Systems and methods for adaptive smart environment automation
원문보기
IPC분류정보
국가/구분
United States(US) Patent
등록
국제특허분류(IPC7판)
G06F-017/18
G06Q-010/00
출원번호
US-0552998
(2009-09-02)
등록번호
US-8417481
(2013-04-09)
발명자
/ 주소
Cook, Diane J.
Rashidi, Parisa
출원인 / 주소
Cook, Diane J.
대리인 / 주소
Lee & Hayes, PLLC
인용정보
피인용 횟수 :
4인용 특허 :
0
초록▼
Several embodiments of systems and methods for adaptive smart environment automation are described herein. In one embodiment, a computer implemented method includes determining a plurality of sequence patterns of data points in a set of input data corresponding to a plurality of sensors in a space.
Several embodiments of systems and methods for adaptive smart environment automation are described herein. In one embodiment, a computer implemented method includes determining a plurality of sequence patterns of data points in a set of input data corresponding to a plurality of sensors in a space. The input data include a plurality of data points corresponding to each of the sensors, and the sequence patterns are at least partially discontinuous. The method also includes generating a plurality of statistical models based on the plurality of sequence patterns, and the individual statistical models corresponding to an activity of a user. The method further includes recognizing the activity of the user based on the statistical models and additional input data from the sensors.
대표청구항▼
1. A computer implemented method, comprising: a processor collecting input data from a plurality of sensors in a space, the input data including a plurality of sequential data points corresponding to the individual sensors, wherein each data point represents a sensor event;a processor analyzing the
1. A computer implemented method, comprising: a processor collecting input data from a plurality of sensors in a space, the input data including a plurality of sequential data points corresponding to the individual sensors, wherein each data point represents a sensor event;a processor analyzing the input data received from the plurality of sensors to determine a sequence pattern of data points from the input data, an instance of the sequence pattern of data points being at least partially discontinuous; anda processor controlling a control element in the space based on the determined sequence pattern of data points. 2. The computer implemented method of claim 1 wherein: the input data are first input data;analyzing the input data includes: calculating a frequency of each of the data points in the first input data;generating second input data from the first input data by eliminating data points with a corresponding frequency less than a preselected threshold;moving a data window across the second input data;determining a sequence pattern based on data points in the data window as the data window moves across the second input data; and correlating the determined sequence pattern to an activity of a user in the space; andcontrolling the control element includes: recognizing the activity of the user based on the determined sequence pattern; andcontrolling the control element in the space based on the recognized activity of the user. 3. The computer implemented method of claim 1 wherein analyzing the input data includes: moving a data window across the input data; anddetermining a sequence pattern based on data points in the data window as the data window moves across the input data, the sequence pattern comprising all variations of a single sequence pattern that occur in the input data and satisfy the following condition: DL(D)(DL(a)+DL(D❘a))*Γa>C where DL is a description length of a corresponding argument, D is a data point, a is a sequence pattern, C is a minimum compression threshold, and Γ is a discontinuity factor. 4. The computer implemented method of claim 1 wherein analyzing the input data includes: moving a data window across the input data, the data window having a length of data points;determining a sequence pattern based on data points in the data window as the data window moves across the input data, the sequence pattern comprising all variations of a single sequence pattern that occur in the input data and satisfy the following condition: DL(D)(DL(a)+DL(D❘a))*Γa>C where DL is a description length of a corresponding argument, D is a data point, a is a sequence pattern, C is a minimum compression threshold, and Γ is a discontinuity factor; and incrementing the length of the data window and repeating moving the data window across the input data and determining a sequence pattern until a preselected number of iterations are reached. 5. The computer implemented method of claim 1 wherein: analyzing the input data includes determining a plurality of sequence patterns based on data points in the input data, the individual sequence patterns comprising all variations of a single sequence pattern that occur in the input data; andthe method further includes grouping the plurality of determined sequence patterns into a plurality of clusters based on a similarity between the sequence patterns. 6. The computer implemented method of claim 1 wherein: analyzing the input data includes: determining a plurality of sequence patterns based on data points in the input data, the individual sequence patterns comprising all variations of a single sequence pattern that occur in the input data and satisfy the following condition: DL(D)(DL(a)+DL(D❘a))*Γa>Cwhere DL is a description length of a corresponding argument, D is a data point, a is a sequence pattern, C is a minimum compression threshold, and Γ is a discontinuity factor; andthe method further includes grouping the plurality of determined sequence patterns into a plurality of clusters based on a similarity between the sequence patterns. 7. The computer implemented method of claim 1 wherein: analyzing the input data includes:determining a plurality of sequence patterns based on data points in the input data; andthe method further includes: computing an edit distance between two sequence patterns in the plurality of sequence patterns; andgrouping the two sequence patterns into a cluster if the computed edit distance is less than a threshold value. 8. The computer implemented method of claim 1 wherein: analyzing the input data includes determining a plurality of sequence patterns based on data points in the input data, the individual sequence patterns comprising all variations of a single sequence pattern that occur in the input data; andthe method further includes: grouping the plurality of determined sequence patterns into a plurality of clusters based on a similarity between the sequence patterns; andgenerating a statistical model for the individual clusters, the statistical models including at least one of a Dynamic Bayes Network, a Naïve Bayes Classifier, a Markov model, and a hidden Markov model. 9. The computer implemented method of claim 1 wherein: analyzing the input data includes: determining a plurality of sequence patterns based on data points in the input data; andthe method further includes: computing an edit distance between two sequence patterns in the plurality of sequence patterns;grouping the plurality of determined sequence patterns into a plurality of clusters based on the computed edit distance; andgenerating a statistical model for the individual clusters, the statistical models including a hidden Markov model. 10. The computer implemented method of claim 1 wherein: analyzing the input data includes: determining a plurality of sequence patterns based on data points in the input data; andthe method further includes: computing an edit distance between two sequence patterns in the plurality of sequence patterns;grouping the plurality of determined sequence patterns into a plurality of clusters based on the computed edit distance;generating a statistical model for the individual clusters, the statistical models including a hidden Markov model;collecting additional input data from the plurality of sensors; andcomputing a statistical probability that the additional input data are correlating to an activity of a user in the space. 11. A computer implemented method, comprising: a computing device determining a plurality of a sequence patterns of data points in a set of input data corresponding to a plurality of sensors in a space, the input data including a plurality of data points corresponding to each of the sensors, wherein each data point represents a sensor event and wherein at least one of the sequence patterns is at least partially discontinuous;a computing device generating a plurality of statistical models based on the plurality of sequence patterns, the individual statistical models corresponding to an activity of a user; anda computing device recognizing the activity of the user based on the statistical models and additional input data from the sensors. 12. The computer implemented method of claim 11 wherein: determining a plurality of sequence patterns of data points includes determining a plurality of sequence patterns of data points in the input data, the individual sequence patterns comprising all variations of a single sequence pattern that occur in the input data; andthe method further includes grouping the plurality of sequence patterns into a plurality of clusters, the individual clusters containing sequence patterns that have a similarity greater than a preselected threshold. 13. The computer implemented method of claim 11 wherein: determining a plurality of sequence patterns of data points includes determining a plurality of sequence patterns of data points in the input data, the individual sequence patterns comprising all variations of a single sequence pattern that occur in the input data; andthe method further includes grouping the plurality of sequence patterns into a plurality of clusters, the individual clusters containing sequence patterns that have an edit distance less than a preselected threshold. 14. The computer implemented method of claim 11, further comprising: grouping the plurality of sequence patterns into a plurality of clusters, the individual clusters containing sequence patterns that have an edit distance less than a preselected threshold; andgenerating a plurality of statistical models includes generating a plurality of statistical models individually based on each of the grouped clusters. 15. The computer implemented method of claim 11, further comprising: grouping the plurality of sequence patterns into a plurality of clusters, the individual clusters containing sequence patterns that have an edit distance less than a preselected threshold; andgenerating a plurality of statistical models includes generating a plurality of hidden Markov models individually based on each of the grouped clusters. 16. The computer implemented method of claim 11, further comprising: grouping the plurality of sequence patterns into a plurality of clusters, the individual clusters containing sequence patterns that have an edit distance less than a preselected threshold; andgenerating a plurality of statistical models includes generating a plurality of hidden Markov models individually based on each of the grouped clusters;wherein recognizing the activity of the user includes: collecting the additional input data from the sensors; andcomputing a probability based on the additional input data from the sensors and the generated hidden Markov Models, the probability indicating a likelihood that the additional input data from the sensors correspond to an activity of a user. 17. The computer implemented method of claim 11, further comprising: grouping the plurality of sequence patterns into a plurality of clusters, the individual clusters containing sequence patterns that have an edit distance less than a preselected threshold; andgenerating a plurality of statistical models includes generating a plurality of hidden Markov models individually based on each of the grouped clusters;wherein recognizing the activity of the user includes: collecting the additional input data from the sensors;computing a probability based on the additional input data from the sensors and the generated hidden Markov Models, the probability indicating a likelihood that the additional input data from the sensors correspond to an activity of a user; andautomating a control element in the space based on the computed probability. 18. The computer implemented method of claim 11, further comprising: grouping the plurality of sequence patterns into a plurality of clusters, the individual clusters containing sequence patterns that have an edit distance less than a preselected threshold; andgenerating a plurality of statistical models includes generating a plurality of hidden Markov models individually based on each of the grouped clusters;wherein recognizing the activity of the user includes: collecting the additional input data from the sensors;computing a probability based on the additional input data from the sensors and the generated hidden Markov Models, the probability indicating a likelihood that the additional input data from the sensors correspond to an activity of a user, the activity having at least one of a start time and a duration;identifying a change in the start time and/or the duration of the activity; andgenerating an alarm to the user about the change. 19. A computer system, comprising: a plurality of sensors installed in a space, the sensors being configured to provide input data including a plurality of data points corresponding to each of the sensors, wherein each data point represents a sensor event; anda controller operatively coupled to the sensors, the controller including:an activity miner configured to analyze the input data from the plurality of sensors to determine a plurality of sequence patterns of data points, the sequence patterns being at least partially discontinuous; andan activity model configured to generate a plurality of statistical models based on the plurality of sequence patterns, the individual statistical models corresponding to an activity of a user. 20. The computer system of claim 19 wherein the activity miner includes: a discontinuous varied-order sequential module configured to determine a plurality of sequence patterns of data points, wherein at least one of the sequence patterns is discontinuous; anda clustering module configured to group the plurality of sequence patterns into a set of clusters based on similarity of the plurality of sequence patterns. 21. The computer system of claim 19 wherein: the activity miner includes (1) a discontinuous varied-order sequential module configured to determine a plurality of sequence patterns of data points, wherein at least one of the sequence patterns is discontinuous and (2) a clustering module configured to group the plurality of sequence patterns into a set of clusters based on similarity of the plurality of sequence patterns; andthe activity model includes a hidden Markov model having a plurality of hidden nodes related to the plurality of sequence patterns via a plurality of corresponding probabilities.
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