Office Action 1: Non-Final Rejection (2024-01-11)
Rejection types: 102 - Lack of Novelty

CLAIMS:
WHAT IS CLAIMED IS:
1\. An image analysis method, comprising:
inputting a target image to an image analysis model composed of at least one expert generated by learning a training image;
analyzing the target image based on an output value from the at least one expert;
determining validity of an analysis result for the target image based on a probability of an input target image to belong to an input category of the at least one expert; and
when it is determined that the analysis result for the target image is not valid, classifying the target image into a new category.
2\. The method of claim 1, wherein the determining of the validity of the analysis result for the target image comprises randomly determining the validity of the analysis result for the target image based on a probability of the input target image to belong to the input category of the at least one expert.
3\. The method of claim 1, wherein the determining of the validity of the analysis result for the target image comprises:
obtaining a likelihood of an input and a class with respect to each of the at least one expert;
obtaining a prior probability of each of the at least one expert; and
obtaining a probability of the input target image to belong to the input category of the at least one expert based on the obtained likelihood and the obtained prior probability.
4\. The method of claim 3, wherein the obtaining the likelihood of an input and a class with respect to each of the at least one expert comprises:
obtaining a first probability of a class with respect to each of the input target image and the at least one expert by using a discriminative model of each of the at least one expert;
obtaining a second probability of an input with respect to each of the at least one expert by using a generative model of each of the at least one expert; and
obtaining the likelihood of an input and a class with respect to each of the at least one expert based on the first probability and the second probability.
5\. The method of claim 4, wherein the obtaining the second probability comprises estimating a lower bound of the second probability as the second probability.
6\. The method of claim 1, further comprising:
storing the target image classified into the new category in a memory; and
when an accumulated capacity of target images stored in the memory is greater than or equal to a predetermined reference capacity, generating an expert for the new category by learning the target images stored in the memory.
7\. The method of claim 6, wherein the generating of the expert for the new category comprises:
labeling the target images stored in the memory; and
generating the expert for the new category by learning the labeled target images.
8\. An image analysis apparatus, comprising:
an image analysis unit configured to analyze a target image based on an image analysis model composed of at least one expert generated by learning training images; and a determination unit configured to determine validity of an analysis result for the target image based on a probability of the target image to belong to an input category of the at least one expert, wherein the image analysis unit classifies the target image into a new category when the analysis result for the target image is determined to be invalid.
9\. The apparatus of claim 8, further comprising:
a memory configured to store the target image classified into the new category.

10\. The apparatus of claim 9, further comprising:
a learning unit configured to, when an accumulated capacity of target images stored in the memory is greater than or equal to a predetermined reference capacity, generate an expert for the new category by learning the target images stored in the memory.

11\. The apparatus of claim 10, wherein the learning unit is further configured to:
labeling the target images stored in the memory, and
generating the expert for the new category by learning the labeled target images.

12\. The apparatus of claim 8, wherein the determination unit is further configured to randomly determine the validity of the analysis result for the target image based on a probability of the input target image to belong to the input category of the at least one expert.

13\. The apparatus of claim 8, wherein the determination unit is further configured to:
obtain a likelihood of an input and a class with respect to each of the at least one expert, obtain a prior probability of each of the at least one expert, and
obtain a probability of the input target image to belong to the input category of the at least one expert based on the obtained likelihood and the obtained prior probability.

14\. The apparatus of claim 13, wherein the determination unit is further configured to:
obtain a first probability of a class with respect to each of the input target image and the at least one expert by using a discriminative model of each of the at least one expert, obtain a second probability of an input with respect to each of the at least one expert by using a generative model of each of the at least one expert, and
obtain the likelihood of an input and a class with respect to each of the at least one expert based on the first probability and the second probability.

15\. The apparatus of claim 14, wherein the determination unit is further configured to estimate a lower bound of the second probability as the second probability.

16\. A method for generating an image analysis model, the method comprising:
continually receiving first training images during a first receiving period;
generating a first expert composed of a discriminative model and a generative model for a first category by learning the first training images during a first learning period right after the first receiving period;
continually receiving second training images during a second receiving period right after the first learning period;
determining whether to learn each of the second training images for the first expert based on a probability of each of the second training images to belong to the first category; and generating a second expert for a second category by learning, during a second learning period right after the second receiving period, a training image determined not to be learned for the first expert among the second training images. -29- -30- -31- -32- -33 .)-

OFFICE ACTION:
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 .
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 09/03/2021 is in compliance with the provisions of 37 CFR 1.97.  Accordingly, the information disclosure statement is being considered by the examiner.
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.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA  35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier.  Such claim limitations are: “image analysis unit”; “determination unit”, “learning unit”, in claims 8-15.
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA  35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA  35 U.S.C. 112, sixth paragraph, applicant may:  (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA  35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA  35 U.S.C. 112, sixth paragraph.
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 (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 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)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1-3, 8-9 and 12-13 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Baughman et al (“Baughman” hereinafter, U.S. Publication No. 2015/0332157 A1).
As per claim 1 Baughman discloses an image analysis method (figure 1, paragraph [0011]: the input dataset include images of historical information, scientific data and other real-time data), comprising: inputting a target image to an image analysis model composed of at least one expert generated by learning a training image (the input data 130 is putted into a sequence of multiple models, which serve as a “mixture of experts”, paragraph [0027]); analyzing the target image based on an output value from the at least one expert (paragraphs [0027] & [0070]: the input data such as an image that is analyzed through at least one expert model based on the numerical value outputted from a model of a particular category); determining validity of an analysis result for the target image based on a probability of an input target image to belong to an input category of the at least one expert (paragraph [0012] & [0027]: “all the features directed to a model are combined to result in a probability aligning with the particular subject determination of the model. Each model within a route of multiple models is a probability density function. The trained models determine and apply confidence values to the plurality of candidate hypotheses or topics from the features of the proximity matrix, and the confidence values or probabilities of the candidate hypotheses can be reported. A report can be produced that includes the top “N” candidate hypotheses, based on the determined probabilities and/or confidence values of the hypotheses”, the validity of the target image analysis based on whether the candidate hypotheses with high confidence value or probability); and when it is determined that the analysis result for the target image is not valid, classifying the target image into a new category (the hypotheses that are not “top candidates” may be considered as “candidates” for another model of another category/topic, paragraph [0033]: “For each type of natural resource, a support vector machine contains an ensemble technique of deep learning and logistic regression, in effect defining a route of feature vectors towards the appropriate models and identifying the best supporting feature vectors, and separating the features to be classified. Ensemble techniques use a sequence of multiple models or “mixed experts”, to obtain better predictive inference performance, predicting future observations based on historical observations and real-time data, producing better results in combination than could be obtained from any of the constituent models alone”, which implies different input data with different categorical values are to be analyzed with different models).
As per claim 2, Baughman discloses wherein the determining of the validity of the analysis result for the target image comprises randomly determining the validity of the analysis result for the target image based on a probability of the input target image to belong to the input category of the at least one expert (paragraph [0030]: “The prior probabilities decorate the feature vectors of each type of input data, and are meant to attribute uncertainty”).
As per claim 3, Baughman discloses wherein the determining of the validity of the analysis result for the target image comprises: obtaining a likelihood of an input and a class with respect to each of the at least one expert; obtaining a prior probability of each of the at least one expert; and obtaining a probability of the input target image to belong to the input category of the at least one expert based on the obtained likelihood and the obtained prior probability (paragraphs [0027]-[0030]).
As per claim 8, see explanation in claim 1, the examiner notes Baughman’s system is a computer-like system, which inherently includes a combination of hardware and software for the claimed “image analysis unit” and “determination unit”.
As per claim 9, see explanation in claim 1.
As per claim 12, see explanation in claim 2.
As per claim 13, see explanation in claim 3.
Allowable Subject Matter
Claims 4-7, 10-11, 14-15 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
Claim 16 is allowed.
Conclusion


NEXT OFFICE ACTION: 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 .
Response to Amendment
The amendment and written response filed 03/21/2024 have been entered and considered.
Claims 1-3 and 6-15 have been amended.
Claims 1-16 are pending.
Response to Arguments
Applicant’s arguments with respect to independent claims 1 and 8 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Upon entry of the claim amendment, the rejection of claims 8-15 under 35 U.S.C. 112(f) has been withdrawn.
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 (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 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)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1-2 and 6-12 are are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Bhatt et al (“Bhatt” hereinafter, U.S. Publication No. 2019/0220967A1).
As per claim 1, Bhatt discloses an image analysis method (abstract: ) comprising: receiving a target image for processing by an image analysis model (paragraph [0041]: step 302: “receive … a plurality of images”) composed of at least one expert generated by learning a training image (paragraph [0044]: step 306: “forward passing the received image through a neural network model pre-trained on a database of a plurality of images pertaining to a varied contexts”); analyzing the target image based on an output value from the at least one expert to generate an analysis result (paragraph [0053]: “the pre-trained tagger models 130a facilitate classifying of the one or more classes identified in the first level of classification into the one or more sub-classes at step 312b”); determining validity of the analysis result for the target image based on a probability of an input target image belonging to an input category of the at least one expert (paragraph [0053]: “a confidence level may be computed for the classified one or more sub-classes, at step 312c, based on which the pre-trained tagger models 130a may be re-trained”); and responsive to determining that the analysis result for the target image is not valid (paragraph [0054]: “the classification module 130f may be further configured to evaluate the received images associated with the confidence level below the pre-defined threshold with the obtained metadata prior to re-training the pre-trained tagger models 130a at step 312d to ensure a valid reason for re-training based on expert tagging”), classifying the target image into a new category so that a new expert for the new category is trained using the target image (paragraph [0049]: “The step 308 of clustering may also flag a new class in the absence of similarity between the extracted features of the received images and the features associated with the one or more classes identified at step 308b-1”; paragraph [0053]: “The re-trained tagger models represent the classification models 130d. The re-training and associated self-learning makes the system 200 intelligent and adaptable to computer vision requirements with minimal human intervention that occurs only in the initial iterations”; paragraph [0116]: “as the cluster wise classifiers are independent of each other, if a new class has to be introduced, only a classifier of the images belonging to sub-classes of that cluster needs to be trained, instead of re-training the whole classification system”).
As per claim 2, Bhatt discloses wherein the determining of the validity of the analysis result for the target image comprises randomly determining the validity of the analysis result for the target image based on a probability of the input image belonging to the input category of the at least one expert (paragraph [0054]: “the classification module 130f may be further configured to evaluate the received images associated with the confidence level below the pre-defined threshold with the obtained metadata prior to re-training the pre-trained tagger models 130a at step 312d to ensure a valid reason for re-training based on expert tagging”).
As per claim 6, Bhatt discloses storing the target image classified into the new category in a memory; and wherein an accumulated capacity of image including the target image and stored in the memory is greater than or equal to a predetermined reference capacity, generating the new expert for the new category by performing training using the image stored in the memory (as explained above, the received images are stored in a memory and the confidence level of the images are below a threshold are used to retrain a classifier as a new class. The examiner notes the claimed “predetermined reference capacity” is inherently to be more than 1 image).
As per claim 7, Bhatt discloses wherein the generating of the expert for the new category comprises: labeling the image stored in the memory; and generating the new expert for the new category by training the new expert using the labeled images (paragraph [0053]: “The re-trained tagger models represent the classification models 130d. The re-training and associated self-learning makes the system 200 intelligent and adaptable to computer vision requirements with minimal human intervention that occurs only in the initial iterations”. Although it is minimal human intervention, some of the images are trained through human expert labeling).
As per claim 8, see explanation in claim 1. Bhatt’s system is a computer system, which inherently includes a processor and a memory.
As per claim 9, see explanation in claim 2.
As per claim 10, see explanation in claim 2.
As per claim 11, see explanation in claim 7.
As per claim 12, Bhatt teaches the received images are randomly subject to image analysis classification, and the images with confidence level below a threshold are used to retrain a classifier.
Allowable Subject Matter
Claims 3-5 and 13-15 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
Claim 16 is allowed.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action.  Accordingly, THIS ACTION IS MADE FINAL.  See MPEP § 706.07(a).  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 extension fee 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 date of this final action.

CITED REFERENCE SUMMARIES: Ref US20150332157A1: Claims:
A method for locating a natural resource, the method comprising: generating, by a computer processor, a topic-based dataset based on parsing content received from a plurality of information sources, wherein the plurality of information sources includes historical data and scientific data, both of which are associated with a location of a natural resource; generating, by the computer processor, a plurality of clusters respectively corresponding to like-topic data of the topic-based dataset from the plurality of information sources; determining, by the computer processor, a plurality of hypotheses, respectively corresponding to the plurality of clusters of the like-topic data, wherein the plurality of hypotheses are based on one or more features extracted from the plurality of clusters of the like-topic data; combining, by the computer processor, one or more pairs of clusters of the plurality of clusters based, at least in part, on a similarity heuristic applied to the one or more pairs of clusters; and determining, by the computer processor, a plurality of probabilities respectively corresponding to a validity of each hypothesis of the plurality of hypotheses associated with the location of a natural resource.
generating, by a computer processor, a topic-based dataset based on parsing content received from a plurality of information sources, wherein the plurality of information sources includes historical data and scientific data, both of which are associated with a location of a natural resource;
generating, by the computer processor, a plurality of clusters respectively corresponding to like-topic data of the topic-based dataset from the plurality of information sources;
determining, by the computer processor, a plurality of hypotheses, respectively corresponding to the plurality of clusters of the like-topic data, wherein the plurality of hypotheses are based on one or more features extracted from the plurality of clusters of the like-topic data;
combining, by the computer processor, one or more pairs of clusters of the plurality of clusters based, at least in part, on a similarity heuristic applied to the one or more pairs of clusters; and
determining, by the computer processor, a plurality of probabilities respectively corresponding to a validity of each hypothesis of the plurality of hypotheses associated with the location of a natural resource.
The method of claim 1 , wherein the topic-based dataset generated from the plurality of information sources includes multimedia data.
The method of claim 1 , wherein the topic-based dataset generated from the plurality of information sources further includes substantially real-time data.
The method of claim 1 , further comprising: transforming, by the computer processor, the topic-based dataset into a summarized format output, based, at least in part, on processing by one or more analytic engines.
transforming, by the computer processor, the topic-based dataset into a summarized format output, based, at least in part, on processing by one or more analytic engines.
The method of claim 1 , wherein the topic-based dataset is weighted based on respective beta distributions of the topic-based data.
The method of claim 1 , further comprising: combining, by the computer processor, hypotheses corresponding to the one or more pairs of clusters that are combined.
combining, by the computer processor, hypotheses corresponding to the one or more pairs of clusters that are combined.
The method of claim 1 , further comprising: generating, by the computer processor, one or more feature vectors from the one or more features of the plurality of clusters; generating, by the computer processor, a plurality of cluster spaces, which include the one or more pairs of clusters of the plurality of clusters that are combined, wherein each cluster space is based on a disparate threshold of similarity; determining, by the computer processor, a cluster space of the plurality of cluster spaces that is favorable, based on a score of the cluster space and the disparate threshold of similarity; and generating, by the computer processor, one or more proximity matrices from the one or more features vectors, based on the cluster space that is favorable.
generating, by the computer processor, one or more feature vectors from the one or more features of the plurality of clusters;
generating, by the computer processor, a plurality of cluster spaces, which include the one or more pairs of clusters of the plurality of clusters that are combined, wherein each cluster space is based on a disparate threshold of similarity;
determining, by the computer processor, a cluster space of the plurality of cluster spaces that is favorable, based on a score of the cluster space and the disparate threshold of similarity; and
generating, by the computer processor, one or more proximity matrices from the one or more features vectors, based on the cluster space that is favorable.
The method of claim 7 , wherein determining the cluster space that is favorable further comprises: determining, by the computer processor, a limit of combining clusters of the plurality of clusters based on the disparate threshold of similarity which produces the score of the cluster space that is favorable.
determining, by the computer processor, a limit of combining clusters of the plurality of clusters based on the disparate threshold of similarity which produces the score of the cluster space that is favorable.
The method of claim 7 , wherein generating one or more proximity matrices from the one or more feature vectors, further comprises: generating, by the computer processor, the one or more proximity matrices, based on a probability density function of the one or more features of the plurality of clusters that are combined.
generating, by the computer processor, the one or more proximity matrices, based on a probability density function of the one or more features of the plurality of clusters that are combined.
The method of claim 7 , further comprising: generating, by the computer processor, a sequence of models associated with the location of the natural resource, based, at least in part, on the one or more proximity matrices.
generating, by the computer processor, a sequence of models associated with the location of the natural resource, based, at least in part, on the one or more proximity matrices.
The method of claim 10 , wherein generating a sequence of models associated with location of the natural resource, further comprises: training, by the computer processor, the sequence of models based on clustering and successive refinement of supervised learning, wherein the sequence of models is based, at least in part, on the one or more proximity matrices.
training, by the computer processor, the sequence of models based on clustering and successive refinement of supervised learning, wherein the sequence of models is based, at least in part, on the one or more proximity matrices.
The method of claim 10 , wherein an output of a first model of the sequence of models is used as input to a second model of the mixture of models.
The method of claim 12 , further comprising: determining, by the computer processor, a route through the sequence of models for each hypothesis of the plurality of hypotheses, based on dimensions of evidence, wherein the dimensions of evidence include groupings of the one or more features.
determining, by the computer processor, a route through the sequence of models for each hypothesis of the plurality of hypotheses, based on dimensions of evidence, wherein the dimensions of evidence include groupings of the one or more features.
The method of claim 1 , further comprising: representing, by the computer processor, the plurality of probabilities respectively corresponding to the validity of each hypothesis of the plurality of hypotheses as a heat map, wherein a first element of the heat map corresponds to a first probability of a hypothesis of the plurality of hypotheses, and disparate from a second element of the heat map corresponding to a second probability of a hypothesis of the plurality of hypotheses.
representing, by the computer processor, the plurality of probabilities respectively corresponding to the validity of each hypothesis of the plurality of hypotheses as a heat map, wherein a first element of the heat map corresponds to a first probability of a hypothesis of the plurality of hypotheses, and disparate from a second element of the heat map corresponding to a second probability of a hypothesis of the plurality of hypotheses.
A computer program product for locating a natural resource, the computer program product comprising: a computer readable storage medium having program instructions embodied therewith, the program instructions executable by a computer processor to cause the computer processor to perform a method comprising: generating a topic-based dataset based on parsing content received from a plurality of information sources, wherein the plurality of information sources includes historical data and scientific data, both of which are associated with a location of a natural resource; generating a plurality of clusters respectively corresponding to like-topic data of the topic-based dataset from the plurality of information sources; determining a plurality of hypotheses, respectively corresponding to the plurality of clusters of the like-topic data, wherein the plurality of hypotheses are based on one or more features extracted from the plurality of clusters of the like-topic data; combining one or more pairs of clusters of the plurality of clusters based, at least in part, on a similarity heuristic applied to the one or more pairs of clusters; and determining a plurality of probabilities respectively corresponding to a validity of each hypothesis of the plurality of hypotheses, associated with the location of a natural resource.
a computer readable storage medium having program instructions embodied therewith, the program instructions executable by a computer processor to cause the computer processor to perform a method comprising:
generating a topic-based dataset based on parsing content received from a plurality of information sources, wherein the plurality of information sources includes historical data and scientific data, both of which are associated with a location of a natural resource;
generating a plurality of clusters respectively corresponding to like-topic data of the topic-based dataset from the plurality of information sources;
determining a plurality of hypotheses, respectively corresponding to the plurality of clusters of the like-topic data, wherein the plurality of hypotheses are based on one or more features extracted from the plurality of clusters of the like-topic data;
combining one or more pairs of clusters of the plurality of clusters based, at least in part, on a similarity heuristic applied to the one or more pairs of clusters; and
determining a plurality of probabilities respectively corresponding to a validity of each hypothesis of the plurality of hypotheses, associated with the location of a natural resource.
The computer program product of claim 15 , further comprising: generating one or more feature vectors from the one or more features of the plurality of clusters; generating a plurality of cluster spaces, which include the one or more pairs of clusters of the plurality of clusters that are combined, wherein each cluster space is based on a disparate threshold of similarity; determining a cluster space that is favorable, based on a score of a cluster space of the plurality of cluster spaces and the disparate threshold of similarity; and generating one or more proximity matrices from the one or more features vectors, based on the cluster space that is favorable.
generating one or more feature vectors from the one or more features of the plurality of clusters;
generating a plurality of cluster spaces, which include the one or more pairs of clusters of the plurality of clusters that are combined, wherein each cluster space is based on a disparate threshold of similarity;
determining a cluster space that is favorable, based on a score of a cluster space of the plurality of cluster spaces and the disparate threshold of similarity; and
generating one or more proximity matrices from the one or more features vectors, based on the cluster space that is favorable.
The computer program product of claim 16 , further comprising: generating an sequence of models associated with the location of the natural resource, based, at least in part, on the one or more proximity matrices.
generating an sequence of models associated with the location of the natural resource, based, at least in part, on the one or more proximity matrices.
The computer program product of claim 17 , wherein generating a sequence of models associated with the location of the natural resource, further comprises: training a sequence of models, based on clustering and successive refinement of supervised learning, wherein the sequence of models is based, at least in part, on the one or more proximity matrices.
training a sequence of models, based on clustering and successive refinement of supervised learning, wherein the sequence of models is based, at least in part, on the one or more proximity matrices.
A computer system for locating a natural resource, the computer system comprising: one or more computer processors; one or more computer readable storage media; program instructions stored on the computer readable storage media for execution by at least one of the one or more processors, the program instructions comprising: program instructions to generate a topic-based dataset based on parsing content received from a plurality of information sources, wherein the plurality of information sources includes historical data and scientific data, both of which are associated with a location of a natural resource; program instructions to generate a plurality of clusters respectively corresponding to like-topic data of the topic-based dataset from the plurality of information sources; program instructions to determine a plurality of hypotheses, respectively corresponding to the plurality of clusters of the like-topic data, wherein the plurality of hypotheses are based on one or more features extracted from the plurality of clusters of the like-topic data; program instructions to combine one or more pairs of clusters of the plurality of clusters based, at least in part, on a similarity heuristic applied to the one or more pairs of clusters; and program instructions to determine a plurality of probabilities respectively corresponding to a validity of each hypothesis of the plurality of hypotheses, associated with the location of a natural resource.
one or more computer processors;
one or more computer readable storage media;
program instructions stored on the computer readable storage media for execution by at least one of the one or more processors, the program instructions comprising:
program instructions to generate a topic-based dataset based on parsing content received from a plurality of information sources, wherein the plurality of information sources includes historical data and scientific data, both of which are associated with a location of a natural resource;
program instructions to generate a plurality of clusters respectively corresponding to like-topic data of the topic-based dataset from the plurality of information sources;
program instructions to determine a plurality of hypotheses, respectively corresponding to the plurality of clusters of the like-topic data, wherein the plurality of hypotheses are based on one or more features extracted from the plurality of clusters of the like-topic data;
program instructions to combine one or more pairs of clusters of the plurality of clusters based, at least in part, on a similarity heuristic applied to the one or more pairs of clusters; and
program instructions to determine a plurality of probabilities respectively corresponding to a validity of each hypothesis of the plurality of hypotheses, associated with the location of a natural resource.
The computer system of claim 19 , wherein the topic-based dataset is weighted based on respective beta distributions of the topic-based data.
Ref US20160162492A1: Claims:
A method, in a data processing system comprising a processor and a memory configured to implement a question and answer system (QA), for providing confidence rankings based on temporal semantics, the method comprising: responsive to receiving an input question, identifying a set of candidate answers from a knowledge domain based on a correlation between an identified one or more predicates and an identified one or more arguments to the knowledge domain; associating a confidence score with each of the candidate answers; refining each confidence score associated with each candidate answer based on a set of temporal characteristics identified in the input question, wherein each confidence score is refined based on the set of temporal characteristics using a temporal confidence function computed against a temporal interval between a reference time of the question and a respective reference time associated with the set of candidate answers; and providing a set of temporally refined candidate answers to the user.
responsive to receiving an input question, identifying a set of candidate answers from a knowledge domain based on a correlation between an identified one or more predicates and an identified one or more arguments to the knowledge domain;
associating a confidence score with each of the candidate answers;
refining each confidence score associated with each candidate answer based on a set of temporal characteristics identified in the input question, wherein each confidence score is refined based on the set of temporal characteristics using a temporal confidence function computed against a temporal interval between a reference time of the question and a respective reference time associated with the set of candidate answers; and
providing a set of temporally refined candidate answers to the user.
The method of claim 1 , wherein the reference time of the question is identified from the set of temporal characteristics.
The method of claim 1 , wherein in the reference time is a current time if no temporal characteristics are identifiable in the input question.
The method of claim 1 , wherein the temporal confidence function is a linear decay function where the confidence of the candidate answer fades smoothly over time.
The method of claim 1 , wherein the temporal confidence function is a step decay function where the confidence of the candidate answer decays slowly over a predetermined time period.
The method of claim 1 , wherein the temporal confidence function is a variable decay function based on the knowledge domain.
The method of claim 1 , wherein the temporal confidence function is populated via at least one of hand-coding or inferred by machine learning techniques.
- 20 . (canceled)
Ref US20190258647A1: Claims:
A method for predicting a location of a natural resource, the method comprising: generating, by a computer processor, a topic-based dataset by filtering out non-topic related content received from a plurality of information sources based on N-gram techniques applied to the received content associated with known locations of a natural resource; generating, by the computer processor, a plurality of clusters respectively including like-topic data of the received content, wherein features corresponding to characteristics of respective topics of the received content are extracted respectively; storing, by the one or more processors, the features as feature vectors in a proximity matrix that includes applying a threshold of aggregation and separation of the clusters as determined by unsupervised learning, wherein a feature vector
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