DETAILED ACTION
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 .
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
Request for continued examination
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 10/30/2025 has been entered.
Restriction
Newly submitted claims 21 and 24 directed to an invention that is independent or distinct from the invention originally claimed for the following reasons:
The application consists of plural distinct combinations requiring a subcombination common to each combination. Inventions 2, 21 and 24 are directed to related systems. The related inventions are distinct if: (1) the inventions as claimed are either not capable of use together or can have a materially different design, mode of operation, function, or effect; (2) the inventions do not overlap in scope, i.e., are mutually exclusive; and (3) the inventions as claimed are not obvious variants. See MPEP § 806.05(j). In the instant case, the inventions as claimed comprise the computing system of claim 1, wherein the instructions, when executed by the computing device processor, however claim 2 is directed to: receive a selection of a pathway from the plurality of potential lifecycle emissions pathways for the selected animal, the pathway comprising an entry point corresponding to a start date and an exit point corresponding to an end date, wherein the animal-centric emissions model is based on the pathway, claim 21 is directed to executes a training module configured to apply a machine-learning algorithm, and claim 24 is directed to computing system comprises a distributed computing architecture. These claims all provide a different mode of operation and function. Furthermore, the inventions as claimed do not encompass overlapping subject matter and there is nothing of record to show them to be obvious variants.
Claim 1 link(s) inventions 2, 21, and 24. The restriction requirement between the linked inventions is subject to the nonallowance of the linking claim(s), claims 21 and 24. Upon the indication of allowability of the linking claim(s), the restriction requirement as to the linked inventions shall be withdrawn and any claim(s) depending from or otherwise requiring all the limitations of the allowable linking claim(s) will be rejoined and fully examined for patentability in accordance with 37 CFR 1.104 Claims that require all the limitations of an allowable linking claim will be entered as a matter of right if the amendment is presented prior to final rejection or allowance, whichever is earlier. Amendments submitted after final rejection are governed by 37 CFR 1.116; amendments submitted after allowance are governed by 37 CFR 1.312.
Applicant(s) are advised that if any claim presented in a divisional application is anticipated by, or includes all the limitations of, the allowable linking claim, such claim may be subject to provisional statutory and/or nonstatutory double patenting rejections over the claims of the instant application. Where a restriction requirement is withdrawn, the provisions of 35 U.S.C. 121 are no longer applicable. In re Ziegler, 443 F.2d 1211, 1215, 170 USPQ 129, 131-32 (CCPA 1971). See also MPEP § 804.01.
Since applicant has received an action on the merits for the originally presented invention, this invention has been constructively elected by original presentation for prosecution on the merits. Accordingly, claim 21 and 24 withdrawn from consideration as being directed to a non-elected invention. See 37 CFR 1.142(b) and MPEP § 821.03.
Should applicant traverse on the ground that the inventions are not patentably distinct, applicant should submit evidence or identify such evidence now of record showing the inventions to be obvious variants or clearly admit on the record that this is the case. In either instance, if the examiner finds one of the inventions unpatentable over the prior art, the evidence or admission may be used in a rejection under 35 U.S.C. 103 or pre-AIA 35 U.S.C. 103(a) of the other invention.
Claim Status
Claims 1-25 are pending.
Claims 21 and 24 is withdrawn from further consideration pursuant to 37 CFR 1.142(b) as being drawn to a non-elected invention, as described above.
Claims 1-20, 22-23, and 25 are under examination.
Claims 1-20, 22-23, and 25 are rejected.
Priority
The instant Application is a Continuation of US provisional application 17/098415, filed 11/15/2020, which claims benefit of US non-provisional 62/935774 filed 11/15/2019. Accordingly, each of claims 1-20 are afforded the effective filing date of the 11/15/2019.
Information Disclosure Statement
The information disclosure statements (IDS) filed on 12/20/2024, 08/10/2024, and 10/02/2024 follows the provisions of 37 CFR 1.97 and has been considered in full by the examiner. A signed copy of the IDS is included in the 12/19/2024 Office Action.
Drawings
The Drawings submitted 12/10/2021 are accepted.
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 1-20, 22-23, and 25 are rejected under 35 U.S.C. 101 because the claimed invention is directed to one or more judicial exceptions without significantly more.
MPEP 2106 organizes judicial exception analysis into Steps 1, 2A (Prongs One and Two) and 2B as follows below. MPEP 2106 and the following USPTO website provide further explanation and case law citations: uspto.gov/patent/laws-and-regulations/examination-policy/examination-guidance-and-training-materials.
Framework with which to Evaluate Subject Matter Eligibility:
Step 1: Are the claims directed to a process, machine, manufacture, or composition of matter;
Step 2A, Prong One: Do the claims recite a judicially recognized exception, i.e. a law of nature, a natural phenomenon, or an abstract idea;
Step 2A, Prong Two: If the claims recite a judicial exception under Prong One, then is the judicial exception integrated into a practical application (Prong Two); and
Step 2B: If the claims do not integrate the judicial exception, do the claims provide an inventive concept.
Framework Analysis as Pertains to the Instant Claims:
Step 1
With respect to Step 1: yes, the claims are directed to system, process, and computer program products i.e., a process, machine, or manufacture within the above 101 categories [Step 1: YES; See MPEP § 2106.03].
Step 2A, Prong One
With respect to Step 2A, Prong One, the claims recite judicial exceptions in the form of abstract ideas. The MPEP at 2106.04(a)(2) further explains that abstract ideas are defined as:
mathematical concepts (mathematical formulas or equations, mathematical relationships and mathematical calculations);
certain methods of organizing human activity (fundamental economic practices or principles, managing personal behavior or relationships or interactions between people); and/or
mental processes (procedures for observing, evaluating, analyzing/ judging and organizing information).
With respect to the instant claims, under the Step 2A, Prong One evaluation, the claims are found to recite abstract ideas that fall into the grouping of mental processes (in particular procedures for observing, analyzing and organizing information) and mathematical concepts (in particular mathematical relationships and formulas) are as follows:
Independent claim 1:
identify, by the computing device processor of the computing system, a plurality of equation components based on the historic animal data, individual equation components configured to quantify an amount of emissions,
generate, by the computing device processor of the computing system, an emissions model comprising the plurality of equation components, the emissions model quantifying a total amount of emissions by a group of animals for an emissions lifecycle of the group of animals, wherein the emissions lifecycle includes a plurality of potential lifecycle emissions pathways
identify, by the computing device processor of the computing system, one or more data variables associated with at least one equation component of the plurality of equation components of the emissions lifecycle based on the performance data, and
apply, by the computing device processor of the computing system, at least one adjustment to the at least one equation component to generate an animal-centric emissions model, the animal-centric emissions model quantifying an amount of emissions by the selected animal during the emissions lifecycle of the selected animal.
Dependent claim 3:
identify, by the computing device processor of the computing system, equation components associated with the pathway, wherein the animal-centric emissions model is based on the equation components.
Dependent claim 4:
determine, by the computing device processor of the computing system, the amount of emissions by the selected animal during the emissions lifecycle of the selected animal by evaluating the animal-centric emissions model on the historic animal data and the performance data.
Dependent claim 7:
associate at least one certification with the selected animal based on the amount of emissions by the selected animal during the emissions lifecycle of the selected animal.
Dependent claim 9:
iteratively update the animal-centric emissions model based on additional data from the plurality of sensors.
Dependent claim 13:
generate control instructions to control an appliance to alter at least one task affecting the amount of emissions by the selected animal during the emissions lifecycle of the selected animal.
Independent claim 14:
identifying, by the computing device processor, a plurality of equation components based on the historic animal data, individual equation components configured to quantify an amount of emissions,
generating, by the computing device processor, an emissions model comprising the plurality of equation components, the emissions model quantifying a total amount of emissions by a group of animals for an emissions lifecycle of the group of animals, wherein the emissions lifecycle includes a plurality of potential lifecycle emissions pathways
identifying, by the computing device processor, one or more data variables associated with at least one equation component of the plurality of equation components of the emissions lifecycle based on the performance data, and
apply, by the computing device processor, at least one adjustment to the at least one equation component to generate an animal-centric emissions model,
the animal-centric emissions model quantifying an amount of emissions by the selected animal during the emissions lifecycle of the selected animal.
Dependent claim 16:
identifying, by the computing device processor, equation components associated with the pathway, wherein the animal-centric emissions model is based on the equation components.
Dependent claim 17:
determining, by the computing device processor, the amount of emissions by the selected animal during the emissions lifecycle of the selected animal by evaluating the animal-centric emissions model on the historic animal data and the performance data.
Dependent claim 18:
determining, by the computing device processor, an emissions offset based on the total amount of emissions by the group of animals and the amount of emissions by the selected animal.
Independent claim 20:
identify, by the computing device processor, a plurality of equation components based on the historic animal data, individual equation components configured to quantify an amount of emissions,
generate, by the computing device processor, an emissions model comprising the plurality of equation components, the emissions model quantifying a total amount of emissions by a group of animals for an emissions lifecycle of the group of animals, wherein the emissions lifecycle includes a plurality of potential lifecycle emissions pathways
identify, by the computing device processor, one or more data variables associated with at least one equation component of the plurality of equation components of the emissions lifecycle based on the performance data, and
apply, by the computing device processor, at least one adjustment to the at least one equation component to generate an animal-centric emissions model,
the animal-centric emissions model quantifying an amount of emissions by the selected animal during the emissions lifecycle of the selected animal.
Independent claim 22:
transmits control instructions generated from the animal-centric emissions model to at least one appliance thereby automatically altering an operation of the farm to reduce the determined emissions by the selected animal.
Independent claim 23:
automatically and continuously updating, by the computing device processor, the animal-centric emissions model based on the real-time sensor data received from the at least one sensor, wherein the at least one sensor comprises a gas sensor, a feed-intake sensor, a movement sensor, a body-composition sensor or any combination thereof, whereby the updated model causes modification of a control instruction output to alter a farm-management operation to reduce the determined emission by the selected animal.
Dependent claims 5, 8, 10-12, and 19 recite further steps that limit the judicial exceptions in independent claims 1 and 14 and, as such, also are directed to those abstract ideas. For example, claim 5 further limits the lifecycle emission of claim 1, claim 8 further limits the certification of claim 7, claim 10 further limits the emissions model of claim 9 to including a machine learning technique, and claim 11 further limits the emissions model of claim 1 to including a machine learning technique.
The abstract ideas recited in the claims are evaluated under the Broadest Reasonable Interpretation (BRI) and determined to each cover performance in the mind because the method only requires a user to manually identify(ing), generate(ing), apply(ing), determine(ing), associate, alter, and update. Without further detail as to the methodology involved in “identify, by the computing device processor of the computing system, a plurality of equation components”, “generate, by the computing device processor of the computing system, an emissions model”, “ identify, by the computing device processor of the computing system, one or more data variables”, “apply, by the computing device processor of the computing system, at least one adjustment”, “identify, by the computing device processor of the computing system, equation components”, “associate at least one certification”, “iteratively update the animal-centric emissions model”, “generate control instructions “, “identify(ing), by the computing device processor, a plurality of equation components”, “generate(ing), by the computing device processor, an emissions model”, “ identify(ing), by the computing device processor, one or more data variables”, “apply(ing), by the computing device processor, at least one adjustment”, and “determining, by the computing device processor, an emissions offset” under the BRI, one may simply, for example, use pen and paper to determine emission from animal production.
The abstract ideas recited in the claims are evaluated under the Broadest Reasonable Interpretation (BRI) and determined to each cover performance either in the mind and/or by mathematical operation “determine, by the computing device processor of the computing system, the amount of emissions” as disclosed in the specification [0011, 0061, and 0113].
Therefore, claims 1, 14, and 20 and those claims dependent therefrom recite an abstract idea [Step 2A, Prong 1: YES; See MPEP § 2106.04].
Step 2A, Prong Two
Because the claims do recite judicial exceptions, direction under Step 2A, Prong Two, provides that the claims must be examined further to determine whether they integrate the judicial exceptions into a practical application (MPEP 2106.04(d)). A claim can be said to integrate a judicial exception into a practical application when it applies, relies on, or uses the judicial exception in a manner that imposes a meaningful limit on the judicial exception. This is performed by analyzing the additional elements of the claim to determine if the judicial exceptions are integrated into a practical application (MPEP 2106.04(d).I.; MPEP 2106.05(a-h)). If the claim contains no additional elements beyond the judicial exceptions, the claim is said to fail to integrate the judicial exceptions into a practical application (MPEP 2106.04(d).III).
Additional elements, Step 2A, Prong Two
With respect to the instant recitations, the claims recite the following additional elements:
Independent claim 1:
obtain, by the computing device processor of the computing system, historic animal data from a plurality of different disaggregated sources
receive a selection of an animal associated with the group of animals to identify a selected animal, the animal associated with a unique identifier identifying the selected animal,
obtain in real-time from a database, by the computing device processor of the computing system, wherein the database is comprised of information obtained by at least one sensor of a plurality of sensors monitoring the selected animal, performance data associated with the unique identifier of the selected animal
Dependent claim 2:
receive a selection of a pathway from the plurality of potential lifecycle emissions pathways for the selected animal, the pathway comprising an entry point corresponding to a start date and an exit point corresponding to an end date, wherein the animal-centric emissions model is based on the pathway.
Dependent claim 6:
display, for the selected animal associated with the unique identifier, in a graphical user interface one or more views of the amount of emissions during the emissions lifecycle.
Independent claims 14 and 20:
obtain(ing), by the computing device processor, historic animal data from a plurality of different disaggregated sources
receive(ing) a selection of an animal associated with the group of animals to identify a selected animal, the animal associated with a unique identifier identifying the selected animal,
obtain(ing) in real-time from a database, by the computing device processor, wherein the database is comprised of information obtained by at least one sensor of a plurality of sensors monitoring the selected animal, performance data associated with the unique identifier of the selected animal
Dependent claim 15:
receive a selection of a pathway from the plurality of potential lifecycle emissions pathways for the selected animal, the pathway comprising an entry point corresponding to a start date and an exit point corresponding to an end date, wherein the animal-centric emissions model is based on the pathway.
Dependent claims 12 and 19 recite further steps that limit the additional element in independent claim 1and, as such, also are directed to those abstract ideas. For example, claim 12 further limits the sensor of claim 1 and claim 19 further limits the database of claim 14.
Dependent claim 22:
an appliance
The claims also include non-abstract computing elements. For example, independent claim 1 includes a computing system comprising: a computing device processor: and a memory device including instructions. Independent claim 14 includes a computer-implemented method. Independent claim 20 includes a non-transitory computer readable storage medium.
Considerations under Step 2A, Prong Two
With respect to Step 2A, Prong Two, the additional elements of the claims do not integrate the judicial exceptions into a practical application for the following reasons. Those steps directed to data gathering, such as “obtain(ing)”, and “receive(ing)”, and to data outputting, such as “display”, perform functions of collecting and outputting the data needed to carry out the judicial exceptions. Data gathering and outputting do not impose any meaningful limitation on the judicial exceptions, or on how the judicial exceptions are performed. Data gathering and outputting steps are not sufficient to integrate judicial exceptions into a practical application (MPEP 2106.05(g)).
Further steps directed to additional non-abstract elements of “a computing system, a computer-implemented model, and a non-transitory computer readable storage medium” do not describe any specific computational steps by which the “computer parts” perform or carry out the judicial exceptions, nor do they provide any details of how specific structures of the computer, such as the computer-readable recording media, are used to implement these functions. The claims state nothing more than a generic computer which performs the functions that constitute the judicial exceptions. Hence, these are mere instructions to apply the judicial exceptions using a computer, and therefore the claim does not integrate that judicial exceptions into a practical application. The courts have weighed in and consistently maintained that when, for example, a memory, display, processor, machine, etc.… are recited so generically (i.e., no details are provided) that they represent no more than mere instructions to apply the judicial exception on a computer, and these limitations may be viewed as nothing more than generally linking the use of the judicial exception to the technological environment of a computer (MPEP 2106.05(f)). Further, the courts have found that receiving and outputting data are well-understood, routine, and conventional functions of a computer when claimed in a merely generic manner or as insignificant extra-solution activity (see Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information), buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network), Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015), and OIP Techs., 788 F.3d at 1363, 115 USPQ2d at 1092-93, as discussed in MPEP 2106.05(d)(II)(i)). As such, the claims simply append well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception (MPEP2106.05(d)). The data gathering steps as recited in the instant claims constitute a general link to a technological environment which is insufficient to constitute an inventive concept which would render the claims significantly more than the judicial exception (MPEP2106.05(g)&(h)).
Thus, none of the claims recite additional elements which would integrate a judicial exception into a practical application, and the claims are directed to one or more judicial exceptions [Step 2A, Prong 2: NO; See MPEP § 2106.04(d)].
Step 2B (MPEP 2106.05.A i-vi)
According to analysis so far, the additional elements described above do not provide significantly more than the judicial exception. A determination of whether additional elements provide significantly more also rests on whether the additional elements or a combination of elements represents other than what is well-understood, routine, and conventional. Conventionality is a question of fact and may be evidenced as: a citation to an express statement in the specification or to a statement made by an applicant during prosecution that demonstrates a well-understood, routine or conventional nature of the additional element(s); a citation to one or more of the court decisions as discussed in MPEP 2106(d)(II) as noting the well-understood, routine, conventional nature of the additional element(s); a citation to a publication that demonstrates the well-understood, routine, conventional nature of the additional element(s); and/or a statement that the examiner is taking official notice with respect to the well-understood, routine, conventional nature of the additional element(s).
With respect to the instant claims, the courts have found that receiving and outputting data are well-understood, routine, and conventional functions of a computer when claimed in a merely generic manner or as insignificant extra-solution activity (see Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information), buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network), Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015), and OIP Techs., 788 F.3d at 1363, 115 USPQ2d at 1092-93, as discussed in MPEP 2106.05(d)(II)(i)). As such, the claims simply append well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception (MPEP2106.05(d)). The data gathering steps as recited in the instant claims constitute a general link to a technological environment which is insufficient to constitute an inventive concept which would render the claims significantly more than the judicial exception (MPEP2106.05(g)&(h)).
With respect to claims 1, 14, and 20 and those claims dependent therefrom, the computer-related elements or the general purpose computer do not rise to the level of significantly more than the judicial exception. The claims state nothing more than a generic computer which performs the functions that constitute the judicial exceptions. Hence, these are mere instructions to apply the judicial exceptions using a computer, which the courts have found to not provide significantly more when recited in a claim with a judicial exception (see MPEP 2106.06(A)). The specification also notes that computer processors and systems, as example, are commercially available or widely used at [0148]. The additional elements are set forth at such a high level of generality that they can be met by a general purpose computer. Therefore, the computer components constitute no more than a general link to a technological environment, which is insufficient to constitute an inventive concept that would render the claims significantly more than the judicial exceptions (see MPEP 2106.05(b)I-III).
Taken alone, the additional elements do not amount to significantly more than the above-identified judicial exception(s). Even when viewed as a combination, the additional elements fail to transform the exception into a patent-eligible application of that exception. Thus, the claims as a whole do not amount to significantly more than the exception itself [Step 2B: NO; See MPEP § 2106.05].
Therefore, the instant claims are not drawn to eligible subject matter as they are directed to one or more judicial exceptions without significantly more. For additional guidance, applicant is directed generally to the MPEP § 2106.
Response to Applicant Arguments
Applicant submits that the amended claims provide a practical application and includes additional elements that integrate the judicial exception into a practical application [p.11, par. 2].
It is respectfully submitted that this is not found persuasive. As set forth in the above 101 rejection, the said improvements are tied to a computer system to the abstract idea of predicting life-cycle emissions. MPEP 2106.05(b)I-III sets forth:
Merely adding a generic computer, generic computer components, or a programmed computer to perform generic computer functions does not automatically overcome an eligibility rejection. Alice Corp. Pty. Ltd. v. CLS Bank Int’l, 573 U.S. 208, 223-24, 110 USPQ2d 1976, 1983-84 (2014). See In re Alappat, 33 F.3d 1526, 1545, 31 USPQ2d 1545, 1558 (Fed. Cir. 1994); In re Bilski, 545 F.3d 943, 88 USPQ2d 1385 (Fed. Cir. 2008).
Additionally, the steps of data gathering do not integrate the judicial exception into a practical application. The courts have found that receiving and outputting data are well-understood, routine, and conventional functions of a computer when claimed in a merely generic manner or as insignificant extra-solution activity (see Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information), buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network), Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015), and OIP Techs., 788 F.3d at 1363, 115 USPQ2d at 1092-93, as discussed in MPEP 2106.05(d)(II)(i)). As such, the claims simply append well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception (MPEP2106.05(d)). The data gathering steps as recited in the instant claims constitute a general link to a technological environment which is insufficient to constitute an inventive concept which would render the claims significantly more than the judicial exception (MPEP2106.05(g)&(h)). Additionally, calculat[ing] an emissions model for that specific and individual animal and any product produced therefrom” is interesting and useful information, but it is not a practical application. The practical application must be found among the additional elements of the claims; ie elements that are not judicial exceptions (MPEP 2106.04(d)). Mathematical calculations are judicial exceptions, so no matter how useful or beneficial those calculations are, they are not a practical application. In regards to claims 7 and 8, the certification is merely an output of the judicial exception which does not integrate into a practical application. Claim 13 does include a farm control integration but that can also be metal activity or an appliance can be anything, even a human changing a routine. Claims 9-11 can also be done as a mental process. Claim 19 is directed to a database which is an abstract idea or storage.
Applicant submits claims recite significantly more than generic computing [p. 12, par. 2].
It is respectfully found not persuasive. The specification discloses a general architecture for some of these machines may be described herein in order to illustrate one or more exemplary means by which a given unit of functionality may be implemented. According to specific embodiments, at least some of the features or functionalities of the various embodiments disclosed herein may be implemented on one or more general- purpose computers associated with one or more networks, such as for example an end-user computer system, a client computer, a network server or other server system, a mobile computing device (e.g., tablet computing device, mobile phone, smartphone, laptop, or other appropriate computing device), a consumer electronic device, a music player, or any other suitable electronic device, router, switch, or other suitable device, or any combination thereof. In at least some embodiments, at least some of the features or functionalities of the various embodiments disclosed herein may be implemented in one or more virtualized computing environments (e.g., network computing clouds, virtual machines hosted on one or more physical computing machines, or other appropriate virtual environments) [0148]. There is no mention of non-conventional features for the computer system.
Applicant submits these claims are directed to a technological improvement, not an abstract idea, and integrate any alleged abstraction into a practical, concrete application [p. 12, par. 4].
It is respectfully found not persuasive. It is important to keep in mind that an improvement in the abstract idea itself (e.g. a recited fundamental economic concept) is not an improvement in technology. Furthermore, it is important to note, the judicial exception alone cannot provide the improvement. The improvement can be provided by one or more additional elements or by the additional element(s) in combination with the recited judicial exception. See MPEP 2106.05(a).
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1-20, 22-23, and 25 are rejected under 35 U.S.C. 102(a)(1) and 102(a)(2) as being anticipated by Zimmerman et al. (US 2011/0192213 A1, published 08/11/2011, cited on IDS dated 12/20/2021).
Claim 1 discloses a computing system for generating emissions models, the computing system comprising: a computing device processor: and a memory device including instructions when executed by the computing device processor, enables the computing system to perform a method, claim 14 discloses a computer-implemented method for generating animal-centric emissions models, and claim 20 discloses a non-transitory computer readable storage medium storing instructions that, when executed by a computing device processor of a computing system, causes the computing system.
Zimmerman discloses the invention provide an implementation of an animal monitoring station that can measure methane emissions and/or emissions of carbon dioxide and/or other metabolic gases such as hydrogen and hydrogen sulfide [0017]. Zimmerman further discloses changes in the ratios of methane compared to carbon dioxide may be used to indicate changes in metabolic efficiency, and these measured emission ratios and changes in metabolic efficiencies may then be tracked in some embodiments along with additional data which is subsequently stored for an individual animal and/or on a herd basis in the system's memory or data storage [0017]. Zimmerman also discloses, this data can be routed to a computer where numerical models or other calculations may be performed (e.g., with software programs or modules run by the computer) to transform the data into methane fluxes, fluxes of carbon dioxide, and fluxes of other metabolic gases that can be measured in the animal monitoring station [0017].
Claims 1, 14, and 20 discloses:
obtain(ing), by the computing device processor (of the computing system), historic animal data from a plurality of different disaggregated sources,
Zimmerman discloses the invention provide an implementation of an animal monitoring station that can measure methane emissions and/or emissions of carbon dioxide and/or other metabolic gases such as hydrogen and hydro gen Sulfide [0017]. Zimmerman further discloses changes in the ratios of methane compared to carbon dioxide may be used to indicate changes in metabolic efficiency, and these measured emission ratios and changes in metabolic efficiencies may then be tracked in some embodiments along with additional data which is subsequently stored for an individual animal and/or on a herd basis in the system's memory or data storage [0017]. Zimmerman also discloses each GreenFeed system may include a Software tool(s) that functions to record and analyze specific ruminant's CH and CO emissions and other available process parameters (e.g., time of day, animal weight, animal temperature, and so on) [0135].
identify(ing), by the computing device processor (of the computing system), a plurality of equation components based on the historic animal data, individual equation components configured to quantify an amount of emissions,
Zimmerman discloses further, this data can be routed to a computer where numerical models or other calculations may be performed (e.g., with Software programs or modules run by the computer) to transform the data into methane fluxes, fluxes of carbon dioxide, and fluxes of other metabolic gases that can be measured in the animal monitoring station. In addition, either an internal (e.g., from the animal) or an external (e.g., from an external source) tracer can be incorporated into the system [0017].
generate(ing), by the computing device processor (of the computing system), an emissions model comprising the plurality of equation components, the emissions model quantifying a total amount of emissions by a group of animals for an emissions lifecycle of the group of animals, wherein the emissions lifecycle includes a plurality of potential lifecycle emissions pathways,
Zimmerman discloses the GreenFeed system and its data analysis system/software may be used to track for each individual cow of a herd (or a monitored subset) the methane to carbon dioxide ratio over time [0141].
receive(ing) a selection of an animal associated with the group of animals to identify a selected animal, the animal associated with a unique identifier identifying the selected animal,
Zimmerman discloses the GreenFeed monitoring system (or to only monitor and control emissions from Such animals based on identification of this subset of the ruminants via eartag/RFID or other animal identification) [0087]. Zimmerman further discloses a GreenFeed system may include an RFID or other identification system to identify individual animals such as particular cattle in a herd for monitoring and for control of feed and supplements to that particular animal [0135].
obtain(ing) in real-time from a database, by the computing device processor (of the computing system), wherein the database is comprised of information obtained by at least one sensor of a plurality of sensors monitoring the selected animal, performance data associated with the unique identifier of the selected animal,
Zimmerman discloses the GreenFeed system may be thought of as including an instrumented feeder station that measures real time CO and CH emissions from ruminant's nose and mouth Such as a dairy cow's nose and mouth [0135]. Zimmerman further discloses a GreenFeed system may include an RFID or other identification system to identify individual animals such as particular cattle in a herd for monitoring and for control of feed and Supplements to that particular animal [0135].
identify(ing), by the computing device processor (of the computing system), one or more data variables associated with at least one equation component of the plurality of equation components of the emissions lifecycle based on the performance data, and
Zimmerman discloses methane and carbon dioxide mass fluxes can be used along with numerical models to estimate dry-matter intake, digestibility, and animal efficiency [0036]. Zimmerman further discloses this data can be used along with production data to select breeding stock that pro duces more meat and milk on less feed resulting in lower emissions of greenhouse gases and improved animal welfare and global sustainability [0036].
apply(ing), by the computing device processor (of the computing system), at least one adjustment to the at least one equation component to generate an animal-centric emissions model, the animal-centric emissions model quantifying an amount of emissions by the selected animal during the emissions lifecycle of the selected animal.
Zimmerman discloses these data may then be transferred to a computer program or series of programs in which numerical models are run such as within the data analyzing station to result in or produce decisions about the types and amounts of specific antibiotics, and/or nutrient supplements to dispense at step in the next or current feeding of animal or access of a feed station (e.g., provide a particular “prescription” or “diet' of supplements and the like to dispense at this time to this particular animal based, typically, on the methane emissions detected and/or on metabolic efficiency of the animal) [0080].
Claims 2 and 15 are directed to wherein the instructions, when executed by the computing device processor, further enables the computing system to: receive a selection of a pathway from the plurality of potential lifecycle emissions pathways for the selected animal, the pathway comprising an entry point corresponding to a start date and an exit point corresponding to an end date, wherein the animal-centric emissions model is based on the pathway.
Zimmerman discloses the user has selected a set or group of animals which may be an entire herd or a subset of a herd of ruminants [0179]. Zimmerman further discloses then, within the selected group of animals the user can use a drop down or other entry device to select all (herd average values and so on) or to choose to inspect a particular animal [0179]. Zimmerman also disclose the data selection area may also be used to select a particular day or range of days (or a time period) for the data to be retrieved [0179].
Claims 3 and 16 are directed to the computing system of claim 2, wherein the instructions, when executed by the computing device processor, further enable the computing system to: identify, by the computing device processor of the computing system, equation components associated with the pathway, wherein the animal-centric emissions model is based on the equation components.
Zimmerman discloses software modules are designed to operate the GreenFeed unit, monitor operational variables and collect data from all sensors [0138].
Claims 4 and 17 are directed to the computing system of claim 1, wherein the instructions, when executed by the computing device processor, further enable the computing system to: determine, by the computing device processor of the computing system, the amount of emissions by the selected animal during the emissions lifecycle of the selected animal by evaluating the animal-centric emissions model on the historic animal data and the performance data.
Zimmerman discloses a system designed to restrict atmospheric mixing; sensors to quantify air flow rates; tracers to characterize breath capture rates under various atmospheric conditions and animal head positions; the potential for the conditional delivery of a specified feed, supplement, or water at specified times or when specified conditions occur; and the ability to use the data in near-real time to identify animals that do not meet performance boundaries (set for each individual or set for the entire herd) [0053].
Claim 5 is directed to the computing system of claim 1, wherein the amount of emissions by the selected animal is for a particular lifecycle emissions.
Zimmerman further discloses then, within the selected group of animals the user can use a drop down or other entry device to select all (herd average values and so on) or to choose to inspect a particular animal [0179]. Zimmerman also disclose the data selection area may also be used to select a particular day or range of days (or a time period) for the data to be retrieved [0179].
Claim 6 is directed to the computing system of claim 1, wherein the instructions, when executed by the computing device processor, further enable the computing system to: display, for the selected animal associated with the unique identifier, in a graphical user interface one or more views of the amount of emissions during the emissions lifecycle.
Zimmerman discloses results of the data analysis provided by the GreenFeed system in the performed test provides a graph that may be generated by the GreenFeed system and displayed (or output) to a user computer system (e.g., in a GUI on a monitor) communicating with the GreenFeed host server [0140].
Claim 7 is directed to the computing system of claim 1, wherein the instructions, when executed by the computing device processor, further enable the computing system to:associate at least one certification with the selected animal based on the amount of emissions by the selected animal during the emissions lifecycle of the selected animal.
Zimmerman discloses providing linkage between the GreenFeed system and other systems, the ruminant monitoring and emission control data may be transformed into carbon credits (e.g., C-Lock certified carbon credits or the like) that may be transparent and verifiable [0083].
Claim 8 is directed to the computing system of claim 7, wherein the at least one certification indicates the amount of emissions that the animal has emitted or is expected to emit.
Zimmerman discloses the methane monitoring and emission control or GreenFeed system may incorporate a telemetry system to transmit data to a remote computer ( or data analyzing station where it may be stored in computer memory or data storage (such as in a database with supplement and methane emission data collected at the data logger for each animal) and/or further processed for a plurality of animals and/or stations [0083]. Zimmerman further discloses to cause the computer( s) or their processor to perform particular functions) to process data and aggregate the collected and logged data to generate a report of emission reductions and performance efficiency for each individual animal [0083].
Claim 9 is directed to the computing system of claim 1, wherein the instructions, when executed by the computing device processor, further enable the computing system to: iteratively update the animal-centric emissions model based on additional data from the plurality of sensors.
Zimmerman discloses the database storing GHG and other monitored/analyzed data for each animal is updated to reflect the most recent feeding and monitoring of the animal with the collected/analyzed data being linked to the animal's ID (e.g., a record may be maintained for each animal with fields for each type of tracked information) [0119].
Claim 10 is directed to the computing system of claim 9, wherein a machine learning technique is utilized to iteratively update the animal-centric emissions model.
Zimmerman discloses the measured data (and other animal data) may be recorded in a local data logger and/or after transmission to a data analyzing station [0119]. Zimmerman further discloses at ,the feeding station resets and awaits another animal [0119]. Zimmerman also discloses at the method continues with the data monitored at the individual feed or other station being analyzed by software/hardware provided at a data analyzing station ( or locally at the feed station or other station in some cases) [0119]. Zimmerman further discloses the GreenFeed system included data analysis software that calculated background concentrations of CH4 and CO2 [0138]. In view of the specification, a search of the term machine learning has been performed, and it appears to provide a list of options for assessing data. Those options including expert practitioner formulations, regression analysis, statistical analysis, sensitivity analysis, Monte Carlo simulation, experimental trials, artificial intelligence, machine learning, training algorithms [0061], and do not appear to provide any new or unique machine learning models or specific known machine learning algorithms.
Claim 11 is directed to the computing system of claim 1, wherein a machine learning technique is utilized to generate the emissions model.
Zimmerman discloses the measured data (and other animal data) may be recorded in a local data logger and/or after transmission to a data analyzing station [0119]. Zimmerman further discloses at ,the feeding station resets and awaits another animal [0119]. Zimmerman also discloses at the method continues with the data monitored at the individual feed or other station being analyzed by software/hardware provided at a data analyzing station ( or locally at the feed station or other station in some cases) [0119]. Zimmerman further discloses the GreenFeed system included data analysis software that calculated background concentrations of CH4 and CO2 [0138]. In view of the specification, a search of the term machine learning has been performed, and it appears to provide a list of options for assessing data. Those options including expert practitioner formulations, regression analysis, statistical analysis, sensitivity analysis, Monte Carlo simulation, experimental trials, artificial intelligence, machine learning, training algorithms [0061], and do not appear to provide any new or unique machine learning models or specific known machine learning algorithms.
Claim 12 is directed to the computing system of claim 1, wherein the plurality of sensors includes at least one of a camera, a scale, a ruler, a timer, a feeder, a temperature sensor, a pressure sensor, a flow meter, an electrical sensor, a radiation sensor, a gas sensor, a liquid sensor, a humidity sensor, a movement sensor, a global positioning sensor (GPS), a soil composition sensor, a pH sensor, a body composition sensor, a health sensor, animal identification sensor, facial identification sensor, biomedical sensor, an x-ray sensor, nuclear magnetic resonance sensor, or an ultrasound sensor, and wherein the performance data includes expected progeny performance data, expected progeny differences data, genotypic data, phenotypic data, and farm practices management data associated with the selected animal.
Zimmerman discloses methane and carbon dioxide and other metabolic gas emission measurements are obtained using methods such as solid-state sensors, tunable diode laser absorption spectroscopy (TD LAS), open-path Fourier transform infrared spectroscopy (FTIR), other infrared-based methods, miniaturized gas chromatography/flame ionization detection (GC/FID), proton transfer reactor mass spectroscopy, cavity ring-down spectroscopy, or other miniaturized mass spectrometry [0018]. Zimmerman further discloses if pressure sensors detect a change in the animal weight distribution coupled with a change (likely a decrease) in rumen methane and carbon dioxide and (possibly an increase) in respiratory carbon dioxide, the animal is marked and the operators are notified that closer inspection for lameness is warranted [0032]. Zimmerman also discloses a feeder [0038], camera [0199], scale [0200], moisture, temperature, pressure and velocity [0204], and GPS [0026].
Claim 13 is directed to the computing system of claim 1, wherein the instructions, when executed by the computing device processor, further enable the computing system to:generate control instructions to control an appliance to alter at least one task affecting the amount of emissions by the selected animal during the emissions lifecycle of the selected animal.
Zimmerman discloses in addition to the generation of high value GHG offsets, the system may serve as a livestock management tool [0100]. Zimmerman further discloses the feed may be chosen based on a prior breath analysis for the animal to try to control GHG production/emission or to control animal production [0118]. Zimmerman also discloses the dispensed feed, for example, may include a particular mixture of two, three, or more feeds and/or supplements that have been determined by a data analyzing station as appropriate for the identified animal in controlling their GHG emissions (or achieving an animal production goal such as weight gain, milk production, or the like) [0118].
Claim 18 is directed to the computer-implemented method of claim 14 comprising: determining, by the computing device processor, an emissions offset based on the total amount of emissions by the group of animals and the amount of emissions by the selected animal.
Zimmerman discloses in addition to the generation of high value GHG offsets, the system may serve as a livestock management tool [0036].
Claim 22 is directed to the computing system of claim 1, wherein the computing device processor transmits control instructions generated from the animal-centric emissions model to at least one appliance thereby automatically altering an operation of the farm to reduce the determined emissions by the selected animal.
Zimmerman discloses in addition to the generation of high value GHG offsets, the system may serve as a livestock management tool [0100]. Zimmerman further discloses the feed may be chosen based on a prior breath analysis for the animal to try to control GHG production/emission or to control animal production [0118]. Zimmerman also discloses the dispensed feed, for example, may include a particular mixture of two, three, or more feeds and/or supplements that have been determined by a data analyzing station as appropriate for the identified animal in controlling their GHG emissions (or achieving an animal production goal such as weight gain, milk production, or the like) [0118].
Claim 23 is directed to the computing system of claim 1, further comprising automatically and continuously updating, by the computing device processor, the animal-centric emissions model based on the real-time sensor data received from the at least one sensor, wherein the at least one sensor comprises a gas sensor, a feed-intake sensor, a movement sensor, a body-composition sensor or any combination thereof, whereby the updated model causes modification of a control instruction output to alter a farm-management operation to reduce the determined emission by the selected animal.
Zimmerman discloses the GreenFeed system may be thought of as including an instrumented feeder station that measures real time CO2 and CH4 emissions from ruminant's nose and mouth such as a dairy cow's nose and mouth [0135]. Zimmerman further discloses a GreenFeed system may include an RFID or other identification system to identify individual animals such as particular cattle in a herd for monitoring and for control of feed and supplements to that particular animal [0135].
Claim 25 is directed to the computing system of claim 1, wherein the wherein the computing device processor is further configured to generate a certification record based on the animal-centric emissions model for the selected animal, the certification record comprising a quantitative emissions score associated with the selected animal and stored in a database linked to the unique identifier of the selected animal, and wherein the certification record is accessible for product labeling, supply-chain traceability, regulatory reporting, or any combination thereof to indicate the amount of emissions that the animal has emitted or is expected to emit during its emissions lifecycle is below a threshold value.
Zimmerman discloses the methane monitoring and emission control or GreenFeed system may incorporate a telemetry system to transmit data to a remote computer ( or data analyzing station where it may be stored in computer memory or data storage (such as in a database with supplement and methane emission data collected at the data logger for each animal) and/or further processed for a plurality of animals and/or stations [0083]. Zimmerman further discloses to cause the computer( s) or their processor to perform particular functions) to process data and aggregate the collected and logged data to generate a report of emission reductions and performance efficiency for each individual animal [0083].
Claim Rejections - 35 USC § 103
The previous 103 rejection has been withdrawn in view of the amendments and remarks.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
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.
A. Claim(s) 19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Zimmerman, as applied to claim 14 above, and in further view of Kamilaris et al (Kamilaris, Andreas, Agusti Fonts, and Francesc X. Prenafeta-Boldύ. "The rise of blockchain technology in agriculture and food supply chains." Trends in food science & technology 91 (2019), previously cited).
Zimmerman discloses the method of claim 14.
Claim 19 discloses the computer-implemented method of claim 14, wherein the database comprises a blockchain database.
Zimmerman is silent a blockchain database.
However, Kamilaris discloses the rise of blockchain technology in agriculture and food supply chains (title). Kamilaris further discloses a query of Blockchain AND [Agriculture OR Food OR “Food Supply” OR “Food Supply Chain”] resulting in 49 initiatives were divided into categories with waste reduction and environmental awareness comprising of 5 projects initiatives, or 10% of the results (p. 643, col. 1, par. 2).
Regarding claim 19, it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine, in the course of routine experimentation and with a reasonable expectation of success, Zimmerman with Kamilaris as they disclose precision livestock farming including food supply. The motivation would have been to overcome current challenges because blockchain is a promising technology towards a transparent supply chain of food, with many ongoing initiatives in various food products and food-related issues as disclosed by Kamilaris (abstract).
Nonstatutory Double Patenting
The instant rejection of claims 1-20 are maintained from the previous Office Action.
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/apply/applying-online/eterminal-disclaimer.
Claims 1-2, 4, 7-9, 12-15, 17, and 20 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-2, 4, 8, 12, and 17 of U.S. Patent No. 11209419. Although the claims at issue are not identical, they are not patentably distinct from each other because the claims of the instant application are broader in scope than the claims of the ‘419 patent and are therefore anticipated by the claims of the ‘419 patent, as set forth in the following table:
Instant Application # 17/556493
Patent No. 11209419
Claim(s)
Limitation(s)
Claim(s)
Limitation(s)
1-2, 4
Claim 1: A computing system for generating emissions models, the computing system comprising: a computer device processor; and a memory device including instructions that, when executed by the computing device processor, enables the computing system to: obtain, by the computing device processor of the computing system, historic animal data from a plurality of different disaggregated sources, identify, by the computing device processor of the computing system, a plurality of equation components based on the historic animal data, individual equation components configured to quantify an amount of emissions, generate, by the computing device processor of the computing system, an emissions model comprising the plurality of equation components, the emissions model quantifying a total amount of emissions by a group of animals for an emissions lifecycle of the group of animals, wherein the emissions lifecycle includes a plurality of potential lifecycle emissions pathways, receive a selection of an animal associated with the group of animals to identify a selected animal, the animal associated with a unique identifier identifying the selected animal, obtain in real-time from a database, by the computing device processor of the computing system, wherein the database is comprised of information obtained by at least one sensor of a plurality of sensors monitoring the selected animal, performance data associated with the unique identifier of the selected animal, identify, by the computing device processor of the computing system, one or more data variables associated with at least one equation component of the plurality of equation components of the emissions lifecycle based on the performance data, and apply, by the computing device processor of the computing system, at least one adjustment to the at least one equation component to generate an animal-centric emissions model, the animal-centric emissions model quantifying an amount of emissions by the selected animal during the emissions lifecycle of the selected animal.
Claim 2: The computing system of claim 1, wherein the instructions, when executed by the computing device processor, further enables the computing system to: receive a selection of a pathway from the plurality of potential lifecycle emissions pathways for the selected animal, the pathway comprising an entry point corresponding to a start date and an exit point corresponding to an end date, wherein the animal-centric emissions model is based on the pathway.
Claim 4: The computing system of claim 2, wherein the instructions, when executed by the computing device processor, further enables the computing system to: identify, by the computing device processor of the computing system, equation components associated with the pathway, wherein the animal-centric emissions model is based on the equation components.
1
A computing system for generating animal-centric emissions models, the computing system comprising: a computing device processor; and a memory device including instructions that, when executed by the computing device processor, enables the computing system to: obtain, by the computing device processor of the computing system, historic animal data from a plurality of different disaggregated sources by scanning an application programming interface (API), the historic animal data associated with a plurality of input parameters of a baseline emissions model; identify, by the computing device processor of the computing system, a plurality of equation components corresponding to the plurality of input parameters, individual equation components configured to quantify an amount of emissions; generate, by the computing device processor of the computing system, a the baseline emissions model comprising the plurality of equation components, the baseline emissions model quantifying an amount of emissions by a group of animals for an emissions lifecycle of the group of animals, the emissions lifecycle including a plurality of potential lifecycle emissions pathways; receive a first user selection of an animal associated with the group of animals to identify a selected animal, the animal associated with a unique identifier identifying the selected animal; receive a second user selection of a segment of the emissions lifecycle for the selected animal comprising an entry point corresponding to a start date and an exit point corresponding to an end date, the segment associated with one of the plurality of potential lifecycle emissions pathways; identify, by the computing device processor of the computing system, equation components associated with the segment; obtain in real-time from a database, by the computing device processor of the computing system and at least one sensor of a plurality of sensors monitoring the selected animal, performance data associated with the unique identifier of the selected animal for the segment, wherein the performance data includes expected progeny performance data, expected progeny differences data, genotypic data, phenotypic data, and or farm practices management data[[;]]associated with the selected animal; identify, by the computing device processor of the computing system, one or more data variables associated with at least one equation component of the plurality of equation components of the baseline emissions model for the segment based on the performance data; apply, by the computing device processor of the computing system, at least one adjustment to the at least one equation component to generate an animal-centric emissions model, the animal-centric emissions model quantifying an amount of emissions by the selected animal during the segment of the emissions lifecycle of the selected animal; and determine, by the computing device processor of the computing system, an amount of emissions by the selected animal during the segment of the emissions lifecycle of the selected animal by evaluating the animal-centric emissions model on the historic animal data and the, performance data; and, display, for the selected animal associated with the unique identifier, in a graphical user interface one or more views of the amount of emissions during the segment of the emissions lifecycle.
7-8
Claim 7 : The computing system of claim 1, wherein the instructions, when executed by the computing device processor, further enables the computing system to: associate at least one certification with the selected animal based on the amount of emissions by the selected animal during the emissions lifecycle of the selected animal.
Claim 8: The computing system of claim 7, wherein the at least one certification indicates the amount of emissions that the animal has emitted or is expected to emit.
2
The computer-implemented method of claim 8, further comprising: comparing the amount of emissions to a threshold level of emissions; determining the amount of emissions satisfies the threshold level of emissions; and associating at least one certification with the selected animal.
12
The computing system of claim 1, wherein the plurality of sensors includes at least one of a camera, a scale, a ruler, a timer, a feeder, a temperature sensor, a pressure sensor, a flow meter, an electrical sensor, a radiation sensor, a gas sensor, a liquid sensor, a humidity sensor, a movement sensor, a global positioning sensor (GPS), a soil composition sensor, a pH sensor, a body composition sensor, a health sensor, animal identification sensor, facial identification sensor, biomedical sensor, an x-ray sensor, nuclear magnetic resonance sensor, or an ultrasound sensor, and wherein the performance data includes expected progeny performance data, expected progeny differences data, genotypic data, phenotypic data, and farm practices management data associated with the selected animal.
12
The computer-implemented method of claim 10, wherein the plurality of sensors includes at least one of a camera, a scale, a ruler, a timer, a feeder, a temperature sensor, a pressure sensor, a flow meter, an electrical sensor, a radiation sensor, a gas sensor, a liquid sensor, a humidity sensor, a movement sensor, a global positioning sensor (GPS), a soil composition sensor, a pH sensor, a body composition sensor, a health sensor, animal identification sensor, biomedical sensor, an x-ray sensor, nuclear magnetic resonance sensor, or an ultrasound sensor.
13
The computing system of claim 1, wherein the instructions, when executed by the computing device processor, further enables the computing system to: generate control instructions to control an appliance to alter at least one task affecting the amount of emissions by the selected animal during the emissions lifecycle of the selected animal.
4
The computing system of claim 1, wherein the instructions, when executed by the computing device processor, further enables the computing system to: compare the amount of emissions to a threshold level of emissions; determine the amount of emissions fails to satisfy the threshold level of emissions; and generate control instructions to control an appliance to alter a farm management task.
14-15, and 17
A computer-implemented method for generating animal-centric emissions models, comprising: obtaining, by a computing device processor, historic animal data from a plurality of different disaggregated sources, identifying, by the computing device processor, a plurality of equation components based on the historic animal data, individual equation components configured to quantify an amount of emissions, generating, by the computing device processor, an emissions model comprising the plurality of equation components, the emissions model quantifying a total amount of emissions by a group of animals for an emissions lifecycle of the group of animals, wherein the emissions lifecycle includes a plurality of potential lifecycle emissions pathways, receiving a selection of an animal associated with the group of animals to identify a selected animal, the animal associated with a unique identifier identifying the selected animal, obtaining in real-time from a database, by the computing device processor, wherein the database is comprised of information obtained by at least one sensor of a plurality of sensors monitoring the selected animal, performance data associated with the unique identifier of the selected animal, identifying, by the computing device processor, one or more data variables associated with at least one equation component of the plurality of equation components of the emissions lifecycle based on the performance data, and applying, by the computing device processor, at least one adjustment to the at least one equation component to generate an animal-centric emissions model, the animal-centric emissions model quantifying an amount of emissions by the selected animal during the emissions lifecycle of the selected animal.
8
A computer-implemented method for generating animal-centric emissions models, comprising: obtaining, by a computing device processor, historic animal data from a plurality of different disaggregated sources by scanning an application programming interface (API), the historic animal data associated with a plurality of input parameters of a baseline emissions model; identifying, by the computing device processor, a plurality of equation components corresponding to the plurality of input parameters, individual equation components configured to quantify an amount of emissions; generating, by the computing device processor, a the baseline emissions model comprising the plurality of equation components, the baseline emissions model quantifying an amount of emissions by a group of animals for an emissions lifecycle of the group of animals, the emissions lifecycle including a plurality of potential lifecycle emissions pathways; receiving a first user selection of an animal associated with the group of animals to identify a selected animal, the animal associated with a unique identifier identifying the selected animal; receiving a second user selection of a segment of the emissions lifecycle for the selected animal comprising an entry point corresponding to a start date and an exit point corresponding to an end date, , the segment associated with one of the plurality of potential lifecycle emissions pathways; identifying, by the computing device processor, equation components associated with the segment; obtaining in real-time from a database, by the computing device processor and at least one sensor of a plurality of sensors monitoring the selected animal, performance data associated with the unique identifier of the selected animal for the segment, wherein the performance data includes expected progeny performance data, expected progeny differences data, genotypic data, phenotypic data, and or farm practices management data; associated with the selected animal; identifying, by the computing device processor, one or more data variables associated with at least one equation component of the plurality of equation components of the baseline emissions model for the segment based on the performance data; applying, by the computing device processor, at least one adjustment to the at least one equation component to generate an animal-centric emissions model, the animal-centric emissions model quantifying an amount of emissions by the selected animal during the segment of the emissions lifecycle of the selected animal; [[and]] determining, by the computing device processor, an amount of emissions by the selected animal during the segment of the emissions lifecycle of the selected animal by evaluating the animal-centric emissions model on the historic animal data and th
20
A non-transitory computer readable storage medium storing instructions that, when executed by a computing device processor of a computing system, causes the computing system to: obtain, by the computing device processor, historic animal data from a plurality of different disaggregated sources, identify, by the computing device processor, a plurality of equation components based on the historic animal data, individual equation components configured to quantify an amount of emissions, generate, by the computing device processor, an emissions model comprising the plurality of equation components, the emissions model quantifying a total amount of emissions by a group of animals for an emissions lifecycle of the group of animals, wherein the emissions lifecycle includes a plurality of potential lifecycle emissions pathways, receive a selection of an animal associated with the group of animals to identify a selected animal, the animal associated with a unique identifier identifying the selected animal, obtain in real-time from a database, by the computing device processor, wherein the database is comprised of information obtained by at least one sensor of a plurality of sensors monitoring the selected animal, performance data associated with the unique identifier of the selected animal, identify, by the computing device processor, one or more data variables associated with at least one equation component of the plurality of equation components of the emissions lifecycle based on the performance data, and apply, by the computing device processor, at least one adjustment to the at least one equation component to generate an animal-centric emissions model, the animal- centric emissions model quantifying an amount of emissions by the selected animal during the emissions lifecycle of the selected animal.
17
A non-transitory computer readable storage medium storing instructions that, when executed by at least one processor of a computing system, causes the computing system to: obtain, by the computing device processor of the computing system, historic animal data from a plurality of different disaggregated sources by scanning an application programming interface (API), the historic animal data associated with a plurality of input parameters of a baseline emissions model; identify, by the computing device processor of the computing system, a plurality of equation components corresponding to the plurality of input parameters, individual equation components configured to quantify an amount of emissions; generate, by the computing device processor of the computing system, a the baseline emissions model comprising the plurality of equation components, the baseline emissions model quantifying an amount of emissions by a group of animals for an emissions lifecycle of the group of animals, the emissions lifecycle including a plurality of potential lifecycle emissions pathways; receive a first user selection of an animal associated with the group of animals to identify a selected animal, the animal associated with a unique identifier identifying the selected animal; receive a second user selection of a segment of the emissions lifecycle for the selected animal comprising an entry point corresponding to a start date and an exit point corresponding to an end date, the segment associated with one of the plurality of potential lifecycle emissions pathways; identify, by the computing device processor of the computing system, equation components associated with the segment; obtain in real-time from a database, by the computing device processor of the computing system and at least one sensor of a plurality of sensors monitoring the selected animal, performance data associated with the unique identifier of the selected animal for the segment, wherein the performance data includes expected progeny performance data, expected progeny differences data, genotypic data, phenotypic data, and or farm practices management data; associated with the selected animal; identify, by the computing device processor of the computing system, one or more data variables associated with at least one equation component of the plurality of equation components of the baseline emissions model for the segment based on the performance data; apply, by the computing device processor of the computing system, at least one adjustment to the at least one equation component to generate an animal-centric emissions model, the animal-centric emissions model quantifying an amount of emissions by the selected animal during the segment of the emissions lifecycle of the selected animal; and determine, by the computing device processor of the computing system, an amount of emissions by the selected animal during the segment of the emissions lifecycle of the selected animal by evaluating the animal-centric emissions model on the historic animal data and the, performance data; and, display, for the selected animal associated with the unique identifier, in a graphical user interface one or more views of the amount of emissions during the segment of the emissions lifecycle.
22
wherein the computing device processor transmits control instructions generated from the animal-centric emissions model to at least one appliance thereby automatically altering an operation of the farm to reduce the determined emissions by the selected animal.
9
generating control instructions to control an appliance to alter a farm management task.
25
wherein the wherein the computing device processor is further configured to generate a certification record based on the animal-centric emissions model for the selected animal, the certification record comprising a quantitative emissions score associated with the selected animal and stored in a database linked to the unique identifier of the selected animal, and wherein the certification record is accessible for product labeling, supply-chain traceability, regulatory reporting, or any combination thereof to indicate the amount of emissions that the animal has emitted or is expected to emit during its emissions lifecycle is below a threshold value.
7 and 8
wherein the instructions, when executed by the computing device processor, further enables the computing system to: associate at least one certification, label, emissions limit/cap, emissions trade, emissions offset/credit, or other emissions transaction with the selected product based on the amount of emissions by the selected product during the emissions assessment cycle of the selected product.
8. The computing system of claim 7, wherein the at least one certification or other transaction indicates the amount of emissions that the product has emitted or is expected to emit.
A. Claims 3, 5, 9-11, 16, 18, and 23 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1, 8, 12 and 14 of U.S. Patent No. 11209419, as applied to claims 1, 8, 12, and 14 above, in view of Zimmerman et al. (US2011/0192213 A1, published 08/11/2011, cited on IDS dated 12/20/2021).
Claims 3 and 16 are directed to the computing system of claim 2, wherein the instructions, when executed by the computing device processor, further enable the computing system to: identify, by the computing device processor of the computing system, equation components associated with the pathway, wherein the animal-centric emissions model is based on the equation components.
Zimmerman discloses software modules are designed to operate the GreenFeed unit, monitor operational variables and collect data from all sensors [0138].
Claim 5 is directed to the computing system of claim 1, wherein the amount of emissions by the selected animal is for a particular lifecycle emissions.
Zimmerman further discloses then, within the selected group of animals the user can use a drop down or other entry device to select all (herd average values and so on) or to choose to inspect a particular animal [0179]. Zimmerman also disclose the data selection area may also be used to select a particular day or range of days (or a time period) for the data to be retrieved [0179].
Claim 9 is directed to the computing system of claim 1, wherein the instructions, when executed by the computing device processor, further enable the computing system to: iteratively update the animal-centric emissions model based on additional data from the plurality of sensors.
Zimmerman discloses the database storing GHG and other monitored/analyzed data for each animal is updated to reflect the most recent feeding and monitoring of the animal with the collected/analyzed data being linked to the animal's ID (e.g., a record may be maintained for each animal with fields for each type of tracked information) [0119].
Claim 10 is directed to the computing system of claim 9, wherein a machine learning technique is utilized to iteratively update the animal-centric emissions model.
Zimmerman discloses the measured data (and other animal data) may be recorded in a local data logger and/or after transmission to a data analyzing station [0119]. Zimmerman further discloses at ,the feeding station resets and awaits another animal [0119]. Zimmerman also discloses at the method continues with the data monitored at the individual feed or other station being analyzed by software/hardware provided at a data analyzing station ( or locally at the feed station or other station in some cases) [0119]. Zimmerman further discloses the GreenFeed system included data analysis software that calculated background concentrations of CH4 and CO2 [0138]. In view of the specification, a search of the term machine learning has been performed, and it appears to provide a list of options for assessing data. Those options including expert practitioner formulations, regression analysis, statistical analysis, sensitivity analysis, Monte Carlo simulation, experimental trials, artificial intelligence, machine learning, training algorithms [0061], and do not appear to provide any new or unique machine learning models or specific known machine learning algorithms.
Claim 11 is directed to the computing system of claim 1, wherein a machine learning technique is utilized to generate the emissions model.
Zimmerman discloses the measured data (and other animal data) may be recorded in a local data logger and/or after transmission to a data analyzing station [0119]. Zimmerman further discloses at ,the feeding station resets and awaits another animal [0119]. Zimmerman also discloses at the method continues with the data monitored at the individual feed or other station being analyzed by software/hardware provided at a data analyzing station ( or locally at the feed station or other station in some cases) [0119]. Zimmerman further discloses the GreenFeed system included data analysis software that calculated background concentrations of CH4 and CO2 [0138]. In view of the specification, a search of the term machine learning has been performed, and it appears to provide a list of options for assessing data. Those options including expert practitioner formulations, regression analysis, statistical analysis, sensitivity analysis, Monte Carlo simulation, experimental trials, artificial intelligence, machine learning, training algorithms [0061], and do not appear to provide any new or unique machine learning models or specific known machine learning algorithms.
Claim 18 is directed to the computer-implemented method of claim 14 comprising: determining, by the computing device processor, an emissions offset based on the total amount of emissions by the group of animals and the amount of emissions by the selected animal.
Zimmerman discloses in addition to the generation of high value GHG offsets, the system may serve as a livestock management tool [0036].
Claim 23 is directed to the computing system of claim 1, further comprising automatically and continuously updating, by the computing device processor, the animal-centric emissions model based on the real-time sensor data received from the at least one sensor, wherein the at least one sensor comprises a gas sensor, a feed-intake sensor, a movement sensor, a body-composition sensor or any combination thereof, whereby the updated model causes modification of a control instruction output to alter a farm-management operation to reduce the determined emission by the selected animal.
Zimmerman discloses the GreenFeed system may be thought of as including an instrumented feeder station that measures real time CO2 and CH4 emissions from ruminant's nose and mouth such as a dairy cow's nose and mouth [0135]. Zimmerman further discloses a GreenFeed system may include an RFID or other identification system to identify individual animals such as particular cattle in a herd for monitoring and for control of feed and supplements to that particular animal [0135].
Regarding claims 3, 5, 9-11, 16, 18, and 23, it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine, in the course of routine experimentation and with a reasonable expectation of success, US Patent 11209419 and Zimmerman as both disclose tracking animal performance data for prediction models. The motivation would have been to reduce greenhouse gas (GHG) emissions (abstract).
B. Claim(s) 19 is/are rejected under 35 U.S.C. 103 as being as being unpatentable over claims 1, 8, 12 and 14 of U.S. Patent No. 11209419, as applied to claims 1, 8, 12, and 14 above, in view of Zimmerman and in further view of Kamilaris et al (Kamilaris, Andreas, Agusti Fonts, and Francesc X. Prenafeta-Boldύ. "The rise of blockchain technology in agriculture and food supply chains." Trends in food science & technology 91 (2019), previously cited).
Zimmerman discloses the method of claim 14.
Claim 19 discloses the computer-implemented method of claim 14, wherein the database comprises a blockchain database.
Zimmerman is silent a blockchain database.
However, Kamilaris discloses the rise of blockchain technology in agriculture and food supply chains (title). Kamilaris further discloses a query of Blockchain AND [Agriculture OR Food OR “Food Supply” OR “Food Supply Chain”] resulting in 49 initiatives were divided into categories with waste reduction and environmental awareness comprising of 5 projects initiatives, or 10% of the results (p. 643, col. 1, par. 2).
Regarding claim 19, it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine, in the course of routine experimentation and with a reasonable expectation of success, Zimmerman with Kamilaris as they disclose precision livestock farming including food supply. The motivation would have been to overcome current challenges because blockchain is a promising technology towards a transparent supply chain of food, with many ongoing initiatives in various food products and food-related issues as disclosed by Kamilaris (abstract).
Provisional Double Patenting
The instant rejection of claims 1-20 are maintained from the previous Office Action.
Claims 1-20,provisionally rejected on the ground of nonstatutory double patenting as being unpatentable over claim 1-20 of copending Application No. 17748709 in view of Zimmerman et al. (US2011/0192213 A1, published 08/11/2011, cited on IDS dated 12/20/2021).
This is a provisional nonstatutory double patenting rejection.
Instant Application # 17/556493
U.S. Application 17748709
Claim(s)
Limitation(s)
Claim(s)
Limitation(s)
1
A computing system for generating emissions models, the computing system comprising: a computer device processor; and a a memory device including instructions that, when executed by the computing device processor, enables the computing system to: obtain, by the computing device processor of the computing system, historic animal data from a plurality of different disaggregated sources, identify, by the computing device processor of the computing system, a plurality of equation components based on the historic animal data, individual equation components configured to quantify an amount of emissions, generate, by the computing device processor of the computing system, an emissions model comprising the plurality of equation components, the emissions model quantifying a total amount of emissions by a group of animals for an emissions lifecycle of the group of animals, wherein the emissions lifecycle includes a plurality of potential lifecycle emissions pathways, receive a selection of an animal associated with the group of animals to identify a selected animal, the animal associated with a unique identifier identifying the selected animal, obtain in real-time from a database, by the computing device processor of the computing system, wherein the database is comprised of information obtained by at least one sensor of a plurality of sensors monitoring the selected animal, performance data associated with the unique identifier of the selected animal, identify, by the computing device processor of the computing system, one or more data variables associated with at least one equation component of the plurality of equation components of the emissions lifecycle based on the performance data, and apply, by the computing device processor of the computing system, at least one adjustment to the at least one equation component to generate an animal-centric emissions model, the animal-centric emissions model quantifying an amount of emissions by the selected animal during the emissions lifecycle of the selected animal.
1
a memory device including instructions that, when executed by the computing device processor, enables the computing system to: obtain, by the computing device processor of the computing system, historic product data from a plurality of different disaggregated sources, identify, by the computing device processor of the computing system, a plurality of equation components based on the historic product data, individual equation components configured to quantify an amount of emissions, generate, by the computing device processor of the computing system, an emissions model comprising the plurality of equation components, the emissions model quantifying a total amount of emissions by a group of products for an emissions lifecycle of the group of products, wherein the emissions lifecycle includes a plurality of potential assessment emissions pathways, receive a selection of a product associated with the group of products to identify a selected product, the product associated with a unique identifier identifying the selected product, obtain in real-time from a database, by the computing device processor of the computing system, wherein the database is comprised of information obtained by at least one sensor of a plurality of sensors monitoring the selected product, performance data associated with the unique identifier of the selected product, identify, by the computing device processor of the computing system, one or more data variables associated with at least one equation component of the plurality of equation components of the emissions lifecycle based on the performance data, and apply, by the computing device processor of the computing system, at least one adjustment to the at least one equation component to generate a product-centric emissions model, the product-centric emissions model quantifying an amount of emissions by the selected product during an emissions assessment cycle of the selected product.
2
The computing system of claim 1, wherein the instructions, when executed by the computing device processor, further enables the computing system to: receive a selection of a pathway from the plurality of potential lifecycle emissions pathways for the selected animal, the pathway comprising an entry point corresponding to a start date and an exit point corresponding to an end date, wherein the animal-centric emissions model is based on the pathway.
2
The computing system of claim 1, wherein the instructions, when executed by the computing device processor, further enables the computing system to: receive a selection of a pathway from the plurality of potential assessment emissions pathways for the selected product, the pathway comprising an entry point corresponding to a start date and an exit point corresponding to an end date, wherein the product-centric emissions model is based on the pathway.
3
The computing system of claim 2, wherein the instructions, when executed by the computing device processor, further enables the computing system to: identify, by the computing device processor of the computing system, equation components associated with the pathway, wherein the animal-centric emissions model is based on the equation components.
3
The computing system of claim 2, wherein the instructions, when executed by the computing device processor, further enables the computing system to: identify, by the computing device processor of the computing system, equation components associated with the pathway, wherein the product-centric emissions model is based on the equation components.
4
The computing system of claim 1, wherein the instructions, when executed by the computing device processor, further enables the computing system to: determine, by the computing device processor of the computing system, the amount of emissions by the selected animal during the emissions lifecycle of the selected animal by evaluating the animal-centric emissions model on the historic animal data and the performance data.
4
The computing system of claim 1, wherein the instructions, when executed by the computing device processor, further enables the computing system to: determine, by the computing device processor of the computing system, the amount of emissions by the selected product during the emissions assessment cycle of the selected product by evaluating the product-centric emissions model on the historic product data and the performance data.
5
The computing system of claim 1, wherein the amount of emissions by the selected animal is for a particular lifecycle emissions pathway.
5
The computing system of claim 1, wherein the amount of emissions by the selected product is for a particular assessment emissions pathway
6
The computing system of claim 1, wherein the instructions, when executed by the computing device processor, further enables the computing system to: display, for the selected animal associated with the unique identifier, in a graphical user interface one or more views of the amount of emissions during the emissions lifecycle.
6
The computing system of claim 1, wherein the instructions, when executed by the computing device processor, further enables the computing system to: display, for the selected product associated with the unique identifier, in a graphical user interface, one or more views of the amount of emissions during the emissions assessment cycle.
7
The computing system of claim 1, wherein the instructions, when executed by the computing device processor, further enables the computing system to: associate at least one certification with the selected animal based on the amount of emissions by the selected animal during the emissions lifecycle of the selected animal.
7
The computing system of claim 1, wherein the instructions, when executed by the computing device processor, further enables the computing system to: associate at least one certification, label, emissions limit/cap, emissions trade, emissions offset/credit, or other emissions transaction with the selected product based on the amount of emissions by the selected product during the emissions assessment cycle of the selected product.
8
The computing system of claim 7, wherein the at least one certification indicates the amount of emissions that the animal has emitted or is expected to emit.
8
The computing system of claim 7, wherein the at least one certification or other transaction indicates the amount of emissions that the product has emitted or is expected to emit.
9
The computing system of claim 1, wherein the instructions, when executed by the computing device processor, further enables the computing system to: iteratively update the animal-centric emissions model based on additional data from the plurality of sensors.
9
The computing system of claim 1, wherein the instructions, when executed by the computing device processor, further enables the computing system to: iteratively update the product-centric emissions model based on additional data from the plurality of sensors.
10
The computing system of claim 9, wherein a machine learning technique is utilized to iteratively update the animal-centric emissions model.
10
The computing system of claim 9, wherein a machine learning technique is utilized to iteratively update the product-centric emissions model.
11
The computing system of claim 1, wherein a machine learning technique is utilized to generate the emissions model.
11
The computing system of claim 1, wherein a machine learning technique is utilized to generate the emissions model.
12
The computing system of claim 1, wherein the plurality of sensors includes at least one of a camera, a scale, a ruler, a timer, a feeder, a temperature sensor, a pressure sensor, a flow meter, an electrical sensor, a radiation sensor, a gas sensor, a liquid sensor, a humidity sensor, a movement sensor, a global positioning sensor (GPS), a soil composition sensor, a pH sensor, a body composition sensor, a health sensor, animal identification sensor, facial identification sensor, biomedical sensor, an x-ray sensor, nuclear magnetic resonance sensor, or an ultrasound sensor, and wherein the performance data includes expected progeny performance data, expected progeny differences data, genotypic data, phenotypic data, and farm practices management data associated with the selected animal.
12
The computing system of claim 1, wherein the plurality of sensors includes at least one of a camera, a scale, a ruler, a timer, a feeder, a temperature sensor, a pressure sensor, a flow meter, an electrical sensor, a radiation sensor, a gas sensor, a liquid sensor, a humidity sensor, a movement sensor, a global positioning sensor (GPS), a soil composition sensor, a pH sensor, a body composition sensor, a health sensor, animal identification sensor, crop identification sensor, energy carrier identification sensor, material identification senso, facial identification sensor, biomedical sensor, an x-ray sensor, nuclear magnetic resonance sensor, or an ultrasound sensor, and wherein the performance data includes expected progeny performance data, expected progeny differences data, genetic data, phenotypic data, properties data and on-site practices management data associated with the selected product.
13
The computing system of claim 1, wherein the instructions, when executed by the computing device processor, further enables the computing system to: generate control instructions to control an appliance to alter at least one task affecting the amount of emissions by the selected animal during the emissions lifecycle of the selected animal.
13
The computing system of claim 1, wherein the instructions, when executed by the computing device processor, further enables the computing system to: generate control instructions to control an appliance to alter at least one task affecting the amount of emissions by the selected product during the emissions assessment cycle of the selected product.
14
A computer-implemented method for generating animal-centric emissions models, comprising: obtaining, by a computing device processor, historic animal data from a plurality of different disaggregated sources, identifying, by the computing device processor, a plurality of equation components based on the historic animal data, individual equation components configured to quantify an amount of emissions, generating, by the computing device processor, an emissions model comprising the plurality of equation components, the emissions model quantifying a total amount of emissions by a group of animals for an emissions lifecycle of the group of animals, wherein the emissions lifecycle includes a plurality of potential lifecycle emissions pathways, receiving a selection of an animal associated with the group of animals to identify a selected animal, the animal associated with a unique identifier identifying the selected animal, obtaining in real-time from a database, by the computing device processor, wherein the database is comprised of information obtained by at least one sensor of a plurality of sensors monitoring the selected animal, performance data associated with the unique identifier of the selected animal, identifying, by the computing device processor, one or more data variables associated with at least one equation component of the plurality of equation components of the emissions lifecycle based on the performance data, and applying, by the computing device processor, at least one adjustment to the at least one equation component to generate an animal-centric emissions model, the animal-centric emissions model quantifying an amount of emissions by the selected animal during the emissions lifecycle of the selected animal.
14
A computer-implemented method for generating product-centric emissions models, comprising: obtaining, by a computing device processor, historic product data from a plurality of different disaggregated sources, identifying, by the computing device processor, a plurality of equation components based on the historic product data, individual equation components configured to quantify an amount of emissions, generating, by the computing device processor, an emissions model comprising the plurality of equation components, the emissions model quantifying a total amount of emissions by a group of products for an emissions assessment cycle of the group of products, wherein the emissions assessment cycle includes a plurality of potential assessment emissions pathways, receiving a selection of a product associated with the group of products to identify a selected product, the product associated with a unique identifier identifying the selected product, obtaining in real-time from a database, by the computing device processor, wherein the database is comprised of information obtained by at least one sensor of a plurality of sensors monitoring the selected product, performance data associated with the unique identifier of the selected product, identifying, by the computing device processor, one or more data variables associated with at least one equation component of the plurality of equation components of the emissions assessment cycle based on the performance data, and applying, by the computing device processor, at least one adjustment to the at least one equation component to generate a product-centric emissions model, the product-centric emissions model quantifying an amount of emissions by the selected product during the emissions assessment cycle of the selected product.
15
The computer implemented method of claim 14 further comprising: receiving a selection of a pathway from the plurality of potential lifecycle emissions pathways for the selected animal, the pathway comprising an entry point corresponding to a start date and an exit point corresponding to an end date, wherein the animal-centric emissions model is based on the pathway.
15
The computer implemented method of claim 14 further comprising: receiving a selection of a pathway from the plurality of potential assessment emissions pathways for the selected product, the pathway comprising an entry point corresponding to a start date and an exit point corresponding to an end date, wherein the product-centric emissions model is based on the pathway.
16
The computer implemented method of claim 15 further comprising: identifying, by the computing device processor, equation components associated with the pathway, wherein the animal-centric emissions model is based on the equation components.
16
The computer implemented method of claim 15 further comprising: identifying, by the computing device processor, equation components associated with the pathway, wherein the product-centric emissions model is based on the equation components.
17
The computer implemented method of claim 14 further comprising: determining, by the computing device processor, the amount of emissions by the selected animal during the emissions lifecycle of the selected animal by evaluating the animal-centric emissions model on the historic animal data and the performance data
17
The computer implemented method of claim 14 further comprising: determining, by the computing device processor, the amount of emissions by the selected product during the emissions assessment cycle of the selected product by evaluating the product-centric emissions model on the historic product data and the performance data
18
The computer implemented method of claim 17 further comprising: determining, by the computing device processor, an emissions offset based on the total amount of emissions by the group of animals and the amount of emissions by the selected animal.
18
The computer implemented method of claim 17 further comprising: determining, by the computing device processor, an emissions offset based on the total amount of emissions by the group of products and the amount of emissions by the selected product.
19
The computer-implemented method of claim 14, wherein the database comprises a blockchain database.
19
The computer-implemented method of claim 14, wherein the database comprises a blockchain database.
20
A non-transitory computer readable storage medium storing instructions that, when executed by a computing device processor of a computing system, causes the computing system to: obtain, by the computing device processor, historic animal data from a plurality of different disaggregated sources, identify, by the computing device processor, a plurality of equation components based on the historic animal data, individual equation components configured to quantify an amount of emissions, generate, by the computing device processor, an emissions model comprising the plurality of equation components, the emissions model quantifying a total amount of emissions by a group of animals for an emissions lifecycle of the group of animals, wherein the emissions lifecycle includes a plurality of potential lifecycle emissions pathways, receive a selection of an animal associated with the group of animals to identify a selected animal, the animal associated with a unique identifier identifying the selected animal, obtain in real-time from a database, by the computing device processor, wherein the database is comprised of information obtained by at least one sensor of a plurality of sensors monitoring the selected animal, performance data associated with the unique identifier of the selected animal, identify, by the computing device processor, one or more data variables associated with at least one equation component of the plurality of equation components of the emissions lifecycle based on the performance data, and apply, by the computing device processor, at least one adjustment to the at least one equation component to generate an animal-centric emissions model, the animal- centric emissions model quantifying an amount of emissions by the selected animal during the emissions lifecycle of the selected animal.
20
A non-transitory computer readable storage medium storing instructions that, when executed by a computing device processor of a computing system, causes the computing system to: obtain, by the computing device processor, historic product data from a plurality of different disaggregated sources, identify, by the computing device processor, a plurality of equation components based on the historic product data, individual equation components configured to quantify an amount of emissions, generate, by the computing device processor, an emissions model comprising the plurality of equation components, the emissions model quantifying a total amount of emissions by a group of products for an emissions assessment cycle of the group of products, wherein the emissions assessment cycle includes a plurality of potential assessment emissions pathways, receive a selection of a product associated with the group of products to identify a selected product, the product associated with a unique identifier identifying the selected product, obtain in real-time from a database, by the computing device processor, wherein the database is comprised of information obtained by at least one sensor of a plurality of sensors monitoring the selected product, performance data associated with the unique identifier of the selected product, identify, by the computing device processor, one or more data variables associated with at least one equation component of the plurality of equation components of the emissions assessment cycle based on the performance data, and apply, by the computing device processor, at least one adjustment to the at least one equation component to generate a product-centric emissions model, the product- centric emissions model quantifying an amount of emissions by the selected product during the emissions assessment cycle of the selected product.
A. Claims 22-23 and 25 are rejected on the ground of nonstatutory double patenting as being unpatentable over U.S. Patent Application 17/748709, as applied to claims 1-20 above, in view of Zimmerman et al. (US2011/0192213 A1, published 08/11/2011, cited on IDS dated 12/20/2021).
Claim 22 is directed to the computing system of claim 1, wherein the computing device processor transmits control instructions generated from the animal-centric emissions model to at least one appliance thereby automatically altering an operation of the farm to reduce the determined emissions by the selected animal.
Zimmerman discloses in addition to the generation of high value GHG offsets, the system may serve as a livestock management tool [0100]. Zimmerman further discloses the feed may be chosen based on a prior breath analysis for the animal to try to control GHG production/emission or to control animal production [0118]. Zimmerman also discloses the dispensed feed, for example, may include a particular mixture of two, three, or more feeds and/or supplements that have been determined by a data analyzing station as appropriate for the identified animal in controlling their GHG emissions (or achieving an animal production goal such as weight gain, milk production, or the like) [0118].
Claim 23 is directed to the computing system of claim 1, further comprising automatically and continuously updating, by the computing device processor, the animal-centric emissions model based on the real-time sensor data received from the at least one sensor, wherein the at least one sensor comprises a gas sensor, a feed-intake sensor, a movement sensor, a body-composition sensor or any combination thereof, whereby the updated model causes modification of a control instruction output to alter a farm-management operation to reduce the determined emission by the selected animal.
Zimmerman discloses the GreenFeed system may be thought of as including an instrumented feeder station that measures real time CO2 and CH4 emissions from ruminant's nose and mouth such as a dairy cow's nose and mouth [0135]. Zimmerman further discloses a GreenFeed system may include an RFID or other identification system to identify individual animals such as particular cattle in a herd for monitoring and for control of feed and supplements to that particular animal [0135].
Claim 25 is directed to the computing system of claim 1, wherein the wherein the computing device processor is further configured to generate a certification record based on the animal-centric emissions model for the selected animal, the certification record comprising a quantitative emissions score associated with the selected animal and stored in a database linked to the unique identifier of the selected animal, and wherein the certification record is accessible for product labeling, supply-chain traceability, regulatory reporting, or any combination thereof to indicate the amount of emissions that the animal has emitted or is expected to emit during its emissions lifecycle is below a threshold value.
Zimmerman discloses the methane monitoring and emission control or GreenFeed system may incorporate a telemetry system to transmit data to a remote computer ( or data analyzing station where it may be stored in computer memory or data storage (such as in a database with supplement and methane emission data collected at the data logger for each animal) and/or further processed for a plurality of animals and/or stations [0083]. Zimmerman further discloses to cause the computer( s) or their processor to perform particular functions) to process data and aggregate the collected and logged data to generate a report of emission reductions and performance efficiency for each individual animal [0083].
Regarding claims 22-23, and 25, it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine, in the course of routine experimentation and with a reasonable expectation of success, 17/748709 with Zimmerman as they disclose precision livestock farming including food supply. Zimmerman as both disclose tracking animal performance data for prediction models. The motivation would have been to reduce greenhouse gas (GHG) emissions (abstract).
Conclusion
No claims are allowed.
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/D.M.B./Examiner, Art Unit 1685
/Soren Harward/Primary Examiner, TC 1600