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 .
Notice of Pre-AIA or AIA Status
Claims 1-7 were pending and were rejected in the previous office action. Claims 1-7 were amended. Claims 1-7 remain pending and are examined in this office action.
Response to Amendment/Arguments
Applicant's arguments filed on 02/19/2026 have been fully considered as below.
35 USC 112 Rejection:
Applicant's arguments regarding rejection under 35 USC 112 of claims 1-7, see pages 5-6 of Remarks, have been fully considered and are persuasive in view of the amendments. The rejection under 35 USC 112 of claims 1-7 has been withdrawn.
35 USC 101 Rejection:
Applicant's arguments regarding the previous 35 USC 101 rejection have been fully considered but they are not persuasive.
Applicant has argued that limitations of the independent claims do not represent an abstract idea because the claim involves a process that “[a] human mind, even with pen and paper, is not equipped to efficiently receive and parse diverse VRP data, dynamically construct an extensible "VRP general form" data structure encapsulating various problem-specific conditions, and then apply a computationally intensive column generation method that continuously interacts with this complex, multifaceted data model.” This argument is not persuasive as neither “an extensive “VRP general form” data structure” nor “a computationally intensive column generation method” is specified within the claims themselves. Furthermore, the claimed limitations do not exclude small sample size data. A human can possibly solve the general form of the Vehicle Routing Problem (VRP) by hand and paper using column generation for a small problem size. The activities recited in the claims are recited at a high level of generality and as such are performable within the human mind and therefore represent an abstract idea in according with Step 2A Prong One.
The applicant has argued the abstract idea recited in the claims are integrated into a practical application and therefore should be eligible via Step 2A Prong Two. This argument is not persuasive as that step is based on the additional elements recited in the claims, i.e. the elements of the claims that are not part of the abstract idea. The additional elements recited in the independent claims represent only mere generic components such as a non-transitory computer-readable recording medium or insignificant extra-solution activity such as data collection/transmission (“receiving vehicle routing problem (VRP) data”, “outputting a solution result of the route selection problem”). Neither of these additional elements are sufficient to integrate the abstract idea into a practical application and therefore Step 2A Prong Two is “No”.
The applicant has further argued that the claim represents significantly more than the judicial exception. However, this argument is not persuasive as this step also refers to additional elements presented in the claims and uses them to determine whether the claim as-a-whole represents significantly more than the judicial exception alone. This is not a case in these claims as the additional elements recited represent generic components or insignificant extra-solution activity and therefore do not amount to an inventive concept and therefore do not cause the claim as-a-whole to amount to significantly more than the judicial exception alone. The recited “column generation method” is well-understood, routine, and conventional mathematical optimization techniques previously known to the industry, specified at a high level of generality, to the judicial exception, e.g., a claim to an abstract idea requiring no more than a generic computer to perform generic computer functions that are well-understood, routine and conventional activities previously known to the industry, as discussed in Alice Corp., 573 U.S. at 225, 110 USPQ2d at 1984 (see MPEP § 2106.05(d)).
Therefore, the rejection under 35 USC 101 to the claims is maintained and updated as appropriate below to reflect the claim amendments.
35 USC 102 Rejection:
Regarding the rejection under 35 USC 102 to the claims, Applicant's arguments have been fully considered but they are not persuasive. The Applicant has argued:
“Yarkony, however, appear to be exclusively focused on specific, pre-defined VRP variations, such as the Capacitated Vehicle Routing Problem (CVRP) and Multi-Robot Routing (MRR). For example, Yarkony discloses in paragraph [0045] various optimization methods applied "in the context of the capacitated vehicle routing problem (CVRP)" and in paragraph [0061] "in the context of the multi-robot routing (MRR) problem." These disclosures consistently detail problem-specific formulations and solutions, where parameters akin to "task," "route constraint," "mobile body number constraint," "route cost," and "travel time calculation method" are inherently integrated into the model for that distinct problem type. Yarkony does not appear to teach or suggest a system that actively "receiving vehicle routing problem (VRP) data" nor contemplate "generating a VRP general form based on the received VRP data," a VRP general form specifically characterized by "a plurality of conditions including: a task, a route constraint, a mobile body number constraint, a route cost and a travel time calculation method" as claimed. Instead, Yarkony appears to consistently position these elements as fixed attributes of a particular problem, rather than configurable parameters dynamically derived from input data.
The assertion that Yarkony discloses the claimed subject matter does not fully appreciate the scope and purpose of these features. While Yarkony discusses components that might be analogous to "task," "route constraint," "mobile body number constraint," "route cost," and "travel time calculation method" within its specific VRP formulations (e.g., item demand and capacity as constraints in [0045], total distance as route cost in [0050], a fixed number of available vehicles K as a constraint in [0052], and time windows in] [0076]), these are invariably presented as static, inherent elements of particular problems. Yarkony does not appear to teach the dynamic "generation of a VRP general form" that includes these conditions as distinct, configurable parameters derived from external "received VRP data."
The claimed invention presents a novel architectural approach to VRP solving, which transcends the limitations of rigid, problem-specific solvers to provide a highly flexible framework. This framework commences with the distinct step of acquiring external VRP data, subsequently transforms this data into a standardized and generalized VRP model (the "VRP general form') comprising explicitly defined and configurable conditions including task specifics, various route constraints, mobile body number constraints, route cost definitions, and travel time calculation methodologies. Only then does it proceed to apply a column generation method to this highly adaptable general form. This design empowers the system to accommodate a wide array of VRP scenarios, including those with potentially unique or unforeseen constraints, without necessitating a complete re-engineering of the solution mechanism for each new problem variant. In stark contrast, Yarkony is configured to address specific, well-defined VRPs (such as CVRP and MRR) with their intrinsic, fixed constraints, rather than dynamically constructing a generalized problem definition from variable input data.
Furthermore, the claims recite "to the VRP general form." This underscores that the column generation method is applied directly to the dynamically generated VRP general form, which is an explicitly configured data structure embracing the specified flexible conditions. This constitutes a crucial distinction. In Yarkony, the column generation method is applied directly to the problem formulation of a specific VRP, where all conditions are already concretely embedded within that very formulation. Yarkony does not appear to teach or suggest an intermediate step of first generating a "VRP general form" that functions as an abstract, configurable representation of diverse VRP data, prior to the application of the column generation method.”
The Examiner respectfully disagrees. The claims are not directed to “configurable parameters dynamically derived from input data,” “the dynamic "generation of a VRP general form" that includes these conditions as distinct, configurable parameters derived from external "received VRP data," “a highly flexible framework. This framework commences with the distinct step of acquiring external VRP data, subsequently transforms this data into a standardized and generalized VRP model (the "VRP general form') comprising explicitly defined and configurable conditions,” and “an intermediate step of first generating a "VRP general form" that functions as an abstract, configurable representation of diverse VRP data, prior to the application of the column generation method” (emphasis added). Under Broadest Reasonable Interpretation (BRI), “VRP general form” could be any structured mechanism for specifying the input and constraints required to create or generate a vehicle routing problem. Applicant has further argued that “Yarkony discloses the claimed subject matter does not fully appreciate the scope and purpose of these features. While Yarkony discusses components that might be analogous to "task," "route constraint," "mobile body number constraint," "route cost," and "travel time calculation method" within its specific VRP formulations (e.g., item demand and capacity as constraints in [0045], total distance as route cost in [0050], a fixed number of available vehicles K as a constraint in [0052], and time windows in] [0076]), these are invariably presented as static, inherent elements of particular problems. Yarkony does not appear to teach the dynamic "generation of a VRP general form" that includes these conditions as distinct, configurable parameters derived from external "received VRP data."” Applicant further argues that “Yarkony does not appear to teach or suggest an intermediate step of first generating a "VRP general form" that functions as an abstract, configurable representation of diverse VRP data, prior to the application of the column generation method.”” The claims are not directed to “a system that actively "receiving vehicle routing problem (VRP) data"” and “contemplate "generating a VRP general form based on the received VRP data" (emphasis added). Under Broadest Reasonable Interpretation (BRI), “VRP general form” could be any structured mechanism for specifying the input and constraints required to create or generate a vehicle routing problem. Yarkony teaches to receive information regarding vehicle routing problem (Yarkony, see at least Fig. 9, step 910, par. [0180]; par. [0101], “In an example, the system includes J homogenous robots each with the capacity to service D orders where J, D are user defined”). Yarkony further teaches to generate, based on the received information regarding vehicle routing problem, a vehicle routing problem (Yarkony, see at least Fig. 9, step 920, par. [0181]), and generating routes in a case where a plurality of mobile bodies that have started from a specific node passes through a plurality of nodes and returns to the specific node, which satisfy the plurality of conditions defined in the VRP general form, from a set of the plurality of routes that indicates order in which the plurality of mobile bodies visit the plurality of nodes, by applying a column generation method to the general form (Yarkony, see at least par. [0076], “the described CG-based optimization solutions can be applied in the context of the CVRP with time windows and the MRR problem with time windows. In an example, the CVRP with time windows imposes the additional constraint that each item can be serviced by a vehicle only within a specified time window, i.e., after a predetermined starting time and before a predetermined ending time. Similarly, the MRR problem with time windows imposes the additional constraint that every robot must return to the launcher item before a predetermined time limit”; Fig. 8 and par. [0178]; Fig. 9, step 950, par. [0184]). Yarkony further teaches column generation is used to provide an optimal solution of a linear programming relaxation by constructing a sufficient subset of a set of routes that satisfy a plurality of constraints, such as a “task”, a “route constraint”, a “transport vehicle number constraint”, a “route cost”, and a “travel time calculation method” (Yarkony, see at least pars. [0045-0056]). Thus, Yarkony teaches the above limitations.
The rejection under 35 USC 102 is maintained.
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 non-statutory subject matter of abstract ideas under the mental processes and mathematical concept groupings without significantly more.
Step 1: Statutory category – Yes
Claim 1 represents an article of manufacture. Claim 6 represents a process. Claim 7 represents a machine.
Claims 1, 6, and 7 recites:
Claim 1: A non-transitory computer-readable recording medium storing an information processing program for causing a computer to execute processing comprising:
Claim 6: An information processing method implemented by a computer, the information processing method comprising:
Claim 7: An information processing apparatus comprising: a memory; and a processor coupled to the memory, the processor being configured to perform processing comprising:
receiving vehicle routing problem (VRP) data;
generating a VRP general form based on the received VRP data, the VRP general form includes a plurality of conditions including: a task, a route constraint, a mobile body number constraint, a route cost and a travel time calculation method;
generating routes in a case where a plurality of mobile bodies that have started from a specific node passes through a plurality of nodes and returns to the specific node, which satisfy the plurality of conditions defined in the VRP general form, from a set of the plurality of routes that indicates order in which the plurality of mobile bodies visit the plurality of nodes, by applying a column generation method to the general form;
generating a route selection problem by using the generated routes;
solving the generated route selection problem; and
outputting a solution result of the route selection problem.
Step 2A Prong one evaluation: Judicial Exception – Yes – Mental processes.
The claims are to be analyzed to determine whether it recites subject matter that falls within one of the following groups of abstract ideas: a) mathematical concepts, b) mental processes, and/or c) certain methods of organizing human activity.
The Office submits that the foregoing bolded limitation(s) constitutes judicial exceptions in terms of “mental processes” because under its broadest reasonable interpretation, the claim covers performance using mental processes.
The claims recite the limitations of “generating a VRP general form …”, “generating routes …”, “generating a route selection problem”, and “solving the generated route selection problem”. These limitations, as drafted, are simple processes that, under its broadest reasonable interpretation, cover performance of the limitations in the mind, but for the limitation that a computer can be programmed to perform the tasks. That is, nothing in the claim elements precludes the limitation from practically being performed in the mind. For example, but for the “computer”, “memory”, and “processor” language, the claim encompasses a person looking at a set of data/requirements for determining a plurality of routes by applying a column generation method to the vehicle routing problem general form, e.g., using table, and performing the steps of “solving” and “outputting” a resolution result of a route selection using pen and paper. The mere nominal recitation of “computer”, “memory”, and “processor” does not take the claim limitations out of the mental processes grouping. Thus, the claims recite mental processes.
Step 2A Prong two evaluation: Practical Application - No
The claims are evaluated whether as a whole it integrates the recited judicial exception into a practical application. As noted in the 2019 PEG, it must be determined whether any additional elements in the claim beyond the abstract idea integrate the exception into a practical application in a manner that imposes a meaningful limit on the judicial exception. The courts have indicated that additional elements merely using a computer to implement an abstract idea, adding insignificant extra solution activity, or generally linking use of a judicial exception to a particular technological environment or field of use do not integrate a judicial exception into a “practical application.”
In the present case, the additional limitations beyond the above-noted abstract idea are as follows (where the underlined portions are the “additional limitations” while the bolded portions continue to represent the “abstract idea”).
The claim recites additional element memory medium, processor, and mobile bodies. The additional limitation is recited at a high level of generality and is merely automates the “receiving”, “generating”, “solving” and “outputting” steps, therefore acting as a generic computer or program to perform the abstract idea. The computer is claimed generically and is operating in ordinary capacity and does not use the judicial exception in a manner that imposes a meaningful limit on the judicial exception, such that the claim is more than a drafting effort designed to monopolize the exception. The additional limitation is no more than mere instruction to apply the judicial exception using a computer.
The claim recites additional steps of receiving data and outputting a solution result. The additional steps are directed to mere data transmit and output, which are an insignificant extra post solution activity per MPEP 2106.05 (g). The steps do not place the identified abstraction into practicality. According, even in combination, the additional limitation does not integrate the abstract idea into a practical application because they do not impose any meaningful limit on practicing the abstract idea. Thus, the claims are directed to the abstract idea.
Step 2B evaluation: Inventive concept - No
Claim 1 is evaluated whether the claim as a whole amounts to significantly more than the recited exception, i.e., whether any additional element, or combination of additional elements, adds an inventive concept to the claim.
As discussed with respect to Step 2A Prong Two, the additional elements in the claim amount to no more than mere instructions to apply the exception using a generic computer component. The same analysis applies here in 2B, i.e., mere instructions to apply an exception on a generic computer cannot integrate a judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B.
Under the 2019 PEG, a conclusion that additional element in Step 2A should be re-evaluated in Step 2B. Here, the limitation of “computer” is considered in Step 2A, and thus the limitation is re-evaluated in Step 2B to determine if it is more than what is well-understood, routine, conventional activity in the field. The recited “column generation method” is well-understood, routine, and conventional mathematical optimization techniques previously known to the industry, specified at a high level of generality, to the judicial exception, e.g., a claim to an abstract idea requiring no more than a generic computer to perform generic computer functions that are well-understood, routine and conventional activities previously known to the industry, as discussed in Alice Corp., 573 U.S. at 225, 110 USPQ2d at 1984 (see MPEP § 2106.05(d)).
For these reasons, there is no inventive concept in the claim, and thus the claim is ineligible.
Claims 2-5 do not recite any additional limitation that amounts more than the judicial exception. Therefore, dependent claims 2-5 are not patent eligible under the same rationale as provided in the rejection of claim 1.
Therefore, claims 1-7 are ineligible under 35 U.S.C. 101.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1-7 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Yarkony et al. (US 20230075128 A1, hereinafter “Yarkony”).
Regarding claims 1, 6, and 7, Yarkony discloses:
(claim 1) A non-transitory computer-readable recording medium storing an information processing program for causing a computer to execute processing (Yarkony, see at least par. [0193]) comprising:
(claim 6) An information processing method implemented by a computer (Yarkony, see at least Figs. 8, 9, par. [0178], “The hardware platform 800 may include a processor 802 that can execute code to implement a method described in this document (e.g., method 900 shown in FIG. 9)”), the information processing method comprising:
(claim 7) An information processing apparatus (Yarkony, see at least Fig. 8, par. [0178], hardware platform 800) comprising: a memory (Yarkony, see at least Fig. 8, par. [0179], memory 804); and a processor coupled to the memory (Yarkony, see at least Fig. 8, par. [0178], processor 802), the processor being configured to perform processing (Yarkony, see at least Fig. 8, par. [0178], “The hardware platform 800 may include a processor 802 that can execute code to implement a method described in this document (e.g., method 900 shown in FIG. 9)”) comprising:
receiving vehicle routing problem (VRP) data (Yarkony, see at least Fig. 9, par. [910], “The method 900 includes, at operation 910, receiving information associated with the starting depot location, the ending depot location, and the items associated with the plurality of customers”; par. [0101], “In an example, the system includes J homogenous robots each with the capacity to service D orders where J, D are user defined”);
generating a VRP general form based on the received VRP data, the VRP general form includes a plurality of conditions including: a task, a route constraint, a mobile body number constraint, a route cost and a travel time calculation method (Yarkony, see at least Fig. 9, par. [920], “The method 900 includes, at operation 920, generating, based on the starting depot location, the ending depot location, the integer demand and the item location of each of the items, and a capacity of the vehicle, a vehicle routing problem”; par. [0061-0063]);
generating routes in a case where a plurality of mobile bodies (Yarkony, see at least Fig. 2, par. [0008], generating routes for a plurality of vehicles/robots) that have started from a specific node (Yarkony, see at least par. [0008], “… starts and ends at the depot location …”; Fig. 2, par. [0061], “In the MRR problem, each of a fleet of mobile warehouse robots that enter the warehouse floor from a single location, called the launcher”) passes through a plurality of nodes (Yarkony, see at least par. [0008], “…the item location of each of the plurality of items …”; Fig. 2, par. [0061], “…pick up one or multiple items inside the warehouse…”) and returns to the specific node (Yarkony, see at least par. [0008], “… starts and ends at the depot location …”; Fig. 2, par. [0061], “In the MRR problem, each of a fleet of mobile warehouse robots that enter the warehouse floor from a single location, called the launcher”), which satisfy the plurality of conditions defined in the VRP general form (Yarkony, see at least Fig. 9, par. [0182], “The method 900 includes, at operation 930, splitting the vehicle routing problem into a master problem and a subproblem, wherein the master problem and the subproblem comprise constraints associated with the vehicle routing problem”; par. [0008, 0077], “In some embodiments, the CVRP with time windows is defined such that the starting depot is the same as the ending depot. In this scenario, a solution to the CVRP corresponds to a solution to the vehicle routing problem that minimizes a sum of a distance of each route of a plurality of routes, wherein each route of a plurality of routes for each of the plurality of vehicles (a) starts and ends at the depot location, (b) visits an item location no more than once and within a corresponding window start time and a corresponding window end time, and (c) services a capacity that does not exceed each vehicle's capacity to carry items”, par. [0063], “To service an item, a robot must travel to the specific cell where the item is located during the item's associated serviceable time window and pick it up for delivery to the launcher. Servicing an item consumes a portion of the robot's capacity, which is refreshed once it travels back to the launcher. The complete path a specific robot takes, which necessarily ends at the launcher, is called a route”), from a set of the plurality of routes (Yarkony, see at least Fig. 2, par. [0007, 0058, 0061], set of routes for the plurality of mobile bodies, i.e. vehicles/robots) that indicates order in which the plurality of mobile bodies visit the plurality of nodes (Yarkony, see at least Fig. 2, par. [0007], “… a particular route with a topological ordering of items serviced by a particular vehicle on the particular route …”), by applying a column generation method to the general form (Yarkony, see at least Fig. 9, par. [0186], “In some embodiments, the column generation method comprises adding a column to the master problem as part of solving the subproblem, wherein the column corresponds to a particular route with a topological ordering of items serviced by a particular vehicle on the particular route”);
generating a route selection problem by using the generated routes (Yarkony, see at least Fig. 9, par. [0182], “at operation 930, splitting the vehicle routing problem into a master problem and a subproblem, wherein the master problem and the subproblem comprise constraints associated with the vehicle routing problem”);
solving the generated route selection problem (Yarkony, see at least Fig. 9, par. [0183], “at operation 940, computing, for the subproblem, a plurality of costs associated with distances between one or more item locations such that each of the distances is less than a first threshold”); and
outputting a solution result of the route selection problem (Yarkony, see at least Fig. 9, par. [0185], “at operation 960, transmitting, to the vehicle, the optimized route, which minimizes a distance of the route”).
Regarding claim 2, Yarkony teaches all the limitations of claim 1 as discussed above. Yarkony further teaches wherein the route constraint indicates a constraint condition for each route, which does not depend on another route (Yarkony, see at least par. [0007], “… (c) visits each item location no more than once, and (d) services a capacity that does not exceed the capacity of the vehicle to carry items…”; par. [0045], “… Each item is associated with an integer demand (e.g., indicating a size of the item or the cost of the item) and each of the vehicles have a capacity … The constraints applied ensure that no vehicle services more demand than it has capacity …”), and in the processing of generating the routes, routes that satisfy the route constraint and minimize a value of an objective function related to the routes are generated (Yarkony, see at least par. [0052-0053], “A set of routes provides a feasible solution to CVRP if it services every item at least once and uses no more than K routes, where K denotes the number of vehicles available … The selection of the optimal solution is determined as: … Herein, (2.1.3a) minimizes the total cost of the routes selected, and (2.1.3b) ensures every item is covered at least once”).
Regarding claim 3, Yarkony teaches all the limitations of claims 1 and 2 as discussed above. Yarkony further teaches wherein the mobile body number constraint relates to the number of mobile bodies that pass through a route (Yarkony, see at least par. [0045], “…Solving the CVRP corresponds to partitioning the items into ordered lists of items called routes, each of which is serviced by a unique vehicle so as to minimize the total distance traveled. The constraints applied ensure … that the number of vehicles used is bounded”), and in the processing of generating the routes, routes that minimize the value of the objective function within a range that satisfies the mobile body number constraint are generated (Yarkony, see at least par. [0052], “… A set of routes provides a feasible solution to CVRP if it services every item at least once and uses no more than K routes, where K denotes the number of vehicles available”; par. [0053], see objective function 2.1.3c).
Regarding claim 4, Yarkony teaches all the limitations of claims 1-3 as discussed above. Yarkony further teaches wherein in the processing of generating the routes, routes that minimize the value of the objective function are generated by using the route cost (Yarkony, see at least par. [0050], “…The cost of a route is denoted cl and is defined as the total distance traveled …”; par. [0052-0053], “… (2.1.3a) minimizes the total cost of the routes selected …”).
Regarding claim 5, Yarkony teaches all the limitations of claims 1-4 as discussed above. Yarkony further teaches wherein the VRP general form includes a definition of a travel time of the route, and in the processing of generating the routes (Yarkony, see at least par. [0076], “… In some embodiments, the described CG-based optimization solutions can be applied in the context of the CVRP with time windows and the MRR problem with time windows. In an example, the CVRP with time windows imposes the additional constraint that each item can be serviced by a vehicle only within a specified time window, i.e., after a predetermined starting time and before a predetermined ending time. Similarly, the MRR problem with time windows imposes the additional constraint that every robot must return to the launcher item before a predetermined time limit. Alternatively, or additionally, the MRR problem with time windows imposes the additional constraint that each item may be picked up only within a specific time window …”), the routes are generated by further using a cost of the route based on the travel time (Yarkony, see at least par. [0079], “…a solution to the robot routing problem that maximizes a sum of rewards associated with picking up each of a plurality of items within a corresponding window start time and a corresponding window end time …”), the mobile body number constraint based on the travel time, and a route constraint based on the travel time (Yarkony, see at least par. [0079], “…wherein each robot of a plurality of robots (a) starts and ends at the launcher location and (b) is associated with exactly one route of a plurality of routes, and (c) services a capacity that does not exceed each robot's capacity to carry items, wherein each of the plurality of items is serviced exactly once, wherein a number of the plurality of robots is less than or equal to a maximum number of robots, and wherein no more than one robot of the plurality of robots can occupy a given space-time location”).
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/TRANG DANG/Examiner, Art Unit 3656 /KHOI H TRAN/Supervisory Patent Examiner, Art Unit 3656
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