include predictions for a portion of the field well dynamics behavior data (210) that fails to satisfy the predetermined threshold. The predictions in the updated well dynamics behavior data (250) may supplement or replace the original data that failed to satisfy the original quality threshold. The digital twin manager (160) may train or retrain the physics constrained machine learning algorithms (200) such that the updated well dynamics behavior data (250) satisfies the predetermined quality threshold.”
Elkhomri discloses wherein the steps are performed by at least one processor and at least one memory storing executable computer program instructions.
Elkhomri: [0004] “Furthermore, embodiments of the invention relate to a non-transitory computer readable medium storing instructions executable by a computer processor.”
Regarding Claim 10 and 16, the same ground of rejection is made as discussed above for substantially similar rationale of claim 1.
Claims 3 and 12
Elkhomri discloses wherein the plurality of data sources comprises an operational data source, a test data source, and a synthetic data source.
Elkhomri [0024] “Keeping with FIG. 1, in some embodiments, the digital twin manager (160) may include hardware and/or software with functionality for determining an updated well dynamics behavior (e.g., mass flow, pressure, and temperature) using a ML algorithm (162) based on the modeled well dynamics behavior data (e.g., physics-based well dynamics behavior data (164)) obtained from the physics-based model server (130) and obtained well dynamics behavior data (163) from the well system (e.g., well mass flow data A (111), well pressure data A (112), well temperature data A (113), well mass flow data B (121), well pressure data B (122), well temperature data B (123)). …. Thus, different inputs (e.g., types of data or different data sources) may provide the initial setup of a particular monitoring criterion, where the data inputs may be customized by a data preprocessing module (168) according to different physics-based models to better arrange a monitoring criterion.”
[0026] “… In some embodiments, a digital twin manager (160) may generate augmented or synthetic data to produce a large amount of interpreted data for training a particular model.”
Claims 4, 13 and 18
Elkhomri discloses wherein identifying training data further comprises computing respective suitability scores for a plurality of datasets associated with the infrastructure based on the use case for which the model is to be used for representing the infrastructure and identifying the training data based on the computed suitability scores.
Elkhomri [0040-0041] “Furthermore, the physics constrained machine learning algorithms (200) may include an activation function in a ReLU layer (e.g., hidden layer F (286)) to calculate the misfit function based on the difference between the updated well dynamics behavior data (250) and a ground truth (e.g., field well dynamics behavior data (210) obtained from the well system). In some embodiments, a physics constrained machine learning algorithm (200) may use a simple data split technique to separate the input dynamics behavior data (e.g., physics-based well dynamics behavior data (205) and field well dynamics behavior data (210)) used for the training, validation, and testing of the physics constrained machine learning models. [correspond to computing suitability scores] An example, the data split technique may consider 70% of the obtained well dynamics behavior data for model training (e.g., tuning of the model parameters), 15% of the obtained well dynamics behavior data for validation (e.g., performance validation for each different set of model parameters), and 15% of the obtained well dynamics behavior data for testing the final trained model. [correspond to use case] … Furthermore, the physics constrained machine learning algorithms (200) may apply a nested k-fold inner/outer cross-validation to tune and validate the optimal parameters of the ML model. … In some example embodiments, the nested inner/outer cross-validation defines one or more physics constrained machine learning algorithms and their corresponding models in a grid and evaluates one or more performance metrics of interest (e.g., area under curve (AUC), accuracy, geometric mean, f1 score, mean absolute error, mean squared error, sensitivity, specificity, etc.) to find the optimal parameters of the physics constrained machine learning model.”
Claims 5, 14 and 19
Elkhomri discloses wherein identifying different training data further comprises recomputing respective suitability scores for the plurality of datasets associated with the infrastructure based on the monitoring being indicative of at least one of a performance and an accuracy being below a given threshold.
Elkhomri [0041] “Furthermore, the physics constrained machine learning algorithms (200) may apply a nested k-fold inner/outer cross-validation to tune and validate the optimal parameters of the ML model. In one or more embodiments, the nested stratified inner/outer cross-validation may be a software and/or hardware system that includes functionality to mitigate the over-fitting problem of the ML model by applying a k-fold inner cross-validation and a k-fold outer cross-validation. The k-fold inner cross-validation and the k-fold outer cross-validation may have different values of the “k” parameter. In some example embodiments, the nested inner/outer cross-validation defines one or more physics constrained machine learning algorithms and their corresponding models in a grid and evaluates one or more performance metrics of interest (e.g., area under curve (AUC), accuracy, geometric mean, f1 score, mean absolute error, mean squared error, sensitivity, specificity, etc.) to find the optimal parameters of the physics constrained machine learning model.” See Fig. 3.
Claim 7. The method of claim 1, Elkhomri discloses wherein the use case corresponds to at least one attribute associated with the infrastructure.
Elkhomri [0040] “… An example, the data split technique may consider 70% of the obtained well dynamics behavior data for model training (e.g., tuning of the model parameters), 15% of the obtained well dynamics behavior data for validation (e.g., performance validation for each different set of model parameters), and 15% of the obtained well dynamics behavior data for testing the final trained model.”
Claim 8. The method of claim 1, Elkhomri discloses wherein the model comprises an artificial intelligence-driven model.
Elkhomri: [0026] “In some embodiments, for example, the digital twin manager (160) applies one or more ML algorithms (162) (e.g., an artificial neural network) to train a ML model (165) to determine well dynamics behavior at a well (e.g., well site A (110), well site B (120)). Likewise, the digital twin manager (160) includes a model validation module (167) to validate the ML model (165). In some embodiments, a digital twin manager (160) may generate augmented or synthetic data to produce a large amount of interpreted data for training a particular model. …”
Claim 9. The method of claim 1, Elkhomri discloses wherein the virtual representation comprises at least one digital twin.
Elkhomri: [0026] “In some embodiments, for example, the digital twin manager (160) applies one or more ML algorithms (162) (e.g., an artificial neural network) to train a ML model (165) to determine well dynamics behavior at a well (e.g., well site A (110), well site B (120)). Likewise, the digital twin manager (160) includes a model validation module (167) to validate the ML model (165). In some embodiments, a digital twin manager (160) may generate augmented or synthetic data to produce a large amount of interpreted data for training a particular model. …”
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.
Claim(s) 2, 6, 11, 15, 17 and 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Elkhomri (US 2023/0144359 A1), in view of Violeta (NPL: A Digital Twin-based Privacy Enhancement Mechanism for the Automotive Industry, 2018).
Claims 2, 11 and 17
Elkhomri does not appear to explicitly disclose further comprising applying data anonymization to one or more of the identified training data and the identified different training data prior to training and retraining the model, respectively.
However, Violeta discloses applying data anonymization to one or more of the identified training data and the identified different training data prior to training and retraining the model, respectively (page 278) “Finally, in step 5, we use data anonymization to minimize the privacy risks and protect subjects from privacy breaches. For data anonymization we use syntactic approaches that operates under the k-anonymity principles, as suggested in [47-48].” See Fig. 2.
Elkhomri and Violeta are analogous art because they are from the “same field of endeavor” digital twin analysis.
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art, having the teachings of Elkhomri and Violeta before him or her, to modify the method of Elkhomri to include the data anonymization feature of Violeta because this combination minimizes privacy risk.
The suggestion/motivation for doing so would have beenVBioleta (Abstract) “We also perform data anonymization to minimize privacy risks and enable actions such as sending an automatic informed consent to the stakeholders.”
Therefore, it would have been obvious to combine Elkhomri and Violeta to obtain the invention as specified in the instant claim(s).
Claims 6, 15 and 20
Elkhomri does not appear to explicitly disclose further comprising adjusting data anonymization applied to one or more of the identified training data and the identified different training data based on one or more of the suitability scores.
However, Violeta discloses adjusting data anonymization applied to one or more of the identified training data and the identified different training data based on one or more of the suitability scores (page 278) “Finally, in step 5, we use data anonymization to minimize the privacy risks and protect subjects from privacy breaches. For data anonymization we use syntactic approaches that operates under the k-anonymity principles, as suggested in [47-48].” See Fig. 4 for example of adjusting data anonymization applied to one or more of the identified training data and the identified different training data.
Elkhomri and Violeta are analogous art because they are from the “same field of endeavor” digital twin analysis.
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art, having the teachings of Elkhomri and Violeta before him or her, to modify the method of Elkhomri to include the data anonymization feature of Violeta because this combination minimizes privacy risk.
The suggestion/motivation for doing so would have beenVBioleta (Abstract) “We also perform data anonymization to minimize privacy risks and enable actions such as sending an automatic informed consent to the stakeholders.”
Therefore, it would have been obvious to combine Elkhomri and Violeta to obtain the invention as specified in the instant claim(s).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to CHUEN-MEEI GAN whose telephone number is (469)295-9127. The examiner can normally be reached Monday-Friday 9:00 am to 4:00 pm EST.
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/CHUEN-MEEI GAN/Primary Examiner, Art Unit 2189
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