DETAILED ACTION
This office action is in response to application filed on August 18, 2023.
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 .
Information Disclosure Statement
The information disclosure statements (IDS) submitted on 08/18/2023 and 08/20/2025 are in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statements are being considered by the examiner.
Claim Objections
Claim 1 is objected to because of the following informalities:
Claim language “A scientific instrument, comprising:” should read “A scientific instrument[[,]] comprising:” in order to correct for minor informalities.
Claim language “a determination component that determines, based on an electronic counter or readback sensor associated with the chromatograph-equipped mass spectrometer failing to satisfy a threshold …” should read “a determination component that determines, based on data from an electronic counter or readback sensor associated with the chromatograph-equipped mass spectrometer failing to satisfy a threshold …” in order to clarify the recited subject matter (e.g., data is compared to thresholds according to the disclosure, see specification at [0041]).
Appropriate correction is required.
Claim 9 is objected to because of the following informalities:
Claim language “A computer-implemented method, comprising:” should read “A computer-implemented method[[,]] comprising:” in order to correct for minor informalities.
Appropriate correction is required.
Examiner’s Note
The examiner notes that claim 18 recites “A computer program product for facilitating intelligent maintenance for scientific instruments, the computer program product comprising a non-transitory computer-readable memory having program instructions embodied therewith …”, therefore, claims 18-20 are considered to recite statutory subject matter under Step 1 of the SUBJECT MATTER ELIGIBILITY TEST FOR PRODUCTS AND PROCESSES described in the 2024 Guidance Update on Patent Subject Matter Eligibility, Including on Artificial Intelligence (see also 2019 Revised Patent Subject Matter Eligibility Guidance; see also specification at [0196] and [0214]).
Claims 1-20 were evaluated for patent eligibility under 35 U.S.C. 101 using the SUBJECT MATTER ELIGIBILITY TEST FOR PRODUCTS AND PROCESSES described in the 2024 Guidance Update on Patent Subject Matter Eligibility, Including on Artificial Intelligence (see also 2019 Revised Patent Subject Matter Eligibility Guidance) to determine patent eligibility under 35 U.S.C. 101.
Regarding claim 1, the examiner submits that under Step 1 of the test for evaluating claims for eligibility under 35 U.S.C. 101, the claim is to a machine, which is one of the statutory categories of invention.
Continuing with the analysis, under Step 2A - Prong One of the test (see italic text):
the limitation “a determination component that determines, based on an electronic counter or readback sensor associated with the chromatograph-equipped mass spectrometer failing to satisfy a threshold, whether performance of a maintenance task on the chromatograph-equipped mass spectrometer is warranted” is a process that, under its broadest reasonable interpretation in light of the specification, covers performance of the limitation using mental processes and/or mathematical concepts to compare data and obtain additional information (e.g., determine whether performance of a maintenance task is warranted based on data comparisons with thresholds, see specification at [0041]). Except for the recitation of the extra-solution activities (e.g., source/type of data being evaluated), the addition of generic computer elements (i.e., a determination component) used to facilitate the application of the judicial exception, and/or the field of use, the limitation in the context of this claim mainly refers to performing mental evaluations and/or applying mathematical concepts to compare data and obtain a result.
the limitation “a scheduling component that schedules, in response to a determination that the performance of the maintenance task is warranted, a time or date for the performance of the maintenance task, wherein the time or date is predicted by a machine learning model based on an operational history of the chromatograph-equipped mass spectrometer” is a process that, under its broadest reasonable interpretation in light of the specification, covers performance of the limitation using mental processes and/or mathematical concepts to evaluate information and obtain additional data (e.g., based on maintenance being warranted, determine a time or date of service based on additional data, see specification at [0042]-[0043]). Except for the recitation of the extra-solution activities (e.g., source/type of data being evaluated), the addition of generic computer elements (i.e., a scheduling component) and generic computer implementation (i.e., a machine learning model) used to facilitate the application of the judicial exception, and/or the field of use, the limitation in the context of this claim mainly refers to performing mental evaluations and/or applying mathematical concepts to determine a result based on additional data manipulation.
Therefore, the claim recites a judicial exception under Step 2A - Prong One of the test.
Furthermore, under Step 2A - Prong Two of the test, the additional elements recited in the claim (see non-italic text):
“A scientific instrument, comprising:
a chromatograph-equipped mass spectrometer; and
a processor that executes computer-executable components stored in a non-transitory computer-readable memory, wherein the computer-executable components comprise:
a determination component that determines, based on an electronic counter or readback sensor associated with the chromatograph-equipped mass spectrometer failing to satisfy a threshold, whether performance of a maintenance task on the chromatograph-equipped mass spectrometer is warranted; and
a scheduling component that schedules, in response to a determination that the performance of the maintenance task is warranted, a time or date for the performance of the maintenance task, wherein the time or date is predicted by a machine learning model based on an operational history of the chromatograph-equipped mass spectrometer”,
when considered individually and in combination, integrate the judicial exception into a practical application, when viewing the claim as a whole, by reflecting an improvement to other technology or technical field (e.g., scheduling a time or date for the performance of a maintenance task of a chromatograph-equipped mass spectrometer; see MPEP 2106.05(a)). The claim, when considered as a whole, is eligible at Prong Two of the Revised Step 2A (see 2019 Revised Patent Subject Matter Eligibility Guidance – Revised Step 2A, see also MPEP 2106.04(d)).
Similarly, independent claims 9 and 18 are directed to patent eligible subject matter as explained above with regards to claim 1.
Regarding the dependent claims 2-8, 10-17 and 19-20, they were found to be patent eligible under 35 U.S.C. 101 by incorporating the eligible subject matter of their corresponding independent claims.
Claim Rejections - 35 USC § 103
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 (i.e., changing from AIA to pre-AIA ) 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.
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, 4-6, 8-9 and 13-19 are rejected under 35 U.S.C. 103 as being unpatentable over Shawish (US 20230184794 A1, IDS reference), hereinafter ‘Shawish’, in view of Yeardley (Aaron S. Yeardley, Jude O. Ejeh, Louis Allen, Solomon F. Brown, Joan Cordiner, Integrating machine learning techniques into optimal maintenance scheduling, Computers & Chemical Engineering, Volume 166, 2022, 107958, ISSN 0098-1354, https://doi.org/10.1016/j.compchemeng.2022.107958), hereinafter ‘Yeardley’.
Regarding claim 1.
Shawish discloses:
A scientific instrument (Fig. 1; [0035]: an analytic laboratory system is presented), comprising:
a chromatograph-equipped mass spectrometer (Fig. 1, items 104 and 112 – “liquid chromatograph” and “mass spectrometer”; [0036]-[0037]: the analytic laboratory system includes analytical instruments such as a liquid chromatograph and a mass spectrometer for analysis of samples); and
a processor (Fig. 8, item 812 – ‘processor’) that executes computer-executable components stored in a non-transitory computer-readable memory (Fig. 8, item 822 – memory’; [0045]: a kiosk (see Fig. 1, item 136), implemented as a computing device having a processor and memory storing instructions (see [0049], [0096], [0147] and [0152]), hosts a health monitoring logic for monitoring conditions in the data from the analytical instruments (see also [0006])), wherein the computer-executable components comprise:
a determination component (Fig. 1, item 700 – “health monitoring logic”; [0045]: the kiosk hosts a health monitoring logic for monitoring conditions in the data from the analytical instruments) that determines, based on an electronic counter or readback sensor associated with the chromatograph-equipped mass spectrometer failing to satisfy a threshold, whether performance of a maintenance task on the chromatograph-equipped mass spectrometer is warranted ([0049]-[0055]: sensor readings that monitor different aspects of the analytical instruments, are compared with thresholds in order to determine diagnostic issues and corresponding maintenance).
Shawish does not disclose:
a scheduling component that schedules, in response to a determination that the performance of the maintenance task is warranted, a time or date for the performance of the maintenance task, wherein the time or date is predicted by a machine learning model based on an operational history of the chromatograph-equipped mass spectrometer.
Yeardley teaches:
“This manuscript aims to improve the standard smart maintenance scheduling by considering both predictive maintenance and maintenance time estimation modelling. The standard smart maintenance scheduling approach uses predictive maintenance and optimisation to schedule maintenance tasks. However, the authors believe this concept
can be improved by additionally considering the time required to complete each maintenance task. This new framework predicts when maintenance is required and how long each task will take before optimising the schedule. Thus, the integration of machine learning techniques creates an optimal maintenance schedule that is robust and
reliable … Predictive maintenance utilises machine learning on real time sensor data to provide estimations of when maintenance is required on a machine (Yan et al., 2017). The most common predictive maintenance techniques use machine learning classification to predict a fault or failure occurring” (p. 1, col. 2, par. 1-2: prediction of when maintenance is required (analogous to time or date for the performance of the maintenance task) is achieved by using machine learning techniques while considering predictive maintenance, which predicts a fault or failure (analogous to determination that the performance of the maintenance task is warranted) (see also Shawish at [0065] regarding adjusting maintenance schedules)); and
“The standard smart maintenance process involves using predictive maintenance with optimization to build a maintenance schedule. This schedule however cannot be accurately constructed without an appreciation for the time required to conduct maintenance. Using machine learning to accurately forecast this time enables a more accurate schedule … Here, we present a novel methodology that can analyse the collection of machine sensor data to provide an optimum maintenance schedule through the combination of multiple machine learning techniques. We aim to provide a data-driven approach that automates the learning of models for predictive maintenance and maintenance time estimation. The main objective of this work, therefore, is to build
such a workflow that implements machine learning and optimisation in an approach that produces an optimum maintenance schedule” (p. 2, col. 2, par. 2-3: machine sensor data (analogous to operational history of the chromatograph-equipped mass spectrometer) is analyzed with machine learning techniques in order to predict a maintenance schedule (see also p. 2-3, section “2. Maintenance policy method”)).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Shawish in view of Yeardley to incorporate a scheduling component that schedules, in response to a determination that the performance of the maintenance task is warranted, a time or date for the performance of the maintenance task, wherein the time or date is predicted by a machine learning model based on an operational history of the chromatograph-equipped mass spectrometer, in order to create an optimal maintenance schedule that is robust and reliable, as discussed by Yeardley (see p. 1, col. 2, par. 1; p. 2, col. 2, par. 2-3).
Regarding claim 4.
Shawish in view of Yeardley discloses all the features of claim 1 as described above.
Shawish does not disclose:
the machine learning model predicts the time or date further based on one or more scheduling preferences of a technician.
Yeardley further teaches:
“The objective of the maintenance schedule optimisation is to minimize the costs to the plant given system constraints resulting from plant procedures, plant layout data, and other operational considerations. Here, we present the mathematical optimisation model with a full nomenclature. The problem is posed as follows:
Given:
a set of machines (devices) in a plant;
a set of possible faults per machine, the (predicted) time of occurrence and whether or not it causes a plant to be shutdown;
estimated maintenance times required by each fault per machine before and after failure occurs;
cost of parts and engineering personnel for each fault that occurs within a machine;
downtime cost of the plant and machine;
maximum number of available engineers for maintenance activities;
Determine:
the maintenance schedule for each fault that occurs on a machine
within the plant” (p. 5, section “3.4.1. Mathematical formulation”: maintenance schedule is determined based on maximum number of available engineers for maintenance activities (analogous to one or more scheduling preferences of a technician)).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Shawish in view of Yeardley to configure the machine learning model to predict the time or date further based on one or more scheduling preferences of a technician, in order to create an optimal maintenance schedule that is robust and reliable, as discussed by Yeardley (see p. 1, col. 2, par. 1; p. 2, col. 2, par. 2-3).
Regarding claim 5.
Shawish in view of Yeardley discloses all the features of claim 1 as described above.
Shawish does not disclose:
the machine learning model predicts the time or date further based on a priority level of the maintenance task.
Yeardley further teaches:
“The objective of the maintenance schedule optimisation is to minimize the costs to the plant given system constraints resulting from plant procedures, plant layout data, and other operational considerations. Here, we present the mathematical optimisation model with a full nomenclature. The problem is posed as follows:
Given:
a set of machines (devices) in a plant;
a set of possible faults per machine, the (predicted) time of occurrence and whether or not it causes a plant to be shutdown;
estimated maintenance times required by each fault per machine before and after failure occurs;
cost of parts and engineering personnel for each fault that occurs within a machine;
downtime cost of the plant and machine;
maximum number of available engineers for maintenance activities;
Determine:
the maintenance schedule for each fault that occurs on a machine
within the plant” (p. 5, section “3.4.1. Mathematical formulation”: maintenance schedule is determined based on a set of possible faults per machine, the (predicted) time of occurrence and whether or not it causes a plant to be shutdown (analogous to a priority level of the maintenance task)).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Shawish in view of Yeardley to configure the machine learning model to predict the time or date further based on a priority level of the maintenance task, in order to create an optimal maintenance schedule that is robust and reliable, as discussed by Yeardley (see p. 1, col. 2, par. 1; p. 2, col. 2, par. 2-3).
Regarding claim 6.
Shawish in view of Yeardley discloses all the features of claim 1 as described above.
Shawish does not disclose:
the machine learning model predicts the time or date further based on an expected duration of the maintenance task.
Yeardley further teaches:
“The objective of the maintenance schedule optimisation is to minimize the costs to the plant given system constraints resulting from plant procedures, plant layout data, and other operational considerations. Here, we present the mathematical optimisation model with a full nomenclature. The problem is posed as follows:
Given:
a set of machines (devices) in a plant;
a set of possible faults per machine, the (predicted) time of occurrence and whether or not it causes a plant to be shutdown;
estimated maintenance times required by each fault per machine before and after failure occurs;
cost of parts and engineering personnel for each fault that occurs within a machine;
downtime cost of the plant and machine;
maximum number of available engineers for maintenance activities;
Determine:
the maintenance schedule for each fault that occurs on a machine
within the plant” (p. 5, section “3.4.1. Mathematical formulation”: maintenance schedule is determined based on estimated maintenance times required by each fault per machine before and after failure occurs (an expected duration of the maintenance task)).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Shawish in view of Yeardley to configure the machine learning model to predict the time or date further based on an expected duration of the maintenance task, in order to create an optimal maintenance schedule that is robust and reliable, as discussed by Yeardley (see p. 1, col. 2, par. 1; p. 2, col. 2, par. 2-3).
Regarding claim 8.
Shawish in view of Yeardley discloses all the features of claim 1 as described above.
Shawish does not explicitly disclose:
the computer-executable components comprise: a preparation component that prepares for the performance of the maintenance task, by adjusting, prior to the time or date, actuatable hardware of the chromatograph-equipped mass spectrometer.
However, Shawish teaches:
“A kiosk 136 may interact with the laboratory analytical instrument(s) in the analytical chemistry system. The kiosk 136 may host health monitoring logic 700 configured to monitor for certain conditions or patterns in the data being received from the laboratory analytical instruments. When certain triggering conditions are met, the kiosk 136 may look up a related issue in a health condition database 138 (see FIG. 2) and retrieve explanatory text to be displayed on a GUI. When a user selects the explanatory text in the GUI, a workflow retrieved from the health condition data base 138 may be automatically executed. The workflow may include one or more actions configured to resolve the issue that triggered the notification. At least some of the actions may be automatically executed without further user intervention” ([0045]: once a trigger condition is met, a workflow of actions is performed to correct the condition, with some actions corresponding to adjustments of the equipment (see [0014], [0057]-[0058], [0060], [0139]; see also Yeardley (at p. 5, section “3.4.1. Mathematical formulation”) regarding maintenance schedule being determined based on estimated maintenance time); examiner notes that in order to perform certain maintenance such as checking the column heater or leaks in the gas chromatogram, the instrument needs to be adjusted in advance (e.g., adjusting temperatures, closing valves, etc.)).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Shawish in view of Yeardley to configure the computer-executable components to comprise: a preparation component that prepares for the performance of the maintenance task, by adjusting, prior to the time or date, actuatable hardware of the chromatograph-equipped mass spectrometer, in order to solve issues more quickly and reliably, as discussed by Shawish (see [0015]).
Regarding claim 9.
Shawish discloses:
A computer-implemented method (Fig. 7; [0007], [0129]: a computer implemented method is presented), comprising:
scheduling, by a scientific instrument (Fig. 1) comprising a chromatograph-equipped mass spectrometer (Fig. 1, items 104 and 112 – “liquid chromatograph” and “mass spectrometer”; [0035]-[0037]: an analytic laboratory system includes analytical instruments such as a liquid chromatograph and a mass spectrometer for analysis of samples), performance of a maintenance task on the chromatograph-equipped mass spectrometer (Fig. 7, item 720; [0138]-[0139]: actions of a workflow are performed for maintenance purposes (see [0045]-[0046]; see also [0065] regarding adjusting maintenance schedules));
identifying, by the scientific instrument, a hardware actuator of the chromatograph-equipped mass spectrometer that is associated with the maintenance task ([0057]-[0058]: actions include automatic adjustments of equipment such as closing valves, which implies first the identification of the corresponding equipment to be adjusted (see also [0140])); and
adjusting, by the scientific instrument, the hardware actuator in preparation for the maintenance task ([0057]-[0058]: actions include automatic adjustments of equipment such as closing valves (see also [0140])).
Shawish does not explicitly disclose (see italic text):
scheduling a time or date for performance of a maintenance task; and
adjusting, in response to there being less than a threshold amount of time remaining until the time or date occurs, the hardware actuator in preparation for the maintenance task.
Regarding “scheduling a time or date for performance of a maintenance task”, Yeardley teaches:
“This manuscript aims to improve the standard smart maintenance scheduling by considering both predictive maintenance and maintenance time estimation modelling. The standard smart maintenance scheduling approach uses predictive maintenance and optimisation to schedule maintenance tasks. However, the authors believe this concept
can be improved by additionally considering the time required to complete each maintenance task. This new framework predicts when maintenance is required and how long each task will take before optimising the schedule. Thus, the integration of machine learning techniques creates an optimal maintenance schedule that is robust and
reliable … Predictive maintenance utilises machine learning on real time sensor data to provide estimations of when maintenance is required on a machine (Yan et al., 2017). The most common predictive maintenance techniques use machine learning classification to predict a fault or failure occurring” (p. 1, col. 2, par. 1-2: prediction of when maintenance is required (analogous to time or date for performance of a maintenance task) is achieved by using machine learning techniques while considering predictive maintenance, which predicts a fault or failure (see also Shawish at [0065] regarding adjusting maintenance schedules)).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Shawish in view of Yeardley to schedule a time or date for performance of a maintenance task, in order to create an optimal maintenance schedule that is robust and reliable, as discussed by Yeardley (see p. 1, col. 2, par. 1; p. 2, col. 2, par. 2-3).
Regarding “adjusting, in response to there being less than a threshold amount of time remaining until the time or date occurs, the hardware actuator in preparation for the maintenance task”, Shawish teaches:
“A kiosk 136 may interact with the laboratory analytical instrument(s) in the analytical chemistry system. The kiosk 136 may host health monitoring logic 700 configured to monitor for certain conditions or patterns in the data being received from the laboratory analytical instruments. When certain triggering conditions are met, the kiosk 136 may look up a related issue in a health condition database 138 (see FIG. 2) and retrieve explanatory text to be displayed on a GUI. When a user selects the explanatory text in the GUI, a workflow retrieved from the health condition data base 138 may be automatically executed. The workflow may include one or more actions configured to resolve the issue that triggered the notification. At least some of the actions may be automatically executed without further user intervention” ([0045]: once a trigger condition is met, a workflow of actions is performed to correct the condition, with some actions corresponding to adjustments of the equipment (see [0014], [0057]-[0058], [0060], [0139]; see also Yeardley (at p. 5, section “3.4.1. Mathematical formulation”) regarding maintenance schedule being determined based on estimated maintenance time); examiner notes that in order to perform certain maintenance such as checking the column heater or leaks in the gas chromatogram, the instrument needs to be adjusted in advance (e.g., adjusting temperatures, closing valves, etc.)).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Shawish in view of Yeardley to adjust, in response to there being less than a threshold amount of time remaining until the time or date occurs, the hardware actuator in preparation for the maintenance task, in order to solve issues more quickly and reliably, as discussed by Shawish (see [0015]).
Regarding claim 13.
Shawish in view of Yeardley discloses all the features of claim 9 as described above.
Shawish further discloses:
the hardware actuator is a fluid valve or a fluid pump of the chromatograph-equipped mass spectrometer ([0057]-[0058]: actions include automatic adjustments of equipment such as closing valves (see also [0014], [0046]-[0047], [0060])).
Regarding claim 14.
Shawish in view of Yeardley discloses all the features of claim 13 as described above.
Shawish does not explicitly disclose:
the adjusting causes a vacuum of the chromatograph-equipped mass spectrometer to be vented during the threshold amount of time.
However, Shawish further teaches:
“A kiosk 136 may interact with the laboratory analytical instrument(s) in the analytical chemistry system. The kiosk 136 may host health monitoring logic 700 configured to monitor for certain conditions or patterns in the data being received from the laboratory analytical instruments. When certain triggering conditions are met, the kiosk 136 may look up a related issue in a health condition database 138 (see FIG. 2) and retrieve explanatory text to be displayed on a GUI. When a user selects the explanatory text in the GUI, a workflow retrieved from the health condition data base 138 may be automatically executed. The workflow may include one or more actions configured to resolve the issue that triggered the notification. At least some of the actions may be automatically executed without further user intervention” ([0045]: once a trigger condition is met, a workflow of actions is performed to correct the condition, with some actions corresponding to adjustments of the equipment (see [0014], [0057]-[0058], [0060], [0139]; see also Yeardley (at p. 5, section “3.4.1. Mathematical formulation”) regarding maintenance schedule being determined based on estimated maintenance time); examiner notes that in order to perform certain maintenance such as checking the column heater in the gas chromatogram, the instrument needs to be adjusted in advance (e.g., venting vacuum)); and
“The actions may include logic or source code configured to make an automatic adjustment to a laboratory analytical instrument (e.g., a command to close a valve, an instruction to adjust a pump speed, an invocation of a power-down or power-up method, etc.)” ([0058]: actions include adjusting pump speed (analogous to causing a vacuum to be vented or cleaned; see also [0036] regarding pumps being used for pumping sample through a column for separation and [0060] regarding cleaning filters)).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Shawish in view of Yeardley to configure the adjusting to cause a vacuum of the chromatograph-equipped mass spectrometer to be vented during the threshold amount of time, in order to check different components of the system for accurate maintenance performance.
Regarding claim 15.
Shawish in view of Yeardley discloses all the features of claim 9 as described above.
Shawish does not explicitly disclose:
the hardware actuator is a circuit switch of the chromatograph-equipped mass spectrometer.
However, Shawish further teaches:
“The actions may include logic or source code configured to make an automatic adjustment to a laboratory analytical instrument (e.g., a command to close a valve, an instruction to adjust a pump speed, an invocation of a power-down or power-up method, etc.)” ([0058]: actions includes a command to close a valve (analogous to a circuit switch)).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Shawish in view of Yeardley to implement the hardware actuator as a circuit switch of the chromatograph-equipped mass spectrometer, in order to check different components of the system for accurate maintenance performance.
Regarding claim 16.
Shawish in view of Yeardley discloses all the features of claim 15 as described above.
Shawish does not explicitly disclose:
the adjusting opens an electrical circuit of the chromatograph-equipped mass spectrometer during the threshold amount of time.
However, Shawish further teaches:
“A kiosk 136 may interact with the laboratory analytical instrument(s) in the analytical chemistry system. The kiosk 136 may host health monitoring logic 700 configured to monitor for certain conditions or patterns in the data being received from the laboratory analytical instruments. When certain triggering conditions are met, the kiosk 136 may look up a related issue in a health condition database 138 (see FIG. 2) and retrieve explanatory text to be displayed on a GUI. When a user selects the explanatory text in the GUI, a workflow retrieved from the health condition data base 138 may be automatically executed. The workflow may include one or more actions configured to resolve the issue that triggered the notification. At least some of the actions may be automatically executed without further user intervention” ([0045]: once a trigger condition is met, a workflow of actions is performed to correct the condition, with some actions corresponding to adjustments of the equipment (see [0014], [0057]-[0058], [0060], [0139]; see also Yeardley (at p. 5, section “3.4.1. Mathematical formulation”) regarding maintenance schedule being determined based on estimated maintenance time); examiner notes that in order to perform certain maintenance such as checking the column heater in the gas chromatogram, the instrument needs to be adjusted in advance (e.g., open an electric switch)).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Shawish in view of Yeardley to configure the adjusting to open an electrical circuit of the chromatograph-equipped mass spectrometer during the threshold amount of time, in order to check different components of the system for accurate maintenance performance.
Regarding claim 17.
Shawish in view of Yeardley discloses all the features of claim 9 as described above.
Shawish does not explicitly disclose:
the threshold amount of time depends upon the maintenance task.
Yeardley further teaches:
“The objective of the maintenance schedule optimisation is to minimize the costs to the plant given system constraints resulting from plant procedures, plant layout data, and other operational considerations. Here, we present the mathematical optimisation model with a full nomenclature. The problem is posed as follows:
Given:
a set of machines (devices) in a plant;
a set of possible faults per machine, the (predicted) time of occurrence and whether or not it causes a plant to be shutdown;
estimated maintenance times required by each fault per machine before and after failure occurs;
cost of parts and engineering personnel for each fault that occurs within a machine;
downtime cost of the plant and machine;
maximum number of available engineers for maintenance activities;
Determine:
the maintenance schedule for each fault that occurs on a machine
within the plant” (p. 5, section “3.4.1. Mathematical formulation”: maintenance schedule is determined based on estimated maintenance times required by each fault per machine before and after failure occurs; examiner notes that in order to perform certain maintenance, the instrument needs to be adjusted in advance accordingly with the maintenance task to be performed).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Shawish in view of Yeardley to configure the threshold amount of time depending upon the maintenance task, in order to create an optimal maintenance schedule that is robust and reliable, as discussed by Yeardley (see p. 1, col. 2, par. 1; p. 2, col. 2, par. 2-3).
Regarding claim 18.
Shawish discloses:
A computer program product for facilitating intelligent maintenance for scientific instruments (Fig. 1; [0006]-[0010], [0035]: non-transitory computer readable media storing instructions for performing diagnostics and maintenance on an analytic laboratory system (Fig. 1) are presented), the computer program product comprising a non-transitory computer-readable memory (Fig. 8, item 822 – memory’) having program instructions embodied therewith, the program instructions executable by a processor (Fig. 8, item 812 – ‘processor’) to cause the processor ([0045]: a kiosk (see Fig. 1, item 136), implemented as a computing device having a processor and memory storing instructions (see [0049], [0096], [0147] and [0152]), hosts a health monitoring logic for monitoring conditions in the data from the analytical instruments) to:
predict, via execution of a machine learning model on a log of graphical user interface interactions experienced by a mass spectrometer, performance of a maintenance task on the mass spectrometer (Fig. 1, item 112 – “mass spectrometer”; [0049]-[0055]: sensor readings that monitor different aspects of the analytical instruments, are compared with thresholds in order to determine diagnostic issues and corresponding maintenance (see [0141]-[0143] regarding recording user interactions with the equipment while performing maintenance as a log and using the log with machine learning algorithms for addressing issues more effectively in the future )); and
prepare the mass spectrometer (Fig. 1, item 112 – “mass spectrometer”; [0036]-[0037]: the analytic laboratory system includes analytical instruments such as a mass spectrometer for analysis of samples) for the maintenance task, by altering an interior temperature, a fluid flow rate, or an electric voltage of the mass spectrometer ([0045]: once a trigger condition is met, a workflow of actions is performed to correct the condition, with some actions corresponding to adjustments of the equipment such as stopping flow of a fluid or adjusting a pump speed (see [0014], [0057]-[0058], [0060]).
Shawish does not explicitly disclose (see italic text):
predict a time or date for performance of a maintenance task; and
prepare, in response to there being less than a threshold amount of time remaining until the time or date occurs, the mass spectrometer for the maintenance task.
Regarding “predict a time or date for performance of a maintenance task”, Yeardley teaches:
“This manuscript aims to improve the standard smart maintenance scheduling by considering both predictive maintenance and maintenance time estimation modelling. The standard smart maintenance scheduling approach uses predictive maintenance and optimisation to schedule maintenance tasks. However, the authors believe this concept
can be improved by additionally considering the time required to complete each maintenance task. This new framework predicts when maintenance is required and how long each task will take before optimising the schedule. Thus, the integration of machine learning techniques creates an optimal maintenance schedule that is robust and
reliable … Predictive maintenance utilises machine learning on real time sensor data to provide estimations of when maintenance is required on a machine (Yan et al., 2017). The most common predictive maintenance techniques use machine learning classification to predict a fault or failure occurring” (p. 1, col. 2, par. 1-2: prediction of when maintenance is required (analogous to time or date for the performance of a maintenance task) is achieved by using machine learning techniques while considering predictive maintenance, which predicts a fault or failure (see also Shawish at [0065] regarding adjusting maintenance schedules)); and
“The standard smart maintenance process involves using predictive maintenance with optimization to build a maintenance schedule. This schedule however cannot be accurately constructed without an appreciation for the time required to conduct maintenance. Using machine learning to accurately forecast this time enables a more accurate schedule … Here, we present a novel methodology that can analyse the collection of machine sensor data to provide an optimum maintenance schedule through the combination of multiple machine learning techniques. We aim to provide a data-driven approach that automates the learning of models for predictive maintenance and maintenance time estimation. The main objective of this work, therefore, is to build
such a workflow that implements machine learning and optimisation in an approach that produces an optimum maintenance schedule” (p. 2, col. 2, par. 2-3: machine sensor data (analogous to log) is analyzed with machine learning techniques in order to predict a maintenance schedule (see also p. 2-3, section “2. Maintenance policy method”)).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Shawish in view of Yeardley to predict a time or date for performance of a maintenance task, in order to create an optimal maintenance schedule that is robust and reliable, as discussed by Yeardley (see p. 1, col. 2, par. 1; p. 2, col. 2, par. 2-3).
Regarding “prepare, in response to there being less than a threshold amount of time remaining until the time or date occurs, the mass spectrometer for the maintenance task”, Shawish teaches:
“A kiosk 136 may interact with the laboratory analytical instrument(s) in the analytical chemistry system. The kiosk 136 may host health monitoring logic 700 configured to monitor for certain conditions or patterns in the data being received from the laboratory analytical instruments. When certain triggering conditions are met, the kiosk 136 may look up a related issue in a health condition database 138 (see FIG. 2) and retrieve explanatory text to be displayed on a GUI. When a user selects the explanatory text in the GUI, a workflow retrieved from the health condition data base 138 may be automatically executed. The workflow may include one or more actions configured to resolve the issue that triggered the notification. At least some of the actions may be automatically executed without further user intervention” ([0045]: once a trigger condition is met, a workflow of actions is performed to correct the condition, with some actions corresponding to adjustments of the equipment (see [0014], [0057]-[0058], [0060], [0139]; see also Yeardley (at p. 5, section “3.4.1. Mathematical formulation”) regarding maintenance schedule being determined based on estimated maintenance time); examiner notes that in order to perform certain maintenance such as checking the column heater or leaks in the gas chromatogram, the instrument needs to be adjusted in advance (e.g., adjusting temperatures, closing valves, etc.)).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Shawish in view of Yeardley to prepare, in response to there being less than a threshold amount of time remaining until the time or date occurs, the mass spectrometer for the maintenance task, in order to solve issues more quickly and reliably, as discussed by Shawish (see [0015]).
Regarding claim 19.
Shawish in view of Yeardley discloses all the features of claim 18 as described above.
Shawish further discloses:
the program instructions are further executable to cause the processor to: visually render, on an electronic display of the mass spectrometer, instructions explaining how to perform the maintenance task ([0045]: once a trigger condition is met, explanatory text is displayed on a GUI for user selection in order to perform a workflow of actions to correct the condition (see also [0014], [0058], [0060]).
Shawish does not explicitly disclose (see italic text):
visually render, at the time or date, instructions explaining how to perform the maintenance task.
Yeardley further teaches:
“This manuscript aims to improve the standard smart maintenance scheduling by considering both predictive maintenance and maintenance time estimation modelling. The standard smart maintenance scheduling approach uses predictive maintenance and optimisation to schedule maintenance tasks. However, the authors believe this concept
can be improved by additionally considering the time required to complete each maintenance task. This new framework predicts when maintenance is required and how long each task will take before optimising the schedule. Thus, the integration of machine learning techniques creates an optimal maintenance schedule that is robust and
reliable … Predictive maintenance utilises machine learning on real time sensor data to provide estimations of when maintenance is required on a machine (Yan et al., 2017). The most common predictive maintenance techniques use machine learning classification to predict a fault or failure occurring” (p. 1, col. 2, par. 1-2: prediction of when maintenance is required (analogous to the time or date) is achieved by using machine learning techniques while considering predictive maintenance, which predicts a fault or failure (see also Shawish at [0065] regarding adjusting maintenance schedules)).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Shawish in view of Yeardley to visually render, at the time or date, instructions explaining how to perform the maintenance task, in order to create an optimal maintenance schedule that is robust and reliable, as discussed by Yeardley (see p. 1, col. 2, par. 1; p. 2, col. 2, par. 2-3).
Claims 2-3, 7, 10-12 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Shawish, in view of Yeardley, and in further view of Novaes-Card (US 20240011954 A1), hereinafter ‘Novaes’.
Regarding claim 2.
Shawish in view of Yeardley discloses all the features of claim 1 as described above.
Shawish does not explicitly disclose:
the operational history comprises previous times or dates during which the chromatograph-equipped mass spectrometer ran samples.
Novaes teaches:
“In various embodiments, the diagnostic and predictive module 118 can also incorporate machine learning to teach the GC system 100 that certain sample data and/or instrument data is associated with a particular failure or maintenance issue of the GC system 100 or with a limited number of likely issues. That is, the diagnostic and predictive module 118 can analyze past chromatographic performance monitoring results, sample data, instrument data, data from diagnostic tests, and/or simulated chromatograms with different performed maintenance tasks to correlate instrument failure and performed maintenance. As such, the diagnostic and predictive module 118 can learn that certain sample data and/or instrument data indicate one or more failures or maintenance issues of the GC system 100” ([0069]: information regarding sample data is analyzed in order to predict failure or maintenance issues of a gas chromatography (GC) system (analogous to chromatograph-equipped mass spectrometer) to perform specific maintenance tasks for correcting issues (see [0022])).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Shawish, in view of Yeardley, and in further view of Novaes, to incorporate the operational history comprising previous times or dates during which the chromatograph-equipped mass spectrometer ran samples, in order to learn that certain sample data indicate one or more failures or maintenance issues while reducing unexpected downtime by determining which maintenance tasks are more likely to correct the upcoming failure or maintenance issue, as discussed by Novaes (see [0022], [0069]).
Regarding claim 3.
Shawish in view of Yeardley discloses all the features of claim 1 as described above.
Shawish does not disclose:
the operational history comprises previous times or dates during which a technician interacted with a graphical user interface of the chromatograph-equipped mass spectrometer.
Novaes teaches:
“In some embodiments, the diagnostic and predictive maintenance tools of the present disclosure utilize chromatographic performance monitoring, chromatographic modelling, and an automated GC troubleshooting procedure combined with chromatographic performance evaluations … control charting, user input, diagnostic test results … and/or instrument sensor data (e.g., temperature, pressure, gas flow, valve state, motor step, sample injection count, motor current value, etc.) to predict future GC system performance and/or maintenance issues” ([0023]: data including user input (analogous to previous times or dates during which a technician interacted with a graphical user interface of the chromatograph-equipped mass spectrometer) is evaluated to predict performance or maintenance issues for correction (see [0022]))).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Shawish, in view of Yeardley, and in further view of Novaes, to incorporate the operational history comprising previous times or dates during which a technician interacted with a graphical user interface of the chromatograph-equipped mass spectrometer, in order to predict future system performance and/or maintenance issues while reducing unexpected downtime by determining which maintenance tasks are more likely to correct the upcoming failure or maintenance issue, as discussed by Novaes (see [0022]-[0023]).
Regarding claim 7.
Shawish in view of Yeardley discloses all the features of claim 1 as described above.
Shawish does not disclose:
the machine learning model receives as input the operational history, wherein the machine learning model produces as output a plurality of possible times or dates for the performance of the maintenance task, and wherein the scheduling component: receives input from a graphical user interface of the chromatograph-equipped mass spectrometer selecting the time or date from among the plurality of possible times or dates.
Regarding “the machine learning model receives as input the operational history”, Yeardley further teaches:
“The standard smart maintenance process involves using predictive maintenance with optimization to build a maintenance schedule. This schedule however cannot be accurately constructed without an appreciation for the time required to conduct maintenance. Using machine learning to accurately forecast this time enables a more accurate schedule … Here, we present a novel methodology that can analyse the collection of machine sensor data to provide an optimum maintenance schedule through the combination of multiple machine learning techniques. We aim to provide a data-driven approach that automates the learning of models for predictive maintenance and maintenance time estimation. The main objective of this work, therefore, is to build
such a workflow that implements machine learning and optimisation in an approach that produces an optimum maintenance schedule” (p. 2, col. 2, par. 2-3: machine sensor data (analogous to operational history) is analyzed with machine learning techniques in order to predict a maintenance schedule (see also p. 2-3, section “2. Maintenance policy method”) (see Figs. 8, 10 and 12 regarding times of maintenance for different cases)).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Shawish in view of Yeardley to configure the machine learning model to receive as input the operational history, in order to create an optimal maintenance schedule that is robust and reliable, as discussed by Yeardley (see p. 1, col. 2, par. 1; p. 2, col. 2, par. 2-3).
Regarding “wherein the machine learning model produces as output a plurality of possible times or dates for the performance of the maintenance task, and wherein the scheduling component: receives input from a graphical user interface of the chromatograph-equipped mass spectrometer selecting the time or date from among the plurality of possible times or dates”, Novaes teaches:
“One advantage provided by the diagnostic and predictive module 118 is the ability to predict a timeframe of a future performance degradation and/or maintenance issue of the GC system 100 and to predict a failure mode associated with the cause of the future performance degradation and/or maintenance issue. That is, the diagnostic and predictive module 118 can determine when for example, after how many injections and/or after a specified amount of instrument run time a failure will occur and what maintenance task to perform to correct the failure. As such, the user can plan when they want to perform maintenance on the GC system instead of having a failure and/or maintenance issue occur in the middle of a sample run or analysis” ([0036]: predicting a timeframe of a performance degradation or maintenance issue (analogous to a plurality of possible times or dates for the performance of the maintenance task) in a GC system is done in order to allow the user to plan when they want to perform preventive maintenance (analogous to receiving input from a graphical user interface to select the time or date from among the plurality of possible times or dates; see also [0024]; see also Shawish at [0065] regarding adjusting maintenance schedules)).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Shawish, in view of Yeardley, and in further view of Novaes, to configure the machine learning model to produce as output a plurality of possible times or dates for the performance of the maintenance task, and wherein the scheduling component: receives input from a graphical user interface of the chromatograph-equipped mass spectrometer selecting the time or date from among the plurality of possible times or dates, in order to improve reliability and reduce unexpected downtime of the systems, as discussed by Novaes (see [0036]).
Regarding claim 10.
Shawish in view of Yeardley discloses all the features of claim 9 as described above.
Shawish does not disclose:
the hardware actuator is a temperature heater or a temperature cooler of the chromatograph-equipped mass spectrometer.
Novaes teaches:
“The GC system 100 further includes a column heater 108, such as an oven, a convection heater, a conduction heater, an air bath, or other such heating device for heating certain GC system components. More specifically, the column heater 108 can be controlled, via a controller 110, to heat or cool the column 104 and other flow path components to desired temperatures … Additionally, the column heater 108 can be configured with a cryogenic cooling system to cool the column to below ambient temperatures” ([0030]: gas chromatogram system includes a column heater, which can develop issues (see [0056]-[0057])); and
“For example, certain sensor values may not match the setpoints (i.e., column heater temperature does not match the setpoint, inlet pressure sensor does not match setpoint, or expected gas flow does not match setpoint) … As such, the automated GC troubleshooting procedure may guide the user to a part of the decision tree to further investigate components of the GC system, such as heaters, flow control, modules, or other components. Diagnostic tests could be implemented to further narrow down the issue and/or confirm an issue. Additionally or alternatively, the automated GC troubleshooting procedure may recommend replacement or servicing a piece of hardware of the GC system (e.g. cleaning, adjusting, etc.) or changing a setpoint as the most likely maintenance item to fix the issue” ([0063]: when sensor data does not match setpoints, issues arise on the components and corrective action is performed (see also [0143])).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Shawish, in view of Yeardley, and in further view of Novaes, to incorporate the hardware actuator as a temperature heater or a temperature cooler of the chromatograph-equipped mass spectrometer, in order to check different components of the system for accurate maintenance performance.
Regarding claim 11.
Shawish, in view of Yeardley and Novaes, discloses all the features of claim 10 as described above.
Shawish does not explicitly disclose:
the adjusting causes an internal temperature of the chromatograph-equipped mass spectrometer to transition, during the threshold amount of time, from an operating temperature value to a room temperature value.
However, Shawish teaches:
“A kiosk 136 may interact with the laboratory analytical instrument(s) in the analytical chemistry system. The kiosk 136 may host health monitoring logic 700 configured to monitor for certain conditions or patterns in the data being received from the laboratory analytical instruments. When certain triggering conditions are met, the kiosk 136 may look up a related issue in a health condition database 138 (see FIG. 2) and retrieve explanatory text to be displayed on a GUI. When a user selects the explanatory text in the GUI, a workflow retrieved from the health condition data base 138 may be automatically executed. The workflow may include one or more actions configured to resolve the issue that triggered the notification. At least some of the actions may be automatically executed without further user intervention” ([0045]: once a trigger condition is met, a workflow of actions is performed to correct the condition, with some actions corresponding to adjustments of the equipment (see [0014], [0057]-[0058], [0060], [0139]; see also Yeardley (at p. 5, section “3.4.1. Mathematical formulation”) regarding maintenance schedule being determined based on estimated maintenance time); examiner notes that in order to perform certain maintenance such as checking the column heater in the gas chromatogram, the instrument needs to be adjusted in advance (e.g., adjusting temperatures, etc.; see also Novaes at [0030] regarding controlling the column heater to remain at desired temperatures)).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Shawish, in view of Yeardley and Novaes, configure the adjusting to cause an internal temperature of the chromatograph-equipped mass spectrometer to transition, during the threshold amount of time, from an operating temperature value to a room temperature value, in order to solve issues more quickly and reliably, as discussed by Shawish (see [0015]).
Regarding claim 12.
Shawish, in view of Yeardley and Novaes, discloses all the features of claim 10 as described above.
Shawish does not explicitly disclose:
the adjusting causes an internal temperature of the chromatograph-equipped mass spectrometer to transition, during the threshold amount of time, from an operating temperature value to a non-room temperature value.
However, Shawish teaches:
“A kiosk 136 may interact with the laboratory analytical instrument(s) in the analytical chemistry system. The kiosk 136 may host health monitoring logic 700 configured to monitor for certain conditions or patterns in the data being received from the laboratory analytical instruments. When certain triggering conditions are met, the kiosk 136 may look up a related issue in a health condition database 138 (see FIG. 2) and retrieve explanatory text to be displayed on a GUI. When a user selects the explanatory text in the GUI, a workflow retrieved from the health condition data base 138 may be automatically executed. The workflow may include one or more actions configured to resolve the issue that triggered the notification. At least some of the actions may be automatically executed without further user intervention” ([0045]: once a trigger condition is met, a workflow of actions is performed to correct the condition, with some actions corresponding to adjustments of the equipment (see [0014], [0057]-[0058], [0060], [0139]; see also Yeardley (at p. 5, section “3.4.1. Mathematical formulation”) regarding maintenance schedule being determined based on estimated maintenance time); examiner notes that in order to perform certain maintenance such as checking the column heater in the gas chromatogram, the instrument needs to be adjusted in advance (e.g., adjusting temperatures, etc.; see also Novaes at [0030] regarding controlling the column heater to cool the column below ambient temperatures)).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Shawish, in view of Yeardley and Novaes, configure the adjusting to cause an internal temperature of the chromatograph-equipped mass spectrometer to transition, during the threshold amount of time, from an operating temperature value to a non-room temperature value, in order to solve issues more quickly and reliably, as discussed by Shawish (see [0015]).
Regarding claim 20.
Shawish in view of Yeardley discloses all the features of claim 18 as described above.
Shawish does not explicitly disclose:
the program instructions are further executable to cause the processor to: visually render, on an electronic display of the mass spectrometer and prior to the time or date, a reminder that the maintenance task will be performed on the time or date.
Novaes teaches:
“The method also comprises performing an automated troubleshooting procedure that uses results of the chromatographic performance monitoring and the chromatographic model to predict an expected maintenance task and transmitting a maintenance notification of the GC system including the expected maintenance task … For example, the maintenance notification can be transmitted to an external electronic device such as a smart phone, a computer, a tablet, or other such electronic device” ([0007]: a maintenance notification including the expected maintenance task is transmitted to the user (analogous to visually render on an electronic display a reminder that the maintenance task will be performed)).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Shawish, in view of Yeardley, and in further view of Novaes, to configure the program instructions to cause the processor to: visually render, on an electronic display of the mass spectrometer and prior to the time or date, a reminder that the maintenance task will be performed on the time or date, in order to efficiently communicate status of maintenance projects to appropriate users.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
CARLISLE; Daniel et al., US 20240014020 A1¸ SYSTEMS AND METHODS FOR IMPROVED MASS ANALYSIS INSTRUMENT OPERATIONS
Reference discloses determination of contamination or degradation in mass spectrometers.
Gordon; David et al., US 20230326733 A1, SYSTEM FOR DETERMINING THE CLEANLINESS OF MASS SPECTROMETER ION OPTICS
Reference discloses determination of cleanliness of mass spectrometer ion optics.
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/LINA CORDERO/Primary Examiner, Art Unit 2857