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 .
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
The term “"highly correlated"” in claims 2,is a relative term which renders the claim indefinite. The term “highly correlated"” is not defined by the claim, the specification does not provide a standard for ascertaining the requisite degree, and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention. The viscosity of the slurry being "highly correlated" fails to identify the specifics of how it relates to the invention. For the purposes of examination it is interpreted to mean viscosity data.
Claim 4 is rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention. The terms “final actual”, “preprocessed” followed by “predetermined” are unclear as to how they limit the claim in terms of the viscosity and how it is being determined for use in the machine learning models. For the purposes of examination, claim 4 is interpreted to mean “using an algorithm (I.E. Ridge model), calculate multiple top contributing factors to the viscosity of the slurry, to generate a second model for the prediction of viscosity of the slurry.
Claim 6 is rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention. The terms “final actual viscosity” and “final predictive viscosity” are unclear as to how these differ from the “actual viscosity” and “predictive viscosity”. For the purposes of examination these limitations are being interpreted to be the same.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-6 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. Below is an analysis in accordance with the Subject Matter Eligibility Test for Products and Process found in MPEP 2106(III).
Claim 1: An apparatus for controlling viscosity of a slurry, comprising:
a mixing module mixing raw materials for a secondary battery; and
a processor performing machine learning for prediction of viscosity of the slurry,
predicting viscosity of the slurry in real time during a mixing process through machine learning, and
adjusting conditions of the mixing process performed by the mixing module such that a predictive viscosity of the slurry meets a target viscosity.
STEP 1:
The claim recites an apparatus; thus, the claim is directed to a statutory category of invention
STEP 2A Prong One:
As is evident from applicant’s disclosure, the claimed steps (a)-(d) fall into the “Mental Processes” group of abstract ideas because the recited steps are evaluations (predicting viscosity of the slurry in real time during a mixing process through machine learning) and judgments(adjusting conditions of the mixing process performed by the mixing module such that a predictive viscosity of the slurry meets a target viscosity) that are simple enough that they can be practically performed in the human mind. Note that even if most humans would use a physical aid to help them complete the recited calculation or observation, the use of such physical aid does not negate the mental nature of these limitations because the claim here merely uses general purpose computer as a tool to perform the otherwise mental process.
Step 2A Prong Two:
Besides the abstract idea, the claim recites the additional elements of “a mixing module mixing raw materials for a secondary battery” and “a processor performing machine learning for prediction of viscosity of the slurry”
The “a mixing module mixing raw materials for a secondary battery” and “a processor performing machine learning for prediction of viscosity of the slurry” is recited at a high-level of generality such that they amount to no more than mere instructions to apply the exception using a generic component (MPEP 2106.05(f)).
Step 2B:
The claim as a whole does not amount to significantly more than the recited exception. As discussed above with respect to the integration of the abstract idea into a practical application, the additional elements “a mixing module mixing raw materials for a secondary battery” and “a processor performing machine learning for prediction of viscosity of the slurry” are recited at a high-level of generality such that they amount to no more than mere instructions to apply the exception using a generic computer component (MPEP 2106.05(f)).
Thus, when taken alone, the individual elements do not amount to significantly more than the above-identified judicial exception (the abstract idea). Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology.
Claims 2, 3 and 6 are directed to further apply the judicial exception to generic devices as recited in claim 1. Therefore, claims 2, 3 and 6 are directed to a judicial exception that is not integrated into practical application. There are no additional limitations in the claim to apply, rely on, or use the judicial exception. The claims are not more than a drafting effort designed to monopolize the exception. The claims also do not include additional elements that integrate the judicial exception into a practical application that would be sufficient to amount to more than the judicial exception. Thus, claims 2, 3 and 6 are not patent eligible.
Regarding claim 4, the claim limitations appear to be directed to an abstract idea using “mathematical concepts”. Claim 4 in view of figure 3 appears to be merely applying a ridge mathematical model which would fall under the “mathematical concepts” grouping.
Regarding claim 5, the claim limitations depend on a claim that contains a “mathematical concept” grouping and in view of figure 2, the flow chat of the threshold check is merely performing a mathematical calculation which would also fall under the “mathematical concepts grouping” The recited “the processor” is recited at a high-level of generality such that they amount to no more than mere instructions to apply the exception using a generic computer component (MPEP 2106.05(f)).
Claims 7-12 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. Below is an analysis in accordance with the Subject Matter Eligibility Test for Products and Process found in MPEP 2106(III).
Claim 7: A method for controlling viscosity of a slurry, comprising: controlling, by
a processor, a mixing module to mix raw materials for a secondary battery;
performing, by the processer, machine learning for prediction of viscosity of the slurry and predicting viscosity of the slurry in real time during a mixing process through machine learning; and
adjusting, by the processor, conditions of the mixing process performed by the mixing module such that a predictive viscosity of the slurry meets a target viscosity.
Step 1:
The claim recites a method. A method is a process. Thus, the claim is a statutory category of invention.
Step 2A Prong One:
As is evident from the applicant’s disclosure, the claimed step (a) falls into the “Mathematical Concepts, Mathematical Calculations” group of the abstract idea because the recited step perform a mathematical operation may also be considered mathematical calculations when the broadest reasonable interpretation of the claim in light of the specification encompasses a mathematical calculation. In view of figures 2 and 3, which depict the mathematical concept that is being performed on the processor and mixing modules in limitation (a), step (b) and (c) are performing calculations to ensure that the viscosity is meeting expectations through a mathematical model (I.E. the Ridge model). Steps (b) and (c) recite a mathematical calculation, when the steps are given their broadest reasonable interpretation in light of the specification, will be considered as falling within the "mathematical concepts" grouping.
Step 2A Prong Two:
Besides the abstract idea, the claim recites the additional elements of “a processor, a mixing module to mix raw materials for a secondary battery; ”
The “a processor, a mixing module to mix raw materials for a secondary battery; ” is recited at a high-level of generality such that they amount to no more than mere instructions to apply the exception using a generic component (MPEP 2106.05(f)).
Step 2B
The claim as a whole does not amount to significantly more than the recited exception. As discussed above with respect to the integration of the abstract idea into a practical application, the additional elements “a processor, a mixing module to mix raw materials for a secondary battery; ” are recited at a high-level of generality such that they amount to no more than mere instructions to apply the exception using a generic computer component (MPEP 2106.05(f)).
Thus, when taken alone, the individual elements do not amount to significantly more than the above-identified judicial exception (the abstract idea). Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology.
Regarding claims 8, 9 and 12 are directed to further apply the judicial exception to generic devices as recited in claim 1. Therefore, claims 8, 9 and 12 are directed to a judicial exception that is not integrated into practical application. There are no additional limitations in the claim to apply, rely on, or use the judicial exception. The claims are not more than a drafting effort designed to monopolize the exception. The claims also do not include additional elements that integrate the judicial exception into a practical application that would be sufficient to amount to more than the judicial exception. Thus, claims 8, 9 and 12 are not patent eligible.
Regarding claim 10, the claim limitations appear to be directed to an abstract idea using “mathematical concepts”. Claim 10 in view of figure 3 appears to be merely applying a ridge mathematical model which would fall under the “mathematical concepts” grouping.
Regarding claim 11, the claim limitations depend on a claim that contains a “mathematical concept” grouping and in view of figure 2, the flow chat of the threshold check is merely performing a mathematical calculation which would also fall under the “mathematical concepts grouping” The recited “the processor” is recited at a high-level of generality such that they amount to no more than mere instructions to apply the exception using a generic computer component (MPEP 2106.05(f)).
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.
Claims 1-3, 7-9 are rejected under 35 U.S.C. 103 as being unpatentable over PENG CN-116297020-A published 2023-06-23 in view of Oehme US-20220410099-A1 Published 2022-12-29. (hereinafter PENG in view of Oehme)
Regarding claims 1 and 7, PENG discloses an apparatus for controlling viscosity of a slurry, comprising: a mixing module mixing raw materials for a secondary battery (¶n0038 mixer data being collected to predict slurry viscosity); and
a processor (¶n0024 a processor, memory) performing machine learning for prediction of viscosity of the slurry, predicting viscosity of the slurry in real time during a mixing process through machine learning (¶n0038-40 real-time prediction request, a first and second prediction of the target slurry based on a correction slurry, to ensure that the slurry viscosity is accurately predicted while the mixer is stirring avoiding not reaching the target viscosity, inputting and real-time data and retraining to achieve the targeted viscosity), and
adjusting conditions of a machine learning model such that a predictive viscosity of the slurry meets a target viscosity (¶n0041” Based on the actual viscosity data corresponding to the operating parameters of the mixer and the predicted viscosity data of the slurry, the model parameters of the slurry viscosity prediction model are adjusted, and the step of inputting the operating parameters of the mixer in the training data into the slurry viscosity prediction model is continued until the preset training conditions are met to obtain the trained slurry viscosity prediction model.” ).
PENG does not disclose expressly adjusting conditions performed by the mixing module such that a predictive viscosity of the slurry meets a target viscosity.
Oehme discloses adjusting conditions performed (regulator unit regulating a process parameter) by the mixing module (the extruder) such that the predictive viscosity meets the target (compared to the threshold value and creates a recommendation) (¶68-70 and 98-105 disclose an influential variable affect the viscosity and training a model based on it. The model then compares the prediction to the threshold and makes control recommendations).
PENG and Oehme are analogous art because they are from the same field of mixing materials using machine learning to achieve a predicted viscosity.
At the time of the invention, it would have been prima facie obvious to one of ordinary skill, in the art as of the effective filing date, to implement the changing of operating parameters as disclosed in Oehem into the models of PENG to change the settings of the mixer to better match the target viscosity.
The suggestion/motivation for doing so would have been to decrease or avoid waiting periods and increase production capacity (Oehme ¶73 discloses “The methods and systems according to the present invention may provide a large number of advantages over known methods and systems for controlling polymer viscosity quality in a compounding process of polymers. Thus, specifically, a method for controlling polymer viscosity quality in a compounding process of polymers using at least one extruder as suggested in the present invention may reduce or even avoid waiting periods and/or idle times and may increase production capacity.”)
Therefore, it would have been prima facie obvious to one of ordinary skill, in the art as of the effective filing date, to combine PENG and Oehme for the benefit of adjusting the mixer settings to achieve the desired viscosity to obtain the invention as specified in the claims 1 and 7.
Regarding claims 2 and 8, the limitations of claims 1 and 7 are discussed above, wherein the processor extracts at least one mixing process datum highly correlated with viscosity of the slurry from multiple mixing process data through machine learning and performs prediction of viscosity of the slurry based on the at least one mixing process datum (PENG in view of Oehme discloses extracting viscosity datum and using previous data to produce a new prediction. PENG ¶n0045 discloses “By setting the slurry viscosity prediction model to y＝ f(R,X<sub>k</sub>,X<sub>k-1</sub>, X<sub>k-2</sub>,t), the sampling results of the two times before the prediction time point are also added to the slurry viscosity prediction model when predicting the concentration of the target slurry, thereby improving the accuracy of the slurry viscosity prediction model.” This is interpreted to mean it takes in two previous viscosity samples (multiple mixing process data) and is using them to produce a more accurate prediction, ¶ n0038 -41 discloses making the prediction with the updated model).
Regarding claims 3 and 9, the limitations of claims 1 and 7 are discussed above, wherein the processor generates a first model through training with N mixing process data and N final actual viscosity data using a predetermined algorithm, the first model being a model for extraction of mixing process data highly correlated with a final viscosity of the slurry and prediction of viscosity of the slurry (PENG in view of Oehme discloses generating a model using mixing process data and the actual viscosity (the final viscosity) and testing if the model produces the expected results. PENG ¶n0038-41 disclose the first model trained on the actual data and the mixing data).
Claims 4, 5, 6, 8, 10 and 12. is/are rejected under 35 U.S.C. 103 as being unpatentable over PENG in view of Oehme further in view of Arora US-20220139558-A1 published 2022-05-05. (hereinafter Peng in view of Oehme in view of Arora)
Regarding claims 4 and 10, the limitations of claims 1 and 7 are discussed above, the limitations of “final actual viscosity”, “highly correlated”, “predetermined” and “preprocessed” are unclear, for the purposes of examination the limitations have been interpreted to mean using an algorithm (I.E. Ridge model), calculate multiple top contributing factors to generate a second model for the prediction of viscosity of the slurry.
PENG in view of Oehme discloses using a first model using the actual viscosity and mixing parameters to predict the viscosity (PENG ¶n0038-41)).
PENG in view of Oehme does not disclose expressly at least one topmost factor, calculate multiple top factors and generate a second model using the multiple factors.
Arora discloses using ridge regression models to identify the topmost factor(¶97-98 highest ranked feature). Then taking the multiple most important factors and machine learning models to predict the viscosity (¶102 top performing features used to predict viscosity). Generating a second model to predict the viscosity and retraining the model until it reaches the expected outcomes to properly predict the viscosity (¶106-109 training using one or more features, to build one or more models, and retraining if not at desired accuracy.) PENG and Oehme are analogous art because they are from the same field of mixing materials using machine learning to achieve a predicted viscosity.
PENG and Oehme are analogous art because they are from the same field of mixing materials using machine learning to achieve a predicted viscosity.
At the time of the invention, it would have been prima facie obvious to one of ordinary skill, in the art as of the effective filing date, to implement the changing of operating parameters as disclosed in Oehem into the models of PENG to change the settings of the mixer to better match the target viscosity.
The suggestion/motivation for doing so would have been to decrease or avoid waiting periods and increase production capacity (Oehme ¶73 discloses “The methods and systems according to the present invention may provide a large number of advantages over known methods and systems for controlling polymer viscosity quality in a compounding process of polymers. Thus, specifically, a method for controlling polymer viscosity quality in a compounding process of polymers using at least one extruder as suggested in the present invention may reduce or even avoid waiting periods and/or idle times and may increase production capacity.”)
PENG in view of Oehme and Arora are analogous art because they are from the same field of endeavor of using machine learning models to predict the viscosity.
At the time of the invention, it would have been prima facie obvious to one of ordinary skill, in the art as of the effective filing date, to modify Peng in view of Oehme to take the teachings of Arora and use a second model that uses ridge regression models to identify the top factor contributing to the prediction of viscosity and produce a second model to further predict the viscosity. If it is not acceptable to the threshold to keep retraining the models.
The suggestion/motivation for doing so would have been machine learning models can be used to decrease time needed to predict viscosity where it would otherwise be costly (Arora ¶6 “Most of the above-mentioned techniques to measure and predict viscosity and aggregation rates are costly, time consuming, and require physical material. Hence, development of novel experimental and/or in silica tools to predict viscosity values and aggregation propensity or to rank antibodies based on their developability is indispensable”).
Therefore, it would have been prima facie obvious to one of ordinary skill, in the art as of the effective filing date, to combine PENG in view of Oehme and Arora for the benefit of one topmost factor, calculating multiple top factors and generate a second model using the multiple factors. to obtain the invention as specified in the claims 4 and 8.
Regarding claims 5 and 11, the limitations of claims 4 and 10 are discussed above, wherein: the processor checks prediction accuracy of the second model; and
If the prediction accuracy of the second model is greater than or equal to a predetermined rate, the processor retrains or updates the second model each time new data is generated and (Peng in view of Oehme in view of Arora disclose checking the accuracy of the model and retraining the second model. Arora ¶102-103 as features are identified by the ridge model (this is read as each time new data is generated), ¶108-109 using a first machine learning model to build a second predictive machine learning model, when a desired accuracy level is reached, the training ends…),
If the prediction accuracy of the second model is less than the predetermined rate, the processor changes the algorithm (Peng in view of Oehme and further in view of Arora discloses when the model is less than the predetermined rate, to change the algorithm, Arora ¶109 discloses both when the accuracy is above the desired result and below the desired threshold, PENG ¶13 “the model parameters of the slurry viscosity prediction model are adjusted” The parameters are read as part of an algorithm, One skilled in the art would know that you could either retrain a machine learning or alter the parameters (change the algorithm) based on a threshold to achieve the limitation of claim 5 in view of “Peng in view of Oehme in view of Arora”).
Regarding claims 6 and 12, the limitations of claims 1 and 7 are discussed above, the terms “final actual viscosity” and “final predictive viscosity” are unclear as to how these differ from the “actual viscosity” and “predictive viscosity”. For the purposes of examination these limitations are interpreted to be the same.
Wherein the processor controls the mixing module by: calculating a final predictive viscosity by inputting mixing process data to a second model upon start of mixing (Peng in view of Oehme in view of Arora disclose creating a second model to calculate the predictive viscosity, Arora ¶108-109);
calculating a new final predictive viscosity by changing settings of mixing process data identified to be highly correlated with a final actual viscosity of the slurry from a first model and the second model and inputting the mixing process data changed due to change of the settings to the second model, if the final predictive viscosity does not meet the target viscosity (Peng in view of Oehme in view of Arora disclose changing the settings of the mixing process in the predictive models and inputting the data into the second predictive model. Peng ¶n0038-45 and Oehme ¶ 98-105 discloses adjusting the settings of the mixing process and inputting those changes into the model by changing the parameters, Arora ¶108 discloses “one or more models” being used to update the second predictive model. One skilled in the art would know they change the settings of the second model. ); and
calculating settings of mixing process factors ensuring that the final predictive viscosity meets the target viscosity by repeating a procedure of changing settings of mixing process data and calculating a final predictive viscosity until the final predictive viscosity meets the target viscosity (Peng in view of Oehme in view of Arora disclose changing the settings of the mixing process data until the target viscosity is met. Arora ¶97-109 discloses determining the factors (features) and retraining the models. Peng ¶n0038-45 and Oehme ¶ 98-105 discloses repeating changes until the viscosity meets the target. One skilled in the art would know they could use the factors (features) of Arora to make changes to identify when the desired threshold of the viscosity is met).
Conclusion
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/C.D.C./Examiner, Art Unit 2115
/KAMINI S SHAH/Supervisory Patent Examiner, Art Unit 2115
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