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 .
DETAILED ACTION
The action is in response to the Applicant’s communication filed on 11/15/2023.
Claims 1-12 are pending, where claims 1 and 11-12 are independent.
This application claims the priority benefit of the international application no. PCT/JP2022/021295 filed on 05/24//2022 incorporated herein.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 11/15/2023 and 10/10/2024 has been filed on/after the filing date of the application. The submission is in-compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
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 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.
The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
Claims 1-12 are rejected under AIA 35 U.S.C. 103 as being unpatentable over Megyesi, et al. (“-- sensor error model on stability and controllability - unmanned aircraft" cited in IDS) in view of Tikito, et al. (USPGPub No. 20150081048 A1).
As to claim 1, Megyesi discloses An information processing device (Megyesi [page 1-4] “automatic control - current and desired position evaluate - Inertial Measurement Unit (IMU) - form a subset of the INS system - many sources of error interfere with their final measurement of quantities - noise into the measurement - decrease in the stability of the automatic control system - measurement errors eliminated” [abstract] see Fig. 1-11) comprising:
[a memory; and a processor --- acquiring a first controller model --- control condition is satisfied;] and
outputting a second controller model, the second controller model being capable of tolerating a measurement value including a measurement error, wherein the second controller model includes information indicating a control condition based on the information indicating the control condition included in the first controller model, and information indicating a control action based on the information indicating the control condition and the information indicating the control action included in the first controller model (Megyesi [page 1-4] “Inertial Measurement Unit (IMU) - form a subset of the INS system - many sources of error interfere with their final measurement of quantities - noise into the measurement - decrease in the stability of the automatic control system - measurement errors eliminated - sensor error model involved in the feedback - feedback to the automatic control system supplied by sensors that measure the actual values of the individual quantities - then compares them with the setpoint values and determine whether the setpoint reached or needs to be adjusted” [abstract] “creation of a three-axis error model - sensor installed - created error model takes into account the basic errors affecting the sensor - Misalignment error and Non-orthogonality error - noise mechanisms performed - verify correct functioning of error model of the sensor - filters remove noise components from sensors information - increase the stability and controllability” see Fig. 1-11, sensor error model involved in the feedback as shown in Fig.10 obviously provides outputting second controller model).
However, Tikito discloses a memory; and a processor configured to execute a process including: acquiring a first controller model, the first controller model including information indicating a control condition based on a measurement value, and information indicating a control action defining an action of a control target when the control condition is satisfied; (Tikito [0025-66] “constraint database 11 for storing a premise constraint file 11a, an input unit 12, a constraint generator 13, a constraint evaluation indicator setting unit 14, a constraint evaluation indicator database 15, a constraint satisfaction solution retrieve unit 16, a display 17, a constraint relaxing unit 18, and a system controller 19 - plurality of existing constraints - plurality of premise constraints - input unit 12 receives input of a control parameter value for the FA control apparatus from a user - constraint generator 13 generates a new constraint ("additional constraint") to be satisfied by the control parameter - evaluation indicator that indicates priority degree (importance degree) to each of the plurality of additional constraints” [002-10] see Fig. 1-14, controller, database for storing, sensor, input unit, setting unit, premise constraint file, constraint generator, constraint evaluation indicator setting unit and database, plurality of existing constraints, plurality of premise constraints, control parameter value, new or additional constraint to be satisfied by the control parameter, plurality of additional constraints obviously provides a memory; and a processor configured to execute a process including: acquiring a first controller model, the first controller model including information indicating a control condition based on a measurement value, and information indicating a control action defining an action of a control target when the control condition is satisfied).
Megyesi and Tikito are analogous arts from the same field of endeavor and contain overlapping structural and functional similarities and both contain automatic control system.
Therefore, at the time the invention was made, it would have been obvious to a person of ordinary skill in the art to modify the above functionalities memory; and a processor and acquiring first controller model, as taught by Megyesi, and incorporating controller, sensor, database, constraint file, plurality of constraints, control parameter value, as taught by Tikito.
As to claim 2, the combination of Megyesi and Tikito disclose all the limitations of the base claims as outlined above.
The combination further discloses The information processing device according to claim 1, wherein the process further includes: selecting one subset from all subsets of the control conditions included in the first controller model, (Tikito [0025-66] “constraint database 11 for storing a premise constraint file 11a, an input unit 12, a constraint generator 13, a constraint evaluation indicator setting unit 14, a constraint evaluation indicator database 15, a constraint satisfaction solution retrieve unit 16, a display 17, a constraint relaxing unit 18, and a system controller 19 - plurality of existing constraints - plurality of premise constraints” [002-10] see Fig. 1-14, input unit, setting unit, premise constraint file, constraint generator, constraint evaluation indicator setting unit and database, plurality of existing constraints, plurality of premise constraints, control parameter value, new or additional constraint to be satisfied by the control parameter, plurality of additional constraints obviously provides selecting one subset from all subsets of the control conditions included in the first controller model) and
generating information indicating a condition that all possible true values estimated from a measurement value are included in the selected subset, as information indicating the control condition included in the second controller model (Megyesi [page 1-4] “Inertial Measurement Unit (IMU) - form a subset of the INS system - many sources of error interfere with their final measurement of quantities - noise into the measurement - decrease in the stability of the automatic control system - measurement errors eliminated - sensor error model involved in the feedback - feedback to the automatic control system supplied by sensors that measure the actual values of the individual quantities - then compares them with the setpoint values and determine whether the setpoint reached or needs to be adjusted” [abstract] “creation of a three-axis error model - sensor installed - created error model takes into account the basic errors affecting the sensor - Misalignment error and Non-orthogonality error - noise mechanisms performed - verify correct functioning of error model of the sensor - filters remove noise components from sensors information - increase the stability and controllability” see Fig. 1-11, sensor error model involved in the feedback model as shown in Fig.10 obviously provides generating information indicating a condition that all possible true values estimated from a measurement value are included in the selected subset, as information indicating the control condition included in the second controller model).
As to claim 3, the combination of Megyesi and Tikito disclose all the limitations of the base claims as outlined above.
The combination further discloses The information processing device according to claim 1, wherein the process further includes: selecting one subset from all subsets of the control conditions included in the first controller model (Tikito [0025-66] “constraint database 11 for storing a premise constraint file 11a, an input unit 12, a constraint generator 13, a constraint evaluation indicator setting unit 14, a constraint evaluation indicator database 15, a constraint satisfaction solution retrieve unit 16, a display 17, a constraint relaxing unit 18, and a system controller 19 - plurality of existing constraints - plurality of premise constraints” [002-10] see Fig. 1-14, input unit, setting unit, premise constraint file, constraint generator, constraint evaluation indicator setting unit and database, plurality of existing constraints, plurality of premise constraints, control parameter value, new or additional constraint to be satisfied by the control parameter, plurality of additional constraints obviously provides the limitations),
and generating information indicating an action common to all control actions of the first controller model under each control condition included in the selected subset, as information indicating the control action included in the second controller model (Megyesi [page 1-4] “Inertial Measurement Unit (IMU) - form a subset of the INS system - many sources of error interfere with their final measurement of quantities - noise into the measurement - decrease in the stability of the automatic control system - measurement errors eliminated - sensor error model involved in the feedback - feedback to the automatic control system supplied by sensors that measure the actual values of the individual quantities - then compares them with the setpoint values and determine whether the setpoint reached or needs to be adjusted” [abstract] “creation of a three-axis error model - sensor installed - created error model takes into account the basic errors affecting the sensor - Misalignment error and Non-orthogonality error - noise mechanisms performed - verify correct functioning of error model of the sensor - filters remove noise components from sensors information - increase the stability and controllability” see Fig. 1-11, sensor error model involved in the feedback model as shown in Fig.10 obviously provides the limitations).
As to claim 4, the combination of Megyesi and Tikito disclose all the limitations of the base claims as outlined above.
The combination further discloses The information processing device according to claim 1, wherein the first controller model further includes information indicating a parameter restriction indicating a condition to be satisfied by a parameter included in the control action, and wherein the process further includes: generating information indicating a parameter restriction included in the second controller model, based on information indicating the control condition, the parameter restriction, and the control action included in the first controller model (Megyesi [page 1-4] “Inertial Measurement Unit (IMU) - form a subset of the INS system - many sources of error interfere with their final measurement of quantities - noise into the measurement - decrease in the stability of the automatic control system - measurement errors eliminated - sensor error model involved in the feedback - feedback to the automatic control system supplied by sensors that measure the actual values of the individual quantities - then compares them with the setpoint values and determine whether the setpoint reached or needs to be adjusted” [abstract] “creation of a three-axis error model - sensor installed - created error model takes into account the basic errors affecting the sensor - Misalignment error and Non-orthogonality error - noise mechanisms performed - verify correct functioning of error model of the sensor - filters remove noise components from sensors information - increase the stability and controllability” see Fig. 1-11, sensor error model involved in the feedback model as shown in Fig.10 obviously provides the limitations).
As to claim 5, the combination of Megyesi and Tikito disclose all the limitations of the base claims as outlined above.
The combination further discloses The information processing device according to claim 4, wherein the first controller model includes information indicating a safety condition that is a condition of safety to be guaranteed, and wherein the generating information indicating a parameter restriction includes selecting one subset from all subsets of the control conditions included in the first controller model , (Tikito [0025-66] “constraint database 11 for storing a premise constraint file 11a, an input unit 12, a constraint generator 13, a constraint evaluation indicator setting unit 14, a constraint evaluation indicator database 15, a constraint satisfaction solution retrieve unit 16, a display 17, a constraint relaxing unit 18, and a system controller 19 - plurality of existing constraints - plurality of premise constraints” [002-10] see Fig. 1-14, input unit, setting unit, premise constraint file, constraint generator, constraint evaluation indicator setting unit and database, plurality of existing constraints, plurality of premise constraints, control parameter value, new or additional constraint to be satisfied by the control parameter, plurality of additional constraints obviously provides the limitations), and
generating information indicating a parameter restriction indicating that all parameter restrictions of the first controller model under each control condition included in the selected subset are satisfied, an action common to the control actions of the first controller model corresponding to all the control conditions included in the selected subset is present, and the safety condition is satisfied in a state controlled by the common action (Megyesi [page 1-4] “Inertial Measurement Unit (IMU) - form a subset of the INS system - many sources of error interfere with their final measurement of quantities - noise into the measurement - decrease in the stability of the automatic control system - measurement errors eliminated - sensor error model involved in the feedback - feedback to the automatic control system supplied by sensors that measure the actual values of the individual quantities - then compares them with the setpoint values and determine whether the setpoint reached or needs to be adjusted” [abstract] “creation of a three-axis error model - sensor installed - created error model takes into account the basic errors affecting the sensor - Misalignment error and Non-orthogonality error - noise mechanisms performed - verify correct functioning of error model of the sensor - filters remove noise components from sensors information - increase the stability and controllability” see Fig. 1-11, sensor error model involved in the feedback model as shown in Fig.10 obviously provides the limitations).
As to claim 6, the combination of Megyesi and Tikito disclose all the limitations of the base claims as outlined above.
The combination further discloses The information processing device according to claim 1, wherein the first controller model further includes information indicating a parameter restriction indicating a condition to be satisfied by a parameter included in the control action, and information indicating a safety condition that is a condition of safety to be guaranteed, and wherein the process further includes: selecting one subset from all subsets of the control conditions included in the first controller model, and generating, as information included in the second controller model, information indicating a parameter restriction that any of the parameter restrictions of the first controller model under each control condition included in the selected subset is satisfied; and selecting one subset from all subsets of the control conditions included in the first controller model, , (Tikito [0025-66] “constraint database 11 for storing a premise constraint file 11a, an input unit 12, a constraint generator 13, a constraint evaluation indicator setting unit 14, a constraint evaluation indicator database 15, a constraint satisfaction solution retrieve unit 16, a display 17, a constraint relaxing unit 18, and a system controller 19 - plurality of existing constraints - plurality of premise constraints” [002-10] see Fig. 1-14, input unit, setting unit, premise constraint file, constraint generator, constraint evaluation indicator setting unit and database, plurality of existing constraints, plurality of premise constraints, control parameter value, new or additional constraint to be satisfied by the control parameter, plurality of additional constraints obviously provides the limitations)and
generating, as information indicating the control action included in the second controller model, an expression indicating any one of the control actions of the first controller model under each control condition included in the selected subset (Megyesi [page 1-4] “Inertial Measurement Unit (IMU) - form a subset of the INS system - many sources of error interfere with their final measurement of quantities - noise into the measurement - decrease in the stability of the automatic control system - measurement errors eliminated - sensor error model involved in the feedback - feedback to the automatic control system supplied by sensors that measure the actual values of the individual quantities - then compares them with the setpoint values and determine whether the setpoint reached or needs to be adjusted” [abstract] “creation of a three-axis error model - sensor installed - created error model takes into account the basic errors affecting the sensor - Misalignment error and Non-orthogonality error - noise mechanisms performed - verify correct functioning of error model of the sensor - filters remove noise components from sensors information - increase the stability and controllability” see Fig. 1-11, sensor error model involved in the feedback model as shown in Fig.10 obviously provides the limitations).
As to claim 7, the combination of Megyesi and Tikito disclose all the limitations of the base claims as outlined above.
The combination further discloses The information processing device according to claim 4, wherein information indicating the control condition, the parameter restriction, and the control action included in the second controller model is generated in a format in which a characteristic of the measurement error is capable of being added later (Megyesi [page 1-4] “Inertial Measurement Unit (IMU) - form a subset of the INS system - many sources of error interfere with their final measurement of quantities - noise into the measurement - decrease in the stability of the automatic control system - measurement errors eliminated - sensor error model involved in the feedback - feedback to the automatic control system supplied by sensors that measure the actual values of the individual quantities - then compares them with the setpoint values and determine whether the setpoint reached or needs to be adjusted” [abstract] “creation of a three-axis error model - sensor installed - created error model takes into account the basic errors affecting the sensor - Misalignment error and Non-orthogonality error - noise mechanisms performed - verify correct functioning of error model of the sensor - filters remove noise components from sensors information - increase the stability and controllability” see Fig. 1-11, sensor error model involved in the feedback model as shown in Fig.10 obviously provides the limitations).
As to claim 8, the combination of Megyesi and Tikito disclose all the limitations of the base claims as outlined above.
The combination further discloses The information processing device according to claim 4, wherein information indicating the control condition, the parameter restriction, and the control action included in the second controller model is generated based on information indicating a characteristic of the measurement error (Megyesi [page 1-4] “Inertial Measurement Unit (IMU) - form a subset of the INS system - many sources of error interfere with their final measurement of quantities - noise into the measurement - decrease in the stability of the automatic control system - measurement errors eliminated - sensor error model involved in the feedback - feedback to the automatic control system supplied by sensors that measure the actual values of the individual quantities - then compares them with the setpoint values and determine whether the setpoint reached or needs to be adjusted” [abstract] “creation of a three-axis error model - sensor installed - created error model takes into account the basic errors affecting the sensor - Misalignment error and Non-orthogonality error - noise mechanisms performed - verify correct functioning of error model of the sensor - filters remove noise components from sensors information - increase the stability and controllability” see Fig. 1-11, sensor error model involved in the feedback model as shown in Fig.10 obviously provides the limitations).
As to claim 9, the combination of Megyesi and Tikito disclose all the limitations of the base claims as outlined above.
The combination further discloses The information processing device according to claim 4, wherein the process further includes: outputting robustness condition data indicating a condition serving as a limit of a tolerable measurement error of the second controller model based on information indicating the control condition and the parameter restriction included in the second controller model (Megyesi [page 1-4] “Inertial Measurement Unit (IMU) - form a subset of the INS system - many sources of error interfere with their final measurement of quantities - noise into the measurement - decrease in the stability of the automatic control system - measurement errors eliminated - sensor error model involved in the feedback - feedback to the automatic control system supplied by sensors that measure the actual values of the individual quantities - then compares them with the setpoint values and determine whether the setpoint reached or needs to be adjusted” [abstract] “creation of a three-axis error model - sensor installed - created error model takes into account the basic errors affecting the sensor - Misalignment error and Non-orthogonality error - noise mechanisms performed - verify correct functioning of error model of the sensor - filters remove noise components from sensors information - increase the stability and controllability” see Fig. 1-11, sensor error model involved in the feedback model as shown in Fig.10 obviously provides the limitations).
As to claim 10, the combination of Megyesi and Tikito disclose all the limitations of the base claims as outlined above.
The combination further discloses The information processing device according to claim 9, wherein the process further includes: a robustness condition generation unit configured to select one subset from all subsets of the control conditions included in the first controller model , (Tikito [0025-66] “constraint database 11 for storing a premise constraint file 11a, an input unit 12, a constraint generator 13, a constraint evaluation indicator setting unit 14, a constraint evaluation indicator database 15, a constraint satisfaction solution retrieve unit 16, a display 17, a constraint relaxing unit 18, and a system controller 19 - plurality of existing constraints - plurality of premise constraints” [002-10] see Fig. 1-14, input unit, setting unit, premise constraint file, constraint generator, constraint evaluation indicator setting unit and database, plurality of existing constraints, plurality of premise constraints, control parameter value, new or additional constraint to be satisfied by the control parameter, plurality of additional constraints obviously provides the limitations), and
generate, as the robustness condition data, information indicating that a condition, in which a parameter satisfying the parameter restriction included in the second controller model for all measurement values satisfying each control condition included in the second controller model corresponding to the selected subset is present, is satisfied for all the subsets (Megyesi [page 1-4] “Inertial Measurement Unit (IMU) - form a subset of the INS system - many sources of error interfere with their final measurement of quantities - noise into the measurement - decrease in the stability of the automatic control system - measurement errors eliminated - sensor error model involved in the feedback - feedback to the automatic control system supplied by sensors that measure the actual values of the individual quantities - then compares them with the setpoint values and determine whether the setpoint reached or needs to be adjusted” [abstract] “creation of a three-axis error model - sensor installed - created error model takes into account the basic errors affecting the sensor - Misalignment error and Non-orthogonality error - noise mechanisms performed - verify correct functioning of error model of the sensor - filters remove noise components from sensors information - increase the stability and controllability” see Fig. 1-11, sensor error model involved in the feedback model as shown in Fig.10 obviously provides the limitations).
Citation of Pertinent Prior Art
It is noted that any citations to specific, pages, columns, lines, or figures in the prior art references and any interpretation of the reference 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. See MPEP 2141.02 VI. PRIOR ART MUST BE CONSIDERED IN ITS ENTIRETY, i.e., as a whole and 2123.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. The prior art made of record:
Shwartz, et al. “On a formal model ---self driving car” cited in IDS discloses a self-driving car control system.
Matsubara, et al. USPGPub No. 20200057416 A1 discloses an apparatus includes plurality of agents of a plurality of devices in a facility, acquires state data, acquires control condition of each target device, uses learning data and outputs control condition recommended in response to the input of state data.
Hatakeyama, et al. USPGPub No. 20230214717 A1 discloses a rule generation apparatus generates a rule group for dividing a training into a plurality of clusters related to target values using a rule base model to satisfy “first constraint”, where feature value and target included in each measured value.
Jeong, et al. USPGPub No. 20240242009 A1 discloses an apparatus for reducing an error of a physical model using an artificial intelligence algorithm of a process including error terms representing a modeling error and correct the physical model deriving error terms from the physical model using real data.
Inoue, USPGPub No. 20220107177 A1 discloses an error determination apparatus for determining a measurement error occurs in a coordinate measuring machine, and outputs the measurement error by determination part.
Kakizaki USPGPub No. 20220006824 A1 discloses a system for controlling a plurality of equipment of a control system used within an important infrastructure facility and implementation of appropriate security assessment.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Md Azad whose telephone @(571)272-0553 or email: md.azad@uspto.gov. The examiner can normally be reached on Mon-Thu 9AM-5PM.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Mohammad Ali can be reached on (571)272-4105. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/Md Azad/
Primary Examiner, Art Unit 2119