DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
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.
Claims 1-6 and 10-11 are rejected under 35 U.S.C. 103 as being unpatentable over SONG WO 2019018015 A1 in view of Ruschival US 20190041832 A1 and LIU CN 113110330 A.
Regarding claim 1, SONG teaches a method for automating at least part of a process plant comprising a plurality of field components configured as self-organizing agents where each agent is configured to communicate with adjacently physically connected agents (Fig. 3 [0028] multi-agent control system for flow control will a plurality of devices each controlled by an agent, [0032] each agent communicates with neighboring agents), the method comprising:
generating requests by requesting agents corresponding to respective present needs of the requesting agents (Fig. 4 [0047] – [0050] each agents communicate its neighbor agents a set of preferred parameter values i.e. “requests by requesting agents corresponding to respective present needs of the requesting agents”),
forwarding each request from the respective requesting agent to one or more responsible agents capable of reacting on the request via one or more paths in each case (each agent communicates its neighbor agents a set of preferred parameter values),
reacting on the requests by responsible agents assigned based on a prioritization mechanism, wherein the prioritization mechanism is established using a topological representation of the process plant by a process (Fig. 4 [0017] [0041] [0042] [0050] each agent includes an optimization layer to use information from the configuration layer including network topological connections to optimize the parameter values for each agent requested to achieve optimal benefit for the entire system).
SONG does not explicitly further teach:
when forwarding each request, a path is defined as a chain of adjacently physically connected agents;
assigning a penalty value to each request;
forwarding the request via one or more paths defined by the topological representation by evaluating an increment of the penalty value at each agent that forwards or reacts on the request depending on a type of the request and/or a type of the agent,
whereby a penalty matrix is created indicative of how much penalty would be incurred if a particular request is reacted on by a given responsible agent, and
using the penalty matrix to assign requests to responsible agents such that a total penalty value is minimized.
Ruschival explicitly teaches in an analogous art:
when forwarding each request, a path is defined as a chain of adjacently physically connected agents ([0044] – [0045] production modules communicate tasks to adjacent production modules in the process chain); and
forwarding the request via one or more paths defined by the topological representation after assessing the request and adding assessed results to the request ([0044] – [0045] production modules evaluate the tasks and forward the tasks they cannot perform of conditions required for these tasks and communicate tasks to adjacent production modules in the process chain);
LIU explicitly teaches in an analogous art:
assigning a penalty value to each request (page 2 paragraphs 11-13, penalties of AGVs taking the tasks are calculated and assigned, the penalty values are depending on whether the task involves idle load, QC/AGV interaction, AGV/ASC interaction, and the length of interaction time), and
the assessing the request and adding assessed results to the request including evaluating an increment of the penalty value at each agent that forwards or reacts on the request depending on a type of the request and/or a type of the agent (page 2 paragraphs 11-13, penalty of AGVs take the tasks are calculated and assigned, the penalty values are depending on whether the task involving idle load, QC/AGV interaction, AGV/ASC interaction, and the length of interaction time, i.e. “type of the request”), and
whereby a penalty matrix is created indicative of how much penalty would be incurred if a particular request is reacted on by a given responsible agent, and using the penalty matrix to assign requests to responsible agents such that a total penalty value is minimized (page 7, paragraphs 3 – 14, a penalty matrix is created based on the penalty values assigned to tasks associated AGVs, optimal task matching list with least whole penalty is output for execution).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified SONG to incorporate the teachings of Ruschival and LIU, because they all directed to task/request orchestration among a plurality of agents, to make the method wherein when forwarding each request, a path is defined as a chain of adjacently physically connected agents; assigning a penalty value to each request; forwarding the request via one or more paths defined by the topological representation by evaluating an increment of the penalty value at each agent that forwards or reacts on the request depending on a type of the request and/or a type of the agent, whereby a penalty matrix is created indicative of how much penalty would be incurred if a particular request is reacted on by a given responsible agent, and using the penalty matrix to assign requests to responsible agents such that a total penalty value is minimized. One of ordinary skill in the art would have been motivated to do this modification so that a performable task can be removed from the list of tasks when forwarding the list, as Ruschival teaches in [0016], and the best tasks dispatching instructions can be generated to improve the overall production efficiency, as LIU teaches in Abstract.
Regarding claim 2, SONG further teaches the plurality of field components configured as agents comprise sensors, actuators and pipe nodes (Fig. 3 [0028] agents comprise pipe nodes device 201 A-D, sensors, actuators).
Regarding claim 3, SONG further teaches the requesting agents comprise sensors, and wherein each request is generated based on a deviation of a measured value from a setpoint, wherein the setpoint is defined by an operator of the process plant or, in an automated way, by a superordinate system of the process plant ([0029] [0031] the calculate a local optimized flow plan based on change to data from sensor i.e. “based on a deviation of a measured value from a setpoint”).
Regarding claim 4, SONG further teaches the responsible agents comprise actuators (Fig. 3 [0028] agents comprise pipe nodes device 201 A-D, sensors, actuators).
Regarding claim 5, SONG in view of Ruschival further teaches at least some of the pipe nodes of the process plant are configured as agents capable of translating the requests received from adjacently physically connected agents and forwarding the translated requests to other adjacently physically connected agents, the translation of a received request being dependent on a type of the respective received request and a type of the respective pipe node ([0044] the production modules evaluate the tasks based its specification and parameters and its own physical and process properties, and communicate the tasks with indication of which they cannot perform or conditions required for these tasks).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified SONG to incorporate the teachings of Ruschival, because they all directed to task/request orchestration among a plurality of agents, to make the method wherein at least some of the pipe nodes of the process plant are configured as agents capable of translating the requests received from adjacently physically connected agents and forwarding the translated requests to other adjacently physically connected agents, the translation of a received request being dependent on a type of the respective received request and a type of the respective pipe node. One of ordinary skill in the art would have been motivated to do this modification so as to confirm the tasks in the list that each production module can perform, as Ruschival teaches in [0048].
Regarding claim 6, SONG further teaches adaptively establishing the prioritization mechanism by updating the penalty matrix in the instance of:
a topology of the process plant being changed (the configuration layer and information is updated when adding a device or agent), or
control of a field component being taken over by an operator of the process plant, or
a new request being generated by a field component.
Regarding claim 10, SONG further teaches the prioritization mechanism is implemented as a software module running on a superordinate system of the process plant, or on an edge device, or on one of the field components of the process plant ([0049] [0050] the optimization solver in each agent).
Regarding claim 11, SONG in view of LIU further teaches assigning requests to responsible agents comprises using the penalty as an input to a mixed integer optimization routine to derive an optimal assignment of the requests to the responsible agents to minimize the total penalty value (paragraph 8 programing the mixed integer), subject to the constraints:
all requests reach at least one responsible agent, and no responsible agent is assigned to more than one request (page 2 S2, only available AGVs within a certain time are included for the task assignment, inherently each available AGV can only take one task at a time).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified SONG to incorporate the teachings of LIU, because they all directed to task/request orchestration among a plurality of agents, to make the method wherein assigning requests to responsible agents comprises using the penalty as an input to a mixed integer optimization routine to derive an optimal assignment of the requests to the responsible agents to minimize the total penalty value, subject to the constraints: all requests reach at least one responsible agent, and no responsible agent is assigned to more than one request. One of ordinary skill in the art would have been motivated to do this modification so as to confirm the tasks in the list that each production module can perform, as Ruschival teaches in [0048].
Claims 14-15 are rejected under 35 U.S.C. 103 as being unpatentable over SONG in view of Ruschival and LIU as applied to claims 1-6 and 10-11 above, further in view of Drees US 20180046164 A1.
Regarding claim 14, SONG in view of Ruschival and LIU teaches similar method steps to that of claim 1 therefore is rejected on the same basis.
The combination of SONG, Ruschival and LIU does not explicitly further teach utilizing a simulation server to simulate the method steps.
Drees explicitly teaches in an analogous art utilizing a simulation server to simulate the method steps ([0189] server 1102 connected to a simulated plant to simulate plant operation).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified SONG to incorporate the teachings of Drees, because they all directed to plant control, to make the method wherein utilizing a simulation server to simulate the method steps. One of ordinary skill in the art would have been motivated to do this modification so as to gain understanding of how the system operates, as Drees teaches in [0189].
Regarding claim 15, SONG in view of HEINEN further teaches a non-transitory computer-readable storage medium including instructions that, when processed by a simulation server, configure the simulation server to perform the method ([0189] server 1102 with memory storing the instructions).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified SONG to incorporate the teachings of Drees, because they all directed to plant control, to make the method wherein a non-transitory computer-readable storage medium including instructions that, when processed by a simulation server, configure the simulation server to perform the method. One of ordinary skill in the art would have been motivated to do this modification so as to gain understanding of how the system operates, as Drees teaches in [0189].
Allowable Subject Matter
Claims 7-9 and 12-13 are objected to as being dependent upon rejected base claims, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
The following is a statement of reasons for the indication of allowable subject matter:
Regarding claim 7, claim 7 depends on claim 2, SONG, Ruschival and LIU together teach the claim limitations of claim 2. However, SONG, Ruschival and LIU do not teach or suggest individually or in combination:
wherein evaluating an increment of the penalty value at each agent that forwards or reacts on the request depending on a type of the request and/or a type of the agent comprises:
for a sensor, not increasing the penalty value of a request if the sensor forwards the request,
for a pipe node, increasing the penalty value of a request if the request is forwarded from a main pipe to two or more pipe branches,
for an actuator,
if the actuator is a valve, then not increasing the penalty value of a mass flow request if the valve reacts on it, and increasing the penalty value of a pressure request if the valve reacts on it,
if the actuator is a pump, then not increasing the penalty value of a pressure request if the pump reacts on it, and increasing the penalty value of a mass flow request if the pump reacts on it, and
not increasing the penalty value of a request which is forwarded by the actuator without being reacted on.
Regarding claim 8, claim 8 depends on claim 1, SONG, Ruschival and LIU together teach the claim limitations of claim 1. However, SONG, Ruschival and LIU do not teach or suggest individually or in combination:
wherein, in each path defined by the topological representation, the forwarding of a request is stopped at an actuator that is capable of reacting on the request, wherein an actuator is considered as capable of reacting on the request if:
the actuator is not occupied already by another request at a lower penalty value, or
the corresponding request has reached another actuator, via another path, wherein said other actuator can react on the corresponding request at a lower penalty value.
Regarding claim 9, claim 9 depends on claim 1, SONG, Ruschival and LIU together teach the claim limitations of claim 1. However, SONG, Ruschival and LIU do not teach or suggest individually or in combination:
wherein, in each path defined by the topological representation, the forwarding of a request is stopped after finding n actuators that are capable of reacting on the request, n being a natural number greater than 1, wherein an actuator is considered as capable of reacting on the request if:
the actuator is not occupied already by another request at a lower penalty value, or
the corresponding request has reached another actuator, via another path, wherein said other actuator can react on the corresponding request at a lower penalty value.
Regarding claim 12, claim 12 depends on claim 1, SONG, Ruschival and LIU together teach the claim limitations of claim 1. However, SONG, Ruschival and LIU do not teach or suggest individually or in combination:
wherein assigning requests to responsible agents comprises:
analyzing the penalty matrix to identify a multiple-input multiple-output condition where a plurality of requests have the same set of responsible agents assigned,
adjusting the penalty matrix by lumping the plurality of requests as a single request and lumping the set of responsible agents as a single responsible agent,
using the adjusted penalty as an input to a mixed integer optimization routine to derive an optimal assignment of the requests to the responsible agents to minimize the total penalty value, subject to the constraints:
all requests reach at least one responsible agent, and
no responsible agent is assigned to more than one request.
Regarding claim 13, claim 13 depends on claim 11, SONG, Ruschival and LIU together teach the claim limitations of claim 11. Song further teaches the assignment of requests to responsible agents using the penalty matrix is carried out at an agent level by each responsible agent ([0049] [0050] the optimization solver in each agent). However, SONG, Ruschival and LIU do not teach or suggest individually or in combination:
wherein the mixed integer optimization routine is triggered in the event of a conflicting assignment at the agent level.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Caviglia US 20060104274 A1 teaches accumulating cost when propagating request in process plant.
HEINEN US 20220398122 A1 teaches virtual machine to simulate plant process control.
JETLEY US 20220066423 A1 teaches change requests traverse a path in graph of process plant.
Liu US 20120134298 A1 teaches adding link cost to accumulated cost or not based on the broadcasting type in network.
THIELE GB 2430764 A teaches penalty matrix for controlled moves of field devices.
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/M.T./ Examiner, Art Unit 2115
/KAMINI S SHAH/ Supervisory Patent Examiner, Art Unit 2115
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