DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
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)(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.
Claims 1-4, 7, 8, 10-13, 16 and 17 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Dominic (US 6,975,253).
Consider claim 1, Dominic discloses a system for intelligent data access and analysis, comprising: a computing device comprising a memory, a processor, and a non-volatile data storage device; an access engine comprising a first plurality of programming instructions that, when operating on the processor, cause the computing device to: process data queries directed to organized data stores; create structured representations of reference information; traverse the structured representations to identify target data elements; and provide the identified data elements for reconstruction; and an analysis module comprising a second plurality of programming instructions that, when operating on the processor, cause the computing device to: estimate initial locations within the organized data; refine the initial locations by: determining boundaries through statistical analysis; and validating sequences at the locations against known patterns; and adjusting the location when invalid patterns are found (Dominic: abstract, background of invention, all three tables, Col. 3 lines 56-67, Col. 5-6 lines 13-53, discloses using variable word length Huffman coding. Using a codebook, incoming data is encoded and sent to a destination as stream of data. Data is decoded using a codebook that also includes computed potential values derived from parameters of the codewords. The system estimates the initial codeword in the stream by trying the first four bits. If these four bits don’t match, then the process goes through a series of operations using the computed potential values using the codebook (collected, organized, interpreted and presented data (statistical data)) to compare the potential values to find the write entry and validate the codeword.).
Consider claim 2, Dominic discloses the system of claim 1, further comprising a data transformation subsystem comprising a third plurality of programming instructions that, when operating on the processor, cause the computing device to: analyze input data properties; create transformation parameters; transform the input data using the parameters generate primary and secondary data flows; and process the primary data flow (Dominic: abstract, background of invention, all three tables, Col. 3 lines 56-67, Col. 5-6 lines 13-53, discloses using variable word length Huffman coding. Using a codebook, incoming data is encoded (transformed) and sent to a destination as stream of data. Data is decoded using a codebook that also includes computed potential values derived from parameters of the codewords. The system estimates the initial codeword in the stream by trying the first four bits. If these four bits don’t match, then the process goes through a series of operations using the computed potential values using the codebook (collected, organized, interpreted and presented data (statistical data)) to compare the potential values to find the write entry and validate the codeword. There are data streams for incoming data.).
Consider claim 3, Dominic discloses the system of claim 2, further comprising a pattern processing model comprising a fourth plurality of programming instructions that, when operating on the processor, cause the computing device to: receive processed data; generate condensed representations; analyze relationships between the condensed representations; and reconstruct output data from the analyzed representations (Dominic: abstract, background of invention, all three tables, Col. 3 lines 56-67, Col. 5-6 lines 13-53, discloses using variable word length Huffman coding. Using a codebook, incoming data is encoded (transformed) and sent to a destination as stream of data. Data is decoded using a codebook that also includes computed potential values derived from parameters of the codewords. The system estimates the initial codeword in the stream by trying the first four bits. If these four bits don’t match, then the process goes through a series of operations using the computed potential values using the codebook (collected, organized, interpreted and presented data (statistical data)) to compare the potential values to find the write entry and validate the codeword. There are data streams for incoming data.).
Consider claim 4, Dominic discloses the system of claim 1, wherein the access engine uses the refined locations from the analysis module for traversal operations (Dominic: abstract, background of invention, all three tables, Col. 3 lines 56-67, Col. 5-6 lines 13-53, discloses that the codebook is traversed to refine the exact location of the correct codeword.).
Consider claim 7, Dominic discloses the system of claim 2, wherein the data transformation module adapts its parameters based on input characteristics (Dominic: abstract, background of invention, all three tables, Col. 3 lines 56-67, Col. 5-6 lines 13-53, discloses the use of Huffman coding which can transform data into variable sized codewords based on the properties of the data and transform the data back to its original form.).
Consider claim 8, Dominic discloses the system of claim 3, wherein the pattern processing model adjusts based on transformation outputs (Dominic: abstract, background of invention, all three tables, Col. 3 lines 56-67, Col. 5-6 lines 13-53, discloses that the codeword stream is processed based on the largest size a codeword can be in binary.).
Claims 10-13, 16 and 17 are the method claims to system claims 1-4, 7 and 8 above and are rejected using the same rationale.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 5, 6, 9, 14, 15 and 18 is/are rejected under 35 U.S.C. 103 as being unpatentable over Dominic (US 6,975,253), and further in view of “Chapter 11 Lossless Data Compression Method Using Deep Learning” henceforth known as Barman et al.
Consider claim 5, Dominic discloses the system of claim 4, however, Dominic does not explicitly disclose training and therefore does not alone teach: “further comprising a training system configured to coordinate improvement of system components.”. However, Barman et al. discloses incorporating deep learning into data compression (Barman et al.: 3. Proposed System first three paragraphs and 5. Conclusion.).
It would have been obvious to a person of ordinary skill in the art at the time the invention was made to modify the system of Dominic to have it be performed using machine learning because machine learning allows a system to keep improving its performance over time and can easily deal with complex data (Barman et al.: 5. Conclusion.) along with automation of repetitive tasks (no manual labor or human intervention) and pattern recognition.
Consider claim 6, Dominic in view of Barman et al. discloses the system of claim 5, wherein the training system optimizes performance across components from initial transformation through final access capabilities (Barman et al.: 3. Proposed System first three paragraphs and 5. Conclusion.).
Consider claim 9, Dominic discloses the system of claim 4, however, Dominic does not explicitly disclose training and therefore does not alone teach: “wherein the analysis module uses learning techniques to improve location estimation.”. However, Barman et al. discloses incorporating deep learning into data compression (Barman et al.: 3. Proposed System first three paragraphs and 5. Conclusion.).
It would have been obvious to a person of ordinary skill in the art at the time the invention was made to modify the system of Dominic to have it be performed using machine learning because machine learning allows a system to keep improving its performance over time and can easily deal with complex data (Barman et al.: 5. Conclusion.) along with automation of repetitive tasks (no manual labor or human intervention) and pattern recognition.
Claims 14, 15 and 18 are the method claims to system claims 5, 6 and 9 above and are rejected using the same rationale.
Conclusion
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/MICHAEL ALSIP/Primary Examiner, Art Unit 2139