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 .
DETAILED ACTION
Claims 15-20 have been canceled. Claims 21-26 have been added. Claims 1-14 and 21-26 are pending. Claims 1-14 and 21-26 have been examined and rejected.
Response to Arguments
Applicant's arguments filed 4/20/2026 regarding the 35 USC 101 rejections of claims 1-20 have been fully considered but they are not persuasive. The limitation “adjusting an amount of power applied … based on the predicted performance characteristic” is regarded as insignificant extra-solution activity, applying it, which does not amount significantly more to abstract ideas. All pending claims are rejected under 35 USC 101 for being directed to abstract ideas, see the 35 USC 101 rejection section below for detail.
Applicant’s arguments with respect to claims 1-14 and 21-26 have been considered but are moot because the new ground of rejection rely on additional references applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
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 1-14 and 21-26 are rejected under 35 USC 101 for being directed to abstract ideas.
Claim 1 is a method claim and recites:
A method of generating a digital twin of an environment comprising a semiconductor processing system that comprises a gas delivery system comprising one or more conduits and one or more heaters, the method comprising:
generating one or more mathematical-based variables based on a mathematical model of the environment and sensor data from one or more sensors of the environment; (mathematical concepts)
generating one or more machine learning-based variables based on a machine learning-based model of the environment and the sensor data; and (mathematical concepts)
stacking the one or more mathematical-based variables and the one or more machine learning-based variables based on a meta-learning model to generate a machine learning input for predicting a performance characteristic of the environment; and (mathematical concepts)
adjusting an amount of power applied to the one or more heaters of the semiconductor processing system based on the predicted performance characteristic. (insignificant extra-solution activity, applying it MPEP 2106.05(f))
Step 2A, prong 1: limitations are grouped into abstract ideas as indicated above.
Step 2A, prong 2: the claim does not recite any limitation to integrate a practical application into abstract ideas.
Step 2B: no additional elements are recited. Limitation “adjusting an amount of power …” is determined to be insignificant extra-solution activity as indicated above.
Claim 2 is a method claim depending on claim 1 and recites:
The method according to Claim 1, wherein the mathematical model is a thermodynamic model of the environment. (mathematical concepts)
Step 2A, prong 1: limitations are grouped into abstract ideas as indicated above.
Step 2A, prong 2: the claim does not recite any limitation to integrate a practical application into abstract ideas.
Step 2B: no additional elements are recited.
Claim 3 is a method claim depending on claim 1 and recites:
The method according to Claim 1, wherein the machine learning-based model is one of a random forest regression model and a gradient boosting regression model. (mathematical concepts)
Step 2A, prong 1: limitations are grouped into abstract ideas as indicated above.
Step 2A, prong 2: the claim does not recite any limitation to integrate a practical application into abstract ideas.
Step 2B: no additional elements are recited.
Claim 4 is a method claim depending on claim 3 and recites:
The method according to Claim 3, wherein the gradient boosting regression model is a support vector machine regression model. (mathematical concepts)
Step 2A, prong 1: limitations are grouped into abstract ideas as indicated above.
Step 2A, prong 2: the claim does not recite any limitation to integrate a practical application into abstract ideas.
Step 2B: no additional elements are recited.
Claim 5 is a method claim depending on claim 1 and recites:
The method according to Claim 1, wherein the meta-learning model is a gradient boosting regression model. (mathematical concepts)
Step 2A, prong 1: limitations are grouped into abstract ideas as indicated above.
Step 2A, prong 2: the claim does not recite any limitation to integrate a practical application into abstract ideas.
Step 2B: no additional elements are recited.
Claim 6 is a method claim depending on claim 1 and recites:
The method according to Claim 1, wherein the sensor data indicates a gas flow rate, a gas temperature, a conduit temperature, a heater characteristic, a conduit heat flux, or a combination thereof. (insignificant extra-solution activity, data gathering MPEP 2106.05(g))
Step 2A, prong 1: limitations are grouped into abstract ideas as indicated above.
Step 2A, prong 2: the claim does not recite any limitation to integrate a practical application into abstract ideas.
Step 2B: no additional elements are recited. A limitation is determined to be insignificant extra-solution activity as indicated above.
Claim 7 is a method claim depending on claim 1 and recites:
The method according to Claim 1, wherein the one or more mathematical-based variables and the one or more machine learning-based variables indicate an upstream temperature and a downstream temperature relative to a sensor from among the one or more sensors configured to generate the sensor data. (mathematical concepts)
Step 2A, prong 1: limitation is grouped into abstract ideas as indicated above.
Step 2A, prong 2: the claim does not recite any limitation to integrate a practical application into abstract ideas.
Step 2B: no additional elements are recited.
Claim 8 is a method claim and recites:
A method comprising:
generating one or more mathematical-based variables based on a mathematical model of an environment and sensor data from one or more sensors of the environment, wherein the environment comprises a semiconductor processing system that comprises one or more heaters; (mathematical concepts)
generating one or more machine learning-based variables based on a machine learning-based model of the environment and the sensor data; (mathematical concepts)
stacking the one or more mathematical-based variables and the one or more machine learning-based variables based on a meta-learning model to generate a machine learning input, wherein the machine learning input includes a material deposit characteristic machine learning (MDCML) input, a sensor characteristic machine learning (SCML) input, a heater characteristic machine learning (HCML) input, or a combination thereof; and (mathematical concepts)
predicting a performance characteristic of the environment based on the machine learning input, wherein the performance characteristic of the environment includes an amount of material deposit within a conduit of the environment based on the MDCML input, a sensor state of the one or more sensors based on the SCML input, a heater state of a heater of the environment based on the HCML input, or a combination thereof; and (predicting a performance as recited in this limitation is interpreted as generating predictive outputs from the mathematics predictive model using inputs, so the limitation is mathematical concepts)
adjusting an amount of power applied to the one or more heaters based on the predicted performance characteristic. (insignificant extra-solution activity, applying it MPEP 2106.05(f))
Step 2A, prong 1: limitations are grouped into abstract ideas as indicated above.
Step 2A, prong 2: the claim does not recite any limitation to integrate a practical application into abstract ideas.
Step 2B: no additional elements are recited. Limitation “adjusting an amount of power …” is determined to be insignificant extra-solution activity as indicated above.
Claims 9-14 recite limitations analogous to those in claims 2-7. They are, hence, rejected for the same reasons.
Claim 21 is a system claim and recites:
A system comprising:
one or more processors; and (generic computer component)
one or more nontransitory computer-readable mediums comprising instructions that are executable by the one or more processors, wherein the instructions comprise: (generic computer component)
generating one or more mathematical-based variables based on a mathematical model of an environment and sensor data from one or more sensors of the environment, wherein the environment comprises a manufacturing system that comprises one or more heaters; (mathematical concepts)
generating one or more machine learning-based variables based on a machine learning-based model of the environment and the sensor data; (mathematical concepts)
stacking the one or more mathematical-based variables and the one or more machine learning-based variables based on a meta-learning model to generate a machine learning input for predicting a performance characteristic of the environment; and (mathematical concepts)
adjusting an amount of power applied to the one or more heaters of the manufacturing system based on the predicted performance characteristic. (insignificant extra-solution activity, applying it MPEP 2106.05(f))
Step 2A, prong 1: limitations are grouped into abstract ideas as indicated above.
Step 2A, prong 2: the claim does not recite any limitation to integrate a practical application into abstract ideas.
Step 2B: the claim recites additional elements include processors and non-transitory computer-readable mediums at generic level to perform functions, which do not amount significantly more to abstract ideas.
Claims 22-26 recite limitations analogous to those in claims 2-5 and 7, respectively. They are, hence, rejected for the same reasons.
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.
Claims 1-2, 6, 8-9, 13, 21-22, and 25 are rejected under 35 U.S.C. 103 as being unpatentable over Huang et al. (A Two-Stage Transfer Learning-Based Deep Learning Approach for Production Progress Prediction in IoT-Based Manufacturing, IEEE Internet Of Things Journal, Vol. 6, No. 6, Dec. 2019) in view of Rao et al. (US 2023/0078146).
As per claim 1, Rao teaches a method of generating an environment comprising an comprising a semiconductor processing system that comprises a gas delivery system comprising one or more conduits and one or more heaters (¶ 0014, 0016, 0042), the method comprising:
generating one or more mathematical-based variables based on a mathematical model of the environment and sensor data from one or more sensors of the environment (¶ 0015-0016, 0023; Rao teaches generating physics-based parameters based on physics-based model of the environment and sensor data from sensors of the environment; a physics-based model corresponds to a mathematical model);
generating one or more machine learning-based variables based on a machine learning-based model of the environment and the sensor data (¶ 0005, 0019-0020; Rao teaches training a machine learning model using measurements, corresponding to sensor data, as inputs; training a machine learning model of the environment inherently means the machine learning model of the environment has to be created comprising one or more machine learning-based variables based on a machine learning-based model of the environment and the sensor data);
adjusting an amount of power applied to the one or more heaters of the semiconductor processing system based on the predicted performance characteristic (¶ 0016, 0024, 0035-0037, 0116); Rao teaches physics-based model containing features of a manufacturing chamber of interest, being run multiples times reflecting a range of processing parameters comprising power to heaters, which are predicted for performance and adjusted).
Rao does not teach:
stacking the one or more mathematical-based variables and the one or more machine learning-based variables based on a meta-learning model to generate a machine learning input for predicting a performance characteristic of the environment.
However, Huang teaches:
stacking the one or more mathematical-based variables and the one or more machine learning-based variables based on a meta-learning model to generate a machine learning input for predicting a performance characteristic of the environment (p. 10627 right col. ¶ 2, p. 10631 Fig. 2, left col. ¶ 2 – right col. ¶ 1; Huang teaches feeding both variables discussed above as inputs, corresponding to stacking one or more mathematical-based variables and the one or more machine learning-based variables, to DBN-based meta learner (PM) for prediction of a complex manufacturing system).
Rao and Huang are analogous art because they are in the same field of employing read data from an environment to perform objective prediction using machine learning system. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of Rao and Huang. One of ordinary skill in the art would have been motivated to make such a combination because Huang’s teachings would have provided a deep autoencoder (DAE) model with transfer learning designed to extract the generalized features of target order in the first stage to avoid over fitting (Huang, p. 10627 Abstract).
As per claim 2, Rao and Huang in combination teach the method according to Claim 1,
Rao further teaches:
wherein the mathematical model is a thermodynamic model of the environment (¶ 0110; Rao teaches physics-based model using equations describing fluid flow, thermodynamics, heat transfer; this physics-based model is a thermodynamic model of the environment.
As per claim 6, Rao and Huang in combination teach the method according to Claim 1,
Rao further teaches:
wherein the sensor data indicates a gas flow rate,
As per claim 8, a method comprising:
generating one or more mathematical-based variables based on a mathematical model of an environment and sensor data from one or more sensors of the environment, wherein the environment comprises a semiconductor processing system that comprises one or more heaters (¶ 0014-0016, 0023, 0042; Rao teaches generating physics-based parameters based on physics-based model of the environment, comprising a semiconductor processing system that comprises one or more heaters, and sensor data from sensors of the environment; a physics-based model corresponds to a mathematical model);
generating one or more machine learning-based variables based on a machine learning-based model of the environment and the sensor data (¶ 0005, 0019-0020; Rao teaches training a machine learning model using measurements, corresponding to sensor data, as inputs; training a machine learning model of the environment inherently means the machine learning model of the environment has to be created comprising one or more machine learning-based variables based on a machine learning-based model of the environment and the sensor data); and
adjusting an amount of power applied to the one or more heaters based on the predicted performance characteristic (¶ 0016, 0024, 0035-0037, 0116); Rao teaches physics-based model containing features of a manufacturing chamber of interest, being run multiples times reflecting a range of processing parameters comprising power to heaters, which are predicted for performance and adjusted).
Rao does not teach:
stacking the one or more mathematical-based variables and the one or more machine learning-based variables based on a meta-learning model to generate a machine learning input, wherein the machine learning input includes a material deposit characteristic machine learning (MDCML) input, a sensor characteristic machine learning (SCML) input, a heater characteristic machine learning (HCML) input, or a combination thereof.
However, Huang teaches:
stacking the one or more mathematical-based variables and the one or more machine learning-based variables based on a meta-learning model to generate a machine learning input, wherein the machine learning input includes
Rao and Huang are analogous art because they are in the same field of employing read data from an environment to perform objective prediction using machine learning system. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of Rao and Huang. One of ordinary skill in the art would have been motivated to make such a combination because Huang’s teachings would have provided a deep autoencoder (DAE) model with transfer learning designed to extract the generalized features of target order in the first stage to avoid over fitting (Huang, p. 10627 Abstract).
As per claim 9, these limitations have been discussed in claim 2. They are, hence, rejected for the same reasons.
As per claim 13, these limitations have been discussed in claim 6. They are, hence, rejected for the same reasons.
As per claim 21, Rao teaches a system comprising:
one or more processors (¶ 0119); and
one or more nontransitory computer-readable mediums comprising instructions that are executable by the one or more processors (¶ 0121), wherein the instructions comprise:
generating one or more mathematical-based variables based on a mathematical model of an environment and sensor data from one or more sensors of the environment, wherein the environment comprises a manufacturing system that comprises one or more heaters ();
generating one or more machine learning-based variables based on a machine learning-based model of the environment and the sensor data (¶ 0014-0016, 0023, 0042; Rao teaches generating physics-based parameters based on physics-based model of the environment, comprising a semiconductor processing system that comprises one or more heaters, and sensor data from sensors of the environment; a physics-based model corresponds to a mathematical model);
adjusting an amount of power applied to the one or more heaters of the manufacturing system based on the predicted performance characteristic (¶ 0016, 0024, 0035-0037, 0116); Rao teaches physics-based model containing features of a manufacturing chamber of interest, being run multiples times reflecting a range of processing parameters comprising power to heaters, which are predicted for performance and adjusted).
Rao does not teach:
stacking the one or more mathematical-based variables and the one or more machine learning-based variables based on a meta-learning model to generate a machine learning input for predicting a performance characteristic of the environment (p. 10627 right col. ¶ 2, p. 10631 Fig. 2, left col. ¶ 2 – right col. ¶ 1; Huang teaches feeding both variables discussed above as inputs, corresponding to stacking one or more mathematical-based variables and the one or more machine learning-based variables, to DBN-based meta learner (PM) for prediction of a complex manufacturing system).
Rao and Huang are analogous art because they are in the same field of employing read data from an environment to perform objective prediction using machine learning system. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of Rao and Huang. One of ordinary skill in the art would have been motivated to make such a combination because Huang’s teachings would have provided a deep autoencoder (DAE) model with transfer learning designed to extract the generalized features of target order in the first stage to avoid over fitting (Huang, p. 10627 Abstract).
As per claim 22, these limitations have been discussed in claim 2. They are, hence, rejected for the same reasons.
As per claim 25, these limitations have been discussed in claim 6. They are, hence, rejected for the same reasons.
Claims 3-5, 10-12, and 23-24 are rejected under 35 U.S.C. 103 as being unpatentable over Rao et al. in view of Huang et al. as applied to claims 1, 8, and 15 above, and further in view of Cella et al. (US 2022/0197306).
As per claim 3, Rao and Huang in combination teach the method according to Claim 1,
Rao and Huang do not teach:
wherein the machine learning-based model is one of a random forest regression model and a gradient boosting regression model.
However, Cella teaches:
the machine learning-based model is one of
Rao, Huang, and Cella are analogous art because they are in the same field of employing read data from an environment to perform objective prediction using machine learning system. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of Rao, Huang, and Cella. One of ordinary skill in the art would have been motivated to make such a combination because Cella’s teachings would have improved any of process and application outputs and outcomes and facilitated automated learning and improvement of prediction (Cella, ¶ 0288).
As per claim 4, this limitation has been discussed in claim 3. It is, hence, rejected for the same reasons.
As per claim 5, this limitation has been discussed in claim 3. It is, hence, rejected for the same reasons.
As per claim 10, these limitations have been discussed in claim 3. They are, hence, rejected for the same reasons.
As per claim 11, these limitations have been discussed in claim 4. They are, hence, rejected for the same reasons.
As per claim 12, these limitations have been discussed in claim 5. They are, hence, rejected for the same reasons.
As per claim 23, these limitations have been discussed in claim 3. They are, hence, rejected for the same reasons.
As per claim 24, these limitations have been discussed in claim 5. They are, hence, rejected for the same reasons.
Claims 7, 14, and 26 are rejected under 35 U.S.C. 103 as being unpatentable over Rao et al. in view of Huang et al. as applied to claims 1, 8, and 21 above, and further in view of Winkler et al. (WO 2022/058408).
As per claim 7, Rao and Huang in combination teach the method according to Claim 1,
Rao and Huang do not teach:
wherein the one or more mathematical-based variables and the one or more machine learning-based variables indicate an upstream temperature and a downstream temperature relative to a sensor from among the one or more sensors configured to generate the sensor data.
However, Winkler teaches:
wherein the one or more mathematical-based variables and the one or more machine learning-based variables indicate an upstream temperature and a downstream temperature relative to a sensor from among the one or more sensors configured to generate the sensor data (p. 13 lines 6-35, p. 22 line 29 – p. 23 line 12; Winkler teaches data for math model and machine learning including an upstream temperature and a downstream temperature relative to a sensor from among the one or more sensors configured to generate the sensor data; this teaching reads onto this limitation).
Rao, Huang, and Winkler are analogous art because they are in the same field of employing read data from an environment to perform objective prediction using machine learning system. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of Rao, Huang, and Winkler. One of ordinary skill in the art would have been motivated to make such a combination because Winkler’s teachings would have improved control and production stability of articles in manufacturing processes (Winkler, p. 2 lines 22-24).
As per claim 14, these limitations have been discussed in claim 7. They are, hence, rejected for the same reasons.
As per claim 26, these limitations have been discussed in claim 7. They are, hence, rejected for the same reasons.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Cuong Van Luu whose telephone number is 571-272-8572. The examiner can normally be reached on Monday - Friday from 8:30 to 5:00.
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/CUONG V LUU/Examiner, Art Unit 2189
/REHANA PERVEEN/Supervisory Patent Examiner, Art Unit 2189
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