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 . 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 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.
DETAILED ACTION
This Non-Final Office Action is in response Applicant communication filled on 01/30/2026.
Status of Claims
Claims 1, 2 and 7-9 have been amended and Claim 3 has been canceled by Applicant.
Claims 1, 2, and 4-9 are currently pending and have been rejected as follows.
Response to Amendments / Arguments
Applicant’s 01/02/2026 amendment necessitated new grounds of rejection in this action.
Response on Applicant’s rebuttal of 112 and 101 rejections
-> 112(b) rebuttal argument: Remarks 01/02/2026 p.9 ¶1 states the claims were amended based on recommendations at Final Act 10/01/2025 p. 8. Additionally, expression “work machine existence area” has been simplified to just “existence area”. Examiner finds the Applicant’s 112(b) argument persuasive and withdraws the 112(b) grounds of rejection from the prior act.
-> 101 rebuttal argument: Remarks 01/02/2026 p.9 ¶2 states the claims recite non-mental, thus non-abstract hardware-driven process of: upon receiving a hazard-map request with a work-machine ID, obtaining the machine's position based on the ID, deriving its existence area, identifying target areas from a database whose rainfall can affect that area, and retrieving a time-series rainfall pattern for those target areas from a weather information database. It then consults a database that associates past target-area rainfall patterns with past disaster states in the existence area to predict a time series of the machine's disaster susceptibility, generates a hazard map shading the prediction level across subareas of the existence area, and outputs hazard map to the client.
Examiner considered 101 rebuttal argument but disagrees finding it unpersuasive.
First, as a finding of fact, the Examiner notes that the current Original Disclosure mentions under the Background Art chapter of the current Original Specification ¶ [0002]-¶ [0005] existing Patent Literature 1,2,3, to assess disaster risk flooding using hardware such as telemeter to predict and display disaster-affected area (Original Specification ¶ [0002]), such as for a road (Original Specification ¶ [0003]) or for an affected water storage or drainage facility (Original Specification ¶ [0004]). Examiner acknowledges this and further adds that well known services or tools such as National Oceanic and Atmospheric Administration (NOAA) Flood Inundation Mapping (FIM) takes meteorological forecast and overlay them onto topographic data to show exactly where water will pool at a street or neighborhood level. Analogous features of using flood zones and real time weather forecasts are provided by the national weather services. All these are confirmed and/or corroborated by at least the following references reincorporated herein:
- Flood Inundation Mapping FIM Program, usgs, archives org, July 3, 2021, noting excerpt below
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- Quantitative precipitation forecast, wikipedia, archives org, June 8, 2021 disclosing
Nowcasting as the forecasting of the precipitation within the next six hours. In this time range it is possible to forecast smaller features such as individual showers and thunderstorms with reasonable accuracy, as well as other features too small to be resolved by a computer model. A human given the latest radar, satellite and observational data will be able to make a better analysis of the small-scale features present and so will be able to make a more accurate forecast for the following few hours. There are now expert systems using those data and mesoscale numerical model to make better extrapolation, including evolution of those features in time.
- floodmapp webpages, archives org, June 14, 2021, noting precited asset 57 below at risk
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Second, as a finding of law, Examiner points to MPEP 2106.04(a)(2) III C noting that:
# 1. Performing a mental process on a generic computer,
# 2. Performing a mental process in a computer environment and
# 3. Using a computer as a tool to perform a mental process
-> are all situations where the claim is considered to recite the abstract exception.
Following the MPEP 2106.04 (a)(2) III C #1,#2, #3 test, the Examiner finds that here, the cognitive assessment or prediction of a time series of the machine's disaster susceptibility (i.e. flood), is aided by support processing elements, processor, databases, and output interface, as equivalent generic computer components as stated by MPEP 2106.04(a)(2) III C #1, or as computer environments as stated by MPEP 2106.04(a)(2) III C #2 and/or as computer tools as stated by MPEP 2106.04(a)(2) III C #3 to aid in the combination of collecting, obtaining or receiving of an identifier of an asset such as a work-machine as well as its position in a hazard-map and time-series rainfall pattern to further evaluate or analyze or predict machine's disaster susceptibility and display certain results as a hazard map shading of the collection and analysis. Such collection, analysis and display of certain results of the collection and analysis are tested per MPEP 2106.04(a)(2) III A, 5th bullet point1 and found to stills recite, describe or set forth the abstract idea. This aided computerization however, does not necessarily preclude the current claims to recite, describe or set forth the abstract mental processes identified above under MPEP 2106.04(a)(2) III C. In fact, even when more granularly testing such elements at Step 2A prong two of the analysis, their analysis or assessment functionality would represent mere examples of a [predictive] algorithm being applied on a general purpose computer [MPEP 2106.05(f)(2)(i)] followed by a requirement to use computer components such as computer software or equivalents, to tailor information and provide it to the user on a generic computer, which is expressed here by use of databases and “output interface”], which as tested per MPEP 2106.05(f)(2)(v), do not integrate the abstract idea into practical application, because they would represent mere invocation of machinery or computer components to apply the abstract idea.
These findings are important because according to MPEP 2106.04 I citation “Myriad, 569 U.S. at 591, 106 USPQ2d at 1979” even a “groundbreaking, innovative, or even brilliant discovery does not by itself satisfy the §101 inquiry”. Based on to such test, the Examiner submits, in arguendo, that even a groundbreaking, innovative or brilliant apparatus, method and product that would recognize or associate past target-area rainfall patterns with past disaster states in the existence area to predict a time series of the machine's disaster susceptibility, and then to generate a hazard map shading the prediction level across subareas of the existence area, and output hazard map to the client, as argued by Applicant at Remarks 01/02/2026 p.9 ¶2, should similarly not render the claims eligible. The “Myriad” rationale was corroborated by SAP Am, Inc v InvestPic as cited by MPEP 2106.04 (a)(2) I.C (i). Specifically, in SAP Am Inc v InvestPic, LLC, 898 F.3d 1161, 127 U.S.P.Q.2d 1597 (Fed. Cir. 2018), the Federal Circuit ruled that “even if one assumes that the techniques claimed are groundbreaking, innovative, or even brilliant those features are not enough for eligibility because their innovation is innovation in ineligible subject matter. An advance of that nature is ineligible for patenting”. “no matter how much of an advance in the field the claims [would] recite, the advance [would still] lie entirely in the realm of abstract ideas with no plausibly alleged innovation in non-abstract application realm.
In conclusion the claims still recite, describe or at least set forth the abstract exception with no additional, computer-based elements capable to integrate said abstract idea into a practical application or provide significantly more given same rationale. Thus, the claims are determined to be ineligible.
II. Response to Applicant’s rebuttal of 103 rejection
Remarks 01/30/2026 p.10-p.11 ¶2 enumerates the rejection standards under 35 USC 103, then at Remarks 01/30/2026 p.11 ¶3-p.15 ¶1argues none of the prior art teaches or suggests:
- requesting position information indicating an existence position of the work machine identified by a work machine identifier included in a received hazard map request;
- recognizing the work machine’s existence position and existence area;
- recognizing, with reference to a weather information database, the time series pattern of the amount of rainfall in a target area associated with the existence area; and,
- with reference to a database in which time series patterns of precipitation in the target area and time series patterns of disasters in the existence area are registered
- predicting the work machine's disaster susceptibility as a time series pattern and rendering the prediction results with shading on a hazard map for output
Applicant’s prior art argument was considered but is moot in view of new grounds of rejection.
Wani et al, US 20190316309 A1 is now relied upon by Examiner to teach:
- requesting position information indicating an existence position of the [asset] identified by a [asset] identifier included in a received hazard map request;
(Wani ¶ [0249] 4th sentence: machine 3100 include sensors 3121, such as GPS sensor, with
Wani ¶ [0200] tracking assets in the geographical region and providing in the user interface, flooding information for the assets in the geographical region. Figs.8-9, ¶ [0102] 2nd-3rd sentences: user interface 900 is another example of asset tracking, in this case for chemical and power plants. A map 902 shows the inundation levels in the area around X power plant. An information box 906 shows several alarms for the X power plant. ¶ [0129] In map 1402, the user has turned on the impacted-assets layer, and the map 1402 shows a large number of impacted assets. The manager may select any of the assets and obtain additional information. In the example of Fig.14, two different charts 1408, 1410 are presented for two different assets, as examples of some of the different types of available information)
- recognizing the [asset] ;
(Wani ¶ [0072] arrows in the water areas represent water-flow velocity, where the longer the arrow, the faster the water is flowing. Properties 316 impacted by the flood are represented by triangles with an exclamation mark ! inside. the triangles are yellow, but other colors may also be utilized i.e. red. The properties may be critical infrastructure [interpreted as assets].
Wani ¶ [0081] As compared to map 302 for flooding at 12 hours, the map 402 shows that flooding spread to areas 412,414. As flooding grows, more properties 416 are shown as impacted
Wani ¶ [0083] Fig. 5 shows map 502 for live prediction of inundation at 84 hours. As seen in user interface 500, the map 502 shows that the inundation is spreading and more assets are shown as impacted. timeline bar 308 indicates that the prediction is for the 84-hours timeframe
Wani ¶ [0095] Fig.8 user interface 800 tracks critical infrastructure and provides options for tracking critical assets and evaluating mitigation options 804. In this example, the timeline bar 308 shows 36 hours, and map 802 shows the areas predicted to be inundated by that time
Wani ¶ [0129] In map 1402, user turned on impacted-assets layer, and map 1402 shows a large number of impacted assets. The manager may select any of the assets and obtain additional information. In the example of Fig.14, two different charts 1408, 1410 are presented for two different assets, as examples of some of the different types of available information.
Wani ¶ [0204] the user interface includes options for presenting rainfall level, color-coded water depth, assets impacted by flooding, and estimate of loss caused by flooding)
- recognizing, with reference to a weather information database, the time series pattern of the amount of rainfall in a target area associated with the existence area;
(Wani teaches several comprehensive examples as follow:
Wani ¶ [0057] 2nd-3rd sentences: weather data 204 includes weather prediction data, such as weather data generated by National Weather Service, but any other source of weather info may also be utilized. weather data 204 include rainfall estimates by area, satellite pictures, weather warnings, etc. ¶ [0058] 1st sentence: historical data 206 includes historical weather-related data as well as flooding data. Similarly, ¶ [0053] historical data from the stream gauges 110 or FEMA's flood-risk maps 112 are used to prepare for a response. ¶ [0149] flood analysis system 1902 includes databases that store geographic and historical data that include weather patterns, rainfall averages, rainfall data for certain catastrophic events, satellite imagery, etc.).
Wani ¶ [0063] 1st-2nd sentences: Hydrology considers quantifying surface water flow and solute transport. Some of the methods for measuring flow, once water has reached a river, include stream gauge and tracer techniques. ¶ [0095] Fig.8 user interface 800 tracks critical infrastructure and provides options for tracking critical assets. In this example, the timeline bar 308 shows 36 hours, and map 802 shows the areas predicted to be inundated by that time. ¶ [0099] information box 806 presents the stream gauge readings of the dam. There are 8 gates to control the flow of water out of the dam and 8 corresponding stream gauges. Based on the stream gauge readings, the flood analysis system creates a health indicator index for each of the gates, then represented as a color-coded icon next to the gate name that represents the health indicator index. ¶ [0153] After the hydrological model calculates the discharge/streamflow map, hydraulic model 1910 is used to determine detailed inundation maps by taking into consideration the flow of water over the surface, as described in detail below with reference to Figs.22-23.
Wani ¶ [0202] In one example, generating the prediction of the inflow and outflow of each cell further comprises accessing data for a river network, the data for the river network including cells in the river network and statistical parameters of the river network; and generating the prediction of the inflow and outflow of each cell based on the data for the river network.
Wani ¶ [0081], ¶ [0083] Fig.5 shows a map 502 for the live prediction of inundation at 84 hours, within a user interface 500, according to some example embodiments. As seen in the user interface 500, the map 502 shows that the inundation is spreading and more assets are shown as impacted. The timeline bar 308 indicates that the prediction is for the 84-hours timeframe.
Wani ¶ [0097] The flood analysis system collects and stores information regarding critical assets, such as the type of asset… and asset-specific data… ¶ [0149] flood analysis system 1902 includes databases that store historical data, geographic data. The historical data include weather patterns, rainfall averages, rainfall data for certain catastrophic [interpreted as disaster] events, satellite imagery, etc.). ¶ [0098] 1st sentence: By clicking one of the assets tracked in map 802, the manager is able to quickly gather info about the asset
Wani ¶ [0083] Fig.5 shows map 502 for the live prediction of inundation at 84 hours, within user interface 500 where map 502 shows that the inundation is spreading and more assets are shown as impacted. timeline bar 308 indicates that the prediction is for the 84-hours timeframe
Wani ¶ [0084] in Figs.3-5, the live prediction of inundation, together with the water velocity and damage indicators, provides valuable information to managers to plan responses. By simply selecting a time period on the timeline bar, the manager is able to see the evolution of the inundation in the future and what critical infrastructures are affected.
Wani ¶ [0090] 1st sentence: After the calculations, a before box 608 and an after box 610 provide details on the differences between the number of assets impacted
Wani ¶ [0095] Fig.8 user interface 800 tracks critical infrastructure and provides options for tracking critical assets and evaluating mitigation options 804. In this example, the timeline bar 308 shows 36 hours, and map 802 shows the areas predicted to be inundated by that time
Wani ¶ [0102] 1st-3rd sentences: Fig.9 shows detail for critical infrastructure tracking, according to some embodiments. user interface 900 is another example of asset tracking, for chemical and power plants. A map 902 shows inundation levels in the area around X power plant
Wani ¶ [0129] In the map 1402, the user turned on the impacted-assets layer, and the map 1402 shows a large number of impacted assets. The manager select any of the assets and obtain additional information. In the example of Fig.14, two different charts 1408, 1410 are presented for two different assets, as examples of some of the different types of available information
Wani ¶ [0151] flood monitor 1906 is used to create inundation runoff data, also referred to herein as runoff maps 1914, and the river routing model 1908 predicts the flow 1916 for each grid cell, which is the amount of water that moves through a river channel or some other waterway. The runoff maps detail the amount of free-running water on the surface. The hydrological model 210 of FIG. 2 includes the flood monitor 1906 and the river routing model 1908.
Wani ¶[0153] After hydrological model calculates discharge/streamflow map, the hydraulic model 1910 is used to determine detailed inundation maps by taking into consideration the flow of water over the surface, as described in more detail below with reference to Figs.22-23.
Wani ¶ [0156] flood monitor 1906 simulates what happens on the land surface, including determining the energy and moisture fluxes. The flood monitor 1906 then generates the inundation runoff maps 1914, which represent the amount of freely running water on the surface. The amount of water running on the surface depends on the amount of rain falling on the grid cell and how much water stays on the surface, as some of the water may infiltrate into the land
Wani ¶ [0162] The river routing model 1908 generates the flow 1916 for each grid cell, which includes the inflow and outflow of the grid cells, where the inflow and outflow refer to the amount of water that comes in or comes out of the grid cell, respectively.
Wani ¶ [0163] Fig.22 illustrates the functions of the flood inundation model 1910, according to some example embodiments. Once the information in the time series of the inflow and outflow for each location at the river is determined, the flood inundation model 1910 is executed.
Wani ¶ [0165] 1st sentence: flood inundation model 1910 simulates what's happening inside each of the grid cells. In the previous operations, the simulation was performed at a larger scale, e.g., at a grid-cell scale. ¶ [0171] 5th sentence: noting an example where there will be more water flowing into certain areas and less water flowing into other areas.
Wani ¶ [0193] From operation 2604, the method flows to operation 2606, where the processors of the flood analysis system generate a prediction of inflow and outflow of water between cells. From operation 2606, the method flows to operation 2608 for calculating, for a plurality of sub-cells of each cell in the geographical region, a predicted water depth in each sub-cell based on the prediction of the inflow and outflow between cells and a hydraulic model) “and”,
- “with reference to a database in which time series patterns of precipitation in the target area and time series patterns of disasters in the existence area are registered”
(Wani ¶ [0149] flood analysis system 1902 includes databases that store historical data, geographic data and results from previous simulations. The historical data include weather patterns, rainfall averages, rainfall data for certain catastrophic events, satellite imagery, etc.
Wani ¶ [0152] 2nd-3rd sentences: 100-year map would show the inundation for a flood event that happens once in 100 years. flood analysis system 1902 takes into account not only historical and statistical data, but also the weather information 1904 to calculate the inundation maps.
Wani ¶ [0057] 1st sentence: flood estimation module 202 utilizes several inputs, including… historical data 206… to generate flood estimation maps 216. ¶ [0058] historical data 206 includes historical weather-related data and flooding data. historical data 206 then identify levels of rainfall at different times for a given location (e.g. region), as well as flood levels and the places where flooding occurred),
- predicting the [asset]
(Wani ¶ [0106] simulation run under different conditions, and results of multiple simulations may be combined into one single flood-risk map. Thus, many simulations may be performed under many different conditions (e.g., hundreds of different weather patterns) and also on historical data, accounting for climate change. ¶ [0107] By taking into consideration climate change, it is possible to analyze for future weather patterns that will be different from historical weather patterns. The inputs include spatial maps over time steps under different weather conditions, high-resolution rainfall maps, wind-fields data, relative humidity, and incoming shortwave and longwave radiation.
Wani ¶ [0151] similarly discloses a flood monitor 1906 used to create inundation runoff data, also referred to herein as runoff maps 1914, and river routing model 1908 predicts the flow 1916 for each grid cell, which is the amount of water that moves through a river channel or some other waterway. The runoff maps detail the amount of free-running water on the surface. The hydrological model 210 of Fig.2 includes the flood monitor 1906 and the river routing model 1908. Wani ¶ [0152] hydraulic models used to determine the flood inundation maps, which are calculated based on historical statistical data. For example, the 100-year map would show the inundation for a flood event that happens once in 100 years. On the other hand, the flood analysis system 1902 takes into account not only the historical and statistical data, but also the weather info 1904 to calculate the inundation [or hazard] maps. Thus, the flood analysis system 1902 predicts inundation not just based on past data, but also based on the future-weather information for an incoming storm. More details for the flood monitor 1906 are provided with reference to Fig.20, and more details for river routing model 1908 are provided below with reference to Fig.21. Wani ¶ [0153] After the hydrological model calculates the discharge/streamflow map, the hydraulic model 1910 is used to determine detailed inundation maps by taking into consideration the flow of water over the surface, as described in more detail with reference to Figs.22-23.
Wani ¶ [0226] noting other example where simulation manager 2908 generates 2912 the overall flood-risk map 1602 by combining info provided by flood inundation maps 1912 resulting from each of the scenarios. In some example embodiments, the overall flood risk map is calculated by averaging values from the inundation maps for each of the simulations. In our example embodiments, a probability is assigned to each of the scenarios, and the overall flood risk map is calculated by adding the inundation maps weighted by their probability. In other example embodiments, the overall flood risk map 1602 is calculated by selecting one or more scenarios with the worst flooding and then combining the selected one or more scenarios. In some example embodiments, the simulations from each of the scenarios are combined by averaging the top 5th percentile in severity values, but other ways to combine the scenarios are also possible.
Wani ¶ [0227] In other approaches, such as that of the flood maps generated by FEMA, flood maps with return periods of 100 years and 500 years are calculated. This means that the flood is expected to happen once in 100 or 500 years based on historical statistical data.
Wani ¶ [0228] The FEMA model is based on the statistical analysis of past weather in flooding data. However, in many places, this type of data is only available for the last 20 or 30 years. Therefore, the statistical analysis extrapolates the data to calculate the 100-year event).
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Wani Figs.5, 8-9 in support of rejection arguments
* While *
Wani exemplifies his asset as power chemical or power plant, which may or may not be argued as work machine Wani does not explicitly recite his chemical or power plant as work machine
* However *
Byk er al, US 20140278708 A1 in analogous assessing flood disaster or risk for an asset using asset database, local baseline database and government GIS database, (¶ [0043],[0052]-[0054]) teaches or suggest the monitoring and risk predicting for a “work machine”
(Byk ¶ [0008] consider the operations of a railroad or municipal transportation authority. Knowing where to store operating equipment and stage spare equipment (rail, railcars, electrical transformers, and the like) can be critical to reducing downtime in event of a catastrophic event, such as the storm surge that impacted the New York Subway system as a result of Tropical Storm Sandy in 2012. Figs.1A-B, 2A-B, ¶ [0031]-¶ [0032],¶ [0046] noting the asset is a train or truck as an example of work machine. ¶ [0055] 4th-5th sentences noting the presence of hazardous waste, chemicals, increases risk exposure value of separate, but related risks. For example, for a train carrying hazardous waste with ¶ [0059] 3rd sentence disclosing that a geographic representation point associated with a train rail crossing a river have higher weight. Another example at ¶ [0004])
Thus, the prior art teaches or at least suggests the contested features.
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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 1,2, and 4-9 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 pre-AIA the applicant regards as the invention.
Claims 1,7 are independent and have been amended to each recite, among others:
receive a hazard map request including a work machine identifier for identifying a work machine from an input interface in a client operated by a user [and then]
… with reference to a database in which existence areas and target areas are registered in association based on the existence area and recognize a time series pattern of an amount of rainfall in the target area with reference to a weather information database based on the target
… with reference to a database in which past time series patterns of an amount of rainfall in the target area and past time series patterns of a disaster-affected state in the existence area are registered in association [and then]
output the hazard map to an output interface in a client…
Claims 1,7 are rendered vague and indefinite because it is unclear if “a client” and
“a database” as subsequently recited at each of said claims, relates back to “a client” and
“a database” as antecedently recited as said claims.
Claims 1,7 are recommended to be amended to each recite, among others, by example
receive a hazard map request including a work machine identifier for identifying a work machine from an input interface in a client operated by a user
… with reference to a database in which existence areas and target areas are registered in association based on the existence area and recognize a time series pattern of an amount of rainfall in the target area with reference to a weather information database based on the target
… with reference to [[a]] the database in which past time series patterns of an amount of rainfall in the target area and past time series patterns of a disaster-affected state in the existence area are registered in association
output the hazard map to an output interface in [[a]] the client…
Claims 2,4-6,8 are dependent and rejected based on rejected parent independent Claim 1.
Claim 9, is dependent and rejected based on rejected parent independent Claim 7. Further
Claim 7 as amended recites: “an information acquisition step executed by a processor for receiving a hazard map request including a work machine identifier for identifying a work machine from an input interface in a client operated by a user based on communication with the client, transmitting a position information request to request position information indicating an existence position of the work machine identified by the work machine identifier, receiving the position information of the work machine so as to recognize an existence position of a work machine”….
Claim 7 is rendered vague and indefinite because it is unclear if:
- an existence position of a work machine as subsequently recited in said claim relates back to
- an existence position of the work machine, as antecedently recited in said claim.
Claim 7 is recommended to be amended to recite, as an example only: an information acquisition step executed by a processor for receiving a hazard map request including a work machine identifier for identifying a work machine from an input interface in a client operated by a user based on communication with the client, transmitting a position information request to request position information indicating an existence position of the work machine identified by the work machine identifier, receiving the position information of the work machine so as to recognize [[an]] the existence position of [[a]] the work machine…. etc.
Claim 9, is dependent and rejected based on rejected parent independent Claim 7.
Clarification and/or correction is/are required.
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 1-2, 4-9 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea, here abstract idea) without significantly more. The claim(s) recite(s) describe, or set forth abstract Certain Methods of Organizing Human Activities namely providing information to “user” about risk: i. “possibility of the existence area” where a “work machine” is positioned “being affected by a disaster depending on an amount of rainfall in the target area” and
ii. “possibility of a work machine being affected by a disaster in the exitance area”
These are tested under the risk mitigation of MPEP 2106.04(a)(2) II and/or2 the computer aided mental processes of MPEP 2106.04 (a)(2) III C achieved by observation, evaluation and judgment as enumerated by MPEP 2106.04(a)(2) III ¶2. Examiner further justifies such rationale by pointing to MPEP 2106.04(a)(2) III D which cited Electric Power Group, 830 F.3d at 1351 and n1 119 USPQ2d at 1740 and n.1, to state that a wide-area real-time performance monitoring system for monitoring and assessing dynamic stability of an electric power grid was integral the abstract exception. This was later echoed in TDE Petroleum Data Sols., Inc v. AKM Enter., Inc 657 Fed. Appx 991 (Fed. Cir. 2016), where the Court found determining well operation state as an abstract idea: “As we discussed at greater length in Electric Power, the claims of the '812 patent recite the what of the invention, but none of the how that is necessary to turn the abstract idea into a patent-eligible application. Electric Power [2016 BL 247416] 2016 U.S. App. LEXIS 13861 [2016 BL 247416], 2016 WL 4073318, at *4-5. Therefore, we find that claim 1 is patent-ineligible under § 1011”. Here, similar to Electric Power and TDE Petroleum the current claims recite similar operations of observation, evaluation and judgment, which according to MPEP 2106.04(a)(2) III C #1,2,3 can be computer aided. Indeed, such functions can be aided by pen and paper, or even by computer aided evaluation and judgment based on observation (MPEP 2106.04(a)(2) III), implemented by a methodologist using predicted meteorological weather lines such as called isobars, fronts, and troughs, squall convergence lines or zones on a paper drawn weather map, also known as synoptic weather chart, to predict weather hazard to working vehicles such as trucks, vessels and commercial or military airplanes.
The computer aided observation is set forth here as: “recognize” [akin to computer aided visual observation] “the existence position of the work machine and an existence area of the work machine that includes the existence position of the work machine”, “recognize” [akin to computer aided visual observation] “a target area where rainwater flows from the target area in which it rained to the existence area, which is spaced apart from the target area, with a possibility of the existence area being affected by a disaster depending on an amount of rainfall in the target area”, “recognize” [akin to computer aided visual observation] “a time series pattern of an amount of rainfall in the target area” (independent Claims 1,7) “recognize” [akin computer aided visual observation] “as a disaster factor at least one of a ground level and a geological condition at each of a plurality of points in at least one area of the target area and the existence area” (dependent Claim 2), “recognize” [akin to computer aided visual observation] “designated line segment” “connecting two points in the hazard map outputted” (dependent Claim 4), “wherein with respect to external water flooding an area including a downstream side of a river is recognized” [akin computer aided visual observation] “wherein with respect to external water flooding, an area including a downstream side of a river is recognized” [akin to computer aided visual observation] “as the existence area and an area including an upstream side of the river is recognized” [akin to computer aided visual observation] “as the target area, or with respect to internal water flooding, an area where there exists a rain water storage facility consecutive to a drainage channel included in the existence area, is recognized” [akin to computer aided visual observation] “as the target area” (dependent Claims 8,9).
The computer aided evaluation and judgement are set forth here as: “predict the possibility of the work machine being affected by the disaster in the existence area with reference to a database in which past time series patterns of an amount of rainfall in the target area and past time series patterns of a disaster-affected state in the existence area are registered in association based on the existence position of the work machine and the time series pattern of the amount of rainfall in the target area”, “generate a hazard map representing in shaded levels of a result of the prediction of the time series pattern of the possibility of the work machine being affected by the disaster in the each of a plurality of areas” (independent Claims 1,7), “generate the hazard map based on the existence position of the work machine, the amount of rainfall in the target area, and the disaster factor at each of the plurality of points in at least one area of the target area and the work machine existence area” (dependent Claim 2), “generate a designated topographical sectional view along the designated line segment”, “and output the designated topographical sectional view” (dependent Claim 4).
Examiner also points to MPEP 2106.04(a)(2) III A, 5th bullet point which states that a claim to directed to combination of: collecting information, analyzing it, and displaying certain results of the collection and analysis3, still recite the abstract, mental processes. with MPEP 2106.04(a)(2) III. C. stating that merely claiming [an abstract] concept performed: #1) on generic computer, #2) in computer environment, or #3) using the computer as tool to perform the [abstract] concept, does not preclude the claims from reciting, describing or setting forth the abstract exception.
It then follows that here, similar to examples 1-3 the claiming of the abstract concept as identified above, in a computer environment, as represented by: “processor”, “reference” “database”, “weather information database” (Claims 1,7), “first” and “second” support processing elements (Claims 1,2,4), “output interface in a client based on communication with the client” (independent Claims 1,7), “the client comprises a remote operation apparatus configured to remotely operate the work machine” (dependent Claim 5), “the disaster countermeasure support server” (dependent Claim 6), and possibly “with reference to a database in which past time series patterns of an amount of rainfall in the target area and past time series patterns of a disaster-affected state in the existence area are registered in association” (independent Claims 1,7) would also not preclude the claims to recite, describe or set forth the abstract exception. Examiner also finds that MPEP 2106.04(a)(2) II ¶6, 4th sentence provides a similar rationale stating that its respective abstract sub-groupings encompass both activity of a single person following a set of instructions and activity that involves multiple people, and thus, certain activity between a person and a computer may still fall within the certain methods of organizing human activity grouping. It then follows that here, recitations of: “input interface in a client operated by a user based on communication with the client” “for identifying a work machine” and “output interface in a client based on communication with the client” (Claims 1,4,7), would also not preclude the claims from reciting, describing or setting forth the abstract exception. While such computer aids will be more granularly investigated below, for now, it is clear, that given the preponderance of legal evidence above, the character as a whole of the claims is undeniably abstract. Step 2A prong one.
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This judicial exception is not integrated into a practical application because per Step 2A prong two, the individual, or combination, of the additional, computer-based elements are/is found, per MPEP 2106.05(f)(2), to merely apply the above abstract idea and/or narrow the abstract idea to a field of use or technological environment per MPEP 2106.05(h). Here, Examiner identified the computer aids above as tools, computer environment etc. to aid in the performance of abstract processes. Now, even when more granular tested, the aforementioned computerization could be characterized as additional, computer-based elements represented by “processor” along with the “reference” and “weather information” databases, and similarly the “input” and “output” interfaces, (Claims 1,7), as well as the first and second support processing elements (Claims 1,2,4) as read in light of Original Specification ¶ [0013] 5th sentence, and also the “output interface in a client based on communication with the client” (independent Claims 1, 7), “wherein the client comprises a remote operation apparatus configured to remotely operate the work machine” (dependent Claim 5), “the disaster countermeasure support server” (dependent Claim 6), and possibly “with reference to a database in which past time series patterns of an amount of rainfall in the target area and past time series patterns of a disaster-affected state in the existence area are registered in association” (independent Claims 1,7). When tested per MPEP 2106.05(f)(2) such additional computer-based elements would represent mere tools that apply the above abstract concepts, in a manner not meaningfully different than how the combination of a server and a telephone unit were used in TLI Communications, to receive data, extract classification info from the received data, and store data based on the extracted info. See MPEP 2106.05(f)(2) citing, among others, TLI Communications LLC v. AV Auto, LLC, 823 F.3d 607, 613, 118 USPQ2d 1744-1748 (Fed. Cir. 2016). Indeed, as stated by MPEP 2106.05(f)(2) ¶1, use of a computer or other machinery in its ordinary capacity to receive, transmit, store, data, does not integrate a judicial exception into a practical application4. Step 2A prong two.
Equally important, as stated by MPEP 2106.05(f)(2) ¶1, even claiming the improved speed or efficiency inherent with applying the abstract idea on a computer does not integrate a judicial exception into a practical application or provide an inventive concept5. It would then follow, in the arguendo, that
here, even claiming the improved speed or efficiency inherent with applying the abstract idea on the aforementioned additional, computer-based elements would not integrate the identified abstract concepts into a practical application. In fact, MPEP 2106.05(f)(2) (i) and (iii) are clear that an algorithm [her predictive algorithm] being applied on a general purpose computer, and a process for monitoring audit log data that is executed on a general-purpose computer where the increased speed in the process comes solely from the capabilities of general-purpose computer6 are examples of invoking computers or machinery as a tool to perform the abstract process which does not integrate the abstract idea into a practical application. Step 2A prong two. In a similar vein, MPEP 2106.05(f)(2) v. cites Intellectual Ventures I LLC v. Capital One Bank (USA), 792 F.3d 1363,1370-71,115 USPQ2d 1636, 1642 (Fed Cir. 2015) to similarly state that requiring use of computer components to tailor information and provide it on a generic computer, represents mere invocation of computers or other machinery merely as a tool to perform an existing process, which again does not integrate the abstract idea into a practical application. It would then follow that here, the capabilities of an “input interface in a client operated by a user based on communication with the client” “for identifying a work machine” and “an output interface in a client based on communication with the client” to be output[ted] the hazard map (independent Claims 1,7) and be output[ted] the designated topographical sectional view (dependent Claim 4) would similarly not integrate the abstract idea into a practical application.
More to the point, according to MPEP 2106.05(h) vi, even limiting the combination of collecting information, analyzing it, and displaying certain results of the collection and analysis to data related to a field of use or technological environment does not integrate the abstract idea into a practical application7. It would then follow that here, the narrowing of the combination of collecting information, analyzing it, and displaying certain results of the collection and analysis, as identified at the prior step, to a technological environment as represented by “processor” along with the “reference” and “weather information” databases, and similarly the “input” and “output” interfaces, (Claims 1,7) “first and second support processing elements” (Claims 1,2,4), “output interface in a client based on communication with the client” (independent Claims 1, 7), “wherein the client comprises a remote operation apparatus configured to remotely operate the work machine” (dependent Claim 5), “the disaster countermeasure support server” (dependent Claim 6), and possibly “with reference to a database in which past time series patterns of an amount of rainfall in the target area and past time series patterns of a disaster-affected state in the existence area are registered in association” (independent Claims 1,7), would similarly not integrate the abstract idea into a practical application. Also, given breadth of Claim 5 the expression, “the client comprises a remote operation apparatus configured to remotely operate the work machine”, can be further argued as an example of generality of the application of the judicial exception, which according to MPEP 2106.05(f)(3) also does not integrate the abstract exception.
Thus here, there is a preponderance of legal evidence showing that no additional computer-based elements integrate, either alone or in combination, the abstract idea into a practical application.
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The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because as shown above, the additional computer-based elements merely apply the already recited abstract idea and/or link it to a field of use or technological environment. Specifically, Examiner follows MPEP 2106.05 (d) II guidelines and carries over the findings of MPEP 2106.05 (f) and/or (h) above, to further submit that the additional computer-based elements also do not provide significantly more as sufficient legal evidence without having to rely on the conventionally test of MPEP 2106.05(d). Yet, assuming arguendo, further evidence would be required to demonstrate conventionality of the additional computer-based elements, per MPEP 2106.05(d), the Examiner would point as evidence to the high level of generality of the additional computer-based elements, as identified above and read in light of Original Disclosure, tested per MPEP 2106.05(d).I.2: …”in many instances, the specification of the application may indicate that additional elements are well-known or conventional”. Here,
* Original Specification ¶ [0013] recites at high level: “disaster countermeasure support server 10 comprises a database 102, a first support processing element 121, and a second support processing element 122. The database 102 stores and holds picked-up image data or the like. The database 102 may be constituted by a database server separate from the disaster countermeasure support server 10. Each of the support processing elements is constituted by an arithmetic processing unit (a single core processor or a multi-core processor or a processor core constituting the processor), and reads required data and software from a storage device such as a memory and performs arithmetic processing, described below, conforming to the software with the data used as a target”.
* Original Specification ¶ [0014] recites at high level of generality: “The remote operation apparatus 20 comprises a remote control device 200, a remote input interface 210, and a remote output interface 220. The remote control device 200 is constituted by an arithmetic processing unit (a single core processor or a multi-core processor or a processor core constituting the processor), and reads required data and software from a storage device such as a memory and performs arithmetic processing conforming to the software with the data used as a target. The remote input interface 210
comprises a remote operation mechanism 211. The remote output interface 220 comprises an image output device 221 and remote wireless communication equipment 222”.
- Additionally, or alternatively per -
MPEP 2106.05(d)(II) the following computer functions are well‐understood, routine, and conventional functions: receiving/transmitting data over network including use of intermediary computer to forward information8 and gathering statistics9 / electronic recordkeeping10 / storing and retrieving information in memory11. It would then follow that the capabilities to “reference to a database in which past time series patterns of an amount of rainfall in the target area and past time series patterns of a disaster-affected state in the existence area are registered in association, based on the existence position of the work machine and the time series pattern of the amount of rainfall in the target area, which have been recognized by the first support processing element” (independent Claims 1,7), “output interface in a client based on communication with the client” (independent Claims 1, 7), “wherein the client comprises a remote operation apparatus configured to remotely operate the work machine” (dependent Claim 5), “the disaster countermeasure support server” (dependent Claim 6), would not provide significantly more.
MPEP 2106.05(d)(II) shows the following computer functions are well‐understood, routine, and conventional functions: performing repetitive calculations12 and determining an estimated outcome13. It then follows that the capabilities of second support processing element executed by the processor to predict the possibility of the work machine being affected by the disaster in the existence area with reference to a database in which past time series patterns of an amount of rainfall in the target area and past time series patterns of a disaster-affected state in the existence area are registered in association, based on the existence position of the work machine and the time series pattern of the amount of rainfall in the target area, which have been recognized by the first support processing element at Claim 1 would also not provide significantly more.
All of these do not provide significantly more when considered in light of MPEP 2106.05(d).
Based on the preponderance of legal and/or factual evidence above, Examiner finds that Claims 1-2, 4-9 although directed to statutory categories (“server” or machine at Claims 1,2,4,5,8, “system” or machine at Claim 6, and “method” or process at Claims 7,9) they still recite, or at least set forth the abstract idea (Step 2A prong one), no additional, computer-based elements capable to not integrate the abstract idea into a practical application (Step 2A prong two) or providing significantly more than the abstract idea itself (Step 2B). Thus, Claims 1-2, 4-9 are not ineligible.
Rejections under 35 § U.S.C. 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 of this title, 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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
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 1,2,4, and 6-9 are rejected under 35 U.S.C. 103 as being unpatentable over:
Wani et al, US 20190316309 A1 hereinafter Wani in view of
Byk et al, US 20140278708 A1 hereinafter Byk. As per,
Claims 1,6,7. Wani teaches “A disaster countermeasure support system comprising the disaster countermeasure support server according to claim 1 and the client” (Claim 6) / “A disaster countermeasure support method comprising: (Claim 7) / “A disaster countermeasure support server comprising: a processor”; (Claim 1) (Wani ¶ [0247]-[0262])
“a first support processing element executed by the processor to”/”information acquisition step for
- “receive a hazard map request including a” {asset} “” {asset} “” (Wani ¶ [0200] tracking assets in the geographical region and providing in the user interface, flooding information for the assets in the geographical region. Specifically per, ¶ [0098] By clicking one of assets tracked in the map 802, the manager is able to quickly gather information about the asset. Similarly, ¶ [0076] 1st-3rd sentences: At bottom right corner, input area 304 provides map options that might be selected by the user. A first option indicates whether to tum on or off the display of impacted assets. A second option provides for turning on or off the presentation of the rainfall level on the map 302. Also, ¶ [0129] 1st sentence: In map 1402, the user has turned on the impacted-assets layer, and map 1402 shows a large number of impacted assets. Also Figs.8-9, ¶ [0102] 2nd-3rd sentences: user interface 900 of asset tracking, in this case for chemical and power plants. map 902 shows inundation levels in area around X power plant. An information box 906 shows several alarms for the X power plant. ¶ [0129] In map 1402, the user has turned on the impacted-assets layer, and the map 1402 shows a large number of impacted assets. The manager may select any of the assets and obtain additional information. In the example of Fig.14, two different charts 1408, 1410 are presented for two different assets, as examples of some of the different types of available information) “transmit a position information request to request position information indicating an existence position of the” {asset} “” {asset} “” {asset} “recognize the existence position of the” {asset}
“an existence area of the” {asset} “the existence position of the” {asset} “(Wani ¶ [0204] the user interface includes options for presenting rainfall level, color-coded water depth, assets impacted by flooding. ¶ [0168] 1st, 3rd sentence: DEM [Digital Elevation Model] defines land elevation data in the area. shapefiles for critical assets may also be received to provide more detailed analysis and more specific information for those critical assets. ¶ [0075] On top left corner, message 306 provides information about the forecast. In Fig.3, the message 306 predicts between 266 and 312 impacted assets. In other example embodiments, other informational messages may be provided. ¶ [0083] 2nd sentence: As seen in the user interface 500, the map 502 shows that the inundation is spreading and more assets are shown as impacted) “recognize a target area where rainwater flows from the target area in which it rained to the” {asset} “ (Wani ¶ [0099] information box 806 presents the stream gauge readings of the dam. There are 8 gates to control the flow of water out of the dam and 8 corresponding stream gauges. Based on the stream gauge readings, the flood analysis system creates a health indicator index for each of the gates, then represented as a color-coded icon next to the gate name that represents the health indicator index. ¶ [0153] After the hydrological model calculates the discharge/streamflow map, hydraulic model 1910 is used to determine detailed inundation maps by taking into consideration the flow of water over the surface, as described with reference to Figs.22-23. ¶ [0202] In one example, generating the prediction of the inflow and outflow of each cell further comprises accessing data for a river network, the data for the river network including cells in the river network and statistical parameters of the river network; and generating the prediction of the inflow and outflow of each cell based on the data for the river network. ¶ [0081], ¶ [0083] Fig.5 shows a map 502 for the live prediction of inundation at 84 hours, within a user interface 500, according to some example embodiments. As seen in the user interface 500, the map 502 shows that the inundation is spreading and more assets are shown as impacted. The timeline bar 308 indicates that the prediction is for the 84-hours timeframe), “with reference to a database in which existence areas and target areas are registered in association based on the existence area and recognize a time series pattern of an amount of rainfall in the target area with reference to a weather information database based on the target area” (Wani ¶ [0097] the flood analysis system collects and stores information regarding critical assets, ¶ [0149] flood analysis system 1902 includes databases that store geographic and historical data that include weather patterns, rainfall averages, rainfall data for certain catastrophic events, satellite imagery, etc.). Also ¶ [0097] The flood analysis system collects and stores information regarding critical assets, ¶ [0084] in Figs.3-5, the live prediction of inundation, together with the water velocity and damage indicators, provides valuable information to managers to plan responses. By simply selecting a time period on the timeline bar, the manager is able to see the evolution of the inundation in the future and what critical infrastructures are affected. ¶ [0095] Fig.8 user interface 800 tracks critical infrastructure and provides options for tracking critical assets and evaluating mitigation options 804. In this example, the timeline bar 308 shows 36 hours, and map 802 shows the areas predicted to be inundated by that time. Additional
¶ [0110]-¶ [0111]. Also ¶ [0130] 2nd-4th sentences: chart 1410 shows the predicted time series of water level at the selected location as a function of time. As used herein, time series refers to a set of data points indexed over time. For example, the time series for water depth at a given location includes the water depth at the location for a plurality of times. ¶ [0161] Based on the inundation runoff map 1914 and the spatial map 2102, the river network 2104 is generated with a time series of how much water is running as a function of time for each of the locations (e.g., grid cells) in the river channel. ¶ [0163] Fig.22 illustrates the functions of the flood inundation model 1910, according to some example embodiments. Once the information in the time series of the inflow and outflow for each location at the river is determined, the flood inundation model 1910 is executed. ¶ [0165] 1st sentence: flood inundation model 1910 simulates what's happening inside each of the grid cells. In the previous operations, the simulation was performed at a larger scale, e.g., at a grid-cell scale. ¶ [0171] 5th sentence: noting an example where there will be more water flowing into certain areas and less water flowing into other areas. ¶ [0178] In some example embodiments, the mesh 2314 in the maps 2308 and 2312 is color-coded, as indicated by a color legend table 2316. The color coding allows the manager to get a better perspective on the elevation of the mesh cells. For example, a darker color for the lower areas shows where the waterways are, a green color may indicate low areas next to the levy, and a bright color (e.g., yellow or red) indicates higher-elevation areas that may have lower risk of flooding. The color coding helps in quickly identifying which buildings are at higher risk of flooding) “and”
a second support processing element executed by the processor to/information provision step for
- “predict the possibility of the” {asset} “with reference to a database in which past time series patterns of an amount of rainfall in the target area and past time series patterns of a disaster-affected state in the existence area are registered in association, based on the existence position of the” {asset} “the time series pattern of the amount of rainfall in the target area, which have been recognized by the first support processing element” (Wani ¶ [0058] historical data 206 includes historical weather-related data as well as flooding data. The historical data 206 then identify levels of rainfall at different times for a given location, as well as flood levels and the places where flooding occurred. ¶ [0110] flood input interface 1106, including map 1102, provides different options to set up the flood simulations to be used under the selected weather scenario. Flooding events, rainfall fields, and climate scenarios utilize data (e.g. a rainfall map) from past flooding events. ¶ [0112] 1st sentence: The flooding events option provides templates for past inundations. ¶ [0113], ¶ [0135] flood risk map 1602 represents the probabilities that locations in the map will be inundated within a certain period (e.g., within next 10,50,100 years). In some example embodiments, the time period is in the range from 10 to 100 years, but other time periods may also be utilized. The risk is color coded according to the risk level. In some example embodiments, different risk-level categories are defined, and each risk category is assigned a specific color. In the example embodiment of Fig.16, 4 risk categories have been identified: levels 1-4. The risk level is determined based on the frequency of flooding that happened in the past and the frequency of flooding estimated for the future (e.g. for a period of 30,50 or 100 years. ¶ [0152] 3rd-4th sentences: flood analysis system 1902 predicts inundation not just based on past data, but also based on the weather info 1904 such as future-weather info for an incoming storm. Indeed per ¶ [0185] 2nd sentence: the weather info 1904 may be updated for flood monitor 1906. The live data 2510 may be of different types, such as satellite imagery of the region or readings from the stream gauges) “generate a hazard map representing in shaded levels of a result of the prediction of the time series pattern of the possibility of the” {asset} “(Wani ¶ [0070] Fig.3 is a diagram illustrating user interface 300 including map 302 for a live prediction of inundation. In map 302, areas 310, 312, and 314 are different shades of a color (e.g., blue) that represents the water, where the darker the color, the deeper the water level. ¶ [0072] The arrows in the water areas represent water-flow velocity, where the longer the arrow, the faster the water is flowing. Properties 316 impacted by the flood are represented by triangles with a exclamation mark ! inside. In some example embodiments, the triangles are yellow, but other colors may also be utilized, such as red. The properties may be buildings or some other critical infrastructure. Similarly, ¶ [0076] 6th sentence-¶ [0077] 1st sentence, ¶ [0122] 4th – 5th sentences: The different color areas, as indicated by the different shadings, show the water depth in the different blocks. The darker lines show the waterways, and the colors in the map indicate the water depth. Also ¶ [0078] 2nd sentence: Other embodiments may utilize different colors, different options, different locations of the information window, etc. ¶ [0104] 3rd-5th sentences: flood-risk map 1002 provides a color-coded risk indicator for a scenario identified for the simulation. The result of the simulation shows that the risk level is higher on the top right corner of the map than it is towards the middle of the map. The manager may zoom in on the desired area to get further details on the risk, such as at the block level. ¶ [0135] The flood risk map 1602 represents the probabilities that locations in the map will be inundated within a certain period (e.g., within the next 10 years, within the next 50 years, within the next 100 years). In some example embodiments, the time period is in the range from 10 to 100 years, but other time periods may also be utilized. The risk is color coded according to the risk level. In some example embodiments, different risk-level categories are defined, and each risk category is assigned a specific color. In the example embodiment of Fig.16, four risk categories have been identified: levels 1-4. The risk level is determined based on the frequency of flooding that happened in the past and the frequency of flooding estimated for the future (e.g., for a period of 30, 50, or 100 years) “and output the hazard map to an output interface in a client based on communication with the client so as to make the user of the client recognize the possibility of the” {asset} “ (Wani ¶ [0081], ¶ [0083] Fig.5 shows map 502 for live prediction of inundation at 84 hours, within user interface 500 where the map 502 shows that inundation is spreading and more assets are shown as impacted. timeline bar 308 indicates that the prediction is for the 84-hours timeframe. ¶ [0084] in Figs.3-5, the live prediction of inundation, together with the water velocity and damage indicators, provides valuable information to managers to plan responses. By simply selecting a time period on the timeline bar, the manager is able to see the evolution of the inundation in the future and what critical infrastructures are affected. ¶ [0178] In some example embodiments, the mesh 2314 in the maps 2308 and 2312 is color-coded, as indicated by a color legend table 2316. The color coding allows the manager to get a better perspective on the elevation of the mesh cells. For example, a darker color for the lower areas shows where the waterways are, a green color may indicate low areas next to the levy, and a bright color (e.g., yellow or red) indicates higher-elevation areas that may have lower risk of flooding. The color coding helps in quickly identifying which buildings are at higher risk of flooding)
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Wani Figs.5, 8-9 in support of rejection arguments
- While -
Wani exemplifies his asset as chemical or power plant, which may or may not be argued as work machine, Wani does not explicitly recite his chemical or power plant as work machine
- However -
Byk in analogous assessing flood disaster or risk for an asset using asset and GIS databases, (Byk ¶ [0043],[0052]-[0054]) teaches or suggest the monitoring and risk predicting for a
- “work machine”
(Byk ¶ [0008] consider the operations of a railroad or municipal transportation authority. Knowing where to store operating equipment and stage spare equipment (rail, railcars, electrical transformers, and the like) can be critical to reducing downtime in event of a catastrophic event, such as the storm surge that impacted the New York Subway system as a result of Tropical Storm Sandy in 2012. Figs.1A-B, 2A-B, ¶ [0031]-¶ [0032],¶ [0046] noting the asset is a train or truck as an example of work machine. ¶ [0055] 4th-5th sentences noting the presence of hazardous waste, chemicals, increases risk exposure value of separate, but related risks. For example, for a train carrying hazardous waste with ¶ [0059] 3rd sentence disclosing that a geographic representation point associated with a train rail crossing a river have higher weight. Another example at ¶ [0004])
It would have been obvious to one skilled in the art, before the effective filling date of the claimed invention, to have modified Wani’s teachings to have included Byk’s teaching/ suggestion
in order to have more effectively reduced downtime in event of a catastrophic event no matter if the asset would be the chemical plant or the work transportation vehicle or machine transporting the chemicals present or heading to the chemical plant at risk of the storm or flood event (Byk ¶ [0008], [0055] in view of MPEP 2143 G and/or F). The predictability of such modification would have been corroborated by the broad level of skills of one of ordinary skills in the art as articulated by Wani ¶ [0037], ¶ [0263], ¶ [0266] in view of Byk ¶ [0070]- ¶ [0073]
Further, the claimed invention could have also been viewed as a mere combination of old elements in a similar field of endeavor assessing flood disaster or risk for an asset to be mitigated. In such combination each element merely would have performed same analytical or mitigation function as it did separately. Thus, one of ordinary skill in the art would have recognized that, given existing technical ability to combine the elements evidenced by Wani in view of Byk, the to be combined elements would have fitted together, like puzzle pieces in a logical, complementary, technologically feasible and/or economically desirable manner. Thus, it would have been reasoned that the combination results would have been predictable (MPEP 2143 A).
Claim 2 Wani/Byk teaches all the limitations in claim 1 above. Wani teaches/suggests “wherein - “the first support processing element is executed by the processor to further recognize as a disaster factor at least one of a ground level” (Wani ¶ [0167]: a mesh based on a Digital Elevation Model (DEM), which represents the elevation of the surface. ¶ [0174] 1st sentence: flood inundation model 1910 keeps information for each sub-cell, such as land elevation… Similarly, ¶ [0198] identifying water level at each sub-cell of the mesh based on an elevation and a type of surface of the sub-cell. ¶ [0220] identifying water level at each sub-cell of the mesh based on an elevation…, wherein updating the topography data includes updating …the elevation and the type of surface of the sub-cell. ¶ [0073] 1st sentence: A building [asset] has been impacted during a flood event when the elevation of the building floor is below the water level, e.g., water is coming into the building and the building is being flooded) “and a geological condition at each of a plurality of points in at least one area of the target area and the” {asset} “(Wani ¶[0062] a phenomenon related to hydrology is infiltration as the process by which water enters the soil. Some of the water is absorbed, and the rest percolates down to the water table. The infiltration capacity, the maximum rate at which the soil can absorb water, depends on several factors. The layer that is already saturated provides a resistance that is proportional to its thickness, while that plus the depth of water above the soil provides the driving force (hydraulic head). Dry soil can allow rapid infiltration by capillary action, which diminishes as the soil becomes wet. Ground compaction reduces the porosity and the pore sizes. Further, surface cover increases capacity by retarding runoff, reducing compaction and other processes. ¶ [0063] 1st,4th sentences: Hydrology considers quantifying surface water flow and solute transport. Precipitation can be measured in various ways, such as by… land cover and use, and soil moisture. ¶ [0174] 1st sentence: flood inundation model 1910 keeps information for each sub-cell, such as…surface characteristics (e.g. roughness). Similarly, ¶ [0198] identifying water level at each sub-cell of the mesh based on … a type of surface of the sub-cell. ¶ [0220] identifying water level at each sub-cell of the mesh based on …a type of surface of the sub-cell, wherein updating the topography data includes updating…the type of surface of sub-cell) “and”
- “the second support processing element is executed by the processor to generate the hazard map based on the existence position of the” {asset} “(Wani ¶ [0042] 3rd-4th sentences: runoff data includes a predicted amount of free-running water on a surface of each cell of the geographical region. includes operations for generating a prediction of inflow and outflow of water between cells, and for calculating, for a plurality of sub-cells of each cell in the geographical region, a predicted water depth in each sub-cell based on the prediction of inflow and outflow between cells and a hydraulic model.
Wani ¶ [0063] 4th sentence: Precipitation can be measured in various ways, such as by a disdrometer for precipitation characteristics at a fine time scale… rain rate estimation, and hail and snow detection; rain gauge for routine accurate measurements of rain and snowfall; and satellite for rainy area identification, rain rate estimation, land cover and use, and soil moisture.
Wani ¶ [0070] Fig.3 is a diagram illustrating user interface 300 including map 302 for a live prediction of inundation. In map 302, areas 310,312,314 are different shades of a color (e.g., blue) that represents the water, where the darker the color, the deeper the water level.
Wani ¶ [0072] The arrows in the water areas represent water-flow velocity, where the longer the arrow, the faster the water is flowing. Properties 316 impacted by the flood are represented by triangles with a exclamation mark ! inside. In some example embodiments, the triangles are yellow, but other colors may also be utilized, such as red. The properties may be buildings or some other critical infrastructure [or assets].
Wani ¶ [0073] A building has been impacted during a flood event when the elevation of the building floor is below the water level, e.g., water is coming into the building and the building is being flooded. Another factor related to floods is water velocity, because if the water has high velocity, the building may be damaged or swept away by the rushing waters.
Wani ¶ [0076] 6th sentence-¶ [0077] 1st sentence, ¶ [0122] 4th-5th sentences: The different color areas, as indicated by the different shadings, show the water depth in the different blocks. The darker lines show the waterways, and the colors in the map indicate the water depth. Also
Wani ¶ [0078] 2nd sentence: Other embodiments utilize different colors, different options, different locations of the information window, etc. ¶ [0104] 3rd-5th sentences: flood-risk map 1002 provides a color-coded risk indicator for a scenario identified for the simulation. The result of the simulation shows that the risk level is higher on the top right corner of the map than it is towards the middle of the map. The manager may zoom in on the desired area to get further details on the risk, such as at the block level. ¶ [0135] flood risk map 1602 represents the probabilities that locations in the map will be inundated within a certain period. The risk is color coded according to the risk level. In some example embodiments, different risk-level categories are defined, and each risk category is assigned a specific color. In the example embodiment of Fig.16, four risk categories have been identified: levels 1-4. The risk level is determined based on the frequency of flooding that happened in the past and the frequency of flooding estimated for the future
Wani ¶ [0150] The flood analysis system 1902 includes … a flood inundation model 1910 (also referred to herein as the hydraulic model). ¶ [0151] 1st sentence: flood monitor 1906 is used to create inundation runoff data, also referred to herein as runoff maps 1914, and the river routing model 1908 predicts the flow 1916 for each grid cell, which is the amount of water that moves through a river channel or some other waterway.
Wani ¶ [0153] After the hydrological model calculates the discharge/streamflow map, the hydraulic model 1910 is used to determine detailed inundation maps by taking into consideration the flow of water over the surface, as described in more detail with reference to Figs.22-23.
Wani ¶ [0156] flood monitor 1906 simulates what happens on the land surface, including determining the energy and moisture fluxes. The flood monitor 1906 then generates the inundation runoff maps 1914, which represent the amount of freely running water on the surface. The amount of water running on the surface depends on the amount of rain falling on the grid cell and how much water stays on the surface, as some of the water may infiltrate into the land.
Wani ¶ [0163] Fig.22 illustrates the functions of the flood inundation model 1910, according to some example embodiments. Once the information in the time series of the inflow and outflow for each location at the river is determined, the flood inundation model 1910 is executed.
Wani ¶ [0164] 2nd-3rd sentences: The flood inundation model 1910 is executed once a signal is received from the river routing model 1908 indicating that the time series at one or more places has exceeded the flood stage. In other words, the flood inundation model 1910 is executed when there is a flood condition.
Wani ¶ [0165] 1st sentence: flood inundation model 1910 simulates what's happening inside each of the grid cells. ¶ [0174] The flood inundation model 1910 keeps information for each sub-cell, such as land elevation and surface characteristics (e.g., roughness). ¶ 0178] In some example embodiments, the mesh 2314 in the maps 2308 and 2312 is color-coded, as indicated by a color legend table 2316. The color coding allows the manager to get a better perspective on the elevation of the mesh cells. For example, a darker color for the lower areas shows where the waterways are, a green color may indicate low areas next to the levy, and a bright color (e.g., yellow or red) indicates higher-elevation areas that may have lower risk of flooding. The color coding helps in quickly identifying which buildings are at higher risk of flooding.
* While *
Wani exemplifies his asset as chemical or power plant, which may or may not be argued as work machine, he does not explicitly recite his chemical or power plant as work machine. Yet,
Byk in analogous assessing flood disaster or risk for an asset using asset and GIS databases, (Byk ¶ [0043],[0052]-[0054]) teaches or suggest the monitoring and risk predicting for a
- “work machine”
(Byk ¶ [0008] consider the operations of a railroad or municipal transportation authority. Knowing where to store operating equipment and stage spare equipment (rail, railcars, electrical transformers, and the like) can be critical to reducing downtime in event of a catastrophic event, such as the storm surge that impacted the New York Subway system as a result of Tropical Storm Sandy in 2012. Figs.1A-B, 2A-B, ¶ [0031]-¶ [0032],¶ [0046] noting the asset is a train or truck as an example of work machine. ¶ [0055] 4th-5th sentences noting the presence of hazardous waste, chemicals, increases risk exposure value of separate, but related risks. For example, for a train carrying hazardous waste with ¶ [0059] 3rd sentence disclosing that a geographic representation point associated with a train rail crossing a river have higher weight. Another example at ¶ [0004]).
Rationales to have modified/combined Wani / Byk are above and reincorporated.
Claim 4. Wani/Byk teaches all the limitations in claim 1 above.
Wani further teaches “wherein”
- “the first support processing element is executed by the processor to recognize a designated line segment, which has been designated through an input interface in the client,
connecting two points in the hazard map outputted to the output interface based on communication with the client” (Wani ¶ [0167] input 2202 includes: the inflow and outflow of water for each of the cells (e.g. flow 1916; and shapefiles for critical infrastructure which according to
¶ [0172] 3rd sentence is a geospatial vector data of latitude/longitude and points, lines) “and”
- “the second support processing element is executed by the processor to generate a designated topographical sectional view along the designated line segment recognized by the first support processing element”, (Wani Fig.3 and ¶ [0072] The arrows or vectors in the water areas represent water-flow velocity, where the longer the arrow, the faster the water is flowing. Properties 316 impacted by the flood are represented by triangles with a “!” (exclamation mark) inside. In some example embodiments, the triangles are yellow, but other colors may also be utilized, such as red. The properties may be buildings or some other critical infrastructure).
- “output the designated topographical sectional view to the output interface in the client based on communication with the client” (Wani Figs.3-5, ¶ [0105] 2nd sentence: flood-risk map 1002 is showing the inundation areas, up to block-by-block-resolution inundation maps, which include direction and actual flow speed of the water inundation or flooding. For example at Fig.4 and ¶ [0081]: As compared to the map 302 for flooding at 12 hours, the map 402 shows that the flooding has spread to areas 412 and 414. As the flooding grows, more properties 416 are shown as impacted. ¶ [0083] Fig.5 shows a map 502 for the live prediction of inundation at 84 hours, within a user interface 500, according to some example embodiments. As seen in the user interface 500, the map 502 shows that the inundation is spreading and more assets are shown as impacted. The timeline bar 308 indicates that the prediction is for the 84-hours timeframe).
Claims 8, 9 Wani/Byk teaches all the limitations in claims 1,7 above.
Wani teaches / suggests: “wherein with respect to external water flooding, an area including a downstream side of a river is recognized as the existence area” (Wani ¶ [0162] river routing model 1908 generates flow 1916 for each grid cell, which includes the outflow of the grid cells referring to amount of water that comes out of grid cell. For example [0081] map 402 shows flooding spread to areas 412, 414. As flooding grows, more properties 416 are shown impacted.
Similar ¶ [0071] 3rd sentence: Areas 312 and 314 represent inundation levels, which means that the water has gone beyond the riverbed, where area 312 is darker than area 314. ¶ [0083] Fig.5 shows a map 502 for live prediction of inundation at 84 hours, within user interface 500. As seen in user interface 500, map 502 shows that the inundation spread and more assets are shown as impacted. The timeline bar 308 indicates that prediction is for 84-hours timeframe) “and an area including an upstream side of the river is recognized as the target area” (Wani ¶ [0162] river routing model 1908 generates flow 1916 for each grid cell, which includes the inflow of the grid cells, where the inflow refer to the amount of water that comes in of the grid cell. For example at ¶ [0099] information box 806 presents the stream gauge readings of the dam. There are 8 gates to control the flow of water out of the dam and 8 corresponding stream gauges. Based on the stream gauge readings, the flood analysis system creates a health indicator index for each of the gates, then represented as a color-coded icon next to the gate name that represents the health indicator index. ¶ [0202] In one example, generating the prediction of the inflow and outflow of each cell further comprises accessing data for a river network, the data for the river network including cells in the river network and statistical parameters of the river network; and generating the prediction of the inflow and outflow of each cell based on the data for the river network), “or with respect to internal water flooding, an area where there exists a rain water storage facility consecutive to a drainage channel included in the existence area, is recognized as the target area” (Wani ¶ [0073] 1st sentence: A building impacted during a flood event when the elevation of the building floor is below the water level, e.g., water is coming into the building and the building is being flooded. ¶ [0099] the information box 806 presents the stream gauge readings of the dam. There are eight gates to control the flow of water out of the dam and eight corresponding stream gauges. Based on the stream gauge readings, the flood analysis system creates a health indicator index for each of the gates, which is then represented as a color-coded icon next to the gate name that represents the health indicator index. Similarly, ¶ [0159] The free-running waters eventually reach the river, and if the amount of water that reaches the river exceeds the river capacity, then flooding takes place)
Byk also teaches or suggests “wherein with respect to external water flooding, an area including a downstream side of a river is recognized as the existence area and an area including an upstream side of the river is recognized as the target area” (Byk ¶ [0037] As shown, the stream 204 flows by switching yard 108, indicating the locations of bridges and significant infrastructure relative to other portions of the assets. The addition of the 20-year flood zone 208 to the combined map 200, which can be accessed using a publicly available GIS database, indicates different levels of flooding risk to the sub-assets. That is, because switching yard 108 and the maintenance facility 112 are in 20-year flood zone 208 surrounding the stream 204 will reflect a higher flooding risk (and therefore a higher risk exposure value) compared to the customer loading site 116, which is outside the flood zone. ¶ [0040] 3rd – 5th sentences: Returning to Fig.2, a multi-segment line can be used in the baseline map to trace the path of the stream 204 and one or more polygons can be used in the baseline map to identify the limits of the 20-year flood zone 208. Meta-data associated with the lines representing stream 204 can include, for example, geographic end-points of line-segments of the stream, its average flow rate, its flood stage flow rate, its flooding frequency, and other similar risk factors. Similarly, meta-data associated with the polygon representing the 20-year flood zone can include flooding frequency, flooding probability as a function of location within the flood zone, typical flooding dates, distance from local emergency services, and other similar information. ¶ [0059] 3rd sentence: a geographic representation point associated with a train rail crossing a river can have a higher weight than a rail crossing an infrequently used or geographically remote road) “or with respect to internal water flooding, an area where there exists a rain water storage facility consecutive to a drainage channel included in the existence area, is recognized as the target area”
Rationales to have modified/combined Wani/Byk are above and reincorporated.
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Claim 5 is rejected under 35 U.S.C. 103 as being unpatentable over:
Wani/Byk as applied to claim 1 above, in further view of
Wei et al, US 20190055715 A1 hereinafter Wei. As per,
Claim 5 Wani/Byk teaches all the limitations in claim 1 above. Wani/Byk as a combination does not explicitly recite: “wherein the client comprises a remote operation apparatus configured to remotely operate the work machine” as claimed. Yet,
Wei in analogues providing information about work vehicles teaches/suggest: “the client comprises a remote operation apparatus configured to remotely operate the work machine”
(Wei ¶ [0021] 2nd–3rd sentences: the multiple input devices receive operational commands from either the operator station 106 or a remote control device (not shown), to control operation of the machine 102 and operate the work implement 202 of the machine 102. The machine 102 can be operated either autonomously or semi-autonomously).
It would have been obvious to one skilled in the art, before the effective filling date of the claimed invention, to have modified Wani/Byk’s “server” to have included Wei’s teachings to have provided a more efficient way to generate work plans for machines requiring large number of workers thereby minimizing operational cost (Wei ¶ [0070] in view of MPEP 2143 G and/or F)
Further, the claimed invention could have also been viewed as a mere combination of old elements in a similar field of endeavor providing information about assets. In such combination each element would have merely performed same analytical, data transmission and operation function as it did separately. Thus, and one of ordinary skill in the art would have recognized that, given existing technical ability to combine the elements evidenced by Wani/Byk in view of Wei the to be combined elements would have fitted together, like puzzle pieces in logical, complementary, technologically feasible and/or economically desirable manner. Thus, it would have been reasoned that the results of the combination would have been predictable (MPEP 2143 A).
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Conclusion
The following art is made of record and considered pertinent to Applicant's disclosure:
- Fall Set al, Application of weather prediction models for hazard mitigation planning: a case study of heavy off-season rains in Senegal. Natural Hazards. 41 pp, 227-43, Apr 2007
- EP 0851240 A2 teaching Meteorological precipitation pattern prediction, teaching starting with p.2 lines 33-39 the prediction of flood damage for shuttles and airplanes with emphasis of Figs. 2, 8A-B, 14-16, 25-29, 34-40 and associated text
- WO 2015112892 A1 teaching utility resource asset management
- US 20190212470 A1 emphasis on Figs. 1, 3A-B, ¶ [0038] Fig.3A is a graph illustrating the relationship between a maximum wave height in the observation position 1012 and a flood depth in one of the land-based areas 1001. The sets (Hik, Djk) in Table 1 of the maximum wave height Hik (lowercase letter “k” refers to the tsunami identification number) in Wi (i=1, 2,…), which is the observation position 1012, and the flood depth Djk in Gj (j=1, 2,…), which is one of the land-based areas 1001 are represented in the graph of FIG. 3A indicating multiple tsunamis. The individual data points in FIG. 3A denote (Hik, Djk) for each tsunami k. As illustrated in FIG. 3A, as the flood depth Djk rises, the extent of the damage also changes in that, on land, flooding occurs, buildings get destroyed and swept away by the flood. ¶ [0039] On land, when the scale of the tsunami increases and the maximum wave height Hik of the tsunami is greater than or equal to the constant value “A”, flooding occurs. This constant value “A” is “0” at a position where the elevation at the shore is 0 [m] and this value progressively increases the further inland the target land-based areas 1001 are or the higher the elevation is of the land-based areas 1001. Therefore, regarding the relationship between the maximum wave height Hik and the flood depth Djk, when the maximum wave height Hik is less than the constant value “A”, the flood depth Djk is 0 [m], whereas when the maximum wave height Hik is greater than or equal to the constant value “A”, the flood depth Djk is a depth [m] that is proportional to the value remaining after subtraction of the constant value “A” from the maximum wave height Hik. Also, in a distribution map indicating the relationship between the maximum wave height Hik and the flood depth Djk, the maximum wave height Hik at the constant value “A” becomes a kinked bent line as illustrated in Fig.3A.
- US 20030107490 A1 GIS-based Automated Weather Alert Notification System reciting
¶ [0062] As shown in FIG. 8, the track operated by the railroad is divided into 1200 individual segments 83-94 referred to as "sections". Twelve dispatchers (69-72, 74-77 and 79-82) are divided into three groups and oversee and control the entire length of the railroad's track. A different set of track segments are managed by each dispatcher. A supervisor 68, 73, 78 is assigned to each group of dispatchers. The weather alert system of the present invention monitors weather conditions potentially affecting each of the 1200 railroad sections. When troublesome weather conditions are predicted for a particular section, the weather alert system issues an alert only to the dispatcher responsible for that particular segment of track. If the dispatcher fails to acknowledge the message during a predetermined period of time, a message is then sent to the dispatcher's supervisor.
¶ [0070] The file server 67 automatically maps the position of detected storms and plots their speed and direction. Based upon the relative position of the storm and the various section of track, the file server 67 can determine which track sections might be affected by the storm and when the storm will impact that section. Not only is the file server 67 able to predict the nature of and time at which storms will impact sections of track, the system is also able to provide alerts for flooding and warnings related to temperature extremes based upon warnings, advisories and data received from the NWS and elsewhere
Any inquiry concerning this communication or earlier communications from the examiner should be directed to OCTAVIAN ROTARU whose telephone number is (571)270-7950. The examiner can normally be reached on 571.270.7950 from 9AM to 6PM. If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, PATRICIA H MUNSON, can be reached at telephone number (571)270-5396. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300. Information regarding the status of an application may be obtained from Patent Center. Status information for published applications may be obtained from Patent Center. Status information for unpublished applications is available through Patent Center for authorized users only. Should you have questions about access to Patent Center, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) Form at https://www.uspto.gov/patents/uspto-automated- interview-request-air-form.
/Octavian Rotaru/
Primary Examiner, Art Unit 3624 A
June 21st, 2026
1 Electric Power Group v. Alstom, S.A., 830 F.3d 1350, 1353-54, 119 USPQ2d 1739, 1741-42 (Fed. Cir. 2016);
2 MPEP 2106.04(a) last ¶: “…examiners should identify at least one abstract idea grouping, but preferably identify all groupings to the extent possible, if a claim limitation(s) is determined to fall within multiple groupings…”
3 Electric Power Group v. Alstom, S.A., 830 F.3d 1350, 1353-54, 119 USPQ2d 1739, 1741-42 (Fed. Cir. 2016)
4 Affinity Labs v. DirecTV, 838 F.3d 1253, 1262, 120 USPQ2d 1201, 1207 (Fed. Cir. 2016);
TLI Communications LLC v. AV Auto, LLC, 823 F.3d 607, 613, 118 USPQ2d 1744, 1748 (Fed. Cir. 2016)
5 Intellectual Ventures I LLC v. Capital One Bank (USA), 792 F.3d 1363, 1367, 115 USPQ2d 1636, 1639 (Fed. Cir. 2015).
6 FairWarning IP, LLC v. Iatric Sys., 839 F.3d 1089, 1095, 120 USPQ2d 1293, 1296 (Fed. Cir. 2016)
7 Electric Power Group, LLC v. Alstom S.A., 830 F.3d 1350, 1354, 119 USPQ2d 1739, 1742 (Fed. Cir. 2016)
8 Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362
TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016);
OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015);
buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014)
9 OIP Techs., 788 F.3d at 1362-63, 115 USPQ2d at 1092-93;
10 Alice Corp. Pty. Ltd. v. CLS Bank Int'l, 573 U.S. 208, 225, 110 USPQ2d 1984 (2014);
Ultramercial, 772 F.3d at 716, 112 USPQ2d at 1755;
11 Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015);
OIP Techs., 788 F.3d at 1363, 115 USPQ2d at 1092-93;
12 Flook, 437 U.S. at 594, 198 USPQ2d at 199); and Bancorp Services v. Sun Life, 687 F.3d 1266, 1278, 103 USPQ2d 1425, 1433 (Fed. Cir. 2012)
13 OIP Techs., 788 F.3d at 1362-63, 115 USPQ2d at 1092-93
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