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 Objections
Claims 9-11 and 14-26 are objected to because of the following informalities:
In claim 9, lines 1-2, “the identified features” should be --the extracted features-- to avoid the issue of lack of antecedent basis.
In claim 10, lines 1-2, “the identified features” should be --the extracted features-- to avoid the issue of lack of antecedent basis.
In claim 11, line 2, “the identified features” should be --the extracted features-- to avoid the issue of lack of antecedent basis.
In claim 14, lines 9-10, “each selected classification category of the material” should be --each of the selected classification categories of the material-- for better clarity and to avoid being too broad without clear boundaries.
The other claim(s) not discussed above, or depending on the above claim(s), are objected to for inheriting the issue(s) from their linking claim(s).
Appropriate correction is required.
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 3 and 16 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.
Regarding claim 3, it recites “a displacement (linear, angular) sensor” in lines 2-3. It is unclear whether it is meant to be a linear or angular displacement sensor, or a linear sensor and an angular displacement sensor. For examination purpose, --a displacement sensor-- is assumed.
Regarding claim 16, it has the same issue as claim 3 above. The same assumption is made for examination purpose.
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.
MPEP 2106 outlines a two-part analysis for Subject Matter Eligibility as shown in the chart below.
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Step 1, the claimed invention must be to one of the four statutory categories. 35 U.S.C. 101 defines the four categories of invention that Congress deemed to be the appropriate subject matter of a patent: processes, machines, manufactures and compositions of matter.
Step 2, the claimed invention also must qualify as patent-eligible subject matter, i.e., the claim must not be directed to a judicial exception unless the claim as a whole includes additional limitations amounting to significantly more than the exception.
Step 2A is a two-prong inquiry, as shown in the chart below.
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Prong One asks does the claim recite an abstract idea, law of nature, or natural phenomenon? In Prong One examiners evaluate whether the claim recites a judicial exception, i.e. whether a law of nature, natural phenomenon, or abstract idea is set forth or described in the claim. If the claim recites a judicial exception (i.e., an abstract idea enumerated in MPEP § 2106.04(a), a law of nature, or a natural phenomenon), the claim requires further analysis in Prong Two. If the claim does not recite a judicial exception (a law of nature, natural phenomenon, or abstract idea), then the claim cannot be directed to a judicial exception (Step 2A: NO), and thus the claim is eligible at Pathway B without further analysis. Abstract ideas can be grouped as, e.g., mathematical concepts, certain methods of organizing human activity, and mental processes.
Prong Two asks does the claim recite additional elements that integrate the judicial exception into a practical application? If the additional elements in the claim integrate the recited exception into a practical application of the exception, then the claim is not directed to the judicial exception (Step 2A: NO) and thus is eligible at Pathway B. This concludes the eligibility analysis. If, however, the additional elements do not integrate the exception into a practical application, then the claim is directed to the recited judicial exception (Step 2A: YES), and requires further analysis under Step 2B.
Claims 1-29 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Regarding claim 1, Step 1: Is the claim to a process, machine, manufacture or composition of matter? Yes.
Step 2A: Is the claim directed to a law of nature, a natural phenomenon, or an abstract idea (judicially recognized exceptions)? Yes (see analysis below).
Prong one: Whether the claim recites a judicial exception? (Yes).
The claim recites:
1. A method for classifying excavation media, comprising:
obtaining sensor signals from one or more proprioceptive sensors on a machine interacting with the material;
using a processor to process the sensor signals, wherein processing includes extracting features from the sensor signals;
selecting one or more classification categories corresponding to physical characteristics of the material;
using the extracted features as inputs to a classifier;
wherein the classifier uses one or more algorithms to classify the extracted features into the selected classification categories; and
outputting a result indicating at least one classification category relating to a physical characteristic of the material.
The claim is directed to an abstract idea regarding the above bold-faced limitations. These limitations are directed to mathematical concepts – mathematical relationships, mathematical formulas or equations, mathematical calculations; and/or mental processes – concepts performed in the human mind (or with a pen and paper).
Prong two: Whether the claim recites additional elements that integrate the exception into a practical application of that exception? (No). The claim recites additional elements as underlined in the claim above. The “obtaining” and “outputting” steps are recited in general for collecting data for the abstract idea and outputting a result. There are insignificant extra-solution activities. See MPEP 2106.05(g). The use of a processor is to invoke a generic computer for its computing power to facilitate the application of the abstract idea. See MPEP 2106.05(f). Note that the “proprioceptive sensors on a machine interacting with the material” is recited to indicate the data source or environment, which is a field of use. See MPEP 2106.05(h). Accordingly, the additional elements are insufficient to integrate the abstract idea into a practical application of the abstract idea.
Step 2B: Does the claim recite additional elements (other than the judicial exception) that amount to significantly more than the judicial exception? No (see analysis below).
The claim does not include additional elements that are sufficient to make the claim significantly more than the judicial exception. As discussed with respect to Step 2A Prong Two above, the additional element(s) in the claim are insignificant extra-solution activities, to invoke a generic computer for its computing power to facilitate the application of the abstract idea, and/or to indicate the data source or environment, which is a field of use. Also, it is routine and conventional to invoke a computer for data processing. See MPEP 2106.05(d). Considered as a whole, the claim does not amount to significantly more than the abstract idea.
Claims 14 and 27are similarly rejected by analogy to claim 1.
Dependent claims 2-13, 15-26, 28, and 29 when analyzed as a whole respectively are held to be patent ineligible under 35 U.S.C. 101 because they either extend (or add more details to) the abstract idea or the additional recited limitation(s) (if any) fail(s) to establish that the claim(s) is/are not directed to an abstract idea, as discussed below: there is no additional element(s) in the dependent claims that sufficiently integrates the abstract idea into a practical application of, or makes the claims significantly more than, the judicial exception (abstract idea). The additional element(s) (if any) are mere instructions to apply an except, field of use, and/or insignificant extra-solution activities (applied to Step 2A_Prong Two and Step 2B; see MPEP 2016.05(f)-(h)) and/or well-understood, routine, or conventional (applied to Step 2B; see MPEP 2106.05(d)) to facilitate the application of the abstract idea.
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.
Claims 1-9, 12, 13, and 27-29 are rejected under 35 U.S.C. 103 as being unpatentable over Melander et al. ("Classifying soil stoniness based on the excavator boom vibration data in mounding operations" Silva Fennica vol. 53 no. 2 article id 10068, 2019; hereinafter “Melander”).
Regarding claim 1, Melander teaches a method for classifying excavation media (i.e., “automatic measurements of soil stoniness during an excavator-based mounding operation”; see Abstract), comprising:
obtaining sensor signals from one or more proprioceptive sensors on a machine interacting with the material (i.e., “The vibrations of the excavator boom were measured using a special-purpose measurement device that was attached to the boom near the mounding blade (FIG. 2)”; see p. 5, ¶ 2);
(i.e., “The features for the IMU signals were calculated again for the mounding windows”; see p. 8, ¶ 5);
selecting one or more classification categories corresponding to physical characteristics of the material (i.e., for classifying stoniness; see Abstract);
using the extracted features as inputs to a classifier (i.e., “Fig. 1 shows the overall classification process from the acquired raw data to the final stoniness class.”; see steps 5 and 6 in FIG. 1 (p. 4) for inputting the features);
wherein the classifier uses one or more algorithms to classify the extracted features into the selected classification categories (i.e., “the stoniness classes of the unlabeled dataset was predicted using all the models”; see p. 9, ¶ 1); and
outputting a result indicating at least one classification category relating to a physical characteristic of the material (i.e., “The output of a stoniness classification model is a probability distribution for the three stoni-ness classes (low, medium, high)… The stoniness class with the highest probability is chosen as the predicted class for the mounding point”; see p. 9, ¶ 2).
Melander does not explicitly disclose (see only the underlined):
using a processor to process the sensor signals, wherein processing includes extracting features from the sensor signals.
However, it is well-known to use a processor to process data. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Melander by using a processor to process the sensor signals, wherein processing includes extracting features from the sensor signals, as claimed. The rationale would be to facilitate the data processing.
Regarding claim 2, Melander further teaches:
wherein the excavation media comprises fragmented rock, gravel,
sand, soil, or mixtures thereof (soil and stone mixture; see Abstract).
Regarding claim 3, Melander further teaches:
wherein the one or more proprioceptive sensor comprises at least one of a force sensor, a pressure sensor, an inertial measurement unit (IMU) sensor, a displacement (linear, angular) sensor, a current sensor and a voltage sensor (i.e., “inertial measurement unit”; see Abstract).
Regarding claim 4, Melander further teaches:
wherein the machine comprises an excavator, haulage equipment, a load haul dump (LHD) machine, or a conveyor (i.e., “excavator”; see Abstract).
Regarding claim 5, Melander further teaches:
wherein the machine is operating in an application selected from underground mining, surface mining, construction, material handling, material preparation, and space exploration and development (i.e., soil excavation or mounding operation; see Abstract).
Regarding claim 6, Melander further teaches:
wherein the machine interacts with the material manually, partially autonomously, or fully autonomously (i.e., “excavator-based mounding operation”; see Abstract; note that the operation needs to be at least one of manually, partially automatically or fully autonomously).
Regarding claim 7, Melander further teaches:
wherein processing the sensor signals and extracting features includes an analysis with respect to time, frequency, amplitude, or a combination thereof (see tables 1, 2 and 4).
Regarding claim 8, Melander further teaches:
wherein processing the sensor signals and extracting features includes a statistical analysis, a stochastic analysis, a fractal analysis, a wavelet analysis, a spectral analysis, or a combination of two or more thereof (see tables 1, 2 and 4).
Regarding claim 9, Melander further teaches:
wherein the classifier uses supervised learning to classify the identified features according to the selected classification categories (i.e., “supervised machine learning”; see Abstract).
Regarding claim 12, Melander further teaches:
obtaining and processing sensor signals from one or more exteroceptive sensors (i.e., “The development of an excavator-activity recognition algorithm was done with the aid of a video recording of the mounding work”; see p. 7, ¶ 2).
Regarding claim 13, Melander further teaches:
wherein the one or more exteroceptive sensors are selected from cameras and laser scanners (i.e., “The development of an excavator-activity recognition algorithm was done with the aid of a video recording of the mounding work”; see p. 7, ¶ 2).
Regarding claim 27, the claim recites the same substantive limitations as claim 1 and is rejected by applying the same teachings.
Regarding claim 28, Melander further teaches:
wherein the classifier uses one or more algorithms selected from a supervised learning algorithm and an unsupervised learning algorithm, or a combination thereof (i.e., “supervised machine learning”; see Abstract).
Regarding claim 29, Melander further teaches:
wherein the classifier uses one or more supervised learning algorithm selected from a k-nearest neighbour (KNN) algorithm and an artificial neural network (ANN) algorithm, or a combination thereof (i.e., KNN, ANN; see p. 8, ¶ 2).
Claims 10, 11, and 14-26 are rejected under 35 U.S.C. 103 as being unpatentable over Melander in view of REIMANN et al. (US 20180177136 A1; hereinafter “REIMANN”).
Regarding claim 10, the prior art applied to the preceding linking claim(s) teaches the features of the linking claim(s).
Melander does not explicitly disclose:
wherein the classifier uses unsupervised learning to classify the identified features according to the selected classification categories.
But REIMANN teaches:
using unsupervised learning (i.e., “classification may be based on feature vectors consisting of measured physical quantities, e.g. hyperspectral wavelengths obtained by a spectrometer.”; see [0020]) to classify the identified features according to the selected classification categories (i.e., “a classification model is trained by a machine learning algorithm, e.g. relying on deep learning for supervised and/or unsupervised learning”; see Abstract; “in so-called ‘unsupervised ML’ the algorithm finds hidden structure in unlabeled data, e.g. by finding groups of data samples sharing similar properties in feature space”; see [0026]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Melander in view of REIMANN, by incorporating the remote sensors and classifier technique such that the classifier uses unsupervised learning to classify the identified features according to the selected classification categories, as claimed. The rationale would be to also help obtain additional soil condition information to facilitate agricultural planning and/or decision making.
Regarding claim 11, Melander further teaches:
wherein the classifier uses (i.e., “supervised machine learning”; see Abstract).
Melander does not explicitly disclose (see only the underlined):
wherein the classifier uses unsupervised and supervised learning to classify the identified features according to the selected classification categories .
But REIMANN teaches:
using unsupervised learning (i.e., “classification may be based on feature vectors consisting of measured physical quantities, e.g. hyperspectral wavelengths obtained by a spectrometer.”; see [0020]) to classify the identified features according to the selected classification categories (i.e., “a classification model is trained by a machine learning algorithm, e.g. relying on deep learning for supervised and/or unsupervised learning”; see Abstract; “in so-called ‘unsupervised ML’ the algorithm finds hidden structure in unlabeled data, e.g. by finding groups of data samples sharing similar properties in feature space”; see [0026]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Melander in view of REIMANN, by incorporating the remote sensors and classifier technique such that the classifier uses unsupervised and supervised learning to classify the identified features according to the selected classification categories, as claimed. The rationale would be to also help obtain additional soil condition information to facilitate agricultural planning and/or decision making.
Regarding claim 14, the claim recites the same substantive limitations as claim 1 and is rejected by applying the same teachings, except for (see only the underlined):
identifies extracted features of the at least one sensor signal that are similar for each selected classification category of the material;
uses the identified features as inputs to a classifier that uses an algorithm to classify the identified features into the selected classification categories.
The difference is that respective features are identified and used for corresponding classification categories.
But REIMANN teaches:
classifying sensor measurement features into soil condition using corresponding features (see Abstract; [0020], [0026]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Melander in view of REIMANN, by incorporating the remote sensors and classifier technique such that the processor identifies extracted features of the at least one sensor signal that are similar for each selected classification category of the material; and uses the identified features as inputs to a classifier that uses an algorithm to classify the identified features into the selected classification categories, as claimed. The rationale would be to also help obtain additional soil condition information to facilitate agricultural planning and/or decision making.
Regarding claim 15, the claim recites the same substantive further limitations as claim 2 and is rejected by further applying the same teachings.
Regarding claim 16, the claim recites the same substantive further limitations as claim 3 and is rejected by further applying the same teachings.
Regarding claim 17, the claim recites the same substantive further limitations as claim 4 and is rejected by further applying the same teachings.
Regarding claim 18, the claim recites the same substantive further limitations as claim 5 and is rejected by further applying the same teachings.
Regarding claim 19, the claim recites the same substantive further limitations as claim 6 and is rejected by further applying the same teachings.
Regarding claim 20, the claim recites the same substantive further limitations as claim 7 and is rejected by further applying the same teachings.
Regarding claim 21, the claim recites the same substantive further limitations as claim 8 and is rejected by further applying the same teachings.
Regarding claim 22, the claim recites the same substantive further limitations as claim 9 and is rejected by further applying the same teachings.
Regarding claim 23, the claim recites the same substantive further limitations as claim 10 and is rejected by further applying the same teachings.
Regarding claim 24, the claim recites the same substantive further limitations as claim 11 and is rejected by further applying the same teachings.
Regarding claim 25, the claim recites the same substantive further limitations as claim 12 and is rejected by further applying the same teachings.
Regarding claim 26, the claim recites the same substantive further limitations as claim 13 and is rejected by further applying the same teachings.
Prior Art
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
OKUZAWA (JP 2020147923 A) teaches a method of evaluating a natural ground around a tunnel, based on oil feed pressure, drilling distance, and drilling speed from sensors incorporated in a drilling system.
HASEBE et al. (JP 2015067957 A) teaches a method of evaluating bedrock quality based on information obtained from a percussion drill in drilling.
XU et al. (CN 109826626 A) teaches a method of adjusting mining cutting pattern based on classification of geological condition using measured running speed of a motor of the cutting machine, and current and voltage for the motor.
DUENNE et al. (DE 102004010827 B4) teaches a method for controlling a mobile soil cultivation device, involving classifying the nature of the soil to be processed based on movement behavior of a harrow and motor current of at least one drive motor of a drive unit and/or a soil treatment unit.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JOHN C KUAN whose telephone number is (571)270-7066. The examiner can normally be reached M-F: 9:00AM-5:30PM.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Andrew Schechter can be reached at (571) 272-2302. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/JOHN C KUAN/Primary Examiner, Art Unit 2857
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