DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claims 1-20 are pending.
Response to Amendments
The amendment filed April 9th, 2026 has been entered. Claims 1- 20 remain pending in the application.
Response to Arguments
Applicant’s arguments filed 04/09/2026 have been fully considered but they are not persuasive.
Applicant’ arguments on page 10, applicant argues “In rejecting claims 1 and 10, the Office action cites to paragraph [0065] of Sobalvarro as allegedly disclosing "calculating a number of the plurality of transport units arranged on the transport path, such that operation rates of the plurality of transport units are within a designated range." As the Office action notes, however, paragraph [0065] of Sobalvarro relates to an optimizing the travel path length and congestion of an intelligent mobile robot (IMR). Optimizing path length and congestion of mobile robots, however, is not the same as "calculating a number of the plurality of transport units arranged on the transport path, such that an operation rate of the plurality of transport units is within a designated range, wherein the operation rates are defined as a time during which the transport units operate for a designated time period." Indeed, Sobalvarro appears to be entirely silent regarding operation rates being within a designated range, where operations are a time during which the transport units operate for a designated time period. Thus, Sobalvarro does not appear to disclose, or even suggest, each of the features of claims 1 and 10”.
Examiner respectfully disagrees because SOBALVARRO teaches optimizing factory scheduling that allocates tasks in time for each product unit’s task sequence (see Paragraph [0066]), and wherein scheduling optimization is bounded by rules as described in Paragraphs [0067-0088], which include preventing overlapping tasks in a workcell, assigning tasks to a workcell when it is idle, task being scheduled when resources are scheduled to arrive at a workcell, whether IMR is available to transport a WIP, IMR being assigned pick up and drop off WIP, task starting after arrival of IMR, and ascertaining that the paths of IMRs are not intersected. Therefore, examiner interpreted scheduling of tasks, and scheduling of transportation as calculating a number of transport units arranged on the transport path, such that the operation rates of transport units, that being a time during which the transport units operate for a designated time period, is within a designated range.
Claim Rejections - 35 USC § 102
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)(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 1-20 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by SOBALVARRO et al. USPGPUB 2022/0121181 (hereinafter “SOBALVARRO”).
Regarding claim 1, SOBALVARRO teaches an apparatus for manufacturing a display device (Paragraph [0013] “the invention pertains to a method of scheduling, for execution on a factory floor having a layout, a plurality of interrelated manufacturing processes”, wherein examiner interpreted manufacturing processes as including manufacturing for a display device), the apparatus comprising:
a plurality of processing lines comprising a plurality of processing units (Paragraph [0013] “Accordingly, in a first aspect, the invention pertains to a method of scheduling, for execution on a factory floor having a layout, a plurality of interrelated manufacturing processes”, and Paragraph [0020], and Paragraph [0043] “The simulations performed in accordance herewith utilize “factory objects” to represent the various human and machine elements actively involved in the process to be optimized”, wherein examiner interpreted executing manufacturing processes on the factory floor as including a plurality of processing lines comprising plurality of processing units, wherein examiner interpreted factory objects as a plurality of processing units);
a transport line between the plurality of processing lines, the transport line comprising a plurality of transport units configured to move along a transport path to take in or take out transport goods to or from the plurality of processing units ([TABLE 1] describes mobile transport machine/robot that moves raw material or workpiece from point to point as it is used to perform various manufacturing processes as described in TABLE 1, wherein examiner interpreted mobile transport machine/robot moving material or workpiece to other manufacturing processes as a transport line between the plurality of processing lines, and the mobile transport machine/robot as plurality of transport units that is configured to move along a transport path to take in or take out transport goods to or from the plurality of processing units); and
a controller configured to control the plurality of transport units (Paragraph [0012], Paragraph [0081-0088], Paragraph [0105] “The single processing unit serves as a supervisory controller, which keeps track of the states of the IMRs, computes trajectories for the fleet, and prevents collisions”, wherein examiner interpreted supervisory controller keeping track of states of IMRs, computes trajectories for the fleet, and prevents collisions as a controller configured to control the plurality of transport units),
wherein the controller comprises a transport line simulator configured to simulate a transport line distribution model for distributing the plurality of transport units (Paragraph [0013] “the method comprises the steps of (a) storing, in a computer memory, a plurality of factory object data structures each corresponding to a machine or a human and containing data and/or instructions for simulating behavior in (i) carrying out a factory task, (ii) any allowed movement about the manufacturing floor layout, (iii) speed of operation and movement based on inherent characteristics of the machine or the human and proximity thereof to other objects, the object data structures including parameter values constraining the simulated behavior; (b) receiving, by a computational simulator, a work order specifying factory inputs and outputs and an objective function specifying a quantity to be maximized; and (c) based on the work order, simulating, using the computational simulator and the stored object data structures, operation of the factory”, and Paragraph [0063] “Simulation of thousands of possible workcell layouts using standard optimization algorithms (such as Coffman-Graham or Turán's brick factory algorithm) indicates the layout in FIG. 5 to be one of the least efficient under the metric of (for example) total IMR travel time or minimization of expected IMR collisions”, wherein examiner interpreted computer that is used to simulate movement of factory objects including layout and IMR as a controller comprising a transport line simulator configured to simulate a transport line distribution model for distributing the plurality of transport units, and wherein examiner interpreted simulating layouts to minimize IMR travel time and IMR collisions as distributing the plurality of transport units), by calculating a number of the plurality of transport units arranged on the transport path, such that an operation rate of the plurality of transport units is within a designated range (Paragraph [0065] “The simulation reveals that the layout shown in FIG. 10 is close to optimal under the constraint of minimizing IMR travel and potential collisions (the path length is 148 and the congestion penalty is 15). This layout is similar to that of FIG. 5, but the workcells are aligned in a manner similar to that shown in FIG. 9. This exploits the precedence graph and creates a structure that reduces IMR congestion and IMR travel time”, Paragraph [0011], “The optimization functions used to evaluate the performance of the layout design when the factory operates with specific scheduling and routing algorithms may reflect multiple and possibly conflicting goals. These goals can be classified into two different groups: maximization and minimization. The first group may comprise maximization goals including performance, revenue, system reliability (e.g., number of redundant workcells, backup workcells), resource utilization (e.g., workcell throughput, transportation utilization), and flexibility (e.g., production of multiple product variations)”, and Paragraph [0012] “The second group may comprise minimization goals including operational costs related to material handling and energy consumption, upfront investment (e.g., fewer machines/equipment/workers), inventory/storage requirements, production/assembly time for each different product variation (the combination of workcell sequences), path distance for IMRs for item pickup and delivery, IMR (intelligent mobile robot) travel time (assuming travel safety is guaranteed) for item pickup and delivery, the number of transportation units, the number of workers, number of unserved requests, the average and maximum number of simultaneous requests, work-in-progress waiting time, work-in-progress line length across the factory, and traffic congestion; and for multiple deliveries/requests from same transportation unit, minimizing the time the last destination is served (makespan) and minimizing the total completion time (latency)”, wherein examiner interpreted simulation revealing layout that is close to optimal for minimizing travel and potential collisions of IMR as calculating number of plurality of transport units arranged on the transport path, such that operation rates of the plurality of transport units are within a designated range, wherein examiner interpreted optimization of the layout by maximizing and minimizing goals as operation rates of the plurality of transport units being within a designated range), wherein the operation rates are defined as a time during which the transport units operate for a designated time period (Paragraph [0040] “The scheduling subsystem 155 takes the optimized layout produced by the layout subsystem 150, or a fixed layout provided by the user, and produces an optimized schedule. The layout and scheduling subsystems 150, 155 can work in tandem (rather than sequentially) to produce an optimized layout and schedule; in this way, scheduling considerations can influence layout optimization”, Paragraph [0066] “A schedule is built by allocating tasks in time for each product unit's task sequence. Hence, scheduling is layout-dependent and scheduling optimization generally follows layout optimization. This need not be the case, however; the processes can be combined, with scheduling optimization applied to intermediate layout candidates. For example, if the objective function maximizes throughput, one approach to optimization is to derive an optimal schedule as described below for a candidate layout and modify the layout in a manner likely to improve the schedule. For example, certain parameters (e.g., the workcell orientation) of the candidate layout may be varied randomly and the schedule simulation re-run and scored. If scheduling improves, the revised layout is retained”, Paragraphs [0067-0088], Paragraphs [0108-0110], and Paragraph [0012-0013], wherein examiner interpreted creating an optimized layout and schedule, wherein the schedule includes allocated tasks for each product unit’s task sequence as time during which the transport units operate for a designated time period, wherein the schedule includes scheduling tasks, and transportation of IMR, and the time it takes for IMR to arrive at its destination, wherein examiner interpreted an optimized schedule as operation rate being a time where transport unit operate for a designated time period).
Regarding claim 2, SOBALVARRO teaches wherein the transport line simulator comprises: a transport line distribution model generator configured to generate an initial transport line distribution model (Paragraph [0046] “In step 210, the layout subsystem selects a factory layout template or an initial set of factory objects from the template store 120. In the latter case, the layout subsystem 150 may define the set of all possible layouts based on the selected factory objects. One of these may be chosen (step 220) as the current layout, either at random or based on initial application of the provided constraints; this may involve selection of attribute values of the factory objects within their allowed ranges. The performance of the current layout is simulated, and the results of the simulation are evaluated (step 230) based on the objective function. Another layout is chosen, or the current layout is modified, based on an optimization algorithm”, and Paragraph [0063] “Simulation of thousands of possible workcell layouts using standard optimization algorithms (such as Coffman-Graham or Turán's brick factory algorithm) indicates the layout in FIG. 5 to be one of the least efficient under the metric of (for example) total IMR travel time or minimization of expected IMR collisions”, wherein examiner interpreted selecting and simulating layout of factory objects including simulating to minimize IMR travel time or minimizing IMR collisions as a transport line distribution model generator configured to generate an initial transport line distribution model, and Paragraphs [0060-0065]); and
a transport line distribution line distribution model modifier configured to modify the initial transport line distribution model to a first-order modified transport line distribution model (Paragraph [0063] “Simulation of thousands of possible workcell layouts using standard optimization algorithms (such as Coffman-Graham or Turán's brick factory algorithm) indicates the layout in FIG. 5 to be one of the least efficient under the metric of (for example) total IMR travel time or minimization of expected IMR collisions”, wherein examiner interpreted simulations of thousands of work cell layouts to minimize IMR travel time or minimization of expected IMR collisions, wherein Paragraphs [0064-0065] describes other layouts simulated as modifying the initial transport line distribution model to a first-order modified transport line distribution model).
Regarding claim 3, SOBALVARRO teaches wherein the transport line distribution model generator is configured to generate the initial transport line distribution model based on an operation plan of the processing lines (Paragraph [0046] “The flowchart 200 shown in FIG. 2 illustrates the operation of a representative layout subsystem 150. In a first step 210, a work order is received. The work order specifies the constraints and requirements 140, including the operations to be performed and the objective function to be maximized (e.g., throughput) or minimized (e.g., power consumption or emissions). In step 210, the layout subsystem selects a factory layout template or an initial set of factory objects from the template store 120. In the latter case, the layout subsystem 150 may define the set of all possible layouts based on the selected factory objects. One of these may be chosen (step 220) as the current layout, either at random or based on initial application of the provided constraints; this may involve selection of attribute values of the factory objects within their allowed ranges. The performance of the current layout is simulated, and the results of the simulation are evaluated (step 230) based on the objective function”, wherein examiner interpreted the work order specifying constraints and requirements, including operations to be performed and the object function and simulating layout based on the objective function as transport line distribution model generator is configured to generate the initial transport line distribution model based on an operation plan of the processing lines, wherein examiner interpreted work order including constraints and requirements, and the objective function to be maximized or minimized as operation plan of the processing lines).
Regarding claim 4, SOBALVARRO teaches wherein the transport line distribution model modifier is configured to remodify the first-order modified transport line distribution model to a second-order modified transport line distribution model (Paragraph [0063] “Simulation of thousands of possible workcell layouts using standard optimization algorithms (such as Coffman-Graham or Turán's brick factory algorithm) indicates the layout in FIG. 5 to be one of the least efficient under the metric of (for example) total IMR travel time or minimization of expected IMR collisions”, Paragraph [0064] “Another layout attempted by the simulation, illustrated in FIG. 9, shows the workcells laid out in a 3×3 arrangement. The rows of workcells are aligned entrance-to-exit, providing a natural structure for the IMRs to use for “out-of-order” tasks without creating significant congestion”, and Paragraph [0065] “The simulation reveals that the layout shown in FIG. 10 is close to optimal under the constraint of minimizing IMR travel and potential collisions (the path length is 148 and the congestion penalty is 15)”, wherein examiner interpreted simulating various different workcell layouts to find the optimal layout to minimize IMR travel time and collisions as the transport line distribution model modifier configured to remodify the first-order modified transport line distribution model to a second-order modified transport line distribution model, wherein the various simulations are interpreted as modifying first-order modified model to a second-order modified model).
Regarding claim 5, SOBALVARRO teaches wherein the transport line includes a plurality of transport lines (Paragraphs [0063-0065], FIGS. 8-10 shows various transport lines), and
the controller further comprises a process simulator configured to simulate a process distribution model for distributing the plurality of transport units between the plurality of transport lines (Paragraph [0013] “the method comprises the steps of (a) storing, in a computer memory, a plurality of factory object data structures each corresponding to a machine or a human and containing data and/or instructions for simulating behavior in (i) carrying out a factory task, (ii) any allowed movement about the manufacturing floor layout, (iii) speed of operation and movement based on inherent characteristics of the machine or the human and proximity thereof to other objects, the object data structures including parameter values constraining the simulated behavior; (b) receiving, by a computational simulator, a work order specifying factory inputs and outputs and an objective function specifying a quantity to be maximized; and (c) based on the work order, simulating, using the computational simulator and the stored object data structures, operation of the factory”, Paragraph [0059] “Once a precedence graph has been established, the groups are mapped to workcell objects corresponding to physical workcells with dimensions and locations on the factory floor. Suppose that the parts to be assembled are brought into the factory (as represented by a factory object, with attributes relating to workspace area, available utilities, physical dimensions, etc.) from the left and leave the factory, assembled, on the right as shown in FIG. 4. At the bottom of the factory is space for IMRs to queue. Each of the IMRs is represented by an IMR object with attributes specifying speed, payload, geometric format, battery life, etc.”, Paragraph [0061] “The next step is to optimize the layout of workcells. Assume that the optimal number of workcells is represented by FIG. 5. Optimizing the layout means accounting for optimal IMR motion—minimizing, for example, total cycle time or IMR total travel. Optimization can exploit the flexible order among tasks and minimize IMR congestion among the workcells in order to optimize the overall efficiency of the factory”, Paragraph [0063] “Simulation of thousands of possible workcell layouts using standard optimization algorithms (such as Coffman-Graham or Turán's brick factory algorithm) indicates the layout in FIG. 5 to be one of the least efficient under the metric of (for example) total IMR travel time or minimization of expected IMR collisions”, wherein examiner interpreted workcell layouts being simulated using a computer to minimize IMR travel time and IMR collisions as controller further comprises a process simulator configured to simulate a process distribution model for distributing the plurality of transport units between the plurality of transport lines).
Regarding claim 6, SOBALVARRO teaches wherein the process simulator comprises: a process distribution model generator configured to generate an initial process distribution model (Paragraph [0046] “In step 210, the layout subsystem selects a factory layout template or an initial set of factory objects from the template store 120. In the latter case, the layout subsystem 150 may define the set of all possible layouts based on the selected factory objects. One of these may be chosen (step 220) as the current layout, either at random or based on initial application of the provided constraints; this may involve selection of attribute values of the factory objects within their allowed ranges. The performance of the current layout is simulated, and the results of the simulation are evaluated (step 230) based on the objective function. Another layout is chosen, or the current layout is modified, based on an optimization algorithm”, and Paragraph [0063] “Simulation of thousands of possible workcell layouts using standard optimization algorithms (such as Coffman-Graham or Turán's brick factory algorithm) indicates the layout in FIG. 5 to be one of the least efficient under the metric of (for example) total IMR travel time or minimization of expected IMR collisions”, and Paragraph [0060] “The initial mapping of groups to workcells (i.e., workcell objects) begins with the precedence graph as shown in FIG. 5. The next step is to optimize the number of workcells per group so as to optimize the objective function—e.g., maximize a profit function (for example, minimize cycle time) or minimize a cost function (for example, capital expenditures or occupied factory floor space)”, wherein examiner interpreted selecting and simulating layout of factory objects or workcells including simulating to minimize IMR travel time or minimizing IMR collisions as a process distribution model generator configured to generate an initial process distribution model, wherein examiner interpreted initial mapping of groups to workcells as an initial process distribution model); and
a process distribution model modifier configured to modify the initial process distribution model to a first-order modified process distribution model (Paragraph [0061] “The next step is to optimize the layout of workcells. Assume that the optimal number of workcells is represented by FIG. 5. Optimizing the layout means accounting for optimal IMR motion—minimizing, for example, total cycle time or IMR total travel. Optimization can exploit the flexible order among tasks and minimize IMR congestion among the workcells in order to optimize the overall efficiency of the factory“, Paragraph [0063] “Simulation of thousands of possible workcell layouts using standard optimization algorithms (such as Coffman-Graham or Turán's brick factory algorithm) indicates the layout in FIG. 5 to be one of the least efficient under the metric of (for example) total IMR travel time or minimization of expected IMR collisions”, wherein examiner interpreted simulations of thousands of work cell layouts, and wherein Paragraphs [0064-0065] describes other layouts simulated as a process distribution model modifier configured to modify the initial process distribution model to a first-order modified process distribution model).
Regarding claim 7, SOBALVARRO teaches wherein the process distribution model modifier is further configured to remodify the first-order modified process distribution model to a second-order modified process distribution model (Paragraph [0063] “Simulation of thousands of possible workcell layouts using standard optimization algorithms (such as Coffman-Graham or Turán's brick factory algorithm) indicates the layout in FIG. 5 to be one of the least efficient under the metric of (for example) total IMR travel time or minimization of expected IMR collisions”, Paragraph [0064] “Another layout attempted by the simulation, illustrated in FIG. 9, shows the workcells laid out in a 3×3 arrangement. The rows of workcells are aligned entrance-to-exit, providing a natural structure for the IMRs to use for “out-of-order” tasks without creating significant congestion”, and Paragraph [0065] “The simulation reveals that the layout shown in FIG. 10 is close to optimal under the constraint of minimizing IMR travel and potential collisions (the path length is 148 and the congestion penalty is 15)”, wherein examiner interpreted simulating various different workcell layouts to find the optimal layout as the process distribution model modifier is further configured to remodify the first-order modified process distribution model to a second-order modified process distribution model, wherein the various simulations are interpreted as modifying first-order modified model to a second-order modified model).
Regarding claim 8, SOBALVARRO teaches wherein at least two of the plurality of transport lines are on different layers from each other (FIGS 8-10 describes the travel paths used by IMRs to travel from one workcell to another workcell, wherein examiner interpreted various paths the IMRs take as including at least two of the plurality of transport lines being on different layers from each other).
Regarding claim 9, SOBALVARRO teaches wherein at least one of the plurality of transport units is configured to move between the transport path and a stage (Paragraph [0059] “Once a precedence graph has been established, the groups are mapped to workcell objects corresponding to physical workcells with dimensions and locations on the factory floor. Suppose that the parts to be assembled are brought into the factory (as represented by a factory object, with attributes relating to workspace area, available utilities, physical dimensions, etc.) from the left and leave the factory, assembled, on the right as shown in FIG. 4. At the bottom of the factory is space for IMRs to queue. Each of the IMRs is represented by an IMR object with attributes specifying speed, payload, geometric format, battery life, etc.”, wherein examiner interpreted IMRs traveling to workcells as at least one of the plurality of transport units is configured to move between the transport path and a stage, wherein examiner interpreted IMRs as transport units, and workcells as stage).
Regarding claim 10, SOBALVARRO teaches a method of manufacturing a display apparatus, the method (Paragraph [0013] “the invention pertains to a method of scheduling, for execution on a factory floor having a layout, a plurality of interrelated manufacturing processes”, wherein examiner interpreted manufacturing processes as including a method of manufacturing a display apparatus) comprising:
arranging a transport line comprising a plurality of transport units, between a plurality of transport lines ([TABLE 1] describes mobile transport machine/robot that moves raw material or workpiece from point to point as it is used to perform various manufacturing processes as described in TABLE 1, wherein examiner interpreted mobile transport machine/robot moving material or workpiece to other manufacturing processes as arranging a transport line comprising a plurality of transport units between a plurality of transport lines); and
simulating a transport line distribution model for distributing the plurality of transport units (Paragraph [0013] “the method comprises the steps of (a) storing, in a computer memory, a plurality of factory object data structures each corresponding to a machine or a human and containing data and/or instructions for simulating behavior in (i) carrying out a factory task, (ii) any allowed movement about the manufacturing floor layout, (iii) speed of operation and movement based on inherent characteristics of the machine or the human and proximity thereof to other objects, the object data structures including parameter values constraining the simulated behavior; (b) receiving, by a computational simulator, a work order specifying factory inputs and outputs and an objective function specifying a quantity to be maximized; and (c) based on the work order, simulating, using the computational simulator and the stored object data structures, operation of the factory”, and Paragraph [0063] “Simulation of thousands of possible workcell layouts using standard optimization algorithms (such as Coffman-Graham or Turán's brick factory algorithm) indicates the layout in FIG. 5 to be one of the least efficient under the metric of (for example) total IMR travel time or minimization of expected IMR collisions”, wherein examiner interpreted computer that is used to simulate movement of factory objects including layout and IMR as simulating a transport line distribution model for distributing the plurality of transport units, and wherein examiner interpreted simulating layouts to minimize IMR travel time and IMR collisions as distributing the plurality of transport units), such that an operation rate of the plurality of transport units is within a designated range (Paragraph [0065] “The simulation reveals that the layout shown in FIG. 10 is close to optimal under the constraint of minimizing IMR travel and potential collisions (the path length is 148 and the congestion penalty is 15). This layout is similar to that of FIG. 5, but the workcells are aligned in a manner similar to that shown in FIG. 9. This exploits the precedence graph and creates a structure that reduces IMR congestion and IMR travel time”, Paragraph [0011], “The optimization functions used to evaluate the performance of the layout design when the factory operates with specific scheduling and routing algorithms may reflect multiple and possibly conflicting goals. These goals can be classified into two different groups: maximization and minimization. The first group may comprise maximization goals including performance, revenue, system reliability (e.g., number of redundant workcells, backup workcells), resource utilization (e.g., workcell throughput, transportation utilization), and flexibility (e.g., production of multiple product variations)”, and Paragraph [0012] “The second group may comprise minimization goals including operational costs related to material handling and energy consumption, upfront investment (e.g., fewer machines/equipment/workers), inventory/storage requirements, production/assembly time for each different product variation (the combination of workcell sequences), path distance for IMRs for item pickup and delivery, IMR (intelligent mobile robot) travel time (assuming travel safety is guaranteed) for item pickup and delivery, the number of transportation units, the number of workers, number of unserved requests, the average and maximum number of simultaneous requests, work-in-progress waiting time, work-in-progress line length across the factory, and traffic congestion; and for multiple deliveries/requests from same transportation unit, minimizing the time the last destination is served (makespan) and minimizing the total completion time (latency)”, wherein examiner interpreted simulation revealing layout that is close to optimal for minimizing travel and potential collisions of IMR as simulating a transport line distribution model for distributing the plurality of transport units, such that operation rates of the plurality of transport units are within a designated range, wherein examiner interpreted optimization of the layout by maximizing and minimizing goals as operation rates of the plurality of transport units being within a designated range), wherein the operation rates are defined as a time during which the transport units operate (Paragraph [0040] “The scheduling subsystem 155 takes the optimized layout produced by the layout subsystem 150, or a fixed layout provided by the user, and produces an optimized schedule. The layout and scheduling subsystems 150, 155 can work in tandem (rather than sequentially) to produce an optimized layout and schedule; in this way, scheduling considerations can influence layout optimization”, Paragraph [0066] “A schedule is built by allocating tasks in time for each product unit's task sequence. Hence, scheduling is layout-dependent and scheduling optimization generally follows layout optimization. This need not be the case, however; the processes can be combined, with scheduling optimization applied to intermediate layout candidates. For example, if the objective function maximizes throughput, one approach to optimization is to derive an optimal schedule as described below for a candidate layout and modify the layout in a manner likely to improve the schedule. For example, certain parameters (e.g., the workcell orientation) of the candidate layout may be varied randomly and the schedule simulation re-run and scored. If scheduling improves, the revised layout is retained”, Paragraphs [0067-0088], Paragraphs [0108-0110], and Paragraph [0012-0013], wherein examiner interpreted creating an optimized layout and schedule, wherein the schedule includes allocated tasks for each product unit’s task sequence as time during which the transport units operate for a designated time period, wherein the schedule includes scheduling tasks, and transportation of IMR, and the time it takes for IMR to arrive at its destination, wherein examiner interpreted an optimized schedule as operation rate being a time where transport unit operate for a designated time period).
Regarding claim 11, SOBALVARRO teaches further comprising: generating an initial transport line distribution model (Paragraph [0046] “In step 210, the layout subsystem selects a factory layout template or an initial set of factory objects from the template store 120. In the latter case, the layout subsystem 150 may define the set of all possible layouts based on the selected factory objects. One of these may be chosen (step 220) as the current layout, either at random or based on initial application of the provided constraints; this may involve selection of attribute values of the factory objects within their allowed ranges. The performance of the current layout is simulated, and the results of the simulation are evaluated (step 230) based on the objective function. Another layout is chosen, or the current layout is modified, based on an optimization algorithm”, and Paragraph [0063] “Simulation of thousands of possible workcell layouts using standard optimization algorithms (such as Coffman-Graham or Turán's brick factory algorithm) indicates the layout in FIG. 5 to be one of the least efficient under the metric of (for example) total IMR travel time or minimization of expected IMR collisions”, wherein examiner interpreted selecting and simulating layout of factory objects including simulating to minimize IMR travel time or minimizing IMR collisions as generating an initial transport line distribution model, and Paragraphs [0060-0065]); and
distributing the plurality of transport units according to the initial transport line distribution model (Paragraph [0063] “Simulation of thousands of possible workcell layouts using standard optimization algorithms (such as Coffman-Graham or Turán's brick factory algorithm) indicates the layout in FIG. 5 to be one of the least efficient under the metric of (for example) total IMR travel time or minimization of expected IMR collisions”, wherein examiner interpreted simulating workcell layouts to minimize IMR travel time and minimizing IMR collisions as distributing the plurality of transport units according to the initial transport line distribution model).
Regarding claim 12, SOBALVARRO teaches further comprising: calculating the operation rates of the plurality of transport units (Paragraph [0061] “The next step is to optimize the layout of workcells. Assume that the optimal number of workcells is represented by FIG. 5. Optimizing the layout means accounting for optimal IMR motion—minimizing, for example, total cycle time or IMR total travel. Optimization can exploit the flexible order among tasks and minimize IMR congestion among the workcells in order to optimize the overall efficiency of the factory”, and Paragraph [0063] “Simulation of thousands of possible workcell layouts using standard optimization algorithms (such as Coffman-Graham or Turán's brick factory algorithm) indicates the layout in FIG. 5 to be one of the least efficient under the metric of (for example) total IMR travel time or minimization of expected IMR collisions”, wherein examiner interpreted optimizing layout of workcells to minimize IMR travel time and minimizing IMR collisions as calculating the operation rates of the plurality of transport units);
based on the operation rates of the plurality of transport units not being within the designated range, modifying the initial transport line distribution model to a first-order modified transport line distribution model (Paragraph [0063] “Simulation of thousands of possible workcell layouts using standard optimization algorithms (such as Coffman-Graham or Turán's brick factory algorithm) indicates the layout in FIG. 5 to be one of the least efficient under the metric of (for example) total IMR travel time or minimization of expected IMR collisions”, wherein examiner interpreted simulations of thousands of work cell layouts to minimize IMR travel time or minimization of expected IMR collisions, wherein Paragraphs [0064-0065] describes other layouts simulated as modifying the initial transport line distribution model to a first-order modified transport line distribution model, wherein examiner interpreted the layouts not being optimal as the operation rates of the plurality of transport units not being within the designated range); and
distributing the plurality of transport units according to the first-order modified transport line distribution model (Paragraph [0060] “The initial mapping of groups to workcells (i.e., workcell objects) begins with the precedence graph as shown in FIG. 5. The next step is to optimize the number of workcells per group so as to optimize the objective function—e.g., maximize a profit function (for example, minimize cycle time) or minimize a cost function (for example, capital expenditures or occupied factory floor space)”, Paragraph [0063] “Simulation of thousands of possible workcell layouts using standard optimization algorithms (such as Coffman-Graham or Turán's brick factory algorithm) indicates the layout in FIG. 5 to be one of the least efficient under the metric of (for example) total IMR travel time or minimization of expected IMR collisions”, wherein examiner interpreted simulating workcell layouts for IMRs as distributing the plurality of transport units according to the first-order modified transport line distribution model).
Regarding claim 13, SOBALVARRO teaches further comprising, based on the operation rates of the plurality of transport units not being within the designated range, remodifying the first-order modified transport line distribution model to a second-order modified transport line distribution model (Paragraph [0063] “Simulation of thousands of possible workcell layouts using standard optimization algorithms (such as Coffman-Graham or Turán's brick factory algorithm) indicates the layout in FIG. 5 to be one of the least efficient under the metric of (for example) total IMR travel time or minimization of expected IMR collisions”, Paragraph [0064] “Another layout attempted by the simulation, illustrated in FIG. 9, shows the workcells laid out in a 3×3 arrangement. The rows of workcells are aligned entrance-to-exit, providing a natural structure for the IMRs to use for “out-of-order” tasks without creating significant congestion”, and Paragraph [0065] “The simulation reveals that the layout shown in FIG. 10 is close to optimal under the constraint of minimizing IMR travel and potential collisions (the path length is 148 and the congestion penalty is 15)”, wherein examiner interpreted simulating various different workcell layouts to find the optimal layout to minimize IMR travel time and collisions as remodifying the first-order modified transport line distribution model to a second-order modified transport line distribution model, wherein the various simulations are interpreted as modifying first-order modified model to a second-order modified model, and wherein the simulations not resulting in an optimal workcell layout to minimize IMR travel time and IMR collisions as the operation rates of the plurality of transport units not being within the designated range).
Regarding claim 14, SOBALVARRO teaches further comprising generating the initial transport line distribution model based on an operation plan of a processing line (Paragraph [0046] “The flowchart 200 shown in FIG. 2 illustrates the operation of a representative layout subsystem 150. In a first step 210, a work order is received. The work order specifies the constraints and requirements 140, including the operations to be performed and the objective function to be maximized (e.g., throughput) or minimized (e.g., power consumption or emissions). In step 210, the layout subsystem selects a factory layout template or an initial set of factory objects from the template store 120. In the latter case, the layout subsystem 150 may define the set of all possible layouts based on the selected factory objects. One of these may be chosen (step 220) as the current layout, either at random or based on initial application of the provided constraints; this may involve selection of attribute values of the factory objects within their allowed ranges. The performance of the current layout is simulated, and the results of the simulation are evaluated (step 230) based on the objective function”, wherein examiner interpreted the work order specifying constraints and requirements, including operations to be performed and the object function and simulating layout based on the objective function as generating the initial transport line distribution model based on an operation plan of the processing lines, wherein examiner interpreted work order including constraints and requirements, and the objective function to be maximized or minimized as operation plan of the processing lines).
Regarding claim 15, SOBALVARRO teaches wherein the transport line includes a plurality of transport lines (Paragraphs [0063-0065], FIGS. 8-10 shows various transport lines), and
the method further comprises simulating a process distribution model for distributing the plurality of transport units between the plurality of transport lines (Paragraph [0013] “the method comprises the steps of (a) storing, in a computer memory, a plurality of factory object data structures each corresponding to a machine or a human and containing data and/or instructions for simulating behavior in (i) carrying out a factory task, (ii) any allowed movement about the manufacturing floor layout, (iii) speed of operation and movement based on inherent characteristics of the machine or the human and proximity thereof to other objects, the object data structures including parameter values constraining the simulated behavior; (b) receiving, by a computational simulator, a work order specifying factory inputs and outputs and an objective function specifying a quantity to be maximized; and (c) based on the work order, simulating, using the computational simulator and the stored object data structures, operation of the factory”, Paragraph [0059] “Once a precedence graph has been established, the groups are mapped to workcell objects corresponding to physical workcells with dimensions and locations on the factory floor. Suppose that the parts to be assembled are brought into the factory (as represented by a factory object, with attributes relating to workspace area, available utilities, physical dimensions, etc.) from the left and leave the factory, assembled, on the right as shown in FIG. 4. At the bottom of the factory is space for IMRs to queue. Each of the IMRs is represented by an IMR object with attributes specifying speed, payload, geometric format, battery life, etc.”, Paragraph [0061] “The next step is to optimize the layout of workcells. Assume that the optimal number of workcells is represented by FIG. 5. Optimizing the layout means accounting for optimal IMR motion—minimizing, for example, total cycle time or IMR total travel. Optimization can exploit the flexible order among tasks and minimize IMR congestion among the workcells in order to optimize the overall efficiency of the factory”, Paragraph [0063] “Simulation of thousands of possible workcell layouts using standard optimization algorithms (such as Coffman-Graham or Turán's brick factory algorithm) indicates the layout in FIG. 5 to be one of the least efficient under the metric of (for example) total IMR travel time or minimization of expected IMR collisions”, wherein examiner interpreted workcell layouts being simulated using a computer to minimize IMR travel time and IMR collisions as simulating a process distribution model for distributing the plurality of transport units between the plurality of transport lines).
Regarding claim 16, SOBALVARRO teaches further comprising simulating the process distribution model for distributing the plurality of transport units between the plurality of transport lines, based on a transport line distribution model of each of the plurality of transport lines (Paragraph [0013] “the method comprises the steps of (a) storing, in a computer memory, a plurality of factory object data structures each corresponding to a machine or a human and containing data and/or instructions for simulating behavior in (i) carrying out a factory task, (ii) any allowed movement about the manufacturing floor layout, (iii) speed of operation and movement based on inherent characteristics of the machine or the human and proximity thereof to other objects, the object data structures including parameter values constraining the simulated behavior; (b) receiving, by a computational simulator, a work order specifying factory inputs and outputs and an objective function specifying a quantity to be maximized; and (c) based on the work order, simulating, using the computational simulator and the stored object data structures, operation of the factory”, Paragraph [0059] “Once a precedence graph has been established, the groups are mapped to workcell objects corresponding to physical workcells with dimensions and locations on the factory floor. Suppose that the parts to be assembled are brought into the factory (as represented by a factory object, with attributes relating to workspace area, available utilities, physical dimensions, etc.) from the left and leave the factory, assembled, on the right as shown in FIG. 4. At the bottom of the factory is space for IMRs to queue. Each of the IMRs is represented by an IMR object with attributes specifying speed, payload, geometric format, battery life, etc.”, Paragraph [0061] “The next step is to optimize the layout of workcells. Assume that the optimal number of workcells is represented by FIG. 5. Optimizing the layout means accounting for optimal IMR motion—minimizing, for example, total cycle time or IMR total travel. Optimization can exploit the flexible order among tasks and minimize IMR congestion among the workcells in order to optimize the overall efficiency of the factory”, Paragraph [0063] “Simulation of thousands of possible workcell layouts using standard optimization algorithms (such as Coffman-Graham or Turán's brick factory algorithm) indicates the layout in FIG. 5 to be one of the least efficient under the metric of (for example) total IMR travel time or minimization of expected IMR collisions”, wherein examiner interpreted workcell layouts being simulated using a computer to minimize IMR travel time and IMR collisions as simulating the process distribution model for distributing the plurality of transport units between the plurality of transport lines, based on a transport line distribution model of each of the plurality of transport lines).
Regarding claim 17, SOBALVARRO teaches further comprising: generating an initial process distribution model (Paragraph [0046] “In step 210, the layout subsystem selects a factory layout template or an initial set of factory objects from the template store 120. In the latter case, the layout subsystem 150 may define the set of all possible layouts based on the selected factory objects. One of these may be chosen (step 220) as the current layout, either at random or based on initial application of the provided constraints; this may involve selection of attribute values of the factory objects within their allowed ranges. The performance of the current layout is simulated, and the results of the simulation are evaluated (step 230) based on the objective function. Another layout is chosen, or the current layout is modified, based on an optimization algorithm”, and Paragraph [0063] “Simulation of thousands of possible workcell layouts using standard optimization algorithms (such as Coffman-Graham or Turán's brick factory algorithm) indicates the layout in FIG. 5 to be one of the least efficient under the metric of (for example) total IMR travel time or minimization of expected IMR collisions”, and Paragraph [0060] “The initial mapping of groups to workcells (i.e., workcell objects) begins with the precedence graph as shown in FIG. 5. The next step is to optimize the number of workcells per group so as to optimize the objective function—e.g., maximize a profit function (for example, minimize cycle time) or minimize a cost function (for example, capital expenditures or occupied factory floor space)”, wherein examiner interpreted selecting and simulating layout of factory objects or workcells including simulating to minimize IMR travel time or minimizing IMR collisions as generating an initial process distribution model, wherein examiner interpreted initial mapping of groups to workcells as an initial process distribution model); and
distributing the plurality of transport units according to the initial process distribution model (Paragraph [0063] “Simulation of thousands of possible workcell layouts using standard optimization algorithms (such as Coffman-Graham or Turán's brick factory algorithm) indicates the layout in FIG. 5 to be one of the least efficient under the metric of (for example) total IMR travel time or minimization of expected IMR collisions”, wherein examiner interpreted simulating workcell layouts to minimize IMR travel time and minimizing IMR collisions as distributing the plurality of transport units according to the initial process distribution model).
Regarding claim 18, SOBALVARRO teaches further comprising: calculating the operation rates of the plurality of transport units (Paragraph [0061] “The next step is to optimize the layout of workcells. Assume that the optimal number of workcells is represented by FIG. 5. Optimizing the layout means accounting for optimal IMR motion—minimizing, for example, total cycle time or IMR total travel. Optimization can exploit the flexible order among tasks and minimize IMR congestion among the workcells in order to optimize the overall efficiency of the factory”, and Paragraph [0063] “Simulation of thousands of possible workcell layouts using standard optimization algorithms (such as Coffman-Graham or Turán's brick factory algorithm) indicates the layout in FIG. 5 to be one of the least efficient under the metric of (for example) total IMR travel time or minimization of expected IMR collisions”, wherein examiner interpreted optimizing layout of workcells to minimize IMR travel time and minimizing IMR collisions as calculating the operation rates of the plurality of transport units);
based on the operation rates of the plurality of transport units not being within the designated range, modifying the initial process distribution model to a first-order modified process distribution model (Paragraph [0061] “The next step is to optimize the layout of workcells. Assume that the optimal number of workcells is represented by FIG. 5. Optimizing the layout means accounting for optimal IMR motion—minimizing, for example, total cycle time or IMR total travel. Optimization can exploit the flexible order among tasks and minimize IMR congestion among the workcells in order to optimize the overall efficiency of the factory“, Paragraph [0063] “Simulation of thousands of possible workcell layouts using standard optimization algorithms (such as Coffman-Graham or Turán's brick factory algorithm) indicates the layout in FIG. 5 to be one of the least efficient under the metric of (for example) total IMR travel time or minimization of expected IMR collisions”, wherein examiner interpreted simulations of thousands of work cell layouts, and wherein Paragraphs [0064-0065] describes other layouts simulated as a process distribution model modifier configured to modify the initial process distribution model to a first-order modified process distribution model, and wherein examiner interpreted the layouts not being optimal as the operation rates of the plurality of transport units not being within the designated range); and
distributing the plurality of transport units according to the first-order modified process distribution model (Paragraph [0060] “The initial mapping of groups to workcells (i.e., workcell objects) begins with the precedence graph as shown in FIG. 5. The next step is to optimize the number of workcells per group so as to optimize the objective function—e.g., maximize a profit function (for example, minimize cycle time) or minimize a cost function (for example, capital expenditures or occupied factory floor space)”, Paragraph [0063] “Simulation of thousands of possible workcell layouts using standard optimization algorithms (such as Coffman-Graham or Turán's brick factory algorithm) indicates the layout in FIG. 5 to be one of the least efficient under the metric of (for example) total IMR travel time or minimization of expected IMR collisions”, wherein examiner interpreted simulating workcell layouts for IMRs as distributing the plurality of transport units according to the first-order modified process distribution model).
Regarding claim 19, SOBALVARRO teaches further comprising, based on the operation rates of the plurality of transport units not being within the designated range, remodifying the first-order modified process distribution model to a second-order modified process distribution model (Paragraph [0063] “Simulation of thousands of possible workcell layouts using standard optimization algorithms (such as Coffman-Graham or Turán's brick factory algorithm) indicates the layout in FIG. 5 to be one of the least efficient under the metric of (for example) total IMR travel time or minimization of expected IMR collisions”, Paragraph [0064] “Another layout attempted by the simulation, illustrated in FIG. 9, shows the workcells laid out in a 3×3 arrangement. The rows of workcells are aligned entrance-to-exit, providing a natural structure for the IMRs to use for “out-of-order” tasks without creating significant congestion”, and Paragraph [0065] “The simulation reveals that the layout shown in FIG. 10 is close to optimal under the constraint of minimizing IMR travel and potential collisions (the path length is 148 and the congestion penalty is 15)”, wherein examiner interpreted simulating various different workcell layouts to find the optimal layout as remodifying the first-order modified process distribution model to a second-order modified process distribution model, wherein the various simulations are interpreted as modifying first-order modified model to a second-order modified model, and wherein the simulations not resulting in an optimal workcell layout to minimize IMR travel time and IMR collisions as the operation rates of the plurality of transport units not being within the designated range).
Regarding claim 20, SOBALVARRO teaches wherein at least two of the plurality of transport lines are on different layers from each other (FIGS 8-10 describes the travel paths used by IMRs to travel from one workcell to another workcell, wherein examiner interpreted various paths the IMRs take as including at least two of the plurality of transport lines being on different layers from each other), and
the method further comprises moving at least one of the plurality of transport units between the plurality of transport lines on the different layers from each other (Paragraph [0059] “Once a precedence graph has been established, the groups are mapped to workcell objects corresponding to physical workcells with dimensions and locations on the factory floor. Suppose that the parts to be assembled are brought into the factory (as represented by a factory object, with attributes relating to workspace area, available utilities, physical dimensions, etc.) from the left and leave the factory, assembled, on the right as shown in FIG. 4. At the bottom of the factory is space for IMRs to queue. Each of the IMRs is represented by an IMR object with attributes specifying speed, payload, geometric format, battery life, etc.”, and FIGS. 8-10, wherein examiner interpreted IMRs traveling to workcells via different paths as shown in FIGS. 8-10 as moving at least one of the plurality of transport units between the plurality of transport lines on the different layers from each other).
Citation of Pertinent Prior Art
The prior art made of record and on the attached PTO Form 892 but not relied upon is considered pertinent to applicant's disclosure.
CHUNG WOO et al. [KR 20200065151 A] teaches an automated transfer system and a job distribution method.
Asakawa et al. [USPGPUB 2014/0018955] teaches a conveyance system comprising conveyance apparatuses for conveying the objects between the plurality of processing apparatuses.
Cella et al. [USPGPUB 2022/0197306] discloses A robot fleet management platform including a job parsing system that applies filters to identify portions of a job request suitable for robot automation.
SHIMAKAWA [USPGPUB 2022/0317647] teaches a simulation device that estimates a behavior of a system including a control device that controls a target.
Bharadwaj et al. [USPGPUB 2023/0213922] teaches a data processing system enabling a user to select performance indicators in terms of which a plant operation is to be optimize.
Conclusion
THIS ACTION IS MADE FINAL. 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 nonprovisional extension fee (37 CFR 1.17(a)) 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 mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to DHRUVKUMAR PATEL whose telephone number is (571)272-5814. The examiner can normally be reached 7:30 AM to 5:30 AM.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Mohammad Ali can be reached at (571)272-4105. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/D.P./ Examiner, Art Unit 2119
/MOHAMMAD ALI/ Supervisory Patent Examiner, Art Unit 2119
Prosecution Insights