CTNF 18/420,783 CTNF 93092 DETAILED ACTION 07-03-aia AIA 15-10-aia The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA. Claims 1-20 are pending. Priority Receipt is acknowledged of certified copies of papers required by 37 CFR 1.55 for Application No. IN 202311005595 filed on 01/27/2023. Information Disclosure Statement The references cited in the information disclosure statements (IDS) submitted on 02/10/2025 and 05/05/2025 have been considered by the examiner. Claim Objections The following claims are objected to for informalities, lack of antecedent support, or for redundancies. The Examiner recommends the following changes: Claim 1, line 9, add “and” and the end of the line. Claim 11, line 8, add “and” and the end of the line. Appropriate correction is respectfully requested. Claim Rejections - 35 USC § 101 07-04-01 AIA 07-04 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 a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more. (Step 2A, Prong One) Independent claim 1 recites, “determine whether the battery manufacturing operation parameter indicator satisfies a battery manufacturing parameter threshold indicator, wherein the battery manufacturing parameter threshold indicator is associated with the battery manufacturing operation indicator; in response to determining that the battery manufacturing operation parameter indicator does not satisfy the battery manufacturing parameter threshold indicator: ...” Under its broadest reasonable interpretation, if a claim limitation covers performance that can be executed in the human mind, but for the recitation of generic electronic devices or generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Under their broadest reasonable interpretation and based on the description provided in the Specification, such as paragraph [0130] and FIG. 3, for instance, the determine function is a mental process that can be performed through observation, evaluation and judgement based on a received operation parameter data and a threshold data. That is, a person may perform, through observation, evaluation and judgement, the features enunciated above. Accordingly, the claim recites an abstract idea. (Step 2A, Prong Two) This judicial exception is not integrated into a practical application. In particular, the claim recites the additional limitations of, “at least one processor and at least one non-transitory memory comprising a computer program code”, “receive a battery manufacturing operation parameter indicator, wherein the battery manufacturing operation parameter indicator is associated with a battery manufacturing batch indicator and a battery manufacturing operation indicator” and “generate a battery manufacturing deviation event data object, wherein the battery manufacturing deviation event data object comprises the battery manufacturing operation parameter indicator and a plurality of additional battery manufacturing operation parameter indicators that is associated with the battery manufacturing batch indicator, and generate a battery manufacturing adjustment data object based at least in part on inputting the battery manufacturing deviation event data object to one or more machine learning models.” The additional limitation “at least one processor and at least one non-transitory memory comprising a computer program code” as recited in the claim that are configured to carry out the additional and abstract idea limitations may be tools that are used to determine as recited in the claim, but recited so generically that they represent no more than mere instructions “to apply” the judicial exceptions on or using generic electronic or computer components. Implementing an abstract idea on generic electronic or computer components as tools to perform an abstract idea is not indicative of integration into a practical application. see MPEP 2106.05(f) The additional limitation of “receive a battery manufacturing operation parameter indicator, wherein the battery manufacturing operation parameter indicator is associated with a battery manufacturing batch indicator and a battery manufacturing operation indicator” is an insignificant extra-solution activity under MPEP 2106.05(g), without imposing meaningful limits. The limitation amounts to necessary data gathering. (i.e., all uses of the recited judicial exception require such data gathering or data output). The practical application requires an additional element or a combination of additional elements in the claim to apply, rely on, or use the judicial exception in a manner that imposes a meaningful limit on the judicial exception, such that the claim is more than a drafting effort designed to monopolize the exception. When so evaluated, the additional limitation of “generate a battery manufacturing deviation event data object, wherein the battery manufacturing deviation event data object comprises the battery manufacturing operation parameter indicator and a plurality of additional battery manufacturing operation parameter indicators that is associated with the battery manufacturing batch indicator, and generate a battery manufacturing adjustment data object based at least in part on inputting the battery manufacturing deviation event data object to one or more machine learning models” is merely adding the word generate with the judicial exception that attempts to cover a solution to an identified problem with no restriction on how the result is accomplished and no description of the mechanism for accomplishing the result, does not integrate a judicial exception into a practical application or provide significantly more because this type of recitation is equivalent to the words "apply it", and does not apply, rely on, or use the judicial exception in a manner that imposes a meaningful limit on the judicial exception, and therefore is not indicative of integration into a practical application, see MPEP 2106.05(f). The claim does not recite an improvement in a technology as set forth in MPEP 2106.04(d) and MPEP 2106.05(a). Accordingly, the additional limitations recited in the claim do not integrate the abstract idea into a practical application. In view of the foregoing, the additional limitations are not sufficient to demonstrate integration of a judicial exception into a practical application. (Step 2B) The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The additional features including “at least one processor and at least one non-transitory memory comprising a computer program code”, as recited in the claim that are configured to carry out the additional and abstract idea limitation may be tools that are used for the functions recited in the claim, but recited so generically that they represent no more than mere instructions “to apply” the judicial exceptions on or using a generic electronic or computer component. Implementing an abstract idea on generic electronic or computer components as tools to perform an abstract idea does not amount to significantly more. The receive function represents a function that is recognized as well-understood, routine, and conventional, for instance, as demonstrated in Reitinger et al. (US 2025/0053143 A1) paragraph [0026] (“Some embodiments of the teachings herein include a method for determining controllable process parameters for a battery production system. First, measurement values of production parameters in the battery production system are ascertained by means of sensors. Furthermore, at least one quality value of at least one battery cell produced in the battery production system with the ascertained measurement values is also ascertained. The quality value is in this case associated with the measurement values. The at least one quality value and the measurement values associated therewith are transferred to a computing unit. ”), and Zhao et al. (US 2024/0004355 A1) paragraph [0014] (“… One such embodiment automatically receives sensor data from an ongoing batch production run of the industrial process (the live process). …”) and paragraph [0060] (“Another embodiment of the method 220 further includes receiving input indicating a selected signature type. …”) The additional limitation of “generate a battery manufacturing deviation event data object, wherein the battery manufacturing deviation event data object comprises the battery manufacturing operation parameter indicator and a plurality of additional battery manufacturing operation parameter indicators that is associated with the battery manufacturing batch indicator, and generate a battery manufacturing adjustment data object based at least in part on inputting the battery manufacturing deviation event data object to one or more machine learning models” is merely adding the word generate (or “to apply”) with the judicial exception, and does not impose a meaningful limit on practicing the abstract idea, see MPEP 2106.05(f). Thus, when taken alone, the individual additional limitations do not amount to significantly more than the above-identified judicial exception (the abstract idea). Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. Therefore, the additional claimed features do not amount to significantly more and the claim is not patent eligible. Claim 11 recite similar limitations as for claim 1, and accordingly, claim 11 is not patent eligible for similar reasons above as for the claim 1. The recitations of claims 2-5, 7-8 and 10, and claims 12-15, 17-18 and 20 simply add more detail to or are cumulative to the insignificant extra-solution activity of claim 1 and 11, respectively. The recitations of claims 6 and 9, and claims 16 and 19 simply add more detail to or are cumulative to the insignificant extra-solution activity and generic applying of abstract idea of claim 1 and 11, respectively. Claim Rejections - 35 USC § 103 07-20-aia AIA 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. 07-21-aia AIA Claim s 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over Reitinger et al. (US 2025/0053143 A1) (“Reitinger”), in view of Zhao et al. (US 2024/0004355 A1) (“Zhao”) . Regarding independent claim 1, Reitinger teaches: An apparatus … , cause the apparatus to: (Reitinger: [0020] “As another example, some embodiments include a battery production system (1) with a computing unit (2) configured to perform one or more of the methods described herein.”) [The computing unit (2) reads on “[a]n apparatus”.] receive a battery manufacturing operation parameter indicator, wherein the battery manufacturing operation parameter indicator is associated with a battery manufacturing batch indicator and a battery manufacturing operation indicator; determine whether the battery manufacturing operation parameter indicator satisfies a battery manufacturing parameter threshold indicator, wherein the battery manufacturing parameter threshold indicator is associated with the battery manufacturing operation indicator; (Reitinger: [0006] “The teachings of the present disclosure include systems and methods for battery production which reduce the rejection rates during battery production. For example, some embodiments include a method for determining controllable process parameters (x) for a battery production system (1), including: ascertaining measurement values of production parameters (A, B) in the battery production system (1) by means of sensors (3), ascertaining at least one quality value of at least one battery cell produced in the battery production system (1) with the ascertained measurement values, wherein the quality value is associated with the measurement values, transferring the at least one quality value and the measurement values to a computing unit (2), ascertaining the dependency of the at least one quality value on the measurement values in the computing unit (2), ascertaining a dependency of the at least one quality value on changed production parameters which differ in value from the measurement values, in the computing unit, wherein a machine learning method is performed in the computing unit (2), and an improved quality value, wherein the ascertaining takes place on the basis of a parameter optimization for the machine learning method, wherein a Bayesian optimization for the ascertained dependency is used as parameter optimization, wherein measurement values with associated quality values are included in the optimization as reference points (12, 13, 14).”) (Reitinger: [0009] “In some embodiments, material properties, temperatures, air humidity, dust concentration, airflow, delivery information and/or batch information are used as production parameters.”) (Reitinger: [0013] “In some embodiments, the quality value is determined by measuring an idle voltage, a deformation of the battery cell, an internal resistance, a cell capacity and/or a weight of the battery cell.”) (Reitinger: [0029] “Using the methods described herein, it is possible to determine complex relationships between production parameters and a quality value and to subsequently identify controllable process parameters. By producing batteries using these process parameters, battery cells can be produced which satisfy a minimum requirement for the quality value. In other words, it is thus possible to set the battery production system automatically. The methods speed up the process of setting the production parameters of a battery production system, in particular r in terms of a design-of-experiment. Thus fewer optimization steps are required to find an optimal setting of the production parameters of the battery production system. This enables the battery production system to be optimized more quickly, so that the rejection rate of the battery production system is reduced.”) [The quality value reads on “a battery manufacturing operation parameter indicator”, and measuring the production parameters associated with the quality value reads on “receive …”. Any one of the production parameters reads on “a battery manufacturing operation indicator”. The quality value having dependency on the production parameters, including the batch information, reads on “… associated with a battery manufacturing batch indicator and a battery manufacturing operation indicator”. The minimum required quality value reads on “a battery manufacturing parameter threshold indicator”, and determining if quality value meets the minimum required quality value reads on “determine whether … satisfies a battery manufacturing parameter threshold indicator”.] in response to determining that the battery manufacturing operation parameter indicator does not satisfy the battery manufacturing parameter threshold indicator: generate a battery manufacturing deviation event data object, wherein the battery manufacturing deviation event data object comprises the battery manufacturing operation parameter indicator and a plurality of additional battery manufacturing operation parameter indicators that is associated with the battery manufacturing batch indicator, and (Reitinger: [0006] and [0029] as discussed above) (Reitinger: [0036] “Thus, a plurality of production parameters may be detected. Furthermore, a plurality of production parameters is correlated with a quality value. Furthermore, the production parameters may be used to identify controllable process parameters which can further improve the quality value. Thus it is possible to correlate the complex relationships during battery production with the quality value of the batteries produced, and thus to identify controllable process parameters which improve the quality value.”) [Not meeting the minimum required quality value requiring improvement reads on “determining … does not satisfy …”. The identifying the controllable process parameter that improves or satisfy the minimum required quality value reads on “generate a battery manufacturing deviation event data object”. All of the identified controllable process parameters or the production parameters reads on “… comprises the battery manufacturing operation parameter indicator and a plurality of additional battery manufacturing operation parameter indicators …”.] generate a battery manufacturing adjustment data object based at least in part on inputting the battery manufacturing deviation event data object to one or more machine learning models. (Reitinger: [0006] as discussed above) (Reitinger: [0018] “In some embodiments, a changed process parameter ascertained as described herein is set and battery cells are produced with these changed process parameters.”) (Reitinger: [0052] “A first controllable process parameter pair 31, a second controllable process parameter pair 32, a third controllable process parameter pair 33 and a fourth controllable process parameter pair 34 are ascertained on the basis of the machine learning method. It is now possible to set a selected controllable process parameter pair in the battery production system and in turn to measure measurement values and the quality value, to transfer them to the computing unit, in turn to establish dependencies which are evaluated using a machine learning method, in turn to ascertain new production parameters and controllable process parameters based thereon which can be used in a nest step. In other words the method thus makes it possible to realize a design-of-experiment with which an improvement in the quality values of the battery storage units in production and thus also a reduction in the rejection rate of the battery storage units produced is enabled.”) [The new production parameters and controllable process parameters reads on “generate a battery manufacturing adjustment data object”. Ascertaining the optimized/changed process parameter using the machine learning with the dependency of the quality value and the production parameters reads on “… based at least in part on inputting the battery manufacturing deviation event data object”.] Reitinger does not expressly teach: An apparatus comprising at least one processor and at least one non-transitory memory comprising a computer program code, the at least one non-transitory memory and the computer program code configured to, with the at least one processor … Zhao teaches: An apparatus comprising at least one processor and at least one non-transitory memory comprising a computer program code, the at least one non-transitory memory and the computer program code configured to, with the at least one processor … (Zhao: [0007] “One such example embodiment is directed to a computer-implemented method for creating a machine learning predictive model for real-world batch production industrial process monitoring and optimization. The method obtains historical operating data from a plurality of batch production runs of an industrial process and standardizes the obtained historical operating data for each run of the plurality of batch production runs. In turn, for each batch production run of the plurality, the method (i) partitions standardized operating data corresponding to the batch production run into one or more stages and (ii) determines one or more signature(s) for each of the one or more stages using the partitioned standardized operating data corresponding to the one or more stages. Each determined signature is associated with a class label based upon output of a batch production run of the plurality corresponding to the determined signature conforming with operational standards or not conforming with the operational standards. The method trains a machine learning predictive pattern model with at least a subset of the determined operational signatures as inputs and associated class labels as outputs. The training configures the model to predict, based on operating data from a real-world batch production process, whether output of the real-world batch production process will conform or not conform with the operational standards. The method is computer implemented and, as such, the functionality, the obtaining, standardizing, partitioning, determining, associating, and training, are automatically implemented by one or more processors.”) (Zhao: [0112] “In one embodiment, the processor routines 92 and data 94 are a computer program product (generally referenced 92), including a computer readable medium (e.g., a removable storage medium such as one or more DVD-ROM's, CD-ROM's, diskettes, tapes, etc.) that provides at least a portion of the software instructions for the invention system. Computer program product 92 can be installed by any suitable software installation procedure, as is well known in the art.”) Accordingly, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, having the teachings of Reitinger and Zhao before them, to modify the computing unit used for a battery production system, to incorporate computer implemented system for real-world batch production industrial process monitoring and optimization. One of ordinary skill in the art before the effective filing date of the claimed invention would have been motivated to do this modification because it would allow for performing all of the software-based batch production functions as well known in the art. (Zhao: [0007]) Regarding claim 2, Reitinger and Zhao teach all the claimed features of claim 1. Reitinger further teaches: wherein the plurality of additional battery manufacturing operation parameter indicators is associated with a plurality of additional battery manufacturing operation indicators, wherein the plurality of additional battery manufacturing operation indicators is different from the battery manufacturing operation indicator. (Reitinger: [0043] FIG. 1 shows a battery production system 1 incorporating teachings of the present disclosure which is connected to a computing unit 2. The battery production system 1 comprises at least one device with a raw solution 4 for an electrode layer 5. It also comprises a coating system for the production of the electrode layer 5. It likewise comprises a winding unit 6 for winding the electrodes to produce a lithium-based energy storage unit. The battery production system 1 also comprises a forming unit 7 for the initial charging and discharging of the battery cell. It also comprises an end-of-line testing unit 8 for determining quality values of the battery cell. The battery production system 1 also comprises sensors 3.”) (Reitinger: [0044] “These sensors 3 measure production parameters, such as in particular properties of the raw solution (e.g. viscosity, temperature), of the electrode layer (layer thickness, rawness) and of the winding system (temperature, winding speed). Furthermore, sensors measure the temperature, the dust content, the airflow, and the air humidity in the space of the battery production system. In particular, optical (imaging) sensors and hyperspectral cameras are also employed as sensors. The production systems and sensors mentioned are not exhaustively mentioned here. In particular, sensors are associated with each production step. The sensors generate data, which is transferred to the computing unit 1 as measurement values.”) [The different sensors associated with different production steps reads on “… is different from …”.] Regarding claim 3, Reitinger and Zhao teach all the claimed features of claims 1-2. Reitinger further teaches: wherein the battery manufacturing adjustment data object comprises an adjusted battery manufacturing operation parameter indicator. (Reitinger: [0052] as discussed in claim 1) [The quality value as a result of the new production parameters and controllable process parameters reads on “an adjusted battery manufacturing operation parameter indicator”.] Regarding claim 4, Reitinger and Zhao teach all the claimed features of claim 1. Zhao further teaches: wherein the one or more machine learning models comprise a deviation event similarity determination machine learning model, wherein the at least one non-transitory memory and the computer program code are configured to, with the at least one processor, cause the apparatus to: receive, from a battery manufacturing deviation event repository, a plurality of historical battery manufacturing deviation event data objects; input the battery manufacturing deviation event data object and the plurality of historical battery manufacturing deviation event data objects to the deviation event similarity determination machine learning model; and receive, from the deviation event similarity determination machine learning model, a plurality of deviation event similarity indicators. (Zhao: [0070] “An embodiment determines the corrective actions by using the trained KNN model to look in a historical database to find a small group of “similar” batch runs to the batch run being analyzed (based on signatures up to the current point in time). The small group of identified “similar” batches is split into two groups: “good” and “bad” batch groups according to whether each batch run end-product quality is acceptable or not acceptable. These good and bad similar batches are compared to identify the differences after the current time point in operation. This allows such an embodiment to statistically identify how bad batches deviated from good batches. For example, such an analysis may identify that one or more features e.g., dryer temperature, pressure, longer/short cooking/drying time, etc., of the bad batches deviated from those features of the good batches. These findings are then inserted into the recommendation report (e.g., automatically using numerically quantitative calculations) to generate the recommendation report. Further, an embodiment can automatically carry out these recommended actions.”) [The historical database reads on “a battery manufacturing deviation event repository”.] The motivation to combine Reitinger and Zhao as described in claim 1 is incorporated herein. Regarding claim 5, Reitinger and Zhao teach all the claimed features of claims 1 and 4. Zhao further teaches: wherein each of the plurality of deviation event similarity indicators indicates a corresponding deviation event similarity level between the battery manufacturing deviation event data object and one of the plurality of historical battery manufacturing deviation event data objects. (Zhao: [0068] “In yet another embodiment, if the determined prediction indicates the output is non-conforming and (ii) the indication of statistical probability in the determined prediction is above a threshold, the method 220 determines, from among the historical operating data, at least one K-nearest neighbor batch to the ongoing batch production run. Then, based on the determined at least one K-nearest neighbor batch, a comparative analysis between the at least one K-nearest neighbor batch and a standard reference batch is performed by using at least one multivariate statistical model. In such an embodiment, the K-nearest neighbor batch is similar to the current batch being analyzed. As such, based on the comparison between the K-nearest neighbor batch and a standard reference batch, such an embodiment can identify, for example, what is wrong with the current batch. This information can be used to provide recommendations to fix the current batch.”) [The K-nearest neighbor batch reads on “a corresponding deviation event similarity level”.] The motivation to combine Reitinger and Zhao as described in claim 1 is incorporated herein. Regarding claim 6, Reitinger and Zhao teach all the claimed features of claims 1 and 4. Zhao further teaches: wherein a deviation event similarity indicator is associated with the battery manufacturing deviation event data object and a historical battery manufacturing deviation event data object from the plurality of historical battery manufacturing deviation event data objects, wherein the at least one non-transitory memory and the computer program code are configured to, with the at least one processor, cause the apparatus to: in response to determining that the deviation event similarity indicator satisfies a deviation event similarity threshold indicator: receive, from the battery manufacturing deviation event repository, a historical battery manufacturing adjustment data object corresponding to the historical battery manufacturing deviation event data object; and generate the battery manufacturing adjustment data object based at least in part on the historical battery manufacturing adjustment data object. (Zhao: [0068] and [0070] as discussed in claims 4 and 5) The motivation to combine Reitinger and Zhao as described in claim 1 is incorporated herein. Regarding claim 7, Reitinger and Zhao teach all the claimed features of claims 1 and 4. Zhao further teaches: wherein the one or more machine learning models comprise a deviation event classification estimation machine learning model, wherein the at least one non-transitory memory and the computer program code are configured to, with the at least one processor, cause the apparatus to: in response to determining that the plurality of deviation event similarity indicators does not satisfy a deviation event similarity threshold indicator: input the battery manufacturing deviation event data object to the deviation event classification estimation machine learning model; and receive, from the deviation event classification estimation machine learning model, an estimated deviation event classification indicator associated with the battery manufacturing deviation event data object and a plurality of candidate battery manufacturing adjustment data objects. (Zhao: [0068] and [0070] as discussed in claims 4 and 5) (Zhao: [0100] “In contrast, embodiments use both “in-spec” and “out-of-spec” batch data. Thus, embodiments are able to learn and predict end-product quality of a currently running batch. Based on comparisons and analytic data analyses on “out-of-spec” batch data, embodiments are able to further help plant operators to find out what are possible root-causes. For instance, if a batch process is predicted to fail, an embodiment can identify similar “out-of-spec” historical batches and from comparison between the current batch and historical batch to identify causes of the failure. Further, if a batch is predicted to fail, an embodiment can identify similar historical batches that yielded in-spec products and identify differences between the current batch, which is predicted to fail, and the historical batches that are similar, but ultimately produced desirable results. These differences can then be used to modify the current batch operation. For example, if it is determined that a previous similar batch, which was successful, operated at a temperature of 100° C. and the current batch, which is predicted to fail, is operating at 90° C., the current operating temperature of the current batch can be increased in an attempt to fix the ongoing process. In such a way, embodiments make full use of historical batch data, extract more information than prior art modeling and online monitoring methods, and provide users with improved batch product quality predictions.”) [The KNN model to look at a historical database to search for similar batches with that are grouped in good and bad batch groups read on “deviation event classification estimation machine learning model”. The possible root causes that may require adjustment to the temperature, as an example, reads on “a plurality of candidate battery manufacturing adjustment data objects”.] The motivation to combine Reitinger and Zhao as described in claim 1 is incorporated herein. Regarding claim 8, Reitinger and Zhao teach all the claimed features of claims 1, 4 and 7. Zhao further teaches: wherein each of the plurality of candidate battery manufacturing adjustment data objects comprises at least one candidate adjusted battery manufacturing operation parameter indicator. (Zhao: [0068], [0070] and [0100] as discussed in claims 4, 5 and 7) [The temperature reads on “at least one candidate adjusted battery manufacturing operation parameter indicator”.] The motivation to combine Reitinger and Zhao as described in claim 1 is incorporated herein. Regarding claim 9, Reitinger and Zhao teach all the claimed features of claims 1, 4 and 7. Zhao further teaches: wherein the one or more machine learning models comprise a battery manufacturing outcome prediction machine learning model, wherein the at least one non-transitory memory and the computer program code are configured to, with the at least one processor, cause the apparatus to: input the plurality of candidate battery manufacturing adjustment data objects to the battery manufacturing outcome prediction machine learning model; receive, from the battery manufacturing outcome prediction machine learning model, a plurality of predicted battery manufacturing outcome data objects associated with the plurality of candidate battery manufacturing adjustment data objects; and generate the battery manufacturing adjustment data object based at least in part on the plurality of candidate battery manufacturing adjustment data objects and the plurality of predicted battery manufacturing outcome data objects. (Zhao: [0068] and [0070] as discussed in claims 4 and 5) (Zhao: [0066] “Embodiments of the method 220 may also deploy the predictive model trained at step 225. One such embodiment automatically receives sensor data from an ongoing batch production run of the industrial process. The received sensor data is processed with the trained machine learning predictive pattern model to determine a prediction of output quality of the ongoing batch production run, i.e., predict whether output of the batch will conform or not conform with operational standards. In an example embodiment, the determined prediction includes an indication of statistical probability, i.e., confidence, in the determined prediction.”) [The predictive model reads on “a battery manufacturing outcome prediction machine learning model”.] The motivation to combine Reitinger and Zhao as described in claim 1 is incorporated herein. Regarding claim 10, Reitinger and Zhao teach all the claimed features of claims 1, 4, 7 and 9. Zhao further teaches: wherein the plurality of predicted battery manufacturing outcome data objects is associated with a plurality of predicted battery manufacturing outcome confidence-to-risk indicators. (Zhao: [0066], [0068] and [0070] as discussed in claims 4, 5 and 9) (Zhao: [0088] “At step 553-2 embodiments proceed with training a model with one or more machine-learning algorithms. According to an embodiment, this model training includes: (i) loading a cleaned and feature calculated batch dataset (from 553-1), (ii) splitting the batch dataset into a training dataset and a testing dataset, and (iii) applying one or more supervised machine learning classifier model training algorithms, such as KNN or SVM. The result of training is a classifier model that predicts a batch's success (in-spec) or failure (out-of-spec) given a (partial or complete) set of feature values as inputs. The classifier is also configured to provide a probability with the predictions. These probabilities can be presented to users to allow users to (i) assess the risk of a running batch moving into failure and (ii) make early decisions to reduce the number of batch failures.”) [The statistical probabilities with the risk factors read on “a plurality of predicted battery manufacturing outcome confidence-to-risk indicators”.] The motivation to combine Reitinger and Zhao as described in claim 1 is incorporated herein. Regarding independent claim 11: The claim recites similar limitations as corresponding claim 1 and is rejected using the same teachings and rationale. Regarding claim 12, Reitinger and Zhao teach all the claimed features of claim 11. The claim recites similar limitations as corresponding claim 2 and is rejected using the same teachings and rationale. Regarding claim 13, Reitinger and Zhao teach all the claimed features of claims 11-12. The claim recites similar limitations as corresponding claim 3 and is rejected using the same teachings and rationale. Regarding claim 14, Reitinger and Zhao teach all the claimed features of claim 11. The claim recites similar limitations as corresponding claim 4 and is rejected using the same teachings and rationale. Regarding claim 15, Reitinger and Zhao teach all the claimed features of claims 11 and 14. The claim recites similar limitations as corresponding claim 5 and is rejected using the same teachings and rationale. Regarding claim 16, Reitinger and Zhao teach all the claimed features of claims 11 and 14. The claim recites similar limitations as corresponding claim 6 and is rejected using the same teachings and rationale. Regarding claim 17, Reitinger and Zhao teach all the claimed features of claims 11 and 14. The claim recites similar limitations as corresponding claim 7 and is rejected using the same teachings and rationale. Regarding claim 18, Reitinger and Zhao teach all the claimed features of claims 11, 14 and 17. The claim recites similar limitations as corresponding claim 8 and is rejected using the same teachings and rationale. Regarding claim 19, Reitinger and Zhao teach all the claimed features of claims 11, 14 and 17. The claim recites similar limitations as corresponding claim 9 and is rejected using the same teachings and rationale. Regarding claim 20, Reitinger and Zhao teach all the claimed features of claims 11, 14, 17 and 19. The claim recites similar limitations as corresponding claim 10 and is rejected using the same teachings and rationale. It is noted that any citations to specific, pages, columns, lines, or figures in the prior art references and any interpretation of the reference should not be considered to be limiting in any way. A reference is relevant for all it contains and may be relied upon for all that it would have reasonably suggested to one having ordinary skill in the art. See MPEP 2123. Conclusion Any inquiry concerning this communication or earlier communications from the examiner should be directed to MICHAEL W CHOI whose telephone number is (571)270-5069. The examiner can normally be reached Monday-Friday 8am-5pm. Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice. If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Kenneth Lo can be reached at (571) 272-9774. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300. Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000. /MICHAEL W CHOI/Primary Examiner, Art Unit 2116 Application/Control Number: 18/420,783 Page 2 Art Unit: 2116 Application/Control Number: 18/420,783 Page 3 Art Unit: 2116 Application/Control Number: 18/420,783 Page 4 Art Unit: 2116 Application/Control Number: 18/420,783 Page 5 Art Unit: 2116 Application/Control Number: 18/420,783 Page 6 Art Unit: 2116 Application/Control Number: 18/420,783 Page 7 Art Unit: 2116 Application/Control Number: 18/420,783 Page 8 Art Unit: 2116 Application/Control Number: 18/420,783 Page 9 Art Unit: 2116 Application/Control Number: 18/420,783 Page 10 Art Unit: 2116 Application/Control Number: 18/420,783 Page 11 Art Unit: 2116 Application/Control Number: 18/420,783 Page 12 Art Unit: 2116 Application/Control Number: 18/420,783 Page 13 Art Unit: 2116 Application/Control Number: 18/420,783 Page 14 Art Unit: 2116 Application/Control Number: 18/420,783 Page 15 Art Unit: 2116 Application/Control Number: 18/420,783 Page 16 Art Unit: 2116 Application/Control Number: 18/420,783 Page 17 Art Unit: 2116 Application/Control Number: 18/420,783 Page 18 Art Unit: 2116 Application/Control Number: 18/420,783 Page 20 Art Unit: 2116 Application/Control Number: 18/420,783 Page 21 Art Unit: 2116
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