,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 .
Response to Arguments
The reply filed on 4/03/2026 has been entered. The applicant’s arguments with respect to claims 1-22 have been considered but are moot in view of new ground(s) of rejection caused by the amendments, all other arguments have been addressed below. Claims 1-22 are pending in this application and have been considered below.
Priority
Receipt is acknowledged of certified copies of papers required by 37 CFR 1.55.
Information Disclosure Statement
The IDSs dated 8/08/2023, 7/09/2024, 9/26/2024, 4/08/2025, 6/12/2025, and 1/06/2026 have been considered and placed in the application file.
1st 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.
The factual inquiries 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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1, 5, and 22 are rejected under 35 U.S.C. 103 as obvious over US Patent Publication 2019 0376991 A1, (Rudorfer et al.) in view of US Patent Publication 2015 0088434 A1, (Grabau et al.).
Claim 1
Regarding claim 1, Rudorfer et al. teach a method of predicting a fault in a diagnostic laboratory system, comprising:
providing one or more sensors associated with at least one module of the diagnostic laboratory system; ("The in vitro instrument maintenance apparatus 100 includes one or more monitoring devices 102A, 102B configured to monitor condition-based parameters of one or more instrument components of one or more instruments," par. 19) generating data using the one or more sensors, ("data sampled over time," par. 24) the data including at least one of aspiration pressure data, dispense pressure data, motor current data, image data of a specimen or specimen container, or combinations thereof; ("Condition of pumps, motors, or other electrical components may be monitored by current," par. 20) and the at least one module of the diagnostic laboratory system ("The in vitro instrument maintenance apparatus 100 includes one or more monitoring devices 102A, 102B configured to monitor condition-based parameters of one or more instrument components of one or more instruments," par. 19).
Rudorfer et al. do not explicitly teach all of inputting the data into an artificial intelligence algorithm, the artificial intelligence algorithm configured to model an operational state of the diagnostic laboratory system based on the generated data; and predicting at least one fault using the artificial intelligence algorithm.
However, Grabau et al. teach inputting the data into an artificial intelligence algorithm, the artificial intelligence algorithm configured to model an operational state of the diagnostic laboratory system based on the generated data; ("the integrated diagnostics module 124 may derive these algorithms from documented average or minimum service life of multiple process control devices of the same type and construction materials, as used in a given application, from laboratory data collected in a manner that most nearly approximates field service conditions (e.g., operating environment)," par. 37) and predicting at least one fault using the artificial intelligence algorithm ("the integrated diagnostics module 400 may be configured to provide more frequent warning messages as the projected failure point nears," par. 68).
Therefore, taking the teachings of Rudorfer et al. and Grabau et al. as a whole, it would have been obvious to a person having ordinary skill in the art before the time of the effective filing date of the claimed invention of the instant application to modify the monitoring of in vitro instruments as taught by Rudorfer et al. to use the algorithmic diagnostics module as taught by Grabau et al. The suggestion/motivation for doing so would have been that, “The integrated diagnostics module 124 assembles prognostics algorithms for components that form the process control device and from which usable remaining lifetime (e.g., remaining cycle life time, projected maintenance date) data may be determined” as noted by the Grabau et al. disclosure in paragraph [0037], which also motivates combination because the combination would predictably have an additional utility as there is a reasonable expectation that more accurate and timely fault detection for the in vitro instruments, ultimately reducing downtime and enhancing system efficiency; and/or because doing so merely combines prior art elements according to known methods to yield predictable results.
Claim 5
Regarding claim 5, Rudorfer et al. and Grabau et al. teach the method of claim 1 as noted above.
Rudorfer et al. do not explicitly teach all of wherein at least one of the one or more sensors is configured to measure light intensity and wherein the generating data comprises generating data indicative of light intensity.
However, Grabau et al. teach wherein at least one of the one or more sensors is configured to measure light intensity and wherein the generating data comprises generating data indicative of light intensity ("Monitoring by the monitoring devices 102A, 102B may be by way of sensors or current and/or voltage sensors/taps on electrical circuits, or other suitable devices. Sensors may be used, for example, to monitor time, distance, position, strain, drift, load, resistance (friction or electrical), speed, acceleration, temperature, # of cycles, component level, light presence, intensity, and/or gradients, pressure and/or vacuum levels, fluid level, flow, leaks, or fluid presence or absence, fluid constituent concentration or condition, bubbles, vibration, noise, capacitance, contamination, contact, closure, state, proximity, or the like of various subcomponents," par. 20).
Rudorfer et al. and Grabau et al. are combined as per claim 1.
Claim 22
Regarding claim 22, Rudorfer et al. teach a diagnostic laboratory system, comprising: one or more sensors associated with a component of the diagnostic laboratory system ("The in vitro instrument maintenance apparatus 100 includes one or more monitoring devices 102A, 102B configured to monitor condition-based parameters of one or more instrument components of one or more instruments," par. 19) configured to generate sensor data, ("data sampled over time," par. 24) the generated sensor data including at least one of aspiration pressure data, dispense pressure data, motor current data, acoustic data, vibration data, position data, image data of a specimen or specimen container, or combinations thereof; ("Condition of pumps, motors, or other electrical components may be monitored by current," par. 20) and the component of the diagnostic laboratory system ("The in vitro instrument maintenance apparatus 100 includes one or more monitoring devices 102A, 102B configured to monitor condition-based parameters of one or more instrument components of one or more instruments," par. 19).
Rudorfer et al. do not explicitly teach all of a computer configured to execute an artificial intelligence algorithm, the artificial intelligence algorithm configured to: receive the generated sensor data; model an operational state of the diagnostic laboratory system based on the generated data; and predict at least one in response to the data and the modeled operational state.
However, Grabau et al. teach a computer configured to execute an artificial intelligence algorithm, the artificial intelligence algorithm configured to: receive the generated sensor data; model an operational state of the diagnostic laboratory system based on the generated data; ("the integrated diagnostics module 124 may derive these algorithms from documented average or minimum service life of multiple process control devices of the same type and construction materials, as used in a given application, from laboratory data collected in a manner that most nearly approximates field service conditions (e.g., operating environment)," par. 37) and predict at least one fault ("the integrated diagnostics module 400 may be configured to provide more frequent warning messages as the projected failure point nears," par. 68) in response to the data and the modeled operational state ("Such algorithms, therefore, may take into account those components that normally fail by mechanical wear or fatigue and which can be characterized as having a fixed or average lifetime when new. For example, when projecting cycle life, the integrated diagnostics module 124 may decrement a fixed or average cycle life, by each cycle experienced during operation," par. 37).
Rudorfer et al. and Grabau et al. are combined as per claim 1.
2nd Claim Rejections - 35 USC § 103
Claims 2 and 3 are rejected under 35 U.S.C. 103 as obvious over US Patent Publication 2019 0376991 A1, (Rudorfer et al.) and US Patent Publication 2015 0088434 A1, (Grabau et al.) in view of US Patent Publication 2009 0075386 A1, (Dunfee et al.).
Claim 2
Regarding claim 2, Rudorfer et al. and Grabau et al. teach the method of claim 1 as noted above.
Rudorfer et al. and Grabau et al. do not explicitly teach wherein at least one of the one or more sensors is configured to measure aspiration pressure and wherein the generating data comprises generating data including a pressure trace as a function of time indicative of aspiration system performance.
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However, Dunfee et al. teach wherein at least one of the one or more sensors is configured to measure aspiration pressure ("Aspiration pressure transducer 28 data are recorded throughout the entire aspiration process but only the pressure data gathered during the first predetermined period of time," par. 31) and wherein the generating data comprises generating data including a pressure trace as a function of time indicative of aspiration system performance ("In accordance with the present invention, aspiration pressure control 30 and pressure sense device 28 are controlled and analyzed by computer 24 so as to determine the quality of the aspirated sample liquid 14 through analysis of a pressure profile generated during the aspiration process. The aspiration quality verification method has the ability to detect adverse events such as a full or partially clogged pipette tip,” par. 30).
Therefore, taking the teachings of Rudorfer et al., Grabau et al., and Dunfee et al. as a whole, it would have been obvious to a person having ordinary skill in the art before the time of the effective filing date of the claimed invention of the instant application to modify the monitoring of in vitro instruments as taught by Rudorfer et al. and the algorithmic diagnostics module as taught by Grabau et al. to use the aspiration/dispensing pressure monitoring system as taught by Dunfee et al. The suggestion/motivation for doing so would have been that, “It is desirable, when aspirating a liquid, to accurately determine if any abnormalities or non-uniformities within the liquid have adversely affected the overall quality of the aspiration process. Non-uniformities such as clogs or clots, bubbles, foam, insufficient volume, etc, may exist in samples, particularly when the sample is a body fluid as these are frequently a non-uniform composition” as noted by the Dunfee et al. disclosure in paragraph [0004], which also motivates combination because the combination would predictably have an additional utility as there is a reasonable expectation that the combination would improve the reliability and accuracy of detecting sample quality issues during the aspiration process by leveraging the specific pressure monitoring techniques, thereby achieving a more robust and efficient automated diagnosis; and/or because doing so merely combines prior art elements according to known methods to yield predictable results.
Claim 3
Regarding claim 3, Rudorfer et al. and Grabau et al. teach the method of claim 1 as noted above.
Rudorfer et al. and Grabau et al. do not explicitly teach wherein at least one of the one or more sensors is configured to measure dispense pressure and wherein the generating data comprises generating data including a pressure trace as a function of time indicative of dispense system performance.
However, Dunfee et al. teach wherein at least one of the one or more sensors is configured to measure dispense pressure ("The pressure within the connecting fluid conduit is measured shortly after the start of the aspiration or dispensation of a sample volume," par. 9) and wherein the generating data comprises generating data including a pressure trace as a function of time ("the average dispensing pressure ADPend measured during the second predetermined period of time," par. 35) indicative of dispense system performance ("it is determined that the average dispensing pressure ADPend measured during the second predetermined period of time DPend exceeds the predetermined min-DCLOG, then the dispensing process is faulty," par. 35) (“The aspiration quality verification method has the ability to detect adverse events such as a full or partially clogged pipette tip,” par. 30).
Rudorfer et al., Grabau et al., and Dunfee et al. are combined as per claim 2.
3rd Claim Rejections - 35 USC § 103
Claim 4 is rejected under 35 U.S.C. 103 as obvious over US Patent Publication 2019 0376991 A1, (Rudorfer et al.) and US Patent Publication 2015 0088434 A1, (Grabau et al.) in view of US Patent Publication 2019 0034461 A1, (Flinsenberg et al.).
Claim 4
Regarding claim 4, Rudorfer et al. and Grabau et al. teach the method of claim 1 as noted above.
Rudorfer et al. teach the diagnostic laboratory system ("The in vitro instrument maintenance apparatus 100 includes one or more monitoring devices 102A, 102B configured to monitor condition-based parameters of one or more instrument components of one or more instruments," par. 19).
Rudorfer et al. and Grabau et al. do not explicitly teach wherein at least one of the one or more sensors is configured to measure electric current drawn by a motor associated with a transport conveyor or a robot arm of the diagnostic laboratory system, and wherein the generating data comprises generating motor current data indicative of an impending fault.
However, Flinsenberg et al. teach wherein at least one of the one or more sensors is configured to measure electric current ("a second sensor 304, 354 may measure the (electrical) power usage of the motor" par. 32) drawn by a motor associated with a transport conveyor or a robot arm ("the variables are: temperature of the motor, power usage of the motor, speed of the conveyor belt, weight of good on conveyor belt and amount of dust near motor," par. 32), and wherein the generating data comprises generating motor current data indicative of an impending fault ("the sensors may sense different characteristics that may be relevant to predict the operational status of the motors," par. 32).
Therefore, taking the teachings of Rudorfer et al., Grabau et al., and Flinsenberg et al. as a whole, it would have been obvious to a person having ordinary skill in the art before the time of the effective filing date of the claimed invention of the instant application to modify the monitoring of in vitro instruments as taught by Rudorfer et al. and the algorithmic diagnostics module as taught by Grabau et al. to use the electric motor fault prediction method as taught by Flinsenberg et al. The suggestion/motivation for doing so would have been that, “At regular moments in time, the operational status of the motor may be determined and at the same moments all sensor sense their specific characteristic” as noted by the Flinsenberg et al. disclosure in paragraph [0032], which also motivates combination because the combination would predictably have an additional utility as there is a reasonable expectation that the accuracy, reliability, and predictive capabilities of the in vitro diagnostic instrument diagnostics by providing specific, timely, and data-driven insights into motor health, thereby enhancing the overall reliability of the diagnostic results; and/or because doing so merely combines prior art elements according to known methods to yield predictable results.
4th Claim Rejections - 35 USC § 103
Claims 6, 7, 8, and 12 are rejected under 35 U.S.C. 103 as obvious over US Patent Publication 2019 0376991 A1, (Rudorfer et al.) and US Patent Publication 2015 0088434 A1, (Grabau et al.) in view of US Patent Publication 2020 0151498 A1, (Sun et al.).
Claim 6
Regarding claim 6, Rudorfer et al. and Grabau et al. teach the method of claim 1 as noted above.
Rudorfer et al. and Grabau et al. do not explicitly teach wherein at least one of the one or more sensors is configured to measure light frequency and wherein the generating data comprises generating data indicative of light frequency.
However, Sun et al. teach wherein at least one of the one or more sensors is configured to measure light frequency and wherein the generating data comprises generating data indicative of light frequency ("The images may be obtained, in some embodiments, by multiple image capture devices located so as to capture images from multiple viewpoints (e.g., multiple lateral viewpoints). The multiple images may be obtained at the quality check module, and may be captured at multiple exposures (e.g., exposure times) while providing illumination (e.g., backlighting) at multiple spectra having different nominal wavelengths. The multiple spectra of illumination may include emitted lighting of red (R), green (G), blue (B), white (W), IR and near IR (NIR), for example," par. 86).
Therefore, taking the teachings of Rudorfer et al., Grabau et al., and Sun et al. as a whole, it would have been obvious to a person having ordinary skill in the art before the time of the effective filing date of the claimed invention of the instant application to modify the monitoring of in vitro instruments as taught by Rudorfer et al. and the algorithmic diagnostics module as taught by Grabau et al. to use the image data acquiring methods as taught by Sun et al. and the result of combining prior art elements according to known methods to yield predictable results. More specifically, the monitoring of in vitro instruments as taught by Rudorfer et al. and the algorithmic diagnostics module as taught by Grabau et al. to use the image data acquiring methods as taught by Sun et al. can yield a predictable result of processing image data to predict malfunctions in machines. Thus, a person of ordinary skill would have appreciated including in the ability to do the image data capturing methods of Sun et al. since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Claim 7
Regarding claim 7, Rudorfer et al. and Grabau et al. teach the method of claim 1 as noted above.
Rudorfer et al. and Grabau et al. do not explicitly teach wherein at least one of the one or more sensors is configured to generate image data of a specimen and wherein the generating data comprises generating image data of the specimen indicative of whether the specimen is in condition for analysis.
However, Sun et al. teach wherein at least one of the one or more sensors is configured to generate image data of a specimen and wherein the generating data comprises generating image data of the specimen ("image capture devices arranged around the track and configured to capture multiple images of a specimen container and the serum or plasma portion of the specimen from multiple viewpoints," par. 17) indicative of whether the specimen is in condition for analysis ("The quality check module 130 is configured to pre-screen and carry out the characterization methods described herein, and is configured to automatically determine a presence of, and possibly an extent of H, I, and/or L contained in a specimen 212 or whether the specimen is normal (N). If found to contain effectively-low amounts of H, I and/or L, so as to be considered normal (N), the specimen 212 may continue on the track 121 and then may be analyzed," par. 17).
Rudorfer et al., Grabau et al., and Sun et al. are combined as per claim 6.
Claim 8
Regarding claim 8, Rudorfer et al. and Grabau et al. teach the method of claim 1 as noted above.
Rudorfer et al. and Grabau et al. do not explicitly teach wherein at least one of the one or more sensors is configured to generate image data of a specimen container and wherein the generating data comprises generating image data of the specimen container indicative of physical dimensions of the specimen container.
However, Sun et al. teach wherein at least one of the one or more sensors is configured to generate image data of a specimen container and wherein the generating data comprises generating image data of the specimen container ("image capture devices arranged around the track and configured to capture multiple images of a specimen container and the serum or plasma portion of the specimen from multiple viewpoints," par. 17) indicative of physical dimensions of the specimen container ("the quality check module 130 may be used to quantify geometry of the specimen container 102, i.e., quantify certain physical dimensional characteristics of the specimen container," par. 115).
Rudorfer et al., Grabau et al., and Sun et al. are combined as per claim 6.
Claim 12
Regarding claim 12, Rudorfer et al. and Grabau et al. teach the method of claim 1 as noted above.
Rudorfer et al. do not explicitly wherein the predicting comprises encoding data from the one or more sensors into an array of values indicative of the operational state of the diagnostic laboratory system, and wherein the inputting the data comprises inputting the array of values into the artificial intelligence algorithm.
However, Grabau et al. teach wherein the predicting comprises encoding data from the one or more sensors into an array of values ("data sets may be provided in the form of data matrices," par. 87).
Additionally, Sun et al. teach indicative of the operational state of the diagnostic laboratory system, and wherein the inputting the data comprises inputting the array of values into the artificial intelligence algorithm ("the integrated diagnostics module 124 may derive these algorithms from documented average or minimum service life of multiple process control devices of the same type and construction materials, as used in a given application, from laboratory data collected in a manner that most nearly approximates field service conditions (e.g., operating environment)," par. 37).
Rudorfer et al., Grabau et al., and Sun et al. are combined as per claim 6.
5th Claim Rejections - 35 USC § 103
Claims 9, 10, 13, 14, 16, 17, 18, 19, 20, and 21 are rejected under 35 U.S.C. 103 as obvious over US Patent Publication 2019 0376991 A1, (Rudorfer et al.) and US Patent Publication 2015 0088434 A1, (Grabau et al.) in view of US Patent Publication 2020 0250109 A1, (Yaacov et al.).
Claim 9
Regarding claim 9, Rudorfer et al. and Grabau et al. teach the method of claim 1 as noted above.
Rudorfer et al. and Grabau et al. do not explicitly teach wherein at least one of the one or more sensors is configured to measure temperature and wherein the generating data comprises generating data indicative of temperature.
However, Yaacov et al. teach wherein at least one of the one or more sensors is configured to measure temperature and wherein the generating data comprises generating data indicative of temperature ("Sensors monitor metrics of the peripheral machines and/or conduits between the machines during operation. Optionally, other sensors monitor the peripheral machine operating conditions (e.g. ambient temperature, humidity, oil pressure, current, power, voltage, etc.). The sensor data is provided over a communication network to the control system. Examples of metrics which may be measured by the sensors include but are not limited to: temperature, pressure, volume and power," par. 129).
Therefore, taking the teachings of Rudorfer et al., Grabau et al., and Yaacov et al. as a whole, it would have been obvious to a person having ordinary skill in the art before the time of the effective filing date of the claimed invention of the instant application to modify the monitoring of in vitro instruments as taught by Rudorfer et al. and the algorithmic diagnostics module as taught by Grabau et al. to use the malfunction prediction method as taught by Yaacov et al. The suggestion/motivation for doing so would have been that, “other sensors monitor the peripheral machine operating conditions (e.g. ambient temperature, humidity, oil pressure, current, power, voltage, etc.). The sensor data is provided over a communication network to the control system” as noted by the Yaacov et al. disclosure in paragraph [0129], which also motivates combination because the combination would predictably have an additional utility as there is a reasonable expectation that the monitoring of peripheral machine operating conditions (such as ambient temperature, humidity, pressure, or current) in conjunction with an algorithmic diagnostics module would result in earlier detection of instrument malfunctions, increased uptime, and/or improved accuracy of in vitro diagnostic results; and/or because doing so merely combines prior art elements according to known methods to yield predictable results.
Claim 10
Regarding claim 10, Rudorfer et al. and Grabau et al. teach the method of claim 1 as noted above.
Rudorfer et al. and Grabau et al. do not explicitly teach wherein at least one of the one or more sensors is configured to measure humidity and wherein the generating data comprises generating data indicative of humidity.
However, Yaacov et al. teach wherein at least one of the one or more sensors is configured to measure humidity and wherein the generating data comprises generating data indicative of humidity ("Sensors monitor metrics of the peripheral machines and/or conduits between the machines during operation. Optionally, other sensors monitor the peripheral machine operating conditions (e.g. ambient temperature, humidity, oil pressure, current, power, voltage, etc.). The sensor data is provided over a communication network to the control system. Examples of metrics which may be measured by the sensors include but are not limited to: temperature, pressure, volume and power," par. 129).
Rudorfer et al., Grabau et al., and Yaacov et al. are combined as per claim 9.
Claim 13
Regarding claim 13, Rudorfer et al. and Grabau et al. teach the method of claim 1 as noted above.
Rudorfer et al. teach the diagnostic laboratory system ("malfunction diagnosis in the in vitro diagnostic instruments," par. 24).
Rudorfer et al. and Grabau et al. do not explicitly teach wherein the predicting comprises calculating a probability that a fault will occur within a predetermined period of time.
However, Yaacov et al. teach wherein the predicting comprises calculating a probability that a fault will occur within a predetermined period of time ("Malfunction predictor 1104 triggers (with a measure of certainty and occurrence in time) the operation plan modeler 1106 to re-evaluate the plan based on the upcoming machine malfunction," par. 287).
Rudorfer et al., Grabau et al., and Yaacov et al. are combined as per claim 9.
Claim 14
Regarding claim 14, Rudorfer et al. and Grabau et al. teach the method of claim 1 as noted above.
Rudorfer et al. teach a module in the diagnostic laboratory system ("The in vitro instrument maintenance apparatus 100 includes one or more monitoring devices 102A, 102B configured to monitor condition-based parameters of one or more instrument components of one or more instruments," par. 19).
Rudorfer et al. and Grabau et al. do not explicitly teach wherein the predicting comprises calculating a probability that a fault will occur within a predetermined period of time.
However, Yaacov et al. teach wherein the predicting comprises calculating a probability that a fault will occur within a predetermined period of time ("Malfunction predictor 1104 triggers (with a measure of certainty and occurrence in time) the operation plan modeler 1106 to re-evaluate the plan based on the upcoming machine malfunction," par. 287).
Rudorfer et al., Grabau et al., and Yaacov et al. are combined as per claim 9.
Claim 16
Regarding claim 16, Rudorfer et al. and Grabau et al. teach the method of claim 1 as noted above.
Rudorfer et al. teach a component in a module of the diagnostic laboratory system ("The in vitro instrument maintenance apparatus 100 includes one or more monitoring devices 102A, 102B configured to monitor condition-based parameters of one or more instrument components of one or more instruments," par. 19).
Rudorfer et al. and Grabau et al. do not explicitly teach wherein the predicting comprises predicting a probability that the system will experience a fault within a predetermined period of time.
However, Yaacov et al. teach wherein the predicting comprises predicting a probability that the system will experience a fault within a predetermined period of time ("Malfunction predictor 1104 triggers (with a measure of certainty and occurrence in time) the operation plan modeler 1106 to re-evaluate the plan based on the upcoming machine malfunction," par. 287).
Rudorfer et al., Grabau et al., and Yaacov et al. are combined as per claim 9.
Claim 17
Regarding claim 17, Rudorfer et al. and Grabau et al. teach the method of claim 1 as noted above.
Rudorfer et al. teach a module within the diagnostic laboratory ("The in vitro instrument maintenance apparatus 100 includes one or more monitoring devices 102A, 102B configured to monitor condition-based parameters of one or more instrument components of one or more instruments," par. 19).
Rudorfer et al. and Grabau et al. do not explicitly teach wherein the predicting comprises predicting a time when a system will experience a fault.
However, Yaacov et al. teach wherein the predicting comprises predicting a time when a system will experience a fault ("Malfunction predictor 1104 triggers (with a measure of certainty and occurrence in time) the operation plan modeler 1106 to re-evaluate the plan based on the upcoming machine malfunction," par. 287).
Rudorfer et al., Grabau et al., and Yaacov et al. are combined as per claim 9.
Claim 18
Regarding claim 18, Rudorfer et al. and Grabau et al. teach the method of claim 1 as noted above.
Rudorfer et al. teach a component of a module within the diagnostic laboratory system ("The in vitro instrument maintenance apparatus 100 includes one or more monitoring devices 102A, 102B configured to monitor condition-based parameters of one or more instrument components of one or more instruments," par. 19).
Rudorfer et al. and Grabau et al. do not explicitly teach wherein the predicting comprises predicting a time when a system will experience a fault.
However, Yaacov et al. teach wherein the predicting comprises predicting a time when a system will experience a fault ("Malfunction predictor 1104 triggers (with a measure of certainty and occurrence in time) the operation plan modeler 1106 to re-evaluate the plan based on the upcoming machine malfunction," par. 287).
Rudorfer et al., Grabau et al., and Yaacov et al. are combined as per claim 9.
Claim 19
Regarding claim 19, Rudorfer et al. and Grabau et al. teach the method of claim 1 as noted above.
Rudorfer et al. and Grabau et al. do not explicitly teach further comprising training the artificial intelligence algorithm.
However, Yaacov et al. teach further comprising training the artificial intelligence algorithm ("Malfunction predictor component 1104 uses a mass training data model," par. 287).
Rudorfer et al., Grabau et al., and Yaacov et al. are combined as per claim 9.
Claim 20
Regarding claim 20, Rudorfer et al. and Grabau et al. teach the method of claim 1 as noted above.
Rudorfer et al. and Grabau et al. do not explicitly teach wherein the artificial intelligence algorithm comprises a generative network.
However, Yaacov et al. teach wherein the artificial intelligence algorithm comprises a generative network ("The optimization and weights adjustments module 1103 uses a neural network model with predefined values for weights to generate suitable suggestions," par. 287).
Rudorfer et al., Grabau et al., and Yaacov et al. are combined as per claim 9.
Claim 21
Regarding claim 21, Rudorfer et al. teach a method of predicting a fault in a component of a module in a diagnostic laboratory system, comprising: providing one or more sensors in the module of the diagnostic laboratory system; ("The in vitro instrument maintenance apparatus 100 includes one or more monitoring devices 102A, 102B configured to monitor condition-based parameters of one or more instrument components of one or more instruments," par. 19) and generating data using the one or more sensors ("data sampled over time," par. 24) including at least one of aspiration pressure data, dispense pressure data, motor current data, acoustic data, vibration data, position data, image data of a specimen or specimen container, or combinations thereof ("Condition of pumps, motors, or other electrical components may be monitored by current," par. 20).
Rudorfer et al. do not explicitly teach inputting generated the data into an artificial intelligence algorithm, the artificial intelligence algorithm configured to predict a fault of the component in response to the data; and predicting a probability of a fault in the component using the artificial intelligence algorithm.
However, Grabau et al. teach inputting generated the data into an artificial intelligence algorithm, ("the integrated diagnostics module 124 may derive these algorithms from documented average or minimum service life of multiple process control devices of the same type and construction materials, as used in a given application, from laboratory data collected in a manner that most nearly approximates field service conditions (e.g., operating environment)," par. 37) the artificial intelligence algorithm configured to predict a fault of the component in response to the data ("the integrated diagnostics module 400 may be configured to provide more frequent warning messages as the projected failure point nears," par. 68).
Additionally, Yaacov et al. teach predicting a probability of a fault in the component using the artificial intelligence algorithm ("Malfunction predictor 1104 triggers (with a measure of certainty and occurrence in time) the operation plan modeler 1106 to re-evaluate the plan based on the upcoming machine malfunction," par. 287).
Rudorfer et al., Grabau et al., and Yaacov et al. are combined as per claim 9.
6th Claim Rejections - 35 USC § 103
Claim 11 is rejected under 35 U.S.C. 103 as obvious over US Patent Publication 2019 0376991 A1, (Rudorfer et al.) and US Patent Publication 2015 0088434 A1, (Grabau et al.) in view of US Patent Publication 2020 0166398 A1, (Collister et al.).
Claim 11
Regarding claim 11, Rudorfer et al. and Grabau et al. teach the method of claim 1 as noted above.
Rudorfer et al. and Grabau et al. do not explicitly teach wherein at least one of the one or more sensors is configured to measure sound and wherein the generating data comprises generating data indicative of sound.
However, Collister et al. teach wherein at least one of the one or more sensors is configured to measure sound and wherein the generating data comprises generating data indicative of sound (“Transducers 112 and 114 are acoustic transceivers, and more particularly ultrasonic transceivers. The ultrasonic transducers 112, 114 both generate and receive acoustic signals having frequencies above about 20 kilohertz … upon being struck by an acoustic signal, the receiving piezoelectric element vibrates and generates an electrical signal (e.g., a sinusoidal signal) that is detected, digitized, and analyzed by the electronics,” par. 38).
Therefore, taking the teachings of Rudorfer et al., Grabau et al., and Collister et al. as a whole, it would have been obvious to a person having ordinary skill in the art before the time of the effective filing date of the claimed invention of the instant application to modify the monitoring of in vitro instruments as taught by Rudorfer et al. and the algorithmic diagnostics module as taught by Grabau et al. to use measuring sound as an indication of a predicted failure as taught by Collister et al. The suggestion/motivation for doing so would have been that, “upon being struck by an acoustic signal, the receiving piezoelectric element vibrates and generates an electrical signal” as noted by the Collister et al. disclosure in paragraph [0038], which also motivates combination because the combination would predictably have an additional utility as there is a reasonable expectation that incorporating the piezoelectric failure detection would enable the real-time, accurate identification of instrument malfunctions by converting mechanical vibrations and acoustic signals into electrical notification signals, thereby providing a more automated, reliable, and instantaneous alert system for maintenance or diagnostic failures; and/or because doing so merely combines prior art elements according to known methods to yield predictable results.
7th Claim Rejections - 35 USC § 103
Claim 15 is rejected under 35 U.S.C. 103 as obvious over US Patent Publication 2019 0376991 A1, (Rudorfer et al.) and US Patent Publication 2015 0088434 A1, (Grabau et al.), US Patent Publication 2020 0250109 A1, (Yaacov et al.) in view of US Patent Publication 2020 10528858 B1, (Murphy et al.).
Claim 15
Regarding Claim 15, Rudorfer et al., Grabau et al., and Yaacov et al. teach the method of claim 14 as noted above.
Rudorfer et al. and Grabau et al. do not explicitly teach further comprising generating a notification in response to the probability being greater than a predetermined value.
However, Yaacov et al. teach generating a notification ("The control system described herein additional benefits, including but not limited to: i) Immediate notification of alerts and machines failures," par. 19).
Additionally, Murphy et al. teach the probability being greater than a predetermined value (“The malfunction detection logic circuitry 1015 may compare the probability threshold to the error or probability output by the neural network 1018 in response to the purchase to determine if proactive action should occur in response to the probability output. For example, in response to a probability output that reaches or exceeds a probability threshold, the malfunction detection logic circuitry 1015 may determine that one or more of the payment interfaces of the customer's payment instrument has failed or otherwise malfunctioned,” par. 52).
Therefore, taking the teachings of Rudorfer et al., Grabau et al., Yaacov et al., and Murphy et al. as a whole, it would have been obvious to a person having ordinary skill in the art before the time of the effective filing date of the claimed invention of the instant application to modify the monitoring of in vitro instruments as taught by Rudorfer et al. and the algorithmic diagnostics module as taught by Grabau et al., and the malfunction prediction method as taught by Yaacov et al., to use payment failure detection threshold notifications as taught by Murphy et al. The suggestion/motivation for doing so would have been that, “The logic circuitry coupled with the memory may compare the probability of the malfunction against a threshold and determine whether the at least one interface is malfunctioning based comparison of the probability of the malfunction against the threshold” as noted by the Murphy et al. disclosure in paragraph [0004], which also motivates combination because the combination would predictably have a greater accuracy as there is a reasonable expectation that incorporating a threshold-based notification system into the in vitro diagnostic device would improve the speed and reliability of malfunction detection, thereby reducing device downtime and optimizing diagnostic performance; and/or because doing so merely combines prior art elements according to known methods to yield predictable results.
Conclusion
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/Karsten F. Lantz/Examiner, Art Unit 2664
Date: 5/8/2026
/JENNIFER MEHMOOD/Supervisory Patent Examiner, Art Unit 2664
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