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
The following non-final action is in response to application 18/590,356 filed on 02/28/2024. The communication is the first action on the merits.
Status of Claims
Claims 1-20 are currently pending and have been rejected as follows.
Drawings
The drawings filed on 02/28/2024 are accepted.
Domestic Benefit/National Stage
Applicant’s claim to domestic benefit/national stage has been acknowledged and the corresponding documents have been received.
IDS
The IDS has been received, and the documents within it have been considered.
Claim Rejections - 35 USC § 112(b)
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 5-6 and 15-16 are rejected under 35 U.S.C. 112(b) or pre-AIA 35 U.S.C. 112, 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.
Claims 5-6 and 15-16 are indefinite because the claim language “enhanced” is considered to be relative terminology and is unclear in terms of enhanced in what degree, in what way, from what starting point, etc. For the purposes of compact prosecution, examiner is interpreting the claim language as enhanced with contextual words to train the clustering algorithm, e.g. spec. [0008].
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 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception without significantly more. A subject matter eligibility analysis is set forth below. See MPEP 2106.
Claim 1 recites:
A system for identifying commonality among different descriptions, the system comprising:
one or more physical processors configured by machine-readable instructions to:
obtain description information, the description information defining descriptions of one or more wells in a subsurface region, individual descriptions including one or more words;
generate a similarity matrix for the descriptions of the one or more wells in the subsurface region, the similarity matrix including values to indicate similarity between different pairs of the descriptions of the one or more wells in the subsurface region;
generate a clustered similarity matrix based on clustering of the values of the similarity matrix, the clustered similarity matrix including groupings of pair-wise similarities between the descriptions of the one or more wells in the subsurface region;
identify commonalities between the descriptions of the one or more wells in the subsurface region based on the groupings of the pair-wise similarities in the clustered similarity matrix;
and determine characteristics of the one or more wells in the subsurface region based on the commonalities between the descriptions of the one or more wells in the subsurface region.
The bolded language in the claim limitations indicate abstract ideas, and the remaining limitations are considered to be additional elements.
Under Step 1 of the analysis, claim 1 does belong to a statutory category, namely it is a machine claim. Claim 11 is a process claim.
Under Step 2A, Prong One: This part of the eligibility analysis evaluates whether the claim recites a judicial exception. As explained in MPEP 2106.04, subsection II, a claim “recites” a judicial exception when the judicial exception is “set forth” or “described” in the claim.
Under Step 2A, Prong One, the broadest reasonable interpretation consistent with the specification of the limitations recited in Claim 1 recite at least one judicial exception, that being a mental process (observations/evaluation/judgement/ or opinion). and a mathematical concept (mathematical calculations/relationships/formulas/ or equations).
According to the specification, “generating a similarity matrix for the descriptions of the one or more wells in the subsurface region” involves embedding arrays for individual descriptions being converted to similarity values using a cosine similarity metric [0032]. Generating a similarity matrix may include calculating the similarity matrix [0046]. This claim limitation recites a mathematical concept in that mathematical calculations are involved in the “generating” process via a cosine similarity metric, for example.
According to the specification, “generating a clustered similarity matrix based on clustering of the values of the similarity matrix” involves the values of the similarity matrix being clustered using spectral clustering, where the matrix normalized Laplacian is computed and then decomposed into eigenvalues and eigenvectors. The Silhoutte and Calinski-Harabasz scores may be calculated on the results of k-means clustering using a variable number of eigenvectors (k). Maxima in both scores may be used to determine how many clusters will be formed, i.e., what value of “k” should be used [0034]. This claim limitation recites a mathematical concept in that mathematical calculations are involved in the “generating” process via the Laplacian normalization and the Silhoutte and Calinski-Harabasz scores being calculated, for example.
According to the specification, “identifying commonalities” involves individual groupings of pair-wise similarities in the clustered similarity matrix being identified as a commonality between the descriptions. Individual clusters in the clustered similarity matrix may be identified as a commonality between the descriptions. Thus, commonalities between disparate descriptions of a thing may be found in the clustered similarity matrix [Fig. 7]. For example, the rock types/lithotypes that are described in disparate descriptions of a well may be found in the clustered similarity matrix [0060] where identifying a commonality between the descriptions of a thing may include ascertaining, approximating, calculating, determining, establishing, estimating, finding, obtaining, quantifying, selecting, setting, and/or otherwise identifying the commonality between the descriptions of the thing [0059] where commonalities/concepts are identified within the descriptions, and the descriptions may be key words, phrases, full paragraphs [0058]. This claim limitation recites the abstract idea of a mental process given that one of ordinary skill in the art would be capable of mentally evaluating the descriptions of a given well in the subsurface region and evaluating the groupings of the pair-wise similarities in the clustered similarity matrix and making a judgement/opinion regarding the commonalities between different wells.
According to the specification, “determining characteristics” may involve ascertaining, approximating, calculating, establishing, estimating, finding, identifying, obtaining, quantifying, selecting, setting, and/or otherwise determining the characteristic of the thing. A characteristic of a thing may refer to an attribute, a feature, a quality, and/or other characteristic of the thing. The characteristics of the thing(s) may be determined based on the commonalities between the descriptions of thing(s) and/or other information [0062] where a characteristic of a well may include a rock type or a depositional environment type and the commonalities between the descriptions of the well may be used to determine the rock type or the depositional environment type of the well [0063]. This claim limitation recites the abstract idea of a mental process given that one of ordinary skill in the art would be capable of mentally evaluating the commonalities between the descriptions of a well and making a judgement/opinion as to what type of rock or depositional environment may be characteristic of that well.
Step 2A, Prong Two of the eligibility analysis evaluates whether the claim as a whole integrates the recited judicial exception(s) into a practical application of the exception. This evaluation is performed by (a) identifying whether there are any additional elements recited in the claim beyond the judicial exception, and (b) evaluating those additional elements individually and in combination to determine whether the claim as a whole integrates the exception into a practical application. 2019 PEG Section III(A)(2), 84 Fed. Reg. at 54-55.
The additional elements in the preambles of all independent claims are recited in generality and represent insignificant extra-solution activity (field-of-use limitations) that is not meaningful to indicate a practical application.
Claim 1 recites the following additional elements:
“obtaining description information”
Claim 11 recites similar additional elements:
These claim limitations generically recite collecting/outputting by sensors/devices measurement data (all independent claims), which represents the insignificant extra-solution activity of mere data gathering/outputting results. 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”.
Claim 1 also recites the additional elements:
“one or more physical processors configured by machine-readable instructions”
Claim 11 recites similar additional elements:
These additional elements are computer components recited in generality and are not meaningful and, therefore, are not qualified as particular machines to indicate a practical application.
Under Step 2B, the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. When re-evaluated under Step 2B, the claim limitations are found to be well-understood, routine, and conventional as explained by MPEP 2106.05(d)(II) (describing conventional activities that include transmitting and receiving data over a communication network) , specifically pertaining to the bulleted lists of additional elements above.
Therefore, the combination and arrangement of the above identified additional elements when analyzed under Step 2B also fails to necessitate a conclusion that claims 1 and 11 amount to significantly more than the abstract idea.
With regards to dependent claims 2-10 and 12-20, they provide additional features/steps which are part of an expanded abstract idea and/or generic additional elements that are not meaningful and therefore not qualified as particular machines to indicate a practical application. The same is true of the independent claims (additionally comprising 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, claims 2-3 further limit the abstract idea through an interpretation of rock characteristics including rock type or a depositional environment type.
Claims 4-10 further limit the abstract ideas through upscaling of descriptions to a higher level of classification, enhancing the similarity matrix and natural language model, vectorizing the descriptions for determining similarity, modifying similarity matrix values based on a threshold, using a threshold based on a curvature of a cumulative distribution function of the similarity matrix, and clustering the values of the similarity matrix using spectral clustering.
Therefore, the dependent claims in their current state do not recite meaningful additional elements that are required for integrating the judicial exceptions into a practical application.
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 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 1-8, 10-18 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Peredriy (WO 2022216311 A1) in view of Nefedov (US 20230074771 A1).
Regarding claim 1, Peredriy teaches a system for identifying commonality among different descriptions, the system comprising:
one or more physical processors configured by machine-readable instructions to (A system comprising: one or more processors; and one or more memory devices including instructions that are executable by the one or more processors for causing the one or more processors to [Claim 1]):
obtain description information, the description information defining descriptions of one or more wells in a subsurface region (a system can receive well log records associated with a group of wellbores drilled through geological layers [0035]), individual descriptions including one or more words (The training logs can be well log records that are annotated (e.g. manually annotated by a geologist) with labels indicating the geological tops of geological layers and the types of the geological layers [00158]);
generate a similarity matrix for the descriptions of the one or more wells in a subsurface region, the similarity matrix including values to indicate similarity between different pairs of the descriptions of the one or more wells in a subsurface region (Each similarity value in the similarity matrix can represent a similarity between a pair of well log records [00184]);
generate a clustered similarity matrix based on clustering of the values of the similarity matrix, the clustered similarity matrix including groupings of pair-wise similarities between the descriptions of the one or more wells in a subsurface region (The clustering process can involve generating a similarity matrix based on the statistical distances and the geographical distances, the similarity matrix including similarity values indicating a respective similarity between each pair of wellbores in the plurality of wellbores where the clustering process can involve determining the plurality of well clusters using the similarity matrix [0005]);
identify commonalities between the descriptions of the one or more wells in a subsurface region based on the groupings of the pair-wise similarities in the clustered similarity matrix (The automated clustering process can group the wellbores 1304a-k into the well clusters based on any suitable criteria, such as the geographical distances (e.g., physical distances in real space) between the wellbores, the statistical similarities between the corresponding well log records, or both of these [00163]);
and determine characteristics of the one or more wells in a subsurface region based on the commonalities between the descriptions of the one or more wells in a subsurface region (The system can automatically correlate the geological layers to one another across two or more well log records. Specifically, the system can identify the same geological top in multiple well log records based on (i) similarities between the depth of the geological top in each well log record, (ii) the adjacent geological layers surrounding the geological top in each well log record, (iii) the types and other characteristics of the adjacent geological layers, or (iv) any combination of these. The system can map the geological top across the multiple well log records, which may provide more holistic and useful information about the geological top and corresponding layers than can be derived from any individual well-log record alone [0037] where these characteristics include locations, tops, types, thicknesses, and geometries of geological layers [00191]).
Peredriy does not explicitly teach:
generating a similarity matrix for the descriptions (the methods comprise generating a similarity matrix among documents of documents' features for a corpus of documents [0005] where the similarity matrices may be constructed based on the features [0023]),
the similarity matrix including values to indicate similarity between different pairs of the descriptions (Similarity matrices, which characterize the pair-wise similarities among documents within the corpus of documents based on content and context, can be computed from vector representations of the corpus of documents…where each document is represented by a vector and each dimension of the vector corresponds to a particular term or word in the corpus of documents [0036]);
generating a clustered similarity matrix based on clustering of the values of the similarity matrix (the methods comprise applying a clustering algorithm to the similarity matrix to generate a graph corresponding to the corpus of documents [0005] (i.e. “clustered similarity matrix”)),
the clustered similarity matrix including groupings of pair-wise similarities between the descriptions (Since the similarity matrices characterize the pair-wise similarities [0036], and the graph (i.e. “clustered similarity matrix”) is created by applying the clustering algorithm to the similarity matrices [0005], the clustered similarity matrix includes pair-wise similarities… the clustering engine 122 may process the similarity matrix to decompose or factorize the same into clusters of submatrices, which can be represented as clusters of nodes of a graph. For example, the clustering engine 122 may apply a clustering algorithm to the similarity matrix to represent the matrix as a graph where the nodes represent documents and the edges represent similarities between the documents [0040]);
identifying commonalities between the descriptions based on the groupings of the pair-wise similarities in the clustered similarity matrix (In such cases, densely connected nodes represent documents that are strongly similar to each other while weak or missing connections represent less similar or dissimilar documents [0040]);
and determining characteristics…based on the commonalities between the descriptions (the clustering algorithm employs statistical methods that are configured to analyze the similarity matrices to discover whether documents or features of the respective matrices belong to the same class or category (e.g., cluster) [0041]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Peredriy with the teachings of Nefedov to use individual descriptions of wellbores including one or more words and generate a clustered similarity matrix including groupings of pair-wise similarities between the descriptions to determine characteristics based on the commonalities between the descriptions to improve scalability and reduce the difficulty, time and bias associated with manually reviewing and matching different descriptions of wells.
Regarding claim 2, Peredriy in view of Nefedov teach the system of claim 1, and Peredriy further teaches wherein a given description of a given well includes interpretation of rock characteristics in the given well (The well log records can include variable values, which may be directly measured or derived from the measurements. Examples of the variables can include measured depth, porosity (FOR), water saturation (WS), volume of shale (VSG), permeability (Penn), gamma-ray corrected shale volume (VSGC), corrected gamma-ray (EGR), compensated neutron porosity (CNLC), borehole compensated sonic (BHC), etc. [00182]); (The computing system 1310 can automatically determine the characteristics (e.g., locations, tops, types, thicknesses, and geometries) of geological layers represented in multiple well-log records and correlate this information together, for example to generate multidimensional models of the geological layers [00191]).
Regarding claim 3, Peredriy in view of Nefedov teach the system of claim 2, and Peredriy further teaches wherein a given characteristic of the given well includes a rock type of the given well (Examples of the layer types can include various types of limestone, shale, sandstone, and composites [00170]).
Regarding claim 4, Peredriy in view of Nefedov teach the system of claim 3, and Peredriy further teaches wherein conversion of the given description of the given well into the rock type of the given well enables upscaling of a well core description of the given well into a higher level classification of the given well (The computing system 1310 can automatically determine the characteristics (e.g., locations, tops, types, thicknesses, and geometries) of geological layers represented in multiple well-log records and correlate this information together, for example to generate multidimensional models of the geological layers [00191] where the computing system 1310 can determine the probability-weighted center of each of the two adjacent geological layers [00193] and the computing system 1310 can estimate the depth of the top of layer B (below layer A) based on the probability-weighted centers of the geological layers [00194]).
Regarding claim 5, Peredriy in view of Nefedov teach the system of claim 2, and Peredriy further teaches wherein the interpretation of the rock characteristics in the given well is enhanced with context for the generation of the similarity matrix (The system can automatically correlate the geological layers to one another across two or more well log records. Specifically, the system can identify the same geological top in multiple well log records based on (i) similarities between the depth of the geological top in each well log record, (ii) the adjacent geological layers surrounding the geological top in each well log record, (iii) the types and other characteristics of the adjacent geological layers, or (iv) any combination of these. The system can map the geological top across the multiple well log records, which may provide more holistic and useful information about the geological top and corresponding layers than can be derived from any individual well-log record alone [0037] where each similarity value in the similarity matrix can represent a similarity between a pair of well log records [00184]; see also [0157] and [0135]).
Peredriy does not explicitly teach the generation of the similarity matrix.
Nefedov teaches the generation of the similarity matrix (Examples of documents' features may include single or multiple words, phrases [0005] where the similarity matrices may be constructed based on the features [0023] and similarity matrices, which characterize the pair-wise similarities among documents within the corpus of documents based on content and context, can be computed… [0036]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Peredriy with the teachings of Nefedov to enhance the interpretation of rock characteristics in the given well with contextual words for the generation of the similarity matrix in order to more accurately interpret rock characteristics as contextual words can provide additional insights into specific similarities and differences of the rock characteristics, and the generation of the similarity matrix improves scalability and reduces the difficulty, time and biases associated with manually reviewing and matching different descriptions of wells .
Regarding claim 6, Peredriy in view of Nefedov teach the system of claim 5, and Peredriy further teaches wherein the interpretation of the rock characteristics in the given well is enhanced with context (see [0157] and [0135]; also The system can automatically correlate the geological layers to one another across two or more well log records. Specifically, the system can identify the same geological top in multiple well log records based on (i) similarities between the depth of the geological top in each well log record, (ii) the adjacent geological layers surrounding the geological top in each well log record, (iii) the types and other characteristics of the adjacent geological layers, or (iv) any combination of these. The system can map the geological top across the multiple well log records, which may provide more holistic and useful information about the geological top and corresponding layers than can be derived from any individual well-log record alone [0037]).
Regarding claim 7, Peredriy in view of Nefedov teach the system of claim 1, and Peredriy further teaches wherein vectorized embeddings of the descriptions of the one or more wells in the subsurface region are generated to determine the similarity between the different pairs of the descriptions of the one or more wells in the subsurface region (In operation 1708, the computing system 1310 refines the similarity matrix (SM) based on the geographical distance matrix. For example, the computing system 1310 can set a similarity value in the similarity matrix to a predefined value when a certain condition is met. One example of such a condition may be that the geographical distance between the two wellbores corresponding to the similarity value exceeds a predefined threshold. The degree matrix may be computed based on the similarity matrix and the Laplacian matrix may be computed based on the degree matrix [00186-00188, Fig. 17], and in operation 1714, the computing system 1310 generates an eigenvector matrix (EM) where eigenvalues are sorted in numerical order and X eigenvectors are selected ...,u.sub.x of the Laplacian matrix corresponding to the A' smallest eigenvalues. The computing system 1310 can then generate the eigenvector matrix using the vectors [00189, Fig. 17], and in operation 1716, the computing system 1310 applies a clustering algorithm to the rows of the eigenvector matrix to generate the group of well clusters [00190, Fig. 17] where the computing system 1310 can implement operations 1406-1422 based on the well clusters. Using these techniques, the computing system 1310 can automatically determine the characteristics (e.g., locations, tops, types, thicknesses, and geometries) of geological layers represented in multiple well-log records and correlate this information together, for example to generate multidimensional models of the geological layers [00191]).
Regarding claim 8, Peredriy in view of Nefedov teach the system of claim 1, and Peredriy further teaches wherein the values of the similarity matrix are modified based on comparison to a threshold value before the clustering of the values of the similarity matrix (In operation 1708, the computing system 1310 refines the similarity matrix (SM) based on the geographical distance matrix. For example, the computing system 1310 can set a similarity value in the similarity matrix to a predefined value when a certain condition is met. One example of such a condition may be that the geographical distance between the two wellbores corresponding to the similarity value exceeds a predefined threshold [00186] and then in operation 1716, the computing system 1310 applies a clustering algorithm to the rows of the eigenvector matrix to generate the group of well clusters [00190, Fig. 17]).
Regarding claim 10, Peredriy in view of Nefedov teach the system of claim 1, and Peredriy further teaches wherein the clustering of the values of the similarity matrix includes spectral clustering of the values of the similarity matrix (To generate the group of well clusters, the computing system 1310 can execute an automated clustering process. In some examples, the automated clustering process can be a modified spectral clustering process [0163]).
Regarding claim 11, Peredriy in view of Nefedov teach the limitations of claim 11 based on the explanation provided for claim 1.
Regarding claim 12, Peredriy in view of Nefedov teach the method of claim 11, and Peredriy in view of Nefedov further teach the limitations of claim 12 based on the explanation provided for claim 2.
Regarding claim 13, Peredriy in view of Nefedov teach the method of claim 12, and Peredriy in view of Nefedov further teach the limitations of claim 13 based on the explanation provided for claim 3.
Regarding claim 14, Peredriy in view of Nefedov teach the method of claim 13, and Peredriy in view of Nefedov further teach the limitations of claim 14 based on the explanation provided for claim 4.
Regarding claim 15, Peredriy in view of Nefedov teach the method of claim 12, and Peredriy in view of Nefedov further teach the limitations of claim 15 based on the explanation provided for claim 5.
Regarding claim 16, Peredriy in view of Nefedov teach the method of claim 15, and Peredriy in view of Nefedov further teach the limitations of claim 16 based on the explanation provided for claim 6.
Regarding claim 17, Peredriy in view of Nefedov teach the method of claim 11, and Peredriy in view of Nefedov further teach the limitations of claim 17 based on the explanation provided for claim 7.
Regarding claim 18, Peredriy in view of Nefedov teach the method of claim 11, and Peredriy in view of Nefedov further teach the limitations of claim 18 based on the explanation provided for claim 8.
Regarding claim 20, Peredriy in view of Nefedov teach the method of claim 11, and Peredriy in view of Nefedov further teach the limitations of claim 20 based on the explanation provided for claim 10.
Claims 9 and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Peredriy (WO 2022216311 A1) in view of Nefedov (US 20230074771 A1) further in view of Whitaker (US 20150355353 A1).
Regarding claim 9, Peredriy in view of Nefedov teach the system of claim 8, and Peredriy further teaches the threshold value of claim 8, but does not explicitly teach wherein the threshold value is determined based on curvature of a cumulative distribution function of the values of the similarity matrix.
Whitaker teaches wherein the threshold value is determined based on curvature of a cumulative distribution function of the values of the similarity matrix (Because interesting regions are assumed to occur sparingly, they can be treated as outliers with regards to the statistical distribution of the patterns of the entire image [0061]); (In PCA, the distribution of a set of attributes, such as seismic descriptors, is examined. The attributes can be obtained as intensity patches in the given image, or as patches of derived attributes, or as a set of collocated attribute values. Because of the correlation between attributes, the distribution of the vectors of descriptors in the associated high-dimensional space tends be concentrated along a low-dimensional manifold [0062]);
(Another technique that can be used to detect the statistical outliers that indicate regions of interest is the statistical analysis of histogram descriptors. In this approach, multi-dimensional histograms are used to estimate the distribution of the seismic descriptors using a coarse binning strategy. Although this approach can only handle a few descriptors at a time, because of the difficulty in estimating the histogram descriptors, it can characterize much wider spatial areas than the PCA-based approach. Thus, the information in the large area is captured into a small number of elements in the histogram [0064]); (After capturing the information into the histograms, non-parametric hypothesis testing is performed to determine if a specific histogram is an outlier. This can be done by comparing the distribution of mass in the specific histogram to the mean and standard deviation of the mass distribution over all of the histograms. A large number of attributes may be used as the descriptors for this stage. The selection of these attributes controls the type of features detected [0065]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Peredriy with the teachings of Nefedov and Whitaker to determine the threshold value based on curvature of a cumulative distribution function of the values of the similarity matrix to more effectively separate meaningful signals or true clusters from background noise and avoid arbitrary, manual cutoffs by identifying the exact point where the underlying data distribution naturally breaks.
Regarding claim 19, Peredriy in view of Nefedov teach the method of claim 18, and Peredriy in view of Nefedov further in view of Whitaker further teach the limitations of claim 19 based on the explanation provided for claim 9.
Pertinent Prior Art
US 20160146973 A1: A method of processing geological data is provided for input to a geostatistical modelling algorithm to predict a value for a parameter relating to a physical property of the Earth. An input data set corresponding to a measured geological parameter is processed to determine a characteristic function of the input data with respect to a geological measure. The input data is transformed to reduce spatial bias with respect to the geological distance measure by applying an inverse function. A statistical weighting is calculated for the transformation and the transformation and weighting are used to predict a representative value of the physical property corresponding to the measured geological parameter. A data processing apparatus and computer program product are also provided.
US 20210342540 A1: Data characterizing a document including a target word and a plurality of potential meanings for the target word is received. A first set of context words is determined using a language model. The first set of context words is for the target word. A second set of context words is determined using a knowledge base and the language model. The second set of context words is for the plurality of potential meanings of the target word. A score is determined for each of the plurality of potential meanings by at least comparing the first set of context words and the second set of context words. A potential meaning selected from the plurality of potential meanings that has a highest score is selected as a disambiguation of the first word.
US 20190034407 A1: A multilingual named-entity recognition system according to an embodiment includes an acquisition unit configured to acquire an annotated sample of a source language and a sample of a target language, a first generation unit configured to generate an annotated named-entity recognition model of the source language by applying Conditional Random Field sequence labeling to the annotated sample of the source language and obtaining an optimum weight for each annotated named entity of the source language, a calculation unit configured to calculate similarity between the annotated sample of the source language and the sample of the target language, and a second generation unit configured to generate a named-entity recognition model of the target language based on the annotated named-entity recognition model of the source language and the similarity.
Conclusion
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/LOGAN D COONS/Examiner, Art Unit 2857
/SHELBY A TURNER/Supervisory Patent Examiner, Art Unit 2857
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