DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Response to Arguments
Applicant’s arguments with respect to claim(s) 1-20 have been considered but are moot in view of the revised grounds of rejection necessitated by the applicant’s amendments to the claims. Although the same art was used, new sections of secondary reference Jain et al were highlighted to illustrate its “condition-based selection” teachings.
The applicant’s 06/24/26 amendments have overcome the previous 35 U.S.C. 101 rejection. The examiner considered the claims, as a whole, to represent an improvement to the functioning of a computer, or to any other technology or technical field (see MPEP 2106.05(a)). As such, the claims are not directed to a judicial exception. They qualify as eligible subject matter under 35 U.S.C. 101.
As agreed upon in the 06/17/26 interview, because this action is not an allowance, it is hereby NON-FINAL.
Drawings
As discussed in a previous action, the drawing amendments filed on 08/21/25 are accepted.
Claim Objections
Claim 1 is objected to because of the following informalities:
Claim 1 discloses the following:
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This appears to be a single limitation that looks like two different limitations, due to both clauses being indented.
A similar limitation in claim 8 states:
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Here, only the first word of the limitation is indented, making it clear that the two clauses go together.
For the purposes of examination, it will be construed that the limitation in claim 1 should be formatted more like its corresponding limitation is in claim 8.
Appropriate correction is required.
Examiner’s Note - 35 USC § 101
Claims 1-20 qualify as eligible subject matter under 35 U.S.C. 101.
In view of the applicant’s 06/24/26 claim amendments, the examiner determined that, as a whole, independent claims 1, 8, and 15 are sufficiently indicative of integration into a practical application because they improve the functioning of a computer, or to any other technology or technical field (see MPEP 2106.05(a)).
The examiner determined that the amended claims, as a whole, formed a solution that combined hydrology data, weather data, and real-time sensor/IoT data into a specific AI-based forecasting workflow that improved water-quality forecasting.
Claims 2-7, 9-14, and 16-20 depend on independent claims 1, 8, and 15 and are also not directed to a judicial exception, as a result of their dependency.
Claims 1-20 therefore qualify as eligible subject matter under 35 U.S.C. 101.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 1-4, 6-11, 13-18, and 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Mustafa et al NPL (Mustafa, Hauwa Mohammed; Hayder, Gasim; Mustpha, Aisha; and Salisu, Abdullahi – “Applications of IoT and Artificial Intelligence in Water Quality Monitoring and Prediction: A Review”; Proceedings of the Sixth International Conference on Inventive Computation Technologies (ICICT). IEEE, 2021.) in view of Jain et al (US Pat 11504011).
With respect to claim 1, Mustafa et al NPL discloses:
A method (abstract; page 971, column 1, first paragraph under section “C. Application of ANN in Water Quality Monitoring” states, “Recent reports have shown a significant increase in the application of ANN algorithms methods in water quality monitoring and forecasting.”) comprising:
receiving, at a computer system from at least one public database (page 969, column 2, last paragraph states, “Granata et al. [27] applied machine learning algorithms (support vector regression (SVR) and regression tree) in predicting, estimating and forecasting the wastewater indicators of data collected from National Stormwater Quality Database (NSQD).”; page 972, column 1, first paragraph states, “The system was developed by embedding ArcGIS engine module, and ANN module with a dynamic database in order to integrate spatial presentation and water quality prediction through the GIS and ANN models respectively.”), hydrology data for a predefined geographic region (suggested by disclosure of National Stormwater Quality Database taught on page 969, as the NSQD has records from many geographical locations. Furthermore, page 972, paragraph 1 explicitly discloses hydrology data for a predefined geographic region, where it states, “Lu et al. [51] applied a geographical information system (GIS) based ANN for prediction of total phosphorous level in Lake Champlain. The system was developed by embedding ArcGIS engine module, and ANN module with a dynamic database in order to integrate spatial presentation and water quality prediction through the GIS and ANN models respectively.”)
receiving, at the computer system from at least one Internet of Things (IOT) device, real-time sensor data associated with water quality within the predefined geographic region (page 968, column 2, paragraph 1 states, “the advent of the Internet of Things (IoT) provides opportunities for reporting operational data in real-time conditions to ensure a good quality outflow from the water bodies.”)
performing, via at least one processor of the computer system, continuous model training of a family of models used by an Artificial Intelligence (AI) algorithm, wherein data for the continuous model training comprises at least one of sensor data or IOT data (abstract; page 969, column 2, first paragraph of section “III. ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING” states, “the application of multiple AI models for prediction is better than using only one AI model …” (the disclosure of using multiple AI models will be broadly construed to serve as a “family of models); page 969, column 2, last paragraph states, “ML algorithms such as ANN [24], DLNN [25], SVM [26], ANFIS and decision tree are the new black box methods used in the field of water management processes [11], [12].”; page 968, column 2, paragraph 2 states, “In hydrology, IoT devices are used to collect real-time data from a given water sample or station. At the same time, AI deals with the evaluation, simulation and prediction of data for easy interpretation and future use. Also, the review focuses on artificial neural network (ANN) …” Mustafa’s teachings of “real-time” are construed to suggest “continuous,” since the models work on data, and if the data keeps coming in, in real-time, the training will be construed to be broadly “continuous,” especially since the claims do not specifically define what “continuous” entails.)
initiating, via the computer system, execution of the AI algorithm using the hydrology data and the real-time sensor data (abstract; page 969, column 2, last paragraph states, “ML algorithms such as ANN [24], DLNN [25], SVM [26], ANFIS and decision tree are the new black box methods used in the field of water management processes [11], [12].”; page 968, column 2, paragraph 2 states, “In hydrology, IoT devices are used to collect real-time data from a given water sample or station. At the same time, AI deals with the evaluation, simulation and prediction of data for easy interpretation and future use. Also, the review focuses on artificial neural network (ANN) …”)
With respect to claim 1, Mustafa et al NPL differs from the claimed invention in that it does not explicitly disclose:
generating initial water quality scores for sub-geographic regions within a predefined geographic region
the hydrology data comprising:
a hydrology map which identifies how water flows between sub-geographic regions within the predefined geographic region, the hydrology map comprising elevation of specific points within the predefined geographic region
weather data of the predefined geographic region
a private Internet of Things (IOT) device
wherein the family of models generated by the continuous model training are configured for condition-based selection
initiating, via the computer system, execution of the AI algorithm using the family of models
wherein initiating execution of the AI algorithm comprises selecting, from the family of models, a model based on how the real-time sensor data compares to predetermined thresholds
receiving, at the computer system, output of the Al algorithm, the output comprising the initial water quality score scores for each sub-geographic region within the predefined geographic region, the initial water quality scores based on hydrological movement defined by the hydrology data and the real-time sensor data
modifying the initial water quality scores based on known contaminants within the predefined geographic region by:
receiving, for each sub-geographic region within the predefined geographic region, a list of contaminants for that sub-geographic region, the list of contaminants based on uses of land within each sub-geographic area, resulting in a location-based breakdown of contaminants within the predefined geographic region
adjusting, via the computer system, the initial water quality scores based on the location-based breakdown of contaminants within the predefined geographic region, resulting in updated water quality scores
predicting, via the Al algorithm, contaminant transport within the predefined geographic region based on the hydrological movement defined by the hydrology data, wherein the predicting of the contaminant transport uses the hydrology map to determine how the contaminants within the predefined geographic region will travel between sub-geographic regions and arrive at a downstream location within the predefined geographic region
transmitting, via the computer system, the updated water quality scores and the contaminant transport to a user device
With respect to claim 1, Jain et al discloses:
generating initial water quality scores for sub-geographic regions within a predefined geographic region (Jain abstract states, “Methods, systems, and apparatus, including computer programs encoded on computer-storage media, for geofencing and location tracking for predicting and limiting disease exposure … Disease transmission scores are assigned to first location tags representing visits of a first user. A disease exposure score is determined for a second user whose user device is determined.”; column 130, line 30 – column 131, line 32 states, “The data collected and used by the computer system 110 (e.g., to generate feature values for input to predictive models, to train predictive models, to validate and select actions and recommendations, to evaluate to determine whether to initiate interactions with users, to assign or determine disease transmission and exposure scores, etc.) can include various other types of data including … Environmental data (e.g., air quality data, ozone data, weather data, water-quality data …) …”; Column 60, lines 39 – 48 state, “The system can also use health records data, e.g., to generate an initial baseline using prior measurements … The system can also use disease exposure data for both the individual and the community, e.g., community infection level information, contact tracing, data indicating disease hotspots, and so on.”; column 26, lines 53-56 state, “The techniques discussed herein can use artificial intelligence, machine learning, and natural language processing, whether through established methods or new methodology.” As seen, Jain et al discloses the broad principles of tracking different data (including water quality data) at different locations, assigning a score to the data, and applying artificial intelligence (AI) and machine learning techniques to process the data. The claimed limitation is obvious in view of applying the principles of Jain et al to the specific water quality tracking context of Mustafa et al NPL.)
the hydrology data (discussed above in Mustafa et al NPL) comprising:
a hydrology map which identifies how water flows between sub-geographic regions within the predefined geographic region, the hydrology map comprising elevation of specific points within the predefined geographic region (Jain figures 17-18; column 2, lines 58-63 state, “Machine learning models can be trained using rich data about geography and behavior in different communities. The examples used for training can include data describing different places, data characterizing occupancy and traffic over time, geographic relationships such as map data, and so on …” Jain et al further teaches “mapping” throughout its disclosure, such as in column 21, lines 4-15; column 37, lines 25-29; column 37, lines 45-50; and column 49, lines 39-44, amongst many other teachings. Jain et al discloses mapping data related to various ways in which disease might be transmitted. As discussed above, Jain et al also discloses water quality as one type of data that might be tracked. One of ordinary skill in the art would recognize that it would be obvious to apply Jain’s broad principles of mapping to a specific hydrology map, as disease is often transmitted through water.)
weather data of the predefined geographic region (As discussed above, column 131, line 31 specifically discloses “weather data.”)
a private Internet of Things (IOT) device (Jain column 77, lines 9-11 state, “The application sends user entered data 1411 to the computer system 110 over a communication network 1430, which can be a public or private network and can include the Internet.”)
wherein the family of models generated by the continuous model training are configured for condition-based selection (Please note that the limitation does not give detail, as to what constitutes “condition-based selection”; Jain figure 3, reference 230 shows model training of multiple prediction model(s), which are broadly construed to represent the “family” of models; column 50, lines 31-66 state, “Markers or indicators may be more complex, involving evaluation with respect to personalized references, involving combinations of measurement types or data types, having conditional applicability … a marker may have conditional properties. A marker may have criteria that involve a certain data measure … occurring in a predetermined context or set of contexts … A data item may be considered to occur in a certain context … when the measurement or condition represented by the data item occurs within a predetermined level of proximity in time and/or location of the context. The module 112 may include risk assessment components, such as predictive models, which can be refined through machine learning.” Here, Jain accounts for different conditions, which will broadly be construed to read on the claimed “condition-based selection.”)
initiating, via the computer system, execution of the AI algorithm using the family of models (obvious in view of combination; Both Mustafa et al NPL and Jain disclose general execution of AI algorithms.)
wherein initiating execution of the AI algorithm comprises selecting, from the family of models, a model based on how the real-time sensor data compares to predetermined thresholds (obvious in view of combination; As seen above, Jain discloses the consideration of different conditions. Both Mustafa et al NPL and Jain disclose real-time. Jain also discloses consideration of various thresholds, such as in column 3, lines 33-36; column 4, line 63; column 11, lines 56-58; and column 17, lines 39-41. Please note that the claim does not define what the “predetermined thresholds” are.)
receiving, at the computer system, output of the Al algorithm, the output comprising the initial water quality score scores for each sub-geographic region within the predefined geographic region, the initial water quality scores based on hydrological movement defined by the hydrology data and the real-time sensor data (Jain column 10, lines 1-5 state, “derived from the monitoring data for the community … and providing output indicating the one or more regions identified using the one or more predictive models.” The claimed limitation is obvious in view of combination. Both Mustafa et al NPL and Jain et al disclose using machine learning/AI to process water quality data. Both Mustafa et al NPL and Jain et al also disclose real time tracking (Mustafa et al NPL, column 2, paragraph 1 and Jain et al, column 2, lines 14-22 and column 6, lines 64-65.)
modifying the initial water quality scores based on known contaminants within the predefined geographic region (obvious in view of combination; Jain et al discloses modifying or adjusting scores. For example, column 4, lines 5-7 state, “Exposure scores can be adjusted or weighted based on the amount of time elapsed …” ) by:
receiving, for each sub-geographic region within the predefined geographic region, a list of contaminants for that sub-geographic region, the list of contaminants based on uses of land within each sub-geographic area, resulting in a location-based breakdown of contaminants within the predefined geographic region (obvious in view of combination; Jain et al column 4, lines 28-36 state, “location tags representing disease exposure events or risks can be based on symptom reports or infection likelihood predictions, in addition to or instead of disease testing results. For example, a location tag representing a visit of a person can be assigned a disease transmission score based on symptoms reported by a user, physiological measures tracked …” Mustafa et al NPL column 1, paragraph 1 states, “The authors’ concluded that the IoT system can be useful in identifying and analyzing the potential source of contaminants in river water.” Please also note various art that Mustafa et al NPL incorporates by reference. It would be obvious to one of ordinary skill in the art, given a wide variety of medical and detected sensor data, to receive the data listed in the claimed limitation.)
adjusting, via the computer system, the initial water quality scores based on the location-based breakdown of contaminants within the predefined geographic region, resulting in updated water quality scores (obvious in view of the score adjustment teachings of Jain et al and the real time teachings of both Mustafa et al NPL and Jain et al, as discussed above.)
predicting, via the Al algorithm, contaminant transport within the predefined geographic region based on the hydrological movement defined by the hydrology data, wherein the predicting of the contaminant transport uses the hydrology map to determine how the contaminants within the predefined geographic region will travel between sub-geographic regions and arrive at a downstream location within the predefined geographic region (obvious in view of combination; Jain et al column 2, lines 58-63 state, “Machine learning models can be trained using rich data about geography and behavior in different communities. The examples used for training can include data describing specific places, data characterizing occupancy and traffic over time, geographic relationships such as map data, and so on.” Jain et al column 21, lines 4-15 state, “selecting the disease prevention option that stored mapping data specifies as corresponding to the activity type of the intended activity, a location type for a location of the intended activity … The mapping data associates different disease prevention options with different values of one or more of activity types, location types, disease transmission levels, user disease susceptibility measures, and/or user characteristics. Jain et al teaches mapping various types of data for various applications. Among the many different types of data that Jain considers is water-quality data (column 131, lines 30-31). Mustafa discloses water quality monitoring and prediction. It would have been obvious to one of ordinary skill in the art to apply the mapping and scoring principles of Jain to the prediction teachings of Mustafa, to arrive at the claimed limitation.)
transmitting, via the computer system, the updated water quality scores and the contaminant transport to a user device (obvious to combination; Jain et al discloses transferring data over a communication network 108 to various user devices 104a-104b (figure 1; column 27, lines 37-40).)
With respect to claim 1, it would have been obvious to one having ordinary skill in the art before the effective filing date of the invention to incorporate the teachings of Jain et al into the invention of Mustafa et al NPL. The motivation for the skilled artisan in doing so is to gain the benefit of more effectively tracking potential sources of disease due to a lack of water quality.
Independent claims 8 and 15 represent system and non-transitory computer-readable storage medium versions of method claim 1 and are rejected for similar reasons as those given with respect to claim 1 above. Mustafa et al NPL also teaches the unique limitations that claims 8 and 15 present.
With respect to claim 8, Mustafa et al NPL discloses:
A system (Mustafa et al NPL abstract)
at least one processor (Mustafa et al NPL page 969, column 1, paragraph 1 under section “II. Internet of Thing in Water Quality Monitoring” states, “data from a network of different chipboard such as the ARM-based Raspberry pi microcomputer, ARDUINO microprocessors, ESP8266 and ESP32 are attached to sensors to collect real-time data which can be viewed via a server computer and interpreted through Artificial Intelligence such as machine learning tools …”)
a non-transitory computer-readable storage medium having instructions stored which, when executed by the at least one processor, cause the at least one processor to perform operations (Mustafa et al NPL abstract teaches computer; Mustafa et al NPL page 969, column 1, paragraph 1 under section “II. Internet of Thing in Water Quality Monitoring” states, “data from a network of different chipboard such as the ARM-based Raspberry pi microcomputer, ARDUINO microprocessors, ESP8266 and ESP32 are attached to sensors to collect real-time data which can be viewed via a server computer and interpreted through Artificial Intelligence such as machine learning tools …”; limitation suggested by disclosure of computer)
With respect to claim 15, Mustafa et al NPL discloses:
A non-transitory computer-readable storage medium having instructions stored which, when executed by at least one processor, cause at least one processor to perform operations (Mustafa et al NPL abstract; page 969, column 1, paragraph 1 under section “II. Internet of Thing in Water Quality Monitoring”)
With respect to claims 2, 9, and 16, Mustafa et al NPL, as modified, discloses:
wherein: the Al algorithm, when generating the initial water quality scores, receives as inputs:
the hydrology data (Mustafa et al NPL page 968 states, “In hydrology, IoT devices are used to collect real-time data from a given water sample or station)
the real-time sensor data (Mustafa et al NPL page 968 states, “In hydrology, IoT devices are used to collect real-time data from a given water sample or station. As discussed above, Jain et al also discloses real time data.)
a period of time for which a predicted water quality score is desired (Mustafa et al NPL page 972, column 1, paragraph 2 states, “Also, ANN tools have the ability to evaluate large amount of historical data collected from different river stations and wastewater treatment plants with minimum errors within a short period of time.” As discussed above, assigning a “score” to the water quality prediction operations would be obvious to one of ordinary skill in the art in view of the combination.)
the Al algorithm outputs:
the initial water quality score for the period of time (obvious in view of combination; outputting water quality results of the model would be obvious to one of ordinary skill in the art);
and wherein the Al algorithm:
uses the hydrology data and the real-time sensor data to predict water levels within the predefined geographic region, resulting in predicted water levels for the period of time (This is an obvious operation in view of the robust and complex artificial intelligence models disclosed by modified Mustafa et al NPL. One of ordinary skill in the art recognizes the vast amount of applications that are encompassed by the use of a robust artificial intelligence model. All of the associated variables necessary to the model to perform the claimed operation are disclosed or suggested, as discussed above.);
use the predicted water levels and the hydrology data to predict a predicted spread of water for the period of time (obvious in view of combination; One of ordinary skill in the art would recognize Jain’s teachings of predicting disease spread (abstract) to encompass the spread of water, especially in view of Mustafa’s expansive teachings.); and
uses the predicted spread of water and the real-time sensor data to predict water quality for the period of time, resulting in the initial water quality scores (obvious in view of combination, for similar reasons as those discussed above)
With respect to claims 3, 10, and 17, Mustafa et al NPL, as modified, discloses:
wherein the known contaminants within the predefined geographic region comprise wastewater (Mustafa et al NPL page 969, column 2, last paragraph states, “ML algorithms … predicting, estimating and forecasting the wastewater indicators of data …”)
With respect to claims 4, 11, and 18, Mustafa et al NPL, as modified, discloses:
transmitting, via the computer system, the updated water quality scores to a mobile computing device, wherein the mobile computing device belongs to a civilian consumer (obvious in view of combination; The abstract of Mustafa et al NPL states, “to achieve a safe and improved water quality for users.” Both civilians and non-civilians drink water and required water quality, so this limitation suggests that water quality applies to both civilian consumers and non-civilian consumers. Furthermore, Jain et al column 10, lines 11-14 state, “In another general aspect, a method performed by one or more computers includes: receiving monitoring data for a community generated using mobile devices of individuals in the community.” Jain further teaches transmission of data through mobile devices, throughout its disclosure.)
With respect to claims 6, 13, and 20, Mustafa et al NPL, as modified, discloses:
detecting, via a contaminant sensor, a discrepancy between a predicted contaminant amount and an actual contaminant amount (Mustafa et al NPL discloses the principle of comparing actual and predicted data for a model. For example, page 972, paragraph 1 states, “The performance of the model was determined by comparing the actual and predicted loads …” Jain et al also teaches predicted versus actual data (see figure 12, reference 1240).)
modifying the Al algorithm based on the discrepancy (This limitation is obvious in view of the combination. Jain et al discloses adjustments. Figure 19, reference 1912 states, “select or adjust disease monitoring, prevention, testing, and treatment for individuals based on the one or more disease-related predictions for the community.” Adjusting based on actual versus predicted data would be obvious to one or ordinary skill in the art.)
With respect to claims 7 and 14, Mustafa et al NPL discloses:
wherein the modifying of the Al algorithm comprises replacing, in memory, at least one piece of data which resulted in the actual contaminant amount (This limitation is obvious in view of the combination. Mustafa et al NPL page 972, column 1, last paragraph – column 2, first paragraph states, “ANN learns to solve a problem by creating a memory that can correlate a large number of input patterns with the output set.”; One of ordinary skill in the art would recognize that it would be obvious to execute updating of an ANN model through various techniques, such as the claimed technique. It should also be noted that the applicant’s own specification does not appear to specifically disclose the claimed language in the way that it is currently written. The examiner could not find any disclosure of the term “replacing, in memory” in the applicant’s specification. The closest support that the examiner found was in paragraph 0039 of the specification, which states, “systems configured as disclosed herein can implement a feedback system, such that the mechanisms used to make predictions of missing data can be periodically updated … because the factors used to make the predictions have been modified.” The examiner broadly interpreted claims 7 and 14 to be reflective of modifications to the model, as represented by paragraph 0039 of the applicant’s specification. This type of behavior is what Mustafa et al NPL, as modified, teaches. Also, as discussed above, Jain et al discloses adjusting.)
Claim(s) 5, 12, and 19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Mustafa et al NPL (Mustafa, Hauwa Mohammed; Hayder, Gasim; Mustpha, Aisha; and Salisu, Abdullahi – “Applications of IoT and Artificial Intelligence in Water Quality Monitoring and Prediction: A Review”; Proceedings of the Sixth International Conference on Inventive Computation Technologies (ICICT). IEEE, 2021.) in view of Jain et al (US Pat 11504011), as applied to claims 1-4, 6-11, 13-18, and 20 above, and further in view of Al Aani et al NPL (Al Aani, Saif; Bonny, Talal; Hasan, Shadi W.; and Hilal, Nidal – “Can machine language and artificial intelligence revolutionize process automation for water treatment and desalination?”; Desalination, vol. 458, no. February, pp 84-96, 2019.) (Please note that this reference was incorporated by reference into Mustafa et al NPL as reference [15]).
With respect to claims 5, 12, and 19, Mustafa et al NPL, as modified discloses:
{claim 5} The method of claim 1 (as applied to claim 1 above)
{claim 12} The system of claim 8 (as applied to claim 8 above)
{claim 19} The non-transitory computer-readable storage medium of claim 15 (as applied to claim 15 above)
With respect to claims 5, 12, and 19, Mustafa et al NPL, as modified, differs from the claimed invention in that it does not explicitly disclose:
wherein the adjusting of the initial water quality scores uses solubility and weight of the contaminants to predict contaminant diffusion of the known contaminants within the predefined geographic region
With respect to claims 5, 12, and 19, Al Aani et al NPL discloses:
wherein the adjusting of the initial water quality scores uses solubility and weight of the contaminants to predict contaminant diffusion of the known contaminants within the predefined geographic region (page 89, column 1, paragraph 2 states, “ANN model consisting of one input layer, two hidden layers, and one output layer for a steady-state contaminant elimination … The investigation was performed under a set of operating conditions such as flux, feed water recovery, contaminant recovery, and feed water quality parameters including TDS concentration, pH, contaminant concentration, and where possible the diffusion coefficients were utilized as inputs for modelling the ratio of permeate/feed concentration of the target containment.”; As seen in the nomenclature chart on page 85, TDS stands for “Total dissolved solids,” which suggests solubility. One of ordinary skill in the art would also recognize that diffusion characteristics are dependent on both solubility and weight, among other factors. One of ordinary skill in the art would also recognize weight as being an obvious factor to contaminant concentration. The claimed limitation is obvious to the combination of modified Mustafa et al NPL in view of Al Aani et al NPL, as modified Mustafa et al NPL teaches the predictive models based on various parameters, and Al Aani et al NPL teaches consideration of parameters that are dependent on factors, such as solubility and weight of the contaminants. Also, as mentioned, Mustafa et al NPL directly incorporates Al Aani et al NPL by reference.)
With respect to claims 5, 12, and 19, it would have been obvious to one having ordinary skill in the art before the effective filing date of the invention to incorporate the teachings of Al Aani et al NPL into the invention of modified Mustafa et al NPL. The motivation for the skilled artisan in doing so is to gain the benefit of forming accurate predictive models based on contaminant details.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Green et al (US PgPub 20140229420) discloses systems and methods for quantifying flood risk.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to LEONARD S LIANG whose telephone number is (571)272-2148. The examiner can normally be reached M-F 10:00 AM - 7 PM.
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) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, ARLEEN M VAZQUEZ can be reached at (571)272-2619. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/LEONARD S LIANG/ Examiner, Art Unit 2857 09/26/26
/ARLEEN M VAZQUEZ/Supervisory Patent Examiner, Art Unit 2857
