DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
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.
Response to Arguments
Applicant’s response to Office action was received on December 29, 2025.
In response to Applicant’s amendment of the claims, all of the indefiniteness rejections, from the previous Office action, are hereby withdrawn.
In response to Applicant’s amendment of the claims, the corresponding prior art claim rejections, from the previous Office action, have been correspondingly amended, below in this Office action.
In response to Applicant’s amendment of the claims, please note the new claim objections, below in this Office action.
In response to Applicant’s amendment of the claims, the corresponding 101 Alice-type claim rejections, from the previous Office action, have been correspondingly amended, below in this Office action.
In response to Applicant’s amendment of the claims, the other 101 claim rejections, from the previous Office action, have been amended, below in this Office action. Note that the transitory-computer-readable-medium-based 101 rejection of claim 15, from the previous Office action, has been withdrawn.
Applicant argues that claims 14-15 and 17 have been amended to overcome their respective non-Alice 101 rejections, from the previous Office action. Please note that this is only the case with claim 15. Note that, as explained in the 101 rejections, below in this Office action, claim 14 continues to be directed to software by itself, which is not patent-eligible under 101. Claim 17 continues to be directed to a neural network, which is information and thus does not fall within a 101 statutory class.
Regarding the 101 Alice-type rejections, Applicant argues that representative claim 1’s features provide a specific computational pipeline that produces a processor-derived control parameter for manufacturing process optimization/control, and is not merely organizing commercial activity or advising a human. In response, a significant amount of claim 1 is data processing which ultimately results in a control parameter for manufacturing processes. The information processing qualifies as an abstract idea in the form of “Certain method(s) of organizing human activity” because (1) this determination relates to how to operate a manufacturing process, which is commonly a commercial endeavor; and (2) the data processes manage personal behavior or relationships or interactions between people because they help generate instructions that allow humans to better operate a manufacturing process. (Note that the independent claim language does not require direct control over manufacturing equipment by the computing system determining the control parameter.)
The use of a computer processor to perform the data processing of the abstract idea is not helpful for rendering eligibility here because it qualifies as potentially a mere generic/general-purpose computing processor. This is addressed in MPEP 2106.05(f) (“Mere Instructions to Apply an Exception”). For example, MPEP 2106.05(f)(2) states: “Use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., a fundamental economic practice or mathematical equation) does not integrate a judicial exception into a practical application or provide significantly more.” As for the controlling of the manufacturing processes, the current claim language does not require direct control of the manufacturing processes by the claimed computing system. Thus, representative claim 1 may amount to merely a generic computing system which processes input data and outputs a control parameter which humans then use to program manufacturing equipment. This break in automated control is significant because it is the difference between a self-optimizing manufacturing equipment system and a separate, independent generic computing system which merely processes data and generates recommendations. Thus, Examiner currently dismisses the manufacturing controlling feature as insignificant extrasolution activity that is also well-understood, routine, and conventional activity. (See MPEP 2106.05(g) and MPEP 2106.05(d).) Therefore, Examiner does not find this Applicant argument to be persuasive.
Applicant argues against the 101 rejections with respect to Step 2A, Prong One, by focusing on the specific details of the data processing that lead to the calculation of the control parameter -- Applicant concludes that these are features relating to how computerized processing is performed (model coupling plus bounded evaluation) and what it produces (a control parameter used for process optimization/control), rather than a claim directed to organizing human activity. In response, there is no reason that the claim cannot both have the specific models and details that lead to the calculation of the control parameter and also be a certain method of organizing human activity. As an example, consider a product pricing algorithm which results in a price that is used for a sales transaction. The product pricing algorithm could involve chained models, similar to Applicant’s independent claims, and be quite complex. However, as part of calculating a price for a product sale, the algorithm is still part of a commercial interaction and thus a certain method of organizing human activity. Therefore, Examiner does not find this Applicant argument to be persuasive.
Regarding Step 2A, Prong Two, Applicant first argues that the generic computing components cannot be dismissed because of the various data processing that they perform, such as the mapping, bounded evaluation, and calculation of a control parameter. However, the data processing here is part of the abstract idea and does not make the computing components any less generic. Merely performing an abstract idea on generic computing components, without more, does not render eligibility to a claim to such an abstract idea. Second, Applicant argues that the use of the control parameter to control manufacturing processes is not in fact dismissible as simply output information that a human then uses to set the manufacturing equipment. Examiner disagrees. For example, the exact language at issue in representative claim 1 states: “calculating, by the at least one processor, a control parameter from the output data, which is used to perform optimization and controlling of the manufacturing processes”. The control parameter here is clearly calculated by at least one processor, and not a human. However, note that the language does not state who or what then uses the control parameter to perform optimization and controlling of the manufacturing processes; that is left open-ended, which means that it encompasses humans doing so. In Examiner’s view, this detail significantly weakens the case for eligibility here, because the computer may simply output a control parameter and have to rely on a human to make the connection to feeding that control parameter to manufacturing equipment, in order to actually extend the claimed subject matter beyond merely a generic computing system processing data in a particular way. Therefore, Examiner does not find this Applicant argument to be persuasive.
Applicant next argues that the claims are eligible under Step 2B as other than well-understood, routine, and conventional. Examiner disagrees. When applying the consideration of well-understood, routine, and conventional, there must be meaningful contribution to the consideration from additional elements beyond the abstract idea/judicial exception. See MPEP 2106.05(d), which states: “Another consideration when determining whether a claim recites significantly more than a judicial exception is whether the additional element(s) are well-understood, routine, conventional activities previously known to the industry. This consideration is only evaluated in Step 2B of the eligibility analysis.” While Step 2B analysis involves consideration of the claim as a whole, it is also clear that the inventive concept cannot be furnished by an abstract idea by itself. See the discussion in MPEP 2106.05(I). This is what Examiner means by needing meaningful contribution to the Step 2B consideration from the additional elements beyond the abstract idea/judicial exception. Looking at the additional elements in current representative claim 1, we have two types: (1) the generic/general-purpose computing components which do not contribute to being other than well-understood, routine, and conventional for that very reason, and (2) “using a control parameter to control manufacturing processes” which Examiner addressed as insignificant extrasolution activity for the reasoning provided. Under 101 guidance, Examiner was then required to explicitly address (2) under well-understood, routine, and conventional analysis, including providing Berkheimer evidence. Examiner did so in the 101 rejections, below in this Office action. Therefore, Examiner has established that such additional elements do not provide the meaningful contribution that would be needed for eligibility under the “other than well-understood, routine, and conventional” rationale. Thus, Examiner does not find Applicant’s Step 2B argument to be persuasive.
Examiner notes Applicant’s arguments regarding that Examiner did not provide Berkheimer evidence in the previous version of the 101 Alice-type rejections. Examiner was not required to do so. The additional elements in the previous claim listing all qualified as generic computing components which were dismissed as mere instructions to apply an exception under MPEP 2106.05(f) and not as well-understood, routine, and conventional under MPEP 2106.05(d). The Berkheimer analysis is for well-understood, routine, and conventional under MPEP 2106.05(d) and not mere instructions to apply an exception under MPEP 2106.05(f).
Examiner is not persuaded by Applicant’s BASCOM argument. In BASCOM, eligibility was found via the overall combination of elements, which included locating a filter at a particular advantageous physical location in the network. Therefore, there was a distinct contribution to the eligibility rationale from the additional elements in BASCOM beyond any judicial exception. In contrast, Applicant’s BASCOM arguments reference a specific arrangement and interaction of model-connection, bounded evaluation, and control-parameter generation. However, note that these features are all part of the judicial exception/abstract idea, and not the additional elements beyond such judicial exception/abstract idea. This distinguishes Applicant’s claims from the BASCOM situation, so the BASCOM rationale does not apply here.
Applicant next argues based on the Desjardins decision. In response, a key piece of guidance from the Desjardins decision is that machine-learning itself may be treated as a technology that may be improved for purposes of the technological or computing improvement consideration of MPEP 2106.05(a). In making the Desjardins argument, Applicant focuses on three claim features: (1) the upstream-downstream mapping between models, (2) bounded-range evaluation, and (3) the calculated control parameter.
Regarding (1), the upstream-downstream mapping between models is a natural consequence of using multiple models in sequence. For example, if one is using a pricing algorithm model to establish a price for a product, and then using a sales forecast model based on price, it would be important that a price is output from the first model because the second model requires price for an input. Using multiple models is simply an aggregation of using one model, so it seems unlikely that mapping inputs/outputs to use multiple models in sequence represents an improvement in machine-learning technology at the time of Applicant’s priority date.
Regarding (2), bounded-range evaluation is essentially the concept of having constraints. Constraints on particular values in a mathematical problem is a long-time concept in mathematics. Therefore, it seems unlikely that bounded-range evaluation represents an improvement in machine-learning technology at the time of Applicant’s priority date.
Regarding (3), calculating a control parameter here amounts to solving the system of models for a particular value, for example, a control parameter that optimizes the system in a desired way. Determining solutions that optimize systems is a core concept of optimization, for example in engineering-type design situations. Therefore, it seems unlikely that calculating the control parameter represents an improvement in machine-learning technology at the time of Applicant’s priority date, and Examiner does not find the Desjardins arguments to be persuasive.
Regarding the 103 rejections, Applicant first argues against the combination of Wikipedia with Floren. Examiner used Wikipedia to disclose “wherein the models are normalized”. Applicant argues that Wikipedia has “batch normalization” which is used in training deep neural networks by normalizing intermediate activations over mini-batches. Applicant further argues that the rejection does not identify where Floren’s cited models are deep neural networks for which batch normalization would even be applicable, nor does it explain how a person of skill in the art would apply a neural-network training technique to arrive at the claimed normalized environmental impact calculation model. Applicant further argues that the asserted motivation (normalization may improve performance) is stated at too high a level of generality to supply the required reasoned rationale for modifying Floren’s simulation/optimization framework in the specific manner.
In response, it is useful to first consider what Applicant’s application means when it refers to normalization. Examiner performed a text-string search of Applicant’s specification for “normal” to attempt to gather such information. Examiner was unable to determine, from such search, a definition or precise description of the meaning that Applicant intends for normalized or normalization to have. Typical usage of the terms in Applicant’s specification are similar to the usage in the claims, in that models were simply referred to as “normalized”. Without a limiting definition, that opened the door for Examiner to use a reasonable meaning for “normalized”, drawn from the prior art.
This led Examiner to the Wikipedia “Batch normalization” reference. The Wikipedia reference gets into a significant amount of detail, but it also provides the following basics about “Batch normalization”: (a) It’s a technique for improving the speed, performance, and stability of artificial neural networks (first page); (b) While Applicant argues that Wikipedia’s “Batch normalization” is specifically for “deep neural networks” (and it is true that Wikipedia references “deep neural networks”), Wikipedia also refers to “Batch normalization” with respect to simply “neural networks”. See the first page, roughly one-third through the page, where Wikipedia states: “Others sustain that batch normalization achieves length-direction decoupling, and thereby accelerates neural networks.”
Having established that Wikipedia describes batch normalization as a technique that may be applied to neural networks and provides various advantages, we see that batch normalization is thus applicable in the 103 rejections. Applicant’s claims themselves recite neural networks (see claim 16). Floren also discloses neural networks, including Floren, paragraph [0154], which states: “In an embodiment, a plurality of black box and/or known models may be optimally chained together through use of a backpropagation through time (BPTT) algorithm and a RNN 705. A RNN 705 (e.g., a neural network with one or more loops 704) may include the plurality of black box and/or known models 702 in which each model may further include parameter output nodes 703 and parameter input nodes 701. The BPTT may be applied by the artificial intelligence training system 402 to train the RNN 705.” This is an example of how the models in Floren are disclosed as being implementable via neural networks.
If the models in Floren are disclosed as being implementable via neural networks, and Wikipedia describes that neural networks may be normalized to achieve advantages, then it would be reasonable to modify Floren with Wikipedia such that the models in Floren are normalized. This is what was done in the 103 rejections. While there are certainly many other details about the normalization covered in the Wikipedia article, Examiner is not aware of any specific incompatibilities between the neural networks described in Floren and the normalization described in Wikipedia. As for the motivation to combine being high-level or simplistic, Examiner does not believe that this renders the motivation to be improper here. For example, imagine modifying a network connection from wired to wireless for the convenience of not having movement of a computer being restricted by the cable length. This motivation could be described as high-level and simplistic as well, yet that does not make the motivation invalid. Therefore, Examiner does not find these Applicant arguments to be persuasive.
Applicant next argues that Floren fails to disclose “calculating, by the at least one processor, a control parameter from the output data, which is used to perform optimization and controlling of the manufacturing processes”. Examiner disagrees. Floren, paragraph [0013], provides a good foundation for this discussion, where it states:
In some of the embodiments, the methods and systems described herein may receive input from one or more real-world systems and may also provide output to one or more real-world systems. In some of these embodiments, (measured) parameter values are obtained from measuring devices or sensors in a (technical) real-world system, the parameter values may be used, for example, to train one or more models (e.g., based on machine learning) or a basic model is already provided and the parameter values are used to adapt to the real-world system and/or to further refine the model. The model then allows to simulate the (technical) real-word system and the insights/predictions obtained via the simulation may again be used for monitoring and/or controlling the real-world system, e.g., using actuators, via the interactive and dynamic graphical user interfaces that are described herein. Such an approach may be employed, for example, to monitor and/or control a water treatment physical system or any other real-world system as will be explained in more detail below.
From the immediately preceding passage, we see many important points. First, Floren models inputs and outputs of real-world systems. Second, some of the model parameters may be associated with devices or sensors in such real-world systems. Third, the models may be used to control the real-world systems. The passage uses a water treatment physical system as an example, but also allows for other possible real-world systems.
Floren, paragraph [0061], describes that the real-world system may be a supply chain. A supply chain supports the form of Applicant’s models because a supply chain may be a series of parts or product manufacturers that operate in sequence to create a final product. This would fit Applicant’s model of a chain of manufacturing stations being modeled. However, Floren, paragraph [0061], also states: “In another example, the real-world system 102 may be a technical system, e.g., a manufacturing site, such as a location in which machinery is used to make articles.” This statement can be used both alone and in combination with the supply chain disclosure to support the use of the modeling in Floren for a manufacturing system.
Floren, paragraph [0064], provides further support for the modeling being of a manufacturing system, where it states: “Another example may include a manufacturing site physical system that may include sensors and/or measuring devices coupled to a machinery physical subsystem so that monitoring of the operation of machinery at the manufacturing site and variations in manufacturing conditions, such as temperature, efficiency, output, etc., and/or the like may occur.” Not only does this reinforce that the modeled system may be a manufacturing system, but it also explicitly discusses that the parameters of the manufacturing processes may be monitored and incorporated into Floren’s method.
Floren, paragraphs [0089]-[0090], discusses simulating a modeled system in order to determine actions to accomplish an optimization goal. Floren, paragraphs [0241]-[0242], provides similar subject matter. Floren, paragraph [0256], also discusses optimization, and states: “Each action may include parameters, such as a name, description, logic (e.g., parameters that are searched in an optimization), a log of uses of the action, and/or the like. Thus, the optimization may present one or more recommended actions that optimize the business goals.” Note that if one is intending to optimize a manufacturing process in which various manufacturing system parameters are monitored, the “parameters that are searched in an optimization” (as in the immediately preceding quotation) would include parameters to operate the manufacturing system at, that would optimize toward a desired goal. Thus, when Floren discusses determining actions based on its optimizations, these actions may include such parameters to operate the manufacturing system at, that would optimize toward a desired goal, as such quoted “parameters that are searched in an optimization”.
Therefore, to summarize the above discussion, in Floren, a manufacturing system may be monitored, simulated, and optimized using model(s). In the optimization, operating parameters of the manufacturing system may be determined that are expected to result in the optimal progress toward desired goal(s). The manufacturing system may then be controlled to operate at those determined optimized operating parameters. Thus, Floren does indeed disclose “calculating, by the at least one processor, a control parameter from the output data, which is used to perform optimization and controlling of the manufacturing processes”, since one of such determined optimized operating parameters would fit the meaning of what the limitation calls a “control parameter” here.
Applicant’s next argument states that certain aspects of Floren do not cure issues with the Wikipedia reference or with Floren disclosing the control parameter limitation. However, Examiner explained above why these alleged issues do not actually exist. Therefore, this Applicant argument is not applicable.
Therefore, Examiner does not find Applicant’s arguments to be persuasive.
Claim Objections
Claims 9 and 15 are objected to because of the following informalities:
a. In the twenty-ninth line of claim 9, please replace the word “comprising” with --comprises-- to correct a grammatical error.
b. In the second line of claim 15, immediately after the word “stored”, please insert -- thereon-- to clarify that it is the non-transitory computer-readable storage medium itself on which the computer element is stored (and not something else).
Appropriate correction is required.
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.
Claim(s) 1-2 and 4-17 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
As per Claim(s) 1 and 9, Claim(s) 1 and 9 recite(s):
- collaborative environmental impact optimization of manufacturing processes;
- receiving first input data relating to at least one chemical product comprising environmental impact metrics data related to environmental impact metrics and product property data related to chemical or physical properties of the at least one chemical product; and second input data relating to at least one chemical product comprising environmental impact metrics data related to environmental impact metrics and product property data related to chemical or physical properties of the at least one chemical product;
- determining a first normalized environmental impact calculation model for the first input data, the first normalized environmental impact calculation model describing a functional relationship between the environmental impact metrics data and the product property data; and determining a second normalized environmental impact calculation model for the second input data, the second normalized environmental impact calculation model describing a functional relationship between the environmental impact metrics data and the product property data;
- connecting the first normalized environmental impact calculation model and the second normalized environmental impact calculation model to a connected normalized environmental impact calculation model, wherein the step of connecting the first normalized environmental impact calculation model and the second normalized environmental impact calculation model to the connected normalized environmental impact calculation model comprises mapping selected product properties as output variables from at least one upstream model based on the first normalized environmental impact calculation model to input variables of at least one downstream model based on the second normalized environmental impact calculation model;
- providing output data of the connected normalized environmental impact calculation model over a variable value range, wherein the step of providing output data of the connected normalized environmental impact calculation model over the variable value range comprises restricting the variable value range to a valid and/or a predetermined variable value range;
- calculating a control parameter from the output data, which is used to perform optimization of the manufacturing processes.
Each of the above limitations falls within the abstract-idea category of “Certain methods of organizing human activity.” Specifically, those limitations relate to the following subject matter that is grouped into the category of “Certain methods of organizing human activity”:
- commercial or legal interactions (including agreements in the form of contracts; legal obligations; advertising, marketing or sales activities or behaviors; business relations): relates to how to operate a manufacturing process, which is commonly a commercial endeavor;
- managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions): helps generate instructions that allow humans to better operate a manufacturing process.
To the extent that any of these limitations are recited alongside recitations of generic computer components, as described below in this rejection: If a claim limitation, under its broadest reasonable interpretation, covers subject matter recognized as certain methods of organizing human activity but for the recitation of generic computer components, then it falls within the “Certain method of organizing human activity” grouping of abstract ideas. Accordingly, the claim(s) recite an abstract idea.
This judicial exception is not integrated into a practical application because the additional elements when considered both individually and as an ordered combination do not integrate the abstract idea into a practical application. The claim(s) recite the following additional elements/limitations, each of which are addressed in the list below with the reason(s) why they do not integrate the abstract idea into a practical application:
- computer-implemented; at least one processor; a communication interface; an apparatus; one or more computing nodes and one or more computer-readable media having thereon computer-executable instructions that are structured such that, when executed by the one or more computing nodes, cause the apparatus to perform: These element(s)/limitation(s) amount to mere instructions to apply an exception. See MPEP 2106.05(f). In making this determination, examiners may consider whether the claim invokes computers or other machinery merely as a tool to perform an existing process. Mere instructions to apply an exception is a consideration with respect to both integration of an abstract idea into a practical application and significantly more. MPEP 2106.05(f)(2) states: “Use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., a fundamental economic practice or mathematical equation) does not provide significantly more. See Affinity Labs v. DirecTV, 838 F.3d 1253, 1262, 120 USPQ2d 1201, 1207 (Fed. Cir. 2016) (cellular telephone); TLI Communications LLC v. AV Auto, LLC, 823 F.3d 607, 613, 118 USPQ2d 1744, 1748 (Fed. Cir. 2016) (computer server and telephone unit).” This is the case with these particular claim element(s)/limitation(s). Those elements/limitations do not meaningfully limit the claim because implementing an abstract idea on a generic computer does not integrate the abstract idea into a practical application, similar to how the recitation of the computer in the claim in Alice amounted to mere instructions to apply the abstract idea of intermediated settlement on a generic computer. Therefore, these particular claim element(s)/limitation(s) do not integrate the abstract idea into a practical application for at least this reason.
- using a control parameter to control manufacturing processes: These element(s)/limitation(s) amount to mere insignificant extra-solution activity. See MPEP 2106.05(g). MPEP 2106.05(g) states: “The term "extra-solution activity" can be understood as activities incidental to the primary process or product that are merely a nominal or tangential addition to the claim. Extra-solution activity includes both pre-solution and post-solution activity.” These particular element(s)/limitation(s) do not meaningfully limit the claim because “using a control parameter to control manufacturing processes” amounts to insignificant application as the control parameter is simply actually used as one would expect following its calculation; note that it could also require a human intermediary to implement the control parameter on equipment, thereby not being direct control. Therefore, these particular claim element(s)/limitation(s) do not integrate the abstract idea into a practical application for at least this reason.
Examiner presents the following examples of activities that the courts have found to be insignificant extra-solution activity, as relevant to these particular element(s)/limitation(s):
Insignificant application:
Cutting hair after first determining the hair style, In re Brown, 645 Fed. App'x 1014, 1016-1017 (Fed. Cir. 2016) (non-precedential).
Printing or downloading generated menus, Apple, Inc. v. Ameranth, Inc., 842 F.3d 1229, 1241-42, 120 USPQ2d 1844, 1854-55 (Fed. Cir. 2016).
Thus, taken alone, the additional elements do not integrate the abstract idea into a practical application. Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology.
Accordingly, the additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claim(s) are directed to an abstract idea.
The claim(s) do not include additional elements that are sufficient to amount to significantly more than the judicial exception, either individually or as an ordered combination. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements of computer-related components amount to no more than mere instructions to apply the exception using generic computer components. Mere instructions to apply an exception using generic computer components cannot provide an inventive concept. As also discussed above with respect to integration of the abstract idea into a practical application, the additional element of “using a control parameter to control manufacturing processes” amounts to insignificant extra-solution activity, which does not provide an inventive concept.
If an examiner previously concludes under Step 2A that an additional element is insignificant extra-solution activity, the examiner should evaluate whether that additional element is more than what is well-understood, routine, and conventional in the field, in step 2B. Examiner addresses below why that element was well-understood, routine, and conventional in the field:
- using a control parameter to control manufacturing processes: See Ketterle, US 20190346824 A1, paragraph [0002], which states: “Industrial control networks are ubiquitous in many areas of industry and manufacturing, and may comprise a plurality of industrial controller units, wherein each of the industrial controller units stores a designated control software program and possibly control parameters adapted to control a machinery or group of machinery connected to the respective industrial controller unit. Industrial controller units may be integrated into the machinery, or may be stand-alone control devices.” Thus, “using a control parameter to control manufacturing processes” was well-understood, routine, and conventional activity.
The claim(s) are not patent eligible.
As per dependent claim(s) 2, 4-8, and 10-17, these claim(s) incorporate the above abstract idea via their dependencies on the respective independent claim(s). The additional element(s)/limitation(s) of the respective independent claim(s) do not integrate the abstract idea into a practical application, nor do they add significantly more, with respect to those dependent claim(s), under the same reasoning as above with respect to the respective independent claim(s).
Those dependent claim(s) add the following generic computer components, which do not integrate the abstract idea into a practical application, nor add significantly more, under the same reasoning as given above with respect to generic computer components in the independent claim(s). Those additional generic computer components and their corresponding dependent claim(s) are as follows:
- uploading (Claims 5-6 and 11);
- a platform node (Claims 5-6 and 11);
- secured computing enclave (Claim 6);
- user interface (Claims 8 and 13);
- a computer element with instructions, which when executed on one or more computing node(s) is configured to carry out the steps (Claim 14);
- a non-transitory computer-readable storage medium having stored the computer element (Claim 15).
The remaining added elements/limitations of those dependent claim(s) do not integrate the abstract idea into a practical application nor add significantly more because they all merely add further functional step(s) and/or detail to the abstract idea; as part of the abstract idea, they cannot integrate into a practical application or be significantly more than the abstract idea of which they are a part. For example, Claim 4 is merely directed to who controls certain data.
Thus, taken alone, the additional elements do not integrate the abstract idea into a practical application, nor add significantly more. Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology.
Claim(s) 1-2 and 4-17 are therefore not drawn to eligible subject matter as they are directed to an abstract idea that is not integrated into a practical application and is without significantly more.
Claims 14 and 17 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. The claim(s) does/do not fall within at least one of the four categories of patent eligible subject matter because:
As per Claim 14, the preamble of this claim describes Claim 14 as being directed to “A computer element”. Claim 15, which depends from Claim 14, refers to “A non-transitory computer-readable storage medium having stored the computer element of claim 14”. Therefore, it appears that Claim 14 is intended to be directed to a computer program by itself. Software by itself is neither a process, a machine, a manufacture, nor a composition of matter. Therefore, Claim 14 is rejected under 35 U.S.C. 101 as not directed to a statutory class.
As per Claim 17, the preamble of this claim describes Claim 17 as being directed to “A trained neural network”. A neural network by itself is neither a process, a machine, a manufacture, nor a composition of matter. Therefore, Claim 17 is rejected under 35 U.S.C. 101 as not directed to a statutory class.
Claim Rejections - 35 USC § 103
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) 1-2, 4, 7-10, and 12-17 is/are rejected under 35 U.S.C. 103 as being unpatentable over Floren, US 20220075515 A1, in view of Wikipedia, “Batch normalization,” www.wikipedia.org, version of article dated June 30, 2020, retrieved on August 23, 2025.
As per Claims 1 and 9, Floren discloses:
- a computer-implemented method (or apparatus) for collaborative environmental impact optimization of manufacturing processes (paragraph [0006] (“Disclosed herein are various systems, computer program products, and computer-implemented methods for visualizing and interacting with various inputs, outputs, and other data resulting from a simulation of multiple models that collectively represent a real-world system, e.g., a technical or physical real-world system.”); paragraph [0061] (“In another example, the real-world system 102 may be a technical system, e.g., a manufacturing site, such as a location in which machinery is used to make articles.”); paragraph [0064] (“Another example may include a manufacturing site physical system that may include sensors and/or measuring devices coupled to a machinery physical subsystem so that monitoring of the operation of machinery at the manufacturing site and variations in manufacturing conditions, such as temperature, efficiency, output, etc., and/or the like may occur.”); paragraph [0167] (“In the example user interface 800, a supply chain is represented, including node representing parts and goods suppliers, manufacturing plants, distributors, consumers (e.g., hospitals), and the like.”); paragraph [0175] (“For example, if a supplier is modeled as having a reduced supply in the future, the manufacturer may model a reduced output, and a supplier may model a shortage of inventory, assuming a modeled constant or increasing demand for goods.”); paragraph [0236] (“A “carbon cost” can also be included, which provides an estimation of the carbon footprint/impact of the changes.”); paragraph [0237] (“Besides manually making changes and seeing the results, the system may automatically run one or more simulations or scenarios and generate recommendations for changes to remediate the alert. In various implementations, the system may optimize for a particular target, e.g., for an optimum cost, an optimum time, or an optimum sustainability (e.g., a minimal carbon footprint). The system may also optimize globally, which may comprise some configurable weighting of cost, time, and/or sustainability.”); paragraph [0249] (“In various implementations, simulations, optimizations, or models may be saved and/or shared with other users of the system, e.g., for review, approval, and/or implementation.”));
- receiving, by at least one processor and via a communication interface, first input data relating to at least one chemical product comprising environmental impact metrics data related to environmental impact metrics and product property data related to chemical or physical properties of the at least one chemical product; and second input data relating to at least one chemical product comprising environmental impact metrics data related to environmental impact metrics and product property data related to chemical or physical properties of the at least one chemical product (paragraph [0006] (“Disclosed herein are various systems, computer program products, and computer-implemented methods for visualizing and interacting with various inputs, outputs, and other data resulting from a simulation of multiple models that collectively represent a real-world system, e.g., a technical or physical real-world system.”); paragraph [0013] (“In some of these embodiments, (measured) parameter values are obtained from measuring devices or sensors in a (technical) real-world system, the parameter values may be used, for example, to train one or more models (e.g., based on machine learning) or a basic model is already provided and the parameter values are used to adapt to the real-world system and/or to further refine the model.”); paragraph [0041] (“For example, live sensor data can be provided as an input to one or more of the simulated models which represent, for example, a technical system in the real world. In response, graphical user interfaces (“GUIs”) may be generated that can include, for example, graph-based GUIs, map-based GUIs, and panel-based GUIs, among others. The GUIs may include one or more panels to display data including technical data objects (also referred to herein as “objects”) (e.g., pumps, compressors, valves, machinery, welding stations, vats, containers, products or items, organizations, countries, counties, factories, customers, hospitals, etc.), technical object properties (e.g., flow rate, suction temperature, volume, capacity, order volume, sales amounts, sales quantity during a time period (e.g., a day, a week, a year, etc.), population density, patient volume, etc.), simulations, alerts, recommendations, and the like.”; “The technical objects and technical object properties may represent the inputs and outputs of the simulated models. Various GUIs may further comprise at least one of information, trend, simulation, mapping, schematic, time, equipment, and toolbar panels. Various panels may display the objects, object properties, inputs, and outputs of the simulated models.”); paragraph [0061] (“In another example, the real-world system 102 may be a technical system, e.g., a manufacturing site, such as a location in which machinery is used to make articles.”); paragraph [0064] (“Another example may include a manufacturing site physical system that may include sensors and/or measuring devices coupled to a machinery physical subsystem so that monitoring of the operation of machinery at the manufacturing site and variations in manufacturing conditions, such as temperature, efficiency, output, etc., and/or the like may occur.”); paragraph [0136] (“The model connector 404 may connect two or more models together via chaining, where the chaining occurs by linking predicted nodal relationships of parameter output nodes of one model with parameter input nodes of another model. In an embodiment, a link between a parameter output node of one model and parameter input node of another model may be established by the model connector 404 based on similar or matching parameter output nodes and parameter input nodes (e.g., the parameter output node of one model matches the parameter input node of another model), where the nodes may include model specific data comprised of subsystems, objects, and/or object properties (e.g., property types and/or property values).”); paragraph [0145] (“At (21), the model connector 404 chains the first and second models by linking and/or re-linking the predicted output and input node relationships. The model connector 404 then transmits the current node relationship data to the artificial intelligence training system 402 at (22) to further predict new node relationships based on the current relationships.”); paragraphs [0140]-[0143] (models of individual subsystems)); paragraph [0175] (“For example, if a supplier is modeled as having a reduced supply in the future, the manufacturer may model a reduced output, and a supplier may model a shortage of inventory, assuming a modeled constant or increasing demand for goods.”); paragraph [0236] (“A “carbon cost” can also be included, which provides an estimation of the carbon footprint/impact of the changes.”); paragraph [0237] (“Besides manually making changes and seeing the results, the system may automatically run one or more simulations or scenarios and generate recommendations for changes to remediate the alert. In various implementations, the system may optimize for a particular target, e.g., for an optimum cost, an optimum time, or an optimum sustainability (e.g., a minimal carbon footprint). The system may also optimize globally, which may comprise some configurable weighting of cost, time, and/or sustainability.”); paragraph [0251] (“In response to adding a simulated value, the chain of models is executed and values of downstream objects are simulated.”));
- determining a first environmental impact calculation model for the first input data, the first environmental impact calculation model describing a functional relationship between the environmental impact metrics data and the product property data; and determining a second environmental impact calculation model for the second input data, the second environmental impact calculation model describing a functional relationship between the environmental impact metrics data and the product property data (paragraph [0006] (“Disclosed herein are various systems, computer program products, and computer-implemented methods for visualizing and interacting with various inputs, outputs, and other data resulting from a simulation of multiple models that collectively represent a real-world system, e.g., a technical or physical real-world system.”); paragraph [0013] (“In some of these embodiments, (measured) parameter values are obtained from measuring devices or sensors in a (technical) real-world system, the parameter values may be used, for example, to train one or more models (e.g., based on machine learning) or a basic model is already provided and the parameter values are used to adapt to the real-world system and/or to further refine the model.”); paragraph [0041] (“For example, live sensor data can be provided as an input to one or more of the simulated models which represent, for example, a technical system in the real world. In response, graphical user interfaces (“GUIs”) may be generated that can include, for example, graph-based GUIs, map-based GUIs, and panel-based GUIs, among others. The GUIs may include one or more panels to display data including technical data objects (also referred to herein as “objects”) (e.g., pumps, compressors, valves, machinery, welding stations, vats, containers, products or items, organizations, countries, counties, factories, customers, hospitals, etc.), technical object properties (e.g., flow rate, suction temperature, volume, capacity, order volume, sales amounts, sales quantity during a time period (e.g., a day, a week, a year, etc.), population density, patient volume, etc.), simulations, alerts, recommendations, and the like.”; “The technical objects and technical object properties may represent the inputs and outputs of the simulated models. Various GUIs may further comprise at least one of information, trend, simulation, mapping, schematic, time, equipment, and toolbar panels. Various panels may display the objects, object properties, inputs, and outputs of the simulated models.”); paragraph [0061] (“In another example, the real-world system 102 may be a technical system, e.g., a manufacturing site, such as a location in which machinery is used to make articles.”); paragraph [0064] (“Another example may include a manufacturing site physical system that may include sensors and/or measuring devices coupled to a machinery physical subsystem so that monitoring of the operation of machinery at the manufacturing site and variations in manufacturing conditions, such as temperature, efficiency, output, etc., and/or the like may occur.”); paragraph [0136] (“The model connector 404 may connect two or more models together via chaining, where the chaining occurs by linking predicted nodal relationships of parameter output nodes of one model with parameter input nodes of another model. In an embodiment, a link between a parameter output node of one model and parameter input node of another model may be established by the model connector 404 based on similar or matching parameter output nodes and parameter input nodes (e.g., the parameter output node of one model matches the parameter input node of another model), where the nodes may include model specific data comprised of subsystems, objects, and/or object properties (e.g., property types and/or property values).”); paragraph [0145] (“At (21), the model connector 404 chains the first and second models by linking and/or re-linking the predicted output and input node relationships. The model connector 404 then transmits the current node relationship data to the artificial intelligence training system 402 at (22) to further predict new node relationships based on the current relationships.”); paragraphs [0140]-[0143] (models of individual subsystems)); paragraph [0175] (“For example, if a supplier is modeled as having a reduced supply in the future, the manufacturer may model a reduced output, and a supplier may model a shortage of inventory, assuming a modeled constant or increasing demand for goods.”); paragraph [0236] (“A “carbon cost” can also be included, which provides an estimation of the carbon footprint/impact of the changes.”); paragraph [0237] (“Besides manually making changes and seeing the results, the system may automatically run one or more simulations or scenarios and generate recommendations for changes to remediate the alert. In various implementations, the system may optimize for a particular target, e.g., for an optimum cost, an optimum time, or an optimum sustainability (e.g., a minimal carbon footprint). The system may also optimize globally, which may comprise some configurable weighting of cost, time, and/or sustainability.”); paragraph [0251] (“In response to adding a simulated value, the chain of models is executed and values of downstream objects are simulated.”));
- connecting the first environmental impact calculation model and the second environmental impact calculation model to a connected environmental impact calculation model, wherein the step of connecting the first environmental impact calculation model and the second environmental impact calculation model to the connected environmental impact calculation model comprises mapping selected product properties as output variables from at least one upstream model based on the first environmental impact calculation model to input variables of at least one downstream model based on the second environmental impact calculation model (paragraph [0006] (“Disclosed herein are various systems, computer program products, and computer-implemented methods for visualizing and interacting with various inputs, outputs, and other data resulting from a simulation of multiple models that collectively represent a real-world system, e.g., a technical or physical real-world system.”; “The objects and object properties may represent the inputs and outputs of the simulated models.”; “These panels may display the objects, object properties, inputs, and outputs of the simulated models.”); paragraph [0013] (“In some of the embodiments, the methods and systems described herein may receive input from one or more real-world systems and may also provide output to one or more real-world systems.”; “In some of these embodiments, (measured) parameter values are obtained from measuring devices or sensors in a (technical) real-world system, the parameter values may be used, for example, to train one or more models (e.g., based on machine learning) or a basic model is already provided and the parameter values are used to adapt to the real-world system and/or to further refine the model.”); paragraph [0034] (“FIG. 11B shows a flowchart illustrating an example process for determining relationships between one or more outputs of a first model and one or more inputs of a second model, and chaining the models together, according to one or more embodiments”); paragraph [0041] (“To satisfy the technical challenges outlined above, among others, disclosed herein are various computer systems, computer program products, and computer-implemented methods for visualizing and interacting with various inputs and outputs, running simulations, optimizing simulations, and automatically determining and implementing recommendations.”; “For example, live sensor data can be provided as an input to one or more of the simulated models which represent, for example, a technical system in the real world. In response, graphical user interfaces (“GUIs”) may be generated that can include, for example, graph-based GUIs, map-based GUIs, and panel-based GUIs, among others. The GUIs may include one or more panels to display data including technical data objects (also referred to herein as “objects”) (e.g., pumps, compressors, valves, machinery, welding stations, vats, containers, products or items, organizations, countries, counties, factories, customers, hospitals, etc.), technical object properties (e.g., flow rate, suction temperature, volume, capacity, order volume, sales amounts, sales quantity during a time period (e.g., a day, a week, a year, etc.), population density, patient volume, etc.), simulations, alerts, recommendations, and the like.”; “The technical objects and technical object properties may represent the inputs and outputs of the simulated models. Various GUIs may further comprise at least one of information, trend, simulation, mapping, schematic, time, equipment, and toolbar panels. Various panels may display the objects, object properties, inputs, and outputs of the simulated models.”); paragraph [0061] (“The real-world system 102 may be a logical system, such as a representation of a supply chain.”; “In another example, the real-world system 102 may be a technical system, e.g., a manufacturing site, such as a location in which machinery is used to make articles.”); paragraph [0064] (“For example, a supply chain system may include one or more logical computations associated with the supply chain itself.”; “Another example may include a manufacturing site physical system that may include sensors and/or measuring devices coupled to a machinery physical subsystem so that monitoring of the operation of machinery at the manufacturing site and variations in manufacturing conditions, such as temperature, efficiency, output, etc., and/or the like may occur.”); paragraph [0136] (“The model connector 404 may connect two or more models together via chaining, where the chaining occurs by linking predicted nodal relationships of parameter output nodes of one model with parameter input nodes of another model. In an embodiment, a link between a parameter output node of one model and parameter input node of another model may be established by the model connector 404 based on similar or matching parameter output nodes and parameter input nodes (e.g., the parameter output node of one model matches the parameter input node of another model), where the nodes may include model specific data comprised of subsystems, objects, and/or object properties (e.g., property types and/or property values).”; “For example, the artificial intelligence training system 402 may use an RNN to accurately and recurrently classify the nodes by using the nodes as training examples. The nodes may then link and/or re-link based on which classified parameter input nodes and parameter output nodes are most similar.”); paragraph [0138] (most of paragraph); paragraph [0145] (“At (21), the model connector 404 chains the first and second models by linking and/or re-linking the predicted output and input node relationships. The model connector 404 then transmits the current node relationship data to the artificial intelligence training system 402 at (22) to further predict new node relationships based on the current relationships.”); paragraphs [0140]-[0143] (models of individual subsystems)); paragraph [0175] (“For example, if a supplier is modeled as having a reduced supply in the future, the manufacturer may model a reduced output, and a supplier may model a shortage of inventory, assuming a modeled constant or increasing demand for goods.”); paragraph [0216] (“FIG. 11B is a flowchart 1120 illustrating an example process for determining relationships between one or more parameter outputs nodes of a first model and one or more parameter inputs nodes of a second model that collectively represent a real-world system, and chaining the models together, according to one or more embodiments.”); paragraph [0236] (“A “carbon cost” can also be included, which provides an estimation of the carbon footprint/impact of the changes.”); paragraph [0237] (“Besides manually making changes and seeing the results, the system may automatically run one or more simulations or scenarios and generate recommendations for changes to remediate the alert. In various implementations, the system may optimize for a particular target, e.g., for an optimum cost, an optimum time, or an optimum sustainability (e.g., a minimal carbon footprint). The system may also optimize globally, which may comprise some configurable weighting of cost, time, and/or sustainability.”); paragraph [0251] (“In response to adding a simulated value, the chain of models is executed and values of downstream objects are simulated.”; some of rest of paragraph));
- providing, by the at least one processor, output data of the connected environmental impact calculation model over a variable value range, wherein the step of providing output data of the connected environmental impact calculation model over the variable value range comprises restricting the variable value range to a valid and/or a predetermined variable value range (paragraph [0006] (“Disclosed herein are various systems, computer program products, and computer-implemented methods for visualizing and interacting with various inputs, outputs, and other data resulting from a simulation of multiple models that collectively represent a real-world system, e.g., a technical or physical real-world system.”); paragraph [0061] (“In another example, the real-world system 102 may be a technical system, e.g., a manufacturing site, such as a location in which machinery is used to make articles.”); paragraph [0226] (“In various implementations, the user may select the one or more models, and one or more parameters (e.g., inputs and outputs) for the respective models. Further, the user may set certain parameter values, which may comprise constraints on the simulation/model.”); paragraph [0236] (“A “carbon cost” can also be included, which provides an estimation of the carbon footprint/impact of the changes.”); paragraph [0237] (“Besides manually making changes and seeing the results, the system may automatically run one or more simulations or scenarios and generate recommendations for changes to remediate the alert. In various implementations, the system may optimize for a particular target, e.g., for an optimum cost, an optimum time, or an optimum sustainability (e.g., a minimal carbon footprint). The system may also optimize globally, which may comprise some configurable weighting of cost, time, and/or sustainability.”); paragraph [0243] (“Referring to FIG. 14A, an example user interface 1400 includes many elements similar to previously discussed GUIs, and additionally includes an “optimization” panel 1402, also similar to optimizations GUIs described herein. Via panel 1402 the user may execute various scenarios/simulations, and may optimize for various goals or KPIs. For example, the user may select an objective/goal for which the system is to optimize via controls 1404, may add input variables/parameters via table 1406, and may add one or more constraints via table 1408. As described above, a column of the tables can show current/real values for the parameters, and one or more additional columns can show simulation results for the parameters. The example user interface 1400 shows optimization results that are automatically developed by the optimization component 116 (and/or other aspects of the system optimization server 104) in response to key metrics provided by a user indicating a desire to optimize revenue without going under inventory limits.”); paragraph [0251] (“In response to adding a simulated value, the chain of models is executed and values of downstream objects are simulated.”; some of the rest of paragraph));
- calculating, by the at least one processor, a control parameter from the output data, which is used to perform optimization and controlling of the manufacturing processes (paragraph [0007] (“The highly configurable and editable nature of the GUI allows any user the ability to visualize, simulate, and interact with the models that collectively represent a real-world system in order to assist the user in monitoring and/or controlling the real-world system by means of a continued and/or guided human-machine interaction process. In some of the embodiments the monitoring and/or controlling is performed in real-time.”); paragraph [0013] (whole paragraph); paragraph [0047] (whole paragraph); paragraph [0061] (“In another example, the real-world system 102 may be a technical system, e.g., a manufacturing site, such as a location in which machinery is used to make articles.”); paragraph [0064] (“Another example may include a manufacturing site physical system that may include sensors and/or measuring devices coupled to a machinery physical subsystem so that monitoring of the operation of machinery at the manufacturing site and variations in manufacturing conditions, such as temperature, efficiency, output, etc., and/or the like may occur.”); paragraphs [0089]-[0090] (optimization); paragraph [0167] (“In the example user interface 800, a supply chain is represented, including node representing parts and goods suppliers, manufacturing plants, distributors, consumers (e.g., hospitals), and the like.”); paragraph [0198] (whole paragraph); paragraphs [0241]-[0242] (optimization); paragraph [0243] (whole paragraph); paragraph [0256] (whole paragraph));
- one or more computing nodes and one or more computer-readable media having thereon computer-executable instructions that are structured such that, when executed by the one or more computing nodes, cause the apparatus to perform (paragraph [0043] (software)).
Floren fails to disclose wherein the models are normalized. Wikipedia discloses wherein the models are normalized (first page of reference, first two paragraphs). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Floren such that the models are normalized, as disclosed by Wikipedia. Motivation for the modification is provided by Wikipedia in that normalization may improve the performance of the models (first page of reference, first two paragraphs).
As per Claim 2, Floren further discloses wherein the first and second input data relating to the at least one chemical product further comprises process data (paragraph [0041]; paragraph [0057]; paragraph [0061]; paragraph [0134]; paragraph [0136]; paragraph [0138]).
As per Claims 4 and 10, Floren further discloses wherein the input variables of at least one downstream model are controlled by a first data owner or a second data owner; and/or wherein the input variables of at least one upstream model are controlled by the first data owner or the second data owner (paragraph [0034]; paragraph [0041]; paragraph [0061]; paragraph [0064]; paragraph [0167]; paragraph [0251]).
As per Claims 7 and 12, Floren further discloses wherein the method further comprises the steps of: determining a third environmental impact calculation model for third input data based on the environmental impact metrics data and the product property data, the third environmental impact calculation model describing a functional relationship between the environmental impact metrics data and the product property data; and connecting the first environmental impact calculation model for the first input data and the second environmental impact calculation model for the second input data and the third environmental impact calculation model for the third input data to a connected normalized environmental impact calculation model (paragraph [0006]; paragraph [0013]; paragraph [0041]; paragraph [0061]; paragraph [0064]; paragraph [0136] (“The model connector 404 may connect two or more models together via chaining, where the chaining occurs by linking predicted nodal relationships of parameter output nodes of one model with parameter input nodes of another model.”); paragraph [0145]; paragraphs [0140]-[0143]; paragraph [0175]; paragraph [0236]; paragraph [0237]; paragraph [0251]).
The modified Floren fails to disclose wherein the models are normalized. Wikipedia further discloses wherein the models are normalized (first page of reference, first two paragraphs). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of the modified Floren such that the models are normalized, as disclosed by Wikipedia. Motivation for the modification is provided by Wikipedia in that normalization may improve the performance of the models (first page of reference, first two paragraphs).
As per Claims 8 and 13, Floren further discloses generating, by the at least one processor, a user interface enabling evaluation of the connected environmental impact calculation model over the variable value range in terms of summing up the environmental impact metrics data and the product property data (Figure 13E; paragraph [0041]; paragraph [0047]; paragraph [0073]; paragraph [0226]; paragraph [0236]; paragraph [0237]; paragraph [0243]; paragraph [0251]; paragraph [0255]; paragraph [0259]).
The modified Floren fails to disclose wherein the models are normalized. Wikipedia further discloses wherein the models are normalized (first page of reference, first two paragraphs). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of the modified Floren such that the models are normalized, as disclosed by Wikipedia. Motivation for the modification is provided by Wikipedia in that normalization may improve the performance of the models (first page of reference, first two paragraphs).
As per Claim 14, Floren further discloses a computer element with instructions, which when executed on one or more computing node(s) is configured to carry out the steps of the method (paragraph [0043]).
As per Claim 15, Floren further discloses a non-transitory computer-readable storage medium having stored the computer element (paragraph [0017]; paragraph [0043]; paragraph [0284]).
As per Claim 16, Floren further discloses a neural network training method for training a neural network, the method comprising: obtaining training data; and training, using the training data, the neural network such that the trained neural network is adapted to perform the method, wherein the training uses the training data to train multivariate models in terms of costs and environmental impact reductions and product properties (paragraph [0041]; paragraph [0133]; paragraph [0135]; paragraph [0137]; paragraph [0143]; paragraph [0144]; paragraph [0153]; paragraph [0154]; paragraph [0236]; paragraph [0237]; paragraph [0243]; paragraph [0255]).
As per Claim 17, Floren further discloses a trained neural network produced by the method (paragraph [0133]; paragraph [0135]; paragraph [0137]; paragraph [0144]; paragraph [0153]; paragraph [0154]).
Claim(s) 5-6 and 11 is/are rejected under 35 U.S.C. 103 as being unpatentable over Floren in view of Wikipedia in further view of Williams, United Kingdom Patent Reference No. GB 2589312 A.
As per Claims 5 and 11, Floren further discloses wherein the input data is of at least one chemical product data (paragraph [0041]; paragraph [0061]; paragraph [0064]; paragraph [00307]; paragraph [0308]).
The modified Floren fails to disclose uploading of the input data using a platform node. Williams discloses uploading of the input data using a platform node (paragraph [0010]; paragraph [0310]; paragraph [0401]). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of the modified Floren such that the invention uploads the input data using a platform node, as disclosed by Williams, since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
As per Claim 6, Floren further discloses statistical models are fitted by using statistical modeling code provided by an independent third party (paragraph [0072]).
The modified Floren fails to disclose uploading the input data to a secured computing enclave. Williams further discloses uploading the input data to a secured computing enclave (paragraph [0010]; paragraph [0141]; paragraph [0258]; paragraph [0282]; paragraph [0310]; paragraph [0401]; paragraph [0435]). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of the modified Floren such that the invention uploads the input data to a secured computing enclave, as disclosed by Williams, since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure: Alhasan, US 20190338622 A1 (multi-period and dynamic long term planning optimization model for a network of gas oil separation plants).
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.
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/NATHAN ERB/Primary Examiner, Art Unit 3628
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