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
Response to Arguments
1. Applicant's arguments filed 08/04/2026 have been fully considered but they are not persuasive.
2. Applicant argues in section I. of their remarks the office action fails to provide adequate description of the rejection. The examiner respectfully disagrees. The applicant states correctly that "the particular part relied on must be designated as nearly as practicable" and "[t]he pertinence of each reference, if not apparent, must be clearly explained." However the pertinence of the reference is apparent and was designated as nearly as practicable. The entire reference is 10 pages, only 8 once the journal header and citations are excluded. Approximately tables and figures occupy the space of 2-3 pages, leaving only 5-6 pages of text to read. On the subject of machine learning in EELS spectroscopy and at the ordinary skill of the art that is a trivial amount of reading and tables/figures to consider. All of it was relevant, as all of it did describe how a machine learning system was applied to edge detection in spectroscopic data. Citations that narrowed consideration would not have been practicable and stood a considerable chance of causing confusion as it might have directed the applicant to think that portion alone was sufficient without being informed of the rest of the context. Those of ordinary skill in the art are sophisticated enough to expect that a scientific publication of that length is already specific and pertinent enough, and understand that they are written in a way that they must be read and considered in whole.
That last point is finely demonstrated in the example the applicant themselves argued. “This is particularly problematic because del-Pozo-Bueno is not directed to the presently claimed edge-confirmation approach, but instead to support vector machine classification for
EELS oxidation-state determination.” EELS oxidation-state determination is itself edge-confirmation, as demonstrated unambiguously in the background section of the application.
“The most commonly studied ionization edges in EELS are the K, L, and M edges, which correspond to the ionization of the 1s, 2s-2p, and 3s-3p-3d orbitals, respectively. The position and intensity of these edges depend on the atomic number of the element and its chemical bonding environment. The fine structure of the edges is also important in EELS analysis, as it provides information about the electronic states of the sample. For example, post edge features in the spectrum correspond to the transitions between the ground state and excited states of the sample. These features can be used to study the local electronic structure and bonding of the material, such as the oxidation state of a metal atom or the coordination of a ligand around a metal center.”
This language is very similar to what is found in the introduction section of the reference.
“One of the most significant advantages of EELS… is the sensitivity to different electronic configurations and in particular, the capability to probe the oxidation state of transition metals (TM) at atomic resolution, through the detailed analysis of the ELNES [Energy-Loss Near Edge Structures] of the oxygen K edge and the L3 and L2 white lines characteristic of these metals. Several specific methods to identify oxidation states have been proposed, such as the normalized oxygen K pre-peak intensity, the energy separation between pre-peak and main peak, the ratio of the L3 to L2 white lines, or studying the fine structures of the edges
At the start of section 2 of their arguments the applicant’s themselves recognize the disclosure of edge positions in EELS disproving their position (and integrated intensities, distances, ratios are also admitted which will be elsewhere pertinent).
3. Applicant argues in section 2 that the reference does not teach the specific configuration of the claims. The examiner respectfully disagrees.
4. The deficiencies named that the examiner could clearly identify the applicant providing supporting arguments for were: providing the location of the ionization edge as an input parameter a numerical model for outputting simulated EELS spectra, fitting to the measured EELS spectrum and providing a fitted location of the ionization edge, using a statistical model to test for confirmation.
5. With regard to providing the ionization edge as an input, in this the applicant made a factual error in their representation of being limited to intensity/energy (histogram) data. In their determination of inputs for the statistical model, del-Pozo-Bueno actually describes a much wider number of inputs. The inputs disclosed are at least the reference and test spectrums, which are not just intensity values per energy channel as the applicant’s claimed but also labels, pre-processing elements, PCA outputs, optimization and hyperparameters, noise and chemical shifts data, performance ranking and comparison, etc. The reference does refer to a process of breaking the histogram data into input vectors, which is perhaps the basis for their confusion and also another argument for why the examiner’s citation of the whole reference is justified, because del-Pozo-Bueno is describing just one of their many steps and can’t be read in isolation for that reason. The applicant appears to recognize this when they cited that labels were part of the input data, confounding their later characterization. The ionization edge as input is specifically recognizably addressed in the last paragraph of the introduction, when they describe using the Python Library HyperSpy to isolate the edge and remove the background, using 30eV of length and 300 histogram channels for the edge. Thus the claimed spectrum with the ionization edge was provided and made an input parameter for the model. This is easy to see, because even if applicant’s whole characterization is correct then providing the EELS spectrum is merely represented as histogram data that is the input data for the model, then the fact that it contains the ionization edge makes it an input parameter. This combination of claim elements would only fail to be taught if the only data provided to the model under the circumstance did not in any step or sense include an ionization peak (from which the ionization edge is identified). But that the model was trained specifically on those peaks/ ionization edges means it had it, that it is later used to identify those same features also means these claimed elements are provided. This makes sense because the paper describes training a ML/SVM model on the data to investigate if it can be relied upon as an identification system for future use and reported the results favorably.
6. The applicant’s remark that the outputted simulated EELS spectrum is a bit of a non sequitur. Their argument’s don’t provide clear support as to their reasons why, and regardless their claim as written doesn’t require simulated EELS spectrum as a necessary result. Rather a model is provided for this, the method doesn’t claim the step of producing the output. Nevertheless, this is taught in the prior art by the data matching in the model.
7. The applicant’s next argument is about the fitting the model to the spectrum and providing a fitted location. Most of the applicant’s arguments in this regard the examiner can make little sense of for they cite portions of the paper’s description of the training process and how they measured and verified the model was able to successfully find and identify the location of these edges with sufficient accuracy; which all itself is undeniably ‘fitting said numerical model to said measured EELS spectrum and providing a fitted location of the ionization edge; and using a statistical test for confirming the ionization edge as a true ionization edge if the statistical test passes a statistical threshold value.’ Their argument that the reported ability to successfully ‘classify spectra despite energy shifts using edge shapes’ is proof that it isn’t fit and providing a fitted location for the ionization edge is simply not understandable to the examiner. That the model is able to identify these features despite the energy shifts is proof cited by the authors, because it recognizes the feature by shape and therefore can find it even when it isn’t numerically supposed to be which means that it is resilient against calibration and noise issues. This is exactly what the applicant’s should expect, the model was given a lot of accurate and inaccurate histograms of this feature as part of teaching it what it ought to recognize as the shape of the ionization edge so it can identify if and where that shape is present in subsequent data. Similarly,
8. Applicant’s last point is one the examiner will include in full for it cannot be paraphrased.
“del-Pozo-Bueno also does not disclose "using a statistical test for confirming the ionization edge as a true ionization edge if the statistical test passes a statistical threshold value," as claimed. Evaluation of SVM classification accuracy, resistance to noise, or resistance to energy shifts is not "using a statistical test for confirming the ionization edge as a true ionization edge if the statistical test passes a statistical threshold value." Thus, the claimed statistical test is applied to determine whether the ionization edge itself is confirmed as a true ionization edge. del-Pozo-Bueno instead determines an oxidation-state class for a spectrum.”
Applicant’s position is rebutted by their own statement about the reference. “del-Pozo-Bueno instead determines an oxidation-state class for a spectrum.” The oxidation state as already addressed is recognized in the background section of the applicant’s own specification as an ionization state that can be identified from the true ionization edge. By applicant’s own admission the reference teaches this claim feature.
Claim Rejections - 35 USC § 102
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 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 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) 10-23 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by del-Pozo-Bueno et al. “Support vector machine for EELS oxidation state determination.”
Regarding Claim(s) 10, del-Pozo-Bueno et al. teaches: A method for confirming an ionization edge within a measured EELS spectrum, the method comprising the steps of: providing a measured EELS spectrum containing the ionization edge; providing a numerical model for outputting simulated EELS spectra, said numerical model having at least a location of the ionization edge as an input parameter; fitting said numerical model to said measured EELS spectrum and providing a fitted location of the ionization edge; and using a statistical test for confirming the ionization edge as a true ionization edge if the statistical test passes a statistical threshold value. (del-Pozo-Bueno et al. – whole reference but summarized best in the Introduction)
Regarding Claim(s) 11, del-Pozo-Bueno et al. teaches: further comprising providing a plurality of ionization edges as an input parameter. (del-Pozo-Bueno et al. – whole reference but summarized best in the Introduction)
Regarding Claim(s) 12, del-Pozo-Bueno et al. teaches: wherein the step of providing the plurality of ionization edges as an input parameter is used for identifying a single ionization edge. (del-Pozo-Bueno et al. – whole reference but summarized best in the Introduction)
Regarding Claim(s) 13, del-Pozo-Bueno et al. teaches: further comprising the step of dividing the measured EELS spectrum into a plurality of subregions. (del-Pozo-Bueno et al. – whole reference but summarized best in the Introduction)
Regarding Claim(s) 14-16, del-Pozo-Bueno et al. teaches: wherein at least one of the subregions spans a maximum of 750 eV, 500 eV, 250 eV. (del-Pozo-Bueno et al. – whole reference but summarized best in the Introduction)
Regarding Claim(s) 17, del-Pozo-Bueno et al. teaches: comprising the step of providing a plurality of ionization edges within a selected subregion as an input parameter, and identifying a single ionization edge within the selected subregion. (del-Pozo-Bueno et al. – whole reference but summarized best in the Introduction)
Regarding Claim(s) 18, del-Pozo-Bueno et al. teaches: comprising the step of using said fitted location and the measured EELS spectrum, wherein said statistical test and/or said statistical threshold value is based on one or more of: a surplus value of the measured EELS spectrum at said fitted location with respect to a base background value, a derivative method and an integrated method. (del-Pozo-Bueno et al. – whole reference but summarized best in the Introduction)
Regarding Claim(s) 19, del-Pozo-Bueno et al. teaches: comprising the step of using said fitted location and the measured EELS spectrum, wherein said statistical test and/or said statistical threshold value is based on one or more of a surplus value of the measured EELS spectrum at said fitted location with respect to a base background value. (del-Pozo-Bueno et al. – whole reference but summarized best in the Introduction)
Regarding Claim(s) 20, del-Pozo-Bueno et al. teaches: further comprising the step of using said fitted location and the measured EELS spectrum, wherein said statistical test and/or said statistical threshold value is based on a derivative method. (del-Pozo-Bueno et al. – whole reference but summarized best in the Introduction)
Regarding Claim(s) 21, del-Pozo-Bueno et al. teaches: further comprising the step of using said fitted location and the measured EELS spectrum, wherein said statistical test and/or said statistical threshold value is based on an integrated method. (del-Pozo-Bueno et al. – whole reference but summarized best in the Introduction)
Regarding Claim(s) 22, del-Pozo-Bueno et al. teaches: wherein the method comprises the step of receiving, from an input device, the statistical threshold value. (del-Pozo-Bueno et al. – whole reference but summarized best in the Introduction)
Regarding Claim(s) 23, del-Pozo-Bueno et al. teaches: A device for confirming an ionization edge within a measured EELS spectrum, comprising:
a processing unit, wherein the processing unit is arranged to: receive a measured EELS spectrum containing the ionization edge; output simulated EELS spectra using a numerical model, said numerical model having at least a location of an ionization edge as an input parameter; fit said numerical model to said measured EELS spectrum and provide a fitted location of the ionization edge; perform a statistical test and confirm said ionization edge as a true ionization edge if the statistical test passes a statistical threshold value; and output said fitted location of the ionization edge as a true ionization edge when the statistical test has passed the statistical threshold value. (del-Pozo-Bueno et al. – whole reference but summarized best in the Introduction)
Conclusion
1. THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to SEAN LUCK whose telephone number is (571)272-6493. The examiner can normally be reached 8-5 M-F.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Georgia Epps can be reached at (571) 272-2328. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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SEAN LUCK
Examiner
Art Unit 2878
/SEAN LUCK/Examiner, Art Unit 2878
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