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Mtoper

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Patent-Prompt-v1

like 1

G06N504

Please help me predict the CPC LABEL for this patentPATENT ABSTRACT: Systems and methods may provide for conducting an interest analysis of data associated with a user, wherein the interest analysis distinguishes between abstract interests and social interests. Additionally, one or more recommendations may be generated for the user based on the interest analysis and a current context of the user, wherein the one or more recommendations may be presented to the user. In one example, the abstract interests identify types of topics and types of objects, and the social interests identify types of social groups.

A method for determining a power outage probability of an electrical power grid for a time period, the method comprising the following steps carried by a processor of a data processing unit: dividing the time period into several time intervals, determining a power generation capacity of a power generation facility and an energy storage unit size for an energy storage unit for said time period, determining an effective load unit demand for each time interval from a load unit demand of a load unit for each time interval, and computing the power outage probability using a grid parameter that comprises the power generation capacity, the energy storage unit size and the effective load unit demand, wherein the grid parameter is optimized for its maximum value with respect to all time intervals of the time period

Please help me write a proper abstract based on the patent claims. CLAIM: 1. A method for determining a power outage probability of an electrical power grid, in particular a smart grid, wherein a power generation facility and an energy storage unit are used to distribute power to at least one load unit, for a time period comprising several time intervals, the method comprising the following steps carried by a processor of a data processing unit: determining an effective load unit demand for each time interval from a load unit demand of the load unit for each time interval, respectively, wherein the load unit demand for each time interval is read-out from a database or obtained by reading out a power meter and the effective load unit takes a value located between a peak demand and an average demand of the load unit, and computing the power outage probability using a grid parameter that depends on a power generation capacity of the power generation facility, an energy storage unit size for the energy storage unit for said time period and the effective load unit demand, wherein the grid parameter is optimized for its maximum value with respect to all time intervals of the time period. 2. The method according to claim 1, wherein the load unit demand comprises several load unit demand distributions for each time interval, determining the effective load unit demand comprises determining an effective load unit demand distribution for each time interval from each load unit demand distribution, respectively, wherein the load unit demand distributions for each time interval are read-out from the database, and a multiplex parameter is determined that aggregates the several load unit demand distributions, wherein the grid parameter comprises the multiplex parameter and is further optimized for its minimum value with respect to the multiplex parameter by the processor of the data processing unit, 3. The method according to claim 2, wherein the load unit demand distributions are stochastic distributions, 4. The method according to claim 2, wherein the bad unit demand distributions are provided as predetermined power usage profiles. 5. The method according to claim 4, wherein the power usage profiles are related to a daily, weekly, monthly or yearly power usage. 6. The method according to claim 2, wherein the load unit demand distributions are provided as measurement values that are measured in real-time and provided to the database by a meter. 7. The method according to claim 6, wherein the measurement values are measured at each load unit or at an energy storage unit. 8. A method for an adaptation of a power generation capacity of an electrical power grid, in particular a smart grid, the method comprising the following steps: a) determining the power generation capacity, an energy storage unit size and a load unit demand, wherein the power generation capacity and/or the energy unit size and/or the load unit demand are read-out from a database or obtained by reading out a power meter, b) computing a power outage probability depending on the power generation capacity, the energy storage unit size and the load unit demand according to the method according to claim 1 by a processor of a data processing unit, c) comparing the power outage probability with a target reliability threshold, d) adjusting the power generation capacity if the power outage probability is smaller than the target reliability threshold, e) computing a new power outage probability depending on the adjusted power generation capacity, the energy storage unit size and the load unit demand according to the method according to claim 1 by the processor of the data processing unit, f) comparing the new power outage probability with the target reliability threshold, and g) repeating the steps d) to f) until the new power outage probability is equal or larger than the target reliability threshold. 9. A method for determining an energy storage unit size for an electrical power grid, in particular a smart grid, comprising a power generation facility and a load unit, the method comprising the steps: a) determining a power generation capacity and a load unit demand, wherein the load unit demand is determined from a predetermined power usage profile that is read-out from a database, b) computing a power outage probability depending on the power generation capacity, the energy storage unit size and the load unit demand according to the method according to claim 1 by a processor of a data processing unit, c) comparing the power outage probability with a target reliability threshold, d) adjusting the energy storage unit size if the power outage probability is smaller than the target reliability threshold, e) computing a new power outage probability depending on the adjusted energy storage unit size, the power generation capacity, and the load unit demand according to the method according to claim 1 by the processor of the data processing unit, f) comparing the new power outage probability with the target reliability threshold, and g) repeating the steps d) to f) until the new power outage probability is equal or larger than the target reliability threshold. 10. A data processing unit for determining a power outage probability of an electrical power grid, in particular a smart grid, wherein a power generation facility and an energy storage unit are used to distribute power to at least one load unit, for a time period comprising several time intervals, the data processing unit comprising a processor for: determining an effective load unit demand for each time interval from a load unit demand of the load unit for each time interval, respectively, wherein the load unit demand for each time interval is read-out from a database or obtained by reading out a power meter and the effective load unit takes a value located between a peak demand and an average demand of the load unit, and computing the power outage probability using a grid parameter that depends on a power generation capacity of the power generation facility, an energy storage unit size for the energy storage unit for said time period and the effective load unit demand, wherein the grid parameter is optimized for its maximum value with respect to all time intervals of the time period. 11. The data processing unit according to claim 10, wherein the power generation capacity comprises a stochastic power generation distribution. 12. The data processing unit according to claim 10, wherein the power generation capacity comprises several individual power generation capacities. 13. The data processing unit according to claim 10, wherein the power generation capacity refers to at least one power generation plant of the following group: nuclear power plant, coal power plant, oil power plant, gas power plant, solar power plant, hydro power plant and wind power plant. 14. The data processing unit according to claim 10, wherein the energy storage unit size comprises several individual energy storage unit sizes.

REJECTED

Please predict whether this patent is acceptable.PATENT ABSTRACT: A method for determining a power outage probability of an electrical power grid for a time period, the method comprising the following steps carried by a processor of a data processing unit: dividing the time period into several time intervals, determining a power generation capacity of a power generation facility and an energy storage unit size for an energy storage unit for said time period, determining an effective load unit demand for each time interval from a load unit demand of a load unit for each time interval, and computing the power outage probability using a grid parameter that comprises the power generation capacity, the energy storage unit size and the effective load unit demand, wherein the grid parameter is optimized for its maximum value with respect to all time intervals of the time period

G06N700

Please help me predict the CPC LABEL for this patentPATENT ABSTRACT: A method for determining a power outage probability of an electrical power grid for a time period, the method comprising the following steps carried by a processor of a data processing unit: dividing the time period into several time intervals, determining a power generation capacity of a power generation facility and an energy storage unit size for an energy storage unit for said time period, determining an effective load unit demand for each time interval from a load unit demand of a load unit for each time interval, and computing the power outage probability using a grid parameter that comprises the power generation capacity, the energy storage unit size and the effective load unit demand, wherein the grid parameter is optimized for its maximum value with respect to all time intervals of the time period

An electronic system, e.g. a power supply, includes elements, and the elements include devices that limit reliability of the electronic system. A system that can monitor parameters that affect electronic system reliability such as temperature, and parameters that can predict power supply failure such as bulk capacitor ESR, includes a monitoring system measuring and monitoring at least one reliability limiting parameter of at least one of the devices connected to the monitoring system. A method for estimating and predicting a failure of the electronic system includes: measuring parameters affecting or associating the reliability of the device by sensors, collecting the measured sensor data and/or other data by a communications unit, and communicating the data to a computing device for processing and predicting a failure of the device and alerting to the failure.

Please help me write a proper abstract based on the patent claims. CLAIM: 1. An electronic system comprising elements and the elements comprising devices that limit reliability of the electronic system, wherein at least one of the devices is connected to a monitoring system measuring and monitoring at least one reliability limiting parameter. 2. The electronic system according to claim 1, wherein the electronic system comprises a power supply, the elements comprise an AC-DC converter, a power factor correction, a bus converter, and a point of load regulation, and one of said elements is connected to the monitoring system measuring and monitoring at least one reliability limiting parameter. 3. The electronic system according to claim 1, wherein the monitoring system comprises sensors for measuring device parameters, a communications unit communicating with the sensors, a computing unit connected to the communications unit, and a storage means associated with the computing unit. 4. The electronic system according to claim 3, wherein the communications unit is connected to a local embedded host by a local communications bus, and the embedded host is located within a facility where the monitoring system is located. 5. The electronic system according to claim 3, wherein the computing unit and the storage means are located within a facility where the at least one of the devices connected to the monitoring system is located. 6. The electronic system according to claim 3, wherein the computing unit and the storage means are located outside a facility where the at least one of the devices connected to the monitoring system is located. 7. The electronic system according to claim 6, wherein the computing unit and the storage means are located in a different facility than where the at least one of the devices connected to the monitoring system is located. 8. The electronic system according to claim 8, wherein the computing unit and the storage means are located at a remote data-center. 9. The electronic system according to claim 2, wherein the monitoring system is connected over cloud computing means with other power supplies and sensors of the other power supplies building up a database of parameters. 10. The electronic system according to claim 3, wherein the computing unit comprises an ASIC or a FPGA. 11. The electronic system according to claim 3, wherein the computing unit is connected to indicator function means. 12. The electronic system according to claim 11 wherein the indicator function means comprises at least one of a light emitting diode or a status register. 13. The electronic system according to claim 1, wherein the monitoring system is incorporated into a digital power control IC or a power management integrated circuit (PMIC) comprising all power controllers, sensors, estimators, observers and communications and processing logic. 14. A method for estimating and predicting a reliability limiting failure of an electronic system comprising the following steps: measuring parameters affecting or associating reliability of a device by sensors, collecting measured sensor data and/or other data by a communications unit, communicating the data to a computing unit for processing, and predicting a failure of the device and alerting to the failure. 15. The method for estimating and predicting a reliability limiting failure of an electronic system according to claim 11, wherein the computing unit runs a machine learning program for estimating, learning and predicting the failure of the device. 16. The method for estimating and predicting a reliability limiting failure of an electronic system according to claim 12, wherein the machine learning program processes the collected and communicated sensor data and/or other data. 17. The method for estimating and predicting a reliability limiting failure of an electronic system according to claim 11, wherein the machine learning program uses at least one of the following algorithms: Anomaly Detection, Neural Network, K-Nearest Neighbor, Linear Regression, Markov Chain Monte Carlo, Hidden Markov Modelling, Naive Bayes or Decision Trees. 18. The method for estimating and predicting a reliability limiting failure of an electronic system according to claim 11, wherein the computing unit is used in a cloud based environment, and is configured via a web interface.

PENDING

Please predict whether this patent is acceptable.PATENT ABSTRACT: An electronic system, e.g. a power supply, includes elements, and the elements include devices that limit reliability of the electronic system. A system that can monitor parameters that affect electronic system reliability such as temperature, and parameters that can predict power supply failure such as bulk capacitor ESR, includes a monitoring system measuring and monitoring at least one reliability limiting parameter of at least one of the devices connected to the monitoring system. A method for estimating and predicting a failure of the electronic system includes: measuring parameters affecting or associating the reliability of the device by sensors, collecting the measured sensor data and/or other data by a communications unit, and communicating the data to a computing device for processing and predicting a failure of the device and alerting to the failure.

G06N5048

Please help me predict the CPC LABEL for this patentPATENT ABSTRACT: An electronic system, e.g. a power supply, includes elements, and the elements include devices that limit reliability of the electronic system. A system that can monitor parameters that affect electronic system reliability such as temperature, and parameters that can predict power supply failure such as bulk capacitor ESR, includes a monitoring system measuring and monitoring at least one reliability limiting parameter of at least one of the devices connected to the monitoring system. A method for estimating and predicting a failure of the electronic system includes: measuring parameters affecting or associating the reliability of the device by sensors, collecting the measured sensor data and/or other data by a communications unit, and communicating the data to a computing device for processing and predicting a failure of the device and alerting to the failure.

In an approach to assisting database management, a computer generates one or more combinations of values of one or more database configuration parameters. The computer associates each of the one or more generated combinations of values with an incident probability. The computer defines relationships between the one or more generated combinations and the associated incident probabilities. The computer stores the defined relationships into an object representable as a multi-dimensional matrix, whose dimensions correspond to a plurality of database configuration parameters used to generate the combinations of values. The computer traverses the object to identify a path in the matrix. The computer returns the identified path for enabling subsequent interpretation thereof as a rule for passing from a first database configuration, corresponding to the first one of the one or more generated combinations, to a second database configuration, corresponding to the second one of the one or more generated combinations.

Please help me write a proper abstract based on the patent claims. CLAIM: 1. A method for assisting database management, the method comprising: generating, via one or more processing elements, one or more combinations of values of one or more database configuration parameters, wherein each of the one or more generated combinations of values is interpretable as a potential database configuration; associating, via the one or more processing elements, each of the one or more generated combinations of values with an incident probability, wherein the incident probability estimates a probability for each of the one or more generated combinations of values to lead to a database incident; defining, via the one or more processing elements, relationships between the one or more generated combinations of values and the associated incident probabilities; storing, via the one or more processing elements, the defined relationships into an object representable as a multi-dimensional matrix, whose dimensions correspond to a plurality of database configuration parameters used to generate the one or more combinations of values; traversing, via the one or more processing elements, the object to identify a path in the matrix, from a first one of the one or more generated combinations of values to a second one of the one or more generated combinations of values; and returning, via the one or more processing elements, the identified path for enabling subsequent interpretation thereof as a rule for passing from a first database configuration, corresponding to the first one of the one or more generated combinations of values, to a second database configuration, corresponding to the second one of the one or more generated combinations of values. 2. The method of claim 1, wherein traversing the object further comprises: identifying, via the one or more processing elements, in the object, a cluster of contiguous matrix elements that are associated to a value of incident probability in a same given range, or with a same value of incident probability, wherein, the identified path originates from or points at one of the contiguous matrix elements. 3. The method of claim 1, wherein traversing the object further comprises: identifying, via the one or more processing elements, in the object, two distinct clusters of contiguous matrix elements, such that two distinct clusters are associated with distinct ranges of incident probabilities, or with distinct values of incident probabilities; and identifying, via the one or more processing elements, one or more paths, each of the identified paths going from a matrix element of a first one of the two distinct clusters to a matrix element of a second one of the two distinct clusters. 4. The method of claim 3, wherein identifying the two distinct clusters maximizes a size of each of the two distinct clusters. 5. The method of claim 4, wherein the identified two distinct clusters are contiguous in the matrix. 6. The method of claim 1, wherein the incident probability associated with the first one of the one or more generated combinations of values is higher than the incident probability associated to the second one of the one or more generated combinations of values. 7. The method of claim 1, further comprising, modifying, via the one or more processing elements, a configuration of an actual database according to a rule corresponding to a returned path. 8. The method of claim 1, wherein associating each of the one or more generated combinations of values to an incident probability further comprises applying, via the one or more processing elements, a statistical model of database configuration incidents to the one or more generated combinations of values. 9. The method of claim 8, wherein applying the statistical model to the one or more generated combinations of values associates each of the one or more generated combinations of values to an incident probability selected from a discrete set of incident probabilities. 10. The method of claim 8, wherein applying the statistical model to the one or more generated combinations of values associates each of the one or more generated combinations of values to an incident probability selected from two incident probabilities. 11. The method of claim 1, further comprising, prior to generating the one or more combinations: selecting, via the one or more processing elements, from a set of one or more database configuration parameters, a subset of database configuration parameters that are related to database incidents; and generating, via the one or more processing elements, the set of one or more combinations using the selected subset of one or more database configuration parameters as database configuration parameters. 12. The method of claim 1, further comprising, prior to generating the one or more combinations: selecting, via the one or more processing elements, a subset of most representative values for each of the database configuration parameters; and generating, via the one or more processing elements, the one or more combinations using the selected subset of most representative values of the database configuration parameters. 13. The method of claim 1, further comprising, prior to generating the one or more combinations: selecting, via the one or more processing elements, from a set of database configuration parameters a subset of database configuration parameters that are related to database incidents; and for each parameter of the selected subset, selecting, via the one or more processing elements, a subset of most representative values thereof, wherein, generating the one or more combinations of values comprises generating combinations of values of the selected subset of most representative values of the sole parameters of said subset of database configuration parameters. 14. The method of claim 1, further comprising, prior to generating the combinations of values, classifying, via the one or more processing elements, one or more actual database configurations by making use of a statistical model, wherein the statistical model is based on actual database configuration incident data. 15. The method of claim 14, wherein the statistical model used for classifying the one or more actual database configurations is trained while classifying the one or more actual database configurations and is subsequently used for associating each of the generated combinations of values to an incident probability. 16. The method of claim 14, wherein each of the one or more actual database configurations is classified as a problematic database configuration or a non-problematic database configuration. 17. The method of claim 14, further comprising, prior to classifying, combining, via the one or more processing elements, a plurality of different sources of database configuration incident data to obtain the database configuration incident data. 18. A computer program product for assisting database management, the computer program product comprising: one or more computer readable storage device and program instructions stored on the one or more computer readable storage device, the stored program instructions comprising: program instructions to generate one or more combinations of values of one or more database configuration parameters, wherein each of the one or more generated combinations of values is interpretable as a potential database configuration; program instructions to associate each of the one or more generated combinations of values with an incident probability, wherein the incident probability estimates a probability for each of the one or more generated combinations of values to lead to a database incident; program instructions to define relationships between the one or more generated combinations of values and the associated incident probabilities; program instructions to store the defined relationships into an object representable as a multi-dimensional matrix, whose dimensions correspond to a plurality of database configuration parameters used to generate the one or more combinations of values; program instructions to traverse the object to identify a path in the matrix, from a first one of the one or more generated combinations of values to a second one of the one or more generated combinations of values; and program instructions to return the identified path for enabling subsequent interpretation thereof as a rule for passing from a first database configuration, corresponding to the first one of the one or more generated combinations of values, to a second database configuration, corresponding to the second one of the one or more generated combinations of values. 19. The computer program product of claim 18, further comprising, modifying, via the one or more processing elements, a configuration of an actual database according to a rule corresponding to a returned path. 20. A computer system for assisting database management, the computer system comprising: one or more computer processors; one or more computer readable storage device; program instructions stored on the one or more computer readable storage device for execution by at least one of the one or more computer processors, the stored program instructions comprising: program instructions to generate one or more combinations of values of one or more database configuration parameters, wherein each of the one or more generated combinations of values is interpretable as a potential database configuration; program instructions to associate each of the one or more generated combinations of values with an incident probability, wherein the incident probability estimates a probability for each of the one or more generated combinations of values to lead to a database incident; program instructions to define relationships between the one or more generated combinations of values and the associated incident probabilities; program instructions to store the defined relationships into an object representable as a multi-dimensional matrix, whose dimensions correspond to a plurality of database configuration parameters used to generate the one or more combinations of values; program instructions to traverse the object to identify a path in the matrix, from a first one of the one or more generated combinations of values to a second one of the one or more generated combinations of values; and program instructions to return the identified path for enabling subsequent interpretation thereof as a rule for passing from a first database configuration, corresponding to the first one of the one or more generated combinations of values, to a second database configuration, corresponding to the second one of the one or more generated combinations of values.

PENDING

Please predict whether this patent is acceptable.PATENT ABSTRACT: In an approach to assisting database management, a computer generates one or more combinations of values of one or more database configuration parameters. The computer associates each of the one or more generated combinations of values with an incident probability. The computer defines relationships between the one or more generated combinations and the associated incident probabilities. The computer stores the defined relationships into an object representable as a multi-dimensional matrix, whose dimensions correspond to a plurality of database configuration parameters used to generate the combinations of values. The computer traverses the object to identify a path in the matrix. The computer returns the identified path for enabling subsequent interpretation thereof as a rule for passing from a first database configuration, corresponding to the first one of the one or more generated combinations, to a second database configuration, corresponding to the second one of the one or more generated combinations.

G06N7005

Please help me predict the CPC LABEL for this patentPATENT ABSTRACT: In an approach to assisting database management, a computer generates one or more combinations of values of one or more database configuration parameters. The computer associates each of the one or more generated combinations of values with an incident probability. The computer defines relationships between the one or more generated combinations and the associated incident probabilities. The computer stores the defined relationships into an object representable as a multi-dimensional matrix, whose dimensions correspond to a plurality of database configuration parameters used to generate the combinations of values. The computer traverses the object to identify a path in the matrix. The computer returns the identified path for enabling subsequent interpretation thereof as a rule for passing from a first database configuration, corresponding to the first one of the one or more generated combinations, to a second database configuration, corresponding to the second one of the one or more generated combinations.

A general framework for cross-validation of any supervised learning algorithm on a distributed database comprises a multi-layer software architecture that implements training, prediction and metric functions in a C++ layer and iterates processing of different subsets of a data set with a plurality of different models in a Python layer. The best model is determined to be the one with the smallest average prediction error across all database segments.

Please help me write a proper abstract based on the patent claims. CLAIM: 1. A method of cross-validation of a supervised machine learning algorithm within a distributed database having a plurality of database segments in which data are stored, comprising: partitioning a data set within said database into a training subset and a validation subset, wherein the partitioning data set comprises partitioning the data set according to randomly sorted data to create two data subsets that are independent and statistically equivalent; determining coefficients of a first model of said supervised machine learning algorithm using the training subset; predicting a value of a data element in said validation subset using said first model; determining a prediction error based at least in part on a difference between said predicted value and the actual value of said data element; successively repeating said partitioning k times to form k different partitions, wherein at least a subset of the k different partitions have different training and validation subsets; determining corresponding k prediction errors based at least in part on iteratively determining the coefficients, predicting the value of the data element, and determining the prediction error for each of said k partitions; and evaluating the performance of said first model using said k prediction errors. 2. The method of claim 1, further comprising repeating said method for each of a plurality of other different models, and identifying as the best model the model having the smallest corresponding prediction error. 3. The method of claim 2, wherein each of said models is defined by a parameter set of one or more parameters, and wherein the method further comprises providing in the database a plurality of such parameter sets for establishing said plurality of models. 4. The method of claim 2, further comprising performing said method in parallel on each of said database segments, and wherein said identifying comprises identifying the best model using the results of the k partitions on all segments. 5. The method of claim 1, wherein said partitioning comprises partitioning said data set into a small subset and a large subset, said large subset comprising said training subset, and said small subset comprising said validation subset. 6. The method of claim 1, wherein said determining coefficients comprises using said training subset to select coefficients of said first model that minimize a target function of said supervised machine learning algorithm. 7. The method of claim 1, wherein said predicting comprises predicting said value of said data element using a prediction function of said supervised machine learning algorithm, the coefficients of said first model comprising coefficients of said prediction function. 8. The method of claim 1, wherein said data set comprises table data, and said partitioning comprises running database SQL processing operations to randomly sort said table data set into sorted table data, to attach an index each row of said sorted table data, and to separate using the indices said sorted table data into said training and said validation subsets. 9. The method of claim 1, wherein said supervised learning algorithm comprises a target function, a prediction function, and a metric function, the target function establishing coefficient values that are used by said prediction and said metric functions, and wherein said functions are embodied in application programs in a first application program layer within said database, said functions being called by cross-validation functions in a second application layer within said database system. 10. The method of claim 1, wherein said functions in said first application layer have formats which define arguments and parameters of the functions using generic elements, and wherein said cross validation functions dynamically replace said generic elements with particular elements. 11. A computer program product comprising a non-transitory computer readable medium storing executable instructions for controlling the operation of a computer in a distributed database having a plurality of database segments to perform a method of cross-validation of a supervised machine learning algorithm, the method comprising: partitioning a data set within said database into a training subset and a validation subset, wherein the partitioning data set comprises partitioning the data set according to randomly sorted data to create two data subsets that are independent and statistically equivalent; determining coefficients of a first model of said supervised machine learning algorithm using the training subset; predicting a value of a data element in said validation subset using said first model; determining a prediction error based at least in part on a difference between said predicted value and the actual value of said data element; wherein at least a subset of the k different partitions have partition having different training and validation subsets; determining corresponding k prediction errors based at least in part on iteratively determining the coefficients, predicting the value of the data element, and determining the prediction error for each of said k partitions; and evaluating the performance of said first model using said k prediction errors. 12. The computer program product of claim 11, further comprising instructions for repeating said method for each of a plurality of other different models, and identifying as the best model the model having the smallest corresponding prediction error. 13. The computer program product of claim 12, wherein each of said models is defined by a parameter set of one or more parameters, and wherein the method further comprises providing in the database a plurality of such parameter sets for establishing said plurality of models. 14. The computer program product of claim 11 further comprising performing said method in parallel on each of said database segments, and wherein said identifying comprises identifying the best model using the results of the k partitions on all segments. 15. The computer program product of claim 11, wherein said partitioning comprises partitioning said data set into a small subset and a large subset, said large subset comprising said training subset, and said small subset comprising said validation subset. 16. The computer program product of claim 11, wherein said determining coefficients comprises using said training subset to select coefficients of said first model that minimize a target function of said supervised machine learning algorithm. 17. The computer program product of claim 11, wherein said predicting comprises predicting said value of said data element using a prediction function of said supervised machine learning algorithm, the coefficients of said first model comprising coefficients of said prediction function. 18. The computer program product of claim 11, wherein said data set comprises table data, and said partitioning comprises running database SQL processing operations to randomly sort said table data set into sorted table data, to attach an index each row of said sorted table data, and to separate using the indices said sorted table data into said training and said validation subsets. 19. The method of claim 11, wherein said supervised learning algorithm comprises a target function, a prediction function, and a metric function, the target function establishing coefficient values that are used by said prediction and said metric functions, and wherein said functions are embodied in application programs in a first application program layer within said database, said functions being called by cross-validation functions in a second application layer within said database system. 20. The computer program product of claim 11, wherein said functions in said first application layer have formats which define arguments and parameters of the functions using generic elements, and wherein said cross validation functions dynamically replace said generic elements with particular elements.

PENDING

Please predict whether this patent is acceptable.PATENT ABSTRACT: A general framework for cross-validation of any supervised learning algorithm on a distributed database comprises a multi-layer software architecture that implements training, prediction and metric functions in a C++ layer and iterates processing of different subsets of a data set with a plurality of different models in a Python layer. The best model is determined to be the one with the smallest average prediction error across all database segments.

G06N99005

Please help me predict the CPC LABEL for this patentPATENT ABSTRACT: A general framework for cross-validation of any supervised learning algorithm on a distributed database comprises a multi-layer software architecture that implements training, prediction and metric functions in a C++ layer and iterates processing of different subsets of a data set with a plurality of different models in a Python layer. The best model is determined to be the one with the smallest average prediction error across all database segments.

This document discloses a system and method for consolidating threat intelligence data for a computer and its related networks. Massive volumes of raw threat intelligence data are collected from a plurality of sources and are partitioned into a common format for cluster analysis whereby the clustering of the data is done using unsupervised machine learning algorithms. The resulting organized threat intelligence data subsequently undergoes a weighted asset based threat severity level correlation process. All the intermediary network vulnerabilities of a particular computer network are utilized as the critical consolidation parameters of this process. The final processed intelligence data gathered through this high speed automated process is then formatted into predefined formats prior to transmission to third parties.

Please help me write a proper abstract based on the patent claims. CLAIM: 1. A method of consolidating threat intelligence data for a computer network, the method to be performed by a computer system comprising: collecting threat intelligence data from a plurality of sources and normalising the collected threat intelligence data into a uniform data format; grouping normalised threat intelligence data into clusters using unsupervised machine learning algorithms, wherein each cluster comprises a group of data that represents an attribute of the threat intelligence data; categorizing clusters that are severe to the computer network; comparing the clusters categorized as severe with a security posture of the computer network to determine clusters that are of interest to the computer system; and formatting the clusters determined to be of interest to the computer system to a predefined format of the computer network. 2. The method according to claim 1 wherein the categorizing the clusters that are severe to the computer network comprises: retrieving a list of computer assets associated with the computer network; identifying clusters that affect a computing feature of the computer assets; and classifying identified clusters that affect a computing feature of the computer asset as severe to the computer network. 3. The method according to claim 2 further comprising retrieving severity weightage values accorded to each of the computer assets associated with the computer network; summing the retrieved severity weightage values; and allocating the summed severity weightage value to the computer network. 4. The method according to claim 2 wherein the computing feature comprises an operating system or a network protocol of a computer asset. 5. The method according to claim 1, wherein before the comparing the clusters categorized as severe with a security posture of the computer network to determine clusters of interest to the computer system, the method further comprises: generating the security posture of the computer network. 6. The method according to claim 5 wherein the generating the security posture of the computer network comprises: creating an object model representing the computer network, wherein the object model includes computer security information of computer assets contained within the computer network; and executing an analysis program operative to run vulnerability testing of each of the computer assets in the computer network using the object model, wherein the results of the vulnerability testing are used to determine the security posture of the computer network. 7. The method according to claim 6, wherein the vulnerability testing of each of the computer assets in the computer network using the object model comprises tests pertaining to system level and topology vulnerabilities of the computer network, and node level vulnerabilities of the computer assets. 8. The method according to claim 1 wherein the grouping normalised threat intelligence data into clusters machine learning algorithms, wherein each cluster comprises a group of data that represents an attribute of the threat intelligence data further comprises: validating the clusters using threat intelligence data in each cluster. 9. The method according to claim 8 wherein the validating the clusters comprises: assigning weightage values to each record contained in the clusters, wherein a record originating from an open source is assigned a lower weightage value as compared to a weightage value assigned to a record originating from a commercial source; summing the weightage values of records contained in each cluster; and validating clusters that have summed weightage values that exceed a predefined threshold. 10. The method according to claim 1 further comprising: using the formatted clusters to update the security posture of the computer network. 11. The method according to claim 1 wherein the attribute of the threat intelligence data comprises a computer security threat or an Internet Protocol (IP) address. 12. A system for consolidating threat intelligence data for a computer network comprising: a processing unit; and a non-transitory media readable by the processing unit, the media storing instructions that when executed by the processing unit, cause the processing unit to; collect threat intelligence data from a plurality of sources and normalise the collected threat intelligence data into a uniform data format; group normalised threat intelligence data into clusters using unsupervised machine learning algorithms, wherein each cluster comprises a group of data that represents an attribute of the threat intelligence data; categorize dusters that are severe to the computer network; compare the clusters categorized as severe with a security posture of the computer network to determine clusters that are of interest to the computer system; and format the clusters determined to be of interest to the computer system to a predefined format of the computer network. 13. The system according to claim 12 wherein the instructions to categorize the clusters that are severe to the computer network comprises: instructions for directing the processing unit to: retrieve a list of computer assets associated with the computer network; identify dusters that affect a computing feature of the computer assets; and classify identified clusters that affect a computing feature of the computer asset as severe to the computer network. 14. The system according to claim 12 further comprising: instructions for directing the processing unit to: retrieve severity weightage values accorded to each of the computer assets associated with the computer network; sum the retrieved severity weightage values; and allocate the summed severity weightage value to the computer network. 15. The system according to claim 12 wherein the computing feature comprises an operating system or a network protocol of a computer asset. 16. The system according to claim 12, wherein before the instructions to compare the clusters categorized as severe with a security posture of the computer network to determine clusters of interest to the computer system, the system further comprises: instructions for directing the processing unit to: generate the security posture of the computer network. 17. The system according to claim 16 wherein the instructions to generate the security posture of the computer network comprises: instructions for directing the processing unit to: create an object model representing the computer network, wherein the object model includes computer security information of computer assets contained within the computer network; and execute an analysis program operative to run vulnerability testing of each of the computer assets in the computer network using the object model, wherein the results of the vulnerability testing are used to determine the security posture of the computer network. 18. The system according to claim 17, wherein the vulnerability testing of each of the computer assets in the computer network using the object model comprises tests pertaining to system level and topology vulnerabilities of the computer network, and node level vulnerabilities of the computer assets. 19. The system according to claim 12 wherein the instructions to group normalised threat intelligence data into clusters machine learning algorithms, wherein each cluster comprises a group of data that represents an attribute of the threat intelligence data further comprises: instructions for directing the processing unit to: validate the clusters using threat intelligence data in each cluster. 20. The system according to claim 19 wherein the instructions to validate the clusters comprises: instructions for directing the processing unit to: assign weightage values to each record contained in the clusters, wherein a record originating from an open source is assigned a lower weightage value as compared to a weightage value assigned to a record originating from a commercial source; sum the weightage values of records contained in each cluster; and validate clusters that have summed weightage values that exceed a predefined threshold. 21. The system according to claim 12 further comprising: instructions for directing the processing unit to: use the formatted clusters to update the security posture of the computer network. 22. The system according to claim 12 wherein the attribute of the threat intelligence data comprises a computer security threat or an Internet Protocol (IP) address. 23. A system for consolidating threat intelligence data for a computer network comprising: circuitry configured to collect threat intelligence data from a plurality of sources and normalise the collected threat intelligence data into a uniform data format; circuitry configured to group normalised threat intelligence data into clusters using unsupervised machine learning algorithms, wherein each cluster comprises a group of data that represents an attribute of the threat intelligence data; circuitry configured to categorize clusters that are severe to the computer network; circuitry configured to compare the clusters categorized as severe with a security posture of the computer network to determine clusters that are of interest to the computer system; and circuitry configured to format the clusters determined to be of interest to the computer system to a predefined format of the computer network. 24. The system according to claim 23 wherein the circuitry configured to categorize the clusters that are severe to the computer network comprises: circuitry configured to retrieve a list of computer assets associated with the computer network; circuitry configured to identify clusters that affect a computing feature of the computer assets; and circuitry configured to classify identified clusters that affect a computing feature of the computer asset as severe to the computer network. 25. The system according to claim 24 further comprising: circuitry configured to retrieve severity weightage values accorded to each of the computer assets associated with the computer network; circuitry configured to sum the retrieved severity weightage values; and circuitry configured to allocate the summed severity weightage value to the computer network. 26. The system according to claim 24 wherein the computing feature comprises an operating system or a network protocol of a computer asset. 27. The system according to claim 23, wherein before the circuitry configured to compare the clusters categorized as severe with a security posture of the computer network to determine clusters of interest to the computer system, the system further comprises: circuitry configured to generate the security posture of the computer network. 28. The system according to claim 27 wherein the circuitry configured to generate the security posture of the computer network comprises: circuitry configured to create an object model representing the computer network, wherein the object model includes computer security information of computer assets contained within the computer network; and circuitry configured to execute an analysis program operative to run vulnerability testing of each of the computer assets in the computer network using the object model, wherein the results of the vulnerability testing are used to determine the security posture of the computer network. 29. The system according to claim 28, wherein the vulnerability testing of each of the computer assets in the computer network using the object model comprises tests pertaining to system level and topology vulnerabilities of the computer network, and node level vulnerabilities of the computer assets. 30. The system according to claim 23 wherein the circuitry configured to group normalised threat intelligence data into clusters machine learning algorithms, wherein each cluster comprises a group of data that represents an attribute of the threat intelligence data further comprises: circuitry configured to validate the clusters using threat intelligence data in each cluster. 31. The system according to claim 30 wherein the circuitry configured to validate the clusters comprises: circuitry configured to assign weightage values to each record contained in the clusters, wherein a record originating from an open source is assigned a lower weightage value as compared to a weightage value assigned to a record originating from a commercial source; circuitry configured to sum the weightage values of records contained in each cluster; and circuitry configured to validate clusters that have summed weightage values that exceed a predefined threshold. 32. The system according to claim 23 further comprising: circuitry configured to use the formatted clusters to update the security posture of the computer network. 33. The system according to claim 23 wherein the attribute of the threat intelligence data comprises a computer security threat or an Internet Protocol (IP) address.

PENDING

Please predict whether this patent is acceptable.PATENT ABSTRACT: This document discloses a system and method for consolidating threat intelligence data for a computer and its related networks. Massive volumes of raw threat intelligence data are collected from a plurality of sources and are partitioned into a common format for cluster analysis whereby the clustering of the data is done using unsupervised machine learning algorithms. The resulting organized threat intelligence data subsequently undergoes a weighted asset based threat severity level correlation process. All the intermediary network vulnerabilities of a particular computer network are utilized as the critical consolidation parameters of this process. The final processed intelligence data gathered through this high speed automated process is then formatted into predefined formats prior to transmission to third parties.

G06N99005

Please help me predict the CPC LABEL for this patentPATENT ABSTRACT: This document discloses a system and method for consolidating threat intelligence data for a computer and its related networks. Massive volumes of raw threat intelligence data are collected from a plurality of sources and are partitioned into a common format for cluster analysis whereby the clustering of the data is done using unsupervised machine learning algorithms. The resulting organized threat intelligence data subsequently undergoes a weighted asset based threat severity level correlation process. All the intermediary network vulnerabilities of a particular computer network are utilized as the critical consolidation parameters of this process. The final processed intelligence data gathered through this high speed automated process is then formatted into predefined formats prior to transmission to third parties.

A method, system and program product comprise monitoring inputs, into a system, for detected actions from a user. At least one detected action is identified. At least a first database storage location associated with the user is accessed. The first database storage location is at least configured for storage of emotional profiles of the user. At least a second storage location associated with the system is accessed. The second storage location is at least configured for storage of emotional profiles of the system. The at least one detected action is processed in a computational machine using data from the first storage location to determine a status of the user. A response to the status of the user is calculated. The response is at least in part determined by the status of the user and data contained in the second storage location.

Please help me write a proper abstract based on the patent claims. CLAIM: 1. A method comprising the steps of: monitoring at least one input into a system and detecting at least one action from a user; identifying at least one detected action; accessing at least a first machine readable memory location to retrieve at least one emotional behavior associated with said system, said at least one emotional behavior being based at least in part upon a certain emotional profile associated with said system; processing the at least one detected action in a computational machine using data from said first emotional profile information to determine a status of the user; and calculating a response to the status of the user, said response comprising an emotional response that is at least partly determined based on the status of the user and data contained in said first emotional profile information. 2. The method as recited in claim 1, further comprising the step of: generating emotional profile related information at least partially associated with said user; identifying at least one user detected action; processing said at least one detected action using data from said user emotional profile to determine a status of the user; and calculating a response to said status of the user, said response being determined at least in partially based upon said status of the user and data in said system emotional profile. 3. The method as recited in claim 1, in which the response comprises further comprise the step of visually displaying of one or more emotions. 4. The method as recited in claim 1, further comprising the step of generating experiential emotional memories based at least in part upon said user monitoring, said calculated user status, and/or said detected at least one action from the user, said emotional memories being stored into a computer readable storage location. 5. The method as recited in claim 1, in which said monitoring further comprises the steps of using pitch and harmonic of the voice to infer an emotional assessment of said user. 6. The method as recited in claim 1, in which said monitoring further comprises facial recognition. 7. The method as recited in claim 1, in which said monitoring further comprises voice recognition. 8. The method as recited in claim 1, in which said monitoring further comprises bio sensor detection. 9. A system comprising: at least one sensor unit, said at least one sensor unit being configure for detecting an action from a user; a first computer readable storage location configured to store at least one emotional behavior associated with said system; and a processing unit in communication with said at least one sensor and said first computer readable storage location, said processing unit being configured for: identifying at least one detected action, and responding to said at least one detected action, said response being at least partially based upon using emotional behavior related data from said first computer readable storage location to determine a status of the user, and calculating a response to said status of the user, said response at least in part being based upon by said status of the user and emotional behavior related data contained in said first computer readable storage location. 10. The system as recited in claim 9, further comprising a second storage location, said storage location being configured with emotional profile related information at least partially associated with said user, and in which said processing unit is configured to be in communication with said second storage location storing said user related emotional profile, said processing unit being configured for: identifying at least one detected action; processing said at least one detected action using data from said user emotional profile to determine a status of the user; and calculating a response to said status of the user, said response at least in part being determined by said status of the user and data contained in said second computer readable storage location. 11. The system as recited in claim 10, in which said processing unit is further configured for storing said status of the user and said response in said system emotional profile and updating said second computer readable storage location, in which said at least one sensor unit further comprises a facial recognition unit, a voice recognition unit, and a bio sensor detection unit, in which said response comprises a visual display of one or more emotions, control data for one or more devices, and a communication with one or more computational devices. 12. A non-transitory computer-readable storage medium with an executable program stored thereon, wherein the program instructs one or more processors to perform the following steps: monitoring inputs, into a system, for detected actions from a user; identifying at least one detected action; accessing at least a system emotional profile associated with the user, the system emotional profile at least configured for storage of emotional profiles of the user; accessing at least a second computer readable storage location associated with the system, the second computer readable storage location at least configured for storage of emotional profiles of the system; processing the at least one detected action in a computational machine using data from the system emotional profile to determine a status of the user; and calculating a response to the status of the user, the response at least in part being determined by the status of the user and data contained in the second computer readable storage location. 13. The program instructing the processor as recited in claim 12, further comprising the step of storing the status of the user and the response in the system emotional profile. 14. The program instructing the processor as recited in claim 13, further comprising the step of updating the second computer readable storage location. 15. The program instructing the processor as recited in claim 12, in which the response comprises a visual display of one or more emotions. 16. The program instructing the processor as recited in claim 12, in which the response comprises control data for one or more devices. 17. The program instructing the processor as recited in claim 12, in which the response comprises a communication with one or more computational devices. 18. The program instructing the processor as recited in claim 12, in which said monitoring further comprises facial recognition. 19. The program instructing the processor as recited in claim 12, in which said monitoring further comprises voice recognition. 20. The program instructing the processor as recited in claim 12, in which said monitoring further comprises bio sensor detection.

PENDING

Please predict whether this patent is acceptable.PATENT ABSTRACT: A method, system and program product comprise monitoring inputs, into a system, for detected actions from a user. At least one detected action is identified. At least a first database storage location associated with the user is accessed. The first database storage location is at least configured for storage of emotional profiles of the user. At least a second storage location associated with the system is accessed. The second storage location is at least configured for storage of emotional profiles of the system. The at least one detected action is processed in a computational machine using data from the first storage location to determine a status of the user. A response to the status of the user is calculated. The response is at least in part determined by the status of the user and data contained in the second storage location.

G06N504

Please help me predict the CPC LABEL for this patentPATENT ABSTRACT: A method, system and program product comprise monitoring inputs, into a system, for detected actions from a user. At least one detected action is identified. At least a first database storage location associated with the user is accessed. The first database storage location is at least configured for storage of emotional profiles of the user. At least a second storage location associated with the system is accessed. The second storage location is at least configured for storage of emotional profiles of the system. The at least one detected action is processed in a computational machine using data from the first storage location to determine a status of the user. A response to the status of the user is calculated. The response is at least in part determined by the status of the user and data contained in the second storage location.

The invention is methods and apparatus for: a) performing nonlinear time warp invariant sequence recognition using a back-off procedure; and b) recognizing complex sequences, using physically embodied computer memories that represent information using a sparse distributed representational (SDR) format. Recognition of complex sequences often requires that multiple equally plausible hypotheses (multiple competing hypotheses, MCHs) can be simultaneously physically active in memory until disambiguating information arrives whereupon only hypotheses that are consistent with the new information are active. The invention is the first description of both back-off and MCH-handling methods in combination with representing information using a sparse distributed representation (SDR) format.

Please help me write a proper abstract based on the patent claims. CLAIM: 1. A computer implemented method for recognizing an input sequence that is a time-warped instance of any of one or more previously learned sequences stored in a memory module M, where M represents information, i.e., the items of the sequences, using a sparse distributed representation (SDR) format, the method comprising: a) for each successive item of the input sequence, activating a code in M, which represents the item in the context of the preceding items of the sequence, and b) where M consists of a plurality of Q winner-take-all competitive modules (CMs), each consisting of K representational units (RUs) and the process of activating a code is carried out by choosing a winning RU (winner) in each CM, such that the chosen (activated) code consists of Q active winners, one per CM, and c) where the process of choosing a winner in a CM involves first producing a probability distribution over the K units of the CM, and then choosing a winner either: i) as a draw from the distribution (soft max), or ii) by selecting the unit with the max probability (hard max). 2. The method of claim 1, wherein: a) one or more sources of input to M are used in determining the code for the item, whereby we mean, more specifically, that the one or more input sources are used to generate the Q probability distributions, one for each of the Q CMs, from which the winners will be picked, and b) if an input sequence is recognized as an instance of a stored sequence, S, then the code activated to represent the last item of the input sequence will be the same as or closest to the code of the last item of S, and c) where the similarity measure over code space is intersection size. 3. The method of claim 2, wherein: a) one or more of the input sources to M represents information about the current input item, referred to as the “U” source in the Detailed Description, and b) one or more of the input sources to M represents information about the history of the sequence of items processed up to the current item, where two such sources were described in the Detailed Description, i) one referred to as the “H” source, which carries information about the previous code active in M and possibly the previous codes active in additional memory modules at the same hierarchical level of an overall possibly multi-level network of memory modules, which by recursion, carries information about the history of preceding items from the start of the input sequence up to and including the previous item, and ii) one referred to as the “D” source, which carries information about previous and or currently active codes in other higher-level memory modules, which also carry information about the history of the sequence thus far, and iii) these H and D sources being instances of what is commonly referred to in the field as “recurrent” sources, and c) where there can be arbitrarily many input sources, and where any of the sources, e.g., U, H, and D, may be further partitioned into different sensory modalities, e.g., the U source might be partitioned into a 2D vector representing an image at one pixel granularity and another 2D vector representing the image at another pixel granularity, both which supply signals concurrently to M. 4. The method of claim 3, wherein the use of the input sources to determine a code is a staged, conditional process, which we call the “Back-off” process, wherein, for each successive item of the input sequence: a) a series of estimates of the familiarity, G, of the item is generated, where b) the production of each estimate of G is achieved by multiplying a subset of all available input sources to M to produce a set of Q CM distributions of support values, i.e., “support distributions”, over the cells comprising each CM, and computing G as a particular measure on that set of support distributions, where in one embodiment that measure is the average maximum support value across the Q CMs, and where c) we denote the estimate of G by subscripting it with the set of input sources used to compute it, e.g., GUD, if U and D are used, GU if only U is used, etc., and where d) the estimate is then compared to a threshold, Γ, which may be specific to the set of sources used to compute it, e.g., compare GUD to ΓUD, compare GU to ΓU, etc., and where e) if the threshold is attained, the G estimate is used to nonlinearly transform the set of Q support distributions (generated in step 4b) into a set of Q probability distributions (in Steps 9-11 of Table 1 of the Background section), from which the winners will be drawn, yielding the code, and f) if the threshold is not attained, the process is repeated for the next G estimate in the prescribed series, proceeding to the end of the series if needed, 5. The method of claim 4, wherein the prescribed series will generally proceed from the G estimate that use all available input sources (the most stringent familiarity test), and then consider subsets of progressively smaller size (progressively less stringent familiarity tests), e.g., starting with GHUD, then if necessary trying GHU and GUD, then if necessary trying GU (note that not all possible subsets need be considered and the specific set of subsets tried and the order in which they are tried are prescribed and can depend on the particular application). 6. The method of claim 5, where M uses an alternative SDR coding format in which the entire field of R representational units is treated as a Z-winner-take-all (Z-WTA) field, where the choosing of a particular code is the process of choosing Z winners from the R units, where Z is much smaller R, e.g., 0.1%, 1%, 5%, and where in one embodiment, G would be defined as the average maximum value of the top Z values of the support distribution, and the actual choosing of the code would be either: a) making Z draws w/o replacement from the single distribution over the R units comprising the field, or b) choosing the units with the top Z probability values in the distribution. 7. A non-transitory computer readable storage medium storing instructions, which when executed implement the functionality described in claims 1-6. 8. The method of claim 3, where in determining the code to activate for item, T, of an input sequence, a) for each of the Q CMs, 1 to q, the number, ζq, of units tied (or approximately tied, i.e., within a predefinable epsilon) for the maximal probability of winning in CM q, and where that maximal probability is within a threshold of 1/ζq, e.g., greater than 0.9×1/ζq (the idea being that the ζq units are tied for their chance of winning and that chance is significantly greater than the chances of any of the other K-ζq units in CM q), is computed, and where b) the average, ζ, of ζq across the Q CMs, rounded to the nearest integer, is computed. 9. The method of claim 8, wherein if ζ≧2, i.e., if in all Q CMs, there are ζ tied units that are significantly more likely to win than the rest of the units, that indicates that, upon being presented with item T of the input sequence, ζ of the sequences stored in M, S1 to Sζ, are equally and maximally likely, i.e., one of the set of ζ maximally likely units in each CM is contained in the code of S1, a different one of that set is in the code of S2, etc., which we refer to as a “multiple competing hypotheses” (MCH) condition, and which is a fundamentally ambiguous condition given M's set of learned (stored) sequences and the current input sequence up to and including item T of the input sequence. 10. The method of claim 9, wherein when an MCH condition exists in M, the process of selecting winners is expected to result in the unit that was contained in S1 being chosen (activated) in approximately 1/ζ of Q CMs, the unit that was contained in S2, being chosen in different approximately 1/ζ of the Q CMs, . . . , the unit that was contained in Sζ being chosen in a further different 1/ζ of the Q CMs; in other words, the ζ equally and maximally likely hypotheses, i.e., the hypothesis that the input sequence up to and including item T is the same as stored sequence S1, that it is the same as stored sequence S2, . . . , that it is the same as the stored sequence Sζ, are physically represented by a 1/ζ fraction of their codes being simultaneously active (modulo variances). 11. The method of claim 10, wherein outgoing signals from the active units comprising the code active in M at T are multiplied in strength by ζ. 12. The method of claim 11, where M uses an alternative SDR coding format in which the entire field of R representational units is treated as a Z-WTA field, where the choosing of a particular code is the process of choosing Z winners from the R units, where Z is much smaller R, e.g., 0.1%, 1%, 5%, and where in one embodiment, the process of choosing a code is to make Z draws w/o replacement from the single distribution over the R units, in which case, if an MCH condition exists in M, then that selection process is expected to result in the unit that was contained in S1 being chosen (activated) in approximately 1/ζ of Q CMs, the unit that was contained in S2, being chosen in different approximately 1/ζ of the Q CMs, . . . , the unit that was contained in Sζ being chosen in a further different 1/ζ of the Q CMs, and in which case, the outgoing signals from the active units comprising the code active in M at T are multiplied in strength by ζ. 13. A non-transitory computer readable storage medium storing instructions, which when executed implement the functionality described in claims 1-3 and 8-12.

PENDING

Please predict whether this patent is acceptable.PATENT ABSTRACT: The invention is methods and apparatus for: a) performing nonlinear time warp invariant sequence recognition using a back-off procedure; and b) recognizing complex sequences, using physically embodied computer memories that represent information using a sparse distributed representational (SDR) format. Recognition of complex sequences often requires that multiple equally plausible hypotheses (multiple competing hypotheses, MCHs) can be simultaneously physically active in memory until disambiguating information arrives whereupon only hypotheses that are consistent with the new information are active. The invention is the first description of both back-off and MCH-handling methods in combination with representing information using a sparse distributed representation (SDR) format.

G06N5047

Please help me predict the CPC LABEL for this patentPATENT ABSTRACT: The invention is methods and apparatus for: a) performing nonlinear time warp invariant sequence recognition using a back-off procedure; and b) recognizing complex sequences, using physically embodied computer memories that represent information using a sparse distributed representational (SDR) format. Recognition of complex sequences often requires that multiple equally plausible hypotheses (multiple competing hypotheses, MCHs) can be simultaneously physically active in memory until disambiguating information arrives whereupon only hypotheses that are consistent with the new information are active. The invention is the first description of both back-off and MCH-handling methods in combination with representing information using a sparse distributed representation (SDR) format.

Systems and methods are described for predicting and/or detecting occupancy of an enclosure, such as a dwelling or other building, which can be used for a number of applications. An a priori stochastic model of occupancy patterns based on information of the enclosure and/or the expected occupants of the enclosure is used to pre-seed an occupancy prediction engine. Along with data from an occupancy sensor, the occupancy prediction engine predicts future occupancy of the enclosure. Various systems and methods for detecting occupancy of an enclosure, such as a dwelling, are also described.

Please help me write a proper abstract based on the patent claims. CLAIM: 1. A system for predicting occupancy of an enclosure comprising: a model of occupancy patterns based in part on information regarding the enclosure and/or the expected occupants of the enclosure; a sensor configured to detect occupancy within the enclosure; and an occupancy predictor configured to predict future occupancy of the enclosure based at least in part on the model and the occupancy detected by the sensor. 2. The system of claim 1 wherein the model is an a priori stochastic model of human occupancy. 3. The system of claim 2 wherein the a priori stochastic model is a comfort and spatial occupancy model that includes one or more statistical profiles. 4. The system of claim 2 wherein the a priori stochastic model includes behavior modeling of activity, itinerary, and/or thermal behavior. 5. The system of claim 1 wherein the model is based at least in part on information selected from the group consisting of: a type of the enclosure, geometrical data about the enclosure, structural data about the enclosure, geographic location of the enclosure, an expected type of occupant of the enclosure, an expected number of occupants of the enclosure, the relational attributes of the occupants of the enclosure, seasons of the year, days of the week, types of day, and times of day. 6. The system of claim 1 wherein the sensor is selected from a group consisting of: motion detector, powerline sensor, network traffic monitor, radio traffic monitor, microphone, infrared sensor, accelerometer, ultrasonic sensor, pressure sensor, smart utility meter, and light sensor. 7. The system of claim 1 further comprising a second sensor, wherein the occupancy predictor is configured to predict future occupancy of the enclosure based at least in part on the model, the sensor, and the second sensor. 8. A method for predicting occupancy of an enclosure comprising: receiving a model of occupancy patterns based in part on information regarding the enclosure and/or the expected occupants of the enclosure; receiving occupancy data from a sensor configured to detect occupancy within the enclosure, the occupancy data being indicative of the occupancy detected by the sensor; and predicting, by a computing device, future occupancy of the enclosure based at least in part on the model and the occupancy data. 9. The method of claim 8 further comprising receiving user inputted data, wherein the future occupancy of the enclosure is predicted further based in part on the user inputted data. 10. The method of claim 9 wherein the user inputted data includes occupancy information directly inputted by an occupant of the enclosure and/or calendar information. 11. The method of claim 9 further comprising detecting periodicities in the user inputted data, wherein the future occupancy of the enclosure is predicted further based in part on the detected periodicities in the user inputted data. 12. The method of claim 8 further comprising: comparing the predicted future occupancy of the enclosure with the occupancy data from the sensor; and updating the model of occupancy patterns based at least in part on the result of the comparison. 13. The method of claim 8 wherein the sensor is one of a plurality of sensors arranged at different sub-regions of the enclosure, receiving occupancy data from a sensor includes receiving occupancy data from the plurality of sensors, and predicting future occupancy of the enclosure includes predicting future occupancy of the enclosure based at least in part on the occupancy data received from the plurality of sensors. 14. The method of claim 8 wherein the future occupancy predictions are based at least in part on a maximum-likelihood approach. 15. A tangible non-transitory computer-readable storage medium having instructions stored thereon that, when executed by a computing device, cause the computing device to perform operations comprising: receiving a model of occupancy patterns based in part on information regarding the enclosure and/or the expected occupants of the enclosure; receiving occupancy data from a sensor configured to detect occupancy within the enclosure, the occupancy data being indicative of the occupancy detected by the sensor; and predicting future occupancy of the enclosure based at least in part on the model and the occupancy data. 16. The storage medium of claim 15 wherein the model is based at least in part on an expected occupant type. 17. The storage medium of claim 16 wherein the expected occupant type depends on one or more occupant attributes selected from a group consisting of: age, school enrollment status, marital status, relationships status with other occupants, and retirement status. 18. The storage medium of claim 16 wherein the expected occupant type is selected from a group consisting of: preschool children, school-age children, seniors, retirees, working-age adults, non-coupled adults, vacationers, office workers, and retail store occupants. 19. The storage medium of claim 15 wherein the model of occupancy patterns includes one or more types of models selected from a group consisting of: Bayesian Network, Hidden Markov Model, Hidden Semi-Markov Model, variant of Markov model, and Partially Observable Markov Decision Process. 20. The storage medium of claim 15 wherein the future occupancy prediction is used in one or more systems of a type selected from a group consisting of: HVAC system, hot water heating, home automation, home security, lighting management, and charging of rechargeable batteries.

ACCEPTED

Please predict whether this patent is acceptable.PATENT ABSTRACT: Systems and methods are described for predicting and/or detecting occupancy of an enclosure, such as a dwelling or other building, which can be used for a number of applications. An a priori stochastic model of occupancy patterns based on information of the enclosure and/or the expected occupants of the enclosure is used to pre-seed an occupancy prediction engine. Along with data from an occupancy sensor, the occupancy prediction engine predicts future occupancy of the enclosure. Various systems and methods for detecting occupancy of an enclosure, such as a dwelling, are also described.

G06N5048

Please help me predict the CPC LABEL for this patentPATENT ABSTRACT: Systems and methods are described for predicting and/or detecting occupancy of an enclosure, such as a dwelling or other building, which can be used for a number of applications. An a priori stochastic model of occupancy patterns based on information of the enclosure and/or the expected occupants of the enclosure is used to pre-seed an occupancy prediction engine. Along with data from an occupancy sensor, the occupancy prediction engine predicts future occupancy of the enclosure. Various systems and methods for detecting occupancy of an enclosure, such as a dwelling, are also described.

Technologies for a human computation framework suitable for answering common sense questions that are difficult for computers to answer but easy for humans to answer. The technologies support solving general common sense problems without a priori knowledge of the problems; support for determining whether an answer is from a bot or human so as to screen out spurious answers from bots; support for distilling answers collected from human users to ensure high quality solutions to the questions asked; and support for preventing malicious elements in or out of the system from attacking other system elements or contaminating the solutions produced by the system, and preventing users from being compensated without contributing answers.

Please help me write a proper abstract based on the patent claims. CLAIM: 1. A method performed on a computing device, the method comprising: selecting, by the computing device, a common-sense problem from a first source; receiving, by the computing device, answers to the common-sense problem from a second source; identifying, by the computing device, any of the received answers that are arbitrary answers; removing, by the computing device, the identified arbitrary answers from the received answers; and designating, by the computing device in response to the removing, as final answers any remaining received answers. 2. The method of claim 1 further comprising sending, in response to the designating, the final answers to the first source. 3. The method of claim 1 where the computing device is configured for performing the method without a priori knowledge of the common-sense problem. 4. The method of claim 1 further comprising inhibiting compensation to a source that does not contribute an answer to the common-sense problem. 5. The method of claim 1 where the first source and the second source are the same source. 6. The method of claim 1 where the second source comprises at least one human. 7. The method of claim 1 where the identifying the arbitrary answers is based on modeling the arbitrary answers as a uniform distribution. 8. A computing device comprising: memory; a processor coupled to the memory and via which the computing device: orders answers according to their frequency of occurrence; determines a relative difference for each neighboring pair of the ordered answers, the relative distance based on the frequency of occurrence of each ordered answer of the each neighboring pair; and designates as final answers any of the ordered answers that have a frequency of occurrence that is greater than a frequency of occurrence of an ordered answer of a neighboring pair that has a greatest relative distance of the neighboring pairs. 9. The computing device of claim 8 where the relative distance is determined based on calculating a slope. 10. The computing device of claim 8 where the answers are directed to labeling an image. 11. The computing device of claim 10 where the labeling comprises a process including a plurality of refining stages. 12. The computing device of claim 11 where the plurality of refining stages comprise collecting candidate labels. 13. The computing device of claim 12 where the plurality of refining stages comprise further refining the candidate labels based on multiple choices. 14. The computing device of claim 12 where the plurality of refining stages comprise further refining based on locating an object in the image that corresponds to at least one of the refined candidate labels 15. At least one computer storage device that comprises computer-executable instructions that, based on execution by a computing device, configure the computing device to perform actions comprising: ordering, by a computing device, answers according to their frequency of occurrence; determining, by a computing device, a relative difference for each neighboring pair of the ordered answers, the relative distance based on the frequency of occurrence of each ordered answer of the each neighboring pair; and designating, by a computing device, as final answers any of the ordered answers that have a frequency of occurrence that is greater than a frequency of occurrence of an ordered answer of a neighboring pair that has a greatest relative distance of the neighboring pairs. 16. The at least one computer storage device of claim 15 where the determining the relative distance comprises calculating a slope. 17. The at least one computer storage device of claim 15 where the answers are directed to labeling an image. 18. The at least one computer storage device of claim 17 where the labeling comprises a process including a plurality of refining stages. 19. The at least one computer storage device of claim 18 where the plurality of refining stages comprise collecting candidate labels. 20. The at least one computer storage device of claim 19 where the plurality of refining stages comprise further refining the candidate labels based on multiple choices, and further refining based on locating an object in the image that corresponds to at least one of the refined candidate labels.

PENDING

Please predict whether this patent is acceptable.PATENT ABSTRACT: Technologies for a human computation framework suitable for answering common sense questions that are difficult for computers to answer but easy for humans to answer. The technologies support solving general common sense problems without a priori knowledge of the problems; support for determining whether an answer is from a bot or human so as to screen out spurious answers from bots; support for distilling answers collected from human users to ensure high quality solutions to the questions asked; and support for preventing malicious elements in or out of the system from attacking other system elements or contaminating the solutions produced by the system, and preventing users from being compensated without contributing answers.

G06N5022

Please help me predict the CPC LABEL for this patentPATENT ABSTRACT: Technologies for a human computation framework suitable for answering common sense questions that are difficult for computers to answer but easy for humans to answer. The technologies support solving general common sense problems without a priori knowledge of the problems; support for determining whether an answer is from a bot or human so as to screen out spurious answers from bots; support for distilling answers collected from human users to ensure high quality solutions to the questions asked; and support for preventing malicious elements in or out of the system from attacking other system elements or contaminating the solutions produced by the system, and preventing users from being compensated without contributing answers.

In one embodiment, a method includes receiving event information associated with an event listing, the event information being inputted by a user of an online event management system at a client device; transmitting the event information to the online event management system, the event listing being synchronously categorized with the sending of the event information; and receiving from the online event management system one or more categories associated with the event listing for display to the user at the client device, the categories being automatically determined based on categorization models of the online event management system in response to the transmitted event information.

Please help me write a proper abstract based on the patent claims. CLAIM: 1. A method comprising: receiving, at a client device, event information associated with an event listing, the event information being inputted by a user of an online event management system at the client device; transmitting, to the online event management system, the event information associated with the event listing, the event listing being synchronously categorized with the sending of the event information; and receiving, at the client device from the online event management system, one or more categories associated with the event listing for display to the user, the one or more categories being automatically determined based on one or more categorization models of the online event management system in response to the transmitted event information. 2. The method of claim 1, wherein the event information is inputted by the user on a webpage hosted by the online event management system. 3. The method of claim 1, wherein the event information is inputted by the user in an application associated with the online event management system. 4. The method of claim 1, wherein the one or more categories are displayed on a webpage for creating the event listing hosted by the online event management system, the one or more categories being displayed automatically in response to the event information being inputted by the user. 5. The method of claim 1, further comprising: receiving, at the client device, one or more user-identified categories associated with the event listing, and wherein the one or more categories are automatically determined further based on the one or more user-identified categories. 6. The method of claim 1, further comprising: calculating, at the online event management system, one or more category probabilities for the event listing using one or more categorization models, respectively, each categorization model calculating a category probability based at least in part on the event information associated with the event listing, each category probability corresponding to a category, the category probability being equal to the probability that the event listing falls within the category; and for each category probability greater than or equal to a threshold probability, associating with the event listing the category corresponding to the category probability. 7. The method of claim 6, wherein each categorization model includes one or more variables based on: a preset list of one or more features; or a list of one or more values of features being associated with the event listing. 8. The method of claim 1, wherein one or more categorization models are refined by one or more learning algorithms, the learning algorithms being logistic regression, binary classification, multiclass classification, or maximum likelihood estimation. 9. The method of claim 1, wherein: the event information identifies one or more of: a title of the event; a description of the event; a self-identified category of the event; a derived category of the event; a keyword of the event; an instructions for the event; an attendee of the event; an organizer of the event; a venue of the event; a ticket type of the event; a ticket price of the event; or a geographic location of the event; and calculating the one or more category probabilities is further based at least in part on one or more of: the title of the event; the description of the event; the self-identified category of the event; the derived category of the event; the keyword of the event; the instructions for the event; the attendee of the event; the organizer of the event; the venue of the event; the ticket type of the event; the ticket price of the event; or the geographic location of the event. 10. The method of claim 1, wherein the category probability for the event listing is a percentile rank equal to the percentage of event listings that have a category probability the same or lower than the event listing. 11. The method of claim 1, wherein calculating the category probability based at least in part on the event information associated with the event listing comprises: accessing a list comprising a plurality of features; comparing the list to the event information; for each feature present in the event information, calculating a value for the feature; and calculating the category probability based at least in part on one or more of the values of the features. 12. The method of claim 11, wherein calculating the category probability based at least in part on the event information associated with the event listing further comprises: for each feature not present in the event information, calculating a value of for the feature equal to zero. 13. The method of claim 11, wherein the features present in the event information are represented as a matrix. 14. The method of claim 11, calculating the one or more category c probabilities fc(z) wherein: f c  ( z ) = 1 1 + e - z ; and z=β0+β1x1+β2x2+ . . . +βkxk, wherein z is the result of a comparison between a set of features associated with an event listing and the weights associated with those features, β is a factor associated with a particular feature, and x is an indication of a particular feature in the event listing. 15. The method of claim 11, wherein the list comprising a plurality of features is determined by a method comprising: accessing D training event informations, each training event information being associated with a training event listing; calculating a term frequency for each term i in the D training event informations; and for each term i with a term frequency greater than or equal to a threshold term frequency, categorizing the term as a feature. 16. The method of claim 15, wherein the term frequency for each term i in the D training event informations equals (tf-idf)i,j, wherein: (tf=idf)i,j=tfi,j×idfi; tf i , j = n ij ∑ k   n k , j , wherein tfi,j is the term frequency of term i in document j, ni,j is the number of times term i appears in document j, and k is the total number of terms; and idf i = log   D   { d  :   t i ∈ d }  , wherein idfi is the inverse document frequency of term i, and d is the current document. 17. An apparatus comprising: one or more processors; and a memory coupled to the processors comprising instructions executable by the processors, the processors operable when executing the instructions to: receive, at a client device, event information associated with an event listing, the event information being inputted by a user of an online event management system at the client device; transmit, to the online event management system, the event information associated with the event listing, the event listing being synchronously categorized with the sending of the event information; and receive, at the client device from the online event management system, one or more categories associated with the event listing for display to the user, the one or more categories being automatically determined based on one or more categorization models of the online event management system in response to the transmitted event information. 18. One or more computer-readable non-transitory storage media embodying instructions that are operable when executed to: receive, at a client device, event information associated with an event listing, the event information being inputted by a user of an online event management system at the client device; transmit, to the online event management system, the event information associated with the event listing, the event listing being synchronously categorized with the sending of the event information; and receive, at the client device from the online event management system, one or more categories associated with the event listing for display to the user, the one or more categories being automatically determined based on one or more categorization models of the online event management system in response to the transmitted event information.

ACCEPTED

Please predict whether this patent is acceptable.PATENT ABSTRACT: In one embodiment, a method includes receiving event information associated with an event listing, the event information being inputted by a user of an online event management system at a client device; transmitting the event information to the online event management system, the event listing being synchronously categorized with the sending of the event information; and receiving from the online event management system one or more categories associated with the event listing for display to the user at the client device, the categories being automatically determined based on categorization models of the online event management system in response to the transmitted event information.

G06N702

Please help me predict the CPC LABEL for this patentPATENT ABSTRACT: In one embodiment, a method includes receiving event information associated with an event listing, the event information being inputted by a user of an online event management system at a client device; transmitting the event information to the online event management system, the event listing being synchronously categorized with the sending of the event information; and receiving from the online event management system one or more categories associated with the event listing for display to the user at the client device, the categories being automatically determined based on categorization models of the online event management system in response to the transmitted event information.