DETAILED ACTION
This communication is a Non-Final Office Action rejection on the merits. Claims 1-8 are currently pending and have been addressed below.
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Priority
Receipt is acknowledged of certified copies of papers required by 37 CFR 1.55.
Information Disclosure Statement (IDS)
The information disclosure statement(s) filed on 03/06/2024 comply with the provisions 37 CFR 1.97, 1.98, and MPEP 609 and is considered by the Examiner.
Election/Restrictions
Applicant’s election without traverse of claims 1-8 in the reply filed on 07/09/2026 is acknowledged.
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 1-8 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., an abstract idea) without reciting significantly more.
Independent Claim 1
Step One - First, pursuant to step 1 in the January 2019 Revised Patent Subject Matter Eligibility Guidance (“2019 PEG”) on 84 Fed. Reg. 53, the claim 1 is directed to a method which is a statutory category.
Step 2A, Prong One - Claim 1 recites: A management method for a manufacturing line, the management method comprising: obtaining a fluctuation characteristic including at least one of an arrival fluctuation characteristic of a lot to a process area, a capability fluctuation characteristic of the process area, or a stay fluctuation characteristic of the lot in the process area in a manufacturing line in which multiple process areas including the process area are arranged, the multiple process areas each including multiple resources; and obtaining inventory information regarding an inventory to be provided in the process area depending on the fluctuation characteristic. These claim elements are considered to be abstract ideas because they are directed to “mathematical concepts” which include “mathematical relationships.” The claim is directed to calculating fluctuation characteristics of different processes/steps in the manufacturing line. In this case, generating a distribution based a relationship between a fluctuation characteristic and inventory in each process/step is considered a mathematical relationship. If a claim limitation, under its broadest reasonable interpretation, covers mathematical relationships, then it falls within the “mathematical concepts” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
Step 2A Prong 2 - The judicial exception is not integrated into a practical application. Claim 1 does not recite any additional elements to consider under Step 2A – Prong Two. Specifically, there are no additional elements that may integrate the abstract idea into practical application by imposing meaningful limitations on practicing the abstract idea. Thus, claim 21 is directed to an abstract idea.
Step 2B - The claim does not include additional elements that are sufficient to amount significantly more than the judicial exception. Claim 1 does not include additional elements to consider under Step 2B and therefore viewed as a whole, the claim does not provide meaningful limitations to transform the abstract idea into a patent eligible application of the abstract idea such that the claims amount to significantly more than the abstract idea itself.
Dependent claims 2-8 are not directed to any additional claim elements. Rather, these claims offer further descriptive limitations of the abstract idea mentioned above - such as: obtaining the stay fluctuation characteristic of the lot in the process area on a basis of the arrival fluctuation characteristic of the lot to the process area and the capability fluctuation characteristic of the process area; and steps for determining an appropriate capacity of the inventory (e.g., depending on the average processing time and the occurrence probability of an operation loss). These processes are similar to the abstract idea noted in the independent claim because they further the limitations of the independent claim which are directed to “mathematical concepts” which include “mathematical relationships.” In addition, there are no additional elements to consider at Step 2A Prong 2 and Step 2B. Therefore, the claims still recite an abstract idea that can be grouped into “mathematical concepts.”
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1-4 are rejected under 35 U.S.C. 102(a)(1) as being anticipated Shi et al. (US 2020/0096981 A1).
Regarding claim 1, Shi et al. discloses a management method for a manufacturing line, the management method comprising (Paragraph 0002, The present disclosure relates to a system and method to control the manufacture process of semiconductor products, in particular, to a system and method that may improve the simulation accuracy of manufacture plants):
obtaining a fluctuation characteristic including at least one of an arrival fluctuation characteristic of a lot to a process area (Paragraph 0019, The manufacturing simulator is a software application implemented on a computer system, either as part of the MES or as an external software package to the MES, to mimic the operation of the manufacturing process in the manufacturing plant. The simulator can be a discrete-event driven simulation software, in which discrete events are generated and processed by a computer to produce a simulation result. For example, an event can be wafers or wafer lots arriving at a machine, wafers or wafer lots that start being processed by a machine, wafers or wafer lots that finish being processed by a machine, or wafers or wafer lots that exit a machine to go to a next machine, etc.), a capability fluctuation characteristic of the process area (Paragraph 0021, The simulator may use the capacity function C(M, S, P) which specifies the amount of time a particular machine M takes to process a particular step S of a particular product P. The simulator may use this information to decide how much time a particular machine needs to devote to a wafer or wafers or wafer lots and generate a production plan for the FAB; Paragraph 0022, The capacity function C(M, S, P) may be obtained from the specification of the machine, or may be obtained from the action taken, or may be estimated from the operating the machine performs, or may be measured from past production statistics, such as when a wafer enters the machine and when a wafer leaves the machine, and what is the time difference between two consecutive wafers leaving the machine, etc.; Paragraph 0028, The capacity function is used in the simulation to determine the number of wafers or wafer lots each machine may process per unit time. The elapsed time function is used in the simulation to determine the amount of time each wafer or wafer lot spend inside a machine; As stated in Paragraph 0040 of Applicant’s specification, the capability of the resource corresponds to a processing rate of a lot by the resource. In this case, Examiner notes that the “number of wafers or wafer lots each machine may process per unit time” is the same as the “processing rate”), or a stay fluctuation characteristic of the lot in the process area in a manufacturing line in which multiple process areas including the process area are arranged, the multiple process areas each including multiple resources (Paragraph 0027, Implementations of the disclosure may further introduce another variable W representing the WIP waiting in front of the machine, to form a WIP-dependent elapsed time function, which is the augmented elapsed time function: T(M, S, P, W). The function T(M, S, P, W) specifies the elapsed time of a wafer or a wafer lot of step S of product P by machine M, when the WIP waiting in front of the machine is W. For different W, T(M, S, P, W) may be different. If recipe R is used to uniquely identify the process, the augmented elapsed time function may be T(M, R, W), where M is the machine, R is the recipe, and W is the WIP waiting in front of M; Paragraph 0067, The simulation may include simulation of machines (or groups of machines) to process wafers (or wafer lots) through different steps. Implementations of the present disclosure provide a technical solution that includes a system and method to model the time taken by machines to process wafers or lots, at different steps to achieve the overall accurate simulation of the semiconductor manufacture plant, where the processing device is a hardware processor (“controller”) to simulate the operation of the semiconductor plants; Examiner interprets the “WIP” as the “stay fluctuation characteristic of the lot in the process area”);
and obtaining inventory information regarding an inventory to be provided in the process area depending on the fluctuation characteristic (Paragraph 0023, For example, machines tend to operate more efficiently when the number of wafers or wafer lots (referred to as work-in-process (WIP)) waiting before the machine is high based on a number of reasons. For example, when the WIP in front of a machine is high, the number of wafers available to the same step S and product P is high. Therefore, the machine may continuously process the same step S for product P for a longer time, before changing to a different step or a different product, thus saving the extra time normally required to adjust the machine when changing to a different step or a different product. For another example, in manual plants or semi-automatic plants, high WIP motivates the operators of the machines to work more efficiently, thus resulting in more efficient operation of the machines; Paragraph 0027, Implementations of the disclosure may further introduce another variable W representing the WIP waiting in front of the machine, to form a WIP-dependent elapsed time function, which is the augmented elapsed time function: T(M, S, P, W). The function T(M, S, P, W) specifies the elapsed time of a wafer or a wafer lot of step S of product P by machine M, when the WIP waiting in front of the machine is W. For different W, T(M, S, P, W) may be different. If recipe R is used to uniquely identify the process, the augmented elapsed time function may be T(M, R, W), where M is the machine, R is the recipe, and W is the WIP waiting in front of M; In this case, the inventory (e.g., WIP) may be high or low depending on the lots arriving to the machine and the processing time (e.g. time taken by the machine to process wafers or lots)).
Regarding claim 2, which is dependent of claim 1, Shi et al. discloses all the limitations in claim 1. Shi et al. further discloses wherein the obtaining the fluctuation characteristic includes: obtaining the stay fluctuation characteristic of the lot in the process area on a basis of the arrival fluctuation characteristic of the lot to the process area and the capability fluctuation characteristic of the process area (Paragraph 0019, The manufacturing simulator is a software application implemented on a computer system, either as part of the MES or as an external software package to the MES, to mimic the operation of the manufacturing process in the manufacturing plant. The simulator can be a discrete-event driven simulation software, in which discrete events are generated and processed by a computer to produce a simulation result. For example, an event can be wafers or wafer lots arriving at a machine, wafers or wafer lots that start being processed by a machine, wafers or wafer lots that finish being processed by a machine, or wafers or wafer lots that exit a machine to go to a next machine, etc.); Paragraph 0027, Implementations of the disclosure may further introduce another variable W representing the WIP waiting in front of the machine, to form a WIP-dependent elapsed time function, which is the augmented elapsed time function: T(M, S, P, W). The function T(M, S, P, W) specifies the elapsed time of a wafer or a wafer lot of step S of product P by machine M, when the WIP waiting in front of the machine is W. For different W, T(M, S, P, W) may be different. If recipe R is used to uniquely identify the process, the augmented elapsed time function may be T(M, R, W), where M is the machine, R is the recipe, and W is the WIP waiting in front of M; Paragraph 0067, The simulation may include simulation of machines (or groups of machines) to process wafers (or wafer lots) through different steps. Implementations of the present disclosure provide a technical solution that includes a system and method to model the time taken by machines to process wafers or lots, at different steps to achieve the overall accurate simulation of the semiconductor manufacture plant, where the processing device is a hardware processor (“controller”) to simulate the operation of the semiconductor plants; In this case, the inventory (e.g., WIP) may be high or low depending on the lots arriving to the machine and the processing time (e.g. time taken by the machine to process wafers or lots)).
Regarding claim 3, which is dependent of claim 2, Shi et al. discloses all the limitations in claim 2. Shi et al. further discloses wherein the obtaining the inventory information includes: obtaining an appropriate capacity of the inventory that enables the resources of the process area to continuously operate depending on the stay fluctuation characteristic (Paragraph 0023, In some implementations, the capacity functions are specified in terms of parameters characterizing the machines without taking into consideration other factors. However, in real applications, factors other than those characterizing the machines may also influence the efficiencies of the machine utilization. For example, machines tend to operate more efficiently when the number of wafers or wafer lots (referred to as work-in-process (WIP)) waiting before the machine is high based on a number of reasons. For example, when the WIP in front of a machine is high, the number of wafers available to the same step S and product P is high. Therefore, the machine may continuously process the same step S for product P for a longer time, before changing to a different step or a different product, thus saving the extra time normally required to adjust the machine when changing to a different step or a different product. For another example, in manual plants or semi-automatic plants, high WIP motivates the operators of the machines to work more efficiently, thus resulting in more efficient operation of the machines; Paragraph 0028, The capacity function is used in the simulation to determine the number of wafers or wafer lots each machine may process per unit time. The elapsed time function is used in the simulation to determine the amount of time each wafer or wafer lot spend inside a machine. The simulation may need both functions to mimic the machine behavior and the wafer or wafer lot behavior during processing; Examiner interprets “having enough WIP to continuously process the same step” as the “appropriate capacity.” Therefore, avoiding operation loss of resources since the machines are operating at a 100% utilization rate).
Regarding claim 4, which is dependent of claim 2, Shi et al. discloses all the limitations in claim 2. Shi et al. further discloses wherein the obtaining the inventory information includes: obtaining an appropriate capacity of the inventory with which an operation loss of resources of the process area becomes zero depending on the stay fluctuation characteristic (Paragraph 0011, Any operation of the FAB such as starting new wafers, turning off a machine to perform maintenance, selecting a certain product to process at a certain machine, etc., may cause costly mistakes. The mistakes can be that a wafer becomes defective, an expensive machine becomes idle, or a product completion time is delayed. To reduce the risk of causing costly mistakes in operating the FAB, the operation of the FAB is commonly simulated in advance before turning on the actual operation; Paragraph 0023, In some implementations, the capacity functions are specified in terms of parameters characterizing the machines without taking into consideration other factors. However, in real applications, factors other than those characterizing the machines may also influence the efficiencies of the machine utilization. For example, machines tend to operate more efficiently when the number of wafers or wafer lots (referred to as work-in-process (WIP)) waiting before the machine is high based on a number of reasons. For example, when the WIP in front of a machine is high, the number of wafers available to the same step S and product P is high. Therefore, the machine may continuously process the same step S for product P for a longer time, before changing to a different step or a different product, thus saving the extra time normally required to adjust the machine when changing to a different step or a different product. For another example, in manual plants or semi-automatic plants, high WIP motivates the operators of the machines to work more efficiently, thus resulting in more efficient operation of the machines; Paragraph 0028, The capacity function is used in the simulation to determine the number of wafers or wafer lots each machine may process per unit time. The elapsed time function is used in the simulation to determine the amount of time each wafer or wafer lot spend inside a machine. The simulation may need both functions to mimic the machine behavior and the wafer or wafer lot behavior during processing; Examiner interprets “having enough WIP to continuously process the same step” as the “appropriate capacity.” Therefore, avoiding operation loss of resources since the machines are operating at a 100% utilization rate).
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
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:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 5-6 are rejected under 35 U.S.C. 103 as being unpatentable over Shi et al. (US 2020/0096981 A1), in view of Xenos et al. (US 2025/0085698 A1).
Regarding claim 5, which is dependent of claim 2, Shi et al. discloses all the limitations in claim 2. Shi et al. further discloses wherein the obtaining an appropriate capacity of the inventory includes: obtaining an average processing time of resources in the process area on a basis of the capability fluctuation characteristic (Paragraph 0061, The capacity may be computed from historical information. In one implementation, historical event may be provided as a number of (TIME, LOTID, PRODUCTID, STEP, EQPID, EVENT) lines, where the fields may not necessarily be in the order and the fields may not necessarily use these names, where TIME is time the event happens, LOTID is a unique ID for a lot involved in the event, PRODUCTID is a unique product ID for the lot, STEP is the unique step ID of the process, EQPID is the unique ID for the equipment (machine) that processes the lot, and EVENT maybe “process_start” or “process_end”; Paragraph 0063, If a machine has an internal pipeline, the time taken by the machine M to process LOTID may be defined as the minimum of {T.sub.1, T.sub.2}, where T.sub.1 is the time between “process_start” and “process_end” of the LOTID as described above. Value T.sub.2 is the time difference between “process_end” of the LOTID, with EQPID=M, PRODUCTID=P and STEP=S, and the most recent “process_end” of a lot. Averaging over all instances of LOTID of the same M, S, and P in the history file provides the capacity function C(M, S, P));
obtaining an occurrence … of an operation loss of the resources of the process area on a basis of the stay fluctuation characteristic (Paragraph 0011, Any operation of the FAB such as starting new wafers, turning off a machine to perform maintenance, selecting a certain product to process at a certain machine, etc., may cause costly mistakes. The mistakes can be that a wafer becomes defective, an expensive machine becomes idle, or a product completion time is delayed. To reduce the risk of causing costly mistakes in operating the FAB, the operation of the FAB is commonly simulated in advance before turning on the actual operation. A simulator may be implemented on a computer system to mimic the operation of the FAB. The simulation may help identify issues in operating the FAB, thus improving the operational efficiency and avoiding mistakes. The simulation may include simulation of the machine operations in the FAB; Paragraph 0065, The capacity may be computed by summing all time when a machine is “busy” or “processing” from the history file, over a time period, and then dividing the time by the number of wafers or lots processed the machine during the same time period; Paragraph 0069, The simulation can be flow-like computer simulation, in which flows of wafers are fed into group of machines to generate the outcome. In this application, a certain amount of WIPs are delivered to a machine group, and after a certain time period, some WIPs are processed and move to the next machine group; Paragraph 0070, At 304, the processing device may further generate a production plan for the plant based on the improved simulation results);
and determining an appropriate capacity of the inventory depending on the average processing time … (Paragraph 0063, If a machine has an internal pipeline, the time taken by the machine M to process LOTID may be defined as the minimum of {T.sub.1, T.sub.2}, where T.sub.1 is the time between “process_start” and “process_end” of the LOTID as described above. Value T.sub.2 is the time difference between “process_end” of the LOTID, with EQPID=M, PRODUCTID=P and STEP=S, and the most recent “process_end” of a lot. Averaging over all instances of LOTID of the same M, S, and P in the history file provides the capacity function C(M, S, P); Paragraph 0065, The capacity may be computed by summing all time when a machine is “busy” or “processing” from the history file, over a time period, and then dividing the time by the number of wafers or lots processed the machine during the same time period).
Although Shi et al. discloses simulating an operation of the manufacturing line in order to obtain an occurrence of an operation loss of the resources of the process area on a basis of the stay fluctuation characteristic (e.g., performing a simulation to identify issues such as idle machines and/or delays in the process based on the fluctuation characteristic), Shi et al. does not specifically disclose an occurrence probability of an operation loss.
However, Xenos et al. discloses wherein the obtaining an appropriate capacity of the inventory includes: obtaining an average processing time of resources in the process area on a basis of the capability fluctuation characteristic; obtaining an occurrence probability of an operation loss of the resources of the process area on a basis of the stay fluctuation characteristic; and determining an appropriate capacity of the inventory depending on the average processing time and the occurrence probability (Paragraph 0011, By conducting simulations for each of the manufacturing processes, the various constraints associated to each sequence of manufacturing steps can be assessed to determine the possibility or probability of a real-world breach in the form of a manufacturing or process defect. Based on the results of the simulations, manufacturing scheduling data can be generated which can be used provide an optimal ordered set of tasks per machine which ensure that predicted violations of constraints are minimized; Paragraph 0012, Accordingly, the resulting generated schedules can reduce the probability of scrap/long cycle times and can keep the probability of scrap/breaching constraints within user-defined acceptable values; Paragraph 0034, By generating a schedule having performed one or more simulations of the outcomes to determine whether any options for the schedule result in a substantially better outcome (for example measured against a set of constraints/objectives), the output schedule can allow for more optimal use of resources in a manufacturing facility or other facility (such as a wafer fabrication plant) having multiple (identical and/or different) machines and also multiple output products that can be produced. In other embodiments, performing simulations allows for identification of breaches of any constraints, such as time link constraints, that would result in lots failing to meet quality control requirements and this allows for removal or deprioritising of lots when preparing scheduling for manufacturing tools/machines; Paragraph 0138, The multi-machine model 225 takes the lots input from the lot release process 220 and prepares a manufacturing schedule for the lots. In one embodiment, the multi-machine model 225 prepares a schedule using a heuristic scheduler (also referred to as an iterative simulation). The heuristic scheduler considers multiple future steps, calculates the total waiting time of a lot for its next steps and calculates a score indicative of how much bottleneck is anticipated for its next steps. Then, the weights used for scheduling are modified iteratively to force the lot to follow a different route (e.g. using different machines, or performing steps with different start times or finish times or in different orders where possible) or to reprioritise a lot compared to other lots to either benefit that lot or the other lot(s). The total objective function is then evaluated in each iteration to determine if a better average cycle time across the lots from the lot release is achieved. The final output is a schedule for all of the input lots, across all manufacturing tools/machines/resources in the factory/fabrication plant; Paragraph 0149, In an embodiment deployed in a wafer fabrication facility, a number (n) of fab-state scenarios are created using Monte-Carlo methods from the distributions of: processing times; transfer times; time to failure (of machines in the fab) and time to recovery (of machines in the fab); Paragraph 0184, Time link constraints endorse the necessity for a global fab scheduler (or multi-machine scheduler) as described in this embodiment and in other aspects and embodiments in this specification. These production constraints mean that toolsets become tightly coupled and must be optimised as a single entity. Without doing so, the work in progress (WIP) flow cannot be controlled well and we may end up creating bottlenecks at downstream tools, thus resulting in time link violations due to a queue build-up; Paragraph 0185, For example, maximising throughput at the upstream toolset may cause too much WIP to arrive at the downstream toolset to process before the time link expires; Paragraph 0186, On one hand, the WIP needs to flow freely through downstream toolsets with minimal queuing however on the other hand, operators cannot afford to be overly conservative and stifle throughput as a result of keeping downstream tools unnecessarily idle waiting for transiting lots. This problem enforces the need for so-called “global” or “multi-machine” scheduling of the fab that considers upstream and downstream toolsets simultaneously; Paragraph 0231, Optionally guidance data/input 1030 is provided along with the fab-wide schedule. The optional guidance data/input 1030 can include WIP targets; priorities; due dates; release data and other relevant data; Examiner interprets “having a WIP that doesn’t breach any constraints” as the “appropriate capacity.” Therefore, avoiding operation loss of resources).
It would have been obvious to one ordinary skill in the art before the effective filing date to modify the method for management a manufacturing line, wherein an appropriate capacity of the inventory is determined based on an occurrence of an operation loss of the resources of the process area of the invention of Shi et al. to further incorporate an occurrence probability of an operation loss of the invention of Xenos et al. because doing so would allow the method to determine the possibility or probability of a real-world breach in the form of a manufacturing or process defect. Based on the results of the simulations, manufacturing scheduling data can be generated which can be used provide an optimal ordered set of tasks per machine which ensure that predicted violations of constraints are minimized (see Xenos et al., Paragraph 0011). Further, the claimed invention is merely a combination of old elements, and in combination each element would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Regarding claim 6, which is dependent of claim 5, the combination of Shi et al. and Xenos et al. discloses all the limitations in claim 5. Shi et al. further discloses wherein the determining the appropriate capacity of the inventory includes: obtaining a number of lots with which an operation rate of the resources in the process area becomes zero by using the average processing time and the occurrence …; and setting the appropriate capacity of the inventory to greater than or equal to the number of lots that has been obtained (Paragraph 0011, Any operation of the FAB such as starting new wafers, turning off a machine to perform maintenance, selecting a certain product to process at a certain machine, etc., may cause costly mistakes. The mistakes can be that a wafer becomes defective, an expensive machine becomes idle, or a product completion time is delayed. To reduce the risk of causing costly mistakes in operating the FAB, the operation of the FAB is commonly simulated in advance before turning on the actual operation. A simulator may be implemented on a computer system to mimic the operation of the FAB. The simulation may help identify issues in operating the FAB, thus improving the operational efficiency and avoiding mistakes. The simulation may include simulation of the machine operations in the FAB; Paragraph 0063, If a machine has an internal pipeline, the time taken by the machine M to process LOTID may be defined as the minimum of {T.sub.1, T.sub.2}, where T.sub.1 is the time between “process_start” and “process_end” of the LOTID as described above. Value T.sub.2 is the time difference between “process_end” of the LOTID, with EQPID=M, PRODUCTID=P and STEP=S, and the most recent “process_end” of a lot. Averaging over all instances of LOTID of the same M, S, and P in the history file provides the capacity function C(M, S, P); Paragraph 0065, The capacity may be computed by summing all time when a machine is “busy” or “processing” from the history file, over a time period, and then dividing the time by the number of wafers or lots processed the machine during the same time period; Paragraph 0065, The capacity may be computed by summing all time when a machine is “busy” or “processing” from the history file, over a time period, and then dividing the time by the number of wafers or lots processed the machine during the same time period; Paragraph 0069, The simulation can be flow-like computer simulation, in which flows of wafers are fed into group of machines to generate the outcome. In this application, a certain amount of WIPs are delivered to a machine group, and after a certain time period, some WIPs are processed and move to the next machine group; Paragraph 0070, At 304, the processing device may further generate a production plan for the plant based on the improved simulation results; Examiner interprets “having enough WIP to continuously process the same step” as the “appropriate capacity.” Therefore, avoiding operation loss of resources since the machines are operating at a 100% utilization rate).
Although Shi et al. discloses simulating an operation of the manufacturing line in order to obtain an occurrence of an operation loss of the resources of the process area on a basis of the stay fluctuation characteristic (e.g., performing a simulation to identify issues such as idle machines and/or delays in the process based on the fluctuation characteristic), Shi et al. does not specifically disclose an occurrence probability of an operation loss.
However, Xenos et al. discloses wherein the determining the appropriate capacity of the inventory includes: obtaining a number of lots with which an operation rate of the resources in the process area becomes zero by using the average processing time and the occurrence probability; and setting the appropriate capacity of the inventory to greater than or equal to the number of lots that has been obtained (Paragraph 0011, By conducting simulations for each of the manufacturing processes, the various constraints associated to each sequence of manufacturing steps can be assessed to determine the possibility or probability of a real-world breach in the form of a manufacturing or process defect. Based on the results of the simulations, manufacturing scheduling data can be generated which can be used provide an optimal ordered set of tasks per machine which ensure that predicted violations of constraints are minimized; Paragraph 0012, Accordingly, the resulting generated schedules can reduce the probability of scrap/long cycle times and can keep the probability of scrap/breaching constraints within user-defined acceptable values; Paragraph 0138, The multi-machine model 225 takes the lots input from the lot release process 220 and prepares a manufacturing schedule for the lots. In one embodiment, the multi-machine model 225 prepares a schedule using a heuristic scheduler (also referred to as an iterative simulation). The heuristic scheduler considers multiple future steps, calculates the total waiting time of a lot for its next steps and calculates a score indicative of how much bottleneck is anticipated for its next steps. Then, the weights used for scheduling are modified iteratively to force the lot to follow a different route (e.g. using different machines, or performing steps with different start times or finish times or in different orders where possible) or to reprioritise a lot compared to other lots to either benefit that lot or the other lot(s). The total objective function is then evaluated in each iteration to determine if a better average cycle time across the lots from the lot release is achieved. The final output is a schedule for all of the input lots, across all manufacturing tools/machines/resources in the factory/fabrication plant; Paragraph 0149, In an embodiment deployed in a wafer fabrication facility, a number (n) of fab-state scenarios are created using Monte-Carlo methods from the distributions of: processing times; transfer times; time to failure (of machines in the fab) and time to recovery (of machines in the fab); Paragraph 0184, Time link constraints endorse the necessity for a global fab scheduler (or multi-machine scheduler) as described in this embodiment and in other aspects and embodiments in this specification. These production constraints mean that toolsets become tightly coupled and must be optimised as a single entity. Without doing so, the work in progress (WIP) flow cannot be controlled well and we may end up creating bottlenecks at downstream tools, thus resulting in time link violations due to a queue build-up; Paragraph 0185, For example, maximising throughput at the upstream toolset may cause too much WIP to arrive at the downstream toolset to process before the time link expires; Paragraph 0186, On one hand, the WIP needs to flow freely through downstream toolsets with minimal queuing however on the other hand, operators cannot afford to be overly conservative and stifle throughput as a result of keeping downstream tools unnecessarily idle waiting for transiting lots. This problem enforces the need for so-called “global” or “multi-machine” scheduling of the fab that considers upstream and downstream toolsets simultaneously; Paragraph 0231, Optionally guidance data/input 1030 is provided along with the fab-wide schedule. The optional guidance data/input 1030 can include WIP targets; priorities; due dates; release data and other relevant data; Examiner interprets “having a WIP that doesn’t breach any constraints” as the “appropriate capacity.” Therefore, avoiding operation loss of resources).
It would have been obvious to one ordinary skill in the art before the effective filing date to modify the method for management a manufacturing line, wherein an appropriate capacity of the inventory is determined based on an occurrence of an operation loss of the resources of the process area of the invention of Shi et al. to further incorporate an occurrence probability of an operation loss of the invention of Xenos et al. because doing so would allow the method to determine the possibility or probability of a real-world breach in the form of a manufacturing or process defect. Based on the results of the simulations, manufacturing scheduling data can be generated which can be used provide an optimal ordered set of tasks per machine which ensure that predicted violations of constraints are minimized (see Xenos et al., Paragraph 0011). Further, the claimed invention is merely a combination of old elements, and in combination each element would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Claims 7-8 are rejected under 35 U.S.C. 103 as being unpatentable over Shi et al. (US 2020/0096981 A1), in view of Xenos et al. (US 2025/0085698 A1), in further view of Kuninobu et al. (US 2016/0033961 A1) and Knicker et al. (US 2022/0244713 A1).
Regarding claim 7, which is dependent of claim 5, the combination of Shi et al. and Xenos et al. discloses all the limitations in claim 5. Although the combination of Shi et al. and Xenos et al. discloses an occurrence probability of the operation loss of the resources of the process area on a basis of the stay fluctuation characteristic (see Xenos et al., Paragraph 0011, determine the possibility or probability of a real-world breach in the form of a manufacturing or process defect), the combination of Shi et al. and Xenos et al. does not specifically disclose how the stay fluctuation characteristics is calculated.
However, Kuninobu et al. discloses wherein the obtaining the fluctuation characteristic includes: obtaining a distribution of occurrence probabilities of a number of staying lots; … (Paragraph 0030, The state probability distribution P(k) represents a probability that the number of semi-finished products within the production line is k; Paragraph 0031, Herein, the probability distribution P(w) is calculated by calculating probability distributions P.sub.n(w) of the respective processes and adding up the distributions thus calculated across all of the processes).
It would have been obvious to one ordinary skill in the art before the effective filing date to modify the method for management a manufacturing line, wherein an occurrence probability of the operation loss of the resources of the process area on a basis of the stay fluctuation characteristic of the invention of Shi et al. and Xenos et al. to further specify steps of how the stay fluctuation characteristics is calculated of the invention of Kuninobu et al. because doing so would allow the method to calculate probability distribution of the respective processes (see Kuninobu et al., Paragraph 0030). Further, the claimed invention is merely a combination of old elements, and in combination each element would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Although the combination of Shi et al., Xenos et al., and Kuninobu et al. discloses obtaining a fluctuation characteristic (e.g., by obtaining a distribution of occurrence probabilities of a number of staying lots), the combination of Shi et al., Xenos et al., and Kuninobu et al. does not specifically disclose extrapolating the distribution to a side where the number of staying lots is negative.
However, Knicker et al. discloses wherein the obtaining the fluctuation characteristic includes: obtaining a distribution of occurrence probabilities of a …; extrapolating the distribution to a side where the number of staying lots is negative; and extracting a feature of the distribution that has been extrapolated, and the obtaining the occurrence probability … includes: integrating a portion where the … in the distribution that has been extrapolated is negative (Paragraph 0170, FIG. 6 shows an x-axis 600 for the sample values x. A y-axis is not shown but relates to the frequency of the respective sample values. Normal probability distribution function 602 (density function), PDF approximates the histogram of a sample of drug delivery devices 100 a mentioned in the description of FIG. 3 or of other devices. A truncation threshold 604 corresponds to a technical limit TL, for instance to value zero, see also FIG. 3. An area 606 corresponds to a value of cumulative distribution function F at cutoff value ξ.sub.L or truncation value ξ.sub.L. An area 608 corresponds to the remainder of cumulative distribution function F if the truncated area 606 is subtracted, i.e., to the value 1−F(ξ.sub.L, p)).
It would have been obvious to one ordinary skill in the art before the effective filing date to modify the method for management a manufacturing line, wherein an occurrence probability of the operation loss of the resources of the process area on a basis of the stay fluctuation characteristic of the invention of Shi et al., Xenos et al., and Kuninobu et al. to further specify steps of how the stay fluctuation characteristics is calculated of the invention of Knicker et al. because doing so would allow the method to truncate values of a probability density fucntion based on a truncation limit, for instance to value zero (see Knicker et al., Paragraph 0170). Further, the claimed invention is merely a combination of old elements, and in combination each element would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Regarding claim 8, which is dependent of claim 7, the combination of Shi et al., Xenos et al., Kuninobu et al., and Knicker et al. discloses all the limitations in claim 5. Although the combination of Shi et al., Xenos et al., and Knicker et al. discloses a portion where the number of staying lots is negative in the distribution that has been extrapolated (see Knicker et al., Paragraph 0170, a truncation threshold), the combination of Shi et al., Xenos et al., and Knicker et al. does not specifically disclose wherein the distribution includes a fluctuation coefficient.
However, Kuninobu et al. further discloses wherein the extracting the feature includes: obtaining a fluctuation coefficient of the distribution, and the integrating includes: integrating … in the distribution … using the fluctuation coefficient (Paragraph 0028, The blocking probability calculator 14 is connected to the process sequence configuration data storage 11, the machine capability data storage 12 and the arrival data storage 13. The blocking probability calculator 14 obtains, based on data of these storages, P.sub.B(k) as a relationship between k and the blocking probability. The blocking probability calculator 14, with the WIP upper bound being set, calculates the coefficient of variation (the statistical dispersion) to the arrival interval of the lots arriving at each process. Different functions corresponding to each region of the WIP upper bound value are calculated by making use of the coefficient of variation, thereby calculating a relationship between the WIP upper bound and the blocking probability per region. I; As stated in Paragraph 0168 of Applicant’s specification, the fluctuation coefficient may be an arrival fluctuation coefficient).
It would have been obvious to one ordinary skill in the art before the effective filing date to modify the method for management a manufacturing line, wherein an occurrence probability of the operation loss of the resources of the process area on a basis of the stay fluctuation characteristic of the invention of Shi et al. and Xenos et al. to further specify steps of how the stay fluctuation characteristics is calculated of the invention of Kuninobu et al. because doing so would allow the method to calculate probability distribution of the respective processes based on a coefficient of variation to the arrival interval of the lots arriving at each process (see Kuninobu et al., Paragraphs 0028-0030). Further, the claimed invention is merely a combination of old elements, and in combination each element would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant’s disclosure.
Yang et al. (US 2013/0346024 A1) – discloses computing quantities of the historical WIPs appearing in the respective intervals in accordance with the output times of the historical WIPs recorded in each set of historical WIP data, thereby obtaining a plurality of output probability density data series regarding the product generated in the respective historical periods. In general, the frequency of WIPs regarding the product appearing in the respective intervals of each historical period is shown as a histogram (see at least Paragraphs 0024-0025).
國信 茂太 (JP 6290029 B2) – discloses device capability data includes, for example, the average lot processing time of the device in each process and its variation coefficient. The coefficient of variation is a value obtained by dividing the standard deviation by the average. Since the standard deviation is the positive square root of the variance, the standard deviation or variance may be used instead of the coefficient of variation. The coefficient of variation, standard deviation, or variance is an example of an index representing variation, and may be collectively referred to as a variation value (see at least Description).
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/MARJORIE PUJOLS-CRUZ/Examiner, Art Unit 3624
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