DETAILED ACTION
This action is responsive to the Applicant’s response filed 11/18/25.
As indicated in Applicant’s response, claims 1, 4, 18 have been amended. Claims 1-20 are pending a next office action.
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 1-15, 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 Cao et al, CN 110531649 (translation), 9-29-2020, 9 pgs (herein Cao).
As per claim 1, Qin discloses a server device (see monitoring server from below) 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 (sensor configured for collecting - pg. 5; remote sensing ... acquired data is transmitted - pg. 5-6; optical sensor - bottom, pg. 4; water quality sensor - top pg. 5), 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); and
a purpose-specific software generation circuit (data center, said pre-processing comprises the
following steps - pg. 6) configured to generate an identification program (see prediction, assessment, disaster/loss realizing, hazard evaluation from below) as software (building 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) to be used for a second purpose (prediction data - 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)
A) Qin does not explicitly disclose generating identification program to be used for a second purpose as data center service to:
transmit the software to a second electronic device different from the first electronic device.
Qin, however, discloses a common platform of hazard prediction and warning in terms of internet-based software platform operating as a public platform permitting communication and/or interaction between the monitoring, the data mining system (through a site) and users of the system (top pg. 18; top pg. 19) in accordance to possibility for the user (calculating workstation for three dimensional model run – claim 13, pg. 25) to operate the three-dimensional numerical value model (user can operate, user intervention, using prediction information - pg. 10), using a web page to load/display dimensional numerical model related to a forecasting period, time and condition information, so that the user (user centre, advanced user, manipulate … three-dimensional numerical model – pg. 18-19) can set to modify the model (pg. 19) or manipulate the 3-dimensional numerical model (top pg. 20) with which simulation of the model, via the website can realize the forecasting related to public pre-warning or prediction (claim 1, pg. 21) on algae harmful
information or water related disaster.
Hence, distribution of a numerical model generated from a data center using a website associated with a public platform enabling users at a user center to manipulate the 3D numerical model for simulation via the website to realize forecast on algae disaster or harmful information obtained via the monitoring and data mining system entails transfer to a website or user center of a 3D numerical model for simulating or forecasting prediction on harmful impacts by the submerged objects related data collected from the sensing system is recognized; i.e. the website and the user center being different device from the data center.
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 a 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 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 of the software - a numerical model - associated with effect of data center
transmitting or distributing the software - 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 use of plural host or modules to execute a
distributed numerical model; 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 analytics 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 per claim 2, Qin discloses server device according to claim 1, wherein the purpose-specific software generation circuit (see data center in claim 1) 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 (refer to preprocessing, pre-treatment and interpolation; backup, classification 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: generating of preprocessing/pre-treatment code or classification/interpolation code from database stored numerical model, or time interpolation data therein reads on software generated at the data center for a first preprocessing/interpolation purpose using pre-existing database information as captured data for a second purpose - numerical model, time/spatial numerical data, hazard forecasting - related to a submerged object) regarding a submerged object (refer to claim l) acquired for the second purpose (refer to claim 1).
As per claims 3-4, Qin discloses server device according to claim 1, wherein the purpose-specific software generation circuit configured to generate software to be used for the first purpose (refer to claim 4) 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; classification and storing in a 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 (refer to claim l; 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 (classification - pg. 18; classifying and storing - pg. 7; pre-processing of the received data - pg. 6) for identifying the submerged object (algae in Abstract; suspended substance, chlorophyll - pg. 5) in software (refer to claim 1) to be used for the first purpose and the software (refer to claim 1) to be used for the second purpose.
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 used to generate the identification program (classification per claim 6) 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 1).
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 Note3) performed when the target submerged object is detected is different between the first purpose (refer to claim 1) and the second purpose (refer to claim 1 – 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)
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 (refer to claim 1);
generating software (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) to be used for a second purpose (refer to claim 1) different from the first purpose on a basis of (refer to claim 1) the first data; and
transmitting the software to a second electronic device different from the first
electronic device (refer to rationale A 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, 10) acquired by a first electronic device (refer to first electronic device in claim 1) for a first purpose (refer to claim 1);
generating software (refer to claim 1, 10) to be used by a second electronic device (refer to rationale A of claim 1), different from the first electronic device, for a second purpose (refer to claim 1) different from the first purpose on a basis of the first data (see above); 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).
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 (refer to claim 1) 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) 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 13, Qin discloses a database generation method comprising:
acquiring first data (refer to claim 1, 10, 11) regarding a submerged object (refer to claim 1)
acquired by a first electronic device (refer to claim 1) for a first purpose (see preprocessing, pretreatment, interpolation and classification in claim 1);
extracting, from the first data, a first data portion (pre-processing of the received data ...
performs interpolation processing, time interpolation, spatial interpolation, replacing the abnormal
data ... after the preprocessing, transmitting to the database - claim 1, pg. 21; after pre-treatment,
original data ... transmitted ... database storage, building numerical value model, stored in the
database - pg. 6; spectrum extracting quality index of effective information comprising temperature,
transparency, chlorophyll and algae area strength - pg. 5) necessary for generating software (building 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) to be used by a second electronic device (refer to rationale A of claim 1), different from the first
electronic device for a second purpose (refer to claim 1) different from the first purpose (see above);
and
storing all or part of the first data in a database (classifying and storing - pg. 7; database storing - claim 15, pg. 25) such that the first data portion is identifiable.
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 (Note5: 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 comprising a non-transitory computer readable medium storing the database (refer to database on claim 13) according to claim 13.
As per claim 19, Qin discloses server device according to claim 1, wherein the server device is connected to a plurality of electronic devices over a network (authority user, website, advanced
user, user centre, set to modify the model, manipulate three-dimensional numerical model - pg. 19-
20), the plurality of electronic devices including the first electronic device (remote sensing ...
acquired data is transmitted - pg. 5-6; optical sensor - bottom, pg. 4; water quality sensor - top pg. 5;
user centre - ) and the second electronic device (refer to rationale A of claim 1).
As per claim 20, Qin discloses a non-transitory computer-readable medium storing a program that, when executed by a computer, (computer, workstation - pg. 4), causes the computer (refer to claim 10) to perform the method of claim 10.
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 Cao et al, CN 110531649
(translation), 9-29-2020, 9 pgs (herein Cao) further in view of O'Hara, USPubN: 2019/0087533
(herein OHara) and Jiao et al, CN 1811792, (translation) 08-02-2006, 6 pgs (herein Jiao)
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) 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 terms 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.
As for (i) and (ii),
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 regard to 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); where to increase
confidence in classification, overlapping results from two runs can be used (para 0276) as a trimmed
down combination to reduce complexity in comparing data sets by the classifier run in regard to risk
probability threats by genomic and pathogen sets.
Hence, use of machine learning technique in finetuning a classifier type training in assessing
probability of risk between on microorganism samples of real data with those in database with
possibility to improve upon a decision table set on basis of a confidence factor associated with each
classifier or feedback thereof via action like adjustment, merging, resizing the genetic training set to
facilitate their comparison as part of the training is recognized.
As for (iii)
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
tide biological image automatic identifying device for acquiring of the image of the microsample
(harmful algae) through a light (pg. 2), for extracting 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) where the correction
coefficient associated with the comparison/identification can be used to compute the degree of
certainty to implement target classification and identification of respective feature value in the image
database, so to include the characteristic value and the image to the database (item 6, pg. 3; bottom
pg. 4) if a confidence probability exceeds a threshold set by known harmful algae (bottom pg. 3).
Hence, biological imaging for detection of harmful algae with identification technique
performed on sample images in terms of comparing data set between real sample of algae with
known good sample thereof per a classification technique 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 is recognized, i.e. the insights or knowledge committed in database for use toward subsequent discovery or identification models.
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 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;
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 a algae known to be
harmful - and, in response to the confidence rate exceeding a threshold, determine the submerged
object is a known object, 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 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 in the
ecological system or health of a geoeconomic environment via use of predictive model or artificial
classification technique such as machine learning 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 § 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 13, 14-15 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 an 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 following 2 Steps Eligibility Analysis.
I) Claim 13 Analysis:
A) Step one:
The claim is directed to a method as a statutory category.
B) Step two, prong A:
The method claim recites “acquiring first data regarding a submerged object acquire by a first device” (for a purpose); “extracting from the first data portion necessary for generating software” for use by a second device and a second purpose; “storing all or part of the first data in a database”; and as a whole, the method claim is perceived as 1) having a pre-activity step that obtains information on a submerged object, the information construed as displayed on a generic computer; 2) extracting the obtained information for the intended use of generating software or use thereof by another device, the extracting amounting to a Judicial Exception by a mental step that performs extracting part of a displayed information from the pre-activity step; 3) post-activity step resulting from the mental extraction so that part of the information is stored to repository, the post-activity step amounting to non-significance to the act of extracting. Thus, the method as a whole is perceived as a central process step of performing mental extraction preceded by a pre-activity step by which information needed for the mental process is presented, and followed by a post-activity step (of storing data) that does not teach any significant technological augmentation to the mental process or “extracting” step. Claim 13 is thus deemed a subject matter that contains a Abstract Idea type of Judicial Exception in the form of a mental process that perform extracting part of information obtained (via a computer) in regard of an object, and when considered along with the pre-activity and post-activity steps the subject matter of claim 13 in terms of a mental process type Judicial Exception cannot be translated into a Practical Application, since information generated mentally can be retained inside one’s mind.
Step 2, prong B:
The “additional elements” identified for the prong-B Analysis include: database, submerged object, acquired by a first electronic device, first and second purpose, software to be used by second electronic device. However, storing data in a database as post-activity add no significance to the Judicial exception of using mental process to extract information; nor does a first or second purpose contribute significantly to the process of extracting to make it a practical application; nor does the nature of a submerged object add significance to the implementation of “extracting” first data portion; nor do the elements construed as first/second electronic device (for acquiring data or using software) add significant implementation details to the Abstract Idea deficiency of the “extracting” step. In all, the additional elements integrated with the method claim 13 as a whole fail to make the method to amount to significantly more than the Abstract Idea deficiency identified therewith.
Claim 13 is deemed non-eligible a subject matter for it leads to a Judicial Exception of a Abstract Idea type.
II) Prong B analysis of dependent claims.
Claim 14: this claim recites “acquiring second data” (regarding a submerged object), integrating the second data and the first data; and storing second and first data in a database. The acquiring and storing will be treated as pre and post activity steps to the main act of integrating data which can be construed as integration being done mentally; thus, claim 14 cannot render the judicial exception of claim 13 for it to amount to significantly more than the identified Judicial Exception.
Claim 15: this claim recites a computer readable medium being an additional element that does not add more teachings or implementation significance to the act of “extracting” identified in claim 13.
In all, claims 13-15 are deemed non-eligible subject matter due to the Judicial Exception deficiency identified with the step of “extracting” information, the latter being an operation that can be done internal to a human mind and that will not need to be externalized or converted into real-world tangible product.
Response to Arguments
Applicant's arguments filed 11/18/25 have been fully considered but they are not persuasive. Following are the Examiner’s observations in regard thereto.
(A) Applicants have submitted that Qin’s numerical value model as cited in the Office Action to match the software being generated on basis of the first data cannot be same as the “identification program” as now found in the amended language, as none of the hazard simulation/prediction provided as second purpose involves classification/identification from the acquired data (Applicants Remarks, pg. 9, top). The added “identification” limitation has been addressed with adjusted ground of rejection that is extended to show that a model executed in Qin to provide prediction, hazard simulation and assessment, realization of economical and ecological loss reasonably involves effect of a predictive, simulation-type execution that identifies, measures and establishes nature, scale and extent of a damage or loss associated with data, properties acquired from a aquatic/submerged object. Raise of a patentability merits of a newly added limitation is further considered not presenting a proper prima facie type of rebut that otherwise would traverse a rejection that is actually and responsively set to address that limitation.
(B) Applicants have submitted that Cao’s numerical model cannot be same as “identification program” (Applicants Remarks, middle, pg. 9). The newly added feature has been addressed with a adjusted ground of rejection that has been necessitated by the amendment, as set forth in section (A) above.
(C ) Applicants have submitted that for claim 18, with effect of adding information to a table on basis of a confidence rate that fails to exceed a threshold, the teaching by Jiao is rather about adding data for known, confidently identified objects to a DB (Applicants Remarks pg. 9, bottom) and the table pre-processing and editing in Ohara is not analogous to the technical endeavor by Qin.
In Ohara’s consolidation of a decision DB, only objects indicative of lower complexity (in decision making) would be retained whereas objects conducive to higher complexity (for decision) would be trimmed away. In other terms, as degree of complexity signifies a degree of confidence in Ohara, objects determined as falling into a lower degree of complexity are more likely to be committed to the decision database: that is, adding or removing objects to/from a table relies on a confidence level.
The degree of usability of information committed for use in the database by Jiao is based on threshold set to indicate harmful state of algae; so that, if the threshold is exceeded (confidence level insufficient), the harmful algae information would be stored in the purpose for this information (of a harmful object) to be reused as learning knowledge base. In other words, exceeding a “harmful” level threshold (as in Jiao) is equivalent to a non-harmful (higher confidence) threshold not attained or met. In other words, claim 18 has been interpreted as though a DB recording (adding) happens when a good threshold is not attained (confidence value lower than desired) for a given algae instance; i.e. information regarding the deficient/harmful algae is committed to a DB for this knowledge to be recorded for reuse/learning toward further analytic model.
The rejection using Jiao and Ohara is deemed proper, and merits of claim 18 rejection will be maintained.
In all, the claims submitted with this Office Action will stand rejected as set forth above.
Conclusion
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/Tuan A Vu/
Primary Examiner, Art Unit 2193
January 02, 2026