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 Amendment
Claims 1, 14 and 16-17 are amended.
Claims 18-20 are canceled.
Claims 1-17 are pending.
Claim Rejections - 35 USC § 112
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.
Claims 1 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.
Claim 1 recites “a trigger event indicative of a change in at least one operational parameter of the machine” and “wherein the trigger event comprises a detected change in the one or several operational parameters of the running machine”. It is unclear if the “a change in at least one operational parameter of the machine” and “a detected change in the one or several operational parameters of the running machine” are referring to the same or a different change of the machine, as the trigger event is “indicative of a change”, but also comprises “a detected change” presumably the indication is of the detected change, but could also be interpreted as two separate and distinct changes. For the purposes of examining, they are interpreted to be the same change in light of Page 14 Lines 9-12 of applicant’s specification. In addition, examiner recommends consistency with the language “at least one operational parameter” and “the one or several operational parameters” for clarity.
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.
Claims 1-17 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
The claim 1 and similarly 14 recite(s) “transforming, by the at least one processing device, the sensor data into a frequency domain to generate a spectral representation of the sensor data influenced by the one or several operational parameters of the machine; generating, by the at least one processing device, a first compressed sensor data representation based on applying a first compression technique to the spectral representation of the sensor data;”, “in response to receiving the trigger event, modifying the first compression technique to a second compression technique; generating, by the at least one processing device, a second compressed sensor data representation based on applying the second compression technique to the spectral representation of the sensor data at a time the corresponding sensor data was captured;”, “one or several operational parameters of a machine having a rotating component or having a reciprocating component, the one or several operational parameters of the machine influencing the sensor data and being distinct from the sensor data;”, and “wherein an applied compression technique including the first compression technique and the second compression technique is dependent on the one or several received operational parameters of the machine that is running, wherein the trigger event comprises a detected change in the one or several operational parameters of the running machine.” are considered to be mental processes and/or mathematical concepts (examiner notes that while an “improved, particular method of digital data compression” is sufficient to show an improvement in existing technology, looking at the applicants claims in light of the specification, the applicant’s invention appears to be directed to applying known digital data compression technique based on what is considered to be mental processes and/or mathematical algorithms (as can be seen on page 14 Lines 16-17 “adjust the compression, i.e. the mathematical operations”), rather than a particular method of digital data compression).
This judicial exception is not integrated into a practical application because “processing sensor data for transmission”, “receiving, by the at least one processing device, a trigger event indicative of a change in at least one operational parameter of the machine;”, “transmitting, by the at least one processing device, a compressed sensor data representations comprising the first compressed sensor data representation and the second compressed sensor data representation to a computing device;” and “receiving, by the at least one processing device, a trigger event” are considered to be data gathering steps required to use the correlation do not add a meaningful limitation to the method as they are insignificant extra-solution activity. The elements of “at least one processing device”, “a computing device” and “an interface” are considered to be generically recited computer elements do not add a meaningful limitation to the abstract idea because they amount to simply implementing the abstract idea on a computer. The elements of “and wherein the applied compression technique is adjusted as the one or several operational parameters of the running machine vary” are considered to be generally linking the use of a judicial exception to a particular technological environment or field of use.
The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because “receiving, by the at least one processing device, a trigger event indicative of a change in at least one operational parameter of the machine;”, “transmitting, by the at least one processing device, a compressed sensor data representations comprising the first compressed sensor data representation and the second compressed sensor data representation to a computing device;” and “receiving, by the at least one processing device, a trigger event” are considered to be well understood routine and conventional per MPEP 2106.05(d)(i). The elements of “at least one processing device”, “a computing device” and “an interface” are considered to be well-understood, routine, conventional computer functions as recognized by the court decisions listed in MPEP § 2106.05(d). The elements of “and wherein the applied compression technique is adjusted as the one or several operational parameters of the running machine vary” are considered to be merely indicating a field of use or technological environment in which to apply a judicial exception do not amount to significantly more than the exception itself per MPEP 2106.05(h) and are well-understood, routine, and conventional activities/elements previously known to the industry per MPEP 2106.05(d) (see prior art of record).
Claims 2-6, 8-9, 11 and 15 are considered to be further directed to the abstract ideas cited above.
Claim 7 recites “analyzing, the compressed sensor data representation” is considered to be further directed to the abstract ideas cited above. This judicial exception is not integrated into a practical application because “receiving, the compressed sensor data representation” are considered to be data gathering steps required to use the correlation do not add a meaningful limitation to the method as they are insignificant extra-solution activity. The elements of “a data analytics computer” are considered to be generically recited computer elements do not add a meaningful limitation to the abstract idea because they amount to simply implementing the abstract idea on a computer.
The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because “receiving, the compressed sensor data representation” are considered to be well understood routine and conventional per MPEP 2106.05(d)(i). The elements of “a data analytics computer” are considered to be well-understood, routine, conventional computer functions as recognized by the court decisions listed in MPEP § 2106.05(d).
Claim 10 recites “wherein applying the compression technique comprises: applying an alignment transformation that is dependent on the rotation speed to the spectral representation of the sensor data to align the spectral representation of the sensor data with at least one reference spectrum of a set of reference spectra, and determining a set of decomposition coefficients of a linear decomposition of the spectral representation of the sensor data, wherein the set of decomposition coefficients is transmitted in the compressed sensor data representations” and “the one or several operational parameters comprise a rotation speed of the rotating component of the machine” are considered to be further directed to the abstract ideas cited above.
This judicial exception is not integrated into a practical application because “wherein the machine has a rotating component” is considered to be merely indicating a field of use or technological environment in which to apply a judicial exception.
The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because “wherein the machine has a rotating component” are considered to be well understood routine and conventional per MPEP 2106.05(d) (See US 20180188715 A1, US 20190033172 A1, and US 20190174207 A1).
Claims 12-13 and 16-17 recite “wherein the at least one processing device is a field sensor device”, “wherein the machine is a generator or a motor” and “a system, comprising: a machine having a rotating component, in particular a motor or a generator, or a machine having a reciprocating component;” which are not integrated into a practical application or include additional elements that are sufficient to amount to significantly more than the judicial exception because they are is considered to be merely indicating a field of use or technological environment in which to apply a judicial exception and are considered to be well understood routine and conventional per MPEP 2106.05(d) (See US 20180188715 A1, US 20190033172 A1, and US 20190174207 A1).
Claims 16 and similarly 17 recite “determine a compression technique that is to be applied to the sensor data as a function of the one or several operational parameters of the machine, the one or several operational parameters of the machine influencing the sensor data and being distinct from the sensor data; transform the sensor data into a frequency domain to generate a spectral representation of the sensor data influenced by the one or several operational parameters of the machine; generate a first compressed sensor data representation based on applying a first compression technique to the spectral representation of the sensor data; in response to a trigger event, indicative of a detected change in at least one operational parameter of the machine that is running, modify the compression technique that is to be applied to the sensor data as the function of the one or several received operational parameters of the machine to a second compression technique; and generate a second compressed sensor data representation based on applying the second compression technique as modified to the spectral representation of the sensor data;” are considered to be mental processes and/or mathematical concepts (examiner notes that while an “improved, particular method of digital data compression” is sufficient to show an improvement in existing technology, looking at the applicants claims in light of the specification, the applicant’s invention appears to be directed to applying known digital data compression technique based on what is considered to be mental processes and/or mathematical algorithms (as can be seen on page 14 Lines 16-17 “adjust the compression, i.e. the mathematical operations”), rather than a particular method of digital data compression).
This judicial exception is not integrated into a practical application because “a device for processing sensor data for transmission, comprising: an interface adapted to” and “at least one processing circuit adapted to:” are considered to be generically recited computer elements do not add a meaningful limitation to the abstract idea because they amount to simply implementing the abstract idea on a computer. The elements of “receive one or several operational parameters of the machine having the rotating component or having the reciprocating component, the one or several operational parameters being different from the sensor data;” and “output circuitry adapted to transmit the compressed sensor data representation including the first compressed sensor data representation and the second compressed sensor data representation” are considered to be data gathering steps required to use the correlation do not add a meaningful limitation to the method as they are insignificant extra-solution activity. The elements of “wherein the applied compression technique is adjusted as the one or several operational parameters of the running machine vary” are considered to be generally linking the use of a judicial exception to a particular technological environment or field of use.
The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because “receive one or several operational parameters of the machine having the rotating component or having the reciprocating component, the one or several operational parameters being different from the sensor data;” and “output circuitry adapted to transmit the compressed sensor data representation including the first compressed sensor data representation and the second compressed sensor data representation” are considered to be well understood routine and conventional per MPEP 2106.05(d)(i). The elements of “a device for processing sensor data for transmission, comprising: an interface adapted to” and “at least one processing circuit adapted to:” are considered to be well-understood, routine, conventional computer functions as recognized by the court decisions listed in MPEP § 2106.05(d). The elements of “wherein the applied compression technique is adjusted as the one or several operational parameters of the running machine vary” are considered to be merely indicating a field of use or technological environment in which to apply a judicial exception do not amount to significantly more than the exception itself per MPEP 2106.05(h) and are well-understood, routine, and conventional activities/elements previously known to the industry per MPEP 2106.05(d) (see prior art of record).
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)(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.
Claim(s) 1-17 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Cella (US 20180188715 A1).
In claim 1, Cella discloses a method of processing sensor data (Par. 127 “spectral”) for transmission (Par. 196-197, 199), the method comprising: receiving, by at least one processing device (Par. 285 “processor”), one or several operational parameters (Par. 101, 130 “speed”) of a machine (Par. 78 “machinery”) having a rotating component or having a reciprocating component (Par. 78 “rotating components”), the one or several operational parameters of the machine influencing the sensor data (Par. 127 Examiner notes that the speed of rotation effects the spectral data) and being distinct from the sensor data (examiner notes that the spectral data is vibration data); transforming, by the at least one processing device, the sensor data into a frequency domain (Par. 105 “integration is performed in the frequency domain”, “resulting hybrid data can then be transformed back”) to generate a spectral representation of the sensor data influenced by the one or several operational parameters of the machine (Par. 105 “spectral low-end frequency data” “noise floor” “unity gain” “signal to noise ratio”); generating, by the at least one processing device, a first compressed sensor data representation based on applying a first compression technique to (Par. 105, 121, 199 “compression”) to the spectral representation of the sensor data (Par. 28, 105 “spectral” Par. 104-105 127, 157 199 Examiner notes that speed variability requires longer sampling rates, using marker links to allow for rapid and efficient compressed storage and later transmission); receiving, by the at least one processing device, a trigger event (Par. 196, ‘automatically organize how data… is stored… level of… compression…’ ‘This may be improved over time, from an initial configuration, by training the self-organizing facility based on… feedback’) indicative of a change in at least one operational parameter of the machine (Par. 104 “shaft's rotational speed” Par. 173 “adjusting weights, rules, parameters, or the like, based on the feedback”); in response to receiving the trigger event, modifying the first compression technique to a second compression technique (Par. 196, ‘This may be improved over time, from an initial configuration, by training the self-organizing facility based on… feedback’); generating, by the at least one processing device, a second compressed sensor data representation based on applying the second compression technique to the spectral representation of the sensor data at a time the corresponding sensor data was captured (Par. 196, ‘This may be improved over time, from an initial configuration, by training the self-organizing facility based on… feedback’; Par. 172 “Reinforcement learning may include the machine learning systems performing in a dynamic environment and then providing feedback about correct and incorrect decisions” Examiner considers the updated configuration to be said second compressions technique); and
transmitting, by the at least one processing device, a compressed sensor data representations comprising the first compressed sensor data representation and the second compressed sensor data representation to a computing device (Par. 117, 180, 199 “transmitting”, “compression”; Par. 77 “computer”); and wherein an applied compression technique including the first compression technique and the second compression technique is dependent on the one or several operational parameters of the machine that is running (Par. 196, ‘This may be improved over time, from an initial configuration, by training the self-organizing facility based on… feedback’, Par. 105 “spectral low-end frequency data” “noise floor” “unity gain” “signal to noise ratio”, Par. 107 “real time analysis”), wherein the trigger event comprises a detected change in the one or several operational parameters of the running machine (Par. 104 “shaft's rotational speed” Par. 173 “adjusting weights, rules, parameters, or the like, based on the feedback”), and wherein the applied compression technique is adjusted as the one or several operational parameters of the running machine vary (Par. 107 “real time analysis”, Par. 173 “By continuously adjusting parameters to cause outputs to match actual conditions, the machine learning facility may self-organize to provide a highly accurate model of the conditions of an environment (such as for predicting faults, optimizing operational parameters, and the like). This may be used to increase fuel efficiency, to reduce wear, to increase output, to increase operating life, to avoid fault conditions, and for many other purposes”).
In claim 2, Cella discloses wherein the sensor data is time-domain data (Par. 121).
In claim 3, Cella discloses wherein the one or several operational parameters of the machine comprise a rotation speed of the rotating component of the machine or a frequency at which the reciprocating component of the machine reciprocates (Par. 78 “rotating components” Par. 101, 130 “speed”).
In claim 4, Cella discloses wherein the trigger event comprises a change in the rotation speed or a change in the frequency at which the reciprocating component of the machine reciprocates (Par. 104 “shaft's rotational speed” Par. 173 “adjusting weights, rules, parameters, or the like, based on the feedback”).
In claim 5, Cella discloses wherein the applied compression technique is further dependent on one or several machine specifics of the machine (Par. 148).
In claim 6, Cella discloses wherein the one or several machine specifics are selected from a group consisting of fault cases, application type of the machine, ambient conditions (Par. 165, 190-191).
In claim 7, Cella discloses receiving, by a data analytics computer, the compressed sensor data representation (Par. 57) and analyzing, by the data analytics computer, the compressed sensor data representations (Par. 57).
In claim 8, Cella discloses wherein the data analytics computer determines at least one key performance indicator, KPI, of the machine (Par. 201).
In claim 9, Cella discloses wherein the trigger event comprises feedback information from the data analytics computer (Par. 190-191 196).
In claim 10, Cella discloses wherein the machine has the rotating component and the one or several operational parameters comprise a rotation speed of the rotating component of the machine (examiner notes that these are considered to be a contingent limitation thus under BRI only those steps that must be performed are considered in the BRI, so the BRI would not include steps contingent on meeting a certain condition, i.e. a machine having a rotating component or having a reciprocating component” as having “a reciprocating component” is not required to perform the claim) wherein applying the compression technique comprises: applying an alignment transformation that is dependent on the rotation speed to the spectral representation of the sensor data to align the spectral representation of the sensor data with at least one reference spectrum of a set of reference spectra (Par, 91 102 “alignment analysis”), and determining a set of decomposition coefficients of a linear decomposition of the spectral representation of the sensor data, wherein the set of decomposition coefficients is transmitted in the compressed sensor data representations (Par. 101).
In claim 11, Cella discloses wherein the one or several operational parameters comprise a rotation speed of the rotating component of the machine (Par. 101), wherein applying the compression technique comprises identifying, based on the rotation speed, a set of peaks in the spectral representation of the sensor data and determining peak characteristics for each peak in the identified set of peaks (Par. 101), wherein the peak characteristics of the peaks included in the identified set of peaks are transmitted in the compressed sensor data representations (Par. 117, 180, 199 “transmitting”, “compression”).
In claim 12, Cella discloses wherein the at least one processing device is a field sensor device (Par. 173).
In claim 13, Cella discloses wherein the machine is a generator or a motor (Par. 188).
In claim 14, Cella discloses an interface adapted to receive (Par. 188 “interface”), one or several operational parameters (Par. 101, 130 “speed”) of a machine (Par. 78 “machinery”) having a rotating component or having a reciprocating component (Par. 78 “rotating components”), the one or several operational parameters being different from the sensor data (Par. 127 Examiner notes that the speed of rotation effects the spectral data; examiner notes that the spectral data is vibration data); at least one processing circuit (Par. 285 “processor”) adapted to determine a compression technique (Par. 105, 121, 199 “compression”) that is to be applied to the sensor data as a function of the one or several operational parameters of the machine (Par. 28 “spectral”), the one or several operational parameters of the machine influencing the sensor data (Par. 127 Examiner notes that the speed of rotation effects the spectral data) and being distinct from the sensor data (examiner notes that the spectral data is vibration data); transform the sensor data into a frequency domain (Par. 105 “integration is performed in the frequency domain”, “resulting hybrid data can then be transformed back”) to generate a spectral representation of the sensor data influenced by the one or several operational parameters of the machine (Par. 105 “spectral low-end frequency data” “noise floor” “unity gain” “signal to noise ratio”); generate a first compressed sensor data representation (Par. 101) based on applying a first (Par. 105, 121, 199 “compression”) compression technique to the spectral representation of the sensor data (Par. 28, 105 “spectral” Par. 104-105 127, 157 199 Examiner notes that speed variability requires longer sampling rates, using marker links to allow for rapid and efficient compressed storage and later transmission) in response to a trigger event (Par. 196, ‘automatically organize how data… is stored… level of… compression…’ ‘This may be improved over time, from an initial configuration, by training the self-organizing facility based on… feedback’), indicative of a detected change in at least one operational parameter of the machine that is running (Par. 107 “real time analysis” Par. 104 “shaft's rotational speed” Par. 173 “adjusting weights, rules, parameters, or the like, based on the feedback”), modify the compression technique that is to be applied to the sensor data as the function of the one or several received operational parameters of the machine to a second compression technique (Par. 196, ‘This may be improved over time, from an initial configuration, by training the self-organizing facility based on… feedback’);
generate a second compressed sensor data representation based on applying the second compression technique as modified to the spectral representation of the sensor data (Par. 196, ‘This may be improved over time, from an initial configuration, by training the self-organizing facility based on… feedback’; Par. 172 “Reinforcement learning may include the machine learning systems performing in a dynamic environment and then providing feedback about correct and incorrect decisions” Examiner considers the updated configuration to be said second compressions technique); and output circuitry adapted to transmit the compressed sensor data representation including the first compressed sensor data representation and the second compressed sensor data representation (Par. 117, 180, 199 “transmitting”, “compression”), wherein the applied compression technique is adjusted as the one or several operational parameters of the running machine vary (Par. 107 “real time analysis”, Par. 173 “By continuously adjusting parameters to cause outputs to match actual conditions, the machine learning facility may self-organize to provide a highly accurate model of the conditions of an environment (such as for predicting faults, optimizing operational parameters, and the like). This may be used to increase fuel efficiency, to reduce wear, to increase output, to increase operating life, to avoid fault conditions, and for many other purposes”).
In claim 15, Cella discloses wherein the one or several operational parameters of the machine comprise a rotation speed of the rotating component of the machine or a frequency at which the reciprocating component reciprocates (Par. 78 “rotating components” Par. 101, 130 “speed”).
In claim 16, Cella discloses a machine having a rotating component, in particular a motor or a generator, or a machine having a reciprocating component; (Par. 188 “motor” Par. 78 “rotating components” “machine”); a device for processing sensor data (Fig. 2, IOT device) for transmission, comprising: an interface (Par. 188 “interface”) adapted to receive one or several operational parameters (Par. 101, 130 “speed”) of the machine (Par. 78 “machinery”) having the rotating component or having the reciprocating component (Par. 78 “rotating components”), the one or several operational parameters being different from the sensor data (Par. 127 Examiner notes that the speed of rotation effects the spectral data, examiner notes that the spectral data is vibration data); and at least one processing circuit (Par. 285 “processor”) adapted to: determine a compression technique (Par. 105, 121, 199 “compression”) that is to be applied to the sensor data as a function of the one or several operational parameters of the machine, the one or several operational parameters of the machine influencing the sensor data and being distinct from the sensor data (Par. 104-105 127, 157 199 Examiner notes that speed variability requires longer sampling rates, using marker links to allow for rapid and efficient compressed storage and later transmission); transform the sensor data into a frequency domain (Par. 105 “integration is performed in the frequency domain”, “resulting hybrid data can then be transformed back”) to generate a spectral representation of the sensor data influenced by the one or several operational parameters of the machine (Par. 105 “spectral low-end frequency data” “noise floor” “unity gain” “signal to noise ratio”); generate a first compressed sensor data representation (Par. 101) based on applying a first (Par. 105, 121, 199 “compression”) compression technique to the spectral representation of the sensor data (Par. 28, 105 “spectral” Par. 104-105 127, 157 199 Examiner notes that speed variability requires longer sampling rates, using marker links to allow for rapid and efficient compressed storage and later transmission) in response to a trigger event (Par. 196, ‘automatically organize how data… is stored… level of… compression…’ ‘This may be improved over time, from an initial configuration, by training the self-organizing facility based on… feedback’), indicative of a detected change in at least one operational parameter of the machine that is running (Par. 104 “shaft's rotational speed” Par. 173 “adjusting weights, rules, parameters, or the like, based on the feedback”, Par. 107 “real time analysis”), modify the compression technique that is to be applied to the sensor data as the function of the one or several operational parameters of the machine to a second compression technique (Par. 196, ‘This may be improved over time, from an initial configuration, by training the self-organizing facility based on… feedback’);
generate a second compressed sensor data representation based on applying the second compression technique as modified to the spectral representation of the sensor data (Par. 196, ‘This may be improved over time, from an initial configuration, by training the self-organizing facility based on… feedback’; Par. 172 “Reinforcement learning may include the machine learning systems performing in a dynamic environment and then providing feedback about correct and incorrect decisions” Examiner considers the updated configuration to be said second compressions technique); and output circuitry adapted to transmit the compressed sensor data representation including the first compressed sensor data representation and the second compressed sensor data representation (Par. 117, 180, 199 “transmitting”, “compression”), wherein the applied compression technique is adjusted as the one or several operational parameters of the running machine vary (Par. 107 “real time analysis”, Par. 173 “By continuously adjusting parameters to cause outputs to match actual conditions, the machine learning facility may self-organize to provide a highly accurate model of the conditions of an environment (such as for predicting faults, optimizing operational parameters, and the like). This may be used to increase fuel efficiency, to reduce wear, to increase output, to increase operating life, to avoid fault conditions, and for many other purposes”).
In claim 17, Cella discloses A system, comprising: a machine having a rotating component, in particular a motor or a generator, or a machine having a reciprocating component; (Par. 188 “motor” Par. 78 “rotating components” “machine”); a device for processing sensor data for transmission (Fig. 2, IOT device), comprising: an interface (Par. 188 “interface”) adapted to receive one or several operational parameters (Par. 101, 130 “speed”) of the machine (Par. 78 “machinery”) having the rotating component or having the reciprocating component (Par. 78 “rotating components”), the one or several operational parameters being different from the sensor data (Par. 127 Examiner notes that the speed of rotation effects the spectral data, examiner notes that the spectral data is vibration data); and at least one processing circuit (Par. 285 “processor”) adapted to: determine a compression technique (Par. 105, 121, 199 “compression”) that is to be applied to the sensor data as a function of the one or several operational parameters of the machine, the one or several operational parameters of the machine influencing the sensor data and being distinct from the sensor data (Par. 104-105 127, 157 199 Examiner notes that speed variability requires longer sampling rates, using marker links to allow for rapid and efficient compressed storage and later transmission); transform the sensor data into a frequency domain (Par. 105 “integration is performed in the frequency domain”, “resulting hybrid data can then be transformed back”) to generate a spectral representation of the sensor data influenced by the one or several operational parameters of the machine (Par. 105 “spectral low-end frequency data” “noise floor” “unity gain” “signal to noise ratio”); generate a first compressed sensor data representation (Par. 101) based on applying a first (Par. 105, 121, 199 “compression”) compression technique to the spectral representation of the sensor data (Par. 28, 105 “spectral” Par. 104-105 127, 157 199 Examiner notes that speed variability requires longer sampling rates, using marker links to allow for rapid and efficient compressed storage and later transmission) in response to a trigger event (Par. 196, ‘automatically organize how data… is stored… level of… compression…’ ‘This may be improved over time, from an initial configuration, by training the self-organizing facility based on… feedback’), in response to a trigger event (Par. 196, ‘automatically organize how data… is stored… level of… compression…’ ‘This may be improved over time, from an initial configuration, by training the self-organizing facility based on… feedback’), indicative of a change in at least one operational parameter of the machine that is running (Par. 104 “shaft's rotational speed” Par. 173 “adjusting weights, rules, parameters, or the like, based on the feedback” Par. 107 “real time analysis”), modify the compression technique that is to be applied to the sensor data as the function of the one or several received operational parameters of the machine to a second compression technique (Par. 196, ‘This may be improved over time, from an initial configuration, by training the self-organizing facility based on… feedback’);
generate a second compressed sensor data representation based on applying the second compression technique as modified to the spectral representation of the sensor data (Par. 196, ‘This may be improved over time, from an initial configuration, by training the self-organizing facility based on… feedback’; Par. 172 “Reinforcement learning may include the machine learning systems performing in a dynamic environment and then providing feedback about correct and incorrect decisions” Examiner considers the updated configuration to be said second compressions technique); and output circuitry adapted to transmit the compressed sensor data representations (Par. 117, 180, 199 “transmitting”, “compression”); wherein the one or several operational parameters of the machine comprise a rotation speed of the rotating component of the machine or a frequency at which the reciprocating component reciprocates (Par. 104 “shaft's rotational speed” Par. 173 “adjusting weights, rules, parameters, or the like, based on the feedback”), wherein the trigger event comprises a detected change in the one or several operational parameters of the running machine (Par. 104 “shaft's rotational speed” Par. 173 “adjusting weights, rules, parameters, or the like, based on the feedback”), and wherein the applied compression technique is adjusted as the one or several operational parameters of the running machine vary (Par. 107 “real time analysis”, Par. 173 “By continuously adjusting parameters to cause outputs to match actual conditions, the machine learning facility may self-organize to provide a highly accurate model of the conditions of an environment (such as for predicting faults, optimizing operational parameters, and the like). This may be used to increase fuel efficiency, to reduce wear, to increase output, to increase operating life, to avoid fault conditions, and for many other purposes”).
Response to Arguments
Applicant's arguments filed 02/05/2026 have been fully considered but they are not persuasive. Regarding applicant’s 101 arguments on pages 9-10, the examiner respectfully disagrees. Foremost, the examiner notes the claims do not recite a neural network, merely a processing device, thus Example 39 does not apply. Further, per MPEP 2106.04(a)(2)(I)(C) “There is no particular word or set of words that indicates a claim recites a mathematical calculation. That is, a claim does not have to recite the word “calculating” in order to be considered a mathematical calculation. For example, a step of “determining” a variable or number using mathematical methods or “performing” a mathematical operation may also be considered mathematical calculations when the broadest reasonable interpretation of the claim in light of the specification encompasses a mathematical calculation”, thus while the applicant asserts the claims do not recite “mathematical concepts” explicit recitation is not required. In addition, per MPEP 2106.04(a)(2)(III) “The courts do not distinguish between mental processes that are performed entirely in the human mind and mental processes that require a human to use a physical aid (e.g., pen and paper or a slide rule) to perform the claim limitation.”.
Regarding applicant’s 102 arguments on pages 11-12, the examiner respectfully disagrees. While applicant asserts “because Cella does not disclose the "live" (e.g., in real time) modification of the "applied compression technique" based upon the present state of the running machine” Cella explicitly recites that it is done in real time (Par. 107) and continually adjusting parameters to match operating conditions (Par. 173), as cited above. Thus the cited language is disclosed by the prior art under BRI.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. US 20160088088 A1, TECHNIQUE TO MINIMIZE INTER-ELEMENT BANDWIDTH REQUIREMENTS DURING DATA SYNTHESIS ON LARGE NETWORKS; US 20150227862 A1, SYSTEMS AND METHODS FOR SUPERVISING INDUSTRIAL VEHICLES VIA ENCODED VEHICULAR OBJECTS SHOWN ON A MOBILE CLIENT DEVICE; US 20180284758 A1 METHODS AND SYSTEMS FOR INDUSTRIAL INTERNET OF THINGS DATA COLLECTION FOR EQUIPMENT ANALYSIS IN AN UPSTREAM OIL AND GAS ENVIRONMENT.
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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to BRANDON J BECKER whose telephone number is (571)431-0689. The examiner can normally be reached M-F 9:30-5:30.
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, Shelby Turner can be reached at (571) 272-6334. 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.
/B.J.B/ Examiner, Art Unit 2857
/SHELBY A TURNER/ Supervisory Patent Examiner, Art Unit 2857
Prosecution Insights