DETAILED ACTION
This communication is a Final Rejection Office Action in response to the 1/28/2026 submission filed in Application 18/162,587. Claims 1-57 are now presented.
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Response to Arguments
Applicant's arguments filed 1/28/2026 have been fully considered but they are not persuasive.
Regarding the rejections under 101, the Applicant argues ”Applicant respectfully disagrees. With regard to "managing personal behavior," this characterization is improperly provided with no reasoning whatsoever. That is, the examiner merely lists over-simplified selections from the claim, i.e. "identify constraints and terms of at least one objective function; generating one or more optimization problems; and generating at least one candidate production schedule" and asserts that they are directed to "managing personal behavior or relationships or interactions between people." Office Action, p. 4. This is insufficient to establish a primafacie case of ineligibility. See MPEP 2106. In other words, the examiner has failed to demonstrate, nor would it be possible to demonstrate, that the claims recite anything about "personal behavior," "relationships between people," or "interactions between people."”
The Examiner respectfully disagrees. MPEP 2106.04(a)(2) II. States managing personal behavior or relationships or interactions between people, (including social activities, teaching, and following rules or instructions). The Applicant’s specification states “Within the one or more processing or manufacturing plants (or one or more portions thereof), various processing units 306 can be used to process the one or more raw materials 302 or the one or more intermediate products. Each processing unit 306 represents equipment or other assets that can be used to manufacture or process one or more materials in order to perform one or more tasks. In some cases, one or more processing units 306 may be used to represent one or more tasks performed by human personnel, such as when one or more employees or other people perform one or more tasks manually (which may or may not involve the use of monitored or other equipment). The task or tasks performed by each processing unit 306 can vary depending on the application (see para. 54). The Examiner asserts that developing an optimal schedule which involves tasks performed by human personal amounts to managing behavior or interactions between people (including following rules or instructions) and is as such abstract.
Regarding the rejections under 101, the Applicant further argues “The reasoning with regard to "mental processes" is similarly deficient, and at the very least fails to comply with the requirements of MPEP 2106. Even assuming arguendo that the claim limitations could, given infinite time, pens, and paper, be performed "mentally," which Applicant does not concede, the rejection ignores the requirement that to be classified as a mental process a claim it must not only be possible but also practical to perform the claim in the human mind. See MPEP 2106(a)(2)(III)(A). As with the examples provided in MPEP 2106(a)(2)(III)(A), the claims solve problems that the human mind is unequipped to solve practically, and the Office Acton makes no attempt to demonstrate otherwise, as required.””
The Examiner respectfully disagrees. The Examiner maintains that the steps directed to using templates to identify constraints and terms of at least one objective function; generating one or more optimization problems; and generating at least one candidate production schedule for at least the portion of the one or more processing targets using the one or more optimization problems are mental processes. There is nothing the claims that preclude these steps from being performed mentally. The claims have been amended to recite the use of a processing device. However, this is recited broadly an amounts to merely including instructions to implement an abstract idea on a computer. As such, the claims recite abstract ideas.
Regarding the rejections under 101, the Applicant further argues “the claims here are integrated into at least the practical application of improving the generation of optimized production schedules, without the need to write custom code for every individual factory or application. As explained in MPEP 2106.05(a)(II), a practical application can be directed to any improvement in technology, not just to computer hardware. The claims here are analogous to those in McRO (see id., citing McRO, 837 F.3d at 1313), where an automated process was found to be eligible as a practical application because it replaced a previous, manual, process in a way that was not merely automating the same steps of the manual process.”
The examiner respectfully disagrees. On page 23 of McRo the court states “We therefore look to whether the claims in these patents focus on a specific means or method that improves the relevant technology or are instead directed to a result or effect that itself is the abstract idea and merely invoke generic processes and machinery. Enfish, LLC v. Microsoft Corp., 822 F.3d 1327, 1336 (Fed. Cir. 2016) (“Enfish”); see also Rapid Litig. Mgmt. Ltd. v. CellzDirect, Inc., No. 2015-1570, 2016 WL 3606624, at *4(Fed. Cir. July 5, 2016).”
The examiner asserts that in the instant case that claims do not focus on a specific means or method that improves the relevant technology. The examiner asserts that resource task scheduling is not a technology or technical field, but rather a business practice.
Further, on page 22 of McRo the court determined “As the specification confirms, the claimed improvement here is allowing computers to produce “accurate and realistic lip synchronization and facial expressions in animated characters” that previously could only be produced by human animators.”
In the instant case there is no such technical improvement disclosed in the Applicant’s specification.
Further, page 24 of McRo states “Claim 1 of the ’576 patent is focused on a specific asserted improvement in computer animation, i.e., the automatic use of rules of a particular type. We disagree with Defendants’ arguments that the claims simply use a computer as a tool to automate conventional activity. While the rules are embodied in computer software that is processed by general-purpose computers, Defendants provided no evidence that the process previously used by animators is the same as the process required by the claims. See Defs.’ Br. 10–15, 39–40. In support, Defendants point to the background section of the patents, but that information makes no suggestion that animators were previously employing the type of rules required by claim 1. Defendants concede an animator’s process was driven by subjective determinations rather than specific, limited mathematical rules.”
As such, in McRO the process previously used by human animators is a different process that the rules required by the claims. In other words, the computer operated in a different way than a human animator would in order to produce the result. In the instant case, the rules recited in the claims are the same rules that a human user would use to perform the process. As such, the claims here are not similar to the claims at issue in McRO.
Regarding the rejections under 102, the Applicant further argues “Applicant respectfully submits that Gombolay does not disclose at least "wherein at least one of the templates is based on a resource-task network (RTN) representation of resource nodes and task nodes associated with at least the portion of the one or more processing targets." Gombolay discloses using simple task networks (STN) to model robots and schedule the robots to perform tasks based on given constraints. See, e.g., Gombolay, paragraphs [0010]-[0011] and [0025]. Gombolay fails to disclose any RTN. The Office Action, as best understood, appears to equate STNs and RTNs, without explanation. See Office Action, p. 8. This equivalence is improper because STNs and RTNs are, by definition, distinct concepts, and would be understood by one of ordinary skill in the art as such (e.g., STNs check whether events can be scheduled in time without conflict while RTNs determine when and how tasks can run given resource limits and material flows).
The Examiner respectfully disagrees. The claims do not define the templates, the resource-task network or how the templates are based on a resource-task network (RTN) representation of resource nodes and task nodes. As such, the Examiner has applied the broadest reasonable interpretation of the claims. Gombolay para. 8 teaches the method includes at each of a plurality of schedule-able time steps, the scheduler collecting a list of available robots into a set of available robots; and the scheduler performing a plurality of simulations to iteratively select a robot from the set of available robots and attempting to assign one or more tasks to the robot using a Q-network (of states and action), wherein each simulated assignment comprises: building a heterogeneous graph g from states in a graph model (e.g., a simple temporal network (STN)-based model); generating input features for nodes in the heterogeneous graph; and predicting the Q-network using the heterogeneous graph; and selecting the robot using two or more policies selected from the group consisting of a first policy associated with first availability, a second policy associated with a minimum average time on unscheduled tasks, a third policy associated with a minimum time on any one unscheduled task, a fourth policy associated with a minimum average time on all tasks. Para. 15 teaches in some embodiments, the heterogeneous graph is built by generating a base graph comprising a minimum distance graph; adding a plurality of robot nodes to the base graph, wherein each robot node of the plurality of robot nodes is connected to an assigned task node, and wherein each robot node of the plurality of robot nodes is connected to other robot nodes of the plurality of robot nodes; adding a plurality of location nodes to the base graph, wherein each location node of the plurality of location nodes is connected to an assigned task node, and wherein each location node of the plurality of location nodes is connected to other location nodes of the plurality of location nodes; and adding a plurality of state summary nodes to the base graph, wherein each state summary node of the plurality of state summary nodes is connected to a task node, a robot node, and a location node. The Examiner considers the disclosed policies to be templates and the simple task networks to be resource-task network as required by the claim.
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 1-57 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter.
When considering subject matter eligibility under 35 U.S.C. 101, in step 1 it must be determined whether the claim is directed to one of the four statutory categories of invention, i.e., process, machine, manufacture, or composition of matter. If the claim does fall within one of the statutory categories, in step 2A prong 1 it must then be determined whether the claim is recite a judicial exception (i.e., law of nature, natural phenomenon, and abstract idea). If the claim recites a judicial exception, under step 2A prong 2 it must additionally be determined whether the recites additional elements that integrate the judicial exception into a practical application. If a claim does not integrate the Abstract idea into a practical application, under step 2B it must then be determined if the claim provides an inventive concept.
In the Instant case, Claims 1-19 are directed toward a method for generating at least one candidate production schedule for at least the portion of the one or more processing targets using the one or more optimization problems. Claims 20-38 are directed toward an apparatus for generating at least one candidate production schedule for at least the portion of the one or more processing targets using the one or more optimization problems Claims 29-57 are directed toward a computer program product for filtering, by the processor set, based on the minimal user role and a user role of the user, data resulting from running the workflow. As such, each of the Claims is directed to one of the four statutory categories of invention.
MPEP 2106.04 II. A. explains that in step 2A prong 1 Examiners are to determine whether a claim recites a judicial exception. MPEP 2106.04(a) explains that:
To facilitate examination, the Office has set forth an approach to identifying abstract ideas that distills the relevant case law into enumerated groupings of abstract ideas. The enumerated groupings are firmly rooted in Supreme Court precedent as well as Federal Circuit decisions interpreting that precedent, as is explained in MPEP § 2106.04(a)(2). This approach represents a shift from the former case-comparison approach that required examiners to rely on individual judicial cases when determining whether a claim recites an abstract idea. By grouping the abstract ideas, the examiners’ focus has been shifted from relying on individual cases to generally applying the wide body of case law spanning all technologies and claim types.
The enumerated groupings of abstract ideas are defined as:
1) Mathematical concepts – mathematical relationships, mathematical formulas or equations, mathematical calculations (see MPEP § 2106.04(a)(2), subsection I);
2) Certain methods of organizing human activity – fundamental economic principles or practices (including hedging, insurance, mitigating risk); commercial or legal interactions (including agreements in the form of contracts; legal obligations; advertising, marketing or sales activities or behaviors; business relations); managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions) (see MPEP § 2106.04(a)(2), subsection II); and
3) Mental processes – concepts performed in the human mind (including an observation, evaluation, judgment, opinion) (see MPEP § 2106.04(a)(2), subsection III).
As per step 2A prong 1 of the eligibility analysis, claim 1 recites the abstract idea of generating at least one candidate production schedule for at least the portion of the one or more processing targets using the one or more optimization problems which falls into the abstract idea categories of certain methods of organizing human activity and mental processes. The elements of Claim 1 that represent the Abstract idea include:
A method comprising:
using templates to identify constraints and terms of at least one objective function associated with at least a portion of one or more processing targets, wherein at least one of the templates is based on a resource-task network (RTN) representation of resource nodes and task nodes associated with at least the portion of the one or more processing targets;
generating one or more optimization problems, wherein the constraints and the at least one objective function represent at least part of the one or more optimization problems; and
generating at least one candidate production schedule for at least the portion of the one or more processing targets using the one or more optimization problems.
MPEP 2106.04(a)(2) II. states:
The phrase "methods of organizing human activity" is used to describe concepts relating to:
fundamental economic principles or practices (including hedging, insurance, mitigating risk);
commercial or legal interactions (including agreements in the form of contracts, legal obligations, advertising, marketing or sales activities or behaviors, and business relations); and
managing personal behavior or relationships or interactions between people, (including social activities, teaching, and following rules or instructions).
The Supreme Court has identified a number of concepts falling within the "certain methods of organizing human activity" grouping as abstract ideas. In particular, in Alice, the Court concluded that the use of a third party to mediate settlement risk is a ‘‘fundamental economic practice’’ and thus an abstract idea. 573 U.S. at 219–20, 110 USPQ2d at 1982. In addition, the Court in Alice described the concept of risk hedging identified as an abstract idea in Bilski as ‘‘a method of organizing human activity’’. Id. Previously, in Bilski, the Court concluded that hedging is a ‘‘fundamental economic practice’’ and therefore an abstract idea. 561 U.S. at 611–612, 95 USPQ2d at 1010.
In the instant case the steps of using templates to identify constraints and terms of at least one objective function; generating one or more optimization problems; and generating at least one candidate production schedule are directed to managing personal behavior or relationships or interactions between people including following rules or instructions.
MPEP 2106.04(a)(2) states:
The courts consider a mental process (thinking) that "can be performed in the human mind, or by a human using a pen and paper" to be an abstract idea. CyberSource Corp. v. Retail Decisions, Inc., 654 F.3d 1366, 1372, 99 USPQ2d 1690, 1695 (Fed. Cir. 2011). As the Federal Circuit explained, "methods which can be performed mentally, or which are the equivalent of human mental work, are unpatentable abstract ideas the ‘basic tools of scientific and technological work’ that are open to all.’" 654 F.3d at 1371, 99 USPQ2d at 1694 (citing Gottschalk v. Benson, 409 U.S. 63, 175 USPQ 673 (1972)). See also Mayo Collaborative Servs. v. Prometheus Labs. Inc., 566 U.S. 66, 71, 101 USPQ2d 1961, 1965 (2012) ("‘[M]ental processes[] and abstract intellectual concepts are not patentable, as they are the basic tools of scientific and technological work’" (quoting Benson, 409 U.S. at 67, 175 USPQ at 675)); Parker v. Flook, 437 U.S. 584, 589, 198 USPQ 193, 197 (1978) (same).
Accordingly, the "mental processes" abstract idea grouping is defined as concepts performed in the human mind, and examples of mental processes include observations, evaluations, judgments, and opinions
The instant claims recite mental processes including observation, evaluation, judgment, opinion. For example, the steps directed to using templates to identify constraints and terms of at least one objective function; generating one or more optimization problems; and generating at least one candidate production schedule for at least the portion of the one or more processing targets using the one or more optimization problems are mental processes. There is nothing is nothing the claims that preclude these steps from being performed mentally. As such, the claims recite abstract ideas.
Under step 2A prong 2 the examiner must then determine if the recited abstract idea is integrated into a practical application. MPEP 2106.04 states:
Limitations the courts have found indicative that an additional element (or combination of elements) may have integrated the exception into a practical application include:
• An improvement in the functioning of a computer, or an improvement to other technology or technical field, as discussed in MPEP §§ 2106.04(d)(1) and 2106.05(a);
• Applying or using a judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition, as discussed in MPEP § 2106.04(d)(2);
• Implementing a judicial exception with, or using a judicial exception in conjunction with, a particular machine or manufacture that is integral to the claim, as discussed in MPEP § 2106.05(b);
• Effecting a transformation or reduction of a particular article to a different state or thing, as discussed in MPEP § 2106.05(c); and
• Applying or using the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception, as discussed in MPEP § 2106.05(e)
The courts have also identified limitations that did not integrate a judicial exception into a practical application:
• Merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea, as discussed in MPEP § 2106.05(f);
• Adding insignificant extra-solution activity to the judicial exception, as discussed in MPEP § 2106.05(g); and
• Generally linking the use of a judicial exception to a particular technological environment or field of use, as discussed in MPEP § 2106.05(h).
In the instant case, this judicial exception is not integrated into a practical application. In particular, Claim 1 recites the additional elements of:
causing a processing device to use an optimization solver
causing the processing device to use the at least one candidate production schedule to control operation of the one or more processing targets.
Further, Claim 20 recites the additional elements of:
An apparatus comprising: at least one processing device configured to perform the abstract idea
Further, Claim 39 recites the additional elements of:
A non-transitory computer readable medium storing computer readable program code that, when executed by one or more processors, causes the one or more processors to perform the abstract idea
However, the processor is recited at a high-level of generality such that it amounts no more than mere instructions to apply the exception using a generic computer component. Accordingly, the additional elements do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
In step 2B, the examiner must determine whether the claim adds a specific limitation other than what is well-understood, routine, conventional activity in the field - see MPEP 2106.05(d). As discussed with respect to Step 2A Prong Two, the processing circuitry 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. Accordingly, the additional elements do not provide and inventive concept.
Further, Claims 2-19 further limit the mental processes and business practices already rejected in the parent claim, but fail to remedy the deficiencies of the parent claim as they do not impose any additional elements that amount to significantly more than the abstract idea itself.
Accordingly, the Examiner concludes that there are no meaningful limitations in claims 2-19 that transform the judicial exception into a patent eligible application such that the claim amounts to significantly more than the judicial exception itself.
The analysis above applies to all statutory categories of invention. The presentment of claim 1 otherwise styled as a computer program product, or apparatus for example, would be subject to the same analysis. As such, claims 8-20 are also rejected.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claim(s) 1, 11, 16, 17, 19, 20, 30, 35, 36, 38, 39, 49, 54, 55, 57 is/are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Gombolay US 2022/0226994 A1.
As per Claim 1 Gombolay teaches a method comprising:
using templates to identify constraints and terms of at least one objective function associated with at least a portion of one or more processing targets, Gombolay para. 8 teaches the method includes at each of a plurality of schedule-able time steps, the scheduler collecting a list of available robots into a set of available robots; and the scheduler performing a plurality of simulations to iteratively select a robot from the set of available robots and attempting to assign one or more tasks to the robot using a Q-network (of states and action), wherein each simulated assignment comprises: building a heterogeneous graph g from states in a graph model (e.g., a simple temporal network (STN)-based model); generating input features for nodes in the heterogeneous graph; and predicting the Q-network using the heterogeneous graph; and selecting the robot using two or more policies selected from the group consisting of a first policy associated with first availability, a second policy associated with a minimum average time on unscheduled tasks, a third policy associated with a minimum time on any one unscheduled task, a fourth policy associated with a minimum average time on all tasks. The Examiner considers the disclosed policies to be templates.
wherein at least one of the templates is based on a resource-task network (RTN) representation of resource nodes and task nodes associated with at least the portion of the one or more processing targets; Gombolay para.10-11 teaches in some embodiments, the graph model comprises a simple temporal network (STN)-based model that encodes temporal constraints and spatial constraints (e.g., 2D or 3D spatial constraints) into a heterogeneous graph. In some embodiments, the graph model comprises a simple temporal network (STN)-based model that encodes temporal and/or spatial constraints and at least one constraint associated with available robots, robot locations, task locations, and shared resources (e.g., tools), into the heterogeneous graph in a convolutional manner. Para. 25 teaches in some embodiments, the graph model comprises a simple temporal network (STN)-based model that encodes temporal constraints and at least one of available robots, robot locations, task locations, and shared resources (e.g., tools), into a heterogeneous graph in a convolutional manner and a Q-function of the Q-network is estimated based on state-action pairs.
generating one or more optimization problems, wherein the constraints and the at least one objective function represent at least part of the one or more optimization problems; Gombolay para. 41 teaches state x.sub.t at a decision-step t includes the temporal constraints of the problem, represented by an STN, the location information, and all robots' partial schedules constructed so far. Action u=<τ.sub.i,r.sub.j> corresponds to appending unscheduled task τ.sub.i at the end of the partial schedule of robot r.sub.j. Transitions T correspond to deterministically adding the edges associated with the action into the STN and updating the partial schedule of the selected robot. Reward R of a state-action pair is defined as the change in objective values after taking the action, calculated as R=−1×(Z.sub.t+1−Z.sub.t). Z.sub.t denotes the partial objective function at state x.sub.t and is calculated only using scheduled tasks. For example, while minimizing makespan, Z.sub.t=max.sub.if.sub.iτ.sub.i∈{partial schedules}. The reward can be multiplied by −1.0 as the objective is minimization. The scheduler 100 can further divide Z.sub.t by a factor D>1 if x.sub.t is not a termination state. D is used to balance between finding the highest immediate reward (local optimal) and finding the global optimal schedules. If the action results in an infeasible schedule in the next state, a large negative reward M.sub.inf is assigned to Z.sub.t+1.
causing a processing device to use an optimization solver to generate at least one candidate production schedule for at least the portion of the one or more processing targets using the one or more optimization problems. Gombolay para. 26 teaches in another aspect, a non-transitory computer-readable medium is disclosed having instructions stored thereon, wherein the instructions, when executed by a processor, cause the processor to generate a schedule for a plurality of heterogenous robots (e.g., robotic equipment, manufacturing equipment, transport equipment, people with assigned tasks in manufacturing, assembling, distributing environment) performing a set of tasks, wherein the plurality of heterogenous robots includes a first robot of a first type, and a second robot of a second type, wherein the first type and the second type are different (e.g., in being configured for different tasks or can perform the same tasks at different proficiencies). Para. 41 teaches the scheduler 100 can further divide Z.sub.t by a factor D>1 if x.sub.t is not a termination state. D is used to balance between finding the highest immediate reward (local optimal) and finding the global optimal schedules. If the action results in an infeasible schedule in the next state, a large negative reward M.sub.inf is assigned to Z.sub.t+1.
and causing the processing device to use the at least one candidate production schedule to control operation of the one or more processing targets. Gombolay para. 35 teaches [0035] In the example shown in FIG. 1, the heterogenous robots 102 can include equipment (shown as 102a) such as, but not limited to, robotic equipment, manufacturing equipment, transport equipment, as well as people with assignable tasks (shown as “workers” 102b) and other equipment and workers described herein. Schedule 110 is used as parameters in control systems (shown as 112) to control the operation of equipment 102a. Schedule 110 may also be used to generate floor schedules or plans (shown as 114) to direct the operation of the equipment 102a as well as workers 102b.
As per Claim 11 Gombolay teaches the method of Claim 1, wherein at least one of the templates is used multiple times to generate multiple embodiments of a common constraint or term. Gombolay para. 47 teaches When solving a given problem instance. ScheduleNet can execute in parallel each task allocation policy variant for the pickRobot function. Among the feasible policy selectable by the same model with each of the evaluated policies, the one that yields the best objective function score are kept. The ensemble of different robot-picking policy variants proves to find not only more feasible schedules but also schedules with better makespans than any single policy alone, as each policy may work better than another in certain simulated scenarios but not the others.
As per Claim 16 Gombolay teaches the method of Claim 1, wherein:the RTN representation is associated with multiple interconnected processing targets; at least one optimization problem and at least one candidate production schedule are generated for each of the processing targets; and the method further comprises iteratively reconciling the candidate production schedules for the processing targets to generate a final production schedule for the processing targets. (Para. 23 teaches the schedule is generated by, at each of a plurality of schedule-able time steps, the scheduler collecting a list of available robots into a set of available robots (e.g., rj); and the scheduler performing a plurality of simulations to iteratively select a robot from the set of available robots and attempting to assign one or more tasks to the robot using a Q-network (of states x, and action u), wherein each simulated assignment comprises: building a heterogeneous graph g from states (x) in a graph model (e.g., a simple temporal network (STN)-based model) generating input features for nodes in the heterogeneous graph (g); and selecting the robot using i) the heterogenous graph and ii) two or more policies selected from the group consisting of a first policy associated with first availability, a second policy associated with a minimum average time on unscheduled tasks, a third policy associated with a minimum time on any one unscheduled task, a fourth policy associated with a minimum average time on all tasks, wherein the generated schedule is used to direct or control the plurality of heterogenous robots to perform the set of tasks.)
As per Claim 17 Gombolay teaches the method of Claim 1, wherein the RTN representation is scalable and able to represent processing units in at least a portion of a single processing target up to processing units in multiple interconnected processing targets. (See para. 83-84 that teaches Instead, the instant scheduler leverages imitation learning methods that learn from high-quality schedules to accelerate the learning process for quick deployment. In real-world scheduling environments, high-quality, manually-generated schedules from human experts who currently manage the logistics in manufacturing environments may be available. Moreover, it is practical to solve small-scale problems optimally with exact methods. Para. 84 teaches given the scalability of the heterogeneous graph, it is expected that exploiting such expert data on smaller problems to train the ScheduleNet can generalize well towards solving unseen problems, even in a larger scale.)
As per Claim 19 Gombolay teaches the method of Claim 1, wherein the one or more processing targets comprise one or more manufacturing or processing plants. (Para. 22 teaches in another aspect, a scheduler system is disclosed comprising a processor, and a memory operatively coupled to the processor, the memory having instructions stored therein, wherein execution of the instructions by the processor cause the processor to generate a schedule for a plurality of heterogenous robots (e.g., robotic equipment, manufacturing equipment, transport equipment, people with assigned tasks in manufacturing, assembling, distributing environment) performing a set of tasks, wherein the plurality of heterogenous robots includes a first robot of a first type, and a second robot of a second type, wherein the first type and the second type are different (e.g., in being configured for different tasks or can perform the same tasks at different proficiencies)).
Claims 20, 30, 35, 36, 38 recites similar limitation to those recited in Claims 1, 11, 16, 17, 19 and is rejected for similar reasons. Further, Gombolay teaches an apparatus comprising: at least one processing device configured to perform the recited steps(see para. 22).
Claims 39, 49, 54, 55, 57 recites similar limitation to those recited in Claims 1, 11, 16, 17, 19 and is rejected for similar reasons. Further, Gombolay teaches A non-transitory computer readable medium storing computer readable program code that, when executed by one or more processors, causes the one or more processors to perform the recited steps (see para. 26).
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries 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) 2, 21, 40 is/are rejected under 35 U.S.C. 103 as being unpatentable over Gombolay US 2022/0226994 A1 in view of Brook US 20070150329 A1.
As per Claim 2 Gombolay teaches the method of Claim 1, wherein: one or more of the templates used to identify one or more of the constraints include at least one of: a material balance constraint template, an energy balance constraint template, a resource constraint template, a status balance constraint template, and a time balance constraint template; and Gombolay para. 27 teaches the schedule is generated by, at each of a plurality of schedule-able time steps, the scheduler collecting a list of available robots into a set of available robots (e.g., rj); and the scheduler performing a plurality of simulations to iteratively select a robot from the set of available robots and attempting to assign one or more tasks to the robot using a Q-network (of states x, and action i), wherein each simulated assignment comprises: building a heterogeneous graph g from states (x) in a graph model (e.g., a simple temporal network (STN)-based model) generating input features for nodes in the heterogeneous graph (g); and selecting the robot using i) the heterogenous graph and ii) two or more policies selected from the group consisting of a first policy associated with first availability, a second policy associated with a minimum average time on unscheduled tasks, a third policy associated with a minimum time on any one unscheduled task, a fourth policy associated with a minimum average time on all tasks, wherein the generated schedule is used to direct or control the plurality of heterogenous robots to perform the set of tasks. The Examiner considers the policy associated with a minimum average time on all tasks to be a time balance constraint template
Gombolay does not teach one or more of the templates used to identify one or more of the terms of the at least one objective function include at least one of: a revenue template, a cost template, a customer satisfaction template, a risk template, an intangible asset template, and an environmental footprint template. However, Brook para. 150 teaches if the test at 623 returns a non-empty subset of potential next services then method 600 continues to 625 where the current service makes a selection from the subset of the service or device that it prefers to make into the next service of the workflow process. This step 625 may involve further analysis of constraints such as cost, quality, etc that might have been preset in a template or by user input data. Both Gombolay and Brook are directed to task management. Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the Applicant’s invention to modify the teachings of Gombolay to include one or more of the templates used to identify one or more of the terms of the at least one objective function include at least one of: a revenue template, a cost template, a customer satisfaction template, a risk template, an intangible asset template, and an environmental footprint template as taught by Brook to meet the specific goals required by the particular business or enterprise that is developing the schedule.
Claims 21 recites similar limitation to those recited in Claims 2 and is rejected for similar reasons. Further, Gombolay teaches an apparatus comprising: at least one processing device configured to perform the recited steps(see para. 22).
Claims 40 recites similar limitation to those recited in Claims 2 and is rejected for similar reasons. Further, Gombolay teaches A non-transitory computer readable medium storing computer readable program code that, when executed by one or more processors, causes the one or more processors to perform the recited steps (see para. 26).
Claim(s) 3, 22, 41 is/are rejected under 35 U.S.C. 103 as being unpatentable over Gombolay US 2022/0226994 A1 in view of Ji US 2021/0373888 A1.
As per Claim 3 Gombolay does not teach the method of Claim 1, further comprising: using information provided for at least one portion of at least one of the templates to automatically add information to at least one other portion of the at least one template based on one or more dependencies associated with the at least one template. Ji para. 39 teaches in the crossover operation, a new particle that inherits gene characteristics of a father particle and a mother particle is generated. First, two crossover points are randomly selected from a chromosome vector of the father particle, and the new solutions generated by crossing preserve the two crossover points and external genes thereof. The remaining genes located between the two crossover points are rearranged according to a sequence corresponding to the remaining genes on a chromosome of the mother particle. FIG. 4 is a schematic diagram of the crossover operation of a particle having 10 orders and 3 workshops, and the genes from the father particle and the mother particle in the new particle chromosome are respectively represented by thin squares and thick squares. As shown in FIG. 4, the crossover points randomly selected from the father particle are “7” and two “*”. As such, “7”, the two “*″”, and external genes thereof, that is, “7”, the left side gene “2”, two “*”, and the right side genes “1”, “4”, and “10”, are all inherited to the new solutions. In the mother particle, after the genes “7”, “2”, the second “*”, “1”, “4”, and “10” are removed, the remaining genes “6”, “3”, the first “*”, “9”, “5”, and “8” are directly filled in a position between the crossover points “7” and the second “*” in the new solutions according to the sequence in the mother particle. A corresponding relationship of workshop separators “*” in the father particle and the mother particle is determined by a sequence of the “*”. For instance, in this embodiment, the second “*” in the mother particle corresponds to the second “*” in the father particle. Both Gombolay and Ji are directed to task management and production planning. Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the Applicant’s invention to modify the teachings of Gombolay to include using information provided for at least one portion of at least one of the templates to automatically add information to at least one other portion of the at least one template based on one or more dependencies associated with the at least one template as taught by Ji to efficiently determine an optimal solution (as suggested by Ji para. 26).
Claims 22 recites similar limitation to those recited in Claims 3 and is rejected for similar reasons. Further, Gombolay teaches an apparatus comprising: at least one processing device configured to perform the recited steps(see para. 22).
Claims 41 recites similar limitation to those recited in Claims 3 and is rejected for similar reasons. Further, Gombolay teaches A non-transitory computer readable medium storing computer readable program code that, when executed by one or more processors, causes the one or more processors to perform the recited steps (see para. 26).
Claim(s) 4, 23, 42 is/are rejected under 35 U.S.C. 103 as being unpatentable over Gombolay US 2022/0226994 A1 in view of Benayon US 2013/0339079 A1.
As per Claim 4 Gombolay does not teach the method of Claim 1, further comprising: using dependencies associated with the templates to identify one or more conflicts associated with two or more of the constraints or terms; and reconciling the one or more conflicts. However, Benayon para. 46 teaches resource templates 308 are a set of data structures specifying sets of rules for resource usage applicable to elements within simulation 302. A resource template can be associated with several levels within a process hierarchy, including the process level, defining global rules for resource usage; the task level, defining rules for overriding the process rules for a given task; the resource requirement level, overriding the rules for the task at the level of an individual resource requirement and the resource level, defining the rules from the resource perspective. A particular implementation is left to resolve conflicts between hierarchical levels (1-3) and (4). Both Gombolay and Benayon are directed to task management and schedule planning. Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the Applicant’s invention to modify the teachings of Gombolay to include using dependencies associated with the templates to identify one or more conflicts associated with two or more of the constraints or terms; and reconciling the one or more conflicts as taught by Hess to produce an optimal and robust schedule.
Claims 23 recites similar limitation to those recited in Claims 4 and is rejected for similar reasons. Further, Gombolay teaches an apparatus comprising: at least one processing device configured to perform the recited steps(see para. 22).
Claims 42 recites similar limitation to those recited in Claims 4 and is rejected for similar reasons. Further, Gombolay teaches A non-transitory computer readable medium storing computer readable program code that, when executed by one or more processors, causes the one or more processors to perform the recited steps (see para. 26).
Claim(s) 5, 24, 43 is/are rejected under 35 U.S.C. 103 as being unpatentable over Gombolay US 2022/0226994 A1 in view of Benayon US 2013/0339079 A1 as applied to Claim 4 and in further view of Bernelas US 2023/0214680 A1.
As per Claim 5 Gombolay does not teach The method of Claim 4, wherein: using the dependencies associated with the templates comprises using a dependency graph associated with multiple hierarchically-arranged templates, the dependency graph identifying relationships between the hierarchically-arranged templates and dependency information for each of the hierarchically-arranged templates; and However, Bernelas para. 19 teaches In some embodiments, a method and system for computing contrastive explanations is provided for explaining the outcome and alternative choices for a different desired outcome in connection with a hierarchical rule-based decision system. Such a system receives input data that is processed according to several intermediate rules in order to produce an output value representative of a resulting decision. In some embodiments, such a system is represented by an acyclic dependency graph that specifies which decisions and input data influence which other decisions. Each graph node has a set of one or more rules that are the basis of the decision for this node. It may happen that a hierarchical rule-based decision system does not output a desired decision for a given case. Given a case and a range of desired outcomes, disclosed embodiments determine one or more families of cases for which the decision system makes a desired decision. In some embodiments, these families are provided in the form of a report for a user to review. In some embodiments, the families are provided in order by the distance to the given case. Both Gombolay and Bernelas are directed to decision support. Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the Applicant’s invention to modify the teachings of Gombolay to include using the dependencies associated with the templates comprises using a dependency graph associated with multiple hierarchically-arranged templates, the dependency graph identifying relationships between the hierarchically-arranged templates and dependency information for each of the hierarchically-arranged templates as taught by Bernelas to ensure more accurate and complete results (as suggested by para. 29).
Gombolay does not teach reconciling the one or more conflicts comprises reconciling the one or more conflicts based on dependencies of the constraints and terms and their associated variables of the hierarchically-arranged templates on input data to ensure that the constraints, terms, and associated variables are consistent. However, Benayon para. 46 teaches resource templates 308 are a set of data structures specifying sets of rules for resource usage applicable to elements within simulation 302. A resource template can be associated with several levels within a process hierarchy, including the process level, defining global rules for resource usage; the task level, defining rules for overriding the process rules for a given task; the resource requirement level, overriding the rules for the task at the level of an individual resource requirement and the resource level, defining the rules from the resource perspective. A particular implementation is left to resolve conflicts between hierarchical levels (1-3) and (4). Both Gombolay and Benayon are directed to task management and schedule planning. Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the Applicant’s invention to modify the teachings of Gombolay to include reconciling the one or more conflicts comprises reconciling the one or more conflicts based on dependencies of the constraints and terms and their associated variables of the hierarchically-arranged templates on input data to ensure that the constraints, terms, and associated variables are consistent as taught by Hess to produce an optimal and robust schedule.
Claims 24 recites similar limitation to those recited in Claims 5 and is rejected for similar reasons. Further, Gombolay teaches an apparatus comprising: at least one processing device configured to perform the recited steps(see para. 22).
Claims 43 recites similar limitation to those recited in Claims 5 and is rejected for similar reasons. Further, Gombolay teaches A non-transitory computer readable medium storing computer readable program code that, when executed by one or more processors, causes the one or more processors to perform the recited steps (see para. 26).
Claim(s) 10, 29, 48 is/are rejected under 35 U.S.C. 103 as being unpatentable over Gombolay US 2022/0226994 A1 in view of Gabeler-Lee US 20220036306 A1.
As per Claim 10 Gombolay does not teach the method of Claim 1, wherein different ones of the templates are associated with different types of processing units in the one or more processing targets.
However, Gabeler-Lee para. 15 teaches in order to provide for a highly flexible client oriented system an initial or default set of one or more inventory allocation templates are provided. A client is allowed to associate a product with a default template and/or is given the opportunity to revise/modify a default inventory allocation template. In revising a default template the client can revise/modify the default template to client specific mandatory constraints which are to be satisfied and/or client specific preferences to be taken into consideration when performing an allocation. A client can generate different custom templates based on the type of allocation request being made, the time the request is to be implemented, the warehouse where the request is to be satisfied and/or other constraints. Examples of other constraints include a portion of a requested item quantity to be satisfied by a storage location for the location to be considered being used, e.g., in response to an order involving picking items, and/or some other constraint such as that the location be capable of holding a certain number or percentage of items to which a particular template applies in the case of a storage request related template. The time referred to here can be, and sometimes is, expressed relative to a particular period of time such as a holiday and/or non-holiday period. Alternatively a fixed time period can be expressed as a day/time or date range for which a particular template is to be applicable. This allows for a client to provide different templates for different time periods. It should be appreciated that warehouse conditions can vary based on date/time with a warehouse being subject to busy/slow/seasonal conditions for different times, e.g., days of the year and/or hours of the day. Both Gombolay and Gabeler-Lee are directed to task management. Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the Applicant’s invention to modify the teachings of Gombolay to include wherein different ones of the templates are associated with different types of processing units in the one or more processing targets as taught by Gabeler-Lee to provide a more flexible system (as suggested by para. 15).
Claims 29 recites similar limitation to those recited in Claims 10 and is rejected for similar reasons. Further, Gombolay teaches an apparatus comprising: at least one processing device configured to perform the recited steps(see para. 22).
Claims 48 recites similar limitation to those recited in Claims 10 and is rejected for similar reasons. Further, Gombolay teaches A non-transitory computer readable medium storing computer readable program code that, when executed by one or more processors, causes the one or more processors to perform the recited steps (see para. 26).
Claim(s) 12, 31, 50 is/are rejected under 35 U.S.C. 103 as being unpatentable over Gombolay US 2022/0226994 A1 in view of Norman US 2014/0136252 A1.
As per Claim 12 Gombolay does not teach the method of Claim 1, further comprising: decomposing the RTN representation into multiple sub-networks, each sub-network associated with a subset of the resource and task nodes; and using different embodiments of a single template for a constraint for different ones of the sub-networks. However, Norman para. 24 teaches Norman the problem translator sub-module 215 can identify sub-problems for a scheduling problem using the sub-graphs, station subset for each sub-graph, and task subset for each sub-graph. The problem translator sub-module 215 can convert the sub-graphs into the format of sub-problems, for example, using a template 257 and/or configuration data 259 that is stored in the data store 250. The problem translator sub-module 215 can group a station subset and the task subset that corresponds to the station subset as a sub-problem. For example, station subset S1 and task subset T1 may be grouped to form Sub-Problem 1, and station subset S2 and task subset T2 may be grouped to form Sub-Problem 2. The problem translator sub-module 215 can also identify task exceptions associated with the sub-problems for the scheduling problem. For example, the problem translator sub-module 215 may identify task t11 as a task exception associated with Sub-Problem 1 and Sub-Problem 2. The problem translator sub-module 215 can create another set of tasks (e.g., T3) to represent the one or more task exceptions. The smallest number of task exceptions can be an indication of optimal sub-graphs. For example, sub-graph G1 391A and sub-graph G2 391B may result in one task exception. In this example, the sub-graphs G1 and G2 may indicate the optimal cut of the graph. Both Gombolay and Norman are directed to task management. Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the Applicant’s invention to modify the teachings of Gombolay to include decomposing the RTN representation into multiple sub-networks, each sub-network associated with a subset of the resource and task nodes; and using different embodiments of a single template for a constraint for different ones of the sub-networks as taught by Norman to more efficiently solve large scale problems (see para. 2).
Claims 31 recites similar limitation to those recited in Claims 12 and is rejected for similar reasons. Further, Gombolay teaches an apparatus comprising: at least one processing device configured to perform the recited steps(see para. 22).
Claims 50 recites similar limitation to those recited in Claims 12 and is rejected for similar reasons. Further, Gombolay teaches A non-transitory computer readable medium storing computer readable program code that, when executed by one or more processors, causes the one or more processors to perform the recited steps (see para. 26).
Claim(s) 14, 33, 52 is/are rejected under 35 U.S.C. 103 as being unpatentable over Gombolay US 2022/0226994 A1 in view of Norman US 2014/0136252 A1 as applied to claim 12 and in further view of Gabeler-Lee US 20220036306 A1.
As per Claim 14 Gombolay does not teach the method of Claim 12, wherein the different embodiments of the single template are used in one of: a single optimization problem; or multiple optimization problems associated with different ones of the embodiments of the single template.
However, Gabeler-Lee para. 15 teaches in order to provide for a highly flexible client oriented system an initial or default set of one or more inventory allocation templates are provided. A client is allowed to associate a product with a default template and/or is given the opportunity to revise/modify a default inventory allocation template. In revising a default template the client can revise/modify the default template to client specific mandatory constraints which are to be satisfied and/or client specific preferences to be taken into consideration when performing an allocation. A client can generate different custom templates based on the type of allocation request being made, the time the request is to be implemented, the warehouse where the request is to be satisfied and/or other constraints. Examples of other constraints include a portion of a requested item quantity to be satisfied by a storage location for the location to be considered being used, e.g., in response to an order involving picking items, and/or some other constraint such as that the location be capable of holding a certain number or percentage of items to which a particular template applies in the case of a storage request related template. The time referred to here can be, and sometimes is, expressed relative to a particular period of time such as a holiday and/or non-holiday period. Alternatively a fixed time period can be expressed as a day/time or date range for which a particular template is to be applicable. This allows for a client to provide different templates for different time periods. It should be appreciated that warehouse conditions can vary based on date/time with a warehouse being subject to busy/slow/seasonal conditions for different times, e.g., days of the year and/or hours of the day. Both Gombolay and Gabeler-Lee are directed to task management. Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the Applicant’s invention to modify the teachings of Gombolay to include wherein the different embodiments of the single template are used in one of: a single optimization problem; or multiple optimization problems associated with different ones of the embodiments of the single template as taught by Gabeler-Lee to provide a more flexible system (as suggested by para. 15).
Claims 33 recites similar limitation to those recited in Claims 14 and is rejected for similar reasons. Further, Gombolay teaches an apparatus comprising: at least one processing device configured to perform the recited steps(see para. 22).
Claims 52 recites similar limitation to those recited in Claims 14 and is rejected for similar reasons. Further, Gombolay teaches A non-transitory computer readable medium storing computer readable program code that, when executed by one or more processors, causes the one or more processors to perform the recited steps (see para. 26).
Claim(s) 15, 34, 53 is/are rejected under 35 U.S.C. 103 as being unpatentable over Gombolay US 2022/0226994 A1 in view of Norman US 2014/0136252 A1 as applied to claim 12 and in further view of Stevenson US 2020/0019435 A1.
As per Claim 15 Gombolay does not teach the method of Claim 12, wherein:generating the at least one candidate production schedule comprises generating multiple candidate production schedules associated with the multiple sub-networks; and the method further comprises iteratively reconciling the candidate production schedules to generate a final production schedule. However, Stevenson para. 28 teaches each instance of the schedule (also referred to as a particle in the context of a particle swarm optimization algorithm) is passed to optimizer 109. Optimizer 109 is an engine that is constructed, programmed, or otherwise configured, to iteratively optimize each individual schedule instance based on that schedule's local rules. Each particle schedule generator uses a different mission perspective and parameter set to develop each local particle schedule. Adjustment of a schedule instance may include ordering (or re-ordering) the tasks of that schedule. Each schedule instance may have multiple configurations that meet the local rules. Accordingly, optimizer 109 may operate iteratively to have a schedule instance converge to an optimal configuration based on the optimization algorithm of optimizer 109. Each schedule instance that meets its local rules is output as an optimized scheduler instance 110, and passed to evaluator 112. Both Gombolay and Gabeler-Lee are directed to optimize scheduling. Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the Applicant’s invention to modify the teachings of Gombolay to include generating the at least one candidate production schedule comprises generating multiple candidate production schedules associated with the multiple sub-networks; and the method further comprises iteratively reconciling the candidate production schedules to generate a final production schedule as taught by Stevenson to generate near-optimal schedule in near real-time. (as suggested by para. 13).
Claims 34 recites similar limitation to those recited in Claims 15 and is rejected for similar reasons. Further, Gombolay teaches an apparatus comprising: at least one processing device configured to perform the recited steps(see para. 22).
Claims 53 recites similar limitation to those recited in Claims 15 and is rejected for similar reasons. Further, Gombolay teaches A non-transitory computer readable medium storing computer readable program code that, when executed by one or more processors, causes the one or more processors to perform the recited steps (see para. 26).
Claim(s) 18, 37, 56 is/are rejected under 35 U.S.C. 103 as being unpatentable over Gombolay US 2022/0226994 A1 as applied to claim 1 and in further view of Nasir US 2021/0224753 A1.
As per Claim 18 Gombolay does not teach the method of Claim 1, wherein the templates are configured to define constraints and terms of objective functions for processing targets in multiple industries and across different scales within processing targets. However, Nasir para.30] In step 206, planning program 200 generates a plan template for the project. In one embodiment, planning program 200 utilizes database 144 to generate one or more templates for a user project based on the inputs of the user. Generally, a template is a preset format for one or more documents of a set of artifacts or project, used so that the format does not have to be re-created each time a document used. For example, planning program 200 generates a set of artifacts for intermediate project milestones with respect to a final deliverable timeline for general availability. Additionally, artifacts can include documents and spreadsheets that are related to a project that align business objectives, address the needs of sponsors and clients, and properly set the project expectations. In this example, planning program 200 retrieves one or more templates associated with a previous project (identified in step 202) from a knowledge base (e.g., database 144) and populate the one or more templates with data provided by a user (in step 204). Also, the one or more templates can include plans and schedules with milestones for high-level tasks, durations and dependencies specific to a domain (e.g., feature in public, private, or hybrid cloud) of the project. Furthermore, planning program 200 can provide a recommendation to a user to schedule initial meetings with stakeholders and participants based on available time blocks. Both Gombolay Nasir are directed to optimized scheduling. Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the Applicant’s invention to modify the teachings of Gombolay to include wherein the templates are configured to define constraints and terms of objective functions for processing targets in multiple industries and across different scales within processing targets as taught by Nasir to generate templates based on knowledge of past tasks (see para. 24) which results in a more accurate solution.
Claims 20 recites similar limitation to those recited in Claim 18 and is rejected for similar reasons. Further, Gombolay teaches an apparatus comprising: at least one processing device configured to perform the recited steps(see para. 22).
Claims 39 recites similar limitation to those recited in Claim 18 and is rejected for similar reasons. Further, Gombolay teaches A non-transitory computer readable medium storing computer readable program code that, when executed by one or more processors, causes the one or more processors to perform the recited steps (see para. 26).
Conclusion
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/DEIRDRE D HATCHER/Primary Examiner, Art Unit 3625
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