DETAILED ACTION
This action is in response to the application filed on 5/30/2024.
Claims 1-25 are pending.
Acknowledgment is made of a claim for foreign priority. All of the certified copies of the priority documents have been received.
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 references listed on the Information Disclosure Statement submitted on 5/30/2024 has/have been considered by the examiner (see attached PTO-1449).
Claim Mapping Notation
In this office action, following notations are being used to refer to the paragraph numbers or column number and lines of portions of the cited reference.
In this office action, following notations are being used to refer to the paragraph numbers or column number and lines of portions of the cited reference.
[0005] (Paragraph number [0005])
C5 (Column 5)
Pa5 (Page 5)
S5 (Section 5)
Furthermore, unless necessary to distinguish from other references in this action, “et al.” will be omitted when referring to the reference.
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale or otherwise available to the public before the effective filing date of the claimed invention.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1-3, 8, 9, 11, 14, 15, 17, 20, 22 and 23 are rejected under 35 U.S.C. 102(a1) and (a2) as being anticipated by Gordon et al. (US 20220269257 A1).
1. A method for predictive maintenance through automatic prediction of a pump anomaly, performed by a computing system, the method comprising:
receiving a plurality of sensing values of two or more categories, among a plurality of categories, from a plurality of sensors provided in a first pump, the plurality of categories including a Body Power (BP), a Dry Power (DP), a piping pressure, a temperature, a body temperature, a voltage, a body voltage, and a dry voltage;
“[0056] Embodiments, provide a diagnostic system that receives inputs from sensors associated with the cryopumps and enter them into a model that models the operation of the cryopump and predicts future failures from the values and/or the changes in values of the received signals.”
“[0060] During operation sensors (not shown) associated with the vacuum pumps 10 sense operating conditions of the pumps and send signals indicative of these operating conditions to the supervisor node 20. Diagnostic circuitry 22 within the supervisor node 20 samples at least some of the signals and inputs the sampled signals as input data to the diagnostic model. These operating conditions may include the temperature of the first stage of the vacuum pump, the temperature of the second stage of the vacuum pump, the time taken to reach a desired temperature in each of the vacuum pump stages, the speed of the vacuum pump motor and other variables illustrative of the operation of the vacuum pump 10.”
“[0068] The remote or cloud based system is configured to stores data from the operation of many pumps of the same type, in a data base or data lake 34. This data includes but is not limited to temperatures, motor speed, heater inputs, regeneration parameters, age, etc…”
“[0076] Initial pumps' manufacturing tests 120 may also be input and may include the counted pass/fails of the pump, and the aggregated statistics of the passes. Regeneration data 130 may also be entered and may include: Regeneration Step the pump is in; 1st Stage Temperature; 2nd Stage Temperature; Motor Status, Purge Valve Status; Rough Valve Status; Heater 1 Status; Heater 2 Status; Heater 1 Percent On; Heater 2 Percent On…The Base pressure setting for the regen…”
“[0077] At the start of the process for building the original diagnostic model using machine learning techniques, pump data such as that outlined above is collected from several different sources.”
inputting a feature representing each of sensing values, selected among the plurality of sensing values, to a first anomaly prediction model that is machine-learned in advance;
“[0056]…The model is generated from an analysis of historic data collected from sensors associated with a plurality of the same type of cryopumps operating over a time period which includes at least some scheduled maintenance periods. The model may be periodically updated using machine learning techniques by analysing newly received data from cryopumps being diagnosed by the system.”
determining whether a future anomaly of the first pump is predicted to occur, by using data output from the first anomaly prediction model; and
“[0056] Embodiments, provide a diagnostic system that receives inputs from sensors associated with the cryopumps and enter them into a model that models the operation of the cryopump and predicts future failures from the values and/or the changes in values of the received signals.”
providing alarm information based on a determination that the future anomaly is predicted to occur,
“[0089]… the time to cool down to the required temperature 310, and from these values the probability of failure 330 is detected using the diagnostic model of an embodiment. This probability can be trended and monitored and when it crosses certain thresholds warnings/alarms to swap the pump can be generated.”
wherein the first pump belongs to a first pump model group, among a plurality of pump model groups, matched with the first anomaly prediction model.
“[0068] The remote or cloud based system is configured to stores data from the operation of many pumps of the same type, in a data base or data lake 34. This data includes but is not limited to temperatures, motor speed, heater inputs, regeneration parameters, age, etc. The remote or cloud based system 30 comprises logic 33 for generating and updating a diagnostic model using this data. The cloud based logic 33 accesses data lake 34 which contains data indicative of operating conditions of multiple pumps of a same type during predetermined times including times when they are regenerated, serviced and replaced.”
2. The method of claim 1, wherein the first pump is disposed at a first site matched with the first anomaly prediction model.
“[0068] The remote or cloud based system is configured to stores data from the operation of many pumps of the same type, in a data base or data lake 34. This data includes but is not limited to temperatures, motor speed, heater inputs, regeneration parameters, age, etc. The remote or cloud based system 30 comprises logic 33 for generating and updating a diagnostic model using this data. The cloud based logic 33 accesses data lake 34 which contains data indicative of operating conditions of multiple pumps of a same type during predetermined times including times when they are regenerated, serviced and replaced.”
3. The method of claim 2, wherein the first site is a specific line, among a plurality of lines, in a factory that performs a semiconductor manufacturing process.
“[0066] In this embodiment, there is also a connection to a remote or cloud based system 30. In the remote system 30, there is a streaming data management tool 32, which sends data to data storage 34 or to a diagnostic model 33 and which comprises log parser 33a, data storage 33b and a machine learning engine 35…”
“[0016] Although the vacuum system may contain just one cryopump, in many cases there are a plurality of cryopumps within a vacuum system, such as in a system for evacuating a semiconductor processing chamber, and scheduling the replacement and maintenance of these cryopumps is important for the productivity and yield of the system…”
Regarding the claims 8, 9 and 11, 14, 15, 17, 20, 22 and 23 they recite elements that that map to the same portions of the reference as the claims 1-3. Therefore, the same rationale for the rejection of the claims 1-3 applies.
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 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 of this title, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied for establishing a background for determining obviousness under pre-AIA 35 U.S.C. 103(a) 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.
Claims 4, 10, 16 and 21 are rejected under 35 U.S.C. 103 as being unpatentable over Gordon in view of Malik et al. (US 20230325226 A1)
Regarding the claim 4, Gordon discloses the invention substantially as claimed as mentioned above for the claim 1.
Gordon discloses,
4. The method of claim 1, further comprising predicting an expected lifespan of the first pump by…based on an anomaly occurrence history of the first pump and an average lifespan of the first pump model group.
“[0012] While this solution can be of general utility across currently used cryopumps, it may be particularly useful for the next generation of cryopump systems and in particular the ion implant use case where the cryopump life is predicted to be shorter as is the interval between PMs.”
“[0056]…The model is generated from an analysis of historic data collected from sensors associated with a plurality of the same type of cryopumps operating over a time period which includes at least some scheduled maintenance periods. The model may be periodically updated using machine learning techniques by analysing newly received data from cryopumps being diagnosed by the system.”
“[0064] Following replacement of the pump, in some embodiments a service engineer will check the health of the replaced pump and enter information into the supervisor node 20 indicative of the condition of the pump and perhaps its expected lifetime prior to actual failure.”
Gordon does not disclose,
…computing a Bayesian probability…
Malik discloses,
…computing a Bayesian probability…
“[0144]…Intuitively, this makes the classification correct for testing data that is near, but not identical to training data. Other directed and undirected model classification approaches comprise, e.g., naïve Bayes, Bayesian networks, decision trees, neural networks, fuzzy logic models, and probabilistic classification models providing different patterns of independence can be employed. Classification as used herein also is inclusive of statistical regression that is utilized to develop models of priority.”
It would have been obvious to one of ordinary skilled in the art before the effective filing date of the claimed invention to utilize the teachings of Malik and apply them on the teachings of Gordon to predict pump failure using Bayesian probability model and updating the model using the historical data.
One would have been motivated as implementing Bayesian model is a well-known in such a failure monitoring and prediction of vacuum pump systems as demonstrated by Malik.
Unless stated otherwise, the same explanation for the rationale for the following dependent claims applies as given for the independent claim.
Regarding the claims 10, 16 and 21 they recite elements that that map to the same portions of the reference as the claims 4, 4 and 4. Therefore, the same rationale for the rejection of the claims 4 and 4 applies.
Claims 5, 6, 12, 18 and 24 are rejected under 35 U.S.C. 103 as being unpatentable over Gordon in view of Bharadwaj et al. (US 11269752 B1)
Regarding the claim 5, Gordon discloses the invention substantially as claimed as mentioned above for the claim 1.
Gordon discloses,
5. The method of claim 1, further comprising:
…based on an anomaly occurrence history of the first pump and the plurality of sensing values of the first pump; and
“[0056]…The model is generated from an analysis of historic data collected from sensors associated with a plurality of the same type of cryopumps operating over a time period which includes at least some scheduled maintenance periods. The model may be periodically updated using machine learning techniques by analysing newly received data from cryopumps being diagnosed by the system.”
determining a sensing value of the BP, a sensing value of the DP, and a sensing value of the piping pressure, among the plurality of sensing values, as a sensing value related to an anomaly of the first pump…
“[0060] During operation sensors (not shown) associated with the vacuum pumps 10 sense operating conditions of the pumps and send signals indicative of these operating conditions to the supervisor node 20. Diagnostic circuitry 22 within the supervisor node 20 samples at least some of the signals and inputs the sampled signals as input data to the diagnostic model. These operating conditions may include the temperature of the first stage of the vacuum pump, the temperature of the second stage of the vacuum pump, the time taken to reach a desired temperature in each of the vacuum pump stages, the speed of the vacuum pump motor and other variables illustrative of the operation of the vacuum pump 10.”
“[0068] The remote or cloud based system is configured to stores data from the operation of many pumps of the same type, in a data base or data lake 34. This data includes but is not limited to temperatures, motor speed, heater inputs, regeneration parameters, age, etc…”
“[0076] Initial pumps' manufacturing tests 120 may also be input and may include the counted pass/fails of the pump, and the aggregated statistics of the passes. Regeneration data 130 may also be entered and may include: Regeneration Step the pump is in; 1st Stage Temperature; 2nd Stage Temperature; Motor Status, Purge Valve Status; Rough Valve Status; Heater 1 Status; Heater 2 Status; Heater 1 Percent On; Heater 2 Percent On…The Base pressure setting for the regen…”
“[0077] At the start of the process for building the original diagnostic model using machine learning techniques, pump data such as that outlined above is collected from several different sources.”
Gordon does not disclose,
performing principal component analysis (PCA)… based on a result of the principal component analysis.
Bharadwaj discloses,
performing principal component analysis (PCA)… based on a result of the principal component analysis.
C3 “FIG. 3 illustrates the overall architecture of the predictive MLP-MLP GAN model. In the present MLP-MLP GAN model, the generator has 3 hidden layers with 100 neurons each. While the discriminator has 4 hidden layers with 100-100-50-25 neurons in each layer. The dimensionality of the PCA process is decided by the amount of variance of the total data that is captured by the principle components. The dimensionality of latent-space in the case of PCA was fixed to be 2.”
It would have been obvious to one of ordinary skilled in the art before the effective filing date of the claimed invention to utilize the teachings of Bharadwaj and apply them on the teachings of Gordon to predict pump failure using PCA model and updating the model using the historical data.
One would have been motivated as implementing PCA model is a well-known in such a failure monitoring and prediction of pump systems as demonstrated by Bharadwaj.
6. The method of claim 1, wherein the inputting the feature representing each of sensing values comprises:
based on a determination that the first pump belongs to the first pump model group,
Gordon “[0068] The remote or cloud based system is configured to stores data from the operation of many pumps of the same type, in a data base or data lake 34. This data includes but is not limited to temperatures, motor speed, heater inputs, regeneration parameters, age, etc. The remote or cloud based system 30 comprises logic 33 for generating and updating a diagnostic model using this data. The cloud based logic 33 accesses data lake 34 which contains data indicative of operating conditions of multiple pumps of a same type during predetermined times including times when they are regenerated, serviced and replaced.”
inputting a first feature representing a sensing value of the BP, a second feature representing a sensing value of the DP, and a third feature representing a sensing value of the piping pressure.
Gordon “[0060] During operation sensors (not shown) associated with the vacuum pumps 10 sense operating conditions of the pumps and send signals indicative of these operating conditions to the supervisor node 20. Diagnostic circuitry 22 within the supervisor node 20 samples at least some of the signals and inputs the sampled signals as input data to the diagnostic model. These operating conditions may include the temperature of the first stage of the vacuum pump, the temperature of the second stage of the vacuum pump, the time taken to reach a desired temperature in each of the vacuum pump stages, the speed of the vacuum pump motor and other variables illustrative of the operation of the vacuum pump 10.”
“[0068] The remote or cloud based system is configured to stores data from the operation of many pumps of the same type, in a data base or data lake 34. This data includes but is not limited to temperatures, motor speed, heater inputs, regeneration parameters, age, etc…”
“[0076] Initial pumps' manufacturing tests 120 may also be input and may include the counted pass/fails of the pump, and the aggregated statistics of the passes. Regeneration data 130 may also be entered and may include: Regeneration Step the pump is in; 1st Stage Temperature; 2nd Stage Temperature; Motor Status, Purge Valve Status; Rough Valve Status; Heater 1 Status; Heater 2 Status; Heater 1 Percent On; Heater 2 Percent On…The Base pressure setting for the regen…”
“[0077] At the start of the process for building the original diagnostic model using machine learning techniques, pump data such as that outlined above is collected from several different sources.”
Regarding the claims 12, 18 and 24 they recite elements that that map to the same portions of the reference as the claims 5, 5 and 7. Therefore, the same rationale for the rejection of the claims 4 and 4 applies.
Allowable Subject Matter
Claims 7, 13, 19 and 25 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
The following is a statement of reasons for the indication of allowable subject matter:
Regarding the claims 7, 13, 19 and 25, applicants uniquely claimed distinct features, which are not found in the prior art, either singularly or in an obvious combination of all the limitation of the claim, the distinct features being… adjusting prediction sensitivity of the first anomaly prediction model, wherein the adjusting the prediction sensitivity of the first anomaly prediction model includes:
upgrading the prediction sensitivity of the first anomaly prediction model through sequential probability ratio verification based on an anomaly occurrence history of the first pump and the plurality of sensing values of the first pump; and
downgrading the prediction sensitivity of the first anomaly prediction model by adjusting boundary value of a Poisson filter applied for filtering of a false alarm.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Malik et al. (US 20230325226 A) and Bharadwaj et al. (US 11269752 B1) disclose relevant art related to the subject matter of the present invention.
A shortened statutory period for reply to this action is set to expire THREE MONTHS from the mailing date of this action. An extension of time may be obtained under 37 CFR 1.136(a). However, in no event, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JAE N NOH whose telephone number is (571)270-0686. The examiner can normally be reached on Mon-Fri 8:30AM-5PM.
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/JAE N NOH/
Primary Examiner
Art Unit 2481
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