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 .
Information Disclosure Statement
The information disclosure statement (IDS) was submitted on 10/02/2023. The submission is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
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. The claimed invention is directed to the abstract concept of performing abstract steps without significantly more. The claim(s) recite(s) the following abstract concepts in BOLD of
1. A computer-implemented method for predicting power outages for use in vegetation management, the computer-implemented method comprising:
obtaining historical weather data for a geographical location including a utility line;
obtaining historical outage data for the utility line;
training an outage prediction model based at least in part on the historical weather data and the historical outage data;
generating, based on the historical weather data, a future storm data set;
obtaining an outage prediction score for the utility line by inputting the future storm data set into the outage prediction model; and
creating a vegetation trimming score for the utility line based at least in part on the outage prediction score, wherein the vegetation trimming score for the utility line indicates an urgency for performing trimming vegetation adjacent to the utility line.
11. A computer program product having one or more computer readable storage media having computer readable program code collectively stored on the one or more computer readable storage media, the computer readable program code being executed by a processor of a computer system to cause the computer system to perform operations comprising:
obtaining historical weather data for a geographical location including a utility line;
obtaining historical outage data for the utility line;
training an outage prediction model based at least in part on the historical weather data and the historical outage data;
generating, based on the historical weather data, a future storm data set;
obtaining an outage prediction score for the utility line by inputting the future storm data set into the outage prediction model; and
creating a vegetation trimming score for the utility line based at least in part on the outage prediction score, wherein the vegetation trimming score for the utility line indicates an urgency for performing trimming vegetation adjacent to the utility line.
20. A computing system comprising:
a processor;
a memory coupled to the processor; and
one or more computer readable storage media coupled to the processor, the one or more computer readable storage media collectively containing instructions that are executed by the processor via the memory to cause the processor to perform operations comprising:
obtaining historical weather data for a geographical location including a utility line;
obtaining historical outage data for the utility line;
training an outage prediction model based at least in part on the historical weather data and the historical outage data;
generating, based on the historical weather data, a future storm data set;
obtaining an outage prediction score for the utility line by inputting the future storm data set into the outage prediction model; and
creating a vegetation trimming score for the utility line based at least in part on the outage prediction score, wherein the vegetation trimming score for the utility line indicates an urgency for performing trimming vegetation adjacent to the utility line.
Under step 1 of the eligibility analysis, we determine whether the claims are to a statutory category by considering whether the claimed subject matter falls within the four statutory categories of patentable subject matter identified by 35 U.S.C. 101: process, machine, manufacture, or composition of matter. The above claims are considered to be in a statutory category.
Under Step 2A, Prong One, we consider whether the claim recites a judicial exception (abstract idea). In the above claim, the highlighted portion constitutes an abstract idea because, under a broadest reasonable interpretation, it recites limitation the fall into/recite abstract idea exceptions. Specifically, under the 2019 Revised Patent Subject Matter Eligibility Guidance, it falls into the grouping of subject matter that, when recited as such in a claim limitation, covers performing mathematics.
Next, under Step 2A, Prong Two, we consider whether the claim that recites a judicial exception is integrated into a practical application. In this step, we evaluate whether the claim recites additional elements that integrate the exception into a practical application of that exception.
This judicial exception is not integrated into a practical application because there is no improvement to another technology or technical field; improvements to the functioning of the computer itself; a particular machine; effecting a transformation or reduction of a particular article to a different state or thing. Examiner notes that since the claimed methods and system are not tied to a particular machine or apparatus, they do not represent an improvement to another technology or technical field. Similarly there are no other meaningful limitations linking the use to a particular technological environment. Finally, there is nothing in the claims that indicates an improvement to the functioning of the computer itself or transform a particular article to a new state.
Finally, under Step 2B, we consider whether the additional elements are sufficient to amount to significantly more than the abstract idea. Claims 1, 11, and 20 do not include additional elements that are sufficient to amount to significantly more than the judicial exception because obtaining historical weather data and historical outage data is considered necessary data gathering. As recited in MPEP section 2106.05(g), necessary data gathering (i.e. obtaining data) is considered extra solution activity in light of Mayo, 566 U.S. at 79, 101 USPQ2d at 1968; OIP Techs., Inc. v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1092-93 (Fed. Cir. 2015). The processor and memory of Claim 20 and the processor and computer readable storage media of Claim 11 are interpreted under broadest reasonable interpretation to be a generic computer elements. Generic computer elements are not considered significantly more than the abstract idea and do not integrate the abstract idea into a practical application. As recited in the MPEP, 2106.05(b), merely adding a generic computer, generic computer components, or a programmed computer to perform generic computer functions does not automatically overcome an eligibility rejection. Alice Corp. Pty. Ltd. v. CLS Bank Int'l, 134 S. Ct. 2347, 2359-60, 110 USPQ2d 1976, 1984 (2014). See also OIP Techs. v. Amazon.com, 788 F.3d 1359, 1364, 115 USPQ2d 1090, 1093-94.
Claim 2-10 and 13-19 further limit the abstract ideas without integrating the abstract concept into a practical application or including additional limitations that can be considered significantly more than the abstract idea.
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.
Claims 1-3, 5, 9-13, 15, and 19-20 are rejected under 35 U.S.C. 103 as being unpatentable over Sun (US20220236451) in view of Cook (US20240168198), Lubkeman (US20050096856), and Neuenschwander (US20200235559).
In regards to Claims 1, 11, and 20, Sun teaches “a processor (processor – [0019]; computer with memory – [0077]);
a memory coupled to the processor (computer with memory – [0077]); and
one or more computer readable storage media coupled to the processor (computer program product – [0041]), the one or more computer readable storage media collectively containing instructions that are executed by the processor via the memory to cause the processor to perform operations comprising:
obtaining historical weather data for a geographical location including a utility line (“The predetermined region is a service area by a power distribution system and contains a set of components related to overhead distribution lines [i.e. including a utility line]. A hardware or electronic processor can be used to access data, including given weather variables related to wind gust speeds and lightning stroke currents corresponding to a model forcing (also referred as “a set of weather drivers”), and historical data that includes weather and component data, such as weather events corresponding to component outages for the predetermined region, from data storage. The weather can include data including one or a combination of: seasonal data; classifications of impending weather events as one of snow, ice, mixed snow and rain, rain/flood, wind, thunderstorm/lightning, ambient air temperature, wildfire, hurricane, severe weather ratings, severe storm ratings, other mixed events, etc. The component can include data for each component including one or a combination of: age; material type; designated type and design; mounting and support equipment; electrical equipment; vegetation rating; tree limb trimming rating; leaf rating; installation and maintenance logs; severe weather exposure logs; vegetation and tree growth ratings around overhead lines. Wherein the historical weather data and the historical component data that include weather events corresponding to component outages for the predetermined region can be used to define model forcing intensity groups using variables of historical weather events, and configure for machine learning (ML) model for each model forcing intensity group” – [0019]);
obtaining historical outage data for the utility line (historical data that includes weather and component data, such as weather events corresponding to component outages for the predetermined region, i.e. historical outage data for the utility line – [0019]);
training an outage prediction model based at least in part on the historical weather data and the historical outage data (“historical data that includes weather and component data [i.e. historical weather and historical outage data]” – [0019]; “The failure forecasting model can be first trained using historical data” – [0026]);
obtaining an outage prediction score for the utility line by inputting data into the outage prediction model (“A step that generates for the updated ML model, an updated output value predicting a component failure or no component failure for each component for the time period” - [0025])”
Sun is silent with regards to the language of “generating, based on the historical weather data, a future storm data set”
Cook teaches “generating, based on the historical weather data, a future storm data set (“In one embodiment, the systems and methods herein are configured to generate an L-model, which identifies relationships between historical atmospheric and oceanic data and historical severe weather reports to generate forecasts of tornadoes, hail, and severe thunderstorm wind gusts. In another embodiment, relationships between tropical cyclone frequency and historical atmospheric and oceanic conditions are identified and used to create tropical cyclone forecasts. In yet another embodiment, relationships between precipitation and historical atmospheric and oceanic conditions are used to create precipitation forecasts. The forecasts developed using the systems and methods disclosed herein can span various timeframes ranging from a few minutes to one year or more. In various embodiments, forecasts can be created for meta-time periods, such as seasons, years, or even decades” – [0038])”
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Sun to incorporate the teaching of Cook to utilize the historical weather data to perform weather forecasting for future storms. By utilizing the historical weather data for weather forecasting, this is an improvement that yields predictable results for providing extended range predictions of severe weather.
Sun in view of Cook are silent with regards to the language of “obtaining an outage prediction score for the utility line by inputting the future storm data set into the outage prediction model.”
Lubkeman teaches “obtaining an outage prediction score for the utility line by inputting the future storm data set into the outage prediction model (“The weather prediction may include predicted wind speed and duration, a predicted storm duration, a predicted snowfall amount, a predicted icing amount, and a predicted rainfall amount, a predicted storm type (e.g., hurricane, wind, ice, tornado, lighting, etc.), a predicted lightning location and intensity, and the like. The weather prediction may be embodied in or may accompany a Geographic Information System (GIS) file, or the like. Weather prediction service 200 may include a national weather service bureau, a commercial weather service organization, an automated weather prediction service, or the like” – [0037]; “damage prediction engine 120 receives the weather prediction from weather prediction service 200 , which may be in the format of GIS files. Damage prediction engine 120 may convert the weather prediction to an indication of predicted intensity, such as, for example, a number using a simple scaling system. For example, the intensity of the storm may be rated on a scale from 1 to 3, from 1 to 10, and the like. Alternatively, various aspects of the weather, such as, for example, predicted wind speed, predicted rainfall amount, and the like may be rated on such a scale. Alternatively, more complex systems may be used to convert the weather prediction to an indication of predicted intensity. For example, conversions between wind speed and predicted intensity may be done on a smaller geographic basis (e.g., an intensity indication per feeder rather than an intensity indication per power circuit). Conversions may be linear, exponential, logarithmic, and the like. Additionally, a user may input, and damage prediction engine 120 may receive a predicted intensity. In this manner, a user may perform “what-if” analyses for various types of storms. For example, a user may enter a predicted storm intensity of ‘3’ into a system and computing application 85 may determine predicted damages and predicted maintenance parameters (e.g., predicted number of customers, predicted time to restore each customer, etc.) based on the user-entered storm intensity.” – [0039]).”
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Sun in view of Cook to incorporate the teaching of Lubkeman to utilize weather forecasts to determine predicted damage and maintenance needed due to the storms to power circuits. By utilizing the predicted weather forecasts to determine predicted damage, this is an improvement to the efficiency and speed that resource management can be to prepare for potential storm damage and reduce outages.
Sun in view of Cook and Lubkeman are silent with regards to the language of “creating a vegetation trimming score for the utility line based at least in part on the outage prediction score, wherein the vegetation trimming score for the utility line indicates an urgency for performing trimming vegetation adjacent to the utility line”
Neuenschwander teaches “creating a vegetation trimming score for the utility line based at least in part on the outage prediction score, wherein the vegetation trimming score for the utility line indicates an urgency for performing trimming vegetation adjacent to the utility line (“A weighted prioritization can occur for areas to perform maintenance due to a projected outage impact” – [0026]; “An example workflow cycle can include the vegetation management system 100 identifying needed actions, planning such actions, having the actions performed (e.g., tree trimming), auditing the actions to ensure 3rd party crews are in compliance with utility work contracts, updating the outstanding maintenance jobs in the work queue as successfully or unsuccessfully completed, and repeating the workflow by then again having the vegetation management system 100 identifying needed actions. The visual imagery enables identifying the types of vegetation (e.g., types of trees, bushes and vines) and their proximity to line corridors of the power lines and other power distribution assets. Predictive analytics can be used to numerically score regions in order of the predicted time of vegetative encroachment which can indicate relative priority in performing maintenance around the locations of the electrical power lines areas regions and the types of equipment needed to perform maintenance” – [0029])”
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Sun in view of Cook and Lubkeman to incorporate the teaching of Neuenschwander to utilize a vegetation management system to plan for actions and perform actions based on a predictive score. By utilizing the vegetation management system to send out actions to perform, this is an improvement that yields predictable results to improve the safety and reliability of the electricity distribution system.
In regards to Claims 2 and 12, Sun in view of Cook, Lubkeman, and Neuenschwander discloses the claimed invention as detailed above. Sun is silent with regards to the language of “wherein the future storm data set includes expected daily meteorological data corresponding to a plurality of storms in the geographical location including the utility line during a time period.”
Cook further teaches “wherein the future storm data set includes expected daily meteorological data corresponding to a plurality of storms in the geographical location including the utility line during a time period (“In one embodiment, the systems and methods herein are configured to generate an L-model, which identifies relationships between historical atmospheric and oceanic data and historical severe weather reports to generate forecasts of tornadoes, hail, and severe thunderstorm wind gusts, i.e. plurality of storms.” – [0038]; “Forecast output from these systems may exist in the form of daily analyses [i.e. daily meteorological data], monthly (time-averaged) analyses, hourly, daily forecasts, or monthly (time-averaged) forecasts. These forecasts may also comprise similar atmospheric and oceanic variables as those contained in reanalysis datasets (described above) and can also contain calculations of convective available potential energy, vertical wind shear, and storm relative helicity, which can be important for embodiments involving shorter-term forecasts of tornadoes, hail, and damaging wind gusts” – [0053])”
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify un in view of Cook, Lubkeman, and Neuenschwander to incorporate the further teaching of Cook to utilize the historical weather data to perform weather forecasting for future storms. By utilizing the historical weather data for weather forecasting, this is an improvement that yields predictable results for providing extended range predictions of severe weather.
In regards to Claims 3 and 13, Sun in view of Cook, Lubkeman, and Neuenschwander discloses the claimed invention as detailed above. Sun is silent with regards to the language of “wherein the future storm data set is generated using a storm probability score corresponding to each of the plurality of storms, wherein the storm probability score indicates a probability of occurrence each of the plurality of storms in the time period.”
Cook further teaches “wherein the future storm data set is generated using a storm probability score corresponding to each of the plurality of storms, wherein the storm probability score indicates a probability of occurrence each of the plurality of storms in the time period (method automatically generates probabilities, i.e. probability score, for the forecasts, i.e. for each storm – [0159]).”
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify un in view of Cook, Lubkeman, and Neuenschwander to incorporate the further teaching of Cook to utilize probabilities with the forecasts. By utilizing the historical weather data for weather forecasting, this is an improvement that yields predictable results for providing extended range predictions of severe weather.
In regards to Claims 5 and 15, Sun in view of Cook, Lubkeman, and Neuenschwander discloses the claimed invention as detailed above. Sun is silent with regards to the language of “wherein the time period is at least one year”.
Cook further teaches “wherein the time period is at least one year (“In various embodiments, forecasts can be created for meta-time periods, such as seasons, years [i.e. at least one year], or even decades” – [0038])”.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify un in view of Cook, Lubkeman, and Neuenschwander to incorporate the further teaching of Cook to utilize the historical weather data to perform weather forecasting for future storms. By utilizing the historical weather data for weather forecasting, this is an improvement that yields predictable results for providing extended range predictions of severe weather.
In regards to Claim 9, Sun in view of Cook, Lubkeman, and Neuenschwander discloses the claimed invention as detailed above. Sun is silent with regards to the language of “wherein the outage prediction model is a machine learning model employing a random forest classifier.”
Cook further teaches “wherein the outage prediction model is a machine learning model employing a random forest classifier (random decision forest for machine learning algorithm – [0106]).”
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify un in view of Cook, Lubkeman, and Neuenschwander to incorporate the further teaching of Cook to random decision forest as a machine learning algorithm. By utilizing a random decision forest machine learning algorithm this is an improvement that yields predictable results with the operation of machine learning models.
In regards to Claims 10 and 19, Sun in view of Cook, Lubkeman, and Neuenschwander discloses the claimed invention as detailed above. Sun is silent with regards to the language of “wherein the vegetation trimming score is further based on one or more of a vegetation score and a priority score for the utility line.”
Neuenschwander further teaches “wherein the vegetation trimming score is further based on one or more of a vegetation score and a priority score for the utility line (“A weighted prioritization can occur for areas to perform maintenance due to a projected outage impact” – [0026]; “An example workflow cycle can include the vegetation management system 100 identifying needed actions, planning such actions, having the actions performed (e.g., tree trimming), auditing the actions to ensure 3rd party crews are in compliance with utility work contracts, updating the outstanding maintenance jobs in the work queue as successfully or unsuccessfully completed, and repeating the workflow by then again having the vegetation management system 100 identifying needed actions. The visual imagery enables identifying the types of vegetation (e.g., types of trees, bushes and vines) and their proximity to line corridors of the power lines and other power distribution assets. Predictive analytics can be used to numerically score regions in order of the predicted time of vegetative encroachment which can indicate relative priority in performing maintenance around the locations of the electrical power lines areas regions and the types of equipment needed to perform maintenance” – [0029]).”
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Sun in view of Cook and Lubkeman to incorporate the teaching of Neuenschwander to utilize a vegetation management system to plan for actions and perform actions based on a predictive score. By utilizing the vegetation management system to send out actions to perform, this is an improvement that yields predictable results to improve the safety and reliability of the electricity distribution system.
Claims 6-8 and 16-18 are rejected under 35 U.S.C. 103 as being unpatentable over Sun in view of Cook, Lubkeman, and Neuenschwander as applied to claim 2 and 12 above, and further in view of Doostan (Milad Doostan et al., “Predicting Lightning-Related Outages in Power Distribution Systems; A statistical Approach”, 05/06/2020, IEEEAccess, https://ieeexplore.ieee.org/document/9084149)
In regards to Claims 6 and 16, Sun in view of Cook, Lubkeman, and Neuenschwander discloses the claimed invention as detailed above. Sun in view of Cook, Lubkeman, and Neuenschwander are silent with regards to the language of “wherein the outage prediction model is configured to calculate a daily outage score for each day during the time period.”
Doostan teaches “wherein the outage prediction model is configured to calculate a daily outage score for each day during the time period (“approach for estimating the number of wind and lightning-related outages, combined together on a daily basis” – Page 84542, Left Column, 3rd Paragraph; Page 84545 Section V details the likelihood of outages with binomial probability model with the outcomes and likelihood of the occurrence).”
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Sun in view of Cook, Lubkeman, and Neuenschwander to incorporate the teaching of Doostan to utilize daily outages. By utilizing daily outages this is an improvement that yields predictable results to the evaluation of outage prediction in power distribution systems due to storms.
In regards to Claims 7 and 17, Sun in view of Cook, Lubkeman, Neuenschwander, and Doostan discloses the claimed invention as detailed above. Sun is silent with regards to the language “wherein the daily outage score for each day during the time period is determined to have a value of one based on a determination that an outage will occur that day and to have a value of zero based on a determination that no outage will occur that day.”
Doostan further teaches “wherein the daily outage score for each day during the time period is determined to have a value of one based on a determination that an outage will occur that day and to have a value of zero based on a determination that no outage will occur that day (Figure 2 details the calculation of the likelihood of groups of outages using binomial probability model with 0 outages and not zero outages given).”
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Sun in view of Cook, Lubkeman, Neuenschwander, and Doostan to incorporate the further teaching of Doostan to utilize binominal probability model for calculating likelihood of outages. By utilizing daily outages this is an improvement that yields predictable results to the evaluation of outage prediction in power distribution systems due to storms.
In regards to Claims 8 and 18, Sun in view of Cook, Lubkeman, Neuenschwander, and Doostan discloses the claimed invention as detailed above. Sun is silent with regards to the language of “wherein the outage prediction model is configured to calculate the outage prediction score for the utility line based on a combination of the daily outage scores.”
Doostan further teaches “wherein the outage prediction model is configured to calculate the outage prediction score for the utility line based on a combination of the daily outage scores (Figure 2 details the calculation of the likelihood of groups of outages using binomial probability model).”
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Sun in view of Cook, Lubkeman, Neuenschwander, and Doostan to incorporate the further teaching of Doostan to utilize binominal probability model for calculating likelihood of outages. By utilizing daily outages this is an improvement that yields predictable results to the evaluation of outage prediction in power distribution systems due to storms.
Examiner’s Note
Claims 4 and 14 are not rejected under a prior art rejection (35 U.S.C. 102 or 35 U.S.C. 103).
In regards to Claims 4 and 14, Sun in view of Cook, Lubkeman, Neuenschwander, and Doostan are silent with regards to the language of “wherein the outage prediction model is configured to create an outage score for each of the plurality of storms and wherein the outage prediction score for the utility line is sum of the outage scores multiplied by the storm probability score for each of the plurality of storms.”
Conclusion
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/YOSSEF KORANG-BEHESHTI/Examiner, Art Unit 2857