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 1-7 have been reviewed and are under consideration by this office action.
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 12/08/2025 has been entered.
Notice to Applicant
The following is a Non-Final Office action. Applicant amended claims. Claims 1-7 are pending in this application and have been rejected below.
Response to Amendment
Applicant’s amendments are received and acknowledged.
Response to Arguments - 35 USC § 101
Applicant’s arguments with respect to the 35 USC 101 rejections have been fully considered, but they are not persuasive.
Applicant contends that claims are directed towards patent-eligible material as the claims recite a technical solution to a technical problem and further points to generating, in real time… and points to limitations regarding logical partitions and changing slots.
Examiner respectfully disagrees. The real-time (implies a general purpose computer) scheduling and changing of slots (specific position in manufacturing order) are concepts capable of being performed in the human mind applied to the general purpose computing device. While the logical partition does imply an additional element it is performing the steps would be no more than mere instructions to apply the exception using a generic computer component. See MPEP 2106.05(f) and/or amounts to no more than generally linking the use of the judicial exception to a particular technological environment or field of use – see MPEP 2106.05(h). The changing of slots
Applicant contends that the claims are integrated into a practical application.
Examiner respectfully disagrees. The limitations are analyzed both individually as well as in combination and are determined to be performing the steps would be no more than mere instructions to apply the exception using a generic computer component. See MPEP 2106.05(f) and/or amounts to no more than generally linking the use of the judicial exception to a particular technological environment or field of use – see MPEP 2106.05(h).
The 101 Rejection is updated and maintained below.
Response to Arguments - 35 USC § 102/103
Applicant’s amendments have overcome the 102 rejection, but facilitate a new 103 rejection. However, Applicant’s arguments are moot in view of the new line of 103 Rejections as seen below.
The 103 rejection is updated and maintained below.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claim 1, 6, and 7 recites “reconfigure the manufacturing order of the products, in real time, by changing the slot a product” however there is insufficient antecedent basis for “the slot” in the claim. For the purpose of examination, and in light of the specification (para. 64) Examiner interprets the slot to be: “A slot here is a specific position in the manufacturing order of a product to be subjected to a manufacturing process.”
Claims 2-5 inherit the deficiencies of the parents and are therefore rejected similarly.
Appropriate correction is required.
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 1-7 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Step One - First, pursuant to step 1 in the January 2019 Guidance on 84 Fed. Reg. 53, the claim(s) 1-7 is/are directed to statutory categories.
Step 2A, Prong One – The claims are found to recite limitations that set forth the abstract idea(s), namely in independent claims 1, 6, and 7 recite a series of steps for the abstract idea described below:
Regarding Claim(s) 1, (additional elements bolded)
A production plan management device for generating a production plan of a product in real time, the production plan management device comprising:
a processor; and
a memory storing instructions that when executed by the processor, configures the processor to:
acquire product order information including a customer request specifying an item, quantity, and a delivery date of a first product and cost criterion information specifying a cost criterion for costs required to manufacture the first product among a plurality of products,;
store a structural constraint database including structural constraint information specifying a plurality of structural constraints including raw materials, temperature/humidity, time, pre-steps, post-steps, which define conditions for manufacturing processes of manufacturing the first product;
generate, in real time, a production plan that satisfies the customer request, the cost criterion, and the structural constraint and defines manufacturing processes to manufacture the first product, by generating a logical partition to arrange products and define a specific position in a manufacturing order of the products; and
reconfigure the manufacturing order of the products, in real time, by changing the slot a product is manufactured in during a subsequent manufacturing process of the manufacturing processes.
Regarding Claim(s) 6: A production plan management method for generating, in real time, a production plan of a product, the production plan management method comprising: a step of acquiring product order information including a customer request specifying an item, quantity, and a delivery date of a first product among a plurality of products;
a step of acquiring cost criterion information specifying a cost criterion for costs required to manufacture the first product;
a step of acquiring structural constraint information specifying a plurality of structural constraints including raw materials, temperature/humidity, time, pre-steps, post-steps, which define conditions for manufacturing processes of manufacturing the first product;
a step of generating, in real time, a production plan that satisfies the customer request, the cost criterion, and the structural constraints and defines manufacturing processes for manufacturing the first product, by generating logical partition to arrange products and define a specific position in a manufacturing order of the products during each of the manufacturing processes;
a step of acquiring information regarding failure probability of a failure occurring for each of the manufacturing processes and determining a probability of a failure occurring in each of the manufacturing processes included in the production plan by using the information regarding failure probability;
a step of determining, in a case that the probability of a failure occurring with respect to a first manufacturing process included in the production plan exceeds a predetermined failure probability criterion, a first alternate manufacturing process to be executed instead of the first manufacturing process in the production plan; and
a step of changing, in a case that a failure occurs in the first manufacturing process, the first manufacturing process to the first alternate manufacturing process in the production plan reconfiguring the manufacturing order of the products, in real time, by changing the slot a product is manufactured in during a subsequent manufacturing process of the manufacturing processes.
Regarding Claim(s) 7: A production plan management system for generating, in real time, a production plan of a product, the production plan management system comprising: a customer terminal used by a customer requesting a first product;
a distribution terminal configured to manage information regarding distribution of the first product;
a delivery terminal configured to manage information regarding delivery of the first product among a plurality of products;
a management terminal used by an administrator of a factory; and
a production plan management device configured to generate a production plan, wherein:
the customer terminal, the distribution terminal, the delivery terminal, the management terminal, and the production plan management device are connected to each other via a communication network; and
the production management plan management device includes: a processor; and
a memory storing instructions that when executed by the processor, configures the processor to:
receive, from the customer terminal, product order information including a customer request specifying an item, quantity, and a delivery date of the first product,
receive, from the management terminal, cost criterion information specifying a cost criterion for costs required to manufacture the first product, and
receive, from the distribution terminal and/or the delivery terminal, distribution and delivery status information that indicates a respective status of distribution and delivery,
store a structural constraint database including structural constraint information specifying a plurality of structural constraints including raw materials, temperature/humidity, time, pre-steps, post-steps, which define conditions for manufacturing processes of manufacturing the first product in the factory, and
generate, in real time, a production plan that satisfies the customer request, the cost criterion, and the structural constraints and defines manufacturing processes for manufacturing the first product, by generating a logical partition to arrange products and define a specific position in a manufacturing order of the products
calculate an estimated delivery date on which the first product is deliverable to the customer based on the production plan thus generated and a lead calculated from the delivery status information,
transmit the estimated delivery date to the customer terminal, and
reconfigure the manufacturing order of the products, in real time, by changing the slot a product is manufactured in during a subsequent manufacturing process of the manufacturing processes.
As drafted, this is, under its broadest reasonable interpretation, within the Abstract idea groupings of “Mental processes—concepts performed in the human mind” (observation, evaluation, judgment, opinion) as the claims are directed towards acquiring customer information and generating a production plan all of which are concepts capable of being performed in the human mind (i.e. via pen and paper).
Further the claims are directed towards the abstract idea grouping of “Certain methods of organizing human activity” — commercial or legal interactions (including agreements in the form of contracts; legal obligations; advertising, marketing or sales activities or behaviors; business relations) and/or managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions) as the claims are directed towards generating and managing production plans to perform manufacturing processes efficiently. (See Specification, [01-03]).
Step 2A, Prong Two - This judicial exception is not integrated into a practical application. The independent claims utilize at least the additional elements bolded above. The additional elements are performing the steps would be no more than mere instructions to apply the exception using a generic computer component. See MPEP 2106.05(f) and/or amounts to no more than generally linking the use of the judicial exception to a particular technological environment or field of use – see MPEP 2106.05(h).
Step 2B - 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 are just “apply it” on a computer. (See 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) and/or amounts to no more than generally linking the use of the judicial exception to a particular technological environment or field of use – see MPEP 2106.05(h). Further the elements of receive, from the customer terminal; receive, from the management terminal; and receive, from the distribution terminal and/or the delivery terminal are activities that has been recognized by the courts as well-understood, routine, and conventional activity (See MPEP 2106.05(d) i. Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362)
Regarding Claim(s) 2, the claim further recite the additional element(s) of a failure expectation database and an alternate manufacturing process database. This element(s) is performing the steps would be no more than mere instructions to apply the exception using a generic computer component. See MPEP 2106.05(f) and/or amounts to no more than generally linking the use of the judicial exception to a particular technological environment or field of use – see MPEP 2106.05(h) in Steps 2A-Prong 2 and 2B.
Regarding Claim(s) 3-5, the claim further narrows the abstract idea or recite additional elements previously addressed.
Accordingly, the claim 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, adding unconventional steps that confine the claim to a particular useful application, and/or meaningful limitations beyond generally linking the use of an abstract idea to a particular environment. See 84 Fed. Reg. 55. Viewed individually or as a whole, these additional claim element(s) do not provide meaningful limitation(s) to transform the abstract idea into a patent eligible application of the abstract idea such that the claim(s) amounts to significantly more than the abstract idea itself.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
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.
Claim(s) 1-4 and 6 is/are rejected under 35 U.S.C. 103 as being unpatentable over Mahmood et al. (US 20210280287 A1) in view of Dorsch et al. (US 20030233163 A1).
Regarding Claim(s) 1, Mahmood teaches: A production plan management device for generating a production plan of a product in real time, the production plan management device comprising: the production plan management device includes: a processor; and acquire product order information including a customer request specifying an item, quantity, and a delivery date of a first product and cost criterion information specifying a cost criterion for costs required to manufacture the first product among a plurality of products; (Mahmood, [74]; In an aspect, supply chain optimization module 110-2 can acquire order data representing a wide range of ordering information such as shipment destination, shipment origin, storage conditions during transport, required temperature conditions during transport, handling instructions, instructions to place an order with a particular carrier, type of transportation mode (e.g., ground transport, air transport, etc.), sample information (e.g., quantity, type of sample, etc.) and Mahmood, [34, 36]; supply chain optimization module 110-2 can execute scheduling operations corresponding to pickup and delivery of raw materials, final products, biological materials, and other such items by stakeholders… For instance, supply chain optimization module 110-2 can intake data corresponding to scheduling a therapeutic order such as collection site data, contact data, delivery site data, calendar scheduling data, and other such data. In a non-limiting embodiment, supply chain optimization module 110-2 can employ a set of rules and requirements applied against intake data to determine a limited selection of options allowable for scheduling and (Mahmood, [154]; In other aspects, courier capacity module 520 can employ parameters that based determinations, in part on, cost considerations such as courier rates, courier reliability, courier speed of transportation, courier cost for slower transporters that still deliver within required time intervals (e.g., cost benefit analysis), and other such considerations. Furthermore, courier capacity module 520 can execute operations in connection with smart scheduling techniques to automatically select a courier to optimize a cost of delivery and Mahmood, [33]; supply chain events such as point-of-care collection (e.g., collection of samples that meet threshold quality standard requirements), …scheduling couriers, raw material delivery, tracking kits and materials and raw materials), ordering and scheduling activities and Mahmood, [108]; In an aspect, processor 230 can comprise one or more processor configured to perform one or more operations (of at least one module of client individualized medicine module 180) using hardware. As such, processor 230 can include hardware elements 240 that may be configured as processors, functional blocks, and so forth). Examiner notes that the delivery is for delivery to customers as well as delivery of raw materials to manufacture.
store a structural constraint database including structural constraint information specifying a plurality of structural constraints including raw materials, temperature/humidity, time, pre-steps, post-steps, which define conditions for manufacturing processes of manufacturing the first product; and (Mahmood, [45]; manufacturing capacity component can access data from multiple data stores to allow scheduler component to provide a unified view of manufacturing capacity by therapy, region, site, any arbitrary tag applied to a site or other segment to ascertain capacity capabilities and constraints. Furthermore, in a non-limiting embodiment, manufacturing site capacity data can be stored in a registry at server device(s) 102 (e.g., database(s) 160) in order to accommodate data from a variety of manufacturing site sources (e.g., data sourced via integrations such as Manufacturing Execution Systems (MES) systems and Mahmood, [36]; For instance, one or more rules can correspond to existing courier scheduling dates, therapy-specific conditions for transportation and delivery time constraints (i.e. time constraint and further post-step constraint), logistical restrictions, and other such requirements and Mahmood, [37]; In another aspect, various constraints, requirements, and rules can be applied against intake data by supply chain optimization module 110-2 in order to effectuate occurrence of timely ordering and scheduling events. For instance, for fresh blood-based therapies, once collected, patient blood has shelf-lives and expiration dates after which the material is no longer usable to manufacture (i.e. raw material constraint) therapeutic products. For cryopreserved material, the shelf-life is longer. However, the effects of cryopreservation on the collected cells are still not fully understood and it introduces further complexity due to freezing and then thawing of material and Mahmood, [41]; For instance, supply chain optimization module 110-2 can intake data such as therapy data, study number data (if applicable), Chain of Identity (COI) information, shipment verification data, collection data, receiving documentation data (e.g., is the raw material kit shipped properly such as inside a specialized pouch, was the raw material stored at required temperatures such as temperatures to preserve a vapor phase of liquid nitrogen as per some material requirements (i.e. temperature constraint), missing items, mismatching numbers, damages, etc. and Mahmood, [156]; distributed scheduling engine module 110-1 can balance scheduling activities to satisfy available capacity slots at each respective site for a particular date based on estimations of time intervals corresponding to each capacity slot (e.g., time interval required to complete each intermediary manufacturing step (i.e. pre-step constraint) for manufacturing activities on particular dates and corresponding capacity during such time durations).
generate, in real time, a production plan that satisfies the customer request, the cost criterion, and the structural constraint and defines manufacturing processes to manufacture the first product… (Mahmood, [29]; embodiments disclosed herein include distributed scheduling systems that execute distributed scheduling operations in connection with capacity subsystems and operational constraints associated with a personalized medicine supply chain. In an aspect, an example environment 100A is disclosed in which various aspects described herein can be employed and Mahmood, [126]; Furthermore, data & prediction service 380B can facilitate real-time schedule optimizations using predictive insights. In another aspect, data & prediction service 380B in connection with scheduling service 330B can assign schedule requests to waitlist queues and over-schedule queues based on predictive insights and rescheduling insights based on rescheduling data. Accordingly, scheduling module 310A can utilize center of excellence capacity service 340B, manufacturing and capacity service 350B, customer configuration and scheduling rules 360B, courier capacity service 370B, and/or data and/or prediction service 380B to generate discrete schedules and/or comprehensive scheduling determinations for the entire process associated with individualized medicine therapy supply chains and Mahmood, [135]; scheduling module 310A can couple the booking identifier to the scheduled reservation. For instance, a scheduled reservation can map to an ordering of steps and corresponding reservation status with each step to represent a journey map throughout the individualized medicine production chain or supply chain and Mahmood, [144]; then scheduling module 310A can auto-schedule (based on such configuration) to automatically schedule a user in the new available scheduling slot. Furthermore, scheduling module 310A can notify all respective service databases and client devices of the occurrence of such rescheduling event).
reconfigure the manufacturing order of the products, in real time,…during a subsequent manufacturing process of the manufacturing processes. (Mahmood, [48]; In yet another aspect, supply chain optimization module 110-2 can also execute operations such as cancellation of approved orders (e.g., intake order cancellation request data, etc.), cancellation of in-progress or submitted orders (e.g., patient consent data, order cancellation request data, etc.), rescheduling of tasks (e.g., collection, manufacturing, infusion, etc.), intake of infusion intake data (e.g., product and/or shipment receipt data, transfer product to storage data, condition of shipment data, real-time tracking data, real-time alert notification data, etc.), user management operations (e.g., creation of a user, etc.), change request operations, and other such operations and Mahmood, [126]; data & prediction service 380B can facilitate real-time schedule optimizations using predictive insights. In another aspect, data & prediction service 380B in connection with scheduling service 330B can assign schedule requests to waitlist queues and over-schedule queues based on predictive insights and rescheduling insights based on rescheduling data. Accordingly, scheduling module 310A can utilize center of excellence capacity service 340B, manufacturing and capacity service 350B, customer configuration and scheduling rules 360B, courier capacity service 370B, and/or data and/or prediction service 380B to generate discrete schedules and/or comprehensive scheduling determinations for the entire process associated with individualized medicine therapy supply chains and Mahmood, [98]; distributed scheduling system 103 can employ prediction engine module 150-1 to execute one or more prediction model to estimate a likelihood of rescheduling events to occur in relation to…, manufacturing activities (e.g., manufacturing system integration, final product assembly, etc.), location prediction of items (e.g., location of materials, samples, products, location within manufacturing process, etc.), prediction of time duration to batch multiple orders, and other such predictions. Furthermore, prediction engine module 150-1 can predict delays across the individualized medicine therapeutic supply chain to optimize capacity (e.g., using capacity engine module 130-1) and scheduling operations (e.g., using distributed scheduling engine module 140-1).
While Mahmood teaches manufacturing planning, Mahmood does not appear to explicitly teach logical partitions. However, Mahmood in view of the analogous art of Dorsch (i.e. manufacturing) does teach: by generating a logical partition to arrange products and define a specific position in a manufacturing order of the products. (Dorsch, [16]; Practice of the invention facilitates setting up and identifying standard and custom groups for the Window and Door Industry. These groups include but are not limited to the following items, Glass, IG Units, Muntins, Gas, Frames, Sashes, Window Lines and Window Styles. Such visual software helps set up a window manufacturing sequence criteria used for processing the various groups. and Dorsch, [51]; The items within the groups can be given a manufacturing sequence by accessing a common shared Multi-Level Sort Configuration Setup dialog (FIG. 6). Each group may be given its own individual Multi-Level Sort Configuration. More details of the Multi-Level Sort Configuration Setup are described below and Dorsch, [Fig. 1]; visual representation of logical partition of products and Dorsch, [125]; FIG. 18 depicts a windows manufacturing flow diagram. As seen, the "Production Groups Setup" and "Sort Configuration Setup" modules 334, 336 implement data entry dialogs that form part of the windows manager 314. The production grouping items and the sort configuration items feed directly into an IG sequencer module 340. The sequencer module 340 uses the information set up by the user relating to production groups and sort configuration to generate the manufacturing sequence for the IG units).
While Mahmood teaches real-time reconfiguring of manufacturing processes, Mahmood does not teach through changing slots. Mahmood/Dorsch does teach: by changing the slot a product is manufactured in (Dorsch, [52]; During the scheduling process cycle, the ordered glass lites and 1G Units are separated into their respective groups using the groups priority setting and its selection criteria. The items in each group are then placed in their manufacturing sequence determined by using either the common shared Multi-Level Sort Configuration Setup or the groups own individual Multi-Level Sort Configuration Setup and Dorsch, [85]; The screen depiction of FIG. 6 includes three levels. To create, remove, change a level position or edit a Sort Level Criteria the user clicks on the "New", "Remove", "Move Up", "Move Dn" or "Edit Sort Level" buttons 220, 222, 224, 226, 228).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the disclosed invention to have combined the teachings of Mahmood including manufacturing planning, with the teachings of Dorsch including logical partitioning in order to create a more efficient manufacturing process (Dorsch, [22]; Increased manufacturing efficiency is accomplished by utilizing colors and text to easily and clearly identify the items that belong to a group and a transport system for the group. Since the transport system is specifically configured for the items in the group, this creates a highly structured and dedicated transport system specifically for the items in the group. This allows freedom in the manufacturing process to transport the groups independently throughout the manufacturing plant.
Regarding Claim(s) 2, While Mahmood teaches a plurality of databases (see at least Mahmood, [45, 89]). Mahmood does not appear to explicitly teach. The production plan management device according to claim 1, wherein the processor is configured to store a failure expectation database including, for each of the manufacturing processes, information regarding failure probability of a failure occurring and a failure prevention constraint specifying a condition to avoid the failure, and (Mahmood, [95]; Furthermore, rescheduling engine module 120-1 can utilize such curated capacity data, scheduling data, relational data, predicted rescheduling likelihood data and other relevant data to execute rescheduling of various operations (e.g., patient blood or sample draws, courier pick-up, courier drop-off, cryogenic freezing of sample, transportation of raw materials, manufacturing of therapeutic, infusion of therapeutic, etc.) throughout the individualized medicine supply chain and Mahmood, [97]; For instance, load balancing engine module 140-1 can determine whether there is adequate production capacity available across several distributed manufacturing facilities to meet scheduled manufacturing demands. Furthermore, load balancing engine module 140-1 can determine whether components and sub-assemblies are in stock, available, need be acquired and determine or assume lead times required to provide such components for manufacturing and Mahmood, [81]; smart label device(s) 198 can employ activation sensors and/or actuator sensors such as movement sensors (e.g., gyroscopes, accelerometers, etc.) or pressure sensors to detect pressure or movement information associated with the item. In an aspect, smart label device(s) 198 can facilitate the detection of conditions pursuant to a set of condition rules corresponding to the contents of a package (e.g., biological materials needing to be stored at temperatures within a target temperature range, delicate specimens that must not be exposed to turbulence or target altitudes, etc.). and Mahmood, [89]; Accordingly, distributed scheduling engine module 110-1 can be configured as an abstraction layer that aggregates, curates, and analyzes capacity data and scheduling data sourced from a distributed array of databases and/or data stores)..
an alternate manufacturing process database including, for each of the manufacturing processes, alternate manufacturing process information specifying an alternate manufacturing process; and (Mahmood, [82]; For instance, smart label module 108 can trigger adjustments to control the temperature of cryogenic storage compartments or refrigerated compartments based on a detected condition of the package contents as detected by smart label device(s) 198. In another non-limiting embodiment, smart label module 108 can control manufacturing processes associated with generating a personalized therapy based on detected conditions of package contents (e.g., half-life of biological materials), such controls include adjusting a speed of assembly, adjusting an order of manufacturing operations, suggesting additional steps to improve a quality of the therapeutic (e.g., centrifuging the material). In an aspect, smart module 108 can control such additional devices via communicative coupling to such other devices (e.g., manufacturing equipment, cryogenic storage equipment, etc. and Mahmood, [31]; In an aspect, environment 100A can include one or more server device(s) 102 and one or more computing device(s) 104 that can generate, curate, track, monitor and store data associated with transactional processes of an individualized medicine supply chain and Mahmood, [61]; In another aspect, integration module 140-2 can employ a unified authentication system to execute authentication of an identity of a user (e.g., identity provider) utilizing individualized medicine platform module 106 and integrated systems (e.g., using integration module 140-2) such as a web application, a web service system, a storage service (e.g., data store, database, server, etc.) employed to perform a task in connection with the individualized medicine platform module 106 and Mahmood, [89]; Accordingly, distributed scheduling engine module 110-1 can be configured as an abstraction layer that aggregates, curates, and analyzes capacity data and scheduling data sourced from a distributed array of databases and/or data stores).
generate, in real time, a production plan that satisfies the failure prevention constraint in addition to the customer request, the cost criterion, and the structural constraint and defines the manufacturing processes for manufacturing the first product. (Mahmood, [83]; The smart label module 108, can control and/or configure smart label device(s) 198 and other equipment (e.g., manufacturing equipment, cryogenic storage equipment) to execute an operation (e.g., increase or decrease a temperature or speed of manufacture, etc.) based on the detected data exceeding or registering below a target range. Furthermore, smart module 108 can detect geo-location coordinates to automatically trigger an adjustment of various conditions based on predicted parameter corresponding to such geo-location coordinates. Furthermore, smart module 108 can also access smart label information to display at a display interface of smart label device(s) 198 based on a prediction of geo-location coordinates to be reached at a target time. As such, smart module 108 in connection with smart label device(s) 198 can avert technical problems associated with the deterioration of quality of patient materials due to transportation, storage, and/or manufacturing processes and Mahmood, [89]; For example, distributed scheduling engine module 110-1 can execute a set of rules that determine which entity can generate and store a data structure such that other entities can successfully access and interpret the data included in such data structure... In an aspect, a data structure can include any suitable type of structure in which data can be stored (e.g., array, string, file, bitmap, object, etc.).
Regarding Claim(s) 3, Mahmood teaches: The production plan management device according to claim 2, wherein: the processor is configured to determine probability of a failure occurring in each of the manufacturing processes included in the production plan by using the information regarding failure probability; (Mahmood, [83]; smart device(s) 198 can continuously detect data from respective smart label sensors. Furthermore, smart module 108 can continuously determine package content (e.g., package can refer to a box, blood bag, test tube, vile, or other containment modality used to store biological material) conditions based on an analysis of the continuous detected data (e.g., threshold comparisons and evaluation). The smart label module 108, can control and/or configure smart label device(s) 198 and other equipment (e.g., manufacturing equipment, cryogenic storage equipment) to execute an operation (e.g., increase or decrease a temperature or speed of manufacture, etc.) based on the detected data exceeding or registering below a target range. Furthermore, smart module 108 can detect geo-location coordinates to automatically trigger an adjustment of various conditions based on predicted parameter corresponding to such geo-location coordinates).
in a case where the probability of a failure occurring with respect to a first manufacturing process included in the production plan exceeds a predetermined failure probability criterion, determine a first alternate manufacturing process to be executed instead of the first manufacturing process in the production plan and stores the first alternate manufacturing step in the alternate manufacturing process database; and (Mahmood, [83]; The smart label module 108, can control and/or configure smart label device(s) 198 and other equipment (e.g., manufacturing equipment, cryogenic storage equipment) to execute an operation (e.g., increase or decrease a temperature or speed of manufacture, etc.) based on the detected data exceeding or registering below a target range. Furthermore, smart module 108 can detect geo-location coordinates to automatically trigger an adjustment of various conditions based on predicted parameter corresponding to such geo-location coordinates. Furthermore, smart module 108 can also access smart label information to display at a display interface of smart label device(s) 198 based on a prediction of geo-location coordinates to be reached at a target time. As such, smart module 108 in connection with smart label device(s) 198 can avert technical problems associated with the deterioration of quality of patient materials due to transportation, storage, and/or manufacturing processes and Mahmood, [89]; For example, distributed scheduling engine module 110-1 can execute a set of rules that determine which entity can generate and store a data structure such that other entities can successfully access and interpret the data included in such data structure... In an aspect, a data structure can include any suitable type of structure in which data can be stored (e.g., array, string, file, bitmap, object, etc.). Accordingly, distributed scheduling engine module 110-1 can be configured as an abstraction layer that aggregates, curates, and analyzes capacity data and scheduling data sourced from a distributed array of databases and/or data stores).
in a case that a failure occurs in the first manufacturing process, the production plan generating unit change the first manufacturing process to the first alternate manufacturing process in the production plan. (Mahmood, [83]; The smart label module 108, can control and/or configure smart label device(s) 198 and other equipment (e.g., manufacturing equipment, cryogenic storage equipment) to execute an operation (e.g., increase or decrease a temperature or speed of manufacture, etc.) based on the detected data exceeding or registering below a target range. Furthermore, smart module 108 can detect geo-location coordinates to automatically trigger an adjustment of various conditions based on predicted parameter corresponding to such geo-location coordinates. Furthermore, smart module 108 can also access smart label information to display at a display interface of smart label device(s) 198 based on a prediction of geo-location coordinates to be reached at a target time. As such, smart module 108 in connection with smart label device(s) 198 can avert technical problems associated with the deterioration of quality of patient materials due to transportation, storage, and/or manufacturing processes).
Regarding Claim(s) 4, Mahmood teaches: The production plan management device according to claim 1, wherein the processor is configured to: in a case that a production plan change request requesting a change to the production plan is received, acquires manufacturing line status information indicating a current status of a manufacturing line affected by the production plan change request; (Mahmood, [48]; supply chain optimization module 110-2 can also execute operations such as cancellation of approved orders (e.g., intake order cancellation request data, etc.), cancellation of in-progress or submitted orders (e.g., patient consent data, order cancellation request data, etc.), rescheduling of tasks (e.g., collection, manufacturing, infusion, etc.), intake of infusion intake data (e.g., product and/or shipment receipt data, transfer product to storage data, condition of shipment data, real-time tracking data, real-time alert notification data, etc.), user management operations (e.g., creation of a user, etc.), change request operations, and other such operations and Mahmood, [130]; At FIG. 3C, illustrated is an interactive scheduling interface 300C. At 310C, the interface 300C references the activity to be scheduled and associated data such as apheresis, infusion, manufacturing, location information, date information, and start time information. At 340C, the interface 300C references treatment data, location data, delivery data, and status data. Furthermore, at 320C, illustrated are the current date. Also, at 330C, illustrated are circles around respective dates, where the circles represent an availability of such dates for scheduling a respective activity (e.g., collecting blood). In an aspect, scheduling module 310A can integrate with interactive scheduling interface 300C to render scheduling dates available based on a scheduling model that determines scheduling availabilities based on collection site capacity data, infusion site capacity data, pre-configured or estimated time intervals required to execute a collection operation, pre-configured or estimated time required to transfer a Dewar to a collection site, a pre-configured or estimated time required to transfer materials to a respective manufacturing site(s), a pre-configured or estimated time required to manufacture the final product, a pre-configured or estimated time required to transfer a respective material to an infusion site, and/or a courier capacity to execute a transportation task).
in a case that the production plan is changed by use of the manufacturing line status information as requested by the production plan change request, determine whether or not the production plan satisfies the cost criterion and the structural constraint; and (Mahmood, [126]; Furthermore, data & prediction service 380B can facilitate real-time schedule optimizations using predictive insights. In another aspect, data & prediction service 380B in connection with scheduling service 330B can assign schedule requests to waitlist queues and over-schedule queues based on predictive insights and rescheduling insights based on rescheduling data. Accordingly, scheduling module 310A can utilize center of excellence capacity service 340B, manufacturing and capacity service 350B, customer configuration and scheduling rules 360B, courier capacity service 370B, and/or data and/or prediction service 380B to generate discrete schedules and/or comprehensive scheduling determinations for the entire process associated with individualized medicine therapy supply chains and Mahmood, [144]; then scheduling module 310A can auto-schedule (based on such configuration) to automatically schedule a user in the new available scheduling slot. Furthermore, scheduling module 310A can notify all respective service databases and client devices of the occurrence of such rescheduling event).
in a case where the production plan is changed, upon determining when determine that the production plan satisfies the cost criterion and the structural constraint, change the production plan in accordance with the change of the production plan. (Mahmood, [126]; Furthermore, data & prediction service 380B can facilitate real-time schedule optimizations using predictive insights. In another aspect, data & prediction service 380B in connection with scheduling service 330B can assign schedule requests to waitlist queues and over-schedule queues based on predictive insights and rescheduling insights based on rescheduling data. Accordingly, scheduling module 310A can utilize center of excellence capacity service 340B, manufacturing and capacity service 350B, customer configuration and scheduling rules 360B, courier capacity service 370B, and/or data and/or prediction service 380B to generate discrete schedules and/or comprehensive scheduling determinations for the entire process associated with individualized medicine therapy supply chains and Mahmood, [144]; then scheduling module 310A can auto-schedule (based on such configuration) to automatically schedule a user in the new available scheduling slot. Furthermore, scheduling module 310A can notify all respective service databases and client devices of the occurrence of such rescheduling event).
Regarding Claim(s) 6, Mahmood teaches: A production plan management method for generating, in real time, a production plan of a product, the production plan management method comprising: a step of acquiring product order information including a customer request specifying an item, quantity, and a delivery date of a first product among a plurality of products; ((Mahmood, [74]; In an aspect, supply chain optimization module 110-2 can acquire order data representing a wide range of ordering information such as shipment destination, shipment origin, storage conditions during transport, required temperature conditions during transport, handling instructions, instructions to place an order with a particular carrier, type of transportation mode (e.g., ground transport, air transport, etc.), sample information (e.g., quantity, type of sample, etc.) and Mahmood, [34, 36]; supply chain optimization module 110-2 can execute scheduling operations corresponding to pickup and delivery of raw materials, final products, biological materials, and other such items by stakeholders… For instance, supply chain optimization module 110-2 can intake data corresponding to scheduling a therapeutic order such as collection site data, contact data, delivery site data, calendar scheduling data, and other such data. In a non-limiting embodiment, supply chain optimization module 110-2 can employ a set of rules and requirements applied against intake data to determine a limited selection of options allowable for scheduling and Mahmood, [126]; Furthermore, data & prediction service 380B can facilitate real-time schedule optimizations using predictive insights. In another aspect, data & prediction service 380B in connection with scheduling service 330B can assign schedule requests to waitlist queues and over-schedule queues based on predictive insights and rescheduling insights based on rescheduling data. Accordingly, scheduling module 310A can utilize center of excellence capacity service 340B, manufacturing and capacity service 350B, customer configuration and scheduling rules 360B, courier capacity service 370B, and/or data and/or prediction service 380B to generate discrete schedules and/or comprehensive scheduling determinations for the entire process associated with individualized medicine therapy supply chains
a step of acquiring cost criterion information specifying a cost criterion for costs required to manufacture the first product; (Mahmood, [154]; In other aspects, courier capacity module 520 can employ parameters that based determinations, in part on, cost considerations such as courier rates, courier reliability, courier speed of transportation, courier cost for slower transporters that still deliver within required time intervals (e.g., cost benefit analysis), and other such considerations. Furthermore, courier capacity module 520 can execute operations in connection with smart scheduling techniques to automatically select a courier to optimize a cost of delivery and Mahmood, [33]; supply chain events such as point-of-care collection (e.g., collection of samples that meet threshold quality standard requirements), …scheduling couriers, raw material delivery, tracking kits and materials and raw materials), ordering and scheduling activities. Examiner notes that the delivery is for delivery to customers as well as delivery of raw materials to manufacture.
a step of acquiring cost criterion information specifying a plurality of structural constraints including raw materials, temperature/humidity, time, pre-steps, post-steps, which define defining conditions for manufacturing processes of manufacturing the first product; (Mahmood, [45]; manufacturing capacity component can access data from multiple data stores to allow scheduler component to provide a unified view of manufacturing capacity by therapy, region, site, any arbitrary tag applied to a site or other segment to ascertain capacity capabilities and constraints. Furthermore, in a non-limiting embodiment, manufacturing site capacity data can be stored in a registry at server device(s) 102 (e.g., database(s) 160) in order to accommodate data from a variety of manufacturing site sources (e.g., data sourced via integrations such as Manufacturing Execution Systems (MES) systems and Mahmood, [36]; For instance, one or more rules can correspond to existing courier scheduling dates, therapy-specific conditions for transportation and delivery time constraints (i.e. time constraint and further post-step constraint), logistical restrictions, and other such requirements and Mahmood, [37]; In another aspect, various constraints, requirements, and rules can be applied against intake data by supply chain optimization module 110-2 in order to effectuate occurrence of timely ordering and scheduling events. For instance, for fresh blood-based therapies, once collected, patient blood has shelf-lives and expiration dates after which the material is no longer usable to manufacture (i.e. raw material constraint) therapeutic products. For cryopreserved material, the shelf-life is longer. However, the effects of cryopreservation on the collected cells are still not fully understood and it introduces further complexity due to freezing and then thawing of material and Mahmood, [41]; For instance, supply chain optimization module 110-2 can intake data such as therapy data, study number data (if applicable), Chain of Identity (COI) information, shipment verification data, collection data, receiving documentation data (e.g., is the raw material kit shipped properly such as inside a specialized pouch, was the raw material stored at required temperatures such as temperatures to preserve a vapor phase of liquid nitrogen as per some material requirements (i.e. temperature constraint), missing items, mismatching numbers, damages, etc. and Mahmood, [156]; distributed scheduling engine module 110-1 can balance scheduling activities to satisfy available capacity slots at each respective site for a particular date based on estimations of time intervals corresponding to each capacity slot (e.g., time interval required to complete each intermediary manufacturing step (i.e. pre-step constraint) for manufacturing activities on particular dates and corresponding capacity during such time durations.).
a step of generating, in real time, a production plan that satisfies the customer request, the cost criterion, and the structural constraints and defines manufacturing processes for manufacturing the first product… (Mahmood, [29]; embodiments disclosed herein include distributed scheduling systems that execute distributed scheduling operations in connection with capacity subsystems and operational constraints associated with a personalized medicine supply chain. In an aspect, an example environment 100A is disclosed in which various aspects described herein can be employed and Mahmood, [126]; Furthermore, data & prediction service 380B can facilitate real-time schedule optimizations using predictive insights. In another aspect, data & prediction service 380B in connection with scheduling service 330B can assign schedule requests to waitlist queues and over-schedule queues based on predictive insights and rescheduling insights based on rescheduling data. Accordingly, scheduling module 310A can utilize center of excellence capacity service 340B, manufacturing and capacity service 350B, customer configuration and scheduling rules 360B, courier capacity service 370B, and/or data and/or prediction service 380B to generate discrete schedules and/or comprehensive scheduling determinations for the entire process associated with individualized medicine therapy supply chains and Mahmood, [135]; scheduling module 310A can couple the booking identifier to the scheduled reservation. For instance, a scheduled reservation can map to an ordering of steps and corresponding reservation status with each step to represent a journey map throughout the individualized medicine production chain or supply chain and Mahmood, [144]; then scheduling module 310A can auto-schedule (based on such configuration) to automatically schedule a user in the new available scheduling slot. Furthermore, scheduling module 310A can notify all respective service databases and client devices of the occurrence of such rescheduling event and Mahmood, [125]; data & prediction service 380B can predict the manufacturing time required to perform a target manufacturing step or entire manufacturing process for a therapeutic product based on historical data and prediction models).
a step of acquiring information regarding failure probability of a failure occurring for each of the manufacturing processes and determining a probability of a failure occurring in each of the manufacturing processes included in the production plan by using the information regarding failure probability; (Mahmood, [98]; distributed scheduling system 103 can employ prediction engine module 150-1 to execute one or more prediction model to estimate a likelihood of rescheduling events to occur in relation to patient activities (e.g., blood draws, etc.), time duration for various activities (e.g., material collection, material transportation, other transportation lags, etc.), manufacturing activities (e.g., manufacturing system integration, final product assembly, etc.), location prediction of items (e.g., location of materials, samples, products, location within manufacturing process, etc.), prediction of time duration to batch multiple orders, and other such predictions. Furthermore, prediction engine module 150-1 can predict delays across the individualized medicine therapeutic supply chain to optimize capacity (e.g., using capacity engine module 130-1) and scheduling operations (e.g., using distributed scheduling engine module 140-1).
a step of changing, in a case that a failure occurs in the first manufacturing process, the first manufacturing process to the first alternate manufacturing process in the production plan reconfiguring the manufacturing order of the products, in real time… during a subsequent manufacturing process of the manufacturing processes. ((Mahmood, [48]; In yet another aspect, supply chain optimization module 110-2 can also execute operations such as cancellation of approved orders (e.g., intake order cancellation request data, etc.), cancellation of in-progress or submitted orders (e.g., patient consent data, order cancellation request data, etc.), rescheduling of tasks (e.g., collection, manufacturing, infusion, etc.), intake of infusion intake data (e.g., product and/or shipment receipt data, transfer product to storage data, condition of shipment data, real-time tracking data, real-time alert notification data, etc.), user management operations (e.g., creation of a user, etc.), change request operations, and other such operations and Mahmood, [83]; smart device(s) 198 can continuously detect data from respective smart label sensors. Furthermore, smart module 108 can continuously determine package content (e.g., package can refer to a box, blood bag, test tube, vile, or other containment modality used to store biological material) conditions based on an analysis of the continuous detected data (e.g., threshold comparisons and evaluation). The smart label module 108, can control and/or configure smart label device(s) 198 and other equipment (e.g., manufacturing equipment, cryogenic storage equipment) to execute an operation (e.g., increase or decrease a temperature or speed of manufacture, etc.) based on the detected data exceeding or registering below a target range. Furthermore, smart module 108 can detect geo-location coordinates to automatically trigger an adjustment of various conditions based on predicted parameter…As such, smart module 108 in connection with smart label device(s) 198 can avert technical problems associated with the deterioration of quality of patient materials due to transportation, storage, and/or manufacturing processes and Mahmood, [126]; data & prediction service 380B can facilitate real-time schedule optimizations using predictive insights. In another aspect, data & prediction service 380B in connection with scheduling service 330B can assign schedule requests to waitlist queues and over-schedule queues based on predictive insights and rescheduling insights based on rescheduling data. Accordingly, scheduling module 310A can utilize center of excellence capacity service 340B, manufacturing and capacity service 350B, customer configuration and scheduling rules 360B, courier capacity service 370B, and/or data and/or prediction service 380B to generate discrete schedules and/or comprehensive scheduling determinations for the entire process associated with individualized medicine therapy supply chains and Mahmood, [98]; distributed scheduling system 103 can employ prediction engine module 150-1 to execute one or more prediction model to estimate a likelihood of rescheduling events to occur in relation to…, manufacturing activities (e.g., manufacturing system integration, final product assembly, etc.), location prediction of items (e.g., location of materials, samples, products, location within manufacturing process, etc.), prediction of time duration to batch multiple orders, and other such predictions. Furthermore, prediction engine module 150-1 can predict delays across the individualized medicine therapeutic supply chain to optimize capacity (e.g., using capacity engine module 130-1) and scheduling operations (e.g., using distributed scheduling engine module 140-1).
While Mahmood teaches manufacturing plan, Mahmood does not appear to explicitly teach logical partitions. However, Mahmood in view of the analogous art of Dorsch (i.e. manufacturing) does teach: by generating a logical partition to arrange products and define a specific position in a manufacturing order of the products. (Dorsch, [16]; Practice of the invention facilitates setting up and identifying standard and custom groups for the Window and Door Industry. These groups include but are not limited to the following items, Glass, IG Units, Muntins, Gas, Frames, Sashes, Window Lines and Window Styles. Such visual software helps set up a window manufacturing sequence criteria used for processing the various groups. and Dorsch, [51]; The items within the groups can be given a manufacturing sequence by accessing a common shared Multi-Level Sort Configuration Setup dialog (FIG. 6). Each group may be given its own individual Multi-Level Sort Configuration. More details of the Multi-Level Sort Configuration Setup are described below and Dorsch, [Fig. 1]; visual representation of logical partition of products and Dorsch, [125]; FIG. 18 depicts a windows manufacturing flow diagram. As seen, the "Production Groups Setup" and "Sort Configuration Setup" modules 334, 336 implement data entry dialogs that form part of the windows manager 314. The production grouping items and the sort configuration items feed directly into an IG sequencer module 340. The sequencer module 340 uses the information set up by the user relating to production groups and sort configuration to generate the manufacturing sequence for the IG units).
While Mahmood teaches real-time reconfiguring of manufacturing processes, Mahmood does not teach through changing slots. Mahmood/Dorsch does teach: by changing the slot a product is manufactured in (Dorsch, [52]; During the scheduling process cycle, the ordered glass lites and 1G Units are separated into their respective groups using the groups priority setting and its selection criteria. The items in each group are then placed in their manufacturing sequence determined by using either the common shared Multi-Level Sort Configuration Setup or the groups own individual Multi-Level Sort Configuration Setup and Dorsch, [85]; The screen depiction of FIG. 6 includes three levels. To create, remove, change a level position or edit a Sort Level Criteria the user clicks on the "New", "Remove", "Move Up", "Move Dn" or "Edit Sort Level" buttons 220, 222, 224, 226, 228).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the disclosed invention to have combined the teachings of Mahmood including manufacturing planning, with the teachings of Dorsch including logical partitioning in order to create a more efficient manufacturing process (Dorsch, [22]; Increased manufacturing efficiency is accomplished by utilizing colors and text to easily and clearly identify the items that belong to a group and a transport system for the group. Since the transport system is specifically configured for the items in the group, this creates a highly structured and dedicated transport system specifically for the items in the group. This allows freedom in the manufacturing process to transport the groups independently throughout the manufacturing plant.
Claim(s) 5 is/are rejected under 35 U.S.C. 103 as being unpatentable over Mahmood et al. (US 20210280287 A1) in view of Dorsch et al. (US 20030233163 A1), and Wezter et al. (US 8266066 B1).
Regarding Claim(s) 5, Mahmood teaches a weight that indicates importance of each… (Mahmood, [157]; In another aspect, estimation module 610 can estimate a time of arrival of a therapeutic product at a manufacturing site using long short-term memory (LSTM) network prediction techniques. For instance, an LSTM network can utilize recurrent neural network (RNN) techniques to learn long-term dependencies to provide accurate predictions related to delays for delivery times and manufacturing times. In an aspect, the RNN LSTM architecture can a Back Propagation Through Time technique to train algorithms using historical input feature values (e.g., one week of scheduling values) to update weights in recurrent neural networks to predict the behavior of the most influential features of the scheduling model and capacity model. Furthermore, in an aspect, the accuracy of the model predictions can be evaluated against subsequent scheduling data). Examiner notes that Mahmood teaches both cost criterion and weighting factors but does not appear to explicitly teach the specific cost criterion bracketed above.
While Mahmood teaches cost criterion and weighting factors, Mahmood does not appear to teach The production plan management device according to claim 1, wherein: the cost criterion information includes information on a labor cost, a maintenance cost, an inventory carrying cost, a backlog cost, and an overhead cost, and However, Mahmood in view of the analogous art of Wezter (i.e. production management) does teach the entirety of the limitation. (Wezter, [col. 37, lines 5-20]; Ultimately, since materials are not either waiting to be put into production, or waiting to be shipped, the cost of carrying inventory is greatly reduced. However, as with any inventory control method, JIT can only be successful if it is supported by other solid management policies and procedures… Maintain and Update Inventory Value: The value of inventory is affected by several factors after it is brought into a company: general overhead, contract negotiations, general cost of labor, cost of benefits, etc. As inventory moves through the production process and moves closer to becoming a finished good, its value increases. If inventory remains on the shelves and is not put to productive use within sufficient time to recuperate the money spent and Wezter, [col. 52, lines 45-51]; These engineering change documents are then referenced to all open or pending work orders so that they may be integrated into the work schedule. After the scheduling changes are made the work plan is updated and the approved configuration of the equipment is updated to reflect the changes that have/will be made).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the disclosed invention to have combined the teachings of Mahmood including cost criterion and weighting factors, with the teachings of Wezter including specific cost criterion mention above in order to maintain an accurate baseline for production planning (Wezter, [col. 17, lines 7-10]; This sub-process requires the identification of separable items. This process is required for accurate baseline inventory counts, materials planning, materials ordering, pre-staging and maintenance execution planning).
Claim(s) 7 is/are rejected under 35 U.S.C. 103 as being unpatentable over Mahmood et al. (US 20210280287 A1) in view of Dorsch et al. (US 20030233163 A1), and Kishi et al. (US 20020026371 A1).
Regarding Claim(s) 7, Mahmood teaches: A production plan management system for generating, in real time, a production plan of a product, the production plan management system comprising: (Mahmood, [126]; Furthermore, data & prediction service 380B can facilitate real-time schedule optimizations using predictive insights. In another aspect, data & prediction service 380B in connection with scheduling service 330B can assign schedule requests to waitlist queues and over-schedule queues based on predictive insights and rescheduling insights based on rescheduling data. Accordingly, scheduling module 310A can utilize center of excellence capacity service 340B, manufacturing and capacity service 350B, customer configuration and scheduling rules 360B, courier capacity service 370B, and/or data and/or prediction service 380B to generate discrete schedules and/or comprehensive scheduling determinations for the entire process associated with individualized medicine therapy supply chains
a distribution terminal configured to manage information regarding distribution of the first product among a plurality of products; (Mahmood, [58]; In another non-limiting embodiment, individualized medicine platform module 106 can employ system integrations module 140-2 (for execution by a processor of server device(s) 102) to integrate data from other systems with data generated, acquired, curated, analyzed and stored within database 160 or other data stores of environment 100A. In an aspect, system integrations module 140-2 can perform system integration operations such as acquiring, curating and storing data extracted from courier systems, enterprise resource planning systems, customer relationship management systems, manufacturing execution systems (MES), laboratory information management systems (LIMS), electronic batch record system (EBR), and quality management system (QMS).
a management terminal used by an administrator of a factory; and (Mahmood, [61]; an access management system or identity provider (e.g., employed across one or more server) can be configured to integrate (e.g., using integration module 14) with individualized platform module 106 and other systems in accordance with user (e.g., customer such as pharmaceutical company) criteria to act as an identity authentication mechanism to authenticate user identity and respective user roles (e.g., user permissions to execute a limited set of operations). In an aspect, the access management system can execute operations such as creation, maintenance and management of identity information of users and authentication operations for individualized medicine platform module 106 and integrated systems based on storage of user credential data.
a production plan management device configured to generate a production plan, wherein: the…, the distribution terminal,… the management terminal, and the production plan management device are connected to each other via a communication network; and (Mahmood, [110]; Example environment 200 can enable multiple devices to be interconnected through server device(s) 102, where server device(s) 102 can be local to the multiple devices, remote from the multiple devices, or any combination thereof. In one or more implementations, server device(s) 102 can be configured as a cloud of one or more server computers that are connected to the multiple devices through a network (e.g., using network component 114), the Internet, or other data communication link capable of enabling functionality to be delivered across multiple devices (e.g., several smartphone devices, desktops, tablets, etc.) to provide a common and seamless experience to a user of the multiple devices. Each of the multiple devices may have different physical requirements and capabilities, and the central computing device uses a platform to enable the delivery of an experience to the device that is both tailored to the device and yet common to all devices). Examiner notes that Kishi below is relied upon to explicitly teach the remaining terminals.
the production management plan management device includes: a processor; and
a memory storing instructions that when executed by the processor, configures the processor to: receive, from the customer terminal, product order information including a customer request specifying an item, quantity, and a delivery date of the first product, (Mahmood, [74]; In an aspect, supply chain optimization module 110-2 can acquire order data representing a wide range of ordering information such as shipment destination, shipment origin, storage conditions during transport, required temperature conditions during transport, handling instructions, instructions to place an order with a particular carrier, type of transportation mode (e.g., ground transport, air transport, etc.), sample information (e.g., quantity, type of sample, etc.) and Mahmood, [36]; For instance, supply chain optimization module 110-2 can intake data corresponding to scheduling a therapeutic order such as collection site data, contact data, delivery site data, calendar scheduling data, and other such data. In a non-limiting embodiment, supply chain optimization module 110-2 can employ a set of rules and requirements applied against intake data to determine a limited selection of options allowable for scheduling and Mahmood, [108]; In an aspect, processor 230 can comprise one or more processor configured to perform one or more operations (of at least one module of client individualized medicine module 180) using hardware. As such, processor 230 can include hardware elements 240 that may be configured as processors, functional blocks, and so forth).
receive, from the management terminal, cost criterion information specifying a cost criterion for costs required to manufacture the first product, and (Mahmood, [154]; In other aspects, courier capacity module 520 can employ parameters that based determinations, in part on, cost considerations such as courier rates, courier reliability, courier speed of transportation, courier cost for slower transporters that still deliver within required time intervals (e.g., cost benefit analysis), and other such considerations. Furthermore, courier capacity module 520 can execute operations in connection with smart scheduling techniques to automatically select a courier to optimize a cost of delivery and Mahmood, [33]; supply chain events such as point-of-care collection (e.g., collection of samples that meet threshold quality standard requirements), tracking supply chain events (e.g., as relates to manufacturing a personalized therapeutic for a patient), optimizing resources (e.g., fulfilling manufacturing orders), security and compliance (e.g., ensuring data quality, data validation, and data encryption mechanisms are implemented), supply chain orchestration events (e.g., scheduling couriers, raw material delivery, tracking kits and materials and raw materials), ordering and scheduling activities (e.g., scheduling of sample collections, manufacturing, administration of medicine, etc.). Examiner notes that the delivery is for delivery to customers as well as delivery of raw materials to manufacture.
receive, from the distribution terminal and/or the delivery terminal, distribution and delivery status information that indicates a respective status of distribution and delivery, (Mahmood, [47]; In other aspects, supply chain optimization module 110-2 can also execute operations based on intake of distribution center (e.g., global distribution) intake data (e.g., specimen status upon receipt, shipment verification data, COI data, condition of shipment data, etc.) and order status tracking intake data or presentation data (e.g., shipping status, satellite lab status, manufacturing status, distribution center status, infusion status, etc.). For instance, the collection stage can be presented (e.g., using display module 190) at a user interface with stage data, phase data (e.g., test result stage), shipping stage data (e.g., pending, shipped, complete, etc.), site data (e.g., center of excellence, cryogenic laboratory, test institution, distribution center, hospital, etc.), timeline data, and other such information and Mahmood, [130]; At FIG. 3C, illustrated is an interactive scheduling interface 300C. At 310C, the interface 300C references the activity to be scheduled and associated data such as apheresis, infusion, manufacturing, location information, date information, and start time information. At 340C, the interface 300C references treatment data, location data, delivery data, and status data.
store a structural constraint database including structural constraint information specifying a plurality of structural constraints including raw materials, temperature/humidity, time, pre-steps, post-steps, which define conditions for manufacturing processes of manufacturing the first product in the factory, and (Mahmood, [45]; manufacturing capacity component can access data from multiple data stores to allow scheduler component to provide a unified view of manufacturing capacity by therapy, region, site, any arbitrary tag applied to a site or other segment to ascertain capacity capabilities and constraints. Furthermore, in a non-limiting embodiment, manufacturing site capacity data can be stored in a registry at server device(s) 102 (e.g., database(s) 160) in order to accommodate data from a variety of manufacturing site sources (e.g., data sourced via integrations such as Manufacturing Execution Systems (MES) systems and Mahmood, [36]; For instance, one or more rules can correspond to existing courier scheduling dates, therapy-specific conditions for transportation and delivery time constraints (i.e. time constraint and further post-step constraint), logistical restrictions, and other such requirements and Mahmood, [37]; In another aspect, various constraints, requirements, and rules can be applied against intake data by supply chain optimization module 110-2 in order to effectuate occurrence of timely ordering and scheduling events. For instance, for fresh blood-based therapies, once collected, patient blood has shelf-lives and expiration dates after which the material is no longer usable to manufacture (i.e. raw material constraint) therapeutic products. For cryopreserved material, the shelf-life is longer. However, the effects of cryopreservation on the collected cells are still not fully understood and it introduces further complexity due to freezing and then thawing of material and Mahmood, [41]; For instance, supply chain optimization module 110-2 can intake data such as therapy data, study number data (if applicable), Chain of Identity (COI) information, shipment verification data, collection data, receiving documentation data (e.g., is the raw material kit shipped properly such as inside a specialized pouch, was the raw material stored at required temperatures such as temperatures to preserve a vapor phase of liquid nitrogen as per some material requirements (i.e. temperature constraint), missing items, mismatching numbers, damages, etc. and Mahmood, [156]; distributed scheduling engine module 110-1 can balance scheduling activities to satisfy available capacity slots at each respective site for a particular date based on estimations of time intervals corresponding to each capacity slot (e.g., time interval required to complete each intermediary manufacturing step (i.e. pre-step constraint) for manufacturing activities on particular dates and corresponding capacity during such time durations.).
generate, in real time, a production plan that satisfies the customer request, the cost criterion, and the structural constraints and defines manufacturing processes for manufacturing the first product… (Mahmood, [29]; embodiments disclosed herein include distributed scheduling systems that execute distributed scheduling operations in connection with capacity subsystems and operational constraints associated with a personalized medicine supply chain. In an aspect, an example environment 100A is disclosed in which various aspects described herein can be employed and Mahmood, [126]; Furthermore, data & prediction service 380B can facilitate real-time schedule optimizations using predictive insights. In another aspect, data & prediction service 380B in connection with scheduling service 330B can assign schedule requests to waitlist queues and over-schedule queues based on predictive insights and rescheduling insights based on rescheduling data. Accordingly, scheduling module 310A can utilize center of excellence capacity service 340B, manufacturing and capacity service 350B, customer configuration and scheduling rules 360B, courier capacity service 370B, and/or data and/or prediction service 380B to generate discrete schedules and/or comprehensive scheduling determinations for the entire process associated with individualized medicine therapy supply chains and Mahmood, [135]; scheduling module 310A can couple the booking identifier to the scheduled reservation. For instance, a scheduled reservation can map to an ordering of steps and corresponding reservation status with each step to represent a journey map throughout the individualized medicine production chain or supply chain and Mahmood, [144]; then scheduling module 310A can auto-schedule (based on such configuration) to automatically schedule a user in the new available scheduling slot. Furthermore, scheduling module 310A can notify all respective service databases and client devices of the occurrence of such rescheduling event).
calculate an estimated delivery date on which the first product is deliverable to the customer based on the production plan thus generated and a lead calculated from the delivery status information… (Mahmood, [152]; In an aspect, the determination model can employ a neural network model to propagate layers of determinations to select optimal sites and provide estimated transit times. In an aspect, a transit time may not merely be proportional to a distance between sites but rather may need to consider other factors specific to the mode of transportation (e.g., air, car, rail, etc.), traffic conditions, weather conditions, requirements of the therapeutic or specimen, and other such considerations. With respect to parameters that predict the fastest manufacturing and transportation timings, such predictions can be based on prediction models applied to historical data to estimate future predictions.
reconfigure the manufacturing order of the products, in real time,…during a subsequent manufacturing process of the manufacturing processes. (Mahmood, [48]; In yet another aspect, supply chain optimization module 110-2 can also execute operations such as cancellation of approved orders (e.g., intake order cancellation request data, etc.), cancellation of in-progress or submitted orders (e.g., patient consent data, order cancellation request data, etc.), rescheduling of tasks (e.g., collection, manufacturing, infusion, etc.), intake of infusion intake data (e.g., product and/or shipment receipt data, transfer product to storage data, condition of shipment data, real-time tracking data, real-time alert notification data, etc.), user management operations (e.g., creation of a user, etc.), change request operations, and other such operations and Mahmood, [126]; data & prediction service 380B can facilitate real-time schedule optimizations using predictive insights. In another aspect, data & prediction service 380B in connection with scheduling service 330B can assign schedule requests to waitlist queues and over-schedule queues based on predictive insights and rescheduling insights based on rescheduling data. Accordingly, scheduling module 310A can utilize center of excellence capacity service 340B, manufacturing and capacity service 350B, customer configuration and scheduling rules 360B, courier capacity service 370B, and/or data and/or prediction service 380B to generate discrete schedules and/or comprehensive scheduling determinations for the entire process associated with individualized medicine therapy supply chains and Mahmood, [98]; distributed scheduling system 103 can employ prediction engine module 150-1 to execute one or more prediction model to estimate a likelihood of rescheduling events to occur in relation to…, manufacturing activities (e.g., manufacturing system integration, final product assembly, etc.), location prediction of items (e.g., location of materials, samples, products, location within manufacturing process, etc.), prediction of time duration to batch multiple orders, and other such predictions. Furthermore, prediction engine module 150-1 can predict delays across the individualized medicine therapeutic supply chain to optimize capacity (e.g., using capacity engine module 130-1) and scheduling operations (e.g., using distributed scheduling engine module 140-1).
While Mahmood does teach acquiring customer requests, Mahmood does not appear to explicitly teach a customer terminal. However, Mahmood in view of the analogous art of Kishi (logistics management) does teach: a customer terminal used by a customer requesting a first product; (Kishi, [35]; FIG. 1, the entire network transaction system includes a customer-client 10 as a terminal computer for a customer, a communication network 20, a transaction server 30 as a transaction center, a communication network 50, a retailer-client 61 as a terminal computer for a retailer, a distributor-client 62 as a terminal computer for a distributor, and a delivery service-client 63 as a terminal computer for a delivery service).
a delivery terminal configured to manage information regarding delivery of the first product; (Kishi, [38]; It should be noted that the customer-client 10 is a terminal computer, a portable computer (including a cellular phone) or the like provided for a client. Also, the retailer-client 61, the distributor-client 62 and the delivery service-client 63 are terminal computers, portable computers (including cellular phones) or the like provided for a retailer, a distributor and a delivery service, respectively and Kishi, [76]; A process flow will now be described with reference to FIG. 3. It is assumed that the customer-client 10, the retailer-client 61, the distributor-client 62 and delivery service-client 63 are connected to the transaction server 30 through the Internet.).
While Mahmood does teach estimating delivery dates and providing estimated delivery times. Mahmood does not appear to explicitly teach: transmit the estimated delivery date to the customer terminal. However, Mahmood/Kishi does teach the entirety of the limitation: (Kishi, [167]; a delivery data is determined based on a current total delivery time required delivering the product through all the corporations associated with a manufacture or a procurement of the product to a shipping of the product. Therefore, the transaction server 30 can automatically provide the delivery date to the customer).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the disclosed invention to have combined the teachings of Mahmood does teach acquiring customer requests, estimating delivery dates and providing delivery times with the teachings of Kishi including customer terminals, delivery terminals, and transmitting delivery times in order to allow a customer to make information inquiries regarding progress and allow for delivery management to receive and manage information regarding delivery for the customer. (Kishi, [48]; the customer-client 10 makes an information inquiry, the product and delivery information providing part 33 provides product information, purchase condition information, corporation information and the like. Further, when the customer-client 10 indicates product selection information (showing the product information which the customer considers buying) on an information inquiry window, the customer-client 10 provides delivery schedule including a product name, a price, delivery date and the like in response to the indication from the customer-client 10 and Kishi, [138]; A process flow will now be described with reference to FIG. 3. It is assumed that the customer-client 10, the retailer-client 61, the distributor-client 62 and delivery service-client 63 are connected to the transaction server 30 through the Internet).
While Mahmood teaches manufacturing plan, Mahmood does not appear to explicitly teach logical partitions. However, Mahmood in view of the analogous art of Dorsch (i.e. manufacturing) does teach: by generating a logical partition to arrange products and define a specific position in a manufacturing order of the products. (Dorsch, [48]; Groups are assigned and setup visually at a control screen (FIG. 1) by a manufacturing supervisor. A given customer order can contain multiple line items, each having its own stock number or special tag. The production supervisor can assign and setup multiple groups inside the "Production" or (main) group. Each group can have its own particular identifying color code, transport properties and selection criteria. The selection criteria only defines the items that will be placed into the group. It does not define the manufacturing sequence. The manufacturing sequence is defined using a Multi-Level Sort Configuration setup that is illustrated in FIG. 6 and Dorsch, [51]; The items within the groups can be given a manufacturing sequence by accessing a common shared Multi-Level Sort Configuration Setup dialog (FIG. 6). Each group may be given its own individual Multi-Level Sort Configuration. More details of the Multi-Level Sort Configuration Setup are described below and Dorsch, [79]; FIG. 5 shows a typical production glass breakout screen 180 utilizing a representative grouping method developed by the assignee of the invention. There are lites from five different groups in this breakout display (Laminated, Tempered, Accentrim, WhiteMuntin, and the Base group). In the prior art, this would have taken 5 different sheets of glass and Dorsch, [Fig. 1]; visual representation of logical partition of products).
While Mahmood teaches real-time reconfiguring of manufacturing processes, Mahmood does not teach through changing slots. Mahmood/Dorsch does teach: by changing the slot a product is manufactured in (Dorsch, [52]; During the scheduling process cycle, the ordered glass lites and 1G Units are separated into their respective groups using the groups priority setting and its selection criteria. The items in each group are then placed in their manufacturing sequence determined by using either the common shared Multi-Level Sort Configuration Setup or the groups own individual Multi-Level Sort Configuration Setup and Dorsch, [85]; The screen depiction of FIG. 6 includes three levels. To create, remove, change a level position or edit a Sort Level Criteria the user clicks on the "New", "Remove", "Move Up", "Move Dn" or "Edit Sort Level" buttons 220, 222, 224, 226, 228).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the disclosed invention to have combined the teachings of Mahmood including manufacturing planning, with the teachings of Dorsch including logical partitioning in order to create a more efficient manufacturing process (Dorsch, [22]; Increased manufacturing efficiency is accomplished by utilizing colors and text to easily and clearly identify the items that belong to a group and a transport system for the group. Since the transport system is specifically configured for the items in the group, this creates a highly structured and dedicated transport system specifically for the items in the group. This allows freedom in the manufacturing process to transport the groups independently throughout the manufacturing plant.
Conclusion
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/JEREMY L GUNN/ Examiner, Art Unit 3624