Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim 15 is pending.
Claim 15 is added. Claims 1-14 are cancelled.
Response to Arguments
Applicant’s amendments with respect to the double patenting rejection and Section 112 have been fully considered and are persuasive. The double patenting rejection and Section 112 rejections of claims 1-14 has been withdrawn.
Applicant's arguments regarding section have been fully considered but they are not persuasive. A new rejection addressing the new claim is presented.
Applicant’s arguments with respect to Section 102 and 103 have been considered but are moot in view of the cancellation of all previously pending claims. A new rejection is set forth below. It is noted that the art was previously cited against the dependent claims, and Applicant presented no arguments addressing any particular art of record.
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.
Claim 15 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. Representative claim 15 recites “…. obtain time-stamped livestock data relevant to determining a carbon footprint of the respective livestock entity, the time-stamped livestock data including at least methane output and at least one of food consumption data, grazing history data, speed data, direction data, spatial orientation data, or location data; … determine livestock waste output, and … additional sensor data indicative of livestock feeding behaviors or livestock feeding histories; … receive the time-stamped livestock data from a corresponding live-stock carbon footprint tracker; obtain additional livestock-related data from one or more local systems that include at least a live-stock feeding pen system configured to determine feeding times and feeding amounts for the respective livestock entity; obtain third-party data from one or more third-party data sources that include at least one of a weather source, a vegetation database, or a satellite mapping service; generate, using the time-stamped livestock data, the additional livestock-related data, and the third-party data, a carbon-footprint profile of the respective livestock entity as a time-series record; update the carbon-footprint profile over time as additional time-stamped livestock data is received; and aggregate the carbon-footprint profile via a series of time-series data to determine a lifetime carbon footprint of the respective livestock entity at a specified point in time, and aggregate carbon-footprint profiles across a specified group of livestock entities to generate a group-level livestock carbon footprint assessment.”. Therefore, the claim as a whole is directed to “Carbon Footprint Calculations”, which is an abstract idea because it is a method of organizing human activity, commercial interactions (including agreements in the form of contracts; legal obligations; advertising, marketing or sales activities or behaviors; business relations) and mental processes, including concepts performed in the human mind (including an observation, evaluation, judgment, opinion). “Carbon Footprint Calculations” is considered to be is a method of organizing human activity and mental process because the analysis of data from a sensor and use of such data to perform mathematical calculations to determine carbon footprints or greenhouse gas profile data is a human organized activity performed in order to enable commercial interactions in the form of carbon credit interchange programs. Such analysis processes also are performed in order to provide baseline numbers to market agricultural best practices and meet obligations for data analysis and metric provision for product certifiers. Such business necessary calculations may also be performed mentally or with pen and paper based on the analysis of gathered sensor data. As such, the claims are directed to an abstract idea.
This judicial exception is not integrated into a practical application. In particular, claim 15 recites the following additional element(s): a plurality of live-stock carbon footprint trackers, wherein each live-stock carbon footprint tracker is configured to be attached to a respective livestock entity, each live-stock carbon footprint tracker comprises at least one chemical sensor configured to generate chemical readings, at least one of a head movement sensor, a sound sensor, or a digital camera configured to generate additional sensor data, one or more communications hubs, a base station, and one or more communication networks, wherein the one or more communications hubs and the base station are configured to receive the time-stamped livestock data from the plurality of live- stock carbon footprint trackers via the one or more communication networks; and a server computing device communicatively coupled to the one or more communication networks, wherein the server computing device is configured to execute instructions that, for each respective livestock entity. These additional elements individually or in combination do not integrate the exception into a practical application. That is, the recitations of additional elements amount merely reciting the words ‘‘apply it’’ (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea (see MPEP 2106.05(f)). Use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., a fundamental economic practice or mathematical equation) does not integrate a judicial exception into a practical application or provide significantly more. See Affinity Labs v. DirecTV, 838 F.3d 1253, 1262, 120 USPQ2d 1201, 1207 (Fed. Cir. 2016) (cellular telephone); TLI Communications LLC v. AV Auto, LLC, 823 F.3d 607, 613, 118 USPQ2d 1744, 1748 (Fed. Cir. 2016) (computer server and telephone unit). See MPEP 2106.05(f)(1) and (2). Generally, these computer elements amount to reciting a generic electronic elements, including commercially available elements such as the Zelp monitor coupled to a network and server for data gathering. As such, the additional elements also do no more than generally link the use of a judicial exception to a particular technological environment or field of use (see MPEP 2106.05(h)). Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. Claim 15 is directed to an abstract idea.
Claim 15 does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements, individually and in combination, are merely being used to apply the abstract idea to a technological environment. As noted above, the additional elements recited include generic electronic elements, including commercially available elements such as the Zelp monitor coupled to a network and server for data gathering. The additional elements do not address any technological problem or provide any technological solution. Accordingly, claim 15 is ineligible.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of pre-AIA 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claim 15 is rejected under 35 U.S.C. 102(a)(1) as being anticipated by U.S. Patent Application Publication No. 20220276222 to Beal.
With regards to claim 15, Beal teaches:
a plurality of live-stock carbon footprint trackers, wherein each live-stock carbon footprint tracker is configured to be attached to a respective livestock entity and is configured to obtain time-stamped livestock data relevant to determining a carbon footprint of the respective livestock entity (paragraph [0233], “In yet another example, the model can be dynamically updated based on real-time data 214. For example, sensors 219, 221, and 223 can obtain data associated with one or more animals. The sensors can include, for example, cameras, electronic scales, electronic feeders, temperature sensors, humidity sensors, movement sensors, GPS sensors, body composition sensors, health sensors, ultrasound sensors, gas sensors, feed intake measurement systems, gas sensors or calorimeter, laser-based methane sensors, soil analysis probe sensors, pH sensors, digital thermometers, digital rulers, Biolectric system sensors, ZELP sensors, Herdsy sensors, Allflex RFID sensors, GPI liquid flowmeter, GPI gas flowmeter, Milbank electricity meter, industrial controls, etc. In an embodiment, the animal performance data can include animal consumption, emissions, and behavior data.”), the time-stamped livestock data including at least methane output and at least one of food consumption data, grazing history data, speed data, direction data, spatial orientation data, or location data (paragraph [0145], “Rations can be influenced by the genetic parameters in the model, as described below. Rations for cow-calf pairs can be determined for a predetermined number of time periods (e.g., four time periods: early lactation, late lactation, early gestation, and late gestation) using at least one feed ration technique known in the art. One such technique considers pasture, hay, protein supplements, and corn and a baseline average cow weight of, e.g., 1,400 lbs. Bull rations can be determined for a predetermined number of time periods (e.g., two time periods, e.g., summer and winter) consisting of pasture, hay, and protein supplements assuming a baseline average weight of, e.g., 1,600 lbs.”, where time periods must be measures with time-stamped data; paragraph [0233], “Animal consumption, emissions, and behavior data can be obtained using one or more sensors. For example, sensors can be used to monitor automatically and continuously the consumption, emissions, and the behavior of individual animals in order to predict and determine a variety of conditions relating to health, feed efficiency, animal welfare, performance, and production efficiency enabling determination of individual animal performance on different rations, response to medications, response to feed supplements, response to minerals and trace minerals, response to growth promoting substances, prediction of carcass quality, and determination of greenhouse gas and manure excretion.”);
wherein each live-stock carbon footprint tracker comprises at least one chemical sensor configured to generate chemical readings used to determine livestock waste output (paragraph [0233], “For example, sensors 219, 221, and 223 can obtain data associated with one or more animals. The sensors can include, for example, cameras, electronic scales, electronic feeders, temperature sensors, humidity sensors, movement sensors, GPS sensors, body composition sensors, health sensors, ultrasound sensors, gas sensors, feed intake measurement systems, gas sensors or calorimeter, laser-based methane sensors, soil analysis probe sensors, pH sensors, digital thermometers, digital rulers, Biolectric system sensors, ZELP sensors, Herdsy sensors, Allflex RFID sensors, GPI liquid flowmeter, GPI gas flowmeter, Milbank electricity meter, industrial controls, etc.”), and comprises at least one of a head movement sensor, a sound sensor, or a digital camera configured to generate additional sensor data indicative of livestock feeding behaviors or livestock feeding histories (paragraph [0025], “For example, the models can be used to determine emissions data for each animal or group of animals, crop or group of crop (e.g., one plant, a crop field, a microorganism culture volume, etc.), each energy carrier or group of energy carriers (e.g., an electron, a gallon of gasoline, a megajoule of heat, etc.), each material or group of materials (e.g., iron ore, lithium, water, ammonia, etc.), or other products.”; paragraph [0234], “Such sensors may include, for example, 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.”);
one or more communications hubs, a base station, and one or more communication networks (paragraphs [0111]-[0114]), wherein the one or more communications hubs and the base station are configured to receive the time-stamped livestock data from the plurality of live-stock carbon footprint trackers via the one or more communication networks (paragraph [0114], “In particular embodiments, each system or engine may be a unitary server or may be a distributed server spanning multiple computers or multiple datacenters. Systems, engines, or modules may be of various types, such as, for example and without limitation, web server, news server, mail server, message server, advertising server, file server, application server, exchange server, database server, or proxy server. In particular embodiments, each system, engine or module may include hardware, software, or embedded logic components or a combination of two or more such components for carrying out the appropriate functionalities implemented or supported by their respective servers.”); and
a server computing device communicatively coupled to the one or more communication networks, wherein the server computing device is configured to execute instructions that, for each respective livestock entity (paragraphs [0114]-[0114]), cause the server computing device to:
receive the time-stamped livestock data from a corresponding live-stock carbon footprint tracker (paragraph [0145], “Rations can be influenced by the genetic parameters in the model, as described below. Rations for cow-calf pairs can be determined for a predetermined number of time periods (e.g., four time periods: early lactation, late lactation, early gestation, and late gestation) using at least one feed ration technique known in the art. One such technique considers pasture, hay, protein supplements, and corn and a baseline average cow weight of, e.g., 1,400 lbs. Bull rations can be determined for a predetermined number of time periods (e.g., two time periods, e.g., summer and winter) consisting of pasture, hay, and protein supplements assuming a baseline average weight of, e.g., 1,600 lbs.”, where time periods must be measures with time-stamped data; paragraph [0233], “Animal consumption, emissions, and behavior data can be obtained using one or more sensors. For example, sensors can be used to monitor automatically and continuously the consumption, emissions, and the behavior of individual animals in order to predict and determine a variety of conditions relating to health, feed efficiency, animal welfare, performance, and production efficiency enabling determination of individual animal performance on different rations, response to medications, response to feed supplements, response to minerals and trace minerals, response to growth promoting substances, prediction of carcass quality, and determination of greenhouse gas and manure excretion.”);
obtain additional livestock-related data from one or more local systems that include at least a live-stock feeding pen system configured to determine feeding times and feeding amounts for the respective livestock entity (paragraph [0145], “Rations can be influenced by the genetic parameters in the model, as described below. Rations for cow-calf pairs can be determined for a predetermined number of time periods (e.g., four time periods: early lactation, late lactation, early gestation, and late gestation) using at least one feed ration technique known in the art. One such technique considers pasture, hay, protein supplements, and corn and a baseline average cow weight of, e.g., 1,400 lbs. Bull rations can be determined for a predetermined number of time periods (e.g., two time periods, e.g., summer and winter) consisting of pasture, hay, and protein supplements assuming a baseline average weight of, e.g., 1,600 lbs.”);
obtain third-party data from one or more third-party data sources that include at least one of a weather source, a vegetation database, or a satellite mapping service (paragraph [0294], “For example, the obtained performance data may be used as input data that may be processed to determine expected emissions in accordance with embodiments described herein. This can include, for example, identifying 450 performance data variables that may affect emissions calculation. For example, as described, an equation component can model the impact of certain input parameters on an energy product's lifecycle emissions. In this situation, an equation component can be configured to quantify an amount of emissions based on certain performance data, such as on-site management practice data. For example, the impact of wind turbine design, weather conditions, installation energy, system downtime, transmission losses, etc. can be used.”);
generate, using the time-stamped livestock data, the additional livestock-related data, and the third-party data, a carbon-footprint profile of the respective livestock entity as a time-series record (paragraph [0119], “As described, a model can be dynamically updated based on available data. Example 200 of FIG. 2 illustrates an example pipeline that can be utilized in accordance with various embodiments. In this example, historic data 160 and performance data 139 are obtained and can be used to generate model 206. Model 206 can be used to determine estimated emissions for one or more animals.”; paragraph [0120], “For example, historic data 160 can be used to determine input parameters that impact emissions. The historic data may be compiled from academic papers, scientific literature, trade publications, etc. For example, the historic data may be comprised of information about different parts of animal lifecycle, including, for example, emissions information, economic impact information, etc. More specifically, historic data may include data grass-fed feedlot inputs including arable land data, precipitation data, atmospheric CO2 data, etc. Performance data 139 can include, for example, expected progeny performance data, properties data, genetic data, phenotypic data, and on-site practices management data.”);
update the carbon-footprint profile over time as additional time-stamped livestock data is received (paragraph [0118], “As will be described further in FIG. 2, a model based on the model equations can be dynamically updated based on available data. For example, adjustments can be applied to the model equations based on performance data. The adjustments can be with respect to baseline emissions as determined by, for example, inputs 143 associated with production system 141. For example, the adjustments can be with respect to baseline emissions for a particular breed of animal associated with a particular production system.”); and
aggregate the carbon-footprint profile via a series of time-series data to determine a lifetime carbon footprint of the respective livestock entity at a specified point in time (paragraph [0121], “In some examples, model 206 directly obtains the data and/or obtains the data via one or components and/or processes. Model 206 can include, for example, equation components based on input parameters that models the impact of certain characteristics on an animal's lifecycle emissions. For example, a model may be comprised of model equations that may be derived from historic data 160 and performance data 139.”; paragraph [0122], “odels can be generated to represent the lifecycles (e.g., assessment cycles) or parts of the lifecycles (e.g., assessment cycles) of products being evaluated. This can include models for, e.g., animal products (beef, chicken, pork, milk, eggs, etc.), crops (plants, algae, fungi, cyanobacteria, bacteria, etc.), energy carriers (oil, coal, gas, solar power electricity, wind power electricity, biofuels, etc.), materials (iron, stone, silver, gold, lithium, aluminum, woods, carbon dioxide, ammonia, propylene, etc.), and other products.”), and
aggregate carbon-footprint profiles across a specified group of livestock entities to generate a group-level livestock carbon footprint assessment (paragraph [0110], “A variety of different outputs may be determined by the emissions calculator 128, including, but not limited to values for: carbon dioxide equivalent emissions (CO2e) absorbed on farm credit, respiratory CO2e emissions, manure CO2e emissions, CO2e emissions from enteric CH4, CO2e emissions upstream (upstream emissions are emissions that occur outside of the production process, but are “embedded” in energy or materials that are used in the production process), CO2e emissions from manure N2O, CO2e directly emitted on farm, CO2e emissions from soil N2O, CO2e emissions from manure CH4, CO2e sequestered in soil or other media, CO2e sequestration flows, and other greenhouse gas fluxes.”).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
U.S. Patent Application Publication No. 20220343229 to Gruber et al. discusses automatically generating and tracking a carbon intensity (CI) score assigned to a particular product as the product traverses through a processing plant and discrete steps in a supply chain. In some examples, intermediate CI scores may be assigned to the product as it completes each step in its life cycle. The intermediate CI scores may be aggregated to produce a final CI score. Each intermediate CI score is recorded on a blockchain, such that the CI score is independently verifiable and auditable.
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). 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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Joshua D Schneider whose telephone number is (571)270-7120. The examiner can normally be reached on Monday - Friday, 9am-5pm.
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/J.D.S./Examiner, Art Unit 3626
/JESSICA LEMIEUX/Supervisory Patent Examiner, Art Unit 3626