Notice of Pre-AIA or AIA Status
1. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
2. Claim 1 is pending in this office action. This action is responsive to Applicant’s application filed 02/10/2025.
Priority
3. Applicant’s claim for the benefit of a Continuation of 18469273 , filed 09/18/2023 ,now U.S. Patent # 12235825, 18469273 is a Continuation of 17934001 , filed 09/21/2022 ,now U.S. Patent # 11797514 17934001 is a Continuation of 16794011 , filed 02/18/2020 ,now U.S. Patent # 11487739 16794011 Claims Priority from Provisional Application 62807526 , filed 02/19/2019
is acknowledged.
Since the Continuation application relied on part of the priority document (Continuation), the claim of priority will be considered on a claim-by-claim basis. The priority date of the instant application is at least 02/10/2025 (the filing date), but depending upon the specific material claimed, could be as early as 02/19/2019.
Information Disclosure Statement
4. The references listed in the IDS filed 02/10/2025 has been considered. A copy of the signed or initialed IDS is hereby attached.
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory obviousness-type double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); and In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on a nonstatutory double patenting ground provided the conflicting application or patent either is shown to be commonly owned with this application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement.
Effective January 1, 1994, a registered attorney or agent of record may sign a terminal disclaimer. A terminal disclaimer signed by the assignee must fully comply with 37 CFR 3.73(b).
5. Claim 1 is rejected on the ground of non-statutory obviousness-type double patenting as being unpatentable over claims 1-19 of U.S. Patent No. US Patent 12,235,825. Although the conflicting claims are not identical, they are not patentably distinct from each other because they are substantially similar in scope and they use the same limitations.
The following table shows the claim 1 in Instant Application that are rejected by corresponding claim(s) 1, and 13 in US Patent 12, 235,825 B2.
Instant Application
US 12,235,825
1. A computer system for detecting changes within datasets supplied from external data sources, the computer system comprising:
a transceiver configured to receive a dataset that is generated by at least one external computer system;
a processing system that includes at least one processor, the processing system configured to:
separate the dataset into at least first and second datasets;
execute a plurality of different detector processes against the first and second data sets, wherein the plurality of different detector processes includes at least a first detector process and a second detector process, wherein the first and second detector processes generate, respectively, first and second metrics of a level of difference by using different processing for the first and second datasets;
perform a comparison of the first and second metrics to at least one threshold value; and
determine, by using the performed comparison, whether there is a statistically significant change in data
within the first dataset as compared to the data within the second dataset based on the performed comparison.
1. A computer system for determining changes in datasets that have been produced by computer systems, the computer system comprising:
a transceiver configured to receive a dataset that is generated by at least one data source computer system;
a memory that is coupled to at least one hardware processor that is configured to perform operations comprising:
separating data within dataset into a plurality of different paired subgroups, the plurality of different paired subgroups including a first subgroup that includes at least a first dataset and a second dataset, wherein the first dataset corresponds to a first window over the data in the dataset and the second dataset corresponds to a second window, which is different from the first window, over the data in the dataset;
executing a plurality of different types of detector processes using the first dataset and the second dataset as input, wherein the plurality of different types of detector processes includes at least a first type detector process and a second type detector process, wherein the plurality of different types of detector processes are executed for the plurality of different paired subgroups that have been generated, wherein executing the first type detector process includes executing a first processing model using the first dataset and the second dataset as input to produce at least a first metric that represents a level of difference between the first dataset and the second dataset, wherein executing the second type detector process includes executing a second processing model that is different from the first processing model and using, as input to the second processing model, the first dataset and the second dataset to produce at least a second metric that represents a level of difference between the first dataset and the second dataset; and
determining whether there has been a statistically significant change in data of the dataset that has been generated by the at least one data source computer system by using at least the first metric and the second metric that are respectively associated with the first dataset and the second dataset, wherein the plurality of different types of detector processes are executed in parallel.
13. The computer system of claim 1, wherein the operations further comprise: performing a first comparison of the first metric to a first threshold value; and performing a second comparison of the second metric to a second threshold value, wherein the first threshold value is different than the second threshold value.
Although the conflicting claims are not identical, they are not patentably distinct from each other because they are substantially similar in scope and they use the same limitations.
After analyzing the language of the claims, it is clear that claim 1 is merely an obvious variation of claims 1-19 of US Patent 12,235,825 B2. Therefore, these two sets of claims are not patentably distinct.
Claim Objection
The following is a quotation of the second paragraph of 35 U.S.C. 112:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
6. Regarding claim 1, the claim recites “whether there is a statistically significant change in data.” which is unclear what “there is” corresponding to. Also, there is insufficient antecedent basis for “there is”.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103(a) which forms the basis for all obviousness rejections set forth in this Office action:
(a) A patent may not be obtained though the invention is not identically disclosed or described as set forth in section 102 of this title, if the differences between the subject matter sought to be patented and the prior art are such that the subject matter as a whole would have been obvious at the time the invention was made to a person having ordinary skill in the art to which said subject matter pertains. Patentability shall not be negatived by the manner in which the invention was made.
This application currently names joint inventors. In considering patentability of the claims under 35 U.S.C. 103(a), the examiner presumes that the subject matter of the various claims was commonly owned at the time any inventions covered therein were made absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and invention dates of each claim that was not commonly owned at the time a later invention was made in order for the examiner to consider the applicability of 35 U.S.C. 103(c) and potential 35 U.S.C. 102(e), (f) or (g) prior art under 35 U.S.C. 103(a).
7. Claim 1 is rejected under 35 U.S.C. 103(a) as being unpatentable over Wang et al. (US Patent Publication No. 2017/0347061 A1, hereinafter “Wang”) in view of Zoldi et al. (US Patent Publication No. 2016/0342963 A1, hereinafter “Zoldi”).
As to Claim 1, Wang teaches the claimed limitations:
“A computer system for detecting changes within datasets supplied from external data sources, the computer system comprising:” as the system separates a first section of the video data into single frames, i.e., into a sequence of images at the full Standard Definition (SD) resolution of the video data (paragraphs 0030-0031).
“a transceiver configured to receive a dataset that is generated by at least one external computer system” as one of the selected input sections occur sequentially in time before or after the target section of the lower-quality visual data enables the enhancement to predict a possible future section (paragraphs 0180, 0233). In parallel to the development of hierarchical algorithms, the visual data can be encoded in an encoder in preparation for its transmission across the network, the time taken to prepare both the hierarchical algorithm and the visual data for transmission can be reduced when compared to developing the hierarchical algorithm and encoding the visual data in series (paragraphs 0399, 0581).
“a processing system that includes at least one processor, the processing system configured to:” as machine learning is the field of study where a computer or computers learn to perform classes of tasks using the feedback generated from the experience or data gathered that the machine learning process acquire during computer performance of those tasks (paragraphs 0011, 0043, 0068, 0427).
“separate the dataset into at least first and second datasets” as when initially configuring a machine learning system, particularly when using a supervised machine learning approach, the machine learning algorithm can be provided with some training data or a set of training examples, each example is typically a pair of an input signal/vector and a desired output value, label or signal. The machine learning algorithm analyses the training data and produces a generalized function that can be used with unseen data sets to produce desired output values or signals for the unseen input vectors/signals. The user needs to decide what type of data is to be used as the training data, and to prepare a representative real-world set of data (paragraphs 0030-0031, 0051). When using dictionary learning based super resolution techniques, there is a need for two dictionaries: one for the low-resolution image and a separate dictionary for the high-resolution image. To combine super resolution techniques with dictionary learning, reconstruction models are created to enhance the image based on mapping the coefficients of the low-resolution dictionary to coefficients in the high-resolution dictionary (paragraphs 0063-0064, 0086-0087, 0580).
“execute a plurality of different detector processes against the first and second data sets, wherein the plurality of different detector processes includes at least a first detector process and a second detector process, wherein the first and second detector processes generate, respectively, first and second metrics of a level of difference by using different processing for the first and second datasets” as the plurality of different dataset pairs (paragraphs 0030-0031, 0051). By dividing the visual data into smaller sections, where the sections can be sequences of frames or portions of one or more frames, and where the division can be based on a particular metric for similarity, more efficient models can be selected, in some embodiments multiple sections can be grouped, all of which comprise part of a landscape shot, and one model can be used to reconstruct the scene, i.e., sequence of frames, as opposed to a using a different model for every separate frame in the scene. In some embodiments, if the next scene in the visual data is very different, then the scene can be detected as being very different and a new model can be selected accordingly for the scene (paragraphs 0132, 0381).
Wang does not explicitly teach the claimed limitation “perform a comparison of the first and second metrics to at least one threshold value; and determine, by using the performed comparison, whether there is a statistically significant change in data within the first dataset as compared to the data within the second dataset based on the performed comparison”.
Zoldi teaches methods for detecting fraud and non-fraud pattern changes based on transaction pathway transversal analysis. A decision tree can be built based on a training dataset from a reference dataset (abstract). A weighted average of the three metrics can quantify the difference of pathway distributions to form a single metric in addition to the mean/variance of class probability to generate results on the deviation of the distribution of the new dataset from the reference dataset on a global level, and to generate an alert if the metric exceeds a predetermined criterion. The calculations of the deviation metrics may be in an arbitrary order and some metrics may be not used (paragraphs 0018, 0023, 0027). For a new dataset under investigation, each transaction sample can traverse through the built tree and reach a leaf node to get classified. The likelihood of fraud can be recorded and the mean and variance of the fraud likelihood can be calculated for the entire population or subpopulations of legitimate transactions and fraudulent transactions, an exemplary mean and variance for some data sets over all the pathways. The mean and variance of all the legitimate and fraudulent samples over the entire set of pathways can be calculated respectively. It can be seen that for fraud samples, the mean value can decrease from the in-time test dataset through the out-of-time dataset to the out-of-region dataset. For non-fraud samples, the mean value can change less significantly from the in-time dataset, indicating a change in the fraud behaviors. The two-sample Wilcoxon test between the in-time datasets and out-of-time dataset or out-of-region dataset may be used, and the p-values may be obtained for a significance test. The p-values obtained can be much lower than the significance level 0.05, which can be indicative of significant changes in the mean values of the in-time dataset and out-of-region dataset from the in-time dataset. The changes in the statistical characteristics may be suggestive of the pattern changes in the new datasets from the reference dataset. These pattern changes can indicate that individual pathways should be the next investigation to determine cause (paragraphs 0082-0083). The comparison of the metrics between classes may indicate whether the changes are similar or dissimilar across all the classes. For example, in the bi-modal case, the metrics can be calculated for non-fraud and fraud classes separately, and a difference between the two metrics may show whether the non-fraud and fraud classes change in a similar manner or not. If the difference exceeds some threshold, then the different variation patterns for fraud and non-fraud classes may persist. For multi-modal cases, the metric to measure the difference metrics may be the difference between the maximum and minimum values of the metrics (paragraph 0099-0103). A method for detecting fraud pattern changes in payment transactions on a global level in accordance with some implementations. The flowchart details the procedure in the pathway anomaly evaluator. In some implementations, a development model can be chosen to be a reference dataset, and the pathway density distribution may be obtained by using the test data which is disjointed with the training data. An example reference dataset can include payment transactions in a North American country. The model was developed and used as a reference model. Exemplary new datasets can include payment transactions for the same country but in a different year, which can be called an out-of-time dataset payment transactions for another country also in North America, which can be called an out-of-region dataset. Transaction patterns and fraud patterns may change from time to time in the same region and can also change from region to region. The pathway density distribution difference between the reference dataset and new dataset under investigation may serve as a significant indicator of the likely fraud pattern changes (paragraph 0101; see also figure 5).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention, having the teachings of Wang and Zoldi before him/her, to modify Wang determining whether there has been a statistically significant change in the time series data generated because that would provide a clear indication of which features are most important for prediction or classification, the difference in the pathway frequency distribution may suggest changes in the patterns and providing unique vantage points on changing fraud patterns as taught by Zoldi (paragraph 0006).
Examiner’s Note
Examiner has cited particular columns/paragraph and line numbers in the references applied to the claims above for the convenience of the applicant. Although the specified citations are representative of the teachings of the art and are applied to specific limitations within the individual claim, other passages and figures may apply as well. It is respectfully requested from the applicant in preparing responses, to fully consider the references in entirety as potentially teaching all or part of the claimed invention, as well as the context of the passage as taught by the prior art or disclosed by the Examiner.
In the case of amending the Claimed invention, Applicant is respectfully requested to indicate the portion(s) of the specification which dictate(s) the structure relied on for proper interpretation and also to verify and ascertain the metes and bounds of the claimed invention. This will assist in expediting compact prosecution. MPEP 714.02 recites: “Applicant should also specifically point out the support for any amendments made to the disclosure. See MPEP § 2163.06. An amendment which does not comply with the provisions of 37 CFR 1.121(b), (c), (d), and (h) may be held not fully responsive. See MPEP § 714.” Amendments not pointing to specific support in the disclosure may be deemed as not complying with provisions of 37 C.F.R. 1.131(b), (c), (d), and (h) and therefore held not fully responsive. Generic statements such as “Applicants believe no new matter has been introduced” may be deemed insufficient.
Contact Information
Any inquiry concerning this communication or earlier communications from the examiner should be directed to James Hwa whose telephone number is 571-270-1285 or email address james.hwa#uspto.gov. The examiner can normally be reached on 9:00 am – 5:30 pm EST. If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Ajay Bhatia can be reached on 571-272-3906. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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12/24/2025
/SHYUE JIUNN HWA/
Primary Examiner, Art Unit 2156