Detailed Action
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Notice to Applicant
The following is a Final Office action to Application Serial Number 18/649,002, filed on April 29, 2024. In response to Examiner’s Non-Final Office Action of July 1, 2025, Applicant, on December 1, 2025, amended claims 1, 14, and 16. Claims 1-4, and 6-26 are pending in this application and have been rejected below.
Response to Amendment
Applicant’s amendments are acknowledged.
Regarding 35 U.S.C. § 101 rejection, the amended claims have been considered
and are insufficient to overcome the rejection. Please refer to the 35 U.S.C. § 101 rejection for further explanation and rationale.
The 35 U.S.C. § 103 rejections are hereby amended pursuant to applicants amendments. Updated 35 U.S.C. § 103 rejections have been applied to amended claims. Please refer to the § 103 rejection for further explanation and rationale.
Response to Arguments
Applicant’s arguments filed December 1, 2025 have been fully considered but they are not persuasive and/or are moot in view of the revised rejections. Applicant’s arguments will be addressed herein below in the order in which they appear in the response filed December 1, 2025.
On Pgs. 13-18 of the Remarks, regarding 35 U.S.C. § 103 rejections, Applicant states (1) There is no suggestion that any sort of hierarchy of times or time intervals may be reconciled and (2) it can be seen that these three data series have two types of relationships - time series relationship and item or group of item relationships. Trovero may perhaps disclose reconciliation using one hierarchy, such as on items or groups of items, but does not also disclose reconciliation that further involves the time relationships that are recited for these three data series. Specifically, the claim as a whole essentially requires multiple different hierarchical or overlapping relationships. In response to applicant's argument that the references fail to show certain features of the invention, it is noted that the features upon which applicant relies (i.e., time hierarchy]) are not recited in the rejected claim(s). Although the claims are interpreted in light of the specification, limitations from the specification are not read into the claims. See In re Van Geuns, 988 F.2d 1181, 26 USPQ2d 1057 (Fed. Cir. 1993). Trovero discloses time series data and relationships in Par. 45-47 . Time series data under the broadest interpretation is s a sequence of data points indexed, recorded, or graphed in successive time order, usually at uniform intervals (e.g., hourly, daily). Unlike static data, it tracks changes, trends, and patterns over time, with each point associated with a specific timestamp. Common examples include stock prices, weather, and IoT sensor metrics. . Fig. 4 illustrates the hierarchical nature of the data.. Furthermore the claim language states “data series of said plurality is associated with (a) an item or a group of items, and (b) a time sequence” which mean Trovero is only required to disclose time series data for “one item” and a group of items is not required by stated claim language.
On Pgs. 15-17 of the Remarks, regarding 35 U.S.C. § 103 rejections , Applicant states recites two required relationships: (a)"a requirement that a value of a first data series for a group of items at a particular time in a time sequence is equal to a sum of values of a second plurality of data series for items in the group of items at that particular time, and" (b) a requirement that a value of a third data series for an item or group of items at a particular time in a third time sequence is equal to a sum of values in a fourth data series for said item or group of items for a plurality of times in a fourth time
sequence associated with said particular time" with regards to claim 10. In response, Examiner disagrees. The claim language states “ at least one requirement. Examiner recommends removing the “ at least one” claim language.
On Pgs. 16-20 of the Remarks, regarding 35 U.S.C. § 103 rejections , Applicant states there is nothing in Trovero that discloses any recognition of a need, or any solution to a situation in which forecasts are both over different subsets of items and over different time sequences (i.e., "time scales"), such as over days vs months" with regards to claim 17 and claim 24. In response, Examiner disagrees. Trovero discloses multiple forecast in Par. 45- FIG. 4 shows at 150 an example of data being arranged in different dimensions. In FIG. 4, data is arranged as a product hierarchy, geography hierarchy, etc. Forecasts for the dependent variable sale are generated first at level 2, region/product, and then at level 1, region. The separate forecasts are then reconciled by using a reconciliation process. For further clarification Par. 41 discloses FIG. 1 depicts at 20 an environment wherein users 30 can interact with a forecast reconciliation system 60 to reconcile multiple forecasts (e.g., predictions, etc.) that are at different data dimensions (e.g., different levels in a hierarchy).
On Pgs. 20-21 of the Remarks, regarding 35 U.S.C. § 103 rejections , Applicant states there’s no motivation to combine Trovero and Saha. In response to applicant’s argument that there is no teaching, suggestion, or motivation to combine the references, the examiner recognizes that obviousness may be established by combining or modifying the teachings of the prior art to produce the claimed invention where there is some teaching, suggestion, or motivation to do so found either in the references themselves or in the knowledge generally available to one of ordinary skill in the art. See In re Fine, 837 F.2d 1071, 5 USPQ2d 1596 (Fed. Cir. 1988), In re Jones, 958 F.2d 347, 21 USPQ2d 1941 (Fed. Cir. 1992), and KSR International Co. v. Teleflex, Inc., 550 U.S. 398, 82 USPQ2d 1385 (2007). In this case, both Trovero and Saha are directed to forecasting analysis.
On Pgs. 21 of the Remarks, regarding 35 U.S.C. § 112a rejection, Applicant states the logic behind the claim language. Examiner recommends updating the claim language with language from the specification.
On Pgs. 22-24 of the Remarks, regarding 35 U.S.C. § 101 rejections, Applicant states the claims cannot be performed in the human mind, the claims are integrated into a practical application ,dependent claims are directed to a solution of a technical problem and it is an inventive concept.. Examiner respectfully disagrees. The recitation of “machine-readable medium”, “data processing system”, “system”; “forecast reconciler”; and “storage”, provide nothing in the claim elements to preclude the step from being a “Mental Processes” – evaluation. Using one or more models and/or optimization to satisfy the plurality of relationships and to best approximate the data series according to a similarity criterion is an evaluation. Accordingly, the claim recites an abstract idea. The present claims amount to no more than utilizing generic computer elements as tools to perform computational data analysis. Examiner finds the present claims improve an existing business process of forecasting and there are currently no functional advancement to any technology or technological field, in order for the claim elements to be considered significantly more than the abstract idea itself. Please review updated 101 analysis.
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 22 and 23 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention. Specifically, there is no support in the specification for the data processing procedure for determining the second forecasts does not process the historical data nor automatically making orders for stocking said items based on the second forecasts
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- 4 and 6-24 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. Claims 1-4, 6-13 and 16-25 are directed to a method for forecasting, Claim 14 is directed to an article of manufacture for forecasting and Claim 15 and Claim 26 are directed to a system for forecasting.
Claim 1, Claim 16 and Claim 24 recite a method for forecasting, Claim 14 recites an article of manufacture for forecasting and Claim 15 and Claim 26 recites a system for forecasting, which include determining, as output from a plurality of independent forecasters, first forecasts comprising a plurality of respective data series, wherein each data series of said plurality is associated with (a) an item or a group of items, and (b) a time sequence in which each item in the time sequence represents a time or an interval, and wherein said plurality of data series includes multiple data series associated with different items or groups of items, or include multiple data series associated with different time sequences, and wherein the multiple data series includes a first data series for a first item or group of items in which data items in said series are associated with respective times or time intervals in a first time sequence, a second data series for the first item or group of items in which data items in said series are associated with respective times or time intervals in a second time sequence, wherein times or time intervals in said second time sequence being each associated with multiple times or time intervals in said first time sequence, and a third data series for a second item or group of items in which data items in said series are associated with respective times or time intervals in the first time sequence, wherein the first item or group of items and the second item or group of items are distinct and share at least one item; processing relationship information representing a plurality of relationships, with each relationship relating the items or groups of items or relating time sequences of different of the data series of a forecast, the plurality of relationships including: a first time relationship associating elements of a first time sequence with elements of a second time sequence, including associating each element of the first time sequence with respective multiple elements of the second time sequence, and a first group relationship associating a first group of items with multiple other groups or items, the first group relationship identifying the at least one item shared between the first item or group of items and the second item or group of items, the processing including forming a data representation of each relationship; determining a set of data series requirements based on the received relationship information, the data series requirements including at least a first data series requirement relating a first set of data series to a second set of data series over times or intervals for a time sequence associated with said first and second sets of data series, and a second data series requirement relating values of multiple data series for each time or interval of a time sequence; processing the plurality of data series of the first forecasts to determine second forecasts comprising a plurality of data series comprising a reconciled first data series, a reconciled second data series, and a third reconciled data series corresponding to the first data series, the second data series, and the third data series, respectively, wherein each data series of said second forecasts is associated with an item or a group of items, and (b) a time sequence in which each item in the time sequence represents a point or an interval, the determining of the second forecasts including executing a data processing procedure on a computer processor to determine the reconciled data series of said second forecasts based on the data series of the first forecasts and based the data representations of the relationships, the data processing procedure being configured for the reconciled data series of the second forecasts to satisfy the plurality of relationships and to best approximate the data series of the first forecasts according to a similarity criterion (Claim 1). Claim 14 recites determining first forecasts comprising a plurality of data series, wherein each data series of said plurality is associated with (a) an item or a group of items, and (b) a time sequence in which each item in the time sequence represents a time or an interval, and wherein said plurality of data series includes multiple data series associated with different items or groups of items, or include multiple data series associated with different time sequences; processing relationship information representing a plurality of relationships, with each relationship relating the items or groups of items or relating time sequences of different of the data series of a forecast, the plurality of relationships including at least one of: a first time relationship associating elements of a first time sequence with elements of a second time sequence, and a first group relationship associating a first group of items with multiple other groups or items, the processing including forming a data representation of each relationship; determining a set of data series requirements based on the received relationship information, the data series requirements including at least a first data series requirement relating a first set of data series to a second set of data series over times or intervals for a time sequence associated with said first and second sets of data series, and a second data series requirement relating values of multiple data series for each time or interval of a time sequence; determining second forecasts comprising a plurality of data series, wherein each data series of said second forecasts is associated with (a) an item or a group of items, and (b) a time sequence in which each item in the time sequence represents a point or an interval, the determining of the second forecasts including executing a data processing procedure to determine the data series of said second forecasts based on the data series of the first forecasts and based the data representations of the relationships, the data processing procedure being configured for the data series of the second forecasts to satisfy the plurality of relationships and to best approximate the data series of the first forecasts according to a similarity criterion. Claim 15 recites receive forecasts data from respective forecasters of the plurality of forecasters; each forecaster of the plurality of forecasters is configured to independently determine a forecast of a first plurality of forecasts comprising a plurality of data series, wherein each data series of said plurality is associated with (a) an item or a group of items, and (b) a time sequence in which each item in the time sequence represents a time or an interval, and wherein said plurality of data series includes multiple data series associated with different items or groups of items, or include multiple data series associated with different time sequences; processing relationship information representing the processing including forming a data representation of each relationship; wherein the system is configured to determine the set of data series requirements based on received relationship information, wherein the relationship information represents a plurality of relationships, with each relationship relating the items or groups of items or relating time sequences of different of the data series of a forecast, the plurality of relationships including at least one of a first time relationship associating elements of a first time sequence with elements of a second time sequence, and a first group relationship associating a first group of items with multiple other groups or items, the data series requirements including at least a first data series requirement relating a first set of data series to a second set of data series over times or intervals for a time sequence associated with said first and second sets of data series, and a second data series requirement relating values of multiple data series for each time or interval of a time sequence; determine second forecasts comprising a plurality of data series, wherein each data series of said second forecasts is associated with (a) an item or a group of items, and (b) a time sequence in which each item in the time sequence represents a point or an interval, the determining of the second forecasts including determine the data series of said second forecasts based on the data series of the first forecasts and based the data representations of the relationships, the data processing procedure being configured for the data series of the second forecasts to satisfy the plurality of relationships and to best approximate the data series of the first forecasts according to a similarity criterion. Claim 16 recites form independent forecasts including at least one of (a) a first plurality of forecasts for a respective a plurality of overlapping subsets of a plurality of items and (b) a second plurality of forecasts for a respective plurality of overlapping time points or intervals; forming a data representation of forecast constraints resulting from relationships arising from at least one of the overlapping subsets of the plurality of items and the overlapping time points or intervals; and executing a reconciliation data processing procedure to process the independent forecasts and the data representation of the forecast constrains to form reconciled forecasts satisfying the forecast constraints and approximating the independent forecasts. And Claim 24 recites processing a plurality of independently computed forecasts over a plurality of time sequences spanning a time interval and over a plurality of subsets of a plurality items, each forecast of the plurality of forecasts representing a prediction for a particular time sequence of the plurality of time sequences and a particular subset of items of the plurality of subsets such that the forecast comprises a predicted value for respective periods of the particular time sequence, and the predicted values of said forecast represent an aggregate value over all the items in the particular subset, the method comprising: receiving the plurality of independent forecasts, said forecasts having been computed separately by a plurality of independent forecasters, wherein the plurality of independent forecasts comprises one or both of (a) a first independent forecast and a second independent forecast each comprising predicted values over a first subset of items, with the first independent forecast comprising values over a first time sequence and the second independent forecast comprising values over a second time sequence, wherein at least some time period of the first time sequence overlaps with multiple time periods of the second time sequence, and(b) a third independent forecast and a fourth independent forecast each comprising predicted values over a third time sequence, with the third independent forecast comprising values for a third subset of items and the fourth independent forecast comprising values for a fourth subset of items, wherein at least some items belong to both the third subset and the fourth subset; storing values in a data array representing a plurality of relationships between forecasts including at least one of (c) a relationship for a particular subset of items requiring that an aggregation of values for time periods in a forecast over one time sequence for said subset is equal to a value for another time period in a forecast over another time sequence for said subset, and(d) a relationship for a particular time sequence requiring that a value for a time period of said time sequence for a forecast for a particular subset of items is equal to an aggregation of values for the time period for other forecasts of subsets of items in said particular subset; and after having received the plurality of independent forecasts, and after having stored the values representing the relationships, processing the values of the independent forecasts to yield a plurality of reconciled forecasts that satisfy the plurality of relationships and that approximate the plurality of independent forecasts, wherein the processing of the values of the independent forecasts comprises processing the values of the independent forecasts and the values in the data array to satisfy the relationships and while reducing an approximation error between the reconciled forecasts and the independent forecasts. Claim 26 recites processing a plurality of independently computed forecasts over a plurality of time sequences spanning a time interval and over a plurality of subsets of a plurality items, each forecast of the plurality of forecasts representing a prediction for a particular time sequence of the plurality of time sequences and a particular subset of items of the plurality of subsets such that the forecast comprises a predicted value for respective periods of the particular time sequence, and the predicted values of said forecast represent an aggregate value over all the items in the particular subset; comprising: means for receiving the plurality of independent forecasts, said forecasts having been computed separately by a plurality of independent forecasters, wherein the plurality of independent forecasts comprises one or both of (e) a first independent forecast and a second independent forecast each comprising predicted values over a first subset of items, with the first independent forecast comprising values over a first time sequence and the second independent forecast comprising values over a second time sequence, wherein at least some time period of the first time sequence overlaps with multiple time periods of the second time sequence, and(f) a third independent forecast and a fourth independent forecast each comprising predicted values over a third time sequence, with the third independent forecast comprising values for a third subset of items and the fourth independent forecast comprising values for a fourth subset of items, wherein at least some items belong to both the third subset and the fourth subset; means for storing values in a data array representing a plurality of relationships between forecasts including at least one of (g) a relationship for a particular subset of items requiring that an aggregation of values for time periods in a forecast over one time sequence for said subset is equal to a value for another time period in a forecast over another time sequence for said subset, and(h) a relationship for a particular time sequence requiring that a value for a time period of said time sequence for a forecast for a particular subset of items is equal to an aggregation of values for the time period for other forecasts of subsets of items in said particular subset; and means for, after having received the plurality of independent forecasts, and after having stored the values representing the relationships, processing the values of the independent forecasts to yield a plurality of reconciled forecasts that satisfy the plurality of relationships and that approximate the plurality of independent forecasts, wherein the processing of the values of the independent forecasts comprises processing the values of the independent forecasts and the values in the data array to satisfy the relationships and while reducing an approximation error between the reconciled forecasts and the independent forecasts.
As drafted, this is, under its broadest reasonable interpretation, within the Abstract idea grouping of “Mental Processes” – evaluation. The recitation of “machine-readable medium”, “data processing system”, “system”; “forecast reconciler”; and “storage”, provide nothing in the claim elements to preclude the step from being a “Mental Processes” – evaluation. Accordingly, the claim recites an abstract idea.
This judicial exception is not integrated into a practical application. The claims primarily recite the additional element of using computer components to perform each step. The “machine-readable medium”, “data processing system”, “system”; “forecast reconciler”; and “storage” is recited at a high-level of generality, such that it amounts no more than mere instructions to apply the exception using a computer component. See MPEP 2106.05(f). Accordingly, the additional elements do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claims also fail to recite any improvements to another technology or technical field, improvements to the functioning of the computer itself, use of a particular machine, effecting a transformation or reduction of a particular article to a different state or thing, and/or an additional element applies or uses the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception. See 84 Fed. Reg. 55. In particular, there is a lack of improvement to a computer or technical field in forecasting.
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements when considered both individually and as an ordered 9combination do not amount to significantly more than the abstract idea. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements of “machine-readable medium”, “data processing system”, “system”; “forecast reconciler”; and “storage” is insufficient to amount to significantly more. (See MPEP 2106.05(f) – Mere Instructions to Apply an Exception – “Thus, for example, claims that amount to nothing more than an instruction to apply the abstract idea using a generic computer do not render an abstract idea eligible.” Alice Corp., 134 S. Ct. at 235). Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept.
The claim fails to recite any improvements to another technology or technical field, improvements to the functioning of the computer itself, use of a particular machine, effecting a transformation or reduction of a particular article to a different state or thing, adding unconventional steps that confine the claim to a particular useful application, and/or meaningful limitations beyond generally linking the use of an abstract idea to a particular environment. See 84 Fed. Reg. 55. Viewed individually or 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. With regards to receiving data and step 2B, it is M2106.05(d)- Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information). Examiner concludes that the additional elements in combination fail to amount to significantly more than the abstract idea based on findings that each element merely performs the same function(s) in combination as each element performs separately. The claim is not patent eligible. Thus, taken alone, the additional elements do not amount to significantly more than the above-identified judicial exception (the abstract idea). Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually.
Dependent Claims 2-4, 6-13, 17-23, and 25 recite the additional elements of determining the first forecasts comprises: collecting historical data comprising measurement data for a plurality of items or a group of items at one or more time sequences; and applying at least one forecasting data processing procedure to the collected historical data to yield the first forecasts; using the second forecasts affect operation of a system involving the plurality of items or groups of items; the items comprise retail items, and the second forecasts are used to affect operation of a supply chain system involving said items; the plurality of relationships include both the first time relationship and the first group relationship; a first input data series plurality of input data series comprises values representing a number of an item or a group of items associated with said data series for each time or interval of the time sequence associated with said data series; the time sequences associated with the data series includes a first time sequences comprising a sequence of calendar intervals; the calendar intervals comprise days, weeks, months, or years; the relationship information comprises a plurality of linear relationships between values of the data series; wherein the plurality of linear relationships include at least one of (a) a requirement that a value of a first data series for a group of items at a particular time in a time sequence is equal to a sum of values of a second plurality of data series for items in the group of items at that particular time, and (b) a requirement that a value of a third data series for an item or group of items at a particular time in a third time sequence is equal to a sum of values in a fourth data series for said item or group of items for a plurality of times in a fourth time sequence associated with said particular time; determining the set of data requirements comprise one or more matrix representations of said requirements; determining the second forecasts comprises using an optimization procedure; wherein using the optimization procedure comprises using a Quadratic Programming procedure; wherein the independent forecasts include both (a) the first plurality of forecasts and (b) the second plurality of forecasts; the reconciliation data processing procedure comprises an optimization procedure for reducing a difference between the independent forecasts and the reconciled forecasts; the forecast constraints are linear constraints and the optimization procedure comprises a Quadratic Programming procedure; wherein executing the one or more data processing procedures to form the independent forecasts comprises executing one or more of regression, moving average, and neural network based procedures; wherein the determining of the second forecasts includes processing the first forecasts and the data representing the relationships using the data processing procedure; wherein determining the first forecasts comprises collecting historical data comprising measurement data for a plurality of items or a group of items at one or more time sequences and applying at least one forecasting data processing procedure to the collected historical data to yield the first forecasts, and wherein the data processing procedure for determining the second forecasts does not process the historical data; automatically collecting historical sales information for a plurality of items, and determining the first forecasts from the historical sales information, and automatically making orders for stocking said items based on the second forecasts; wherein the independent forecasters include forecasters that operate according to configurable parameters, and wherein the method further processing historical data to determine the values of configurable parameters for said forecaster, and producing the independent forecasts by the independent forecasters using said values of the configurable parameters, wherein at least the values of the configurable parameters for a first independent forecaster are determined independently of determining the values of the configurable parameters for a second independent forecaster; and further narrowing the abstract idea. These recited limitations in the dependent claims are mere instructions for applying the abstract idea on a computerized system which are operating such that they do not amount to significantly more than the above-identified judicial exceptions in Claims 1, 14, 15, 16 and 24 and 26.
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, 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 1-4, 6-21and 24-26 are rejected under 35 U.S.C. 103 as being unpatentable over Trovero et al., US Publication No. 20120089609A1 [hereinafter Trovero], in view of Saha et al., US Publication No. 20200151748 A1 [hereinafter Saha]
Regarding Claim 1,
Trovero teaches
A method for forecasting a plurality of data series, the method comprising: determining, as output from a plurality of independent forecasters (Trovero Par. 5-“In accordance with the teachings provided herein, systems and methods for operation upon data processing devices are provided for performing statistical forecasts of data that are arranged in a plurality of dimensions. For example, a system and method can be configured to generate a forecast for a dimension based upon the data that is associated with the dimension. The generating step generates a plurality of forecasts for the dimensions such that the forecast of a first dimension is generated independently of other dimensions.”)
determining first forecasts comprising a plurality of respective data series, wherein each data series of said plurality is associated with (a) an item or a group of items,(Trovero Par. 45; Fig. 4; Par. 102) and (b) a time sequence in which each item in the time sequence represents a time or an interval (Trovero Par. 46; Fig5; Par. 1029) , wherein said plurality of data series includes multiple data series associated with different items or groups of items, or include multiple data series associated with different time sequences (Trovero Par. 45-47);
and wherein the multiple data series includes a first data series for a first item or group of items in which data items in said series are associated with respective times or time intervals in a first time sequence, a second data series for the first item or group of items in which data items in said series are associated with respective times or time intervals in a second time sequence, wherein times or time intervals in said second time sequence being each associated with multiple times or time intervals in said first time sequence, and a third data series for a second item or group of items in which data items in said series are associated with respective times or time intervals in the first time sequence, wherein the first item or group of items and the second item or group of items are distinct and share at least one item (Trovero Par. 46-47- “FIG. 5 depicts a graphical user interface at 200, wherein the data has been arranged by levels. For example, there is a geographical region level, a product line level, and a specific product level. The data for one of the levels is shown in the graph of the graphical user interface 200. Time series data is shown in the left portion of the graph, and forecasting is shown in the right portion of the graph.”);
processing relationship information representing a plurality of relationships, with each relationship relating the items or groups of items or relating time sequences of different of the data series of a forecast, the plurality of relationships including: a first time relationship associating elements of a first time sequence with elements of a second time sequence, including associating each element of the first time sequence with respective multiple elements of the second time sequence, and a first group relationship associating a first group of items with multiple other groups or items, the first group relationship identifying the at least one item shared between the first item or group of items and the second item or group of items, the processing including forming a data representation of each relationship (Trovero Par. 5; Par. 46-48- “FIG. 5 depicts a graphical user interface at 200, wherein the data has been arranged by levels. For example, there is a geographical region level, a product line level, and a specific product level. The data for one of the levels is shown in the graph of the graphical user interface 200. Time series data is shown in the left portion of the graph, and forecasting is shown in the right portion of the graph.”; Par. 58);
determining a set of data series requirements based on the relationship information, the data series requirements including at least a first data series requirement relating a first set of data series to a second set of data series over times or intervals for a time sequence associated with said first and second sets of data series, and a second data series requirement relating values of multiple data series for each time or interval of a time sequence; (Trovero Par. 5- “The generating step generates a plurality of forecasts for the dimensions such that the forecast of a first dimension is generated independently of other dimensions. The forecast of the first dimension affects the constraints that are imposed on the forecasts of other dimensions. Other constraints independent of the first dimension can be present. A reconciliation is performed between the forecast of the first dimension and the forecast of the other dimensions in order to determine how the other dimensions' forecasts are influenced by the first dimension's forecast through the constraints. After the reconciliation, reconciled forecasts that satisfy all constraints are provided for analysis, such as but not limited to for use by a decision process system (e.g., planning activities, resource allocation, manpower scheduling, distribution of resources, etc.).”)
processing the plurality of data series of the first forecasts to determining second forecasts comprising a plurality of data series comprising a reconciled first data series, a reconciled second data series, and a third reconciled data series corresponding to the first data series, the second data series, and the third data series, respectively, wherein each data series of said second forecasts is associated with (a) an item or a group of items, and (b) a time sequence in which each item in the time sequence represents a point or an interval, the determining of the second forecasts including executing a data processing procedure on a computer processor to determine the reconciled data series of said second forecasts based on the data series of the first forecasts and based the data representations of the relationships, the data processing procedure being configured for the reconciled data series of the second forecasts to satisfy the plurality of relationships and to best approximate the data series of the first forecasts according to a similarity criterion. (Trovero Par. 5; Par. 41-“ FIG. 1 depicts at 20 an environment wherein users 30 can interact with a forecast reconciliation system 60 to reconcile multiple forecasts (e.g., predictions, etc.) that are at different data dimensions (e.g., different levels in a hierarchy). When the data are organized in different dimensions, there are often constraints that link series at the different dimensions. The forecast reconciliation system 60 addresses such constraints after the forecasting process. In this example, the reconciliation process is the after-the-fact process through which such constraints are enforced. ; Par. 45-49; Fig. 18)
Trovero teaches forecasting and the feature is expounded upon by the teaching in Saha:
…according to a similarity criterion; (Saha- Par. 4-5-“
run a plurality of candidate forecasting models on each shape label associated with each cluster in the second datasets to obtain an average forecast error for each shape label, select a best forecasting model with a lowest average forecast error for each shape label, and assign the best forecasting model to each item in the cluster sharing the shape label for an item prediction... acquire, from the database, a plurality of time series datasets associated with a plurality of items, generate, based on date labels in plurality of time series datasets, a plurality of first datasets comprising shape-based features and effect-based features, and generate, based on the plurality of first datasets, a plurality of second datasets with shape labels, wherein the items in the second datasets are classified into clusters and wherein each cluster shares a shape label; and an item similarity module programmed to: select a reference item for each cluster of items, apply a plurality of search models respectively on shape-based and effect-based features of the reference item in the second datasets to obtain a plurality of similarity values, the plurality of search models comprising a full feature search, a reduced feature search, a model based search, and a fast combined search, and identify top K similar items for the reference item with a decreasing order of the plurality of the similarity values.”)
Trovero and Saha is directed predictive analysis. It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of Trovero to improve upon the data analysis, as taught by Saha, with a reasonable expectation of success of arriving at the claimed invention. One of ordinary skill in the art would have been motivated to make the modification to the teachings of Saha with the motivation of improved item forecasting performance. (Saha Par. 22).
Regarding Claim 2, Trovero in view of Saha teach The method of claim 1,…
Trovero teaches forecasting and the feature is expounded upon by the teaching in Saha:
wherein determining the first forecasts comprises: collecting historical data comprising measurement data for a plurality of items or a group of items at one or more time sequences; and applying at least one forecasting data processing procedure to the collected historical data to yield the first forecasts. (Saha Par.21-22-“ The concepts disclosed herein are directed to systems and methods of forecasting item demand and identifying item similarity based on the underlying shape-based and effect-based features of a time serial historical data. As will be described in greater detail below, embodiments of the invention can identify item attributes associated with temporal variables of certain time serial historical data. The system can capture the comprehensive shape characteristics and effects of the time series historical data.”);
Trovero and Saha is directed predictive analysis. It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of Trovero to improve upon the data analysis, as taught by Saha, with a reasonable expectation of success of arriving at the claimed invention. One of ordinary skill in the art would have been motivated to make the modification to the teachings of Saha with the motivation of improved item forecasting performance. (Saha Par. 22).
Regarding Claim 3,
The method of claim 2, further comprising: using the second forecasts affect operation of a system involving the plurality of items or groups of items. (Trovero Par.5- “In accordance with the teachings provided herein, systems and methods for operation upon data processing devices are provided for performing statistical forecasts of data that are arranged in a plurality of dimensions. For example, a system and method can be configured to generate a forecast for a dimension based upon the data that is associated with the dimension. The generating step generates a plurality of forecasts for the dimensions such that the forecast of a first dimension is generated independently of other dimensions. The forecast of the first dimension affects the constraints that are imposed on the forecasts of other dimensions. Other constraints independent of the first dimension can be present “)
Regarding Claim 4, Trovero in view of Saha teach The method of claim 3,…
Trovero teaches forecasting and the feature is expounded upon by the teaching in Saha:
wherein the items comprise retail items, and the second forecasts are used to affect operation of a supply chain system involving said items. (Saha Par. 3- A merchandise retailer may provide millions of items to customers through a chain of retail stores. Two of the most common problems in retail management may be related to understanding item-item similarity relationships and accurately forecasting item demand. To timely fulfil the item demand of each store, it is important to precisely forecast a number of items for each store-item combination to minimize overstocking and avoid out-of-stock situations. It is also very important to understand similarity relationships between different items.”);
Trovero and Saha is directed predictive analysis. It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of Trovero to improve upon the data analysis, as taught by Saha, with a reasonable expectation of success of arriving at the claimed invention. One of ordinary skill in the art would have been motivated to make the modification to the teachings of Saha with the motivation of improved item forecasting performance. (Saha Par. 22).
Regarding Claim 5, - Cancelled
Regarding Claim 6,
The method of claim 1, wherein a first input data series plurality of input data series comprises values representing a number of an item or a group of items associated with said data series for each time or interval of the time sequence associated with said data series. (Trovero Par. 46- FIG. 5 depicts a graphical user interface at 200, wherein the data has been arranged by levels. For example, there is a geographical region level, a product line level, and a specific product level. The data for one of the levels is shown in the graph of the graphical user interface 200. Time series data is shown in the left portion of the graph, and forecasting is shown in the right portion of the graph.; Par. 58)
Regarding Claim 7,
The method of claim 1, wherein the time sequences associated with the data series includes a first time sequences comprising a sequence of calendar intervals. (Trovero Par. 101-102- (Table 1) with instances 1, 4, and 7 from county-level data (Table 2), because they all have the same value of 01-01-2006 for the instance attribute Date. The processor can then do processing on the Sales values in these instances. For example, if the processor is the reconciliation processor, it may adjust the Sales values for Date=01-01-2006 for County=Wake, County=Orange, and County=Durham to 105, 115, and 80, respectively, so that they add up to the Sales=300 value for State=NC on 01-01-2006. At the end of the processing, the output data-set at the county-level should list instances in the same order as the input dataset as illustrated in Table 3.)
Regarding Claim 8,
The method of claim 7, wherein the calendar intervals comprise days, weeks, months, or years. (Trovero Par. 101-102- (Table 1) with instances 1, 4, and 7 from county-level data (Table 2), because they all have the same value of 01-01-2006 for the instance attribute Date. The processor can then do processing on the Sales values in these instances. For example, if the processor is the reconciliation processor, it may adjust the Sales values for Date=01-01-2006 for County=Wake, County=Orange, and County=Durham to 105, 115, and 80, respectively, so that they add up to the Sales=300 value for State=NC on 01-01-2006. At the end of the processing, the output data-set at the county-level should list instances in the same order as the input dataset as illustrated in Table 3.)
Regarding Claim 9, Trovero in view of Saha teach The method of claim 1,…
Trovero teaches forecasting and the feature is expounded upon by the teaching in Saha:
wherein the relationship information comprises a plurality of linear relationships between values of the data series. (Saha Par. 42- For example, the shape-based features may be created for each item based on the time series historical data. The shape-based features for each item may be extracted as various features, such as autocorrelation, Kurtosis, trend, non-linear, Hurst, Lyapunov, and skewness, etc. Effect-based features may be created by regressing holiday and promotion variables on sales for each item. Standardized coefficients may be calculated to estimate the relative impact of holidays and promotions variables associated with item sales.)
Trovero and Saha is directed predictive analysis. It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of Trovero to improve upon the data analysis, as taught by Saha, with a reasonable expectation of success of arriving at the claimed invention. One of ordinary skill in the art would have been motivated to make the modification to the teachings of Saha with the motivation of improved item forecasting performance. (Saha Par. 22).
Regarding Claim 10, The method of claim 9,wherein the plurality of linear relationships include at least one of (a) a requirement that a value of a first data series for a group of items at a particular time in a time sequence is equal to a sum of values of a second plurality of data series for items in the group of items at that particular time, and (b) a requirement that a value of a third data series for an item or group of items at a particular time in a third time sequence is equal to a sum of values in a fourth data series for said item or group of items for a plurality of times in a fourth time sequence associated with said particular time. (Trovero Par. 47- As shown in FIG. 6, when the data 100 are organized in different dimensions 130, there are often constraints (e.g., constraints 300 and 310) that link the forecasted series at the different dimensions. The forecast reconciliation system addresses such constraints. As an illustration of constraints, retailing companies have often organized their data in a hierarchy that is defined by the product characteristics (e.g., qualities, etc.) and by the geographical location of the stores. The hierarchy structure imposes accounting constraints on the series. The series at the parent node of the hierarchy are typically the sum or the average of the series at the child nodes. This accounting constraint can be termed the aggregation constraint and is an example of an explicit hierarchical constraint 300.)
Regarding Claim 11, The method of claim 9, wherein determining the set of data requirements comprise one or more matrix representations of said requirements. (Trovero Par. 90- The weighting matrix A allows the use of the covariance matrix as weights, thus making the reconciliation efficient (Generalized Least Square (GLS)).
Regarding Claim 12, Trovero in view of Saha teach The method of claim 9,…
Trovero teaches forecasting and the feature is expounded upon by the teaching in Saha:
wherein determining the second forecasts comprises using an optimization procedure (Saha Par. 22- “These features are useful in identifying optimal time series models leading to improved item forecasting performance. In some embodiments, the system can provide a most appropriate forecasting model for each item and each of the pattern-based segments. In some embodiments, the system may efficiently recommend a group of top-K similar items for a particular item.; Par. 51”).
Trovero and Saha is directed predictive analysis. It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of Trovero to improve upon the data analysis, as taught by Saha, with a reasonable expectation of success of arriving at the claimed invention. One of ordinary skill in the art would have been motivated to make the modification to the teachings of Saha with the motivation of improved item forecasting performance. (Saha Par. 22).
Regarding Claim 13, Trovero in view of Saha teach The method of claim 12,…
Trovero teaches forecasting and the feature is expounded upon by the teaching in Saha:
wherein using the optimization procedure comprises using a Quadratic Programming procedure. (Saha Par. 68-69 “For example, the forecasting module 116 may run 5 different forecasting algorithms (e.g., forecasting models) F1, F2, F3, F4, and F5 on shape labels. The forecasting module 116 may be used to implement the following operations, where the objective is to minimize a loss function L.”).
Trovero and Saha is directed predictive analysis. It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of Trovero to improve upon the data analysis, as taught by Saha, with a reasonable expectation of success of arriving at the claimed invention. One of ordinary skill in the art would have been motivated to make the modification to the teachings of Saha with the motivation of improved item forecasting performance. (Saha Par. 22).
Regarding Claim 14,
Trovero teaches
A non-transitory machine-readable medium having instructions stored thereon, the instructions when executed by a data processing system cause said system to perform operations including: determining first forecasts comprising a plurality of data series, wherein each data series of said plurality is associated with (a) an item or a group of items, and (b) a time sequence in which each item in the time sequence represents a time or an interval, and wherein said plurality of data series includes multiple data series associated with different items or groups of items, or include multiple data series associated with different time sequences; (Trovero Par. 46-47- “FIG. 5 depicts a graphical user interface at 200, wherein the data has been arranged by levels. For example, there is a geographical region level, a product line level, and a specific product level. The data for one of the levels is shown in the graph of the graphical user interface 200. Time series data is shown in the left portion of the graph, and forecasting is shown in the right portion of the graph.”; Par. 569-570);
processing relationship information representing a plurality of relationships, with each relationship relating the items or groups of items or relating time sequences of different of the data series of a forecast, the plurality of relationships including at least one of: a first time relationship associating elements of a first time sequence with elements of a second time sequence, and a first group relationship associating a first group of items with multiple other groups or items, the processing including forming a data representation of each relationship; (Trovero Par. 5; Par. 46-48- “FIG. 5 depicts a graphical user interface at 200, wherein the data has been arranged by levels. For example, there is a geographical region level, a product line level, and a specific product level. The data for one of the levels is shown in the graph of the graphical user interface 200. Time series data is shown in the left portion of the graph, and forecasting is shown in the right portion of the graph.”; Par. 58);
determining a set of data series requirements based on the relationship information, the data series requirements including at least a first data series requirement relating a first set of data series to a second set of data series over times or intervals for a time sequence associated with said first and second sets of data series, and a second data series requirement relating values of multiple data series for each time or interval of a time sequence (Trovero Par. 5- “The generating step generates a plurality of forecasts for the dimensions such that the forecast of a first dimension is generated independently of other dimensions. The forecast of the first dimension affects the constraints that are imposed on the forecasts of other dimensions. Other constraints independent of the first dimension can be present. A reconciliation is performed between the forecast of the first dimension and the forecast of the other dimensions in order to determine how the other dimensions' forecasts are influenced by the first dimension's forecast through the constraints. After the reconciliation, reconciled forecasts that satisfy all constraints are provided for analysis, such as but not limited to for use by a decision process system (e.g., planning activities, resource allocation, manpower scheduling, distribution of resources, etc.).”) ;
determining second forecasts comprising a plurality of data series, wherein each data series of said second forecasts is associated with (a) an item or a group of items, and (b) a time sequence in which each item in the time sequence represents a point or an interval, the determining of the second forecasts including executing a data processing procedure on a computer processor to determine the data series of said second forecasts based on the data series of the first forecasts and based the data representations of the relationships, the data processing procedure being configured for the data series of the second forecasts to satisfy the plurality of relationships and to best approximate the data series of the first forecasts according to a similarity criterion. (Trovero Par. 5; Par. 41-“ FIG. 1 depicts at 20 an environment wherein users 30 can interact with a forecast reconciliation system 60 to reconcile multiple forecasts (e.g., predictions, etc.) that are at different data dimensions (e.g., different levels in a hierarchy). When the data are organized in different dimensions, there are often constraints that link series at the different dimensions. The forecast reconciliation system 60 addresses such constraints after the forecasting process. In this example, the reconciliation process is the after-the-fact process through which such constraints are enforced. ; Par. 45-49; Fig. 18)
Trovero teaches forecasting and the feature is expounded upon by the teaching in Saha:
…according to a similarity criterion; (Saha- Par. 4-5-“
run a plurality of candidate forecasting models on each shape label associated with each cluster in the second datasets to obtain an average forecast error for each shape label, select a best forecasting model with a lowest average forecast error for each shape label, and assign the best forecasting model to each item in the cluster sharing the shape label for an item prediction... acquire, from the database, a plurality of time series datasets associated with a plurality of items, generate, based on date labels in plurality of time series datasets, a plurality of first datasets comprising shape-based features and effect-based features, and generate, based on the plurality of first datasets, a plurality of second datasets with shape labels, wherein the items in the second datasets are classified into clusters and wherein each cluster shares a shape label; and an item similarity module programmed to: select a reference item for each cluster of items, apply a plurality of search models respectively on shape-based and effect-based features of the reference item in the second datasets to obtain a plurality of similarity values, the plurality of search models comprising a full feature search, a reduced feature search, a model based search, and a fast combined search, and identify top K similar items for the reference item with a decreasing order of the plurality of the similarity values.”)
Trovero and Saha is directed predictive analysis. It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of Trovero to improve upon the data analysis, as taught by Saha, with a reasonable expectation of success of arriving at the claimed invention. One of ordinary skill in the art would have been motivated to make the modification to the teachings of Saha with the motivation of improved item forecasting performance. (Saha Par. 22).
Regarding Claim 15,
Saha teaches
A system comprising: a plurality of computer implemented forecasters; a computer implemented forecast reconciler coupled to receive forecasts data from respective forecasters of the plurality of forecasters (Trovero Par. 5-“In accordance with the teachings provided herein, systems and methods for operation upon data processing devices are provided for performing statistical forecasts of data that are arranged in a plurality of dimensions. For example, a system and method can be configured to generate a forecast for a dimension based upon the data that is associated with the dimension. The generating step generates a plurality of forecasts for the dimensions such that the forecast of a first dimension is generated independently of other dimensions.”)
and a storage for relationship data comprising a set of data series requirements for use by the forecast reconciler (Trovero Par. 45; Fig. 18; Par. 569);
wherein each forecaster of the plurality of forecasters is configured to independently determine a forecast of a first plurality of forecasts comprising a plurality of data series, wherein each data series of said plurality is associated with (a) an item or a group of items ,(Trovero Par. 45; Fig. 4) , and (b) a time sequence in which each item in the time sequence represents a time or an interval (Trovero Par. 46; Fig5) l, and wherein said plurality of data series includes multiple data series associated with different items or groups of items, or include multiple data series associated with different time sequences (Trovero Par. 45-47);
where the forecast reconciler is configured processing relationship information representing the processing including forming a data representation of each relationship; (Trovero Par. 5; Par. 46-48- “FIG. 5 depicts a graphical user interface at 200, wherein the data has been arranged by levels. For example, there is a geographical region level, a product line level, and a specific product level. The data for one of the levels is shown in the graph of the graphical user interface 200. Time series data is shown in the left portion of the graph, and forecasting is shown in the right portion of the graph.”; Par. 58);
wherein the forecast reconciler is configured to determine second forecasts comprising a plurality of data series, wherein each data series of said second forecasts is associated with (a) an item or a group of items, and (b) a time sequence in which each item in the time sequence represents a point or an interval, the determining of the second forecasts including executing a data processing procedure on a computer processor to determine the data series of said second forecasts based on the data series of the first forecasts and based the data representations of the relationships, the data processing procedure being configured for the data series of the second forecasts to satisfy the plurality of relationships and to best approximate the data series of the first forecasts … (Trovero Par. 5; Par. 41-“ FIG. 1 depicts at 20 an environment wherein users 30 can interact with a forecast reconciliation system 60 to reconcile multiple forecasts (e.g., predictions, etc.) that are at different data dimensions (e.g., different levels in a hierarchy). When the data are organized in different dimensions, there are often constraints that link series at the different dimensions. The forecast reconciliation system 60 addresses such constraints after the forecasting process. In this example, the reconciliation process is the after-the-fact process through which such constraints are enforced. ; Par. 45-49; Fig. 18)
Trovero teaches forecasting and the feature is expounded upon by the teaching in Saha:
…according to a similarity criterion; (Saha- Par. 4-5-“
run a plurality of candidate forecasting models on each shape label associated with each cluster in the second datasets to obtain an average forecast error for each shape label, select a best forecasting model with a lowest average forecast error for each shape label, and assign the best forecasting model to each item in the cluster sharing the shape label for an item prediction... acquire, from the database, a plurality of time series datasets associated with a plurality of items, generate, based on date labels in plurality of time series datasets, a plurality of first datasets comprising shape-based features and effect-based features, and generate, based on the plurality of first datasets, a plurality of second datasets with shape labels, wherein the items in the second datasets are classified into clusters and wherein each cluster shares a shape label; and an item similarity module programmed to: select a reference item for each cluster of items, apply a plurality of search models respectively on shape-based and effect-based features of the reference item in the second datasets to obtain a plurality of similarity values, the plurality of search models comprising a full feature search, a reduced feature search, a model based search, and a fast combined search, and identify top K similar items for the reference item with a decreasing order of the plurality of the similarity values.”)
Trovero and Saha is directed predictive analysis. It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of Trovero to improve upon the data analysis, as taught by Saha, with a reasonable expectation of success of arriving at the claimed invention. One of ordinary skill in the art would have been motivated to make the modification to the teachings of Saha with the motivation of improved item forecasting performance. (Saha Par. 22).
Regarding Claim 16,
Saha teaches
A method for forecasting comprising: executing one or more data processing procedures to form independent forecasts including at least one of (a) a first plurality of forecasts for a respective a plurality of overlapping subsets of a plurality of items and (b) a second plurality of forecasts for a respective plurality of overlapping time points or intervals (Trovero Par. 5; Par. 46-47; Par. 97- The input data may be assumed to be organized such that all the data for a given level is present in a single data set. This data set is ordered by the entity attributes that identify distinct entities at that level. The data corresponding to each entity is assumed to be ordered by an instance attribute that identifies a specific instance of the data for that entity. The entity attributes are assumed to be organized hierarchically such that entity attributes of an upper level are a proper subset of the entity attributes of a lower level. This implies a containment relationship between entities from two levels. An entity at a lower level is said to be a sub-entity of an upper-level entity, if it has the same values for the upper-level entity attributes as those of the upper-level entity.”)
forming a data representation of a plurality of forecast constraints resulting from relationships arising from at least one of the overlapping subsets of the plurality of items and the overlapping time points or intervals; (Trovero Par.41-48-“ FIG. 1 depicts at 20 an environment wherein users 30 can interact with a forecast reconciliation system 60 to reconcile multiple forecasts (e.g., predictions, etc.) that are at different data dimensions (e.g., different levels in a hierarchy). When the data are organized in different dimensions, there are often constraints that link series at the different dimensions. The forecast reconciliation system 60 addresses such constraints after the forecasting process. In this example, the reconciliation process is the after-the-fact process through which such constraints are enforced.);
and executing a reconciliation data processing procedure to process the independent forecasts and the data representation of the plurality of forecast constraints to form reconciled forecasts satisfying the plurality of forecast constraints and … the independent forecasts. (Trovero Par.41-47);
Trovero teaches forecasting and the feature is expounded upon by the teaching in Saha:
… approximating the independent forecasts; (Saha- Par. 77-78)
Trovero and Saha is directed predictive analysis. It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of Trovero to improve upon the data analysis, as taught by Saha, with a reasonable expectation of success of arriving at the claimed invention. One of ordinary skill in the art would have been motivated to make the modification to the teachings of Saha with the motivation of improved item forecasting performance. (Saha Par. 22).
Regarding Claim 17,
The method of claim 16, wherein the independent forecasts include both (a) the first plurality of forecasts and (b) the second plurality of forecasts. (Trovero Par. 5-“In accordance with the teachings provided herein, systems and methods for operation upon data processing devices are provided for performing statistical forecasts of data that are arranged in a plurality of dimensions. For example, a system and method can be configured to generate a forecast for a dimension based upon the data that is associated with the dimension. The generating step generates a plurality of forecasts for the dimensions such that the forecast of a first dimension is generated independently of other dimensions.”; Par. 41-47)
Regarding Claim 18,
The method of claim 16,wherein the reconciliation data processing procedure comprises an optimization procedure for reducing a difference between the independent forecasts and the reconciled forecasts. (Trovero Par.60-“ FIG. 11 illustrates that the forecast reconciliation system 460 can make efficient use of weighting information associated with forecast 420, such as by utilizing the variability information associated with forecast 420. FIG. 12 illustrates that an optimization routine 510 can make use of that forecast weighting information, such as by using the information while performing a minimization of a quadratic loss function. Par. 123; Par. 172);
Regarding Claim 19, Trovero in view of Saha teach The method of claim 18 wherein the forecast constraints are linear constraints the optimization procedure …
,…
Trovero teaches forecasting and the feature is expounded upon by the teaching in Saha:
comprises a Quadratic Programming procedure.. (Saha Par. 68-69 “For example, the forecasting module 116 may run 5 different forecasting algorithms (e.g., forecasting models) F1, F2, F3, F4, and F5 on shape labels. The forecasting module 116 may be used to implement the following operations, where the objective is to minimize a loss function L; Par. 77)
Trovero and Saha is directed predictive analysis. It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of Trovero to improve upon the data analysis, as taught by Saha, with a reasonable expectation of success of arriving at the claimed invention. One of ordinary skill in the art would have been motivated to make the modification to the teachings of Saha with the motivation of improved item forecasting performance. (Saha Par. 22).
Regarding Claim 20, Trovero in view of Saha teach The method of claim 16 ,…
Trovero teaches forecasting and the feature is expounded upon by the teaching in Saha:
wherein executing the one or more data processing procedures to form the independent forecasts comprises executing one or more of regression, moving average, and neural network based procedures. (Saha Claim 13, Par. 42 or example, the shape-based features may be created for each item based on the time series historical data. The shape-based features for each item may be extracted as various features, such as autocorrelation, Kurtosis, trend, non-linear, Hurst, Lyapunov, and skewness, etc. Effect-based features may be created by regressing holiday and promotion variables on sales for each item. Standardized coefficients may be calculated to estimate the relative impact of holidays and promotions variables associated with item sales.)
Trovero and Saha is directed predictive analysis. It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of Trovero to improve upon the data analysis, as taught by Saha, with a reasonable expectation of success of arriving at the claimed invention. One of ordinary skill in the art would have been motivated to make the modification to the teachings of Saha with the motivation of improved item forecasting performance. (Saha Par. 22).
Regarding Claim 21,
The method of claim 1, wherein the determining of the second forecasts includes processing the first forecasts and the data representing the relationships using the data processing procedure. (Trovero Par 5; Par. 41-52)
Regarding Claim 24,
Trovero teaches
A method for processing a plurality of independently computed forecasts over a plurality of time sequences spanning a time interval and over a plurality of subsets of a plurality items, each forecast of the plurality of forecasts representing a prediction for a particular time sequence of the plurality of time sequences and a particular subset of items of the plurality of subsets such that the forecast comprises a predicted value for respective periods of the particular time sequence, and the predicted values of said forecast represent an aggregate value over all the items in the particular subset, the method comprising: receiving the plurality of independent forecasts, said forecasts having been computed separately by a plurality of independent forecasters, (Trovero Par. 5-“In accordance with the teachings provided herein, systems and methods for operation upon data processing devices are provided for performing statistical forecasts of data that are arranged in a plurality of dimensions. For example, a system and method can be configured to generate a forecast for a dimension based upon the data that is associated with the dimension. The generating step generates a plurality of forecasts for the dimensions such that the forecast of a first dimension is generated independently of other dimensions.”; Par 41-47)
wherein the plurality of independent forecasts comprises one or both of (a) a first independent forecast and a second independent forecast each comprising predicted values over a first subset of items, with the first independent forecast comprising values over a first time sequence and the second independent forecast comprising values over a second time sequence, wherein at least some time period of the first time sequence overlaps with multiple time periods of the second time sequence, (Trovero Par. 45-47; Par. 97);
and(b) a third independent forecast and a fourth independent forecast each comprising predicted values over a third time sequence, with the third independent forecast comprising values for a third subset of items and the fourth independent forecast comprising values for a fourth subset of items, wherein at least some items belong to both the third subset and the fourth subset; (Trovero Par. 46-47- “FIG. 5 depicts a graphical user interface at 200, wherein the data has been arranged by levels. For example, there is a geographical region level, a product line level, and a specific product level. The data for one of the levels is shown in the graph of the graphical user interface 200. Time series data is shown in the left portion of the graph, and forecasting is shown in the right portion of the graph.”; Par. 57-58l; Par. 97);
storing values in a data array representing a plurality of relationships between forecasts including at least one of (c) a relationship for a particular subset of items requiring that an aggregation of values for time periods in a forecast over one time sequence for said subset is equal to a value for another time period in a forecast over another time sequence for said subset, and(d) a relationship for a particular time sequence requiring that a value for a time period of said time sequence for a forecast for a particular subset of items is equal to an aggregation of values for the time period for other forecasts of subsets of items in said particular subset; and after having received the plurality of independent forecasts, (Trovero Par. 5- “The generating step generates a plurality of forecasts for the dimensions such that the forecast of a first dimension is generated independently of other dimensions. The forecast of the first dimension affects the constraints that are imposed on the forecasts of other dimensions. Other constraints independent of the first dimension can be present. A reconciliation is performed between the forecast of the first dimension and the forecast of the other dimensions in order to determine how the other dimensions' forecasts are influenced by the first dimension's forecast through the constraints. After the reconciliation, reconciled forecasts that satisfy all constraints are provided for analysis, such as but not limited to for use by a decision process system (e.g., planning activities, resource allocation, manpower scheduling, distribution of resources, etc.).”)
and after having stored the values representing the relationships, processing the values of the independent forecasts to yield a plurality of reconciled forecasts that satisfy the plurality of relationships and that approximate the plurality of independent forecasts, wherein the processing of the values of the independent forecasts comprises processing the values of the independent forecasts and the values in the data array to satisfy the relationships and while reducing an approximation error between the reconciled forecasts and the independent forecasts. Trovero Par.60-“ FIG. 11 illustrates that the forecast reconciliation system 460 can make efficient use of weighting information associated with forecast 420, such as by utilizing the variability information associated with forecast 420. FIG. 12 illustrates that an optimization routine 510 can make use of that forecast weighting information, such as by using the information while performing a minimization of a quadratic loss function. Par. 123; Par. 172);
Trovero teaches forecasting and the feature is expounded upon by the teaching in Saha:
…approximation error..; (Saha- Par. 77-78)
Trovero and Saha is directed predictive analysis. It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of Trovero to improve upon the data analysis, as taught by Saha, with a reasonable expectation of success of arriving at the claimed invention. One of ordinary skill in the art would have been motivated to make the modification to the teachings of Saha with the motivation of improved item forecasting performance. (Saha Par. 22).
Regarding Claim 25,
The method of claim 24, wherein the independent forecasters include forecasters that operate according to configurable parameters, and wherein the method further processing historical data to determine the values of configurable parameters for said forecaster, and producing the independent forecasts by the independent forecasters using said values of the configurable parameters, wherein at least the values of the configurable parameters for a first independent forecaster are determined independently of determining the values of the configurable parameters for a second independent forecaster (Trovero Par 50-“ If it is known that one series is accurately predicted, the system can be configured to require that the reconciliation adjustment be less than for this series than for a series whose prediction is known unreliable. The system can also be configured to seamlessly integrate reconciliation of large hierarchies of statistical forecasts with judgmental forecasts. At the same time, the uncertainty about the statistical prediction can be used efficiently to determine the reconciliation adjustment.; Par. 59”).
Regarding Claim 26,
Trovero teaches
A system for processing a plurality of independently computed forecasts over a plurality of time sequences spanning a time interval and over a plurality of subsets of a plurality items, each forecast of the plurality of forecasts representing a prediction for a particular time sequence of the plurality of time sequences and a particular subset of items of the plurality of subsets such that the forecast comprises a predicted value for respective periods of the particular time sequence, and the predicted values of said forecast represent an aggregate value over all the items in the particular subset, the system comprising: means for receiving the plurality of independent forecasts, said forecasts having been computed separately by a plurality of independent forecasters, (Trovero Par. 5-“In accordance with the teachings provided herein, systems and methods for operation upon data processing devices are provided for performing statistical forecasts of data that are arranged in a plurality of dimensions. For example, a system and method can be configured to generate a forecast for a dimension based upon the data that is associated with the dimension. The generating step generates a plurality of forecasts for the dimensions such that the forecast of a first dimension is generated independently of other dimensions.”; Par 41-47)
wherein the plurality of independent forecasts comprises one or both of (e) a first independent forecast and a second independent forecast each comprising predicted values over a first subset of items, with the first independent forecast comprising values over a first time sequence and the second independent forecast comprising values over a second time sequence, wherein at least some time period of the first time sequence overlaps with multiple time periods of the second time sequence, (Trovero Par. 45-47; Par. 97);
and(f) a third independent forecast and a fourth independent forecast each comprising predicted values over a third time sequence, with the third independent forecast comprising values for a third subset of items and the fourth independent forecast comprising values for a fourth subset of items, wherein at least some items belong to both the third subset and the fourth subset; (Trovero Par. 46-47- “FIG. 5 depicts a graphical user interface at 200, wherein the data has been arranged by levels. For example, there is a geographical region level, a product line level, and a specific product level. The data for one of the levels is shown in the graph of the graphical user interface 200. Time series data is shown in the left portion of the graph, and forecasting is shown in the right portion of the graph.”; Par. 57-58l; Par. 97);
means for storing values in a data array representing a plurality of relationships between forecasts including at least one of (g) a relationship for a particular subset of items requiring that an aggregation of values for time periods in a forecast over one time sequence for said subset is equal to a value for another time period in a forecast over another time sequence for said subset, and(h) a relationship for a particular time sequence requiring that a value for a time period of said time sequence for a forecast for a particular subset of items is equal to an aggregation of values for the time period for other forecasts of subsets of items in said particular subset; (Trovero Par. 5- “The generating step generates a plurality of forecasts for the dimensions such that the forecast of a first dimension is generated independently of other dimensions. The forecast of the first dimension affects the constraints that are imposed on the forecasts of other dimensions. Other constraints independent of the first dimension can be present. A reconciliation is performed between the forecast of the first dimension and the forecast of the other dimensions in order to determine how the other dimensions' forecasts are influenced by the first dimension's forecast through the constraints. After the reconciliation, reconciled forecasts that satisfy all constraints are provided for analysis, such as but not limited to for use by a decision process system (e.g., planning activities, resource allocation, manpower scheduling, distribution of resources, etc.).”)
and means for, after having received the plurality of independent forecasts, and after having stored the values representing the relationships, processing the values of the independent forecasts to yield a plurality of reconciled forecasts that satisfy the plurality of relationships and that approximate the plurality of independent forecasts, wherein the processing of the values of the independent forecasts comprises processing the values of the independent forecasts and the values in the data array to satisfy the relationships and while reducing an approximation error between the reconciled forecasts and the independent forecasts. Trovero Par.60-“ FIG. 11 illustrates that the forecast reconciliation system 460 can make efficient use of weighting information associated with forecast 420, such as by utilizing the variability information associated with forecast 420. FIG. 12 illustrates that an optimization routine 510 can make use of that forecast weighting information, such as by using the information while performing a minimization of a quadratic loss function. Par. 123; Par. 172);
Trovero teaches forecasting and the feature is expounded upon by the teaching in Saha:
…approximation error; (Saha- Par. 77-78)
run a plurality of candidate forecasting models on each shape label associated with each cluster in the second datasets to obtain an average forecast error for each shape label, select a best forecasting model with a lowest average forecast error for each shape label, and assign the best forecasting model to each item in the cluster sharing the shape label for an item prediction... acquire, from the database, a plurality of time series datasets associated with a plurality of items, generate, based on date labels in plurality of time series datasets, a plurality of first datasets comprising shape-based features and effect-based features, and generate, based on the plurality of first datasets, a plurality of second datasets with shape labels, wherein the items in the second datasets are classified into clusters and wherein each cluster shares a shape label; and an item similarity module programmed to: select a reference item for each cluster of items, apply a plurality of search models respectively on shape-based and effect-based features of the reference item in the second datasets to obtain a plurality of similarity values, the plurality of search models comprising a full feature search, a reduced feature search, a model based search, and a fast combined search, and identify top K similar items for the reference item with a decreasing order of the plurality of the similarity values.”)
Trovero and Saha is directed predictive analysis. It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of Trovero to improve upon the data analysis, as taught by Saha, with a reasonable expectation of success of arriving at the claimed invention. One of ordinary skill in the art would have been motivated to make the modification to the teachings of Saha with the motivation of improved item forecasting performance. (Saha Par. 22).
Claim 22 is rejected under 35 U.S.C. 103 as being unpatentable over Trovero et al., US Publication No. 20120089609A1 [hereinafter Trovero], in view of Saha et al., US Publication No. 20200151748 A1 [hereinafter Saha] in further view of Dawarakanath et al., US Patent No. 7739143 B1 [hereinafter Dawarakanath],
Regarding Claim 22, Trovero in view of Saha teach The method of claim 21,…
wherein determining the first forecasts comprises collecting historical data comprising measurement data for a plurality of items or a group of items at one or more time sequences (Saha- Fig. 2; Par. 31-33- The system may utilize at least two year time series historical dataset as the primary input.;)
and applying at least one forecasting data processing procedure to the collected historical data to yield the first forecasts (Saha Par. 46-“The SCBC module 114 may use the machine learning classification model for scoring remaining items (test data) to predict and assign the shape labels for all items in the first shape and effect datasets.),
Trovero in view of Saha fail to teach the following feature taught by Dawarakanath:
and wherein the data processing procedure for determining the second forecasts does not process the historical data. (Dawarakanath. Col 2 Ln62-67 & Col 3 Ln 1-17 - One robust technique provides a relatively accurate forecast of seasonal effects even in the presence of an anomaly in historical data. In contrast to methods using exponential smoothing of entire collections of data, the seasonal profile is derived from a predetermined number of past seasons to ensure that an anomaly will eventually be fully purged. In one embodiment, the predetermined number is a fixed number. Seasonal profiles are calculated for each of these past seasons, and are merged in a manner that effectively excludes anomalies. In one embodiment, the seasonal profile for a season of s observations is comprised of s seasonality factors In.)
Trovero, Saha and Dawarakanath are directed to predictive analysis. It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of Trovero in view of Saha by modifying a forecasting algorithm in the 2nd forecast and exclude historical data, as taught by Dawarakanath, with a reasonable expectation of success of arriving at the claimed invention. One of ordinary skill in the art would have been motivated to make the modification to the teachings of Trovero in view of Saha with the motivation of improved forecasting methodology (Dawarakanath Col.1 Ln 24-26).
Claim 23 is rejected under 35 U.S.C. 103 as being unpatentable over Trovero et al., US Publication No. 20120089609A1 [hereinafter Trovero], in view of Saha et al., US Publication No. 20200151748 A1 [hereinafter Saha] in further view of Popescu et al., US Patent No. 20200074485 A1 [hereinafter Popescu],
Regarding Claim 23, Trovero in view of Saha teach The method of claim 4,…
further comprising automatically collecting historical sales information for a plurality of items, and determining the first forecasts from the historical sales information (Saha Par. 3; Par. 32-33; Fig 2),
Trovero in view of Saha teach forecasting for inventory planning and the feature is expounded upon by Popescu:
and automatically making orders for stocking said items based on the second forecasts. (Popescu Par. 119- “Embodiments compute, based on the inventory level statistics, an inventory level for each day of the week, such that the safety stock accommodates variations in inventory between the different days of the week. Embodiments render, for each of a plurality of items, a stocking level indicative of the target inventory level including the safety stock for each day of the week. Embodiments compute an ordering quantity based on a lead time such that the ordered quantity arrives to satisfy the rendered stocking level on the determined day of the week. Identifying the actual stock levels includes identifying stock levels on the day of the week from previous weeks from the history data, thus focusing on the same day of the week over time, rather than an average of all days in the week.”)
Trovero, Saha and Popescu are directed to predictive analysis. It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of Trovero in view of Saha by improving upon model which impacts inventory levels, as taught by Popescu, with a reasonable expectation of success of arriving at the claimed invention. One of ordinary skill in the art would have been motivated to make the modification to the teachings of Trovero in view of Saha with the motivation of retailers predicting their demand in the future to better manage their inventory or promotion/markdown planning (Popescu Par. 4).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure: US 20190129821A1 to Lee et al - Abstract-“ An anomaly detection platform can be used to monitor the operation performed on or by a computer system to identify potentially anomalous conditions. In response, a corrective action can be taken to address the issue. This can be useful, for example, in improving the efficiency, effectiveness, and reliability of the computer system during operation.”
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/CHESIREE A WALTON/Examiner, Art Unit 3624