DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-3, 5-19, 21 -22 are rejected under 35 U.S.C. § 101 because the claimed subject matter is directed to a judicial exception, namely abstract ideas, and the claims do not recite additional elements that amount to significantly more than the judicial exception.
Step 1 — Statutory Category
With respect to the independent claims, claims 1, 21, and 22 are directed to a method, an electronic device, and a non-transitory storage medium, respectively, and therefore fall within the statutory categories of § 101.
Step 2A, Prong One — Judicial Exception
Claim 1 recites determining replenishment attribute values based on shipment information and time, comparing the replenishment attribute values to thresholds, determining target inventory quantities using a model, and determining replenishment quantities based on inventory levels. These limitations collectively recite mental processes, mathematical concepts, and methods of organizing human activity, including inventory management and supply chain planning.
Specifically, the claim recites abstract concepts such as analyzing historical shipment data, calculating statistical values, applying thresholds, and making inventory decisions, which are concepts that can be performed by the human mind or using pen and paper. Such inventory planning, forecasting, and replenishment determination are longstanding commercial practices and constitute abstract ideas.
Claims 21 and 22 merely recite implementing the same abstract inventory control method of claim 1 using generic computing components, namely a processor and storage medium, and therefore are likewise directed to the same abstract idea.
Accordingly, claims 1, 21, and 22 are directed to an abstract idea under Step 2A, Prong One.
Step 2A, Prong Two — Integration Into a Practical Application
Claims 1, 21, and 22 do not integrate the abstract idea into a practical application. The claims merely apply the abstract inventory management logic using generic computing components without improving the functioning of a computer or another technology. The claims do not recite any particular technical solution to a technical problem, nor do they recite a specific improvement in inventory system architecture, sensor technology, or data processing hardware.
Instead, the claims simply automate conventional inventory decision-making using generic data processing techniques, which constitutes mere instruction to apply the abstract idea using a computer.
Accordingly, claims 1, 21, and 22 do not integrate the abstract idea into a practical application.
Step 2B — Inventive Concept
The additional elements recited in claims 1, 21, and 22, including determining shipment intervals, applying thresholds, selecting estimation methods, and using inventory determination models, amount to no more than well-understood, routine, and conventional activities in inventory management and data analysis. The use of generic processors and storage media to perform these steps does not add an inventive concept.
Therefore, claims 1, 21, and 22 do not recite significantly more than the abstract idea itself.
With respect to the dependent claims, claims 2–19 depend directly or indirectly from claim 1 and therefore include all of the limitations of claim 1.
Claims 2–7 further recite analyzing shipment dates, shipment quantities, shipment interval durations, statistical measures such as mean, variance, and variation coefficients, and selecting items based on threshold comparisons. These limitations recite additional mathematical analysis and data evaluation, which are abstract ideas and do not add any technical improvement or practical application beyond the abstract inventory management process.
Claims 8–17 further recite selecting data processing methods based on distribution characteristics, performing mean or quantile estimation, determining factiles, and fitting empirical distributions. These limitations are directed to mathematical concepts and statistical modeling, which are themselves abstract ideas and merely refine the abstract decision-making process without improving computer functionality or another technical field.
Claims 18 and 19 recite determining replenishment thresholds and using replenishment attribute values as input parameters to inventory determination models. These limitations further recite abstract business logic and mathematical modeling techniques commonly used in inventory planning and do not add significantly more to the abstract idea.
Because claims 2–19 merely add additional abstract limitations or narrow the abstract idea using conventional statistical and business rules, they do not overcome the eligibility deficiencies of claim 1.
Claims 21 and 22 recite generic computing components configured to perform the abstract method of claim 1. The recited processor and storage medium are generic and perform only conventional data processing functions. Merely implementing an abstract idea on a generic computer does not render the claim patent-eligible.
Thus, after considering all claim elements, both individually and in combination and in ordered combination, it has been determined that the claims are not enough to transform the abstract idea into a patent-eligible invention since the claim limitations do not amount to a practical application or significantly more than an abstract idea. Accordingly, claims 1-3, 5-19, and 21-22 are directed to non-statutory subject matter under 35 U.S.C. § 101.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 1-3, 5-19 and 21-22 is/are rejected under 35 U.S.C. 103 as being unpatentable over Essenmacher et al. (US 2019/0138976, hereinafter Essenmacher), in view of Bowman et al. (US 2018/021834, hereinafter Bowman) and further in view of Jacoby (US 2012/0323744, hereinafter Jacoby).
With respect to claim 1, Essenmacher discloses a method for controlling item inventory in which replenishment-related values are determined for items based on current time and shipment quantity-associated information, including shipment history and inventory flow data. Essenmacher further teaches comparing such replenishment-related values against preset thresholds to identify items requiring replenishment action and, in response, determining target inventory quantities using inventory determination or optimization models. Essenmacher also teaches determining replenishment quantities by comparing target inventory quantities with net or current inventory quantities corresponding to the current time (Essenmacher ¶¶ [0008], [0031]–[0034], [0038]–[0046], [0048]–[0050]).
However, Essenmacher does not explicitly disclose deriving replenishment attribute values using shipment quantity-associated information that emphasizes shipment dates and shipment quantities as structured data inputs for replenishment evaluation, nor does Essenmacher expressly describe refining replenishment attribute determination using shipment-based temporal associations as claimed.
Bowman discloses determining replenishment-related attributes based on shipment quantity-associated information that includes shipment dates and shipment quantities and using such shipment-timing information to evaluate replenishment needs and identify target items whose replenishment attributes exceed predetermined thresholds (Bowman ¶¶ [0026], [0030], [0034], [0037]).
Additionally, Essenmacher does not expressly disclose detailed inventory quantity determination models that take replenishment attribute values as direct input parameters for calculating target inventory quantities using predictive or optimization-based techniques.
Jacoby discloses inventory quantity determination models in which replenishment-related parameters are used as input variables to calculate target inventory quantities and corresponding replenishment quantities based on differences between desired inventory levels and current or net inventory levels (Jacoby ¶¶ [0061]–[0065], [0068]–[0071]).
It would have been obvious to one of ordinary skill in the art at the time of the invention to combine Essenmacher with Bowman and Jacoby because all three references are directed to the same field of automated inventory management and replenishment control. Bowman provides improved shipment-based replenishment attribute determination that predictably enhances Essenmacher’s replenishment analysis, while Jacoby provides well-known inventory determination models that improve the accuracy and reliability of Essenmacher’s target inventory and replenishment quantity calculations. The combination merely applies known shipment-data processing techniques and inventory modeling methods to Essenmacher’s inventory control system to achieve predictable results, representing a routine optimization rather than an inventive step.
With respect to claims 2 and 3, Essenmacher further discloses an inventory control method that utilizes shipment history information to evaluate replenishment needs, including shipment quantities, shipment timing, and inventory flow trends derived from historical transaction data. Essenmacher teaches using such shipment-related information to analyze inventory turnover, replenishment cycles, and demand timing as part of determining replenishment-related attributes for items (Essenmacher ¶¶ [0032]–[0036], [0040], [0044]), and
Bowman discloses analyzing shipment dates and shipment quantities to determine shipment interval durations between consecutive shipments for each item and using such interval durations to characterize replenishment behavior and demand regularity (Bowman ¶¶ [0028]–[0033], [0036]–[0039]). Bowman further teaches filtering shipment data using shipment quantity thresholds to identify relevant shipment dates for interval calculation, thereby addressing the limitations of claims 2 and 3.
With respect to claims 5, 6 and 7, Bowman further discloses computing statistical measures including mean shipment interval, variance, and coefficients of variation to identify items with stable or predictable shipment patterns and selecting such items for replenishment processing based on comparison to preset thresholds (Bowman ¶¶ [0041]–[0047], [0050]).
With respect to claims 8 and 9, Bowman further discloses evaluating shipment interval distributions to determine whether shipment timing conforms to a uniform or non-uniform distribution and selecting appropriate statistical processing methods, including mean-based estimation or quantile-based estimation, based on such distribution characteristics (Bowman ¶¶ [0052]–[0057]).
With respect to claims 10, 11, Bowman further discloses identifying shipment dates exceeding shipment quantity thresholds, selecting shipment dates nearest to the current time, and calculating temporal durations used as input to replenishment attribute estimation models, including both mean-based and quantile-based methods (Bowman ¶¶ [0060]–[0066]). Bowman further discloses using shipment interval variance in mean estimation approaches, addressing the subject matter of claims 10 and 11.
With respect to claims 12 -17, Jacoby further discloses quantile-based inventory and demand estimation techniques, including determining quantile fractiles from empirical distributions fitted to historical shipment or demand interval data and selecting estimation approaches based on shipment sample size and quantile thresholds (Jacoby ¶¶ [0069]–[0076], [0079]–[0084]).
With respect to claims 18 -19, Jacoby further discloses inventory determination models that accept replenishment-related parameters as input variables and apply corresponding threshold logic to determine target inventory quantities, including adjusting thresholds based on modeling approach and demand estimation method (Jacoby ¶¶ [0061]–[0065], [0072]–[0075]).
With respect to claims 21 and 22, Essenmacher discloses implementing the disclosed inventory control methods using electronic devices including processors and non-transitory storage media storing computer-executable instructions that cause the processor to perform the inventory control operations (Essenmacher ¶¶ [0016], [0022], [0049]).
Bowman and Jacoby likewise disclose computer-implemented inventory and replenishment control systems executed by processors using stored program instructions (Bowman ¶ [0018]; Jacoby ¶ [0059]). Therefore, claims 21 and 22 merely recite the computer implementation of the unpatentable methods discussed above.
Therefore it would have been obvious to one of ordinary skill in the art at the time of the invention to combine Essenmacher with Bowman and Jacoby because all references are directed to automated inventory replenishment control using shipment history analysis and statistical modeling. Bowman provides known statistical shipment interval analysis techniques that predictably improve Essenmacher’s replenishment attribute determination, while Jacoby provides well-established quantile-based estimation and inventory modeling techniques that enhance Essenmacher’s target inventory and replenishment quantity determination. The combination represents the application of known analytical methods to a known inventory control system to obtain predictable improvements in accuracy and responsiveness, which is a routine design choice under KSR.
Conclusion
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/ROKIB MASUD/Primary Examiner, Art Unit 3627