Notice of Pre-AIA or AIA Status
1. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
DETAILED ACTION
2. This Office Action is sent in response to Applicant’s Communication received on 02/25/2026 for application number 18/031,285.
Response to Amendments
3. The Amendment filed 02/25/2026 has been entered. Claims 1, 5, 6, and 9 have been amended. Claims 2-4 and 8 have been canceled. Claims 1, 5-7, and 9-10 remain pending in the application.
4. Applicant’s amendments to the claim 1 have been fully considered and are persuasive. The amendments provided to overcome the 112(f) issued in the last office action is sufficient. The 35 U.S.C § 112(f) of claims 1, 6-7, 9-10 is respectfully withdrawn.
Response to Arguments
Applicant argues that the present invention limits the hardware processor to processing specific manufacturing data, such as "physical factory processes, equipment states, and input/output lots," rather than simple calculations. And, the present invention functions as a technical means to improve the efficiency of factory operations by optimizing the neural network by reflecting "production target amount" and "an achieved state of the products" in real time.
Examiner respectfully disagrees and notes that claim 1 still recites an abstract idea in the form of mental processes, and the additional elements, including the processor, memory, neural network, simulator, and manufacturing state data amount only to applying that abstract idea in the field of factory scheduling using generic computer and AI tools. The claim does not recite a specific asserted improvement as required for integration into a practical application, and it does not recite significantly more than the abstract idea.
Applicant argues that Gottin fails to teach the amended claim 1. However, the argument is moot since this is a newly presented limitation, thus changes the scope of the claim. However, newly found references, Hubbs and Weatherhead, are applied.
Claim Rejections - 35 USC § 101
5. 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, 5-7, and 9-10 are rejected under 35 U.S.C. 101 because the claimed invention is directed to the abstract idea without significantly more.
Step 1, the claims are directed to a machine.
Step 2A Prong 1, Claim 1 recites, in part
map a plurality of processes of a factory that produces products by performing the processes into a factory workflow, wherein a plurality of works that can be performed in each process of the factory are mapped to a plurality of works of the corresponding process of the factory workflow, and sequences of performing the processes of the factory for producing the products is mapped to sequences of the processes of the factory workflow (Mental processes, observation/evaluation/judgment).
to select an optimal one among a plurality of works of the corresponding process and output it as a next work (Mental processes, evaluation/judgment concept).
extracting reinforcement learning data from the simulation results (Mental processes, observation and evaluation).
Step 2A Prong 2, this judicial exception is not integrated into a practical application.
The additional elements:
a memory configured to store instructions; and one or more processors configured to execute the instructions to (mere instructions to apply the exception using a generic computer component).
configure a neural network for each process of the factory workflow (mere instructions to apply the exception using a generic computer component).
wherein the neural network of each process outputs the next work of the corresponding process when inputting the workflow state of the factory workflow, wherein the workflow state includes the factory state and the state of each process, the factory state includes the production target amount and an achieved state of the products, and the state of each process includes an input lot, an output lot, and the state of each equipment of the corresponding process (mere data gathering and output recited at a high level of generality, and thus are insignificant extra-solution activity).
train the neural network of each process, performing a simulation using a factory simulator, and training the neural network of each process using the extracted reinforcement learning data (mere instructions to apply the exception using a generic computer component).
apply the current state of the factory to the neural network of each process to obtain the next work of the corresponding process (mere data gathering and output recited at a high level of generality, and thus are insignificant extra-solution activity).
the neural network is optimized on the basis of the workflow state, a next work of a corresponding process performed in a corresponding state, a workflow state after a corresponding work is performed, and a reward obtained when a corresponding work is performed (mere instructions to apply the exception using a generic computer component).
a plurality of production episodes are simulated using the factory simulator to extract a workflow state and a work according to time order in each process, extract a reward in each state from the performance of a production episode, and collect reinforcement learning data using the extracted state, work, and reward (mere data gathering and output recited at a high level of generality, and thus are insignificant extra-solution activity).
Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception, either alone or in combination.
The additional elements:
a memory configured to store instructions; and one or more processors configured to execute the instructions to (mere instructions to apply the exception using a generic computer component).
configure a neural network for each process of the factory workflow (mere instructions to apply the exception using a generic computer component).
wherein the neural network of each process outputs the next work of the corresponding process when inputting the workflow state of the factory workflow, wherein the workflow state includes the factory state and the state of each process, the factory state includes the production target amount and an achieved state of the products, and the state of each process includes an input lot, an output lot, and the state of each equipment of the corresponding process (mere data gathering and output recited at a high level of generality, and thus are insignificant extra-solution activity).
train the neural network of each process, performing a simulation using a factory simulator, and training the neural network of each process using the extracted reinforcement learning data (mere instructions to apply the exception using a generic computer component).
apply the current state of the factory to the neural network of each process to obtain the next work of the corresponding process (mere data gathering and output recited at a high level of generality, and thus are insignificant extra-solution activity).
the neural network is optimized on the basis of the workflow state, a next work of a corresponding process performed in a corresponding state, a workflow state after a corresponding work is performed, and a reward obtained when a corresponding work is performed (mere instructions to apply the exception using a generic computer component).
a plurality of production episodes are simulated using the factory simulator to extract a workflow state and a work according to time order in each process, extract a reward in each state from the performance of a production episode, and collect reinforcement learning data using the extracted state, work, and reward (mere data gathering and output recited at a high level of generality, and thus are insignificant extra-solution activity).
Claims 5-7 and 9-10 provide further limitations to the abstract idea (Mathematical concepts and/or Mental processes) as rejected in claim 1, however, they do not disclose any additional elements that would amount to a practical application or significantly more than an abstract idea (data gathering/insignificant extra-solution activity and/or generic computer component).
Claim Rejections – 35 USC § 103
6. 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 of this title, 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.
7. Claims 1, 6, and 9 are rejected under 35 U.S.C. 103 as being unpatentable over Hubbs et al. (U.S. Patent Application Pub. No. US 20220027817 A1) in view of Weatherhead (U.S. Patent Application Pub. No. US 20100050020 A1).
Claim 1: Hubbs teaches a system for factory scheduling (i.e. Herein are described apparatus and methods for solving production scheduling and planning problems; para. [0039]) comprising:
a memory configured to store instructions (i.e. Memory 104 can store program instructions and/or data on which program instructions can operate; para. [0053]); and
one or more processors configured to execute the instructions to (i.e. executed by the one or more processors, cause the computing device to carry out functions that can include the computer-implemented method; para. [0007]):
(a) map a plurality of processes of a factory that produces products by performing the processes into a factory workflow (i.e. A model of a production facility that relates to production of one or more products that are produced at the production facility utilizing one or more input materials to satisfy one or more product requests can be determined … a model of production facility PF1 can be obtained; para. [0006, 0043]), wherein a plurality of works that can be performed in each process of the factory are mapped to a plurality of works of the corresponding process of the factory workflow (i.e. generate a schedule of production actions at the production facility that satisfy the one or more product requests over an interval of time … Example production actions can include, but are not limited to, actions related to how much of each of chemicals A, B, C . . . to produce at times t1, t2, t3 . . . . The agent can interact with a simulation or model of the production facility to take in information regarding inventory levels, orders, production data, maintenance history, and schedule the plant according to historical demand patterns; para. [0010, 0042]), and sequences of performing the processes of the factory for producing the products is to sequences of the processes of the factory workflow (i.e. The herein-described reinforcement learning techniques can be used to train ANNs to solve the problem of generating schedules to control a production facility; para. [0047]);
(b) configure a neural network for each process of the factory workflow (i.e. ANNs trained using the herein-described deep reinforcement learning techniques to account for uncertainty and achieve online, dynamic scheduling. The trained ANNs can then be used for production scheduling. For example, a computational agent can embody and use two multi-layer ANNs for scheduling: a value ANN representing a value function for estimating a value of a state of a production facility, where the state is based an inventory of products produced at the production facility (e.g., chemicals produced a chemical plant) and a policy ANN representing a policy function for scheduling production actions at the production facility; para. [0042]), wherein the neural network of each process outputs the next work of the corresponding process when inputting the workflow state of the factory workflow (i.e. the policy function can map one or more states of the production facility to the production actions … scheduling the particular production action based on the particular state utilizing the policy neural network can include: determining a probability distribution of the production actions to be scheduled at the production facility based on the particular state utilizing the policy neural network; and determining the particular production action based on the probability distribution of the production actions; para. [0200, 0202, 0214]), wherein the workflow state includes the factory state and the state of each process (i.e. information regarding inventory levels, orders, production data, maintenance history … where a state of the one or more states of the production facility can represent a product inventory of the one or more products available at the production facility at a specific time within the interval of time and an input-material inventory; para. [0042, 0200]), the factory state includes the production target amount and an achieved state of the products (i.e. Each product request can specify one or more requested products of the one or more products to be available at the production facility at one or more requested times … determining whether at least part of at least one product request is satisfied by the updated product inventory; para. [0006, 0203]);
(c) train the neural network of each process to select an optimal one among a plurality of works of the corresponding process and output it as a next work (i.e. determining a probability distribution of the production actions to be scheduled at the production facility based on the particular state utilizing the policy neural network; and determining the particular production action based on the probability distribution of the production actions; para. [0043, 0201, 0202]), performing a simulation using a factory simulator (i.e. The agent can interact with a simulation or model of the production facility; para. [0042]), extracting reinforcement learning data from the simulation results (i.e. The ANNs of the agent can use deep reinforcement learning over a number of simulations to learn how to effectively schedule the production facility in order to meet business requirements; para. [0042]), and training the neural network of each process using the extracted reinforcement learning data (i.e. training the neural network to represent the policy function and the value function can include training the neural network to represent the policy function and the value function utilizing a reinforcement learning technique; para. [0206]); and,
(d) apply the current state of the factory to the neural network of each process to obtain the next work of the corresponding process (i.e. where scheduling the particular production action based on the particular state utilizing the trained policy neural network can include: determining a probability distribution of the production actions to be scheduled at the production facility based on the particular state utilizing the trained policy neural network; and determining the particular production action based on the probability distribution of the production actions; para. [0215]),
wherein, in step (c), the neural network is optimized on the basis of the workflow state, a next work of a corresponding process performed in a corresponding state, a workflow state after a corresponding work is performed, and a reward obtained when a corresponding work is performed (i.e. training the policy neural network and the value neural network can include: receiving an input related to a particular state of the one or more states of the production facility at the policy neural network and the value neural network; scheduling a particular production action based on the particular state utilizing the policy neural network; determining an estimated benefit of the particular production action utilizing the value neural network; and updating the policy neural network and the value neural network based on the estimated benefit; para. [0201, 0203, 0207]); and,
in step (c), a plurality of production episodes are simulated using the factory simulator (i.e. training rewards per episode and product availability per episode … The ANNs of the agent can use deep reinforcement learning over a number of simulations; para. [0029, 0042]) to extract a workflow state and a work according to time order in each process (i.e. a state of the one or more states of the production facility can represent a product inventory of the one or more products available at the production facility at a specific time within the interval of time and an input-material inventory of the one or more input materials available at the production facility at the specific time, and where the value function can represent a benefits of products produced after taking production actions and the penalties due to late production; para. [0200]), extract a reward in each state from the performance of a production episode (i.e. determining an estimated benefit of the particular production action utilizing the value neural network; and updating the policy neural network and the value neural network based on the estimated benefit. In some of these embodiments, updating the policy neural network and the value neural network based on the estimated benefit can include: determining an actual benefit for the particular production action; para. [0029, 0201]), and collect reinforcement learning data using the extracted state, work, and reward (i.e. training the policy neural network and the value neural network can include: receiving an input related to a particular state of the one or more states of the production facility at the policy neural network and the value neural network; scheduling a particular production action based on the particular state utilizing the policy neural network; determining an estimated benefit of the particular production action utilizing the value neural network; and updating the policy neural network and the value neural network based on the estimated benefit. In some of these embodiments, updating the policy neural network and the value neural network based on the estimated benefit can include: determining an actual benefit for the particular production action; para. [0201]).
Hubbs does not explicitly teach sequences of performing the processes of the factory for producing the products is mapped to sequences of the processes of the factory workflow; and the state of each process includes an input lot, an output lot, and the state of each equipment of the corresponding process.
However, Weatherhead teaches (a) map a plurality of processes of a factory that produces products by performing the processes into a factory workflow (i.e. one or more functional specification sub-sections can be determined based at least in part on the user requirements. The functional specifications detail a project's desired and/or required operation, and the subsections further define the project's operational objectives. The subsections can include but are not limited to a version subsection, a description subsection; equipment used subsection, a sequence of operation subsection, an exception handling subsection, a process inputs subsection, a process outputs subsection, a process parameters subsection, a process data subsection, an operator interaction subsection, and a sustainability subsection. At 508, a functional specification can be generated based on one more of the subsections; para. [0047, 0066]), wherein a plurality of works that can be performed in each process of the factory are mapped to a plurality of works of the corresponding process of the factory workflow (i.e. The functional specification component 402 which describe the function of the process includes a plurality of subcomponents, including a version component 404, a description component 406, an equipment used component 408, a sequence of operation component 410, an exception handling component 412, a process inputs component 414, a process outputs component 416, a process parameters component 418, a process data component 420, an operator interaction component 422, and a sustainability component 424; para. [0059, 0060]), and sequences of performing the processes of the factory for producing the products is mapped to sequences of the processes of the factory workflow (i.e. The sequence of operation component 410 can determine the sequence of operations necessary to execute the functional component. For example, if the user requirements call for a liquid to be pumped at a rate X, the sequence of operations component can determine the system must receive a rate set point (e.g. X), open a flow valve, start the pump, and adjust the speed of the pump based on feedback from a flow meter; para. [0060]); the state of each process includes an input lot, an output lot, and the state of each equipment of the corresponding process (i.e. The exception handling component 412 determines the manner in which exceptions to the sequence of operations should be handled. For example, if a fault occurs the exception handling component 412 can determine to stop the pump. The process inputs component 414 and process outputs component 416 can determine the process inputs and outputs that are utilized such as lots of materials consumed, WIP lots produced, and so forth. The process parameters component 418 can determine the parameters of the process, such as tolerances, set points, etc. The process data component 420 can determine the data that needs to be generated as a result of the process, including but not limited to errors, set points, tolerances, lot numbers, date and time information, measurements, etc. The operator interaction component 422 can determine an operator interaction schema required to execute the process, including but not limited to displaying information, requiring input, etc; para. [0051, 0060]);
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention to modify the invention of Hubbs to include the feature of Weatherhead. One would have been motivated to make this modification because it improves how production facility is represented and decomposed for automated control and optimization.
Claim 6: Hubbs and Weatherhead teach the system according to claim 1. Hubbs further teaches wherein the factory simulator configures the factory workflow as a simulation model (i.e. The agent can interact with a simulation or model of the production facility; para. [0042]), and the simulation model of each process is modeled on the basis of a facility configuration (i.e. The model can be based on data about PF1 obtained from enterprise resource planning systems and other sources; para. [0043]) and a processing capacity of a corresponding process (i.e. Example production actions can include, but are not limited to, actions related to how much of each of chemicals A, B, C . . . to produce at times t1, t2, t3 . . . . The agent can interact with a simulation or model of the production facility to take in information regarding inventory levels, orders, production data, maintenance history, and schedule the plant according to historical demand patterns; para. [0042]).
Weatherhead further teaches wherein the factory simulator configures the factory workflow as a simulation model (i.e. In addition the testing component may contain emulation requirements, emulating the process in order to achieve the required test results; para. [0050]), and the simulation model of each process is modeled on the basis of a facility configuration (i.e. the hardware design specification component 324 can determine the inputs and outputs (I/O) required for the flow meter, the network(s) that must to be connected to the I/O points, whether a control cabinet is necessary, and so forth; para. [0051, 0059]) and a processing capacity of a corresponding process (i.e. the user requirements can include quality specifications such as the rate at which the widgets need to be manufactured, the size of the widgets, quality tolerances; para. [0027]).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention to modify the invention of Hubbs to include the feature of Weatherhead. One would have been motivated to make this modification because it improves how production facility is represented and decomposed for automated control and optimization.
Claim 9: Hubbs and Weatherhead teach the system according to claim 1. Hubbs further teaches wherein the reinforcement learning module extract a transition (i.e. After carrying out action At, state St+ of environment 520 at a next time step t+1 can be provided to agent 510. At least while agent 510 is being trained, state St+i of environment 520 can be accompanied by (or perhaps include) reward Rt determined after action At is carried out; para. [0126]) configured of a next state St+1 and a reward rt from a current state St and work process ap,t (i.e. At least while agent 510 is being trained, state St+i of environment 520 can be accompanied by (or perhaps include) reward Rt determined after action At is carried out; i.e., reward Rt is a response to action At. Reward Rt can be one or more scalar values signifying rewards or punishments; para. [0126]) using the workflow state, the work, and the reward according to time order in each process (i.e. the policy function can map one or more states of the production facility to the production actions, where a state of the one or more states of the production facility can represent a product inventory of the one or more products available at the production facility at a specific time within the interval of time and an input-material inventory of the one or more input materials available at the production facility at the specific time, and where the value function can represent a benefits of products produced after taking production actions and the penalties due to late production; para. [0200]), and generates the extracted transition as reinforcement learning data (i.e. training the policy neural network and the value neural network can include: receiving an input related to a particular state of the one or more states of the production facility at the policy neural network and the value neural network; scheduling a particular production action based on the particular state utilizing the policy neural network; determining an estimated benefit of the particular production action utilizing the value neural network; and updating the policy neural network and the value neural network based on the estimated benefit; para. [0201]).
8. Claims 5 and 7 are rejected under 35 U.S.C. 103 as being unpatentable over Hubbs in view of Weatherhead and further in view of Gottin et al. (U.S. Patent Application Pub. No. US 20190324822 A1).
Claim 5: Hubbs and Weatherhead teach the system according to claim 1. Hubbs further teaches wherein the workflow state includes a state of each process for all processes or some processes, and a state for the entire factory (i.e. A model of a production facility that relates to production of one or more products that are produced at the production facility utilizing one or more input materials to satisfy one or more product requests can be determined; para. [0006, 0042, 0200]).
Hubbs does not explicitly teach wherein the workflow state includes a state of each process for all processes or some processes.
However, Gottin teaches wherein the workflow state includes a state of each process for all processes or some processes (i.e. an agent traverses a set of states S and a set of actions A per state; para. [0046, 0051, 0102]), and a state for the entire factory (i.e. The exemplary reinforcement learning process 200 also obtains a simulation model of the workflow of the plurality of concurrent workflows during step 215 representing a plurality of different configurations of the control variables of the workflow of the concurrent workflows by mapping the states of the workflow based on a similarity given by one or more state similarity functions; para. [0052, 0061, 0071]).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention to modify the combination of Hubbs and Weatherhead to include the feature of Gottin. One would have been motivated to make this modification because it improves the modeling, optimization, and automated control of operations.
Claim 7: Hubbs and Weatherhead teach the system according to claim 6. Hubbs further teaches wherein the reinforcement learning module sets in advance mapping information (i.e. one or more computing devices can be populated with untrained policy and value ANNs to represent policy and value functions for deep learning; para. [0043])
Hubbs does not explicitly teach between a work of each process and a modeling variable in the simulation model of each process, and determines to which work a processing procedure of the simulation model corresponds using the set mapping information.
However, Gottin teaches wherein the reinforcement learning module sets in advance (i.e. The results of this search are persisted in a data structure (typically, as the state space graph 400); para. [0075]) mapping information between a work of each process and a modeling variable in the simulation model of each process (i.e. The exemplary reinforcement learning process 200 also obtains a simulation model of the workflow of the plurality of concurrent workflows during step 215 representing a plurality of different configurations of the control variables of the workflow of the concurrent workflows by mapping the states of the workflow based on a similarity given by one or more state similarity functions; para. [0047, 0052]), and determines to which work a processing procedure of the simulation model corresponds using the set mapping information (i.e. By mapping each state (defined by a set of snapshots) to the most similar state for each possible value of the control variable(s), a state space graph is generated; para. [0071, 0076]).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention to modify the combination of Hubbs and Weatherhead to include the feature of Gottin. One would have been motivated to make this modification because it improves the modeling, optimization, and automated control of operations.
9. Claim 10 is rejected under 35 U.S.C. 103 as being unpatentable over Hubbs in view of Weatherhead and further in view of Mnih et al. (U.S. Patent Application Pub. No. US 20150100530 A1).
Claim 10: Hubbs and Weatherhead teach the system according to claim 9. Hubbs further teaches wherein the reinforcement learning module samples transitions from the reinforcement learning data and trains the neural network agent to learn using the sampled transitions (i.e. After carrying out action At, state St+ of environment 520 at a next time step t+1 can be provided to agent 510. At least while agent 510 is being trained, state St+i of environment 520 can be accompanied by (or perhaps include) reward Rt determined after action At is carried out; i.e., reward Rt is a response to action At. Reward Rt can be one or more scalar values signifying rewards or punishments. Reward Rt can be defined by a reward or value function—in some examples, the reward or value function can be equivalent to an objective function in an optimization domain; para. [0126]).
Hubbs does not explicitly teach randomly samples.
However, Mnih teaches wherein the reinforcement learning module randomly samples transitions from the reinforcement learning data and trains the neural network agent to learn using the sampled transitions (i.e. In the above algorithms we store the agent's experiences at each time-step, e.sub.t=(s.sub.t, a.sub.t, r.sub.t, s.sub.t+1) in a data-set D=e.sub.1, . . . , e.sub.N, pooled over many episodes into a replay memory. During the inner loop of the algorithm, Q-learning updates, or minibatch updates, are applied to samples of experience, , drawn at random from the pool of stored samples. After performing experience replay, the agent selects and executes an action according to an .epsilon.-greedy policy (where 0.ltoreq..epsilon..ltoreq.1 and may change over time). Since using histories of arbitrary length as inputs to a neural network can be difficult, the Q-function instead works on fixed length representation of histories produced by a function .phi.; para. [0072]).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention to modify the combination of Hubbs and Weatherhead to include the feature of Mnih. One would have been motivated to make this modification because it improves training stability and efficiency.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant’s disclosure.
Sobalvarro et al. (Pub. No. US 11256241 B1), Systems and methods for optimizing factory scheduling, layout or both which represent active factory elements (human and machine) as computational objects and simulate factory operation to optimize a solution.
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 extension fee 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 date of this final action.
It is noted that any citation to specific pages, columns, lines, or figures in the prior art references and any interpretation of the references should not be considered to be limiting in any way. A reference is relevant for all it contains and may be relied upon for all that it would have reasonably suggested to one having ordinary skill in the art. In re Heck, 699 F.2d 1331, 1332-33, 216 U.S.P.Q. 1038, 1039 (Fed. Cir. 1983) (quoting In re Lemelson, 397 F.2d 1006, 1009, 158 U.S.P.Q. 275, 277 (C.C.P.A. 1968)).
Any inquiry concerning this communication or earlier communications from the examiner should be directed to TAN TRAN whose telephone number is (303)297-4266. The examiner can normally be reached on Monday - Thursday - 8:00 am - 5:00 pm MT.
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/TAN H TRAN/Primary Examiner, Art Unit 2141
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