Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claim(s) are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Efros et al. (USPN 8742959B1).
As per claim 1, Efros et al. discloses a storage system monitoring method, comprising: obtaining storage system monitoring data (column 9, lines 13-27 - The data module 108 may provide the raw data 118 to the parameter module 106 in response to request(s) from the parameter module 106. As referenced above, such data streams are known to exist in a variety of circumstances and settings, including, for example, business, industry, healthcare, government, or military settings. To give just a few, more specific, examples, the data sources may output data streams representing or related to (events occurring within or with respect to) network monitoring, network traffic engineering, telecom call records, financial applications, stock market data, sensor networks, manufacturing processes, web logs and click streams, and massive datasets which are streamed as a way of handling the large volume of data. – the data is inclusive of storage system monitoring data);
generating a segment of the storage system monitoring data (column 3, lines 60-63 - The data may be divided into intervals of variable (temporal) length, with the intervals each including different numbers of data points, and regression functions may be determined to approximate each interval.);
determining time information, a compression technique, and a compression parameter set for the segment, wherein a reconstructed segment generated using the compression technique and the compression parameter set satisfies an error condition (column 3, line 63 – column 4, line 20 - The lengths of the intervals and the determined functions may be based on a user-defined maximum tolerable error value and/or maximum allowable error. The maximum tolerable error value, or predetermined error margin, may be per data point, i.e., the regression function per interval may not allow any single data point to be approximated with an error greater than the user-defined maximum tolerable error value. The compression may result in identifiers of the intervals or time series, start and/or end points of intervals or time series, function type identifiers of the intervals or time series, function terms indicating a number of parameters or coefficients in the functions, and/or parameter values such as polynomial orders and/or polynomial coefficients. The function types may include, for example, polynomial functions, sinusoidal functions, exponential functions, logarithmic functions, or asymptotic functions. The terms of the functions, as well as the number of polynomial coefficients or number of frequencies for a sinusoidal function, of the intervals or time series may vary. The compression may include, for example, determining a least number of parameters or coefficients, such as a lowest order polynomial, that will approximate each interval or time series within a maximum allowable error, with the lowest order polynomial being unique and/or different for each interval or time series.; column 11, lines 26-36 - A retrieval component 216 of the database management system 202 may access the compressed data from the data storage unit 212. The retrieval component 216 may decompress the compressed data to approximate the original data. The retrieval component 216 may decompress the compressed data in accordance with functions and processes described herein. The database management system 202 may include a data access interface 218 which communicates with external devices and/or applications. The data access interface 218 may provide the decompressed and/or approximated data to the external devices and/or applications.);
storing the time information, the compression parameter set, and an indication of the compression technique (column 9, lines 34-38 - The compressed data 120 may include segments 122, such as beginning and/or end points of segments, intervals, or time series. The compressed data 120 may also include parameters 124 for each of the intervals or segments, such as coefficients, function type identifiers, and/or function terms. The compressed data 120 may also include identifiers for the intervals or segments and/or functions.);
receiving a user query from a user system, the user query indicating a time interval; in response to the user query, identifying the compression technique and the compression parameter set based on the time interval and the time information and performing at least one of: reconstructing and providing at least a portion of the segment using the time information, the compression technique, and the compression parameter set; or providing the compression parameter set and the indication of the compression technique for reconstruction of the portion (column 12, lines 16-36 - The retrieval component 232 may decompress the compressed data 228 and/or approximate the original data. The retrieval component 232 may decompress the compressed data 228 and/or approximate the original data in accordance with functions and processes described herein. The form of the data retrieved by the retrieval component 232 may include, for example, a set of values or data points, or a structured reply such as Extensible Markup Language (XML) code or a graph. An application logic 234 may query the decompressed and/or approximated data from the retrieval component 232. The retrieval component 232 may decompress the compressed data 228 "just in time," or at the time of the query from the application logic 234. The retrieval component 232 may provide the data (which has been decompressed and/or approximated) to the application logic 234. The application logic 234 may deliver the decompressed and/or approximated data to a user, such as via a server 236, which may include a web server, over a data access interface 238. The application logic 234 may, for example, execute on an application server which may co-host a web server.; column 9, lines 45-51 - The decompressor 126 may decompress data to estimate point values of data, or estimate total values over intervals, such as power usage over a time interval. A point estimator 128 of the decompressor 126 may estimate or approximate point values, and an area estimator 130 of the decompressor estimate or approximate areas such as power usage over a time period.; column 9, lines 52-59 - The point estimator 128 may determine the data value at a certain point, such as a certain point in time. The point estimator 128 may determine the function for the interval, segment, or time series of the independent variable (such as time), as well as the parameters or coefficients. The point estimator 128 may then insert the value of the independent variable into the function, along with the parameters and/or coefficients, to determine the value of the dependent variable.).
As per claim 2, Efros et al. discloses wherein: the compression technique comprises a formula for approximating the segment that takes segment indices and the compression parameter set as parameters (column 4, lines 3-20 - The compression may result in identifiers of the intervals or time series, start and/or end points of intervals or time series, function type identifiers of the intervals or time series, function terms indicating a number of parameters or coefficients in the functions, and/or parameter values such as polynomial orders and/or polynomial coefficients. The function types may include, for example, polynomial functions, sinusoidal functions, exponential functions, logarithmic functions, or asymptotic functions. The terms of the functions, as well as the number of polynomial coefficients or number of frequencies for a sinusoidal function, of the intervals or time series may vary. The compression may include, for example, determining a least number of parameters or coefficients, such as a lowest order polynomial, that will approximate each interval or time series within a maximum allowable error, with the lowest order polynomial being unique and/or different for each interval or time series.).
As per claim 3, Efros et al. discloses wherein: the formula comprises a polynomial approximation formula (column 4, lines 35-38 - The compressor module 102 may determine functions, such as polynomial functions or sinusoidal functions (such as Fourier series) that each approximate an interval or time series within the raw data within the user-defined maximum tolerable error value.).
As per claim 4, Efros et al. discloses wherein: determining the compression technique and the compression parameter set comprises determining parameters for a compression formula and parameters for an inverse formula (column 4, lines 3-20 - The compression may result in identifiers of the intervals or time series, start and/or end points of intervals or time series, function type identifiers of the intervals or time series, function terms indicating a number of parameters or coefficients in the functions, and/or parameter values such as polynomial orders and/or polynomial coefficients.; column 4, lines 38 - 48 - The compressor module 102 may determine functions, such as polynomial functions or sinusoidal functions (such as Fourier series) that each approximate an interval or time series within the raw data within the user-defined maximum tolerable error value. The compressor module 102 may determine, for example, the lowest-order or least complex functions, such as the functions with the fewest coefficients, that approximate the intervals or times within the raw data, thereby reducing the number of values that need to be stored to approximate the raw data within the user-defined maximum tolerable error value. – the parameters are able to be for an inverse formula of a function); and
storing the time information, the compression parameter set, and the indication of the compression technique comprises storing the inverse formula parameters and an indication of the inverse formula (column 9, lines 32-38 - The compressed data 120 may include segments 122, such as beginning and/or end points of segments, intervals, or time series. The compressed data 120 may also include parameters 124 for each of the intervals or segments, such as coefficients, function type identifiers, and/or function terms. The compressed data 120 may also include identifiers for the intervals or segments and/or functions.).
As per claim 5, Efros et al. discloses wherein: the compression formula comprises a frequency domain transform or audio encoding (column 4, lines 12-20 - The terms of the functions, as well as the number of polynomial coefficients or number of frequencies for a sinusoidal function, of the intervals or time series may vary.; column 6, lines 43-46 - In an example of another function type (Fourier series), the function module 114 may perform a discrete Fourier transform on the data to determine a sinusoidal function within a given degree of complexity to approximate the data.).
As per claim 6, Efros et al. discloses wherein: the time information specifies a mapping from segment index to time (column 4, lines 3-9 - The compression may result in identifiers of the intervals or time series, start and/or end points of intervals or time series, function type identifiers of the intervals or time series, function terms indicating a number of parameters or coefficients in the functions, and/or parameter values such as polynomial orders and/or polynomial coefficients.; column 5, lines 43-60 - The function module 114 may select the at least one function from a plurality of functions available to approximate the interval, segment, or time series of data. The function module 114 may approximate the interval, segment, or time series using the selected function, adding data points from a time series to the interval or segment, as long as the selected function approximates the interval, segment, or time series within a maximum allowable error. The function module 114 may add a single data point at a time, adding a single data point and then approximating and determining whether the function approximates the interval, segment, or time series within the maximum allowable error for each added data point, or may add multiple data points, such as two, three, or more data points at a time, and approximate the interval, segment, or time series and determine whether the function approximates the interval, segment, or time series within the maximum allowable error with the multiple added data points.).
As per claim 7, Efros et al. discloses wherein: generating the segment of the storage system monitoring data comprises: segmenting the storage system monitoring data based on at least one of segment size, number of data points, or segment duration (column 3, lines 60-63 - The data may be divided into intervals of variable (temporal) length, with the intervals each including different numbers of data points, and regression functions may be determined to approximate each interval.); segmenting the storage system monitoring data based on at least one of statistics of the storage system monitoring data, a frequency domain representation of the storage system monitoring data, or a wavelet domain representation of the storage system monitoring data; or applying the storage system monitoring data to at least one machine learning model trained to segment the storage system monitoring data.
As per claim 10, Efros et al. discloses wherein: the storage system monitoring data includes CPU I/O wait time, CPU Guest Usage, CPU usage, System Status, number of connected clients, network usage, memory usage, disk usage, read latency, write latency, or operating system load (column 9, lines 13-27 - The data module 108 may provide the raw data 118 to the parameter module 106 in response to request(s) from the parameter module 106. As referenced above, such data streams are known to exist in a variety of circumstances and settings, including, for example, business, industry, healthcare, government, or military settings. To give just a few, more specific, examples, the data sources may output data streams representing or related to (events occurring within or with respect to) network monitoring, network traffic engineering, telecom call records, financial applications, stock market data, sensor networks, manufacturing processes, web logs and click streams, and massive datasets which are streamed as a way of handling the large volume of data. – the data is inclusive of storage system monitoring data disclosed in the claimed limitation).
There is no prior art rejection for claims 8,9 because either no prior art could be found to reject the claims or no reason to combine with prior art found.
There is no prior art rejection for claims 11-20 is the inclusion of the following limitations: ‘determining time information, a compression technique, a compression parameter set for the segment, and a set of reconstruction values for the segment, wherein a reconstructed segment generated using the compression technique, the compression parameter set, and the set of reconstruction values satisfies an error condition based on a difference between values of the segment and values of the reconstructed segment; storing the time information, the compression parameter set, the set of reconstruction values, and an indication of the compression technique; reconstructing and providing at least a portion of the segment using the time information, the compression technique, the compression parameter set, and the set of reconstruction values; or providing the compression parameter set, the set of reconstruction values, and the indication of the compression technique for reconstruction of the portion’.
The closest prior art to claims 11-20 is USPN 20230057444 – paragraph 0011 - Systems and methods for compressing network data are provided. According to one implementation, a method includes the step of collecting raw telemetry data from a network environment. The raw telemetry data is collected as time-series datasets. The method also includes the step of compressing the time-series datasets by deploying the time-series datasets as a Deep Neural Network (DNN) in the network environment itself. The time-series datasets are configured to be substantially reconstructed from the DNN using predictive functionality of the DNN.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Yolanda L Wilson whose telephone number is (571)272-3653. The examiner can normally be reached M-F (7:30 am - 4 pm).
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/Yolanda L Wilson/Primary Examiner, Art Unit 2113