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 .
Priority
Receipt is acknowledged of certified copies of papers required by 37 CFR 1.55.
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
Use of the word “means” (or “step for”) in a claim with functional language creates a rebuttable presumption that the claim element is to be treated in accordance with 35 U.S.C. 112(f) (pre-AIA 35 U.S.C. 112, sixth paragraph). The presumption that 35 U.S.C. 112(f) (pre-AIA 35 U.S.C. 112, sixth paragraph) is invoked is rebutted when the function is recited with sufficient structure, material, or acts within the claim itself to entirely perform the recited function.
Absence of the word “means” (or “step for”) in a claim creates a rebuttable presumption that the claim element is not to be treated in accordance with 35 U.S.C. 112(f) (pre-AIA 35 U.S.C. 112, sixth paragraph). The presumption that 35 U.S.C. 112(f) (pre-AIA 35 U.S.C. 112, sixth paragraph) is not invoked is rebutted when the claim element recites function but fails to recite sufficiently definite structure, material or acts to perform that function.
Claim elements in this application that use the word “means” (or “step for”) are presumed to invoke 35 U.S.C. 112(f) except as otherwise indicated in an Office action. Similarly, claim elements that do not use the word “means” (or “step for”) are presumed not to invoke 35 U.S.C. 112(f) except as otherwise indicated in an Office action.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitation(s) is/are: a smart gas user platform, a smart gas service platform, a smart gas device management platform, a smart gas sensor network platform, and a smart gas object platform in claims 1 and 12.
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
A review of the specification shows that the following appears to be the corresponding structure described in the specification for the 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph limitation, all references are made to the written specification dated 02/26/2024:
a smart gas user platform – may be a platform for interacting with a user or configured as a terminal device (see paragraph section [0028])
a smart gas service platform - may be a platform for communicating the user’s need and control information (see paragraph sections [0033]-[0034])
a smart gas device management platform - may be a platform that integrates and coordinates a connection and collaboration between various functional platforms, gathers all information of the Internet of Things system, and provides functions of perception management and control management for an operation system of the Internet of Things system (see paragraph section [0038])
a smart gas sensor network platform - may be a platform for managing sensor communication. In some embodiments and may realize functions of sensor communication of perception information and sensor communication of control information (see paragraph sections [0045]-[0046])
a smart gas object platform - may be a platform for generating the perception information and executing the control information (see paragraph sections [0047]-[0048]).
In these cases, the specification does not appear to define “platform” as a physical device or component (see paragraph sections [0023]-[0024]). Neither the specification nor the claims appear to define the “platforms” as devices or components having actual corresponding structures. Rather, the “platforms” appear to be programs or algorithms for performing the stated functional language. For examination purposes, these platforms will be interpreted as programs or algorithms without any corresponding structures to the functional language.
If applicant wishes to provide further explanation or dispute the examiner’s interpretation of the corresponding structure, applicant must identify the corresponding structure with reference to the specification by page and line number, and to the drawing, if any, by reference characters in response to this Office action.
If applicant does not intend to have the claim limitation(s) treated under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112 , sixth paragraph, applicant may amend the claim(s) so that it/they will clearly not invoke 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, or present a sufficient showing that the claim recites/recite sufficient structure, material, or acts for performing the claimed function to preclude application of 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
For more information, see MPEP § 2173 et seq. and Supplementary Examination Guidelines for Determining Compliance With 35 U.S.C. 112 and for Treatment of Related Issues in Patent Applications, 76 FR 7162, 7167 (Feb. 9, 2011).
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.
Claims 1-20 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as failing to set forth the subject matter which the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the applicant regards as the invention.
Regarding claim 1, the claim recites “a method for monitoring ultrasonic metering based on smart gas Internet of Things” and “a system for monitoring ultrasonic metering” but does not actually define a method and system for monitoring an ultrasonic metering device nor does the claim appear to disclose any details or structures unique or exclusive to ultrasonic metering devices or methods. The claim is incomplete for omitting essential elements, such omission amounting to a gap between the elements (see MPEP § 2172.01). The omitted elements are: ultrasonic metering devices.
The claim recites “a smart gas user platform”, “a smart gas service platform”, “a smart gas device management platform”, “a smart gas sensor network platform”, and “a smart gas object platform” without disclosing any corresponding structures. Accordingly, these limitations will be interpreted as programs or algorithms including functional language without reciting sufficient structure to perform the recited function, as the generic placeholder (a.k.a., platform) is not preceded by a structural modifier.
The claim recites the method steps of “obtaining”, “judging”, and “performing interference processing” without disclosing the devices or components for performing the stated functions. The claim is incomplete for omitting essential elements, such omission amounting to a gap between the elements (see MPEP § 2172.01). The omitted elements are: the devices or components for performing the method steps of obtaining, judging, and performing interference processing.
The claim recites the method step of “obtaining target monitoring data at a target time point, a first monitoring data sequence within a first preset time period, and a second preset time period within a second preset time period from the smart gas object platform” without disclosing a device or component for obtaining the target monitoring data, the first monitoring data, and the second preset time period. In other words, the claim does not explain whether the target monitoring data and the first monitoring dare are measurements obtained from sensor or downloaded from a database. Furthermore, the phrase “the second monitoring data sequence” lacks proper antecedent basis, as the phrase was never defined in the claim.
The claim recites “judging […] whether the target monitoring data being interference data through an interference data model” without explaining the criteria for the “target monitoring data being interference data”. In order words, the claim does not explain how the “model” determines the interference data based on the first and second monitoring data sequence, first and second sets of time points, and the target time point.
The phrase “in response to a query instruction issued by the smart gas user platform” and “in response to the target monitoring data being the interference data” implies that the method steps only occur when the conditions precedent are met (i.e., when the query instruction is used by the smart gas user platform and when the target monitoring data is the interference data). In this case, the broadest reasonable interpretation of a method (or process) claim having contingent limitations requires only those steps that must be performed and does not include steps that are not required to be performed because the condition(s) precedent are not met (see MPEP 2111.04). Accordingly, these steps are considered contingent and neither are required by the broadest reasonable interpretation of the claim.
Further clarification is respectfully requested
Regarding claim 2, the claim recites “a large number of second training samples” without providing a standard for “a large number”. Accordingly, the term “a large number” is not defined by the claim, and the specification does not provide a standard for ascertaining the requisite degree, and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention. Further clarification is respectfully requested.
Claims 3-11 and 20 are rejected as being dependent on the rejected base claim.
Regarding claim 12, the claim recites “a system for monitoring ultrasonic metering” but does not actually define a system for monitoring an ultrasonic metering device nor does the claim appear to disclose any details or structures unique or exclusive to ultrasonic metering devices or methods. The claim only recites that “the smart gas user platform is configured to send a query instruction of parameter management information of a gas device” and not that the gas device is an ultrasonic metering device or that the gas device is configured to perform any ultrasonic metering. The claim is incomplete for omitting essential elements, such omission amounting to a gap between the elements (see MPEP § 2172.01). The omitted elements are: ultrasonic metering devices.
The claim recites “a smart gas user platform”, “a smart gas service platform”, “a smart gas device management platform”, “a smart gas sensor network platform”, and “a smart gas object platform” without disclosing any corresponding structures. Accordingly, these limitations will be interpreted as programs or algorithms including functional language without reciting sufficient structure to perform the recited function, as the generic placeholder (a.k.a., platform) is not preceded by a structural modifier.
The claim recites the steps of “obtaining”, “judging”, and “performing interference processing” without disclosing the devices or components for performing the stated functions. The claim is incomplete for omitting essential elements, such omission amounting to a gap between the elements (see MPEP § 2172.01). The omitted elements are: the devices or components to obtain data, judge, and perform interference processing.
The claim recites the step of “obtaining target monitoring data at a target time point, a first monitoring data sequence within a first preset time period, and a second preset time period within a second preset time period from the smart gas object platform” without disclosing a device or component for obtaining the target monitoring data, the first monitoring data, and the second preset time period. In other words, the claim does not explain whether the target monitoring data and the first monitoring dare are measurements obtained from sensor or downloaded from a database. In particular, the claim does not explain that the data being obtained from the gas device. Furthermore, the phrase “the second monitoring data sequence” lacks proper antecedent basis, as the phrase was never defined in the claim.
The phrase “in response to a query instruction issued by the smart gas user platform” and “in response to the target monitoring data being the interference data” implies that the method steps only occur when the conditions precedent are met (i.e., when the query instruction is used by the smart gas user platform and when the target monitoring data is the interference data). In this case, the broadest reasonable interpretation of a method (or process) claim having contingent limitations requires only those steps that must be performed and does not include steps that are not required to be performed because the condition(s) precedent are not met (see MPEP 2111.04). Accordingly, the corresponding structures are not present in the claim, and these steps are considered contingent and neither are required by the broadest reasonable interpretation of the claim.
Regarding claim 13, the claim recites “a large number of second training samples” without providing a standard for “a large number”. Accordingly, the term “a large number” is not defined by the claim, and the specification does not provide a standard for ascertaining the requisite degree, and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention. Further clarification is respectfully requested.
Claims 14-19 are rejected as being dependent on the rejected base claim.
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (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 the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1-3, 12-14, and 20 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Shao et al. (Pub. No. US 2022/0163365) (hereafter Shao).
Regarding claim 1, Shao teaches a method for monitoring ultrasonic metering based on smart gas Internet of Things, wherein the method is implemented based on a system for monitoring ultrasonic metering, the system includes a smart gas user platform (i.e., user platform 110) (see Fig. 1), a smart gas service platform (i.e., service platform 120) (see Fig. 1), a smart gas device management platform (i.e., management platform 130) (see Fig. 1), a smart gas sensor network platform (i.e., sense network platform 140) (see Fig. 1), and a smart gas object platform that are connected in sequence (i.e., sense control platform 150) (see Fig. 1), and the method is executed by the smart gas device management platform, comprising:
in response to a query instruction issued by the smart gas user platform (i.e., in response to a query request received by a user platform, a natural gas detection parameter detected by a sense control platform may be obtained via a sense network platform) (see paragraph section [0037]),
obtaining target monitoring data at a target time point, a first monitoring data sequence within a first preset time period, and a second preset time period within a second preset time period from the smart gas object platform (i.e., a natural gas detection parameter detected by a sense control platform may be obtained via a sense network platform) (see paragraph section [0048]),
the monitoring data including at least one of a gas transportation feature and an environmental feature (i.e., the related information of the detection device may refer to information related to the detection device. The related information of the detection device may include working environment of the detection device (e.g., a temperature of the working environment, pressure of the working environment, etc.), detection parameters corresponding to the detection device, device information of the detection device, or the like. The device information of the detection device may include an error range of the detection device, a type of the detection device, a model of the detection device, a maintenance record of the detection device, service time of the detection device, or the like) (see paragraph section [0118]);
wherein the query instruction is sent to the smart gas object platform via the smart gas sensor network platform (i.e., the sense control platform 150 may be used to obtain the natural gas detection parameter) (see paragraph section [0039]);
judging, based on the first monitoring data sequence, the second monitoring data sequence, the target monitoring data,
a set of first time points of the first monitoring data sequence and an interference confidence level of each first time point (i.e., in operation 510, the at least one first detection parameter may be processed based on a predetermined algorithm, and the first energy data may be determined) (see Fig. 5),
a set of second time points of the second monitoring data sequence and an interference confidence level of each second time point (i.e., in operation 520, a second energy data may be determined by processing at least one second detection parameter based on the prediction model, and the second energy data may be determined) (see Fig. 5), and
the target time point of the target monitoring data and an interference confidence level of the target time point (i.e., in operation 530, an abnormal device may be determined based on the first energy data and the second energy data) (see Fig. 5),
whether the target monitoring data being interference data through an interference data determination model (i.e., in response to determining that the abnormal device exists, for each detection device of the at least one first detection device and the at least one second detection device, the processing module 320 may determine a probability that the detection device is abnormal based on related information of the detection device, the first energy data, and the second energy data, and determine the abnormal device based on the probability) (see paragraph sections [0102]-[0112]), and
the interference data determination model being a machine learning model (i.e., abnormality determination model may be a machine learning model) (see paragraph sections [0113]-[0127]); and
in response to the target monitoring data being the interference data, performing interference processing on the interference data (i.e., the comparison operation may be used to determine a difference between the first energy data and the second energy data. A result of the comparison operation may be used to determine whether the abnormal device exists. When the difference between the first energy data and the second energy data exceeds a predetermined threshold, there is an abnormal device in the detection device) (see paragraph sections [0113]-[0127]).
Regarding claim 2, Shao teaches a training process of the interference data determination model includes:
obtaining a large number of second training samples with second labels, each of the second training samples including a sample first monitoring data sequence, a sample second monitoring data sequence, sample target monitoring data, a set of first time points of the sample first monitoring data sequence and an interference confidence level of each first time point, a set of second time points of the sample second monitoring data sequence and an interference confidence level of each second time point, a target time point of the sample target monitoring data, and an interference confidence level of the target time point, and each of the second labels being whether the sample target monitoring data is the interference data; and
obtaining, based on the large number of second training samples with second labels, a trained interference data determination model (i.e., parameters of the abnormality determination model may be obtained by training. In some embodiments, the abnormality determination model may be obtained based on a large number of training samples with labels) (see paragraph sections [0113]-[0127]).
Regarding claim 3, Shao teaches that the interference confidence level is determined by a process including: selecting, from the first monitoring data sequence, the second monitoring data sequence, and the target monitoring data, a first count of the monitoring data at different time points as baseline data and a second count of the monitoring data at different time points as data to be assessed, respectively; and determining an interference confidence level of acquired time points corresponding to the data to be assessed through a confidence level determination model (i.e., the processing module 320 may determine the probability that the detection device is abnormal according to the related information of the detection device, the first energy data, and the second energy data) (see paragraph sections [0113]-[0127]), wherein the confidence level determination model is a trained machine learning model (i.e., the abnormality determination model may be a machine learning model) (see paragraph sections [0113]-[0127]).
Regarding claim 12, Shao teaches a system for monitoring ultrasonic metering based on smart gas Internet of Things, wherein the system includes a smart gas user platform (i.e., user platform 110) (see Fig. 1), a smart gas service platform (i.e., service platform 120) (see Fig. 1), a smart gas device management platform (i.e., management platform 130) (see Fig. 1), a smart gas sensor network platform (i.e., sense network platform 140) (see Fig. 1), and a smart gas object platform that are connected in sequence (i.e., sense control platform 150) (see Fig. 1);
the smart gas user platform is configured to send a query instruction of parameter management information of a gas device to the smart gas device management platform through the smart gas service platform (i.e., in response to a query request received by a user platform, a natural gas detection parameter detected by a sense control platform may be obtained via a sense network platform) (see paragraph section [0037]);
the smart gas device management platform is configured to:
in response to a query instruction issued by the smart gas user platform, obtain target monitoring data at a target time point, a first monitoring data sequence within a first preset time period, and a second preset time period within a second preset time period from the smart gas object platform (i.e., a natural gas detection parameter detected by a sense control platform may be obtained via a sense network platform) (see paragraph section [0048]),
the monitoring data including at least one of a gas transportation feature and an environmental feature (i.e., the related information of the detection device may refer to information related to the detection device. The related information of the detection device may include working environment of the detection device (e.g., a temperature of the working environment, pressure of the working environment, etc.), detection parameters corresponding to the detection device, device information of the detection device, or the like. The device information of the detection device may include an error range of the detection device, a type of the detection device, a model of the detection device, a maintenance record of the detection device, service time of the detection device, or the like) (see paragraph section [0118]);
wherein the query instruction is sent to the smart gas object platform via the smart gas sensor network platform (i.e., the sense control platform 150 may be used to obtain the natural gas detection parameter) (see paragraph section [0039]);
judge, based on the first monitoring data sequence, the second monitoring data sequence, the target monitoring data, a set of first time points of the first monitoring data sequence and an interference confidence level of each first time point (i.e., in operation 510, the at least one first detection parameter may be processed based on a predetermined algorithm, and the first energy data may be determined) (see Fig. 5), a set of second time points of the second monitoring data sequence and an interference confidence level of each second time point, and the target time point of the target monitoring data and an interference confidence level of the target time point (i.e., in operation 520, a second energy data may be determined by processing at least one second detection parameter based on the prediction model, and the second energy data may be determined) (see Fig. 5),
whether the target monitoring data being interference data through an interference data determination model (i.e., in response to determining that the abnormal device exists, for each detection device of the at least one first detection device and the at least one second detection device, the processing module 320 may determine a probability that the detection device is abnormal based on related information of the detection device, the first energy data, and the second energy data, and determine the abnormal device based on the probability) (see paragraph sections [0102]-[0112]), and the interference data determination model being a machine learning model (i.e., abnormality determination model may be a machine learning model) (see paragraph sections [0113]-[0127]); and in response to the target monitoring data being the interference data, perform interference processing on the interference data (i.e., the comparison operation may be used to determine a difference between the first energy data and the second energy data. A result of the comparison operation may be used to determine whether the abnormal device exists. When the difference between the first energy data and the second energy data exceeds a predetermined threshold, there is an abnormal device in the detection device) (see paragraph sections [0113]-[0127]).
Regarding claim 13, Shao teaches a training process of the interference data determination model includes: obtaining a large number of second training samples with second labels, each of the second training samples including a sample first monitoring data sequence, a sample second monitoring data sequence, sample target monitoring data, a set of first time points of the sample first monitoring data sequence and an interference confidence level of each first time point, a set of second time points of the sample second monitoring data sequence and an interference confidence level of each second time point, a target time point of the sample target monitoring data, and an interference confidence level of the target time point, and each of the second labels being whether the sample target monitoring data is the interference data; and obtaining, based on the large number of second training samples with second labels, a trained interference data determination model (i.e., parameters of the abnormality determination model may be obtained by training. In some embodiments, the abnormality determination model may be obtained based on a large number of training samples with labels) (see paragraph sections [0113]-[0127]).
Regarding claim 14, Shao teaches that the smart gas device management platform is further configured to: select, from the first monitoring data sequence, the second monitoring data sequence, and the target monitoring data, a first count of the monitoring data at different time points as baseline data and a second count of the monitoring data at different time points as data to be assessed, respectively; and determine an interference confidence level of acquired time points corresponding to the data to be assessed through a confidence level determination model (i.e., the processing module 320 may determine the probability that the detection device is abnormal according to the related information of the detection device, the first energy data, and the second energy data) (see paragraph sections [0113]-[0127]), wherein the confidence level determination model is a trained machine learning model (i.e., the abnormality determination model may be a machine learning model) (see paragraph sections [0113]-[0127]).
Regarding claim 20, Shao teaches a computer-readable non-transitory storage medium storing computer instructions, wherein a computer operates a method for monitoring ultrasonic metering based on smart gas Internet of Things according to claim 1 when reading the computer instructions (i.e., the computing device 200) (see paragraph section [0043]).
Claims 4-11 and 15-19 are objected to as being dependent on the rejected base claims.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure: see PTO-892.
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/Tran M. Tran/Examiner, Art Unit 2855