DETAILED ACTION
This action is responsive to the Applicant’s response filed 8/11/26.
As indicated in Applicant’s response, claims 1-3, 10-11, 13, 15 have been amended. Claims 1-20 are pending prosecution by an office action as following.
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.
Claim 11 is rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more. Claim(s) 11 is/are directed to Abstract Idea. The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because of the 2-step analysis as follows.
Step I:
This claim is directed to a method/process category.
Step 2A
Prong One:
The action recited as "extracting, as a microscopic biological tactic motion feature amount of the submerged object, a moving speed and movement vectors defining an autonomous stimulus-response taxis..." relies on mathematical/analytical data extraction. Analyzing movement data, identifying speed/vectors, and evaluating taxis characteristics can be performed mentally or with simple pen and paper. See MPEP 2106..04(a)(2)(III)
Deriving "moving speed" and "movement vectors" consists of mathematical calculations and algorithms based on positional data. MPEP 2106.04(a)(2)(I)
The claim recites limitations that fall under two enumerated abstract idea groupings: Mental Processes and Mathematical Concepts.
The method claim recites a Judicial Exception – MPEP 2106.04(a)
Prong Two:
The physical elements such as "electronic device," an "event-based vision sensor," and a "medium" are recited at a high level of generality. The claim uses the first electronic device and sensor merely as generic tools to gather input data for the calculation; and using generic hardware as a tool cannot integrate the Abstract Idea into practical application – MPEP 2106(d)(II)(C)
The elements recited as "acquiring first data..." and storing output ("storing the software in a medium") represent routine construed as pre-and post-solution activities that do not restrict or transform the abstracted information processing itself; i.e. insignificant Extra-solution activity under MPEP 210604(d)(II)(A)
The feature recited as "causes the second electronic device to control a purpose-specific hardware feedback control operation upon detection..." is a generic functional language ("apply it") that describes a desirable result without reciting specific technical steps for how the feedback control operation specifically interacts with physical hardware.
Per MPEP 2106.04(d)(II)(A), nowhere in the claim is there specific showing or limitation that improves the functioning of the computer/hardware itself (e.g., how the sensor functions, memory efficiency, or system speed). Rather, it merely uses a computer to execute data processing.
The additional elements set forth above fail to integrate the abstract idea into a practical application. MPEP 2106.04(d)
Step 2B
In considering the additional elements, it is found that “acquiring data via sensors” are well-understood, routine, and conventional in the sensing art, that “extracting motion metrics (speed/vectors) is a well-understood data processing; that “generating software based on extracted data” is a conventional software compilation/generation technique; that “storing code in a medium to execute feedback control” is no more than standard computer architecture and control system functionality. MPEP 2106.05.(d)
Considering the above in an ordered combination, these elements follow a combination that amounts to nothing more than collecting data from a sensor, performing mathematical data analysis, compiling software based on the results, and storing it for general feedback control. The combination does not yield an inventive technical implementation beyond performing the abstract idea on generic equipment. MPEP 2106.05(f)
In all, the “additional elements” do not add significantly more to the Abstract Idea.
Claims 1 and 10 is rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more. Claim(s) 1 and 10 is/are directed to Abstract Idea. The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because of the 2-step analysis as follows
A. Eligibility of claim 1
Step I:
This claim is directed to a device/product category
Step 2A
Prong One:
The element recited as “acquire event-based vision” for data regarding an object including event-based optical tracking of biological specimens” and “extracting tactic motion feature, moving speed and movement vectors defining stimulus-response taxis” data are viewed as known techniques expressed in high level of generality, and reliance to mathematical/analytical data extraction. Analyzing movement/speed data, identifying speed/vectors, and evaluating taxis characteristics can be performed mentally or with simple pen and paper. MPEP 2106.04(a)(2)(III)
Deriving "moving speed" and "movement vectors" consists of mathematical calculations and algorithms based on positional data. MPEP 2106.04(a)(2)(I)
“Acquiring first data" is generic data gathering, and "generating purpose-specific software... on a basis of the first data" simply represents manipulating data to output instructions (a mental or algorithmic operation)
The claim recites limitations that fall under two enumerated Abstract Idea groups, being the Mental processes and Mathematical concepts of the Judicial Exception.
Prong Two:
The device limitation as to “control a hardware-specific control operation … upon detection of a submerge object” amounts to nominating a function without depicting the “how” of its operation; hence is seen as mere listing of a function in its most generic nomination or its intended use. The elements recited as “circuit”, “server device”, “event-based vision sensor”, “machine-learning based identification program” or “purpose-specific generation circuit” expressed in a high-level of generality are viewed as generic tools with which to gather data or perform mathematical calculations. The limitation of “circuity configured to generate identification program” is considered generic tool to generate identification software expressed in high level of generality. MPEP 2106.04(d)(II) (C )
Further, the activities of “acquire” (sensor data) and “transmit” (software after the extracting) represent pre- and post-solution activities that do not restrict or significantly transform the Abstract processing of prong One. MPEP 2106.04(d)(II)(E)
The claim does not improve the functioning of the computer/HW itself (how the sensor functions, how memory is efficient). Rather, it merely uses a computer to execute data processing.
The limitation recited as “to control a purpose-specific hardware feedback control operation of a device upon detection of the object” is seen as a generic functional language (“apply it”) that merely depicts a desirable result (“control”) without reciting technical steps for how the feedback control operation interacts with the physical HW. MPEP 2106.04(d)(II) (C )
The above elements fail to integrate the Abstract Idea into a practical application.
Step 2B
In regard to the additional elements, it is found that “acquiring data via sensors”, “extracting motion metrics (speed/vectors),“generating software based on extracted data” (used of a purpose) are viewed as conventional components or software compilation/generation technique; that “transmit” the software over the network is post-activity and a “device to control a HW specific purpose” is no more than standard computer architecture and control system functionality. MPEP 2106.05.(d)
Considered in an ordered combination, these additional elements follow a combination that amounts to nothing more than collecting data from a sensor, performing mathematical data analysis, compiling software based on the results, and storing it for general feedback control. The combination does not yield an inventive technical implementation beyond performing the abstract idea on generic equipment. MPEP 2106.05(f)
In all, the “additional elements” do not add significantly more to the Abstract Idea.
Eligibility of claim 10
Step I:
This claim is directed to a method/process category.
Step 2A
Prong One:
The elements recited as "extracting, as a microscopic biological tactic motion feature amount of the submerged object, a moving speed and movement vectors defining an autonomous stimulus-response taxis..." relies on mathematical/analytical data extraction. Analyzing movement data, identifying speed/vectors, and evaluating taxis characteristics can be performed mentally or with simple pen and paper. See MPEP 2106.04(a)(2)(III)
Deriving "moving speed" and "movement vectors" consists of mathematical calculations and algorithms based on positional data. MPEP 2106.04(a)(2)(I)
The claim recites limitations that fall under two enumerated abstract idea groupings: Mental Processes and Mathematical Concepts.
Prong Two:
The activities recited as “acquiring” (data including event-based vision sensor) and “transmitting” (software) software represent pre- and post-solution activities that do not restrict or significantly transform the Abstract processing of prong One. MPEP 2106.04(d)(II)(E)
The elements recited as “vision sensor, “software used for a second purpose”, tactic motion, moving speed and movement vectors, autonomous stimulus-response taxis, all expressed in a high-level of generality are viewed as generic tools with which to gather data or perform mathematical calculations, or to generate identification software expressed in high level of generality. MPEP 2106.04(d)(II) (C )
The above elements fail to integrate the Abstract Idea into a practical application.
Step 2B
In regard to the additional elements, it is found that “acquiring data via sensors”, “extracting motion metrics (speed/vectors)“generating” second purpose software (based on extracted data) are viewed as conventional components or software compilation/generation technique which amounts to no more than standard computer architecture and control system functionality. MPEP 2106.05.(d) whereas “transmitting” the software (to another device) is post-activity of low significance. MPEP 2106.05.(g)
Considered in an ordered combination, these additional elements follow a combination that amounts to nothing more than collecting data from a sensor, performing mathematical data analysis, compiling a generic software based on the results, and transmitting the software. The combination does not yield an inventive technical implementation beyond performing the abstract idea on generic equipment. MPEP 2106.05(f)
In all, the “additional elements” do not add significantly more to the Abstract Idea.
Claim 13 is rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more. Claim(s) 13 is/are directed to Abstract Idea. The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because of the 2-step analysis as follows.
Step I:
Claim 13 is directed to a method/process category.
Step 2A
Prong One:
The method recites “acquiring” first data from vision sensor on event-based microscopic specimens, “extracting” a portion of the first data for a software purpose; “vectorizing” the extracted data into a technical training set; and these activities are viewed as capturing specimens data, identifying their technical properties, and extracting certain characteristics for a software purpose, and reorganizing data into a particular format (“vectorized”) can be performed mentally or with simple pen and paper that fall within the grouping under Mental processes and organization of mental activity. See MPEP 2106.04(a)(2)(III)
Prong Two:
The mention of “event-based vision sensor” and “optical tracking” are viewed as mere tools to enable information or characteristics to be acquired for the processing or reorganizing data included with the Abstract idea. Hence, they fail to integrate the Abstract Idea into a practical application.
The element recited as “generating software for a different purpose” is viewed as a well-understood concepts expressed in a very high level of generality dominated by a mere “purpose”; hence cannot be viewed as a limitation that alters the abstract Idea in a meaningful manner, required under MPEP 2106.04(d)
The “integrating data in a database” amounts to a insignificant extra-solution activity that operates on data resulting from the abstracted steps of acquiring, extracting. MPEP 2106.05(g)
The element recited as “training dataset” formed from “vectorizing” fluorescence wavelength, phototaxis response, tactic motion, stimulus-response taxis, movement vectors is viewed as generic enumeration of well-known techniques in the field of studying specimens, all the listing expressed as nominal functionalities without implementation details; i.e.. mere mention of their functional name without actual description, specification of a internal interaction or specific manipulation that demonstrate any concrete transformation of significance being made in order to build the Abstract Idea into a eligible practical application. The claim merely says applying these techniques to the Abstract Idea in a very broad manner. MPEP 2106.05(f)
The element recited as updating a “recognition parameter of software” by “executing a machine learning algorithm” (to derive a confidence rate) is construed as using an intelligent mathematical tool (e.g. machine learning) to support updating and deriving of information from the training set. Use of a mathematical means as tool to derive new or update data from a training set gathered from extracting and acquiring steps can be subsumed into typical activities described with the Judicial Exception subgroups of Mental processes and Mathematical concepts. The claim merely says applying these intelligent techniques to the Abstract Idea in a very broad manner - MPEP 2106.04(II)(A)(1); MPEP2106.04(a)(2)
The element recited as “performing machine learning using the technical training set” to “generate a new identification program” or “purpose-specific software” by which to update “a recognition parameter” or “the purpose-specific software” is viewed as using a intelligent tool to generate a program with which to achieve a update to parameter or the program itself. The use of machine learning execution is viewed as applying a mathematical tool to the Abstract Idea, and generated software to enable “update” as claimed in a very generic term, is viewed as reciting a well understood concepts - MPEP 2106.05(d) – along with a desired result or outcome (updated parameter or updated software) – MPEP 2106.05(f)
Based on the above, the Abstract Idea is deemed not sufficient to be integrated into a Practical Application.
Step 2B.
The limitation of “generating software for a different purpose” , as additional element, is viewed as a well-understood concepts expressed in a very high level of generality dominated by a mere “purpose”; hence cannot be viewed as a limitation that alters the abstract Idea in a meaningful manner MPEP 2106.05(c )
The additional element of “integrating data in a database” amounts to a insignificant extra-solution activity that operates on data resulting from the abstracted steps of acquiring, extracting. MPEP 2106.05(g)
The “vectorizing” of data (as another additional element) construed as a listing of fluorescence wavelength, phototaxis response, tactic motion, stimulus-response taxis, movement vectors is viewed as mere mention of items strictly in their nominal functionality without actual description, specification of a internal interaction or specific manipulation that demonstrate any concrete transformation of significance being made in order to build the Abstract Idea into a eligible practical application. The claim merely says applying these broad, nominal techniques to the Abstract Idea in a very broad manner. MPEP 2106.05(f)
The additional elements recited as “performing machine learning using the technical training set” to “generate a new identification program” amounts to using a mathematical tool to help a generic software-based component to produce result such as update to a parameter or update to a purpose. Use of mathematical means or conventional components (software expressed in very generic manner) to generate an desired outcome (update) cannot be viewed as providing a technical improvement to a problem or a inventive transformation to the Abstract idea. MPEP 2106.05(a)(c)(e) (f)
Based on the above additional elements, claim 13 is deemed non-eligible under the 35 § 101 statute
Step 2B analysis for dependent claims
Claims 2-3 recite circuit to generate first purpose SW using first and second data from submerged object, the software construed as very high level of generality; hence cannot provide a SW limitation that transforms the Abstract Idea significantly.
Claim 4 recites circuit to generate SW for a first purpose and SW for a second purpose on basis of first and second data; hence this SW construed from high level of generality cannot be sufficient to add significantly to the Abstract Idea step actions of acquiring and extracting set forth with prong one.
Claims 5-6 recite identification program for identifying the submerged object in SW for a first purpose and second purpose. The enumerating of a functional element or SW expressed in a very high level of generality without internal details cannot be viewed as limitations that significantly transform the Abstract Idea of prong one.
Claim 7 recites a DB used to generate identification program on basis of acquired first and second data; hence the storing of first and second data can be viewed as a extra-solution activity of non-significance.
Claims 8-9 recite that part of the target object is different between each purpose and the operation performed is different between each purpose. This minor differentiation in operation for a first or second purpose fails to describe a transformation to the Abstracted steps of acquiring and extracting submerged object data.
Claim 12 recites acquiring data for the second purpose, acquiring identification result from processing data used in the second purpose, and storing the identification result: that is, the claim merely describes acquiring data and storing its result; hence falls into the typical, well-understood data acquiring and storing aspects of Abstract Idea scenario.
Claim 14 recites acquiring object data and integrating first and second portion of the data into a database; and a post-activity that relies of result of a acquiring or identifying cannot add significantly more to the abstract idea.
Claim 15 recites a medium for method claim 13; this fails to add significantly more to the abstract idea.
Claim 16 recites use of a machine learning to generate a identification program using training data from the first data; as the machine learning amounts to mathematical techniques and the integration program is describes in name only; the claim only mentions of a well-understood mathematical means without substantial description of how the internal operation operate; fails to describe a transformation to the Abstracted steps of acquiring and extracting submerged object data.
Claims 17-18 recite capabilities of the identification program such as to detect, derive a confidence rate with respect to threshold, determine nature of a object; and add information of the target object as augmented data for further training (in response to the confidence rate failing a threshold). The recital of a functional component (via a language expressed in a high-level of generality) respective to a reference and a desired result fails to demonstrate how the functional element operates and significantly transforms the Abstract Idea into a inventive application that makes it eligible under the 101 statute.
Claim 19 recites connection of the server device with other electronic devices and this limitation fails to provide a transformation that adds significantly more to the Abstract Idea.
Claim 20 recites a medium to store the program of claim 10; hence fails to add significantly more to the Abstract Idea.
In all, claims 1-12, 16-20 are therefore ineligible under the 35 USC § 101 statute.
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.
Claims 1-12, 19-20 is/are rejected under § 35 U.S.C. 103 as being unpatentable over Qin et
al, CN 107340365B (translation), 04-26-2019, 25 pgs (herein Qin) in view of Thomas et al, USPubN: 2020/0355612 (herein Thomas), KR 101845528 (translation) 04-04-2018, 10 pgs (herein ‘528) and Ni, USPubN: 2018/0286259 (herein Ni) further in view of Cao et al, CN 110531649 (translation), 9-29-2020, 9 pgs (herein Cao) and Brassard et al, USPubN: 2017/0371151 (herein Brassard).
As per claim 1, Qin discloses a server (server and workstation – pg. 4) device comprising:
a data acquisition circuit (monitoring subsystem - pg. 3; monitoring station through a
wireless network, subsystem for receiving comprising a server array, computing workstation, server
and workstation are connected - pg. 4; server in data center, server end to finish the data receiving -pg. 5) configured to acquire, from a first electronic device including an event-based vision sensor (sensor configured for collecting - pg. 5; remote sensing acquired data is transmitted - pg. 5-6; satellite bringing optical sensor - bottom, pg. 5; water quality sensor - top pg. 5; acquired in hydrological instrument … video apparatus – pg. 4), first data (e.g. water index hydrology index, quality index a video image - pg. 5; sensing atmosphere, hydrology, water quality, wind speed, wind direction, air pressure, humidity, solar, rainfall, velocity profile, temperature, dissolved oxygen, conductivity, turbidity, chlorophyll, video image - pg. 6) regarding a submerged object (e.g. lake blue algae - see Abstract; optical sensor transmission spectrum related to algae, index of effective information suspended substance, chlorophyll and algae area - pg. 5) acquired for a first purpose (preprocessing, pre-treatment to be stored in the database pg. 6; classification and storing in a database pg. 6; data backup and data pre-processing pg. 6; interpolation processing, spatial interpolation pg. 6) including real-time event-based optical tracking of microscopic biological specimens (collecting real-time data through … remote sensing… data mining … automatic real-time accurate collecting processing algae disaster information … social and ecological environment – pg. 3; monitoring system … based on water temperature and water quality index bring optical sensor transmission and receiving influence of the spectrum … spectrum extracting quality index of … satellite index – pg. 5; acquired in hydrological instrument … video apparatus – pg. 4) within an underwater environment;
a purpose specific software generation unit to generate identification program used for a second purpose (remote sensing … to facilitate types of data processing program for identification of the original data – pg. 6; three-dimensional numerical value model of the lake, constructing a
water dynamic model - pg. 6; three-dimensional numerical model generation - pg. 7; damage
estimation algorithm - pg. 10; simulation technology and algae hazard assessment, dynamic
simulation function related to life process of algae pg. 11; realize the disaster caused by economic
and ecological loss see Abstract; three-dimensional numerical simulation and algae hazard
evaluation claim 11, pg. 24) different from the first purpose (see preprocessing, pretreatment,
interpolation, classification/storing from above) on a basis of the first data (see above)
Qin does not explicitly disclose a purpose-specific software generation circuit configured to generate software to be used for a second purpose different from the first purpose on a basis of the first data in terms of
(i) a machine-learning based identification program by extracting, as a microscopic biological tactic motion feature amount of the submerged object, a moving speed and movement vectors defining an autonomous stimulus-response taxis with respect to a stimulation source and gravity, and
(ii) circuit to transmit the software over a network to a second electronic device different from the first electronic device to control a purpose-specific hardware feedback control operation of the second electronic device upon detection of the submerged object.
As for (ii)
Cao discloses an ocean nuclear power platform provided as a cloud-based multi-hosted
modules in connection with a separate platform design unit configured for generating of an initial
value model (pg. 2) via distribution of a large task to a pool of servers (pg. 5) for use with a
numerical simulation/optimization software (pg. 6) underlying the numerical maintenance execution
flow initiated from the central module (pg. 4, 5) by which to optimize the numerical model via
successive rounds of correlating feedback from previous rounds (pg. 4) via passes of a removal,
filtering, fusion, cleaning and reclassification by one or more modules arranged under the
maintenance task or clustering software thereof. (pg. 5 bottom top pg. 6); hence a design unit
distributing or transmitting an initial value numerical model to different cloud host modules, or servers operating as cluster software to operate on feedback from a previous task execution stage as part of achieving optimization of the initial numerical model is recognized.
Therefore, based on possibility to manipulate a numerical model and execute a model,
respectively at a user device and at a website as set forth in Qin, it would have been obvious for one
of ordinary skill in the art before the effective filing date of the invention to implement software
generating from a central data center or server in Qin hazard preventive maintenance system so that
the software generated for use in a second purpose - disaster simulation, hazard prediction - would
include distribution-type transmission of the software – e.g. numerical model - to a second electronic device (different from the first data center at which the numerical model is generated), the electronic device receiving the transmitted software as shown in Cao's establishing of plural host or modules to execute a distributed numerical model for a HW/feedback control operation thereby; because
hazard preventive system under hydrographic meteorological conditions in Qin monitoring
platform coupled with predictive analytics operative on collected and pre-processed sensor data from
various sources (lake water, algae, aquatic plants) using a central datacenter to have the collected
data pre-processed and interpolated necessarily includes a post-processing analysis as well as use of
algorithms or simulation software to model potential conditions or scenarios by which to prevent a
hazard or disaster, and use of a distribution paradigm by which software model or algorithm created
at the central source is sent to a plurality of executing platforms or host device or module as set forth
above in Qin, would enable feedback from individual execution data from the respective executing
host or devices remote from the source platform to be correlated by a centralized server or
aggregator platform, which in turn can subject the distributed model/code to further modification at
the central server for the purpose of finetuning of the predicted outcome underlying the overall
predictive analytics approach as set forth above in Cao for carrying out cycles or repeated cycles
of a software model instantiated to provide the most efficient set of forecasted behavior to adopt as
solution to improve the preventive maintenance of a targeted ecological system.
As for (i)
Generating identification by means of input data captured from microscopic features or behavior such as from algae in Qin is shown in Thomas. That is, properties on microorganic groups such as algae, fertilizers being subjected to excitation by UV light is shown in Thomas study of water contaminants for alarm determination (para 0024- 0029) where classification of sensor changepoints (para 0082-0083) by way of machine learning model can predict contamination of the water source, where tracking of fluorescence (TLF) or excitation wavelength (para 0046) for sudden change or discharge (para 0051) provided as training set can assist the machine learning model in refining analysis of the water quality conditions, based on acquired sensor information over microorganisms (para 0055-0059), where processing of sensor stream can yield a binary indicator via such TLF level or fluorescence readings(para 0074) indicative of a contamination event(para 0063); e.g. a reading that is beyond a water alarm threshold on basis of a machine learner calculation (para 0075), the classification by the ML identifying a predicted contamination event. Hence input into a classification ML in terms of preprocessed information that include fluorescence readings and excitation wavelength thereof to generate software effecting sudden threshold-based change indicative of a contamination event; which entails an identification software being obtained via machine learning and classification of preprocessed contaminant data.
Excitation via simulation under a microfluidic device configured for analyzing chemotaxis of microalgae is shown in ‘528 quantitative study, where understanding of the movement in terms of more efficient monitoring, observation and learning of the cell organisms under chemo influence or simulation affecting the culture (e.g. chemoattractant or coin) or diluting the solution(change to concentration gradient, modification of semi-permeable material) in which the microorganism is being experimented (pg. 4), thereby chemotaxis of the micro-algae in the solution is analyzed (pg. 5) including quantitative verification of the metabolic pathways associated with the chemotaxis, and calculation of a chemotaxis index in relevance to establishing of the strongest chemotaxis behavior induced under chemotactic factors (pg. 7-8) in the microfluidic environment. Hence, quantitative establishing of microscopic tactic motion feature of a submerged object defining a stimulus-response taxis with respect to a source of stimulation is recognized.
Use of vectorized constructs to configure a classification model on basis of gravity-induced
motion, trajectorial behavior by suspended objects is shown in Ni training system seeking learning
of real-world ecological situations (para 0006-0007, 0021; environment cues such as gravity - para
0003), where the vectors include training variables about the animated motion and point coordinates
for the movement within an animated space by a predetermined object (para 0022, 0030; Fig. 5) in
consideration of targets and distractors within a gravity simulation environment (para 0008; gravity
is simulated - para 0027) so that properties of the objects and dynamics of the modeled collision
define the objects under the animated cues (para 0035; Fig. 8); hence ecological scenarios-oriented
use of vectors to parameterize a training model for effect of learning behavior and properties of
object subjected to gravity simulation is recognized.
Brassard also discloses configuration of gravitational acceleration as a vector quantity (para
0043-0044) for a numerical method (para 0082) configured for aligning (with gravity) and rotating
microparticles in response to speed simulation, like observing of displacement of microparticles in a
fluid environment (para 0046-0048) according to the Janus micro-fluid particles dispersion technique
(para 0086, 0093), using a simulation to provide estimates of rotational speed for a given physical
shape of particles (non-spherical or spherical) and statistical model for predicting by the numerical
model (para 0097, 0103) their shape and motion behavior (rotational speed, Brownian movement)
within a fluid environment of specific properties. Hence, using of simulation to obtain motion behavior on microparticles in liquid suspension and generating movement vectors with respect to a speed simulation source that affects gravitational movement so to learn on speed, gravitating and shape of a submerged object is recognized.
Therefore, as a second purpose involves executing a identification software by a processor
effecting classification in Qin (classifying and storing data in a database - pg. 7), it would have been
obvious for one of ordinary skill in the art before the effective filing date of the invention to
implement the second purpose software in Qin system so that generating identification program is
a machine-learning based software – as in Thomas - and would be functionally implemented by extracting, as a microscopic biological tactic motion feature amount of the submerged object, a moving speed and movement vectors – as in Brassard - defining an autonomous stimulus-response taxis with respect to a stimulation source – as in ‘528 - and gravity - as per Ni and Brassard; because
vectorization of data on target objects or microorganisms that are properly preprocessed or
extracted from raw sensor data for use into a more intelligent numerical model or artificial intelligence platform would facilitate the processor implementing the model with processing of understandable format and execution of the numerical software aspect of the model on that platform, since machine-processed information related to behavior or change of the micro-organisms (e.g. algae) such as release of fluorescence or reaction of light, micro-movement under effect under simulated gravity can assist an intelligent model like a machine-learning-based classification engine to categorize which species is present, exhibits a typical characteristic or response under some conditions setting by the purpose-specific software whose purpose is for identifying or classifying a given organism type or species (based on a given behavior or response to stimuli); so that ML-based classification model carried out via intelligent analytics by a second purpose software at a given host computing, would be able to provide evaluation of the classification in order affirm the validity of its finding, the validity thereof enabling new data learned from the classification model to be confidently ingested into a species database for further use.
As per claim 2, Qin discloses server device according to claim 1, wherein the purpose-specific software generation circuit is configured to
generate software (pre-treatment to be stored in the database pg. 6; classification and storing in a database pg. 6; data backup and data pre-processing pg. 6) to be used for the first purpose (preprocessing, pre-treatment and interpolation per claim 1) on a basis of second data (three-dimension numerical model see Abstract; three-dimensional numerical model pg. 7-8; time interpolation, spatial interpolation, numerical value model stored in the database pg. 6 - Note1: information extracted from first data - illumination, suspended substance, algae growth, salt circulation, dissolved system - from pre-judging or pre-treatment based on which to build a numerical model or dynamic or hydrodynamic model, damage estimate algorithm - reads on extracting from acquired first data, portion necessary for generating software/algorithm designed for a second purpose such as model prediction, classification that is different from pre-processing or interpolating of the first purpose) regarding a submerged object acquired for the second purpose (refer to claim 1 or 13).
As per claims 3-4, Qin discloses server device according to claim 1,
wherein the purpose-specific software generation circuit is configured to generate software to be used for the first purpose (refer to claim 1) on a basis of the first data;
wherein the purpose-specific software generation circuit (refer to claim 1) is configured to generate software to be used for the first purpose (pre-processing, pre-treatment to be stored in the database pg. 6; data backup and data pre-processing pg. 6) and the software to be used for the second purpose (model prediction data pg. 10; numerical simulation technology and algae hazard assessment, dynamic simulation function related to life process of algae - pg. 11; realize the disaster caused by economic and ecological loss - see Abstract ; three-dimensional numerical simulation and algae hazard evaluation - claim 11, pg. 24) on a basis of the first data (refer to claim 1) and second data (refer to claim 2) regarding a submerged object (algae in Abstract; suspended
substance, chlorophyll - pg. 5) acquired for the second purpose (see above).;
As per claim 5, Qin discloses server device according to claim 1, further comprising an identification program generation circuit configured to generate an identification program (classification - pg. 18; classifying and storing - pg. 7; pre-processing of the received data - pg. 6) for identifying the submerged object in software (refer to claim 1) to be used for the first purpose and the software (numerical model - refer to claim 1) to be used for the second purpose (three-dimensional numerical value model of the lake, constructing a water dynamic model - pg. 6; three-dimensional numerical model generation - pg. 7; damage estimation algorithm - pg. 10; simulation technology and algae hazard assessment, dynamic simulation function related to life process of algae pg. 11; realize the disaster caused by economic and ecological loss).
As per claim 6, Qin discloses server device according to claim 5, wherein the identification program is used in common to the software to be used for the first purpose (refer to claim 1; preprocessing of the received data - pg. 6; in said step (2), classifying and storing - pg. 7) and the software to be used for the second purpose (data after pre-treatment, along with the original data receives the database, building three-dimensional numerical value model
stored in the database constructing a dynamic model using the finite difference solving model,
acquiring the numerical simulation data risk evaluation can be the existing algorithm - pg. 6-7).
As per claim 7, Qin discloses server device according to claim 5, wherein a database (classifying and storing data in a database - pg. 7) used to generate the identification program is generated on a basis of the first data (remote sensing atmosphere, hydrology, water quality, wind speed, wind direction, air pressure, humidity, solar, rainfall, velocity profile, temperature, dissolved oxygen, conductivity, turbidity, chlorophyll, video image - pg. 6; in said step (2), classifying and storing - pg. 7) and second data (three-dimensional numerical value model stored in the database - pg. 6) regarding a submerged object (refer to claim 1) acquired for the second purpose (refer to claim 6).
As per claim 8, Qin discloses server device according to claim 1, wherein at least part of a target submerged object (algae attenuation coefficient and non-algae particles expressed as chlorophyl - pg. 9) is different between the first purpose and the second purpose (Note2: attenuation coefficients for algae and non-algae particles expressed as chlorophyll per a build of a numerical model destined for a second purpose reads on target submerged object in a model evaluation phase expressed with attenuation coefficients for a second purpose - see Note3 from below - being different from parts of the submerged object that have been associated with
camera/sensor capture - per preprocessing of first data in claim 1 - or first purpose such as measurement phase prior to their classification using numerical model).
As per claim 9, Qin discloses server device according to claim 8,
wherein an operation (see below) performed when the target submerged object is detected is different between the first purpose and the second purpose (Note3: algorithm-based simulation using a numerical model - as a second purpose - reads on operation that is different from the pre-processing, pretreatment and identification of a submerged object or suspended substance based their initial detection via remote sensing means associated with pre-processing of the first purpose).
As per claim 10, Qin discloses a generation method comprising:
acquiring first data (e.g. index system water index hydrology index, quality index a video
image - pg. 5; sensing atmosphere, hydrology, water quality, wind speed, wind direction, air
pressure, humidity, solar, rainfall, velocity profile, temperature, dissolved oxygen, conductivity, turbidity, chlorophyll, video image - pg. 6) regarding a submerged object (refer to claim 1) acquired from a first electronic device (refer to first electronic device in claim 1) for a first purpose including an event-based vision sensor (satellite bringing optical sensor - bottom, pg. 5; acquired in hydrological instrument … video apparatus – pg. 4) for a first purpose (refer to claim 1);
generating software to be used for a second purpose (three-dimensional numerical value model of the lake, constructing a water dynamic model - pg. 6; three-dimensional numerical model generation - pg. 7; damage estimation algorithm - pg. 10; refer to claim 1) different from the first purpose on a basis of the first data (see above) by extracting, as a tactic motion feature amount of the submerged object, a moving speed and movement vectors defining an autonomous stimulus-response taxis (refer to rationale A(i) of claim 1) with respect to a stimulation source; and
transmitting the software to a second electronic device different from the first electronic device (refer to rationale A(i) of claim 1).
As per claim 11, Qin discloses an electronic device generation method comprising:
acquiring first data (index system water index hydrology index, quality index a video image - pg. 5; sensing atmosphere, hydrology, water quality, wind speed, wind direction, air pressure, humidity, solar, rainfall, velocity profile, temperature, dissolved oxygen, conductivity, turbidity, chlorophyll, video image - pg. 6) regarding a submerged object (refer to claim 1) acquired by a first electronic device including an event-based vision sensor (sensor configured for collecting - pg. 5; remote sensing acquired data is transmitted - pg. 5-6; optical sensor - bottom, pg. 5; water quality sensor - top pg. 5; acquired in hydrological instrument … video apparatus – pg. 4) for a first purpose (refer to claim 1) including real-time event-based optical (optical sensor pg. 5; acquired in hydrological instrument … video apparatus – pg. 4) tracking of microscopic biological specimens (lake blue algae - see Abstract; optical sensor transmission spectrum related to algae, index of effective information suspended substance, chlorophyll and algae area pg. 5) within an underwater environment;
generating purpose-specific software (refer to claim 1, 10) to be used by a second electronic device (refer to rationale A(i) of claim 1), different from the first electronic device, for a second purpose (refer to claim 1, 10) different from the first purpose on a basis of the first data by extracting, as a microscopic biological tactic motion feature amount of the submerged object, a moving speed and movement vectors defining an autonomous stimulus-response taxis with respect to a stimulation source and gravity (refer to rationale A(i) of claim 1); and
storing the software in a medium (server array - pg. 4-5; using program design language
compilation algorithm program in the server - pg. 10 - Note4: server array having medium of a
computer and equipped with compilation for program design reads on generating software - by a
server - and storing the generated software in a medium of one of the server computers), the software, when executed by the second electronic device, causes the second electronic to control a purpose-specific hardware feedback control operation (refer to rationale A(i) of claim 1) upon detection of the submerged object.
As per claim 12, Qin discloses electronic device generation method according to claim 11, further comprising:
acquiring second data (numerical value model stored in the database - pg. 6) regarding a submerged object acquired for the second purpose (refer to claim 1, 10, 11);
acquiring an identification result (classifying and storing data in a database as follows storing
for the single-point time-continuous data for the data generated by three-dimensional numerical
method for image or video data - see claim 2, pg. 22; refer to data from prediction, assessment, disaster/loss realizing, hazard evaluation from claim 1) obtained by processing the second data (numerical value model stored in the database - pg. 6; three-dimensional numerical simulation and
algae hazard evaluation - claim 11, pg. 24) by using the software (see hazard evaluation, simulation,
numerical model) by using the software to be used for the second purpose (refer to claim 1, 10, 11); and
storing the identification result in the medium (classifying and storing - pg. 7).
As per claim 19, Qin discloses server device according to claim 1, wherein the server device is connected to a plurality of electronic devices (bottom pg. 4) over a network, the plurality of electronic devices including the first electronic device and the second electronic device (server array, computer, workstation, router – pg. 4) .
As per claim 20, Qin discloses non-transitory computer-readable medium storing a
program that, when executed by a computer, causes the computer to perform the method of claim 10 (refer to claim 10)
Claims 13-15 is/are rejected under § 35 U.S.C. 103 as being unpatentable over Qin et al, CN 107340365B (translation), 04-26-2019, 25 pgs (herein Qin) in view of Thomas et al, USPubN: 2020/0355612 (herein Thomas), KR 101845528 (translation) 04-04-2018, 10 pgs (herein ‘528) and Ni, USPubN: 2018/0286259 (herein Ni) further in view of Cao et al, CN 110531649 (translation), 9-29-2020, 9 pgs (herein Cao), Brassard et al, USPubN: 2017/0371151 (herein Brassard), and Apte et al, USPubN: 2019/0080046 (herein Apte)
As per claim 13, Qin discloses a database generation method comprising:
acquiring first data (water index hydrology index, quality index a video image - pg. 5;
sensing atmosphere, hydrology, water quality, wind speed, wind direction, air pressure, humidity,
solar, rainfall, velocity profile, temperature, dissolved oxygen, conductivity, turbidity, chlorophyll,
video image - pg. 6) regarding a submerged object acquired by a first electronic device including an event-based vision sensor (satellite bringing optical sensor - pg. 5; acquired in hydrological instrument … video apparatus – pg. 4) for a first purpose (refer to claim 1) including real-time event-based optical tracking of microscopic biological specimens (lake blue algae - see Abstract; optical sensor transmission spectrum related to algae, index of effective information suspended substance, chlorophyll and algae area pg. 5) within an underwater environment;
extracting, from the first data, a first data portion ((see pre-processing, data judging, building three-dimensional numerical value model of the lake, constructing a water dynamic model, hydrodynamic model, scalar comprises illumination, suspended substance, algae growth, salt circulation, dissolved system - pg. 6; three-dimensional numerical model generation - pg. 7; damage estimation algorithm- pg. 10 – Note5: information extracted from first data - illumination, suspended substance, algae growth, salt circulation, dissolved system - from pre-judging or pre-treatment based on which to build a numerical model or dynamic or hydrodynamic model, damage estimate algorithm - reads on extracting from acquired first data, portion necessary for generating software designed for a second purpose that is different from pre-processing or interpolating, a purpose such as prediction, classification) necessary for generating software (see prediction and simulation from below; classifying and storing database - pg. 7; simulation function related to life process of algae pg. 11) to be used by a second electronic device, different from the first electronic device for a second purpose different from the first purpose;
integrating the extracted data in a database (classifying and storing data in a database - pg. 7) configured to be used for generating purpose-specific software for identifying a submerged object, by vectorizing (see below) the extracted data into a technical training dataset (refer to rationale A(i) of claim 1) including at least one of a fluorescence reaction wavelength, a phototaxis response wavelength, or a tactic motion feature amount of the submerged object defining an autonomous stimulus-response taxis including movement vectors with respect to a stimulation source and gravity (refer to teachings by Thomas, ‘528, Brassard, Ni in rationale A(i)); and
wherein the machine-learning algorithm generates a new identification program (refer to rationale A(ii) of claim 1) by performing machine learning using the technical training dataset as training data,
B) Qin does not explicitly disclose generating identification program in terms of
(i) by performing machine learning using the technical training dataset as training data, such that the purpose-specific software for the second purpose is updated based on the new identification program upon detection of the submerged object;
(ii) automatically updating a recognition parameter of the purpose-specific software by executing a machine-learning algorithm using the technical training dataset to derive a confidence rate for identifying the submerged object as a known species.
As for (i)
Thomas discloses study of water contaminants for alarm determination (para 0024- 0029) where classification of sensor changepoints (para 0082-0083) by way of machine learning model can predict contamination of the water source, where tracking of fluorescence (TLF) or excitation wavelength (para 0046) for sudden change or discharge (para 0051) provided as training set can assist the machine learning model in refining analysis of the water quality conditions, based on acquired sensor information over microorganisms (para 0055-0059), where processing of sensor stream can yield a binary indicator via such TLF level or fluorescence readings(para 0074) indicative of a contamination event(para 0063); e.g. a reading that is beyond a water alarm threshold on basis of a machine learner calculation (para 0075), the classification by the ML identifying a predicted contamination event and where ML outcome (calculated changepoints) is updated daily (para 0075; 0081 ) based on sensed data changes. Hence input into a classification ML in terms of preprocessed information that include fluorescence readings and excitation wavelength thereof to generate software effecting sudden threshold-based change indicative of a contamination event; which entails an identification software being obtained via machine learning for performing classification of preprocessed contaminant data. Hence, performing machine learning using the technical training dataset as training data, resulting in a purpose-specific software such as classification SW whose output is updated based on output instance from cycles of identification operation upon data acquired with metrics detection of the submerged object is recognized.
As for (ii)
Apte discloses classification based on micro-organisms related metrics and calculating of related confidence indicative of accuracy in the microbiome analysis associated with conditions of microorganisms in solution (para 0117) with predictive support from computational methods of machine learning models for determining features and diversity of microbiome features and taxonomy grouping thereof (para 0058), and deriving significant index metrics or propensity scores, experimental deviation (para 0048) for a related microorganism conditions via classification by a ML model, the classification indicative of presence or absence of condition, or condition severity (para 0070-0071) in terms of scores associating a classification (on conditions, behavior, health) with a confidence level representative of one such index metric (para 0037) or a confidence interval across a population of users (para 0063); hence machine-learning based software operating as classifiers to yield grouping of data, rendering of scores associated with microorganisms and sampled experimental conditions thereof in terms of metrics or confidence levels according to which significance of the sampled conditions can be ingested in a micro-organism database (para 0046-0047) such that the persisted taxonomy, micro-organisms statistics, reference features and conditions in said DB can be subjected to update or refinement (para 0050, 0066) from effect of continually correlating the grouping significance with changes observed via the experimenting – e.g. propensity scores max, min updated based on newly processed samples – para 0083 - is recognized.
Therefore, based on Qin use of acquisition of microorganism event-based data and use thereof as input to a classification software for maintaining a database based on result of this SW identification function, it would have been obvious for one of ordinary skill in the art before the effective filing date of the invention to implement identification program as purpose-specific software subsequent to the acquisition and preprocessing stage by way of
(i) performing machine learning using the technical training dataset as training data, such that the purpose-specific software for the second purpose is updated – as in Thomas - based on the new identification program upon detection of the submerged object;
(ii) automatically updating a recognition parameter of the purpose-specific software by executing a machine-learning algorithm – see Apte update to DB metrics and experimental conditions - using the technical training dataset to derive a confidence rate – as in Apte - for identifying the submerged object as a known species as shown in the taxonomy DB by Apte; because
enabling a identification program to be evolve with cycle of machine learning so that each classification result obtained by this program can be dynamically adjusted in response to a change or conditions associated with state of real-time state of micro-organisms within a specific experimental or target environment will improve the intelligent classification software to attain the most accurate identification of a taxonomy group with which to impart metrics describing behavior or metadata identifying the microorganism, as well as conditions in which the metrics or score applied so that a corresponding level of confidence can be imparted with each taxonomy effect; and provision of a database to maintain information descriptive of this micro-organism as derived from the identification software with continual update made to the metrics, experiment condition, and confidence levels associated therewith as set forth above would improve scope of applicability and reusability of knowledge persisted in this database notably when real-world alarming events or dire changes to the habitat/conditions of the micro-organisms necessitate retrieval of the knowledgebase data for use in implementing remedial actions or recovery/treatment techniques to respond to critical states of the ecological system affected by ( or affecting the health status quo of) microorganisms in their environment.
As per claim 14, Qin discloses database generation method according to claim 13, further comprising:
acquiring second data (numerical value model stored in the database - pg. 6) regarding a
submerged object (refer to claim 1) acquired for the second purpose (three-dimensional numerical
simulation and algae hazard evaluation - claim 11, pg. 24; refer to claim 11); and
integrating the second data and the first data portion and storing the integrated second data
and first data portion in the database (refer to claim 13; database storing remote sensing/monitoring,
survey data for three-dimensional numerical simulation and storing the data in single data table
for the two-dimensional data - claim 15, pg. 25; step (2), classifying and storing in a database - pg.
7) as information necessary (Note6: image and video data - pg. 7 processed from remote sensing
and stored as 2/3 -dimensional numerical data table by which to configure simulation model reads
on storing integrated of first and second data portions in a medium as information necessary for
generating software for the simulation purpose) for generating the software to be used for the second
purpose (model simulation per claim 1, 10, 11; hazard forecasting - per claim 1).
As per claim 15, Qin discloses an electronic device (see Abstract) comprising a non-transitory computer readable medium storing the database (refer to claim 13) generated by the method according to claim 13.
Claims 16-18 is/are rejected under § 35 U.S.C. 103 as being unpatentable over Qin et al, CN 107340365B (translation), 04-26-2019, 25 pgs (herein Qin) in view of Thomas et al, USPubN: 2020/0355612 (herein Thomas), KR 101845528 (translation) 04-04-2018, 10 pgs (herein ‘528) and Ni, USPubN: 2018/0286259 (herein Ni) further in view of Cao et al, CN 110531649 (translation), 9-29-2020, 9 pgs (herein Cao) and Brassard et al, USPubN: 2017/0371151 (herein Brassard) and further of O'Hara, USPubN: 2019/0087533 (herein OHara), Jiao et al, CN 1811792, (translation) 08-02-2006, 6 pgs (herein Jiao) and Apte et al, USPubN: 2019/0080046 (herein Apte)
As per claims 16-18, Qin does not explicitly disclose server device according to claim 5, wherein
(i)the identification program is generated by machine learning using a submerged object table as training data, the submerged object table being generated based on at least the first data.
(ii) the identification program detects the submerged object, derives a confidence rate between the detected submerged object and a submerged object in the submerged object table and, in response to the confidence rate exceeding a threshold, determine the submerged object is a known object.
(iii) wherein, in response to the confidence rate failing to exceed the threshold, add detailed information regarding the detected submerged object to the submerged object table to augment the training data for a subsequent generation of the identification program.
As for (i)
Creating a record/table for storing initially processed first data acquired from remote sensor
system on monitored submerged objects and lake water (pg. 3) after a pre-treatment stage is shown
in Qin as storing the pre-processed or interpolated data in database (pre-processing, time/spatial
interpolation, transmitted to database - see Abstract) as part of database assimilation (after pre-
treatment, original data are transmitted database storage - pg. 6), the database also storing a
numerical model (pg. 6) as part of constructed tool for simulation associated with ecological disaster
prevention, the recorded data resulting from classification ( classifying and storing in a database -
pg. 7). Hence identification program in form of a classification software operating on basis of a)
preprocessed information generated based on at least the first data (sensor data) and b) submerged
object table or DB record whose content is affected by the classification is recognized- referred
herein as (*).
As for (ii) and (iii)
The use of confidence score or metric to indicate whether a persisted micro-organism in a database as shown in Apte (para 0037) can induce sufficient confidence from the users associated with use of the database signifies that a larger confidence score gathered from user satisfaction is indicative that the information obtained as knowledge gather useful and most complete set of information deemed useful to the users. And this confidence score established from the users is shown in Apte classification of microbiome (reference microbiome parameter ranges representing … confidence intervals across a population of users – para 0063)
Similar to Qin analyzing impact by water microorganisms like cyanobacteria, Ohara
discloses characterization of risks in microbic environments, via use of sensor data extraction and
processing aligned with inference algorithms (Fig. 1) implemented with models of the infection risk
related to microorganisms such as parasite, fungi, protozoan or bacteria (e.g. cyanobacteria - see
para 0005-0006; para 0015), where characterizing viability of the microorganism in line with assessing risk of infection thereby includes genetic estimates or pathogenic analytics provided via machine learning on basis of collected data (para 0081) where big data sets or population of microbes computed in terms of their pathogens can generate different sample types, that may be differentiated into risk probability threats (para 0135), the probability assessment thereof effected with Bayesian classifier techniques associated with satisfying an assumption set with a probability distribution table to determine estimation of a risk and decision for updating the results in a decision database (para 0172) constituting aspect of probabilistic techniques with statistical scoring realized by assessing matches between real sample and counterparts in database (para 0 170), the matching using machine learning techniques (para 0176) where result from filtering data from respective classification may be used to replace or adjust an entry in the decision database (para 0174-0175); such that, in order to increase confidence in classification, overlapping results from two runs can be trimmed down (para 0276) to reduce complexity in comparing data sets by the classifier run associated with evaluating risk probability threats by genomic and pathogen set. That is, finetuning classification capability of machine learning technique associated with assessing probability of risk between on microorganism samples of real data via evaluating satisfactory level or a confidence metric of each classifier output or feedback thereof via action like adjustment, merging, resizing the genetic training set to improve the deficiency in this confidence factor entails implementing augmentation to the training data via additional adjust made to the decision database and the integration program in response to a confidence score not meeting a satisfactory threshold.
Jiao further discloses identification techniques on harmful effect in marine ecological
system or algae environment using image recognition technique or biological imaging in terms of
automatic identifying device for acquiring of the image of the microsample (harmful algae) through a light (pg. 2), so as to extract characteristic of value from the real sample followed by classification identification per effect of comparing ideal characteristic value in database with a algae known to be harmful according to their similarity (pg. 3), using similarity measure by which to set a confidence probability (relative to a threshold) as basis to commit a set of characteristic value and the sample image to a database, i.e. additional insights or knowledge committed in database for use toward subsequent discovery or identification models in correlation to their confidence value of the classification instance. Hence, committing algae classification DB by Jiao in terms of a determination as whether more information is needed to correct a deficient confidence factor associated with assessing instance of a classification run entails adding more details to the configuration record of a submerged object for implementing an improved classification database in response to a confidence factor not meeting a satisfactory level.
Thus, it would have been obvious for one of ordinary skill in the art before the effective
filing date of the invention to implement evaluation of ecological risk, disaster and forecast of hazard
in microorganism and lake submerged objects in Qin so that the central datacenter server is equipped with
1) artificial techniques such as machine learning to support identification program - similarity
determination technique operative on basis of a submerged object table - as in Ohara comparing real
sample and database sample - the table of microorganisms serving as target to the training, the table
as in Qin being generated based on at least the first data obtained from preprocessing sensed
information from the submerged objects - as in Qin from (*);
2) the identification program being configured to detect the submerged object, derives a
confidence rate between the detected submerged object and a submerged object in the submerged
object table - as per Jiao comparing ideal characteristic value in database with an algae known to be
harmful - and, in response to the confidence rate passing a satisfaction threshold, determine and commit the submerged object as a known/admitted object in the database – as in Apte, so that
3) in response to the confidence rate not exceeding the threshold, add detailed information
regarding the detected submerged object to the submerged object table - as shown in Ohara and Jiao - for committing a characteristic value and image for an algae sample into a database acting a knowledge-base for use toward subsequent discovery or identification models; because
evaluation as to whether a microorganism can cause disastrous or hazardous effect to the
ecological system or health of a geoeconomic environment via use of predictive model or artificial
classification technique such as machine learning SW to carry out training over a set of real sample (
of acquired microorganism data) for comparison to a known set (preestablished table of
microorganism data) in a database reference would enable analysis at each stage of the classification
to derive similarity between the real sample and a reference table in the database, the improved
similarity achieved at each cycle of the training progressively forming a measure of confidence (e.g.
matching a threshold) in categorizing the trained sample as fitting a acceptance criterion with which
to add the set into the database - e.g. by which the database is augmented with data considered
causing acceptable risks or low probability thereof - as opposed to case where similarity between the
compared set falling below a desirable confidence factor/score in which case the sample is to be
returned to the training cycle for further machine learning setting and sample re-adjustment, thereby
augmenting the filtering effect associated with classification of samples and improving the overall
forecasting of risk or disaster prevention related to collected data using the monitoring system and
identification software by Qin.
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 1, 11, 13 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention.
In fact, claim 1 recites “transmit software … to a second electronic device … to control a purpose-specific hardware feedback control operation of the second device upon detection of the submerged object”. No part of the disclosure describes “submerged object detection processing” (e.g. Specs pg. 18, pg. 38) in terms of control a purpose-specific hardware feedback control operation of the second device as result of a transmit.
Claim 11 recites “causes the second electronic device to control a purpose-specific hardware feedback control operation upon detection of the submerged object”. No part of the disclosure expresses “submerged object detection processing” (Specs: pg. 18, 38) in terms of “causes the second electronic device to control a purpose-specific hardware feedback control operation”
Claim 13 recites “such that the purpose-specific software for the second purpose is updated based on the new identification program upon detection of the submerged object”; no part of the described “submerged object detection processing” (Specs: pg. 18, 38) includes “such that the purpose-specific software for the second purpose is updated based on the new identification program”
One skill in the art would find it extremely hard-pressed in making use of ( or replicating) the claimed invention, based on the fact that the inventor is clearly not in possession of this claimed feature.
For interpretation of merits, the “upon detection of the submerged object” limitation is treated with minimal or no weight.
Response to Arguments
Applicant's arguments filed 8/11/26 have been fully considered but they are not persuasive. Following are the Examiner’s observations in regard thereto.
(A) The Applicant has submitted that Qin, Brassard, Ni, and Thomas do not disclose or suggest extracting (from event-based vision sensor) “tactic motion feature amount” defining “autonomous stimulus-response taxis” of living organisms (Applicant's Remarks pg. 11-12). The relied upon feature has been interpreted as taxis movement as capture response (from optical or camera sensor) to stimuli such as chemo or gravity change experimentation; where as shown in the rationale A of claim 1, this stimulus-response taxis is shown in ‘528 as taxis due to chemical stimuli, in Ni object response as due to gravity forces, in Brassard as taxis caused by gravitational stimulation. The argument appears to dissect each individual reference without tying this alleged finding with the very 103 prongs of the Office action rationale, as one currently proffered to meet the latest changes to claim 1, including introduction of additional references. Therefore, merits of the Applicant rebuttal would be deemed misplaced and largely non-commensurate with state of the Office Action currently presented to address change introduced with the amended claim 1.
(B) The Applicant has submitted that in Jiao regarding claim 18, the adding of information by Jiao due to established confidence appears to take place in a direction opposite to the claim (Applicant's Remarks pg. 13 bottom). Jiao has been cited to show that classification information can be committed to a record for classification purpose when confidence factor achieved via similarity comparison between a set of incident characteristic values and those of the sample image to a database is sufficiently met or not; in that additional commit information be added to retrain the classification model in order to improve the similarity compute when this confidence factor fails. In other words, committing a algae classification DB by Jiao includes determination as whether more information is needed for a classification record to correct a deficient confidence factor associated with assessing instance of a classification run and this entails adding more details to the configuration record of a submerged object for implementing an improved classification database in response to a confidence factor not meeting a satisfactory level.
One cannot properly affirm on correctness of the Applicant argument when the alleged remark made against Jiao is only directed at one reference, whereas the 101 rejection applied to claim 18 contains citing of at least 3 references; therefore, demonstration of non-obviousness is deemed incomplete.
( C ) The Applicant has submitted that that due to the added feature in claim 1, such as “event-based vision sensor”, extracting “biological tactic motion amount”, “moving speed and movement vectors” defining “autonomous stimulus-response taxis” with “respect to stimulation source and gravity”, the alleged remark by the Examiner on the lack of hardware tool as a need to cure mootness of Applicant raise of patentability is hereby moot (Applicant's Remarks pg. 14). This remark does not relate to the actual citing of prior art (emphasis here) to meet any particular claimed feature.
( D) The Applicant has submitted that the adjusted ground of rejection for claim 13 now as amended remains unpersuasive due to addition of “autonomous stimulus-response taxis” defined by “tactic motion feature amount of the submerged object” (Applicant's Remarks pg. 15) This remark does not relate to the actual citing of prior art to meet any particular claimed feature.
( E) Applicant's Remarks that as currently amended, claim 10 now includes extracting “biological tactic motion amount”, “moving speed and movement vectors” defining “autonomous stimulus-response taxis” with “respect to stimulation source, and claim 10 is directed to a distinct, patentable scope covering event-based sensor-driven taxis extraction for cross-purpose, and accordingly, claim 10, claims 1 and 11 being a narrower version to claim 10 are believed to be patentable over the cited references of Qin, Cao, Ni, Brassard.(Applicant's Remarks pg. 16). Merely stating that the amended claims are now patentable without factual demonstration as to how the cited references (if any) fail to anticipate or render obvious the allegedly patentable feature is considered largely inconclusive.
(F ) The Applicant has submitted that for prong One and Prong two of the Step 2A in the Abstract Idea rejection, claim 13 is tethered to a specific non-generic physical data pipeline that cannot be replicated by any conventional computer system or human mind, especially due to inclusion of features on “event-based vision sensor”, tracking of “biological tactic motion amount”, of biological specimens, and properties of “autonomous stimulus-response taxis” which makes claim 13, unlike RPI court case, an exclusive transformation pipeline that does not pre-empt the general use of machine learning but defines specific technical solution to operational bottlenecks inherent to fluid dynamic and underwater data (Applicant's Remarks pg. 17-19). The added features to claim 13 have been analyzed and addressed with a proper analysis and pertinent ground to rejection, making the above allegation largely moot.
(G ) The Applicant has submitted that the added limitations to claim 13 now cure to the deficiencies raised by the Examiner response to the Applicants remarks set forth with the last AF response, notably when the machine learning of claim 13 now includes “generates a new identification program by using training dataset such that the purpose-specific software is updated based on the new identification program upon detection of the submerged object” (Applicant's Remarks pg. 20), especially when vectorizing autonomous stimulus-response taxis, event-based vision sensor cannot be a well-understood methodology or a generic computer. The state of the amended features has been analyzed via BRI and merits thereof have been met with detailed presentation of a proper eligibility status analysis.
In all, the claims as amended stand rejected as set forth in the office action.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Tuan A Vu whose telephone number is (571) 272-3735. The examiner can normally be reached on 8AM-4:30PM/Mon-Fri.
If attempts to reach the examiner by telephone are unsuccessful, the examiner's supervisor, Chat Do can be reached on (571)272-3721.
The fax phone number for the organization where this application or proceeding is assigned is (571) 273-3735 ( for non-official correspondence - please consult Examiner before using) or 571-273-8300 ( for official correspondence) or redirected to customer service at 571-272-3609.
Any inquiry of a general nature or relating to the status of this application should be directed to the TC 2100 Group receptionist: 571-272-2100.
/Tuan A Vu/
Primary Examiner, Art Unit 2193
Septembre 15, 2026
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