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 .
This communication is responsive to application filed on 07/05/2022.
Claims 1-14 presented for examination.
Preliminary Amendments
Applicant’s preliminary amendments to the Specification have been fully considered and are entered.
Information Disclosure Statement
The information disclosure statements (IDSs) submitted on 01/26/2022 and 07/05/2022 are in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Claim Objections
Claim 1 is objected to because of the following informalities: Claim recites “measures” that may need to be corrected as “measure” to reflect the previous limitation of “measure of susceptibility” in line 9. Appropriate correction is required.
Specification
The abstract of the disclosure is objected to because it exceeds 15 lines of text or 150 words. A corrected abstract of the disclosure is required and must be presented on a separate sheet, apart from any other text. See MPEP § 608.01(b).
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 1-14 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 1 recites “for each location and for one or more types of infrastructure component, each type having associated infrastructure characteristics identifying features of an infrastructure component of the type, executing a classifier to forecast a measure of susceptibility of infrastructure deployed at the location to one or more operational impediments of the infrastructure in use, the classifier being executed based on each of one or more of the infrastructure characteristics, the location characteristics for the location and the environmental characteristics” which is unclear. Therefore, it is vague and indefinite.
Claim 1 recites the limitation “measures" in line 14. If “measures” is referring to the previous “measure” in line 9, then there is insufficient antecedent basis for this limitation in the claim.
Claim 8 recites “a type of component”. Does this limitation refer to the previous limitation recited in claim 1? If so, then there is insufficient antecedent basis for this limitation in the claim.
Regarding claim 8, the phrase "such as" renders the claim indefinite because it is unclear whether the limitations following the phrase are part of the claimed invention. See MPEP § 2173.05(d).
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.
Step 1 (Does this claim fall within at least one statutory category?): Yes, the claim recites a series of steps and, therefore, is a process.
Step 2A, Prong 1: ((a) identify the specific limitation(s) in the claim that recites an abstract idea: and (b) determine whether the identified limitation(s) falls within at least one of the groups of abstract ideas enumerates in MPEP 2106.04(a)(2)):
Claim 1:
A computer implemented method of defining a deployment specification for one or more infrastructure components as part of a transmission network for a utility service in a defined geographic region, the region having associated environmental characteristics identifying environmental features of the region, and the region including a plurality of locations each having associated location characteristics identifying features of the location, the method comprising:
for each location and for one or more types of infrastructure component, each type having associated infrastructure characteristics identifying features of an infrastructure component of the type [“mental process i.e. concepts performed in the human mind or with pen and paper (including an observation, evaluation judgement, opinion), executing a classifier to forecast a measure of susceptibility of infrastructure deployed at the location to one or more operational impediments of the infrastructure in use, the classifier being executed based on each of one or more of the infrastructure characteristics, the location characteristics for the location and the environmental characteristics [“mental process i.e. concepts performed in the human mind or with pen and paper (including an observation, evaluation judgement, opinion)];
selecting one or more locations in the region based on the forecast susceptibilities to trigger deployment of infrastructure components at the selected locations [“mental process i.e. concepts performed in the human mind or with pen and paper (including an observation, evaluation judgement, opinion)].
Claim 1 recites “identifying”, “executing”, and “selecting” which fall into [“mental process i.e. concepts performed in the human mind or with pen and paper (including an observation, evaluation judgement, opinion)].
Step 2A, Prong 2 (1. Identifying whether there are any additional elements recited in the claim beyond the judicial exception; and 2. Evaluating those additional elements individually and in combination to determine whether the claim as a whole integrates the exception into a practical application): there is no any additional elements that integrate into practical application.
Step 2B: (Does the claim recite additional elements that amount to significantly more than the judicial exception? No): There is no any additional elements that amounts to significantly more than the judicial exception.
As per claim 2, the claim falls into [“mental process i.e. concepts performed in the human mind or with pen and paper (including an observation, evaluation judgement, opinion)].
As per claim 3, the claim falls into [“mental process i.e. concepts performed in the human mind or with pen and paper (including an observation, evaluation judgement, opinion)].
As per claim 4, the claim falls into generic computer component/machine learning.
As per claim 5, the claim falls into [“mental process i.e. concepts performed in the human mind or with pen and paper (including an observation, evaluation judgement, opinion)].
As per claim 6, the claim falls into [“mental process i.e. concepts performed in the human mind or with pen and paper (including an observation, evaluation judgement, opinion) and/or generic computer components].
As per claim 7, the claim falls into [“mental process i.e. concepts performed in the human mind or with pen and paper (including an observation, evaluation judgement, opinion) and/or generic computer components].
As per claim 8, the claim falls into [“mental process i.e. concepts performed in the human mind or with pen and paper (including an observation, evaluation judgement, opinion) and/or generic computer components].
As per claim 9, the claim falls into [“mental process i.e. concepts performed in the human mind or with pen and paper (including an observation, evaluation judgement, opinion) and/or generic computer components].
As per claim 10, the claim falls into [“mental process i.e. concepts performed in the human mind or with pen and paper (including an observation, evaluation judgement, opinion) and/or generic computer components].
As per claim 11, the claim falls into [“mental process i.e. concepts performed in the human mind or with pen and paper (including an observation, evaluation judgement, opinion)].
As per claim 12, the claim falls into [“mental process i.e. concepts performed in the human mind or with pen and paper (including an observation, evaluation judgement, opinion)].
As per claim 13, independent claim 13 recites limitations analogous in scope to those of independent claim 1, and as such are similar rejected. Further, claim 13 recites additional elements of “a processor” and “a memory”. The components recited at a high level of generality (e.g. a generic computer element for performing a generic computer functions) such that it amounts to no more than mere application of the judicial exception using generic computer component(s). Accordingly, the additional element(s) of each of these claims do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. Further, as discussed above with respect to the integration of the abstract into a practical application, the additional elements of “processor” and “memory” amount to no more than mere instructions to apply the judicial exception using generic computer component(s). Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept.
As per Claim 14, claim 14 recites limitations analogous in scope to those of claim 1, and as such is similarly rejected.
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claim 14 is rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. The claim(s) does/do not fall within at least one of the four categories of patent eligible subject matter because product that does not have a physical or tangible form, such as information (often referred to as "data per se") or a computer program per se (often referred to as "software per se") when claimed as a product without any structural recitations.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1-14 are rejected under 35 U.S.C. 103 as being unpatentable over US Publication No. 2019/0303810 A1 issued to Gross et al in view of US Publication No. 2016/0356666 A1 issued to BILAL et al.
1. Gross et al discloses a computer implemented method of defining a deployment specification for one or more infrastructure components as part of a transmission network for a utility service in a defined geographic region, the region having associated environmental characteristics identifying environmental features of the region (See: [0064] The integration of the foregoing risk indices with the results of the network-importance analysis provides significant advantages for performing optimal resource allocation during severe storm events. For example, consider two identical transformers A and B in the grid, which are located in regions that are likely to be impacted by an approaching storm) , and the region including a plurality of locations each having associated location characteristics identifying features of the location (See: [0064] The integration of the foregoing risk indices with the results of the network-importance analysis provides significant advantages for performing optimal resource allocation during severe storm events. For example, consider two identical transformers A and B in the grid, which are located in regions that are likely to be impacted by an approaching storm), the method comprising:
for each location and for one or more types of infrastructure components, each type having associated infrastructure characteristics identifying features of an infrastructure component of the type (See: par [0011] In some embodiments, the different types of nodes in the utility network include: generating plants; transmission lines; and transformers; [0064] The integration of the foregoing risk indices with the results of the network-importance analysis provides significant advantages for performing optimal resource allocation during severe storm events. For example, consider two identical transformers A and B in the grid, which are located in regions that are likely to be impacted by an approaching storm. Suppose that the two identical transformers A and B are both assigned a quantitative risk index of 0.95, but the network-importance analysis reveals that transformer A is “fault tolerant” in the grid architecture and may only impact a one-block area if it fails, whereas transformer B is located in a region of the grid where its failure can cause a series cascade of downstream failures, which could take out a 10-square-mile region containing tens of thousands of homes),;
selecting one or more locations in the region based on the forecast measures of susceptibility to trigger deployment of infrastructure components at the one or more selected locations (See: [0008] In some embodiments, determining the node failure probabilities for nodes in the utility network involves: determining a susceptibility of the nodes in the utility network to weather-induced failures based on the historical weather data and the historical node failure data; determining a node-specific weather forecast for each node in the utility network based on the historical weather forecast information; and determining the node failure probability for each node in the utility network based on the node-specific weather forecast for the node and the susceptibility of the node to a weather-induced failure; par [0064] The integration of the foregoing risk indices with the results of the network-importance analysis provides significant advantages for performing optimal resource allocation during severe storm events. For example, consider two identical transformers A and B in the grid, which are located in regions that are likely to be impacted by an approaching storm. Suppose that the two identical transformers A and B are both assigned a quantitative risk index of 0.95, but the network-importance analysis reveals that transformer A is “fault tolerant” in the grid architecture and may only impact a one-block area if it fails, whereas transformer B is located in a region of the grid where its failure can cause a series cascade of downstream failures, which could take out a 10-square-mile region containing tens of thousands of homes. In this case, the emergency response center would proactively direct a repair crew to transformer B instead of transformer A to prevent the possible cascade of downstream failures).
Gross et al discloses one or more of the infrastructure characteristics, the location characteristics for the location, and the environmental characteristics (See: par [0049] these external stress factors can include: high winds; high temperatures that can cause thermal-accelerated degradation in electronic and electromechanical systems; very high relative humidity, which can cause internal condensation during sudden temperature drops, and can lead to internal shorting; very low relative humidity, which can lead to arcing failures in assets with high potential-difference gradients between parallel conductors; and heavy precipitation, which can accelerate tree-branch failures in warm environments and snow- and ice-related failures in cold environments; [0064] The integration of the foregoing risk indices with the results of the network-importance analysis provides significant advantages for performing optimal resource allocation during severe storm events. For example, consider two identical transformers A and B in the grid, which are located in regions that are likely to be impacted by an approaching storm. Suppose that the two identical transformers A and B are both assigned a quantitative risk index of 0.95, but the network-importance analysis reveals that transformer A is “fault tolerant” in the grid architecture and may only impact a one-block area if it fails, whereas transformer B is located in a region of the grid where its failure can cause a series cascade of downstream failures, which could take out a 10-square-mile region containing tens of thousands of homes).
However, Gross et al does not specify but Bilal et al discloses executing a classifier to forecast a measure of susceptibility of infrastructure deployed at the location to one or more operational impediments of the infrastructure in use (See: Abstract, The sensor node also includes a trained classifier configured to test real-time sensor data received from the one or more sensors and processed by the processor. The sensor node also includes a transmitter configured to forward classified data from the trained classifier to a sink node within the fluid transportation infrastructure; par [0010] The circuitry is further configured to determine a final learned classifier with a maximum generalization capability from the trained one or more classifiers, and run real-time fluid flow data from the fluid transportation infrastructure. The circuitry is further configured to identify leakage signals from the running real-time fluid flow data using the final learned classifier; par [0036] The classifiers are used to detect the status of failure in pipelines; [0169] Embodiments herein also describe a sensor node includes one or more sensors configured to measure sensory information collected from fluid flowing through a fluid transportation infrastructure. The sensor node also includes a processor configured to remove noise and unnecessary data from the collected sensory information and to extract statistical attributes associated with a leakage within the fluid transportation infrastructure. The sensor node also includes a trained classifier configured to test real-time sensor data received from the one or more sensors and processed by the processor. The sensor node also includes a transmitter configured to forward classified data from the trained classifier to a sink node within the fluid transportation infrastructure).
It would have been obvious before the effective filing date to combine intelligent leakage detection system for pipelines as taught by BILAL et al to deploying utility repair assets of Gross et al would be to ensure a safety and maintenance of underground and above ground pipelines (BILAL et al, par [0002]).
2. Bilal et al discloses the method of claim 1 wherein selecting the one or more locations further includes selecting at least one type of infrastructure component for each of the one or more selected locations (See: par [0071] Different node types used inside the network are identified by a device type identifier).
3. Gross et al discloses the method of any preceding claim 1 wherein selecting the one or more locations is further based on at least one predetermined location, the predetermined location being a location at or to which infrastructure is to be deployed (See: [0064] The integration of the foregoing risk indices with the results of the network-importance analysis provides significant advantages for performing optimal resource allocation during severe storm events. For example, consider two identical transformers A and B in the grid, which are located in regions that are likely to be impacted by an approaching storm. Suppose that the two identical transformers A and B are both assigned a quantitative risk index of 0.95, but the network-importance analysis reveals that transformer A is “fault tolerant” in the grid architecture and may only impact a one-block area if it fails, whereas transformer B is located in a region of the grid where its failure can cause a series cascade of downstream failures, which could take out a 10-square-mile region containing tens of thousands of homes).
4. BILAL et al discloses the method of any preceding claim 1 wherein the classifier is trained based on training data items each relating to one or more deployed infrastructure components in respect of which the training data item includes one or more of infrastructure characteristics, location characteristics, and environmental characteristics, and the training data item further includes an indication of one or more operational impediments affecting the one or more deployed infrastructure components (See: [0033] Each sensor node of a first tier is designed and configured to collect ambient data, process and analyze the data for a suspected leak, and transmit the data to a sink node. FIG. 2A illustrates a layout of an exemplary sensor node 200. Sensor node 200 can have a plurality of sensors 210 including, but not limited to a location sensor, a pressure sensor, a temperature sensor, a stress sensor, a corrosion sensor, and a thermal imaging sensor. However, other sensors 210 are contemplated by embodiments described herein, and could depend upon factors such as the type of fluid being transported, the size of the pipeline transportation infrastructure, the geographic area, natural and manmade environmental factors, natural and manmade risk factors, etc. Other units in the sensor node 200 include a transmitter/receiver 220 and a power unit 230. However, other units are contemplated by embodiments described herein and could depend upon factors, such as the factors described above).
5. BILAL et al discloses the method of any preceding claim 1 wherein an operational impediment of infrastructure is an impediment to the operation of, access to, or maintenance of the infrastructure in use (See: [0090] The exemplary remote monitoring software system includes a menu bar and a tool bar to enable performing functionalities, such as data acquisition, and data representation and maintenance).
6. BILAL et al discloses the method of any preceding claim 1 wherein an infrastructure component includes one or more of: a duct; a conduit; a pipe; a cable; a pole; a pylon; and a tower (See: par [0025] a network of autonomous wireless sensor nodes that are designed and configured to detect fluid leakage in its proximity within a fluid pipeline transportation infrastructure).
7. BILAL et al discloses the method of any preceding claim 1 wherein operational impediments include one or more of: erosion; corrosion; rotting; movement; damage; being struck; fracture; perforation; blockage; clogging; collapse; silting; and pest damage (See: Abstract, extract statistical attributes associated with a leakage within the fluid transportation infrastructure; par [0005] a sensor node leakage detection system includes circuitry configured to preprocess fluid flow data from leakage signals and non-leakage signals of a fluid transportation infrastructure; .
8. BILAL et al discloses the method of any preceding claim 1 wherein features of an infrastructure component include one or more of: a type of component including one or more of a duct; a conduit; a pipe; a cable; a pole; a pylon; and a tower; one or more materials of manufacture of the component (See: par [0025] a network of autonomous wireless sensor nodes that are designed and configured to detect fluid leakage in its proximity within a fluid pipeline transportation infrastructure); one or more configurations of the component (See: par [0025] Embodiments herein describe a network of autonomous wireless sensor nodes that are designed and configured to detect fluid leakage in its proximity within a fluid pipeline transportation infrastructure. Multiple sensory nodes are placed along the pipeline infrastructure, which communicate with one or more remote sink nodes. The embodiments can be used for an above-ground pipeline transportation infrastructure or a buried pipeline transportation infrastructure, wherein a buried pipeline infrastructure can be located below the ground surface, below a water surface, or within a deep or buried enclosure below a surrounding ground level); a deployment feature of the component such as being laid or hung (See: par [0025] The embodiments can be used for an above-ground pipeline transportation infrastructure or a buried pipeline transportation infrastructure, wherein a buried pipeline infrastructure can be located below the ground surface, below a water surface, or within a deep or buried enclosure below a surrounding ground level); and one or more physical characteristics of the component including one or more of: [[a]] mass; density; porosity; permeability; cross-sectional shape; rigidity; strength such as tensile or compressive strength; corrosion resistance; flexibility; brittleness; durability; elasticity; resilience; and thermal properties (See: [0033] Each sensor node of a first tier is designed and configured to collect ambient data, process and analyze the data for a suspected leak, and transmit the data to a sink node. FIG. 2A illustrates a layout of an exemplary sensor node 200. Sensor node 200 can have a plurality of sensors 210 including, but not limited to a location sensor, a pressure sensor, a temperature sensor, a stress sensor, a corrosion sensor, and a thermal imaging sensor).
9. Gross et al discloses the method of claim 1 wherein features of a location include one or more of: a topography of the location including one or more of: elevation; altitude; slope; and incline; a longitude and/or latitude of the location; a latitude of the location; a relative water table level or an absolute water table level for the location; water flow information for the location; an identification of one or more of faults, fissures, shafts and voids in the ground at the location; a type of soil at the location; an identification of one or more of mineral and resource deposits at the location; a history of the location including one or more of: prior development at the location; and prior uses of the location; soil salinity; airborne salinity geographic features at or proximate to the location including one or more of: natural, landform, and artificial features; an identification of vegetation at or proximate to the location; an identification of streams, rivers, seas, oceans, or deltas at or proximate to the location; an identification of hills, mountains, and plains at or proximate to the location; an identification of one or more pre-existing infrastructure components at or proximate to the location including: ducts; conduits; pipes; cables; poles; pylons; and towers; and an identification of buildings at or proximate to the location (See: [0011] In some embodiments, the different types of nodes in the utility network include: generating plants; transmission lines; and transformers; [0012] In some embodiments, the weather forecast information comprises specific weather forecasts for geographically distributed weather feed locations; [0052] To apply BCT to optimal utility resource allocation during such weather events, we first create a large database of utility system assets with latitude and longitude coordinates, and a second large database of weather feeds located throughout the region served by the utility (typically more than 10,000 square miles for average utilities) also with latitude and longitude coordinates).
10. HILAL et al discloses the method of any preceding claim 1 wherein environmental features include one or more of: climatic features including a statistical measure of one or more of a statistical measure of: temperature; humidity; pressure; wind; and precipitation (See: [0033] Each sensor node of a first tier is designed and configured to collect ambient data, process and analyze the data for a suspected leak, and transmit the data to a sink node. FIG. 2A illustrates a layout of an exemplary sensor node 200. Sensor node 200 can have a plurality of sensors 210 including, but not limited to a location sensor, a pressure sensor, a temperature sensor, a stress sensor, a corrosion sensor, and a thermal imaging sensor); and weather features including one or more of frequency and severity of one or more of: flooding; storm; excessive wind speed; drought; cold event; snow; and ice (See: par [0033] other sensors 210 are contemplated by embodiments described herein, and could depend upon factors such as the type of fluid being transported, the size of the pipeline transportation infrastructure, the geographic area, natural and manmade environmental factors, natural and manmade risk factors, etc.).
11. BILAL et al discloses the method of any preceding claim 1 wherein selecting one or more locations based on the forecast susceptibilities includes ranking each location based on one or more metrics derived from the forecasting by the classifier for the location (See: Abstract, A pipeline system and method include a sensor node having one or more sensors configured to measure sensory information collected from fluid flowing through a fluid transportation infrastructure. The sensor node also includes a processor configured to remove noise and unnecessary data from the collected sensory information and to extract statistical attributes associated with a leakage within the fluid transportation infrastructure. The sensor node also includes a trained classifier configured to test real-time sensor data received from the one or more sensors and processed by the processor. The sensor node also includes a transmitter configured to forward classified data from the trained classifier to a sink node within the fluid transportation infrastructure; par [0027] Nodes are designed and configured to convert measured metrics from their associated sensors into digital information to be read and processed by a remote monitoring facility. ).
12. BILAL et al discloses the method of claim 11 wherein a metric is evaluated for each location based on a combination of each forecast measure of susceptibility for each of one or more impediments for the location (See: Abstract, A pipeline system and method include a sensor node having one or more sensors configured to measure sensory information collected from fluid flowing through a fluid transportation infrastructure. The sensor node also includes a processor configured to remove noise and unnecessary data from the collected sensory information and to extract statistical attributes associated with a leakage within the fluid transportation infrastructure. The sensor node also includes a trained classifier configured to test real-time sensor data received from the one or more sensors and processed by the processor. The sensor node also includes a transmitter configured to forward classified data from the trained classifier to a sink node within the fluid transportation infrastructure).
As per claims 13-14, claims 13-14 recite limitations analogous in scope to those of claim 1, and as such are similarly rejected.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to KIBROM K GEBRESILASSIE whose telephone number is (571)272-8571. The examiner can normally be reached M-F 9:00 AM-5:30 PM.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Rehana Perveen can be reached at 571 272 3676. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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KIBROM K. GEBRESILASSIE
Primary Examiner
Art Unit 2189
/KIBROM K GEBRESILASSIE/Primary Examiner, Art Unit 2189 10/15/2025