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 § 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 1-13 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.
Specifically, representative Claim 1 recites:
“A method for determining biochar identity, comprising: obtaining physical and chemical property data of a sample to be identified, wherein the sample comprises waste biomass and biochar corresponding to the waste biomass, the biochar is a solid material obtained by carbonizing the waste biomass, and the physical and chemical property data comprises a hydrogen content, an organic carbon concentration, a nitrogen content, a phosphorus content, a potassium content, a pH scale, a specific surface area, and a pore volume; and inputting the physical and chemical property data of the sample into a biochar identity determination model to obtain identity information of the sample, wherein a process for determining the biochar identity determination model comprises the following steps: constructing high-dimensional multi-category biochar sample data, wherein the high-dimensional multi-category biochar sample data comprises an input variable data matrix and an identity multi-category label column vector; performing abnormality detection and standardization processing on the input variable data matrix in the high-dimensional multi-category biochar sample data to obtain a processed input variable data matrix; constructing a feature data matrix based on the processed input variable data matrix; and obtaining a random subspace nearest neighbor clustering ensemble learning classifier based on the feature data matrix, the sample identity multi-category label column vector and a random subspace nearest neighbor clustering ensemble learning algorithm, wherein the random subspace nearest neighbor clustering ensemble learning classifier is the biochar identity determination model.”
The claim limitations in the abstract idea have been highlighted in bold above; the remaining limitations are “additional elements”.
Under the Step 1 of the eligibility analysis, we determine whether the claims are to a statutory category by considering whether the claimed subject matter falls within the four statutory categories of patentable subject matter identified by 35 U.S.C. 101: Process, machine, manufacture, or composition of matter. The above claim is considered to be in a statutory category (process).
Under the Step 2A, Prong One, we consider whether the claim recites a judicial exception (abstract idea). In the above claim, the highlighted portion constitutes an abstract idea because, under a broadest reasonable interpretation, it recites limitations that fall into/recite an abstract idea exceptions. Specifically, under the 2019 Revised Patent Subject matter Eligibility Guidance, it falls into the groupings of subject matter that covers mathematical concepts - mathematical relationships, mathematical formulas or equations, mathematical calculations.
Similar limitations comprise the abstract ideas of Claim 7.
Next, under the Step 2A, Prong Two, we consider whether the above claims that recites a judicial exception are integrated into a practical application.
The above claims comprise the following additional elements:
In Claim 1: A method for determining biochar identity, comprising: obtaining physical and chemical property data of a sample to be identified, wherein the sample comprises waste biomass and biochar corresponding to the waste biomass, the biochar is a solid material obtained by carbonizing the waste biomass, and the physical and chemical property data comprises a hydrogen content, an organic carbon concentration, a nitrogen content, a phosphorus content, a potassium content, a pH scale, a specific surface area, and a pore volume;
In Claim 7: A system for determining biochar identity, comprising: a sample data acquisition module, configured to obtain physical and chemical property data of a sample to be identified, wherein the sample comprises waste biomass and biochar corresponding to the waste biomass, the biochar is a solid material obtained by carbonizing the waste biomass, and the physical and chemical property data comprises a hydrogen content, an organic carbon concentration, a nitrogen content, a phosphorus content, a potassium content, a pH scale, a specific surface area, and a pore volume; and a biochar identity information determining module.
The additional elements in the preambles are recited in generality and represent insignificant extra-solution activity (field-of-use limitations) that is not meaningful to indicate a practical application.
The additional elements in the claims such as a sample data acquisition module (Claim 7) are examples of generic computer equipment (components) that are generally recited and not meaningful and, therefore, are not qualified as particular machines to indicate a practical application. The limitations that generically recite obtaining physical and chemical property data of a sample represent insignificant extra-solution activity of mere data gathering. According to the October update on 2019 SME Guidance such steps are “performed in order to gather data for the mental analysis step, and is a necessary precursor for all uses of the recited exception. It is thus extra-solution activity, and does not integrate the judicial exception into a practical application”.
Therefore, the claims are directed to a judicial exception and require further analysis under the Step 2B.
However, the above claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception (Step 2B analysis) because these additional elements/steps are well-understood and conventional in the relevant art based on the prior art of record.
The independent claims, therefore, are not patent eligible.
With regards to the dependent claims, claims 2-6, 8-13 provide additional features/steps which are part of an expanded abstract idea of the independent claims (additionally comprising mathematical abstract idea steps) and, therefore, these claims are not eligible without meaningful additional elements that reflect a practical application and/or additional elements that qualify for significantly more for substantially similar reasons as discussed with regards to Claim 1.
For example, additional element (“An electronic device, comprising a memory and a processor”) in Claim 8 and its dependent claims is generically recited and not meaningful to indicate a practical application and/or qualify for significantly more.
Examiner Note with Regards to Prior Art of Record
Claims 1-13 are distinguished over prior art of record based on the reasons below.
The following references are considered to be the closest prior art to the claimed invention:
Johannes Lehmann et al. ,” Biochar for environmental management : science and technology / edited by Johannes Lehmann and Stephen Joseph”, Published by Earthscan in the UK and USA in 2009, London-Sterling VA, 12 p., https://biochar-international.org/wp-content/uploads/2018/11/prelim_ch1_2015biocharforenvironmentalmanagement_text.pdf, hereinafter ‘Lehmann’, discloses obtaining physical and chemical property data of a sample comprising a hydrogen content, an organic carbon concentration, a nitrogen content, a phosphorus content, a potassium content, a pH scale, a specific surface area, and a pore volume.
Hannah Larissa Nicholas et al., “Physico-chemical properties of waste derived biochar from community scale faecal sludge treatment plants”, Physico-chemical properties of waste derived biochar from community, National Institute of Health, 2022, https://gatesopenresearch.org/articles/6-96/v2, hereinafter ‘Nicholas”, also discloses obtaining physical and chemical property data of a sample comprising a hydrogen content, an organic carbon concentration, a nitrogen content, a phosphorus content, a potassium content, a pH scale, a specific surface area, and a pore volume.
Kun Zhou et al. (CN 112287862), hereinafter ‘Zhou’, discloses performing abnormality detection and standardization processing on the input variable data matrix in the high-dimensional multi-category sample data to obtain a processed input variable data matrix.
CAO JIANGZHONG et al. (CN 112465062), hereinafter ‘Cao’, discloses constructing a feature data matrix based on the processed input variable data matrix; and obtaining nearest neighbor clustering classifier based on the feature data matrix.
Chatterjee Ajay et al. (US 20200097545) discloses multi-category label column vector and a nearest neighbor clustering learning algorithm.
Yuhong Guo et al., “Probabilistic Multi-Label Classification with Sparse Feature Learning”, Proceedings of the Twenty-Third International Joint Conference on Artificial Intelligence, Beijing, China, 3–9 August 2013, pp. 1373-1379, hereinafter ‘Guo’, discloses obtaining a random subspace nearest neighbor clustering ensemble learning classifier based on the feature data matrix.
Mu Li et al. (US 20060287848) discloses random feature clustering with a classifier ensemble.
However, in regards to Claims 1 and 7, the claims differ from the closest prior art, Lehmann, Nicholas, Zhou, Cao, Ajay, Guo, and Li, either singularly or in combination, because they fail to anticipate or render obvious constructing a feature data matrix based on the processed input variable data matrix; and obtaining a random subspace nearest neighbor clustering ensemble learning classifier based on the feature data matrix, the sample identity multi-category label column vector and a random subspace nearest neighbor clustering ensemble learning algorithm, wherein the random subspace nearest neighbor clustering ensemble learning classifier is the biochar identity determination model, in combination with all other limitations in the claim as claimed and defined by applicant.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Islam Sumaiya et al. (US 20210358571) discloses classifier that is trained by: sequentially providing, for each of a number of fusions, a matrix of feature vectors to the classifier; and sequentially updating, for each fusion included in the number of fusions, weights included in the classifier based on the matrix of feature vectors and a label associated with the fusion.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ALEXANDER SATANOVSKY whose telephone number is (571)270-5819. The examiner can normally be reached on M-F: 9 am-5 pm.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Catherine Rastovski can be reached on (571) 270-0349. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/ALEXANDER SATANOVSKY/
Primary Examiner, Art Unit 2863