DETAILED ACTION
Claims 1-20 are pending and have been examined.
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Drawings
The drawings are objected to because figures 2 and 9 contain shaded black areas, illegible text, and/or lines that are not uniformly thick and well defined. See MPEP §608.02, 37 CFR 1.84 (I), and 37 CFR 1.84 (m).
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. The figure or figure number of an amended drawing should not be labeled as "amended." If a drawing figure is to be canceled, the appropriate figure must be removed from the replacement sheet, and where necessary, the remaining figures must be renumbered and appropriate changes made to the brief description of the several views of the drawings for consistency. Additional replacement sheets may be necessary to show the renumbering of the remaining figures. 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 1.1 21 (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 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 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. The claims are directed to an abstract idea without significantly more.
Here, under step 1 of the Alice analysis, method claims 1-10 are directed to a series of steps, and system claims 11-20 are directed to a hardware processor; and a memory that stores a computer program. Thus the claims are directed to a process and machine, respectively.
Under step 2A Prong One of the analysis, the claimed invention is directed to an abstract idea without significantly more. The claims recite pre-processing time series data, including assigning, determining, comparing, removing, predicting and performing steps.
The limitations of assigning, determining, comparing, removing, predicting and performing, are a process that, under its broadest reasonable interpretation, covers organizing human activity concepts, but for the recitation of generic computer components.
Specifically, the claim elements recite assigning transition events from categorical time series data into a list of transition sets that each include transitions from a respective first category to a respective second category; determining a mean duration and standard deviation, for each transition set, of the respective first category before the transition to the respective second category; comparing a ratio between the mean duration and the standard deviation to a threshold value to identify noisy transition sets; removing noisy transition sets from the list of transition sets to output de-noised transition sets; predicting a probability of an event occurrence using the de-noised transition sets; and performing an action responsive to the probability.
That is, other than reciting a hardware processor; and a memory that stores a computer program in system claims 11-20, the claim limitations merely cover fundamental economic principles or practices, including insurance and mitigating risk, thus falling within the “Certain Methods of Organizing Human Activity” grouping of abstract ideas. Accordingly, the claims recite an abstract idea.
Under Step 2A Prong Two, the eligibility analysis evaluates whether the claim as a whole integrates the recited judicial exception into a practical application of the exception. This judicial exception is not integrated into a practical application. The claims include a hardware processor; and a memory that stores a computer program. The hardware processor; and memory that stores a computer program in the steps is recited at a high-level of generality, such that it amounts no more than mere instructions to apply the exception using a generic computer component. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. As a result, the claims are directed to an abstract idea.
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional element of a hardware processor; and a memory that stores a computer program amounts to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept.
None of the dependent claims recite additional limitations that are sufficient to amount to significantly more than the abstract idea. Claims 2 and 3 further describe determining the mean duration and standard deviation for a transition set and comparing the ratio to the threshold value. Claims 4 and 5 recite and further describe an additional converting step. Claims 6-10 further describe the categorical time series data, predicting the probability, removing the noisy transition, and the action. Similarly, dependent claims 12-20 recite additional details that further restrict/define the abstract idea. A more detailed abstract idea remains an abstract idea.
Under step 2B of the analysis, the claims include, inter alia, a hardware processor; and a memory that stores a computer program.
As discussed with respect to Step 2A Prong Two, the additional elements in the claim amount to no more than mere instructions to apply the exception using a generic computer component. The same analysis applies here in 2B, i.e., mere instructions to apply an exception on a generic computer cannot integrate a judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B.
There isn’t any improvement to another technology or technical field, or the functioning of the computer itself. Moreover, individually, there are not any meaningful limitations beyond generally linking the abstract idea to a particular technological environment, i.e., implementation via a computer system. Further, taken as a combination, the limitations add nothing more than what is present when the limitations are considered individually. There is no indication that the combination provides any effect regarding the functioning of the computer or any improvement to another technology.
In addition, as discussed in paragraphs 0115 of the specification, “Each computer program may be tangibly stored in a machine-readable storage media or device (e.g., program memory or magnetic disk) readable by a general or special purpose programmable computer, for configuring and controlling operation of a computer when the storage media or device is read by the computer to perform the procedures described herein. The inventive system may also be considered to be embodied in a computer-readable storage medium, configured with a computer program, where the storage medium so configured causes a computer to operate in a specific and predefined manner to perform the functions described herein.”
As such, this disclosure supports the finding that no more than a general purpose computer, performing generic computer functions, is required by the claims.
Viewed as a whole, these additional claim element(s) do not provide meaningful limitation(s) to transform the abstract idea into a patent eligible application of the abstract idea such that the claim(s) amounts to significantly more than the abstract idea itself. Therefore, the claim(s) are rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter. See Alice Corporation Pty. Ltd. v. CLS Bank Int’l et al., No. 13-298 (U.S. June 19, 2014).
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 1-6, 9-16, 19 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Achin et al (US 20180046926 A1), in view of Bures et al (US 20200322703 A1).
As per claim 1, Achin et al disclose a computer-implemented method for pre-processing time series data (i.e., Performing the predictive modeling procedure may include performing the pre-processing task, including: (a) obtaining time-series data including one or more data sets, ¶ 0042), comprising:
assigning transition events from categorical time series data into a list of transition sets that each include transitions from a respective first category to a respective second category (i.e., determining the time interval of the time-series data includes: for each of the data sets, determining a respective time interval of the data set; and determining that the time intervals of at least two of the data sets are different, wherein the time interval of the time-series data is determined based, at least in part, on (1) respective proportions of the observations included in each of the data sets, and/or (2) the respective time intervals of each of the data sets, ¶ 0018);
determining a mean duration and standard deviation, for each transition set, of the respective first category before the transition to the respective second category (i.e., determining the respective time interval of the data set includes: determining respective time periods between each pair of successive observations included in the data set; if the time periods between the pairs of successive observations exhibit a plurality of non-uniform durations, the time interval of the data set is determined based, at least in part, on (1) respective proportions of the pairs of successive observations exhibiting each of the non-uniform durations, and/or (2) the durations of the time periods; and if the time periods between the pairs of successive observations are of uniform duration, the time interval of the data set is the duration of each of the time periods, ¶ 0018, wherein The system 100 may calculate the model-independent predictive value of a feature using any suitable techniques, including, without limitation, (1) calculating a statistical measure of the model-specific predictive values (e.g., the mean, median, standard deviation, etc.), ¶ 0409); and
removing noisy transition sets from the list of transition sets to output de-noised transition sets (i.e., The predictive modeling system 100 may automatically check for missing data, outliers, and other important data anomalies, ¶ 0262, wherein the training data may be cross-sectionally down-sampled by removing one or more of the data sets from the training data. In some embodiments, the training data is both temporally down-sampled and cross-sectionally down-sampled, ¶ 0371).
Achin et al does not disclose comparing a ratio between the mean duration and the standard deviation to a threshold value to identify noisy transition sets; predicting a probability of an event occurrence using the de-noised transition sets; and performing an action responsive to the probability.
Bures et al disclose the time-series data for various raw and/or synthetic measurements of the measurement database can be utilized to aggregate and/or summarize selected sets of measurement entries in the measurement database. The statistical measurement functions can include spread measurement functions such as standard deviation, range, interquartile range, absolute deviation, mean absolute difference, distance standard deviation, coefficient of variation, and/or other spread measurement functions utilized for statistical measurement (¶ 0257).
This can be utilized in automatically facilitating various functionality discussed herein and/or enabling user-friendly configurability of various functionality discussed herein, including but not limited to: determining statistical trends; determining correlations and/or cause and effect relationships between various measurement types; training and performing inference functions to predict the occurrence of conditions of interest and/or to predict the values of future measurements; determining optimal weight distribution of unit weights and/or sensor device weights to optimally allocate bandwidth across different multi-sensor units and/or individual sensor devices based on their learned importance in detecting conditions of interest; determining optimal control of controllable environmental factors; determining optimal locations of various equipment, inventory, people, and/or other objects in the facility; and/or otherwise determining optimal conditions for optimal functioning of the facility (¶ 0203).
Achin et al and Bures et al et al are concerned with effective workforce management. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include comparing a ratio between the mean duration and the standard deviation to a threshold value to identify noisy transition sets; predicting a probability of an event occurrence using the de-noised transition sets; and performing an action responsive to the probability in Achin et al, as seen in Bures et al et al, since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
As per claim 2, Achin et al disclose determining a duration for each transition in the transition set that is a length of time a system was in a state corresponding to the first category before the system transitioned a state corresponding to the second category (i.e., determining the respective time interval of the data set includes: determining respective time periods between each pair of successive observations included in the data set; if the time periods between the pairs of successive observations exhibit a plurality of non-uniform durations, the time interval of the data set is determined based, at least in part, on (1) respective proportions of the pairs of successive observations exhibiting each of the non-uniform durations, and/or (2) the durations of the time periods; and if the time periods between the pairs of successive observations are of uniform duration, the time interval of the data set is the duration of each of the time periods, ¶ 0018).
As per claim 3, Achin et al does not disclose identifying a set as a noisy set if the ratio of the mean duration to the standard deviation exceeds the threshold value.
Bures et al disclose the time-series data for various raw and/or synthetic measurements of the measurement database can be utilized to aggregate and/or summarize selected sets of measurement entries in the measurement database. The statistical measurement functions can include spread measurement functions such as standard deviation, range, interquartile range, absolute deviation, mean absolute difference, distance standard deviation, coefficient of variation, and/or other spread measurement functions utilized for statistical measurement (¶ 0257).
Achin et al and Bures et al et al are concerned with effective workforce management. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include identifying a set as a noisy set if the ratio of the mean duration to the standard deviation exceeds the threshold value in Achin et al, as seen in Bures et al et al, since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
As per claim 4, Achin et al disclose converting event information into categorical time series data for a plurality of properties (i.e., down-sampling the observations of the data set, thereby converting the time interval of the data set to the time interval of the time-series data, ¶ 0019).
As per claim 5, Achin et al disclose mapping numerical values onto categorical values (i.e., rules for mapping the target data schemas into the desired dataset schema, ¶ 0179).
As per claim 6, Achin et al disclose multivariate time series information tracking multiple types of event or system state across a shared timeline (i.e., the durations of the non-uniform time intervals. In some embodiments, the modified time interval is the shortest common time period (e.g., the shortest time period that is an integer multiple of each of the non-uniform time periods), ¶ 0359).
As per claim 9, Achin et al disclose removing the noisy transition sets improves an accuracy of predicting the probability (i.e., he predictive modeling system automatically includes data pre-treatment procedures to handle both well-known data anomalies like missing data and outliers, and less widely appreciated anomalies like inliers (repeated observations that are consistent with the data distribution, but erroneous) and postdictors (i.e., extremely predictive covariates that arise from information leakage), the resulting models may be more accurate and more useful, ¶ 0273).
As per claim 10, Achin et al disclose an anomaly relates to a likelihood of a natural disaster occurring at a particular location, and wherein the action includes adjusting an insurance premium associated with the particular location in accordance with the anomaly (i.e., in insurance prediction problems, a dataset may have records of each time a policy holder had a claim. However, in building a model to predict future risk, it may be more useful to consider how many claims a policy-holder has had in the past X years. The engine may detect such situations when it evaluates the dataset (e.g., step 408 of the method 400) by detecting data structure relationships between records corresponding to entities and other records corresponding to events, ¶ 0471).
Claims 11-16, 19 and 20 are rejected based upon the same rationale as the rejection of claims 1-6, 9 and 10, respectively, since they are the system claims corresponding to the method claims.
Conclusion
The prior art made of record and not relied upon, listed in the PTO-892, considered pertinent to applicant's disclosure, discloses time series data analysis and anomaly detection.
With respect to dependent claims 7, 8, 17 and 18, none of the prior art of record, taken individually or in any combination, teach inter alia, wherein predicting the probability includes summing over a Hawkes process for pairs of properties, where the Hawkes process uses a relationship between de-noised transition sets as an input; and wherein predicting the probability has a computational complexity of O(N2), where N is a number of transition sets, such that removing the noisy transition sets improves a speed of predicting the probability, respectively.
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/ANDRE D BOYCE/Primary Examiner, Art Unit 3623 August 7, 2026
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