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 .
DETAILED ACTION
Status of the Application
The following is a Final Office Action.
In response to Examiner's communication of 5/28/2025, Applicant responded on 7/11/2025. Amended claims 1.
Claims 1-12 are pending in this application and have been examined.
Response to Amendment
Applicant's amendments to claims 1 are not sufficient to overcome the 35 USC 101 rejections set forth in the previous action.
Applicant's amendments to claims 1 are not sufficient to overcome the prior art rejections set forth in the previous action.
Response to Arguments – 35 USC § 101
Applicant’s arguments with respect to the rejections have been fully considered, but they are not persuasive.
Applicant submits, “…Applicant's claims are directed to a technological improvement to systems that automatically calculate manufacturing and sales planning that comply with restrictions on greenhouse gas emission volumes.…The technological improvement which is discussed in the specification and claimed is that a numerical model is created based on a plurality of supply chain patterns and a simulation is executed to calculate a maximum profit gained based on each of the supply chain patterns and a volume of greenhouse gases emitted by the processes. For example, the specification describes that simulation processing is executed to create "the profit maximizing model 5 based on the master tables registered in s11 to s15 and obtains output values by executing the profit maximizing model 5 (s17)...a plurality of patterns (supply chain patterns) of the processes of procurement, manufacturing, and sales of a product, the profit maximizing model 5 is executed on each of the supply chain patterns, and only output values for the supply chain pattern with the highest amount of profit are outputted. These output values are outputted to a simulation result DB 510. Results of the execution of the profit maximizing model 5 are also displayed on a simulation execution screen to be described next...when considered as a whole, integrates the alleged abstract idea into a practical application at least because these claims recite additional elements that automatically calculate manufacturing and sales planning that comply with restrictions on greenhouse gas emission volumes…One way to demonstrate such integration is when the claimed invention improves the functioning of a computer or improves another technology or technical field…the improvement is to systems that automatically calculate manufacturing and sales planning that comply with restrictions on greenhouse gas emission volumes. That is, it is the automatic planning system that is improved and the limitations, discussed below, which reflect the improvements do not recite abstract ideas, such as a mental process, a certain method of organizing human activity or a mathematical concept, and are therefore additional elements...This is prohibitively complicated to do using pen and paper and cannot practically be performed in the human mind. Further, at least this limitation does not involve a mathematical concept or a method of organizing human activity. Therefore, at least this limitation is an additional element separate from the alleged abstract ideas mentioned in the Office Action...executing a simulation, which is part of the automatic planning, cannot practically be performed in the human mind, is not a mathematical concept withing the context of MPEP Section 2106.04(a)(2) I and is not a method or organizing human activity within the context of MPEP Section 2106.04(a)(2) II…Applicant's claim 1 is not directed to an abstract idea because claim 1 includes additional elements that integrate the alleged abstract idea into a practical application of the abstract idea demonstrated by the technological improvement to systems that automatically calculate manufacturing and sales planning that comply with restrictions on greenhouse gas emission volumes. Additionally, claim 1 includes additional elements that apply the alleged abstract idea in a meaningful way by creating a numerical model is based on a plurality of supply chain patterns and executing the model to calculate a maximum profit gained based on each of the supply chain patterns and a volume of greenhouse gases emitted by the processes, which goes beyond generally linking the use of the abstract idea to a particular technological environment. Therefore, under the Step 2A Prong Two analysis, claim 1 is not directed to an abstract idea...” The Examiner respectfully disagrees.
By Applicant’s own admission and Applicant’s specifications, the claims and the argued elements, are directed to, …creating a numerical model is based on a plurality of supply chain patterns and executing the model to calculate a maximum profit gained based on each of the supply chain patterns and a volume of greenhouse gases emitted by the processes…, which is a problem directed to organizing human activity (i.e. commercial interactions = managing and modeling supply chain patterns, fundamental economic principles = maximizing profit, humans modeling supply chain business patterns to meet sales demand and to maximize profit) and a mental process (i.e. humans observing and identifying supply chain patterns, humans simulating and modeling supply chain business patterns to meet sales demand and to maximize profit with pen and paper) and mathematical concepts (i.e. humans applying mathematical simulation optimization models to maximize profits), as established in Step 2A Prong 1. This problem does not specifically arise in the realm of computer technology, but rather, this problem existed and was addressed long before the advent of computers. Thus, the claims do not recite a technical improvement to a technical problem or necessarily roots in computing technologies. The alleged solutions are abstract solutions directed to solving abstract problem of “calculate a maximum profit gained based on each of the supply chain patterns and a volume of greenhouse gases emitted by the processes”, which are abstract ideas. Additionally, pursuant to the broadest reasonable interpretation, as an ordered combination, each of the additional elements are computing elements recited at high level of generality implementing the abstract idea, and thus, are no more than applying the abstract idea with generic computer components. Further, these additional elements generally link the abstract idea to a technical environment, namely the environment of a computer, performing extra solution activities. Therefore, as a whole, the additional elements do not integrate the abstract ideas into a practical application in Step 2A Prong 2 or amount to significantly more under Step 2B.
The limitations are abstract elements that are part of and directed to the recited abstract idea as described above with respect to the first prong of Step 2A, i.e. mental process, mathematical concepts and organizing human activities, generally linked to a technical environment, i.e. computer. Even novel and newly discovered judicial exceptions are still exceptions, despite their novelty. July 2015 Update, p. 3; see SAP America Inc. v. Investpic, LLC, No. 2017-2081, slip op. at 2 (Fed Cir. May 15, 2018).
Simply reciting specific limitations that narrow the abstract idea does not make an abstract idea non-abstract. 79 Fed. Reg. 74631; buySAFE Inc. v. Google, Inc., 765 F.3d 1350, 1355 (2014); see SAP America at p. 12. As discussed in SAP America, no matter how much of an advance the claims recite, when “the advance lies entirely in the realm of abstract ideas, with no plausibly alleged innovation in the non-abstract application realm,” “[a]n advance of that nature is ineligible for patenting.” Id. at p. 3.
Response to Arguments – Prior Art
Applicant’s arguments with respect to the rejections have been fully considered, but they are not persuasive.
Applicant submits, “…Saeed does not disclose executing a simulation which includes inputting each of a plurality of created supply chain patterns into the numerical model..., and execute the numerical model to calculate a maximum profit gained based on each of the supply chain patterns. Saeed discloses grouping constraints (as discussed above) and compares "business data" such as marketing data, sales data and production data (i.e., routings, which is a sequence of assets used in production [para. [0016]]) with the claimed plurality of supply chain patterns. See Office Action, pp. 20-21. However, in Saeed "[a] constraint group is a combination of different categorical attributes' values of different attributes" (para. [0020]) and the "categorical attributes define the level of granularity of decision making in the decision system" (para. [0019]. The constraint groups are not comparable to supply chain patterns and Saeed does not disclose executing a simulation for each constraint group. Rather, Saeed executes an optimization engine that finds, using the collected business data, a set of values that satisfy the set of constraints created at S205 and S210 and at the same time maximize the cash contribution measured by the objective function created at S220. Para. [0038] (emphasis added). Therefore, Saeed does not disclose "execute simulation processing including: input each of the created supply chain patterns into the numerical model with the acquired sales destination and the manufactured quantity, and execute the numerical model to calculate a maximum profit gained based on each of the supply chain patterns with the greenhouse gas emission being at or below the predetermined upper limit, and calculate a volume of greenhouse gases emitted by the processes," as set forth in claim 1. Further, resort to neither Jabara or Xu, whether considered individually or in combination, cure the deficiencies in Saeed. For at least the reasons presented herein, the cited combination of documents does not teach or suggest all of the elements of claim 1....” The Examiner respectfully disagrees.
Under the broadest reasonable interpretation, “supply chain patterns” can be interpreted to be the “constraint groups” in Saeed. Therefore, Saeed discloses:
execute simulation processing including: (in at least [0032] At S180, once the attribute-based constraints are generated, an optimization process is carried out. [0038] a linear programming optimization engine is executed to determine the best feasible values of the volumes that satisfy the constraints of the attribute-based constraints. That is, the optimization engine finds, using the collected business data (i.e. supply chain patterns), a set of values that satisfy the set of constraints created at S205 and S210 and at the same time maximize the cash contribution measured by the objective function created at S220 (i.e. maximize profit).)
input each of the created supply chain patterns into the numerical model with the acquired sales destination and the manufactured quantity, and (in at least [0009] a manufacturer may be negotiating with a customer, and the customer may desire a guaranteed minimum quantity commitment for its delivery destinations in China. (i.e. sales destination and manufactured quantity) [0016] A sales order includes price and demand quantity information for a unique combination of product and customer attributes. [0015] constraints above may include, but are not limited to: environmental constraints (e.g., emissions caps, minimum fuel efficiency, etc.), resource constraints (e.g., minimum material commitments, maximum material allocations), asset constraints (e.g., assets' capacities), market constraints such as those set by customers (e.g., minimum quantity for a set of products(i.e. manufactured quantity), maximum price for a set products, etc.), and economic constraints (e.g., revenue targets). These constraints can be grouped according to one or more attributes. For example, a limit on mercury emissions is typically summed across all production activities at a plant. (i.e. supply chain patterns and volume of greenhouse gases emitted by the processes, any changes to constraints or attributes creates a new set of supply chain pattern to input for optimization) [0021] A company decides to restrict its carbon dioxide (CO2) emissions at its factories in the United States (US). The company already trades CO2 emissions credits in the European Union (EU) and it does not consider CO2 emissions in the Asia Pacific (AP) or South America (SA). In this case, a user (e.g., a business manager) selects “CO2 emissions” as the numerical factor to constrain, “SUM” as the constraint calculation function, and “Region” as the categorical attribute. [0022] the user specifies only an upper bound for CO2 emissions in the US and sets the lower bound to a null (or ‘don't care’) value. Thereafter, the method automatically creates a constraint according to the values entered by the user. The constraint ensures that the sum of CO2 emissions for the production of all products on all machines in all plants in the US is less than or equal to the upper bound specified by the user. [0032] At S180, once the attribute-based constraints are generated (i.e. supply chain patterns), an optimization process is carried out. Referring now to FIG. 2, step S180 is described in greater detail. At S205, a mathematical model is initialized with the constructed constraint groups. The attribute-based constraints already include the demand and capacity constraints which are part of any mathematical model of a typical business plan. [0027] A first typically-mandatory attribute-based constraint is the demand constraint. The demand constraints ensure that the quantities stay within the specified minimum and maximum boundaries for each sales order. An exemplary set of demand constraints for a particular sales order may be in following form: minimum commitment≦sales order quantity≦maximum demand (i.e. manufactured quantity) [0028] The creation of demand constraints includes selection of a “Demand” as the factor to constrain, the “SUM” as the constraint calculation function and the categorical attributes “Customer” and “Deliver-to Location”, (i.e. sales destination) “Gauge”, “Grade”, and “Width”. [0029] The attribute values are always specified for each attribute value combination. A “Lower Bound” represents the minimum commitment of the product to the customer at the deliver-to location. An “Upper Bound” represents the maximum demand of the product by the customer at the deliver-to location and is typically mandatory. An example for the attributes' values is shown in Table 3. [0030] the company must provide at least 5,000 and at most 10,000 units to customer C1 at deliver-to location 94705 of a product with Gauge 0.001, Grade A1, and Width 14.00. [0033] At S210, a set of constraints representing structural business factors, such as production flows and shipping is added to the mathematical model. It would be apparent to a person skilled in the art that other types of constraints are possible. The production flow constraint(s) ensure that the products are manufactured according to pre-specified steps. For example, in a paper company, products are created through a sequence of steps including fiber supply, digester, bleach plant, paper machine, finishing machine, and, finally, shipping of the products. These relationships describe the structure of the production system and are not characterized by attribute-based constraints. The production flow may be modeled as described in U.S. application Ser. No. 11/860,473 (hereinafter the '473 application) assigned to common assignee and which is hereby incorporated herein in its entirety by this reference thereto. The shipping constraints ensure that the quantities of a product delivered equals the quantities shipped. These are structural constraints that ensure the proper product flows, and therefore they are not subject to the constraint-by-attributes approach. [0035] At S220, an objective function to measure profit is created. An exemplary and non-limiting objective function that uses cash contribution as a measure of the profit is [0036] a sales order represents the most granular unit of decision making available to the mathematical model's constraints, and the sum is over all sales orders. It should be noted that other objective functions can be used, such as revenue, quantity, profit, return on assets (ROA), and so on [0037] a linear program is formed from the constraints generated at step S210 and the objective function mentioned at S220. It is well known in the art that linear programming problems involve a linear objective function and linear equality and inequality constraints. Other mathematical programming optimization engines, such as a mixed integer-linear programming engine, a nonlinear programming engine, stochastic programming optimization engine, and the likes can also be used to solve the set of constraints and the objective function. [0038] At S230, a linear programming optimization engine is executed to determine the best feasible values of the volumes that satisfy the constraints of the attribute-based constraints. That is, the optimization engine finds, using the collected business data, a set of values that satisfy the set of constraints created at S205 and S210 and at the same time maximize the cash contribution measured by the objective function created at S220.)
execute the numerical model to calculate a maximum profit gained based on each of the supply chain patterns with the greenhouse gas emission being at or below the predetermined upper limits and calculate a volume of greenhouse gases emitted by the processes, and (in at least [0015] constraints above may include, but are not limited to: environmental constraints (e.g., emissions caps, minimum fuel efficiency, etc.), resource constraints (e.g., minimum material commitments, maximum material allocations), asset constraints (e.g., assets' capacities), market constraints such as those set by customers (e.g., minimum quantity for a set of products, maximum price for a set products, etc.), and economic constraints (e.g., revenue targets). These constraints can be grouped according to one or more attributes. For example, a limit on mercury emissions is typically summed across all production activities at a plant. (i.e. supply chain patterns and volume of greenhouse gases emitted by the processes) [0021] A company decides to restrict its carbon dioxide (CO2) emissions at its factories in the United States (US). The company already trades CO2 emissions credits in the European Union (EU) and it does not consider CO2 emissions in the Asia Pacific (AP) or South America (SA). In this case, a user (e.g., a business manager) selects “CO2 emissions” as the numerical factor to constrain, “SUM” as the constraint calculation function, and “Region” as the categorical attribute. [0022] the user specifies only an upper bound for CO2 emissions in the US and sets the lower bound to a null (or ‘don't care’) value. Thereafter, the method automatically creates a constraint according to the values entered by the user. The constraint ensures that the sum of CO2 emissions for the production of all products on all machines in all plants in the US is less than or equal to the upper bound specified by the user. [0032] At S180, once the attribute-based constraints are generated, an optimization process is carried out. Referring now to FIG. 2, step S180 is described in greater detail. At S205, a mathematical model is initialized with the constructed constraint groups. The attribute-based constraints already include the demand and capacity constraints which are part of any mathematical model of a typical business plan. [0033] At S210, a set of constraints representing structural business factors, such as production flows and shipping is added to the mathematical model. It would be apparent to a person skilled in the art that other types of constraints are possible. The production flow constraint(s) ensure that the products are manufactured according to pre-specified steps. For example, in a paper company, products are created through a sequence of steps including fiber supply, digester, bleach plant, paper machine, finishing machine, and, finally, shipping of the products. These relationships describe the structure of the production system and are not characterized by attribute-based constraints. The production flow may be modeled as described in U.S. application Ser. No. 11/860,473 (hereinafter the '473 application) assigned to common assignee and which is hereby incorporated herein in its entirety by this reference thereto. The shipping constraints ensure that the quantities of a product delivered equals the quantities shipped. These are structural constraints that ensure the proper product flows, and therefore they are not subject to the constraint-by-attributes approach. [0035] At S220, an objective function to measure profit is created. An exemplary and non-limiting objective function that uses cash contribution as a measure of the profit is [0036] a sales order represents the most granular unit of decision making available to the mathematical model's constraints, and the sum is over all sales orders. It should be noted that other objective functions can be used, such as revenue, quantity, profit, return on assets (ROA), and so on [0037] a linear program is formed from the constraints generated at step S210 and the objective function mentioned at S220. It is well known in the art that linear programming problems involve a linear objective function and linear equality and inequality constraints. Other mathematical programming optimization engines, such as a mixed integer-linear programming engine, a nonlinear programming engine, stochastic programming optimization engine, and the likes can also be used to solve the set of constraints and the objective function. [0038] At S230, a linear programming optimization engine is executed to determine the best feasible values of the volumes that satisfy the constraints of the attribute-based constraints. That is, the optimization engine finds, using the collected business data, a set of values that satisfy the set of constraints created at S205 and S210 and at the same time maximize the cash contribution measured by the objective function created at S220.(i.e. profit))
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 1-12 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Claim 1 (similarly 7) recites, ”A manufacturing and sales planning support …;
store a table indicating, for each of a plurality of entries, a product to be manufactured, a location where the product is sold, and a sales destination of the product, which are stored in association with each other,
store a numerical model that receives, as input values, a sales destination of a product whose manufacturing and sales involves greenhouse gas emission, a demanded quantity of the product at the sales destination, and processes for the manufacturing and sales of the product, and
calculate, as output values, a maximum profit gained based on the processes with the greenhouse gas emission being at or below a predetermined upper limit and a volume of greenhouse gases emitted by the processes,
acquire information related to the sales destination of the product and the demanded quantity of the product at the sales destination,
create a plurality of supply chain patterns each being information including a pattern of the processes for the manufacturing and sales of the product manufactured in a manufactured quantity corresponding to the demanded quantity, the plurality of supply chain patterns being created by creating combinations of the product to be manufactured, the location where the product is sold, and the sales destination of the product, and for each combination, creates all patterns of a supplier, a manufacturing location, manufacturing equipment, a transportation route, and means of transportation, with the demanded manufactured quantity varying from a minimum value to the demanded manufactured quantity, and
execute simulation processing including:
input each of the created supply chain patterns into the numerical model with the acquired sales destination and the manufactured quantity, and
execute the numerical model to calculate a maximum profit gained based on each of the supply chain patterns with the greenhouse gas emission being at or below the predetermined upper limit, and calculate a volume of greenhouse gases emitted by the processes, and
if the greenhouse gas emission exceeds the predetermined upper limit, receive an input of a reset of the predetermined upper limit of the greenhouse gas emission.”
Analyzing under Step 2A, Prong 1:
The limitations regarding, …store a table indicating, for each of a plurality of entries, a product to be manufactured, a location where the product is sold, and a sales destination of the product, which are stored in association with each other, store a numerical model that receives, as input values, a sales destination of a product whose manufacturing and sales involves greenhouse gas emission, a demanded quantity of the product at the sales destination, and processes for the manufacturing and sales of the product, and calculate, as output values, a maximum profit gained based on the processes with the greenhouse gas emission being at or below a predetermined upper limit and a volume of greenhouse gases emitted by the processes, acquire information related to the sales destination of the product and the demanded quantity of the product at the sales destination, create a plurality of supply chain patterns each being information including a pattern of the processes for the manufacturing and sales of the product manufactured in a manufactured quantity corresponding to the demanded quantity, the plurality of supply chain patterns being created by creating combinations of the product to be manufactured, the location where the product is sold, and the sales destination of the product, and for each combination, creates all patterns of a supplier, a manufacturing location, manufacturing equipment, a transportation route, and means of transportation, with the demanded manufactured quantity varying from a minimum value to the demanded manufactured quantity, input each of the created supply chain patterns into the numerical model with the acquired sales destination and the manufactured quantity, and execute the numerical model to calculate a maximum profit gained based on each of the supply chain patterns with the greenhouse gas emission being at or below the predetermined upper limit, and calculate a volume of greenhouse gases emitted by the processes, and if the greenhouse gas emission exceeds the predetermined upper limit, receive an input of a reset of the predetermined upper limit of the greenhouse gas emission.…, under the broadest reasonable interpretation, can include a human using their mind and using pen and paper to, …store a table indicating, for each of a plurality of entries, a product to be manufactured, a location where the product is sold, and a sales destination of the product, which are stored in association with each other, store a numerical model that receives, as input values, a sales destination of a product whose manufacturing and sales involves greenhouse gas emission, a demanded quantity of the product at the sales destination, and processes for the manufacturing and sales of the product, and calculate, as output values, a maximum profit gained based on the processes with the greenhouse gas emission being at or below a predetermined upper limit and a volume of greenhouse gases emitted by the processes, acquire information related to the sales destination of the product and the demanded quantity of the product at the sales destination, create a plurality of supply chain patterns each being information including a pattern of the processes for the manufacturing and sales of the product manufactured in a manufactured quantity corresponding to the demanded quantity, the plurality of supply chain patterns being created by creating combinations of the product to be manufactured, the location where the product is sold, and the sales destination of the product, and for each combination, creates all patterns of a supplier, a manufacturing location, manufacturing equipment, a transportation route, and means of transportation, with the demanded manufactured quantity varying from a minimum value to the demanded manufactured quantity, input each of the created supply chain patterns into the numerical model with the acquired sales destination and the manufactured quantity, and execute the numerical model to calculate a maximum profit gained based on each of the supply chain patterns with the greenhouse gas emission being at or below the predetermined upper limit, and calculate a volume of greenhouse gases emitted by the processes, and if the greenhouse gas emission exceeds the predetermined upper limit, receive an input of a reset of the predetermined upper limit of the greenhouse gas emission…; therefore, the claims are directed to a mental process.
Further, …store a table indicating, for each of a plurality of entries, a product to be manufactured, a location where the product is sold, and a sales destination of the product, which are stored in association with each other, store a numerical model that receives, as input values, a sales destination of a product whose manufacturing and sales involves greenhouse gas emission, a demanded quantity of the product at the sales destination, and processes for the manufacturing and sales of the product, and calculate, as output values, a maximum profit gained based on the processes with the greenhouse gas emission being at or below a predetermined upper limit and a volume of greenhouse gases emitted by the processes, acquire information related to the sales destination of the product and the demanded quantity of the product at the sales destination, create a plurality of supply chain patterns each being information including a pattern of the processes for the manufacturing and sales of the product manufactured in a manufactured quantity corresponding to the demanded quantity, the plurality of supply chain patterns being created by creating combinations of the product to be manufactured, the location where the product is sold, and the sales destination of the product, and for each combination, creates all patterns of a supplier, a manufacturing location, manufacturing equipment, a transportation route, and means of transportation, with the demanded manufactured quantity varying from a minimum value to the demanded manufactured quantity, input each of the created supply chain patterns into the numerical model with the acquired sales destination and the manufactured quantity, and execute the numerical model to calculate a maximum profit gained based on each of the supply chain patterns with the greenhouse gas emission being at or below the predetermined upper limit, and calculate a volume of greenhouse gases emitted by the processes, and if the greenhouse gas emission exceeds the predetermined upper limit, receive an input of a reset of the predetermined upper limit of the greenhouse gas emission…, under the broadest reasonable interpretation, are humans modeling supply chain business plans to meet sales demand and to maximize profit, therefore it is, commercial interactions, fundamental economic principles or practices. Thus, the claims are directed to certain methods of organizing human activity.
Additionally, …store a table indicating, for each of a plurality of entries, a product to be manufactured, a location where the product is sold, and a sales destination of the product, which are stored in association with each other, store a numerical model that receives, as input values, a sales destination of a product whose manufacturing and sales involves greenhouse gas emission, a demanded quantity of the product at the sales destination, and processes for the manufacturing and sales of the product, and calculate, as output values, a maximum profit gained based on the processes with the greenhouse gas emission being at or below a predetermined upper limit and a volume of greenhouse gases emitted by the processes, acquire information related to the sales destination of the product and the demanded quantity of the product at the sales destination, create a plurality of supply chain patterns each being information including a pattern of the processes for the manufacturing and sales of the product manufactured in a manufactured quantity corresponding to the demanded quantity, the plurality of supply chain patterns being created by creating combinations of the product to be manufactured, the location where the product is sold, and the sales destination of the product, and for each combination, creates all patterns of a supplier, a manufacturing location, manufacturing equipment, a transportation route, and means of transportation, with the demanded manufactured quantity varying from a minimum value to the demanded manufactured quantity, input each of the created supply chain patterns into the numerical model with the acquired sales destination and the manufactured quantity, and execute the numerical model to calculate a maximum profit gained based on each of the supply chain patterns with the greenhouse gas emission being at or below the predetermined upper limit, and calculate a volume of greenhouse gases emitted by the processes, and if the greenhouse gas emission exceeds the predetermined upper limit, receive an input of a reset of the predetermined upper limit of the greenhouse gas emission…, are mathematical concepts.
Accordingly, the claims are directed to a mental process, certain methods of organizing human activity, mathematical concepts, and thus, the claims are directed to an abstract idea under the first prong of Step 2A.
Analyzing under Step 2A, Prong 2:
This judicial exception is not integrated into a practical application under the second prong of Step 2A.
In particular, the claims recite the additional elements beyond the recited abstract idea identified under Step 2A, Prong 1, such as:
Claim 1, 7: apparatus comprising: a processor; a memory, coupled to the processor, the memory storing instructions that when executed by the processor, configure the processor to, an information processing apparatus
Claim 6: a display coupled to the processor
, and pursuant to the broadest reasonable interpretation, as an ordered combination, each of the additional elements are computing elements recited at high level of generality implementing the abstract idea, and thus, are no more than applying the abstract idea with generic computer components. Further, these additional elements generally link the abstract idea to a technical environment, namely the environment of a computer.
Additionally, with respect to, “store…receives… acquire… create… output… …input… display”, these elements do not add a meaningful limitations to integrate the abstract idea into a practical application because they are extra-solution activity, pre and post solution activity - i.e. data gathering – store…receives… acquire…input…, create…, data output – create … output… display…
Analyzing under Step 2B:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception under Step 2B.
As noted above, the aforementioned additional elements beyond the recited abstract idea are not sufficient to amount to significantly more than the recited abstract idea because, as an order combination, the additional elements are no more than mere instructions to implement the idea using generic computer components (i.e. apply it).
Additionally, as an order combination, the additional elements append the recited abstract idea to well-understood, routine, and conventional activities in the field as individually evinced by the applicant’s own disclosure, as required by the Berkheimer Memo, in at least:
[0032] Next, as shown in Fig. 1, the manufacturing and sales planning support apparatus 10 includes the following functional parts (programs): a profit maximizing model storage part 11, a plan information acquisition part 13, a supply chain model creation part 15, a supply chain pattern creation part 17, a simulation execution part 19, and a result display part 21.
[0039] Next, Fig. 13 is a diagram illustrating an example hardware configuration of the manufacturing and sales planning support apparatus 10. The manufacturing and sales planning support apparatus 10 includes a processing device 31 (processor) such as a central processing unit (CPU), a main storage device 32 (memory) such as a random-access memory (RAM) or a read-only memory (ROM), an auxiliary storage device 33 such as a hard disk drive (HDD) or a solid-state drive (SSD), an input device 34 formed by a keyboard, a mouse, a touch panel, and/or the like and an output device 35 which is formed by a monitor (display) or the like and displays screens.
[0040] The functions of the manufacturing and sales planning support apparatus 10 may be implemented when the hardware of the manufacturing and sales planning support apparatus 10 or the processing device 31 of the manufacturing and sales planning support apparatus 10 reads and executes the programs stored in the main storage device 32 or the auxiliary storage device 33. Also, these programs are stored in, for example, a storage device such as a secondary storage device, a non-volatile semiconductor memory, a hard disk drive, or an SSD, or a recording medium readable by an information processing apparatus, such as an IC card, an SD card, or a DVD. Note that, like a virtual server provided by a cloud system, the manufacturing and sales planning support apparatus 10 may be partly or entirely implemented using, for example, virtual information processing resources provided using a virtualization technique, a process space separation technique, or the like. Also, all or some of the functions provided by each information processing apparatus may be implemented by, for example, a service provided by a cloud system via an application programming interface (API) or the like.
[0097] For example, the configuration of each functional part of the manufacturing and sales planning support apparatus 10 is an example. For example, a certain functional part may be provided to a different functional part, or a plurality of functional parts may be integrated into a single functional part. Also, some of the functional parts of the manufacturing and sales planning support apparatus 10 may be provided in another communicable information processing apparatus.
Furthermore, as an ordered combination, these elements amount to generic computer components receiving or transmitting data over a network, performing repetitive calculations, electronic record keeping, and storing and retrieving information in memory, which, as held by the courts, are well-understood, routine, and conventional. See MPEP 2106.05(d).
Moreover, the remaining elements of dependent claims do not transform the recited abstract idea into a patent eligible invention because these remaining elements merely recite further abstract limitations that provide nothing more than simply a narrowing of the abstract idea recited in the independent claims.
Looking at these limitations as an ordered combination adds nothing additional that is sufficient to amount to significantly more than the recited abstract idea because they simply provide instructions to use a generic arrangement of generic computer components to “apply” the recited abstract idea, perform insignificant extra-solution activity, and generally link the abstract idea to a technical environment. Thus, the elements of the claims, considered both individually and as an ordered combination, are not sufficient to ensure that the claim as a whole amounts to significantly more than the abstract idea itself. Since there are no limitations in these claims that transform the exception into a patent eligible application such that these claims amount to significantly more than the exception itself, claims 1-12 are rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.
Claim Rejections – 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, 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 set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
Determining the scope and contents of the prior art.
Ascertaining the differences between the prior art and the claims at issue.
Resolving the level of ordinary skill in the pertinent art.
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.
Claims 1-12 is/are rejected under 35 U.S.C. 103 as being unpatentable by US Patent Publication to US20090216576A1 to Saeed et al., (hereinafter referred to as “Saeed”) in view of US Patent Publication to US20120123953A1 to Jabara., (hereinafter referred to as “Jabara”) in view of US Patent Publication to US20220333236A1 to Xu et al., (hereinafter referred to as “Xu”)
As per Claim 1, Saeed teaches: (Currently amended) A manufacturing and sales planning support apparatus comprising: a processor; a memory, coupled to the processor, the memory storing instructions that when executed by the processor, configure the processor to: ([0046])
store a table indicating, for each of a plurality of entries, a product to be manufactured, a location where the product is sold, and a sales destination of the product, which are stored in association with each other, (in at least [0016] FIG. 1 shows an exemplary and non-limiting flowchart 100 describing the process of optimizing a constrained business plan implemented in accordance with an embodiment of the present invention. At S110, business data of the organization is collected from multiple data sources, such as those described above, including but not limited to, individual spreadsheets, online transaction processing (OLTP) applications, and specialized databases, called operational data stores (ODS). Specifically, the data includes financial data (e.g., production costs, shipping costs, labor costs, etc.), marketing data (e.g., product lists, new product lists, product attributes, etc.), sales data (e.g., customer lists, new customer lists, customer attributes, sales orders, sales order attributes, etc.), and production data (e.g., asset capabilities, asset rates, routings, etc.). In an exemplary embodiment, the term “asset” refers to a machine, the term “asset capability” refers to the product types that each machine can produce, and the term “routing” refers to the sequence of assets used in production. A sales order includes price and demand quantity information for a unique combination of product and customer attributes. The business data may be based on historical or projected details. [0023] The following is another example describing the process for a user to determine the attribute-based constraints of the business plan. A car manufacturer manufactures three different categories of vehicles: passenger cars, light trucks and heavy trucks for the USA and UK markets (i.e. location where product sold, sales destination). For each of these markets, the car manufacturer must produce a mix of cars that meet certain minimum average fuel efficiency by vehicle category. The car manufacturer optimizes its business plans by taking into account the efficiency standards in these countries for those vehicle categories. Each car has two categorical attributes, “Product” and “Vehicle Category” and a numerical attribute, “Miles per Gallon”. The “Product” attribute is the specific vehicle model; the “Vehicle Category” attribute can be either “Passenger Cars”, “Light Trucks”, or “Heavy Trucks”. The numerical attribute “Miles per Gallon” gives the fuel efficiency of a specific vehicle. Vehicles are produced on production lines with the following categorical attributes: “Production Line,” “Plant,” and “Country.” “Production Line” has attribute values such as “Line 1” , “Line 2” , and so on. “Plant” has attribute values such as “Chicago”, “Atlanta”, “Warren A”, and so on. “Country” has attribute values of “USA” and “UK.” [0027] The demand constraints ensure that the quantities stay within the specified minimum and maximum boundaries for each sales order. An exemplary set of demand constraints for a particular sales order may be in following form: minimum commitment≦sales order quantity≦maximum demand [0028] The creation of demand constraints includes selection of a “Demand” as the factor to constrain, the “SUM” as the constraint calculation function and the categorical attributes “Customer” and “Deliver-to Location”, “Gauge”, “Grade”, and “Width”.)
store a numerical model that receives, as input values, a sales destination of a product whose manufacturing and sales involves greenhouse gas emission, a demanded quantity of the product at the sales destination, and processes for the manufacturing and sales of the product, and (in at least [0009] a manufacturer may be interested in restricting its carbon dioxide emissions in the United States, and, in a similar fashion, the manufacturer could test the economic impact of the proposed emissions limit. As yet another example, a manufacturer may be negotiating with a customer, and the customer may desire a guaranteed minimum quantity commitment for its delivery destinations in China. The manufacturer can determine the overall economic impact on the business plan and use this information in negotiating prices with the customer. [0015] constraints above may include, but are not limited to: environmental constraints (e.g., emissions caps, minimum fuel efficiency, etc.), resource constraints (e.g., minimum material commitments, maximum material allocations), asset constraints (e.g., assets' capacities), market constraints such as those set by customers (e.g., minimum quantity for a set of products, maximum price for a set products, etc.), and economic constraints (e.g., revenue targets). These constraints can be grouped according to one or more attributes. For example, a limit on mercury emissions is typically summed across all production activities at a plant. [0032] At S205, a mathematical model is initialized with the constructed constraint groups. The attribute-based constraints already include the demand and capacity constraints which are part of any mathematical model of a typical business plan. [0033] At S210, a set of constraints representing structural business factors, such as production flows and shipping is added to the mathematical model.)
calculate as output values, a maximum profit gained based on the processes with the greenhouse gas emission being at or below a predetermined upper limit and a volume of greenhouse gases emitted by the processes, (in at least [0014] highly efficient in organizations having a complex organizational structure, multiple interactive production flows, many products, many customers and the need to rapidly add, modify and assess attribute-based constraints. It allows managers to make better strategic and tactical decisions regarding their business, thereby maximizing profit while satisfying constraints. [0015] constraints above may include, but are not limited to: environmental constraints (e.g., emissions caps, minimum fuel efficiency, etc.), resource constraints (e.g., minimum material commitments, maximum material allocations), asset constraints (e.g., assets' capacities), market constraints such as those set by customers (e.g., minimum quantity for a set of products, maximum price for a set products, etc.), and economic constraints (e.g., revenue targets). These constraints can be grouped according to one or more attributes. For example, a limit on mercury emissions is typically summed across all production activities at a plant. [0019] At S140, the user selects one or more categorical attributes. The categorical attributes define the level of granularity of decision making in the decision system. These attributes are predefined based on the numerical factor being constrained and may be, but are not limited to, a combination of customer region, a customer industry, a customer market, a sales order, a deliver-to destination, a product market segment, a product group, a product width, a product gauge, a sales person region, an asset, a plant, a production region…attributes may be used in other attribute combinations (e.g., to cap global carbon dioxide emissions in additional to regional carbon dioxide emissions)…[0020] At S155 a table including lower and upper bounds for each constraint group is generated and displayed to the user. [0021] A company decides to restrict its carbon dioxide (CO2) emissions at its factories in the United States (US). The company already trades CO2 emissions credits in the European Union (EU) and it does not consider CO2 emissions in the Asia Pacific (AP) or South America (SA). In this case, a user (e.g., a business manager) selects “CO2 emissions” as the numerical factor to constrain, “SUM” as the constraint calculation function, and “Region” as the categorical attribute. Then, a table of lower and upper bounds are generated as illustrated in Table 1. [0022] The constraint ensures that the sum of CO2 emissions for the production of all products on all machines in all plants in the US is less than or equal to the upper bound specified by the user. [0027] A first typically-mandatory attribute-based constraint is the demand constraint. The demand constraints ensure that the quantities stay within the specified minimum and maximum boundaries for each sales order. An exemplary set of demand constraints for a particular sales order may be in following form: minimum commitment≦sales order quantity≦maximum demand [0029] A “Lower Bound” represents the minimum commitment of the product to the customer at the deliver-to location. An “Upper Bound” represents the maximum demand of the product by the customer at the deliver-to location and is typically mandatory. An example for the attributes' values is shown in Table 3. [0035] an objective function to measure profit is created. An exemplary and non-limiting objective function that uses cash contribution as a measure of the profit [0036] sales order represents the most granular unit of decision making available to the mathematical model's constraints, and the sum is over all sales orders. It should be noted that other objective functions can be used, such as revenue, quantity, profit, return on assets (ROA), and so on. [0038] At S230, a linear programming optimization engine is executed to determine the best feasible values of the volumes that satisfy the constraints of the attribute-based constraints.)
acquire information related to the sales destination of the product and the demanded quantity of the product at the sales destination, (in at least [0009] a manufacturer may be negotiating with a customer, and the customer may desire a guaranteed minimum quantity commitment for its delivery destinations in China. [0016] A sales order includes price and demand quantity information for a unique combination of product and customer attributes. [0027] A first typically-mandatory attribute-based constraint is the demand constraint. The demand constraints ensure that the quantities stay within the specified minimum and maximum boundaries for each sales order. An exemplary set of demand constraints for a particular sales order may be in following form: minimum commitment≦sales order quantity≦maximum demand [0028] The creation of demand constraints includes selection of a “Demand” as the factor to constrain, the “SUM” as the constraint calculation function and the categorical attributes “Customer” and “Deliver-to Location”, “Gauge”, “Grade”, and “Width”. [0029] The attribute values are always specified for each attribute value combination. A “Lower Bound” represents the minimum commitment of the product to the customer at the deliver-to location. An “Upper Bound” represents the maximum demand of the product by the customer at the deliver-to location and is typically mandatory. An example for the attributes' values is shown in Table 3. [0030] the company must provide at least 5,000 and at most 10,000 units to customer C1 at deliver-to location 94705 of a product with Gauge 0.001, Grade A1, and Width 14.00.)
create a plurality of supply chain patterns each being information including a pattern of the processes for the manufacturing and sales of the product manufactured in a manufactured quantity corresponding to the demanded quantity, the plurality of supply chain patterns being created by creating combinations of the product to be manufactured, the location where the product is sold, and the sales destination of the product, and for each combination, creates all patterns of a supplier, a manufacturing location, manufacturing equipment, a transportation …, and … of transportation, with the demanded manufactured quantity varying from a minimum value to the demanded manufactured quantity, and (in at least [0016] S110, business data of the organization is collected from multiple data sources, such as those described above, including but not limited to, individual spreadsheets, online transaction processing (OLTP) applications, and specialized databases, called operational data stores (ODS). Specifically, the data includes financial data (e.g., production costs, shipping costs, labor costs, etc.), marketing data (e.g., product lists, new product lists, product attributes, etc.), sales data (e.g., customer lists, new customer lists, customer attributes, sales orders, sales order attributes, etc.), and production data (e.g., asset capabilities, asset rates, routings, etc.). (i.e. supply chain patterns) In an exemplary embodiment, the term “asset” refers to a machine, the term “asset capability” refers to the product types that each machine can produce (i.e. manufacturing equipment), and the term “routing” refers to the sequence of assets used in production. A sales order includes price and demand quantity information for a unique combination of product and customer attributes. The business data may be based on historical or projected details. (i.e. supply chain patterns) [0023] The following is another example describing the process for a user to determine the attribute-based constraints of the business plan. A car manufacturer manufactures three different categories of vehicles: passenger cars, light trucks and heavy trucks for the USA and UK markets (i.e. sales destination). For each of these markets, the car manufacturer must produce a mix of cars that meet certain minimum average fuel efficiency by vehicle category. The car manufacturer optimizes its business plans by taking into account the efficiency standards in these countries for those vehicle categories. Each car has two categorical attributes, “Product” and “Vehicle Category” and a numerical attribute, “Miles per Gallon”. The “Product” attribute is the specific vehicle model; the “Vehicle Category” attribute can be either “Passenger Cars”, “Light Trucks”, or “Heavy Trucks”. The numerical attribute “Miles per Gallon” gives the fuel efficiency of a specific vehicle. Vehicles are produced on production lines with the following categorical attributes: “Production Line,” “Plant,” and “Country.” “Production Line” has attribute values such as “Line 1” , “Line 2” , and so on. “Plant” has attribute values such as “Chicago”, “Atlanta”, “Warren A”, and so on. “Country” has attribute values of “USA” and “UK.” [0027] The demand constraints ensure that the quantities stay within the specified minimum and maximum boundaries for each sales order. An exemplary set of demand constraints for a particular sales order may be in following form: minimum commitment≦sales order quantity≦maximum demand [0028] The creation of demand constraints includes selection of a “Demand” as the factor to constrain, the “SUM” as the constraint calculation function and the categorical attributes “Customer” and “Deliver-to Location”, “Gauge”, “Grade”, and “Width”. [0033] At S210, a set of constraints representing structural business factors, such as production flows and shipping (i.e. transportation) is added to the mathematical model. It would be apparent to a person skilled in the art that other types of constraints are possible. The production flow constraint(s) ensure that the products are manufactured according to pre-specified steps. For example, in a paper company, products are created through a sequence of steps including fiber supply, digester, bleach plant, paper machine, finishing machine, and, finally, shipping of the products.(i.e patterns) These relationships describe the structure of the production system and are not characterized by attribute-based constraints. The production flow may be modeled as described in U.S. application Ser. No. 11/860,473 (hereinafter the '473 application) assigned to common assignee and which is hereby incorporated herein in its entirety by this reference thereto. The shipping constraints ensure that the quantities of a product delivered equals the quantities shipped. These are structural constraints that ensure the proper product flows, and therefore they are not subject to the constraint-by-attributes approach.)
execute simulation processing including: (in at least [0032] At S180, once the attribute-based constraints are generated, an optimization process is carried out. [0038] a linear programming optimization engine is executed to determine the best feasible values of the volumes that satisfy the constraints of the attribute-based constraints. That is, the optimization engine finds, using the collected business data (i.e. supply chain patterns), a set of values that satisfy the set of constraints created at S205 and S210 and at the same time maximize the cash contribution measured by the objective function created at S220 (i.e. maximize profit).)
input each of the created supply chain patterns into the numerical model with the acquired sales destination and the manufactured quantity, and (in at least [0009] a manufacturer may be negotiating with a customer, and the customer may desire a guaranteed minimum quantity commitment for its delivery destinations in China. (i.e. sales destination and manufactured quantity) [0016] A sales order includes price and demand quantity information for a unique combination of product and customer attributes. [0015] constraints above may include, but are not limited to: environmental constraints (e.g., emissions caps, minimum fuel efficiency, etc.), resource constraints (e.g., minimum material commitments, maximum material allocations), asset constraints (e.g., assets' capacities), market constraints such as those set by customers (e.g., minimum quantity for a set of products(i.e. manufactured quantity), maximum price for a set products, etc.), and economic constraints (e.g., revenue targets). These constraints can be grouped according to one or more attributes. For example, a limit on mercury emissions is typically summed across all production activities at a plant. (i.e. supply chain patterns and volume of greenhouse gases emitted by the processes, any changes to constraints or attributes creates a new set of supply chain pattern to input for optimization) [0021] A company decides to restrict its carbon dioxide (CO2) emissions at its factories in the United States (US). The company already trades CO2 emissions credits in the European Union (EU) and it does not consider CO2 emissions in the Asia Pacific (AP) or South America (SA). In this case, a user (e.g., a business manager) selects “CO2 emissions” as the numerical factor to constrain, “SUM” as the constraint calculation function, and “Region” as the categorical attribute. [0022] the user specifies only an upper bound for CO2 emissions in the US and sets the lower bound to a null (or ‘don't care’) value. Thereafter, the method automatically creates a constraint according to the values entered by the user. The constraint ensures that the sum of CO2 emissions for the production of all products on all machines in all plants in the US is less than or equal to the upper bound specified by the user. [0032] At S180, once the attribute-based constraints are generated (i.e. supply chain patterns), an optimization process is carried out. Referring now to FIG. 2, step S180 is described in greater detail. At S205, a mathematical model is initialized with the constructed constraint groups. The attribute-based constraints already include the demand and capacity constraints which are part of any mathematical model of a typical business plan. [0027] A first typically-mandatory attribute-based constraint is the demand constraint. The demand constraints ensure that the quantities stay within the specified minimum and maximum boundaries for each sales order. An exemplary set of demand constraints for a particular sales order may be in following form: minimum commitment≦sales order quantity≦maximum demand (i.e. manufactured quantity) [0028] The creation of demand constraints includes selection of a “Demand” as the factor to constrain, the “SUM” as the constraint calculation function and the categorical attributes “Customer” and “Deliver-to Location”, (i.e. sales destination) “Gauge”, “Grade”, and “Width”. [0029] The attribute values are always specified for each attribute value combination. A “Lower Bound” represents the minimum commitment of the product to the customer at the deliver-to location. An “Upper Bound” represents the maximum demand of the product by the customer at the deliver-to location and is typically mandatory. An example for the attributes' values is shown in Table 3. [0030] the company must provide at least 5,000 and at most 10,000 units to customer C1 at deliver-to location 94705 of a product with Gauge 0.001, Grade A1, and Width 14.00. [0033] At S210, a set of constraints representing structural business factors, such as production flows and shipping is added to the mathematical model. It would be apparent to a person skilled in the art that other types of constraints are possible. The production flow constraint(s) ensure that the products are manufactured according to pre-specified steps. For example, in a paper company, products are created through a sequence of steps including fiber supply, digester, bleach plant, paper machine, finishing machine, and, finally, shipping of the products. These relationships describe the structure of the production system and are not characterized by attribute-based constraints. The production flow may be modeled as described in U.S. application Ser. No. 11/860,473 (hereinafter the '473 application) assigned to common assignee and which is hereby incorporated herein in its entirety by this reference thereto. The shipping constraints ensure that the quantities of a product delivered equals the quantities shipped. These are structural constraints that ensure the proper product flows, and therefore they are not subject to the constraint-by-attributes approach. [0035] At S220, an objective function to measure profit is created. An exemplary and non-limiting objective function that uses cash contribution as a measure of the profit is [0036] a sales order represents the most granular unit of decision making available to the mathematical model's constraints, and the sum is over all sales orders. It should be noted that other objective functions can be used, such as revenue, quantity, profit, return on assets (ROA), and so on [0037] a linear program is formed from the constraints generated at step S210 and the objective function mentioned at S220. It is well known in the art that linear programming problems involve a linear objective function and linear equality and inequality constraints. Other mathematical programming optimization engines, such as a mixed integer-linear programming engine, a nonlinear programming engine, stochastic programming optimization engine, and the likes can also be used to solve the set of constraints and the objective function. [0038] At S230, a linear programming optimization engine is executed to determine the best feasible values of the volumes that satisfy the constraints of the attribute-based constraints. That is, the optimization engine finds, using the collected business data, a set of values that satisfy the set of constraints created at S205 and S210 and at the same time maximize the cash contribution measured by the objective function created at S220.)
execute the numerical model to calculate a maximum profit gained based on each of the supply chain patterns with the greenhouse gas emission being at or below the predetermined upper limits and calculate a volume of greenhouse gases emitted by the processes, and (in at least [0015] constraints above may include, but are not limited to: environmental constraints (e.g., emissions caps, minimum fuel efficiency, etc.), resource constraints (e.g., minimum material commitments, maximum material allocations), asset constraints (e.g., assets' capacities), market constraints such as those set by customers (e.g., minimum quantity for a set of products, maximum price for a set products, etc.), and economic constraints (e.g., revenue targets). These constraints can be grouped according to one or more attributes. For example, a limit on mercury emissions is typically summed across all production activities at a plant. (i.e. supply chain patterns and volume of greenhouse gases emitted by the processes) [0021] A company decides to restrict its carbon dioxide (CO2) emissions at its factories in the United States (US). The company already trades CO2 emissions credits in the European Union (EU) and it does not consider CO2 emissions in the Asia Pacific (AP) or South America (SA). In this case, a user (e.g., a business manager) selects “CO2 emissions” as the numerical factor to constrain, “SUM” as the constraint calculation function, and “Region” as the categorical attribute. [0022] the user specifies only an upper bound for CO2 emissions in the US and sets the lower bound to a null (or ‘don't care’) value. Thereafter, the method automatically creates a constraint according to the values entered by the user. The constraint ensures that the sum of CO2 emissions for the production of all products on all machines in all plants in the US is less than or equal to the upper bound specified by the user. [0032] At S180, once the attribute-based constraints are generated, an optimization process is carried out. Referring now to FIG. 2, step S180 is described in greater detail. At S205, a mathematical model is initialized with the constructed constraint groups. The attribute-based constraints already include the demand and capacity constraints which are part of any mathematical model of a typical business plan. [0033] At S210, a set of constraints representing structural business factors, such as production flows and shipping is added to the mathematical model. It would be apparent to a person skilled in the art that other types of constraints are possible. The production flow constraint(s) ensure that the products are manufactured according to pre-specified steps. For example, in a paper company, products are created through a sequence of steps including fiber supply, digester, bleach plant, paper machine, finishing machine, and, finally, shipping of the products. These relationships describe the structure of the production system and are not characterized by attribute-based constraints. The production flow may be modeled as described in U.S. application Ser. No. 11/860,473 (hereinafter the '473 application) assigned to common assignee and which is hereby incorporated herein in its entirety by this reference thereto. The shipping constraints ensure that the quantities of a product delivered equals the quantities shipped. These are structural constraints that ensure the proper product flows, and therefore they are not subject to the constraint-by-attributes approach. [0035] At S220, an objective function to measure profit is created. An exemplary and non-limiting objective function that uses cash contribution as a measure of the profit is [0036] a sales order represents the most granular unit of decision making available to the mathematical model's constraints, and the sum is over all sales orders. It should be noted that other objective functions can be used, such as revenue, quantity, profit, return on assets (ROA), and so on [0037] a linear program is formed from the constraints generated at step S210 and the objective function mentioned at S220. It is well known in the art that linear programming problems involve a linear objective function and linear equality and inequality constraints. Other mathematical programming optimization engines, such as a mixed integer-linear programming engine, a nonlinear programming engine, stochastic programming optimization engine, and the likes can also be used to solve the set of constraints and the objective function. [0038] At S230, a linear programming optimization engine is executed to determine the best feasible values of the volumes that satisfy the constraints of the attribute-based constraints. That is, the optimization engine finds, using the collected business data, a set of values that satisfy the set of constraints created at S205 and S210 and at the same time maximize the cash contribution measured by the objective function created at S220.(i.e. profit))
if the greenhouse gas emission … the predetermined upper limit, receive an input of … of the predetermined upper limit of the greenhouse gas emission. (in at least [0015] constraints above may include, but are not limited to: environmental constraints (e.g., emissions caps, minimum fuel efficiency, etc.), resource constraints (e.g., minimum material commitments, maximum material allocations), asset constraints (e.g., assets' capacities), market constraints such as those set by customers (e.g., minimum quantity for a set of products, maximum price for a set products, etc.), and economic constraints (e.g., revenue targets). These constraints can be grouped according to one or more attributes. For example, a limit on mercury emissions is typically summed across all production activities at a plant. [0019] These attributes are predefined based on the numerical factor being constrained and may be, but are not limited to, a combination of customer region, a customer industry, a customer market, a sales order, a deliver-to destination, a product market segment, a product group, a product width, a product gauge, a sales person region, an asset, a plant, a production region, and so on. It should be noted that other types of categorical attributes may be defined by the user, the same attributes may be used in other attribute combinations (e.g., to cap global carbon dioxide emissions in additional to regional carbon dioxide emissions), different attributes may apply to different factors, and the attributes may be different depending on the industry or business. [0021] creating constraint groups and an attribute-based constraint. A company decides to restrict its carbon dioxide (CO2) emissions at its factories in the United States (US). The company already trades CO2 emissions credits in the European Union (EU) and it does not consider CO2 emissions in the Asia Pacific (AP) or South America (SA). In this case, a user (e.g., a business manager) selects “CO2 emissions” as the numerical factor to constrain, “SUM” as the constraint calculation function, and “Region” as the categorical attribute. Then, a table of lower and upper bounds are generated as illustrated in Table 1. [0040] This information is presented in a constraint group value table 300 provided in FIG. 3. The table 300 includes the following fields: one ore more categorical attributes of the constraint group; a lower bound, an upper bound, the calculated value based on the calculation function, an optional weighting factor, and the marginal value.)
Although implied, Saeed does not expressly disclose the following limitations, which however, are taught by Jabara,
….a transportation route, and means of transportation… (in at least [0101] product destination and distribution/transportation data for a kitchen dishwasher machine may indicate that the dishwasher is manufactured in Guangdong, China, transported by rail to Shanghai, China, transported by freighter to San Francisco, Calif., transported by rail to Denver, and transported by truck to a retailer location, where it is sold to a consumer. [0102] product destination and distribution/transportation data may be obtained from manufacturers, vendors, and other entities in a product's supply chain. In some embodiments, general transportation research and models available from life cycle databases are used to find or calculate likely shipping routes, likely transportation modes (sea, land), etc. from the final assembly location to the end user location and to generate product destination and distribution/transportation data. In some embodiments, stage 645 may also include producing destination and distribution/transportation data for components and materials that go into a final product (e.g., supply chain data), such as the circuit boards, motors, pumps, sheet metal, etc. that go into a kitchen dishwasher. In various embodiments, the data produced in this stage may be saved in a database or data structure.)
In analogous fields of invention, at the time the invention was filed, it would have been obvious for one of ordinary skill in the art to have modified the teachings of Saeed with the aforementioned teachings of Jabara, with a reasonable expectation of success if arriving at the claimed invention. One of ordinary skill in the art would have been motivated to make this modification to the teachings of Saeed with the motivation of, …employs relatively clean and environmentally friendly material manufacturing processes in its operations. In another embodiment, manufacturing process environmental impacts for the product can be estimated from published literature on similar products, and manufacturers can be encouraged to submit further information to improve accuracy where manufacturing impacts are a significant factor in the environmental assessment... to develop a product environmental rating system that has sufficient environmental attributes to characterize the full impact of products in a large number of product categories over the products' entire life cycle, with enough sensitivity to address the newly emerging consumer desire to understand and act based on small but meaningful environmental-impact differences between products. In addition, it is desirable to deploy systems and methods that allow for the mass screening of products and categories across multiple environmental attributes and to cover the entire environmental impact of the product lifecycle using a common methodology that does not heavily rely on data supplied by product manufacturers…., as recited in Jabara.
Although implied, Saeed in view of Jabara does not expressly disclose the following limitations, which however, are taught by Xu,
…emission exceeds the predetermined upper limit, receive an input of a reset of the predetermined upper limit… (in at least [0050] In operation 430, one or more operations of the deposition apparatus are adjusted based on a comparison between the contamination level and a predefined cleanliness requirement. In response to the contamination level being higher than the predefined cleanliness requirement, the adjustments can include removing contaminants from the thermal distributor 140 of processing module 102. [0051] after operation 430, the contamination level can be reset based on the adjustment of one or more operations in operation 430. For example, the contamination level can be reset to zero if the decontamination gas substantially removes the contaminants (e.g., the contaminants are completely removed by operation 430.) In some embodiments, the contamination level can be reset to a fraction of the original contamination level (e.g., the contaminants are partially removed by operation 430.))
In analogous fields of invention, at the time the invention was filed, it would have been obvious for one of ordinary skill in the art to have modified the teachings of Saeed in view of Jabara with the aforementioned teachings of Xu, with a reasonable expectation of success if arriving at the claimed invention. One of ordinary skill in the art would have been motivated to make this modification to the teachings of Saeed in view of Jabara with the motivation of, … there has been increasing demand for high yield of the deposition process for manufacturing semiconductor devices. To meet this demand, it is crucial to prevent deposition apparatus failures to ensure a reliable deposition process…An overall yield of manufacturing semiconductor devices depends not only on an accuracy of each fabrication process, but also on a cleanliness of the semiconductor manufacturing apparatuses.… to provide a mechanism to dynamically decontaminate the deposition apparatus, thus improving the production yield of the deposition apparatus with an efficient usage of the decontamination gas.… to improve production yield for a semiconductor device manufacturing process… The quality of semiconductor devices depends on the semiconductor manufacturing apparatuses' performance and ability to consistently achieve a high yield of operable semiconductor devices on semiconductor wafers… improving the production yield of the deposition apparatus with an efficient usage of the decontamination gas..., as recited in Xu.
As per Claim 2, Saeed teaches: (Previously presented) The manufacturing and sales planning support apparatus according to claim 1,
wherein the processor is configured to create a plurality of patterns of the manufactured quantity which is equal to or below the acquired demanded quantity, and (in at least [0017] At S120, one or more numerical factors to constrain are selected. These factors include, but are not limited to, a customer quantity, a specific amount of emissions, an economic value, fuel efficiency for a vehicle category, an amount of a resource (water, electricity, coal, etc.), an amount of a form of waste (scrap, rework, etc.) and so on. It should be noted that other types of factors may be constrained and that different factors may be defined depending on the industry or business. [0019] At S140, the user selects one or more categorical attributes. The categorical attributes define the level of granularity of decision making in the decision system. These attributes are predefined based on the numerical factor being constrained and may be, but are not limited to, a combination of customer region, a customer industry, a customer market, a sales order, a deliver-to destination, a product market segment, a product group, a product width, a product gauge, a sales person region, an asset, a plant, a production region, and so on. It should be noted that other types of categorical attributes may be defined by the user, the same attributes may be used in other attribute combinations (e.g., to cap global carbon dioxide emissions in additional to regional carbon dioxide emissions), different attributes may apply to different factors, and the attributes may be different depending on the industry or business. [0027] A first typically-mandatory attribute-based constraint is the demand constraint. The demand constraints ensure that the quantities stay within the specified minimum and maximum boundaries for each sales order. An exemplary set of demand constraints for a particular sales order may be in following form: minimum commitment≦sales order quantity≦maximum demand [0033] At S210, a set of constraints representing structural business factors, such as production flows and shipping is added to the mathematical model. It would be apparent to a person skilled in the art that other types of constraints are possible. The production flow constraint(s) ensure that the products are manufactured according to pre-specified steps. For example, in a paper company, products are created through a sequence of steps including fiber supply, digester, bleach plant, paper machine, finishing machine, and, finally, shipping of the products. (i.e. patterns) These relationships describe the structure of the production system and are not characterized by attribute-based constraints. The production flow may be modeled as described in U.S. application Ser. No. 11/860,473 (hereinafter the '473 application) assigned to common assignee and which is hereby incorporated herein in its entirety by this reference thereto. The shipping constraints ensure that the quantities of a product delivered equals the quantities shipped. These are structural constraints that ensure the proper product flows, and therefore they are not subject to the constraint-by-attributes approach.)
wherein by input of each of the supply chain patterns related to the created patterns of the manufactured quantity to the numerical model, the maximum profit and the volume of greenhouse gases emitted are calculated. (in at least [0014] It allows managers to make better strategic and tactical decisions regarding their business, thereby maximizing profit while satisfying constraints [0022] The constraint ensures that the sum of CO2 emissions for the production of all products on all machines in all plants in the US is less than or equal to the upper bound specified by the user (i.e. supply chain patterns) [0027] A first typically-mandatory attribute-based constraint is the demand constraint. The demand constraints ensure that the quantities stay within the specified minimum and maximum boundaries for each sales order. An exemplary set of demand constraints for a particular sales order may be in following form: minimum commitment≦sales order quantity≦maximum demand [0029] The attribute values are always specified for each attribute value combination. A “Lower Bound” represents the minimum commitment of the product to the customer at the deliver-to location. An “Upper Bound” represents the maximum demand of the product by the customer at the deliver-to location and is typically mandatory. [0032] At S180, once the attribute-based constraints are generated, an optimization process is carried out. Referring now to FIG. 2, step S180 is described in greater detail. At S205, a mathematical model is initialized with the constructed constraint groups. The attribute-based constraints already include the demand and capacity constraints which are part of any mathematical model of a typical business plan. [0033] At S210, a set of constraints representing structural business factors, such as production flows and shipping is added to the mathematical model. It would be apparent to a person skilled in the art that other types of constraints are possible. The production flow constraint(s) ensure that the products are manufactured according to pre-specified steps. For example, in a paper company, products are created through a sequence of steps including fiber supply, digester, bleach plant, paper machine, finishing machine, and, finally, shipping of the products. These relationships describe the structure of the production system and are not characterized by attribute-based constraints. The production flow may be modeled as described in U.S. application Ser. No. 11/860,473 (hereinafter the '473 application) assigned to common assignee and which is hereby incorporated herein in its entirety by this reference thereto. The shipping constraints ensure that the quantities of a product delivered equals the quantities shipped. These are structural constraints that ensure the proper product flows, and therefore they are not subject to the constraint-by-attributes approach. [0035] At S220, an objective function to measure profit is created. An exemplary and non-limiting objective function that uses cash contribution as a measure of the profit. [0036] a sales order represents the most granular unit of decision making available to the mathematical model's constraints, and the sum is over all sales orders. It should be noted that other objective functions can be used, such as revenue, quantity, profit, return on assets (ROA), and so on.)
As per Claim 3, Saeed teaches: (Previously presented) The manufacturing and sales planning support apparatus according to claim 1,
wherein the processor is configured to create, as the supply chain patterns, a plurality of patterns of the manufacturing location for the product or manufacturing equipment for the product at the manufacturing location. (in at least [0016] production data (e.g., asset capabilities, asset rates, routings, etc.). In an exemplary embodiment, the term “asset” refers to a machine, the term “asset capability” refers to the product types that each machine can produce, and the term “routing” refers to the sequence of assets used in production. [0019] These attributes are predefined based on the numerical factor being constrained and may be, but are not limited to, a combination of customer region, a customer industry, a customer market, a sales order, a deliver-to destination, a product market segment, a product group, a product width, a product gauge, a sales person region, an asset, a plant, a production region, and so on. It should be noted that other types of categorical attributes may be defined by the user, the same attributes may be used in other attribute combinations (e.g., to cap global carbon dioxide emissions in additional to regional carbon dioxide emissions), different attributes may apply to different factors, and the attributes may be different depending on the industry or business. (i.e. supply chain patterns) [0021] A company decides to restrict its carbon dioxide (CO2) emissions at its factories in the United States (US). The company already trades CO2 emissions credits in the European Union (EU) and it does not consider CO2 emissions in the Asia Pacific (AP) or South America (SA). [0033] At S210, a set of constraints representing structural business factors, such as production flows and shipping is added to the mathematical model. It would be apparent to a person skilled in the art that other types of constraints are possible. The production flow constraint(s) ensure that the products are manufactured according to pre-specified steps. For example, in a paper company, products are created through a sequence of steps including fiber supply, digester, bleach plant, paper machine, finishing machine, and, finally, shipping of the products. (i.e. patterns) These relationships describe the structure of the production system and are not characterized by attribute-based constraints. The production flow may be modeled as described in U.S. application Ser. No. 11/860,473 (hereinafter the '473 application) assigned to common assignee and which is hereby incorporated herein in its entirety by this reference thereto. The shipping constraints ensure that the quantities of a product delivered equals the quantities shipped. These are structural constraints that ensure the proper product flows, and therefore they are not subject to the constraint-by-attributes approach.)
As per Claim 4, Saeed teaches: (Previously presented)The manufacturing and sales planning support apparatus according to claim 1,
wherein the processor is configured to create, as the supply chain patterns, a plurality of patterns of a supplier of a material needed to manufacture the product. (in at least [0015] resource constraints (e.g., minimum material commitments, maximum material allocations) [0017] one or more numerical factors to constrain are selected. These factors include, but are not limited to, a customer quantity, a specific amount of emissions, an economic value, fuel efficiency for a vehicle category, an amount of a resource (water, electricity, coal, etc.), an amount of a form of waste (scrap, rework, etc.) (i.e. supply chain patterns) [0033] At S210, a set of constraints representing structural business factors, such as production flows and shipping is added to the mathematical model. It would be apparent to a person skilled in the art that other types of constraints are possible. The production flow constraint(s) ensure that the products are manufactured according to pre-specified steps. For example, in a paper company, products are created through a sequence of steps including fiber supply, digester, bleach plant, paper machine, finishing machine, and, finally, shipping of the products. (i.e. patterns) These relationships describe the structure of the production system and are not characterized by attribute-based constraints. The production flow may be modeled as described in U.S. application Ser. No. 11/860,473 (hereinafter the '473 application) assigned to common assignee and which is hereby incorporated herein in its entirety by this reference thereto. The shipping constraints ensure that the quantities of a product delivered equals the quantities shipped. These are structural constraints that ensure the proper product flows, and therefore they are not subject to the constraint-by-attributes approach [0044] insights apply to other decisions, such as with supplier negotiations of guaranteed quantities)
As per Claim 5, Saeed teaches: (Previously presented) The manufacturing and sales planning support apparatus according to claim 1,
wherein the supply chain patterns are a plurality of patterns of a route of transportation or means of transportation of the product or a material needed to manufacture the product. (in at least [0015] resource constraints (e.g., minimum material commitments, maximum material allocations) [0017] one or more numerical factors to constrain are selected. These factors include, but are not limited to, a customer quantity, a specific amount of emissions, an economic value, fuel efficiency for a vehicle category, an amount of a resource (water, electricity, coal, etc.), an amount of a form of waste (scrap, rework, etc.) [0016] financial data (e.g., production costs, shipping costs, labor costs, etc.) (i.e. supply chain patterns) [0033] At S210, a set of constraints representing structural business factors, such as production flows and shipping is added to the mathematical model. It would be apparent to a person skilled in the art that other types of constraints are possible. The production flow constraint(s) ensure that the products are manufactured according to pre-specified steps. For example, in a paper company, products are created through a sequence of steps including fiber supply, digester, bleach plant, paper machine, finishing machine, and, finally, shipping of the products.(i.e. pattern and transport flow of material fiber supply necessary to manufacture and ship paper product) These relationships describe the structure of the production system and are not characterized by attribute-based constraints. The production flow may be modeled as described in U.S. application Ser. No. 11/860,473 (hereinafter the '473 application) assigned to common assignee and which is hereby incorporated herein in its entirety by this reference thereto. The shipping constraints ensure that the quantities of a product delivered equals the quantities shipped. These are structural constraints that ensure the proper product flows, and therefore they are not subject to the constraint-by-attributes approach. [0039] The plan provides optimized sales order quantities, production quantities, production routings, and so on. The information is arranged in at least four different tables: an asset loading table, a route loading table, a sales order marginal value table, and an asset marginal value table. The information presented in each of these tables is described in greater in the '473 application.)
As per Claim 6, Saeed teaches: (Previously presented) The manufacturing and sales planning support apparatus according to claim 1, further comprising: a display coupled to the processor, ([0045][0046])
wherein the processor is configured to display information on the maximum profit calculated by the numerical model and display information on the processes for the manufacturing and sales of the product, the processes being inputted to the numerical model. (in at least [0014] highly efficient in organizations having a complex organizational structure, multiple interactive production flows, many products, many customers and the need to rapidly add, modify and assess attribute-based constraints. It allows managers to make better strategic and tactical decisions regarding their business, thereby maximizing profit while satisfying constraints. [0015] constraints above may include, but are not limited to: environmental constraints (e.g., emissions caps, minimum fuel efficiency, etc.), resource constraints (e.g., minimum material commitments, maximum material allocations), asset constraints (e.g., assets' capacities), market constraints such as those set by customers (e.g., minimum quantity for a set of products, maximum price for a set products, etc.), and economic constraints (e.g., revenue targets). These constraints can be grouped according to one or more attributes. For example, a limit on mercury emissions is typically summed across all production activities at a plant. A minimum fuel efficiency standard may be for passenger vehicles produced in the United Sates, with a weighted average of fuel efficiency by model, weighted by production volume by car model. A minimum guaranteed quantity to sell to a customer is summed across all delivery destinations (e.g., stores or warehouses) for a customer. A revenue target may be grouped by product and/or region where sales orders' product quantities that were sold are summed. In these examples, a plant, a country (e.g., United States), a customer, a product, and a region are attributes and their values are used for grouping. [0020] At S150, based on the selected categorical attributes, constraints by groups of attributes values (hereinafter “constraint groups”) are created. A constraint group is a combination of different categorical attributes' values of different attributes. In each constraint group, the factor's quantities with identical attribute values across a selected group of attributes are summed or averaged according to the selected calculation function and, optionally, a weighting factor. At S155 a table including lower and upper bounds for each constraint group is generated and displayed to the user. At S160, the user specifies the values of the lower and upper bounds. [0038] At S230, a linear programming optimization engine is executed to determine the best feasible values of the volumes that satisfy the constraints of the attribute-based constraints. That is, the optimization engine finds, using the collected business data, a set of values that satisfy the set of constraints created at S205 and S210 and at the same time maximize the cash contribution measured by the objective function created at S220. [0039] to FIG. 1, where at S190 an optimized business plan is output. The plan provides optimized sales order quantities, production quantities, production routings, and so on. The information is arranged in at least four different tables: an asset loading table, a route loading table, a sales order marginal value table, and an asset marginal value table. [0044] The output tables may be created for other attribute-based constraints and decisions may be taken accordingly. For example, the capacity constraints described discussed above indicates which assets limit profit, and therefore, should have their capacities increased or the minimum operating times decreased, as appropriate. The attribute-based carbon-dioxide and CAFE constraints can be useful for providing estimates of cost impacts (from the Marginal Values) when assessing new regulations or changes to regulations. These forms of insights apply to other decisions, such as with supplier negotiations of guaranteed quantities and the economic impacts of setting targets such as revenue growth.)
As per Claim 7-12 for a method (see at least Saeed [0016]), respectively, substantially recite the subject matter of Claim 1-6 and are rejected based on the same reasoning and rationale.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any extension fee pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the date of this final action.
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/PO HAN LEE/Examiner, Art Unit 3623
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