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 .
Summary
Claims 1-14 are pending. Claims 1-14 are rejected herein. This is a First Action on the Merits.
Claim Objections
Claim(s) 10, 11, and 14 objected to because of the following informalities. Appropriate correction is required.
Regarding claims 10, 11, and 14: These claims are repeats of previous claims and it is clear that these claims are supposed to depend from claim 9 instead of claim 1. That is how they have been treated for the purposes of this office action.
Claim Rejections - 35 USC § 112(b)
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claim(s) 1-14 is/are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Regarding claims 1 and 9: These claims recite “A real-time computer system for automated acoustic anomaly detection in a programmable logic controller (PLC)” (emphasis added). This language sounds like the acoustic anomaly is coming from the PLC itself. However, it is clear from the specification that the sounds are being detected from work products under inspection (para. 12 of the specification as published). The Examiner recommends using language such as “A real-time computer system for automated acoustic anomaly detection [[in]] in conjunction with a programmable logic controller (PLC)” or something similar.
Regarding claims 2-8 and 10-14: These claims are rejected as indefinite for depending from an indefinite claim.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claim(s) 1-14 is/are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Regarding claims 1 and 9: The claim(s) recite(s) data processing on a general-purpose computer. This judicial exception is not integrated into a practical application because there is nothing within the scope of the claims beyond the algorithm processing data on a general purpose-computer. The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because there is no material transformation. There are no also machines within the scope of the claim besides a “computer system.” The PLC and the sensor are not within the scope of the claim. The PLC only states an intended use for the computer and the sensor is only recited as the origin of the data. To overcome this rejection, the Examiner recommends claiming a system comprising a computer system, a PLC, and one or more sensors.
Regarding claims 2-8 and 10-14: These claims are rejected under 35 U.S.C. 101 due to their dependence.
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.
Claim(s) 1-6, 8-12, and 14 is/are rejected under 35 U.S.C. 103 as being unpatentable over GEIB et al. (US 8655571) in view of CLAUSSEN et al. (WO 2020222845).
Regarding claims 1, 3, 4, and 8: As best understood, GEIB discloses: A real-time computer system (24 in FIG. 1), the computer system comprising: at least one processor (28) with a real-time operating system (Inherent in the operation of any computer); and a memory (26) having algorithmic modules stored thereon executable by the processor, the modules comprising: a digital signal processing module configured to window sound signal data captured by a sensor (time domain windows as discussed in col. 4 lines 54-67), the sound signal data representative of sound emitted by an energized work product under quality inspection (gas turbine engine in col. 1 lines 42-53); a feature extraction component is configured to extract acoustic features from each sound window (MFCC coefficients in col. 3 lines 25-39); and an anomaly detector module configured to operate a machine learning-based model to execute acoustic anomaly detection according to results of a classification operation on the acoustic features (col. 4 lines 1-13).
GEIB does not disclose that the computer is deployed onto the backplane of a PLC.
CLAUSSEN however does teach that it is known to install a computer (external module in para. 14-15) on the backplane of a PLC (para. 15) and receive sensor data for an industrial process (para. 14-15). CLAUSSEN also teaches that the at least one processor is configured as an artificial intelligence accelerator comprising: a main central processing unit (CPU) configured to operate the real-time operating system and interface with the PLC control loop (para. 24); and at least one real-time CPU configured to operate algorithms of the anomaly detector module (para. 24), thus meeting the limitations of claim 3. CLAUSSEN also teaches Streaming Hybrid Architecture Vector Engine (SHAVE) cores that use a parallel processing unit specific for neural network evaluation in real-time (para. 24), thus meeting the limitations of claim 4. CLAUSSEN also teaches a USB driver for controlling streaming of sound data received from the sensor (para. 21), thus meeting the limitations of claim 8.
One skilled in the art at the time the application was effectively filed would be motivated to use a computer installed on the backplane of a PLC in this way because it provides a hardware-based protection for the PLC against cyber attacks (para. 14 of CLAUSSEN).
Regarding claims 2 and 10: As best understood, GEIB discloses: the acoustic features include Mel-frequency cepstral coefficients (col. 3 lines 25-39).
Regarding claims 5 and 11: As best understood, GEIB discloses: the anomaly detector module is further configured to: generate a normal classification or an abnormal classification for the acoustic features (fault detection and diagnosis, steps 114 and 116 in FIG. 2; col. 52-67) and treat the abnormal classification as a detected acoustic anomaly, and respond with one or more trigger events (col. 4 lines 18-32).
GEIB does not disclose using the classification as a data point input to the PLC.
CLAUSSEN however does teach using the classification as a data point input to the PLC (para. 18) and sending an abnormal classification as a data point input to the PLC for controlling the automation system (para. 18); and responding with one or more trigger events (para. 18; “Connections 105 may…interface with elements of the industrial processes, including but not limited to valves, sensors, and actuators. Connections 105 may transmit control data to, and receive operating data from, any appropriate elements of the industrial process. Data, such as sensor data, from connections 105 is received on backplane 104, and is used by PLC resources 102 to analyze the operation of the industrial process and to control the elements of the industrial process by transmitting commands to the elements of the industrial process via backplane 104 and connections 105.”).
One skilled in the art at the time the application was effectively filed would be motivated to use the PLC interface of CLAUSSEN to control an industrial process such as the fault detection of GEIB, because it provides an interconnection between the automation hardware and flexibility of programming and processing in computer software (para. 2 of CLAUSSEN).
Regarding claims 6 and 12: As best understood, GEIB discloses: the trigger events include: an alert for display on a human machine interface in response to an anomaly detection (Faults are reported to a maintenance scheduler. Col. 4 lines 18-22).
GEIB does not explicitly disclose a display on a human machine interface, however the Examiner takes Official Notice that it is known in the art for monitoring systems to send alerts to personnel about systems faults and required maintenance so that personnel are informed about the work they are supervising.
Regarding claims 9 and 14: As best understood, GEIB discloses: A real-time computer-based method for automated acoustic anomaly detection (24 in FIG. 1), the method comprising: windowing sound signal data captured by a sensor (time domain windows as discussed in col. 4 lines 54-67), the sound signal data representative of sound emitted by an energized work product under quality inspection (gas turbine engine in col. 1 lines 42-53); extracting acoustic features from each sound window (MFCC coefficients in col. 3 lines 25-39); and operating a machine learning-based model to execute acoustic anomaly detection according to results of a classification operation on the acoustic features (col. 4 lines 1-13).
GEIB does not disclose that the computer is deployed on a PLC.
CLAUSSEN however does teach that it is known to install a computer (external module in para. 14-15) on the backplane of a PLC (para. 15) and receive sensor data for an industrial process (para. 14-15). CLAUSSEN also teaches a USB driver for controlling streaming of sound data received from the sensor (para. 21), thus meeting the limitations of claim 14. CLAUSSEN also teaches that the at least one processor is configured as an artificial intelligence accelerator comprising: a main central processing unit (CPU) configured to operate the real-time operating system and interface with the PLC control loop (para. 24); and at least one real-time CPU configured to operate algorithms of the anomaly detector module (para. 24). CLAUSSEN also teaches Streaming Hybrid Architecture Vector Engine (SHAVE) cores that use a parallel processing unit specific for neural network evaluation in real-time (para. 24).
One skilled in the art at the time the application was effectively filed would be motivated to use a computer installed on the backplane of a PLC in this way because it provides a hardware-based protection for the PLC against cyber attacks (para. 14 of CLAUSSEN).
Claim(s) 7 and 13 is/are rejected under 35 U.S.C. 103 as being unpatentable over GEIB and CLAUSSEN in view of BIRKHOFER et al. (US 20240381881).
Regarding claims 7 and 13: As best understood, GEIB as modified by CLAUSSEN does not teach routing a work product to a production line that performs remedial measures.
BIRKHOFER however does teach using sensor data (para. 36, 41-42) in an automated PLC factory environment (abstract; para. 47) to flag and divert a faulty product (para. 5) to a different production line for remedial measures (repackaging in para. 5).
One skilled in the art at the time the application is effectively filed would be motivated to use acoustic sensors for anomaly detection as taught by GEIB in an automated production environment as taught by BIRKHOFER because acoustic analysis provides a sophisticated way to diagnose faults in complex machines (col. 2 line 56-col. 3 line 9 of GEIB), and automated redirection of faulty product uses time and production resources more efficiently (para. 56-57 of BIRKHOFER), saving material and labor costs.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. QUANCI et al. (US 20170352243) teaches a PLC system that monitors an industrial process with sensors and automatically generates remedial action based on the sensor data.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to NATHANIEL J KOLB whose telephone number is (571)270-7601. The examiner can normally be reached M-F 9-5 EST.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, JESSICA HAN can be reached at (571) 272-2078. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/NATHANIEL J KOLB/Examiner, Art Unit 2896
Prosecution Insights