DETAILED ACTION
This Office Action is sent in response to Applicant’s Communication received 7/14/2026 for application number 18/282,709.
Claims 22-41 are pending.
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim 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 40-42 are rejected under 35 U.S.C. § 101 because the claimed invention is directed to non-statutory subject matter. Claim 40 is directed to “An XGBoost, Random Forest or artificial neural network.” Claim 41 is directed to “A computer program.” Claim 42 is directed to a “data carrier signal.” This allows the claim to encompass software per se or a signal per-se, which is not a “process,” a “machine,” a “manufacture,” or a “composition of matter” as defined in 35 U.S.C. § 101.
Claims 22-41 rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. Independent claim 22, representative of independent claims 3, 40, and 31 recites:
A method for ensuring product quality in a process for producing a product from a material comprising the following steps: acquiring raw material data from at least two different sources for the production process and its relevant parameters by using a Data Collecting computer; using the acquired raw material data from the at least two different sources respectively related to the production process to perform a Process Mapping step using at least two distinct types of the acquired raw material data according to the at least two different sources to create a process structure of the production process by using a Process Mapping computer; assigning respective types of the at least two distinct types of the acquired raw material data related to its separated parameters of the production process to its corresponding process parts of the process structure of the production process by performing a Data Mapping step by using a Data Mapping computer to create a mapped process description; analyzing the therefore mapped process description with a specific software performed on an Analyzing computer thereby identifying and validating one or more existing characteristics related to the quality of the produced product; and generating and executing, by using the identified and validated characteristics to choose the most suitable, available raw material data, an executable instruction to perform a production process thereby improving the resulting product quality.
(2A, prong 1) The underlined portions of the claim recite an abstract idea, specifically a mental process. A human can acquire raw material data (dependent claim 23 explicitly states the data is provided by a human user), map the raw material data to a corresponding step in a production process, mentally judge the process identify quality characteristics of produced product and choose a most suitable raw material to improve product quality.
(2A, prong 2) This judicial exception is not integrated into a practical application. The claim contains the additional element of a computer performing the steps. This additional element is a mere instruction to apply the exception because it merely adds a generic computer after the fact to the mental process. Even when all of the additional elements are considered in ordered combination with the recited abstract idea, the claim as a whole does not integrate the abstract idea into a practical application because it merely amounts to adding generic computers to perform the mental process.
(2B) The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception. The additional element of a computer performing the steps is a mere instruction to apply the exception as explained above. Even when all of the additional elements are considered in ordered combination with the recited abstract idea, the claim as a whole does not amount to significantly more than the abstract idea itself because it merely amounts to adding generic computers to perform the mental process.
For dependent claims 23-26 and 30-32, these claims add additional steps that can be performed mentally by a human. Claims 23-24 recite a human can provide data after previous executions of the process. Claim 25 recites the process map is performed by describing the production process or pre-stages, including components, steps, ingredients, and raw materials; a human can, with the aid of pen and paper, create a production process description for mapping. Claim 26 recites raw material data is assigned to corresponding process components or steps; a human can map these steps mentally. Claim 30-32 specifies a human manually acquires raw material data from two different production sites, the data comprising quality parameters, metal purity and impurity levels, or in-process data.
For dependent claims 27-29 and 39, these claims recite analysis is performed by trained supervised or unsupervised algorithms like a neural network, XGBoost, random forest, etc. (2A, prong 2). This additional element does not integrate the abstract idea into a practical application because it is a mere instruction to apply the exception; specifically, this additional element only recites the idea of an outcome (that these trained algorithms are used for analysis) and not how to accomplish the solution (that is, how to use or train the algorithms for analysis). Even when all of the additional elements are considered in ordered combination with the recited abstract idea, the claim as a whole does not integrate the abstract idea into a practical application because the additional elements are mere instructions to apply the mental process. (2B, prong 2). This additional element does not amount to significantly more than the abstract idea itself because it is a mere instruction to apply the exception, as explained above. Even when all of the additional elements are considered in ordered combination with the recited abstract idea, the claim as a whole does not amount to significantly more than the abstract idea itself because the additional elements are mere instructions to apply the mental process.
For dependent claims 33-34 and 38, these claims recite writing results to a database and displaying the results in a dashboard with the predictions of quality to a user and a computer acquiring raw material data from two other production sites. (2A, prong 2). This additional element does not integrate the abstract idea into a practical application because it is insignificant extra-solution activity that is mere necessary data gathering and outputting for the mental process. Even when all of the additional elements are considered in ordered combination with the recited abstract idea, the claim as a whole does not integrate the abstract idea into a practical application because the additional elements are mere instructions to apply the mental process and insignificant extra-solution activity for the mental process. (2B) This additional element does not amount to significantly more than the abstract idea itself because it is well-understood, routine, and conventional, analogous to presenting offers and gathering statistics, see MPEP 2106.05(d) citing OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1362-63, 115 USPQ2d 1090, 1092-93 (Fed. Cir. 2015). Even when all of the additional elements are considered in ordered combination with the recited abstract idea, the claim as a whole does not amount to significantly more than the abstract idea itself because the additional elements are mere instructions to apply the mental process and insignificant extra-solution activity that is well-understood, routine, and conventional for the mental process.
For dependent claims 35 and 37, these claims recite the process is for semiconductor manufacturing using chemical mechanical planarization, and that the other sites are factories for producing chemicals or pharmaceuticals and a chemical provider or distributor. (2A, prong 2). This additional element does not integrate the abstract idea into a practical application because they are field of use limitations that merely confine the mental process to a particular field of use. Even when all of the additional elements are considered in ordered combination with the recited abstract idea, the claim as a whole does not integrate the abstract idea into a practical application because the additional elements are mere instructions to apply the mental process and field of use limitations for the mental process. (2B) This additional element does not amount to significantly more than the abstract idea itself they are field of use limitations, as explained above. Even when all of the additional elements are considered in ordered combination with the recited abstract idea, the claim as a whole does not amount to significantly more than the abstract idea itself because the additional elements are mere instructions to apply the mental process and field of use limitations for the mental process.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claim(s) 22-41 is/are rejected under 35 U.S.C. 103 as being unpatentable over Yuan et al. (US 2020/0401113 A1) in view of Mrziglod et al. (US 2022/0068440 A1).
In reference to claim 22, Yuan teaches a method (para. 0099) for ensuring product quality in a process for producing a product from a material (para. 0001) comprising the following steps: acquiring raw material data from at least two different sources for the production process and its relevant parameters by using a Data Collecting computer (raw material data from a plurality of suppliers is received, para. 0051); … at least two distinct types of the acquired raw material data according to the at least two different sources … (see different types of raw material that are compared in fig. 6) analyzing the … process description with a specific software performed on an Analyzing computer thereby identifying and validating one or more existing characteristics related to the quality of the produced product; and generating and executing, by using the identified and validated characteristics to choose the most suitable, available raw material data, an executable instruction to perform a production process thereby improving the resulting product quality (raw material data and manufacturing process are used for running predictive models, like machine learning models, to predict the best available raw materials, para. 0063-71, 0052-54).
However, Yuan does not explicitly teach using the acquired raw material data from the at least two different sources respectively related to the production process to perform a Process Mapping step … by using a Process Mapping computer; assigning the acquired raw material data related to its separated parameters of the production process to its corresponding process parts by performing a Data Mapping step using at least two distinct types of the acquired raw material data according to the at least two different sources to create a process structure of the production process by using a Data Mapping computer to create a mapped process description.
Mrziglod teaches using the acquired raw material data from the at least two different sources respectively related to the production process to perform a Process Mapping step using at least two distinct types of the acquired raw material data according to the at least two different sources to create a process structure of the production process to create a process structure of the production process by using a Process Mapping computer; assigning the acquired raw material data related to its separated parameters of the production process to its corresponding process parts by performing a Data Mapping step by using a Data Mapping computer to create a mapped process description (raw materials and manufacturing steps are mapped together, para. 0119-31, for example by receiving a process with sub-processes, or steps, para. 0049-54, then determining the input parameters – which would include the raw materials, para. 0123 – that affect the sub-processes, para. 0059-65, which is mapping parameters to steps).
It would have been obvious to one of ordinary skill in art, having the teachings of Yuan and Mrziglod before the earliest effective filing date, to modify the production process of Yuan to include the mapping of Mrziglod.
One of ordinary skill in the art would have been motivated to modify the production process of Yuan to include the mapping of Mrziglod because it can help improve quality predictions in multistep processes (Mrziglod, para. 0002-06).
In reference to claim 23, Yuan teaches the method of claim 22, wherein for acquiring the raw material data for the production process and its relevant parameters the raw material data is retrieved from a database which is connected to the Data Collecting computer, created by observing the process using data collecting devices, sensors, and/or provided by a human user (raw material data retrieved from manufacturing databases, para. 0030, and materials databases, para. 0051-52; it would be obvious that the data gathered in physical testing would need to be collected from sensors or humans).
In reference to claim 24 Yuan teaches the method of claim 23, wherein acquiring the raw material data is done during previous executions of the process and/or during a current execution after using the identified and validated characteristics (previous testing, para. 0051-52).
In reference to claim 25, Yuan does not explicitly teach the method of claim 22, wherein the Process Mapping is performed by describing the structure of the production process or its pre-stages including necessary components, process sequences or process steps, ingredients, especially the raw material and the like.
Mrziglod teaches the method of claim 22, wherein the Process Mapping is performed by describing the structure of the production process or its pre-stages including at least one of necessary components, process sequences or process steps, ingredients, or the raw material (manufacturing process with sub-processes, or steps, is received with ingredients used in the process, para. 0049-54, 0119-31).
It would have been obvious to one of ordinary skill in art, having the teachings of Yuan and Mrziglod before the earliest effective filing date, to modify the production process of Yuan to include the mapping of Mrziglod.
One of ordinary skill in the art would have been motivated to modify the production process of Yuan to include the mapping of Mrziglod because it can help improve quality predictions in multistep processes (Mrziglod, para. 0002-06).
In reference to claim 26, Yuan does not explicitly teach the method of claim 22, wherein the Data Mapping is performed by assigning the acquired raw material data, like temperature, mixing ratio of the raw material, time, and the like, to its corresponding process components and process sequences or steps.
Mrziglod teaches the method of claim 22, wherein the Data Mapping is performed by assigning at least the acquired raw material data, temperature of the raw material, mixing ratio of the raw material, and time to its corresponding process components and process steps (raw material quality parameters and process parameters like production machine settings and measurements during production, para. 0123, like temperature, para 0106-07, are associated together in the model, para. 0059-65).
It would have been obvious to one of ordinary skill in art, having the teachings of Yuan and Mrziglod before the earliest effective filing date, to modify the production process of Yuan to include the mapping of Mrziglod.
One of ordinary skill in the art would have been motivated to modify the production process of Yuan to include the mapping of Mrziglod because it can help improve quality predictions in multistep processes (Mrziglod, para. 0002-06).
In reference to claim 27, Yuan teaches the method of claim 22, wherein analyzing the mapped process description is performed by the software using supervised algorithms including a data analysis framework with a data model using approaches including Multivariate Analysis including PLS regression, PCA, Random Forest, XGBoost and artificial neural networks, or using supervised and/or unsupervised algorithms (analysis can be performed using ANN, para. 0029).
In reference to claim 28, Yuan teaches the method of claim 27, wherein analyzing the raw material data is performed using mechanistic models, physics based models, models based on either one of differential equations or partial differential equations and models based on quantum chemical computations (the Examiner notes the 112(b) rejection above – it is unclear what data is being analyzed because “the data” lacks antecedent basis. Yuan teaches any simulation model can be used, like physics-based models, para. 0058).
In reference to claim 29, Yuan teaches the method of claim 27, wherein the structure of the supervised algorithms is the result of training the PLS regression, PCA, Random Forest, XGBoost and artificial neural networks, with the results of the process description from the Process and Data Mapping (models are trained, para. 0055).
In reference to claim 30, Yuan teaches the method of claim 22, wherein the raw material data is acquired by examining the at least two different sources either manually by a user who inputs this data in the Data Collecting computer or automatically by a Data Collecting Software performed on either the Data Collecting computer or a separate computer which is connected to it with the Data Collecting Software transmitting it to the Data Collecting computer (raw material data retrieved automatically from databases, para. 0030, 0051-52).
In reference to claim 31, Yuan teaches the method of claim 30, wherein at least two different sources at least two different production sites are used (Yuan teaches different manufacturers, which would be different sites, para. 0030, 0051-52).
In reference to claim 32, Yuan teaches the method according to claim 31, wherein the raw material data from the at least two involved production sites comprises specific quality parameters or metal impurity and purity levels, in-process-data including at least one of temperatures, pressures, flows or P&ID charts (metal quality properties and temperature process data, para. 0051).
In reference to claim 33, Yuan teaches the method of claim 22, wherein a user interface is implemented which uses a data platform on the Analyzing computer for a preprocessing of the acquired raw material data before applying the specific software performed and writes the results to a database from where a dedicated dashboard retrieves the raw material data to provide it to the user for performing a Raw Material Review (UI displayed to user showing the results of the raw material ranking, para. 0075-89, fig. 8; data can be stored in database, para. 0031).
In reference to claim 34, Yuan teaches the method of claim 33, wherein the user interface displays the contributions of different raw materials to the prediction of a certain quality measurement to indicate the most relevant raw materials (see fig. 8, para. 0075-89).
In reference to claim 36, this claim is directed to a system associated with the method claimed in claim 22 and is therefore rejected under a similar rationale.
In reference to claim 37, Yuan does not explicitly teach the System according to claim 36, wherein at least one of the at least two sites is a factory for producing chemicals, pharmaceuticals or the like and at least one of the other sites is a chemical material provider and/or distributor.
Mrziglod teaches the System according to claim 36, wherein at least one of the at least two sites is a factory for producing chemicals, pharmaceuticals or the like and at least one of the other sites is a chemical material provider and/or distributor (pharmaceutical and chemical production and distribution, para. 0014, 0128-30).
It would have been obvious to one of ordinary skill in art, having the teachings of Yuan and Mrziglod before the earliest effective filing date, to modify the production process of Yuan to include the chemical and pharmaceutical production of Mrziglod.
One of ordinary skill in the art would have been motivated to modify the production process of Yuan to include the chemical and pharmaceutical production of Mrziglod because it would allow the quality predictions of Yuan to be used in more industries, like chemicals and pharmaceuticals (Mrziglod, para. 0002-06).
In reference to claim 38, this claim is directed to a system associated with the method claimed in claim 31 and is therefore rejected under a similar rationale.
In reference to claim 39, Yuan teaches the System according to claim 38, wherein the Process Mapping computer and the Data Mapping computer are supporting input terminals for human users to perform the Process Mapping and Data Mapping step (see figs. 5-8), while the Analyzing computer is a server which hosts the software with the supervised and/or unsupervised algorithms and an artificial neural network (ANN, para. 0029), and the Process Performing computer is part of or identical to the respective computer based control terminal for the at least two production sites (it would be obvious that the various computer-implemented steps could be performed on the same computer or different identical computers).
In reference to claim 40, this claim is directed to a machine learning algorithm associated with the method claimed in claim 29 and is therefore rejected under a similar rationale.
In reference to claim 41, this claim is directed to a software associated with the method claimed in claim 22 and is therefore rejected under a similar rationale.
Response to Arguments
Applicant's arguments filed 7/14/2026 have been fully considered but they are not persuasive.
First, with respect to the 103 rejection, Applicant argues that Yuan and Mrziglod are not combinable because it would change the basic operating principle of Yuan and the combination would not result in mapping two different types of raw material to the process steps. The Examiner respectfully disagrees. Yuan is intended to address the problem of determining an optimal raw material (see, e.g., Yuan para. 0001-02), and the predictive models can include, “perform[ing] virtual manufacturing such as process prediction, manufacturing feedback, and the like … the prediction program 124 may execute one or more machine learning models and/or simulation models for determining manufacturing results for individual candidate materials,” and, “predict[ing] product performance for individual candidate material and/or processes,” for scoring and comparing candidate materials, para. 0045-48. However, Yuan not specify precise machine learning or simulation models. Mrziglod is also directed to analyzing product quality including “quality of starting material(s),” para. 0003-06, by mapping raw materials to process steps, para. 0119-31. Mrziglod would be readily and predictably added to Yuan by simply adding the process mapping and analysis of Mrziglod to the plurality of machine learning and simulation models. The combination would result in the different types of raw materials being each sent through the process mapping and analysis of Mrziglod in order to determine the product quality because the point of Yuan is to compare different raw materials.
Next with respect to the 101 rejection, the Applicant argues that the limitation of, “generating and executing, by using the identified and validated characteristics to choose the most suitable, available raw material data, an executable instruction to perform a production process thereby improving the resulting product quality,” is not a mental process step, and is an additional limitation that integrate the abstract idea into a practical application. The Examiner respectfully disagrees: the broadest reasonable interpretation of this limitation is a mental process. A simple example is shown by figure 6 of Yuan: a human can look at characteristics of different available raw materials and mentally judge one of the materials as being the best for integrating into a production process to improve the process. Applicant’s argument that the invention is directed to an improvement in the technical field of creating and performing a production process, or an improved computer for production process execution, is not convincing. First, production processes are not a technical field: a “production process” cover many different technical fields. Second, to the extent that the computer system is improved, the improvement is provided by the mental process alone. See MPEP 2106.05(a).
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Andrew T. Chiusano whose telephone number is (571)272-5231. The examiner can normally be reached M-F, 10am-6pm.
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/ANDREW T CHIUSANO/Primary Examiner, Art Unit 2144
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