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.
Claims 1-12 are 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 pre-AIA the applicant regards as the invention.
In claim 1 recites the limitation "in accordance with a trained deep learning model, estimating a temperature..." recites a function without clearly defining what structure performs this function. The claim fails to specify what hardware component executes the deep learning model, rendering the scope of the claim indefinite (MPEP § 2173.05(g)).
The claim recites "observed current into direct-axis current" as an input to the model, but the same value was previously described as being "converted or transformed." This creates ambiguity about whether these refer to the same value (MPEP § 2173.05(a)).
The claim references "observed or estimated direct-axis voltage" and "observed or estimated quadrature-axis voltage" as inputs to the model, but fails to recite any steps for observing or estimating these voltages, creating uncertainty about how these values are obtained (MPEP § 2173.05(b)).
Also recites the limitations "Truncated back propagation through time technique" is a specialized AI training method that lacks sufficient explanation to inform one of ordinary skill in the art of motor control systems about its specific implementation in this context (MPEP § 2173.05(b)).
In claim 2, This claim depends from claim 1 which recited limitations "flow rate of the coolant," but claim 2 introduces "a stator flow rate and rotor flow rate." This creates indefiniteness about how these two flow rates relate to the single flow rate mentioned in claim 1 (MPEP § 2173.05(c)).
In claims 3-4, recited limitations "the deep learning model comprises..." in both claims is
indefinite because it fails to specify the boundaries of what constitutes the claimed model (MPEP § 2173.05(b)). For claim 4, the phrase "one or more of the following" creates ambiguity about which combinations of networks are included in the scope.
In claim 5, recited limitations "determining an exponentially weighted moving average... based on a mean, a standard deviation, maximum and minimum values" without specifying the mathematical relationship between these statistical measures and the exponentially weighted moving average calculation (MPEP § 2173.05(b)).
In claim 6, recited limitations "first input data" and "second input data" without clearly identifying which inputs from claim 1 these refer to (MPEP § 2173.05(b)).
In claim 8, recited limitations "Scaling the magnitude levels of the signals" is indefinite because it fails to specify the scaling algorithm, factors, or reference values used in the scaling process (MPEP § 2173.05(b)).
In claims 9-10, recited limitations Claim 9 introduces "the deep neural network" which was not previously mentioned in claim 1 (which refers only to a "deep learning model"), creating ambiguity about whether these refer to the same component (MPEP § 2173.05(e)), and "limiting the duration of the input data or a limited number of successive time intervals" fails to specify what threshold constitutes "limiting" or how this limitation is implemented (MPEP § 2173.05(b)). Claim 10 recites training "with input data that is consistent with a truncated-back-propagation-through-time (TBPTT) technique" without defining what "consistent with" means in this technical context (MPEP § 2173.05(b)).In claim 11, recited limitations "transform module," "motion estimator," and "data processor" are functional limitations that invoke 35 U.S.C. 112(f). Without corresponding structure disclosed in the specification for performing these specific functions, these terms render the claim indefinite (MPEP § 2181).
Similar to claim 1, the claim references "observed or estimated direct-axis voltage" and "observed or estimated quadrature-axis voltage" without reciting corresponding sensors or estimators for these voltages (MPEP § 2173.05(b)).
In claim 12, recited limitations "Current adjustment module" is a functional limitation that invokes 35 U.S.C. 112(f). Without sufficient structure disclosed in the specification for performing the adjustment function, this term renders the claim indefinite (MPEP § 2181).
The phrase "the estimated change in the estimated temperature" lacks clarity about what baseline temperature is used for comparison to determine the "change" (MPEP § 2173.05(b)).
The following is a quotation of 35 U.S.C. 112(d):
(d) REFERENCE IN DEPENDENT FORMS.—Subject to subsection (e), a claim in dependent form shall contain a reference to a claim previously set forth and then specify a further limitation of the subject matter claimed. A claim in dependent form shall be construed to incorporate by reference all the limitations of the claim to which it refers.
The following is a quotation of pre-AIA 35 U.S.C. 112, fourth paragraph:
Subject to the following paragraph [i.e., the fifth paragraph of pre-AIA 35 U.S.C. 112], a claim in dependent form shall contain a reference to a claim previously set forth and then specify a further limitation of the subject matter claimed. A claim in dependent form shall be construed to incorporate by reference all the limitations of the claim to which it refers.
Claim 7 is rejected under 35 U.S.C. 112(d) or pre-AIA 35 U.S.C. 112, 4th paragraph, as being of improper dependent form for failing to further limit the subject matter of the claim upon which it depends, or for failing to include all the limitations of the claim upon which it depends. Claim 7 depends from itself.
Claims 9-10 are rejected under 35 U.S.C. 112(d) or pre-AIA 35 U.S.C. 112, 4th paragraph, as being of improper dependent form for failing to further limit the subject matter of the claim upon which it depends, or for failing to include all the limitations of the claim upon which it depends.
Claim 9 depends from claim 1 but introduces “the deep neural network that comprises a long-short-term memory(LSTM) neural network” which contradict claim 4 (which depends from the same claim1 but refers to different network types) creates confusion about the scope of the claims when read together.
Applicant may cancel the claim(s), amend the claim(s) to place the claim(s) in proper dependent form, rewrite the claim(s) in independent form, or present a sufficient showing that the dependent claim(s) complies with the statutory requirements.
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.
Claims 1-12 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Analysis Under the Two-Step Test (Alice/Mayo):
Regarding claim 1:
Subject Matter Eligibility Analysis Step 1:
Claims 1-10 are directed to a method, which is a process. Claim 11 is directed to a system, which is a machine. The claims fall within statutory categories.
Subject Matter Eligibility Analysis Step 2A Prong 1:
The claims are directed to an abstract idea, specifically Mathematical Concepts and Mental Processes.
Mathematical Concepts: Independent Claims 1 and 11 recite steps/elements
such as "converting or transforming... into direct-axis current and observed quadrature axis current" (coordinate transformation equations), "estimating a speed and torque," and using a "trained deep learning model" trained via "truncated back propagation through time." Neural networks, LSTM models (Claim 3), and back-propagation techniques are fundamentally mathematical relationships and algorithms. The step of calculating a temperature based on current, voltage, and flow rate inputs relies on mathematical correlations.
Mental Processes: The claims recite steps of "estimating," "determining,"
"analyzing" (Claim 7), and "selecting." These are concepts that can be performed in the human mind (e.g., observing data and calculating a result), or are mere observations and evaluations of data.
Therefore, the claims are directed to the abstract idea of collecting motor data and calculating/estimating machine states (temperature, torque, speed) using mathematical models.
Subject Matter Eligibility Analysis Step 2A Prong 2: Integration into a Practical Application
Mere Data Gathering: The steps of "measuring observed current," "measuring a
voltage," "sensing a coolant inlet temperature," and "sensing a flow rate" constitute insignificant extra-solution activity. As established in Electric Power Group, LLC v. Alstom S.A., merely collecting information (even via sensors) to be used in a mathematical analysis does not render the claim eligible.
Generic Computer Implementation: The use of a "processor" or "deep learning
model" merely automates the calculation. The claims do not recite an improvement to the functioning of the computer itself, nor do they effect a transformation of a particular article to a different state or thing.
Lack of Control/Actuation (Claims 1 & 11): Independent Claims 1 and 11 conclude with the step of "estimating a temperature." They do not positively recite utilizing this estimated temperature to actively control, adjust, or modify the operation of the motor. The process begins with data and ends with data (an estimate). A method of calculating a number, without a recited use of that number to perform a practical action, remains abstract.
Step 2B: Inventive Concept
The claims do not include an inventive concept sufficient to transform the abstract idea into a patent-eligible application.
The additional elements (sensors, processor, motor components) are recited at a high level of generality and are well-understood, routine, and conventional in the art of motor diagnostics.
The "Deep Learning" and "TBPTT" limitations are part of the abstract idea itself (the mathematical model) and cannot provide the inventive concept.
Viewing the claim elements as an ordered combination, they amount to nothing
more than collecting data and running a mathematical algorithm on a generic computer to generate an estimate. This preempts the use of these mathematical equations for estimating stator temperature.
Dependent Claims:
Claims 2-10: These claims recite specific types of mathematical models (LSTM, CNN), data processing techniques (exponentially weighted moving averages), or variable selections (SHAP analysis). These are further mathematical concepts or data manipulation techniques that do not add a physical inventive concept.
Claim 12: While Claim 12 recites "adjusting a command... to compensate for shaft torque variation," which acts as a specific motor control step, it depends from rejected Claim 11. As currently written, it incorporates the ineligible subject matter of the independent claim.
Therefore, claims 1-12 are rejected as being directed to non-statutory subject matter.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MUHAMMAD S ISLAM whose telephone number is (571)272-8439. The examiner can normally be reached on 9:30am to 6:00pm.
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/MUHAMMAD S ISLAM/Primary Examiner, Art Unit 2846