DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA . Claims 1-20 filed on 6/28/2024 have been reviewed and considered by this office action.
Drawings
The drawings filed on 6/28/2024 have been reviewed and are considered acceptable.
Specification
The specification filed on 6/28/2024 has been reviewed and is considered acceptable.
Claim Objections
Claim 1 is objected to because of the following informalities: The two limitations of, “identify a source level category of greenhouse gas emissions associated with a contributing category of the greenhouse gas emissions, wherein the source level category contributes to overall greenhouse gas emission of the contributing category.” and “obtain a first weighted distribution for the source level category, wherein the first weighted distribution represents a percentage contribution of the greenhouse gas emissions contributed to the contributing category.”, both end in periods, “.”. Per the MPEP, each claim is allowed only one period, “.”. Please correct the punctuation for each of these limitations. Appropriate correction is required.
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.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
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.
Claim 1 recites the following limitations, “identify a source level category of greenhouse gas emissions associated with a contributing category of the greenhouse gas emissions, wherein the source level category contributes to overall greenhouse gas emission of the contributing category.
obtain a first weighted distribution for the source level category, wherein the first weighted distribution represents a percentage contribution of the greenhouse gas emissions contributed to the contributing category.
determine a second weighted distribution for the contributing category, wherein the second weighted distribution represents another percentage contribution of the contributing category to the greenhouse gas emissions produced by an emission source;”.
It is very confusing trying to follow the flow of “greenhouse emissions” associated with a source level category and contributing category. In particular, “greenhouse emissions” is only introduced once in the first limitation listed, however, parsing through the limitations it would appear that there are different levels of emissions being discussed.
For instance, “obtain a first weighted distribution for the source level category, wherein the first weighted distribution represents a percentage contribution of the greenhouse gas emissions contributed to the contributing category”, obtains a first weight for determining a percentage amount of greenhouse emissions produced for a contributing category (i.e. one production process of several processes as defined in the spec).
Then proceeding to the next limitation, “determine a second weighted distribution for the contributing category, wherein the second weighted distribution represents another percentage contribution of the contributing category to the greenhouse gas emissions produced by an emission source”, obtains a second weight that only considers a contributing category’s percentage impact (i.e. potentially one manufacturing process impact from a plurality of processes impact) on a total emission produced from an emission source (i.e. maybe a factory’s total output?), which appears to be a different emission total than the emissions discussed in the previous limitation.
This leads to a plurality of potential antecedent basis issues as “greenhouse gas emissions” is only introduced in the first limitation as, ““identify a source level category of greenhouse gas emissions associated with a contributing category of the greenhouse gas emissions,”.
In order to further prosecution, any prior art that can break down a sources percentage or contribution to a production process of a total emission amount will be considered to read upon the claims until further amendments are provided to correct this issue.
Independent claims 12 and 16 have substantially similar limitations and are rejected using the same rationale as provided above. Further, claims 2-11, 13-15, and 17-20 each depend upon rejected claims 1, 12, and 16 respectively and are thus rejected by virtue of dependency.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed towards an abstract idea without significantly more. Claim 1 recites, “identify a source level category of greenhouse gas emissions associated with a contributing category of the greenhouse gas emissions, wherein the source level category contributes to overall greenhouse gas emission of the contributing category.”, “determine a second weighted distribution for the contributing category, wherein the second weighted distribution represents another percentage contribution of the contributing category to the greenhouse gas emissions produced by an emission source;”, “estimate, for a production plan specifying a predefined quantity of goods to be manufactured, a volume of emission attributable to the source level category and the contributing category based on the first weighted distribution and the second weighted distribution;”, and “determine whether the emission of the source level category is greater than a predefined threshold;”, which analyzed under Step 2A Prong One, includes limitations of identifying a source of greenhouse emissions, determining a weight to apply to contributing category responsible for the source, estimating a volume of emissions for the source, and determining whether the estimation exceeds a threshold which are all limitations that can reasonably be performed in the human mind/with pen and paper and thus, fall within the, “Mental Processes” grouping of abstract ideas.
This judicial exception is not integrated into a practical application. Claim 1 further recites, “cause to modify an operating parameter of an asset contributing to the source level category of the greenhouse gas emissions.”, which analyzed under Step 2A Prong Two, states generally that an operating parameters is modified without providing context as to how that modification actually effects the system, thus, the claim can broadly be interpreted as merely adjusting a value which just generally applies the use of the judicial exception (see MPEP 2106.05(f)). Additionally, claim 1 recites, “obtain a first weighted distribution for the source level category, wherein the first weighted distribution represents a percentage contribution of the greenhouse gas emissions contributed to the contributing category.”, which analyzed under Step 2A Prong Two, adds insignificant extra solution activity in the form of mere data gathering (see MPEP 2106.05(g)). Finally, the limitations of, “a processor” and “a machine-readable storage medium”, as generally recited, merely represent generic computer components for implementing the abstract idea.
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception because as analyzed under Step 2B, the additional elements merely amount to gathering weighted distribution data for greenhouse gas emissions and sending the data over a network. Analyzed under Berkheimer, the act of gathering and sending data over a network has been deemed as well-understood, routine, and conventional by the courts (see MPEP 2106.05(d)(II), “sending/receiving data over a network”).
Independent claims 12 and 16 are substantially similar to claim 1 and are thus rejected using the same rationale as provided above.
Dependent claims 2-11, 13-15, and 17-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed towards an abstract idea without significantly more. Claims 7, 10, and 19 include limitations of, correlating historic data to greenhouse gas emissions being produced from a source and segregating obtained estimated emission values for different organizational levels of an industrial process, which analyzed under Step 2A Prong One, include limitations that can reasonably be performed in the human mind/with pen and paper and thus, fall within the, “Mental Processes” grouping of abstract ideas.
This judicial exception is not integrated into a practical application. Claims 4, 11, 14, 17, and 20, each include limitations of, generating an alert, providing a recommended compensatory action, and rendering a display to show various data points, which analyzed under Step 2A Prong Two, just merely applies the use of the judicial exception (see MPEP 2106.05(f)). Further, claim 13 describes measuring and collecting data using sensors, which analyzed under Step 2A Prong Two, adds insignificant extra solution activity in the form of mere data gathering (see MPEP 2106.05(g)). Finally, claims 2-3, 5-6, 8-9, 15, and 18, include limitations of, describing the various assets of the industrial process, contributing categories, types of emission sources, using scopes 1-3 of the greenhouse gas protocol for direct/indirect source classification, using a machine learning model for estimation of volume of emissions, description of the production plan, and the types of operating parameters considered, which analyzed under Step 2A Prong Two, adds a plurality of limitations providing various definitions/clarifications of specific terms or how information is processed which just generally links the use of the judicial exception to a particular technological environment or field of use (see MPEP 2106.05(h)).
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception because as analyzed under Step 2B, the additional elements merely amount to gathering greenhouse gas emissions data and sending the data over a network. Analyzed under Berkheimer, the act of gathering and sending data over a network has been deemed as well-understood, routine, and conventional by the courts (see MPEP 2106.05(d)(II), “sending/receiving data over a network”).
Allowable Subject Matter
The following is a statement of reasons for the indication of allowable subject matter:
The office would first like to note that the present application currently has an outstanding 35 U.S.C. 112(b), 35 U.S.C. 101 rejection, and claim objections pending that must be resolved prior to considering this application for allowance.
The independent claims recite: “A system comprising: a processor; and a machine-readable storage medium comprising instructions executable by the processor to:
identify a source level category of greenhouse gas emissions associated with a contributing category of the greenhouse gas emissions, wherein the source level category contributes to overall greenhouse gas emission of the contributing category.
obtain a first weighted distribution for the source level category, wherein the first weighted distribution represents a percentage contribution of the greenhouse gas emissions contributed to the contributing category.
determine a second weighted distribution for the contributing category, wherein the second weighted distribution represents another percentage contribution of the contributing category to the greenhouse gas emissions produced by an emission source;
estimate, for a production plan specifying a predefined quantity of goods to be manufactured, a volume of emission attributable to the source level category and the contributing category based on the first weighted distribution and the second weighted distribution;
determine whether the emission of the source level category is greater than a predefined threshold; and
cause to modify an operating parameter of an asset contributing to the source level category of the greenhouse gas emissions.”
This application describes a software-based system for estimating greenhouse gas emissions from a planned production schedule before the production happens. It starts with historical emissions data collected from equipment, assets, or process stages in a facility. The system groups emissions at two levels: a fine-grained “source level category” such as a specific machine or asset, and a broader “contributing category” such as a process stage like venting or combustion. It assigns weighted percentages showing how much each source level category contributes to its process stage, and how much each process stage contributes to the overall emission source. Using those weights, the system estimates future emissions for a proposed production plan and the quantity of goods to be manufactured. It can also break the estimate down by asset and by process stage. If a source-level estimate exceeds a threshold, the system can generate an alert and recommend changes to operating settings on the asset. The operating settings may include temperature, pressure, duty cycle, or load. The disclosure says the approach can be used for Scope 1, Scope 2, and Scope 3 emissions. The system may also display the results on a dashboard for users to review.
The closest prior art of record is Russo (US PGPUB 20240160956). Russo discloses a dashboard display system and method for tracking all direct and indirect sources of carbon emissions for a manufacturing facility. The system utilizes machine learning algorithms to monitor and correlate a plurality of sources of direct and indirect sources of ghg emissions and can further create visual displays breaking down and showing the percentages each source contributes to overall emissions from a ghg emission producing facility. Also, the system is capable of categorizing the various producers of the ghg emissions which can then be compared to other producers to visualize each producers overall impact. However, Russo as well as all other cited prior art of reference, are silent on isolating a specific ghg producing source, determining its percentage impact to a specific part of a production process (i.e. contributing category), determining the specific production processes impact to a total ghg emission total of a plant, and utilizing those two data points to determine if the targeted source exceeds a ghg emission threshold, and if so, adjusting parameters of the source. This provides monitoring of every devices impact during each part of a production process so that the highest ghg producing components can be optimized in order to reduce the carbon footprint during large scale manufacturing processes.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Russo et al. (US PGPUB 20230103187): This application describes a computer-based system for tracking greenhouse gas emissions, especially in logistics and transport. The system gathers greenhouse gas-related data from two different sources for a user or group of users. It then computes metrics for each source and calculates a ratio between them. The application says the data can come from direct transport activity, such as vehicle fuel use, as well as indirect sources, such as electronic invoicing, electricity use, packaging, and loading operations. It can also use databases, user records, and transport records to organize the information. In some embodiments, the system applies conversion factors to translate fuel, miles, electricity, or other inputs into greenhouse gas amounts. It may generate pollutant profiles showing categories of emissions and contributing factors. The system can present the results through a display or graphical user interface. It also supports machine learning to predict emissions and estimate greenhouse gas ratios from training data. The output may be used to provide feedback and reduction plans to users.
Krishna et al (US PGPUB 20240403776): This application describes software that estimates how much fuel, energy, emissions, waste, or other resources a facility or machine should be using under given operating conditions. It compares those AI-predicted baseline values with actual sensor readings to see whether an asset is performing efficiently or wasting resources. The system can do this for one machine, a whole facility, or an entire organization. It can also look across a cohort of similar assets to benchmark one asset against its peers. The software uses historical and real-time time series data, plus operating conditions such as temperature, pressure, production schedule, and maintenance events. It can forecast future resource use and emissions as well. When the system detects inefficiency, it can recommend corrective actions or even trigger adjustments. The application also emphasizes hierarchical aggregation so facility-level predictions are not distorted by simply summing dependent asset values. It includes explainability, model management, and orchestrated workflows for deploying multiple models. It can also answer natural-language questions about resource performance and savings opportunities.
McConnell et al. (US PGPUB 20080255899): This application describes a networked software system for tracking and managing energy use, greenhouse gas emissions, carbon credits, and related costs for business assets. The system can cover one piece of equipment, one site, multiple sites, or an entire enterprise. It stores asset data in a database and uses software modules to calculate energy consumption, emissions, credits, and financial impacts. The system can also generate reports and compare actual performance against budgeted targets. In some embodiments, it identifies operating adjustments that could reduce emissions or costs. It may receive data from utility bills, sensors, or equipment interfaces. The same platform can also track refrigerant loss, service and maintenance, and verification of emissions reductions. Another embodiment calculates the carbon footprint of products, services, or assets based on manufacturing, packaging, transportation, and operational emissions. The system is designed for centralized monitoring across distributed sites. It aims to help users understand both financial and environmental performance in one place.
Rawat et al. (US PGPUB 20180031533): This application describes a way to measure carbon emissions from electricity use in near real time. It works at two levels: the utility substation level and the individual device level. A substation controller communicates with connected devices through a demand response protocol. The system can receive device power-use data, load information, location information, and power-source data. A carbon emissions calculator uses that information to estimate how much carbon each device is causing. In some versions, the calculator is built into the substation controller; in others, it is built into the device controller itself. The system can display the calculated emissions on a user interface. It can also compare emissions before and after a demand response event to estimate emissions reductions. If a device’s emissions stay above a threshold, the substation can take action to reduce emissions, such as sending a demand response event or switching to a renewable power source. The stated goal is to give utilities a more accurate, localized view of carbon emissions than plant-level estimates.
Trout et al. (US PGPUB 20060020502): This application describes a method for determining a target variable for a production process, determining a plurality of characteristics involved during the process related to the target variable, and then creating categories based on the determined characteristics. The method does this by determining each characteristics ghg percentage contribution for a given target variable in the process. The system can then use this to optimize performance of the various characteristics to minimize ghg impacts and provide permits to allow production of goods using the optimized parameters.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to CHRISTOPHER W CARTER whose telephone number is (469)295-9262. The examiner can normally be reached 9-6:30.
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/CHRISTOPHER W CARTER/Examiner, Art Unit 2117
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