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 Amendment
This office action is in response to applicant’s amendment received on 06/11/2026.
Claims 1, 2, 8-16, and 20 have been amended.
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 non-statutory subject matter.
Step 1:
According to the first part of the analysis, in the instant case, claims 1-8 are directed to a method, claims 9-14 are directed to using an apparatus to perform the method, claims 15-20 directed to using a system comprising a memory and a processor to perform the method. Thus, each of the claims falls within one of the four statutory categories (i.e. process, machine, manufacture, or composition of matter).
Regarding claim 1:
A method for detecting water quality, comprising:
obtaining, from a plurality of ultrasonic sensors associated with a water meter, data comprising time-series time-of-flight (ToF) data;
classifying the obtained data to determine a quality of water based on ultrasonic time-of-flight (ToF) change behavior due to a mixed or combination of impurities in the water;
utilizing a sequential learning unit for classification of impurities in the water, wherein classifying the impurities in the water comprises forming, from the obtained data, sequential samples composed of a plurality of time steps in a temporal dimension;
applying the sequential learning unit to the sequential samples to classify the impurities in the water; and
initiating an impurity correction action based on the classified impurities in the water.
Step 2A Prong 1:
“obtaining, from a plurality of ultrasonic sensors associated with a water meter, data comprising time-series time-of-flight (ToF) data” is directed to mental step of data gathering.
“classifying the obtained data to determine a quality of water based on ultrasonic time-of-flight (ToF) change behavior due to a mixed or combination of impurities in the water” is directed to math because when multiple impurities mix, their combined effect on density and compressibility is rarely a simple linear sum. It requires multivariate calculus and matrix equation to model cross-interaction. Categorizing the resulting ToF data into quality tiers relies on linear algebra, statistical regression, or machine learning classifiers that map multi-dimensional time delays to specific impurity concentrations.
“utilizing a sequential learning unit for classification of impurities in the water, wherein classifying the impurities in the water comprises forming, from the obtained data, sequential samples composed of a plurality of time steps in a temporal dimension” is directed to math because sequential learning models like a type of recurrent neural network are used for water quality classification. These models use matrices and vector operations to process sequences of water quality data (e.g. pH, dissolved oxygen, temperature over time) to predict future contamination, achieving high accuracy in classification tasks. Water quality data is often sequential (e.g. sensor reading taken every hour). Analyzing this requires statistical methods to identify patterns, trends, and correlations in the data over time to distinguish between safe and contaminated water. Linear Algebra uses matrices and vectors to organize and transform the sequential data across time steps. Calculus uses derivatives and optimization rules to train the learning unit and minimize classification errors. Probability and statistics measure the likelihood of specific water impurities occurring and tests how accurate the classifications are.
“initiating an impurity correction action based on the classified impurities in the water” is directed to math because water treatment plants use math to figure out the exact amount of treatment chemicals needed based on the volume of water and the level of impurities. Impurity levels are reported in numbers like milligrams per liter. Statistics help technicians review water quality trends and decide when impurity levels cross safety limits. Math helps calculate how fast water moves through treatment systems to ensure proper contact time for purification.
Each limitation recites in the claim is a process that, under BRI covers performance of the limitation in the mind but for the recitation of a generic “sensor and measurement” which is a mere indication of the field of use. Nothing in the claim elements precludes the steps from practically being performed in the mind. Thus, the claim recites a mental process.
Further, the claim recites the step of " classifying the obtained data to determine a quality of water based on ultrasonic time-of-flight (ToF) change behavior due to a mixed or combination of impurities in the water; utilizing a sequential learning unit for classification of impurities in the water, wherein classifying the impurities in the water comprises forming, from the obtained data, sequential samples composed of a plurality of time steps in a temporal dimension; and initiating an impurity correction action based on the classified impurities in the water” which as drafted, under BRI recites a mathematical calculation. The grouping of "mathematical concepts” in the 2019 PED includes "mathematical calculations" as an exemplar of an abstract idea. 2019 PEG Section |, 84 Fed. Reg. at 52. Thus, the recited limitation falls into the "mathematical concept" grouping of abstract ideas. This limitation also falls into the “mental process” group of abstract ideas, because the recited mathematical calculation is simple enough that it can be practically performed in the human mind, e.g., scientists and engineers have been solving the Arrhenius equation in their minds since it was first proposed in 1889.
Note that even if most humans would use a physical aid (e.g., pen and paper, a slide rule, or a calculator) to help them complete the recited calculation, the use of such physical aid does not negate the mental nature of this limitation. See October Update at Section I(C)(i) and (iii).
Additional Elements:
Step 2A Prong 2:
“classifying the obtained data to determine a quality of water based on ultrasonic time-of-flight (ToF) change behavior due to a mixed or combination of impurities in the water” does not integrate the judicial exception into a practical application. This additional element is merely using a computer as a tool to perform an abstract idea (see MPEP 2106.05(h)).
“utilizing a sequential learning unit for classification of impurities in the water, wherein classifying the impurities in the water comprises forming, from the obtained data, sequential samples composed of a plurality of time steps in a temporal dimension” does not integrate the judicial exception into a practical application. This additional element is merely using a computer as a tool to perform an abstract idea (see MPEP 2106.05(h)).
“applying the sequential learning unit to the sequential samples to classify the impurities in the water” does not integrate the judicial exception into a practical application. This additional element is merely using a computer as a tool to perform an abstract idea (see MPEP 2106.05(h)).
“initiating an impurity correction action based on the classified impurities in the water” is directed to insignificant activity and does not integrate the judicial exception into a practical application. See MPEP 2106.05(g).
The claim is merely collecting data, manipulating or analyzing the data using math and mental process, and displaying the results.
This is similar to electric power: MPEP 2106.05(h) vi. Limiting the abstract idea of collecting information, analyzing it, and displaying certain results of the collection and analysis to data related to the electric power grid, because limiting application of the abstract idea to power-grid monitoring is simply an attempt to limit the use of the abstract idea to a particular technological environment, Electric Power Group, LLC v. Alstom S.A., 830 F.3d 1350, 1354, 119 USPQ2d 1739, 1742 (Fed. Cir. 2016).
Whether the claim invokes computers or other machinery merely as a tool to perform an existing process. Use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., a fundamental economic practice or mathematical equation) does not integrate a judicial exception into a practical application or provide significantly more. See Affinity Labs v. DirecTV, 838 F.3d 1253, 1262, 120 USPQ2d 1201, 1207 (Fed. Cir. 2016) (cellular telephone); TLI Communications LLC v. AV Auto, LLC, 823 F.3d 607, 613, 118 USPQ2d 1744, 1748 (Fed. Cir. 2016) (computer server and telephone unit). Similarly, "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 concept. Intellectual Ventures I LLC v. Capital One Bank (USA), 792 F.3d 1363, 1367, 115 USPQ2d 1636, 1639 (Fed. Cir. 2015). In contrast, a claim that purports to improve computer capabilities or to improve an existing technology may integrate a judicial exception into a practical application or provide significantly more. McRO, Inc. v. Bandai Namco Games Am. Inc., 837 F.3d 1299, 1314-15, 120 USPQ2d 1091, 1101-02 (Fed. Cir. 2016); Enfish, LLC v. Microsoft Corp., 822 F.3d 1327, 1335-36, 118 USPQ2d 1684, 1688-89 (Fed. Cir. 2016). See MPEP §§ 2106.04(d)(1) and 2106.05(a) for a discussion of improvements to the functioning of a computer or to another technology or technical field.
Claim 1 recites the additional element(s) of using generic AI/ML technology, i.e. *** utilizing a sequential learning unit ***, to perform data evaluations or calculations, as identified under Prong 1 above. The claims do not recite any details regarding how the AI/ML algorithm or model functions or is trained. Instead, the claims are found to utilize the AI/ML algorithm as a tool that provides nothing more than mere instructions to implement the abstract idea on a general purpose computer. See MPEP 2106.05(f). Additionally, the use of the *** utilizing a sequential learning unit *** merely indicates a field of use or technological environment in which the judicial exception is performed. See MPEP 2106.05(h). Therefore, the use of *** utilizing a sequential learning unit *** to perform steps that are otherwise abstract does not integrate the abstract idea into a practical application. See the 2024 Guidance Update on Patent Subject Matter Eligibility, Including on Artificial Intelligence; and Example 47, ineligible claim 2.
The claim as a whole does not meet any of the following criteria to integrate the judicial exception into a practical application:
An additional element reflects an improvement in the functioning of a computer, or an improvement to other technology or technical field;
an additional element that applies or uses a judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition;
an additional element implements a judicial exception with, or uses a judicial exception in conjunction with, a particular machine or manufacture that is integral to the claim;
an additional element effects a transformation or reduction of a particular article to a different state or thing; and
an additional element applies or uses the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception.
Step 2B:
“classifying the obtained data to determine a quality of water based on ultrasonic time-of-flight (ToF) change behavior due to a mixed or combination of impurities in the water” does not amount to significantly more than the judicial exception in the claim. This additional element is merely using a computer as a tool to perform an abstract idea (see MPEP 2106.05(h)).
“utilizing a sequential learning unit for classification of impurities in the water, wherein classifying the impurities in the water comprises forming, from the obtained data, sequential samples composed of a plurality of time steps in a temporal dimension” does not amount to significantly more than the judicial exception in the claim. This additional element is merely using a computer as a tool to perform an abstract idea (see MPEP 2106.05(h)).
“applying the sequential learning unit to the sequential samples to classify the impurities in the water” does not amount to significantly more than the judicial exception in the claim. This additional element is merely using a computer as a tool to perform an abstract idea (see MPEP 2106.05(h)).
“initiating an impurity correction action based on the classified impurities in the water” is directed to insignificant activity and does not amount to significantly more than the judicial exception in the claim. See MPEP 2106.05(g) and 2106.05(d)(ii), third list, (iv).
The claim is therefore ineligible under 35 USC 101.
Claim 9 is similar to claim 1 but recites an apparatus for detecting water quality. These additional elements fail to integrate the abstract idea into a practical application. These limitations are recited at a high level of generality and do not add significantly more to the judicial exception. These elements are generic computing devices that perform generic functions. Using generic computer elements to perform an abstract idea does not integrate an abstract idea into a practical application. See 2019 Guidance, 84 Fed. Reg. at 55. Moreover, “the mere recitation of a generic computer cannot transform a patent-ineligible abstract idea into a patent-eligible invention.” Alice, 573 U.S. at 223; see also FairWarninglP, LLCv. latric SysInc., 839 F.3d 1089, 1096 (Fed. Cir. 2016) (citation omitted) (“[T]he use of generic computer elements like a microprocessor or user interface do not alone transform an otherwise abstract idea into patent-eligible subject matter”).
On the record before us, we are not persuaded that the hardware of claim 9 integrates the abstract idea into a practical application. Nor are we persuaded that the additional elements are anything more than well-understood, routine, and conventional so as to impart subject matter eligibility to claim 9.
Claim 9 is similar to claim 1 but recites an apparatus for detecting water quality. These additional elements fail to integrate the abstract idea into a practical application. These limitations are recited at a high level of generality and do not add significantly more to the judicial exception. These elements are generic computing devices that perform generic functions. Using generic computer elements to perform an abstract idea does not integrate an abstract idea into a practical application. See 2019 Guidance, 84 Fed. Reg. at 55. Moreover, “the mere recitation of a generic computer cannot transform a patent-ineligible abstract idea into a patent-eligible invention.” Alice, 573 U.S. at 223; see also FairWarninglP, LLCv. latric SysInc., 839 F.3d 1089, 1096 (Fed. Cir. 2016) (citation omitted) (“[T]he use of generic computer elements like a microprocessor or user interface do not alone transform an otherwise abstract idea into patent-eligible subject matter”).
Regarding claims 2 and 16, “obtaining the data indicative of ultrasonic time-of-flight change behavior from a plurality of ultrasonic sensors associated with the water meter” does not integrate the judicial exception into a practical application. It does not amount to significantly more than the judicial exception in the claim. This additional element is merely using a computer as a tool to perform an abstract idea (see MPEP 2106.05(h)).
Regarding claims 3 and 17, “obtaining the data indicative of ultrasonic time-of-flight change behavior from at least two ultrasonic sensors associated with the water meter” does not integrate the judicial exception into a practical application. It does not amount to significantly more than the judicial exception in the claim. This additional element is merely using a computer as a tool to perform an abstract idea (see MPEP 2106.05(h)).
Regarding claims 4 and 10, “classifying with the sequential learning unit the impurities in the water as water quality parameters including at least one of: TDS (Total Dissolved Solids), ph level, chlorine residual data, turbidity information, and total organic carbon values” does not integrate the judicial exception into a practical application. It does not amount to significantly more than the judicial exception in the claim. This additional element is merely using a computer as a tool to perform an abstract idea (see MPEP 2106.05(h)).
Regarding claims 5 and 11, “communicating data indicative of the impurities in the water classified with a machine learning algorithm to a user through a radio frequency frame” does not integrate the judicial exception into a practical application. It does not amount to significantly more than the judicial exception in the claim. This additional element is merely using a computer as a tool to perform an abstract idea (see MPEP 2106.05(h)).
Regarding claims 6, 12, and 18, “classifying with the sequential learning unit the impurities in the water as water quality parameters including at least one of: TDS (Total Dissolved Solids), ph level, chlorine residual data, turbidity information, and total organic carbon values; and communicating the water quality parameters associated with the water to a user through a radio frequency frame” does not integrate the judicial exception into a practical application. It does not amount to significantly more than the judicial exception in the claim. This additional element is merely using a computer as a tool to perform an abstract idea (see MPEP 2106.05(h)).
Regarding claims 7, 13, and 19, “the sequential learning unit comprises a machine learning algorithm.” does not integrate the judicial exception into a practical application. It does not amount to significantly more than the judicial exception in the claim. This additional element is merely using a computer as a tool to perform an abstract idea (see MPEP 2106.05(h)).
Regarding claims 8, 14, and 20, “wherein the obtained data indicative of the classification of the impurities in the water comprising difference in Time-of-flight (DiffToF) and temperature information” does not integrate the judicial exception into a practical application. It does not amount to significantly more than the judicial exception in the claim. This additional element is merely using a computer as a tool to perform an abstract idea (see MPEP 2106.05(h)).
Hence the claims 1-20 are treated as ineligible subject matter under 35 U.S.C. § 101.
Response to Arguments
Applicant's arguments filed 06/11/2026 have been fully considered but they are not persuasive.
-Applicant argues that one or more features of amended independent claims cannot be performed/executed by the human mind. The amended independent claims are not directed to an abstract idea because it is specifically rooted in a technical environment involving ultrasonic sensing within a water meter and recites a concrete sequence of physical and data-processing operations that model real-world signal behavior. The amended independent claims are directed to a practical application of ultrasonic signal propagation and transformation rather than a disembodied mathematical concept or mental process. In particular, the claim requires obtaining time-series time-of-flight (ToF) from a plurality of ultrasonic sensors associated with a water meter, which are measurements derived from physical acoustic wave transmission through water, inherently affected by impurities. This establishes that the claimed invention operates on real-time physical signals representative of a tangible phenomenon (ultrasonic propagation influenced by water quality), and not merely on abstract or arbitrary data.
Response: The examiner respectfully disagrees. Claim 1 recites a method for detecting water quality, comprising:
obtaining, from a plurality of ultrasonic sensors associated with a water meter, data comprising time-series time-of-flight (ToF) data;
classifying the obtained data to determine a quality of water based on ultrasonic time-of-flight (ToF) change behavior due to a mixed or combination of impurities in the water;
utilizing a sequential learning unit for classification of impurities in the water, wherein classifying the impurities in the water comprises forming, from the obtained data, sequential samples composed of a plurality of time steps in a temporal dimension;
applying the sequential learning unit to the sequential samples to classify the impurities in the water; and
initiating an impurity correction action based on the classified impurities in the water.
As identified in in the 35 USC 101 rejection above, step “obtaining, from a plurality of ultrasonic sensors associated with a water meter, data comprising time-series time-of-flight (ToF) data” is directed to mental step of data gathering,
step “classifying the obtained data to determine a quality of water based on ultrasonic time-of-flight (ToF) change behavior due to a mixed or combination of impurities in the water” is directed to math because when multiple impurities mix, their combined effect on density and compressibility is rarely a simple linear sum. It requires multivariate calculus and matrix equation to model cross-interaction. Categorizing the resulting ToF data into quality tiers relies on linear algebra, statistical regression, or machine learning classifiers that map multi-dimensional time delays to specific impurity concentrations, step “utilizing a sequential learning unit for classification of impurities in the water, wherein classifying the impurities in the water comprises forming, from the obtained data, sequential samples composed of a plurality of time steps in a temporal dimension” is directed to math because sequential learning models like a type of recurrent neural network are used for water quality classification. These models use matrices and vector operations to process sequences of water quality data (e.g. pH, dissolved oxygen, temperature over time) to predict future contamination, achieving high accuracy in classification tasks. Water quality data is often sequential (e.g. sensor reading taken every hour). Analyzing this requires statistical methods to identify patterns, trends, and correlations in the data over time to distinguish between safe and contaminated water. Linear Algebra uses matrices and vectors to organize and transform the sequential data across time steps. Calculus uses derivatives and optimization rules to train the learning unit and minimize classification errors. Probability and statistics measure the likelihood of specific water impurities occurring and tests how accurate the classifications are, step “initiating an impurity correction action based on the classified impurities in the water” is directed to math because water treatment plants use math to figure out the exact amount of treatment chemicals needed based on the volume of water and the level of impurities. Impurity levels are reported in numbers like milligrams per liter. Statistics help technicians review water quality trends and decide when impurity levels cross safety limits. Math helps calculate how fast water moves through treatment systems to ensure proper contact time for purification.
Each limitation recites in the claim is a process that, under BRI covers performance of the limitation in the mind but for the recitation of a generic “sensor and measurement” which is a mere indication of the field of use. Nothing in the claim elements precludes the steps from practically being performed in the mind. Thus, the claim recites a mental process.
Further, the claim recites the step of "classifying the obtained data to determine a quality of water based on ultrasonic time-of-flight (ToF) change behavior due to a mixed or combination of impurities in the water; utilizing a sequential learning unit for classification of impurities in the water, wherein classifying the impurities in the water comprises forming, from the obtained data, sequential samples composed of a plurality of time steps in a temporal dimension; and initiating an impurity correction action based on the classified impurities in the water” which as drafted, under BRI recites a mathematical calculation. The grouping of "mathematical concepts” in the 2019 PED includes "mathematical calculations" as an exemplar of an abstract idea. 2019 PEG Section |, 84 Fed. Reg. at 52. Thus, the recited limitation falls into the "mathematical concept" grouping of abstract ideas. This limitation also falls into the “mental process” group of abstract ideas, because the recited mathematical calculation is simple enough that it can be practically performed in the human mind, e.g., scientists and engineers have been solving the Arrhenius equation in their minds since it was first proposed in 1889.
Note that even if most humans would use a physical aid (e.g., pen and paper, a slide rule, or a calculator) to help them complete the recited calculation, the use of such physical aid does not negate the mental nature of this limitation. See October Update at Section I(C)(i) and (iii).
MPEP 2106.04(a):The use of a physical aid (e.g., pencil and paper or a slide rule) to help perform a mental step (e.g., a mathematical calculation) does not negate the mental nature of the limitation, but simply accounts for variations in memory capacity from one person to another. For instance, in CyberSource, the court determined that the step of "constructing a map of credit card numbers" was a limitation that was able to be performed "by writing down a list of credit card transactions made from a particular IP address." In making this determination, the court looked to the specification, which explained that the claimed map was nothing more than a listing of several (e.g., four) credit card transactions. The court concluded that this step was able to be performed mentally with a pen and paper, and therefore, it qualified as a mental process.
Additional Elements:
Step 2A Prong 2:
“classifying the obtained data to determine a quality of water based on ultrasonic time-of-flight (ToF) change behavior due to a mixed or combination of impurities in the water” does not integrate the judicial exception into a practical application. This additional element is merely using a computer as a tool to perform an abstract idea (see MPEP 2106.05(h)).
“utilizing a sequential learning unit for classification of impurities in the water, wherein classifying the impurities in the water comprises forming, from the obtained data, sequential samples composed of a plurality of time steps in a temporal dimension” does not integrate the judicial exception into a practical application. This additional element is merely using a computer as a tool to perform an abstract idea (see MPEP 2106.05(h)).
“applying the sequential learning unit to the sequential samples to classify the impurities in the water” does not integrate the judicial exception into a practical application. This additional element is merely using a computer as a tool to perform an abstract idea (see MPEP 2106.05(h)).
“initiating an impurity correction action based on the classified impurities in the water” is directed to insignificant activity and does not integrate the judicial exception into a practical application. See MPEP 2106.05(g).
The claim is merely collecting data, manipulating or analyzing the data using math and mental process, and displaying the results.
This is similar to electric power: MPEP 2106.05(h) vi. Limiting the abstract idea of collecting information, analyzing it, and displaying certain results of the collection and analysis to data related to the electric power grid, because limiting application of the abstract idea to power-grid monitoring is simply an attempt to limit the use of the abstract idea to a particular technological environment, Electric Power Group, LLC v. Alstom S.A., 830 F.3d 1350, 1354, 119 USPQ2d 1739, 1742 (Fed. Cir. 2016).
Whether the claim invokes computers or other machinery merely as a tool to perform an existing process. Use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., a fundamental economic practice or mathematical equation) does not integrate a judicial exception into a practical application or provide significantly more. See Affinity Labs v. DirecTV, 838 F.3d 1253, 1262, 120 USPQ2d 1201, 1207 (Fed. Cir. 2016) (cellular telephone); TLI Communications LLC v. AV Auto, LLC, 823 F.3d 607, 613, 118 USPQ2d 1744, 1748 (Fed. Cir. 2016) (computer server and telephone unit). Similarly, "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 concept. Intellectual Ventures I LLC v. Capital One Bank (USA), 792 F.3d 1363, 1367, 115 USPQ2d 1636, 1639 (Fed. Cir. 2015). In contrast, a claim that purports to improve computer capabilities or to improve an existing technology may integrate a judicial exception into a practical application or provide significantly more. McRO, Inc. v. Bandai Namco Games Am. Inc., 837 F.3d 1299, 1314-15, 120 USPQ2d 1091, 1101-02 (Fed. Cir. 2016); Enfish, LLC v. Microsoft Corp., 822 F.3d 1327, 1335-36, 118 USPQ2d 1684, 1688-89 (Fed. Cir. 2016). See MPEP §§ 2106.04(d)(1) and 2106.05(a) for a discussion of improvements to the functioning of a computer or to another technology or technical field.
Claim 1 recites the additional element(s) of using generic AI/ML technology, i.e. *** utilizing a sequential learning unit ***, to perform data evaluations or calculations, as identified under Prong 1 above. The claims do not recite any details regarding how the AI/ML algorithm or model functions or is trained. Instead, the claims are found to utilize the AI/ML algorithm as a tool that provides nothing more than mere instructions to implement the abstract idea on a general purpose computer. See MPEP 2106.05(f). Additionally, the use of the *** utilizing a sequential learning unit *** merely indicates a field of use or technological environment in which the judicial exception is performed. See MPEP 2106.05(h). Therefore, the use of *** utilizing a sequential learning unit *** to perform steps that are otherwise abstract does not integrate the abstract idea into a practical application. See the 2024 Guidance Update on Patent Subject Matter Eligibility, Including on Artificial Intelligence; and Example 47, ineligible claim 2.
The claim as a whole does not meet any of the following criteria to integrate the judicial exception into a practical application:
An additional element reflects an improvement in the functioning of a computer, or an improvement to other technology or technical field;
an additional element that applies or uses a judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition;
an additional element implements a judicial exception with, or uses a judicial exception in conjunction with, a particular machine or manufacture that is integral to the claim;
an additional element effects a transformation or reduction of a particular article to a different state or thing; and
an additional element applies or uses the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception.
Step 2B:
“classifying the obtained data to determine a quality of water based on ultrasonic time-of-flight (ToF) change behavior due to a mixed or combination of impurities in the water” does not amount to significantly more than the judicial exception in the claim. This additional element is merely using a computer as a tool to perform an abstract idea (see MPEP 2106.05(h)).
“utilizing a sequential learning unit for classification of impurities in the water, wherein classifying the impurities in the water comprises forming, from the obtained data, sequential samples composed of a plurality of time steps in a temporal dimension” does not amount to significantly more than the judicial exception in the claim. This additional element is merely using a computer as a tool to perform an abstract idea (see MPEP 2106.05(h)).
“applying the sequential learning unit to the sequential samples to classify the impurities in the water” does not amount to significantly more than the judicial exception in the claim. This additional element is merely using a computer as a tool to perform an abstract idea (see MPEP 2106.05(h)).
“initiating an impurity correction action based on the classified impurities in the water” is directed to insignificant activity and does not amount to significantly more than the judicial exception in the claim. See MPEP 2106.05(g) and 2106.05(d)(ii), third list, (iv).
The claim is therefore ineligible under 35 USC 101.
-Applicant argues that the prior art does not teach, “utilizing a sequential learning unit for classification of impurities in the water, wherein classifying the impurities in the water comprises forming, from the obtained data, sequential samples composed of a plurality of time steps in a temporal dimension; applying the sequential learning unit to the sequential samples to classify the impurities in the water; and initiating an impurity correction action based on the classified impurities in the water” as cited in claims 1, 9, and 15.
Response: The Examiner agrees, therefore the rejection under 103 of claims 1, 9, and 15 has been withdrawn.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any extension fee pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the date of this final action.
Contact Information
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JOHN H LE whose telephone number is (571)272-2275. The examiner can normally be reached on Monday-Friday from 7:00am – 3:30pm ET.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Shelby A. Turner can be reached on (571) 272-6334. 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 the Patent Application Information Retrieval (PAIR) system. Status information for published applications may be obtained from either Private PAIR or Public PAIR. Status information for unpublished applications is available through Private PAIR only. For more information about the PAIR system, see http://pair-direct.uspto.gov. Should you have questions on access to the Private PAIR system, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative or access to the automated information system, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/JOHN H LE/Primary Examiner, Art Unit 2857
Prosecution Insights