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 .
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
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Claims 1 – 20 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims of U.S. Patent No. 12,001,931. Although the claims at issue are not identical, they are not patentably distinct from each other because:
Claim 1 of 18/645,758
Claim 1 of 12,001,931
A method for automatically optimizing hyper parameter and feature selection in a machine learning system, the method comprising:
A method for automatically optimizing hyper parameter and feature selection in a machine learning system, the method comprising:
identifying a training data source comprising a plurality of records, wherein each record of the plurality of records comprises data corresponding to a plurality of feature
identifying a training data source comprising a plurality of records, wherein each record of the plurality of records comprises data corresponding to a plurality of features;
initializing a first cycle of the machine learning system by generating a first generation of candidate models, wherein generating each respective first candidate model of the first generation of candidate models comprises selecting a first subset of features, of the plurality of features, for use in the first candidate model;
initializing a first cycle of the machine learning system by generating a first generation of candidate models, wherein generating each respective first candidate model of the first generation of candidate models comprises selecting a first subset of features, of the plurality of features, for use in the first candidate model;
determining, for each first candidate model of the first generation of candidate models, a respective first optimal boosting iterations hyper parameter;
determining, for each first candidate model of the first generation of candidate models, a respective first optimal boosting iterations hyper parameter;
evaluating fitness values for each respective first candidate model in the first generation based on a corresponding subset of features and a corresponding optimal boosting iterations hyperparameter;
evaluating fitness values for each respective first candidate model in the first generation based on a corresponding subset of features and a corresponding optimal boosting iterations hyper parameter;
performing one or more cycles of the machine learning system by iteratively evaluating a plurality of generations of the machine learning system;
performing one or more cycles of the machine learning system by iteratively evaluating a plurality of generations of the machine learning system, wherein evaluating a respective generation of the machine learning system comprises:
generating a second generation of candidate models by performing an evolution process on a respective subset of features associated with selected models of a current generation of candidate models to generate respective second candidate models;
determining, for each second candidate model of the second generation of candidate models, a respective second optimal boosting iterations hyper parameter;
evaluating fitness values for each respective second candidate model in the second generation based on a corresponding subset of features and a corresponding optimal boosting iterations hyper parameter;
subsequent to iteratively evaluating the plurality of generations, determining a selected candidate model of a final generation of candidate models associated with the one or more cycles;
subsequent to iteratively evaluating the plurality of generations, determining a selected candidate model of a final generation of candidate models associated with the one or more cycles;
determining that the selected candidate model breaches a solution constraint;
determining that the selected candidate model breaches a solution constraint, wherein the solution constraint defines a threshold number of selected features and a target optimal boosting iterations range, and the breach is determined when evaluation of the selected candidate model produces a solution that does not satisfy the solution constraint;
adjusting a learning rate of the machine learning system and a subset of features in response to determining that the selected candidate model breaches the solution constraint;
adjusting a learning rate of the machine learning system and a subset of features in response to determining that the selected candidate model breaches the solution constraint;
performing one or more additional cycles of the machine learning system employing the
adjusted learning rate and the adjusted subset of features;
performing one or more additional cycles of the machine learning system employing the adjusted learning rate and the adjusted subset of features;
based on termination criteria, identifying a resulting candidate model of a final cycle of
the machine learning system as an optimized model.
based on termination criteria, identifying a resulting candidate model of a final cycle of the machine learning system as an optimized model.
Claim 2 of 18/645,758
Claim 1 of 12,001,931
The method of claim 1, comprising performing at least one second cycle of the machine learning system, wherein a first generation of the at least one second cycle is generated based on a final generation associated with the first cycle, wherein the resulting candidate model is an output of the at least one second cycle.
The method of claim 1, comprising: performing at least one second cycle of the machine learning system, wherein a first generation of the at least one second cycle is generated based on a final generation associated with the first cycle, wherein the resulting candidate model is an output of the at least one second cycle.
Claim 3 of 18/645,758
Claim 3 of 12,001,931
The method of claim 1, wherein the termination criteria comprises a predetermined number of cycles of the machine learning system.
The method of claim 1, wherein the termination criteria comprises a predetermined number of cycles of the machine learning system.
Claim 4 of 18/645,758
Claim 4 of 12,001,931
The method of claim 1, wherein the termination criteria comprises a threshold fitness value for the resulting candidate model.
The method of claim 1, wherein the termination criteria comprises a threshold fitness value for the resulting candidate model.
Claim 5 of 18/645,758
Claim 5 of 12,001,931
The method of claim 1, wherein generating each respective first candidate model of the first generation of candidate models comprises selecting the first subset of features randomly and based on a maximum allowed features constraint.
The method of claim 1, wherein generating each respective first candidate model of the first generation of candidate models comprises: selecting the first subset of features randomly and based on a maximum allowed features constraint.
Claim 6 of 18/645,758
Claim 6 of 12,001,931
The method of claim 1, wherein determining the first optimal boosting iterations hyper parameter for the first candidate model is based on a maximum number of boosting iterations.
The method of claim 1, wherein determining the first optimal boosting iterations hyper parameter for the first candidate model is based on a maximum number of boosting iterations.
Claim 7 of 18/645,758
Claim 7 of 12,001,931
The method of claim 6, wherein determining the first optimal boosting iterations hyper parameter for the first candidate model comprises: for each number of boosting iterations between an initial number of boosting iterations and the maximum number of boosting iterations, evaluating fitness of the first candidate model using the number of boosting iterations; and selecting the number of boosting iterations that provides a highest fitness for the first candidate model as the first optimal boosting iterations hyper parameter.
The method of claim 6, wherein determining the first optimal boosting iterations hyper parameter for the first candidate model comprises: for each number of boosting iterations between an initial number of boosting iterations and the maximum number of boosting iterations, evaluating fitness of the first candidate model using the number of boosting iterations; and selecting the number of boosting iterations that provides a highest fitness for the first candidate model as the first optimal boosting iterations hyper parameter.
Claim 8 of 18/645,758
Claim 8 of 12,001,931
The method of claim 1, wherein the evolution process employs a crossover function configured to repair candidate solutions that exceed a maximum number of allowed features.
The method of claim 1, wherein the evolution process employs a crossover function configured to repair candidate solutions that exceed a maximum number of allowed features.
Claim 9 of 18/645,758
Claim 9 of 12,001,931
The method of claim 1, wherein the evolution process employs a mutation function configured to reduce a number of features selected in a given candidate model.
The method of claim 1, wherein the evolution process employs a mutation function configured to reduce a number of features selected in a given candidate model.
Claim 10 of 18/645,758
Claim 1 of 12,001,931
The method of claim 1, wherein evaluating a respective generation of the machine
learning system comprises:
A method for automatically optimizing hyper parameter and feature selection in a machine learning system, the method comprising:
identifying a training data source comprising a plurality of records, wherein each record of the plurality of records comprises data corresponding to a plurality of features;
initializing a first cycle of the machine learning system by generating a first generation of candidate models, wherein generating each respective first candidate model of the first generation of candidate models comprises selecting a first subset of features, of the plurality of features, for use in the first candidate model;
determining, for each first candidate model of the first generation of candidate models, a respective first optimal boosting iterations hyper parameter;
evaluating fitness values for each respective first candidate model in the first generation based on a corresponding subset of features and a corresponding optimal boosting iterations hyper parameter;
performing one or more cycles of the machine learning system by iteratively evaluating a plurality of generations of the machine learning system, wherein evaluating a respective generation of the machine learning system comprises:
generating a second generation of candidate models by performing an evolution process
on a respective subset of features associated with selected models of a current generation of
candidate models to generate respective second candidate models;
generating a second generation of candidate models by performing an evolution process on a respective subset of features associated with selected models of a current generation of candidate models to generate respective second candidate models;
determining, for each second candidate model of the second generation of candidate
models, a respective second optimal boosting iterations hyper parameter;
determining, for each second candidate model of the second generation of candidate models, a respective second optimal boosting iterations hyper parameter;
evaluating fitness values for each respective second candidate model in the second
generation based on a corresponding subset of features and a corresponding optimal boosting
iterations hyper parameter.
evaluating fitness values for each respective second candidate model in the second generation based on a corresponding subset of features and a corresponding optimal boosting iterations hyper parameter;
subsequent to iteratively evaluating the plurality of generations, determining a selected candidate model of a final generation of candidate models associated with the one or more cycles;
determining that the selected candidate model breaches a solution constraint, wherein the solution constraint defines a threshold number of selected features and a target optimal boosting iterations range, and the breach is determined when evaluation of the selected candidate model produces a solution that does not satisfy the solution constraint;
adjusting a learning rate of the machine learning system and a subset of features in response to determining that the selected candidate model breaches the solution constraint;
performing one or more additional cycles of the machine learning system employing the adjusted learning rate and the adjusted subset of features;
based on termination criteria, identifying a resulting candidate model of a final cycle of the machine learning system as an optimized model.
Claim 11 of 18/645,758
Claim 10 of 12,001,931
A machine learning system, comprising:
one or more processors; and memory storing instructions that, when executed by the one or more processors, cause the machine learning system to optimize hyper parameter and feature selection by causing the machine learning system to:
A machine learning system, comprising: one or more processors; and memory storing instructions that, when executed by the one or more processors, cause the machine learning system to optimize hyper parameter and feature selection by causing the machine learning system to:
identify a training data source comprising a plurality of records, wherein each record of the plurality of records comprises data corresponding to a plurality of features;
identify a training data source comprising a plurality of records, wherein each record of the plurality of records comprises data corresponding to a plurality of features;
initialize a first cycle by generating a first generation of candidate models, wherein generating each respective first candidate model of the first generation of candidate models comprises selecting a first subset of features, of the plurality of features, for use in the first candidate model;
initialize a first cycle by generating a first generation of candidate models, wherein generating each respective first candidate model of the first generation of candidate models comprises selecting a first subset of features, of the plurality of features, for use in the first candidate model;
determine, for each first candidate model of the first generation of candidate models, a respective first optimal boosting iterations hyper parameter;
determine, for each first candidate model of the first generation of candidate models, a respective first optimal boosting iterations hyper parameter;
evaluate fitness values for each respective first candidate model in the first generation based on a corresponding subset of features and a corresponding optimal boosting iterations hyper parameter;
evaluate fitness values for each respective first candidate model in the first generation based on a corresponding subset of features and a corresponding optimal boosting iterations hyper parameter;
perform the first cycle by iteratively evaluating a plurality of generations of the machine learning system;
perform the first cycle by iteratively evaluating a plurality of generations of the machine learning system, wherein evaluating a respective generation of the machine learning system comprises:
generating a second generation of candidate models by performing an evolution process on a respective subset of features associated with selected models of a current generation of candidate models to generate respective second candidate models;
determining, for each second candidate model of the second generation of candidate models, a respective second optimal boosting iterations hyper parameter;
evaluating fitness values for each respective second candidate model in the second generation based on a corresponding subset of features and a corresponding optimal boosting iterations hyper parameter;
subsequent to iteratively evaluating the plurality of generations, determine a selected candidate model of a final generation of candidate models of the first cycle;
subsequent to iteratively evaluating the plurality of generations, determine a selected candidate model of a final generation of candidate models of the first cycle;
determine that the selected candidate model breaches a solution constraint;
determine that the selected candidate model breaches a solution constraint, wherein the solution constraint defines a threshold number of selected features and a target optimal boosting iterations range, and the breach is determined when evaluation of the selected candidate model produces a solution that does not satisfy the solution constraint;
adjust a learning rate of the machine learning system and a subset of features in response to determining that the selected candidate model breaches the solution constraint;
adjust a learning rate of the machine learning system and a subset of features in response to determining that the selected candidate model breaches the solution constraint;
perform one or more second cycles employing the adjusted learning rate and the adjusted subset of features, wherein a first generation of at least one second cycle of the one or more second cycles is generated based on the final generation of candidate models of the first cycle;
perform one or more second cycles employing the adjusted learning rate and the adjusted subset of features, wherein a first generation of at least one second cycle of the one or more second cycles is generated based on the final generation of the first cycle;
based on termination criteria, identify a resulting candidate model of a final cycle of the one or more second cycles as an optimized model.
based on termination criteria, identify a resulting candidate model of a final cycle of the one or more second cycles as an optimized model.
Claim 12 of 18/645,758
Claim 11 of 12,001,931
The system of claim 11, wherein the termination criteria comprises a predetermined number of cycles of the machine learning system.
The system of claim 1, wherein the termination criteria comprises a predetermined number of cycles of the machine learning system.
Claim 13 of 18/645,758
Claim 12 of 12,001,931
The system of claim 1, wherein the instructions cause the machine learning system to determine the first optimal boosting iterations hyper parameter for the first candidate model based on a maximum number of boosting iterations.
The system of claim 10, wherein the instructions cause the machine learning system to determine the first optimal boosting iterations hyper parameter for the first candidate model based on a maximum number of boosting iterations.
Claim 14 of 18/645,758
Claim 13 of 12,001,931
The system of claim 13, wherein the instructions cause the machine learning system to determine the first optimal boosting iterations hyper parameter for the first candidate model by causing the machine learning system to: for each number of boosting iterations between an initial number of boosting iterations and the maximum number of boosting iterations, evaluate fitness of the first candidate model using the number of boosting iterations; and select the number of boosting iterations that provides a highest fitness for the first candidate model as the first optimal boosting iterations hyper parameter.
The system of claim 12, wherein the instructions cause the machine learning system to determine the first optimal boosting iterations hyper parameter for the first candidate model by causing the machine learning system to: for each number of boosting iterations between an initial number of boosting iterations and the maximum number of boosting iterations, evaluate fitness of the first candidate model using the number of boosting iterations; and select the number of boosting iterations that provides a highest fitness for the first candidate model as the first optimal boosting iterations hyper parameter.
Claim 15 of 18/645,758
Claim 14 of 12,001,931
The system of claim 11, wherein the evolution process employs: a first evolutionary operator configured to repair candidate solutions that exceed a maximum number of allowed features; and a second evolutionary operator configured to reduce a number of features selected in a
given candidate model.
The system of claim 10, wherein the evolution process employs: a first evolutionary operator configured to repair candidate solutions that exceed a maximum number of allowed features; and a second evolutionary operator configured to reduce a number of features selected in a given candidate model.
Claim 16 of 18/645,758
Claim 10 of 12,001,931
The system of claim 1, wherein evaluating a respective generation of the machine
learning system comprises:
A machine learning system, comprising: one or more processors; and memory storing instructions that, when executed by the one or more processors, cause the machine learning system to optimize hyper parameter and feature selection by causing the machine learning system to:
identify a training data source comprising a plurality of records, wherein each record of the plurality of records comprises data corresponding to a plurality of features;
initialize a first cycle by generating a first generation of candidate models, wherein generating each respective first candidate model of the first generation of candidate models comprises selecting a first subset of features, of the plurality of features, for use in the first candidate model;
determine, for each first candidate model of the first generation of candidate models, a respective first optimal boosting iterations hyper parameter;
evaluate fitness values for each respective first candidate model in the first generation based on a corresponding subset of features and a corresponding optimal boosting iterations hyper parameter;
perform the first cycle by iteratively evaluating a plurality of generations of the machine learning system, wherein evaluating a respective generation of the machine learning system comprises:
generating a second generation of candidate models by performing an evolution process
on a respective subset of features associated with selected models of a current generation of
candidate models to generate respective second candidate models;
generating a second generation of candidate models by performing an evolution process on a respective subset of features associated with selected models of a current generation of candidate models to generate respective second candidate models;
determining, for each second candidate model of the second generation of candidate
models, a respective second optimal boosting iterations hyper parameter;
determining, for each second candidate model of the second generation of candidate models, a respective second optimal boosting iterations hyper parameter;
evaluating fitness values for each respective second candidate model in the second
generation based on a corresponding subset of features and a corresponding optimal boosting
iterations hyper parameter.
evaluating fitness values for each respective second candidate model in the second generation based on a corresponding subset of features and a corresponding optimal boosting iterations hyper parameter;
subsequent to iteratively evaluating the plurality of generations, determine a selected candidate model of a final generation of candidate models of the first cycle;
determine that the selected candidate model breaches a solution constraint, wherein the solution constraint defines a threshold number of selected features and a target optimal boosting iterations range, and the breach is determined when evaluation of the selected candidate model produces a solution that does not satisfy the solution constraint;
adjust a learning rate of the machine learning system and a subset of features in response to determining that the selected candidate model breaches the solution constraint;
perform one or more second cycles employing the adjusted learning rate and the adjusted subset of features, wherein a first generation of at least one second cycle of the one or more second cycles is generated based on the final generation of the first cycle;
based on termination criteria, identify a resulting candidate model of a final cycle of the one or more second cycles as an optimized model.
Claim 17 of 18/645,758
Claim 15 of 12,001,931
One or more non-transitory computer readable media storing instructions that,
when executed by one or more processors, cause a machine learning system to perform steps
comprising:
One or more non-transitory computer readable media storing instructions that, when executed by one or more processors, cause a machine learning system to perform steps comprising:
identifying a training data source comprising a plurality of records, wherein each record of the plurality of records comprises data corresponding to a plurality of feature
identifying a training data source comprising a plurality of records, wherein each record of the plurality of records comprises data corresponding to a plurality of features;
initializing a first cycle of the machine learning system by generating a first generation of candidate models, wherein generating each respective first candidate model of the first generation of candidate models comprises selecting a first subset of features, of the plurality of features, for use in the first candidate model;
initializing a first cycle of the machine learning system by generating a first generation of candidate models, wherein generating each respective first candidate model of the first generation of candidate models comprises selecting a first subset of features, of the plurality of features, for use in the first candidate model;
determining, for each first candidate model of the first generation of candidate models, a respective first optimal boosting iterations hyper parameter;
determining, for each first candidate model of the first generation of candidate models, a respective first optimal boosting iterations hyper parameter;
evaluating fitness values for each respective first candidate model in the first generation based on a corresponding subset of features and a corresponding optimal boosting iterations hyperparameter;
evaluating fitness values for each respective first candidate model in the first generation based on a corresponding subset of features and a corresponding optimal boosting iterations hyper parameter;
performing one or more cycles of the machine learning system by iteratively evaluating a plurality of generations of the machine learning system;
performing one or more cycles of the machine learning system by iteratively evaluating a plurality of generations of the machine learning system, wherein evaluating a respective generation of the machine learning system comprises:
generating a second generation of candidate models by performing an evolution process on a respective subset of features associated with selected models of a current generation of candidate models to generate respective second candidate models;
determining, for each second candidate model of the second generation of candidate models, a respective second optimal boosting iterations hyper parameter;
evaluating fitness values for each respective second candidate model in the second generation based on a corresponding subset of features and a corresponding optimal boosting iterations hyper parameter;
subsequent to iteratively evaluating the plurality of generations, determining a selected candidate model of a final generation of candidate models associated with the one or more cycles;
subsequent to iteratively evaluating the plurality of generations, determining a selected candidate model of a final generation of candidate models associated with the one or more cycles;
determining that the selected candidate model breaches a solution constraint;
determining that the selected candidate model breaches a solution constraint, wherein the solution constraint defines a threshold number of selected features and a target optimal boosting iterations range, and the breach is determined when evaluation of the selected candidate model produces a solution that does not satisfy the solution constraint;
adjusting a learning rate of the machine learning system and a subset of features in response to determining that the selected candidate model breaches the solution constraint;
adjusting a learning rate of the machine learning system and a subset of features in response to determining that the selected candidate model breaches the solution constraint;
performing one or more additional cycles of the machine learning system employing the
adjusted learning rate and the adjusted subset of features;
performing one or more additional cycles of the machine learning system employing the adjusted learning rate and the adjusted subset of features;
based on termination criteria, identifying a resulting candidate model of a final cycle of
the machine learning system as an optimized model.
based on termination criteria, identifying a resulting candidate model of a final cycle of the machine learning system as an optimized model.
Claim 18 of 18/645,758
Claim 16 of 12,001,931
The computer readable media of claim 17, wherein the instructions cause the machine learning system to determine the first optimal boosting iterations hyper parameter for the first candidate model by causing the machine learning system to: for each number of boosting iterations between an initial number of boosting iterations and a maximum number of boosting iterations, evaluate fitness of the first candidate model using the number of boosting iterations; and select the number of boosting iterations that provides a highest fitness for the first candidate model as the first optimal boosting iterations hyper parameter.
The computer readable media of claim 15, wherein the instructions cause the machine learning system to determine the first optimal boosting iterations hyper parameter for the first candidate model by causing the machine learning system to: for each number of boosting iterations between an initial number of boosting iterations and a maximum number of boosting iterations, evaluate fitness of the first candidate model using the number of boosting iterations; and select the number of boosting iterations that provides a highest fitness for the first candidate model as the first optimal boosting iterations hyper parameter.
Claim 19 of 18/645,758
Claim 17 of 12,001,931
The computer readable media of claim 15, wherein the evolution process employs: a first evolutionary operator configured to repair candidate solutions that exceed a maximum number of allowed features; and a second evolutionary operator configured to reduce a number of features selected in a given candidate model.
The computer readable media of claim 15, wherein the evolution process employs: a first evolutionary operator configured to repair candidate solutions that exceed a maximum number of allowed features; and a second evolutionary operator configured to reduce a number of features selected in a given candidate model.
Claim 20 of 18/645,758
Claim 15 of 12,001,931
The computer readable media of claim 17, wherein the instructions for evaluating
a respective generation of the machine learning system further cause the machine learning
system to perform steps comprising:
A method for automatically optimizing hyper parameter and feature selection in a machine learning system, the method comprising:
identifying a training data source comprising a plurality of records, wherein each record of the plurality of records comprises data corresponding to a plurality of features;
initializing a first cycle of the machine learning system by generating a first generation of candidate models, wherein generating each respective first candidate model of the first generation of candidate models comprises selecting a first subset of features, of the plurality of features, for use in the first candidate model;
determining, for each first candidate model of the first generation of candidate models, a respective first optimal boosting iterations hyper parameter;
evaluating fitness values for each respective first candidate model in the first generation based on a corresponding subset of features and a corresponding optimal boosting iterations hyper parameter;
performing one or more cycles of the machine learning system by iteratively evaluating a plurality of generations of the machine learning system, wherein evaluating a respective generation of the machine learning system comprises:
generating a second generation of candidate models by performing an evolution process
on a respective subset of features associated with selected models of a current generation of
candidate models to generate respective second candidate models.
generating a second generation of candidate models by performing an evolution process on a respective subset of features associated with selected models of a current generation of candidate models to generate respective second candidate models;
determining, for each second candidate model of the second generation of candidate models, a respective second optimal boosting iterations hyper parameter; and
determining, for each second candidate model of the second generation of candidate models, a respective second optimal boosting iterations hyper parameter;
evaluating fitness values for each respective second candidate model in the second
generation based on a corresponding subset of features and a corresponding optimal boosting
iterations hyper parameter.
evaluating fitness values for each respective second candidate model in the second generation based on a corresponding subset of features and a corresponding optimal boosting iterations hyper parameter;
subsequent to iteratively evaluating the plurality of generations, determining a selected candidate model of a final generation of candidate models associated with the one or more cycles;
determining that the selected candidate model breaches a solution constraint, wherein the solution constraint defines a threshold number of selected features and a target optimal boosting iterations range, and the breach is determined when evaluation of the selected candidate model produces a solution that does not satisfy the solution constraint;
adjusting a learning rate of the machine learning system and a subset of features in response to determining that the selected candidate model breaches the solution constraint;
performing one or more additional cycles of the machine learning system employing the adjusted learning rate and the adjusted subset of features;
based on termination criteria, identifying a resulting candidate model of a final cycle of the machine learning system as an optimized model.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Konig et al (US 2010/0299350) teaches estimating whether an online search query is a news-related query, and if so, outputting news-related results in association with other search results returned in response to the query. The query is processed into features, including by accessing corpora that corresponds to relatively current events, e.g., recently crawled from news and blog articles. A corpus of static reference data, such as an online encyclopedia, may be used to help determine whether the query is less likely to be about current events. Features include frequency-related data and context-related data corresponding to frequency and context information maintained in the corpora. Additional features may be obtained by processing text of the query itself, e.g., "query-only" features.
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/BRANDON S COLE/ Primary Examiner, Art Unit 2128
