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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 5/11/26 has been entered.
Notice to Applicant
The following is a Non-Final Office Action. In response to Examiner’s Final
Rejection of 2/18/26, Applicant, on 5/11/26, amended claims. Claims 1-6
are pending in this application and have been rejected below.
Notice to Applicant
Applicant’s amendments are acknowledged.
The 112(b) rejections are withdrawn based on Applicant’s arguments and amendments, that it is all the same “production order.”
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 1-6 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention. The independent claims 1, 5, and 6 now reach recite: “specifying the processing timings of the plurality of products by executing a target tracking method by using the cumulative sum as a target number of appearances for each specification to generate a production order for the plurality of products in accordance with the specified processing timings, the target tracking method includes sequentially determining an appearance order of the products such that a cumulative number of appearances of each specification over a production line approaches the target number of appearances, the generated production order being configured to reduce product dwell time and increase throughput in the production line.” Examiner is unable to find in the disclosure support for reducing dwell and time and increasing throughput. It is unclear what is being relied upon. Applicant is welcome to explain, amend the claim, and/or cancel the subject matter not supported. Examiner notes that the claim, in its current form, does not even use the term “target” or specify that the final “production order” is finding a best/optimal schedule; so it is unclear how this helps with throughput at this time. While the current language does not appear supported, perhaps Applicant is trying to rely on: [0038] “ target tracking method will be described as an example of a method for determining the production order such that the same specifications are not consecutive as much as possible. The target tracking method is a method of sequentially determining an appearance order of products such that a cumulative number of appearances of each specification over a production line approaches a target number of appearances as much as possible” and some operation from FIG. 13 (S24-S29 – has 4 different loops resulting in “target number of appearances”); and/or perhaps the goal is down in [0115] as published and FIG. 19 “when the product of the slot number 5 is specified, a difference from the target number of appearances is 1.6 2=−0.4 for the specification combination α, 1.6−1=0.6 for the specification combination β, and 0.8−1=−0.2 for the specification combinations a and B. Accordingly, one of the products E, G, and H having the specification B with the largest difference is selected”. It is not clear which portion Applicant is intending to rely upon, Examiner provides this to help Applicant in finding claim language that is supported.
Independent Claims 5-6 are rejected for the same reasons.
Claims 2-4 depend from claim 1 and are rejected for the same reasons.
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 1-6 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.
Step One - First, pursuant to step 1 in MPEP 2106.03, the claim 1 is directed to an article of manufacture which is a statutory category.
Step 2A, Prong One - MPEP 2106.04 - The claim 1 recites–
“… execute … planning a production order in a production line and planning production of different products under product specification constraints and period constraints specific to each product, the processing comprising:
on a condition that each of a plurality of products is processed at any of a plurality of processing timings, in the production order, a production specification is determined for each of the plurality of products (e.g. see Applicant’s [0034] as published “as illustrated in FIG. 1, specification constraints are defined for each product of products A to J. For example, a product A, a product D, a product F, and a product I have to be processed with the same specification a.”), a processing period defined by a user in which processing is performed for each of the plurality of products is determined, and the processing period includes one or more of the processing timings,
calculating a probability representing a likelihood of a particular production specification being suitable for a given processing timing taking into account the period constraints of each product, the probability is obtained as a reciprocal of a number of the processing timings included in the processing period for each of the production specifications of the plurality of products, for each processing timing;
calculating a sum of probabilities of the production specifications for each processing timing; and
calculating a cumulative sum of sums of the probabilities for the processing timing; and
specifying the processing timings of the plurality of products by executing a target tracking method by using the cumulative sum as a target number of appearances for each specification to generate a production order for the plurality of products in accordance with the specified processing timings, the target tracking method includes sequentially determining an appearance order of the products such that a cumulative number of appearances of each specification over a production line approaches the target number of appearances, the generated production order being configured to reduce product dwell time and increase throughput in the production line.”
As drafted, this is, under its broadest reasonable interpretation, within the Abstract idea grouping of “mathematical concepts” (a series of mathematical calculations) and “certain methods of organizing human activity” (managing relationships between people – including… following rules or instructions – giving people a desired planned schedule for manufacturing). Here, we have a series of steps for conditions for objects (e.g. products to be manufactured) in a certain order, with a specification (e.g. any requirements/equipment/constraints), then calculating probabilities representing a likelihood of a particular production specification being suitable for a given processing timing taking into account period constraints, a sum of probabilities for each “specification” (e.g. any constraints), cumulative sum of probabilities for products, where the processing timing have a target number of appearances, sequentially determining an appearance order, to generate a production order (e.g. a plan) where the plan reduces dwell time and increases throughput. See e.g. MPEP 2106.04(a)(2) example of mathematical relationships - iv. organizing information and manipulating information through mathematical correlations, Digitech Image Techs., LLC v. Electronics for Imaging, Inc., 758 F.3d 1344, 1350, 111 USPQ2d 1717, 1721 (Fed. Cir. 2014). The patentee in Digitech claimed methods of generating first and second data by taking existing information, manipulating the data using mathematical functions, and organizing this information into a new form. The court explained that such claims were directed to an abstract idea because they described a process of organizing information through mathematical correlations, like Flook's method of calculating using a mathematical formula. 758 F.3d at 1350, 111 USPQ2d at 1721. The end of the claim is giving a person a recommendation of a schedule order for manufacturing at least two objects/products. Accordingly, claim 1 is directed to an abstract idea.
Step 2A, Prong Two - MPEP 2106.04 - This judicial exception is not integrated into a practical application. Claim 1 recites Additional elements that are:
“A non-transitory computer-readable recording medium storing a computation program for causing a computer to execute computation processing comprising:.”
(Additional elements of computer, computer-recordable medium, causing a computer to perform each step is considered “apply it [abstract idea] on a computer” (See MPEP 2106.05f).
Examiner notes, the final portion of the last limitation with “reduce product dwell time and increase throughput in the production line,” is only from a person following the planned production order; the computer itself does not control the production line.
Accordingly, the additional elements do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claim also fails to recite any improvements to another technology or technical field, improvements to the functioning of the computer itself, use of a particular machine, effecting a transformation or reduction of a particular article to a different state or thing, and/or an additional element applies or uses the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception. See 84 Fed. Reg. 55. The claim is directed to an abstract idea.
Step 2B in MPEP 2106.05 - The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements of a computer and CRM is treated as MPEP 2106.05(f) (Mere Instructions to Apply an Exception – “Thus, for example, claims that amount to nothing more than an instruction to apply the abstract idea using a generic computer do not render an abstract idea eligible.” Alice Corp., 134 S. Ct. at 235). Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept.
Independent claim 5 is directed to a method at step 1, which is a statutory category. Claim 5 recites similar limitations as claim 1 and is rejected for the same reasons at step 2a, prong one, 2a, prong 2, and step 2b. At step 2a, prong two and step 2B the “implemented by a computer” is considered “apply it [abstract idea] on a computer” (See MPEP 2106.05f). The claim is not patent eligible.
Independent claim 6 is directed to an apparatus at step 1, which is a statutory category. Claim 6 recites similar limitations as claim 1 and claim 5 and is rejected for the same reasons at step 2a, prong one; step 2a, prong 2 and step 2b.
Claim 2 narrows the abstract idea by stating the processing periods scheduled overlap.
Claim 3 narrows the abstract idea by stating that an object (e.g. a product to be manufactured) is selected based on a difference between a cumulative number of appearances of “processing specification” (e.g. constraints/equipment/machine). It is a mathematical calculation to recommend to a user scheduling, e.g. how many times a processing specification should be scheduled.
Claim 4 narrows the abstract idea by stating that an object (e.g. a product to be manufactured) is selected based on a difference between a sum of squares of another difference and a relationship between the processing timing and the cumulative sum being minimized. It is interpreted as a mathematical calculation to recommend to a user what the next product that should be scheduled, e.g. how many times a processing specification should be scheduled. [0115] of applicant’s specification may be the corresponding example it appears, where it determines an “appearance order” by comparing “target number of appearances” and “cumulative number of appearances.”
Therefore, the claim(s) are rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.
For more information on 101 rejections, see MPEP 2106.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries 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 1-6 are rejected under 35 U.S.C. 103 as being unpatentable over Fukuda (US 2020/0026757) and Shiho (US 2021/0405627) and Zanchettin, “Robust scheduling and dispatching rules for high-mix collaborative manufacturing systems,” 2022, Flexible Services and Manufacturing Journal, Vol. 34, pages 293-316.
Concerning claim 1, Fukuda discloses:
A non-transitory computer-readable recording medium storing a computation program for causing a computer to execute computation planning a production order in a production line and planning production of different products (Fukuda – see par 82 - The auxiliary storage apparatus 12 is composed of hard disk drives and large-capacity nonvolatile storage devices such as SSD's (Solid State Drives) and is used to retain various kinds of programs and control information for a long period of time. The respective programs including the production plan creation processing program 13 and the production plan display processing program 14 which are stored in the auxiliary storage apparatus 12 are loaded onto the main storage apparatus 11 when activating the production plan supporting apparatus 4 or whenever necessary; and various kinds of processing as the production plan supporting apparatus 4 as a whole as described later is executed by the CPU10 executing the programs loaded onto the main storage apparatus 11) under product specification constraints and period constraints specific to each product, the processing (Fukuda – see par 63 - In this example, it is assumed that working time for the work of each item K1 or K2 in each process is 60 minutes and the work of the item K1 and the work of the item K2 are input to the production process alternately in one lot every hour. Furthermore, it is assumed that: the item K1 is produced through any one of the three routes described earlier with reference to FIG. 1A to FIG. 1C, and the item K2 is produced through a route as illustrated in FIG. 8A and also in FIG. 8B and FIG. 8C so that if a failure occurs in process A, the route proceeds to the repair process R1 or the repair process R2 depending on the type of that failure, and then returns to process B and goes through subsequent processes; see also FIG. 6-7, 11 – showing different time slots for each process (disclosing “period constraints”)) comprising:
on a condition that each of a plurality of products is processed at any of a plurality of processing timings in the production order (Fukuda see par 43 – principle - a case where a product is conducted through four processes “A,” “B,” “C,” and “D” (hereinafter respectively referred to as process A, process B, process C, and process D) as illustrated in FIG. 1A in this order. Process D is, for example, a process to carry the product, which is produced through process A to process C, into a warehouse.; see par49 - Furthermore, “a1” represents the branching rate for the work to proceed from process B to the repair process R1. This branching rate can be set based on the past actual results. For example, let us assume that the past actual results when works of lot L1 to lot L5 are sequentially input, one lot by one lot, by every unit time (every hour in this example) from the time slot “1” to the production process are in a status as illustrated in FIG. 4. Incidentally, a flow of the process is expressed as “time” in FIG. 2 and FIG. 3; however, the actual results have information about actually worked time, the “time” is expressed as “time slots” in FIG. 4 and specific time starting from “9:00” is assigned to each “time slot” column; see par 63 - Next, a case where works of two different types of times (items K1 and K2) are input to the aforementioned production process will be examined. In this example, it is assumed that working time for the work of each item K1 or K2 in each process is 60 minutes and the work of the item K1 and the work of the item K2 are input to the production process alternately in one lot every hour. See par 65 - FIG. 10 illustrates a total existence probability Gantt chart when works of products of the two items K1 and K2 of only five lots, one lot at a time, are input to the production process alternately in this sequential order every hour from “9:00.”; see also FIG. 11), a production specification is determined for each of the plurality of products, a processing period defined by a user (Fukuda see par 85 - The input plan table 15 is a table used to manage the input plan to input each lot to the production process and is created by the user in advance. The input plan table 15 is configured as illustrated in FIG. 17 by including an item name column 15A, a lot ID column 15B, a number-of-works column 15C, and a scheduled input time column 15D) in which processing is performed for each of the plurality of products is determined, and the processing period includes one or more of the processing timings (Fukuda – see par 63 - In this example, it is assumed that working time for the work of each item K1 or K2 in each process is 60 minutes and the work of the item K1 and the work of the item K2 are input to the production process alternately in one lot every hour. Furthermore, it is assumed that: the item K1 is produced through any one of the three routes described earlier with reference to FIG. 1A to FIG. 1C, and the item K2 is produced through a route as illustrated in FIG. 8A and also in FIG. 8B and FIG. 8C so that if a failure occurs in process A, the route proceeds to the repair process R1 or the repair process R2 depending on the type of that failure, and then returns to process B and goes through subsequent processes; see par 88 - The process master table 16 is a table used to manage all the routes for the production process and is created by the user in advance. This process master table 16 is configured as illustrated in FIG. 18 by including a process name column 16A, a post-process column 16B, an item name column 16C, a working time column 16D, a used equipment column 16E, and a worker column 16F.),
calculating a probability representing a likelihood of a particular production specification being suitable for a given processing timing taking into account the period constraints of each product, the probability is obtained as a reciprocal of a number of the processing timings included in the processing period for each of the production specifications of the plurality of products (Fukuda – see par 64 - Since the existence probability model of the work of the item K1 in this case is as explained earlier with reference to FIG. 3, an explanation about it is omitted here. On the other hand, the existence probability model of the item K2 is as illustrated in FIG. 9; The values of these “b1” to “b3” can also be set based on the past actual results. In this example, it is assumed that “b1” is 10%, “b2” is 80%, and “b3” is 10%; see FIG. 10-11 – showing percentages (which are reciprocals/fractions); see par 99 - the production plan creation processing program 13 is a program having a function that generates and updates the total existence probability Gantt chart described earlier with reference to FIG. 6 and FIG. 11 and makes the production plan), for each processing timing;
calculating a sum of probabilities of the production specifications for each processing timing (Fukuda – see par 65 - FIG. 10 illustrates a total existence probability Gantt chart when works of products of the two items K1 and K2 of only five lots, one lot at a time, are input to the production process alternately in this sequential order every hour from “9:00.” Referring to FIG. 10, “L1” to “L5” represent the lot number of the corresponding lot (lot L1 to lot L5). In this example, “L1,” “L3,” and “L5” are lots of the work regarding the product of the item K1 “L2” and “L4” are lots of the work regarding the product of the item K2. See par 66 - Furthermore, FIG. 11 shows the existence probabilities of the respective lots L1 to L5 in the respective time slots of the respective processes, which are calculated by substituting the corresponding branching rates (a1=40%, a1.sup.˜=60%, a2=20%, a2.sup.˜=80%, b1=80%, b2=10%) of the examples of FIG. 1 to FIG. 9 in “a1,” “a1.sup.˜,” “a2,” “a2.sup.˜,” “b1,” and “b2” in FIG. 10, respectively.); and
calculating a cumulative sum of sums of the probabilities for the processing timing (Fukuda – see par 66 - In this example as well, the maximum value of the existence probability of the work in each time slot of each process can be calculated as the total sum of all the existence probabilities indicated in that time slot of that process.);
“specifying the processing timings of the plurality of products by executing a target tracking method… to generate a production order for the plurality of products in accordance with the specified processing timings the target tracking method includes sequentially determining an appearance order of the products…” (Fukuda – see par 49 - For example, let us assume that the past actual results when works of lot L1 to lot L5 are sequentially input, one lot by one lot, by every unit time (every hour in this example) from the time slot “1” to the production process are in a status as illustrated in FIG. 4; see par 58 - FIG. 5 is a Gantt chart generated by stacking existence probability models for the respective lots one over another when the work of one lot (lot L1 to lot L5) was input to the production process every hour in a sequential order from “9:00” (hereinafter referred to as the total existence probability Gantt chart). )
Fukuda discloses that there is a possibility that more than 100% is allocated for a time slot, which creates a bottleneck and disrupts production (See par 68, FIG. 12), and then shifting timing to shit the timing to input a lot, using the heat map of FIG. 12 as a guide (see par 69), and then adjusting the production of some works in a resulting schedule (See par 107-108, FIG. 22).
Shiho discloses the remaining portions of the limitation:
specifying the processing timings of the plurality of products by executing a target tracking method using the cumulative sum as a target number of appearances for each specification to generate a production order for the plurality of products in accordance with the specified processing timings the target tracking method includes sequentially determining an appearance order of the products such that a cumulative number of appearances of each specification over a production line approaches the target number of appearances (Shiho – see par 46 - workload aggregation unit 123 aggregates the total workload distribution information 115 for each predetermined work unit (for example, process and date); Specifically, the workload aggregation unit 123 aggregates the probability distribution of the total workload by the sum of the workloads of all products produced for each date and process. see par 48 - The violation probability estimation unit 125 estimates a probability that the total workload for each date and process violates the constraint of the work capacity. Specifically, the violation probability estimation unit 125 estimates, for the work plan leveled by the workload leveling unit 124, the probability distribution of the total workload which is the sum of the workloads of all products to be produced according to a design item with determined specifications for each combination of dates and execution processes; the violation probability estimation unit 125 estimates the upper/lower limit constraint violation probability of the total workload; par 49 - the violation probability estimation unit 125 estimates a probability that the total workload exceeds upper and lower limit values (a violation probability which is a probability of not being included in the work capacity) in a range specified by the upper limit value and the lower limit value of the work amount indicating the work capacity of each date and process set as the work capacity information 113. For example, assuming that the upper limit of the work capacity is ten hours, and the lower limit is seven hours, the violation probability estimation unit 125 calculates a probability that the workload according to N(8,3) exceeds ten hours and a probability that the workload is less than seven hours).
It is unclear if the work plan leveling for each date is considered a “production order” achieving benefit of reduced dwell time and increased throughput.
Zanchettin discloses:
specifying the processing timings of the plurality of products by executing a target tracking method by using the cumulative sum as a target number of appearances for each specification to generate a production order for the plurality of products in accordance with the specified processing timings, the target tracking method includes sequentially determining an appearance order of the products such that a cumulative number of appearances of each specification over a production line approaches the target number of appearances, “the generated production order being configured to reduce product dwell time and increase throughput in the production line” (Zanchettin – see page 296, Section 2, #2 - the Throughput controller exploits the Plant Digital Twin to forecast the production of the manufacturing system as a function of the possible dispatching and the scheduling decisions and is responsible for selecting the best activities to execute, i.e. the input u(t) to the plant; see page 299, 3rd paragraph -states ̄s ∈ Sext are conditions of the production process corresponding to one or more agents available to start processing new jobs. see page 293, Abstract; page 303, 1st paragraph - sequencing policy is dynamically adjusted by online forecasting the throughput of the facility as a function of the scheduling and dispatching rules; due to the stochastic nature of the Plant Digital Twin, the trajectory e is a stochastic process, and therefore the associated cost c is a stochastic variable. Therefore, in order to attain a robust decision regarding the scheduling and the dispatching rules, a high number N of trajectories are generated).
Fukuda, Shiho, and Zanchettin are analogous art as they are directed to production planning (see Fukuda Abstract; Shiho Abstract; Zanchettin Abstract). 1) Fukuda discloses that there is a possibility that more than 100% is allocated for a time slot, which creates a bottleneck and disrupts production (See par 68, FIG. 12), and then shifting timing to shit the timing to input a lot, using the heat map of FIG. 12 as a guide (see par 69), and then adjusting the production of some works in a resulting schedule (See par 107-108, FIG. 22). Fukuda also discloses sequentially inputting probabilities for respective lots (See par 49, 59) for the production process. Shiho improves upon Fukuda by adding all the time to produce objects/products for a schedule/plan. One of ordinary skill in the art would be motivated to further include using determining total workload to produce items/objects to efficiently improve upon the calculation of time for different operations causing bottlenecks within different periods in Fukuda. 2) Zanchettin improves upon Fukuda and Shiho by dynamically adjusting sequencing/scheduling activities and selecting the best activities for throughput purposes. One of ordinary skill in the art would be motivated to further include sequencing policies and selecting best activities to execute for throughput purposes to efficiently improve upon the calculation of time for different operations causing bottlenecks within different periods in Fukuda and the aggregation of workload for each date and process in Shio.
Accordingly, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the probability of different works occurring in different time slots in Fukuda to further compute overall timings for different objects/products as disclosed in Shiho, to further select the best activities for scheduling/sequencing for throughput of the facility as disclosed in Zanchettin, since the claimed invention is merely a combination of old elements, and in combination each element merely 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 and there is a reasonable expectation of success.
Concerning independent claim 5, Fukuda and Shiho and Zanchettin disclose:
A computer-implemented method of planning a production order in a production line and planning production of different products under product specification constraints and period constraints specific to each product, the computation method comprising (Fukuda – see par 82 - The auxiliary storage apparatus 12 is composed of hard disk drives and large-capacity nonvolatile storage devices such as SSD's (Solid State Drives) and is used to retain various kinds of programs and control information for a long period of time. The respective programs including the production plan creation processing program 13 and the production plan display processing program 14 which are stored in the auxiliary storage apparatus 12 are loaded onto the main storage apparatus 11 when activating the production plan supporting apparatus 4 or whenever necessary; and various kinds of processing as the production plan supporting apparatus 4 as a whole as described later is executed by the CPU10 executing the programs loaded onto the main storage apparatus 11).
The remaining limitations are similar to claim 1 above. It would be obvious to combine Fukuda and Shiho and Zanchettin for the same reasons as claim 1.
Concerning independent claim 6, Fukuda and Shiho and Zanchettin disclose:
An information processing apparatus of planning a production order in a production line and planning production of different products under product specification constraints and period constraints specific to each product, the information processing apparatus (Fukuda – see par 82 - The auxiliary storage apparatus 12 is composed of hard disk drives and large-capacity nonvolatile storage devices such as SSD's (Solid State Drives) and is used to retain various kinds of programs and control information for a long period of time. The respective programs including the production plan creation processing program 13 and the production plan display processing program 14 which are stored in the auxiliary storage apparatus 12 are loaded onto the main storage apparatus 11 when activating the production plan supporting apparatus 4 or whenever necessary; and various kinds of processing as the production plan supporting apparatus 4 as a whole as described later is executed by the CPU10 executing the programs loaded onto the main storage apparatus 11) comprising:
a memory; and
a processor coupled to the memory, the processor being configured to perform processing including: (Fukuda – see par 82 - The auxiliary storage apparatus 12 is composed of hard disk drives and large-capacity nonvolatile storage devices such as SSD's (Solid State Drives) and is used to retain various kinds of programs and control information for a long period of time. The respective programs including the production plan creation processing program 13 and the production plan display processing program 14 which are stored in the auxiliary storage apparatus 12 are loaded onto the main storage apparatus 11 when activating the production plan supporting apparatus 4 or whenever necessary; and various kinds of processing as the production plan supporting apparatus 4 as a whole as described later is executed by the CPU10 executing the programs loaded onto the main storage apparatus 11).
The remaining limitations are similar to claim 1 above. It would be obvious to combine Fukuda and Shiho and Zanchettin for the same reasons as claim 1.
Concerning claim 2, Fukuda discloses:
The non-transitory computer-readable recording medium according to claim 1, wherein
for at least two of the plurality of products, the processing periods partially overlap each other (Applicant’s FIG. 7, [0044] as published gives example, where processing of product E happens in same timeslot (e.g. 5) as product A
Fukuda – see par 65 – Lots L1, L3, L5 are for item K1; Lots L2, L4 are item K2; see FIG. 10 – Lots 1 and 2 “overlap” in a variety of different time slots; see also FIG. 11 – “mixed items existence case”).
Concerning claim 3, Fukuda discloses:
selecting a next product by … when the processing timings of the plurality of products are sequentially specified (Fukuda see par 58 - FIG. 5 is a Gantt chart generated by stacking existence probability models for the respective lots one over another when the work of one lot (lot L1 to lot L5) was input to the production process every hour in a sequential order from “9:00” (hereinafter referred to as the total existence probability Gantt chart).
Fukuda discloses branching to different processes for different time slots and having complementary events based on how process flow occurs (See par 51-53). Shiho discloses that probability distribution of a workflow of product X1 has a “variance of 2” (See par 29).
Shiho discloses:
The non-transitory computer-readable recording medium according to claim 1, the computation processing further comprising:
selecting a next product by using a difference from … appearances of the production specification in a case where the next product is selected by using, as a reference, a relationship between the processing timing and the cumulative sum when the processing timings of the plurality of products are sequentially specified ([0115] as published – “For example, as illustrated in FIG. 19, the tally calculation is a method of sequentially determining an appearance order of the products such that the cumulative number of appearances of a specification combination of each product does not deviate from the target number of appearances over a production line. In this case, when the product of the slot number 5 is specified, a difference from the target number of appearances is 1.6 (in FIG. 19) MINUS 2 (production order?) =−0.4 for the specification combination α, 1.6−1=0.6 for the specification combination β, and 0.8−1=−0.2 for the specification combinations a and B. Accordingly, one of the products E, G, and H having the specification B with the largest difference is selected. The above-described embodiment may be applied to such tally calculation when the production order is specified
Shiho –see par 46 - the workload aggregation unit 123 aggregates the probability distribution of the total workload by the sum of the workloads of all products produced for each date and process; see par 50 - The influence degree calculation unit 126 calculates the degree of influence which is applied on the probability distribution of the workload by determining the design specification item for the product for which the date when the upper/lower limit constraint violation probability becomes equal to or greater than a predetermined threshold is the execution date of the process, and calculates the design specification item of the product as a candidate for preferentially designing. See par 51 - the influence degree calculation unit 126 specifies, as a candidate, the date and the process in which the upper/lower limit constraint violation probability is equal to or greater than the predetermined threshold. Then, the influence degree calculation unit 126 selects a product having a large variation in workload as a candidate to be preferentially designed among the products which are produced on the specified date and process. Then, the influence degree calculation unit 126 calculates the influence degree to be the expected value of the variation reduction amount of the workload at the time of determination for each design specification of the target product. the influence degree calculation unit 126 specifies, as a candidate, the date and the process in which the upper/lower limit constraint violation probability is equal to or greater than the predetermined threshold. Then, the influence degree calculation unit 126 selects a product having a large variation in workload as a candidate to be preferentially designed among the products which are produced on the specified date and process. Then, the influence degree calculation unit 126 calculates the influence degree to be the expected value of the variation reduction amount of the workload at the time of determination for each design specification of the target product. See par 53 - The display control unit 127 extracts a design specification item having a large variation reduction amount (influence degree) of the workload from the target product, and creates and outputs screen information for displaying the design specification item and the influence degree in association with each other).
Zanchettin discloses:
selecting a next product by using a difference from a “cumulative number of appearances” of the production specification in a case where the next product is selected by using, as a reference, a relationship between the processing timing and the “cumulative sum” when the processing timings of the plurality of products are sequentially specified (Zanchettin – see page 299, Section 3, 1st paragraph - This Section defines the optimality principle for finding the best scheduling and dispatching policy 휋∗ that, for each product p within the mix ℙ, will ensure the minimum deviation from the desired throughput; page 301, 2nd paragraph - when the actual production exceeds the desired one (i.e. when 𝜃p(t) > 𝜃0 p(t)), the corresponding reference throughput 휔0 p(t) is set to zero, so to temporarily stop the production of p; See page 304, 2nd paragraph - At the end of the iterative procedure in Algorithm 2, the unique remaining cluster will contain the optimal external state s happening at present time instant t that, for all products p in the mix ℙ, will ensure, at least for N → ∞, a robust and optimal tracking of the reference throughput 휔0 p(t) within the prediction horizon. The same algorithm will be invoked cyclically every time a new scheduling or dispatching rule can be applied)
Obvious to combine Fukuda, Shiho, and Zanchettin for the same reasons as claim 1. In addition, Fukuda discloses branching to different processes for different time slots and having complementary events based on how process flow occurs (See par 51-53). Shiho discloses that probability distribution of a workflow of product X1 has a “variance of 2” (See par 29) and stacking existence probability models for the respective lots for every hour in sequential order (See par 58). Shiho improves upon Fukuda by selecting a next candidate based on expected value of variation reduction in workload (See par 50-53). Zanchettin improves upon Fukuda and Shiho by finding the best scheduling and dispatching policy for a product within a mix (See page 299) and checking if actual production exceeds desired production (See page 301, 304). One of ordinary skill in the art would be motivated to further include selecting a candidate operation to reduce variation in Shio and to further check product within a mix for whether production exceeds desired production in Zanchettin to efficiently improve upon the calculation of time for different operations causing bottlenecks within different periods in Fukuda.
Concerning claim 4, Fukuda discloses:
The non-transitory computer-readable recording medium according to claim 1, the computation processing further comprising:
selecting a next product such that … when the processing timings of the plurality of products are sequentially specified (Fukuda see par 58 - FIG. 5 is a Gantt chart generated by stacking existence probability models for the respective lots one over another when the work of one lot (lot L1 to lot L5) was input to the production process every hour in a sequential order from “9:00” (hereinafter referred to as the total existence probability Gantt chart).
Fukuda discloses branching to different processes for different time slots and having complementary events based on how process flow occurs (See par 51-53). Shiho discloses that probability distribution of a workflow of product X1 has a “variance of 2” (See par 29).
Shiho discloses:
The non-transitory computer-readable recording medium according to claim 1, the computation processing further comprising:
selecting a next product such that a sum of squares of a difference from a cumulative number of appearances of the production specification in a case where the next product is selected by using, as a reference, a relationship between the processing timing and the cumulative sum is minimized when the processing timings of the plurality of products are sequentially specified (Shiho – see par 46 - the workload aggregation unit 123 aggregates the probability distribution of the total workload by the sum of the workloads of all products produced for each date and process; see par 50 - The influence degree calculation unit 126 calculates the degree of influence which is applied on the probability distribution of the workload by determining the design specification item for the product for which the date when the upper/lower limit constraint violation probability becomes equal to or greater than a predetermined threshold is the execution date of the process, and calculates the design specification item of the product as a candidate for preferentially designing. See par 51 - the influence degree calculation unit 126 specifies, as a candidate, the date and the process in which the upper/lower limit constraint violation probability is equal to or greater than the predetermined threshold. Then, the influence degree calculation unit 126 selects a product having a large variation in workload as a candidate to be preferentially designed among the products which are produced on the specified date and process. Then, the influence degree calculation unit 126 calculates the influence degree to be the expected value of the variation reduction amount of the workload at the time of determination for each design specification of the target product. the influence degree calculation unit 126 specifies, as a candidate, the date and the process in which the upper/lower limit constraint violation probability is equal to or greater than the predetermined threshold. Then, the influence degree calculation unit 126 selects a product having a large variation in workload as a candidate to be preferentially designed among the products which are produced on the specified date and process. Then, the influence degree calculation unit 126 calculates the influence degree to be the expected value of the variation reduction amount of the workload at the time of determination for each design specification of the target product. See par 53 - The display control unit 127 extracts a design specification item having a large variation reduction amount (influence degree) of the workload from the target product, and creates and outputs screen information for displaying the design specification item and the influence degree in association with each other).
Zanchettin discloses entire limitation:
selecting a next product such that a “sum of squares” of a difference from a cumulative number of appearances of the production specification in a case where the next product is selected by using, as a reference, a relationship between the processing timing and the cumulative sum is minimized when the processing timings of the plurality of products are sequentially specified (Zanchettin – see page 300, 3rd paragraph – optimisation problem in (2) attempts to find optimal set of scheduling and dispatching policy -∗ at present time instant t by minimising, for all products p ∈ ℙ , the conditional expectation, under -π , of the mean square production error ; see page 307, section 5.2, 2nd paragraph - At each iteration, the scheduler executes a query on the mySQL database to retrieve the updated list of orders and, for each product, updates the corresponding reference production 휃0 p(t) using (5) as well as the reference throughput 휔0 p(t) according to (4); Each simulation is then evaluated in terms of the corresponding cost in (3) by applying Algorithm 1. The whole set of simulations and corresponding costs are clustered according to Algorithm 2 and the corresponding optimal and robust dispatching and scheduling policy 휋∗, i.e. the one satisfying the optimality of (2), is obtained; outcome shown in FIG. 9; facility is able to adjust its pace as well as the resource allocation strategy based on the actual demand; see page 309 , FIG. 8 – using root means square error to quantify variability of production with respect to the desired value).
It would be obvious to combine Fukuda, Shiho, and Zanchettin for the same reasons as claim 1 and 3.
Response to Arguments
Applicant’s arguments 5/11/26 have been considered but are not persuasive and/or moot over the revised rejections necessitated by the amendments.
With regards to 112(a), Applicant argues that the “claimed invention is directed to a computation program, method, and apparatus for planning a production order, not for directly controlling the physical production line. The reduction of product dwell time and increase in throughput are inherent and direct results of implement such a well-planned production order. Remarks, pages 6-7. In response, Examiner respectfully disagrees with the analysis. The disclosure not support such additional actions of “increase throughput” and “reduce product dwell time” as they do not occur for computer operations in claim 1, 5, or 6. The arguments are beyond the scope of what is claimed, because claim 1, 5, and 6 require a computer to “reduce product dwell time and increase throughput in the production line”. Subsequent actions by a person following the plan are not within the scope of the disclosure as claimed – nor is there any disclosure of dwell time or throughput. See also MPEP 2163.07(a) "To establish inherency, the extrinsic evidence ‘must make clear that the missing descriptive matter is necessarily present in the thing described in the reference, and that it would be so recognized by persons of ordinary skill. Inherency, however, may not be established by probabilities or possibilities. The mere fact that a certain thing may result from a given set of circumstances is not sufficient.’" In re Robertson, 169 F.3d 743, 745, 49 USPQ2d 1949, 1950-51 (Fed. Cir. 1999). Here, Applicant is even saying there is a plan… that might be followed by a person correctly. This demonstrates it is not inherently disclosed, and the 112a is still needed. Examiner notes, there is no advantage to this language for 101 purposes either, since the computer itself is only making a plan, so Examiner recommends removing the limitations not supported under 112(a), and instead, for 101 purposes, focusing on “additional elements” under 2a, prong 2 and step 2B.
With respect to 112a, Applicant further argues that [0053] of the Specification stating “by treating the obtained cumulative sum as the target number of appearances and performing the target tracking method, a production order is planned.” Remarks, pages 7-8. This portion still does not address the other questions raised by the revised language, such as how this relates to disclosing reduced dwell time or increased throughput if the plan is followed.
With respect to 112a, Applicant further argues that [0038-0039] of the Specification stating “cumulative number of appearances of each specification over a production line approaches a target number of appearances as much as possible.” Remarks, pages 8. This portion still does not address the other questions raised by the revised language, such as how this relates to disclosing reduced dwell time or increased throughput if the plan is followed.
With regards to 101,Applicant argues the Specification in [0046] highlights technical problem that “try all patterns” is computationally prohibitive, suffering from combinatorial explosion, and [0046] of specification explains is “performing calculations that are qualitatively different and technically advantageous in managing combinatorial complexity. The key inventive step lies in effectively condensing the vast combinatorial search space into a manageable, probabilistically-derived target,” so therefore, is a technical improvement. Remarks, page 11. In response, Examiner respectfully disagrees. First, the calculations are “mathematical relationships” – probabilities, sums, and cumulative sums, which are still directed to abstract ideas. “Calculating a probability… as a reciprocal of a number of the processing timings” as claimed also falls into a mathematical relationships. See MPEP 2106.04(a)(2). Moreover, “planning a production order” is part of the abstract idea of “certain methods of organizing human activity.” The claim does not cover actually controlling other machines; it only outputs a recommended schedule/plan for a user.
Turning to [0046] of Applicant’s specification, it states “to obtain a production order that satisfies a specification constraint while observing a period constraint, it is considered to try all patterns of products to be allocated to each section. However, in this case, the calculation time becomes enormous. For example, in a case of automobile production, since a product having a scale of 1000 vehicles per day is produced, constraints on production periods are set for the respective vehicles, and the respective period constraints may partially overlap each other. For example, in a case of product data in FIG. 8, since product allocation patterns to each section are about 50950×50! patterns, when it is assumed that it takes one second for one target tracking method, calculation takes about 101584 seconds.” However, [0046] does not explain what feature makes the calculation time shorter; nor do the claims restrict a minimum amount of calculations; nor is there some kind of condensing as alleged. The scope of the current claims includes 3 products, determining an order, and doing a few calculations. Later in the specification, [0053], it states “in the planning of the production order in the mixed production method, even when the period constraints partially overlap each other, it is possible to plan a production order that satisfies a specification constraint while observing a period constraint. It is possible to obtain the production order satisfying the period constraint while maintaining the specification constraint in the target tracking method. Since it is not desired to consider the case division or the like of the number of products included in each section, an enormous amount of calculation time is not taken.” Even from this passage, the time of calculation is shorter from “choosing not to do more calculations.” Examiner has carefully reviewed the specification and the claims. Examiner is unable to find some technical way that the math itself is speeding up the calculation; as best understood, it is just “doing less calculations” using a computer.
Applicant now argues (Remarks page 11) that “key inventive step lies in effectively condensing the vast combinatorial search space into a manageable, probabilistically-derived target.” Examiner respectfully disagrees. It is unclear how this is reflected in the claims or the specification.
Applicant now argues (Remarks page 12) that taking probabilities for each time slot, then summing all the probabilities of each specification at each slot [0050-0053] and in FIG. 9 is the “condensing” into a per-slot, per-specification expectation. Examiner respectfully disagrees. The aspects related to mathematical probabilities appear to be in the claim – calculating a probability that is a reciprocal; summing the probabilities. However, these are just mathematical operations; and under 101, do not represent a technical improvement and move from being part of the abstract idea into a technical improvement at step 2a, prong 2 and step 2B. There is no disclosure related to the argued “condensing,” so it is unclear what Applicant is referring to. It appears to Examiner this is just the summary of the fact that one “sums” the probabilities. See e.g. MPEP 2106.04(a)(2) example of mathematical relationships - iv. organizing information and manipulating information through mathematical correlations, Digitech Image Techs., LLC v. Electronics for Imaging, Inc., 758 F.3d 1344, 1350, 111 USPQ2d 1717, 1721 (Fed. Cir. 2014). The patentee in Digitech claimed methods of generating first and second data by taking existing information, manipulating the data using mathematical functions, and organizing this information into a new form. The court explained that such claims were directed to an abstract idea because they described a process of organizing information through mathematical correlations, like Flook's method of calculating using a mathematical formula. 758 F.3d at 1350, 111 USPQ2d at 1721.
Applicant then argues that [0052] “target number of appearances” over entire sequence of slots, creates a “smooth, continuous target progression” that “inherently balances the demand across the constrained periods without requiring explicit enumeration or evaluation of each individual product allocation patter.” Remarks, page 13. Examiner respectfully disagrees. Smooth, continuous, and balance are not in the specification; this argument only supports that the claim is just “refraining from doing other mathematical operations.” Applicant then argues that this allows the system to “efficiently determine when and where each specification should ideally appear to satisfy both period and specification constraints simultaneously.” Examiner respectfully disagrees. First, while a production order and appearance is made, the claim does not appear to state that it ideally chooses periods for each specification, but rather in second limitation that “a processing period defined by a user in which processing is performed for each of the plurality of products is determined.” Again, this appears to be just the mathematical operations for planning a schedule, not a technical improvement.
With respect to 101, Applicant argues the approach directly bypasses the “case division” or “trying all patterns” (Specification, paragraphs [0046, 54]) that leads to combinatorial explosion. Remarks, page 13. In response, Examiner respectfully disagrees. Arguing that “we could have done something more complicated, but chose not to,” is not the kind of reasoning supporting eligibility. This logic would enable all 101 rejections to be avoided just by saying “well, it could have been more complicated.” Applicant further argues: “This technical transformation of the problem’s computational structure is a clear improvement to a technical field analogous to the guidance in MPEP 2106.05a(I)(ii) which considers “Accelerating a process of analyzing audit log data when the increased speed comes solely from the capabilities of a general-purpose computer” as an example of an improvement.” Remarks, page 13. In response, Examiner respectfully disagrees. This argument copied the wrong section – as the portion copied is actually an example of that is not sufficient to show an improvement in computer-functionality:
Examples that the courts have indicated may not be sufficient to show an improvement in computer-functionality:[AltContent: rect]
ii. Accelerating a process of analyzing audit log data when the increased speed comes solely from the capabilities of a general-purpose computer, FairWarning IP, LLC v. Iatric Sys., 839 F.3d 1089, 1095, 120 USPQ2d 1293, 1296 (Fed. Cir. 2016).
In summary, Examiner is unable to find some technical way that the math itself is speeding up the calculation; as best understood, it is just “doing less calculations.” Accordingly, at this time, all we have is a “bare assertion” of an improvement – with no details on how a computer performing the mathematical calculations is an improved speed. Accordingly, at this time, this is viewed as MPEP 2106.04(d)(1) “Conversely, if the specification explicitly sets forth an improvement only in a conclusory manner (i.e., a bare assertion of an improvement without the detail necessary to be apparent to a person of ordinary skill in the art), the examiner should not determine that the claim improves technology or a technical field.”
Applicant then argues that it is a practical application because the math is “specific.” Remarks, page 10. In response, Examiner respectfully disagrees. As stated above, Examiner is unable to find technical details of the improvement; it appears Applicant is stating “the math is specific; therefore the claim is eligible.” Accordingly, MPEP 2106.04 applies - “The Court has held that a claim may not preempt abstract ideas, laws of nature, or natural phenomena, even if the judicial exception is narrow (e.g., a particular mathematical formula such as the Arrhenius equation). See, e.g., Mayo, 566 U.S. at 79-80, 86-87, 101 USPQ2d at 1968-69, 1971 (claims directed to "narrow laws that may have limited applications" held ineligible)”. In addition, eligibility based on 101 is not simply whether any “specific” limitations are recited in the claim – it needs to be a particular solution to “improve a computer or other technology.” Rather, McRo, as explained in MPEP 2106.05(a)(II)(“Improvements to Any Other Technology of Technical Field”), had a specific way to solve the problem of producing accurate and realistic lip synchronization and facial expressions in animated characters.
With regards to 103, Applicant argues “cumulative sum of sums of the probabilities for the probabilities timing” is not disclosed in Fukuda (par 55, 57, 47, 9, 1, 8, 68-69). Remarks, pages 14-15. In response, Examiner respectfully disagrees. Fukuda paragraph 66 was applied as “In this example as well, the maximum value of the existence probability of the work in each time slot of each process can be calculated as the total sum of all the existence probabilities indicated in that time slot of that process.”
Applicant then argues “probability calculation as a reciprocal of the number of processing timings” is not disclosed in Fukuda. Remarks, pages 14-15. In response, Examiner respectfully disagrees. Fukuda paragraphs 64, 99 were not addressed disclosing 10%, 80%, 10%, probability, which are all considered “reciprocals.”
Applicant then argues that Shiho does not disclose “specifying the processing timings of the plurality of products by executing a target tracking method using the cumulative sum as a target number of appearances for each specification… the target tracking method includes sequentially determining an appearance order” because “cumulative sum” pertains to “total workload” in Shiho. Remarks, pages 16. In response, Examiner respectfully disagrees. Shio explicitly is for leveling the workload. It is unclear how the claim language makes it so Shiho does not apply. The arguments are also moot in light of the revised rejection necessitated by the amendments.
Applicant then argues that Zanchettin does not disclose “specifying the processing timings of the plurality of products by executing a target tracking method by using the cumulative sum “as a target number of appearances for each specification to generate a production order for the plurality of products in accordance with the specified processing timings, the target tracking method includes sequentially determining an appearance order of the products such that a cumulative number of appearances of each specification over a production line approaches the target number of appearances, the generated production order being configured to reduce product dwell time and increase throughput in the production line” because it does not disclose “probability as a reciprocal”, nor the “target number of appearances.” Remarks, pages 16-17. In response, Examiner respectfully disagrees. The arguments are also moot in light of the revised rejection necessitated by the amendments. In addition, the reasons point to limitations that are mapped to the other references.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to IVAN R GOLDBERG whose telephone number is (571)270-7949. The examiner can normally be reached 830AM - 430PM.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Anita Coupe can be reached at 571-270-3614. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/IVAN R GOLDBERG/Primary Examiner, Art Unit 3619
Prosecution Insights