The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
DETAILED ACTION
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 09/10/2025 has been entered.
Notice to Applicant
In response to the communication received on 09/10/2025, the following is a Non-Final Office Action for Application No. 17955053.
Status of Claims
Claims 1-3, 5-6, 8-11, and 13-14 are pending.
Claims 4 and 12 are withdrawn.
Claim 7 is cancelled.
Response to Amendments
Applicant’s amendments have been fully considered.
Response to Arguments
Applicant’s arguments with respect to the claims have been considered but are moot in light of the new grounds of rejection, as necessitated by amendment.
As per the 101 rejection, Applicant argues that the claims are in favor of eligibility per Prong One of Step 2A, however Examiner respectfully disagrees. Per Prong One of Step 2A, the identified recitation of an abstract idea falls within at least one of the Abstract Idea Groupings consisting of: Mathematical Concepts, Mental Processes, or Certain Methods of Organizing Human Activity. Particularly, the identified recitation falls within the Mental Processes including concepts performed in the human mind (including an observation, evaluation judgment, opinion) and/or Certain Methods of Organizing Human Activity including managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules of instructions). Since the recitation of the claims falls into at least one of the above Groupings, there is a basis for providing further analysis with regard to Prong Two of Step 2A to determine whether the recitation of an abstract idea is deduced to being directed to an abstract idea. Thus, the rejection is maintained.
Applicant argues that the claims are in favor of eligibility per Prong Two of Step 2A, however Examiner respectfully disagrees. Per Prong Two of Step 2A, this judicial exception is not integrated into a practical application because the claim as a whole does not integrate the identified abstract idea into a practical application. The processor and/or memory is recited at a high level of generality, i.e., as a generic processor performing a generic computer function of processing/transmitting data. This generic processor server limitation is no more than mere instructions to apply the exception using a generic computer component. Further, the processor selects a number of the plurality of forecast modules to be used based on the input is mere instruction to apply an exception using a generic computer component which cannot integrate a judicial exception into a practical application. Accordingly, this/these additional element(s) does/do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. In other words, the present claims use a generic processing device and memory medium to inter alia perform the function of selecting a number of the plurality of forecast modules to be used based on the input which is a concept that can be performed in the human mind. The processor is merely used to perform the function(s), and the processor does not integrate the abstract idea into a practical application since there are no meaningful limits on practicing the abstract idea. Thus, since the claims are directed to the determined judicial exception in view of the two prongs of Step 2A, the 2019 PEG flowchart is directed to Step 2B. Thus, the rejection is maintained.
Applicant argues that the claims are in favor of eligibility per Step 2B, however Examiner respectfully disagrees. Therein, the additional elements and combinations therewith are examined in the claims to determine whether the claims as a whole amounts to significantly more than the judicial exception. It is noted here that the additional elements are to be considered both individually and as an ordered combination. In this case, the claims each at most comprise additional elements of: processor and/or memory. Taken individually, the additional limitations each are generically recited and thus does not add significantly more to the respective limitations. Further, the processor selects a number of the plurality of forecast modules to be used based on the input is mere instruction to apply an exception using a generic computer component which cannot provide an inventive concept in Step 2B (or, looking back to Step 2A, cannot integrate a judicial exception into a practical application). For further support, the Applicant’s specification supports the claims being directed to use of a generic computer/memory type structure. Taken as an ordered combination, the claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the limitations are directed to limitations referenced in Alice Corp. that are not enough to qualify as significantly more when recited in a claim with an abstract idea include the non-limiting or non-exclusive examples of MPEP § 2106.05. Thus, the rejection is maintained.
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 1-3, 5-6, 8-11, and 13-14 are rejected under 35 U.S.C. 101 as directed to non-statutory subject matter.
Claims 1-3, 5-11, and 13-14 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more. In adhering to the 2019 PEG, Step 1 is directed to determining whether or not the claims fall within a statutory class. Herein, the claims fall within statutory class of process or machine or manufacture. Hence, the claims qualify as potentially eligible subject matter under 35 U.S.C §101. With Step 1 being directed to a statutory category, the 2019 PEG flowchart is directed to Step 2. Step 2 is the two-part analysis from Alice Corp. (also called the Mayo test). The 2019 PEG makes two changes in Step 2A: It sets forth new procedure for Step 2A (called “revised Step 2A”) under which a claim is not “directed to” a judicial exception unless the claim satisfies a two-prong inquiry. The two-prong inquiry is as follows: Prong One: evaluate whether the claim recites a judicial exception (an abstract idea enumerated in the 2019 PEG, a law of nature, or a natural phenomenon). If claim recites an exception, then Prong Two: evaluate whether the claim recites additional elements that integrate the exception into a practical application of the exception. The claim(s) recite(s) the following abstract idea indicated by non-boldface font and additional limitations indicated by boldface font:
a user interface an input module configured to receive a set of features comprising data and timeseries to be used by each of a plurality of prediction models for generating the forecast; a display;a processor; anda memory storing a program, wherein:the program, when executed by the processor, causes the processor to function as:a plurality of arbitrary forecast modules, wherein each of the plurality of arbitrary forecast module modules is configured to generate, using the set of features, a plurality of forecast results based on an ensemble of a family of models each including one or more of the plurality of prediction models, different families of models being associated with the plurality of forecast models, respectively two or more families of models;a plurality of optimization modules, each optimization module being configured to optimize the plurality of forecast results associated with a respective forecast module among the plurality of forecast modules, wherein each optimization module is further configured to optimize the plurality of forecast results by minimizing a validation error of an aggregated forecast derived using a plurality of optimization metrices subjected to physical and operational constraints;a forecast result combination module configured to probabilistically combine the outputs of the plurality of optimization modules; andan output module that outputs, to the display, a final forecast based on the combination of the at least two forecast results,the user interface is further configured to receive an input associated with a number of the plurality of forecast modules, and the processor selects the number of the plurality of forecast modules to be used based on the input received through the user interface.
Per Prong One of Step 2A, the identified recitation of an abstract idea falls within at least one of the Abstract Idea Groupings consisting of: Mathematical Concepts, Mental Processes, or Certain Methods of Organizing Human Activity. Particularly, the identified recitation falls within the Mental Processes including concepts performed in the human mind (including an observation, evaluation judgment, opinion) and/or Certain Methods of Organizing Human Activity including managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules of instructions). Per Prong Two of Step 2A, this judicial exception is not integrated into a practical application because the claim as a whole does not integrate the identified abstract idea into a practical application. The processor and/or memory is recited at a high level of generality, i.e., as a generic processor performing a generic computer function of processing/transmitting data. This generic processor and/or memory limitation is no more than mere instructions to apply the exception using a generic computer component. Further, outputs a final forecast by a processor and/or memory is mere instruction to apply an exception using a generic computer component which cannot integrate a judicial exception into a practical application. Accordingly, this/these additional element(s) does/do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. Thus, since the claims are directed to the determined judicial exception in view of the two prongs of Step 2A, the 2019 PEG flowchart is directed to Step 2B. Therein, the additional elements and combinations therewith are examined in the claims to determine whether the claims as a whole amounts to significantly more than the judicial exception. It is noted here that the additional elements are to be considered both individually and as an ordered combination. In this case, the claims each at most comprise additional elements of: processor and memory. Taken individually, the additional limitations each are generically recited and thus does not add significantly more to the respective limitations. Further, outputs a final forecast by a processor and/or memory is mere instruction to apply an exception using a generic computer component which cannot provide an inventive concept in Step 2B (or, looking back to Step 2A, cannot integrate a judicial exception into a practical application). For further support, the Applicant’s specification supports the claims being directed to use of a generic computer/memory type structure at ¶0036 wherein “the processor/controller 102 may include a central processing unit (CPU), a graphics processing unit (GPU), or both.”. Taken as an ordered combination, the claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the limitations are directed to limitations referenced in Alice Corp. that are not enough to qualify as significantly more when recited in a claim with an abstract idea include, as a non-limiting or non-exclusive examples: i. Adding the words "apply it" (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, e.g., a limitation indicating that a particular function such as creating and maintaining electronic records is performed by a computer, as discussed in Alice Corp., 134 S. Ct. at 2360, 110 USPQ2d at 1984 (see MPEP § 2106.05(f));
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ii. Simply appending well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception, e.g., a claim to an abstract idea requiring no more than a generic computer to perform generic computer functions that are well-understood, routine and conventional activities previously known to the industry, as discussed in Alice Corp., 134 S. Ct. at 2359-60, 110 USPQ2d at 1984 (see MPEP § 2106.05(d));
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iii. Adding insignificant extra-solution activity to the judicial exception, e.g., mere data gathering in conjunction with a law of nature or abstract idea such as a step of obtaining information about credit card transactions so that the information can be analyzed by an abstract mental process, as discussed in CyberSource v. Retail Decisions, Inc., 654 F.3d 1366, 1375, 99 USPQ2d 1690, 1694 (Fed. Cir. 2011) (see MPEP § 2106.05(g)); or
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v. Generally linking the use of the judicial exception to a particular technological environment or field of use, e.g., a claim describing how the abstract idea of hedging could be used in the commodities and energy markets, as discussed in Bilski v. Kappos, 561 U.S. 593, 595, 95 USPQ2d 1001, 1010 (2010) or a claim limiting the use of a mathematical formula to the petrochemical and oil-refining fields, as discussed in Parker v. Flook. The courts have recognized the following computer functions inter alia to be well-understood, routine, and conventional functions when they are claimed in a merely generic manner: performing repetitive calculations; receiving, processing, and storing data (e.g., the present claims); electronically scanning or extracting data; electronic recordkeeping; automating mental tasks (e.g., process/machine/manufacture for performing the present claims); and receiving or transmitting data (e.g., the present claims). The dependent claims do not cure the above stated deficiencies, and in particular, the dependent claims further narrow the abstract idea without reciting additional elements that integrate the exception into a practical application of the exception or providing significantly more than the abstract idea. Since there are no elements or ordered combination of elements that amount to significantly more than the judicial exception, the claims are not eligible subject matter under 35 USC §101. Thus, 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.
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 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 of this title, 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 1-3, 5-6, 8-11, and 13-14 are rejected under 35 U.S.C. 103 as being unpatentable over Dan et al. (US 20190034821 A1) hereinafter referred to as Dan in view of Sengupta et al. (US 20200118019 A1) hereinafter referred to as Sengupta in further view of Rangapuram et al. (US 11281969 B1) hereinafter referred to as Rangapuram.
Dan teaches:
Claim 1. A system for generating a forecast of a timeseries, the system comprising:
a user interface configured to receive a set of features comprising data and timeseries to be used by each of a plurality of prediction models for generating the forecast;a display;a processor; anda memory storing a program, wherein: the program, when executed by the processor, causes the processor to function as: (Fig. 4 and ABSTRACT manufacturing and configuring an information handling system, comprising: generating a first level forecast prediction, the first level forecast prediction being based upon seasonal factors and a trend component; generating a second level forecast prediction, the second level forecast prediction being based upon an average error between current time period revenue data and a plurality of previous time periods revenue data; generating a third level forecast prediction, the third level forecast prediction being based upon a remaining portion of a particular time period and data relating to an already completed portion of the particular time period; generating a final forecast prediction, the final forecast prediction being based upon the first level forecast prediction, the second level forecast prediction and the third level forecast prediction ¶0022 FIG. 1 is a generalized illustration of an information handling system 100 that can be used to implement the system and method of the present invention. The information handling system 100 includes a processor (e.g., central processor unit or “CPU”) 102, input/output (I/O) devices 104, such as a display, a keyboard, a mouse, and associated controllers, a hard drive or disk storage 106, and various other subsystems 108. In various embodiments, the information handling system 100 also includes network port 110 operable to connect to a network 140 ¶0025 In certain embodiments, the forecasting operation forecasts run rate revenue with limited and volatile historical data using self-learning blended time series techniques. In certain embodiments, the forecasting operation includes a multi-step approach which uses blended time series techniques to achieve robust, consistent and accurate prediction at a granular level. In certain embodiments, the multi-step approach provides an end to end process of prediction ¶0029 Such a forecasting operation is repeatable. The forecasting operation is repeatable in other areas to forecast using time series data. The forecasting operation can also be applied for bidding prediction (i.e., prediction where there is an offer and acceptance, often present with larger orders) with little customization. Such a forecasting operation is extendable to other business objectives. For example with little or no modification the forecasting operation can organize and manage commit numbers of individual contributors on a weekly basis into a centralized system. The forecasting operation can be leveraged to provide revenue guidance (e.g., an expected revenue range) as the forecast operation can quickly calculate the expected revenue and its volatility.);
a plurality of forecast modules, wherein each of the plurality of forecast modules is configured to generate, using the set of features, a plurality of forecast results based on an ensemble of a family of models each including one or more of the plurality of prediction models, different families of models being associated with the plurality of forecast models, respectively (¶0031 the BMS 240 is implemented on the BMS server 245 to provide business intelligence operations. In certain embodiments, the business intelligence operations include data modeling operations. In certain embodiments, results of the forecasting operation are provided to the BMS 240 to enable the BMS 240 to take into account forecasts provided by the forecasting operation when performing the business intelligence operations. In various embodiments, the manufacturing system 118, the inventory system 220, the manufacturing system 230 and the BMS 240 execute on hardware processors on their respective servers to perform their respective operations);
a plurality of optimization modules, each optimization module being configured to optimize the plurality of forecast results associated with a respective forecast module among the plurality of forecast modules, wherein each optimization module is further configured to optimize the plurality of forecast results by minimizing a validation error of an aggregated forecast derived using a plurality of optimization metrices subjected to physical and operational constraints (Fig. 4 and ABSTRACT manufacturing and configuring an information handling system, comprising: generating a first level forecast prediction, the first level forecast prediction being based upon seasonal factors and a trend component; generating a second level forecast prediction, the second level forecast prediction being based upon an average error between current time period revenue data and a plurality of previous time periods revenue data; generating a third level forecast prediction, the third level forecast prediction being based upon a remaining portion of a particular time period and data relating to an already completed portion of the particular time period; generating a final forecast prediction, the final forecast prediction being based upon the first level forecast prediction, the second level forecast prediction and the third level forecast prediction ¶0028 Such a forecasting operation increases the performance of a forecasting system while reducing prediction error. For example, in one implementation the Mean Absolute Prediction Error (MAPE) for overall worldwide commercial businesses was reduced from 7% (using a known forecast methodology) to 1% (using present forecast methodology). This performance is achieved using existing data extracts only and no additional sources are needed. ¶0114 A final RTG Revenue prediction is obtained from the weighted average of all three revenue estimates taken together. The weights assigned to the three revenue estimates are determined based on the ratio of the reciprocal of Mean Absolute Percentage Error (MAPE) of each revenue estimate with the actual dataset.);
a forecast result combination module configured to probabilistically combine the outputs of the plurality of optimization modules; andan output module that outputs, to the display, a final forecast based on the combination of the at least two forecast results,the user interface is further configured to receive an input associated with a number of the plurality of forecast modules, andthe processor selects a number of the plurality of forecast modules to be used based on the input received through the user interface (Fig. 10 and ABSTRACT manufacturing and configuring an information handling system, comprising: generating a first level forecast prediction, the first level forecast prediction being based upon seasonal factors and a trend component; generating a second level forecast prediction, the second level forecast prediction being based upon an average error between current time period revenue data and a plurality of previous time periods revenue data; generating a third level forecast prediction, the third level forecast prediction being based upon a remaining portion of a particular time period and data relating to an already completed portion of the particular time period; generating a final forecast prediction, the final forecast prediction being based upon the first level forecast prediction, the second level forecast prediction and the third level forecast prediction ¶0025 Such a multi-step approach, addresses the issue of missing data, identifies outliers, decomposes trend and seasonality, extrapolates pattern, assigns weight to immediate past revenues, and incorporates impact of week to date (WTD) revenue of a historical time period. A forecast is generated using the results trend and seasonality decomposition, recent impact adjustment and current quarter weeks to date revenue impact. In certain embodiments, the forecasts are synthesized using weights assigned from individual prediction error to arrive at final prediction. The forecast operation provides a significant improvement in accuracy over the known forecast techniques and also over standard time series models. The forecasting operation is universally suited to other use cases. ¶0031 the business intelligence operations include data modeling operations. In certain embodiments, results of the forecasting operation are provided to the BMS 240 to enable the BMS 240 to take into account forecasts provided by the forecasting operation when performing the business intelligence operations. In various embodiments, the manufacturing system 118, the inventory system 220, the manufacturing system 230 and the BMS 240 execute on hardware processors on their respective servers to perform their respective operations. ¶0063 FIG. 6 shows an example of a result when statistical techniques are applied to identify and outliers and missing values. The forecasting operation then calculates an adjusted de-trended field ‘Detrended_adjusted’. With the adjusted de-trended field if the value of ‘flag_detrended_RTG’ column is FALSE (i.e. the value is not an outlier), then the ‘Detrended_adjusted’ column takes the value of the ‘Detrended_Series_RTG’ column, else the column takes a value of 0. ¶0040 In various embodiments the forecast operation is coded and automated to function for all granular combinations. In certain embodiments, the output of the forecast operation is provided to and integrated with a business management system (BMS) (i.e., a central reporting system) for Predicted RTG RR Revenue in Pipeline Sufficiency reports. Figs. 3,5-11 and ¶0022 FIG. 1 is a generalized illustration of an information handling system 100 that can be used to implement the system and method of the present invention. The information handling system 100 includes a processor (e.g., central processor unit or “CPU”) 102, input/output (I/O) devices 104, such as a display, a keyboard, a mouse, and associated controllers, a hard drive or disk storage 106, and various other subsystems 108).
Although not explicitly taught by Dan, Sengupta teaches in the analogous art of interface for visualizing and improving model performance:
a plurality of forecast modules, wherein each of the plurality of forecast modules is configured to generate, using the set of features, a plurality of forecast results based on an ensemble of a family of models each including one or more of the plurality of prediction models, different families of models being associated with the plurality of forecast models, respectively (¶0054 In some implementations, the GUI display space in FIG. 4 can automatically update in real-time. For example, the new graphical objects can appear in the GUI display space and/or existing graphical object can be replaced with updated graphical objects. The updates can be based on new results generated by the predictive model generator (e.g., generation of new predictive models). For example, when a new candidate model is generated, a graphical object associated with the performance metric of the newly generated candidate model may appear in the plot. Graphical objects associated with available budget, probability of success, highest model accuracy value, lowest false positive value, and lowest false negative value can be updated. ¶0055 a specific class of models may be performing well relative to other classes of models and with a current dataset even though the specific class of models may have not performed as well for similar datasets in the past. This approach can start with a mix of models (e.g., an ordered list of model types to train with the data set) biased to the desired objective (e.g. lowest complexity, highest accuracy). For example, if a user is looking for a low-cost auditable model with real time predictions, the model mix can primarily select algorithms that typically produce smaller models that are auditable and capable of being deployed for real time predictions ¶0057-0060 In some implementation, a small sampling (e.g., one, two, etc.) of complex models can be included to the mix (e.g., ordered list, set, and the like) to determine if the higher complexity models perform significantly better than the simpler models for the given dataset. Other types of models can also run (e.g., be trained) to determine how additional model types perform. While the model mix can be determined by the user's business objectives, other modeling types may be run to determine the optimal model type. For example, the user looking for the highest accuracy might expect a neural net, or deep learning model to produce the best predictions, however, running a few decisions trees, or linear regressions may reveal that the more sophisticated models are only marginally higher accuracy, in this case the user might want to focus further development on simpler models to reduce cost and gain the benefits of less complex models).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the interface for visualizing and improving model performance of Sengupta with the system for forecasting run rate revenue with limited and volatile historical data using self-learning blended time series techniques of Dan for the following reasons:
(1) a finding that there was some teaching, suggestion, or motivation, either in the references themselves or in the knowledge generally available to one of ordinary skill in the art, to modify the reference or to combine reference teachings, e.g. Dan ¶0004 teaches that tracking and managing sales can be especially important for companies that manufacture and/or supply goods and/or services;
(2) a finding that there was reasonable expectation of success since the only difference between the claimed invention and the prior art being the lack of actual combination of the elements in a single prior art reference, e.g. Dan Abstract teaches manufacturing and configuring an information handling system, comprising: generating a first level forecast prediction, a second level forecast prediction, a third level forecast prediction, and a final forecast prediction, and Sengupta Abstract teaches monitoring performance of a generated model while the generated model is being used for classification on live data; and
(3) whatever additional findings based on the Graham factual inquiries may be necessary, in view of the facts of the case under consideration, to explain a conclusion of obviousness, e.g. Dan at least the above cited paragraphs, and Sengupta at least the inclusively cited paragraphs.
Therefore, it would be obvious to one skilled in the art at the time of the invention to combine the interface for visualizing and improving model performance of Sengupta with the system for forecasting run rate revenue with limited and volatile historical data using self-learning blended time series techniques of Dan. The rationale to support a conclusion that the claim would have been obvious is that "a person of ordinary skill in the art would have been motivated to combine the prior art to achieve the claimed invention and whether there would have been a reasonable expectation of success in doing so." DyStar Textilfarben GmbH & Co. Deutschland KG v. C.H. Patrick Co., 464 F.3d 1356, 1360, 80 USPQ2d 1641, 1645 (Fed. Cir. 2006). See MPEP 2143(G).
Although not explicitly taught by Dan in view of Sengupta, Rangapuram teaches in the analogous art of artificial intelligence system combining state space models and neural networks for time series forecasting:
the user interface is further configured to receive an input associated with a number of the plurality of forecast modules, andthe processor selects the number of the plurality of forecast modules to be used based on the input received through the user interface (C.5 L.17 Any of various types of programmatic interfaces may be employed in different embodiments, such as a web-based console, a set of application programming interfaces (APIs), command line tools and/or graphical user interfaces. In one or more programmatic interactions with the forecasting service or tool, a client or user may specify any of several hyper-parameters or meta-parameters for training and/or executing the requested model in some embodiments. For example, in some embodiments the hyper-parameters may include, among others, (a) a time frequency of one or more of the time series used for the training, (b) an indicator of a number of predictions to be generated for one or more of the time series, (c) an indicator of a number of time series time steps to be consumed as input to generate a prediction, (d) an indication of a noise model to be used for uncertainty estimates, (e) a number of training epochs, (f) a cardinality of a categorical feature associated with individual ones of the time series, (g) an embedding dimension to be used to characterize categories of time series, (h) a number of cells within a layer of a recurrent neural network used in a forecasting model, (i) a number of layers of a recurrent neural network used in a forecasting model,…).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the artificial intelligence system combining state space models and neural networks for time series forecasting of Rangapuram with the system for forecasting run rate revenue with limited and volatile historical data using self-learning blended time series techniques of Dan in view of Sengupta for the following reasons:
(1) a finding that there was some teaching, suggestion, or motivation, either in the references themselves or in the knowledge generally available to one of ordinary skill in the art, to modify the reference or to combine reference teachings, e.g. Dan ¶0004 teaches that tracking and managing sales can be especially important for companies that manufacture and/or supply goods and/or services;
(2) a finding that there was reasonable expectation of success since the only difference between the claimed invention and the prior art being the lack of actual combination of the elements in a single prior art reference, e.g. Dan Abstract teaches manufacturing and configuring an information handling system, comprising: generating a first level forecast prediction, a second level forecast prediction, a third level forecast prediction, and a final forecast prediction, and Sengupta Abstract teaches monitoring performance of a generated model while the generated model is being used for classification on live data, and Rangapuram Abstract teaches a composite time series forecasting model comprising a neural network sub-model and one or more state space sub-models corresponding to individual time series is trained; and
(3) whatever additional findings based on the Graham factual inquiries may be necessary, in view of the facts of the case under consideration, to explain a conclusion of obviousness, e.g. Dan in view of Sengupta at least the above cited paragraphs, and Rangapuram at least the inclusively cited paragraphs.
Therefore, it would be obvious to one skilled in the art at the time of the invention to combine the artificial intelligence system combining state space models and neural networks for time series forecasting of Rangapuram with the system for forecasting run rate revenue with limited and volatile historical data using self-learning blended time series techniques of Dan in view of Sengupta. The rationale to support a conclusion that the claim would have been obvious is that "a person of ordinary skill in the art would have been motivated to combine the prior art to achieve the claimed invention and whether there would have been a reasonable expectation of success in doing so." DyStar Textilfarben GmbH & Co. Deutschland KG v. C.H. Patrick Co., 464 F.3d 1356, 1360, 80 USPQ2d 1641, 1645 (Fed. Cir. 2006). See MPEP 2143(G).
Dan teaches:
Claim 2. The system as claimed in claim 1, wherein each forecast module among the plurality of forecast modules comprises an independent ensemble learning model that combines the plurality of prediction models, and wherein each forecast module is independently configurable (¶0025 In certain embodiments, the forecasting operation forecasts run rate revenue with limited and volatile historical data using self-learning blended time series techniques. In certain embodiments, the forecasting operation includes a multi-step approach which uses blended time series techniques to achieve robust, consistent and accurate prediction at a granular level. In certain embodiments, the multi-step approach provides an end to end process of prediction. Such a multi-step approach, addresses the issue of missing data, identifies outliers, decomposes trend and seasonality, extrapolates pattern, assigns weight to immediate past revenues, and incorporates impact of week to date (WTD) revenue of a historical time period ¶0027 Such a forecasting operation is adaptable and discoverable. More specifically, the forecasting operation provides a self-learning solution that is adaptable to constant changes in business scenarios. The forecasting system includes an architecture which is designed to not only capture overall historical pattern but also to account for recent impacts, whether the impacts are from previous time period (e.g., quarter) revenue data and/or current time period revenue data.).
Dan teaches:
Claim 3. The system as claimed in claim 1, wherein the user interface is further configured to: receive one or more inputs associated with selection of the plurality of prediction models and a plurality of hyperparameters for each forecast module (¶0031 the BMS 240 is implemented on the BMS server 245 to provide business intelligence operations. In certain embodiments, the business intelligence operations include data modeling operations. In certain embodiments, results of the forecasting operation are provided to the BMS 240 to enable the BMS 240 to take into account forecasts provided by the forecasting operation when performing the business intelligence operations ¶0110 Next, the forecasting operation uses the first, second and third level run rate revenue forecasts to identify a final run rate revenue forecast. More specifically, a Mean Absolute Percentage Error (MAPE) value is calculated for each of the three revenue estimate levels for a time period t ∈[5, 12 .fwdarw.Z by comparing the estimates with the actuals (which in this case, is stored as ‘ln(RR_RTG_Rev)_adjusted’) of the corresponding quarter. ¶0065 With the values of Trend_RTG and the Seasonal Factors (for each quarter) obtained in the previous steps, the first level of revenue estimate is calculated. More specifically, the first level revenue estimate is calculated as: For t.fwdarw.Z & t≥1; i denoting the quarter number for the particular row into consideration, Rev_Estimate_L1.sub.t=Trend_RTG.sub.t×Seasonal Factor for Qi E.g. for 2018-Q1(where t=13 and i=1), Rev_Estimate_L1.sub.13=Trend_RTG.sub.13×Seasonal Factor for Q1 where Trend_RTG.sub.13=α+β×t (t=13 in this case).).
Dan teaches:
Claim 5. The system as claimed in claim 1, wherein each optimization module has an independent optimization method for each forecast module (Figs. 4-10 and ABSTRACT manufacturing and configuring an information handling system, comprising: generating a first level forecast prediction, the first level forecast prediction being based upon seasonal factors and a trend component; generating a second level forecast prediction, the second level forecast prediction being based upon an average error between current time period revenue data and a plurality of previous time periods revenue data; generating a third level forecast prediction, the third level forecast prediction being based upon a remaining portion of a particular time period and data relating to an already completed portion of the particular time period; generating a final forecast prediction, the final forecast prediction being based upon the first level forecast prediction, the second level forecast prediction and the third level forecast prediction ¶0025 Such a multi-step approach, addresses the issue of missing data, identifies outliers, decomposes trend and seasonality, extrapolates pattern, assigns weight to immediate past revenues, and incorporates impact of week to date (WTD) revenue of a historical time period. A forecast is generated using the results trend and seasonality decomposition, recent impact adjustment and current quarter weeks to date revenue impact. In certain embodiments, the forecasts are synthesized using weights assigned from individual prediction error to arrive at final prediction. The forecast operation provides a significant improvement in accuracy over the known forecast techniques and also over standard time series models. The forecasting operation is universally suited to other use cases. ¶0031 the business intelligence operations include data modeling operations. In certain embodiments, results of the forecasting operation are provided to the BMS 240 to enable the BMS 240 to take into account forecasts provided by the forecasting operation when performing the business intelligence operations. In various embodiments, the manufacturing system 118, the inventory system 220, the manufacturing system 230 and the BMS 240 execute on hardware processors on their respective servers to perform their respective operations. ¶0063 FIG. 6 shows an example of a result when statistical techniques are applied to identify and outliers and missing values. The forecasting operation then calculates an adjusted de-trended field ‘Detrended_adjusted’. With the adjusted de-trended field if the value of ‘flag_detrended_RTG’ column is FALSE (i.e. the value is not an outlier), then the ‘Detrended_adjusted’ column takes the value of the ‘Detrended_Series_RTG’ column, else the column takes a value of 0.).).
Dan teaches:
Claim 6. The system as claimed in claim 1, wherein the forecast result combination module uses a user-defined statistical method (¶0063 FIG. 6 shows an example of a result when statistical techniques are applied to identify and outliers and missing values. The forecasting operation then calculates an adjusted de-trended field ‘Detrended_adjusted’. With the adjusted de-trended field if the value of ‘flag_detrended_RTG’ column is FALSE (i.e. the value is not an outlier), then the ‘Detrended_adjusted’ column takes the value of the ‘Detrended_Series_RTG’ column, else the column takes a value of 0. With the values of Trend_RTG and the Seasonal Factors (for each quarter) obtained in the previous steps, the first level of revenue estimate is calculated).
Dan teaches:
Claim 8. The system as claimed in claim 1, wherein the plurality of optimization metrics is based on Mean Absolute Percentage Error (MAPE), Mean Absolute Error (MAE), variance, maximum error, higher order moments, and one or more user configurable parameters (¶0028 Such a forecasting operation increases the performance of a forecasting system while reducing prediction error. For example, in one implementation the Mean Absolute Prediction Error (MAPE) for overall worldwide commercial businesses was reduced from 7% (using a known forecast methodology) to 1% (using present forecast methodology). ¶0073 The forecasting operation now calculates weightages for values stored within lag columns. More specifically, a Mean Absolute Percentage Error (MAPE) value is calculated for each of the four values of the quarterly lag columns for time period t ∈ [5, 12 .fwdarw.Z by comparing the quarterly lag values with the actuals (which correspond to ‘ln(RR_RTG_Rev)_adjusted’) of the corresponding quarter. Additionally, the reciprocal of the MAPE values for each of the four lag columns are calculated. ¶0046 The forecasting operation identifies a field ‘ln(RR_RTG_Rev)’ and assigns logarithmic transformation values to ‘RR_RTG_Revenue’ column. The logarithmic transformation of the RR_RTG_Revenue value stabilizes any variance that exists in the raw data. FIG. 5 shows an example of the effect of the logarithmic transformation. Next, the forecasting operation creates a time field (t) and assigns values (1, 2, 3, . . . ) against a quarter column.).
Dan teaches:
Claim 9. The system as claimed in claim 1, wherein the plurality of prediction models includes two or more of a linear regression model, a support vector regression model, a ridge regression model, a lasso regression model, an elastic net model, a Bayesian ridge model, a huber regression model, a KNN model, a gradient boost model, a random forest regression model, a neural network including deep architectures with a range of hyperparameters (¶0050 The forecasting operation then performs a linear regression operation with the dependent variable being ln(RR_RTG_Rev)_adjusted and the independent variables being t_adjusted and flag_RTG. The linear regression operation may be represented as: ln(RR_RTGcustom-character)_adjusted=α+β×t_adjusted where α=estimated intercept and β=estimated slope ¶0026 The multi-step, end to end process addresses the problem of missing and negative revenue data, identifies and adjusts for outliers in the estimation methodology, focuses on applying and combining multiple techniques to come up with final prediction. Overall, the forecast operation uses the time series analysis, regression techniques and modelling in presence of limited and volatile data. The whole solution is scalable at a granular level and thus is useful for the business to implement in pipeline sufficiency reports ¶0033 The extrapolate, combine and predict module 320 provides three estimated revenue forecast levels. The step regression process module 330 applies a three step regression process based upon the three estimated revenue forecast levels provided by the extrapolate, combine and predict module 320. ¶0038 At step 460, a final RTG revenue prediction value is obtained as a function of the first, second and third level estimated revenue forecast series. Individual regressions are performed on each of the three estimated revenue series.).
As per claims 10,11,13,14, the method tracks the system of claims 1,3,8,9, respectively, resulting in substantially similar limitations.
Conclusion
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/KURTIS GILLS/Primary Examiner, Art Unit 3624