DETAILED ACTION
Claims 1-13 and 16-19 are pending
Independent claims are 1 and 10
Amended claims are 1-2, 8 and 10-11,
Newly added claims are 16-19
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 06/26/2025 has been entered.
Response to Arguments
Applicant’s arguments, see pg. 9, lines 4-10, filed 06/26/2025, with respect to claims 1, 10 and 14 have been fully considered and are persuasive. The 112 rejection of 02/26/2025 has been withdrawn.
Applicant’s arguments with respect to claims 1-13 and 16-19 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument. The new prior art Arbogast et al (Monitoring Insect Pests in Retail Stores by Trapping and Spatial Analysis, “Arbogast”) teaches the newly added limitations in combination with Yan, Xia, Hopper and Eliopoulos.
In regard to Rejections under 35 U.S.C. 101, see Applicant’s Remarks pgs. 10-15, Applicant argues, “Applicant respectfully submits that the claimed invention, as recited, for example, in the amended independent claims, are not directed to an abstract idea. Specifically, the claims recite far more than "a mental process" as the independent claims recite features that cannot be practically performed in the human mind.” Examiner would like to point out that the amendments a maintenance module to provide maintenance information and maintenance notifications by correlating at least one of the insect behavioural data and the presence value (IPD) to at least one of the pheromone device, the at least one immobilization device, and the substances released by the pheromone device; and is insignificant extra solution activity of mere data gathering or data output, see MPEP 2106.05(g), using components and functions claimed at a high level of generality have been determined by the courts as being well-understood, routine and conventional activities in the field of computer functions (see MPEP 2106.05(d)(II)(i)). Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362).
a management module to determine and control the sampling rate based on at least one of a comparison between the historical data of insect presence and the presence value (IPD), a comparison between the meteorological data and predetermined anomaly levels of meteorological data, and a comparison between the environmental data and predetermined anomaly levels of environmental data,. The management module is mere instructions to apply, see MPEP 2106.05(f)). While the rest of the limitation to determine and control the sampling rate based on at least one of a comparison between the historical data of insect presence and the presence value (IPD), a comparison between the meteorological data and predetermined anomaly levels of meteorological data, and a comparison between the environmental data and predetermined anomaly levels of environmental data, is directed to a mental process. One can mentally process the comparison of the historical insect presence and anomaly levels of data by use of pen and paper to process or detect presence of insects in an area.
Applicant also argues, “Applicant asserts that the human mind cannot practically perform releasing of pheromones to attract insects, immobilizing and capturing insects, detecting and providing meteorological data, detecting and providing environmental data,” Examiner would like to point out that the pheromone device, is generally linking the abstract idea to a field of use (see MPEP 2106.05(h)). As for the meteorological and environmental data detecting and providing, are insignificant extra solution activity of mere data gathering or data output, (see MPEP 2106.05(g)), using components and functions claimed at a high level of generality have been determined by the courts as being well-understood, routine and conventional activities in the field of computer functions (see MPEP 2106.05(d)(II)(i)). Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362).
Lastly, the Applicant lastly argues, “…that the presently claimed invention provide an inventive concept such that the claims amount to significantly more than the purported abstract idea.” Examiner would like to point out that Applicant is stating that the management and maintenance modules are what make the application significantly more. Applicant points to BASCOM, saying modules are non-conventional, and a non-generic arrangement. Examiner would like to point out that the rejection below for the limitations using the management and maintenance module are rejected under the recitation of generic computer components i.e. maintenance/ management module, which are recited at a high level of generality and, therefore, are mere instructions to apply the exception using generic computer components. Therefore, the 35 USC 101 rejection is being maintained.
Claim Objections
Claim 2 objected to because of the following informalities:
Line 9 reads “to a another”, should read “to another”
Appropriate correction is required.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-13 and 16-19 rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Regarding claim 1,
Step 1: Is the claim to a process, machine, manufacture or composition of matter?
Claim 1 is directed to a machine.
Step 1: yes.
Step 2A, prong 1: Does the claim recite an abstract idea, law of nature, or natural phenomenon?
process at least one insect digital image (IM) at a sampling rate to provide a presence value (IPD), representing the presence of insects in an area of interest for insect attack; (limitation is directed to a mental process. One can mentally process the sampling rate by use of pen and paper to process or detect presence of insects in an area.)
process the presence value (IPD) and the insect behavioural data according to a mathematical prediction algorithm to estimate a risk of attack (PRB) to the area of interest, the prediction module being configured to provide said risk of attack (PRB) as output; (limitation is directed to a mental process, One can mentally process the mathematical prediction algorithm by use of pen and paper to process or detect presence of insects in an area.).
determine and control the sampling rate based on at least one of a comparison between the historical data of insect presence and the presence value (IPD), a comparison between the meteorological data and predetermined anomaly levels of meteorological data, and a comparison between the environmental data and predetermined anomaly levels of environmental data. (limitation is directed to a mental process, One can mentally process the comparison of the historical insect presence and anomaly levels of data by use of pen and paper to process or detect presence of insects in an area.).
Step 2A, prong 1: If claim limitations, under their broadest reasonable interpretation, covers performance of the limitations as a mental process but for the recitation of generic computer components, then it falls within the mental process grouping of abstract ideas. According, the claim “recites” an abstract idea.
Step 2A, prong 2: Does the claim recite additional elements that integrate the judicial exception
into a practical application?
a prediction module configured to (e.g., mere instructions to apply, see MPEP 2106.05(f)).
an insect identification module configured to (e.g., mere instructions to apply, see MPEP 2106.05(f)).
at least one trap device to be positioned within an area of interest, the at least one trap device including: (e.g., generally linking the use of a judicial exception to a particular technological environment or field of use, see MPEP 2106.05(h)).
a housing having an inner chamber and at least one opening that extends from the inner chamber to an external environment; (e.g., generally linking the use of a judicial exception to a particular technological environment or field of use, see MPEP 2106.05(h)).
a pheromone device to release substances that are configured to attract the insects to the inner chamber; and (e.g., generally linking the use of a judicial exception to a particular technological environment or field of use, see MPEP 2106.05(h)).
at least one immobilization device positioned within the inner chamber and configured to capture insects; (e.g., generally linking the use of a judicial exception to a particular technological environment or field of use, see MPEP 2106.05(h)).
at least one sensing apparatus housed by the at least one trap device, the at least one sensing apparatus comprising: a digital camera; (e.g., mere instructions to apply using generic computer components, see MPEP 2106.05(f)).
a meteorological sensor to detect and provide meteorological data; and (e.g., insignificant extra solution activity of mere data gathering or data output), see MPEP 2106.05(g)).
an environmental sensor to detect and provide environmental data; (e.g., insignificant extra solution activity of mere data gathering or data output), see MPEP 2106.05(g)).
wherein the digital camera is oriented within the housing to capture and provide at least one insect digital image of the insects that are captured by the immobilization device; (e.g., insignificant extra solution activity of mere data gathering or data output), see MPEP 2106.05(g)).
a data collecting module configured to acquire insect behavioural data associated to said area of
interest and comprises the meteorological data, the environmental data, and the historical data of
insect presence; and (e.g., insignificant extra solution activity of mere data gathering or data output), see MPEP 2106.05(g)).
a maintenance module to provide maintenance information and maintenance notifications by correlating at least one of the insect behavioural data and the presence value (IPD) to at least one of the pheromone device, the at least one immobilization device, and the substances released by the pheromone device; and (e.g., insignificant extra solution activity of mere data gathering or data output), see MPEP 2106.05(g)).
a management module to (e.g., mere instructions to apply, see MPEP 2106.05(f)).
Step 2A, prong 2: Since the claim as a whole, looking at the additional elements individually and in combination, does not contain any other additional elements that are indicative of integration into a practical application, the claim is directed to an abstract idea.
Step 2B: Does the claim recite additional elements that amount to significantly more than the
judicial exception?
a prediction module configured to (e.g., mere instructions to apply, see MPEP 2106.05(f)).
an insect identification module configured to (e.g., mere instructions to apply, see MPEP 2106.05(f)).
at least one trap device to be positioned within an area of interest, the at least one trap device including: (e.g., generally linking the use of a judicial exception to a particular technological environment or field of use, see MPEP 2106.05(h)).
a housing having an inner chamber and at least one opening that extends from the inner chamber to an external environment; (e.g., generally linking the use of a judicial exception to a particular technological environment or field of use, see MPEP 2106.05(h)).
a pheromone device to release substances that are configured to attract the insects to the inner chamber; and (e.g., generally linking the use of a judicial exception to a particular technological environment or field of use, see MPEP 2106.05(h)).
at least one immobilization device positioned within the inner chamber and configured to capture insects; (e.g., generally linking the use of a judicial exception to a particular technological environment or field of use, see MPEP 2106.05(h)).
at least one sensing apparatus to be positioned within an area of interest comprising at least one of: a digital camera; (e.g., mere instructions to apply using generic computer components, see MPEP 2106.05(f)).
a meteorological sensor to detect and provide meteorological data; and (e.g., insignificant extra solution activity of mere data gathering or data output), see MPEP 2106.05(g), using components and functions claimed at a high level of generality have been determined by the courts as being well-understood, routine and conventional activities in the field of computer functions (see MPEP 2106.05(d)(II)(i)). Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362).
an environmental sensor to detect and provide environmental data; and (e.g., insignificant extra solution activity of mere data gathering of data output), see MPEP 2106.05(g), using components and functions claimed at a high level of generality have been determined by the courts as being well-understood, routine and conventional activities in the field of computer functions (see MPEP 2106.05(d)(II)(i)). Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362).
wherein the digital camera is oriented within the housing to capture and provide at least one insect digital image of the insects that are captured by the immobilization device; (e.g., insignificant extra solution activity of mere data gathering or data output), see MPEP 2106.05(g), using components and functions claimed at a high level of generality have been determined by the courts as being well-understood, routine and conventional activities in the field of computer functions (see MPEP 2106.05(d)(II)(i)). Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362).
a data collecting module configured to acquire insect behavioural data associated to said area of
interest and comprises the meteorological data, the environmental data, and the historical data of
insect presence; and (e.g., insignificant extra solution activity of mere data gathering or data output), see MPEP 2106.05(g), using components and functions claimed at a high level of generality have been determined by the courts as being well-understood, routine and conventional activities in the field of computer functions (see MPEP 2106.05(d)(II)(i)). Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362).
a maintenance module to provide maintenance information and maintenance notifications by correlating at least one of the insect behavioural data and the presence value (IPD) to at least one of the pheromone device, the at least one immobilization device, and the substances released by the pheromone device; and (e.g., insignificant extra solution activity of mere data gathering or data output), see MPEP 2106.05(g), using components and functions claimed at a high level of generality have been determined by the courts as being well-understood, routine and conventional activities in the field of computer functions (see MPEP 2106.05(d)(II)(i)). Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362).
a management module to (e.g., mere instructions to apply, see MPEP 2106.05(f)).
Step 2B: Considering the additional elements individually and in combination, and the claim as a whole, the additional elements do not provide significantly more than the abstract idea. Therefore, the claim is not patent eligible.
Regarding claim 2,
Claim 2 incorporates the analysis of the machine of claim 1.
Step 2A, prong 1:
detect differences between the current prediction algorithm definition data set associated to the area of interest with a further prediction algorithm definition data set associated to a another area of interest or to preceding acquisition time; (limitation is directed to a mental process, One can mentally process the further prediction algorithm by use of pen and paper to process or detect presence of insects in an area.).
update the current mathematical prediction algorithm by employing the further prediction algorithm definition data set. (limitation is directed to a mental process, One can mentally process the mathematical prediction algorithm by use of pen and paper to update the algorithm.)
Step 2A, prong 1: yes.
Step 2A, prong 2:
an updating and configuring module configured to define a current mathematical prediction
algorithm by defining a current prediction algorithm definition data set comprising: a prediction model typology, algorithm configuration values, algorithm variable types; and (e.g., mere instructions to apply, see MPEP 2106.05(f)).
a difference matching module configured to (e.g., mere instructions to apply, see MPEP 2106.05(f)).
wherein the updating and configuring module is further configured to (e.g., mere instructions to apply, see MPEP 2106.05(f)).
Step 2A, prong 2: no.
Step 2B:
an updating and configuring module configured to define a current mathematical prediction
algorithm by defining a current prediction algorithm definition data set comprising: a prediction model typology, algorithm configuration values, algorithm variable types; and (e.g., mere instructions to apply, see MPEP 2106.05(f)).
a difference matching module configured to (e.g., mere instructions to apply, see MPEP 2106.05(f)).
wherein the updating and configuring module is further configured to (e.g., mere instructions to apply, see MPEP 2106.05(f)).
Step 2B: Since the claim does not recite additional elements that either integrate the judicial exception into a practical application, nor provide significantly more than the judicial exception, the claim is not patent eligible.
Regarding claim 3,
Claim 3 incorporates the analysis of the machine of claim 1.
Step 2A, Prong 2:
process the at least one insect image and extract entomological measured parameters; and (limitation is directed to a mental process, One can mentally process the insect image and extracted parameters by use of pen and paper to process or detect the insect.).
identify an insect from the extract entomological measured parameters and provide the presence value. (limitation is directed to a mental process, One can mentally process the extracted parmeters by use of pen and paper to process or detect presence of insects in an area.).
Step 2A, Prong 2 and Step 2B:
a visual computer algorithm configured to (e.g., mere instructions to apply, see MPEP 2106.05(f)).
an insect classification algorithm configured to (e.g., mere instructions to apply, see MPEP 2106.05(f)).
Since the claim does not recite additional elements that either integrate the judicial exception into a practical application, nor provide significantly more than the judicial exception, the claim is not patent eligible.
Regarding claim 4,
Claim 4 incorporates the analysis of the machine of claim 1.
Step 2A, Prong 2:
the meteorological data are selected from the following quantities: temperature, humidity,
pressure, moisture level, and leaf hygrometer; (e.g., generally linking the use of a judicial exception to a particular technological environment or field of use.)
the environmental data are selected from the following parameters: quality of air, carbon
dioxide CO2 concentration, carbon monoxide CO concentration, Volatile Organic Compounds
concentration, ammonia concentration, luminosity, sound presence, sound level, long terms seasonal
time, and presence of pesticide; and (e.g., generally linking the use of a judicial exception to a particular technological environment or field of use.)
the historical data for insect presence include data on insect attacks to the area of interest occurred before a current period of time submitted to the risk prediction. (e.g., generally linking the use of a judicial exception to a particular technological environment or field of use.)
Step 2B:
the meteorological data are selected from the following quantities: temperature, humidity,
pressure, moisture level, and leaf hygrometer; (e.g., generally linking the use of a judicial exception to a particular technological environment or field of use.)
the environmental data are selected from the following parameters: quality of air, carbon
dioxide CO2 concentration, carbon monoxide CO concentration, Volatile Organic Compounds
concentration, ammonia concentration, luminosity, sound presence, sound level, long terms seasonal
time, and presence of pesticide; and (e.g., generally linking the use of a judicial exception to a particular technological environment or field of use.)
the historical data for insect presence include data on insect attacks to the area of interest occurred before a current period of time submitted to the risk prediction. (e.g., generally linking the use of a judicial exception to a particular technological environment or field of use.)
Since the claim does not recite additional elements that either integrate the judicial exception into a practical application, nor provide significantly more than the judicial exception, the claim is not patent eligible.
Regarding claim 5,
Claim 5 incorporates the analysis of the machine of claim 1.
wherein the mathematical prediction algorithm and the insect classification algorithm are
algorithms selected from the group consisting of: neural network based model, and non-neural network
based model. (limitation is directed to a mental process (i.e. “judgement”), One can mentally process the selection of the models by use of pen and paper to process or detect the mathematical prediction algorithm.).
Since the claim does not recite additional elements that either integrate the judicial exception into a practical application, nor provide significantly more than the judicial exception, the claim is not patent eligible
Regarding claim 6,
Claim 6 incorporates the analysis of the machine of claim 1.
wherein the plurality of software modules further comprise: a local knowledge module structured to store a current insect behavioural knowledge data set based on a value set assumed by at least one of the following set: entomologic parameters, meteorological quantities, environmental quantities and corresponding insect identified species. (e.g., mere data gathering or data output), see MPEP 2106.05(g)). (receiving or transmitting data in a network, using components and functions claimed at a high level of generality have been determined by the courts as being well-understood, routine and conventional activities in the field of computer functions (see MPEP 2106.05(d)(II)(i)).
Since the claim does not recite additional elements that either integrate the judicial exception into a practical application, nor provide significantly more than the judicial exception, the claim is not patent eligible
Regarding claim 7,
Claim 7 incorporates the analysis of the machine of claim 3.
Step 2A, Prong 2:
said insect classification algorithm is based on a current insect behavioural knowledge data set; and (e.g., generally linking the use of a judicial exception to a particular technological environment or field of use.)
wherein the plurality of software modules comprise an updating module configured to said replace the current insect behavioural knowledge data set with an updated insect behavioural knowledge data set. (e.g., insignificant extra solution activity of mere data gathering or data output), see MPEP 2106.05(g)).
Step 2B:
said insect classification algorithm is based on a current insect behavioural knowledge data set; and (e.g., generally linking the use of a judicial exception to a particular technological environment or field of use.)
wherein the plurality of software modules comprise an updating module configured to said replace the current insect behavioural knowledge data set with an updated insect behavioural knowledge data set. (e.g., insignificant extra solution activity of mere data gathering or data output), see MPEP 2106.05(g)). (receiving or transmitting data in a network, using components and functions claimed at a high level of generality have been determined by the courts as being well-understood, routine and conventional activities in the field of computer functions (see MPEP 2106.05(d)(II)(i)).
Since the claim does not recite additional elements that either integrate the judicial exception into a practical application, nor provide significantly more than the judicial exception, the claim is not patent eligible.
Regarding claim 8,
Claim 8 incorporates the analysis of the machine of claim 6.
Step 2A, prong 1:
detect differences between the current insect behavioural knowledge data set associated to the
area of interest with a further insect behavioural knowledge data set associated to another area of
interest or to preceding acquisition time; and (limitation is directed to a mental process (i.e.
“evaluation”), fidning differences between behavioural knowledge data sets is laid out with pen and paper.).
Step 2A, prong 1: yes.
Step 2A, prong 2:
replace the current insect behavioural knowledge data set with the further insect behavioural
knowledge data set in connection with said area of interest. (e.g., insignificant extra solution activity of mere data gathering or data output)
see MPEP 2106.05(g)).
Step 2A, prong 2: no.
Step 2B:
replace the current insect behavioural knowledge data set with the further insect behavioural
knowledge data set in connection with said area of interest. (receiving or transmitting data in a network,
using components and functions claimed at a high level of generality have been determined by the courts as being well-understood, routine and conventional activities in the field of computer functions (see MPEP 2106.05(d)(II)(i)).
Step 2B: no.
Since the claim does not recite additional elements that either integrate the judicial exception into a practical application, nor provide significantly more than the judicial exception, the claim is not patent eligible
Regarding claim 9,
Claim 9 incorporates the analysis of the machine of claim 5.
the non-neural network based model is logistic regression; (limitation is directed to a
mathematical concept).
the neural network based model is selected from the group consisting of: Convolutional Neural
Network, and Deep Neural Network. (limitation is directed to a mental process (i.e. “judgement”), selecting a neural network based model can be performed in the human mind One can mentally process the selection of a neural network by use of pen and paper to process or detect the type of model).
Since the claim does not recite additional elements that either integrate the judicial exception into a practical application, nor provide significantly more than the judicial exception, the claim is not patent eligible
Regarding claim 10,
Step 1: Is the claim to a process, machine, manufacture or composition of matter?
Claim 10 is directed to a process.
Step 1: yes.
Step 2A, prong 1: Does the claim recite an abstract idea, law of nature, or natural phenomenon?
processing the at least one insect digital image (IM) at a sampling rate to provide a presence value (IPD), representing the presence of insects in the area of interest for insect attack; (limitation is directed to a mental process, One can mentally process sampling rate by use of pen and paper to process or detect presence of the insect.)
processing the presence value (IPD) and the insect behavioural data according to a mathematical prediction algorithm to estimate a risk of attack (PRB) to the area of interest; and (limitation is directed to a mental process, One can mentally process the mathematical prediction algorithm by use of pen and paper to process or detect the presence value and insect behavioural data.)
controlling the sampling rate based on at least one of a comparison between the historical data of insect presence and the presence value (IPD), a comparison between the meteorological data and predetermined anomaly levels of meteorological data, and a comparison between the environmental data and predetermined anomaly levels of environmental data. (limitation is directed to a mental process, One can mentally process sampling rate using comparison by use of pen and paper to process or detect the historical data and presence value.)
Step 2A, prong 1: yes.
Step 2A, prong 2: Does the claim recite additional elements that integrate the judicial exception
into a practical application?
providing at least one trap device with a housing having an inner chamber that carries a pheromone device and at least one immobilization device, the inner chamber having at least one opening that extends from the inner chamber to an external environment; (e.g., generally linking the use of a judicial exception to a particular technological environment or field of use, see MPEP 2106.05(h)).
positioning the at least one trap device within an area of interest; (e.g., generally linking the use of a judicial exception to a particular technological environment or field of use, see MPEP 2106.05(h)).
releasing substances with the pheromone device to attract insects to the immobilization device;(e.g., generally linking the use of a judicial exception to a particular technological environment or field of use, see MPEP 2106.05(h)).
capturing insects with the immobilization device; (e.g., generally linking the use of a judicial exception to a particular technological environment or field of use, see MPEP 2106.05(h)).
capturing at least one insect digital image (IM) of the insects that are captured by the immobilization device; (e.g., insignificant extra solution activity of mere data gathering or data output), see MPEP 2106.05(g)).
detecting at least one meteorological quantity to provide meteorological data; (e.g., insignificant extra solution activity of mere data gathering or data output), see MPEP 2106.05(g)).
detecting at least one environmental quantity to provide environmental data; (e.g., insignificant extra solution activity of mere data gathering or data output), see MPEP 2106.05(g)).
acquiring insect behavioural data associated to said area, the insect behavioural data comprising the meteorological data, the environmental data, and historical data of insect presence; (e.g., insignificant extra solution activity of mere data gathering or data output), see MPEP 2106.05(g)).
providing maintenance information and maintenance notifications by correlating the insect behavioural data and the presence value (IPD) to at least one of the pheromone device, the at least one immobilization device, and the substances released by the pheromone device; and (e.g., insignificant extra solution activity of mere data gathering or data output), see MPEP 2106.05(g)).
producing as output said risk of attack (PRB) to the area of interest; (e.g., insignificant extra solution activity of mere data gathering or data output), see MPEP 2106.05(g)).
Step 2A, prong 2: no
Step 2B: Does the claim recite additional elements that amount to significantly more than the
judicial exception?
providing at least one trap device with a housing having an inner chamber that carries a pheromone device and at least one immobilization device, the inner chamber having at least one opening that extends from the inner chamber to an external environment; (e.g., generally linking the use of a judicial exception to a particular technological environment or field of use, see MPEP 2106.05(h)).
positioning the at least one trap device within an area of interest; (e.g., generally linking the use of a judicial exception to a particular technological environment or field of use, see MPEP 2106.05(h)).
releasing substances with the pheromone device to attract insects to the immobilization device;(e.g., generally linking the use of a judicial exception to a particular technological environment or field of use, see MPEP 2106.05(h)).
capturing insects with the immobilization device; (e.g., generally linking the use of a judicial exception to a particular technological environment or field of use, see MPEP 2106.05(h)).
capturing at least one insect digital image (IM) of the insects that are captured by the immobilization device; (e.g., insignificant extra solution activity of mere data gathering or data output), see MPEP 2106.05(g), using components and functions claimed at a high level of generality have been determined by the courts as being well-understood, routine and conventional activities in the field of computer functions (see MPEP 2106.05(d)(II)(i)). Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362).
detecting at least one meteorological quantity to provide meteorological data; (e.g., insignificant extra solution activity of mere data gathering or data output), see MPEP 2106.05(g), using components and functions claimed at a high level of generality have been determined by the courts as being well-understood, routine and conventional activities in the field of computer functions (see MPEP 2106.05(d)(II)(i)). Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362).
detecting at least one environmental quantity to provide environmental data; (e.g., insignificant extra solution activity of mere data gathering or data output), see MPEP 2106.05(g), using components and functions claimed at a high level of generality have been determined by the courts as being well-understood, routine and conventional activities in the field of computer functions (see MPEP 2106.05(d)(II)(i)). Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362).
acquiring insect behavioural data associated to said area, the insect behavioural data comprising the meteorological data, the environmental data, and historical data of insect presence(e.g., insignificant extra solution activity of mere data gathering or data output), see MPEP 2106.05(g), using components and functions claimed at a high level of generality have been determined by the courts as being well-understood, routine and conventional activities in the field of computer functions (see MPEP 2106.05(d)(II)(i)). Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362).
providing maintenance information and maintenance notifications by correlating the insect behavioural data and the presence value (IPD) to at least one of the pheromone device, the at least one immobilization device, and the substances released by the pheromone device; and (e.g., insignificant extra solution activity of mere data gathering or data output), see MPEP 2106.05(g), using components and functions claimed at a high level of generality have been determined by the courts as being well-understood, routine and conventional activities in the field of computer functions (see MPEP 2106.05(d)(II)(i)). Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362).
producing as output said risk of attack (PRB) to the area of interest; (e.g., insignificant extra solution activity of mere data gathering or data output), see MPEP 2106.05(g), using components and functions claimed at a high level of generality have been determined by the courts as being well-understood, routine and conventional activities in the field of computer functions (see MPEP 2106.05(d)(II)(i)). Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362).
Step 2B: no.
Since the claim does not recite additional elements that either integrate the judicial exception into a practical application, nor provide significantly more than the judicial exception, the claim is not patent eligible
Regarding claim 11,
Claim 11 incorporates the analysis of the machine of claim 1.
Step 2, Prong 2:
wherein the prediction module (e.g., “apply it”, (see MPEP 2106.05(f)).
comprises an alerting module to generate an alerting signal based on the risk of attack (PRB) to
the area of interest. (e.g., insignificant extra solution activity of mere data gathering or data output), see MPEP 2106.05(g)).
Step 2B:
wherein the prediction module (e.g., “apply it”, (see MPEP 2106.05(f)).
comprises an alerting module to generate an alerting signal based on the risk of attack (PRB) to
the area of interest. (e.g., insignificant extra solution activity of mere data gathering or data output), see MPEP 2106.05(g), using components and functions claimed at a high level of generality have been determined by the courts as being well-understood, routine and conventional activities in the field of computer functions (see MPEP 2106.05(d)(II)(i)). Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362).
Since the claim does not recite additional elements that either integrate the judicial exception into a practical application, nor provide significantly more than the judicial exception, the claim is not patent eligible.
Regarding claim 13,
Claim 13 incorporates the analysis of the machine of claim 11.
Step 2A, Prong 2:
wherein the alerting module communicates the alerting signal to at least one external
communication device. (e.g., generally linking the use of a judicial exception to a particular technological environment or field of use, (see MPEP 2106.05(e)).
Step 2B:
wherein the alerting module communicates the alerting signal to at least one external
communication device. (e.g., generally linking the use of a judicial exception to a particular technological environment or field of use, (see MPEP 2106.05(e)).
Since the claim does not recite additional elements that either integrate the judicial exception into a practical application, nor provide significantly more than the judicial exception, the claim is not patent eligible
Regarding claim 16 and analogous claim 18,
Claim 16 incorporates the analysis of the machine of claim 1.
Step 2A, Prong 2:
wherein the sampling rate includes a frequency having an original frequency; and (e.g., mere instructions to apply using generic computer components, see MPEP 2106.05(f)).
wherein the management module changes the frequency by changing the original frequency to an increased frequency upon the presence value (IPD) being significantly greater than the historical data of insect presence. (e.g., mere instructions to apply using generic computer components, see MPEP 2106.05(f)).
Step 2B:
wherein the sampling rate includes a frequency having an original frequency; and (e.g., mere instructions to apply using generic computer components, see MPEP 2106.05(f)).
wherein the management module changes the frequency by changing the original frequency to an increased frequency upon the presence value (IPD) being significantly greater than the historical data of insect presence. (e.g., mere instructions to apply using generic computer components, see MPEP 2106.05(f)).
Since the claim does not recite additional elements that either integrate the judicial exception into a practical application, nor provide significantly more than the judicial exception, the claim is not patent eligible
Regarding claim 17 and analogous claim 19,
Claim 17 incorporates the analysis of the machine of claim 16.
wherein the management module changes the frequency of the sampling rate by changing the increased frequency to the original frequency upon at least one of the meteorological data having a level within the predetermined anomaly levels of meteorological data and the environmental data having a level within the predetermined anomaly levels of environmental data. (under step2A prong II and step 2B e.g., mere instructions to apply using generic computer components, see MPEP 2106.05(f)).
Since the claim does not recite additional elements that either integrate the judicial exception into a practical application, nor provide significantly more than the judicial exception, the claim is not patent eligible
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or non-obviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1-14 and 16-19 are rejected under 35 U.S.C. 103 as being unpatentable over Yan et al (Multiple Regression and Artificial Neural Network for the Prediction of Crop Pest Risks, "Yan"), in view of Xia et al (Insect Detection and Classification Based on an Improved Convolutional Neural Network, "Xia"), Hopper (RISK-SPREADING AND BET-HEDGING IN INSECT POPULATION BIOLOGY) and Eliopoulos et al (A "Smart" Trap Device for Detection of Crawling Insects and Other Arthropods in Urban Environments, "Eliopoulos") and Arbogast et al (Monitoring Insect Pests in Retail Stores by Trapping and Spatial Analysis, “Arbogast”).
In regard to claim 1, Yan teaches a meteorological sensor to detect and provide meteorological data; and (Yan, pg. 77, paragraph 1, “According to prior studies, meteorological variables including monthly mean temperature, monthly mean maximum temperature, monthly mean minimum temperature, monthly mean relative humidity, monthly precipitation amount, and monthly mean wind speed, for both the prediction months and one month prior to the prediction months [meteorological data;], are potential factors affecting development of the pests [16–19].”)
a data collecting module configured to acquire insect behavioural data associated to said area of interest and comprises the meteorological data, environmental data, and historical data of insect presence; (Yan, pg. 77, paragraph 1, “MR models were built to predict monthly T. palmi and monthly P. xylostella populations (dependent variables), which could be used to determine pest risk level and degree of emergency. According to prior studies, meteorological variables including monthly mean temperature, monthly mean maximum temperature [meteorological data;],, monthly mean minimum temperature, monthly mean relative humidity, monthly precipitation amount [environmental data], and monthly mean wind speed, for both the prediction months and one month prior to the prediction months [historical data, one month prior to show development of insects is interpreted as historical data as it is from the past] are potential factors affecting development of the pests [16–19].”)
a prediction module configured to process the presence value (IPD) and the insect behavioural data according to a mathematical prediction algorithm to estimate a risk of attack (PRB) to the area of interest, the prediction module being configured to provide risk of attack (PRB) as output; (Yan, pg. 78-79, 5.1 MR Models, “The fitted MR models for the T. palmi and the T. xylostella are shown by the following equations, respectively. In each of the following equations, some of the 12 independent variables were excluded as they were not statistically significantly related to the given dependent variable according to the stepwise regression… where ZMTmax is the Z-score transformed monthly mean maximum temperature, ZMTmean is the Z-score transformed monthly mean temperature, ZMPA is the Z-score transformed monthly precipitation amount, r denotes the month for prediction, r-1 denotes one month before the prediction month r [a mathematical prediction algorithm to estimate a risk of attack (PRB) to the area of interests.]. All the regression assumptions were satisfied, and the constants and coefficients of the equations were significant at a 0.05 confidence interval. The overall fit of the regression models were significant (F = 16.67, P < 0.00001 for (3); F = 10.24, P = 0.02 for (4)) to provide risk of attack (PRB) as output.]. The correlation of determination R2 for (3) was 0.294 with ZMTmean(r) accounting for 48.3 %, ZMTmean(r) accounting for 43.5 %, and ZMPA(r-1) accounting for 8.2 % of the variation of the monthly T. Palmi population, according to the stepwise regression analysis. [presence value (IPD) and the insect behavioural data]”)
Yan does not explicitly teach at least one trap device to be positioned within an area of interest, the at least one trap device including:
a housing having an inner chamber and at least one opening that extends from the inner chamber to an external environment;
a pheromone device to release substances that are configured to attract the insects to the inner chamber; and
at least one immobilization device positioned within the inner chamber and configured to capture insects;
at least one sensing apparatus housed by the at least one trap device, the at least one sensing apparatus comprising:
a digital camera;
an environmental sensor to detect and provide environmental data;
wherein the digital camera is oriented within the housing to capture and provide at least one insect digital image of the insects that are captured by the immobilization device;
an insect identification module configured to process the at least one insect digital image (IM) to provide a presence value (IPD), representing the presence of insects in the area of interest for insect attack;
a maintenance module to provide maintenance information and maintenance notifications by correlating at least one of the insect behavioural data and the presence value (IPD) to at least one of the pheromone device, the at least one immobilization device, and the substances released by the pheromone device; and
a management module to determine and control the sampling rate based on at least one of a comparison between the historical data of insect presence and the presence value (IPD), a comparison between the meteorological data and predetermined anomaly levels of meteorological data, and a comparison between the environmental data and predetermined anomaly levels of environmental data.
Xia teaches an insect identification module configured to process the at least one insect digital image (IM) at a sampling rate to provide a presence value (IPD), representing the presence of insects in the area of interest for insect attack; (Xia, pg. 3, paragraph 1, “Due to the small size of Xie’s data set, collecting new images was required. The authors manually collected images by search engines, such as Baidu and Google, where similar images were extracted manually. Table 1 lists the information of insect species including Xie’s data set and those collected from crop fields and the Internet. Following exclusion of some images with errors and low quality, 660 images [at least one insect digital image (IM) to provide a presence value (IPD)] were used in this work, where 60 images were randomly selected for the test data set and the remaining 540 images for the training one.” and paragraph 2, “Moreover, to avoid over-fitting of this model, data augmentation was performed on the training data set to increase the number of training samples. Bilinear interpolation [21] was adopted to fix images to the pixel size of 450 _ 750, and all images were then rotated at 90_, 180_, 270_ angles.”)
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention, having the teachings of Yan and Xia before them, to include Xia’s insect image detection in Yan’s system of neural networks for predicting pest risks. One would have been motivated to make such a combination in order to extract features necessary from the insect. (Xia Abstract, “The model can make full use of the advantages of the neural network to comprehensively extract multifaceted insect features.”)
However, Yan and Xia do not explicitly teach at least one trap device to be positioned within an area of interest, the at least one trap device including:
a housing having an inner chamber and at least one opening that extends from the inner chamber to an external environment;
a pheromone device to release substances that are configured to attract the insects to the inner chamber; and
at least one immobilization device positioned within the inner chamber and configured to capture insects;
at least one sensing apparatus housed by the at least one trap device, the at least one sensing apparatus comprising:
a digital camera;
an environmental sensor to detect and provide environmental data;
wherein the digital camera is oriented within the housing to capture and provide at least one insect digital image of the insects that are captured by the immobilization device;
a maintenance module to provide maintenance information and maintenance notifications by correlating at least one of the insect behavioural data and the presence value (IPD) to at least one of the pheromone device, the at least one immobilization device, and the substances released by the pheromone device; and
a management module to determine and control the sampling rate based on at least one of a comparison between the historical data of insect presence and the presence value (IPD), a comparison between the meteorological data and predetermined anomaly levels of meteorological data, and a comparison between the environmental data and predetermined anomaly levels of environmental data.
Hopper teaches an environmental sensor to detect and provide environmental data; (Hopper, pg. 538, paragraph 1, “An example will help clarify this point. Suppose an insect species lives in an environment that fluctuates between either a short, cool growth season or a long, hot growth season [long terms seasonal time,]. Let us assume there are three genotypes: one that does well in short seasons, another that does well in long seasons, and a third that produces both phenotypes in proportions that match the frequencies of each type of season (Figure 1).”)
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention, having the teachings of Yan, Xia and Hopper before them, to include Hopper’s insect risk-spreading in Yan and Xia’s system of neural networks for predicting pest risks and image detection. One would have been motivated to make such a combination in order to develop detailed predictions of risk-spreading. (Hopper, Abstract, “Although risk-spreading theory is voluminous and well developed in some ways, rarely has it been used to generate detailed, testable hypotheses about the evolution of risk-spreading.”)
However, Yan, Xia and Hopper do not explicitly teach at least one trap device to be positioned within an area of interest, the at least one trap device including:
a housing having an inner chamber and at least one opening that extends from the inner chamber to an external environment;
a pheromone device to release substances that are configured to attract the insects to the inner chamber; and
at least one immobilization device positioned within the inner chamber and configured to capture insects;
at least one sensing apparatus housed by the at least one trap device, the at least one sensing apparatus comprising:
a digital camera;
wherein the digital camera is oriented within the housing to capture and provide at least one insect digital image of the insects that are captured by the immobilization device;
a maintenance module to provide maintenance information and maintenance notifications by correlating at least one of the insect behavioural data and the presence value (IPD) to at least one of the pheromone device, the at least one immobilization device, and the substances released by the pheromone device; and
a management module to determine and control the sampling rate based on at least one of a comparison between the historical data of insect presence and the presence value (IPD), a comparison between the meteorological data and predetermined anomaly levels of meteorological data, and a comparison between the environmental data and predetermined anomaly levels of environmental data.
Eliopoulos teaches at least one trap device to be positioned within an area of interest, the at least one trap device including: (Eliopoulos, pg. 3, paragraph 2, “This box-shaped device [one trap device] with dimensions of 21 cm _ 11 cm _ 7.5 cm, including the plastic box and the attached electronic kit (Figure 1) attracts insect pests, senses the entering insect and takes automatically a picture of the internal space of the box. The picture is communicated through the Wi-Fi commonly found in such establishments to an authorized person/stakeholder receiving the picture to take proper action. In this way, continuous, accurate and verifiable, real-time detection is achieved, without the need for human intervention. It is a monitoring device for urban pests in the context of smart homes and smart cities [an area of interest], and is compatible with the emerging discipline of the Internet of Things (IoT) (see Figure 2 for the way we envision its application).”)
a housing having an inner chamber and at least one opening that extends from the inner chamber to an external environment; (Eliopoulos, pg. 3, paragraph 2, “This box-shaped device with dimensions of 21 cm _ 11 cm _ 7.5 cm, including the plastic box and the attached electronic kit (Figure 1) attracts insect pests, senses the entering insect and takes automatically a picture of the internal space of the box [an inner chamber]. The picture is communicated through the Wi-Fi commonly found in such establishments to an authorized person/stakeholder receiving the picture to take proper action. In this way, continuous, accurate and verifiable, real-time detection is achieved, without the need for human intervention. It is a monitoring device for urban pests in the context of smart homes and smart cities, and is compatible with the emerging discipline of the Internet of Things (IoT) (see Figure 2 for the way we envision its application).”)
a pheromone device to release substances that are configured to attract the insects to the inner chamber; and (Eliopoulos, pg. 3, paragraph 3 and 4, “Our smart trap functions like a classic floor trap. Traps may differ greatly in design features (shape, size, surface material, etc.) and the presence of attractants (food, pheromone) [a pheromone device]. All these factors along with placement method influence dramatically the trap efficacy [30].
The box includes a strong attractant to attract the insects and maximize captures. The insect is trapped on a sticky surface that is inside the device, basically a cardboard coated with special insect glue (Tangle-Trap® Sticky Coatings, Tanglefoot, Marysville, Washington, DC, USA) that lasts (remains sticky) for 4–6 months. The sticky floor provides the means for immediate verification of reported results. Captured insects frequently release additional pheromone by themselves and increase the attractiveness. The presence of multiple attractants targeting different insect species simultaneously is also possible and recommended.”)
at least one immobilization device positioned within the inner chamber and configured to capture insects; (Eliopoulos, pg. 3, paragraph 3 and 4, “Our smart trap functions like a classic floor trap. Traps may differ greatly in design features (shape, size, surface material, etc.) and the presence of attractants (food, pheromone). All these factors along with placement method influence dramatically the trap efficacy [30].
The box includes a strong attractant to attract the insects and maximize captures. The insect is trapped on a sticky surface that is inside the device [immobilization device], basically a cardboard coated with special insect glue (Tangle-Trap® Sticky Coatings, Tanglefoot, Marysville, Washington, DC, USA) that lasts (remains sticky) for 4–6 months. The sticky floor provides the means for immediate verification of reported results. Captured insects frequently release additional pheromone by themselves and increase the attractiveness. The presence of multiple attractants targeting different insect species simultaneously is also possible and recommended.”)
at least one sensing apparatus housed by the at least one trap device, the at least one sensing apparatus comprising: (Eliopoulos, pg. 4, paragraph 1, “After 20 s a photograph (Camera OV2640, cmos sensor camera module, Omnivision, Santa Clara, CA, USA) of the internal volume of the trap is taken (Figure 3), time-stamped and delivered to pre-stored mail addresses while a copy of the picture is stored internally in the SD card of the device. The time delay ensures that is given enough time to the insect that follows the chemical signals of the bait to crawl inside. The crossing of the laser beam [at least one sensing apparatus] by an insect effects a voltage drop in the receiver’s amplifier. The drop is analogous to the size of the insect. A minimum and maximum threshold is set during monitoring of the voltage drop based on inactive, targeted pests. For example, when we need to monitor cockroaches we do not want the system to be triggered by the accidental entrance of an ant. Note that a random entrance is uncommon as targeted insects enter the trap because the follow their corresponding pheromones.”)
a digital camera; (Eliopoulos, pg. 4, paragraph 1, “After r 20 s a photograph (Camera OV2640, cmos sensor camera module, Omnivision, Santa Clara, CA, USA) of the internal volume of the trap is taken (Figure 3), time-stamped and delivered to prestored mail addresses while a copy of the picture is stored internally in the SD card of the device.”)
wherein the digital camera is oriented within the housing to capture and provide at least one insect digital image of the insects that are captured by the immobilization device; (Eliopoulos, pg. 4, paragraph 1, “After 20 s a photograph (Camera OV2640, cmos sensor camera module, Omnivision, Santa Clara, CA, USA) of the internal volume of the trap is taken [digital camera is oriented within the housing] (Figure 3), time-stamped and delivered to pre-stored mail addresses while a copy of the picture is stored internally in the SD card of the device. The time delay ensures that is given enough time to the insect that follows the chemical signals of the bait to crawl inside. The crossing of the laser beam by an insect effects a voltage drop in the receiver’s amplifier. The drop is analogous to the size of the insect. A minimum and maximum threshold is set during monitoring of the voltage drop based on inactive, targeted pests. For example, when we need to monitor cockroaches we do not want the system to be triggered by the accidental entrance of an ant. Note that a random entrance is uncommon as targeted insects enter the trap because the follow their corresponding pheromones.”)
a maintenance module to provide maintenance information and maintenance notifications by correlating at least one of the insect behavioural data and the presence value (IPD) to at least one of the pheromone device, the at least one immobilization device, and the substances released by the pheromone device; and (Eliopoulos, pg. 4, paragraph 1, “After 20 s a photograph (Camera OV2640, cmos sensor camera module, Omnivision, Santa Clara, CA, USA) of the internal volume of the trap is taken (Figure 3), time-stamped and delivered to pre-stored mail addresses while a copy of the picture is stored internally in the SD card of the device. The time delay ensures that is given enough time to the insect that follows the chemical signals of the bait to crawl inside. The crossing of the laser beam by an insect effects a voltage drop in the receiver’s amplifier. The drop is analogous to the size of the insect. A minimum and maximum threshold is set during monitoring of the voltage drop based on inactive, targeted pests. For example, when we need to monitor cockroaches we do not want the system to be triggered by the accidental entrance of an ant. Note that a random entrance is uncommon as targeted insects enter the trap because the follow their corresponding pheromones [substances released by the pheromone device]. Similarly, we monitor the time delay of the entering event based on marking the onset and the end of an entering event [insect behavioural data and the presence value (IPD)]. A long delay is atypical for an insect movement and this initiates a possible malfunction notice sent through the Wi-Fi (e.g., an insect blocking the entrance). The speed of the entering pest is also calculated, and atypical speeds are rejected as false alarms.”)
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention, having the teachings of Yan, Xia, Hopper and Eliopoulos before them, to include Eliopoulos’ insect detector in Yan, Xia and Hopper’s system of neural networks for predicting pest risks and image detection. One would have been motivated to make such a combination in order to capture and transmit images of insects to user. (Eliopoulos, abstract, “This triggers a detection event; a picture is taken, and a time-stamp is set before delivering the picture through the Wi-Fi to an authorized person/stakeholder.”)
However, Yan, Xia, Hopper and Eliopoulos does not explicitly teach a management module to determine and control the sampling rate based on at least one of a comparison between the historical data of insect presence and the presence value (IPD), a comparison between the meteorological data and predetermined anomaly levels of meteorological data, and a comparison between the environmental data and predetermined anomaly levels of environmental data.
However Arbogast teaches a management module to determine and control the sampling rate based on at least one of a comparison between the historical data of insect presence and the presence value (IPD), a comparison between the meteorological data and predetermined anomaly levels of meteorological data, and a comparison between the environmental data and predetermined anomaly levels of environmental data. (Arbogast, pg. 1534, Col. 2, paragraph 1 and 2, “Traps that captured the same number of insects were grouped together, and the highest cumulative frequency in the group was assigned to all traps in the group (Table 1) [determine and control the sampling rate]. The assigned cumulative frequency (fi9) [frequency having an original frequency] for any trap, thus indicates the proportion of the total catch represented by the combined catch of traps with an equal or greater number of insects. It estimates the probability that any one trap will capture an equal or greater number of insects, given the size and spatial distribution of the population [between the environmental data and predetermined anomaly levels of environmental data.].
If we assume that the spatial distribution of trap catch respects the spatial distribution of the insect population, we can then use the cumulative frequency distributions derived from trap samples (Table 1) to define areas in which action thresholds for pest management are exceeded [the historical data of insect presence and the presence value (IPD),]. A threshold can be either an insect count typically associated with the maximum tolerable level of damage or contamination (trap threshold), or it can be a proportion of the pest population that must be suppressed [the historical data of insect presence] (Brenner et al. 1998).”)
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention, having the teachings of Yan, Xia, Hopper, Eliopoulos and Arbogast before them, to include Arbogast’s trapping and special analysis system in Yan, Xia, Hopper and Eliopoulos’s system of neural networks for predicting pest risks and image detection. One would have been motivated to make such a combination in order to estimate how many insects will be captured. ((Arbogast, pg. 1534, Col. 2, paragraph 1, “It estimates the probability that any one trap will capture an equal or greater number of insects, given the size and spatial distribution of the population.”)
In regard to claim 2, Yan, Xai, Hopper, Eliopoulos and Arbogast teach the system of claim 1.
Yan further teaches an updating and configuring module configured to define a current mathematical prediction algorithm by defining a current prediction algorithm definition data set comprising: a prediction model typology, algorithm configuration values, algorithm variable types; and (Yan, pg. 78, 4.2 ANN Models, “Individual BP-ANN models [a prediction model typology], one for each pest species, were built to also predict the monthly pest populations, using the same set of independent variables considered in the MR. Data outliers were also removed to improve training accuracy. Given the self-learning capability of ANN, all the independent variables [algorithm variable types] were included in training the BP-ANN models without mathematical transformation. Data collected before 2012 were used as training dataset [algorithm configuration values], of which 20 % were randomly selected and used as an independent set of data records for tracking the errors leading to over-fitting. Data collected in 2012 were used as test dataset.”)
a difference matching module configured to detect differences between the current prediction algorithm definition data set associated to the area of interest with a further prediction algorithm definition data set associated to a another area of interest or to preceding acquisition time; (Yan, pg. 75, 3.1 Multiple Regression, paragraph 1, “Normally, β0 and β1, β2, …, βn need to be derived by the procedure of ordinary least squares. is the random error, which is the difference [detect differences] between desired outputs (i.e., observed values) [to the area of interest with a further prediction algorithm definition data set] and actual outputs (i.e., predicted values) [the current prediction algorithm definition data set] not accounted for by the model. When the regression expression is applied to predictive mode, ε is omitted because its mathematical expectation is zero.”)
wherein the updating and configuring module is further configured to update the current mathematical prediction algorithm by employing the further prediction algorithm definition data set. (Yan, pg. 76, paragraph 1, “The basic idea of BP-ANN is to minimize the error through iterative backward propagation of error signals, and adjusting the connection weights recurrently until the cost function is minimized (i.e., one consecutive step with no decrease in error).”)
In regard to claim 3, Yan, Xai, Hopper, Eliopoulos and Arbogast teach the system of claim 1.
Yan further teaches extract entomological measured parameters; and (Yan, pg. 77, “MR models were built to predict monthly T. palmi and monthly P. xylostella populations (dependent variables), which could be used to determine pest risk level and degree of emergency. According to prior studies, meteorological variables including monthly mean temperature, monthly mean maximum temperature, monthly mean minimum temperature, monthly mean relative humidity, monthly precipitation amount, and monthly mean wind speed, for both the prediction months and one month prior to the prediction months, are potential factors affecting development of the pests [16–19].”)
Xia further teaches a visual computer algorithm configured to process the at least one insect image (Xia, pg.3, “Following exclusion of some images with errors and low quality, 660 images were used in this work, where 60 images were randomly selected for the test data set and the remaining 540 images for the training one.”)
an insect classification algorithm configured to identify an insect from the extract entomological measured parameters and provide the presence value. (Xia, pg. 3, “Moreover, to avoid over-fitting of this model, data augmentation was performed on the training data set to increase the number of training samples. Bilinear interpolation [21] was adopted to fix images to the pixel size of 450 _ 750, and all images were then rotated at 90_, 180_, 270_ angles. Salt and Pepper Noise [22] was also added to the images to ensure the validity of data, which randomly changes pixel values in the images, whitening some pixel points and blackening some other pixel points. As a result, these techniques expanded the number of training samples to eight times the original ones. Meanwhile, an annotation file containing bounding boxes and the categories of each insect were generated for each image.”)
Yan and Xai are combinable for the same rationale as set forth above with respect to claim 1.
In regard to claim 4, Yan, Xai, Hopper, Eliopoulos and Arbogast teach the system of claim 1.
Yan further teaches the meteorological data are selected from the following quantities: temperature, humidity, pressure, moisture level, and leaf hygrometer; (Yan, pg. 77, paragraph 1, “MR models were built to predict monthly T. palmi and monthly P. xylostella populations (dependent variables), which could be used to determine pest risk level and degree of emergency. According to prior studies, meteorological variables including monthly mean temperature [temperature,], monthly mean maximum temperature, monthly mean minimum temperature, monthly mean relative humidity [humidity,], monthly precipitation amount, and monthly mean wind speed, for both the prediction months and one month prior to the prediction months, are potential factors affecting development of the pests [16–19].”)
the historical data for insect presence include data on insect attacks to the area of interest occurred before a current period of time submitted to the risk prediction. (Yan, pg. 76, paragraph 1, “The hidden layers are capable of detecting and learning the relationship between the predictors and dependent variables [data on insect attacks to the area of interest occurred before a current period of time submitted to the risk prediction.], including both linear and non-linear, and both simple and complex relationships. In a BP-ANN learning process, a training input pattern is first given to the input layer, and then the network propagates the input pattern from layer to layer until the output pattern is generated in the output layer. If this pattern is different from the desired output, an error is generated and propagated backwards through the network from the output layer to the input layer for retraining.” And paragraph 2, “The MR and ANN modelling of this study were based on the datasets provided by the Agri-Food & Veterinary Authority (AVA) of Singapore. It includes historical pest surveillance data collected [the historical data for insect presence] in AVA’s farms at Northwest Singapore and a series of meteorological data recorded near the farms.”)
However, Yan and Xia do not explicitly teach the environmental data are selected from the following parameters: quality of air, carbon dioxide CO2 concentration, carbon monoxide CO concentration, Volatile Organic Compounds concentration, ammonia concentration, luminosity, sound presence, sound level, long terms seasonal time, and presence of pesticide;
Hopper teaches the environmental data are selected from the following parameters: quality of air, carbon dioxide CO2 concentration, carbon monoxide CO concentration, Volatile Organic Compounds concentration, ammonia concentration, luminosity, sound presence, sound level, long terms seasonal time, and presence of pesticide; (Hopper, pg. 538, paragraph 1, “An example will help clarify this point. Suppose an insect species lives in an environment that fluctuates between either a short, cool growth season or a long, hot growth season [long terms seasonal time,]. Let us assume there are three genotypes: one that does well in short seasons, another that does well in long seasons, and a third that produces both phenotypes in proportions that match the frequencies of each type of season (Figure 1).”)
Yan, Xai and Hopper are combinable for the same rationale as set forth above with respect to claim 1.
In regard to claim 5, Yan, Xai, Hopper, Eliopoulos and Arbogast teach the system of claim 1.
Yan further teaches wherein the mathematical prediction algorithm and the insect classification algorithm are algorithms selected from the group consisting of: neural network based model and non-neural network based model. (Yan, pg. 75, paragraph 2, “To the best of our knowledge, despite that [12] compares regression model with bioclimatic model in terms of Helicoverpa population prediction, study comparing MR with ANN in terms of pest risk prediction is currently lacking. In the following sections of this paper, a comparison between MR [non-neural network based model.] and ANN [neural network based model] is thus presented.”)
In regard to claim 6, Yan, Xai, Hopper, Eliopoulos and Arbogast teach the system of claim 1.
Yan further teaches wherein the plurality of software modules further comprise: a knowledge module structured to store a current insect behavioural knowledge data set based local on a value set assumed by at least one of the following set: entomologic parameters, meteorological quantities, environmental quantities and corresponding insect identified species. (Yan, pg. 77, 4.1 MR Models, paragraph 1, “MR models were built to predict monthly T. palmi and monthly P. xylostella population (dependent variables), which could be used to determine pest risk level and degree of emergency. According to prior studies, meteorological variables [meteorological quantities,] including monthly mean temperature, monthly mean maximum temperature, monthly mean minimum temperature, monthly mean relative humidity, monthly precipitation amount, and monthly mean wind speed, for both the prediction months and one month prior to the prediction months, are potential factors affecting development of the pests [16–19].”)
In regard to claim 7, Yan, Xai, Hopper, Eliopoulos and Arbogast teach the system of claim 3.
Yan further teaches said insect classification algorithm is based on a current insect behavioural knowledge data set; and (Yan, pg. 77, 4.1 MR Models, “Data collected before 2012 were used as training dataset [current insect behavioural knowledge data set] and data collected in 201 were used as test dataset. Data outliers were removed appropriately and stepwise regression was performed for predictor selection. Base-10 logarithmic transformation wa performed on the dependent variables to meet the assumptions of the regression, and the independent variables were Z-score standardized selectively to eliminate multicollinearity
Problems.”)
wherein the plurality of software modules comprises an updating module configured to replace the current insect behavioural knowledge data set with an updated insect behavioural knowledge data set. (Yan, pg. 77, 4.1 MR Models, “Data collected before 2012 were used as training dataset and data collected [replace the current insect behavioural knowledge data set with an updated insect behavioural knowledge data set.] in 2012 were used as test dataset. Data outliers were removed appropriately and stepwise regression was performed for predictor selection. Base-10 logarithmic transformation was performed on the dependent variables to meet the assumptions of the regression, and the independent variables were Z-score standardized selectively to eliminate multicollinearity Problems.”)
In regard to claim 8, Yan, Xai, Hopper, Eliopoulos and Arbogast teach the system of claim 6.
Arbogast further teaches detect differences between the current insect behavioural knowledge data set associated to the area of interest with a further insect behavioural knowledge data set associated to another area of interest or to preceding acquisition time; and (Arbogast, pg. 1535, Col. 1-2, “define areas in which action thresholds for pest management are exceeded [detect differences between the current insect behavioural knowledge data set associated to the area of interest]. A threshold can be either an insect count typically associated with the maximum tolerable level of damage or contamination (trap threshold), or it can be a proportion of the pest population that must be suppressed (Brenner et al. 1998). Thresholds, which in practice would be chosen on the basis of experience and pest management needs (goals), were used to assign values of P to trap locations [a further insect behavioural knowledge data set associated to another area of interest or to preceding acquisition time]. This was done as follows: P 5 0 when fi9 . threshold and P 5 1 when fi9 # threshold. Because P represents a probability, it can assume any value between 0 and 1(0 # P # 1) at various points in a store.”)
replace the current insect behavioural knowledge data set with the further insect behavioural knowledge data set in connection with said area of interest. (Arbogast, pg. 1535, Col. 1-2, “define areas in which action thresholds for pest management are exceeded. A threshold can be either an insect count typically associated with the maximum tolerable level of damage or contamination (trap threshold), or it can be a proportion of the pest population that must be suppressed (Brenner et al. 1998). Thresholds, which in practice would be chosen on the basis of experience and pest management needs (goals), were used to assign values of P to trap locations [replace the current insect behavioural knowledge data set with the further insect behavioural knowledge data set]. This was done as follows: P 5 0 when fi9 . threshold and P 5 1 when fi9 # threshold. Because P represents a probability, it can assume any value between 0 and 1(0 # P # 1) at various points in a store.”)
In regard to claim 9, Yan, Xai, Hopper, Eliopoulos and Arbogast teach the system of claim 5.
Yan further teaches wherein: the non-neural network based model includes a logistic regression; the neural network based model is selected from the group consisting of: Convolutional Neural Network and Deep Neural Network. (Yan, pg. 75, 3.1 Multiple Regression, “MR is a commonly used linear regression method [the non-neural network based model includes a logistic regression;]…” and 3.2 Artificial Neural Network, “As a supervised learning technique, back-propagation artificial neural network (BP-ANN) is one of the most widely used structures of ANN [13]. [the neural network based model is selected from the group consisting of: Convolutional Neural Network and Deep Neural Network.]”)
In regard to claim 10, Yan teaches detecting at least one meteorological quantity to provide meteorological data; (Yan, pg. 77, paragraph 1, “According to prior studies, meteorological variables including monthly mean temperature, monthly mean maximum temperature, monthly mean minimum temperature, monthly mean relative humidity, monthly precipitation amount, and monthly mean wind speed, for both the prediction months and one month prior to the prediction months [meteorological data;], are potential factors affecting development of the pests [16–19].”)
acquiring insect behavioural data associated to said area, the insect behavioural data comprising the meteorological data, the environmental data, and historical data of insect presence; (Yan, pg. 77, paragraph 1, “MR models were built to predict monthly T. palmi and monthly P. xylostella populations (dependent variables), which could be used to determine pest risk level and degree of emergency. According to prior studies, meteorological variables including monthly mean temperature [meteorological data;],, monthly mean maximum temperature monthly mean minimum temperature, monthly mean relative humidity, monthly precipitation amount [environmental data], and monthly mean wind speed, for both the prediction months and one month prior to the prediction months [historical data], are potential factors affecting development of the pests [16–19].”)
processing the presence value (IPD) and the insect behavioural data according to a mathematical prediction algorithm to estimate a risk of attack (PRB) to the area of interest; and producing as output said risk of attack (PRB) to the area of interest; (Yan, pg. 78-79, 5.1 MR Models, “The fitted MR models for the T. palmi and the T. xylostella are shown by the following equations, respectively. In each of the following equations, some of the 12 independent variables were excluded as they were not statistically significantly related to the given dependent variable according to the stepwise regression… where ZMTmax is the Z-score transformed monthly mean maximum temperature, ZMTmean is the Z-score transformed monthly mean temperature, ZMPA is the Z-score transformed monthly precipitation amount, r denotes the month for prediction, r-1 denotes one month before the prediction month r [a mathematical prediction algorithm to estimate a risk of attack (PRB) to the area of interests.]. All the regression assumptions were satisfied, and the constants and coefficients of the equations were significant at a 0.05 confidence interval. The overall fit of the regression models were significant (F = 16.67, P < 0.00001 for (3); F = 10.24, P = 0.02 for (4)) to provide risk of attack (PRB) as output.]. The correlation of determination R2 for (3) was 0.294 with ZMTmean(r) accounting for 48.3 %, ZMTmean(r) accounting for 43.5 %, and ZMPA(r-1) accounting for 8.2 % of the variation of the monthly T. Palmi population, according to the stepwise regression analysis. [presence value (IPD) and the insect behavioural data]”)
Yan does not explicitly providing at least one trap device with a housing having an inner chamber that carries a pheromone device and at least one immobilization device, the inner chamber having at least one opening that extends from the inner chamber to an external environment;
positioning the at least one trap device within an area of interest;
releasing substances with the pheromone device to attract insects to the immobilization device;
capturing insects with the immobilization device;
capturing at least one insect digital image (IM) of the insects that are captured by the immobilization device;
detecting at least one environmental quantity to provide environmental data;
processing the at least one insect digital image (IM) at a sampling rate to provide a presence value (IPD), representing the presence of insects in the area of interest for insect attack;
providing maintenance information and maintenance notifications by correlating the insect behavioural data and the presence value (IPD) to at least one of the pheromone device, the at least one immobilization device, and the substances released by the pheromone device; and
controlling the sampling rate based on at least one of a comparison between the historical data of insect presence and the presence value (IPD), a comparison between the meteorological data and predetermined anomaly levels of meteorological data, and a comparison between the environmental data and predetermined anomaly levels of environmental data.
Xia teaches processing the at least one insect digital image (IM) at a sampling rate to provide a presence value (IPD), representing the presence of insects in the area of interest for insect attack; (Xia, pg. 3, paragraph 1, “Due to the small size of Xie’s data set, collecting new images was required. The authors manually collected images by search engines, such as Baidu and Google, where similar images were extracted manually. Table 1 lists the information of insect species including Xie’s data set and those collected from crop fields and the Internet. Following exclusion of some images with errors and low quality, 660 images [at least one insect digital image (IM) to provide a presence value (IPD)] were used in this work, where 60 images were randomly selected for the test data set and the remaining 540 images for the training one.” and paragraph 2, “Moreover, to avoid over-fitting of this model, data augmentation was performed on the training data set to increase the number of training samples. Bilinear interpolation [21] was adopted to fix images to the pixel size of 450 _ 750, and all images were then rotated at 90_, 180_, 270_ angles.”)
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention, having the teachings of Yan and Xia before them, to include Xia’s insect image detection in Yan’s system of neural networks for predicting pest risks. One would have been motivated to make such a combination in order to extract features necessary from the insect. (Xia Abstract, “The model can make full use of the advantages of the neural network to comprehensively extract multifaceted insect features.”)
However, Yan and Xia do not explicitly teach providing at least one trap device with a housing having an inner chamber that carries a pheromone device and at least one immobilization device, the inner chamber having at least one opening that extends from the inner chamber to an external environment;
positioning the at least one trap device within an area of interest;
releasing substances with the pheromone device to attract insects to the immobilization device;
capturing insects with the immobilization device;
capturing at least one insect digital image (IM) of the insects that are captured by the immobilization device;
detecting at least one environmental quantity to provide environmental data;
providing maintenance information and maintenance notifications by correlating the insect behavioural data and the presence value (IPD) to at least one of the pheromone device, the at least one immobilization device, and the substances released by the pheromone device; and
controlling the sampling rate based on at least one of a comparison between the historical data of insect presence and the presence value (IPD), a comparison between the meteorological data and predetermined anomaly levels of meteorological data, and a comparison between the environmental data and predetermined anomaly levels of environmental data.
Hopper teaches detecting at least one environmental quantity to provide environmental data; (Hopper, pg. 538, paragraph 1, “An example will help clarify this point. Suppose an insect species lives in an environment that fluctuates between either a short, cool growth season or a long, hot growth season [long terms seasonal time,]. Let us assume there are three genotypes: one that does well in short seasons, another that does well in long seasons, and a third that produces both phenotypes in proportions that match the frequencies of each type of season (Figure 1).”)
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention, having the teachings of Yan, Xia and Hopper before them, to include Hopper’s insect risk-spreading in Yan and Xia’s system of neural networks for predicting pest risks and image detection. One would have been motivated to make such a combination in order to develop detailed predictions of risk-spreading. (Hopper, Abstract, “Although risk-spreading theory is voluminous and well developed in some ways, rarely has it been used to generate detailed, testable hypotheses about the evolution of risk-spreading.”)
However, Yan, Xia and Hopper do not explicitly teach providing at least one trap device with a housing having an inner chamber that carries a pheromone device and at least one immobilization device, the inner chamber having at least one opening that extends from the inner chamber to an external environment;
positioning the at least one trap device within an area of interest;
releasing substances with the pheromone device to attract insects to the immobilization device;
capturing insects with the immobilization device;
capturing at least one insect digital image (IM) of the insects that are captured by the immobilization device;
providing maintenance information and maintenance notifications by correlating the insect behavioural data and the presence value (IPD) to at least one of the pheromone device, the at least one immobilization device, and the substances released by the pheromone device; and
controlling the sampling rate based on at least one of a comparison between the historical data of insect presence and the presence value (IPD), a comparison between the meteorological data and predetermined anomaly levels of meteorological data, and a comparison between the environmental data and predetermined anomaly levels of environmental data.
Eliopoulos teaches providing at least one trap device with a housing having an inner chamber that carries a pheromone device and at least one immobilization device, the inner chamber having at least one opening that extends from the inner chamber to an external environment; (Eliopoulos, pg. 3, paragraph 2, “This box-shaped device [one trap device] with dimensions of 21 cm _ 11 cm _ 7.5 cm, including the plastic box and the attached electronic kit (Figure 1) attracts insect pests, senses the entering insect and takes automatically a picture of the internal space of the box [an inner chamber]. The picture is communicated through the Wi-Fi commonly found in such establishments to an authorized person/stakeholder receiving the picture to take proper action. In this way, continuous, accurate and verifiable, real-time detection is achieved, without the need for human intervention. It is a monitoring device for urban pests in the context of smart homes and smart cities [an area of interest], and is compatible with the emerging discipline of the Internet of Things (IoT) (see Figure 2 for the way we envision its application).” And pg. 3, paragraph 3 and 4, “Our smart trap functions like a classic floor trap. Traps may differ greatly in design features (shape, size, surface material, etc.) and the presence of attractants (food, pheromone) [a pheromone device]. All these factors along with placement method influence dramatically the trap efficacy [30].
The box includes a strong attractant to attract the insects and maximize captures. The insect is trapped on a sticky surface that is inside the device [one immobilization device], basically a cardboard coated with special insect glue (Tangle-Trap® Sticky Coatings, Tanglefoot, Marysville, Washington, DC, USA) that lasts (remains sticky) for 4–6 months. The sticky floor provides the means for immediate verification of reported results. Captured insects frequently release additional pheromone by themselves and increase the attractiveness. The presence of multiple attractants targeting different insect species simultaneously is also possible and recommended.”)
positioning the at least one trap device within an area of interest; (Eliopoulos, pg. 3, paragraph 2, “This box-shaped device [one trap device] with dimensions of 21 cm _ 11 cm _ 7.5 cm, including the plastic box and the attached electronic kit (Figure 1) attracts insect pests, senses the entering insect and takes automatically a picture of the internal space of the box. The picture is communicated through the Wi-Fi commonly found in such establishments to an authorized person/stakeholder receiving the picture to take proper action. In this way, continuous, accurate and verifiable, real-time detection is achieved, without the need for human intervention. It is a monitoring device for urban pests in the context of smart homes and smart cities [an area of interest], and is compatible with the emerging discipline of the Internet of Things (IoT) (see Figure 2 for the way we envision its application).”
releasing substances with the pheromone device to attract insects to the immobilization device; (Eliopoulos, pg. 3, paragraph 3 and 4, “Our smart trap functions like a classic floor trap. Traps may differ greatly in design features (shape, size, surface material, etc.) and the presence of attractants (food, pheromone) [a pheromone device]. All these factors along with placement method influence dramatically the trap efficacy [30].
The box includes a strong attractant to attract the insects and maximize captures. The insect is trapped on a sticky surface that is inside the device [one immobilization device], basically a cardboard coated with special insect glue (Tangle-Trap® Sticky Coatings, Tanglefoot, Marysville, Washington, DC, USA) that lasts (remains sticky) for 4–6 months. The sticky floor provides the means for immediate verification of reported results. Captured insects frequently release additional pheromone by themselves and increase the attractiveness. The presence of multiple attractants targeting different insect species simultaneously is also possible and recommended.”)
capturing insects with the immobilization device; (Eliopoulos, pg. 3, paragraph 3 and 4, “Our smart trap functions like a classic floor trap. Traps may differ greatly in design features (shape, size, surface material, etc.) and the presence of attractants (food, pheromone). All these factors along with placement method influence dramatically the trap efficacy [30].
The box includes a strong attractant to attract the insects and maximize captures. The insect is trapped on a sticky surface that is inside the device [one immobilization device], basically a cardboard coated with special insect glue (Tangle-Trap® Sticky Coatings, Tanglefoot, Marysville, Washington, DC, USA) that lasts (remains sticky) for 4–6 months. The sticky floor provides the means for immediate verification of reported results. Captured insects frequently release additional pheromone by themselves and increase the attractiveness. The presence of multiple attractants targeting different insect species simultaneously is also possible and recommended.”)
capturing at least one insect digital image (IM) of the insects that are captured by the immobilization device; (Eliopoulos, pg. 4, paragraph 1, “After 20 s a photograph (Camera OV2640, cmos sensor camera module, Omnivision, Santa Clara, CA, USA) of the internal volume of the trap is taken (Figure 3), time-stamped and delivered to pre-stored mail addresses while a copy of the picture is stored internally in the SD card of the device. The time delay ensures that is given enough time to the insect that follows the chemical signals of the bait to crawl inside. The crossing of the laser beam by an insect effects a voltage drop in the receiver’s amplifier. The drop is analogous to the size of the insect. A minimum and maximum threshold is set during monitoring of the voltage drop based on inactive, targeted pests. For example, when we need to monitor cockroaches we do not want the system to be triggered by the accidental entrance of an ant. Note that a random entrance is uncommon as targeted insects enter the trap because the follow their corresponding pheromones.”)
providing maintenance information and maintenance notifications by correlating the insect behavioural data and the presence value (IPD) to at least one of the pheromone device, the at least one immobilization device, and the substances released by the pheromone device; and (Eliopoulos, pg. 4, paragraph 1, “After 20 s a photograph (Camera OV2640, cmos sensor camera module, Omnivision, Santa Clara, CA, USA) of the internal volume of the trap is taken (Figure 3), time-stamped and delivered to pre-stored mail addresses while a copy of the picture is stored internally in the SD card of the device. The time delay ensures that is given enough time to the insect that follows the chemical signals of the bait to crawl inside. The crossing of the laser beam by an insect effects a voltage drop in the receiver’s amplifier. The drop is analogous to the size of the insect. A minimum and maximum threshold is set during monitoring of the voltage drop based on inactive, targeted pests. For example, when we need to monitor cockroaches we do not want the system to be triggered by the accidental entrance of an ant. Note that a random entrance is uncommon as targeted insects enter the trap because the follow their corresponding pheromones [substances released by the pheromone device]. Similarly, we monitor the time delay of the entering event based on marking the onset and the end of an entering event [insect behavioural data and the presence value (IPD)]. A long delay is atypical for an insect movement and this initiates a possible malfunction notice sent through the Wi-Fi (e.g., an insect blocking the entrance). The speed of the entering pest is also calculated, and atypical speeds are rejected as false alarms.”)
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention, having the teachings of Yan, Xia, Hopper and Eliopoulos before them, to include Eliopoulos’ insect detector in Yan, Xia and Hopper’s system of neural networks for predicting pest risks and image detection. One would have been motivated to make such a combination in order to capture and transmit images of insects to user. (Eliopoulos, abstract, “This triggers a detection event; a picture is taken, and a time-stamp is set before delivering the picture through the Wi-Fi to an authorized person/stakeholder.”)
However, Yan, Xia, Hopper and Eliopoulos does not explicitly teach controlling the sampling rate based on at least one of a comparison between the historical data of insect presence and the presence value (IPD), a comparison between the meteorological data and predetermined anomaly levels of meteorological data, and a comparison between the environmental data and predetermined anomaly levels of environmental data.
However, Arbogast teaches controlling the sampling rate based on at least one of a comparison between the historical data of insect presence and the presence value (IPD), a comparison between the meteorological data and predetermined anomaly levels of meteorological data, and a comparison between the environmental data and predetermined anomaly levels of environmental data. (Arbogast, pg. 1534, Col. 2, paragraph 1 and 2, “Traps that captured the same number of insects were grouped together, and the highest cumulative frequency in the group was assigned to all traps in the group (Table 1) [controlling the sampling rate]. The assigned cumulative frequency (fi9) for any trap, thus indicates the proportion of the total catch represented by the combined catch of traps with an equal or greater number of insects. It estimates the probability that any one trap will capture an equal or greater number of insects, given the size and spatial distribution of the population [between the environmental data and predetermined anomaly levels of environmental data.].
If we assume that the spatial distribution of trap catch respects the spatial distribution of the insect population, we can then use the cumulative frequency distributions derived from trap samples (Table 1) to define areas in which action thresholds for pest management are exceeded [the historical data of insect presence and the presence value (IPD),]. A threshold can be either an insect count typically associated with the maximum tolerable level of damage or contamination (trap threshold), or it can be a proportion of the pest population that must be suppressed [the historical data of insect presence] (Brenner et al. 1998).”)
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention, having the teachings of Yan, Xia, Hopper, Eliopoulos and Arbogast before them, to include Arbogast’s trapping and special analysis system in Yan, Xia, Hopper and Eliopoulos’s system of neural networks for predicting pest risks and image detection. One would have been motivated to make such a combination in order to estimate how many insects will be captured. ((Arbogast, pg. 1534, Col. 2, paragraph 1, “It estimates the probability that any one trap will capture an equal or greater number of insects, given the size and spatial distribution of the population.”)
In regard to claim 11, Yan, Xai, Hopper, Eliopoulos and Arbogast teach the system of claim 2.
Eliopoulos teaches wherein the prediction module comprises an alerting module to generate an alerting signal based on the risk of attack (PRB) to the area of interest. (Eliopoulos, pg. 5, paragraph 1, “The STM32F767 processors has all the communication buses that are required for the task. The
laser sends 200 Hz pulses of 12 μs duration. The pulses are received by the photodiode and are
amplified. The amplifier’s output is driven to analogue input of the processor. The processor runs an
algorithm that reads the amplitude of the signal and detects if the beam of light has been interrupted.
If so, then, after 20 s a light illuminates the trap and the camera takes a picture. The jpeg picture is
transferred through the 8-BIT DCMI interface to the memory of the processor and stored in the SD
card. The communication of the processor with the SD is done through the 4-BIT SPI interface.
Subsequently, the photograph is sent via the Wi-Fi functionality to predefined e-mails. The e-mails
as well as other parameters are stored in a settings file stored in the SD card of the system. The
software is written in C language using the IAR Embedded workbench.”)
Yan, Xai, Hopper and Eliopoulos are combinable for the same rationale as set forth above with respect to claim 1.
In regard to claim 12, Yan, Xai, Hopper, Eliopoulos and Arbogast teach the system of claim 11.
Eliopoulos teaches wherein the alerting module generates the alerting signal by comparing the risk of attack (PRB) to a pre-established risk threshold. (Eliopoulos, pg. 5, paragraph 1, “The STM32F767 processors has all the communication buses that are required for the task. The laser sends 200 Hz pulses of 12 μs duration. The pulses are received by the photodiode and are amplified. The amplifier’s output is driven to analogue input of the processor. The processor runs an algorithm that reads the amplitude of the signal [comparing the risk of attack (PRB)] and detects if the beam of light has been interrupted [a pre-established risk threshold]. If so, then, after 20 s a light illuminates the trap and the camera takes a picture. The jpeg picture is transferred through the 8-BIT DCMI interface to the memory of the processor and stored in the SD card. The communication of the processor with the SD is done through the 4-BIT SPI interface. Subsequently, the photograph is sent via the Wi-Fi functionality to predefined e-mails. The e-mails as well as other parameters are stored in a settings file stored in the SD card of the system. The software is written in C language using the IAR Embedded workbench.”)
Yan, Xai, Hopper and Eliopoulos are combinable for the same rationale as set forth above with respect to claim 1.
In regard to claim 13, Yan, Xai, Hopper, Eliopoulos and Arbogast teach the system of claim 11.
Eliopoulos teaches wherein the alerting module communicates the alerting signal to at least one external communication device. (Eliopoulos, pg. 5, paragraph 1, “The STM32F767 processors has all the communication buses that are required for the task. The laser sends 200 Hz pulses of 12 μs duration. The pulses are received by the photodiode and are amplified. The amplifier’s output is driven to analogue input of the processor. The processor runs an algorithm that reads the amplitude of the signal and detects if the beam of light has been interrupted. If so, then, after 20 s a light illuminates the trap and the camera takes a picture. The jpeg picture is transferred through the 8-BIT DCMI interface to the memory of the processor and stored in the SD card. The communication of the processor with the SD is done through the 4-BIT SPI interface. Subsequently, the photograph is sent via the Wi-Fi functionality to predefined e-mails. The e-mails as well as other parameters are stored in a settings file stored in the SD card of the system. The software is written in C language using the IAR Embedded workbench.”)
Yan, Xai, Hopper and Eliopoulos are combinable for the same rationale as set forth above with respect to claim 1.
In regard to claim 16 and analogous claim 18, Yan, Xai, Hopper, Eliopoulos and Arbogast teach the system of claim 1.
Arbogast teaches wherein the sampling rate includes a frequency having an original frequency; and wherein the management module changes the frequency by changing the original frequency to an increased frequency upon the presence value (IPD) being significantly greater than the historical data of insect presence. (Arbogast, pg. 1534, Col. 2, paragraph 1 and 2, “Traps that captured the same number of insects were grouped together, and the highest cumulative frequency in the group was assigned to all traps in the group (Table 1). The assigned cumulative frequency (fi9) [frequency having an original frequency] for any trap, thus indicates the proportion of the total catch represented by the combined catch of traps with an equal or greater number of insects. It estimates the probability that any one trap will capture an equal or greater number of insects, given the size and spatial distribution of the population.
If we assume that the spatial distribution of trap catch respects the spatial distribution of the insect population, we can then use the cumulative frequency distributions derived from trap samples (Table 1) to define areas in which action thresholds for pest management are exceeded [an increased frequency upon the presence value (IPD)]. A threshold can be either an insect count typically associated with the maximum tolerable level of damage or contamination (trap threshold), or it can be a proportion of the pest population that must be suppressed [the historical data of insect presence] (Brenner et al. 1998).”)
Yan, Xai, Hopper, Eliopoulos and Arbogast are combinable for the same rationale as set forth above with respect to claim 1.
In regard to claim 17 and analogous claim 19, Yan, Xai, Hopper, Eliopoulos and Arbogast teach the system of claim 16.
Arbogast further teaches wherein the management module changes the frequency of the sampling rate by changing the increased frequency to the original frequency upon at least one of the meteorological data having a level within the predetermined anomaly levels of meteorological data and the environmental data having a level within the predetermined anomaly levels of environmental data. (Arbogast, pg. 1534, Col. 2, paragraph 1 and 2, “Traps that captured the same number of insects were grouped together, and the highest cumulative frequency in the group was assigned to all traps in the group (Table 1) [determine and control the sampling rate]. The assigned cumulative frequency (fi9) [frequency having an original frequency] for any trap, thus indicates the proportion of the total catch represented by the combined catch of traps with an equal or greater number of insects. It estimates the probability that any one trap will capture an equal or greater number of insects, given the size and spatial distribution of the population [between the environmental data and predetermined anomaly levels of environmental data.].
If we assume that the spatial distribution of trap catch respects the spatial distribution of the insect population, we can then use the cumulative frequency distributions derived from trap samples (Table 1) to define areas in which action thresholds for pest management are exceeded [the historical data of insect presence and the presence value (IPD),]. A threshold can be either an insect count typically associated with the maximum tolerable level of damage or contamination (trap threshold), or it can be a proportion of the pest population that must be suppressed [the historical data of insect presence] (Brenner et al. 1998).”)
Yan, Xai, Hopper, Eliopoulos and Arbogast are combinable for the same rationale as set forth above with respect to claim 1.
Conclusion
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/S.K.V./Examiner, Art Unit 2146
/USMAAN SAEED/Supervisory Patent Examiner, Art Unit 2146
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