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 Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims 14-26 in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
Claim Rejections - 35 USC § 103
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.
Claim(s) 1-26 is/are rejected under 35 U.S.C. 103 as being unpatentable over Ebrahimi et al. US 20240310851 A1 “Ebrahimi ”, in view of GORYAEV ET AL: "Reinforcing materials modelling by encodingthe structures of defects in crystalline solidsinto distortion scores",NATURE COMMUNICATIONS, vol. 11,no. 1, 17 September 2020 (2020-09-17), pages1-14, XP055958998,DOI: 10.1038/s41467-020-18282-2Retrieved from the Internet:URL: https:/ /www .nature.com/articles/s41467-020-18282-2 “ GORYAEV”(IDS)
Regarding claim 1, EBRAHIMI discloses a computer-implemented method (EBRAHIMI, para. 44) for processing experimental data of a solid (as cited below) to be characterized (EBRAHIMI, Abstract), formation of device features with high precision and uniformity, which, in turn, necessitates careful monitoring of the fabrication process, including automated examination of the devices while they are still in the form of semiconductor wafers (EBRAHIMI, para. 2).
including atoms and including one or more defects (GORYAEV, Fig. 1, see Pg. 3 Fig. 1 description), said experimental data coming from at least one sensor (GORYAEV, pg. 9, last para.) and having a multimodal distribution (GORYAEV, Fig. 7, pg. 9, sect. discussion), the method comprising: representing, in a descriptor space of dimension K (as cited below, i.e. descriptor), comprised between 10 and 108 , one or more reference solid(s) and said data (page 10, section Methods, "Representation of structural data and training data sets", second paragraph, the number of descriptors is 26); calculating, for at least one portion (as cited below, i.e. …statistical distance from a reference distribution in the feature space of atomic descriptors,) of the atoms of the solid to be characterized, an experimental confidence score (as cited below, see also pg. 6,… Here we suggest using the distortion score to define the confidence region based solely on geometric information of LAEs) in the descriptor space, relative to the atoms of said reference solid (GORYAEV, pg. 2, i.e. distortion score and its correlation with atomic energy); and classifying the atoms of the structure depending on the experimental confidence score (page 8, section Discussion, fourth paragraph: "This metric can serve as a fingerprint for filtering databases with atomic structures to select and/or classify defects").
Regarding claim 2, EBRAHIMI/GORYAEV, for the same motivation of combination, further discloses the method according to claim 1, wherein the experimental data is obtained by a Tomographic Atom Probe (TAP) technique by Transmission Electron Microscopy (TEM), or by X-ray diffraction (GORYAEV, pg. 9, sect. description)
Regarding claim 3, EBRAHIMI/GORYAEV, for the same motivation of combination, further discloses the method according to claim 1, further comprising a prior step of forming the descriptor space and/or one or more descriptor function(s), depending at least on distances between the atoms and/or angles between directions connecting each atom of the solid or the sample which is studied to different neighbours in the network of the solid to be characterized (D 1, page 10, section: Representation of structural data and training data sets).
Regarding claim 4, EBRAHIMI/GORYAEV, for the same motivation of combination, further discloses the method according to claim 1,wherein the representation, in the descriptor space of dimension K, preserves symmetry(symmetries) and the chemical nature of the atomic structure(s) resulting from the experiment and/or used for reference (D 1, page 10, section: Representation of structural data and training data sets).
Regarding claim 5, EBRAHIMI/GORYAEV, for the same motivation of combination, further discloses the method according to claim 1 wherein the representation step is performed using a descriptor which implements, for each atom j, a graph Gj whose nodes are neighbours, more or less close, to the atom j, the graph then being pixelated in a form of a matrix M (D 1, figure 8 and page 10, sect. Representation of structural data and training data sets)
Regarding claim 6, EBRAHIMI/GORYAEV, for the same motivation of combination, further discloses the method according to claim 5, wherein the graph being is a dense, non-directional graph, the nodes or vertices of the graph corresponding to the atoms of the atomic environment of a central atom1 and with edges with weight weighted by interatomic distances (D 1, figure 8 and page 10, sect. Representation of structural data and training data sets).
Regarding claim 7, EBRAHIMI/GORYAEV, for the same motivation of combination, further discloses the method according to claim 5, the lth line (1 < l<nG) of the matrix M concerning the neighbour of order (Z - 1) of the node 0 of the graph (G) (D 1, figure 8 and page 10, sect. Representation of structural data and training data sets).
Regarding claim 8, EBRAHIMI/GORYAEV, for the same motivation of combination, further discloses the method according to claim 1, further comprising a preliminary step of selecting a radius Rc, called the cut-off radius, which defines an environment of one or more atom(s) or of each atom j, the environment including all atoms present in a vicinity of the atom j or of each atom j and which are included in the cut-off radius (D 1, figure 8 and page 10, sect. Representation of structural data and training data sets).
Regarding claim 9, EBRAHIMI/GORYAEV, for the same motivation of combination, further discloses the method according to claim 1, further comprising learning a method for calculating, a statistical distance of said experimental confidence score (GORYAEV, as cited above, see pg. 11 i.e. optimal outlier method)
Regarding claim 10, EBRAHIMI/GORYAEV, for the same motivation of combination, further discloses the method according to claim 9, wherein the step of learning the method for calculating an experimental confidence score implements a machine learning or a deep learning method an anomaly detection or a novelty detection method (as cited above, i.e. GORYAEV, pg. 11), including one or more of a statistical distance calculation, an MCD,_a Mahalanobis type method, a physical statistical distance calculation, an SVM type technique, or a neural network (GORYAEV, pg. 10).
Regarding claim 11, EBRAHIMI/GORYAEV, for the same motivation of combination, further discloses the method according to claim 1 wherein the classification of the atoms implements a classification algorithm of the DBScan a neural network, an SVM, a MCD, or other clustering method type (GORYAEV, pg. 4, section Application 1, see GORYAEV Fig. 6)
Regarding claim 12, EBRAHIMI/GORYAEV, for the same motivation of combination, further discloses the method according to claim 1 further including a step of comprising distributing or grouping the atoms detected by class of defects, by a machine learning or deep learning type method or a clustering and classification method such as DBSCAN or a "Gaussian Mixtures" or neural network type method (GORYAEV, pg. 4, section Application 1)
Regarding claim 13, EBRAHIMI/GORYAEV, for the same motivation of combination, further discloses the method according to claim 1, further including a step of comprising distributing or grouping the atoms detected by class of defects by a convolutional neural network type method (EBRAHIMI, para. 44).
Regarding claim 14, EBRAHIMI/GORYAEV, for the same motivation of combination, discloses a device (40, 50) for processing experimental data from solids to be characterized, including atoms and including one or more defects, said data having a multimodal distribution (see rejection of claim 1), the device including comprising: means configured to represent, in a space called descriptor space of dimension K comprised between 10 and 10 8(see rejection of claim 1), at least one reference solid and said data, means configured to calculate an experimental confidence score (see rejection of claim 1), in the descriptor space, for at least one portion of the atoms of said solid to be characterized, relative to the atoms of said reference solid (see rejection of claim 1); and means configured to classify atoms of a solid depending on said experimental confidence score (see rejection of claim 1).
Regarding claim 15, EBRAHIMI/GORYAEV, for the same motivation of combination, further discloses the device according to claim 14, the device being connected to a detector, being a detector of a Tomographic Atom Probe (TAP) system, X-ray detector associated with a Transmission Electron Microscopy (TEM) system, or by X-ray diffraction (GORYAEV, pg. 9, sect. description)
Regarding claim 16, EBRAHIMI/GORYAEV, for the same motivation of combination, further discloses the device according to claim 14, further including comprising means (50, 52, 53, 55) adapted configured to form or calculate the descriptor space from experimental data, from data of at least one reference sample depending on at least distances between the atoms and angles between directions connecting the atoms of the solid (D 1, page 10, section: Representation of structural data and training data sets).
Regarding claim 17, EBRAHIMI/GORYAEV, for the same motivation of combination, further discloses the device according to claim 14,further comprising means (50, 52, 53, 55) adapted configured to implement a representation step using a descriptor for which, for each atom j, a graph Gj whose nodes are neighbours, more or less close, to the atom j, the graph then being pixelated in the form of a matrix (D 1, figure 8 and page 10, sect. Representation of structural data and training data sets)
Regarding claim 18, EBRAHIMI/GORYAEV, for the same motivation of combination, further discloses the device according to claim 17, wherein the graph being is a dense, non-directional graph, the nodes or vertices of the graph corresponding to the atoms of the atomic environment of a central atom,and with edges with weight weighted by the interatomic distances (D 1, figure 8 and page 10, sect. Representation of structural data and training data sets).
Regarding claim 19, EBRAHIMI/GORYAEV, for the same motivation of combination, further discloses the device according to claim 17, the lth line (1 <l< nG) of the matrix MJ concerning the neighbour of order (l - 1) of the node 0 of the graph (G,) (D 1, figure 8 and page 10, sect. Representation of structural data and training data sets).
Regarding claim 20, EBRAHIMI/GORYAEV, for the same motivation of combination, further discloses the device according to claim 14,further including comprising means configured to implement a step of processing or preprocessing and/or preparing experimental data (GORYAEV, Fig. 1)
Regarding claim 21, EBRAHIMI/GORYAEV, for the same motivation of combination, further discloses the device according to claim 14, further including comprising means adapted configured to implement a step of learning a method for calculating, a statistical distance of said experimental confidence score (GORYAEV, Fig. 1)
Regarding claim 22, EBRAHIMI/GORYAEV, for the same motivation of combination, further discloses the device according to claim 14, further including comprising means configured to implement a machine learning or deep learning method or an anomaly detection or novelty detection method, including a statistical distance calculation or else an MCD or a Mahalanobis type method, a physical statistical distance calculation, an SVM type technique, or a neural network (GORYAEV, pg. 10).
Regarding claim 23, EBRAHIMI/GORYAEV, for the same motivation of combination, further discloses the device according to claim 14, including further comprising means adapted configured to implement a machine learning or deep learning method or an anomaly detection or novelty detection method, by a convolutional neural network EBRAHIMI, para. 44).
Regarding claim 24, EBRAHIMI/GORYAEV, for the same motivation of combination, further discloses the device according to claim 14,further including comprising means adapted configured to implement a classification algorithm of the DBScans neural networks an SVM, a MCD or other "elustering"clustering method type (GORYAEV, pg. 10).
Regarding claim 25, EBRAHIMI/GORYAEV, for the same motivation of combination, further discloses the device according to claim 14,further including comprising means adapted configured to perform a distribution or a grouping of the atoms detected by class of defects, by a machine learning or deep learning type methods or a clustering and classification method (GORYAEV, pg. 4, section Application 1).
Regarding claim 26, EBRAHIMI/GORYAEV, for the same motivation of combination, further discloses the e device according to claim 14, further comprising means for selecting a radius-Recalled cut-off radius, which defines the environment of one or more atom(s) j or of each atom j, this environment including all atoms present in the vicinity of the atom j or of each atom j and which are included in the cut-off radius (D 1, figure 8 and page 10, sect. Representation of structural data and training data sets).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
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/FRANK F HUANG/Primary Examiner, Art Unit 2485
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