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 .
Priority
Examiner acknowledges Applicant’s claim to priority regarding provisional application 62/403,576 filed on 10/03/2016, as a continuation in part to 15/723,554 and 15/724,052 both filed on 10/03/2017, and as a continuation of 15/724,108 filed on 10/03/2017.
Information Disclosure Statement
The information disclosure statement (IDS) filed on 05/13/2025 has been fully considered.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 USC 101 because the claimed invention is directed to a judicial exception (i.e. abstract idea) without anything significantly more.
Step 1: Claims 1-7 are directed to a method, claims 8-14 are directed to a system, and claims 15-20 are directed to a non-transitory computer readable medium.
Step 2A, Prong 1: Independent claims 1, 8, and 15 recite allocating inventory, constituting an abstract idea based on “Certain Methods of Organizing Human Activity” related to commercial interactions including advertising or marketing sales activities or behaviors, as well as business relations. Claim 1 recites limitations, similarly recited in claims 8 and 15, including “receiving one or more forecasts for determining one or more inventory levels; allocating available or future planned inventory for future incoming orders; allocating inventory for each of one or more products to one or more customer groups; allocating the inventory according to an available-to-promise approach and an allocated-available-to-promise approach, wherein each order is checked against available inventory and promised in real time; generating one or more inventory norms; applying one or more inventory policies.” These limitations, as drafted, but for the recitation of “by a computer,” is a process that covers performance of the limitations in the mind but for the recitation of generic computer components. That is, but for the “by a computer” language, nothing in the claim elements preclude the steps from practically being performed in the human mind. For example, with the exception of the “by a computer” language, the claim steps in the context of the claim encompass a user mentally or manually performing the steps of the claim.
Dependent claims 2-7, 9-14, and 16-20 further narrow the abstract idea and do not introduce further additional elements for consideration.
Step 2A, Prong 2: Independent claims 1, 8, and 15 do not integrate the judicial exception into a practical application. Claim 1 recites “a computer-implemented method for managing inventory by a computer comprising a processor and a memory, comprising” within the preamble of the claim. Claim 8 recites a system comprising “a computer comprising a processor and a memory and configured to.” Claim 15 recites “a non-transitory computer-readable medium embodied with software, the software when executed configured for managing inventory by:” within the preamble of the claim. Claims 1, 8, and 15 further recite “shipping the inventory on a day-to-day basis.” These additional elements are mere instructions to implement an abstract idea using a computer in its ordinary capacity, or merely uses the computer as a tool to perform the identified abstract idea. MPEP 2106.05(f) provides the following considerations for determining whether a claim simply recites a judicial exception with the words “apply it” (or an equivalent), such as mere instructions to implement an abstract idea on a computer: (1) whether the claim recites only the idea of a solution or outcome i.e., the claim fails to recite details of how a solution to a problem is accomplished; (2) whether the claim invokes computers or other machinery merely as a tool to perform an existing process; and (3) the particularity or generality of the application of the judicial exception. Use of a computer or other machinery in its ordinary capacity for tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., certain methods of organizing human activity) does not integrate a judicial exception into a practical application. See MPEP 2106.05(f).
Therefore, the additional elements of the independent claims, when considered both individually and in combination, are not sufficient to prove integration into a practical application.
Dependent claims 2-7, 9-14, and 16-20 further narrow the abstract idea and do not introduce further additional elements for consideration, which does not integrate the judicial exception into a practical application.
Step 2B: Independent claims 1, 8, and 15 do not comprise anything significantly more than the judicial exception. Claim 1 recites “a computer-implemented method for managing inventory by a computer comprising a processor and a memory, comprising” within the preamble of the claim. Claim 8 recites a system comprising “a computer comprising a processor and a memory and configured to.” Claim 15 recites “a non-transitory computer-readable medium embodied with software, the software when executed configured for managing inventory by:” within the preamble of the claim. Claims 1, 8, and 15 further recite “shipping the inventory on a day-to-day basis.” These additional elements are mere instructions to implement an abstract idea using a computer in its ordinary capacity, or merely uses the computer as a tool to perform the identified abstract idea. MPEP 2106.05(f) provides the following considerations for determining whether a claim simply recites a judicial exception with the words “apply it” (or an equivalent), such as mere instructions to implement an abstract idea on a computer: (1) whether the claim recites only the idea of a solution or outcome i.e., the claim fails to recite details of how a solution to a problem is accomplished; (2) whether the claim invokes computers or other machinery merely as a tool to perform an existing process; and (3) the particularity or generality of the application of the judicial exception. Use of a computer or other machinery in its ordinary capacity for tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., certain methods of organizing human activity) is not significantly more than the judicial exception. See MPEP 2106.05(f).
Therefore, the additional elements of the independent claims, when considered both individually and in combination, are not anything significantly more than the judicial exception.
Dependent claims 2-7, 9-14, and 16-20 further narrow the abstract idea and do not introduce further additional elements for consideration, which is not significantly more than the judicial exception.
Accordingly, claims 1-20 are rejected under 35 USC 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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
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.
Claim(s) 1-2, 4-9, 11-16, and 18-20 are rejected under 35 U.S.C. 103 as being unpatentable over Meyr ("Customer segmentation, allocation planning and order promising in make-to-stock production." 2009) in view of Lokowandt et al. (US 20100161365 A1).
Regarding claim 1, Meyr teaches a computer-implemented method for managing inventory by a computer comprising a processor and a memory (Pg. 245 teaches computational tests are executed on a personal computer with a processor and RAM, which is used for allocation planning), comprising:
receiving one or more forecasts for determining one or more inventory levels (Pg. 254 teaches priority classes for customer orders are built and available inventory, or ATP quantities, are allocated to these classes and reserved for later consumption by their respective customers, wherein planning is performed by determining a reasonable number of priority classes, clustering assignment of customers and customer orders to these classes, performing allocation planning of the available inventory on hand the planned production quantities (ATP) to the priority classes, and performing an ATP search by successively consuming the allocate ATP for each incoming order, wherein Pg. 230 teaches production has to be planned on the basis of forecasts and customer requests, wherein Pgs. 234-235 teach the modeling environment with customer segmentation includes performing demand planning and outputting forecasts, as well as performing supply planning in order to output ATP inventory information, wherein an overall planning horizon is subdivided into discrete time buckets, wherein at the beginning of planning, on the basis of supply information from the master production schedule, it is calculated how much ATP becomes available in each period, wherein customer orders arrive one after each other at different arrival dates, wherein for each order, it is known how much the customer wants to get and when they want to get this quantity, including the requested delivery quantity, requested delivery date, the time bucket for which the customer requests their order, wherein situation (d) is modeled by a sequence of an allocation planning model that is executed several times including in real time when each new customer order arrives, wherein Pg. 236 teaches the requested demand quantity of certain customer orders have to be satisfied by ATP inventories that become available in discrete periods, such as days,; see also: Pg. 233, 235, 239);
allocating available or future planned inventory for future incoming orders (Pg. 254 teaches priority classes for customer orders are built and available inventory, or ATP quantities, are allocated to these classes and reserved for later consumption by their respective customers, wherein planning is performed by determining a reasonable number of priority classes, clustering assignment of customers and customer orders to these classes, performing allocation planning of the available inventory on hand the planned production quantities (ATP) to the priority classes, and performing an ATP search by successively consuming the allocate ATP for each incoming order, wherein Pgs. 234-235 teach the modeling environment with customer segmentation includes performing demand planning and outputting forecasts, as well as performing supply planning in order to output ATP inventory information, wherein an overall planning horizon is subdivided into discrete time buckets, wherein at the beginning of planning, on the basis of supply information from the master production schedule, it is calculated how much ATP becomes available in each period, wherein customer orders arrive one after each other at different arrival dates, wherein for each order, it is known how much the customer wants to get and when they want to get this quantity, including the requested delivery quantity, requested delivery date, the time bucket for which the customer requests their order, wherein each single order is processed in real time, but it is only allocated to the desired delivery date if enough allocated ATP, or aATP, of its respective customer class is available and can be consumed, wherein the AP model once allocates ATP to the different, a priori known customer classes by means of linear programming, wherein Pg. 241 teaches performing iterative ATP consumption, with customer segmentation, wherein the modeling is based on the ATP that becomes available in a given period and has been allocated to orders with a priority class with a requested date and the ATP that becomes available in the period but has not yet been allocated to any priority class or planned delivery date; see also: Pgs. 232, 239-240, 242);
allocating inventory for each of one or more products to one or more customer groups (Pg. 254 teaches priority classes for customer orders are built and available inventory, or ATP quantities, are allocated to these classes and reserved for later consumption by their respective customers, wherein planning is performed by determining a reasonable number of priority classes, clustering assignment of customers and customer orders to these classes, performing allocation planning of the available inventory on hand the planned production quantities (ATP) to the priority classes, and performing an ATP search by successively consuming the allocate ATP for each incoming order, wherein Pgs. 234-235 teach the modeling environment with customer segmentation includes performing demand planning and outputting forecasts, as well as performing supply planning in order to output ATP inventory information, wherein an overall planning horizon is subdivided into discrete time buckets, wherein at the beginning of planning, on the basis of supply information from the master production schedule, it is calculated how much ATP becomes available in each period, wherein customer orders arrive one after each other at different arrival dates, wherein for each order, it is known how much the customer wants to get and when they want to get this quantity, including the requested delivery quantity, requested delivery date, the time bucket for which the customer requests their order, wherein situation (d) is modeled by a sequence of an allocation planning model that is executed several times including in real time when each new customer order arrives, wherein the allocation planning model once allocates ATP to the different, a priori known customer classes by linear programming, wherein up-to-date forecasts of customer demand within each customer class are necessary, wherein each single order is processed in real time, but it is only allocated to the desired delivery date if enough allocated ATP, or aATP, of its respective customer class is available and can be consumed, wherein model (d) can differentiate different customer classes, allocating ATP to these customer classes, and satisfying customer demand only if enough allocated ATP of the customer’s corresponding class is available, wherein the AP model once allocates ATP to the different, a priori known customer classes by means of linear programming, wherein Pg. 239 teaches the allocation planning model first assigns ATP to a predefined number of customer classes, wherein the subsequent single order consumption SOPA of the class specific ATP is also modeled and solved, wherein with respect to the limited ATP capacity, the model further restricts potential sales to certain customer classes by allocating ATP to the most profitable ones; see also: Pgs. 241-242);
allocating the inventory according to an available-to-promise approach and an allocated-available-to-promise approach (Pg. 254 teaches priority classes for customer orders are built and available inventory, or ATP quantities, are allocated to these classes and reserved for later consumption by their respective customers, wherein planning is performed by determining a reasonable number of priority classes, clustering assignment of customers and customer orders to these classes, performing allocation planning of the available inventory on hand the planned production quantities (ATP) to the priority classes, and performing an ATP search by successively consuming the allocate ATP for each incoming order, wherein Pgs. 234-235 teach the modeling environment with customer segmentation includes performing demand planning and outputting forecasts, as well as performing supply planning in order to output ATP inventory information, wherein an overall planning horizon is subdivided into discrete time buckets, wherein at the beginning of planning, on the basis of supply information from the master production schedule, it is calculated how much ATP becomes available in each period, wherein customer orders arrive one after each other at different arrival dates, wherein for each order, it is known how much the customer wants to get and when they want to get this quantity, including the requested delivery quantity, requested delivery date, the time bucket for which the customer requests their order, wherein situation (d) is modeled by a sequence of an allocation planning model that is executed several times including in real time when each new customer order arrives, wherein the allocation planning model once allocates ATP to the different, a priori known customer classes by linear programming, wherein up-to-date forecasts of customer demand within each customer class are necessary, wherein each single order is processed in real time, but it is only allocated to the desired delivery date if enough allocated ATP, or aATP, of its respective customer class is available and can be consumed, wherein the AP model once allocates ATP to the different, a priori known customer classes by means of linear programming, wherein Pg. 241 teaches performing iterative ATP consumption, after allocation with customer segmentation, wherein the modeling is based on the ATP that becomes available in a given period and has been allocated to orders with a priority class with a requested date and the ATP that becomes available in the period but has not yet been allocated to any priority class or planned delivery date; see also: Pgs. 242),
wherein each order is checked against available inventory and promised in real time (Pg. 254 teaches priority classes for customer orders are built and available inventory, or ATP quantities, are allocated to these classes and reserved for later consumption by their respective customers, wherein planning is performed by determining a reasonable number of priority classes, clustering assignment of customers and customer orders to these classes, performing allocation planning of the available inventory on hand the planned production quantities (ATP) to the priority classes, and performing an ATP search by successively consuming the allocate ATP for each incoming order, wherein Pg. 241 teaches performing iterative ATP consumption, wherein the modeling is based on the ATP that becomes available in a given period and has been allocated to orders with a priority class with a requested date and the ATP that becomes available in the period but has not yet been allocated to any priority class or planned delivery date, wherein the LP model uses these allocated and unallocated ATP quantities as an input for real time single order processing after allocation planning, wherein the order planning is performed in real-time, and wherein Pg. 236 teaches executing allocation planning and a short-term, such as real time for batch order processing level, for ATP consumption; see also: Pgs. 242-243, 246);
generating one or more inventory norms (Pgs. 239-240 teach performing batch order processing with respect to the limited ATP capacity, wherein the model further restricts potential sales to certain customer classes by allocating ATP to the most profitable ones, wherein the allocation planning problem can include maximizing profit based on supply costs including holding costs and backlogging costs, wherein Pg. 236 teaches profit is based on subtracting costs from revenue and punishing the use of ATP from periods earlier, necessitating storage, or later, backlogging, than the customer’s requested delivery date, as well as in Pg. 248 teaches evaluating the profit loss based on inventory or backlogging costs; see also: Pgs. 249-250);
applying one or more inventory policies (Pg. 248 teaches batching orders and increase the batching horizon is advantageous when compared to FCFS single order processing SOP, as well as in Pg. 236 teaches the requested demand quantity of certain customer orders have to be satisfied by ATP inventories that become available in discrete periods, such as days, wherein the order processing can be performed on a batch order processing level, wherein Pg. 237 teaches orders successively arrive with a continuous arrival date and time, wherein the a batch of all orders can be performed for a single day, as well as in Pgs. 238-239 teach performing batch order processing over the batching horizon; see also: Pgs. 230, 235).
However, Meyr does not explicitly teach and shipping the inventory on a day-to-day basis.
From the same or similar field of endeavor, Lokowandt teaches and shipping the inventory on a day-to-day basis ([0285-0288] teach planning with different supply planning areas in order to differentiate the demand, wherein material may be delivered every day, wherein material may be delivered every day, wherein [0193] teaches an ATP check may create sales order confirmations that tells the customer when they can expect their ordered material, wherein [0198-0199] teach the ATP check may be performed against all allocations, wherein an allocation may specify the daily capacity of the bottleneck resource and the ATP check subtracts the downstream lead time from the requested delivery date in order to determine if sufficient unconsumed cumulated allocation is available; see also: [0214, 0242, 0305]).
It would have been obvious to a person of ordinary skill before the effective filing date of the claimed invention to modify Meyr to incorporate the teachings of Lokowandt to include and shipping the inventory on a day-to-day basis. One would have been motivated to do so in order to automatically create order confirmations that provide favorable confirmations for the customer based on the daily availability (Lokowandt, [0214]). By incorporating the teachings of Lokowandt, one would have been able to automatically determine the material requirements in every supply planning area in order to manage different delivery schedules (Lokowandt, [0288]).
Regarding claims 2, 9, and 16, the combination of Meyr and Lokowandt teaches all the limitations of claims 1, 8, and 15 above.
Meyr further teaches wherein the one or more inventory policies comprises one of: batch quantity rules (Pg. 248 teaches batching orders and increase the batching horizon is advantageous when compared to FCFS single order processing SOP, as well as in Pg. 236 teaches the requested demand quantity of certain customer orders have to be satisfied by ATP inventories that become available in discrete periods, such as days, wherein the order processing can be performed on a batch order processing level, wherein Pg. 237 teaches orders successively arrive with a continuous arrival date and time, wherein the a batch of all orders can be performed for a single day, as well as in Pgs. 238-239 teach performing batch order processing over the batching horizon; see also: Pgs. 230, 235).
Regarding claims 4, 11, and 18, the combination of Meyr and Lokowandt teaches all the limitations of claims 1, 8, and 15 above.
Meyr further teaches wherein the one or more inventory norms comprise one of: a cost (Pgs. 239-240 teach performing batch order processing with respect to the limited ATP capacity, wherein the model further restricts potential sales to certain customer classes by allocating ATP to the most profitable ones, wherein the allocation planning problem can include maximizing profit based on supply costs including holding costs and backlogging costs, wherein Pg. 236 teaches profit is based on subtracting costs from revenue and punishing the use of ATP from periods earlier, necessitating storage, or later, backlogging, than the customer’s requested delivery date, as well as in Pg. 248 teaches evaluating the profit loss based on inventory or backlogging costs; see also: Pgs. 249-250).
Regarding claims 5, 12, and 19, the combination of Meyr and Lokowandt teaches all the limitations of claims 1, 8, and 18 above.
Meyr further teaches wherein the inventory is allocated to the one or more customer groups based on one or more predefined rules (Pg. 254 teaches priority classes for customer orders are built and available inventory, or ATP quantities, are allocated to these classes and reserved for later consumption by their respective customers, wherein planning is performed by determining a reasonable number of priority classes, clustering assignment of customers and customer orders to these classes, performing allocation planning of the available inventory on hand the planned production quantities (ATP) to the priority classes, and performing an ATP search by successively consuming the allocate ATP for each incoming order, wherein Pgs. 248-249 teach searching the ATP space based on consumption rules related to the allowed classes based on priority and other information, wherein Pg. 239 teaches the allocation planning model first assigns ATP to a predefined number of customer classes, wherein the class specific ATP is also modeled and solved, wherein with respect to the limited ATP capacity, the model further restricts potential sales to certain customer classes by allocating ATP to the most profitable ones, wherein the APS applies rule-based algorithms for the ATP consumption; see also: Pgs. 234-235, 241-242).
Regarding claims 6, 13, and 20, the combination of Meyr and Lokowandt teaches all the limitations of claims 1, 8, and 15 above.
Meyr further teaches wherein the available-to-promise approach and the allocated-available-to-promise approach reserves inventory and calculates a promise date (Pg. 254 teaches priority classes for customer orders are built and available inventory, or ATP quantities, are allocated to these classes and reserved for later consumption by their respective customers, wherein planning is performed by determining a reasonable number of priority classes, clustering assignment of customers and customer orders to these classes, performing allocation planning of the available inventory on hand the planned production quantities (ATP) to the priority classes, and performing an ATP search by successively consuming the allocate ATP for each incoming order, wherein Pgs. 234-235 teach the modeling environment with customer segmentation includes performing demand planning and outputting forecasts, as well as performing supply planning in order to output ATP inventory information, wherein an overall planning horizon is subdivided into discrete time buckets, wherein at the beginning of planning, on the basis of supply information from the master production schedule, it is calculated how much ATP becomes available in each period, wherein customer orders arrive one after each other at different arrival dates, wherein for each order, it is known how much the customer wants to get and when they want to get this quantity, including the requested delivery quantity, requested delivery date, the time bucket for which the customer requests their order, wherein situation (d) is modeled by a sequence of an allocation planning model that is executed several times including in real time when each new customer order arrives, wherein the allocation planning model once allocates ATP to the different, a priori known customer classes by linear programming, wherein up-to-date forecasts of customer demand within each customer class are necessary, wherein each single order is processed in real time, but it is only allocated to the desired delivery date if enough allocated ATP, or aATP, of its respective customer class is available and can be consumed, wherein the AP model once allocates ATP to the different, a priori known customer classes by means of linear programming, wherein Pg. 241 teaches performing iterative ATP consumption, after allocation with customer segmentation, wherein the modeling is based on the ATP that becomes available in a given period and has been allocated to orders with a priority class with a requested date and the ATP that becomes available in the period but has not yet been allocated to any priority class or planned delivery date; see also: Pgs. 242).
Regarding claims 7 and 14, the combination of Meyr and Lokowandt teaches all the limitations of claims 6 and 13 above.
Meyr further teaches wherein the reserved inventory comprises one or more available warehouse items or future available items based on a supply chain (Pg. 254 teaches priority classes for customer orders are built and available inventory, or ATP quantities, are allocated to these classes and reserved for later consumption by their respective customers, wherein planning is performed by determining a reasonable number of priority classes, clustering assignment of customers and customer orders to these classes, performing allocation planning of the available inventory on hand the planned production quantities (ATP) to the priority classes, and performing an ATP search by successively consuming the allocate ATP for each incoming order, wherein Pg. 232 teaches the ATP is allocated to an order’s corresponding class, or can be substituted to other locations such as distribution centers or regional warehouses, wherein Pgs. 234-235 teach the modeling environment with customer segmentation includes performing demand planning and outputting forecasts, as well as performing supply planning in order to output ATP inventory information, wherein an overall planning horizon is subdivided into discrete time buckets, wherein at the beginning of planning, on the basis of supply information from the master production schedule, it is calculated how much ATP becomes available in each period, wherein customer orders arrive one after each other at different arrival dates, wherein for each order, it is known how much the customer wants to get and when they want to get this quantity, including the requested delivery quantity, requested delivery date, the time bucket for which the customer requests their order, wherein each single order is processed in real time, but it is only allocated to the desired delivery date if enough allocated ATP, or aATP, of its respective customer class is available and can be consumed, wherein the AP model once allocates ATP to the different, a priori known customer classes by means of linear programming, wherein Pg. 241 teaches performing iterative ATP consumption, with customer segmentation, wherein the modeling is based on the ATP that becomes available in a given period and has been allocated to orders with a priority class with a requested date and the ATP that becomes available in the period but has not yet been allocated to any priority class or planned delivery date; see also: Pgs. 239-240, 242).
Claim(s) 3, 10, and 17 are rejected under 35 U.S.C. 103 as being unpatentable over Meyr ("Customer segmentation, allocation planning and order promising in make-to-stock production." 2009) in view of Lokowandt et al. (US 20100161365 A1) in view of Humphries et al. (US 20140200946 A1).
Regarding claims 3, 10, and 17, the combination of Meyr and Lokowandt teaches all the limitations of claims 1, 8, and 15 above.
Meyr further teaches wherein the one or more inventory policies each describe a target quantity (Pg. 248 teaches batching orders and increase the batching horizon is advantageous when compared to FCFS single order processing SOP, wherein Pgs. 254 teaches priority classes for customers are built and available inventory quantities are allocated to these classes and reserved for later consumption by their respective customers, wherein Pg. 237 teaches orders successively arrive with a continuous arrival date and time, wherein the a batch of all orders can be performed for a single day, wherein Pgs. 238-239 teach performing batch order processing over the batching horizon; see also: Pgs. 230, 234-235, 240).
However, Meyr does not explicitly teach wherein the one or more inventory policies each describe a reorder point.
From the same or similar field of endeavor, Humphries teaches wherein the one or more inventory policies each describe a reorder point ([0045-0055] teach optimizing the inventory policy based on the reorder point for a retailer, wherein assumptions for the policy include the reorder point and the demand, wherein [0063-0034] teach providing various inventory policies with different reorder point quantities, as well as in [0073-0074] teach another inventory policy; see also: [0014, 0046]).
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to modify the combination of Meyr and Lokowandt to incorporate the teachings of Humphries to include wherein the one or more inventory policies each describe a reorder point. One would have been motivated to do so in order to avoid high inventory levels that can significantly increase carrying costs and handling costs in a business (Humphries, [0003]). By incorporating the teachings of Humphries, one would have been able to identify an optimized inventory policy based on the present predicted demand and the reorder point of the policy (Humphries, [0048-0055]).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Rodrigues (US 20150254601 A1) discloses improving the performance of the inventory control policy by comparing the effective inventory level and the variable reorder point
Heise et al. (US 20090063215 A1) discloses performing inventory optimization based on the available shipping hours ever day and available to promise scheduling
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/SARA GRACE BROWN/Primary Examiner, Art Unit 3625
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