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 1-20 are pending. Claims 1-20 have been examined and rejected.
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 1-20 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:
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. (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 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; (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. (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)
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.
Claims 9-14 recite limitations analogous to those in claims 2-7. They are, hence, rejected for the same reasons.
Claim 15 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; (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. (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: 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 16-20 recite limitations analogous to those in claims 2-3 and 5-7, respectively. They are, hence, rejected for the same reasons.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claims 1, 8, and 15 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by 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).
As per claim 1, Huang teaches a method of generating a digital twin of an environment 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 (p. 10627 right col. last paragraph, p. 10631 Fig. 2, left col. ¶ 2 – right col. ¶ 1; Huang teaches in the stage-2 the secondary data set D’c generated by DAEs in Stage-1 using 30% of Dc data set, see Fig. 2 upper right side DAEs; the secondary data set D’c corresponds to one or more mathematical-based variables as recited since DAEs, Deep Autoencoders, are mathematical model of the environment as seen in section B. Bagging DAE-Based Feature Extraction on p. 10632, and data sets are sensor data);
generating one or more machine learning-based variables based on a machine learning-based model of the environment and the sensor data (p. 10627 right col. last paragraph, p. 10631 Fig. 2, left col. ¶ 2 – right col. ¶ 1; Huang teaches output from DBN-based meta learner (PM’) generated by a machine learning-based model of the environment and the sensor data; the output from DBN-based meta learner (PM’) correspond to one or more machine learning-based variables as recited); and
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. 10631 Fig. 2, left col. ¶ 2 – right col. ¶ 1; Huang teaches feeding both variables discussed above as inputs to DBN-based meta learner (PM) for prediction).
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 (p. 10627 right col. last paragraph, p. 10631 Fig. 2, left col. ¶ 2 – right col. ¶ 1; Huang teaches in the stage-2 the secondary data set D’c generated by DAEs in Stage-1 using 30% of Dc data set, see Fig. 2 upper right side DAEs; the secondary data set D’c corresponds to one or more mathematical-based variables as recited since DAEs, Deep Autoencoders, are mathematical model of the environment as seen in section B. Bagging DAE-Based Feature Extraction on p. 10632, and data sets are sensor data);
generating one or more machine learning-based variables based on a machine learning-based model of the environment and the sensor data (p. 10627 right col. last paragraph, p. 10631 Fig. 2, left col. ¶ 2 – right col. ¶ 1; Huang teaches output from DBN-based meta learner (PM’) generated by a machine learning-based model of the environment and the sensor data; the output from DBN-based meta learner (PM’) correspond to one or more machine learning-based variables as recited);
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 inputs to DBN-based meta learner (PM) for prediction; Huang also teaches using sensor data having heterogeneous characteristics, which correspond to a sensor characteristic machine learning (SCML) input); and
predicting a performance characteristic of the environment based on the machine learning input, wherein the performance characteristic of the environment includes an
As per claim 15, Huang teaches a system comprising:
one or more processors (p. 10633 right col. last paragraph); and
one or more nontransitory computer-readable mediums comprising instructions that are executable by the one or more processors (p. 10633 right col. last paragraph; Huang teaches an algorithm coded in Python tested on a computer; this teaching reads onto one or more nontransitory computer-readable mediums as recited), 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 (p. 10627 right col. last paragraph, p. 10631 Fig. 2, left col. ¶ 2 – right col. ¶ 1; Huang teaches in the stage-2 the secondary data set D’c generated by DAEs in Stage-1 using 30% of Dc data set, see Fig. 2 upper right side DAEs; the secondary data set D’c corresponds to one or more mathematical-based variables as recited since DAEs, Deep Autoencoders, are mathematical model of the environment as seen in section B. Bagging DAE-Based Feature Extraction on p. 10632, and data sets are sensor data);
generating one or more machine learning-based variables based on a machine learning-based model of the environment and the sensor data (p. 10627 right col. last paragraph, p. 10631 Fig. 2, left col. ¶ 2 – right col. ¶ 1; Huang teaches output from DBN-based meta learner (PM’) generated by a machine learning-based model of the environment and the sensor data; the output from DBN-based meta learner (PM’) correspond to one or more machine learning-based variables as recited);
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
predicting a performance characteristic of the environment based on the machine learning input, wherein the performance characteristic of the environment includes .
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 2-6, 9-13, and 16-19 are rejected under 35 U.S.C. 103 as being unpatentable over 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 2, Huang teaches the method according to Claim 1,
Huang does not teach:
wherein the mathematical model is a thermodynamic model of the environment.
However, Cella teaches:
the mathematical model is a thermodynamic model of the environment (¶ 1016, 1020; Cella teaches an EMP to merge data from multiple data sources including sensor readings into a model and apply one or more thermodynamic equations to the received sensor readings to model the thermodynamic behavior of the environment).
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 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 3, Huang teaches the method according to Claim 1,
Huang does 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
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 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 6, Huang teaches the method according to Claim 1,
Huang does not teach:
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.
However, Cella teaches:
the sensor data indicates
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 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 9, these limitations have been discussed in claim 2. They are, 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 13, these limitations have been discussed in claim 6. They are, hence, rejected for the same reasons.
As per claim 16, these limitations have been discussed in claim 2. They are, hence, rejected for the same reasons.
As per claim 17, these limitations have been discussed in claim 3. They are, hence, rejected for the same reasons.
As per claim 18, these limitations have been discussed in claim 5. They are, hence, rejected for the same reasons.
As per claim 19, these limitations have been discussed in claim 6. They are, hence, rejected for the same reasons.
Claims 7, 14, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Huang et al. as applied to claims 1, 8, and 15 above, and further in view of Winkler et al. (WO 2022/058408)
As per claim 7, Huang teaches the method according to Claim 1,
Huang does 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).
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 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 20, these limitations have been discussed in claim 7. They are, hence, rejected for the same reasons.
Conclusion
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