DETAILED ACTION
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Starting 01/09/2025, Examiner attempted to contact Applicant’s representative multiple times by phone and left voicemails to propose Examiner’s amendment to advance prosecution, however, without a return call.
Claims 1-2, 4-5, 7-9, 11-13, 19-20, 23-25, 28 and 63-66 are pending.
Claims 3, 6, 10, 14-18, 21-22, 26-27 and 29-62 are cancelled.
Response to Amendment
Applicant’s amendments to the claims have overcome each and every objections previously set forth. The objections of the claims have been withdrawn.
Response to Arguments
Applicant’s arguments with respect to the 102 and 103 rejections (see Amendment, Page 8), are directed to that “the emitter is designed to produce the signal just for mapping,” and that “Salonidis and Trayhan do not teach this and do not mention that their sound emitters can be speakers or buzzers.”
Examiner respectfully submits that Salonidis teaches a machine that converts electrical energy into an acoustic energy, which reads on a “speaker”, as described in at least Column 14 lines 8-15 (“In another example, the machines may be recording boards, mixing consoles, sound amplifiers, recording devices, etc., which may operate, for example, as a recording studio. The machines may include electrical components, such as, for example, integrated circuits, printed circuit boards, and the like, and may further include various types of active electrical component such as, for example, transistors, resistors, diodes, and the like.”).
Further, Examiner respectfully submits that Salondis teaching the sensing unit taking readings of acoustic signal of a machine to generate the acoustic signal spatialization map reads on “the emitter is designed to produce the signal … for mapping”, as described in at least Column 2, lines 56-61 (“Embodiments of the present invention are directed to a monitoring system that monitors machines in a machine room, and which can detect and determine a condition of a machine's operation performance on the basis of machine-emitted acoustic or electromagnetic signals, in order to perform targeted corrective action.”), and Column 6, lines 57-62 (“Data collection points 190, as depicted in FIG. 1, represent positions within machine room enclosure 101 at which sensing unit 220 may take readings of acoustic signals, for use in generating acoustic signal spatialization maps, or representations of an operating environment such as machine room enclosure 101.”). While Salondis does not expressly teach that the machine is designed to produce signal just for the mapping, claim 1 also does not recite that “the emitter is designed to produce the signal just for mapping.” (emphasis added) Nothing in the claim recites that the bounding of the usage of the emitter that is just for mapping. A broad and reasonable construction of the claim 1 reciting “… using an emitter to emit a first acoustic test signal; storing a first acoustic map indicative of an acoustic transfer function between the first location and the second location; using the emitter to emit a second acoustic test signal; determining a second acoustic map …”, does not require that such bounding be performed prior to the emitting operation. If Applicant desires to distinguish such recitation from the teachings of Salondis, the Examiner respectfully recommends incorporating such recitation into the claim 1.
For foregoing reasons, Applicant’s arguments are not deemed persuasive, and accordingly, the 102 and 103 rejections are maintained.
Claim Objections
The following claims are objected to for informalities, lack of antecedent support, or for redundancies. The Examiner recommends the following changes:
Claim 1, line 2, replace “which emitter” with “which the emitter”
Claim 19, line 2, replace “which emitter” with “which the emitter”
Appropriate correction is respectfully requested.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(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 1-2, 4-5, 7-9, 12-13, 19-20, 23-25 and 28 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Salonidis et al. (US 9,892,744 B1) (“Salonidis”).
Regarding independent claim 1, Salonidis teaches:
A method of acoustic mapping, the method comprising: (Salonidis: Abstract “Monitoring a plurality of machines located in an operating environment. First and second acoustic signal readings and their respective detecting locations are received from a sensing device. First and second acoustic signal spatialization map containing characteristic data signatures for the machines are generated based on the first and second acoustic signal readings. One or more differences are determined that exceed a predetermined threshold value, between corresponding characteristic data signatures in each of the first and second acoustic signal spatialization maps. At least one of the machines that are associated with the determined differences is identified. A corrective action to perform on the machine is identified, based on the determined one or more differences. Commands are transmitted to a corrective action module in the operating environment to cause the corrective action module to perform the corrective action.”) [Generating the acoustic signal spatialization map containing the characteristic data signatures reads on “[a] method of acoustic mapping”.]
using an emitter to emit a first acoustic test signal, which emitter is disposed at a first location in an enclosure; using a sensor to measure a first acoustic response corresponding to the first acoustic test signal, wherein the sensor is disposed at a second location; (Salonidis: FIG. 1) (Salonidis Column 1, line 59 – Column 2, line 2 “Embodiments of the invention are directed to a method, system, and computer program product for monitoring a plurality of machines located in an operating environment. A first acoustic signal readings and their respective detecting locations is received from a sensing device over a network to a computing system. A first acoustic signal spatialization map containing characteristic data signatures is generated by the computing system, based on the first acoustic signal readings and their respective detecting locations, each of the characteristic data signatures being associated with one or more of the plurality of machines.”) (Salonidis: Column 2, lines 56-61 “Embodiments of the present invention are directed to a monitoring system that monitors machines in a machine room, and which can detect and determine a condition of a machine's operation performance on the basis of machine-emitted acoustic or electromagnetic signals, in order to perform targeted corrective action.”) (Salonidis: Column 6, lines 57-62 “Data collection points 190, as depicted in FIG. 1, represent positions within machine room enclosure 101 at which sensing unit 220 may take readings of acoustic signals, for use in generating acoustic signal spatialization maps, or representations of an operating environment such as machine room enclosure 101.”) [Any one of the operated machines 102s, as illustrated in FIG. 1, that emits acoustic signals reads on “an emitter”, and its location in the machine room enclosure 101, as illustrated in FIG. 1, reads on “which emitter is disposed at a first location in an enclosure”. The first acoustic signal readings and its respective detecting location to generate the first acoustic signal spatialization map containing the characteristic data signatures reads on “using a sensor to measure a first acoustic response”, and the corresponding emitted acoustic signal by the operated machine reads on “a first acoustic test signal”. The combination of any one of the data collection points located in the machine room enclosure 101 and the sensing unit 220 reads on “a sensor… is disposed at a second location”.]
storing a first acoustic map indicative of an acoustic transfer function between the first location and the second location; (Salonidis: Column 1, line 59 – Column 2, line 2 as discussed above) (Salonidis: Column 9, line 45 – Column 10, line 9 “Data characterization and correlation module 244 receives sets of acoustic reading data files for an initial phase and a later monitoring phase, and, for each set of acoustic reading data files, generates an acoustic signal spatialization map which includes characteristic data signatures for each monitored machine 102, and an associated location of each machine in machine room enclosure 101. FIG. 3 is a functional block diagram depicting the data characterization and correlation module 244 of FIG. 2, in accordance with an embodiment of the present invention. Data characterization and correlation module 244 includes data characterization program 302, signature formulation program 304, signature to source location mapping program 306, and signature to machine identity mapping program 308. Data characterization program 302 receives sets of acoustic reading data files, each data file containing signal data and metadata, as previously described, and performs digital signal processing on the signal data and metadata contained in each acoustic reading data file to produce characterized signal data. Data characterization program 302 may perform various types of computations in order to characterize the signal data in terms of one or more time- and/or frequency-domain characteristics of the signal data, or temporal and/or spectral features of the signal data, respectively. Signal data may also be characterized with the time- and/or frequency-domain characteristics, in terms of one or more time-frequency domain characteristics, and/or in terms of one or more statistical measurements. The statistical measurements may be based on the signal data, or based on any of the time- and/or frequency-domain characteristics, or time-frequency domain characteristics of the signal data.”) (Salonidis: Column 12, line 66 – Column 13, line 2 “Data storage 250 may operate to store all data regarding characterized data signatures, as well as associated data and other related data, for retrieval and use by machine operation monitor program 240 and any of its associated modules.”) [The acoustic signal spatialization map containing the characteristic data signatures from the first acoustic signal readings, represented as frequency-domain characteristics reads on “a first acoustic map indicative of an acoustic transfer function …”.]
using the emitter to emit a second acoustic test signal; measuring a second acoustic response corresponding to the second acoustic test signal; determining a second acoustic map; and (Salonidis: Column 2, lines 2-10 “A second acoustic signal readings and their respective detecting locations is received from the sensing device over the network to the computing system. A second acoustic signal spatialization map containing characteristic data signatures is generated by the computing system, based on the second acoustic signal readings and their respective detecting locations, each of the characteristic data signatures being associated with one or more of the plurality of machines.”) [The second acoustic signal readings and its respective detecting location to generate the second acoustic signal spatialization map containing the characteristic data signatures reads on “measuring a second acoustic response … determining a second acoustic map”, and the corresponding emitted acoustic signal by the operated machine reads on “a second acoustic test signal”.]
generating a notification and/or a report when a difference between the second acoustic map and the first acoustic map is greater than a threshold, wherein the emitter is a speaker or a buzzer. (Salonidis: Column 2, lines 10-25 “One or more differences is determined by the computing system that exceeds a predetermined threshold value, between one or more characteristic data signatures in the first acoustic signal spatialization map and corresponding one or more characteristic data signatures in the second acoustic signal spatialization map. At least one of the plurality of machines that are associated with the determined differences is identified. A corrective action to perform on a machine of the plurality of machines is identified by the computing system, based on the determined one or more differences in the generated first and second acoustic signal spatialization maps. Commands are transmitted by the computing system to a corrective action module in the operating environment to cause the corrective action module to perform the identified corrective action.”) (Salonidis: Column 9, lines 22-32 “Machine operation monitor program 240, residing on server 230, represents a computer program which receives and processes data generated by sensing unit 220 to determine conditions as to operation performance of each machine being monitored, and to determine and generate commands to perform corrective action on any of the machines being monitored, accordingly. Machine operation monitor program 240 includes data collection module 242, data characterization and correlation module 244, data anomaly detection module 246, corrective action module 248, and data storage 250.”) (Salonidis: Column 12 lines 57-65 “For example, machine-emitted acoustic signals indicative of a wearing bearing, as identified by data anomaly detection module 246, may cause corrective action module 248 to determine and communicate an appropriate corrective action to perform on the machine to either of CAMs 130A-B, where such corrective action might involve, for example, lubrication of the wearing bearing, or deactivation of the machine, by either of CAMs 130A-B.”) (Salonidis: Column 14 lines 8-15 “In another example, the machines may be recording boards, mixing consoles, sound amplifiers, recording devices, etc., which may operate, for example, as a recording studio. The machines may include electrical components, such as, for example, integrated circuits, printed circuit boards, and the like, and may further include various types of active electrical component such as, for example, transistors, resistors, diodes, and the like.”) [Generating the commands and communicating them to perform corrective actions reads on “generating a notification and/or a report …”. The predetermined threshold value reads on “a threshold”. The machine that uses electrical energy/power, and generates and emits acoustic or electromagnetic signals reads on “a speaker”.]
Regarding claim 2, Salonidis teaches all the claimed features of claim 1. Salonidis further teaches:
controlling at least one apparatus in the enclosure and/or in a facility in which the enclosure is disposed, (Salonidis: Column 8, line 61 – column 9, line 21 “Corrective action performed by CAMs 130A-B may take the form of, for example, lubrication of a machine's bearings or other parts, fastening or replacement of loosened connections between parts or portions of the machine, varying of the machine's operating parameters, such as, for example, varying a drive motor output speed of the machine, or shutting the machine down. Other forms of corrective action might involve, for example, electrical or radio-frequency communication with the machine or components of the machine, so as to vary or control, for example, factors relating to any of the machine's operational parameters. Such corrective action might involve, for example, lubricating one or more components of the machine by opening and closing a valve of the machine, or varying a drive motor output speed of the machine. CAMs 130A-B may include trouble ticketing systems or software database logging systems, which may be communicated between each of CAMs 130A-B, in which a corrective action to be performed may be noted and stored, in order to allow for later performance of the corrective action. For example, CAMs 130A-B may note and store corrective actions to be performed at a later time, when commands regarding the corrective actions are received at a time when CAMs 130A-B are occupied with the performance of other corrective actions. Generally, a corrective action may involve any action or sequence of actions that affect a machine's operation performance, with respect to, for example, maximizing efficiency or reliability, or minimizing unscheduled downtime.”)
wherein the emitter is operatively coupled to a control system; and (Salonidis: FIG. 2) [Combination of the CAM 130 and the SERVER 230 reads on “a control system”. See the operative coupling of the MACHINE 102, the CAM 130 and the SERVER 230, as illustrated in FIG. 2]
the controlling is by the control system. (Salonidis: Column 8, line 61 – column 9, line 21 as discussed above) (Salonidis: Column 8, lines 20-47 “Corrective action modules (CAMs) 130A-B represent devices which perform corrective action on monitored machines. CAMs 130A-B may include internal and external hardware components, as well as network communications components, as depicted and described in further detail below with reference to FIG. 5, allowing them to receive commands from server 230, as will be described in further detail below. CAMs 130A-B may include one or more onboard or remote control systems, as previously described in connection with autonomous spatial positioning and orientation of sensing unit 220, allowing them to execute and perform corrective actions according to received commands, as will be described in further detail below. CAM 130A may include navigation capability, allowing for selective mobile positioning of CAM 130A according to received commands. CAMs 130A-B may include one or more rotatably and/or pivotally attached, cantilevered robotic arms with a number of degrees of freedom, usable to perform corrective action. The one or more robotic arms of the devices may include one or more end effectors, or components usable for grasping, gripping, or otherwise manipulating a machine part, and may also include, for example, one or more electrical or electromechanical devices usable for interfacing, or otherwise interacting, with a machine, such as, for example, by way of radio-frequency communication. Generally, means to perform corrective action by CAMs 130A-B may take on any form that might allow for physical or other interaction with monitored machines.”)
Regarding claim 4, Salonidis teaches all the claimed features of claims 1-2. Salonidis further teaches:
wherein the at least one apparatus comprises a lighting device, a tintable window, another sensor, another emitter, a media display, a dispenser, a processor, a power source, a security system, a fire alarm system, a sound media, a heater, a cooler, a vent, or a heating ventilation and air conditioning system (HVAC). (Salonidis: Column 3, lines 38-50 “In an exemplary embodiment of the present invention, machines 102 and certain components of air handling unit 110 and water control and distribution unit 170 represent, for example, machinery, with rotating and/or reciprocating components, used in the operation of a building's power and utility systems. These types of systems might include, for example, power storage or generation systems, cooling and heating systems, or other types of systems, and might involve the use of machines such as, for example, inertial flywheels, transformers, generators, dynamos, alternators, prime movers such as diesel or gasoline engines, motors, turbines, and fluid conveyors and regulators such as pumps, compressors, fans, and valves.”)
Regarding claim 5, Salonidis teaches all the claimed features of claim 1. Salonidis further teaches:
using the emitter to emit sounds including discrete sounds of a sound spectrum. (Salonidis: Column 13, lines 14-32 “Data characterization program 302, of data characterization and correlation module 244, receives signal data and metadata from data collection module 242, and generates characterized signal data accordingly, in terms of a number of temporal and/or spectral features of the signal data, in order to identify and distinguish between each distinct acoustic signal composing the received signal data (step 404). Data characterization and correlation module 244 may further associate and index such temporal and spectral features with the produced characterized signal data. Signature formulation program 304 receives characterized signal data, and defines and generates characteristic data signatures according to a number of the produced temporal and spectral features of the characterized signal data, by using certain temporal and/or spectral features of the received characterized signal data (step 406). Signature formulation program 304 may associate and index the selected temporal and/or spectral features to the defined characteristic data signatures for further processing.”)
Regarding claim 7, Salonidis teaches all the claimed features of claim 1. Salonidis further teaches:
using the emitter to emit the first acoustic test signal and/or the second acoustic test signal when the enclosure is non-inhabited. (Salonidis: Column 19, lines 6-9 “On-demand self-service: a cloud consumer can unilaterally provision computing capabilities, such as server time and network storage, as needed automatically without requiring human interaction with the service's provider.”)
Regarding claim 8, Salonidis teaches all the claimed features of claim 1. Salonidis further teaches:
using the emitter to emit the second acoustic test signal according to a schedule that considers a change in a Building Information Modeling file of the enclosure or of a facility in which the enclosure is disposed. (Salonidis: Column 7, lines 35-47 “In an exemplary embodiment of the present invention, acoustic signal readings by sensing unit 220 may be performed initially to produce a high accuracy spatialization map. In an exemplary embodiment, between 6 and 60 acoustic signal readings may be taken by sensing unit 220 for this initial phase. In later monitoring phases, fewer acoustic readings may be taken, for example, as few as two. However, as described in more detail below, taking fewer acoustic readings may produce a less accurate spatialization map. Generally, the data collection points 190 at which sensing unit 220 takes acoustic signal readings should allow for the received acoustic signals to include machine-emitted acoustic signals from each of the monitored machines.”) (Salonidis: Column 14, lines 16-34 “Generally, these types of machines may emit electromagnetic radiation or signals due to the nature of their operation. The nature of emitted electromagnetic signals from these types of machines may be affected by factors such as, for example, those relating to an operating environment of a machine, the machine's overall design, the materials used in the machine's construction, and may vary from machine to machine. These types of machines may produce and emit electromagnetic signals which, with continued operation and use, may change in terms of character over time due to, for example, degradation of a machine in the form of “wear and tear” of certain of the machine's components, such as, for example, one or more electrical components of the machine, as previously described. For these types of machines, the detection and characterization of machine-emitted electromagnetic signals, and any changes relating to the machine-emitted electromagnetic signals over time, can be used as a basis for monitoring and determining conditions of a machine's operation performance.”) [The affecting factors of the emitted signals by the machine, such as the operating environment and the machine degradation, reads on “a change in a Building Information Modeling file”. The monitoring incrementally at designated phases reads on “a schedule”.]
Regarding claim 9, Salonidis teaches all the claimed features of claim 1. Salonidis further teaches:
wherein measurement of the second acoustic response is by a same sensor measuring the first acoustic response. (Salonidis: Abstract as discussed in claim 1) [The first and the second acoustic signal readings from the sensing device reads on “… by the same sensor …”.]
Regarding claim 12, Salonidis teaches all the claimed features of claim 1. Salonidis further teaches:
wherein the emitter is a first emitter, and wherein the method further comprises: using a second emitter at a third location to emit a third acoustic test signal; measuring a third acoustic response corresponding to the third acoustic test signal; and (Salonidis: FIG. 1) (Salonidis: Column 1, line 59 – Column 2, line 2; Column 2, lines 56-61; and Column 2, lines 2-10, as discussed in claim 1) [The operated machine as discussed in claim 1 reads on “a first emitter”. Another one of the operated machines 102s, as illustrated in FIG. 1, that emits acoustic signals reads on “a second emitter”, and its location in the machine room enclosure 101, as illustrated in FIG. 1, reads on “at a third location”. The second acoustic signal readings of the another one of the operated machines and its respective detecting location to generate the acoustic signal spatialization map containing the characteristic data signatures reads on “measuring a third acoustic response”, and the corresponding emitted acoustic signal by the operated machine reads on “a third acoustic test signal”.]
comparing the third acoustic response to the acoustic response to the second acoustic test signal to detect a fault in the sensor, in the first emitter, or in the second emitter. (Salonidis: Column 2, lines 10-25; Column 9, lines 22-32; and Column 12 lines 57-65, as discussed in claim 1) [Determining the difference between the first and the second acoustic signal spatialization map reads on “comparing … third … to … second …”. The difference indicating anomaly, such as, worn bearing of the machine, reads on “to detect a fault … in the second emitter.”]
Regarding claim 13, Salonidis teaches all the claimed features of claim 1. Salonidis further teaches:
detecting an irregular sound event in the enclosure utilizing a plurality of sensors that include the sensor; compensating the detected sound event according to a corresponding acoustic transfer function from the first acoustic map and/or the second acoustic map; recognizing an event type utilizing the compensated detected sound event; and generating a notification of the event type to a user. (Salonidis: Column 2, lines 10-25; Column 9, lines 22-32; and Column 12 lines 57-65, as discussed in claim 1) (Salonidis: Column 12, lines 12-25 “Data anomaly detection module 246 receives and compares acoustic signal spatialization maps, generated based on sets of acoustic reading data files, for example, acoustic reading data files for an initial phase and a later monitoring phase, as previously described, to detect differences between corresponding characteristic data signatures contained in each of the acoustic signal spatialization maps, respectively, which exceed a predetermined threshold value. Data anomaly detection module 246 may detect differences in corresponding characteristic data signatures by comparing corresponding pairs of acoustic signal spatialization maps, and computing differences in numerical values, as previously described, representative of the corresponding characteristic data signatures.”) [Determining the difference between the first and the second acoustic signal spatialization map to determine anomaly reads on “detecting an irregular sound event”. Comparing the difference with the predetermined threshold reads on “compensating the detected sound event according to a corresponding … the first acoustic map and/or the second acoustic map”. The difference beyond the predetermined threshold indicating anomaly, such as, worn bearing of the machine, reads on “recognizing an event type.” Generating the commands and communicating them to perform corrective actions for the anomaly by CAM 130 or the machines reads on “generating a notification”. The CAM 130 or the machine that uses the commands to perform the corrective actions reads on “a user”.]
Regarding independent claim 19, Salonidis teaches:
A method of acoustic mapping, the method comprising: (Salonidis: Abstract “Monitoring a plurality of machines located in an operating environment. First and second acoustic signal readings and their respective detecting locations are received from a sensing device. First and second acoustic signal spatialization map containing characteristic data signatures for the machines are generated based on the first and second acoustic signal readings. One or more differences are determined that exceed a predetermined threshold value, between corresponding characteristic data signatures in each of the first and second acoustic signal spatialization maps. At least one of the machines that are associated with the determined differences is identified. A corrective action to perform on the machine is identified, based on the determined one or more differences. Commands are transmitted to a corrective action module in the operating environment to cause the corrective action module to perform the corrective action.”) [Generating the acoustic signal spatialization map containing the characteristic data signatures reads on “[a] method of acoustic mapping”.]
using an emitter to emit an acoustic test signal, which emitter is disposed at a first location in an enclosure; using a sensor to measure an acoustic response corresponding to the acoustic test signal, wherein the sensor is disposed at a second location; and (Salonidis: FIG. 1) (Salonidis Column 1, line 59 – Column 2, line 2 “Embodiments of the invention are directed to a method, system, and computer program product for monitoring a plurality of machines located in an operating environment. A first acoustic signal readings and their respective detecting locations is received from a sensing device over a network to a computing system. A first acoustic signal spatialization map containing characteristic data signatures is generated by the computing system, based on the first acoustic signal readings and their respective detecting locations, each of the characteristic data signatures being associated with one or more of the plurality of machines.”) (Salonidis: Column 2, lines 56-61 “Embodiments of the present invention are directed to a monitoring system that monitors machines in a machine room, and which can detect and determine a condition of a machine's operation performance on the basis of machine-emitted acoustic or electromagnetic signals, in order to perform targeted corrective action.”) (Salonidis: Column 6, lines 57-62 “Data collection points 190, as depicted in FIG. 1, represent positions within machine room enclosure 101 at which sensing unit 220 may take readings of acoustic signals, for use in generating acoustic signal spatialization maps, or representations of an operating environment such as machine room enclosure 101.”) [Any one of the operated machines 102s, as illustrated in FIG. 1, that emits acoustic signals reads on “an emitter”, and its location in the machine room enclosure 101, as illustrated in FIG. 1, reads on “which first emitter is disposed at a first location in an enclosure”. The first acoustic signal readings and its respective detecting location to generate the first acoustic signal spatialization map containing the characteristic data signatures reads on “using a sensor to measure a first acoustic response”, and the corresponding emitted acoustic signal by the operated machine reads on “a first acoustic test signal”. The combination of any one of the data collection points located in the machine room enclosure 101 and the sensing unit 220 reads on “a sensor is disposed at a second location”.]
using information pertaining to an inanimate alteration to generate an acoustic map indicative of an acoustic transfer function between the first location and the second location, which inanimate alteration is projected to affect the acoustic mapping of the enclosure; wherein the emitter is a speaker or a buzzer. (Salonidis: Column 2, lines 2-10 “A second acoustic signal readings and their respective detecting locations is received from the sensing device over the network to the computing system. A second acoustic signal spatialization map containing characteristic data signatures is generated by the computing system, based on the second acoustic signal readings and their respective detecting locations, each of the characteristic data signatures being associated with one or more of the plurality of machines.”) (Salonidis: Column 9, line 45 – Column 10, line 9 “Data characterization and correlation module 244 receives sets of acoustic reading data files for an initial phase and a later monitoring phase, and, for each set of acoustic reading data files, generates an acoustic signal spatialization map which includes characteristic data signatures for each monitored machine 102, and an associated location of each machine in machine room enclosure 101. FIG. 3 is a functional block diagram depicting the data characterization and correlation module 244 of FIG. 2, in accordance with an embodiment of the present invention. Data characterization and correlation module 244 includes data characterization program 302, signature formulation program 304, signature to source location mapping program 306, and signature to machine identity mapping program 308. Data characterization program 302 receives sets of acoustic reading data files, each data file containing signal data and metadata, as previously described, and performs digital signal processing on the signal data and metadata contained in each acoustic reading data file to produce characterized signal data. Data characterization program 302 may perform various types of computations in order to characterize the signal data in terms of one or more time- and/or frequency-domain characteristics of the signal data, or temporal and/or spectral features of the signal data, respectively. Signal data may also be characterized with the time- and/or frequency-domain characteristics, in terms of one or more time-frequency domain characteristics, and/or in terms of one or more statistical measurements. The statistical measurements may be based on the signal data, or based on any of the time- and/or frequency-domain characteristics, or time-frequency domain characteristics of the signal data.”) (Salonidis: Column 7, lines 35-47 “In an exemplary embodiment of the present invention, acoustic signal readings by sensing unit 220 may be performed initially to produce a high accuracy spatialization map. In an exemplary embodiment, between 6 and 60 acoustic signal readings may be taken by sensing unit 220 for this initial phase. In later monitoring phases, fewer acoustic readings may be taken, for example, as few as two. However, as described in more detail below, taking fewer acoustic readings may produce a less accurate spatialization map. Generally, the data collection points 190 at which sensing unit 220 takes acoustic signal readings should allow for the received acoustic signals to include machine-emitted acoustic signals from each of the monitored machines.”) (Salonidis: Column 14, lines 16-34 “Generally, these types of machines may emit electromagnetic radiation or signals due to the nature of their operation. The nature of emitted electromagnetic signals from these types of machines may be affected by factors such as, for example, those relating to an operating environment of a machine, the machine's overall design, the materials used in the machine's construction, and may vary from machine to machine. These types of machines may produce and emit electromagnetic signals which, with continued operation and use, may change in terms of character over time due to, for example, degradation of a machine in the form of “wear and tear” of certain of the machine's components, such as, for example, one or more electrical components of the machine, as previously described. For these types of machines, the detection and characterization of machine-emitted electromagnetic signals, and any changes relating to the machine-emitted electromagnetic signals over time, can be used as a basis for monitoring and determining conditions of a machine's operation performance.”) (Salonidis: Column 14 lines 8-15 “In another example, the machines may be recording boards, mixing consoles, sound amplifiers, recording devices, etc., which may operate, for example, as a recording studio. The machines may include electrical components, such as, for example, integrated circuits, printed circuit boards, and the like, and may further include various types of active electrical component such as, for example, transistors, resistors, diodes, and the like.”) [The affecting factors of the emitted signals by the machine, such as the operating environment and the machine degradation over time, reads on “using information pertaining to an inanimate alteration”. The second acoustic signal spatialization map containing characteristic data signatures, represented as frequency-domain characteristics, reads on “an acoustic transfer function …”. The machine that uses electrical energy/power, and generates and emits acoustic or electromagnetic signals reads on “a speaker”.]
Regarding claim 20, Salonidis teaches all the claimed features of claim 19. Salonidis further teaches:
using the emitter to emit the acoustic test signal according to a schedule. (Salonidis: Column 7, lines 35-47; and Column 14, lines 16-34, as discussed in claim 19) [The monitoring incrementally at designated phases reads on “a schedule”.]
Regarding claim 23, Salonidis teaches all the claimed features of claim 19. Salonidis further teaches:
wherein the sensor is a first sensor, and wherein the method further comprises using a second sensor to measure at least one other acoustic response corresponding to the acoustic test signal, which second sensor is disposed at a third location different from the second location. (Salonidis: FIG. 1) (Salonidis Column 1, line 59 – Column 2, line 2; Column 2, lines 56-61; and Column 6, lines 57-62, as discussed in claim 19) [The combination of any other data collection points located in the machine room enclosure 101 and the sensing unit 220 reads on “a second sensor … disposed at a third location different from the second location”.]
Regarding claim 24, Salonidis teaches all the claimed features of claim 19. Salonidis further teaches:
wherein the information comprises a shape, or a material property of one or more fixtures. (Salonidis: Column 12 lines 57-65 “For example, machine-emitted acoustic signals indicative of a wearing bearing, as identified by data anomaly detection module 246, may cause corrective action module 248 to determine and communicate an appropriate corrective action to perform on the machine to either of CAMs 130A-B, where such corrective action might involve, for example, lubrication of the wearing bearing, or deactivation of the machine, by either of CAMs 130A-B.”)
Regarding claim 25, Salonidis teaches all the claimed features of claim 19. Salonidis further teaches:
wherein the inanimate alteration is of one or more fixtures and/or non-fixtures. (Salonidis: Column 12 lines 57-65 “For example, machine-emitted acoustic signals indicative of a wearing bearing, as identified by data anomaly detection module 246, may cause corrective action module 248 to determine and communicate an appropriate corrective action to perform on the machine to either of CAMs 130A-B, where such corrective action might involve, for example, lubrication of the wearing bearing, or deactivation of the machine, by either of CAMs 130A-B.”)
Regarding claim 28, Salonidis teaches all the claimed features of claim 19. Salonidis further teaches:
wherein generation of the acoustic map utilizes information of sound frequency sweeping, location, and coordination, of the emitter, of the sensor, of the at least one other emitter, and/or of the at least one sensor. (Salonidis: Abstract “Monitoring a plurality of machines located in an operating environment. First and second acoustic signal readings and their respective detecting locations are received from a sensing device. First and second acoustic signal spatialization map containing characteristic data signatures for the machines are generated based on the first and second acoustic signal readings. One or more differences are determined that exceed a predetermined threshold value, between corresponding characteristic data signatures in each of the first and second acoustic signal spatialization maps. At least one of the machines that are associated with the determined differences is identified. A corrective action to perform on the machine is identified, based on the determined one or more differences. Commands are transmitted to a corrective action module in the operating environment to cause the corrective action module to perform the corrective action.”) (Salonidis: Column 9, line 45 – Column 10, line 9 “Data characterization and correlation module 244 receives sets of acoustic reading data files for an initial phase and a later monitoring phase, and, for each set of acoustic reading data files, generates an acoustic signal spatialization map which includes characteristic data signatures for each monitored machine 102, and an associated location of each machine in machine room enclosure 101. FIG. 3 is a functional block diagram depicting the data characterization and correlation module 244 of FIG. 2, in accordance with an embodiment of the present invention. Data characterization and correlation module 244 includes data characterization program 302, signature formulation program 304, signature to source location mapping program 306, and signature to machine identity mapping program 308. Data characterization program 302 receives sets of acoustic reading data files, each data file containing signal data and metadata, as previously described, and performs digital signal processing on the signal data and metadata contained in each acoustic reading data file to produce characterized signal data. Data characterization program 302 may perform various types of computations in order to characterize the signal data in terms of one or more time- and/or frequency-domain characteristics of the signal data, or temporal and/or spectral features of the signal data, respectively. Signal data may also be characterized with the time- and/or frequency-domain characteristics, in terms of one or more time-frequency domain characteristics, and/or in terms of one or more statistical measurements. The statistical measurements may be based on the signal data, or based on any of the time- and/or frequency-domain characteristics, or time-frequency domain characteristics of the signal data.”)
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, 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 11 is rejected under 35 U.S.C. 103 as being unpatentable over Salonidis, in view of Trayhan, JR. et al. (US 2018/0011059 A1) (“Trayhan”).
Regarding claim 11, Salonidis teaches all the claimed features of claim 1. Salonidis does not expressly teach the recitations of claim 11.
Trayhan teaches:
wherein the sensor is a first sensor, and wherein the method further comprises: using a second sensor disposed at a third location to measure a third acoustic response to the second acoustic test signal, wherein the second acoustic response measured is sensed at the second location by the second sensor; and comparing the second acoustic response and the third acoustic response to detect a fault in the emitter or in one of the sensors. (Trayhan: [0027] “In some cases, computing device 200 can combine its analysis of acoustic signature(s) 166 with a positional analysis of other data to identify unknown component(s) 102, 104, 110, 126, 128, within industrial plant 152 which produce anomalous sounds within acoustic signature(s) 166. For example, where detected acoustic signature(s) 166 include anomalous soundwaves produced by an unknown component 102, 104, 110, 126, 128, computing device 200 can identify one or more specific acoustic sensors 164 and/or areas 156, 158, 160 where such sounds were detected. Computing device 200 can cross-reference these determinations with positional data of industrial plant 152 to identify one or more components 102, 104, 110, 126, 128 within industrial plant 152 which may have produced the anomalous sounds in acoustic signature 166. For example, computing device 200 can determine that two acoustic signatures 166 detected by two acoustic sensors 164 detected soundwaves with an amplitude exceeding a baseline amplitude for a particular operation. Computing device 200 can then determine that the two acoustic sensors 164 are proximal to, e.g., area 156 but acoustically isolated from areas 158, 160 of industrial plant 152. Computing device 200 can then derive a source of the anomalous sound by triangulating the position two or more acoustic sensors 164 with component 102 and/or a subcomponent thereof within area 156 of industrial plant 152. In some cases, possible sources of the anomalous sound(s) in acoustic signature(s) 166 can be ruled out by comparing characteristic properties of the anomalous sound(s) with example frequencies, amplitudes, etc., of baseline acoustic signatures. Where one or more acoustic sensors 164 are direction-sensitive (e.g., by including one or more directional microphones therein), computing device 200 can further locate and/or identify components 102, 104, 110 126, 128 which correspond to acoustic signature(s) 166 based on the directional orientation and/or sensitivity of each acoustic sensor 164.”) [One of the two acoustic sensors 164 reads on “a first sensor”, and the other one of the two acoustic sensors 164 located proximately reads on “a second sensor disposed at a third location”. Comparing the characteristic properties of the two acoustic signatures 166 from the two acoustic sensors 164 reads on “comparing …”, and identifying the component of interest emitting the anomalous sound reads on “… to detect a fault in the emitter”.]
Accordingly, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, having the teachings of Salonidis and Trayhan before them, to modify the use of multiple data collection points of the sensor assembly to monitor the acoustics of the machines in the enclosure, to incorporate using the relative positional information of the multiple data collection points of the sensor assembly to identify a specific machine.
One of ordinary skill in the art before the effective filing date of the claimed invention would have been motivated to do this modification because it would allow for identifying a machine that may be unknown. (Trayhan: [0027])
Allowable Subject Matter
Claims 63-66 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.
As allowable subject matter has been indicated, applicant's reply must either comply with all formal requirements or specifically traverse each requirement not complied with. See 37 CFR 1.111(b) and MPEP § 707.07(a).
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). 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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MICHAEL W CHOI whose telephone number is (571)270-5069. The examiner can normally be reached Monday-Friday 8am-5pm.
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, Kenneth Lo can be reached at (571) 272-9774. 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.
/MICHAEL W CHOI/ Primary Examiner, Art Unit 2116