‘’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 .
Response to Arguments
Applicant's arguments filed 5/08/2026 have been fully considered but they are not persuasive.
Regarding applicants arguments for 101 in page 11-12 of applicant’s remarks, the applicant states “The Office Action asserted that the claims recite mental processes (Step 2A, Prong 1) and that none of the additional independent claim elements integrate the alleged judicial exception into a particular application (Step 2A, Prong 2). Without agreeing that the claims are directed to an abstract idea, Applicant has amended independent claims 1, 8, and 15 to further emphasize patent-eligible features.
Therefore, the amended independent claims are not directed to merely analyzing or manipulating data as an end in itself Instead, they recite distinct processing paths that are triggered by objective conditions in the data stream and that control how sequential data is processed at a data handling server and used by a prediction model. Thus, amended independent claims 1, 8, and 15 integrate any alleged abstract idea into a practical application, satisfying MPEP § 2106.05(a) and Step 2A, Prong 2.
For at least the above reasons, Applicant respectfully asserts that claims 1-20 are directed to patent-eligible subject matter and requests that the §101 rejection be withdrawn.” The applicant argues how the amended limitations to independent claim 1 and analogous claims 8 and 15 provide a practical application. Yet the independent claims recite abstract ideas that fail to provide a practical application as mentioned specification paragraph 0002 such as any system glitches resulting in packet loss, outage, etc. The claim only provides the abstract idea of the process for correcting the data. Also MPEP 2106.05(a) states “It is important to note, the judicial exception alone cannot provide the improvement.” The claims lack crucial specificity to provide a practical application. Furthermore, the limitation mentioned in the applicant’s remarks are regarding claim 1 limitations that were amended. The amended limitation has not been examined and the argument is not convincing.
(Examiner Note: To help in overcoming 101 rejection application could provide further specificity on how the data is being used to fill in the data gaps to allow the data to be used is specific fashion that relates to solving some technological problem. However the specification has to support the specific way for filing in the data gaps and how fixing the data gap fixes a technological problem.)
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 1-20 rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention.
Regarding claim 1 and analogous claims 8 and 15, the limitation “determining, in response to the detecting, [[a]]first and second sliding windows respectively associated with the first and second data gaps,” does not have written description support in the specification for a first and second sliding windows to fill in a second data gap. The specification in paragraph 0031 “The data handling server 130 may determine whether the data in the sliding window includes at least one data gap (decision 206). The data gap in the sliding window may also be referred to as a window data gap. As a result of determining the sliding window, the data handling server 130 may utilize the data falling in the sliding window to fill the data gap. As data gaps may or may not occur in the sliding window, the data handling server 130 may determine subsequent operations based on whether there is at least one data gap in the sliding window. [0032] As a result of the data not including a data gap (decision 206, "NO" branch), the data handling server 130 may utilize a prediction model to fill the selected data gap based on the data in the sliding window (step 208). With the data in the sliding window being complete with no data gaps, the prediction model may utilize all the information of this data and output a prediction to fill the selected data gap using any technique as one skilled in the art will of understand. In this manner, the data handling server 130 may take the data of the sliding window directly” and Fig. 2 provide a method by which the method identifies a data gap to fill in. The process provides two alternatives one being the sliding window includes a data gap and the second the sliding windows does not have a data gap associated with the data gap. When the sliding window has a data gap it extracts patters and fills in the data gap using prediction model. If no data gap is present in the sliding window the data gap is filled in using the data of the sliding window. Further specification in paragraph 0038 line 1-2 “In processing the sequence 305, the data handling server 130 may identify the various data gaps. The data handling server 130 may also select one of the data gaps to fill. ” The specification does not have support for a second sliding window to fill a second data gap. All dependent claims inherit the issue.
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.
Claims 1-20 rejected under 35 U.S.C. 101 because the claimed invention is directed to abstract idea without significantly more. The claim(s) recite(s) significantly more. The subject matter eligibility test for products and process is describe below for claim 1 in view of dependent claims.
Regarding claim 1 analogous claims 8 and 15 :
Step 1: Is the claim to a process machine manufacture or composition of matter?
Yes – Claim 1 recites a method, which a method falls under the statutory categories
Step 2A Prong 1: Does the claim recite an abstract idea, law of nature, or natural phenomenon?
Yes – The claim recites the following:
“detecting a first data gap and a second data gap in the sequential data, the first data gap being at a first timestamp and the second data gap being at a second timestamp;” – The limitation recites a mental process of detecting a first and second data gap. (see MPEP 2106.04(a)(2)III).
“determining, in response to the detecting, [[a]]first and second sliding windows respectively associated with the first and second data gaps, ” The limitation recites a mental process of determining a first and second sliding window. (see MPEP 2106.04(a)(2)III).
“as a result of determining that the first set of dependent data does not include a window data gap, filling the first data gap with a first prediction generated by a prediction model, wherein the first prediction is based directly on the first set of dependent data” – The limitation recites a mental process of filling in the first data gap using a prediction based on the first set of dependent data that does not have a window data gap. (see MPEP 2106.04(a)(2)III).
Step 2 Prong 2: Does the claim recite additional elements that integrate the judicial exception into a particular application? No –
The claim includes the additional element(s):
“A computer-implemented method for handling gaps in sequential data transmitted via a network between data exchange devices comprising a data handling server, a network, and a data source device, wherein the gaps are created by events experienced by one or more of the data exchange devices, the method comprising:” - The additional elements fall under “apply it” as using generic computer to handle a data gap in sequential data (See MPEP 2106.05(f)).
“receiving, at the data handling server and via the network, the data streamed as sequential data from the data source device; and” - The additional elements fall under “insignificant extra-solution activity” mere data gathering (See MPEP 2106.05(g))
“in response to the receiving, processing the sequential data by the data handling server, wherein the processing comprises:” - The additional elements fall under “apply it” as using generic computer to process the sequential data (See MPEP 2106.05(f)).
“wherein the first sliding window includes a first set of dependent data from the sequential data for a duration of time preceding the first timestamp, and the second sliding window includes a second set of dependent data from the sequential data for a duration of time preceding the second timestamp” The additional elements fall under “insignificant extra-solution activity” mere data gathering as viewed as a whole by including data that precedes a timestamp (See MPEP 2106.05(g))
“as a result of determining that the second set of dependent data includes at least one window data gap, masking the at least one window data gap, wherein the masking comprises: generating feature maps by performing convolution with kernels based on the second set of dependent data under a masking mechanism that masks missing data from results of the convolution The additional elements fall under “apply it” as using generic computer to mask the at least one window data gap by performing a convolution with kernels and extracting feature maps (See MPEP 2106.05(f)).
“and modifying the second set of dependent data in the sliding window to include the extracted patterns;” - The additional elements fall under “apply it” as using generic computer to modify the dependent data (See MPEP 2106.05(f)).
“filling the second data gap using with a second prediction generated by the prediction model, wherein the second prediction is generated based on the modified second set of dependent data.” The additional elements fall under “apply it” as using generic computer to fill in data gaps using generate predictions (See MPEP 2106.05(f)).
Step 2B: Does the claim recite additional elements that amount to significantly more than the judicial exception?
No - The claim does not include additional elements that are sufficient to amount to a significantly more than the judicial exemption. As an order whole, the claim is directed to a mental process. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements fall under data gathering and apply it and do not limit the claim. The method does not improve on the function of a computer, transforms an article into another article, nor is it applied by a particular machine, making the claim not patent eligible.
Regarding claim 2 and analogous claims 9 and 16:
Step 2A Prong 2, Step 2B: The additional element(s):
“wherein the extracted patterns are generated based on a random convolutional kernel transform algorithm.”
The additional elements fall under “apply it” as using generic computer to use convolutional kernel transform algorithm to generate extracted patterns (See MPEP 2106.05(f)).
Regarding claim 3 and analogous claims 10 and 17:
Step 2A Prong 2, Step 2B: The additional element(s):
“wherein the random convolutional kernel transform algorithm is a multi-variate time-series classification model” - The additional element falls under the “insignificant extra-solution activity”. The judicial exemptions do not integrate into a practical application nor provide an improvement. The process does not provide an inventive concept nor provides a practical application.
Regarding claim 4 and analogous claims 11 and 18:
Step 2A Prong 2, Step 2B: The additional element(s):
“wherein the random convolutional kernel transform algorithm performs [[a]] the convolution with kernels ” - The additional elements fall under “apply it” as using generic computer to perform a convolution of kernels to generate features (See MPEP 2106.05(f)). The judicial exemptions do not integrate into a practical application nor provide an improvement. The process does not provide an inventive concept nor provides a practical application.
Regarding claim 5 and analogous claims 12 and 19:
Step 2A Prong 2, Step 2B: The additional element(s):
“wherein the duration of time of the first and second sliding window immediately precedes the first and second timestamp.” - The additional element falls under the “insignificant extra-solution activity”. The judicial exemptions do not integrate into a practical application nor provide an improvement. The process does not provide an inventive concept nor provides a practical application.
Regarding claim 6 and analogous claims 13 and 20:
Step 2A Prong 1,
“validating the second prediction based on an acceptable threshold for subsequent processing of the sequential data with the second prediction;” – The limitation recites a mental process of validating the prediction. (see MPEP 2106.04(a)(2)III).
Step 2A Prong 2, Step 2B: The additional element(s): “as a result of the second prediction not being validated, performing a feedback by determining a modified operation in determining a further prediction.” The additional elements fall under “apply it” as using generic computer to perform a feedback and determine a modified operation (See MPEP 2106.05(f)). The judicial exemptions do not integrate into a practical application nor provide an improvement. The process does not provide an inventive concept nor provides a practical application.
Regarding claim 7 and analogous claim 14:
Step 2A Prong 2, Step 2B: The additional element(s):
“wherein the modified operation is one of using a different feature extraction method, using a different method for prediction, and estimating at least one of a floor and ceiling accuracy.” - The additional elements fall under “apply it” as using generic computer to perform one of different feature extraction method, using a different method for prediction, and estimating at least one of a floor and ceiling accuracy (See MPEP 2106.05(f)). The judicial exemptions do not integrate into a practical application nor provide an improvement. The process does not provide an inventive concept nor provides a practical application.
Allowable Subject Matter
Claim 1-20 would be allowable if rewritten or amended to overcome the rejection(s) under 35 U.S.C. 101, 112(a) or 35 U.S.C. 112 (pre-AIA ), 1st paragraph, set forth in this Office action.
Regarding independent claim 1 and analogous claims 8 and 15. The independent claims recite a method of filing missing data by “determining, in response to the detecting, [[a]]first and second sliding windows respectively associated with the first and second data gaps, wherein the first sliding window includes a first set of dependent data from the sequential data for a duration of time preceding the first timestamp, and the second sliding window includes a second set of dependent data from the sequential data for a duration of time preceding the second timestamp
as a result of determining that the first set of dependent data does not include a window data gap, filling the first data gap with a first prediction generated by a prediction model, wherein the first prediction is based directly on the first set of dependent data;
as a result of determining that the second set of dependent data includes at least one window data gap, masking the at least one window data gap, wherein the masking comprises”.
Lujic teaches a method for detecting missing data in sequential data and filling in using different methods (Lujic para 664 Fig. 2,
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2 Architecture Model Overview para 3, Edge layer manages data through different stages of EDMFrame, to perform accurate and timely analytics. It is composed of edge nodes, e.g., edge servers and micro data centers [10], aiming to perform data processing closer to data sources.
page 665 Fig 2.
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page 665, 3.1 Data Preparation
The goal of this component is to prepare an incomplete dataset for the recovery process. To this end, we apply a set of operations that detect each gap in the dataset. The data preparation process is described in Algorithm 1. First, line 1 creates an empty vector for indexes of missing values in the dataset. Outliers are identified according to minimum and maximum values, for particular sensors, that can be either application-dependent or predefined by the user. If a data value is out of bounds, it is replaced by a missing value indicator such as NA (Not Available) (line 2), so that the correct value can be efficiently estimated in the recovering cycle. Missing values can occur for different reasons, like system or sensor failures. Once the system/sensor is recovered, the next received data point is stored right after the last generated timestamp. Therefore, to identify a gap, it is necessary to check timestamps. We propose a solution where the monitoring component receives data and stores either corresponding data value or NA for each created timestamp (lines 4-13). Counters i and j (line 3) count data from input and prepared
D
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Page 666 Fig 3,
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Finn teaches a method of filling data gaps by using masking and performing convolutional methods (Finn Page 1 1 Introduction para 2 line 5-10, Such models follow a paradigm of reconstructing future frames from the internal state of the model. In our approach, we propose a method which does not require the model to store the object and background appearance. Such appearance information is directly available in the previous frame. We develop a predictive model which merges appearance information from previous frames with motion predicted by the model. As a result, the model is better able to predict future video sequences for multiple steps, even involving objects not seen at training time.
3 Motion-Focused Predictive Models,
In order to learn about object motion while remaining invariant to appearance, we introduce a class of video prediction models that directly use appearance information from previous frames to construct pixel predictions. Our model computes the next frame by first predicting the motions of image segments, then merges these predictions via masking. In this section, we discuss our novel pixel transformation models, and propose how to effectively merge predicted motion of multiple segments into a single next image prediction. The architecture of the CDNA model is shown in Figure 1. Diagrams of the DNA and STP models are in Appendix B.
Finn Page 3,
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Figure 1: Architecture of the CDNA model, one of the three proposed pixel advection models. We use convolutional LSTMs to process the image, outputting 10 normalized transformation kernels from the smallest middle layer of the network and an 11-channel compositing mask from the last layer (including 1 channel for static background). The kernels are applied to transform the previous image into 10 different transformed images, which are then composited according to the masks. The masks sum to 1 at each pixel due to a channel-wise softmax. Yellow arrows denote skip connections).
The prior art cited Lujic in view of Finn fail to teach the amended limitations specifically “determining, in response to the detecting, [[a]]first and second sliding windows respectively associated with the first and second data gaps,”
“as a result of determining that the first set of dependent data does not include a window data gap, filling,”
and “as a result of determining that the second set of dependent data includes at least one window data gap” as recited in claim 1, in combination with the remaining features and elements of the claimed invention.
Independent claim 8 and 15 would be allowable for the reasons cited for claim 1. The remaining claims are allowable because they depend on one of allowable independent claims 1, 8 and 15.
Pertinent Prior Art
The prior art made of record and not relied upon is considered pertinent to applicant's
disclosure.
Baradaran et al. (US20180307712A1) teaches – teaches in FIG. 5 a method for detecting outliers on a series of data and creating a window of data.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/ALFREDO CAMPOS/Examiner, Art Unit 2129
/MICHAEL J HUNTLEY/Supervisory Patent Examiner, Art Unit 2129
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