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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 03/02/26 has been entered.
Response to Arguments
Applicant’s arguments with respect to claim(s) 1-20 have been considered but are moot in view of the new grounds of rejection necessitated by the applicant’s amendments to the claims.
Nonetheless, the examiner will respond to some of the applicant’s arguments.
The applicant’s amendments have overcome the previous 35 U.S.C. 112 rejection.
With respect to 35 U.S.C. 101, the applicant’s amendments have necessitated the 101 rejection shown below. The applicant argues, “the claims recite execution of an AI model which is using ‘at least one model’ undergoing continuous ‘model training.’ The minds of human beings cannot practically perform such limitations …” While this may be true, the model training uses an AI algorithm and recites abstract mathematical concepts.
With respect to the 35 U.S.C. 103 rejection, the applicant argues:
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This argument is not persuasive because Jain teaches multiple principles, which, when incorporated into the primary reference of Mustafa, will render the claimed limitation obvious. As discussed in the rejection below, Jain teaches mapping principles that apply to a wide variety of data and application contexts. Jain also teaches water quality as one type of data that it considers. Mustafa teaches water quality and monitoring prediction in greater detail. The applicant’s arguments appear to view Mustafa and Jain in a vacuum and overlook what would be obvious to one of ordinary skill in the art, in view of their combination.
The applicant’s other arguments are also unpersuasive for similar reasons, as discussed above.
The rejection is maintained.
Drawings
As discussed in a previous action, the drawing amendments filed on 08/21/25 are accepted.
Claim Objections
Claim 8 is objected to because of the following informalities:
Claim 8 has been amended to include the limitation of, “initiating execution of the algorithm …” Claims 1 and 15 have been similarly amended; however, they state, “initiating execution of the AI algorithm.” For the sake of consistency and because there is no antecedent basis for “the algorithm” (while there is antecedent basis for “the AI algorithm”), the examiner will construe that claim 8 should also state, “initiating execution of the AI algorithm.”
Appropriate correction is required.
Examiner’s Note - 35 USC § 112
The applicant’s 02/04/26 amendments have overcome the previous 112 rejection.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
With respect to step 1 of the patent subject matter eligibility analysis, the claims are directed to a process, machine, manufacture, or composition of matter. Independent claim 1 is directed to a method, which is a process. Independent claim 8 is directed to a system, which is a machine. Independent claim 15 is directed to a non-transitory computer-readable medium. All other claims depend on independent claims 1, 8, and 15. As such, claims 1-20 are directed to a statutory category.
With respect to step 2A, prong one, the claims recite an abstract idea, law of nature, or natural phenomenon. Specifically, the following limitations recite mathematical concepts and/or mental processes.
Claim 1
performing, via at least one processor of the computer system, continuous model training of at least one model used by an Artificial Intelligence (AI) algorithm (This limitation recites an abstract mathematical concept in the form of a mathematical formula or equation. It positively recites an Artificial Intelligence (AI) algorithm.)
initiating execution of the AI algorithm using the hydrology data, the real-time sensor data, and the at least one model (This limitation recites an abstract mathematical concept in the form of a mathematical formula or equation. It specifically and positively recites execution of an algorithm. The hydrology data, the real-time sensor data, and the at least one model serve as input variables that are processed by the algorithm.)
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 (This limitation recites abstract mathematical concepts. The prediction is performed using an AI algorithm, which defines a specific mathematical formula or equation.)
Claim 8
performing continuous model training of at least one model used by an Artificial Intelligence (AI) algorithm (This limitation recites an abstract mathematical concept in the form of a mathematical formula or equation. It positively recites an Artificial Intelligence (AI) algorithm.)
initiating execution of the AI algorithm using the hydrology data, the real-time sensor data, and the at least one model (This limitation recites an abstract mathematical concept in the form of a mathematical formula or equation. It specifically and positively recites execution of an algorithm. The hydrology data, the real-time sensor data, and the at least one model serve as input variables that are processed by the algorithm.)
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 (This limitation recites abstract mathematical concepts. The prediction is performed using an AI algorithm, which defines a specific mathematical formula or equation.)
Claim 15
performing continuous model training of at least one model used by an Artificial Intelligence (AI) algorithm (This limitation recites an abstract mathematical concept in the form of a mathematical formula or equation. It positively recites an Artificial Intelligence (AI) algorithm.)
initiating execution of the AI algorithm using the hydrology data, the real-time sensor data, and the at least one model (This limitation recites an abstract mathematical concept in the form of a mathematical formula or equation. It specifically and positively recites execution of an algorithm. The hydrology data, the real-time sensor data, and the at least one model serve as input variables that are processed by the algorithm.)
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 (This limitation recites abstract mathematical concepts. The prediction is performed using an AI algorithm, which defines a specific mathematical formula or equation.)
All dependent claims depend on independent claims 1, 8, and 15 and also recite their abstract limitations by virtue of their dependence. In addition, some of the dependent claims also recite their own abstract mathematical concepts and/or mental processes.
Dependent claims 2, 9, and 16 disclose the variables that the AI algorithm receives as inputs. This recites mathematical relationships.
Dependent claims 5, 12, and 19 are further directed to the AI algorithm and reflect mathematical relationships.
With respect to step 2A, prong two, the claims do not recite additional elements that integrate the judicial exception into a practical application. The following limitations are considered “additional elements” and explanation will be given as to why these “additional elements” do not integrate the judicial exception into a practical application.
Claim 1
A method (As seen below, the limitations that make up the method are not indicative of integration into a practical application.)
generating initial water quality scores for sub-geographic regions within a predefined geographic region (This limitation is not indicative of integration into a practical application because it merely serves to generally link scoring to the particular technological environment or field of use of water quality for sub-geographic regions within a predefined geographic region. Generally linking the use of the judicial exception to a particular technological environment or field of use is not indicative of integration into a practical application (see MPEP 2106.05(f)).)
receiving, at a computer system from at least one public database, hydrology data for a predefined geographic region (This limitation is not indicative of integration into a practical application for a number of reasons. First, the limitations explicitly disclose using a computer system, and merely using a computer as a tool to perform an abstract idea is not indicative of integration into a practical application (see MPEP 2106.05(f)). Next, simply receiving data to be processed merely adds insignificant extra-solution activity to the judicial exception (see MPEP 2106.05(g)). The “solution” of the current claims is in the data processing. Generic reception of the data that is processed by the data processing serves as “extra-solution activity.” Furthermore, the disclosure of “hydrology data for a predefined geographic region” merely serves to generally link the use of the judicial exception to a particular technological environment or field of use, which is not indicative of integration into a practical application (see MPEP 2106.05(h)).)
the hydrology data (This is a general link to a particular technological environment or field of use. It merely gives context to the type of data that is processed.) 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 (The claims do not positively recite the water flowing between different geographic points. Rather, the claims are directed to processing data about water flowing between different geographic points. The hydrology map is an example of the data that is processed. Its presence merely serves to generally link the use of the judicial exception to a particular technological environment or field of use (see MPEP 2106.05(h)).)
weather data for the predefined geographic region (The weather data is also technological context for the type of data that is processed. It merely serves to generally link the use of the judicial exception to a particular technological environment or field of use (see MPEP 2106.05(h)).)
receiving, at the computer system from at least one private Internet of Things (IOT) device, real-time sensor data associated with water quality within the predefined geographic region (This limitation is not indicative of integration into a practical application for a number of reasons. First, the limitations explicitly disclose using a computer system, and merely using a computer as a tool to perform an abstract idea is not indicative of integration into a practical application (see MPEP 2106.05(f)). Next, simply receiving data to be processed merely adds insignificant extra-solution activity to the judicial exception (see MPEP 2106.05(g)). The “solution” of the current claims is in the data processing. Generic reception of the data that is processed by the data processing serves as “extra-solution activity.” Furthermore, the disclosure of “hydrology data for a predefined geographic region” merely serves to generally link the use of the judicial exception to a particular technological environment or field of use, which is not indicative of integration into a practical application (see MPEP 2106.05(h)). While this limitation does “mention” structure, such as a “private Internet of Things (IOT) device,” it should be noted that the device itself is not positively recited. There is a distinction between the positive recitation of structure and the processing of data about structure. The current limitations fall into the latter category.)
receiving, at the computer system, output of the Al algorithm, the output comprising the initial water quality 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 (This limitation is not indicative of integration into a practical application because it merely uses a computer as a tool to perform an abstract idea. Furthermore, receiving data output from computer processing merely adds insignificant extra-solution activity to the judicial exception. Also, the disclosure of “water quality” merely serves to generally link the use of the judicial exception to a particular technological environment or field of use.)
modifying the initial water quality scores based on known contaminants within the predefined geographic region (As seen below, the sub-steps of this modifying step are not indicative of integration into a practical application.)
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 area (Receiving data merely uses a computer as a tool to perform an abstract idea. The context of the data (i.e. the list of contaminants) merely serve to generally link the use of the judicial exception to a particular technological environment or field of use.)
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 (The claimed adjusting appears to merely be a data processing application that happens entirely on the computer. Merely using a computer as a tool to perform an abstract idea is not indicative of integration into a practical application.)
transmitting, via the computer system, the updated water quality scores and the contaminant transport to a user device (The generic transmission of data merely adds insignificant extra-solution activity to the judicial exception. The claims also disclose “via the computer system.” As discussed above, merely using a computer as a tool to perform an abstract idea is not indicative of integration into a practical application. Although the claims do positively recite a structural element (i.e. a mobile computing device), no details are given about the device other than it receiving data. Here, the mention of the mobile device merely serves to generally link the use of the judicial exception to a particular technological environment or field of use. Similarly, the water quality index is merely a piece of data that generally links the use of the judicial exception to a particular technological environment or field of use. It is not indicative of integration into a practical application.)
Claim 8
A system (The limitations that make up the system are not indicative of integration into a practical application.)
at least one processor (This limitation recites a generic computer component. As discussed above, merely using a computer as a tool to perform an abstract idea is not indicative of integration into a practical application.)
and 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 (This limitation recites a generic computer component. As discussed above, merely using a computer as a tool to perform an abstract idea is not indicative of integration into a practical application.)
generating initial water quality scores for sub-geographic regions within a predefined geographic region (This limitation is not indicative of integration into a practical application because it merely serves to generally link scoring to the particular technological environment or field of use of water quality for sub-geographic regions within a predefined geographic region. Generally linking the use of the judicial exception to a particular technological environment or field of use is not indicative of integration into a practical application (see MPEP 2106.05(f)).)
receiving, from at least one public database, hydrology data for a predefined geographic region (This limitation is not indicative of integration into a practical application for the reasons given with respect to claim 1 above.)
the hydrology data (This is a general link to a particular technological environment or field of use. It merely gives context to the type of data that is processed.) 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 (The claims do not positively recite the water flowing between different geographic points. Rather, the claims are directed to processing data about water flowing between different geographic points. The hydrology map is an example of the data that is processed. Its presence merely serves to generally link the use of the judicial exception to a particular technological environment or field of use (see MPEP 2106.05(h)).)
weather data for the predefined geographic region (The weather data is also technological context for the type of data that is processed. It merely serves to generally link the use of the judicial exception to a particular technological environment or field of use (see MPEP 2106.05(h)).)
receiving, from at least one Internet of Things (IOT) device, real-time sensor data associated with water quality within the predefined geographic region (This limitation is not indicative of integration into a practical application for the reasons given with respect to claim 1 above.)
receiving output of the Al algorithm, the output comprising the initial water quality 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 (This limitation is not indicative of integration into a practical application for the reasons given with respect to claim 1 above.)
modifying the initial water quality scores based on known contaminants within the predefined geographic region (As seen below, the sub-steps of this modifying step are not indicative of integration into a practical application.)
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 area (Receiving data merely uses a computer as a tool to perform an abstract idea. The context of the data (i.e. the list of contaminants) merely serve to generally link the use of the judicial exception to a particular technological environment or field of use.)
adjusting initial water quality scores based on the location-based breakdown of contaminants within the predefined geographic region, resulting in updated water quality scores (The claimed adjusting appears to merely be a data processing application that happens entirely on the computer. Merely using a computer as a tool to perform an abstract idea is not indicative of integration into a practical application.)
transmitting the water quality index score to a mobile computing device (This limitation is not indicative of integration into a practical application for the reasons given with respect to claim 1 above.)
Claim 15
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 (This limitation recites generic computer components. As discussed above, merely using a computer as a tool to perform an abstract idea is not indicative of integration into a practical application.)
generating initial water quality scores for sub-geographic regions within a predefined geographic region (This limitation is not indicative of integration into a practical application because it merely serves to generally link scoring to the particular technological environment or field of use of water quality for sub-geographic regions within a predefined geographic region. Generally linking the use of the judicial exception to a particular technological environment or field of use is not indicative of integration into a practical application (see MPEP 2106.05(f)).)
receiving, from at least one public database, hydrology data for a predefined geographic region (This limitation is not indicative of integration into a practical application for the reasons given with respect to claim 1 above.)
the hydrology data (This is a general link to a particular technological environment or field of use. It merely gives context to the type of data that is processed.) 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 (The claims do not positively recite the water flowing between different geographic points. Rather, the claims are directed to processing data about water flowing between different geographic points. The hydrology map is an example of the data that is processed. Its presence merely serves to generally link the use of the judicial exception to a particular technological environment or field of use (see MPEP 2106.05(h)).)
weather data for the predefined geographic region (The weather data is also technological context for the type of data that is processed. It merely serves to generally link the use of the judicial exception to a particular technological environment or field of use (see MPEP 2106.05(h)).)
receiving, from at least one Internet of Things (IOT) device, real-time sensor data associated with water quality within the predefined geographic region (This limitation is not indicative of integration into a practical application for the reasons given with respect to claim 1 above.)
receiving output of the Al algorithm, the output comprising the initial water quality 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 (This limitation is not indicative of integration into a practical application for the reasons given with respect to claim 1 above.)
modifying the initial water quality scores based on known contaminants within the predefined geographic region (As seen below, the sub-steps of this modifying step are not indicative of integration into a practical application.)
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 area (Receiving data merely uses a computer as a tool to perform an abstract idea. The context of the data (i.e. the list of contaminants) merely serve to generally link the use of the judicial exception to a particular technological environment or field of use.)
adjusting initial water quality scores based on the location-based breakdown of contaminants within the predefined geographic region, resulting in updated water quality scores (The claimed adjusting appears to merely be a data processing application that happens entirely on the computer. Merely using a computer as a tool to perform an abstract idea is not indicative of integration into a practical application.)
transmitting the water quality index score to a mobile computing device (This limitation is not indicative of integration into a practical application for the reasons given with respect to claim 1 above.)
All dependent claims depend on independent claims 1, 8, and 15 and also recite limitations that are not indicative of integration into a practical application by virtue of their dependence. In addition, some of the dependent claims also recite their own limitations that are not indicative of integration into a practical application.
Dependent claims 3, 10, and 17 disclose that contaminants within the predefined geographic region comprise wastewater. This limitation is not indicative of integration into a practical application because it merely serves to generally link the use of the judicial exception to a particular technological environment or field of use.
Dependent claims 4, 11, and 18 disclose that the mobile computing device belongs to a civilian consumer. This limitation is not indicative of integration into a practical application because it merely serves to generally link the use of the judicial exception to a particular technological environment or field of use.
Dependent claims 6, 13, and 20 disclose detecting a discrepancy via a sensor and then modifying the AI algorithm based on the discrepancy. Using a generic sensor to detect discrepancies merely serves to link the use of the judicial exception to a particular technological environment or field of use. The modifying of the AI algorithm appears to be a computer processing operation that alters or replaces data in memory. This merely uses a computer as a tool to perform an abstract idea.
Dependent claims 7 and 14 disclose modifying the AI algorithm by replacing data in memory. This merely uses a computer as a tool to perform an abstract idea.
With respect to step 2B, the claims do not recite additional elements that amount to significantly more than the judicial exception. The claimed invention does not add significantly more because, as discussed above in step 2A, prong two, the claims do nothing more than merely use a computer as a tool to perform an abstract idea; add insignificant extra-solution activity to the judicial exception; and/or generally link the use of the judicial exception to a particular technological environment or field of use. The claims are directed to receiving and processing data. This is well-understood, routine, and conventional. Simply appending well-understood, routine, and conventional activities previously known to the industry, and specified at a high level of generality, to the judicial exception is not indicative of an inventive concept (aka “significantly more”) (see MPEP 2106.05(d) and Berkheimer Memo).
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 at least one model 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, 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, the real-time sensor data, and the at least one model (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
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 index 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 (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 (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 (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.”)
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 (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.
Herzig et al (US PgPub 20130166266) discloses a weather and satellite model for estimating solar irradiance.
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/LEONARD S LIANG/ Examiner, Art Unit 2857 03/20/26