DETAILED ACTION
Claims 1-20 are currently presented for examination.
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 .
Information Disclosure Statement
The information disclosure statement (IDS) has been considered by the Examiner.
Drawings
The drawings are objected to as failing to comply with 37 CFR 1.84(p)(5) because they do not include the following reference sign(s) mentioned in the description: 7020. Corrected drawing sheets in compliance with 37 CFR 1.121(d) are required in reply to the Office action to avoid abandonment of the application. Any amended replacement drawing sheet should include all of the figures appearing on the immediate prior version of the sheet, even if only one figure is being amended. Each drawing sheet submitted after the filing date of an application must be labeled in the top margin as either “Replacement Sheet” or “New Sheet” pursuant to 37 CFR 1.121(d). If the changes are not accepted by the examiner, the applicant will be notified and informed of any required corrective action in the next Office action. The objection to the drawings will not be held in abeyance.
Claim Objections
Claim 1 is objected to because of the following informalities: the claim recites “missing values the observations” which is grammatically incorrect. For the purposes of examination, it will be interpreted as “missing values observations.” Appropriate correction is required.
Claim 6 is objected to because of the following informalities: the claim recites “a window” when it is not the first recitation. Appropriate correction is required.
Claim 11 is objected to because of the following informalities: the claim recites “missing values the observations” which is grammatically incorrect. For the purposes of examination, it will be interpreted as “missing values observations.” Appropriate correction is required.
Claim 16 is objected to because of the following informalities: the claim recites “a window” when it is not the first recitation. Appropriate correction is required.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Regarding claims 1-20, are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e. abstract idea) without anything significantly more.
Step 1: Claims 1-10 are directed to a method, which is a process, which is a statutory category of invention. Claims 18-20 are directed to a non-transitory computer readable medium, which is a manufacture, which is a statutory category of invention. Therefore, claims 1-20 are directed to patent eligible categories of invention.
Step 2A, Prong 1: Claims 1 and 11 recite the abstract idea of determining a loss pattern in a set of data, constituting an abstract idea based on Mental Processes based on concepts performed in the human mind, or with the aid of pencil and paper. The limitation of "identifying loss patterns from historical observations, the loss patterns including block loss patterns and row loss patterns during an offline stage;” covers mental processes including observing a data set and evaluating it to identify loss patterns. Additionally, the limitation of “searching for the loss patterns in on line observations collected during an online stage;” covers mental processes including evaluating another dataset based on the previously found loss patterns. Additionally, the limitation of “selecting an imputation method for each of the loss patterns found in the online observations; and” covers mental processes including making a judgment on how to fix the loss patterns with an imputation method. Additionally, the limitation of “imputing values for missing values the observations corresponding to the loss patterns found in the on line observations.” covers mental processes including making a judgment on how to fix the loss patterns with the imputation method and fixing them, including with pencil and paper. Thus, the claims recite the abstract idea of a mental process performed in the human mind, or with the aid of pencil and paper.
Dependent claims 2-10 and 12-20 further narrow the abstract ideas, identified in the independent claims.
Step 2A, Prong 2: The judicial exception is not integrated into a practical application. In Claim 11, the additional element of “non-transitory storage medium”, and “one or more hardware processors”, merely uses a computer device as a tool to perform the abstract idea. (MPEP 2106.05(f)) Therefore, the judicial exception is not integrated into a practical application.
Dependent claims 2-10 and 12-20 further narrow the abstract ideas, identified in the independent claims, and do not introduce further additional elements for consideration beyond those addressed above.
Step 2B: Claims 1 and 11 do not include additional elements that are sufficient to amount to significantly more than the judicial exception. In Claim 11, the additional element of “non-transitory storage medium”, and “one or more hardware processors”, merely uses a computer device as a tool to perform the abstract idea. (MPEP 2106.05(f)) Therefore, the claim as a whole does not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements, when considered alone or in combination, do not amount to significantly more than the judicial exception. As stated in Section I.B. of the December 16, 2014 101 Examination Guidelines, “[t]o be patent-eligible, a claim that is directed to a judicial exception must include additional features to ensure that the claim describes a process or product that applies the exception in a meaningful way, such that it is more than a drafting effort designed to monopolize the exception.”
The dependent claims include the same abstract ideas recited as recited in the independent claims, and merely incorporate additional details that narrow the abstract ideas and fail to add significantly more to the claims.
Dependent claims 2 and 12 are directed to further defining binarizing the historical observations, which further narrows the abstract idea identified in the independent claim, which is directed to “Mental Processes.”
Dependent claims 3 and 13 are directed to further defining the method of identifying the loss patterns as the use of a matrix, which further narrows the abstract idea identified in the independent claim, which is directed to “Mental Processes.”
Dependent claims 4 and 14 are directed to further defining the generation of candidate block loss patterns, which further narrows the abstract idea identified in the independent claim, which is directed to “Mental Processes.”
Dependent claims 5 and 15 are directed to further defining the generation of candidate block loss patterns using a window, which further narrows the abstract idea identified in the independent claim, which is directed to “Mental Processes.”
Dependent claims 6 and 16 are directed to further defining how a loss pattern is found, which further narrows the abstract idea identified in the independent claim, which is directed to “Mental Processes.”
Dependent claims 7 and 17 are directed to further defining how row loss patterns are found, which further narrows the abstract idea identified in the independent claim, which is directed to “Mental Processes.”
Dependent claims 8 and 18 are directed to further defining the output of the patterns using pencil and paper, which further narrows the abstract idea identified in the independent claim, which is directed to “Mental Processes.”
Dependent claims 9 and 19 are directed to further defining how additional block loss patterns are found, which further narrows the abstract idea identified in the independent claim, which is directed to “Mental Processes.”
Dependent claims 10 and 20 are directed to further defining what patterns are searched for, which further narrows the abstract idea identified in the independent claim, which is directed to “Mental Processes.”
Accordingly, claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e. an abstract idea) without anything significantly more.
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
(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, 10-14 and 20 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Faizin et al. “A Review of Missing Sensor Data Imputation Methods.”
Regarding claim 1, Faizin teaches identifying loss patterns from historical observations, the loss patterns including block loss patterns and row loss patterns during an offline stage; (Section 4, during an offline stage; Figure 1, Section 2, historical data is used to find missing data patterns including block and row loss patterns as shown in figure 1)
searching for the loss patterns in on line observations collected during an online stage; (Sections 3 and 4, loss patterns are searched for during the online stage)
selecting an imputation method for each of the loss patterns found in the online observations; and imputing values for missing values the observations corresponding to the loss patterns found in the online observations. (Abstract, Sections 3 and 4, Table 1, when online data is imputed as soon as it occurs using one of the imputation methods)
Regarding claim 2, Faizin teaches the limitations of claim 1. Faizin teaches further comprising binarizing the historical observations. (Figure 1, the observations are binarized to black and white)
Regarding claim 3, Faizin teaches the limitations of claim 1. Faizin teaches further identifying the loss patterns using a matrix. (Figure 1, the loss patterns are found in a matrix)
Regarding claim 4, Faizin teaches the limitations of claim 3. Faizin teaches further comprising generating candidate block loss patterns. (Figure 1, Section 2, candidate block loss patterns are shown in the matrix from the missing data patterns)
Regarding claim 10, Faizin teaches the limitations of claim 1. Faizin teaches further comprising searching for random element losses and searching for full row and/or full column losses. (Figure 1, Sections 2 and 3, random element losses, full row and full column losses are searched for as see in the figures and described by the algorithms)
In regards to claim 11, it is the computer readable medium embodiment of claim 1 with similar limitations to claim 1, and is such rejected using the same reasoning found in claim 1.
Examiners note: As the algorithms of Faizin use machine learning algorithms, they are run on computers which teaches the additional computer components of claim 11.
In regards to claim 12, it is the computer readable medium embodiment of claim 2 with similar limitations to claim 2, and is such rejected using the same reasoning found in claim 2.
In regards to claim 13, it is the computer readable medium embodiment of claim 3 with similar limitations to claim 3, and is such rejected using the same reasoning found in claim 3.
In regards to claim 14, it is the computer readable medium embodiment of claim 4 with similar limitations to claim 4, and is such rejected using the same reasoning found in claim 4.
In regards to claim 20, it is the computer readable medium embodiment of claim 10 with similar limitations to claim 10, and is such rejected using the same reasoning found in claim 10.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
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.
Claims 5-6 and 15-16 are rejected under 35 U.S.C. 103 as being unpatentable over Faizin in view of Rawassizadeh et al. “Ghost Imputation: Accurately Reconstructing Missing Data of the Off Period.”
Regarding claim 5, Faizin anticipates the limitations of claim 1. Faizin does not explicitly recite further comprising finding the candidate block loss patterns in the historical observations by iterating over observations in the matrix using with a window.
Rawassizadeh teaches further comprising finding the candidate block loss patterns in the historical observations by iterating over observations in the matrix using with a window. (Sections 2 and 3.4, Figure 2, Algorithm 1, an iterative window is used to find the loss patterns in the data)
It would have been obvious to one of ordinary skill in the art, before the effective filing date, to combine the teachings of Faizin with Rawassizadeh as the references deal with reconstructing missing data, in order to implement a system that searches for lost data using a window and identifies a loss pattering by matching a hash of window with the sum of a candidate block loss pattern. Rawassizadeh would modify Faizin by using quantile plots to indicate projected performance. The benefit of doing so is the search space and time complexity of the algorithm is reduced. (Rawassizadeh Section 3.3)
Regarding claim 6, Faizin and Rawassizadeh teach the limitations of claim 5. Faizin does not explicitly recite wherein a loss pattern is found when a hash of a candidate block loss pattern matches a hash of a window and a sum of the candidate block loss pattern matches a sum of the window.
Rawassizadeh teaches wherein a loss pattern is found when a hash of a candidate block loss pattern matches a hash of a window and a sum of the candidate block loss pattern matches a sum of the window. (Figure 2, Sections 3.2-3.4, Algorithm 1, the caching algorithm uses a has window sum to match against previous sums that identify block loss patterns)
See motivation of claim 5
In regards to claim 15, it is the computer readable medium embodiment of claim 5 with similar limitations to claim 5, and is such rejected using the same reasoning found in claim 5.
In regards to claim 16, it is the computer readable medium embodiment of claim 6 with similar limitations to claim 6, and is such rejected using the same reasoning found in claim 6.
Claims 7-8 and 17-18 are rejected under 35 U.S.C. 103 as being unpatentable over Faizin in view of Tan et al. “A Packet Loss Monitoring System for In-Band Network Telemetry: Detection, Localization, Diagnosis and Recovery.”
Regarding claim 7, Faizin anticipates the limitations of claim 3. Faizin teaches further comprising finding the row loss patterns (Figure 1, Section 2, historical data is used to find missing data patterns including block and row loss patterns as shown in figure 1)
Faizin does not explicitly recite one or more allowed gaps.
Tan teaches one or more allowed gaps. (Section 4D, loss is identified over the allowed gap time period)
It would have been obvious to one of ordinary skill in the art, before the effective filing date, to combine the teachings of Faizin with Tan as the references deal with recognizing missing data, in order to implement a system that searches for lost data using an allowed gap. Tan would modify Faizin by searching for lost data using an allowed gap. The benefit of doing so is detection of packet loss events, the system can determine the time and location of the losses, diagnose of the root cause of the losses, and recover of the lost information. This provides excellent performance and extremely low overhead, including detection accuracy and diagnostic precision close to 100%, and detection latency of just milliseconds. Which, gives the algorithm excellent accuracy and reliability. (Tan Abstract)
Regarding claim 8, Faizin and Tan teach the limitations of claim 7. Faizin teaches further comprising storing the block loss patterns and the row loss patterns that are found in the historical observations. (Section 3, machine learning techniques are run on a computer to classify the loss patterns, by running the algorithms on a computer, the loss patterns are stored)
In regards to claim 17, it is the computer readable medium embodiment of claim 7 with similar limitations to claim 7, and is such rejected using the same reasoning found in claim 7.
In regards to claim 18, it is the computer readable medium embodiment of claim 8 with similar limitations to claim 8, and is such rejected using the same reasoning found in claim 8.
Claims 9 and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Faizin in view of Langari et al. “Edge-Guided Image Gap Interpolation Using Multi-scale Transformation.”
Regarding claim 9, Faizin anticipates the limitations of claim 3. Faizin teaches wherein a recent size of the matrix is used in the online stage. (Figure 1, Sections 3 and 4, a size of the matrix is used)
Faizin does not explicitly recite further comprising finding additional block loss patterns based on a new matrix size; and increasing a size of the matrix until no more loss patterns are found,
Langari teaches further comprising finding additional block loss patterns based on a new matrix size; and increasing a size of the matrix until no more loss patterns are found, (Section 2, Figures 1-4, starting with a 2x2 matrix, lost data is found and interpolated to fix the lost data, the size is increased and the process is iterated until the entire image has been enhanced to no longer contain lost data)
It would have been obvious to one of ordinary skill in the art, before the effective filing date, to combine the teachings of Faizin with Langari as the references deal with recognizing missing data, in order to implement a system that increases the size of the matrix until the entire data structure is free of loss patterns. The benefit of doing so is system significantly improves the quality and PSNR of the restored images by using the proposed method. (Langari Abstract, Section 4)
In regards to claim 19, it is the computer readable medium embodiment of claim 9 with similar limitations to claim 9, and is such rejected using the same reasoning found in claim 9.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Huang et al. USPPN 2023/0177397: Also teaches the identification of missing data set parameters in an online and offline system.
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/MICHAEL EDWARD COCCHI/Primary Examiner, Art Unit 2188
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