Prosecution Insights
Last updated: April 19, 2026
Application No. 18/719,417

SYSTEMS AND METHODS FOR PREDICTING ENVIRONMENTAL EMISSIONS BASED ON ANIMAL NUTRITION

Non-Final OA §101§103
Filed
Jun 13, 2024
Examiner
CRANDALL, RICHARD W.
Art Unit
3619
Tech Center
3600 — Transportation & Electronic Commerce
Assignee
Can Technologies Inc.
OA Round
1 (Non-Final)
30%
Grant Probability
At Risk
1-2
OA Rounds
3y 1m
To Grant
64%
With Interview

Examiner Intelligence

Grants only 30% of cases
30%
Career Allow Rate
90 granted / 301 resolved
-22.1% vs TC avg
Strong +34% interview lift
Without
With
+33.8%
Interview Lift
resolved cases with interview
Typical timeline
3y 1m
Avg Prosecution
42 currently pending
Career history
343
Total Applications
across all art units

Statute-Specific Performance

§101
34.6%
-5.4% vs TC avg
§103
37.1%
-2.9% vs TC avg
§102
8.3%
-31.7% vs TC avg
§112
15.4%
-24.6% vs TC avg
Black line = Tech Center average estimate • Based on career data from 301 resolved cases

Office Action

§101 §103
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 . Status of Claims This Office action is in response to correspondence received June 13, 2024. Claims 1-20 are pending and have been examined. 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 to an abstract idea without significantly more. The claim(s) recite(s): Claims 1, 9, and 14, which are similar in scope, recite the following abstract idea: A method comprising: determining a raw material environmental footprint profile for one or more raw materials to be used in a feed formulation for an animal product; determining, an animal feed environmental footprint profile, wherein the animal feed environmental footprint profile is determined based on the feed formulation comprising the one or more raw materials; determining, an animal production and processing environmental footprint profile for producing and processing the animal product; determining, an overall environmental footprint profile by combining the raw material environmental footprint profile, the animal feed environmental footprint profile, and the animal production and processing environmental footprint profile; and adjusting, the feed formulation based on the overall environmental footprint profile to achieve a target emission value. The steps above recite an abstract idea that is a mental process of collecting, comparing, and then performing a step which could also be a mental process of decision-making. The steps up to the adjusting steps are collecting and comparing steps. The adjusting step under a broadest reasonable interpretation is also a mental process of making a decision as there is nothing physical occurring. The adjustment could be a change of a plan in feeding. This is very similar to USPTO guidance from October 2019, “Appendix 1 to the October 2019 Update: Subject Matter Eligibility Life Sciences & Data Processing Examples,” example 46, claim 1 Pages 31-34. This is available here: https://www.uspto.gov/sites/default/files/documents/peg_oct_2019_app1.pdf This is similar to claim 1 in these Subject Matter Eligibility (“SME”) examples. Like claim 1, information about animals is collected and results are determined. The differences between the steps are not pertinent to the 101 analysis as both claim 1 of the SME example and Applicant’s independent claims are taking information about animals and making determinations. Note that sending signals to computers/display steps, etc, would be more similar to claim 1 than claim 2 in the SME example, where a feed dispenser as well as sending signals automatically to the dispenser are required elements and combinations of elements to overcome the 101 rejection. This judicial exception is not integrated into a practical application. Applicant has recited generic computing components in the independent claims and the combination of these elements amounts to no more than instructions to apply the abstract idea to a computer. See MPEP 2106.05(f)(2), Alice, Versata. The additional elements are: Claim 1: by a processor executing computer-readable instructions stored on a memory, Claim 9: A non-transitory computer-readable medium comprising computer-executable instructions stored thereon that when executed by at least one processor, cause the at least one processor to: Claim 14: A system comprising: a memory having computer-executable instructions stored thereon; and a processor that executes the computer-executable instructions to: Therefore these additional elements amount to no more than instructions to apply a computer to the abstract idea. The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because for the same reasons as explained in the practical application section, instructions to apply an abstract idea to a computer (or vice versa) are not significantly more than the abstract idea. The reasoning from above is carried over and applied here, and therefore the claims do not recite significantly more than the abstract idea. Claims 2-8, 19-13, and 15-20 further define the abstract idea with collecting and comparing information steps and therefore further describe the abstract idea. Therefore, for these reasons, claims 1-20 are rejected under 35 USC 101. Claim Rejections - 35 USC § 103 The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action: A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made. The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows: 1. Determining the scope and contents of the prior art. 2. Ascertaining the differences between the prior art and the claims at issue. 3. Resolving the level of ordinary skill in the pertinent art. 4. Considering objective evidence present in the application indicating obviousness or nonobviousness. Claim(s) 1-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Heidari et al., “Proposing a Framework for sustainable feed formulation for laying hens: A systematic review of recent developments and future directions,” Journal of Cleaner Production, 233 (2021) 125585, pages 1-21, available online December 17, 2020 (“Heidari”), in view of Burghardi et al., US PGPUB 20080154568 A1 (“Burghardi”). Per claims 1, 9, and 14, which are similar in scope, Heidari teaches determine a raw material environmental footprint profile for one or more raw materials to be used in a feed formulation for an animal product in Table 2 where the first column Ingredient Type teaches a raw material, such as Fat, or Energy, or Protein, and then the columns Data Source, Impact Assessment Method, and Remarks teach environmental footprint profile because they describe how things have global warming potential or climate change. Heidari then teaches determine an animal feed environmental footprint profile, wherein the animal feed environmental footprint profile is determined based on the feed formulation comprising the one or more raw materials in table 2 where the animal feed is taught in the case study column, “Corn” which comprises the raw material of Energy, or the Carinata and camelina which comprises fat. The environmental footprint profile is aught by the impact categories, Global warming potential, aquatic ecotoxicity (LCA of pulse systems). Heidari then teaches determine an animal production and processing environmental footprint profile for producing and processing the animal product in page 15 where under the header Processing, the energy sources and fuel type most determine the environmental footprint for a feed process. Heidari then teaches determine an overall environmental footprint profile by combining the raw material environmental footprint profile, the animal feed environmental footprint profile, and the animal production and processing environmental footprint profile in page 16, where least cost feed formulation and weighting methods are applied to “each impact type” which teaches for raw material … animal feed environmental … and animal production and processing. Heidari then teaches and adjust the feed formulation based on the overall environmental footprint profile to achieve a target emission value on page 17, item 5, Apply LCIA methods using regionally resolved characterization factors (IMPACT World) and Item 6 integrate regionalized LCIA results for feed inputs into feed formulation tool. See column 2 on page 17, “environmental objectives by advancing the right weighting methods.” See page 2 for GHG emissions being defined as a part of the environment. See also table 2 remarks for feed with certain emissions data. See also page 11: “Broiler feed containing less dietary crude protein but a better balance in amino acid composition results in a better feed conversion ratio and less nitrogen emissions from manure.” Heidari does not teach that steps are performed by a processor executing computer-readable instructions stored on a memory (claim 1), A non-transitory computer-readable medium comprising computer-executable instructions stored thereon that when executed by at least one processor, cause the at least one processor to (claim 9). A system comprising: a memory having computer-executable instructions stored thereon; and a processor that executes the computer-executable instructions to (claim 14). Burghardi teaches a system for generating animal feed formulation. See abstract. Burghardi teaches by a processor executing computer-readable instructions stored on a memory (claim 1), A non-transitory computer-readable medium comprising computer-executable instructions stored thereon that when executed by at least one processor, cause the at least one processor to (claim 9). A system comprising: a memory having computer-executable instructions stored thereon; and a processor that executes the computer-executable instructions to (claim 14) in pars 022-025 where the system is described with multiple computers which makes these elements obvious. It would have been obvious to one ordinarily skilled in the art before the effective filing date of the claimed invention to modify the feed optimization for environmental impact teaching of Heidari with the using computers to carry out steps teaching of Burghardi because one would be motivated to use computers to perform data collection and comparison as these speed up the processes that one would formerly do by hand or with other devices. As this would make data collection and comparison more efficient, one would be motivated to modify Heidari with Burghardi. Per claims 2, 10, and 15, which are similar in scope, Heidari and Burghardi teach the limitations of claims 1, 9, and 14, above. Heidari further teaches receive a plurality of inputs related to the raw material in pages 11 and 14 where x_j are the inputs. Heidari then teaches determine a value of one or more environmental indicators for each of the plurality of inputs in page 15 where weights (w_i,k) teach environmental indicators. Heidari then teaches and create the raw material environmental footprint profile for the raw material based on the value of the one or more environmental indicators in page 15 where the F is minimized which teaches the raw material environmental footprint profile. Heidari does not teach by the processor or other generic computing components. Burghardi teaches by the processor and other generic computing components in pars 022-025 where the system is described with multiple computers which makes these elements obvious. It would have been obvious to one ordinarily skilled in the art before the effective filing date of the claimed invention to modify the feed optimization for environmental impact teaching of Heidari with the using computers to carry out steps teaching of Burghardi because one would be motivated to use computers to perform data collection and comparison as these speed up the processes that one would formerly do by hand or with other devices. As this would make data collection and comparison more efficient, one would be motivated to modify Heidari with Burghardi. Per claims 3 and 16, which are similar in scope, Heidari and Burghardi teach the limitations of claims 2 and 15, above. Heidari further teaches wherein the plurality of inputs comprise at least one of a supplier input, a species input, a country of origin input, or a transportation input in page 15 where transportation is taught. Per claims 4, 11, and 17, which are similar in scope, Heidari and Burghardi teach the limitations of claims 1, 9, and 14, above. Heidari further teaches wherein the feed formulation comprises a percentage of each of the one or more raw materials used in the feed formulation, and wherein determining the animal feed environmental footprint profile comprises computing a product of the percentage of each of the one or more raw materials with a value of an environmental indicator for the each of the one or more raw materials, wherein the value is determined based on the raw material environmental footprint profile in page 18 where assessment methods are taught which teaches LCA studies of feed input supply chains. Heidari does not teach by the processor. Burghardi teaches by the processor and other generic computing components in pars 022-025 where the system is described with multiple computers which makes these elements obvious. It would have been obvious to one ordinarily skilled in the art before the effective filing date of the claimed invention to modify the feed optimization for environmental impact teaching of Heidari with the using computers to carry out steps teaching of Burghardi because one would be motivated to use computers to perform data collection and comparison as these speed up the processes that one would formerly do by hand or with other devices. As this would make data collection and comparison more efficient, one would be motivated to modify Heidari with Burghardi. Per claims 5, 12, and 18, which are similar in scope, Heidari and Burghardi teach the limitations of claims 1, 12, and 14, above. Heidari further teaches wherein determining the animal production and processing environmental footprint profile further comprises: receiving a plurality of inputs in pages 11 and 14 where x_j are the inputs. Heidari then teaches determining a value of one or more environmental indicators based for each of the plurality of inputs in page 15 where weights (w_i,k) teach environmental indicators. Heidari then teaches and creating the animal production and processing environmental footprint profile based on the value of the one or more environmental indicators in page 15 where the F is minimized which teaches the animal production and processing environmental footprint profile footprint profile. Heidari does not teach by the processor or other generic computing components. Burghardi teaches by the processor and other generic computing components in pars 022-025 where the system is described with multiple computers which makes these elements obvious. It would have been obvious to one ordinarily skilled in the art before the effective filing date of the claimed invention to modify the feed optimization for environmental impact teaching of Heidari with the using computers to carry out steps teaching of Burghardi because one would be motivated to use computers to perform data collection and comparison as these speed up the processes that one would formerly do by hand or with other devices. As this would make data collection and comparison more efficient, one would be motivated to modify Heidari with Burghardi. Per claim 6, Heidari and Burghardi teach the limitations of claim 5, above. Heidari further teaches wherein the plurality of inputs comprise at least one of a chemical input, a transportation input, an energy input, a facilities input, a life stage input, or a parasite input in page 15 where transportation is taught. Per claims 7 and 19, which are similar in scope, Heidari and Burghardi teach the limitations of claims 1 and 14, above. Heidari further teaches wherein adjusting the feed formulation comprises changing a percentage of at least one of the one or more raw materials in the feed formulation on page 17, item 5, Apply LCIA methods using regionally resolved characterization factors (IMPACT World) and Item 6 integrate regionalized LCIA results for feed inputs into feed formulation tool, which under a broadest reasonable interpretation teaches changing percentages. Per claims 8 and 20, which are similar in scope, Heidari and Burghardi teach the limitations of claims 1 and 14, above. Heidari further teaches wherein adjusting the feed formulation comprising replacing at least one of the one or more raw materials in the feed formulation with another raw material in page 15: “The impacts characteristic of specific raw materials also vary by raw material type. Taking into account the quantity of each ingredient in a feed formulation, canola, corn, and soybean accounted for the largest share of both mass and environmental impacts in several reported formulations (Pacheco et al., 2018; Pelletier, 2006). However, on an equivalent mass basis and taking into account feed input type, animal fats and meals typically have the highest environmental impacts, while energy crops such as corn have proportionately lower environmental impacts (Ellingsen and Aanondsen, 2006; Pelletier, 2008; Pelletier et al., 2008) (Review Question 2a). For example, poultry fat (11–35 times), poultry by-product meal (6.5–18.5 times), and fishmeal (1.4–3.5 times) have higher environmental impacts compared to crop ingredients (Papatryphon et al., 2004; Pelletier, 2008; Silva et al., 2018).” Animal fats and meals are substituted for energy crops. Per claim 13, Heidari and Burghardi teach the limitations of claim 9, above. Heidari further teaches wherein adjusting the feed formulation comprises changing a percentage of at least one of the one or more raw materials in the feed formulation on page 17, item 5, Apply LCIA methods using regionally resolved characterization factors (IMPACT World) and Item 6 integrate regionalized LCIA results for feed inputs into feed formulation tool, which under a broadest reasonable interpretation teaches changing percentages. Heidari then teaches wherein adjusting the feed formulation comprising replacing at least one of the one or more raw materials in the feed formulation with another raw material in page 15: “The impacts characteristic of specific raw materials also vary by raw material type. Taking into account the quantity of each ingredient in a feed formulation, canola, corn, and soybean accounted for the largest share of both mass and environmental impacts in several reported formulations (Pacheco et al., 2018; Pelletier, 2006). However, on an equivalent mass basis and taking into account feed input type, animal fats and meals typically have the highest environmental impacts, while energy crops such as corn have proportionately lower environmental impacts (Ellingsen and Aanondsen, 2006; Pelletier, 2008; Pelletier et al., 2008) (Review Question 2a). For example, poultry fat (11–35 times), poultry by-product meal (6.5–18.5 times), and fishmeal (1.4–3.5 times) have higher environmental impacts compared to crop ingredients (Papatryphon et al., 2004; Pelletier, 2008; Silva et al., 2018).” Animal fats and meals are substituted for energy crops. Therefore, claims 1-20 are rejected under 35 USC 103. Prior Art Considered Relevant The following prior art is considered relevant to Applicant’s disclosure but is not relied upon in the above rejection: Burghardi et al., CA 2573901 A1, teaches in par 058 an optimization engine which uses feed ingredient information in order to optimize the environmental impact of feed. Conclusion Any inquiry concerning this communication or earlier communications from the examiner should be directed to RICHARD W. CRANDALL whose telephone number is (313)446-6562. The examiner can normally be reached M - F, 8:00 AM - 5:00 PM. Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice. If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Anita Coupe can be reached at (571) 270-3614. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300. Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000. /RICHARD W. CRANDALL/ Primary Examiner, Art Unit 3619
Read full office action

Prosecution Timeline

Jun 13, 2024
Application Filed
Jan 26, 2026
Non-Final Rejection — §101, §103 (current)

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Study what changed to get past this examiner. Based on 5 most recent grants.

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Prosecution Projections

1-2
Expected OA Rounds
30%
Grant Probability
64%
With Interview (+33.8%)
3y 1m
Median Time to Grant
Low
PTA Risk
Based on 301 resolved cases by this examiner. Grant probability derived from career allow rate.

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