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 7 and 9 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 applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
The term “proper energy stores” in claims 7 and 9 is a relative term which renders the claim indefinite. The term “proper energy stores” 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. It is unclear what specifications or requirements make an energy store “proper” versus just a normal or irregular energy store.
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 1-15 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Under step 1, claims 1 and 15 belong to statutory categories.
Under Step 2A prong 1, the claims as a whole are identified as being directed to a judicial exception as claim 1 and similarly 15 recite(s) “determining an anomaly of a behavior of an electrical energy store in a technical device” and “determining at least one feature from the sensed operating variable profile of the at least one operating variable of the electrical energy store; evaluating an anomaly detection model using an autoencoder with a supplied input vector that includes or depends on the determined at least one feature, in order to determine a reconstructed input vector;” which are directed to mathematical concepts and/or mental processes in view of applicant’s specification Par. 53-57 and 60-66.
Under Step 2A prong 2, evaluating whether the claim as a whole integrates the exception into a practical application of that exception, the judicial exception is not integrated into a practical application because “A computer-implemented method for” and in claim 15 “a control unit” are considered to be generically recited computer elements do not add a meaningful limitation to the abstract idea because they amount to simply implementing the abstract idea on a computer. The elements of “comprising: sensing an operating variable profile of at least one operating variable of the electrical energy store;” and “signaling an error based on a reconstruction error between the reconstructed input vector and the supplied input vector” are considered to be data gathering steps required to use the correlation do not add a meaningful limitation to the method as they are insignificant extra-solution activity.
Under Step 2B, evaluating additional elements to determine whether they amount to an inventive concept both individually and in combination, the claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because “A computer-implemented method for” and in claim 15 “a control unit” are well-understood, routine, conventional computer functions as recognized by the court decisions listed in MPEP § 2106.05(d). The elements of “comprising: sensing an operating variable profile of at least one operating variable of the electrical energy store;” and “signaling an error based on a reconstruction error between the reconstructed input vector and the supplied input vector” are considered to be adding insignificant extra-solution activity to the judicial exception per MPEP 2106.05(g)(ii) and are well-understood, routine, conventional activities/elements previously known to the industry per MPEP 2106.05(d)(i)(also see prior art of record).
Claims 1-15 recites the additional element(s) of using generic AI/ML technology, i.e. “an autoencoder”, to perform data evaluations or calculations, as identified under Prong 1 above. The claims do not recite any details regarding how the AI/ML algorithm or model functions or is trained. Instead, the claims are found to utilize the AI/ML algorithm as a tool that provides nothing more than mere instructions to implement the abstract idea on a general-purpose computer. See MPEP 2106.05(f). Additionally, the use of the “autoencoder” merely indicates a field of use or technological environment in which the judicial exception is performed. See MPEP 2106.05(h). Therefore, the use of “an autoencoder” to perform steps that are otherwise abstract does not integrate the abstract idea into a practical application. See the 2024 Guidance Update on Patent Subject Matter Eligibility, Including on Artificial Intelligence; and Example 47, ineligible claim 2.
Claims 1-11 are considered to further describe the abstract ideas cited above.
In claim 12, the elements “the electrical energy store is used to operate the technical device, and the technical device is a motor vehicle, a pedelec, an aircraft, a drone, a machine tool, a consumer electronics device, a mobile phone, an autonomous robot, or a household appliance” are not integrated into a practical application and do not include additional elements that are sufficient to amount to significantly more than the judicial exception because they are generally linking the use of a judicial exception to a particular technological environment or field of use and are considered to be merely indicating a field of use or technological environment in which to apply a judicial exception do not amount to significantly more than the exception itself per MPEP 2106.05(h) and are well-understood, routine, and conventional activities/elements previously known to the industry per MPEP 2106.05(d) (see prior art of record).
In claims 13-14, the elements are not integrated into a practical application and do not include additional elements that are sufficient to amount to significantly more than the judicial exception because they are generically recited computer elements do not add a meaningful limitation to the abstract idea because they amount to simply implementing the abstract idea on a computer and are well-understood, routine, conventional computer functions as recognized by the court decisions listed in MPEP § 2106.05(d).
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.
Claim(s) 1-15 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Cella (US 20190121340 A1).
In claim 1, Cella discloses a computer-implemented (Par. 204 “computer”) method for determining an anomaly of a behavior (Par. 630, 694, 743) of an electrical energy store (Par. 324, 903 “battery” “batteries”) in a technical device (Par. 324 “industrial machines or components”) comprising: sensing an operating variable profile of at least one operating variable of the electrical energy store (Par. 26, 698, claim 13 “a data storage profile of the plurality of variable groups”); determining at least one feature from the sensed operating variable profile of the at least one operating variable of the electrical energy store (Par. 545, 548, 630, 663 “monitor conditions” “monitoring the condition of various components” “monitoring the same or different aspects of the component/piece of equipment” “recognizing a pattern (e.g., a pattern indicating a problem or fault condition)”); evaluating an anomaly detection model (Par. 745 “a neural net model given a desired goal” “an operational efficiency/failure rate” “an avoidance of a dangerous condition or catastrophic failure”) using an autoencoder (Par. 646 “autoencoder neural networks”) with a supplied input vector that includes or depends on the determined at least one feature (Par. 658-659, 663 “a vector of input values” “real-valued input vectors”), in order to determine a reconstructed input vector (Par. 659 “each non-input unit may compute its current activation as a nonlinear function of the weighted sum of the activations of all units from which it receives connections”); and signaling an error based on a reconstruction error between the reconstructed input vector and the supplied input vector (Par. 663 “recognizing a pattern (e.g., a pattern indicating a problem or fault condition)”).
In claim 2, Cella further discloses wherein determining the at least one feature comprises: determining at least one operating feature from the sensed operating variable profile of the at least one operating variable of the electrical energy store as an aggregate variable (Par. 659 “each non-input unit may compute its current activation as a nonlinear function of the weighted sum of the activations of all units from which it receives connections”).
In claim 3, Cella discloses all of claim 2. Cella further discloses wherein determining the at least one feature comprises: determining a load-state vector from the determined at least one operating feature using a main component analysis or a kernel main component analysis (Par. 657-658, “RBF kernel function” 229, 233, 576, 626 “load”), wherein the autoencoder is trained with the load-state vectors from a plurality of the electrical energy stores (Par. 324 “batteries”), and wherein the load-state vector is evaluated as a supplied input vector in the autoencoder (Par. 324 “a data pool might take streams of data from various machines or components in an environment, such as turbines, compressors, batteries”).
In claim 4, Cella further discloses wherein determining the at least one feature comprises: determining at least one error feature resulting from a statistical evaluation of a difference between a variable modeled using a battery performance model and a variable determined or measured by a further method or model for a predetermined time interval (Par. 663 “recognizing a pattern (e.g., a pattern indicating a problem or fault condition)”); and evaluating a difference between a modeled battery voltage and a measured battery voltage (Par. 663-664 “configured to stream voltage values that represent analog vibration sensor data voltage values,”, 743) and/or a difference between an amount of cyclable lithium modeled using the battery performance model and an amount of cyclable lithium resulting from an evaluation of an operating variable profile using a physical aging state model.
In claim 5, Cella further discloses comparing the reconstruction error to a first error threshold value in order to signal a warning of a possible malfunction (Par. 645 “alert”) of the electrical energy store when the reconstruction error exceeds the first error threshold value (Par. 663 “recognizing a pattern (e.g., a pattern indicating a problem or fault condition)”).
In claim 6, Cella discloses all of claim 5. Cella further discloses comparing the reconstruction error to a second, higher error threshold value in order to signal the error of the energy store when the reconstruction error exceeds the second error threshold value (Par. 604 “detected values exceeding thresholds or predetermined values, but detected values may also fall below thresholds or predetermined values—for example where an amount of change in the detected value is expected to occur”).
In claim 7, Cella discloses all of claim 6. Cella further discloses performing a statistical evaluation of the reconstruction errors of training data sets, which have been used by proper energy stores for training the autoencoder (Par. 659 “each non-input unit may compute its current activation as a nonlinear function of the weighted sum of the activations of all units from which it receives connections”); and selecting the first or the second error threshold value as the maximally occurring reconstruction error, based on a distribution of the reconstruction errors resulting from the evaluation of the training data sets using the trained autoencoder (Par. 604).
In claim 8, Cella further discloses selecting an energy store-specific execution frequency of the method based on the reconstruction error (Par. 868 “frequency”).
In claim 9, Cella further discloses training the autoencoder using training data sets formed by respectively proper electrical energy stores (Par. 652 “be trained, such as by a human operator or supervisor, or based on a data set, model, or the like”).
In claim 10, Cella discloses all of claim 3. Cella further discloses providing an aging state model that uses the at least one operating feature or the load-state vector to determine an aging state in a data-based model (Par. 715 “The machine learning system may learn system alarm condition patterns, such as alarm conditions expected under normal operating conditions, under peak operating conditions, expected over time based on age of components”).
In claim 11, Cella further discloses wherein signaling the error comprises: automatically shutting down the technical device, transferring the electrical energy store into a safe state via rapid discharge, or planning of a workshop visit for inspection of the technical device or the electrical energy store (Par. 501 “scheduling preventive maintenance”).
In claim 12, Cella further discloses wherein: the electrical energy store is used to operate the technical device, and the technical device is a motor vehicle, a pedelec, an aircraft, a drone, a machine tool, a consumer electronics device, a mobile phone, an autonomous robot, or a household appliance (Par. 513 “vehicle” 1152 “aircraft”, 161 “electronic device”, 329 “drone”, 982 “appliance”, “television”, 34 “heavy industrial machine” 338 “mobile phone”).
In claim 13, Cella further discloses wherein a computer program product comprises instructions that, when the computer program product is executed by at least one data processing device, causes the at least one data processing device to perform the method (Par. 204 “computer”).
In claim 14, Cella further discloses wherein the computer program product is stored on a non-transitory machine-readable storage medium (Par. 705 “non-transitory computer-readable media”).
In claim 15, Cella discloses an apparatus (Par. 204 “computer”) for determining an anomaly of a behavior (Par. 630, 694, 743) of an electrical energy store (Par. 324, 903 “battery” “batteries”) in a technical device (Par. 324 “industrial machines or components”), the apparatus comprising: a control unit (Par. 16 “controller”) configured to: sense an operating variable profile of at least one operating variable of the electrical energy store (Par. 26, 698, claim 13 “a data storage profile of the plurality of variable groups”); determine at least one feature from the sensed operating variable profile of the at least one operating variable of the electrical energy store (Par. 545, 548, 630, 663 “monitor conditions” “monitoring the condition of various components” “monitoring the same or different aspects of the component/piece of equipment” “recognizing a pattern (e.g., a pattern indicating a problem or fault condition)”); evaluate an anomaly detection model (Par. 745 “a neural net model given a desired goal” “an operational efficiency/failure rate” “an avoidance of a dangerous condition or catastrophic failure”) using an autoencoder (Par. 646 “autoencoder neural networks”) with a supplied input vector that includes or depends on the determined at least one feature (Par. 658-659, 663 “a vector of input values” “real-valued input vectors”), in order to determine a reconstructed input vector (Par. 659 “each non-input unit may compute its current activation as a nonlinear function of the weighted sum of the activations of all units from which it receives connections”); and signal an error based on a reconstruction error between the reconstructed input vector and the supplied input vector (Par. 663 “recognizing a pattern (e.g., a pattern indicating a problem or fault condition)”).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. US 20190129410 A1, SYSTEMS AND METHODS FOR DATA COLLECTION UTILIZING ADAPTIVE SCHEDULING OF A MULTIPLEXER; US 20220108262 A1 INDUSTRIAL DIGITAL TWIN SYSTEMS AND METHODS WITH ECHELONS OF EXECUTIVE, ADVISORY AND OPERATIONS MESSAGING AND VISUALIZATION.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to BRANDON J BECKER whose telephone number is (571)431-0689. The examiner can normally be reached M-F 9:30-5:30.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Shelby Turner can be reached at (571) 272-6334. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/B.J.B/ Examiner, Art Unit 2857
/SHELBY A TURNER/ Supervisory Patent Examiner, Art Unit 2857