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 .
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.
Examiner’s Note
This Office Action is in response to application filed 6/13/2023 and response to restriction requirement filed on 2/2/2026, where claims 1-7 and 14-20 are elected; claims 8-13 are withdrawn; and claims 1-7 and 14-20 are currently pending.
Response to Arguments
Applicant elected claims 1-7 and 14-20 (Group I) in response to the restriction requirement with traverse. Applicant argued that, see pg. 1-2, there is enough overlap between Group I and Group II that there would not be a serious burden on the Examiner to examine both groups. However, Examiner respectfully disagrees. As indicated in the restriction requirement, two groups are classified in different CPC codes; therefore, they are directed to two different technology fields, and require different field of search and/or different search strategies. As such, it imposes a serious burden on the Examiner.
Allowable Subject Matter
Claims 7 and 20 are objected to as being dependent upon a rejected base claim, 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 claims 7 and 20, the primary reason it is allowable over the US Patent Application Pub. No. 20180267489 (Tango), the US Patent Application Pub. No. 20220106061 (Strickland), the US Patent Application Pub. No. 20170158329 (Liu), the US Patent Application Pub. No. 20230356698 (Shi), the US Patent Application Pub. No. 20150220867 (Christensen), the US Patent Application Pub. No. 20230011225 (Armbruster, JR.), and the US Patent Application Pub. No. 20200368795 (Che), is because the cited prior art teach similar limitations of utilizing machine learning to optimize the function of measuring cleanliness of a target, determine when the target needs cleaning based on the measured cleanliness, and execute the cleaning protocol; however, neither alone nor in combination, the cited prior art teach using a first machine learning algorithm to generate first and second electronic cleanliness measurements, and using a second machine learning algorithm to generate first cleaning system protocol and updated version of the first cleaning system protocol. Therefore, the cited prior art do not teach each and every limitation in the specific combination as recited in claims 7 and 20.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale or otherwise available to the public before the effective filing date of the claimed invention.
Claims 1-6 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Tango et al., (US 20180267489 A1) (hereinafter Tango).
Referring to claim 1, Tango teaches a controller operable to perform cleaning system control operations comprising:
generating, using a processor system (¶ [0057], computer), a first electronic cleanliness measurement (ECM) associated with a cleaning target (¶ [0024], “when the object to be cleaned is the machine tool, the first measurement device can measure the entire machine tool or at least one point of interest in the machine tool from a predetermined position. For example, when the object to be cleaned is the workpiece, the first measurement device can measure the entire workpiece or at least one point of interest thereof from a predetermined position.”); and
based at least in part on a determination that the first ECM exceeds a threshold, generating, using the processor system, a first cleaning system protocol (¶ [0028], “the learning unit 26 can gradually approach to an optimum solution of a correlation between a current state of an object to be cleaned and the action of any cleaning condition under which cleaning needs to be performed for the object to be cleaned in the current state.”);
wherein the processor system executes the first cleaning system protocol to control a cleaning system operable to clean debris from the cleaning target (¶ [0028], “a learning result repeatedly output by the learning unit 26 can be used to perform selection (that is, decision making) on action such as any cleaning condition under which cleaning needs to be performed for the object to be cleaned in the current state (that is, contamination state).” ¶ [0060], “The decision-making unit 52 displays the cleaning condition when cleaning the object to be cleaned learned by the learning unit 26 to the worker, or generates a command value C with respect to an industrial machine, which performs cleaning based on the cleaning condition when cleaning the object to be cleaned learned by the learning unit 26, and outputs the generated command value C.”)
Referring to claim 2, Tango further teaches the controller of claim 1, wherein the control operations further comprise, subsequent to using the first cleaning system protocol to control a cleaning system operable to clean debris from the cleaning target, generating a second ECM associated with the cleaning target (¶ [0026], “re-measurement of a contamination state of the object to be cleaned after cleaning using the first measurement device are implemented under the environment while the machine learning device 20 of the cleaning process optimization device 10 proceeds learning.”)
Referring to claim 3, Tango further teaches the controller of claim 2, wherein the control operations further comprise, based at least in part on a determination that the second ECM exceeds a threshold, generating an updated version of the first cleaning system protocol (¶ [0027], “The learning unit 26 can repeatedly execute learning based on data set including the above-described state variable S and determination data D for a plurality of objects to be cleaned. During the repetition of a learning cycle for the plurality of objects to be cleaned, the cleaning condition data S1 in the state variable S is a cleaning condition obtained in the learning cycle up to the previous time”).
Referring to claim 4, Tango further teaches the controller of claim 3, wherein the control operations further comprise using the updated version of the first cleaning system protocol to control the cleaning system operable to clean debris from the cleaning target (¶ [0028], “By repeating such a learning cycle, the learning unit 26 can automatically identify a feature that implies the correlation between the contamination state of the object to be cleaned (the contamination state data S2) and the cleaning condition of cleaning on the object to be cleaned.”)
Referring to claim 5, Tango further teaches the controller of claim 4, wherein the control operations further comprise, based at least in part on a determination that the first ECM does not exceed the threshold, not initiating operation of the cleaning system (¶ [0028], “the learning unit 26 can gradually approach to an optimum solution of a correlation between a current state of an object to be cleaned and the action of any cleaning condition under which cleaning needs to be performed for the object to be cleaned in the current state.” Based on the disclosure, it is implied that the cleaning does not perform when it is not needed based on the correlation between the current state and the action of cleaning condition in the current state.)
Referring to claim 6, Tango further teaches the controller of claim 4, wherein the control operations further comprise, based at least in part on a determination that the second ECM does not exceed the threshold, terminating operation of the cleaning system (¶ [0028], “the learning unit 26 can gradually approach to an optimum solution of a correlation between a current state of an object to be cleaned and the action of any cleaning condition under which cleaning needs to be performed for the object to be cleaned in the current state.” Based on the disclosure, it is implied that the cleaning does not perform, e.g., terminate, when it is not needed based on the correlation between the current state and the action of cleaning condition in the current state.)
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.
Claims 14 and 17-19 are rejected under 35 U.S.C. 103 as being unpatentable over Strickland et al., (US 20220106061 A1) (hereinafter Strickland) in view of Tango et al., (US 20180267489 A1) (hereinafter Tango).
Referring to claim 14, Strickland teaches A controller operable to perform cleaning system control operations comprising:
…an extravehicular mobility unit (EMU) having debris (¶ [0003], “a system to remove dust from an extravehicular mobility unit (EMU) worn by an astronaut”).
…wherein the processor system executes the first cleaning system protocol to control a cleaning system operable to clean some or all of the debris from the EMU (¶ [0035], “An exemplary ionic shower unit 130 is shown above the astronaut. Ion generation is well-known and not detailed herein…Generally, a magnetic field is created between two electrodes, and material (e.g., air) flowing through the magnetic field is ionized. Negatively charged ions (i.e., anions 137) and positively charged ions (i.e., cations 135) are generated. While some known ion generators create a localized ionic discharge, the ionic shower unit 130 includes a fan 125 to direct the flow of cations 135 and anions 137 to the EMU 120, as shown. When the airlock 105 is first entered, it is essentially a vacuum which is then filled with gases (e.g., oxygen O.sub.2, nitrogen N.sub.2, carbon dioxide CO.sub.2). According to one or more embodiments, the inflow of these gases may be through the electric field created by the one or more ionic shower units 130.” Examiner notes, the cleaning system protocol is executed in order for the cleaning process to begin after the astronaut entered the airlock.)
Strickland teaches cleaning debris off a extravehicular mobility unit as discussed above. However, Strickland does not explicitly teach generating, using a processor system, a first electronic cleanliness measurement (ECM)…; and
based at least in part on a determination that the first ECM exceeds a threshold, generating, using the processor system, a first cleaning system protocol.
Tango teaches generating, using a processor system (¶ [0057], computer), a first electronic cleanliness measurement (ECM)…( ¶ [0024], “when the object to be cleaned is the machine tool, the first measurement device can measure the entire machine tool or at least one point of interest in the machine tool from a predetermined position. For example, when the object to be cleaned is the workpiece, the first measurement device can measure the entire workpiece or at least one point of interest thereof from a predetermined position.”); and
based at least in part on a determination that the first ECM exceeds a threshold, generating, using the processor system, a first cleaning system protocol (¶ [0028], “the learning unit 26 can gradually approach to an optimum solution of a correlation between a current state of an object to be cleaned and the action of any cleaning condition under which cleaning needs to be performed for the object to be cleaned in the current state.”)
Strickland and Tango are analogous art to the claimed invention because they are concerning with interface for cleaning debris off a target (i.e., same field of endeavor).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention having Strickland and Tango before them to modify the method of dust removal off extravehicular mobility unit of Strickland to incorporate the function of measuring an object to be cleaned to determine the cleaning condition under which cleaning needs to be performed by Tango. One of ordinary skill in the art would have combined the elements as claimed by known methods as disclosed by Tango (¶ [0020]-[0071]), because the function of measuring an object to be cleaned to determine the cleaning condition under which cleaning needs to be performed does not depend on the method of dust removal off extravehicular mobility unit. That is the function of measuring an object to be cleaned to determine the cleaning condition under which cleaning needs to be performed performs the same function independent on which interface it is incorporated onto, and therefore, the result of the combination would have been predictable to one of ordinary skill in the art. The motivation to combine would have been to provide precise cleaning while finely adjusting the cleaning condition, which would save time and effort as suggested by Tango (¶ [0004]).
Referring to claim 17, Strickland teaches the controller of claim 14. However, Strickland does not explicitly teach the controller operations further comprise:
subsequent to using the first cleaning system protocol to control a cleaning system operable to clean some or all of the debris from the target, generating a second ECM associated with the target;
based at least in part on a determination that the second ECM exceeds a threshold, generating an updated version of the first cleaning system protocol; and
using the updated version of the first cleaning system protocol to control the cleaning system operable to clean some or all of the debris from the target.
Tango further teaches the controller operations further comprise:
subsequent to using the first cleaning system protocol to control a cleaning system operable to clean some or all of the debris from the target, generating a second ECM associated with the target (¶ [0026], “re-measurement of a contamination state of the object to be cleaned after cleaning using the first measurement device are implemented under the environment while the machine learning device 20 of the cleaning process optimization device 10 proceeds learning.”);
based at least in part on a determination that the second ECM exceeds a threshold, generating an updated version of the first cleaning system protocol (¶ [0027], “The learning unit 26 can repeatedly execute learning based on data set including the above-described state variable S and determination data D for a plurality of objects to be cleaned. During the repetition of a learning cycle for the plurality of objects to be cleaned, the cleaning condition data S1 in the state variable S is a cleaning condition obtained in the learning cycle up to the previous time”); and
using the updated version of the first cleaning system protocol to control the cleaning system operable to clean some or all of the debris from the target (¶ [0028], “By repeating such a learning cycle, the learning unit 26 can automatically identify a feature that implies the correlation between the contamination state of the object to be cleaned (the contamination state data S2) and the cleaning condition of cleaning on the object to be cleaned.”)
Referring to claim 18, Strickland teaches the limitations above. However, Strickland does not explicitly teach the control operations further comprise, based at least in part on a determination that the first ECM does not exceed the threshold, not initiating operation of the cleaning system.
Tango further teaches the control operations further comprise, based at least in part on a determination that the first ECM does not exceed the threshold, not initiating operation of the cleaning system (¶ [0028], “the learning unit 26 can gradually approach to an optimum solution of a correlation between a current state of an object to be cleaned and the action of any cleaning condition under which cleaning needs to be performed for the object to be cleaned in the current state.” Based on the disclosure, it is implied that the cleaning does not perform when it is not needed based on the correlation between the current state and the action of cleaning condition in the current state.)
Referring to claim 19, Strickland teaches the limitations above. However, Strickland does not explicitly teach the control operations further comprise, based at least in part on a determination that the second ECM does not exceed the threshold, terminating operation of the cleaning system.
Tango further teaches the control operations further comprise, based at least in part on a determination that the second ECM does not exceed the threshold, terminating operation of the cleaning system (¶ [0028], “the learning unit 26 can gradually approach to an optimum solution of a correlation between a current state of an object to be cleaned and the action of any cleaning condition under which cleaning needs to be performed for the object to be cleaned in the current state.” Based on the disclosure, it is implied that the cleaning does not perform, e.g., terminate, when it is not needed based on the correlation between the current state and the action of cleaning condition in the current state.)
Claim 15 is rejected under 35 U.S.C. 103 as being unpatentable over Strickland et in view of Tango as applied to claim 14 above, and further in view of Liu et al., (US 20170158329 A1) (hereinafter Liu).
Referring to claim 15, Strickland in view of Tango teach the controller of claim 14. However, Strickland in view of Tango do not explicitly teach the control operations further comprise terminating the first cleaning system protocol based at least in part on a determination that the cleaning system attempted to clean a prohibited region...
Liu teaches the control operations further comprise terminating the first cleaning system protocol based at least in part on a determination that the cleaning system attempted to clean a prohibited region...(¶ [0192], “Step S122 is recognizing the current region as a cleaning prohibition region if there is a cleaning prohibition identifier.” ¶ [0193], “When the UAV recognizes the current region as a cleaning prohibition region, the UAV stops the cleaning of the current region.”)
Strickland, Tango, and Liu are analogous art to the claimed invention because they are concerning with interface for cleaning debris off a target (i.e., same field of endeavor).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention having Strickland in view of Tango and Liu before them to modify the method of dust removal off extravehicular mobility unit of Strickland in view of Tango to incorporate the function of identifying cleaning prohibition identifier by Liu. One of ordinary skill in the art would have combined the elements as claimed by known methods as disclosed by Liu (¶ [0140]-[0314]), because the function of identifying cleaning prohibition identifier does not depend on the method of dust removal off extravehicular mobility unit. That is the function of identifying cleaning prohibition identifier performs the same function independent on which interface it is incorporated onto, and therefore, the result of the combination would have been predictable to one of ordinary skill in the art. The motivation to combine would have been to improve automation in cleaning as suggested by Liu (¶ [0014]).
Claim 16 is rejected under 35 U.S.C. 103 as being unpatentable over Strickland in view of Tango, and further in view of Shi, (US 20230356698 A1) (hereinafter Shi).
Referring to claim 16, Strickland in view of Tango teach the controller of claim 14. However, Strickland in view of Tango do not explicitly teach the control operations further comprise terminating the first cleaning system protocol based at least in part on a determination that the target is outside a safe region.
Shi teaches the control operations further comprise terminating the first cleaning system protocol based at least in part on a determination that the target is outside a safe region (¶ [0102], “A safe car washing area is set, where the safe car washing area is a car washing area selected for active safety.” ¶ [0103], “The cleaning rack is moved to the corresponding cleaning position based on the car washing instruction, a distance between the cleaning rack and a side obstacle is acquired when the cleaning position is located in the safe car washing area, whether the distance is within a preset safe distance is determined, and movement of the cleaning rack is stopped if the distance is not within the safe distance”.)
Strickland, Tango, and Shi are analogous art to the claimed invention because they are concerning with interface for cleaning debris off a target (i.e., same field of endeavor).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention having Strickland in view of Tango and Shi before them to modify the method of dust removal off extravehicular mobility unit of Strickland in view of Tango to incorporate the function of setting a safe washing area by Shi. One of ordinary skill in the art would have combined the elements as claimed by known methods as disclosed by Shi (¶ [0028]-[0134]), because the function of setting a safe washing area does not depend on the method of dust removal off extravehicular mobility unit. That is the function of setting a safe washing area performs the same function independent on which interface it is incorporated onto, and therefore, the result of the combination would have been predictable to one of ordinary skill in the art. The motivation to combine would have been to improve safety and avoid unnecessary loss during washing process as suggested by Shi (¶ [0012]).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant’s disclosure.
US 20220234545 (Herse) – discloses mechanism for cleaning sensors.
US 20220339675 (Jones) – discloses automated clean referencing system.
US 20240050991 (Uno) – discloses cleaning apparatus that utilizes machine learning.
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/MONG-SHUNE CHUNG/
Primary Examiner, Art Unit 2118