Updated DETAILED ACTION
This action is responsive to the claims filed on May 19, 2022. Claims 1-20 are under examination.
Claims 1-20 are rejected for non-statutory double patenting over US App. No. 17/556,493 (Provisional DP) and US Patent 11,209,419.
Claims 1-20 are rejected under 35 USC 112(b) for indefiniteness.
Claims 1-20 are rejected under 35 USC 101 as ineligible.
Claims 1-18 and 20 are rejected under 35 USC 103 over Alcock, Neethirajan, and Negussie.
Claim 19 is rejected under 35 USC 103 over Alcock, Neethirajan, Negussie, and Kamilaris.
Response To Arguments/Amendments
Nonstatutory Double Patenting: The Applicant’s arguments and amendments have been considered, but are not persuasive. The animal recited in the other applications is a species of the genus, product. A species teaches the genus in art determinations and double-patenting determinations. Also, these rejections are not held in abeyance. The Applicant is required to file a terminal disclaimer.
35 USC 112(b) Rejections: The Applicant’s arguments and amendments have been considered and are not persuasive. The Applicant argues that real-time isa known term, and its context should be derived from the specification. However, as drafted, there are steps that precede, as least in the claim as presented, the “obtain in real-time” step. For at least these reasons, it is unclear whether all of the steps of the method are performed contemporaneously with the “obtain in real-time” step, the only step the method asserts is in real-time. Real-time is a relative term and, even if used in a typical way in the art, can be rendered unclear if recited with respect to a single step of a method where the relative timing of the other steps is unknown. It may be more reasonable to place the real-time qualifier in the preamble, where one can understand that all of the steps in the method are performed substantially contemporaneously. The 35 USC 112(b) rejections are maintained.
35 USC 101 Rejections: The Applicant’s arguments and amendments have been considered and are not persuasive. The Applicant’s arguments will be addressed in the order presented in the Applicant’s response.
Individual Products: The Applicant appears to interpret the term “specific product,” more narrowly than a broader and equally reasonable interpretation. For example, a specific product could refer to cow as a type, as in all of the cows in the aggregate. The claim language does not dispel this interpretation in any way. Because the claim does not, by its terms, distinguish between an aggregate metric for a type of product and an individual metric for an instance of a product, a person of ordinary skill in the art would be reasonable in interpreting the claim either way. Should the Applicant wish to specify that the specific product is an individual instance of a type of product, it must be done in an amendment.
Also, the assertion that animals and their emissions are not modeled individually in the art is incorrect. Modeling emissions based on individual animals is a longstanding practice and is well-understood, routine and conventional. For example, please see the following references made of record: McAuliffe et al.; Moraes et al.; Lassey; Alcock et al.; and Negussie et al. The Applicant’s argument is not persuasive.
The Claims Recite Features That Are Not Elements of The Abstract Idea: As indicated in the very specific rejections, some elements are identified as additional limitations. The inquiry for whether a claim is directed to the abstract idea is whether the additional limitations integrate the abstract idea into a practical application. The Applicant failed to demonstrate any additional limitations of the independent claims that integrate the abstract idea into a practical application, so the Applicant has failed to make a prima facie showing of integration into a practical application at Step 2A, Prong 2. The Applicant does address, very generically, elements of the dependent claims, but does not provide any rebuttal of the characterization of those elements as failing to confer eligibility as presented in detail in the rejection. This argument is not persuasive.
The Claims Allegedly Recite Significantly More Than Generic Computing: As a note, this is not the test articulated at Step 2B. The test at Step 2B is whether the additional limitations in combination with the rest of the claim provide significantly more than the abstract idea as to confer an inventive concept. Also, the Applicant makes a list of the types of data considered but fails to attempt to rebut the rejection that these types of data merely limit the abstract idea to a particular field under MPEP 2106.05(h). The Applicant has not rebutted the identification of the elements as elements of the abstract idea and other additional limitations and demonstrated why the combination of them provides more than the elements identified as abstract ideas.
Courts Recognize Improvements To Computing: The Applicant points to Examples 37 and 40 which address improvements to the computer processes themselves. Claim 37 addresses improvements to machine learning, and claim 40 addresses improvements to network packet routing, a core function of a computer. By contrast, the Applicant recites machine learning at a high level of generality without any particular improvement thereto. The Applicant merely inputs data and responsively receives output data. The independent claims do not even train the machine learning algorithm but use it as a generic tool that could be replaced by a practical exercise of the mind or a mind with the use of simple aids such as pen and paper. Any asserted improvement the Applicant claims is an element of the abstract idea, not to computing.
The Claims Are Allegedly Not Directed To An Abstract Idea: The test for whether a claim is directed to an abstract idea hinges on whether the additional limitations integrate the abstract idea into a practical application, The Applicant has failed to specify which elements integrate the abstract idea into a particular application and how. That is, the use of sensors is merely an element of the data gathering and is WURC activity (See MPEP 2106.05(g) examples: “i. Performing clinical tests on individuals to obtain input for an equation” “vi. Determining the level of a biomarker in blood”; and MPEP 2106.05(d) examples: “i. Determining the level of a biomarker in blood by any means” “ii. Using polymerase chain reaction to amplify and detect DNA” “iii. Detecting DNA or enzymes in a sample” “i. Recording a customer’s order”; Also, see the references of record that demonstrate the routine and conventional nature of how the claims utilize the machine learning element of the claims.) Applying adjustment to equations based on data is a mental process and mathematical operation, elements of an abstract idea, as demonstrated in greater detail below.
The Applicant continues by stating that the claim provides a specific technological solution, but the solution is provided entirely within the abstract idea. As such, the claims are directed to the abstract idea.
The Claims are Allegedly Integrated Into A Practical Application: Again, this is the standard for whether claims are directed to the abstract idea. The Applicant reiterates the reliance on Examples 37 and 40, which, as previously demonstrated, are non-analogous direct improvements to the functioning of a computer. The Applicant’s independent claims merely select data for input into a machine learning model to determine a mathematical expression. The dynamic creation of new math is still the creation of new math. This belongs to the public, not to an Applicant who merely recites input to and output from a generic machine learning model. Also, the brief high level recitation of features in the dependent claims without defining which elements are additional limitations that integrate the abstract idea into a practical application represent a failure to make a prima facie case that the any of the dependent claim features integrate the abstract idea into a practical application.
The Claims Allegedly Recite Significantly More: The Applicant argues that more than one type of data is input, including sensor data, into a generic model, and that the model outputs useful information. This is similar to the Electric Power Group Case. In order for a claim to represent more than the abstract idea, the additional limitations have to contribute something to the claim that makes it inventive. As demonstrated in the Office Action, the claims do not recite additional limitations that combine with the other elements of the claim to provide significantly more than the abstract idea.
Federal Precedent: The Applicant attempts to analogize the claims to Enfish. However, Enfish represented a computer data structure with a self-referential feature. This is an improvement to an element of the computer itself. The Applicant then continues to make high level analogies to McRo DDr, and Visual Memory LLC by stating that the Applicant’s claim provides some benefit. However, as previously stated, the alleged benefit provided by the claim is provided entirely by the abstract idea itself. The claim recites no improvement to computing. It merely specifies input and output data with a conventional relationship to a generic computing element, a machine learning model. That the claim merely ingests data and outputs data, without any specific improvement to the computing elements themselves or the environment in which the data is sensed, demonstrates that the claim relies entirely on the abstract idea to provide any benefit. Accordingly, the Applicant has failed to demonstrate that any of the additional limitations confer eligibility at either Step 2A, Prong 2 or Step 2B.
USPTO Guidance: The Applicant misapplies the claim to the Examples. Example 37 was already discussed. Example 38 is a very specific example for modeling a computing component that has been interpreted very narrowly. The Applicant’s claim is not remotely analogous if, for no other reason, that it does not involve a simulation. Example 45 results in a physical change in the environment of the sensing, a transformation, and is most likely preempted by the decision in Recentive. With respect to Example 46, the Applicant’s claims are most analogous to claim 1, which merely outputs data, and is ineligible. Under the terms of the Example, Example claim 2 is eligible as an improvement because it actually affects the environment in which it is being implemented. It should also be noted that Example 36 is also most likely preempted by the decision in Recentive.
Conclusion: Accordingly, the Applicant’s arguments and amendments are not persuasive. The rejections are maintained.
35 USC 103: The Applicant’s arguments and amendments have been considered but are not persuasive. The Applicant’s arguments will be addressed in the order presented in the Applicant’s response.
Particular Product: The Applicant has relied a great deal on the claims’ recitation of “selected product” from a group of products. However, as previously demonstrated, the claim terms are susceptible to the reasonable interpretation that the selected product a selected type of product. For example, the group of products could be agricultural and the particular product (type) could reasonably construed to mean the type cow, for which an aggregate metric could be reasonably determined. Should the Applicant wish for the claim term “selected product” to mean what is asserted in the response, the Applicant is invited to amend, with specified support from the specification, that the selected product is an individual product instance, rather than the reasonable interpretation that the “selected product” is a selected product type.
Also, as demonstrated in the previous 35 USC 103 rejection, Negussie is relied upon to teach the feature of selecting an individual product. Negussie explicitly states that the emission considerations will have to be done on a cow-by-cow basis. (Negussie Abstract “Efforts to reduce the carbon footprint of milk production through selection and management of low-emitting cows require accurate and large-scale measurements of methane (CH4) emissions from individual cows.” – The assessment of individual cows demonstrates the selection of each animal the emissions of which are being determined.) The metrics are determined for each cow, so the Negussie reference clearly teaches “selected product” as the Applicant asserts in the response. Also, Alcock teaches on Page 26, Left Column, Second Paragraph “Interventions to animal genotypic traits that reduce emissions intensities include selective breeding of animals with greater feed efficiency (lower than expected feed intake relative to the size and performance of the animal; FE) and/or with lower methane yield (MY) per unit dry matter intake. Differences in FE of individual animals represent a divergence between the efficiency of ingested feed used by the animal for maintenance and for production, primarily due to differences in digestion and metabolism (Waghorn and Hegarty, 2011). More efficient animals require less feed than average and produce less methane per unit product compared with the population average when expressed at a similar level of production. Ongoing research has stressed a need for productive individuals with high FE and low MY to reduce emissions intensities (Waghorn and Hegarty, 2011).” This teaches modeling of the emissions impact of each individual cow. Alcock also teaches on Page 26, Right Column, Second Paragraph “The scale with which imposed strategies are assessed is important. Management strategies effective in reducing emissions at the individual animal level may be less effective in reducing emissions at the enterprise level if stocking rates are modified such that surplus feed is also consumed (Hegarty et al., 2010). For example, selecting animals with high FE may lower methane emissions per animal (Waghorn and Hegarty, 2011), but if more animals are retained on farm to eat the surplus feed, there may be no change or even an increase in net emissions (Harrison et al., 2014b). New GHG mitigation technologies should be evaluated in terms of their effects on whole-enterprise net emissions and emissions intensity, not just on their effects on individual animals.” This teaches modeling emissions of individuals and also rebuts the Applicant’s assertion that it is advantageous to model single instances of a product individually rather than as a product as a whole.
Also, the claim recites, “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 quantifying an amount of emissions by the selected product during an emissions assessment cycle of the selected product.” However, entry of a parameter value is an adjustment to an equation component. Rather than a generic variable, the adjustment could reasonably be interpreted by a person of ordinary skill in the art to be a value of the variable to replace the variable, the value specific to a particular cow. For example, a parameter representing cow weight could be adjusted to be 400kg, or whatever the weight of that cow is. The Applicant has not distinguished by the terms of the claim from any other consideration of individuals in a model in which parameter values of a single product can be ingested.
The Applicant’s assertion that none of the references teach all of the features of the claim is accurate because the references were provided in a motivated combination, the motivation for which the Applicant has not rebutted. The references are relied upon to teach the features of the claims in combination. These arguments amount to an attack on individual references that does not consider the undisputedly motivated whole combination.
With regard to claim 19, these arguments apply based on the dependency of claim 19 on the independent claims for which the arguments have been presented.
Accordingly, the rejections are maintained.
Double Patenting
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 1-4, 6-7, 14-16, 18, and 20 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1, 2, 5, 8 and 17 of U.S. Patent No. 11,209,419. Although the claims at issue are not identical, they are not patentably distinct from each other because they recite similar but not exactly identical subject matter.
Claim 1-20 are provisionally rejected on the ground of nonstatutory double patenting as being unpatentable over respective claims 1-20 of copending Application No. 17/556,493 (reference application). Although the claims at issue are not identical, they are not patentably distinct from each other because They recite essentially the same subject matter with minor variations identifying an animal v. a product, obvious variants of one another in the context of the claims. This is a provisional nonstatutory double patenting rejection because the patentably indistinct claims have not in fact been patented.
Here is a Mapping:
17/748,709
17/556,493
17/098,415 / 11,209,419
Claim 1
1. A computing system for generating product-centric emissions models, the computing system comprising: 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
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
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 based on the historic product data, individual equation components configured to quantify an amount of product centric emissions,
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 animal centric emissions,
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, 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,
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,
generate, by the computing device processor of the computing system, 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 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,
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,
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 exitpoint corresponding to an end date, the segment associated with one of the plurality of potential lifecycle emissions pathways;
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,
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, 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 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 emissions lifecycle based on the performance data, and
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
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 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.
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.
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; 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.
Claim 2
The computing system of claim 1, wherein the instructions, when executed by the computing device processor, further enable 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.
2. The computing system of claim 1, wherein the instructions, when executed by the computing device processor, further enable 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.
1. […] 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;
Claim 3
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 product-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 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.
1. […] 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; 5.[…] and wherein individual input parameters are associated with respective model equations.
Claim 4
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 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.
4. The computing system of claim 1, wherein the instructions, whenexecuted 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.
No Rejection
Claim 5
The computing system of claim 1, wherein the amount of emissions by the selected product is for a particular assessment emissions pathway.
5. The computing system of claim 1, wherein the amount of emissions by the selected animal is for a particular lifecycle emissions pathway.
No Rejection
Claim 6
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 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.
6. 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.
1. […] 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.
Claim 7
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, 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.
7. 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.
2. 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 satisfies the threshold level of emissions; and
associate at least one certification with the selected animal.
Claim 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.
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.
No Rejection
Claim 9
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 product-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 enable the computing system to:
iteratively update the animal-centric emissions model based on additional data from the plurality of sensors.
No Rejection
Claim 10
The computing system of claim 9, wherein a machine learning technique is utilized to iteratively update the product-centric emissions model.
10. The computing system of claim 9, wherein a machine learning technique is utilized to iteratively update the animal-centric emissions model.
Claim 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.
Claim 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 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, genetic data, phenotypic data, properties data and on-site practices management data associated with the selected product.
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.
Claim 13
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 product during the emissions assessment cycle of the selected product.
13. The computing system of claim 1, wherein the instructions, whenexecuted 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.
Claim 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,
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
8. A computer-implemented method for generating animal-centric emissions models, comprising:
obtaining , 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;
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,
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,
identifying, 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;
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,
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,
generating, by the computing device processor of the computing system,
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 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,
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,
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; 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 exitpoint corresponding to an end date, the segment associated with one of the plurality of potential lifecycle emissions pathways;
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,
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,
obtaining 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 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 emissions assessment cycle based on the performance data, and
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
identifying, 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;
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.
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.
applying, 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; 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.
Claim 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.
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.
1. […]
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;
Claim 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.
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.
1. […]
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; 5.[…] and wherein individual input parameters are associated with respective model equations.
Claim 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.
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.
No Rejection
Claim 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.
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.
2. 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 satisfies the threshold level of emissions; and associate at least one certification with the selected animal.
Claim 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.
Claim 20
A non-transitory computer readable storage medium storing instructions that, when executed by a computing device processor of a computing system, cause the computing system to:
obtain, by the computing device processor, historic product data from a plurality of different disaggregated sources,
20. A non-transitory computer readable storage medium storing instructions that, when executed by a computing device processor of a computing system, cause the computing system to:
obtain, by the computing device processor, historic animal data from a plurality of different disaggregated sources,
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, a plurality of equation components based on the historic product data,
individual equation components configured to quantify an amount of emissions,
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,
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,
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,
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,
generate, by the computing device processor of the computing system,
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 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,
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,
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;
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,
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,
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,
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
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
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, 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.
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.
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;
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
Claims 1-20 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
real-time
Claims 1, 14, and 20 recite, “real time,” but it is unclear relative to what operations of the claim the obtain/obtaining operation of the claims are conducted. For example, is the real-time relative to: The sensing by the sensors? The retrieval of the data from the database? Without specifying this in the claim, the metes and bounds of “real-time” are unclear.
The dependent claims are rejected for depending from rejected claims.
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.
The claimed invention is directed to an abstract idea without significantly more.
Independent Claims
Claim 1 (Statutory Category – Machine)
Step 2A – Prong 1: Judicial Exception Recited?
Yes, the claims recite a mental process and a mathematical concept, which are abstract ideas.
Claim 1 Recites:
[…]
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, (Evaluation, Mental Process – Identification of specific data is a mental evaluation a person can perform mentally or with aid of pen, paper, or a calculator.)
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, (Evaluation, Mental Process – Assembling equation components to meaningfully model a process such as emissions is a mental evaluation a person can perform mentally or with aid of pen, paper, or a calculator.)
[…]
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 (Evaluation, Mental Process; Mathematical Concept – Assembling equation components, including data variables, to meaningfully model a process such as emissions is a mental evaluation a person can perform mentally or with aid of pen, paper, or a calculator. Also, assembling an equation from components and variables is the design of a mathematical calculation, a mathematical concept)
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. (Evaluation, Mental Process; Mathematical Concept – Assembling equation components, including data variables, to meaningfully model a process such as emissions is a mental evaluation a person can perform mentally or with aid of pen, paper, or a calculator. Also, adjusting an equation from components and variables is the design of a mathematical calculation and yielding (or having a configuration to yield) a quantified result from a model including a mathematical calculation are mathematical concepts).
Mental processes and mathematical concepts are abstract ideas.
Claim 1 recites an abstract idea.
Step 2A – Prong 2: Integrated into a Practical Application?
No.
The Additional limitations:
A computing system for generating 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:
[…] , by the computing device processor of the computing system, […]
[…] product-centric emissions model […]
[…] a/the plurality of sensors
These are generic computing elements recited a high level of generality and, under MPEP 2106.05(f), fail to integrate the abstract idea into a practical application at Step 2A, Prong 2.
obtain, by the computing device processor of the computing system, historic product data from a plurality of different disaggregated sources,
[…]
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, The obtain and receive steps are mere data gathering activities similar to the MPEP 2106.05(g) insignificant extra-solution activity examples: “e.g., a step of obtaining information about credit card transactions, which is recited as part of a claimed process of analyzing and manipulating the gathered information by a series of steps in order to detect whether the transactions were fraudulent.” “iv. Obtaining information about transactions using the Internet to verify credit card transactions” “vi. Determining the level of a biomarker in blood” “iii. Selecting information, based on types of information and availability of information in a power-grid environment, for collection, analysis and display.” Because the obtain and receive steps are insignificant extra-solution activity, under MPEP 2106.05(g), the steps fail to integrate the abstract idea into a practical application at Step 2A, Prong 2.
Also, any specific details about the parameters the data recited represent, the parameters merely limit the abstract idea to a particular technological environment and, under MPEP 2106.05(h), fail to integrate the abstract idea into a practical application at Step 2A, Prong 2.
The additional limitations recited in claim 1 fail to integrate the abstract idea into a practical application at Step 2A, Prong 2.
Claim 1 is directed to the Abstract idea.
Step 2B: Claim provides an Inventive Concept?
No.
The additional limitations:
A computing system for generating 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:
[…] , by the computing device processor of the computing system, […]
[…] product-centric emissions model […]
[…] a/the plurality of sensors
These are generic computing elements recited a high level of generality and, under MPEP 2106.05(f), fail to combine with other elements of the claim to provide significantly more that would confer an inventive concept at Step 2B.
obtain, by the computing device processor of the computing system, historic product data from a plurality of different disaggregated sources,
[…]
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, The obtain and receive steps are well-understood, routine, and conventional activity similar to the MPEP 2106.05(d) examples: “i. Receiving or transmitting data over a network,” “iii. Electronic recordkeeping” “iv. Storing and retrieving information in memory” “i. Determining the level of a biomarker in blood by any means” (sensors) “vi. Arranging a hierarchy of groups, sorting information, eliminating less restrictive pricing information and determining the price.” Because the obtain and receive steps are WURC and, as previously demonstrated, insignificant extra-solution activity, under MPEP 2106.05(d) and MPEP 2106.05(g), the steps fail to combine with other elements of the claim to provide significantly more that would confer an inventive concept at Step 2B.
Also, any specific details about the parameters the data recited represent, the parameters merely limit the abstract idea to a particular technological environment and, under MPEP 2106.05(h), fail to combine with other elements of the claim to provide significantly more that would confer an inventive concept at Step 2B.
The additional limitations fail to combine with other elements of the claim to provide significantly more that would confer an inventive concept at Step 2B.
Claim 1 is ineligible.
Claim 14
Claim 14 (Statutory Category – Process)
Claim 14 substantially recites the method performed by the system of claim 1 and is rejected for at least the same reasons as claim 1.
Claim 14 is ineligible.
Claim 20
Claim 20 (Statutory Category – Machine)
Claim 20 substantially recites the method performed by the system of claim 1 and is rejected for at least the same reasons as claim 1.
Claim 20 is ineligible.
Dependent Claims
The dependent claims fail to provide any additional limitations that would confer eligibility at Step 2A, Prong 2 and Step 2B.
NOTE: For all of the dependent claims, the parameters the data represents merely limit the abstract idea to a particular technological field and fail to confer eligibility under MPEP 2106.05(g). Also, all recited computing elements or the use thereof are recited at a high level of generality and represent generic computing processes, so, under MPEP 2106.05(f), these fail to confer eligibility.
Claims 2 and 15
receive/ing 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.
This receive/ing step is mere data gathering and fails to confer eligibility for at least the same reasons as the receive/ing and obtain/ing steps of the independent claims.
Claims 2 and 15 fail to recite any additional limitations that confer eligibility.
Claims 2 and 15 are ineligible.
Claims 3 and 16
Identify/ing, by the computing device processor, equation components associated with the pathway, wherein the product-centric emissions model is based on the equation components.
This identify/ing step is a mental process for at least the same reasons as the identifying steps of the independent claims and is, therefore, an element of the abstract idea.
Claims 3 and 16 fail to recite any additional limitations that confer eligibility.
Claims 3 and 16 are ineligible.
Claims 4 and 17
Determine/ing, 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.
This determination is an evaluation practically performable in the mind or with aid of pen, paper, and/or a calculator, so the determine/ing step is an element of the abstract idea.
Claims 4 and 17 fail to recite any additional limitations that confer eligibility.
Claims 4 and 17 are ineligible.
Claim 5
wherein the amount of emissions by the selected product is for a particular assessment emissions pathway.
This describes the nature of the data, which, under MPEP 2106.05(h) merely limits the abstract idea to a particular technological field.
Claim 5 fails to recite any additional limitations that confer eligibility.
Claim 5 is ineligible.
Claim 6
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.
Display of determined or retrieved data is a generic computing operation recited at a high level, and, under MPEP 2106.05(f) fails to confer eligibility.
Claim 6 fails to recite any additional limitations that confer eligibility.
Claim 6 is ineligible.
Claims 7 and 18
associate/ing 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.
Comparisons of emission levels to determine another parameter is an evaluation practically performable in the mind or with aid of pen, paper, or a calculator, so it is a mental process, an element of the abstract idea.
Claims 7 and 18 fail to provide any additional limitations that confer eligibility.
Claims 7 and 18 are ineligible.
Claim 8
wherein the at least one certification or other transaction indicates the amount of emissions that the product has emitted or is expected to emit.
This describes the nature of the data, which, under MPEP 2106.05(h) merely limits the abstract idea to a particular technological field.
Claim 8 fails to recite any additional limitations that confer eligibility.
Claim 8 is ineligible.
Claim 9
iteratively update the product-centric emissions model based on additional data from the plurality of sensors.
Iterative determinations/calculations are practically performable in the mind or with the aid of pen, paper, and/or a calculator, so this step is an element of the abstract idea.
Claim 9 fails to recite any additional limitations that confer eligibility.
Claim 9 is ineligible.
Claim 10
wherein a machine learning technique is utilized to iteratively update the product-centric emissions model.
Machine learning, recited at a high level, is a generic computing element/operation and, under MPEP 2106.05(f), fails to confer eligibility.
Claim 10 fails to recite any additional limitations that confer eligibility.
Claim 10 is ineligible.
Claim 11
wherein a machine learning technique is utilized to generate the emissions model.
Machine learning, recited at a high level, is a generic computing element/operation and, under MPEP 2106.05(f), fails to confer eligibility.
Claim 11 fails to recite any additional limitations that confer eligibility.
Claim 11 is ineligible.
Claim 12
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.
These are just types of sensors that, as data sources, are properties of the data sensed (the sensing itself, not recited as an element of the claim), and descriptions of the performance data. These merely limit the abstract idea to a particular technological field and, under MPEP 2106.05(h), fail to confer eligibility.
Also, the type of sensors used are recited as utilized in a way that the sensors are generic computing elements and, under MPEP 2106.05(f), fail to confer eligibility.
Claim 12 fails to recite any additional limitations that confer eligibility.
Claim 12 is ineligible.
Claim 13
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.
The generation of instructions is an evaluation practically performable in the mind or with the aid of pen, paper, and/or a calculator, so it is an element of the abstract idea.
Should it be found otherwise, this merely generally applies the result of the abstract idea in a general way similar to the MPEP 2106.05(f) “apply it” example: “vi. A method of assigning hair designs to balance head shape with a final step of using a tool (scissors) to cut the hair.”
Claim 13 fails to recite any additional limitations that confer eligibility.
Claim 13 is ineligible.
Claim 19
wherein the database comprises a blockchain database.
The generic use of blockchain technology recited at a high level is the use of a generic computing element that, under MPEP 2106.05(f), fails to confer eligibility.
Should it be found otherwise, the use of blockchain technology merely limits the abstract idea to a particular field and, under MPEP 2106.05(h), fails to confer eligibility.
Claim 19 fails to recite any additional limitations that confer eligibility.
Claim 19 is ineligible.
Claim Rejections - 35 USC § 103
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 (i.e., changing from AIA to pre-AIA ) 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.
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 under 35 U.S.C. 103, the examiner presumes that the subject matter of the various claims was commonly owned at the time any inventions covered therein were made absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and invention dates of each claim that was not commonly owned at the time a later invention was made in order for the examiner to consider the applicability of pre-AIA 35 U.S.C. 103(c) and potential pre-AIA 35 U.S.C. 102(e), (f) or (g) prior art under pre-AIA 35 U.S.C. 103(a).
Claims 1-18 and 20 : Alcock, Neethirajan, and Negussie
Claims 1-18 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over NPL: “Can Animal Genetics and Flock Management Be Used to Reduce Greenhouse Gas Emissions but Also Maintain Productivity of Wool-Producing Enterprises?” by Alcock et al. (Alcock) in view of NPL: “Recent Advances in wearable sensors for animal health management” by Neethirajan et al. (Neethirajan) and NPL: “Invited Review: Large-scale indirect measurements for enteric methane emissions in dairy cattle: A review of proxies and other potential for use in management and breeding decisions” by Negussie et al. (Negussie).
Claims 1, 14, and 20
Regarding claim 1, Alcock teaches:
obtain historic product data from a plurality of different disaggregated sources, (Alcock Page 27, Left Column, Second Paragraph – Right Column, Third Paragraph - Alcock discloses historical data, and data regarding wool production of the animals (performance) are provided such as time of lambing, mating of maiden ewes at a younger age, animal genotypic traits, fleece weight methane yield, feed conversion efficiency etc. Page 27, Left Column, 2.3.1 “Baseline conception rate parameters for a spring lambing were set in accordance with historically observed data from several farmer surveys” Page 27, Left Column, 2.3.3 “The range in conception rates simulated (103– 164%) was designed to align with ranges reported in case studies of flocks covering many Australian wool-producing regions (Lifetime Wool project;” – The data are from disaggregated sources.)
identifya plurality of equation components based on the historic product data, individual equation components configured to quantify an amount of product-centric emissions, (Alcock Page 25, Right Column, 2.1 “methane production is estimated using the equations of Blaxter and Clapperton (1965) as described by Freer et al. (1997). Simulations were conducted for a representative farm at Hamilton in south-west Victoria (37°50′S, 142°04′E), a prominent region of Australian wool and prime lamb production (DEPI, 2013; LFMP, 2011). GrassGro has been extensively parameterised and simulations validated for pasture and animal data on sites throughout south-western Victoria in previous work (Cayley et al., 1998; Clark et al., 2003; Harrison et al., 2014b; Mokany et al., 2010), with validations demonstrating credible capacity to simulate biophysical data for sites in this region (Moore and Harrison, 2011). […] Pasture and soil parameters in GrassGro simulations were set to those typical of south-western Victoria (for further information on soil data see Harrison et al., 2014a). Botanical compositions included perennial and annual ryegrass (Lolium spp.) and subterranean clover (Trifolium subterraneum cv. Leura), with root depths set to the default value for the soil type (780 mm, 250mm and 250 mm, respectively). The soil A horizon was 250 mm deep and consisted of clay loam (bulk density 1.06 Mg/m3, plant available water capacity 19% v/v), overlaying a B horizon consisting of clay (bulk density 1.33 Mg/m3, plant available water capacity 15% v/v) to a total soil depth of 1000 mm.” Page 28, 2.3.5 “Each management and genotype permutation was compared with the baseline enterprises using a range of performance and methane emissions metrics. Total enterprise enteric methane emissions as well as wool emissions intensity (kg CO2-eq/kg clean fleece) were calculated along with abatement cost per CO2-eq. Wool emissions intensity was calculated for each simulation by allocating a proportion of total methane emissions to wool according to long term average relative gross annual income from wool and meat. Averaged across simulations, emissions apportioned to wool were 52% and 54% in the baseline yearling and weaner enterprises, respectively. Across interventions methane emissions attributed to wool ranged from 46% for the weaner enterprise at the highest fecundity level to 59% for the weaner enterprise lambing in April.” See Table 3. Also See Equation (1) in 2.3.6. Also See Table 5 with parameters for calculating emission depending on age. Also, See Page 27, Left Column, Second Paragraph – Right Column, Third Paragraph – The equations for determining emissions are built on components of different factors, such as botanical compositions, soil composition, water availability, draining, soil fertility, wool, weaner, and fecundity. Table 3 and Table 5 show parameters for this calculation. Equation (1) then uses those equation components to calculate a break-even carbon price.)
generate 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, (Alcock Page 28 “The GrassGro model (Freer et al., 1997; Moore et al., 1997) was used to conduct all simulations. Herbage availability and dry matter intake are simulated in GrassGro as a function of pasture characteristics, and methane production is estimated using the equations of Blaxter and Clapperton (1965) as described by Freer et al. (1997). Simulations were conducted for a representative farm at Hamilton in south-west Victoria (37°50′S, 142°04′E), a prominent region of Australian wool and prime lamb production (DEPI, 2013; LFMP, 2011). GrassGro has been extensively parameterised and simulations validated for pasture and animal data on sites throughout south-western Victoria in previous work (Cayley et al., 1998; Clark et al., 2003; Harrison et al., 2014b; Mokany et al., 2010), with validations demonstrating credible capacity to simulate biophysical data for sites in this region (Moore and Harrison, 2011).” – The GrassGro model models emissions based on several factors and alternative emissions pathways with different parameters and parameter values, including conditions of the pasture. Also, See the equations from the Blaxter and Freer cited in Alcock and made of record for the some of the specific equations and parameters used by GrassGro in Alcock.)
,
identify, 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 (Alcock Page 26, Right Column, 2.1 “Herbage availability and dry matter intake are simulated in GrassGro as a function of pasture characteristics, and methane production is estimated using the equations of Blaxter and Clapperton.” – Herbage availability and dry matter intake are data variables associated with at least one equation component that affects performance/methane emissions.)
apply 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 during an emissions assessment cycle of the selected product. (Alcock Page 29, Right Column, 3.2 “When stocking rates were adjusted to achieve equivalent profit to the baseline simulations (iso-economic simulations; see methods) reductions in absolute emissions of 0.26 and 0.36 t CO2-eq/ha were observed for the yearling and weaner enterprises, respectively. Using Eq. (1) to calculate the break-even carbon price at these reduced stocking rates the methane emissions abated would need to be valued at more than $150/t CO2-eq for the yearling enterprise ($37/ha) and $267/t CO2-eq ($94/ha) for the weaner enterprise to compensate for the income foregone from reduced livestock carrying capacity.” – The equations are adjusted for stocking rates. Also, in 3.3-3.4 on pages 29-30, the equation are adjusted for wool emissions intensity.)
Alcock appears to fail to expressly teach, but Alcock in view of Neethirajan teaches:
A computing system for generating product-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: […] by the computing device processor of the computing system […] (Neethirajan Page 23, 2.12 “Usually, biosensors require a lot of power. However, this platform is capable of transmitting information to laptops and smartphones, where the information can be processed and stored.” Page 25, FIG. 3, See Integrated Management System
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the product associated with a unique identifier identifying the product, (Neethirajan Page 18, 2.4 “A recognition method for farming operations using Radio-frequency identification (RFID) has also been proposed. RFID tags attached to farm animals can record simple farming tasks. RFID tags can also be attached to or embedded in animal bodies, tracking such health control factors as fattening management, milking management, and behavior [24]. Tagging animals has now become a trend, as millions of fish, bees and even racing pigeons have been tagged to keep tabs on their locations [2].” – The RFID is an identifier unique to an animal/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 product, performance data associated with the unique identifier of the product, (Neethirajan Page 15, Abstract “However, there is a need to integrate all the available sensors and create an efficient online monitoring system so that animal health status can be monitored in real time, without delay.” Page 26, Left Column, First Paragraph “A nanotechnology-based array of sensors has been tailored for detection of M. bovis-infected cattle via breath, which allows real-time cattle monitoring” Page 27, 2.16 “Several ingenious sensing devices and concepts have been demonstrated and also proposed in recent past years, but building a miniaturized device that can transmit data in a real-time fashion and also can simultaneously detect multiple target molecules remains a bottleneck.” Page 27, 3. Conclusions “However, there is a need to integrate all the available sensors and create an efficient online monitoring system, so that animal health can be monitored in real time, without delay.” – Real time sensors are used to take appropriate measurements of the animals. Fig. 3 - Shows data exchanged between computing items, illustrating there is some database from and/or to which the data is transferred.)
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It would have been obvious to a person of ordinary skill in the art before the effective filing date of the application to modify the emissions determinations of Alcock by the real-time computerized measurement systems of Neethirajan because the person of ordinary skill in the art would be motivated by the express aim of Alcock to achieve improvements in emissions, production, and profitability of a farm to look to the real-time detection and computational methods of Neethirajan that provide efficiency and eliminate delay. (Alcock Abstract “Divergence between the relative effects of alternative strategies on farm economics, production and wool emissions intensities suggests that farm adaptations will depend on the goal of the individual farmer. If the goal is to increase profitability, flock management interventions are most beneficial; if the goal is to reduce emissions intensity, superior breeds containing improvements in several genetic traits have the greatest potential. We demonstrate that no intervention– to farm management, animal genotype or otherwise – is likely to achieve simultaneous improvements in all of production, profitability, net farm emissions and wool emissions intensity.”; Neethirajan Abstract “Precision livestock farming techniques, which include a wide span of technologies, are being applied, along with advanced technologies like microfluidics, sound analyzers, image-detection techniques, sweat and salivary sensing, serodiagnosis, and others. However, there is a need to integrate all the available sensors and create an efficient online monitoring system so that animal health status can be monitored in real time, without delay.”)
Alcock in view of Neethirajan appears to fail to explicitly teach, but Alcock in view of Neethirajan and Negussie teaches:
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,
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. (Negussie Abstract “Efforts to reduce the carbon footprint of milk production through selection and management of low-emitting cows require accurate and large-scale measurements of methane (CH4) emissions from individual cows.” – The assessment of individual cows demonstrates the selection of each animal the emissions of which are being determined.)
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claims to modify the emissions determinations of Alcock by the individual selective emission proxy measurements of animals in Negussie because the person of ordinary skill in the art would be motivated by the express aim of Alcock to achieve improvements in emissions, production, and profitability of a farm to incorporate the emission proxy measurements of individual animals of Negussie that, in combination to improve accuracy of emissions determinations. (Alcock Abstract “”; Negussie Abstract “The most important applications of CH4 proxies are in dairy cattle management and breeding for lower environmental impact. When breeding for traits of lower environmental impact, single or multiple proxies can be used as indirect criteria for the breeding objective, but care should be taken to avoid unfavorable correlated responses. Finally, although combinations of proxies appear to provide the most accurate estimates of CH4,”)
Regarding claim 14, claim 14 substantially recites the method executed by the system of claim 1 and so is rejected for at least the same reasons as claim 1.
Regarding claim 20, claim 20 recites a CRM that is essentially an implementation of the memory of claim 1 and so is rejected for at least the same reasons as claim 1.
Claims 2 and 15
Regarding claim 2, Alcock in view of Neethirajan and Negussie teaches the features of claim 1. Alcock further teaches:
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. (Alcock Page 26, Right Column, 2.1 “The GrassGro model (Freer et al., 1997; Moore et al., 1997) was used to conduct all simulations. Herbage availability and dry matter intake are simulated in GrassGro as a function of pasture characteristics, and methane production is estimated using the equations of Blaxter and Clapperton (1965) as described by Freer et al. (1997). Simulations were conducted for a representative farm at Hamilton in south-west Victoria (37°50′S, 142°04′E), a prominent region of Australian wool and prime lamb production (DEPI, 2013; LFMP, 2011). GrassGro has been extensively parameterised and simulations validated for pasture and animal data on sites throughout south-western Victoria in previous work (Cayley et al., 1998; Clark et al., 2003; Harrison et al., 2014b; Mokany et al., 2010), with validations demonstrating credible capacity to simulate biophysical data for sites in this region (Moore and Harrison, 2011). Hamilton has an average annual rainfall of 649 mm and a winter dominant rainfall pattern with cold winters and warm summers (Supplementary Fig. 1). All simulations were conducted for the period 1978–2012 using the GrassGro default weather set constructed from Bureau of Meteorology data. The 35-year simulation period was chosen to provide a sufficient time frame to capture the impacts of climate variably while being sufficiently recent to be of relevance to the experience of the current farming community. Pasture and soil parameters in GrassGro simulations were set to those typical of south-western Victoria (for further information on soil data see Harrison et al., 2014a). – The pathways represent different approaches to raising the animals, whether that be variables of the environment, approaches to rearing or weaning the animals, among other considerations. Page 31, 4.1 “For weaner enterprises the profitability of lambing time largely depends on the interaction between achieving high conception rates at mating, having sufficient pasture available at lambing and having sufficient time before the end of the growing season to maximise the total liveweight of young animals sold (Fig. 1). Wool emissions intensity in the weaner enterprise was minimised at the lambing time yielding the highest profit. Data of Croker et al. (2009) indicates that around half the Victorian sheep flock commences lambing before August and around 40% lamb before the end of May. Our results therefore indicate that the wool emissions intensity of ∼40% of Victorian weaner enterprises lambing in April/May could be reduced by up to 8% by changing lambing time to August (cf. April to August lambing in Fig. 1b).” – This includes an entry date/start point and an end date/exit point (during the course of a season). The model is based on this in that it determines the duration of the emissions determination. The computing elements are taught by Neethirajan, and the selection of the animal is taught by Negussie, as mapped already in the independent claims.)
Regarding claim 15, claim 15 teaches the method executed by the system of claim 2, so it is rejected for at least the same reasons as claim 2.
Claims 3 and 16
Regarding claim 3, Alcock in view of Neethirajan and Negussie teaches the features of claim 2. Alcock further teaches:
Identify […] equation components associated with the pathway, wherein the product-centric emissions model is based on the equation components. (Alcock Page 26, 2.1 “The GrassGro model (Freer et al., 1997; Moore et al., 1997) was used to conduct all simulations. Herbage availability and dry matter intake are simulated in GrassGro as a function of pasture characteristics, and methane production is estimated using the equations of Blaxter and Clapperton (1965) as described by Freer et al. (1997). Simulations were conducted for a representative farm at Hamilton in south-west Victoria (37°50′S, 142°04′E), a prominent region of Australian wool and prime lamb production (DEPI, 2013; LFMP, 2011). GrassGro has been extensively parameterised and simulations validated for pasture and animal data on sites throughout south-western Victoria in previous work (Cayley et al., 1998; Clark et al., 2003; Harrison et al., 2014b; Mokany et al., 2010), with validations demonstrating credible capacity to simulate biophysical data for sites in this region (Moore and Harrison, 2011). Hamilton has an average annual rainfall of 649 mm and a winter dominant rainfall pattern with cold winters and warm summers (Supplementary Fig. 1). All simulations were conducted for the period 1978–2012 using the GrassGro default weather set constructed from Bureau of Meteorology data. The 35-year simulation period was chosen to provide a sufficient time frame to capture the impacts of climate variably while being sufficiently recent to be of relevance to the experience of the current farming community. Pasture and soil parameters in GrassGro simulations were set to those typical of south-western Victoria (for further information on soil data see Harrison et al., 2014a). Botanical compositions included perennial and annual ryegrass (Lolium spp.) and subterranean clover (Trifolium subterraneum cv. Leura), with root depths set to the default value for the soil type (780 mm, 250mm and 250 mm, respectively). The soil A horizon was 250 mm deep and consisted of clay loam (bulk density 1.06 Mg/m3, plant available water capacity 19% v/v), overlaying a B horizon consisting of clay (bulk density 1.33 Mg/m3, plant available water capacity 15% v/v) to a total soil depth of 1000 mm. Sub-soils were relatively poorly drained with 1 mm/h saturated hydraulic conductivity compared to 8.3 mm/h in the top soil. In GrassGro soil fertility is represented by a simple fertility scalar (0 and 1 representing minimum and maximum values respectively); the modelled fertility level was selected in line with previous work that identified values resulting in long-term average annual pasture shoot growth rates that matched empirically determined annual pasture growth at known rates of phosphate fertiliser application (Mokany et al., 2010). Based on this information, local expertise (Clark, pers. comm.) and previous studies using GrassGro for sites at Hamilton (Cayley et al., 1998; Mokany et al., 2010; Moore and Harrison, 2011;Warn et al., 2006), the fertility scalar was set to 0.8; seasonal fertiliser application would be required only for maintenance of this fertility level.” – Each of these design options are modeled by individual equation components that can be deployed as needed to combine to form an emissions equation to model emissions. The computing elements are taught by Neethirajan, and the selection of the animal is taught by Negussie, as mapped already in the independent claims.)
Regarding claim 16, claim 16 teaches the method executed by the system of claim 3, so it is rejected for at least the same reasons as claim 3.
Claims 4 and 17
Regarding claim 4, Alcock in view of Neethirajan and Negussie teaches the features of claim 1. Alcock further teaches:
Determine […] the amount of emissions by the […] 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. (Alcock Page 26, 2.1 “The GrassGro model (Freer et al., 1997; Moore et al., 1997) was used to conduct all simulations. Herbage availability and dry matter intake are simulated in GrassGro as a function of pasture characteristics, and methane production is estimated using the equations of Blaxter and Clapperton (1965) as described by Freer et al. (1997). Simulations were conducted for a representative farm at Hamilton in south-west Victoria (37°50′S, 142°04′E), a prominent region of Australian wool and prime lamb production (DEPI, 2013; LFMP, 2011). GrassGro has been extensively parameterised and simulations validated for pasture and animal data on sites throughout south-western Victoria in previous work (Cayley et al., 1998; Clark et al., 2003; Harrison et al., 2014b; Mokany et al., 2010), with validations demonstrating credible capacity to simulate biophysical data for sites in this region (Moore and Harrison, 2011). Hamilton has an average annual rainfall of 649 mm and a winter dominant rainfall pattern with cold winters and warm summers (Supplementary Fig. 1). All simulations were conducted for the period 1978–2012 using the GrassGro default weather set constructed from Bureau of Meteorology data. The 35-year simulation period was chosen to provide a sufficient time frame to capture the impacts of climate variably while being sufficiently recent to be of relevance to the experience of the current farming community. Pasture and soil parameters in GrassGro simulations were set to those typical of south-western Victoria (for further information on soil data see Harrison et al., 2014a). Botanical compositions included perennial and annual ryegrass (Lolium spp.) and subterranean clover (Trifolium subterraneum cv. Leura), with root depths set to the default value for the soil type (780 mm, 250mm and 250 mm, respectively). The soil A horizon was 250 mm deep and consisted of clay loam (bulk density 1.06 Mg/m3, plant available water capacity 19% v/v), overlaying a B horizon consisting of clay (bulk density 1.33 Mg/m3, plant available water capacity 15% v/v) to a total soil depth of 1000 mm. Sub-soils were relatively poorly drained with 1 mm/h saturated hydraulic conductivity compared to 8.3 mm/h in the top soil. In GrassGro soil fertility is represented by a simple fertility scalar (0 and 1 representing minimum and maximum values respectively); the modelled fertility level was selected in line with previous work that identified values resulting in long-term average annual pasture shoot growth rates that matched empirically determined annual pasture growth at known rates of phosphate fertiliser application (Mokany et al., 2010). Based on this information, local expertise (Clark, pers. comm.) and previous studies using GrassGro for sites at Hamilton (Cayley et al., 1998; Mokany et al., 2010; Moore and Harrison, 2011;Warn et al., 2006), the fertility scalar was set to 0.8; seasonal fertiliser application would be required only for maintenance of this fertility level.” – The GrassGro system of Alcock considers multiple equation components of an emissions model that factor in all manor of historic product data and performance data of one or more emissions pathways. See Also the equations from the Blaxter and Freer references cited in Alcock and made of record. Also, the computing and sensing elements are taught by Neethirajan, and the selection of the animal is taught by Negussie, as mapped already in the independent claims.)
Regarding claim 17, claim 17 teaches the method executed by the system of claim 4, so it is rejected for at least the same reasons as claim 4.
Claim 5
Regarding claim 5, Alcock in view of Neethirajan and Negussie teaches the features of claim 1. Alcock further teaches:
wherein the amount of emissions by the selected product is for a particular assessment emissions pathway. (Alcock Page 26, Right Column, 2.1 “The GrassGro model (Freer et al., 1997; Moore et al., 1997) was used to conduct all simulations. Herbage availability and dry matter intake are simulated in GrassGro as a function of pasture characteristics, and methane production is estimated using the equations of Blaxter and Clapperton (1965) as described by Freer et al. (1997). Simulations were conducted for a representative farm at Hamilton in south-west Victoria (37°50′S, 142°04′E), a prominent region of Australian wool and prime lamb production (DEPI, 2013; LFMP, 2011). GrassGro has been extensively parameterised and simulations validated for pasture and animal data on sites throughout south-western Victoria in previous work (Cayley et al., 1998; Clark et al., 2003; Harrison et al., 2014b; Mokany et al., 2010), with validations demonstrating credible capacity to simulate biophysical data for sites in this region (Moore and Harrison, 2011). Hamilton has an average annual rainfall of 649 mm and a winter dominant rainfall pattern with cold winters and warm summers (Supplementary Fig. 1). All simulations were conducted for the period 1978–2012 using the GrassGro default weather set constructed from Bureau of Meteorology data. The 35-year simulation period was chosen to provide a sufficient time frame to capture the impacts of climate variably while being sufficiently recent to be of relevance to the experience of the current farming community. Pasture and soil parameters in GrassGro simulations were set to those typical of south-western Victoria (for further information on soil data see Harrison et al., 2014a). – The pathways represent different approaches to raising the animals, whether that be variables of the environment, approaches to rearing or weaning the animals, among other considerations.)
Claim 6
Regarding claim 6, Alcock in view of Neethirajan and Negussie teaches the features of claim 1. Neethirajan further teaches:
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. (Neethirajan Page 23, 2.1.1 “Coupling these technologies with user-friendly interfaces can lead to development of new portable gadgets for farmers, contributing to PLF [7,100].” Page 22, 2.6 “This system of visual streaming data can analyze the activities of weaning and individual pigs as well [88,89].” Also See Fig. 5, which shows a pigsty administrator looking at an alarm on a UI. – This teaches using a UI and a display to see the emissions numbers and even receive alerts about them. Also, the computing and sensing elements and RFID are taught by Neethirajan, and the selection of the animal is taught by Negussie, as mapped already in the independent claims.)
Claims 7 and 18
Regarding claim 7, Alcock in view of Neethirajan and Negussie teaches the features of claim 1.
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. (Alcock Page 28, 2.3.6 “To determine the break-even price (or opportunity cost) of emissions for management and genetic strategies that improved profit and emissions intensity (Tables 5 and 6), selected simulations were repeated at a reduced stocking rate that matched that of the baseline profit (iso-economic simulations). The break-even carbon price ($/t CO2-eq) was then calculated using Eq. (1):” See Also Equation (1) on Page 28.” – Equation (1) is an emissions transaction based on the amount of emissions determined using the equations from Alcock. Also, the computing and sensing elements and RFID are taught by Neethirajan, and the selection of the animal is taught by Negussie, as mapped already in the independent claims.)
Regarding claim 18, claim 18 teaches the method executed by the system of claim 7, so it is rejected for at least the same reasons as claim 7.
Claim 8
Regarding claim 8, Alcock in view of Neethirajan and Negussie teaches the features of claim 1. Alcock further teaches:
wherein the at least one certification or other transaction indicates the amount of emissions that the product has emitted or is expected to emit. (Alcock Page 28, 2.3.6 “To determine the break-even price (or opportunity cost) of emissions for management and genetic strategies that improved profit and emissions intensity (Tables 5 and 6), selected simulations were repeated at a reduced stocking rate that matched that of the baseline profit (iso-economic simulations). The break-even carbon price ($/t CO2-eq) was then calculated using Eq. (1):” See Also Equation (1) on Page 28.” – Equation (1) is an emissions transaction based on the amount of emissions determined using the equations from Alcock. This is explicitly indicated in Equation (1). Also, the computing and sensing elements and RFID are taught by Neethirajan, and the selection of the animal is taught by Negussie, as mapped already in the independent claims.)
Claim 9
Regarding claim 9, Alcock in view of Neethirajan and Negussie teaches the features of claim 1. Neethirajan further teaches:
iteratively update the product-centric emissions model based on additional data from the plurality of sensors. (Neethirajan Abstract “Now, these innovative technologies are being considered for their future use in livestock development and welfare. Precision livestock farming techniques, which include a wide span of technologies, are being applied, along with advanced technologies like microfluidics, sound analyzers, image-detection techniques, sweat and salivary sensing, serodiagnosis, and others. However, there is a need to integrate all the available sensors and create an efficient online monitoring system so that animal health status can be monitored in real time, without delay.” – The sensors provide live data updates to update the parameters of the Alcock model in real time, which is an iterative update. Also, the computing and sensing elements and RFID are taught by Neethirajan, and the selection of the animal is taught by Negussie, as mapped already in the independent claims.)
Claim 10
Regarding claim 10, Alcock in view of Neethirajan and Negussie teaches the features of claim 1. Neethirajan futher teaches.
wherein a machine learning technique is utilized to iteratively update the product-centric emissions model. (Neethirajan Page 22, 2.7 “An inexpensive and automatic prototype has hence been developed recently to notify farmers to stress levels using sound data. Its structure consists of three binary-classifier support vector machines. The sound emitted by the hens is first detected; then the classification module identifies and classifies the stress in the sound. An experimental evaluation is then prepared, using real-time sound data from an audio surveillance system.” – Neethrijan uses a support vector machine, which is a machine learning model, to provide sound data for use as a stress component in the model of Alcock. Also, the computing and sensing elements and RFID are taught by Neethirajan, and the selection of the animal is taught by Negussie, as mapped already in the independent claims.)
Claim 11
Regarding claim 11, Alcock in view of Neethirajan and Negussie teaches the features of claim 1. Neethirajan futher teaches:
wherein a machine learning technique is utilized to generate the emissions model. (Neethirajan Page 22, 2.7 “An inexpensive and automatic prototype has hence been developed recently to notify farmers to stress levels using sound data. Its structure consists of three binary-classifier support vector machines. The sound emitted by the hens is first detected; then the classification module identifies and classifies the stress in the sound. An experimental evaluation is then prepared, using real-time sound data from an audio surveillance system.” – Neethrijan uses a support vector machine, which is a machine learning model, to provide sound data for use as a stress component in the model of Alcock. Also, the computing and sensing elements and RFID are taught by Neethirajan, and the selection of the animal is taught by Negussie, as mapped already in the independent claims.)
Claim 12
Regarding claim 12, Alcock in view of Neethirajan and Negussie teaches the features of claim 1. Alcock in view of Neethirajan and Negussie futher teaches:
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 (Neethirajan Page 21, 2.6 “For instance, a top-view camera may be used to analyze motion and detect low weight in pigs.” Page 16, 1. Introduction “Sensors and wearable technologies can be implanted on animals to detect their sweat constituents [2–4], measure body temperature [5– 7], observe behavior and movement [8,9], detect stress [10], analyze sound [11–16], detect pH [17], prevent disease [18], detect analytes and detect presence of viruses and pathogens [19–23].”)
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. (Alcock Page 27, 2.3.3.1 “Ewe fecundity. Increased ewe fecundity in prime lamb enterprises has been shown to increase lamb sales, reduce emissions per lamb sold and reduce emissions intensity of animal product (Harrison et al., 2014b). Such effects occur because (1) growing animals have higher feed-conversion efficiency and lower emissions intensity than adults, and (2) there are more animals on farm during periods of feed abundance and fewer animals when feed supply is lacking (Harrison et al., 2014a, b). Parameters for the percentage of ewes conceiving singles and twins at body condition score 3 were raised incrementally to generate weaning rates at higher levels than those used in the baseline simulations (Table 2). Conceptions rates were increased by reducing the number of empty ewes or ewes conceiving a single lamb and increasing the number of ewes conceiving twins and triplets (Table 2). This approach is in line with industry pregnancy scanning data observed nationally (Chris Shands, unpublished data). The range in conception rates simulated (103–164%) was designed to align with ranges reported in case studies of flocks covering many Australian wool-producing regions (Lifetime Wool project; http://www.lifetimewool.com.au/Ewe%20Management/conception.aspx#response).” Page 27, 2.3.1 “Lambing time influences feed-use efficiency through its effect on peak energy requirements (feed demand) and its (mis-)alignment with feed supply of the flock (Harrison et al., 2014a). Optimising the balance between feed demand and feed supply increases the efficiency of conversion of dry matter into animal product and reduces supplementary feeding; together this can increase whole farm production efficiency and reduce emissions intensity (Moore and Ghahramani, 2013).” – The information used by the model to determine emission includes genetic data and data regarding expected progeny. Also, the computing and sensing elements and RFID are taught by Neethirajan, and the selection of the animal is taught by Negussie, as mapped already in the independent claims.)
Claim 13
Regarding claim 13, Alcock in view of Neethirajan and Negussie teaches the features of claim 1. Alcock in view of Neethirajan and Negussie futher teaches:
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. (Neethirajan Page 26, Fig. 4 – Figure 4 shows using control of water and feed to effect the outputs, including pollutants/emissions. Also, the computing and sensing elements and RFID are taught by Neethirajan, and the selection of the animal is taught by Negussie, as mapped already in the independent claims.)
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Claim 19: Alcock, Neethirajan, Negussie, and Kamilaris
Claim 19 is rejected under 35 U.S.C. 103 as being unpatentable over NPL: “Can Animal Genetics and Flock Management Be Used to Reduce Greenhouse Gas Emissions but Also Maintain Productivity of Wool-Producing Enterprises?” by Alcock et al. (Alcock) in view of NPL: “Recent Advances in wearable sensors for animal health management” by Neethirajan et al. (Neethirajan), NPL: “Invited Review: Large-scale indirect measurements for enteric methane emissions in dairy cattle: A review of proxies and other potential for use in management and breeding decisions” by Negussie et al. (Negussie), and NPL “The Rise of Blockchain Technology in Agriculture and Food Supply Chains” by Kamilaris et al. (Kamilaris).
Claim 19
Regarding claim 19, Alcock in view of Neethirajan and Negussie teaches the features of claim 14. Alcock in view of Neethirajan and Negussie appear to fail to teach, but Alcock in view of Neethirajan, Negussie, and Kamilaris teaches:
wherein the database comprises a blockchain database. (Kamilaris Abstract “Our findings indicate that blockchain is a promising technology towards a transparent supply chain of food, with many ongoing initiatives in various food products and food-related issues“ Page 4, 2. Food Supply Chain, 1. Production “Production: The production phase represents all agricultural activities implemented within the farm. The farmer uses raw and organic material (fertilizers, seeds, animal breeds and feeds) to grow crops and livestock. Throughout the year, depending on the cultivations and/or animal production cycle, we can have one or more harvest/yield.” Page 4, Last Paragraph “Thus, there is much space for optimization of the supply chains, by effectively reducing the operating costs.” Page 6, Second Paragraph – Item 2 “At every stage of the trajectory of food (defined with numbers 1-6 in Figure 2), different technologies are involved and different information is written to the blockchain, as described below for each of these stages: 1. Provider: Information about the crops, pesticide and fertilizers used, machinery involved etc. The transactions with the producer/farmer are recorded. 2. Producer: Information about the farm and the farming practices employed. Additional info about the crop cultivation process, weather conditions, or animals and their welfare is also possible to be added.” – Kamilaris teaches using blockchain to store all data on the process of raising animals.)
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claims to modify the parameters representing equation components of the model in Alcock by the blockchain storage of Kamilaris because the person of ordinary skill in the art would be motivated by the expressed desire in Alcock to increase profitability profitability to look to the block chain data storage system of Kamilaris based on its reduction of operating costs that increases its profitability. (Alcock Abstract “Increasing weaning rate and introducing genotypes with lower methane yield afforded the greatest reductions in wool emissions intensities. Divergence between the relative effects of alternative strategies on farm economics, production and wool emissions intensities suggests that farm adaptations will depend on the goal of the individual farmer. If the goal is to increase profitability, flock management interventions are most beneficial; if the goal is to reduce emissions intensity, superior breeds containing improvements in several genetic traits have the greatest potential. We demonstrate that no intervention – to farm management, animal genotype or otherwise – is likely to achieve simultaneous improvements in all of production, profitability, net farm emissions and wool emissions intensity. Under current carbon prices, subsidies greater than $150/t CO2-eq would be required if economic returns from GHG abatement were to equal those from increased productivity, suggesting there would be little incentive for wool producers to participate in the Carbon Farming Initiative under the intervention strategies modelled here.”; Kamilaris Page 4, Last Paragraph “Exchange of good are based on complex and paper-heavy settlement processes while these processes are not much transparent, with high risks between buyers and sellers during exchange of value. As transactions are vulnerable to fraud, intermediaries get involved, increasing the overall costs of the transfers (Lierow, Herzog and Oest 2017). It is estimated that the cost of operating supply chains makes up two thirds of the final cost of goods. Thus, there is much space for optimization of the supply chains, by effectively reducing the operating costs.”)
Conclusion
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
US 2011/0192213 A1 to Zimmerman et al. (Teaches using blockchain and RFID in the context of emission determinations).
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/J.M.W./Examiner, Art Unit 2188
/RYAN F PITARO/Supervisory Patent Examiner, Art Unit 2188