DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Response to Arguments
Applicant’s arguments, see pgs. 12-21, filed November 25, 2025, with respect to the interpretations and rejection(s) of claim(s) 1-20 under 35 U.S.C. 112(f), 35 U.S.C. 112(b), 35 U.S.C. 101, and 35 U.S.C. 103 have been fully considered and are discussed below.
Applicant argues on pg. 12, regarding the 35 U.S.C. 112(f) interpretation cited in the previous office action, that:
“the Applicant has amended the claims in the spirit of expediting patent prosecution to remove any doubt that these claims should not be interpreted under 35 U.S.C. 112(f).”
In response, the examiner finds the arguments persuasive and agrees. Therefore the 35 U.S.C. 112(f) interpretation cited in the previous office action is withdrawn.
Applicant argues on pg. 13, regarding the 35 U.S.C. 112(b) rejections presented in the previous office action, that:
“Applicant has amended the claims for clarification, without narrowing the scope of the claims, to satisfy the concerns of this rejection. Favorable consideration is respectfully requested.”
In response, the examiner finds the argument persuasive and agrees. Therefore the 35 U.S.C. 112(b) rejections presented in the previous office action are withdrawn.
Applicant argues on pgs. 13-14, regarding the 35 U.S.C. 101 rejection presented in the previous office action, that:
“the Applicant has amended the pending claims to recite “at least one processor” or “a computer” to support the position of the Applicant that the claims do not relate to “mental steps’ or mere data gathering”.
In arguendo and in view of MPEP 2106.05(g), at a minimum, the claims (as amended) relate to a “significant extra-solution activity” because:
The recitations of the claims are not well known, as evidenced by the weakness of the prior art rejection (discussed below).
The recitation of the claims are significant, as evidence from both the weakness of the prior art rejections and the practical applications of the embodiments covered by the claims.
The recitations of the claims are significantly more than “necessary data gathering and outputting.”
In view of the above, favorable reconsideration and withdraw[al] sic of the rejections under 35 U.S.C. 101 are respectfully requested.”
In response, the examiner finds the arguments not persuasive and respectfully disagrees.
To the first point of utilizing a generic processor - The recitation of “computer-implemented” in claims 1, 19, and 20 does not represent integration of the abstract idea into a practical application because employing well-known computer functions to execute an abstract idea, even when limiting the use of the idea to one particular environment, does not integrate the exception into a practical application or add significantly more. See MPEP 2106.07(a).II.
To the second point of “the claims are not well known.” The 35 U.S.C. 101 rejection presented in the previous office action did not rely on a rejection of well-understood, routine, and/or conventional. Instead, the 35 U.S.C. 101 rejection presented in the previous office action disclosed the abstract idea, implemented by a generic processor, having insignificant extra-solution activity. Wherein insignificant extra-solution activity includes insignificant post-solution activity; e.g., outputting a signal corresponding to a derived value.
To the third point of “the claims are significant.” This argument fails to indicate what in the claims is significant, how it is significant, and how that significance overcomes the 35 U.S.C. 101 rejection. Therefore the argument is rendered moot.
To the fourth point of “not directed towards necessary data gathering and outputting.” The independent claims 1, 19, and 20 all disclose one component to perform the functions of the claim, which is a processor. None of claims 1, 19, or 20 are capable of performing more than mere data gathering as there is not an instrument disclosed that collects the data. The data is then analyzed and an output is produced. There is no limitation disclosed for claims 1, 19, or 20 that perform any function beyond mere data gathering, calculation, and insignificant extra-solution activity.
Applicant argues on pgs. 14-20, regarding the 35 U.S.C. 103 rejection presented in the previous office action, that:
“These claims now recite (as amended for clarification) acquiring “…group information representing a grouping of the plurality of measurement data representing the state of the target having a higher correlation than a reference among the plurality of types of the plurality of measurement data representing the state of the target…”
“Accordingly, Kasahara is deficient in teaching or suggesting all the claim recitations.”
In response, the examiner finds the arguments persuasive and agrees insofar as Kasahara is not relied upon as explicitly disclosing amended subject matter. Therefore, the 35 U.S.C. 103 rejection presented in the previous office action is withdrawn. However, upon further consideration, a new ground(s) of rejection is made in view of Horrell et al. (US 2019/0087256 A1) in view of Gugaliya et al. (US 2022/0004176 A1).
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 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
The claims are evaluated for patent subject matter eligibility under 35 U.S.C. 101 using the 2019 Revised Patent Subject Matter Eligibility Guidance (2019 PEG) as follows:
Step 1:
Claims 1-18 are directed to an apparatus and therefore falls within the four statutory categories of subject matter.
Step 2A:
This step asks if the claim is directed to a law of nature, a natural phenomenon (product of nature) or an abstract idea. Step 2A is a two-prong inquiry: in prong 1 it is determined whether a claim recites a judicial exception, and if so, then in prong 2 it is determined if the recited judicial exception is integrated into a practical application of that exception.
Analyzing claim 1 under prong 1 of step 2A, the abstract idea in bold:
An apparatus comprising at least one processor, wherein
the at least one processor acquires a plurality of types of a plurality of measurement data representing a state of a target;
the at least one processor acquires group information representing a grouping of the plurality of measurement data representing the state of the target having a higher correlation than a reference among the plurality of types of the plurality of measurement data representing the state of the target;
the at least one processor specifies abnormal measurement data among the plurality of measurement data representing the state of the target acquired by the at least one processor for each group represented by the group information;
the at least one processor supplies each of the abnormal measurement data specified for each group by the at least one processor to a model;
the model outputs, in response to any one type of the plurality of measurement data representing the state of the target being input for each group represented by the group information, a state index value representing a quality of a state of the target; and
the at least one processor outputs a signal corresponding to the state index value output from the model.
has a scope that encompasses mental steps, e.g., concepts that may be performed in the human mind; e.g., human observation/performable with pen and paper/mere data gathering. Claim 1 discloses acquires a plurality of types of a plurality of measurement data representing a state of a target; construed by the examiner as a mental step; e.g., mere data gathering; acquires group information representing a grouping of the plurality of measurement data representing the state of the target having a higher correlation than a reference among the plurality of types of the plurality of measurement data representing the state of the target; construed by the examiner as a mental step; e.g., performable with pen and paper; specifies abnormal measurement data among the plurality of measurement data representing the state of the target for each group represented by the group information; construed as a mental step; e.g., observation; supplies each of the abnormal measurement data specified for each group to a model; construed as a mental step; e.g., performable with pen and paper; the model outputs, in response to any one type of the plurality of measurement data representing the state of the target being input for each group represented by the group information, a state index value representing a quality of a state of the target; and; construed by the examiner as performable with pen and paper. The broadest reasonable interpretation of the abovementioned steps in light of the specification has a scope that encompasses steps that may be performed in the human mind. It is therefore concluded under prong 1 of step 2A that claim 1 recites a judicial exception in the form of an abstract idea, i.e., mental steps. See MPEP 2106.04(a)(2)(A-C) and MPEP 2106.05(f).
In prong 2 of step 2A it is determined whether the recited judicial exception is integrated into a practical application of that exception by: (1) identifying whether there are any additional elements recited in the claim beyond judicial exception(s); and (2) evaluating those additional elements individually and in combination to determine whether they integrate the exception into a practical application.
Analyzing claim 1 under prong 2 of step 2A, in addition to the abstract ideas described above, claim 1 further recites:
at least one processor,
the at least one processor
the at least one processor
the at least one processor
acquired by the at least one processor
the at least one processor
by the at least one processor
the at least one processor
Analyzing these additional elements of claim 1 under prong 2 of step 2A, these additional elements appear to merely recite the use of a generic processor/computer as a tool to implement the abstract idea and/or to perform functions in its ordinary capacity, e.g., receive, store, or transmit data. However, use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general-purpose computer or computer component after the fact to an abstract idea (e.g., a fundamental economic practice or mathematical equation) does not integrate a judicial exception into a practical application or provide significantly more. See MPEP 2106.05(f).
outputs a signal corresponding to the state index value output from the model.
Analyzing this additional element of claim 1 under prong 2 of step 2A, this additional element appears to merely collect and interpolate mathematical data, interpreted by the examiner as insignificant extra-solution activity. The term “extra-solution activity” can be understood as activities incidental to the primary process or product that are merely a nominal or tangential addition to the claim. Extra-solution activity includes both pre-solution and post-solution activity. An example of pre-solution activity is a step of gathering data for use in a claimed process, which is recited as part of a claimed process of analyzing and manipulating the gathered information by a series of steps. An example of post-solution activity is an element that is not integrated into the claim as a whole, which is recited in a claim to analyze and manipulate information. See MPEP 2016.05(g). Also, employing well-known computer functions to execute an abstract idea, even when limiting the use of the idea to one particular environment, does not integrate the exception into a practical application or add significantly more. See MPEP 2106.07(a).II.
Step 2B:
In step 2B it is determined whether the claim recites additional elements that amount to significantly more than the judicial exception. The additional elements discussed above in connection with prong 2 of step 2A merely represents implementation of the abstract idea using a generic processor/computer and use of a generic processor/computer. However, use of a computer or other machine in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general-purpose computer or computer components after the fact to an abstract idea (e.g., a fundamental economic practice or mathematical equation) does not integrate a judicial exception into a practical application or provide significantly more. See MPEP 2106.05(f).
The further additional elements discussed above in connection with prong 2 of step 2A also merely represents insignificant extra-solution activity. The term “extra-solution activity” can be understood as activities incidental to the primary process or product that are merely a nominal or tangential addition to the claim. Extra-solution activity includes both pre-solution and post-solution activity. An example of pre-solution activity is a step of gathering data for use in a claimed process, which is recited as part of a claimed process of analyzing and manipulating the gathered information by a series of steps. An example of post solution activity is an element that is not integrated into the claim as a whole, which is recited in a claim to analyze and manipulate information. See MPEP 2016.05(g).
It is therefore concluded under step 2B that claim 1 does not recite additional elements that amount to significantly more than the judicial exception.
Dependent claims 2-18 merely recite further details of the abstract idea of claim 1 and therefore do not represent any additional elements that would integrate the abstract idea into a practical application or represent significantly more than the abstract idea itself.
Step 1:
Claim 19 is directed to a method and therefore falls within the four statutory categories of subject matter.
Step 2A:
This step asks if the claim is directed to a law of nature, a natural phenomenon (product of nature) or an abstract idea. Step 2A is a two-prong inquiry: in prong 1 it is determined whether a claim recites a judicial exception, and if so, then in prong 2 it is determined if the recited judicial exception is integrated into a practical application of that exception.
Analyzing claim 19 under prong 1 of step 2A, the abstract idea in bold:
A method performed by at least one processor comprising:
acquiring, by the at least one processor, a plurality of types of a plurality of measurement data representing a state of a target;
acquiring, by the at least one processor, group information representing a grouping of the plurality of measurement data representing the state of the target having a higher correlation than a reference among the plurality of types of the plurality of measurement data representing the state of the target;
specifying, by the at least one processor, abnormal measurement data among the plurality of measurement data representing the state of the target acquired by the acquiring the plurality of types of the plurality of measurement data, for each group represented by the group information;
supplying, by the at least one processor, each of the abnormal measurement data specified for each group by the specifying the abnormal measurement data to a model, wherein the model outputs, in response to any one type of the plurality of measurement data representing the state of the target being input for each group represented by the group information, a state index value representing a quality of a state of the target; and
outputting, by the at least one processor, a signal corresponding to the state index value output from the model.
has a scope that encompasses mental steps, e.g., concepts that may be performed in the human mind; e.g., human observation/performable with pen and paper/mere data gathering. Claim 19 discloses acquiring, a plurality of types of a plurality of measurement data representing a state of a target; construed by the examiner as a mental step; e.g., mere data gathering; acquiring, group information representing a grouping of the plurality of measurement data representing the state of the target having a higher correlation than a reference among the plurality of types of the plurality of measurement data representing the state of the target; construed by the examiner as a mental step; e.g., observation and/or performable with pen and paper; specifying, abnormal measurement data among the plurality of measurement data representing the state of the target acquired by the acquiring the plurality of types of the plurality of measurement data, for each group represented by the group information; construed by the examiner as a mental step; e.g., performable with pen and paper; supplying, each of the abnormal measurement data specified for each group by the specifying the abnormal measurement data to a model, wherein the model outputs, in response to any one type of the plurality of measurement data representing the state of the target being input for each group represented by the group information, a state index value representing a quality of a state of the target; and; construed by the examiner as a mental step; e.g., performable with pen and paper. The broadest reasonable interpretation of the abovementioned steps in light of the specification has a scope that encompasses steps that may be performed in the human mind. It is therefore concluded under prong 1 of step 2A that claim 19 recites a judicial exception in the form of an abstract idea, i.e., mental steps. See MPEP 2106.04(a)(2)(A-C) and MPEP 2106.05(f).
In prong 2 of step 2A it is determined whether the recited judicial exception is integrated into a practical application of that exception by: (1) identifying whether there are any additional elements recited in the claim beyond judicial exception(s); and (2) evaluating those additional elements individually and in combination to determine whether they integrate the exception into a practical application.
Analyzing claim 19 under prong 2 of step 2A, in addition to the abstract ideas described above, claim 19 further recites:
performed by at least one processor
by the at least one processor,
by the at least one processor,
by the at least one processor,
by the at least one processor,
by the at least one processor,
Analyzing these additional elements of claim 19 under prong 2 of step 2A, these additional elements appear to merely recite the use of a generic processor/computer as a tool to implement the abstract idea and/or to perform functions in its ordinary capacity, e.g., receive, store, or transmit data. However, use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general-purpose computer or computer component after the fact to an abstract idea (e.g., a fundamental economic practice or mathematical equation) does not integrate a judicial exception into a practical application or provide significantly more. See MPEP 2106.05(f).
outputting, a signal corresponding to the state index value output from the model.
Analyzing this additional element of claim 19 under prong 2 of step 2A, this additional element appears to merely collect and interpolate mathematical data, interpreted by the examiner as insignificant extra-solution activity. The term “extra-solution activity” can be understood as activities incidental to the primary process or product that are merely a nominal or tangential addition to the claim. Extra-solution activity includes both pre-solution and post-solution activity. An example of pre-solution activity is a step of gathering data for use in a claimed process, which is recited as part of a claimed process of analyzing and manipulating the gathered information by a series of steps. An example of post-solution activity is an element that is not integrated into the claim as a whole, which is recited in a claim to analyze and manipulate information. See MPEP 2016.05(g). Also, employing well-known computer functions to execute an abstract idea, even when limiting the use of the idea to one particular environment, does not integrate the exception into a practical application or add significantly more. See MPEP 2106.07(a).II.
Step 2B:
In step 2B it is determined whether the claim recites additional elements that amount to significantly more than the judicial exception. The additional elements discussed above in connection with prong 2 of step 2A merely represents implementation of the abstract idea using a generic processor/computer and use of a generic processor/computer. However, use of a computer or other machine in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general-purpose computer or computer components after the fact to an abstract idea (e.g., a fundamental economic practice or mathematical equation) does not integrate a judicial exception into a practical application or provide significantly more. See MPEP 2106.05(f).
The further additional elements discussed above in connection with prong 2 of step 2A also merely represents insignificant extra-solution activity. The term “extra-solution activity” can be understood as activities incidental to the primary process or product that are merely a nominal or tangential addition to the claim. Extra-solution activity includes both pre-solution and post-solution activity. An example of pre-solution activity is a step of gathering data for use in a claimed process, which is recited as part of a claimed process of analyzing and manipulating the gathered information by a series of steps. An example of post solution activity is an element that is not integrated into the claim as a whole, which is recited in a claim to analyze and manipulate information. See MPEP 2016.05(g).
It is therefore concluded under step 2B that claim 19 does not recite additional elements that amount to significantly more than the judicial exception.
Step 1:
Claim 20 is directed to a device and therefore falls within the four statutory categories of subject matter.
Step 2A:
This step asks if the claim is directed to a law of nature, a natural phenomenon (product of nature) or an abstract idea. Step 2A is a two-prong inquiry: in prong 1 it is determined whether a claim recites a judicial exception, and if so, then in prong 2 it is determined if the recited judicial exception is integrated into a practical application of that exception.
Analyzing claim 20 under prong 1 of step 2A, the abstract idea in bold:
A non-transitory computer readable medium having a program recorded thereon for causing a computer to:
acquire a plurality of types of a plurality of measurement data representing a state of a target;
acquire group information representing a grouping of the plurality of measurement data representing the state of the target having a higher correlation than a reference among the plurality of types of the plurality of measurement data representing the state of the target;
specify abnormal measurement data among the plurality of measurement data representing the state of the target acquired for each group represented by the group information;
supply each of the abnormal measurement data specified for each group to a model and output, in response to any one type of the plurality of measurement data representing the state of the target being input for each group represented by the group information, a state index value representing a quality of a state of the target; and
output a signal corresponding to the state index value output from the model.
has a scope that encompasses mental steps, e.g., concepts that may be performed in the human mind; e.g., human observation/performable with pen and paper/mere data gathering. Claim 20 discloses acquire a plurality of types of a plurality of measurement data representing a state of a target; construed as a mental step; e.g., mere data gathering; acquire group information representing a grouping of the plurality of measurement data representing the state of the target having a higher correlation than a reference among the plurality of types of the plurality of measurement data representing the state of the target; construed as a mental step; e.g., mere data gathering and/or performable with pen and paper; specify abnormal measurement data among the plurality of measurement data representing the state of the target acquired for each group represented by the group information; construed as a mental step; e.g., observation; supply each of the abnormal measurement data specified for each group to a model and output, in response to any one type of the plurality of measurement data representing the state of the target being input for each group represented by the group information, a state index value representing a quality of a state of the target; and; construed as a mental step; e.g., performable with pen and paper. The broadest reasonable interpretation of the abovementioned steps in light of the specification has a scope that encompasses steps that may be performed in the human mind. It is therefore concluded under prong 1 of step 2A that claim 20 recites a judicial exception in the form of an abstract idea, i.e., mental steps. See MPEP 2106.04(a)(2)(A-C) and MPEP 2106.05(f).
In prong 2 of step 2A it is determined whether the recited judicial exception is integrated into a practical application of that exception by: (1) identifying whether there are any additional elements recited in the claim beyond judicial exception(s); and (2) evaluating those additional elements individually and in combination to determine whether they integrate the exception into a practical application.
Analyzing claim 20 under prong 2 of step 2A, in addition to the abstract ideas described above, claim 20 further recites:
A non-transitory computer readable medium having a program recorded thereon for causing a computer to:
Analyzing these additional elements of claim 20 under prong 2 of step 2A, these additional elements appear to merely recite the use of a generic processor/computer as a tool to implement the abstract idea and/or to perform functions in its ordinary capacity, e.g., receive, store, or transmit data. However, use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general-purpose computer or computer component after the fact to an abstract idea (e.g., a fundamental economic practice or mathematical equation) does not integrate a judicial exception into a practical application or provide significantly more. See MPEP 2106.05(f).
output a signal corresponding to the state index value output from the model.
Analyzing this additional element of claim 20 under prong 2 of step 2A, this additional element appears to merely collect and interpolate mathematical data, interpreted by the examiner as insignificant extra-solution activity. The term “extra-solution activity” can be understood as activities incidental to the primary process or product that are merely a nominal or tangential addition to the claim. Extra-solution activity includes both pre-solution and post-solution activity. An example of pre-solution activity is a step of gathering data for use in a claimed process, which is recited as part of a claimed process of analyzing and manipulating the gathered information by a series of steps. An example of post-solution activity is an element that is not integrated into the claim as a whole, which is recited in a claim to analyze and manipulate information. See MPEP 2016.05(g). Also, employing well-known computer functions to execute an abstract idea, even when limiting the use of the idea to one particular environment, does not integrate the exception into a practical application or add significantly more. See MPEP 2106.07(a).II.
Step 2B:
In step 2B it is determined whether the claim recites additional elements that amount to significantly more than the judicial exception. The additional elements discussed above in connection with prong 2 of step 2A merely represents implementation of the abstract idea using a generic processor/computer and use of a generic processor/computer. However, use of a computer or other machine in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general-purpose computer or computer components after the fact to an abstract idea (e.g., a fundamental economic practice or mathematical equation) does not integrate a judicial exception into a practical application or provide significantly more. See MPEP 2106.05(f).
The further additional elements discussed above in connection with prong 2 of step 2A also merely represents insignificant extra-solution activity. The term “extra-solution activity” can be understood as activities incidental to the primary process or product that are merely a nominal or tangential addition to the claim. Extra-solution activity includes both pre-solution and post-solution activity. An example of pre-solution activity is a step of gathering data for use in a claimed process, which is recited as part of a claimed process of analyzing and manipulating the gathered information by a series of steps. An example of post solution activity is an element that is not integrated into the claim as a whole, which is recited in a claim to analyze and manipulate information. See MPEP 2016.05(g).
It is therefore concluded under step 2B that claim 20 does not recite additional elements that amount to significantly more than the judicial exception.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-2 and 19-20 are rejected under 35 U.S.C. 103 as being unpatentable over Horrell et al. (US 2019/0087256 A1), hereinafter Horrell, in view of Gugaliya et al. (US 2022/0004176 A1), hereinafter Gugaliya.
Regarding claim 1, Horrell discloses An apparatus comprising at least one processor, wherein: (Horrell, e.g., see fig. 2 illustrating processing unit (206) as communicatively connected to sensors (204), subsystem(s) (202), and data storage (208) of asset (200); see also paras. [0076]-[0077] disclosing a processing unit and its functions).
the at least one processor acquires a plurality of types of a plurality of measurement data representing a state of a target; (Horrell, e.g., see rejection as applied above; see also paras. [0072]-[0075] disclosing sensors (204) of fig. 2, specifically that various sensors (204) are configured to monitor operating conditions of the asset (200), and a group of sensors (204) may be configured to monitor operating conditions of a particular subsystem (202). Sensors (204) may be configured to detect a physical property, indicative of one or more operating conditions of the asset (200), wherein the sensors may obtain measurements continuously, periodically, and/or in response to a triggering event. The sensors may perform measurements in accordance with operating parameters provided by the processing unit (206). Additionally, various sensors (204) may be configured to measure other operating conditions of the asset (200) such as temperature, pressures, speeds, friction, power usages, fuel usages, fluid levels, runtimes, voltages and currents, magnetic fields, electric fields, and power generation, among other examples. Additional or few sensors may be used depending on the industrial application or specific asset; examiner notes operating conditions are construed as a “state of a target.”).
the at least one processor acquires group information representing a grouping of the plurality of measurement data representing the state of the target having a higher correlation than a reference among the plurality of types of the plurality of measurement data representing the state of the target; (Horrell, e.g., see rejection as applied above; see also fig. 3 illustrating a conceptual illustration of example abnormal-condition indicators and respective sensor criteria for an asset; see also paras. [0080]-[0082] disclosing columns (302), (304), and (306) corresponding to sensors A, B, and C, respectively, and also rows (308), (310), and (312) corresponding to fault codes 1, 2, and 3, respectively of fig. 3. Entries (314) then specify sensor criteria (e.g., sensor value thresholds) that correspond to the given fault codes. For example, Fault Code (1) will be triggered when Sensor A detects a rotational measurement greater than (135) revolutions per minute (RPM) and Sensor C detects a temperature measurement greater than 65˚ Celsius (C), Fault Code (2) will be triggered when Sensor B detects a voltage measurement greater than 1000 Volts (V) and a temperature measurement less than 55˚ C, and Fault Code (3) will be triggered when Sensor A detects a rotational measurement greater than 100 RPM, a voltage measurement greater than 750 V, and a temperature measurement greater than 60˚ C. Numerous other fault codes and/or sensor criteria are possible; see also paras. [0105]-[0110] disclosing groupings of sensor data; e.g., Sensors A and C, Sensor B and/or other sensors, Sensor B and C, etc., wherein the operating data is based on a fault-code rule or instruction provided by the analytics system (400) which determines a correlation between that which a sensor is measuring that which caused the Fault Code (1) to be triggered in the first place. Sensor data may include one or more sensor measurements from each sensor of interest based on a particular time of interest, based on a number of factors, sampling rate, or an abnormal-condition indicator is triggered; examiner notes that table 300 is construed as representing a grouping of the plurality of measurement data, wherein the fault code is representative of the state of the target, and wherein the reference is explicitly disclosed as greater than; e.g., a degree Celsius, a voltage, an RPM, etc.).
the at least one processor specifies abnormal measurement data among the plurality of measurement data representing the state of the target acquired by the at least one processor for each group represented by the group information; (Horrell, e.g., see rejection as applied above disclosing the plurality of measurement data representing the state of the target acquired by the at least one processor, especially with regard to fig. 3 and paras. [0105]-[0110]; wherein paras. [0109]-[0110] discloses similar to sensor data, the abnormal-condition data may take various forms to include an indicator that is operable to uniquely identify a particular abnormal condition that occurred at the asset (200) from all other abnormal conditions that may occur at the asset (200), or the form of an alphabetic, numeric, or alphanumeric identifier, among other examples, and/or the form of a string of words that is descriptive of the abnormal condition, such as “Overheated Engine” or “Out of Fuel”, among other examples; see also para. [0126] disclosing a given failure may be associated with one or multiple abnormal-condition indicators, such as fault codes. That is, when the given failure occurs, one or multiple abnormal-condition indicators may be triggered. As such, abnormal-condition indicators may be reflective of an underlying symptom of a given failure; see also fig. 6 illustrating a conceptual illustration of data utilized to define a model; see also para. [0130] disclosing plot (600) may correspond to a segment of historical sensor data that originated from some (e.g., Sensor A and Sensor B) or all of the sensors (204). The plot (600) includes time on the x-axis (602), sensor measurement values on the y-axis (604), and sensor data (606) corresponding to Sensor A and sensor data (608) corresponding to Sensor B, each of which includes various data-points representing sensor measurements at particular points in time,
T
f
. Moreover, the plot (600) includes an indication of an occurrence of a failure (610) that occurred at a past time,
T
f
(e.., “time of failure”)).
the at least one processor supplies each of the abnormal measurement data specified for each group by the at least one processor to a model; (Horrell, e.g., see rejection as applied above; see also paras. [0114]-[0119] disclosing determining a health metric may involve two phases: (1) a “modeling” phase during which the data science system (404) defines a model for predicting the likelihood of failures occurring and (2) an asset-monitoring phase during which the data science system (404) utilizes the model defined in the machine learning phase and operating data for a given asset to determine a health metric for a given asset; e.g., see fig. 5A illustrating a flow diagram (500) depicting one possible example of a modeling phase that may be used for determining a health metric, wherein paras. [0116]-[0119] disclose steps (502), (504), (506), (508), and (510) of the model depicted in fig. 5A, which outputs a defined model; see also fig. 5B illustrating a flow diagram (520) depicting one possible example of an asset-monitoring phase that may be used for determining a health metric; e.g., see paras. [0151]-[0160] disclosing steps (522); e.g., receive operating data, (524); e.g., identify model inputs, (526); e.g., execute model, and (528); e.g., convert to health score; see also para. [0162] disclosing causing one or more of the output systems (108) to output various information regarding an asset in operation, such as an indication of the health metric and perhaps an indication of fault codes and/or sensor data as well; see also para. [0175] disclosing the set of failures may be defined from abnormal-condition indicators, such as fault codes, associated with a subsystem. In general, a subsystem may have one or multiple indicators associated with it. for example, Fault codes 1-3 of fig. 3 may all be associated with a given subsystem (202)).
the model outputs, in response to any one type of the plurality of measurement data representing the state of the target being input for each group represented by the group information, a health metric representing a quality of a health score; and (Horrell, e.g., see rejection as applied above, specifically with regard to fig. 5B and step (526); e.g., execute model and (528); e.g., convert to health score; see also para. [0160] disclosing at block (528), the data science system (404) may convert the probability of a failure occurring into the health score that may take the form of a single, aggregated parameter that reflects the likelihood that no failures will occur at the asset within the give[n] sic timeframe in the future (e.g., two weeks). Converting the failure probability into the health metric may involve the data science system (404) determining the complement of the failure probability. Specifically, the overall failure probability may take the form of a value ranging from zero to one; the health metric may be determined by subtracting one by that number. Other examples of converting the failure probability into the health metric are also possible; see also fig. 7 illustrating a health metric (702); see also para. [0163] disclosing this graphical user interface (700) shown to include various information about a given asset (e.g., a vehicle asset). The graphical user interface (700) may include a health metric display (702) that shows the asset’s overall health metric (outlined by the dashed, block box). Here, the health-metric display (702) takes the form of a percentage and a dial-like visualization).
the at least one processor outputs a signal corresponding to the health metric output from the model. (Horrell, e.g., see rejection as applied above; see also paras. [0168]-[0171] disclosing examples of the health metric triggering one of a plurality of actions based on meeting a threshold; e.g., outputting a signal to display a warning or alert, generating a list of one or more recommended actions that may help increase the health metric, cause a work-order system to generate a work order to repair the asset, and/or transmit to the asset (200) one or more commands that facilitate modifying one or more operating conditions of the asset (200)).
Horrell is not relied upon as explicitly disclosing a state index value representing a quality of a state target health wherein the state index value is output.
However, Gugaliya further discloses: a state index value representing a quality of a state target health wherein the state index value is output. (Gugaliya, e.g., see para. [0031] disclosing the condition of the process equipment is detected by calculating an index value for each of the plurality of faults associated with the process equipment based on the real-time values and corresponding weight of the plurality of parameters from the weight matrix; examiner notes that an index value for each of the plurality of faults is construed as a state index value and a process equipment condition corresponding to the weight of the plurality of parameters is construed as a quality of a state target; see also fig. 5c illustrating step (509) of outputting the generated weight matrix comprising plurality of fault, plurality of parameters, and weights corresponding to plurality of parameters for each of plurality of faults; construed as outputting a state index value).
Accordingly, it would be prima facie obvious to one of ordinary skill in the art, at the time the invention was effectively filed, to have modified Horrell with Gugaliya’s state index value representing a quality of a state target health for at least the reasons that it is known to correspond identified parameters in an abnormal state when an index value is greater than the threshold attribute; e.g., see para. [0031]).
Regarding claim 2, Horrell in view of Gugaliya is not relied upon as explicitly disclosing: The apparatus according to claim 1, wherein: the at least one processor designates a target time point of the state index value, the at least one processor specifies the abnormal measurement data at the target time point, and the at least one processor supplies the abnormal measurement data at the target time point to the model.
However, Gugaliya further discloses: the at least one processor designates a target time point of the state index value, (see rejection as applied to claim 1, specifically Gugaliya; e.g., para. [0031] disclosing the condition of the process equipment is detected by calculating an index value for each of the plurality of faults associated with the process equipment based on the real-time values and corresponding weight of the plurality of parameters from the weight matrix. The index value is compared with the threshold attribute of the corresponding parameter to identify the corresponding parameter to be in an abnormal state; see also fig. 3 illustrating a predefined duration of time of historic values of identified faults to include a first, second and third fault, wherein the abnormality measurement; e.g., the fault, is produced at time points t1, t2, and t3, which are then used to produce the state index value; see also para. [0036] disclosing the server (204) is configured to identify occurrence of one or more faults associated with the turbine unit (201.2) in the plurality of historic values. Consider the fault to be identified is leakage fault in the turbine unit (201.2). Occurrence of the leakage fault in the turbine unit (201.2) at one or more time instances in the predefined duration of time is identified).
the at least one processor specifies the abnormal measurement data at the target time point, and (Gugaliya, e.g., see rejection as applied above, wherein the first, second, and third fault are identified with an amplitude of a graph; construed as the abnormal measurement data, wherein t1, t2, and t3 are construed as the target time point; see also para. [0036] disclosing the first data segment include data in the predefined time window before occurrence of the first fault at the time instance ‘t1’. The second data segment include data in the predefined time window before occurrence of the second fault at the time instance ‘t2’. The third data segment include data in the predefined time window before occurrence of the third fault at the time instance ‘t3’.).
the at least one processor supplies the abnormal measurement data at the target time point to the model. (Gugaliya, e.g., see rejection above of fig. 3 and para. [0036] disclosing data segments of t1, t2, and t3; see also para. [0038] disclosing based on the data segments, a decision tree model corresponding to the turbine unit (201.2) is generated by the server (2040 using the data in the data segments and the plurality of faults).
Accordingly, it would be prima facie obvious to one of ordinary skill in the art, at the time the invention was effectively filed, to have modified Horrell in view of Gugaliya’s apparatus with Gugaliya’s the at least one processor designates a target time point of the state index value, the at least one processor specifies the abnormal measurement data at the target time point, and the at least one processor supplies the abnormal measurement data at the target time point to the model for at least the reasons that models may be utilized to identify one or more parameters influencing faults, as taught by Gugaliya; e.g., see para. [0038].
Regarding claim 19, Claim 19 recites A method performed by at least one processor comprising: acquiring, by the at least one processor, a plurality of types of a plurality of measurement data representing a state of a target; acquiring, by the at least one processor, group information representing a grouping of the plurality of measurement data representing the state of the target having a higher correlation than a reference among the plurality of types of the plurality of measurement data representing the state of the target; specifying, by the at least one processor, an abnormal measurement data among the plurality of measurement data representing the state of the target acquired by the acquiring the plurality of types of the plurality of measurement data, for each group represented by the group information; supplying, by the at least one processor, each of the abnormal measurement data specified for each group by the specifying the abnormal measurement data to a model, wherein the model outputs, in response to any one type of the plurality of measurement data representing the state of the target being input for each group represented by the group information, a state index value representing a quality of a state of the target; and outputting, by the at least one processor, a signal corresponding to the state index value output from the model., and is rejected under 35 U.S.C. 103 as being unpatentable by Horrell in view of Gugaliya for reasons analogous to those set forth in connection with claim 1.
Regarding claim 20, Claim 20 discloses A non-transitory computer readable medium having a program recorded thereon for causing a computer to: acquire a plurality of types of a plurality of measurement data representing a state of a target; acquire group information representing a grouping of the plurality of measurement data representing the state of the target having a higher correlation than a reference among the plurality of types of the plurality of measurement data representing the state of the target; specify abnormal measurement data among the plurality of measurement data representing the state of the target acquired for each group represented by the group information; supply each of the abnormal measurement data specified for each group to a model and output, in response to any one type of the plurality of measurement data representing the state of the target being input for each group represented by the group information, a state index value representing a quality of a state of the target; and output a signal corresponding to the state index value output from the model., and is rejected under 35 U.S.C. 103 as being unpatentable by Horrell in view of Gugaliya for reasons analogous to those set forth in connection with claim 1. Claim 20 is different than claim 1 in the claim recitation disclosing: A non-transitory computer readable medium having a program recorded thereon for causing a computer to, wherein Horrell discloses (Horrell, e.g., see fig. 2 illustrating data storage (208); see also paras. [0076]-[0079] disclosing data storage (208) and program instructions).
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.
The prior art made of record and not relied upon is considered pertinent to applicant’s disclosure.
US 2022/0253677 A1 to Dan et al. relates to behavior learning system, behavior learning method, and program.
US 12,327,186 B2 to Tsutsui relates to an abnormality detecting device and abnormality detecting method.
US 2021/0064606 A1 to Akkaku et al. relates to an analysis apparatus, analysis method, and storage medium.
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/E.S.V./Examiner, Art Unit 2863
/Catherine T. Rastovski/Supervisory Primary Examiner, Art Unit 2863
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