DETAILED ACTION
This Office Action is in response to Applicant's response to application filed on 20 August 2025. Currently, claims 1-18 are pending. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Information Disclosure Statement
The information disclosure statement (IDS) submitted is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/apply/applying-online/eterminal-disclaimer.
Claims 1-18 are provisionally rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-18 of copending Application No. 19/304,733. Although the claims at issue are not identical, they are not patentably distinct from each other because the claims are identical except for the additional limitations of "extract, by the processor, weather data from a weather database, the weather data related to source locations, destination locations and shipment dates within the historical lead time data" and "extract, by the processor, economic indicators data from an economic data base the economic indicators data related to the source locations, the destination locations and the shipment dates within the historical lead time data" and “generate, by the processor, processed weather data by selection of one or more weather features and generate, by the processor, processed economic indicators data by selection of one or more economic indicators features” that are in claims 1, 7 and 13 in the current application, which is shown to be obvious based on Nadella et al. (US 2018/0101814 A1) at para [0020]-[0029].
This is a provisional nonstatutory double patenting rejection because the patentably indistinct claims have not in fact been patented.
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 1-18 are clearly drawn to at least one of the four categories of patent eligible subject matter recited in 35 U.S.C. 101 (system, non-transitory computer readable storage medium and method). Claims 1-18 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more. Claims 1, 7 and 13 recite the abstract idea of receiving historical lead time data and generating processed historical lead time data by: removal of outlier data from the historical lead time data and selecting one or more features of the historical lead time data and constructing a time series data based on the processed historical lead time data and generating a set of features associated with the time series data and generating processed weather data by selection of one or more weather features and generating processed economic indicators data by selection of one or more economic indicators features and separating the time series data into one or more groups based on a time density of data points and performing clustering on each of the one or more groups to provide a plurality of clusters within each group. The claims are directed to a type of processing of historical lead time data. Under prong 1 of Step 2A, these claims are considered abstract because the claims are certain methods of organizing human activity such as business relations. Applicant’s claims are organizing human activity because the claims show receiving historical lead time (which can be considered business/human activity data) and that data is organized by the processing and separating and clustering of the data. Under prong 2 of Step 2A, the judicial exception is not integrated into a practical application because the claims (the judicial exception and any additional elements individually or in combination such as a processor and projecting, by the processor, a full feature space onto a 2-dimensional space, computer-implemented, a system comprising a processor; and a memory storing instructions that, when executed by the processor, configure the system to perform steps, a non-transitory computer-readable storage medium, the computer-readable storage medium including instructions that when executed by a computer, cause the computer to perform steps) are not an improvement to a computer or a technology, the claims do not apply the judicial exception with a particular machine, the claims do not effect a transformation or reduction of a particular article to a different state or thing nor do the claims apply 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 claims as a whole is more than a drafting effort designed to monopolize the exception. These limitations at best are merely implementing an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f). Under Step 2B, the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements individually or in combination such as a processor and projecting, by the processor, a full feature space onto a 2-dimensional space, computer-implemented, a system comprising a processor; and a memory storing instructions that, when executed by the processor, configure the system to perform steps, a non-transitory computer-readable storage medium, the computer-readable storage medium including instructions that when executed by a computer, cause the computer to perform steps (as evidenced by para [0014], [0065], [0072]-[0076], [0151]-[0155] of applicant’s own specification) are well understood, routine and conventional in the field. Dependent claims 2-6, 8-12, 14-18 also do not include additional elements that integrate the judicial exception into a practical application because the additional elements either individually or in combination are merely an extension of the abstract idea itself by further showing wherein the time series data is separated into a sparse group, a rich group and a flat group and wherein the full feature space is defined as a space comprising data and two or more features with orthogonality between all of the data and the two or more features and wherein the set of features associated with the time features includes seasonality and linearity and wherein the set of features associated with the time features includes seasonality and upward linearity, flat linearity and downward linearity and wherein the instructions further configure the system to separate the historical lead time data into a plurality of tolerance zones; separate, by the processor, the plurality of clusters in accordance with a respective tolerance zone of each group and further separate the plurality of clusters according to one or more lead time identifiers to provide one or more further separated clusters and adjust planned lead times for future purchase orders based on the further separated clusters. Dependent claims 6, 12 and 18 do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements individually or in combination such as a processor (as evidenced by para [0014], [0065], [0072]-[0076], [0151]-[0155] of applicant’s own specification) are well understood, routine and conventional in the field.
Claim Rejections - 35 USC § 103
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.
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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1-5, 7-11, 14-17 are rejected under 35 U.S.C. 103 as being unpatentable over Nadella et al. (US 2018/0101814 A1) (hereinafter Nadella) in view of Chan et al. (US 2014/0122179 A1) (hereinafter Chan) in view of Ethington et al. (US 2019/0156298 A1) (hereinafter Ethington).
Claims 1, 7 and 13:
Nadella, as shown, discloses the following limitations of claims 1, 7 and 13:
A system (and corresponding non-transitory computer readable medium and computer implemented method – see para [0012]-[0014], showing equivalent computing functionality and components) comprising: a processor; and a memory storing instructions that, when executed by the processor, configure the system (see para [0012]-[0014], showing equivalent computing functionality and components) to: receive, by a processor, historical lead time data (see para [0004], "In an embodiment, a dynamic supply chain management system comprises a historical causality database comprising historical data related to external factors that affected a supply chain; a historical order information database comprising historical data related to order information in the supply chain; a current causality database comprising current or predicted data related to external factors affecting the supply chain; a current order information database comprising order data related to a current order in the supply chain; a supply chain management engine communicatively coupled with the historical causality database, the historical order information database, the current causality database, and the current order information database and configured to: decompose the historical data related to external factors that affected a supply chain and the historical data related to order information in the supply chain to determine a plurality of factors, each of the plurality of factors having affected at least one of historical lead time and historical fill rate variability, determine a current order lead time and a current order fill rate from the current or predicted data from the current causality database and the order data from the current order information database, and apply an algorithm based on the plurality of factors to the determined current order lead time and current order fill rate to determine a dynamic order lead time and a dynamic order fill rate variability for the current order.");
extract, by the processor, weather data from a weather database, the weather data related to source locations, destination locations and shipment dates within the historical lead time data (see para [0025], " Once determined, variability in the historical order lead time, order fill rate, and other measures can be identified and decomposed by application of a statistical and machine learning algorithm by data staging engine 140, such as at 220 in FIG. 2. In one embodiment, the historical measures can be decomposed with respect to external factors that affected a supply chain and the historical data related to order information in the supply chain, in order to determine one or a plurality of factors that, in historical actuality, affected supply chain efficiency or effectiveness. Decomposition can involve using models to explain variability that then can be applied to new data to achieve dynamic lead time and dynamic fill rates. Decomposition techniques can be useful in the parallel processing of large amounts of data, which can be the case in system 100 in which a high number of historical orders may be available and a high number of current orders may be processed. The result of decomposition can be identification of particular factors of impact or influence on different types of orders that can be explained by selected causality factors. This data can be stored in one or more knowledge database(s) 222. The knowledge database(s) contain raw data and processed knowledge. For example, snow fall amount is raw data, and based on different locations the relative significance of snow fall amount is calculated and stored in the knowledge database(s) with leading and lagging effects. Interaction effects among different events can be realized partially in the knowledge database(s) and partially from the statistical and machine learning algorithms. These then can be applied by system 100 to current orders with predicted events that may impact the supply chain of those orders to better and more effectively manage current orders.");
extract, by the processor, economic indicators data from an economic data base the economic indicators data related to the source locations, the destination locations and the shipment dates within the historical lead time data (see para [0020], showing price data, etc. and see para [0023], showing receiving such database data);
select one or more features of the historical lead time data (see para [0032], "At 320, the compiled historical data is decomposed to identify causality factors. These are factors, whether internal or external, that influenced past orders and can be associated with some form or amount of variability such that the factors can be applied to similar current or future orders. For example, weather influenced orders placed from a particular supplier during a particular time of year.");
generate, by the processor, processed weather data by selection of one or more weather features (see para [0024]-[0025], "In some embodiments, aggregated determined historical lead time and fill rate data can be stored in a historical data database 210. Because of the huge volumes of historical data that can be implicated in these analyses, storing aggregated data can make this data available for further use by system 100 as well as for scheduled or periodic updating. For example, five years of historical data can be analyzed and stored in database 210, and then this data can be updated (e.g., daily, weekly, monthly, quarterly, etc.) as new historical data becomes available with the passage of time. Once determined, variability in the historical order lead time, order fill rate, and other measures can be identified and decomposed by application of a statistical and machine learning algorithm by data staging engine 140, such as at 220 in FIG. 2. In one embodiment, the historical measures can be decomposed with respect to external factors that affected a supply chain and the historical data related to order information in the supply chain, in order to determine one or a plurality of factors that, in historical actuality, affected supply chain efficiency or effectiveness. Decomposition can involve using models to explain variability that then can be applied to new data to achieve dynamic lead time and dynamic fill rates. Decomposition techniques can be useful in the parallel processing of large amounts of data, which can be the case in system 100 in which a high number of historical orders may be available and a high number of current orders may be processed. The result of decomposition can be identification of particular factors of impact or influence on different types of orders that can be explained by selected causality factors. This data can be stored in one or more knowledge database(s) 222. The knowledge database(s) contain raw data and processed knowledge. For example, snow fall amount is raw data, and based on different locations the relative significance of snow fall amount is calculated and stored in the knowledge database(s) with leading and lagging effects. Interaction effects among different events can be realized partially in the knowledge database(s) and partially from the statistical and machine learning algorithms. These then can be applied by system 100 to current orders with predicted events that may impact the supply chain of those orders to better and more effectively manage current orders.) and
generate, by the processor, processed economic indicators data by selection of one or more economic indicators features (see para [0028-[0029], showing adjusting order quantity because of production facility capacity and processing the adjustments)
Nadella, however, does not specifically disclose generate, by the processor, processed historical lead time data by: removal of outlier data from the historical lead time data. In analogous art, Chan discloses the following limitations:
generate, by the processor, processed historical lead time data by: removal of outlier data from the historical lead time data (see para [0045]-[0069], where removing the trend effect from a seasonal factor calculations can be considered to show removal of outlier data given broadest reasonable interpretation and Fig 7)
construct, by the processor, a time series data based on the processed historical lead time data (Fig. 4, showing time series data for the processed historical sales data where it would be obvious to one of ordinary skill in the art that the historical sales data could be substituted with the historical lead data shown in Nadella);
generate, by the processor, a set of features associated with the time series data (see para [0046]-[0048] and Fig 5B, showing calculating seasonal factors calculated from the data);
project, by the processor, a full feature space onto a 2-dimensional space (Fig. 5B, showing the seasonal factors on a 2d space with a Y and X axis)
It would have been obvious to one or ordinary skill in the art at the time of the invention to combine the teachings of Chan with Nadella because removing outlier data enables more accurate determinations to be made for business activities related to inventory control and replenishment (see Chan, para [0009]-[0012]).
Moreover, it would have been obvious to one of ordinary skill in the art at the time of the invention to include the system for forecasting product demand as taught by Chan in the dynamic supply chain management system of Nadella, since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Nadella and Chan, however, do not specifically disclose separate, by the processor, the time series data into one or more groups based on a time density of data points. In analogous art, Ethington discloses the following limitations:
separate, by the processor, the time series data into one or more groups based on a time density of data points (see para [0024], "In some examples, Ward's method of agglomerative clustering may be used. Systems may be grouped together by the mathematical distance between selected repair data parameters. In such an example, a dendrogram may be produced. At each level of the dendrogram the systems are divided into some set of clusters, the highest level having only one cluster that includes all systems and the lowest level having a cluster for each system. Clusters at a predetermined level of the dendrogram may be selected, or clusters at multiple levels may be analyzed prior to selecting a set of clusters." where density is a well know mathematical distance that can be applied for the grouping);
perform, by the processor, clustering on each of the one or more groups to provide a plurality of clusters within each group (see para [0023], "At step 14, process 10 includes automatically categorizing the systems into clusters using an unsupervised machine learning method. That is, a cluster analysis may be performed on the repair data parameters of the historical dataset. Any appropriate clustering analysis may be performed and/or any effective clustering algorithm may be used. A set of clusters may be selected from the results of the clustering analysis before proceeding with the next step." and see para [0046]-[0048], "Clustering module 112 is configured to divide the set of aircraft of historical dataset 124A into multiple subsets, or clusters. As shown in FIG. 3, cluster assignments 138 are added to working dataset 124, and each aircraft identifier 126 is associated with a cluster. Module 112 may utilize any suitable form of unsupervised machine learning, applied to the repair parameters of historical dataset 124A to perform the clustering. That is, the module may perform a cluster analysis using a technique such as hierarchical, centroid-based, or distribution-based clustering. For example, the clustering module may use Ward's method of agglomerative clustering. The aircraft may be divided into a plurality of clusters (e.g., at least two clusters). In some examples, the cluster count may be at least greater than a selected number of clusters, no more than a selected number of clusters, and/or the number of clusters may be determined automatically by the clustering method. Some clustering methods may have associated parameters, which may be pre-selected or selected by a user of the machine learning system. In some examples, clustering module 112 may include a library of clustering techniques and a technique appropriate to each dataset may be selected by the module or by the user.")
It would have been obvious to one or ordinary skill in the art at the time of the invention to combine the teachings of Ethington with Nadella and Chan because separating the time series data into groups enhances enables more specific forecasting decisions to be made on the specific groups (see Ethington, para [0002]-[0004]).
Moreover, it would have been obvious to one of ordinary skill in the art at the time of the invention to include the system for machine learning based repair forecasting as taught by Ethington in the Nadella and Chan combination, since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Claims 2-5, 8-11 and 14-17:
Nadella does not specifically disclose wherein the time series data is separated into a sparse group, a rich group and a flat group. In analogous art, Chan discloses the following limitations:
wherein the time series data is separated into a sparse group, a rich group and a flat group (see para [0030], "Seasonal Profile: The Seasonal Profile module, also referred to as the Intelligent Profile Clustering (IPC) module, 212 automatically calculates seasonal selling patterns at all levels of merchandise and location. This module draws on historical sales data to automatically create seasonal models for groups of items with similar seasonal patterns. The model might contain the effects of promotions, markdowns, and items with different seasonal tendencies." where groups with similar seasonal patterns can be considered a flat group)
Moreover, examiner notes that the specific groups is considered non-functional descriptive language and not given patentable weight. See MPEP 2111.05: "To be given patentable weight, the printed matter and associated product must be in a functional relationship. A functional relationship can be found where the printed matter performs some function with respect to the product to which it is associated. See Lowry, 32 F.3d at 1584, 32 USPQ2d at 1035 (citing Gulack, 703 F.2d at 1386, 217 USPQ at 404). For instance, indicia on a measuring cup perform the function of indicating volume within that measuring cup. See In re Miller, 418 F.2d 1392, 1396, 164 USPQ 46, 49 (CCPA 1969). A functional relationship can also be found where the product performs some function with respect to the printed matter to which it is associated. For instance, where a hatband places a string of numbers in a certain physical relationship to each other such that a claimed algorithm is satisfied due to the physical structure of the of the hatband, the hatband performs a function with respect to the string of numbers. See Gulack, 703 F.2d at 1386-87, 217 USPQ at 405.")
wherein the full feature space is defined as a space comprising data and two or more features with orthogonality between all of the data and the two or more features (see para [0097], "FIG. 11 is a graph providing a comparison of long range product demand forecasts including a forecast for product demand beyond 160 weeks determined without application of the present invention and seen to have a trend opposite the actual demand for weeks 0 through 160, identified by reference numeral 906; a forecast determined with trend effects removed from historical demand data 1108; and a forecast with a trend factor applied to the long range product demand forecast in accordance with the present invention 1106, also shown linearly by line 1112. The graph, based on actual customer data and results, presents a comparison between the forecasting methodology prior to incorporation of the trending logic discussed above, and the improved long range forecasting methodology including the trending logic. Actual demand for weeks 0 through 160 is identified by reference numeral 1102, and shown linearly by line 1110. The new long range forecast is seen to improve forecast accuracy and the predictive power of DCM forecast engine, with little additional computational effort or effect upon the scalability of the DCM forecasting application." and Fig 11).
wherein the set of features associated with the time features includes seasonality and linearity (see para [0031], "Demand Forecasting: The Demand Forecasting module 213 provides store/SKU level forecasting that responds to unique local customer demand. This module considers both an item's seasonality and its rate of sales (sales trend) to generate an accurate long range demand forecast (up to the next 65 weeks). The module continually compares historical and current demand data and utilizes several methods to determine the best product demand forecast.")
wherein the set of features associated with the time features includes seasonality and upward linearity, flat linearity and downward linearity (see Fig 4 and 5B and see para [0045]-[0047], "The seasonal pattern associated with a product is a major factor in the accuracy of product long range forecasts, contributing up to 50% of the total forecast accuracy. The DCM seasonal profiling module 212, also referred to as Intelligent Profile Clustering module, or IPC module, is used to group similar items together and generate seasonal factors (patterns). The seasonal factors created by the IPC module are thereafter used in the DCM Automated Replenishment (AR) module 215 to generate product long range sales and order forecasts. The IPC module uses up to four years of actual sales history as input for seasonal factor calculations. However, this sales history may contain year-over-year trend contribution on top of the seasonal patterns. As the former IPC module docs not include any de- trending logic to filter out the year-over-year trending effect, the seasonal factors created by this IPC module have the trending factor included. Leaving the year- over-year trend contribution in the seasonal factors will cause two major issues. First, as illustrated in FIG. 4, not all products (SKUs) within a seasonal model have the same trend. Therefore, the effect on the forecast accuracy for each individual SKU within a seasonal model may vary. FIG. 4 provides an illustration of two products, PROD A and PROD B, having the same seasonal pattern. The sales history for PROD A, including a downward trend, is illustrated in graph 402 and, linearly, in graph 404; while the sales history for PROD B, including an upward trend, is illustrated in graph 406 and, linearly, in graph 408." and see para [0051], "Graph 602 shows flat sales of 10 units pcr week for weeks 3 through 29 with increasing sales from week 30 through 48, followed by a steep drop in sales to begin the next year. The pattern for weeks 1 through 52 is identical to the pattern shown for weeks 52 through 104, having the same flat sales values for the first half of each year, and an increase to the same peak sales value during the second half of each year. Line 604 shows the average product sales over the 104 week period represented as a straight line having a slope of 0.07." Moreover, examiner notes that the specific features is considered non-functional descriptive language and not given patentable weight. See MPEP 2111.05: "To be given patentable weight, the printed matter and associated product must be in a functional relationship. A functional relationship can be found where the printed matter performs some function with respect to the product to which it is associated. See Lowry, 32 F.3d at 1584, 32 USPQ2d at 1035 (citing Gulack, 703 F.2d at 1386, 217 USPQ at 404). For instance, indicia on a measuring cup perform the function of indicating volume within that measuring cup. See In re Miller, 418 F.2d 1392, 1396, 164 USPQ 46, 49 (CCPA 1969). A functional relationship can also be found where the product performs some function with respect to the printed matter to which it is associated. For instance, where a hatband places a string of numbers in a certain physical relationship to each other such that a claimed algorithm is satisfied due to the physical structure of the hatband, the hatband performs a function with respect to the string of numbers. See Gulack, 703 F.2d at 1386-87, 217 USPQ at 405."))
It would have been obvious to one of ordinary skill in the art at the time of the invention to include the system for forecasting product demand as taught by Chan in the dynamic supply chain management system of Nadella, since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Allowable Subject Matter
Claims 6, 12 and 18 would be allowable if rewritten to overcome the rejections under 35 U.S.C. 101, set forth in this Office action and to include all of the limitations of the base claim and any intervening claims.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Tomoyasu et al. (JP 2003160228 A), a product stock monitoring system that is capable of calculating the accurate number of stocks according to a change in demand fluctuation by conditions at the time of production and shipment scheduling by eliminating dispersion of number of stocks by degree of experience of a person in charge
Chapman et al. (US 2014/0379422 A1), a method for selectively adjusting a price associated with an inventory stock keeping unit (SKU) where each of the inventory SKUs has associated therewith one or more sales transaction history records and an original price value
Jingang et al. "Outlier detection and sequence reconstruction in continuous time series of ocean observation data based on difference analysis and the Dixon criterion", a paper on detecting and removing data outliers where a correlation analysis and the test results show that a proposed new method can effectively instantiate a sequence of outliers characterized by different phases
Any inquiry concerning this communication or earlier communications from the examiner should be directed to SUJAY KONERU whose telephone number is 571-270-3409. The examiner can normally be reached on Monday-Friday, 9 am to 5 pm.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Patricia Munson can be reached on 571- 270-5396. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/SUJAY KONERU/
Primary Examiner, Art Unit 3624
