DETAILED ACTION
This Non-Final Office action is in response to Applicant’s filing on 07/10/2024. Claims 1-8 are pending. The effective filing date of the claimed invention is 11/22/2021.
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 § 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 1-8 are rejected under 35 U.S.C. 101 because the claims are found to be directed to abstract idea.
Step 1 – Claims 1-4 are process claims; claims 5-8 are machine claims. Step 1 is passed.
Step 2A Prong 1 – Claim 1 and 5 recite the following abstract idea: A method for interpolating missing values, the method comprising:
by a data processor, collecting a data set by selecting a complete signal without a missing part among a plurality of unit signals constituting a sensor signal (see MPEP 2106.04(a)(2)(III)(A) Electric Power Group);
by a learning unit, through the data set, training an interpolation network for interpolating a missing part in a missing signal in which at least a part of the sensor signal is missing (see e.g. MPEP 2106.04(a)(2)(III); see also Recentive v. Fox, Appeal No. 2023-2437 (Fed. Cir. 4/18/25));
by an interpolation unit, receiving as an input the missing signal from the data processor (see MPEP 2106.04(a)(2)(III)(A) Electric Power Group); and
by the interpolation unit, generating an interpolated signal by interpolating the missing part through the interpolation network (see MPEP 2106.04(a)(2)(III)).
When viewed alone and in ordered combination, these limitations are found to recite abstract idea.
Step 2A Prong 2 – Claims 1 and 5 do not integrate the abstract idea into practical application. Claim 1 recites the additional limitations of a data processor that collects data and sends/receives data; sensor signal; a learning unit that trains; interpolation unit that receives data and genereates an interpolated signal. The examiner finds that these additional limitations are recited in an “apply it” manner, at a high level of generality, and without reciting the “how” in how the functions are being performed by the various additional limitations. The examiner recommends amending the claim (with proper support) to get more into the “how” in each claim limitation. For instance, for the last limitation of claim 1, how is this happening? Please recite in the claim language.
When viewed alone and in ordered combination, these limitations are not found to integrate the abstract idea into practical application.
Step 2B – Claims 1 and 5 are not found to recite significantly more. Another consideration when determining whether a claim recites significantly more than a judicial exception is whether the additional element(s) are well-understood, routine, conventional activities previously known to the industry. This consideration is only evaluated in Step 2B of the eligibility analysis. See MPEP 2106.05(d).
Claim 1 includes the following WURC activity:
Receiving/collecting/transmitting data – found to WURC MPEP 2106.05(d)(II)(i).
Performing repetitive calculations – found to be WURC MPEP 2106.05(d)(II)(ii).
When viewed alone and in ordered combination, these limitations of claim 1 are not found to include significantly more. Claims 1 and 5 are thereby found to be directed to abstract idea.
Dependent Claims – Claims 2 and 6 recite more abstract idea. See MPEP 2106.04(a)(2)(III). Claims 3 and 7 recites more abstract idea through “apply it”. See MPEP 2106.04(a)(2)(III). Claims 4 and 8 recites at a very high level more abstract idea. See MPEP 2106.04(a)(2)(III).
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claim(s) 1-8 are rejected under 35 U.S.C. 103 as being unpatentable over U.S. Pat. Pub. No. 2018/0275658 to Iandola et al. (“Iandola”) in view of U.S. Pat. Pub. No. 2021/0064034 to Ouyang et al. (“Ouyang”).
With regard to claims 1, 5, Iandola further discloses method for interpolating missing values, the method comprising:
by a data processor, collecting a data set by selecting a complete signal without a missing part among a plurality of unit signals constituting a sensor signal (see e.g. [0005-6]);
by a learning unit, through the data set, training an interpolation network for interpolating a missing part in a missing signal in which at least a part of the sensor signal is missing (Iandola e.g. [0007], [0028-30], [0058] In many cases, these scenarios can result in sensor data in which portions of the sensor data are incomplete or missing. The reconstruction models reconstruct portions of incomplete or missing data from a sensor based on information received from other sensors to generate a refined version of the sensor data with respect to the sensing characteristics of the sensor. For example, the reconstruction module 315 may reconstruct the incomplete information of the dark-colored car in the LIDAR point-cloud and fill in these points for the LIDAR sensor using mutual information from other sensors (e.g. camera); [0060] In one embodiment, the reconstruction module 315 constructs the training data set by removing portions of sensor data for one or more sensors; Iandola may not disclose “a missing part in a missing signal in which at least a part of the [received] sensor signal is missing.” See Ouyang which deals with aquaculture sensing platforms and expressly discusses missing D.O. sensor values, “NaN” missing values in raw data, complete data used for RNN training, and machine learning prediction of missing D.O. values. See Ouyang at
[0077] - The LSTM model does not operate satisfactorily when there is incomplete data (i.e., gaps in the time series data). Incomplete data or missing data can be expected to frequently occur in the field due to interferences from various sources.
[0078] As such, a novel approach is adopted herein to train a feed-forward neural network to learn the D.O. variation trend of the body(ies) of water 122 and to predict the missing values. The LSTM network is trained using the fused dataset. Also, a bias is introduced in the LSTM model to reduce false negatives (e.g., the predicted D.O. level is higher than the actual D.O. level).
[0080], [0083], [0085] [0088] Fig. 5 etc. Therefore, one of ordinary skill in the missing value art before the effective filing date of the claimed invention would modify Iandola to include the ability to receive a signal with missing data and then predict the missing value, using machine learning, as taught by Ouyang, where this is beneficial in that missing values can be determined with some level of accuracy. See Ouyang [0093]);
by an interpolation unit, receiving as an input the missing signal from the data processor (Iandola e,g, [0059] Based on the training data set, the reconstruction module 315 can train reconstruction models to refine incomplete sensor data based on sensor data from the set of other sensors); and
by the interpolation unit, generating an interpolated signal by interpolating the missing part through the interpolation network (Iandola, e.g. [0061-62]).
With regard to claims 2, 6, Iandola further discloses collecting the data set includes: by the data processor, collecting the sensor signal composed of the plurality of unit signals (see [0053] where “each instance of sensor data” reads on the claimed unit signals) and having information greater than a predetermined length (see [0053] each instance has a length greater than 0); by the data processor, selecting the complete signal without the missing part among the unit signals (e.g. [0032-33], [0060-61] dsicusses pairing incomplete/missing sensor data with corresponding original sensor data without the missing portions); and by the data processor, accumulating the complete signals in the data set until a number of the selected complete signals is greater than or equal to a predetermined number (e.g. [0027] [0080]).
With regard to claims 3, 7, Iandola further discloses where training the interpolation network includes: after accumulating the complete signals in the data set, by the learning unit, generating the missing signal from the complete signal in the data set (see e.g. [0060-61); when the learning unit inputs the generated missing signal to the interpolation network, the interpolation network generates an interpolated signal in which the missing part is interpolated through a plurality of operations to which weights between layers are applied (e.g. [0048] [0058-63] reconstruction modules train one or more neural network models as reconstruction models, and those models receive missing or incomplete sensor data. Reconstruction models reconstruct signals); by the learning unit, calculating an interpolation loss representing a difference between the interpolated signal and the complete signal which is a label of the missing signal (see e.g. [0032-33] [0060-61] the pairing of the original with the missing data portion, where the loss is inherent in the comparison of the two); and by the learning unit, performing optimization to update the weights of the interpolation network to minimize the interpolation loss (see e.g. [0026] [0053-56] [0059-61]).
With regard to claims 4, 8, Iandola further discloses generating the missing signal includes: by the learning unit, erasing a part of the complete signal to generate the missing signal having the missing part (see e.g. [0060] published claim 6, 7 - omitting at least a portion of the sensor measurements.); and by the learning unit, setting the generated missing signal as an input value for the interpolation network (see e.g. published claims 7, 18) and labeling the complete signal, which is an original of the generated missing signal, as a target value for the generated missing signal (e.g. [0030] [0054-57] etc.).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Peter Ludwig whose telephone number is (571)270-5599. The examiner can normally be reached Mon-Fri 9-5.
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/PETER LUDWIG/Primary Examiner, Art Unit 3627
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