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 .
DETAILED ACTION
Status of the Application
The following is a non-Final Office Action.
In response to Examiner's communication of 1/10/2025, Applicant responded on 5/12/2025. Amended Claims 1, 13, 24, 28.
Claims 1, 3-10, 13, 15, 24-25 and 28-33 are pending in this application and have been examined.
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 5/12/2025 has been entered.
Response to Amendment
Applicant's amendments to claims 1, 13, 24, 28 are not sufficient to overcome the 35 USC 101 rejections set forth in the previous action.
Applicant's amendments to claims 1, 13, 24, 28 are sufficient to overcome the 35 USC 103 rejections set forth in the previous action.
Response to Arguments - 35 USC § 101
Applicant’s arguments with respect to the rejections have been fully considered, but they are not persuasive.
Applicant submits, “...The Office Action states that claims 1, 3-10, 13, 15, 24-25, 27 and 29-33 are rejected under 35 U.S.C. § 101 because the claimed subject-matter is directed to an abstract idea without significantly more.…Without acquiescing to these rejections of the Office Action, the Applicant has amended independent claims 1, 13 and 14. It is respectfully submitted that these allegations are now moot. …” The Examiner respectfully disagrees.
While Applicant’s amendments further prosecution, the amendments to the independent claims are not sufficient to overcome the 101 rejections as detailed below.
Response to Arguments – Prior Art
Applicant’s arguments with respect to the rejections have been fully considered.
The closest prior art are US Patent Publication US20190228218A1 to James et al., (hereinafter referred to as “James”) in view of CN Patent to CN103324839A to LEE et al., (hereinafter referred to as “LEE”) in view of US Patent Publication US20150164965A1 to Moshitzky et al., (hereinafter referred to as “Moshitzky”) in view of US Patent US10191489B1 to Rapoport et al., (hereinafter referred to as “Rapoport”).
The teachings of the references do not teach the specific ordered sequence of limitations of independent claims 1, 13, 24:
a container; a structure facing the container and having a camera with a field of view orientable towards a second sample of said population of shrimps, and being configured for acquiring an image of said second sample at a second moment in time; and
a controller having a memory and a processor configured to perform the steps of:
accessing said image;
using a growth indication quantity determination engine being stored on said memory and said accessed image, determining at least a second value of the growth indication quantity being associated to the second sample of said population;
using a growth prediction engine, and based on at least the first and second values of the growth indication quantity, executing at least one of: one or more trained artificial neural networks, one or more support vector machines, and one or more capsule-based networks to determine at least a predicted value of said growth indication quantity for said population at least at a subsequent moment in time; and
on a display screen communicatively coupled to the controller, displaying a graphical user interface including a prediction value graph having the predicted value of said growth indication quantity for said population at the subsequent moment in time; and
updating the graphical user interface to display a harvesting graphical overlay indicating whether harvesting of the shrimps of the population is to be performed at the subsequent moment in time or at another moment in time based on the predicted value of said growth indication quantity.
…
receiving a first sample of said population of shrimps into a container at a first moment in time;
imaging said first sample of said population to obtain a first image;
using a growth indication quantity determination engine and said first image, determining at least a first value of the growth indication quantity being associated to the first sample of said population;
receiving a second sample of said population of shrimps into the container at a subsequent, second moment in time;
imaging said second sample of said population to obtain a second image; using the growth indication quantity determination engine and said second image, determining at least a second value of the growth indication quantity being associated to the second sample of said population; using a growth prediction engine, and based on at least the first and second values of the growth indication quantity, executing at least one of: one or more trained artificial neural networks, one or more support vector machines, and one or more capsule-based networks to determine at least a predicted value of said growth indication quantity for said population at least at a subsequent moment in time; and
on a display screen communicatively coupled to the growth prediction engine, displaying a graphical user interface including a prediction value graph having the predicted value of said growth indication quantity for said population at the subsequent moment in time; and
updating the graphical user interface to display a harvesting graphical overlay indicating whether harvesting of the shrimps of the population is to be performed at the subsequent moment in time or at another moment in time based on the predicted value of said growth indication quantity.
…
receiving a first sample of said population of shrimps into a container at a first moment in time;
imaging said first sample of said population to obtain a first image;
using a growth indication quantity determination engine and said first image, determining at least a first value of the growth indication quantity being associated to the first sample of said population;
using a growth prediction engine, accessing prior experimental data pertaining to a species of said shrimps, executing at least one of: one or more trained artificial neural networks, one or more support vector machines, and one or more capsule-based networks to determine at least a predicted value of said growth indication quantity for said population at least at a subsequent moment in time based on said prior experimental data, said first value of the growth indication quantity and said first moment in time; and
on a display screen communicatively coupled to the growth prediction engine displaying a graphical user interface a prediction value graph having the predicted value of said growth indication quantity for said population at the subsequent moment in time; and
updating the graphical user interface to display a harvesting graphical overlay indicating whether harvesting of the shrimps of the population is to be performed at the subsequent moment in time or at another moment in time based on the predicted value of said growth indication quantity.
Furthermore, Non-Patent Literature, “Determining Individual Variation in Growth and Its Implication for Life-History and Population Processes Using the Empirical Bayes Method” to Vincenzi et al, 9/11/2014, hereinafter Vincenzi discloses, — The differences in demographic and life-history processes between organisms living in the same population have important consequences for ecological and evolutionary dynamics. Modern statistical and computational methods allow the investigation of individual and shared (among homogeneous groups) determinants of the observed variation in growth. We use an Empirical Bayes approach to estimate individual and shared variation in somatic growth using a von Bertalanffy growth model with random effects. To illustrate the power and generality of the method, we consider two populations of marble trout Salmo marmoratus living in Slovenian streams, where individually tagged fish have been sampled for more than 15 years. We use year-of-birth cohort, population density during the first year of life, and individual random effects as potential predictors of the von Bertalanffy growth function's parameters k (rate of growth) and Inline graphic (asymptotic size). Our results showed that size ranks were largely maintained throughout marble trout lifetime in both populations. According to the Akaike Information Criterion (AIC), the best models showed different growth patterns for year-of-birth cohorts as well as the existence of substantial individual variation in growth trajectories after accounting for the cohort effect. For both populations, models including density during the first year of life showed that growth tended to decrease with increasing population density early in life. Model validation showed that predictions of individual growth trajectories using the random-effects model were more accurate than predictions based on mean size-at-age of fish. However, Vincenzi does not teach the specific ordered sequence of limitations of independent claims 1, 13, 24, nor otherwise cure the deficiencies of James, LEE, Moshitzky.
However, while James, LEE, Moshitzky, Vincenzi, do not teach the specific ordered sequence of limitations of independent claims 1, 13, 24, as claimed, Applicant’s specifications do not support all of the claimed elements.
Claim Rejections - 35 USC § 112(a)
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 1, 3-10, 13, 15, 24-25 and 28-33 is/are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement.
The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for pre-AIA the inventor(s), at the time the application was filed, had possession of the claimed invention.
Claim 1, 13, 24 recites, “…updating the graphical user interface to display a harvesting graphical overlay indicating whether harvesting of the shrimps of the population is to be performed at the subsequent moment in time…”. However, Applicant’s Specification does not expressly or inherently require, “…updating the graphical user interface to display a … indicating whether harvesting of the shrimps of the population is to be performed at the subsequent moment in time or at another moment in time based on the predicted value of said growth indication quantity…”, and “a harvesting graphical overlay”, as recited in claim 1, 13, 24.
In order to satisfy the written description requirement, each claim limitation must be expressly or inherently supported by the disclosure. MPEP 2163 (emphasis added). "The 'written description' requirement implements the principle that a patent must describe the technology that is sought to be patented; the requirement serves both to satisfy the inventor's obligation to disclose the technologic knowledge upon which the patent is based, and to demonstrate that the patentee was in possession of the invention that is claimed." Capon v. Eshhar, 76 USPQ2d 1078, 1084 (Fed. Cir. 2005). Further, the written description requirement promotes the progress of the useful arts by ensuring that patentees adequately describe their inventions in their patent specifications in exchange for the right to exclude others from practicing the invention for the duration of the patent's term. See MPEP 2163. For claims directed toward computer-implemented functions, like the presently claimed invention, "[i]f the specification does not provide a disclosure of the computer and algorithm in sufficient detail to demonstrate to one of ordinary skill in the art that the inventor possessed the invention including how to program the disclosed computer to perform the claimed function, a rejection under 35 U.S.C. 112(a) or pre-AIA 35 U.S.C. 112, first paragraph, for lack of written description must be made." MPEP 2161.01.
Applicant’s specification discloses,
[0017] Fig. 5 is a histogram showing a distribution of values of the growth indication quantity at a given moment in time, with a shape being fitted to the distribution of values, in accordance with an embodiment;
[0030] As such, the so-determined predicted value(s) of the growth indication quantity can enable aquaculture farmer(s) to make informed decision concerning whether the population is ready to be harvested.
[0031] In further embodiments, the predicted values of the growth indication quantity can be used to determine whether the population should be harvested and sold now or wait to a later moment in time, in order to maximize profits based on the market demand. In these embodiments, market prices being function of the species of the organisms can be involved and updated as function of the market or of the time of the year for instance. Other economical parameters can be used in the profitability optimization such as market price for organisms in specific weight ranges, fixed plant operation costs, weekly plant operation costs, feed costs and the like.
[0055] As can be understood, the software application 300 needs not to determine both the first value 314a and the second value 314b. For instance, in some embodiments, the first value 314a and second value 314b are determined using different but similar systems 100. In some other embodiments, the first value 314a is received from a network or a remote memory where it has been stored for a given period of time. The first value 314a need not to be determined by a growth indication quantity determination engine either. For instance, in alternate embodiments, the growth indication quantity of the first sample is determined by visual inspection, inputted via a user interface, and then stored onto a memory which will be later accessible to the software application 300 for computational purposes.
[0056] The software application 300 is further configured to perform the step of, using a growth prediction engine 316, determining at least a predicted value 318 of the growth indication quantity for the population at least at a subsequent moment in time based on the first and second values 314a and 314b.
[0057] In some embodiments, the software application 300 can be configured to output a single predicted value 318 of the growth indication quantity at a predetermined subsequent period of time, e.g., one week, two weeks or one month later. However, in some other embodiments, it was found convenient to configure the growth prediction engine 316 to determine a plurality of predicted values 318 of the growth indication quantity for a plurality of subsequent moments in time. These predicted values 318 can be provided in the form of a continuous function varying as a function of time in some alternate embodiments. In any case, it can be preferred to determine the predicted values 318 for about 1 to 4 weeks after the last value measurement.
[0058] It can be appreciated that the predicted value(s) of the growth indication quantity determined by growth prediction engine can increase in accuracy with an increasing number of value(s) of the growth indication quantity. In other words, the accuracy of the growth prediction engine increases with the number of times that the population of organisms is tested over time to determine its corresponding value of the growth indication quantity. Accordingly, while the above-described embodiment describes the first and second values 314a and 314b, it is envisaged that a plurality of such value measurements can preferably be made and used in the determination of the predicted value(s) of the growth indication quantity. Additionally, as new values of the growth indication quantity are measured, the growth prediction engine can calculate new predicted values of the growth indication quantity taking into account the new measurements.
[0059] Fig. 4 is a graph of a growth indication quantity, in this case average body weight, as a function of time expressed in term of days. Data points being indicative of the first value 310a, the second value 310b and a plurality of other values 310c through 310j, and the moment in time at which they have been measured, are shown. As depicted, in this specific embodiment, the predicted values 318 are provided in the form of a curve 350 fitting the data points.
[0060] In some embodiments, in addition to the predicted values of the growth indication quantity, the growth prediction engine can also be configured to predict a confidence interval for the predicted values of the growth indication quantity. The confidence interval can be indicative of the certainty (or uncertainty) of the growth prediction engine on the predicted values, for instance an interval in which it is probable at 95% that the predicted values might lay. Examples of such confidence intervals are shown in dashed lines in Fig. 4 for predicted values of the average body weight. As depicted, these confidence intervals can act as boundaries within which the predicted values of the average body weight might lay
[0061] The computing device 200 and the software application 300 described above are meant to be examples only. Other suitable embodiments of the controller 112 can also be provided, as it will be apparent to the skilled reader. In alternate embodiments, the controller 112 need not to be mounted to the structure 108, or to the lid 109, of the system 100. In these embodiments, the controller 112 can be remotely located relative to the system 100 via a network such as the Internet for instance.
[00116] Fig. 7 shows an example of a system 400 for predicting growth of a population of organisms, in accordance with an embodiment. As depicted, the system 400 has a camera 410 having a field of view 420 orientable towards the population 404 of organisms 402 and which is configured for acquiring an image of the one or more organisms 402 of the population 404. The system 400 has a controller 412 having a memory on which are stored the software program such as the one described with reference to Fig. 3. In this particular embodiment, the system 400 has a structure 408, or frame, to which the camera 410 and the controller 412 are mounted. More specifically, the structure 408 is provided in the form of a mobile device, such as a smart phone or an electronic tablet.
[00117] Accordingly, in this embodiment, the system 400 can be handled by a user to acquire an image 428 of the population 404, after which the acquired image 428 can be processed by the controller 412 to determine one or more values of a growth indication quantity concerning the organisms 402 of the population 404 on the go. As can be noticed, the sample of organisms 402 that is imaged in this embodiment is provided in the form of a large portion, if not the entirety, of the population 404 of organisms 402. The so-determined values of the growth indication quantity can thus be used to determine the predicted values of the growth indication quantity using the growth prediction engine.
[00118] In contrast with the embodiment of Fig. 1, which provided a predetermined, constant distance d between the camera and the sample, the manoeuvrability of the system 400 may cause challenges in suitably referencing the population 404 with respect to the system 400. Accordingly, at least one reference object 491 may be positioned in the field of view 420 of the camera 410 proximate to the population 404so that a sample referencing engine being stored on the memory of the controller 412 can localize a position and an orientation of the population 404 relative to the camera 410 based on the accessed image 428 including the reference object(s) 491. In some embodiments, the sample referencing engine is trained using either supervised learning or unsupervised learning. As such, the growth indication quantity determination engine can factor in the position and the orientation of the sample relative to the camera 410, which can impact the determination of the growth indication quantity. As can be understood, the sample referencing engine can be trained using training images showing organism representations which are associated to different position and orientation truth values. An example of such sample referencing engine is shown at 347 in the software program 300 of Fig. 3.
However, the paragraph and figures does not expressly or inherently require or support “…updating the graphical user interface to display a … indicating whether harvesting of the shrimps of the population is to be performed at the subsequent moment in time or at another moment in time based on the predicted value of said growth indication quantity…”, and “a harvesting graphical overlay”, as recited claim 1, 13, 24.
Claims 3-10, 15, 25 and 28-33 depend on claim 1, 13, 24 and do not cure the aforementioned deficiencies of claim 1, 13, 24, and thus, claims 3-10, 15, 25 and 28-33 is rejected for the reasons set forth above regarding claim 1, 13, 24 as a result.
Claim Rejections - 35 USC § 112(b)
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claim 28, 29, 30, 31 are rejected under is/are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as failing to set forth the subject matter which the inventor or a joint inventor, or for pre-AIA the applicant(s) regard as their invention.
Claim 28, 29, 30, 31 recite “The method of claim 1”, however Claim 1 is a system. Further, these elements lack antecedent basis. 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, 3-10, 13, 15, 24-25, 27, 29-33 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Claim 1 recites, ”A … for predicting growth of a population of shrimps, using at least a first value of a growth indicator quantity being associated to a first sample of said population at a first moment in time, the … comprising:
…with a field of view orientable towards a second sample of said population of shrimps, and being configured for acquiring an image of said second sample at a second moment in time; and
…to perform the steps of:
accessing said image;
using a growth indication quantity determination engine being stored on said … and said accessed image, determining at least a second value of the growth indication quantity being associated to the second sample of said population;
using a growth prediction engine, and based on at least the first and second values of the growth indication quantity, … to determine at least a predicted value of said growth indication quantity for said population at least at a subsequent moment in time; and
on a …, displaying … including a prediction value graph having the predicted value of said growth indication quantity for said population at the subsequent moment in time; and
updating the … to display … indicating whether harvesting of the shrimps of the population is to be performed at the subsequent moment in time or at another moment in time based on the predicted value of said growth indication quantity.”
Claim 13 recites, “A method for predicting growth of a population of shrimps, the method comprising:
receiving a first sample of said population of shrimps into a container at a first moment in time;
imaging said first sample of said population to obtain a first image;
using a growth indication quantity determination engine and said first image, determining at least a first value of the growth indication quantity being associated to the first sample of said population;
receiving a second sample of said population of shrimps into the container at a subsequent, second moment in time;
imaging said second sample of said population to obtain a second image;
using the growth indication quantity determination engine and said second image, determining at least a second value of the growth indication quantity being associated to the second sample of said population;
using a growth prediction engine, and based on at least the first and second values of the growth indication quantity, … to determine at least a predicted value of said growth indication quantity for said population at least at a subsequent moment in time; and
on a … communicatively coupled to the growth prediction engine, … including a prediction value graph having the predicted value of said growth indication quantity for said population at the subsequent moment in time; and
updating the … to display a … indicating whether harvesting of the shrimps of the population is to be performed at the subsequent moment in time or at another moment in time based on the predicted value of said growth indication quantity.”
Claim 24 recites, “ A method for predicting growth of a population of shrimps, the method comprising:
receiving a first sample of said population of shrimps into a container at a first moment in time;
imaging said first sample of said population to obtain a first image;
using a growth indication quantity determination engine and said first image, determining at least a first value of the growth indication quantity being associated to the first sample of said population;
using a growth prediction engine, accessing prior experimental data pertaining to a species of said shrimps, … to determine at least a predicted value of said growth indication quantity for said population at least at a subsequent moment in time based on said prior experimental data, said first value of the growth indication quantity and said first moment in time; and
on a … communicatively coupled to the growth prediction engine displaying a … a prediction value graph having the predicted value of said growth indication quantity for said population at the subsequent moment in time; and
updating the … to display a … indicating whether harvesting of the shrimps of the population is to be performed at the subsequent moment in time or at another moment in time based on the predicted value of said growth indication quantity.”
Analyzing under Step 2A, Prong 1:
The limitations regarding, …predicting growth of a population of shrimps, using at least a first value of a growth indicator quantity being associated to a first sample of said population at a first moment in time… with a field of view orientable towards a second sample of said population of shrimps, and being configured for acquiring an image of said second sample at a second moment in time… accessing said image; using a growth indication quantity determination engine being stored on said … and said accessed image, determining at least a second value of the growth indication quantity being associated to the second sample of said population; using a growth prediction engine, and based on at least the first and second values of the growth indication quantity, … to determine at least a predicted value of said growth indication quantity for said population at least at a subsequent moment in time; and on a …, displaying … including a prediction value graph having the predicted value of said growth indication quantity for said population at the subsequent moment in time; and updating the … to display … indicating whether harvesting of the shrimps of the population is to be performed at the subsequent moment in time or at another moment in time based on the predicted value of said growth indication quantity… receiving a first sample of said population of shrimps into a container at a first moment in time; imaging said first sample of said population to obtain a first image; using a growth indication quantity determination engine and said first image, determining at least a first value of the growth indication quantity being associated to the first sample of said population; receiving a second sample of said population of shrimps into the container at a subsequent, second moment in time; imaging said second sample of said population to obtain a second image; using the growth indication quantity determination engine and said second image, determining at least a second value of the growth indication quantity being associated to the second sample of said population; using a growth prediction engine, and based on at least the first and second values of the growth indication quantity, … to determine at least a predicted value of said growth indication quantity for said population at least at a subsequent moment in time; and on a … communicatively coupled to the growth prediction engine, … including a prediction value graph having the predicted value of said growth indication quantity for said population at the subsequent moment in time; and updating the … to display a … indicating whether harvesting of the shrimps of the population is to be performed at the subsequent moment in time or at another moment in time based on the predicted value of said growth indication quantity…receiving a first sample of said population of shrimps into a container at a first moment in time; imaging said first sample of said population to obtain a first image; using a growth indication quantity determination engine and said first image, determining at least a first value of the growth indication quantity being associated to the first sample of said population; using a growth prediction engine, accessing prior experimental data pertaining to a species of said shrimps, … to determine at least a predicted value of said growth indication quantity for said population at least at a subsequent moment in time based on said prior experimental data, said first value of the growth indication quantity and said first moment in time; and on a … communicatively coupled to the growth prediction engine displaying a … a prediction value graph having the predicted value of said growth indication quantity for said population at the subsequent moment in time; and updating the … to display a … indicating whether harvesting of the shrimps of the population is to be performed at the subsequent moment in time or at another moment in time based on the predicted value of said growth indication quantity…, under the broadest reasonable interpretation, can include a human using their mind and using pen and paper to, … predicting growth of a population of shrimps, using at least a first value of a growth indicator quantity being associated to a first sample of said population at a first moment in time… with a field of view orientable towards a second sample of said population of shrimps, and being configured for acquiring an image of said second sample at a second moment in time… accessing said image; using a growth indication quantity determination engine being stored on said … and said accessed image, determining at least a second value of the growth indication quantity being associated to the second sample of said population; using a growth prediction engine, and based on at least the first and second values of the growth indication quantity, … to determine at least a predicted value of said growth indication quantity for said population at least at a subsequent moment in time; and on a …, displaying … including a prediction value graph having the predicted value of said growth indication quantity for said population at the subsequent moment in time; and updating the … to display … indicating whether harvesting of the shrimps of the population is to be performed at the subsequent moment in time or at another moment in time based on the predicted value of said growth indication quantity… receiving a first sample of said population of shrimps into a container at a first moment in time; imaging said first sample of said population to obtain a first image; using a growth indication quantity determination engine and said first image, determining at least a first value of the growth indication quantity being associated to the first sample of said population; receiving a second sample of said population of shrimps into the container at a subsequent, second moment in time; imaging said second sample of said population to obtain a second image; using the growth indication quantity determination engine and said second image, determining at least a second value of the growth indication quantity being associated to the second sample of said population; using a growth prediction engine, and based on at least the first and second values of the growth indication quantity, … to determine at least a predicted value of said growth indication quantity for said population at least at a subsequent moment in time; and on a … communicatively coupled to the growth prediction engine, … including a prediction value graph having the predicted value of said growth indication quantity for said population at the subsequent moment in time; and updating the … to display a … indicating whether harvesting of the shrimps of the population is to be performed at the subsequent moment in time or at another moment in time based on the predicted value of said growth indication quantity…receiving a first sample of said population of shrimps into a container at a first moment in time; imaging said first sample of said population to obtain a first image; using a growth indication quantity determination engine and said first image, determining at least a first value of the growth indication quantity being associated to the first sample of said population; using a growth prediction engine, accessing prior experimental data pertaining to a species of said shrimps, … to determine at least a predicted value of said growth indication quantity for said population at least at a subsequent moment in time based on said prior experimental data, said first value of the growth indication quantity and said first moment in time; and on a … communicatively coupled to the growth prediction engine displaying a … a prediction value graph having the predicted value of said growth indication quantity for said population at the subsequent moment in time; and updating the … to display a … indicating whether harvesting of the shrimps of the population is to be performed at the subsequent moment in time or at another moment in time based on the predicted value of said growth indication quantity……; therefore, the claims are directed to a mental process.
Further, … predicting growth of a population of shrimps, using at least a first value of a growth indicator quantity being associated to a first sample of said population at a first moment in time… with a field of view orientable towards a second sample of said population of shrimps, and being configured for acquiring an image of said second sample at a second moment in time… accessing said image; using a growth indication quantity determination engine being stored on said … and said accessed image, determining at least a second value of the growth indication quantity being associated to the second sample of said population; using a growth prediction engine, and based on at least the first and second values of the growth indication quantity, … to determine at least a predicted value of said growth indication quantity for said population at least at a subsequent moment in time; and on a …, displaying … including a prediction value graph having the predicted value of said growth indication quantity for said population at the subsequent moment in time; and updating the … to display … indicating whether harvesting of the shrimps of the population is to be performed at the subsequent moment in time or at another moment in time based on the predicted value of said growth indication quantity… receiving a first sample of said population of shrimps into a container at a first moment in time; imaging said first sample of said population to obtain a first image; using a growth indication quantity determination engine and said first image, determining at least a first value of the growth indication quantity being associated to the first sample of said population; receiving a second sample of said population of shrimps into the container at a subsequent, second moment in time; imaging said second sample of said population to obtain a second image; using the growth indication quantity determination engine and said second image, determining at least a second value of the growth indication quantity being associated to the second sample of said population; using a growth prediction engine, and based on at least the first and second values of the growth indication quantity, … to determine at least a predicted value of said growth indication quantity for said population at least at a subsequent moment in time; and on a … communicatively coupled to the growth prediction engine, … including a prediction value graph having the predicted value of said growth indication quantity for said population at the subsequent moment in time; and updating the … to display a … indicating whether harvesting of the shrimps of the population is to be performed at the subsequent moment in time or at another moment in time based on the predicted value of said growth indication quantity…receiving a first sample of said population of shrimps into a container at a first moment in time; imaging said first sample of said population to obtain a first image; using a growth indication quantity determination engine and said first image, determining at least a first value of the growth indication quantity being associated to the first sample of said population; using a growth prediction engine, accessing prior experimental data pertaining to a species of said shrimps, … to determine at least a predicted value of said growth indication quantity for said population at least at a subsequent moment in time based on said prior experimental data, said first value of the growth indication quantity and said first moment in time; and on a … communicatively coupled to the growth prediction engine displaying a … a prediction value graph having the predicted value of said growth indication quantity for said population at the subsequent moment in time; and updating the … to display a … indicating whether harvesting of the shrimps of the population is to be performed at the subsequent moment in time or at another moment in time based on the predicted value of said growth indication quantity……, under the broadest reasonable interpretation, are instructions for human aquaculture farmers managing and predicting shrimp population growth in a shrimp farm to be ready for harvesting to maximize profitability, therefore it is, commercial interactions, fundamental economic principles or practices. Thus, the claims are directed to certain methods of organizing human activity.
Additionally, the limitations, …predicting growth of a population of shrimps, using at least a first value of a growth indicator quantity being associated to a first sample of said population at a first moment in time… with a field of view orientable towards a second sample of said population of shrimps, and being configured for acquiring an image of said second sample at a second moment in time… accessing said image; using a growth indication quantity determination engine being stored on said … and said accessed image, determining at least a second value of the growth indication quantity being associated to the second sample of said population; using a growth prediction engine, and based on at least the first and second values of the growth indication quantity, … to determine at least a predicted value of said growth indication quantity for said population at least at a subsequent moment in time; and on a …, displaying … including a prediction value graph having the predicted value of said growth indication quantity for said population at the subsequent moment in time; and updating the … to display … indicating whether harvesting of the shrimps of the population is to be performed at the subsequent moment in time or at another moment in time based on the predicted value of said growth indication quantity… receiving a first sample of said population of shrimps into a container at a first moment in time; imaging said first sample of said population to obtain a first image; using a growth indication quantity determination engine and said first image, determining at least a first value of the growth indication quantity being associated to the first sample of said population; receiving a second sample of said population of shrimps into the container at a subsequent, second moment in time; imaging said second sample of said population to obtain a second image; using the growth indication quantity determination engine and said second image, determining at least a second value of the growth indication quantity being associated to the second sample of said population; using a growth prediction engine, and based on at least the first and second values of the growth indication quantity, … to determine at least a predicted value of said growth indication quantity for said population at least at a subsequent moment in time; and on a … communicatively coupled to the growth prediction engine, … including a prediction value graph having the predicted value of said growth indication quantity for said population at the subsequent moment in time; and updating the … to display a … indicating whether harvesting of the shrimps of the population is to be performed at the subsequent moment in time or at another moment in time based on the predicted value of said growth indication quantity…receiving a first sample of said population of shrimps into a container at a first moment in time; imaging said first sample of said population to obtain a first image; using a growth indication quantity determination engine and said first image, determining at least a first value of the growth indication quantity being associated to the first sample of said population; using a growth prediction engine, accessing prior experimental data pertaining to a species of said shrimps, … to determine at least a predicted value of said growth indication quantity for said population at least at a subsequent moment in time based on said prior experimental data, said first value of the growth indication quantity and said first moment in time; and on a … communicatively coupled to the growth prediction engine displaying a … a prediction value graph having the predicted value of said growth indication quantity for said population at the subsequent moment in time; and updating the … to display a … indicating whether harvesting of the shrimps of the population is to be performed at the subsequent moment in time or at another moment in time based on the predicted value of said growth indication quantity…, are mathematical concepts.
Accordingly, the claims are directed to a mental process, certain methods of organizing human activity, mathematical concepts, and thus, the claims are directed to an abstract idea under the first prong of Step 2A.
Analyzing under Step 2A, Prong 2:
This judicial exception is not integrated into a practical application under the second prong of Step 2A.
In particular, the claims recite the additional elements beyond the recited abstract idea identified under Step 2A, Prong 1, such as:
Claim 1, 13, 24: system, a container; a structure facing the container and having a camera, a controller having a memory and a processor configured, executing at least one of: one or more trained artificial neural networks, one or more support vector machines, and one or more capsule-based networks, display screen communicatively coupled to the controller, a graphical user interface, graphical user interface to display a harvesting graphical overlay,
, and pursuant to the broadest reasonable interpretation, as an ordered combination, each of the additional elements are computing elements recited at high level of generality implementing the abstract idea, and thus, are no more than applying the abstract idea with generic computer components. Further, these additional elements generally link the abstract idea to a technical environment, namely the environment of a computer.
With respect to, “container”, “structure facing the container”, are field of use additional elements that generally links the abstract idea to a field of use.
Additionally, with respect to, “acquiring…accessing… imaging… indicating… updating… display…”, these elements do not add a meaningful limitations to integrate the abstract idea into a practical application because they are extra-solution activity, pre and post solution activity - i.e. data gathering – acquiring…accessing… imaging …, data output – indicating… updating… display
Analyzing under Step 2B:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception under Step 2B.
As noted above, the aforementioned additional elements beyond the recited abstract idea are not sufficient to amount to significantly more than the recited abstract idea because, as an order combination, the additional elements are no more than mere instructions to implement the idea using generic computer components (i.e. apply it).
Additionally, as an order combination, the additional elements append the recited abstract idea to well-understood, routine, and conventional activities in the field as individually evinced by the applicant’s own disclosure, as required by the Berkheimer Memo, in at least:
[0030] As such, the so-determined predicted value(s) of the growth indication quantity can enable aquaculture farmer(s) to make informed decision concerning whether the population is ready to be harvested.
[0031] In further embodiments, the predicted values of the growth indication quantity can be used to determine whether the population should be harvested and sold now or wait to a later moment in time, in order to maximize profits based on the market demand. In these embodiments, market prices being function of the species of the organisms can be involved and updated as function of the market or of the time of the year for instance. Other economical parameters can be used in the profitability optimization such as market price for organisms in specific weight ranges, fixed plant operation costs, weekly plant operation costs, feed costs and the like.
[0036] In this specific embodiment, the system 100 includes a visual indicator 121 which can be operable to visual indicator the predicted value(s) of the growth indication quantity upon determination by the controller 112. However, in other embodiments, the visual indicator may be remotely located relative to the system 100. For instance, the visual indicator may be provided in the form of a computer application, a web page, a web application, or a mobile application accessible through a network such as the Internet.
[0037] The controller 112 can be provided as a combination of hardware and software components. The hardware components can be implemented in the form of a computing device 200, an example of which is described with reference to Fig. 2. Moreover, the software components of the controller 112 can be implemented in the form of a software application 300, an example of which is described with reference to Fig. 3.
[0038] More specifically, and referring now to Fig. 2, the computing device 200 can have a processor 230, a memory 232, and I/O interface 234.Instructions 236 for predicting growth of a population of organisms can be stored on the memory 232 and accessible by the processor 230.
[0039] The processor 230 can be, for example, a general-purpose microprocessor or microcontroller, a digital signal processing (DSP) processor, an integrated circuit, a field programmable gate array (FPGA), a reconfigurable processor, a programmable read- only memory (PROM), or any combination thereof.
[0040] The memory 232 can include a suitable combination of any type of computer- readable memory that is located either internally or ext