DETAILED ACTION
Claims 1 and 3-10 are presented for examination. Claims 1 and 3-10 stand currently amended.
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Finality of Office Action
The following is a brief summary description of new ground(s) of rejection (if any) and the reason why those new ground(s) are made necessary by this amendment:
Claim 1 is almost entirely re-written.
A new grounds of prior art rejection is made over Liu, Q., et al. "Hierarchical Random Walk Inference in Knowledge Graphs" SIGIR '16, pp. 445-454 (2016) [herein “Liu”] in view of US patent 11,327,989 B2 Wu, et al. [herein “Wu”]. This new grounds of rejection was necessitated by the extensive re-write of claim 1, including correction of various §112 indefiniteness issues.
A new §112 rejection is made over claim 9 based on the amendment to claim 9.
A new §101 rejection is made over claim 1 based on amendments made to claim 1.
Respective dependent claim rejections are adjusted accordingly.
Response to Arguments
Applicant's remarks filed 19 March 2026 have been fully considered and Examiner’s response is as follows:
Applicant remarks state the features of claim 2 was incorporated into claim 1.
Specification
The Specification has been appropriately corrected. Accordingly, Examiner's objection(s) to the specification is withdrawn.
Claim Objections
Claims 6 and 10 have been appropriately corrected. Accordingly, Examiner's objection(s) to the claim(s) are withdrawn. However, the following new objections are made as follows:
Claims 6 and 9 are objected to because of the following informalities:
Claim 6 recites “track key position video monitoring information are sent back to the processor.” The “are sent back” appears to be grammatical error. Examiner suggests amending to recite “track key position video monitoring information and send the key position video monitoring information
Claim 9 equations subscripts are not legible. The subscripts are legible in the originally filed claims so Examiner assumes the equation subscripts are unchanged.
Appropriate correction is required.
Claim Rejections - 35 USC § 112 – Indefiniteness
The claims have been appropriately corrected. Accordingly, Examiner's rejection(s) of the claim(s) is/are partially withdrawn. However, a new rejection is made as follows:
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claim 9 is rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor, or for pre-AIA the applicant regards as the invention.
Claim 9 recites “the complicated actual water body environment according to the warning information.” There is a lack of antecedent basis for “the complicated actual water body environment.”
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, 3, 5, 7, 8, and 10 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
To determine if a claim is directed to patent ineligible subject matter, the Court has guided the Office to apply the Alice/Mayo test, which requires:
1. Determining if the claim falls within a statutory category;
2A. Determining if the claim is directed to a patent ineligible judicial exception consisting of a law of nature, a natural phenomenon, or abstract idea; and
2B. If the claim is directed to a judicial exception, determining if the claim recites limitations or elements that amount to significantly more than the judicial exception.
See MPEP §2106.
Step 2A is a two prong inquiry. MPEP §2106.04(II)(A). Under 2A(i), the first prong, examiners evaluate whether a law of nature, natural phenomenon, or abstract idea is set forth or described in the claim. Abstract ideas include mathematical concepts, certain methods of organizing human activity, and mental processes. MPEP §2106.04(a)(2). Under 2A(ii), the second prong, examiners determine whether any additional limitations integrates the judicial exception into a practical application. MPEP §2106.04(d).
Claim 1 step 2A(i):
Claim 1 recites:
1. …
perform category classification, extraction and feature definition on the collected multi-source heterogeneous data to form a multi-category feature set, then perform heterogeneous knowledge fusion on the multi-source multi-category feature set and acquire a fusion feature by supervised random walk, and perform iterative reasoning on problems according to the fusion feature and by differentiable reasoning, construct a water circulation intelligent sensing and monitoring feature knowledge graph and store the water circulation intelligent sensing and monitoring feature knowledge graph in a feature knowledge base; and
perform water environment monitoring diagnosis, early warning or decision-making control services to realize cyclic control, optimization and updating, according to the water circulation intelligent sensing and monitoring feature knowledge graph.
Performing category classification, extraction, and feature definition to form a feature set and constructing a knowledge graph are capable of being performed mentally as evaluation of respective calculations.
Monitoring diagnosis and/or decision-making are recitations of mental processes in the form of evaluation, judgment, or opinion.
This falls within the mental processes grouping of abstract ideas. See MPEP §2106.04(a)(2).
Claim 1 step 2A(ii):
This judicial exception is not integrated into a practical application because:
Claim 1 recites:
1. A water circulation intelligent sensing and monitoring system based on differentiable reasoning, comprising
a processor and a memory storing instructions that, when executed by the processor, cause the processor to:
collect multi-source heterogeneous data;
… .
The processor and memory are recited at a high-level of generality (i.e., as a generic processor performing generic computer functions) such that it amounts no more than mere instructions to apply the exception using generic computer components. Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. See MPEP §2106.05(b) (“Merely adding a generic computer, generic computer components, or a programmed computer to perform generic computer functions does not automatically overcome an eligibility rejection. Alice Corp. Pty. Ltd. v. CLS Bank Int’l, 573 U.S. 208, 223-24, 110 USPQ2d 1976, 1983-84 (2014).”).
Collecting heterogeneous data, recited at a high level of generality, is extra solution activity in the form of data gathering. See MPEP §2106.05(g).
Claim 1 step 2B:
The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception, when considered individually and in combination, because:
Claim 1 recites:
1. A water circulation intelligent sensing and monitoring system based on differentiable reasoning, comprising
a processor and a memory storing instructions that, when executed by the processor, cause the processor to:
collect multi-source heterogeneous data;
… .
The processor and memory analyzed under MPEP §2106.05(b) in step 2A(ii) above is analyzed the same here under step 2B.
Regarding collecting heterogeneous data, MPEP §2106.05(d) provides examples:
i. Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information);
iv. Storing and retrieving information in memory, Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015)
These data gathering examples are encompassed by the generic recitation of data gathering recited by the claim. Accordingly, the claim recitation here is at least as abstract as the examples given in the MPEP.
When further considering the claims as a whole and as an ordered combination the claims fail to amount to significantly more than the judicially excepted abstract idea.
Claim 3 step 2A(i):
Dependent claims recite at least the identified judicially excepted subject matter of their parent claim(s).
The claim(s) recite:
3. The system according to Claim 1, wherein the processor is configured to handle the problems, and to perform the following steps:
S1: converting a received problem q into a distributed vector through input terminal preprocessing to obtain a context character string (cw1, cw2, …, cwm), wherein (h, r1, r2, …, rm-2, t) is used to express a fact triple and the problem is represented as
q
=
c
w
←
1
,
c
w
←
M
, the context character string is linearly transformed into a position awareness vector
q
i
i
=
1,2
,
…
,
n
, and
q
i
∈
R
q
is used to reflect a related problem of the ith reasoning step,
and wherein q refers to the received problem, m refers to the length of the character string, (cw1, cw2, …, cwm) refers to the context character string, the problem q is expressed as
q
=
c
w
←
1
,
c
w
←
M
,
q
i
i
=
1,2
,
…
,
n
represents a problem position awareness vector, h represents a head entity,
r
1
,
r
2
, …,
r
m
-
2
represent several relationships/attributes, t represents a tail entity/attribute value, and
q
i
∈
R
q
is used to represent a set of the problem position awareness vectors;
S2: transmitting a reasoning task in the first step to a differentiable recurrent neural network, beginning to perform iterative reasoning for many times, wherein the differentiable recurrent neural network is composed of n differentiable recurrent neurons, each differentiable recurrent neuron participates in a current reasoning task, and each differentiable recurrent neuron includes a controller, an identification element and a memory element; for a reasoning process in the ith step (i=1,2, …, n), receiving a reasoning task awareness vector
q
i
in the ith step and a memory vector
m
i
-
1
obtained in a memory element in the
i
-
1
t
h
step, processing by the controller to obtain a control vector
c
i
=
q
i
,
m
i
-
1
and transferring the control vector to the identification element, associating global or local knowledge graph path planning with a current reasoning task control vector
c
i
by the identification element based on a given knowledge base, performing walking on the knowledge graph (KG) based on category feature of
c
i
to extract a representative path
L
l
1
,
l
2
…
l
l
, inferring and identifying a matched value P of the control vector
c
i
and the representative path L by a content similarity evaluation function and performing sorting to select an optimal path to obtain a solution vector
A
i
=
c
i
,
p
m
a
x
, integrating
c
i
,
m
i
-
1
and
A
i
by the memory element, storing an integrated value into the memory
m
i
=
c
i
,
m
i
-
1
,
A
i
and transferring to a next reasoning task
c
i
+
1
, iteratively performing the previous steps to obtain an iterative calculation answer through n steps of reasoning processes, wherein a storage structure is arranged in the memory element; storing an intermediate state in the reasoning process into a memory gate, inserting the intermediate state into
m
i
-
1
and the to-be-generated
m
i
in the subsequent reasoning process, determining the similarity with the previous reasoning task, and if the similarity is high, skipping the reasoning step, directly calling the stored memory state, dynamically adjusting the length of the reasoning process and reducing the reasoning times,
wherein
P
m
a
x
refers to the optimal path; and
….
Converting the received problem into a vector is a mathematical operation. Linear transformation of the position awareness vector is further mathematical operation. The mathematical description of S1 conversion is further mathematical subject matter.
Inputting the reasoning task into the recurrent neural network to perform further mathematical operations is further recitation of mathematical subject matter.
The mathematical operations of each recurrent neuron are further description of mathematical subject matter. The reasoning process applied to the reasoning task awareness vector and memory vector are mathematical operations. Performing walking on the knowledge graph corresponds with mathematical algorithm. Extracting a path using mathematical algorithm is further recitation of mathematical calculation. Iterating to determine “a content similarity evaluation function and performing sorting” and integrating the values is further mathematical operation. Here, the “storage structure” is interpreted as corresponding data structure and not hardware structure. Thus, storing the intermediate state in the reasoning into a memory gate is interpreted as articulating steps of a mathematical algorithm rather than physical operation of hardware. The respective indices represent mathematical indices rather than any indication of physical location in hardware. Operating on a plurality of indexed numerical values is further recitation of mathematical operation. Lastly, using the similarity evaluation function to achieve a reduction in reasoning time or length corresponds with achieving a convergence of the iterative process and encompasses mathematical subject matter.
The optimal path corresponds with a final calculated result of the identified mathematical subject matter.
This falls within the mathematical concepts grouping of abstract ideas. See MPEP §2106.04(a)(2). Combining mental process steps with mathematical concepts is a combined abstract idea which remains an abstract idea.
Claim 3 step 2A(ii):
This judicial exception is not integrated into a practical application because:
The claim 3 further recites:
S3: outputting a final answer by an output terminal according to the problem q and the memory result
m
n
in the final reasoning process.
Outputting a result to a terminal corresponds with insignificant extra solution activity. See MPEP §2106.05(g).
Claim 3 step 2B:
The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception, when considered individually and in combination, because:
Limitations evaluated under MPEP §2106.05(b) are analyzed the same under step 2B as under step 2A(ii) above.
Claim 3 further recites:
S3: outputting a final answer by an output terminal according to the problem q and the memory result
m
n
in the final reasoning process.
Outputting a result to a generic computer “output terminal” is an outputting not limited to any particular form or function. MPEP §2106.05(d) states “The courts have recognized the following computer functions as well‐understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity. i. Receiving or transmitting data over a network, e.g., using the Internet to gather data.” Accordingly, a generic recitation of outputting by transmitting to a generic terminal is recognized as well‐understood, routine, and conventional.
When further considering the claims as a whole and as an ordered combination the claims fail to amount to significantly more than the judicially excepted abstract idea.
Claim 5 step 2A(i):
Dependent claims recite at least the identified judicially excepted subject matter of their parent claim(s).
This falls within the mental processes grouping of abstract ideas. See MPEP §2106.04(a)(2).
Claim 5 step 2A(ii):
This judicial exception is not integrated into a practical application because:
The claim(s) recite:
5. The system according to Claim 1, wherein the processor is configured to be connected to a monitoring robot (3-5).
The processor remains recited at a high level of generality, as a generic processor. Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. See MPEP §2106.05(b) (“Merely adding a generic computer, generic computer components, or a programmed computer to perform generic computer functions does not automatically overcome an eligibility rejection. Alice Corp. Pty. Ltd. v. CLS Bank Int’l, 573 U.S. 208, 223-24, 110 USPQ2d 1976, 1983-84 (2014).”).
A monitoring robot is not actively recited as the configuration of the processor does not require the monitoring robot to be present for the processor to be configured for a subsequent connection. Accordingly, no physical monitoring robot is actively recited by claim 5.
Claim 5 step 2B:
The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception, when considered individually and in combination, because:
The processor and memory analyzed under MPEP §2106.05(b) in step 2A(ii) above is analyzed the same here under step 2B.
When further considering the claims as a whole and as an ordered combination the claims fail to amount to significantly more than the judicially excepted abstract idea.
Claim 7 step 2A(i):
Dependent claims recite at least the identified judicially excepted subject matter of their parent claim(s).
The claim(s) recite:
7. The system according to Claim 5, wherein when the water environment of the monitoring position is diagnosed, the processor is configured to perform the following steps:
firstly, standardizing and normalizing environment parameter monitoring data
a
1
,
a
2
,
…
,
a
j
,
…
,
a
a
in the acquisition period at a monitoring point to obtain
a
h
j
'
, and using weighting average and processing each environment parameter to obtain
a
j
'
; secondly, linking with differentiable reasoning to obtain geographic information of a monitoring position, and considering subjective and objective factors and performing reasoning to obtain a weight value
W
v
j
of each index; finally, constructing a parameter threshold model
V
a
by using each index value and weight value, in addition, establishing a grade evaluation set
V
f
'
(wherein
f
is a corresponding grade), performing normalization and standardization to obtain a combined threshold evaluation value
V
f
, connecting an output value of the parameter threshold model with the combined threshold evaluation value, and diagnosing the water environment state of the monitoring position, wherein related formulas are as follows:
a
h
j
'
=
m
a
x
a
h
j
-
a
h
j
m
a
x
a
h
j
-
m
i
n
a
h
j
,
h
∈
0
,
H
a
j
'
=
∑
j
=
1
H
a
h
j
'
H
W
v
=
W
v
1
,
W
v
2
,
…
,
W
v
j
,
…
,
W
v
a
,
W
v
j
∈
0,1
V
a
=
∑
j
=
1
a
W
v
j
a
j
'
a
V
f
=
V
1
,
V
2
,
V
3
,
V
4
,
V
5
=
excellent, very good, good, poor, very poor
f
is a grade value
,
wherein
a
h
j
'
refers to an index value after the hth sample of the ith parameter is treated, a refers to an index value of the jth parameter, and j refers to jth environment parameter and h refers to hth sample of each environment parameter.
Standardizing and normalizing parameter data is a mathematical operation. Determining a weighting average is mathematical calculation.
Performing “differentiable reasoning” (e.g. using a recurrent neural network training) to obtain weight values is further mathematical calculation of respective weights.
Constructing a parameter threshold model is recitation of mathematical algorithm. Calculating a grade evaluation as a numerical value corresponds with further mathematical operation.
Diagnosing water environment state using the recited formulae is explicit recitation of mathematical formula.
This falls within the mathematical concept grouping of abstract ideas. See MPEP §2106.04(a)(2). Combining mental process steps with mathematical concepts is a combined abstract idea which remains an abstract idea.
Claim 7 step 2A(ii):
This judicial exception is not integrated into a practical application because:
Claim(s) do not recite any “additional” limitations.
Claim 7 step 2B:
The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception, when considered individually and in combination, because:
Claim(s) do not recite any “additional” limitations.
When further considering the claims as a whole and as an ordered combination the claims fail to amount to significantly more than the judicially excepted abstract idea.
Claim 8 step 2A(i):
Dependent claims recite at least the identified judicially excepted subject matter of their parent claim(s).
This falls within the mental processes grouping of abstract ideas. See MPEP §2106.04(a)(2).
Claim 8 step 2A(ii):
This judicial exception is not integrated into a practical application because:
The claim(s) recite:
8. The system according to Claim 1, wherein the processor is connected to an input terminal of a D/A converter (4-3); and output terminal of the D/A converter (4-3) is connected to an input terminal of a switching controller, and the switching controller is configured to implement a control scheme, including dosing control and aeration control.
The processor and D/A converters are recited at a high-level of generality (i.e., as a generic processor performing generic computer functions) such that it amounts no more than mere instructions to apply the exception using generic computer components. Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. See MPEP §2106.05(b) (“Merely adding a generic computer, generic computer components, or a programmed computer to perform generic computer functions does not automatically overcome an eligibility rejection. Alice Corp. Pty. Ltd. v. CLS Bank Int’l, 573 U.S. 208, 223-24, 110 USPQ2d 1976, 1983-84 (2014).”).
Instructions to “implement” a dosing control and aeration control are mere instructions to “apply” the abstract idea. See MPEP §2106.05(f),
Claim 8 step 2B:
The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception, when considered individually and in combination, because:
The limitations analyzed under MPEP §2106.05(b) and (f) in step 2A(ii) above is analyzed the same here under step 2B.
When further considering the claims as a whole and as an ordered combination the claims fail to amount to significantly more than the judicially excepted abstract idea.
Claim 10 step 2A(i):
Dependent claims recite at least the identified judicially excepted subject matter of their parent claim(s).
The claim(s) recite:
10. The system according to Claim 1, wherein the processor is configured to establish a three-dimensional model of a monitoring site, such that the environment parameters and geographic features of the actual monitoring site are effectively linked with the model, thereby being capable of intuitively observing changes of data, control schemes and results in the monitoring site water circulation intelligent sensing and monitoring process in time and space dimensions.
Establishing a 3D model of the site corresponds with a mathematical construction involving mental processes deciding the form and manner of the modeling in the form of evaluation, judgment, or opinion.
Observing changes in the model is further recitation of mental processes in the form of observation.
This falls within the mental processes grouping of abstract ideas. See MPEP §2106.04(a)(2).
Claim 10 step 2A(ii):
This judicial exception is not integrated into a practical application because:
Claim(s) do not recite any “additional” limitations.
Claim 10 step 2B:
The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception, when considered individually and in combination, because:
Claim(s) do not recite any “additional” limitations.
When further considering the claims as a whole and as an ordered combination the claims fail to amount to significantly more than the judicially excepted abstract idea.
Subject Matter Eligibility of Claims 4, 6, and 9
Claim 4 recites “treat a water body.” This is a physical transformation. See MPEP §2106.05(c). Accordingly, Examiner finds claim 4 is eligible under §101.
Claim 6 recites real-time sensing of a physical monitoring robot. Examiner finds this real-time data sensing is recited with specificity including identifying specific sensing devices. Accordingly, this data gathering is specific and not a generic data gathering nor mere field of use. See MPEP §2106.05(b), (g), and (h). Accordingly, Examiner finds claim 6 eligible under §101.
Claim 9 recites “controlling and monitoring the water environment, evaluating the actual control effect of the water environment, verifying the feasibility of the scheme.” Controlling is a physical step effectuating respective engineering steps. This is a physical transformation. See MPEP §2106.05(c). Accordingly, Examiner finds claim 9 is eligible under §101.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claim 1
Claim 1 is rejected under 35 U.S.C. 103 as being unpatentable over Liu, Q., et al. "Hierarchical Random Walk Inference in Knowledge Graphs" SIGIR '16, pp. 445-454 (2016) [herein “Liu”] in view of US patent 11,327,989 B2 Wu, et al. [herein “Wu”].
Claim 1 recites “1. A water circulation intelligent sensing and monitoring system based on differentiable reasoning.” Liu abstract discloses “we propose a hierarchical random-walk inference algorithm for relational learning in large scale graph-structured knowledge bases.” Inference relational learning in graph-structured knowledge bases corresponds with a system based on differentiable reasoning.
Liu does not explicitly disclose water circulation; however, in analogous art of managing industrial knowledge graph data, Wu column 2 lines 22-24 teach “Examples of industrial operations include synthesizing a particular set of chemicals, fabricating semiconductor wafers, and performing water treatment.” Water treatment corresponds with a water circulation.
Wu column 5 lines 54-62 teach:
FIG. 2, the customized industrial graph knowledge base may include a graph database 232, intermediate data repositories 220, and a platform and application interface 234. Specifically, entities and relationships may be further derived from the data elements extracted from the baseline, domain-specific, and implementation-specific data sources. These entities and relationships may be stored in the graph database 232 as nodes and edges. A graph database is an effectively way to store entity and relationship network.
Storing the industrial graph knowledge base corresponds with having constructed and now storing the respective sensing and monitoring knowledge graph in a knowledge base.
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to combine Liu and Wu. One having ordinary skill in the art would have found motivation to use water treatment monitoring into the system of knowledge graph inference method for the advantageous purpose “to provide intelligent, accurate, and efficient data services to the operators and controllers of the industrial operation.” See Wu column 2 line 67 to column 3 line 2.
Claim 1 further recites “comprising a processor and a memory storing instructions that, when executed by the processor.” Liu does not explicitly disclose processor and memory; however, in analogous art of managing industrial knowledge graph data, Wu column 5 lines 6-9 teach “system circuitry 104 may include one or more instruction processors 118 and memories 120. The memories 120 stores, for example, control instructions 124 and an operating system 122.”
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to combine Liu and Wu. One having ordinary skill in the art would have found motivation to use water treatment monitoring into the system of knowledge graph inference method for the advantageous purpose “to provide intelligent, accurate, and efficient data services to the operators and controllers of the industrial operation.” See Wu column 2 line 67 to column 3 line 2.
Claim 1 further recites “cause the processor to: collect multi-source heterogeneous data.” Wu column 2 lines 28-39 teach:
The operation of a specific industrial plant may involve a large number of entities having complex relationships. These entities, for example, may include physical as well as abstract items of disparate nature and characteristics, including but not limited to domain processes, facilities, equipment, sensors/sensor parameters, personnel hierarchies, supply chains, raw materials, intermediate products, final products, key performance measures, customers, power consumptions, emissions, and regulation compliances. Data representing some or all of these entities and their relationships may be used to build a customized knowledge base for the plant.
This data forming the knowledge base for the plant including sensors corresponds to collecting multi-source heterogeneous data.
Claim 1 further recites “perform category classification, extraction and feature definition on the collected multi-source heterogeneous data to form a multi-category feature set.” Liu page 447 section 3.1 last line discloses “this classification method.” A classification method corresponds with performing a category classification.
Liu page 445 section 1 first paragraph discloses:
The goal of relational inference research is to infer new knowledge (facts) from the existed knowledge bases[6]. This paper considers the problem of relational inference on large-scale graph-structured knowledge bases (GKBs, a.k.a. knowledge graphs)
Inference from the graph knowledge bases corresponds to an extraction on collected multi-source heterogeneous data.
Liu page 448 section 3.3 discloses:
In this paper, we use a revised PRA algorithm for recognizing path features and for learning the global inference models for each …. we use undirected graph for path feature discovery. The direct effect is to increase the chance of finding more plausible features.
The recognized features and found plausible features correspond with a feature set.
Claim 1 further recites “then perform heterogeneous knowledge fusion on the multi-source multi-category feature set and acquire a fusion feature by supervised random walk.” Liu title discloses “Hierarchical Random Walk Inference in Knowledge Graphs.” A hierarchical random walk performing inference on knowledge graph corresponds with performing a heterogeneous knowledge fusion by supervised random walk.
Claim 1 further recites “and perform iterative reasoning on problems according to the fusion feature and by differentiable reasoning.” Liu page 448 figure 3 shows:
PNG
media_image1.png
200
400
media_image1.png
Greyscale
The box “Learning and Reasoning” corresponds with performing an iterative and differentiable reasoning. The box “reasoning results fusion” corresponds with fusion feature.
Claim 1 further recites “construct a water circulation intelligent sensing and monitoring feature knowledge graph and store the water circulation intelligent sensing and monitoring feature knowledge graph in a feature knowledge base.” Liu does not explicitly disclose water circulation; however, in analogous art of managing industrial knowledge graph data, Wu column 2 lines 22-24 teach “Examples of industrial operations include synthesizing a particular set of chemicals, fabricating semiconductor wafers, and performing water treatment.” Water treatment corresponds with a water circulation.
Wu column 3 lines 56-59 teach “These applications may provide efficient data queries and data services for monitoring, controlling, and optimizing the specific industrial operation.” Wu column 2 lines 30-35 teach “These entities, for example, may include physical as well as abstract items of disparate nature and characteristics, including … sensors/sensor parameters, …, key performance measures.” Sensor information corresponds with a sensing.
Wu column 5 lines 54-62 teach:
FIG. 2, the customized industrial graph knowledge base may include a graph database 232, intermediate data repositories 220, and a platform and application interface 234. Specifically, entities and relationships may be further derived from the data elements extracted from the baseline, domain-specific, and implementation-specific data sources. These entities and relationships may be stored in the graph database 232 as nodes and edges. A graph database is an effectively way to store entity and relationship network.
Storing the industrial graph knowledge base corresponds with having constructed and now storing the respective sensing and monitoring knowledge graph in a knowledge base.
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to combine Liu and Wu. One having ordinary skill in the art would have found motivation to use water treatment monitoring into the system of knowledge graph inference method for the advantageous purpose “to provide intelligent, accurate, and efficient data services to the operators and controllers of the industrial operation.” See Wu column 2 line 67 to column 3 line 2.
Claim 1 further recites “and perform water environment monitoring diagnosis, early warning or decision-making control services to realize cyclic control, optimization and updating, according to the water circulation intelligent sensing and monitoring feature knowledge graph.” Wu column 3 lines 56-59 teach “These applications may provide efficient data queries and data services for monitoring, controlling, and optimizing the specific industrial operation.” Monitoring corresponds with a monitoring diagnosis. Controlling corresponds with a decision-making control service. The combination monitoring, controlling, and optimizing corresponds to realizing a cyclic control, optimization, and updating.
Dependent Claims 4 and 5
Claims 4 and 5 are rejected under 35 U.S.C. 103 as being unpatentable over Liu and Wu as applied to claim 1 above, and further in view of US patent 11,498,858 B2 Jorden, et al. [herein “Jorden”].
Claim 4 further recites “4. The system according to Claim 1, wherein the processor is configured to treat a water body that does not comply with a standard, thereby making the water environment comply with the standard.” Liu does not explicitly disclose treating a water body to comply with a standard; however, in analogous art of water treatment, Jorden column 40 lines 16-27 teaches:
A significant strategy for detecting changes in raw water quality and an indicated related need for a corresponding coagulant(s) dosage(s) change is inherent in a continued dose dithering sequence, at an appropriate time interval. Such an action could especially be of benefit where raw water changes present operational challenges to continuously meet effluent turbidity goals. A dose dither strategy in combination with the decision table of FIG. 24 foretells that such a strategy is amenable to evolving a software based automated or semi-automated coagulant dosage control system, as the value of dose dithering becomes more widely detected.
Meeting effluent turbidity goals is complying with a respective water standard. Optimizing the coagulant dosage of a coagulant dosage control system is linking the corresponding sensing with a control module to make the water comply with the standard.
Jorden column 39 lines 35-41 teach:
any time the metal dosage is changed, e.g. in response to a TOC (total organic carbon) raw water change, it is necessary to also re-optimize polymer dosage for peak filter performance. Effectively optimizing both metal and polymer dosing is a never-ending job, especially for plants experiencing changes in raw water chemistry.
Changing water chemistry triggering the need to re-optimize corresponds with the control being based on, and thus linked with, a corresponding sensing.
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to combine Liu, Wu, and Jorden. One having ordinary skill in the art would have found motivation to use optimizing coagulant dosage into the system of knowledge graph inference method for the advantageous purpose of keeping within a relatively narrow optimum-dose zone to ensure pathogen removal by filtration. See Jorden column 39 lines 19-20.
Claim 5 further recites “5. The system according to Claim 1, wherein the processor is configured to be connected to a monitoring robot (3-5).” Jorden column 11 lines 50-56 teach the early warning output as discussed immediately above.
Jorden column 23 lines 3-8 teach:
The controller 7 may include a processor and software. The controller may be coupled to the optical sensor 100, the mixer 15, and/or the valves 144 and 145. Power and communication may be supplied by cabling connected to and coordinated with the overall plant operation through a plant process network.
The cabling connections and plant process network are communications and connections of respective inputs and outputs of the plant. The sensors are a monitoring of a machine and thus correspond with a monitoring robot.
Dependent Claim 6
Claim 6 is rejected under 35 U.S.C. 103 as being unpatentable over Liu, Wu, and Jorden as applied to claim 5 above, and further in view of Baek, S., et al. “Prediction of Water Level and Water Quality Using a CNN-LSTM Combined Deep Learning Approach” Water, vol. 12, issue 12, no. 3399 (2020) [herein “Baek”].
Claim 6 further recites “6. The system according to Claim 5, wherein the monitoring robot (3-5) performs intelligent sensing on the surrounding environment based on a millimeter wave radar, a laser radar and visible light and infrared devices, so as to acquire environment parameters of specified positions in real time.” Liu does not explicitly disclose radar devices; however, in analogous art of deep learning water quality prediction, Baek abstract discloses “and the Real-Time Water Quality Information, respectively. The rainfall radar image and operation information of estuary barrage were also collected from the Korea Meteorological Administration.” A radar image of rainfall is a radar imaging of an acquired environment parameters with corresponding rainfall positions. Real-time water quality information is data obtained in real-time.
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to combine Liu, Wu, Jorden, and Baek. One having ordinary skill in the art would have found motivation to use rainfall radar images into the system of knowledge graph inference method for the advantageous purpose “to accurately simulate the water level and water quality.” See Baek abstract.
Claim 6 further recites “and for the heterogeneous multi-source characteristics of the acquired information, multiple-sensor information is fused to improve the environment sensing accuracy and robustness.” Wu column 2 lines 28-39 teach:
The operation of a specific industrial plant may involve a large number of entities having complex relationships. These entities, for example, may include physical as well as abstract items of disparate nature and characteristics, including but not limited to domain processes, facilities, equipment, sensors/sensor parameters, personnel hierarchies, supply chains, raw materials, intermediate products, final products, key performance measures, customers, power consumptions, emissions, and regulation compliances. Data representing some or all of these entities and their relationships may be used to build a customized knowledge base for the plant.
This data forming the knowledge base for the plant including sensors corresponds to collecting multi-source heterogeneous data. Building the customized knowledge base for the plant corresponds with fusing the multiple disparate data of the acquired information.
Claim 6 further recites “a real-time monitoring state, environment sensing information.” Wu column 3 lines 56-59 teach “These applications may provide efficient data queries and data services for monitoring, controlling, and optimizing the specific industrial operation.” Wu column 6 lines 21-22 teach “real-time prediction of performance of the plant 246.” Real-time prediction performance of the plant corresponds with a real-time monitoring.
Claim 6 further recites “track key position video monitoring information are sent back to the processor in real time as real-time data.” Wu column 6 lines 21-22 teach “real-time prediction of performance of the plant 246.”
Liu nor Wu explicitly disclose track key position video monitoring; however, in analogous art of water treatment, Jorden column 5 lines 38-42 teaches:
The digital-camera-based instrument (suspended particle characterization system) shown at 100 in FIG. 1 can:
a) Measure a surrogate of pollutant-concentration removal success.
b) Work in water treatment facilities that remove pollutants.”
A digital-camera providing real-time measurement is a video monitoring.
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to combine Liu, Wu, and Jorden. One having ordinary skill in the art would have found motivation to use digital-camera characterization of suspended particles into the system of knowledge graph inference method for the advantageous purpose of “c) Provide real-time feedback of pollutant-removal progress.” See Jorden column 5 line 50.
Claim 6 further recites “and the real-time data is processed by the processor to perform prediction calculation and warning prompt based on the real-time data.” Wu column 3 lines 56-59 teach “These applications may provide efficient data queries and data services for monitoring, controlling, and optimizing the specific industrial operation.” Monitoring corresponds with a monitoring diagnosis.
Neither Liu nor Wu explicitly disclose a warning prompt; however, in analogous art of water treatment, Jorden column 11 lines 50-56 teach:
The system 100 can have preprogrammed limits and provide alarm signals if particle characteristics exceed a threshold. Such a warning can be triggered when a particle characteristic rate of change exceeds a predefined rate. Thresholds can be defined by a user or established by the computing engine 111 based on historical data and pattern recognition algorithms.
Providing alarm signals and triggering a warning corresponds with an early warning output.
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to combine Liu, Wu, and Jorden. One having ordinary skill in the art would have found motivation to use water treatment with alarm and warning systems into the system of knowledge graph inference method for the advantageous purpose to “produce critical new information for facility operation and control.” See Jorden column 11 lines 28-29.
Claim 8 further recites “8. The system according to Claim 1, wherein the processor is connected to an input terminal of a D/A converter (4-3).” Jorden column 19 lines 44-49 teach:
The central processing unit 180 can output data to analog communication protocols 181 though digital to analog conversion hardware. Analog communication protocols 181 can be used to communicate with external devices, such as SCADA networks, programmable logic controllers, data loggers, and alarms.
Claim 8 further recites “an output terminal of the D/A converter (4-3) is connected to an input terminal of a switching controller; and the switching controller is configured to implement a control scheme, including dosing control and aeration control.” Jorden column 19 lines 44-49 teach:
The central processing unit 180 can output data to analog communication protocols 181 though digital to analog conversion hardware. Analog communication protocols 181 can be used to communicate with external devices, such as SCADA networks, programmable logic controllers, data loggers, and alarms.
A programmable logic controller corresponds with a control unit configured to implement a control scheme of the dosing control.
Jorden column 35 lines 3-9 teach:
The use of a stepper motor 195 and control circuitry, 196 and 197, facilitates the precise control of mixing rotational speed (as measured in revolutions per minute) and direction (clockwise or counterclockwise) in response to elapsed time and empirical data from the floe particles in the water sample in the chamber 210.
A controlled mixing corresponds with an aeration control of the dosing control.
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to combine Liu, Wu, and Jorden. One having ordinary skill in the art would have found motivation to use water treatment with alarm and warning systems into the system of knowledge graph inference method for the advantageous purpose to “produce critical new information for facility operation and control.” See Jorden column 11 lines 28-29.
Dependent Claim 10
Claim 10 is rejected under 35 U.S.C. 103 as being unpatentable over Liu and Wu as applied to claim 1 above, and further in view of US patent 11,039,226 B2 Fleishman [herein “Fleishman”].
Claim 10 further recites “10. The system according to Claim 1, wherein the processor is configured to establish a three-dimensional model of a monitoring site, such that the environment parameters and geographic features of the actual monitoring site are effectively linked with the model, thereby being capable of intuitively observing changes of data, control schemes and results in the monitoring site water circulation intelligent sensing and monitoring process in time and space dimensions.” Wu column 3 lines 56-59 teach “These applications may provide efficient data queries and data services for monitoring, controlling, and optimizing the specific industrial operation.” A combination of monitoring and controlling for optimizing corresponds to linking monitoring of observed changes to control schemes with respective time and space dimensions.
But neither Liu nor Wu explicitly disclose a 3D model of the site; however, in analogous art of online water quality monitoring, Fleishman column 13 lines 28-33 teach “At step 810 real-time (or near real-time) data from the server computer is outputted to a geographic information system (GIS) user interface. The outputted data includes a visual depiction of the supply network including color coded indicia which indicate the health level of the water in the depicted locations.” A GIS corresponds with a model of the real-world site. A color coded display of water health level in a GIS user interface corresponds with a visualization of the monitored sites including the environment parameter (i.e. health) and the geographic features. See further Fleishman figure 5. A visualization enables observing respective changes in data.
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to combine Liu, Wu, and Fleishman. One having ordinary skill in the art would have found motivation to use GIS user interface into the system of knowledge graph inference method for the advantageous purpose of displaying water quality information with easy to read color-coded indicia. See Fleishman column 13 lines 28-47.
Allowable Subject Matter
Claims 3 and 7 would be allowable if rewritten to overcome the rejection(s) under 35 U.S.C. §101, set forth in this Office action and to include all of the limitations of the base claim and any intervening claims.
Claim 9 would be allowable if rewritten to overcome the rejection(s) under 35 U.S.C. §112, set forth in this Office action and to include all of the limitations of the base claim and any intervening claims.
The following is a statement of reasons for the indication of allowable subject matter:
Baek, S., et al. “Prediction of Water Level and Water Quality Using a CNN-LSTM Combined Deep Learning Approach” Water, vol. 12, issue 12, no. 3399 (2020) [herein “Baek”] teaches predicting water level and water quality using CNN-LSTM combined deep learning.
Zhang, J. & Tao, D. “Empowering Things With Intelligence: A Survey of the Progress, Challenges, and Opportunities in Artificial Intelligence of Things” IEEE: Internet of Things J., vol. 8, no. 10 (2021) [herein “Zhang”] teaches AI empowering real-time data collection of Internet-of-Things devices. Zhang page 7804 teaches technology background of knowledge graphs and reasoning. Zhang page 7792 figure 3 shows computing layers of the Cloud and Edge for AIoT.
US patent 11,498,858 B2 Jorden, et al. [herein “Jorden”] teaches optimization of water treatment coagulant dosing.
US patent 11,039,226 B2 Fleishman [herein “Fleishman”] teaches online water quality and safety monitoring.
US patent 11,886,821 B2 Sengupta, et al. [herein “Sengupta”] teaches inferring from knowledge graphs. Sengupta abstract teaches “a Hierarchical Recurrent Path Encoder (HRPE).”
US patent 11,365 140 B2 Whalen, et al. [herein “Whalen”] teaches a decision support system for water treatment.
US patent 10,928,501 B2 Santra, et al. [herein “Santra”] teaches radar used in rainfall and snowfall conditions.
Liu, P., et al. “Analysis and Prediction of Water Quality Using LSTM Deep Neural Networks in IoT Environment” Sustainability, vol. 11, no. 2058 (2019) [herein “Liu”] teaches predicting water quality with LSTM in the Yangzhou area.
Qolomany, B., et al. “Leveraging Machine Learning and Big Data for Smart Buildings: A Comprehensive Survey” IEEEAccess, ACCESS.2019.2926642 (2019) [herein “Qolomany”] teaches technology background of machine learning used in control tasks.
Sit, M., et al. “A comprehensive review of deep learning applications in hydrology and water resources” Water Science & Tech., vol. 82, no. 12 (2020) [herein “Sit”] teaches technology background of different DNN techniques used with forecasting water level and water resource management.
Barzegar, R., et al. “Short-term water quality variable prediction using a hybrid CNN–LSTM deep learning model” Stochastic Environmental Research & Risk Assessment, vol. 34, pp. 415-433 (2020) [herein “Barzegar”] teaches a hybrid CNN-LSTM model to predict water quality variables of dissolved oxygen and chlorophyll-a.
US patent 10,402,690 B2 Vernaza et al. [herein “Vernaza”] teaches a random walk of training images for semantic segmentation.
Liu, Q., et al. "Hierarchical Random Walk Inference in Knowledge Graphs" SIGIR '16, pp. 445-454 (2016) [herein “Liu”] abstract discloses “we propose a hierarchical random-walk inference algorithm for relational learning in large scale graph-structured knowledge bases.”
US patent 11,327,989 B2 Wu, et al. [herein “Wu”] teaches multi-dimensional industrial knowledge graph. Wu column 3 lines 56-59 teach “These applications may provide efficient data queries and data services for monitoring, controlling, and optimizing the specific industrial operation.”
Regarding claim 3:
US patent 11,886,821 B2 Sengupta, et al. [herein “Sengupta”] column 7 lines 14-16 teaches “the hierarchical recurrent encoder-decoder model (HRED) that handles sequence of queries and generates context-aware suggestions for users.” But Sengupta fails to teach a position awareness vector.
Liu, Q., et al. "Hierarchical Random Walk Inference in Knowledge Graphs" SIGIR '16, pp. 445-454 (2016) [herein “Liu”] page 446 right column second paragraph teaches “The basic idea of the latent factor models is to obtain a vectorized representation for each of the entities. But Liu fails to teach a position awareness vector.
None of the references taken either alone or in combination with the prior art of record disclose “the context character string is linearly transformed into a position awareness vector
q
i
i
=
1,2
,
…
,
n
” in combination with the remaining elements and features of the claimed invention.
Regarding claim 7:
None of the references taken either alone or in combination with the prior art of record disclose “establishing a grade evaluation set
V
f
'
(wherein
f
is a corresponding grade)” … “
V
f
=
V
1
,
V
2
,
V
3
,
V
4
,
V
5
=
excellent, very good, good, poor, very poor
” in combination with the remaining elements and features of the claimed invention.
Regarding claim 9:
US patent 11,365 140 B2 Whalen, et al. [herein “Whalen”] abstract teaches “the decision support system uses machine learning applied to (i) historical data from a selected water system and/or (ii) data from other water systems to modify the rules or algorithms used to analyze current data from a selected water system.” Whalen column 7 lines 22-28 teach:
a weighted average within the subset may be used, with better performing (i.e. typically lower BLD effluent) water systems weighted more heavily. The decision tree for the failed system is then adjusted to recommend operation within the 95% confidence interval. In addition, the failed water system operator may be provided with a workplan with instructions for how to adjust the water system to have cATP and MLSS within the optimal ranges.
None of the references taken either alone or in combination with the prior art of record disclose “inputting the schemes into the decision-making model, introducing a virtual task, controlling a process virtual simulation by constructing a distribution sequence and in combination with the water environment under the intelligent control scheme, measuring and evaluating the virtual restoration effect by a human-machine synergistic precise group decision-making mode, then screening and sorting the comprehensive priority values of the virtual simulation effects of the k alternative schemes K under different attributes S, and finally outputting an optimal control scheme and implementing the optimal control scheme, thereby reducing secondary pollution to the environment” in combination with the remaining elements and features of the claimed invention.
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 nonprovisional extension fee (37 CFR 1.17(a)) 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 mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Jay B Hann whose telephone number is (571)272-3330. The examiner can normally be reached M-F 10am-7pm EDT.
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/Jay Hann/Primary Examiner, Art Unit 2186 8 June 2026
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