DETAILED ACTION
This communication is a Non-Final Rejection Office Action in response to the 6/25/2024 preliminary amendment filed in Application 18/751,665. Claims 1-3, 5-8, 11, 16-18, 22-29, 31, 35-38, 41 are now presented.
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitation(s) is/are: a dry matter intake (DMI) module; a milk production (MP) module in claims 1, 11.
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof. The Applicants specification states “Computing device 1 may include a controller 2 that may be, for example, a central processing unit (CPU) processor, a chip or any suitable computing or computing device, an operating system 3, a memory 4, executable code 5, a storage system 6, input device(s) 7 and output device(s) 8. Controller 2 (or one or more controllers or processors, possibly across multiple units or devices) may be configured to carry out methods described herein, and/or to execute or act as the various modules, e.g., a DMI module and/or an MP module, units, etc. More than one computing device 1 may be included in, and one or more computing devices 1 may act as the components of, a system according to embodiments of the invention.”
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
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 28, 29 recites the limitation "the IoF and the NIMP". There is insufficient antecedent basis for this limitation in the claim.
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 1-3, 5-8, 11, 16-18, 22-29, 31, 35-38, 41 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter.
When considering subject matter eligibility under 35 U.S.C. 101, in step 1 it must be determined whether the claim is directed to one of the four statutory categories of invention, i.e., process, machine, manufacture, or composition of matter. If the claim does fall within one of the statutory categories, in step 2A prong 1 it must then be determined whether the claim is recite a judicial exception (i.e., law of nature, natural phenomenon, and abstract idea). If the claim recites a judicial exception, under step 2A prong 2 it must additionally be determined whether the recites additional elements that integrate the judicial exception into a practical application. If a claim does not integrate the Abstract idea into a practical application, under step 2B it must then be determined if the claim provides an inventive concept.
In the Instant case, Claims 1-3, 5-8, 11, 16-18 are directed toward systems for classifying an individual herd member. Claims 22-20, 31, 36-38, 41 are directed toward methods for classifying an individual herd member and timing the removal of at least one individual herd member from a herd. As such, each of the Claims is directed to one of the four statutory categories of invention.
MPEP 2106.04 II. A. explains that in step 2A prong 1 Examiners are to determine whether a claim recites a judicial exception. MPEP 2106.04(a) explains that:
To facilitate examination, the Office has set forth an approach to identifying abstract ideas that distills the relevant case law into enumerated groupings of abstract ideas. The enumerated groupings are firmly rooted in Supreme Court precedent as well as Federal Circuit decisions interpreting that precedent, as is explained in MPEP § 2106.04(a)(2). This approach represents a shift from the former case-comparison approach that required examiners to rely on individual judicial cases when determining whether a claim recites an abstract idea. By grouping the abstract ideas, the examiners’ focus has been shifted from relying on individual cases to generally applying the wide body of case law spanning all technologies and claim types.
The enumerated groupings of abstract ideas are defined as:
1) Mathematical concepts – mathematical relationships, mathematical formulas or equations, mathematical calculations (see MPEP § 2106.04(a)(2), subsection I);
2) Certain methods of organizing human activity – fundamental economic principles or practices (including hedging, insurance, mitigating risk); commercial or legal interactions (including agreements in the form of contracts; legal obligations; advertising, marketing or sales activities or behaviors; business relations); managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions) (see MPEP § 2106.04(a)(2), subsection II); and
3) Mental processes – concepts performed in the human mind (including an observation, evaluation, judgment, opinion) (see MPEP § 2106.04(a)(2), subsection III).
As per step 2A prong 1 of the eligibility analysis, claim 11 is directed to the abstract idea classifying an individual herd member based on dry matter intake and milk production which falls into the abstract idea categories of certain methods of organizing human activity and mental.
The limitations of Claim 11 that represent the Abstract idea include:
calculate the feed efficiency (FE) of the individual herd member over the first pre-determined period of time, based on the DMI over the first pre- determined time period and the MP over the first pre-determined time period;
classify the individual herd member according to the FE; and
wherein if the individual herd member is classified to be removed from the herd, or group within the herd or group within the herd, the DMI module is further configured to obtain the DMI or DMI-related data of the individual herd member, over a second pre-determined period of time;
the MP module is further configured to obtain the MP or MP-related data of the individual herd member, over the second pre-determined period of time; and
calculate an NIMP or an IoF of the individual herd member, based on the monetary data, the DMI over the second pre-determined time period, and the MP over the second predetermined time period; and
indicate a time for removal of the individual herd member from the herd, or group within the herd, wherein the time indicated is a time at which the NIMP is lower than a pre-determined NIMP value or a time at which the IoF is lower than a pre-determined IoF value.
MPEP 2106.04(a)(2) II. states:
The phrase "methods of organizing human activity" is used to describe concepts relating to:
fundamental economic principles or practices (including hedging, insurance, mitigating risk);
commercial or legal interactions (including agreements in the form of contracts, legal obligations, advertising, marketing or sales activities or behaviors, and business relations); and
managing personal behavior or relationships or interactions between people, (including social activities, teaching, and following rules or instructions).
The Supreme Court has identified a number of concepts falling within the "certain methods of organizing human activity" grouping as abstract ideas. In particular, in Alice, the Court concluded that the use of a third party to mediate settlement risk is a ‘‘fundamental economic practice’’ and thus an abstract idea. 573 U.S. at 219–20, 110 USPQ2d at 1982. In addition, the Court in Alice described the concept of risk hedging identified as an abstract idea in Bilski as ‘‘a method of organizing human activity’’. Id. Previously, in Bilski, the Court concluded that hedging is a ‘‘fundamental economic practice’’ and therefore an abstract idea. 561 U.S. at 611–612, 95 USPQ2d at 1010.
The instant claims are directed to analyzing livestock related data including DMI and MP to maximize operational efficiency of a cattle herd which relates to marketing or sales activities or behaviors, and business relations which falls in the abstract method or organizing human activity bucket.
MPEP 2106.04(a)(2) states:
The courts consider a mental process (thinking) that "can be performed in the human mind, or by a human using a pen and paper" to be an abstract idea. CyberSource Corp. v. Retail Decisions, Inc., 654 F.3d 1366, 1372, 99 USPQ2d 1690, 1695 (Fed. Cir. 2011). As the Federal Circuit explained, "methods which can be performed mentally, or which are the equivalent of human mental work, are unpatentable abstract ideas the ‘basic tools of scientific and technological work’ that are open to all.’" 654 F.3d at 1371, 99 USPQ2d at 1694 (citing Gottschalk v. Benson, 409 U.S. 63, 175 USPQ 673 (1972)). See also Mayo Collaborative Servs. v. Prometheus Labs. Inc., 566 U.S. 66, 71, 101 USPQ2d 1961, 1965 (2012) ("‘[M]ental processes[] and abstract intellectual concepts are not patentable, as they are the basic tools of scientific and technological work’" (quoting Benson, 409 U.S. at 67, 175 USPQ at 675)); Parker v. Flook, 437 U.S. 584, 589, 198 USPQ 193, 197 (1978) (same).
Accordingly, the "mental processes" abstract idea grouping is defined as concepts performed in the human mind, and examples of mental processes include observations, evaluations, judgments, and opinions
In the instant case, the limitations of calculate the feed efficiency (FE) of the individual herd member over the first pre-determined period of time, based on the DMI over the first pre- determined time period and the MP over the first pre-determined time period; classify the individual herd member according to the FE; and wherein if the individual herd member is classified to be removed from the herd, or group within the herd or group within the herd, the DMI module is further configured to obtain the DMI or DMI-related data of the individual herd member, over a second pre-determined period of time; the MP module is further configured to obtain the MP or MP-related data of the individual herd member, over the second pre-determined period of time; and calculate an NIMP or an IoF of the individual herd member, based on the monetary data, the DMI over the second pre-determined time period, and the MP over the second predetermined time period; and indicate a time for removal of the individual herd member from the herd, or group within the herd, wherein the time indicated is a time at which the NIMP is lower than a pre-determined NIMP value or a time at which the IoF is lower than a pre-determined IoF value cover observations and evaluations that can be performed in the mind but for the recitation of generic computer components. That is, other than reciting “a processor” nothing in the claim precludes the steps from being performed in the human mind.
Under step 2A prong 2 the examiner must then determine if the recited abstract idea is integrated into a practical application. MPEP 2106.04 states:
Limitations the courts have found indicative that an additional element (or combination of elements) may have integrated the exception into a practical application include:
• An improvement in the functioning of a computer, or an improvement to other technology or technical field, as discussed in MPEP §§ 2106.04(d)(1) and 2106.05(a);
• Applying or using a judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition, as discussed in MPEP § 2106.04(d)(2);
• Implementing a judicial exception with, or using a judicial exception in conjunction with, a particular machine or manufacture that is integral to the claim, as discussed in MPEP § 2106.05(b);
• Effecting a transformation or reduction of a particular article to a different state or thing, as discussed in MPEP § 2106.05(c); and
• Applying or using the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception, as discussed in MPEP § 2106.05(e)
The courts have also identified limitations that did not integrate a judicial exception into a practical application:
• 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, as discussed in MPEP § 2106.05(f);
• Adding insignificant extra-solution activity to the judicial exception, as discussed in MPEP § 2106.05(g); and
• Generally linking the use of a judicial exception to a particular technological environment or field of use, as discussed in MPEP § 2106.05(h).
In the instant case, this judicial exception is not integrated into a practical application. In particular, Claim 11 recites the additional elements of:
A system for classifying an individual herd member and for further indicating a time at which the individual herd member is to be removed from a herd, or group within the herd, said system comprising:
a dry matter intake (DMI) module configured to obtain the DMI or DMI-related data of the individual herd member, over a first pre-determined period of time;
a milk production (MP) module, configured to obtain the MP or MP-related data of the individual herd member, over the first pre-determined period of time;
a central processing unit (CPU) comprising a processor in communication with a memory module, the memory module having stored thereon a program code, the program code executable by the processor to:
obtain the DMI from the DMI module, or obtain the DMI-related data from the DMI module and to further obtain the DMI from the DMI-related data, over the first pre-determined period of time;
the program code is further executable by the processor to:
obtain the DMI from the DMI module, or obtain the DMI-related data from the DMI module and to further obtain the DMI from the DMI-related data, over the second pre-determined period of time;
obtain the MP from the MP module, or obtain the MP-related data from the MP module and to further obtain the MP from the MP-related data, over the second pre-determined period of time;
receive monetary data; obtain the MP from the MP module, or obtain the MP-related data from the MP module and to further obtain the MP from the MP-related data, over the first pre-determined period of time;
However, the computer elements (central processing unit and the recited modules) are recited at a high-level of generality (i.e., as a generic processor performing a generic computer functions) such that it amounts no more than mere instructions to apply the exception using a generic computer component.
Further, obtaining DMI or DMO related data and MP or MP related data amounts to insignificant data gathering. The claim does not state how data id obtained. As such, under the broadest reasonable interpretation, this amounts to insignificant data gathering.
When viewing the generic computer in combination with the insignificant data gathering does not add more than when viewing the elements individually. Accordingly, the 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.
In step 2B, the examiner must determine whether the claim adds a specific limitation other than what is well-understood, routine, conventional activity in the field - see MPEP 2106.05(d). As discussed with respect to Step 2A Prong Two, the additional elements in the claim amount to no more than mere instructions to apply the exception using a generic computer component. The same analysis applies here in 2B, i.e., mere instructions to apply an exception on a generic computer cannot integrate a judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B.
Further, MPEP 2106.05(d) states storing and retrieving information in memory is conventional when claimed generically (see Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015); OIP Techs., 788 F.3d at 1363, 115 USPQ2d at 1092-93).
Further, MPEP 2106.05(d) also states receiving or transmitting data over a network, e.g., using the Internet to gather data is conventional when recited broadly (see Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016) (using a telephone for image transmission); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350). In the instant case, the receipt of diagnostic data is recited broadly and such does not provide an inventive concept.
When viewing the generic computer in combination with the insignificant data gathering does not add more than when viewing the elements individually. Accordingly, the additional elements do provide and inventive concept.
Further, Claims 16-18 further limit the mental processes and methods of human activity already rejected in the parent claim, but fail to remedy the deficiencies of the parent claim as they do not impose any additional elements that amount to significantly more than the abstract idea itself.
Accordingly, the Examiner concludes that there are no meaningful limitations in claims 11, 16-18 that transform the judicial exception into a patent eligible application such that the claim amounts to significantly more than the judicial exception itself.
Claim 1 recites similar limitation to claim 11 an is also rejected.
Claims 2, 3 further recites that additional elements of a sensor comprising an accelerometer, wherein the sensor is positioned on, or in the vicinity, of the individual herd member; and wherein the sensor is configured to collect at least one type of behavioral data of the individual herd member and is further configured to transmit signals related to the at least one type of behavioral data to the CPU; and wherein the sensor is positioned on, or in the vicinity, of a head of the individual herd member. However, the sensor are recited broadly such that it amounts to conventional data gathering.
The analysis above applies to all statutory categories of invention. The presentment of claim 1 otherwise styled as a computer program product or system, for example, would be subject to the same analysis. As such, claims 8-20 are also rejected.
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, 5, 6, 7, 11, 16, 17, 22, 26, 27, 29, 31, 35, 36 is/are rejected under 35 U.S.C. 103 as being unpatentable over Kopic US 2014/0116341 A1 in view of Huisma US 20200323172 A1.
As per Claim 1 Kopic teaches A system for classifying an individual herd member, said system comprising:
a dry matter intake (DMI) module configured to obtain the DMI or DMI-related data of the individual herd member, over a pre-determined period of time; Kopic para. 24 teaches For example, for one or more individual animal within the animal group, Block S110 can collect information relating to any one or more of birth date, auction or purchase date, feed history (e.g., schedule, content, quantity), nutritional demand, reproductive history (e.g., inseminated, expected pregnant, diagnosed pregnant, birth pending, recent birth), immunological history, exposure to weather (e.g., drought, rain, indoor and outdoor temperature, indoor and outdoor humidity, indoor and outdoor oxygen level, light, heat stress index, moon phase), weight, age, number of days in milk, days carrying calf, growth rate, maturity rate, life stage (e.g., milk heifer, dry cow, dry heifer, calf, etc.), lactation stage, size, weight, type, housing need, or any other relevant life data for one or more individual animal within the group. Further, para. 84 teaches once the target feed volume and nutritional content is set for the group, Block S150 can select a particular feed material or a combination of feed materials--from the list of available feed materials of known nutritional content--to achieve the target feed volume and nutritional content. For example, Block S150 can select a volume or mass of each of a subset of the available feed materials to approximate each of the dry matter, energy, ash, starch, sugar, soluble fiber, beta glucan, crude protein, rumen digestible protein, rumen indigestible protein, usable protein, crude fat, ADF, NDF, lignin, and/or micro element content targets for the feed. Block S150 can also select quantities (e.g., volumes, masses, weights) of various supplements to add to the feed, such as vitamin, mineral, and antibiotic supplements for the feed.
a milk production (MP) module, configured to obtain the MP or MP-related data of the individual herd member, over the pre-determined period of time; Kopic para. 53 teaches Block S140 can collect milking data on a daily, weekly, or other timed schedule, or in real-time during each scheduled milking event for the animal group. Block S140 can further assemble these milking data for various milking events into a timeline of milk production for the animal group. For example, for a particular milking event defining a milking period for animals within the animal group, Block S140 can receive, for each individual animal within the animal group, a milk file corresponding to a singular milking animal within the animal group and define a quality and quantity of milk output by the singular animal. In this example, Block S140 can combine the quantity values and average the quality values of milk output by all individual animal within the animal group to generate a milk quality and quantity metric for the animal group for the particular milking event. Block S140 can repeat this process for each milking event and aggregate milk quality and quantity metrics for each milking period into a milk production timeline. Finally, Block S140 can pass this milk production timeline to Block S130, and Block S130 can apply feed, environment, gynecological, and/or other timelines described above to extrapolate trends in milk production with respect to one or more variables and thus identify effects of the one or more variables on milk production within the animal group.
a central processing unit (CPU) comprising a processor in communication with a memory module, the memory module having stored thereon a program code, the program code executable by the processor to: (see para. 98)
obtain the DMI from the DMI module, or obtain the DMI-related data from the DMI module and to further obtain the DMI from the DMI-related data, over the pre-determined period of time; Para. 24-25 teach
Block S110 of the method S100 recites receiving a set of life events of an animal group, the set of life events including a gynecological status. (Block S110 can similarly recite receiving a production status of a group of milking animals). Generally, Block S110 functions to collect various data pertaining to a group of milking animals, such as feeding, milking, health, fertility, and milk production events, any of which may trigger changes in lactation, gynecological and health status, etc. Block S110 can then pass any of these data to Block S130 as milk production-related variables to generate a multi-variable model of milk production within the group. For example, for one or more individual animal within the animal group, Block S110 can collect information relating to any one or more of birth date, auction or purchase date, feed history (e.g., schedule, content, quantity), nutritional demand, reproductive history (e.g., inseminated, expected pregnant, diagnosed pregnant, birth pending, recent birth), immunological history, exposure to weather (e.g., drought, rain, indoor and outdoor temperature, indoor and outdoor humidity, indoor and outdoor oxygen level, light, heat stress index, moon phase), weight, age, number of days in milk, days carrying calf, growth rate, maturity rate, life stage (e.g., milk heifer, dry cow, dry heifer, calf, etc.), lactation stage, size, weight, type, housing need, or any other relevant life data for one or more individual animal within the group. Block S110 can therefore collect, store, and deliver relevant animal information to assemble a foundation for dairy herd management through a milk production model for one or a group of milking animals.
In one implementation, Block S110 retrieves previous feed schedules and corresponding feed periods (i.e., times) assigned to the animal group and assembles a timeline of nutrition supplied to the animal group based on feed nutrition data collected in Block S120 (described below). For example, Block S110 can generate a chart of moisture, dry matter, different kind of energy (i.e., UE, ME, NEL, NEG), ashes, different kind carbohydrates, (i.e., starch, sugar, soluble fibers), proteins (i.e., CP, RDP, RUP, RUP digestible, usable protein), raw fat, ADF, NDF, lignin, micro elements, etc.) assigned or distributed to or consumed by one animal or the animal group on a certain day or over a certain time. In this implementation, Block S110 can additionally or alternatively interface with a feed storage facility database, a feed manager dashboard, or other electronic server or interface to collect processed feed orders for (i.e., feed elements fed to) the animal group. Block S110 can also receive manual feed inputs, such as from the farmer or from a farm manager, over time and assemble these data into the feed timeline.
obtain the MP from the MP module, or obtain the MP-related data from the MP module and to further obtain the MP from the MP-related data, over the pre-determined period of time; Kopic para. 53 teaches Block S140 can collect milking data on a daily, weekly, or other timed schedule, or in real-time during each scheduled milking event for the animal group.
classify the individual herd member according to the FE. Kopic para.41 teaches in one example implementation, the method S100 can generate a feed schedule for a group of milking animals to achieve a target milk production value (i.e., milk output quantity and/or quality), such as based on life events, milking records, and other animal group data collected in Block S110 and electronic feed data (as described below). However, Block S140 can collect subsequent milk records for the group of animals (as described below), and Block S102 can detect deviation from the target milk production value by a particular milking animal in the group of milking animals. Block S102 can respond to this detected deviation by identifying a more suitable animal group for the particular milking animal, such as by selecting an alternative group with characteristics more compatible with the particular milking animal. For example, Block S102 can identify milk production volume output by the particular milking animal that falls short of a target milk production quantity (e.g., volume, mass, weight) by a preset quantity threshold (e.g., more than 8%) over a set of milking periods (e.g., three consecutive days) and selecting the alternative group of milking animals that produces similar milk quantities for similar feed schedules. Alternatively, in this example, Block S102 can specify the particular milking animal for culling (i.e., removal from the herd) based on the deviation from the target milk production by the herd. In this example, Block S102 can also account for the age of the particular milking animal, the genetic potential of the particular milking animal to continue profitable milk production, etc. to determine if the particular milking animal should be culled or moved to an alternative group characterized by lower milk production than the particular milking animal's current group. Block S102 can then prompt placement of the particular milking animal into the alternatively group of milking animals, such as by issuing a notification within a farmer's dashboard. Alternatively, Block S102 can generate a notification (or work order) to move or cull the particular milking animal based on a unique animal identifier associated with the particular milking animal (and then transmit the work order to a farmhand for implementation). Block S102 can implement similar functionality to move a particular milking animal to another group characterized by higher milk production, to a group awaiting insemination, to a group pending or recently completing a birth, etc. based on the milk production figures, age, weight, and/or other data amassed in Blocks S110, S140, etc.
Kopic dos not explicitly disclose calculate the feed efficiency (FE) of the individual herd member over the pre-determined period of time, based on the DMI, over the pre-determined time period, and the MP, over the pre-determined time period; and However, Huisma para. 2 teaches In the dairy industry the effectiveness of cows in producing milk is sometimes referred to as dairy efficiency or feed efficiency. Dairy efficiency can be defined simply as a ratio of the weight of milk produced to the weight dry matter or “feed” consumed. Monitoring dairy efficiency in the dairy industry has not been used as a common benchmark for monitoring profitability and evaluating dry matter intake relative to milk yield. Para. 7 teaches The present invention relates to a feeding system which can be utilized with the system and method for determining animal behavioral phenotypes as described in U.S. 62/468,634, the description thereof being fully incorporated herein by reference thereto. The feeding system according to the present invention includes weighing devices for measuring the weight of the feed in the feed trough. The weight measurements are transmitted to a computer which records and analyses the collected data. From the collected and analyzed data over a period of time, the computer can then determine and monitor an animal's weight and gain, growth rate and the weight of feed/water consumed, e.g., feed/water intake by the animal over a period of time. Ultimately the weight data can be used to determine, among others, the residual feed intake and the feed/water retention of an animal. Both Kopic and Huisma are directed to livestock management. Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the Applicant’s invention to modify the teachings of Kopic to include calculate the feed efficiency (FE) of the individual herd member over the pre-determined period of time, based on the DMI, over the pre-determined time period, and the MP, over the pre-determined time period as taught by Huisma to provide a more accurate dairy efficiency value (see para. 3).
As per Claim 5 Kopic does not teach The system according to claim 1, wherein the FE is calculated according to the following formula: FE = MP/DMI. However, Huisma para. 2 teaches in the dairy industry the effectiveness of cows in producing milk is sometimes referred to as dairy efficiency or feed efficiency. Dairy efficiency can be defined simply as a ratio of the weight of milk produced to the weight dry matter or “feed” consumed. Monitoring dairy efficiency in the dairy industry has not been used as a common benchmark for monitoring profitability and evaluating dry matter intake relative to milk yield. Both Kopic and Huisma are directed to livestock management. Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the Applicant’s invention to modify the teachings of Kopic to include wherein the FE is calculated according to the following formula:FE = MP/DMI as taught by Huisma to provide a more accurate dairy efficiency value (see para. 3).
As per Claim 6 Kopic teaches the system according to claim 1, wherein the program code is further executable by the processor to classify the individual herd member according to the MP. Kopic para. 41 teaches in one example implementation, the method S100 can generate a feed schedule for a group of milking animals to achieve a target milk production value (i.e., milk output quantity and/or quality), such as based on life events, milking records, and other animal group data collected in Block S110 and electronic feed data (as described below). However, Block S140 can collect subsequent milk records for the group of animals (as described below), and Block S102 can detect deviation from the target milk production value by a particular milking animal in the group of milking animals. Block S102 can respond to this detected deviation by identifying a more suitable animal group for the particular milking animal, such as by selecting an alternative group with characteristics more compatible with the particular milking animal. For example, Block S102 can identify milk production volume output by the particular milking animal that falls short of a target milk production quantity (e.g., volume, mass, weight) by a preset quantity threshold (e.g., more than 8%) over a set of milking periods (e.g., three consecutive days) and selecting the alternative group of milking animals that produces similar milk quantities for similar feed schedules. Alternatively, in this example, Block S102 can specify the particular milking animal for culling (i.e., removal from the herd) based on the deviation from the target milk production by the herd. In this example, Block S102 can also account for the age of the particular milking animal, the genetic potential of the particular milking animal to continue profitable milk production, etc. to determine if the particular milking animal should be culled or moved to an alternative group characterized by lower milk production than the particular milking animal's current group. Block S102 can then prompt placement of the particular milking animal into the alternatively group of milking animals, such as by issuing a notification within a farmer's dashboard. Alternatively, Block S102 can generate a notification (or work order) to move or cull the particular milking animal based on a unique animal identifier associated with the particular milking animal (and then transmit the work order to a farmhand for implementation). Block S102 can implement similar functionality to move a particular milking animal to another group characterized by higher milk production, to a group awaiting insemination, to a group pending or recently completing a birth, etc. based on the milk production figures, age, weight, and/or other data amassed in Blocks S110, S140, etc.
As per Claim 7 Kopic teaches the system according to claim 1 wherein the CPU is configured to receive monetary data, and wherein program code is further executable by the processor to classify the individual herd member according to a net income from milk production (NIMP) or an income over feed (IoF) of the individual herd member, wherein the NIMP or IoF is based on the monetary data, the DMI and the MP. Kopic para. 62 teaches block S130 can therefore function to generate a milk production model that defines a group of milking animals as a profit center with costs associated with milk production and incomes associated with milk, manure, calf, and salvage revenues. Block S130 can similarly generate a milk production model that defines a single milking animal as a profit center and then group multiple milk production models for various unique milking animals to simulate production, costs, and incomes for a set, group, farm, region, etc. of milking animals.
As per Claim 11 Kopic teaches A system for classifying an individual herd member and for further indicating a time at which the individual herd member is to be removed from a herd, or group within the herd, said system comprising:
a dry matter intake (DMI) module configured to obtain the DMI or DMI-related data of the individual herd member, over a first pre-determined period of time; Kopic para. 24 teaches for example, for one or more individual animal within the animal group, Block S110 can collect information relating to any one or more of birth date, auction or purchase date, feed history (e.g., schedule, content, quantity), nutritional demand, reproductive history (e.g., inseminated, expected pregnant, diagnosed pregnant, birth pending, recent birth), immunological history, exposure to weather (e.g., drought, rain, indoor and outdoor temperature, indoor and outdoor humidity, indoor and outdoor oxygen level, light, heat stress index, moon phase), weight, age, number of days in milk, days carrying calf, growth rate, maturity rate, life stage (e.g., milk heifer, dry cow, dry heifer, calf, etc.), lactation stage, size, weight, type, housing need, or any other relevant life data for one or more individual animal within the group. Further, para. 84 teaches once the target feed volume and nutritional content is set for the group, Block S150 can select a particular feed material or a combination of feed materials--from the list of available feed materials of known nutritional content--to achieve the target feed volume and nutritional content. For example, Block S150 can select a volume or mass of each of a subset of the available feed materials to approximate each of the dry matter, energy, ash, starch, sugar, soluble fiber, beta glucan, crude protein, rumen digestible protein, rumen indigestible protein, usable protein, crude fat, ADF, NDF, lignin, and/or micro element content targets for the feed. Block S150 can also select quantities (e.g., volumes, masses, weights) of various supplements to add to the feed, such as vitamin, mineral, and antibiotic supplements for the feed.
a milk production (MP) module, configured to obtain the MP or MP-related data of the individual herd member, over the first pre-determined period of time;
Kopic para. 53 teaches Block S140 can collect milking data on a daily, weekly, or other timed schedule, or in real-time during each scheduled milking event for the animal group.
a central processing unit (CPU) comprising a processor in communication with a memory module, the memory module having stored thereon a program code, the program code executable by the processor to: (see para. 98).
obtain the DMI from the DMI module, or obtain the DMI-related data from the DMI module and to further obtain the DMI from the DMI-related data, over the first pre-determined period of time; Para. 24-25 teach
Block S110 of the method S100 recites receiving a set of life events of an animal group, the set of life events including a gynecological status. (Block S110 can similarly recite receiving a production status of a group of milking animals). Generally, Block S110 functions to collect various data pertaining to a group of milking animals, such as feeding, milking, health, fertility, and milk production events, any of which may trigger changes in lactation, gynecological and health status, etc. Block S110 can then pass any of these data to Block S130 as milk production-related variables to generate a multi-variable model of milk production within the group. For example, for one or more individual animal within the animal group, Block S110 can collect information relating to any one or more of birth date, auction or purchase date, feed history (e.g., schedule, content, quantity), nutritional demand, reproductive history (e.g., inseminated, expected pregnant, diagnosed pregnant, birth pending, recent birth), immunological history, exposure to weather (e.g., drought, rain, indoor and outdoor temperature, indoor and outdoor humidity, indoor and outdoor oxygen level, light, heat stress index, moon phase), weight, age, number of days in milk, days carrying calf, growth rate, maturity rate, life stage (e.g., milk heifer, dry cow, dry heifer, calf, etc.), lactation stage, size, weight, type, housing need, or any other relevant life data for one or more individual animal within the group. Block S110 can therefore collect, store, and deliver relevant animal information to assemble a foundation for dairy herd management through a milk production model for one or a group of milking animals.
In one implementation, Block S110 retrieves previous feed schedules and corresponding feed periods (i.e., times) assigned to the animal group and assembles a timeline of nutrition supplied to the animal group based on feed nutrition data collected in Block S120 (described below). For example, Block S110 can generate a chart of moisture, dry matter, different kind of energy (i.e., UE, ME, NEL, NEG), ashes, different kind carbohydrates, (i.e., starch, sugar, soluble fibers), proteins (i.e., CP, RDP, RUP, RUP digestible, usable protein), raw fat, ADF, NDF, lignin, micro elements, etc.) assigned or distributed to or consumed by one animal or the animal group on a certain day or over a certain time. In this implementation, Block S110 can additionally or alternatively interface with a feed storage facility database, a feed manager dashboard, or other electronic server or interface to collect processed feed orders for (i.e., feed elements fed to) the animal group. Block S110 can also receive manual feed inputs, such as from the farmer or from a farm manager, over time and assemble these data into the feed timeline.
obtain the MP from the MP module, or obtain the MP-related data from the MP module and to further obtain the MP from the MP-related data, over the first pre-determined period of time; Kopic para. 53 teaches Block S140 can collect milking data on a daily, weekly, or other timed schedule, or in real-time during each scheduled milking event for the animal group.
classify the individual herd member according to the FE; and Kopic para.41 teaches in one example implementation, the method S100 can generate a feed schedule for a group of milking animals to achieve a target milk production value (i.e., milk output quantity and/or quality), such as based on life events, milking records, and other animal group data collected in Block S110 and electronic feed data (as described below). However, Block S140 can collect subsequent milk records for the group of animals (as described below), and Block S102 can detect deviation from the target milk production value by a particular milking animal in the group of milking animals. Block S102 can respond to this detected deviation by identifying a more suitable animal group for the particular milking animal, such as by selecting an alternative group with characteristics more compatible with the particular milking animal. For example, Block S102 can identify milk production volume output by the particular milking animal that falls short of a target milk production quantity (e.g., volume, mass, weight) by a preset quantity threshold (e.g., more than 8%) over a set of milking periods (e.g., three consecutive days) and selecting the alternative group of milking animals that produces similar milk quantities for similar feed schedules. Alternatively, in this example, Block S102 can specify the particular milking animal for culling (i.e., removal from the herd) based on the deviation from the target milk production by the herd. In this example, Block S102 can also account for the age of the particular milking animal, the genetic potential of the particular milking animal to continue profitable milk production, etc. to determine if the particular milking animal should be culled or moved to an alternative group characterized by lower milk production than the particular milking animal's current group. Block S102 can then prompt placement of the particular milking animal into the alternatively group of milking animals, such as by issuing a notification within a farmer's dashboard. Alternatively, Block S102 can generate a notification (or work order) to move or cull the particular milking animal based on a unique animal identifier associated with the particular milking animal (and then transmit the work order to a farmhand for implementation). Block S102 can implement similar functionality to move a particular milking animal to another group characterized by higher milk production, to a group awaiting insemination, to a group pending or recently completing a birth, etc. based on the milk production figures, age, weight, and/or other data amassed in Blocks S110, S140, etc.
wherein if the individual herd member is classified to be removed from the herd, or group within the herd or group within the herd, the DMI module is further configured to obtain the DMI or DMI-related data of the individual herd member, over a second pre-determined period of time; Kopic para. 53 teaches Block S140 can collect milking data on a daily, weekly, or other timed schedule, or in real-time during each scheduled milking event for the animal group. Block S140 can further assemble these milking data for various milking events into a timeline of milk production for the animal group. For example, for a particular milking event defining a milking period for animals within the animal group, Block S140 can receive, for each individual animal within the animal group, a milk file corresponding to a singular milking animal within the animal group and define a quality and quantity of milk output by the singular animal. In this example, Block S140 can combine the quantity values and average the quality values of milk output by all individual animal within the animal group to generate a milk quality and quantity metric for the animal group for the particular milking event. Block S140 can repeat this process for each milking event and aggregate milk quality and quantity metrics for each milking period into a milk production timeline. Finally, Block S140 can pass this milk production timeline to Block S130, and Block S130 can apply feed, environment, gynecological, and/or other timelines described above to extrapolate trends in milk production with respect to one or more variables and thus identify effects of the one or more variables on milk production within the animal group.
the MP module is further configured to obtain the MP or MP-related data of the individual herd member, over the second pre-determined period of time; and Kopic para. 53 teaches Block S140 can collect milking data on a daily, weekly, or other timed schedule, or in real-time during each scheduled milking event for the animal group.
the program code is further executable by the processor to:
obtain the DMI from the DMI module, or obtain the DMI-related data from the DMI module and to further obtain the DMI from the DMI-related data, over the second pre-determined period of time; Kopic para. 24 teaches For example, for one or more individual animal within the animal group, Block S110 can collect information relating to any one or more of birth date, auction or purchase date, feed history (e.g., schedule, content, quantity), nutritional demand, reproductive history (e.g., inseminated, expected pregnant, diagnosed pregnant, birth pending, recent birth), immunological history, exposure to weather (e.g., drought, rain, indoor and outdoor temperature, indoor and outdoor humidity, indoor and outdoor oxygen level, light, heat stress index, moon phase), weight, age, number of days in milk, days carrying calf, growth rate, maturity rate, life stage (e.g., milk heifer, dry cow, dry heifer, calf, etc.), lactation stage, size, weight, type, housing need, or any other relevant life data for one or more individual animal within the group. Further, para. 84 teaches once the target feed volume and nutritional content is set for the group, Block S150 can select a particular feed material or a combination of feed materials--from the list of available feed materials of known nutritional content--to achieve the target feed volume and nutritional content. For example, Block S150 can select a volume or mass of each of a subset of the available feed materials to approximate each of the dry matter, energy, ash, starch, sugar, soluble fiber, beta glucan, crude protein, rumen digestible protein, rumen indigestible protein, usable protein, crude fat, ADF, NDF, lignin, and/or micro element content targets for the feed. Block S150 can also select quantities (e.g., volumes, masses, weights) of various supplements to add to the feed, such as vitamin, mineral, and antibiotic supplements for the feed.
obtain the MP from the MP module, or obtain the MP-related data from the MP module and to further obtain the MP from the MP-related data, over the second pre-determined period of time; Kopic para. 53 teaches Block S140 can collect milking data on a daily, weekly, or other timed schedule, or in real-time during each scheduled milking event for the animal group.
receive monetary data; calculate an NIMP or an IoF of the individual herd member, based on the monetary data, the DMI over the second pre-determined time period, and the MP over the second predetermined time period; and Kopic para. 62 teaches block S130 can therefore function to generate a milk production model that defines a group of milking animals as a profit center with costs associated with milk production and incomes associated with milk, manure, calf, and salvage revenues. Block S130 can similarly generate a milk production model that defines a single milking animal as a profit center and then group multiple milk production models for various unique milking animals to simulate production, costs, and incomes for a set, group, farm, region, etc. of milking animals.
indicate a time for removal of the individual herd member from the herd, or group within the herd, wherein the time indicated is a time at which the NIMP is lower than a pre-determined NIMP value or a time at which the IoF is lower than a pre-determined IoF value. Para. 41 teaches For example, Block S102 can identify milk production volume output by the particular milking animal that falls short of a target milk production quantity (e.g., volume, mass, weight) by a preset quantity threshold (e.g., more than 8%) over a set of milking periods (e.g., three consecutive days) and selecting the alternative group of milking animals that produces similar milk quantities for similar feed schedules. Alternatively, in this example, Block S102 can specify the particular milking animal for culling (i.e., removal from the herd) based on the deviation from the target milk production by the herd. In this example, Block S102 can also account for the age of the particular milking animal, the genetic potential of the particular milking animal to continue profitable milk production, etc. to determine if the particular milking animal should be culled or moved to an alternative group characterized by lower milk production than the particular milking animal's current group. Block S102 can then prompt placement of the particular milking animal into the alternatively group of milking animals, such as by issuing a notification within a farmer's dashboard. Alternatively, Block S102 can generate a notification (or work order) to move or cull the particular milking animal based on a unique animal identifier associated with the particular milking animal (and then transmit the work order to a farmhand for implementation). Block S102 can implement similar functionality to move a particular milking animal to another group characterized by higher milk production, to a group awaiting insemination, to a group pending or recently completing a birth, etc. based on the milk production figures, age, weight, and/or other data amassed in Blocks S110, S140, etc.
Kopic does not teach calculate the feed efficiency (FE) of the individual herd member over the first pre-determined period of time, based on the DMI over the first pre- determined time period and the MP over the first pre-determined time period; However, Huisma para. 2 teaches In the dairy industry the effectiveness of cows in producing milk is sometimes referred to as dairy efficiency or feed efficiency. Dairy efficiency can be defined simply as a ratio of the weight of milk produced to the weight dry matter or “feed” consumed. Monitoring dairy efficiency in the dairy industry has not been used as a common benchmark for monitoring profitability and evaluating dry matter intake relative to milk yield. Para. 7 teaches The present invention relates to a feeding system which can be utilized with the system and method for determining animal behavioral phenotypes as described in U.S. 62/468,634, the description thereof being fully incorporated herein by reference thereto. The feeding system according to the present invention includes weighing devices for measuring the weight of the feed in the feed trough. The weight measurements are transmitted to a computer which records and analyses the collected data. From the collected and analyzed data over a period of time, the computer can then determine and monitor an animal's weight and gain, growth rate and the weight of feed/water consumed, e.g., feed/water intake by the animal over a period of time. Ultimately the weight data can be used to determine, among others, the residual feed intake and the feed/water retention of an animal. Both Kopic and Huisma are directed to livestock management. Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the Applicant’s invention to modify the teachings of Kopic to include calculate the feed efficiency (FE) of the individual herd member over the first pre-determined period of time, based on the DMI over the first pre- determined time period and the MP over the first pre-determined time period as taught by Huisma to provide a more accurate dairy efficiency value (see para. 3).
As per Claim 16 Kopic teaches The system according to claim 11, wherein the program code is further executable by the processor to classify the individual herd member according to the MP. Kopic para. 41 teaches in one example implementation, the method S100 can generate a feed schedule for a group of milking animals to achieve a target milk production value (i.e., milk output quantity and/or quality), such as based on life events, milking records, and other animal group data collected in Block S110 and electronic feed data (as described below). However, Block S140 can collect subsequent milk records for the group of animals (as described below), and Block S102 can detect deviation from the target milk production value by a particular milking animal in the group of milking animals. Block S102 can respond to this detected deviation by identifying a more suitable animal group for the particular milking animal, such as by selecting an alternative group with characteristics more compatible with the particular milking animal. For example, Block S102 can identify milk production volume output by the particular milking animal that falls short of a target milk production quantity (e.g., volume, mass, weight) by a preset quantity threshold (e.g., more than 8%) over a set of milking periods (e.g., three consecutive days) and selecting the alternative group of milking animals that produces similar milk quantities for similar feed schedules. Alternatively, in this example, Block S102 can specify the particular milking animal for culling (i.e., removal from the herd) based on the deviation from the target milk production by the herd. In this example, Block S102 can also account for the age of the particular milking animal, the genetic potential of the particular milking animal to continue profitable milk production, etc. to determine if the particular milking animal should be culled or moved to an alternative group characterized by lower milk production than the particular milking animal's current group. Block S102 can then prompt placement of the particular milking animal into the alternatively group of milking animals, such as by issuing a notification within a farmer's dashboard. Alternatively, Block S102 can generate a notification (or work order) to move or cull the particular milking animal based on a unique animal identifier associated with the particular milking animal (and then transmit the work order to a farmhand for implementation). Block S102 can implement similar functionality to move a particular milking animal to another group characterized by higher milk production, to a group awaiting insemination, to a group pending or recently completing a birth, etc. based on the milk production figures, age, weight, and/or other data amassed in Blocks S110, S140, etc.
As per Claim 17 Kopic teaches the system according to claim 11, wherein the CPU is configured to receive monetary data, and wherein program code is further executable by the processor to classify the individual herd member according to a net income from milk production (NIMP) or an income over feed (IoF) of the individual herd member, wherein the NIMP or the IoF is based on the monetary data, the DMI and the MP. Kopic 62 Block S130 can therefore function to generate a milk production model that defines a group of milking animals as a profit center with costs associated with milk production and incomes associated with milk, manure, calf, and salvage revenues. Block S130 can similarly generate a milk production model that defines a single milking animal as a profit center and then group multiple milk production models for various unique milking animals to simulate production, costs, and incomes for a set, group, farm, region, etc. of milking animals.
As per Claim 22 Kopic teaches A method for classifying an individual herd member, said method comprising:
obtaining a dry matter intake (DMI) of the individual herd member over a pre- determined period of time;
Kopic para. 24 teaches For example, for one or more individual animal within the animal group, Block S110 can collect information relating to any one or more of birth date, auction or purchase date, feed history (e.g., schedule, content, quantity), nutritional demand, reproductive history (e.g., inseminated, expected pregnant, diagnosed pregnant, birth pending, recent birth), immunological history, exposure to weather (e.g., drought, rain, indoor and outdoor temperature, indoor and outdoor humidity, indoor and outdoor oxygen level, light, heat stress index, moon phase), weight, age, number of days in milk, days carrying calf, growth rate, maturity rate, life stage (e.g., milk heifer, dry cow, dry heifer, calf, etc.), lactation stage, size, weight, type, housing need, or any other relevant life data for one or more individual animal within the group. Further, para. 84 teaches once the target feed volume and nutritional content is set for the group, Block S150 can select a particular feed material or a combination of feed materials--from the list of available feed materials of known nutritional content--to achieve the target feed volume and nutritional content. For example, Block S150 can select a volume or mass of each of a subset of the available feed materials to approximate each of the dry matter, energy, ash, starch, sugar, soluble fiber, beta glucan, crude protein, rumen digestible protein, rumen indigestible protein, usable protein, crude fat, ADF, NDF, lignin, and/or micro element content targets for the feed. Block S150 can also select quantities (e.g., volumes, masses, weights) of various supplements to add to the feed, such as vitamin, mineral, and antibiotic supplements for the feed.
obtaining a milk production (MP) of the individual herd member over the pre- determined period of time; Kopic para. 53 teaches Block S140 can collect milking data on a daily, weekly, or other timed schedule, or in real-time during each scheduled milking event for the animal group. Block S140 can further assemble these milking data for various milking events into a timeline of milk production for the animal group. For example, for a particular milking event defining a milking period for animals within the animal group, Block S140 can receive, for each individual animal within the animal group, a milk file corresponding to a singular milking animal within the animal group and define a quality and quantity of milk output by the singular animal. In this example, Block S140 can combine the quantity values and average the quality values of milk output by all individual animal within the animal group to generate a milk quality and quantity metric for the animal group for the particular milking event. Block S140 can repeat this process for each milking event and aggregate milk quality and quantity metrics for each milking period into a milk production timeline. Finally, Block S140 can pass this milk production timeline to Block S130, and Block S130 can apply feed, environment, gynecological, and/or other timelines described above to extrapolate trends in milk production with respect to one or more variables and thus identify effects of the one or more variables on milk production within the animal group.
classifying the individual herd member according to the FE. Kopic para.41 teaches in one example implementation, the method S100 can generate a feed schedule for a group of milking animals to achieve a target milk production value (i.e., milk output quantity and/or quality), such as based on life events, milking records, and other animal group data collected in Block S110 and electronic feed data (as described below). However, Block S140 can collect subsequent milk records for the group of animals (as described below), and Block S102 can detect deviation from the target milk production value by a particular milking animal in the group of milking animals. Block S102 can respond to this detected deviation by identifying a more suitable animal group for the particular milking animal, such as by selecting an alternative group with characteristics more compatible with the particular milking animal. For example, Block S102 can identify milk production volume output by the particular milking animal that falls short of a target milk production quantity (e.g., volume, mass, weight) by a preset quantity threshold (e.g., more than 8%) over a set of milking periods (e.g., three consecutive days) and selecting the alternative group of milking animals that produces similar milk quantities for similar feed schedules. Alternatively, in this example, Block S102 can specify the particular milking animal for culling (i.e., removal from the herd) based on the deviation from the target milk production by the herd. In this example, Block S102 can also account for the age of the particular milking animal, the genetic potential of the particular milking animal to continue profitable milk production, etc. to determine if the particular milking animal should be culled or moved to an alternative group characterized by lower milk production than the particular milking animal's current group. Block S102 can then prompt placement of the particular milking animal into the alternatively group of milking animals, such as by issuing a notification within a farmer's dashboard. Alternatively, Block S102 can generate a notification (or work order) to move or cull the particular milking animal based on a unique animal identifier associated with the particular milking animal (and then transmit the work order to a farmhand for implementation). Block S102 can implement similar functionality to move a particular milking animal to another group characterized by higher milk production, to a group awaiting insemination, to a group pending or recently completing a birth, etc. based on the milk production figures, age, weight, and/or other data amassed in Blocks S110, S140, etc.
Kopic does not teach calculating the feed efficiency (FE) of the individual herd member over the pre- determined period of time, based on the DMI, over the pre-determined period of time, and the MP, over the pre-determined period of time; and However, Huisma para. 2 teaches In the dairy industry the effectiveness of cows in producing milk is sometimes referred to as dairy efficiency or feed efficiency. Dairy efficiency can be defined simply as a ratio of the weight of milk produced to the weight dry matter or “feed” consumed. Monitoring dairy efficiency in the dairy industry has not been used as a common benchmark for monitoring profitability and evaluating dry matter intake relative to milk yield. Para. 7 teaches The present invention relates to a feeding system which can be utilized with the system and method for determining animal behavioral phenotypes as described in U.S. 62/468,634, the description thereof being fully incorporated herein by reference thereto. The feeding system according to the present invention includes weighing devices for measuring the weight of the feed in the feed trough. The weight measurements are transmitted to a computer which records and analyses the collected data. From the collected and analyzed data over a period of time, the computer can then determine and monitor an animal's weight and gain, growth rate and the weight of feed/water consumed, e.g., feed/water intake by the animal over a period of time. Ultimately the weight data can be used to determine, among others, the residual feed intake and the feed/water retention of an animal. Both Kopic and Huisma are directed to livestock management. Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the Applicant’s invention to modify the teachings of Kopic to include calculate the feed efficiency (FE) of the individual herd member over the first pre-determined period of time, based on the DMI over the first pre- determined time period and the MP over the first pre-determined time period as taught by Huisma to provide a more accurate dairy efficiency value (see para. 3).
As per Claim 26 Kopic teaches The method according to claim 22 wherein the method further comprises classifying the individual herd member according to the MP over the pre-determined period of time, wherein the classifying according to the MP is performed before, at the same time, or after classifying according to the FE. Kopic para. 41 teaches In one example implementation, the method S100 can generate a feed schedule for a group of milking animals to achieve a target milk production value (i.e., milk output quantity and/or quality), such as based on life events, milking records, and other animal group data collected in Block S110 and electronic feed data (as described below). However, Block S140 can collect subsequent milk records for the group of animals (as described below), and Block S102 can detect deviation from the target milk production value by a particular milking animal in the group of milking animals. Block S102 can respond to this detected deviation by identifying a more suitable animal group for the particular milking animal, such as by selecting an alternative group with characteristics more compatible with the particular milking animal. For example, Block S102 can identify milk production volume output by the particular milking animal that falls short of a target milk production quantity (e.g., volume, mass, weight) by a preset quantity threshold (e.g., more than 8%) over a set of milking periods (e.g., three consecutive days) and selecting the alternative group of milking animals that produces similar milk quantities for similar feed schedules. Alternatively, in this example, Block S102 can specify the particular milking animal for culling (i.e., removal from the herd) based on the deviation from the target milk production by the herd. In this example, Block S102 can also account for the age of the particular milking animal, the genetic potential of the particular milking animal to continue profitable milk production, etc. to determine if the particular milking animal should be culled or moved to an alternative group characterized by lower milk production than the particular milking animal's current group. Block S102 can then prompt placement of the particular milking animal into the alternatively group of milking animals, such as by issuing a notification within a farmer's dashboard. Alternatively, Block S102 can generate a notification (or work order) to move or cull the particular milking animal based on a unique animal identifier associated with the particular milking animal (and then transmit the work order to a farmhand for implementation). Block S102 can implement similar functionality to move a particular milking animal to another group characterized by higher milk production, to a group awaiting insemination, to a group pending or recently completing a birth, etc. based on the milk production figures, age, weight, and/or other data amassed in Blocks S110, S140, etc.
As per Claim 27 Kopic teaches The method according to claim 22 wherein the method further comprises receiving monetary data. Kopic 62 Block S130 can therefore function to generate a milk production model that defines a group of milking animals as a profit center with costs associated with milk production and incomes associated with milk, manure, calf, and salvage revenues. Block S130 can similarly generate a milk production model that defines a single milking animal as a profit center and then group multiple milk production models for various unique milking animals to simulate production, costs, and incomes for a set, group, farm, region, etc. of milking animals.
As per Claim 29 Kopic teaches The method according to claim 22 wherein the method further comprises classifying the individual herd member according to the NIMP or the IoF, wherein the classifying according to the NIMP or the IoF is performed before, at the same time, or after classifying according to the FE and/or the MP, if the individual herd member is further classified according to the MP. Kopic para. 41 teaches In one example implementation, the method S100 can generate a feed schedule for a group of milking animals to achieve a target milk production value (i.e., milk output quantity and/or quality), such as based on life events, milking records, and other animal group data collected in Block S110 and electronic feed data (as described below). However, Block S140 can collect subsequent milk records for the group of animals (as described below), and Block S102 can detect deviation from the target milk production value by a particular milking animal in the group of milking animals. Block S102 can respond to this detected deviation by identifying a more suitable animal group for the particular milking animal, such as by selecting an alternative group with characteristics more compatible with the particular milking animal. For example, Block S102 can identify milk production volume output by the particular milking animal that falls short of a target milk production quantity (e.g., volume, mass, weight) by a preset quantity threshold (e.g., more than 8%) over a set of milking periods (e.g., three consecutive days) and selecting the alternative group of milking animals that produces similar milk quantities for similar feed schedules. Alternatively, in this example, Block S102 can specify the particular milking animal for culling (i.e., removal from the herd) based on the deviation from the target milk production by the herd. In this example, Block S102 can also account for the age of the particular milking animal, the genetic potential of the particular milking animal to continue profitable milk production, etc. to determine if the particular milking animal should be culled or moved to an alternative group characterized by lower milk production than the particular milking animal's current group. Block S102 can then prompt placement of the particular milking animal into the alternatively group of milking animals, such as by issuing a notification within a farmer's dashboard. Alternatively, Block S102 can generate a notification (or work order) to move or cull the particular milking animal based on a unique animal identifier associated with the particular milking animal (and then transmit the work order to a farmhand for implementation). Block S102 can implement similar functionality to move a particular milking animal to another group characterized by higher milk production, to a group awaiting insemination, to a group pending or recently completing a birth, etc. based on the milk production figures, age, weight, and/or other data amassed in Blocks S110, S140, etc.
As per Claim 31 Kopic teaches A method for classifying an individual herd member, said method comprising:
obtaining a dry matter intake (DMI) of the individual herd member over a first pre-determined period of time; Kopic para. 24 teaches For example, for one or more individual animal within the animal group, Block S110 can collect information relating to any one or more of birth date, auction or purchase date, feed history (e.g., schedule, content, quantity), nutritional demand, reproductive history (e.g., inseminated, expected pregnant, diagnosed pregnant, birth pending, recent birth), immunological history, exposure to weather (e.g., drought, rain, indoor and outdoor temperature, indoor and outdoor humidity, indoor and outdoor oxygen level, light, heat stress index, moon phase), weight, age, number of days in milk, days carrying calf, growth rate, maturity rate, life stage (e.g., milk heifer, dry cow, dry heifer, calf, etc.), lactation stage, size, weight, type, housing need, or any other relevant life data for one or more individual animal within the group. Further, para. 84 teaches once the target feed volume and nutritional content is set for the group, Block S150 can select a particular feed material or a combination of feed materials--from the list of available feed materials of known nutritional content--to achieve the target feed volume and nutritional content. For example, Block S150 can select a volume or mass of each of a subset of the available feed materials to approximate each of the dry matter, energy, ash, starch, sugar, soluble fiber, beta glucan, crude protein, rumen digestible protein, rumen indigestible protein, usable protein, crude fat, ADF, NDF, lignin, and/or micro element content targets for the feed. Block S150 can also select quantities (e.g., volumes, masses, weights) of various supplements to add to the feed, such as vitamin, mineral, and antibiotic supplements for the feed.
obtaining a milk production (MP) of the individual herd member over the first pre-determined period of time,; Kopic para. 53 teaches Block S140 can collect milking data on a daily, weekly, or other timed schedule, or in real-time during each scheduled milking event for the animal group. Block S140 can further assemble these milking data for various milking events into a timeline of milk production for the animal group. For example, for a particular milking event defining a milking period for animals within the animal group, Block S140 can receive, for each individual animal within the animal group, a milk file corresponding to a singular milking animal within the animal group and define a quality and quantity of milk output by the singular animal. In this example, Block S140 can combine the quantity values and average the quality values of milk output by all individual animal within the animal group to generate a milk quality and quantity metric for the animal group for the particular milking event. Block S140 can repeat this process for each milking event and aggregate milk quality and quantity metrics for each milking period into a milk production timeline. Finally, Block S140 can pass this milk production timeline to Block S130, and Block S130 can apply feed, environment, gynecological, and/or other timelines described above to extrapolate trends in milk production with respect to one or more variables and thus identify effects of the one or more variables on milk production within the animal group.
classifying the individual herd member according to the FE; Kopic para.41 teaches in one example implementation, the method S100 can generate a feed schedule for a group of milking animals to achieve a target milk production value (i.e., milk output quantity and/or quality), such as based on life events, milking records, and other animal group data collected in Block S110 and electronic feed data (as described below). However, Block S140 can collect subsequent milk records for the group of animals (as described below), and Block S102 can detect deviation from the target milk production value by a particular milking animal in the group of milking animals. Block S102 can respond to this detected deviation by identifying a more suitable animal group for the particular milking animal, such as by selecting an alternative group with characteristics more compatible with the particular milking animal. For example, Block S102 can identify milk production volume output by the particular milking animal that falls short of a target milk production quantity (e.g., volume, mass, weight) by a preset quantity threshold (e.g., more than 8%) over a set of milking periods (e.g., three consecutive days) and selecting the alternative group of milking animals that produces similar milk quantities for similar feed schedules. Alternatively, in this example, Block S102 can specify the particular milking animal for culling (i.e., removal from the herd) based on the deviation from the target milk production by the herd. In this example, Block S102 can also account for the age of the particular milking animal, the genetic potential of the particular milking animal to continue profitable milk production, etc. to determine if the particular milking animal should be culled or moved to an alternative group characterized by lower milk production than the particular milking animal's current group. Block S102 can then prompt placement of the particular milking animal into the alternatively group of milking animals, such as by issuing a notification within a farmer's dashboard. Alternatively, Block S102 can generate a notification (or work order) to move or cull the particular milking animal based on a unique animal identifier associated with the particular milking animal (and then transmit the work order to a farmhand for implementation). Block S102 can implement similar functionality to move a particular milking animal to another group characterized by higher milk production, to a group awaiting insemination, to a group pending or recently completing a birth, etc. based on the milk production figures, age, weight, and/or other data amassed in Blocks S110, S140, etc.
wherein, if the individual herd member is classified to be removed from the herd, or group within the herd, the method further comprises;
selecting the individual herd member to be removed from the herd, or group within the herd;
Kopic 41 teaches Alternatively, in this example, Block S102 can specify the particular milking animal for culling (i.e., removal from the herd) based on the deviation from the target milk production by the herd. In this example, Block S102 can also account for the age of the particular milking animal, the genetic potential of the particular milking animal to continue profitable milk production, etc. to determine if the particular milking animal should be culled or moved to an alternative group characterized by lower milk production than the particular milking animal's current group. Block S102 can then prompt placement of the particular milking animal into the alternatively group of milking animals, such as by issuing a notification within a farmer's dashboard. Alternatively, Block S102 can generate a notification (or work order) to move or cull the particular milking animal based on a unique animal identifier associated with the particular milking animal (and then transmit the work order to a farmhand for implementation). Block S102 can implement similar functionality to move a particular milking animal to another group characterized by higher milk production, to a group awaiting insemination, to a group pending or recently completing a birth, etc. based on the milk production figures, age, weight, and/or other data amassed in Blocks S110, S140, etc.
obtaining a dry matter intake (DMI) of the individual herd member over a second pre-determined period of time; obtaining a milk production (MP) of the individual herd member over the second pre-determined period of time; Kopic para. 53 teaches Block S140 can collect milking data on a daily, weekly, or other timed schedule, or in real-time during each scheduled milking event for the animal group. Block S140 can further assemble these milking data for various milking events into a timeline of milk production for the animal group. For example, for a particular milking event defining a milking period for animals within the animal group, Block S140 can receive, for each individual animal within the animal group, a milk file corresponding to a singular milking animal within the animal group and define a quality and quantity of milk output by the singular animal. In this example, Block S140 can combine the quantity values and average the quality values of milk output by all individual animal within the animal group to generate a milk quality and quantity metric for the animal group for the particular milking event. Block S140 can repeat this process for each milking event and aggregate milk quality and quantity metrics for each milking period into a milk production timeline. Finally, Block S140 can pass this milk production timeline to Block S130, and Block S130 can apply feed, environment, gynecological, and/or other timelines described above to extrapolate trends in milk production with respect to one or more variables and thus identify effects of the one or more variables on milk production within the animal group.
receiving monetary data; Kopic 62 Block S130 can therefore function to generate a milk production model that defines a group of milking animals as a profit center with costs associated with milk production and incomes associated with milk, manure, calf, and salvage revenues. Block S130 can similarly generate a milk production model that defines a single milking animal as a profit center and then group multiple milk production models for various unique milking animals to simulate production, costs, and incomes for a set, group, farm, region, etc. of milking animals.
calculating an NIMP or an IoF of the individual herd member, based on the monetary data, the DMI, over the second pre-determined period of time, and the MP over the second pre-determined period of time;
Kopic para. 62 teaches block S130 can therefore function to generate a milk production model that defines a group of milking animals as a profit center with costs associated with milk production and incomes associated with milk, manure, calf, and salvage revenues. Block S130 can similarly generate a milk production model that defines a single milking animal as a profit center and then group multiple milk production models for various unique milking animals to simulate production, costs, and incomes for a set, group, farm, region, etc. of milking animals.
and removing the individual herd member from the herd, or group within the herd, at a time when the NIMP is lower than a pre-determined NIMP value or at a time when the IoF is lower than a pre-determined IoF value. Para. 41 teaches For example, Block S102 can identify milk production volume output by the particular milking animal that falls short of a target milk production quantity (e.g., volume, mass, weight) by a preset quantity threshold (e.g., more than 8%) over a set of milking periods (e.g., three consecutive days) and selecting the alternative group of milking animals that produces similar milk quantities for similar feed schedules. Alternatively, in this example, Block S102 can specify the particular milking animal for culling (i.e., removal from the herd) based on the deviation from the target milk production by the herd. In this example, Block S102 can also account for the age of the particular milking animal, the genetic potential of the particular milking animal to continue profitable milk production, etc. to determine if the particular milking animal should be culled or moved to an alternative group characterized by lower milk production than the particular milking animal's current group. Block S102 can then prompt placement of the particular milking animal into the alternatively group of milking animals, such as by issuing a notification within a farmer's dashboard. Alternatively, Block S102 can generate a notification (or work order) to move or cull the particular milking animal based on a unique animal identifier associated with the particular milking animal (and then transmit the work order to a farmhand for implementation). Block S102 can implement similar functionality to move a particular milking animal to another group characterized by higher milk production, to a group awaiting insemination, to a group pending or recently completing a birth, etc. based on the milk production figures, age, weight, and/or other data amassed in Blocks S110, S140, etc.
Kopic does not teach calculating the feed efficiency (FE) of the individual herd member over the first pre-determined period of time, based on the DMI,over the first pre-determined period of time, and the MP, over the first pre-determined period of time; However, Huisma para. 2 teaches In the dairy industry the effectiveness of cows in producing milk is sometimes referred to as dairy efficiency or feed efficiency. Dairy efficiency can be defined simply as a ratio of the weight of milk produced to the weight dry matter or “feed” consumed. Monitoring dairy efficiency in the dairy industry has not been used as a common benchmark for monitoring profitability and evaluating dry matter intake relative to milk yield. Para. 7 teaches The present invention relates to a feeding system which can be utilized with the system and method for determining animal behavioral phenotypes as described in U.S. 62/468,634, the description thereof being fully incorporated herein by reference thereto. The feeding system according to the present invention includes weighing devices for measuring the weight of the feed in the feed trough. The weight measurements are transmitted to a computer which records and analyses the collected data. From the collected and analyzed data over a period of time, the computer can then determine and monitor an animal's weight and gain, growth rate and the weight of feed/water consumed, e.g., feed/water intake by the animal over a period of time. Ultimately the weight data can be used to determine, among others, the residual feed intake and the feed/water retention of an animal. Both Kopic and Huisma are directed to livestock management. Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the Applicant’s invention to modify the teachings of Kopic to include calculating the feed efficiency (FE) of the individual herd member over the first pre-determined period of time, based on the DMI,over the first pre-determined period of time, and the MP, over the first pre-determined period of time as taught by Huisma to provide a more accurate dairy efficiency value (see para.
As per Claim 35 Kopic teaches The method according to claim 31 wherein the method further comprises classifying the individual herd member according to the MP over the first pre-determined time period, wherein the classifying according to the MP is performed before, at the same time, or after classifying according to the FE. Kopic para. 41 teaches In one example implementation, the method S100 can generate a feed schedule for a group of milking animals to achieve a target milk production value (i.e., milk output quantity and/or quality), such as based on life events, milking records, and other animal group data collected in Block S110 and electronic feed data (as described below). However, Block S140 can collect subsequent milk records for the group of animals (as described below), and Block S102 can detect deviation from the target milk production value by a particular milking animal in the group of milking animals. Block S102 can respond to this detected deviation by identifying a more suitable animal group for the particular milking animal, such as by selecting an alternative group with characteristics more compatible with the particular milking animal. For example, Block S102 can identify milk production volume output by the particular milking animal that falls short of a target milk production quantity (e.g., volume, mass, weight) by a preset quantity threshold (e.g., more than 8%) over a set of milking periods (e.g., three consecutive days) and selecting the alternative group of milking animals that produces similar milk quantities for similar feed schedules. Alternatively, in this example, Block S102 can specify the particular milking animal for culling (i.e., removal from the herd) based on the deviation from the target milk production by the herd. In this example, Block S102 can also account for the age of the particular milking animal, the genetic potential of the particular milking animal to continue profitable milk production, etc. to determine if the particular milking animal should be culled or moved to an alternative group characterized by lower milk production than the particular milking animal's current group. Block S102 can then prompt placement of the particular milking animal into the alternatively group of milking animals, such as by issuing a notification within a farmer's dashboard. Alternatively, Block S102 can generate a notification (or work order) to move or cull the particular milking animal based on a unique animal identifier associated with the particular milking animal (and then transmit the work order to a farmhand for implementation). Block S102 can implement similar functionality to move a particular milking animal to another group characterized by higher milk production, to a group awaiting insemination, to a group pending or recently completing a birth, etc. based on the milk production figures, age, weight, and/or other data amassed in Blocks S110, S140, etc.
As per Claim 36 Kopic teaches The method according to claim 31,wherein the method further comprises receiving monetary data. Kopic 62 Block S130 can therefore function to generate a milk production model that defines a group of milking animals as a profit center with costs associated with milk production and incomes associated with milk, manure, calf, and salvage revenues. Block S130 can similarly generate a milk production model that defines a single milking animal as a profit center and then group multiple milk production models for various unique milking animals to simulate production, costs, and incomes for a set, group, farm, region, etc. of milking animals.
Claim(s) 2, 3, 23, 24, 25 is/are rejected under 35 U.S.C. 103 as being unpatentable over Kopic US 2014/0116341 A1 in view of Huisma US 2020/0323172 A1 as applied to claims 1, 22 and in further view of Biffert US 2024/0407338 A1.
As per Claim 2 Kopic does not teach The system according to claim 1, wherein the DMI module comprises: a sensor comprising an accelerometer, wherein the sensor is positioned on, or in the vicinity, of the individual herd member; and wherein the sensor is configured to collect at least one type of behavioral data of the individual herd member and is further configured to transmit signals related to the at least one type of behavioral data to the CPU. However, Biffert para. 42-43 teaches In another example, the system can also record an amount of time the animal's head is in a feed trough or a water source, which helps identify feed efficiencies of the animals (how much they take in and how much they gain). Deviations in the amount of time these animals spend at water and at feed are also illness indicators. This determination implicitly includes the service provider 10 comparing the location of the animal to a known location of a feed trough 16 or water source 17 to determine how long the animal spent at these locations. In instances where the sensor 14 is located on an car or the animal, and the sensor 14 omits signals frequently. These signals can be used to distinguish the action of the animal by changes in the angle/height measurements (e.g., is the movement indicative of eating, ruminating, or drinking). These data can be combined with accelerometer data from the sensor (for the tags that have it equipped, some may not) where it can determine, based on head/tag movements if the animal is eating (not just standing still but actually chewing). Both Kopic and Biffert are directed to managing livestock. Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the Applicant’s invention to modify the teachings of Kopic to include wherein the DMI module comprises: a sensor comprising an accelerometer, wherein the sensor is positioned on, or in the vicinity, of the individual herd member; and wherein the sensor is configured to collect at least one type of behavioral data of the individual herd member and is further configured to transmit signals related to the at least one type of behavioral data to the CPU as taught by Biffert to help better identify feed efficiencies of the animals (see para. 42)
As per Claim 3 Kopic does not teach The system according to claim 2, wherein the at least one type of behavioral data includes head movements, and wherein the sensor is positioned on, or in the vicinity, of a head of the individual herd member. However, Biffert para. 42-43 teaches In another example, the system can also record an amount of time the animal's head is in a feed trough or a water source, which helps identify feed efficiencies of the animals (how much they take in and how much they gain). Deviations in the amount of time these animals spend at water and at feed are also illness indicators. This determination implicitly includes the service provider 10 comparing the location of the animal to a known location of a feed trough 16 or water source 17 to determine how long the animal spent at these locations. In instances where the sensor 14 is located on an car or the animal, and the sensor 14 omits signals frequently. These signals can be used to distinguish the action of the animal by changes in the angle/height measurements (e.g., is the movement indicative of eating, ruminating, or drinking). These data can be combined with accelerometer data from the sensor (for the tags that have it equipped, some may not) where it can determine, based on head/tag movements if the animal is eating (not just standing still but actually chewing). Both Kopic and Biffert are directed to managing livestock. Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the Applicant’s invention to modify the teachings of Kopic to include wherein the at least one type of behavioral data includes head movements, and wherein the sensor is positioned on, or in the vicinity, of a head of the individual herd member as taught by Biffert to help better identify feed efficiencies of the animals (see para. 42).
As per Claim 23 Kopic does not teach The method according to claim 22, wherein the DMI is obtained based on at least one type of behavioral data of the individual herd member that is collected by a sensor comprising an accelerometer, wherein the sensor is positioned on, or in the vicinity, of the individual herd member. However, Biffert para. 42-43 teaches In another example, the system can also record an amount of time the animal's head is in a feed trough or a water source, which helps identify feed efficiencies of the animals (how much they take in and how much they gain). Deviations in the amount of time these animals spend at water and at feed are also illness indicators. This determination implicitly includes the service provider 10 comparing the location of the animal to a known location of a feed trough 16 or water source 17 to determine how long the animal spent at these locations. In instances where the sensor 14 is located on an car or the animal, and the sensor 14 omits signals frequently. These signals can be used to distinguish the action of the animal by changes in the angle/height measurements (e.g., is the movement indicative of eating, ruminating, or drinking). These data can be combined with accelerometer data from the sensor (for the tags that have it equipped, some may not) where it can determine, based on head/tag movements if the animal is eating (not just standing still but actually chewing). Both Kopic and Biffert are directed to managing livestock. Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the Applicant’s invention to modify the teachings of Kopic to include wherein the DMI is obtained based on at least one type of behavioral data of the individual herd member that is collected by a sensor comprising an accelerometer, wherein the sensor is positioned on, or in the vicinity, of the individual herd member as taught by Biffert to help better identify feed efficiencies of the animals (see para. 42)
As per Claim 24 Kopic does not teach the method according to claim 23, wherein the at least one type of behavioral data includes head movements, and wherein the sensor is positioned on, or in the vicinity, of a head of the individual herd member. However, Biffert para. 42-43 teaches In another example, the system can also record an amount of time the animal's head is in a feed trough or a water source, which helps identify feed efficiencies of the animals (how much they take in and how much they gain). Deviations in the amount of time these animals spend at water and at feed are also illness indicators. This determination implicitly includes the service provider 10 comparing the location of the animal to a known location of a feed trough 16 or water source 17 to determine how long the animal spent at these locations. In instances where the sensor 14 is located on an car or the animal, and the sensor 14 omits signals frequently. These signals can be used to distinguish the action of the animal by changes in the angle/height measurements (e.g., is the movement indicative of eating, ruminating, or drinking). These data can be combined with accelerometer data from the sensor (for the tags that have it equipped, some may not) where it can determine, based on head/tag movements if the animal is eating (not just standing still but actually chewing). Both Kopic and Biffert are directed to managing livestock. Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the Applicant’s invention to modify the teachings of Kopic to include the method according to claim 23, wherein the at least one type of behavioral data includes head movements, and wherein the sensor is positioned on, or in the vicinity, of a head of the individual herd member as taught by Biffert to help better identify feed efficiencies of the animals (see para. 42).
As per Claim 25 Kopic does not teach he method according to claim 24, wherein the sensor is positioned in, or on, a collar or ear tag of the individual herd member. However, Biffert para. 48 teaches Some embodiments include implantable RFID chips and rumen boluses. The implantable RFID chips typically go in the ear of the animal, as meat processors may not allow for alternative placement on the animal so as to keep the sensor out of the food supply. The rumen boluses are stored in the stomach with a magnet that never allows them to get digested. This known technique is applicable to the system of Kopic as they are both directed to providing a user interface that displays that context of a current work situation to a user. One of ordinary skill in the art before the effective filing date of the Applicant’s invention would have recognized that applying the known technique of Biffert would have yielded predictable results and resulted in an improved system. It would have been recognized that applying the technique of Biffert to the teachings of Kopic would have yielded predictable results because the level of ordinary skill in the art demonstrated by the references applied shows the ability to incorporate placing a sensor on in, or on, a collar or ear tag of the individual herd member into similar systems. Further, incorporating the placing a sensor on in, or on, a collar or ear tag of the individual herd member taught by Biffert to the system taught by Kopic would result in an improved system that provides a reliable and well understood way to monitor livestock.
Claim(s) 8, 18, 28, 37, 38 is/are rejected under 35 U.S.C. 103 as being unpatentable over Kopic US 2014/0116341 A1 in view of Huisma US 20200323172 A1 as applied to claims 1, 11, 22, 31 and in further view of Gross US 2023/0263124 A1.
As per Claim 8 Kopic teaches (Original) The system according to claim 7, wherein the monetary data includes at least the price of milk and the cost of feed, and Kopic par. 64 teaches In one implementation, Block S130 receives a current local milk price, such as by automatically retrieving a current local milk price from an electronic agricultural database or by prompting the farmer to enter the current milk price into the dashboard within the corresponding dairy production account. Block S130 can then insert current milk price into the milk production model--along with current feed prices, master production cost data, etc.--to identify one or more financial milestones in milk production within the animal group (or herd, subset of the herd, etc.). For example, Block S130 can identify a financial "break-even" milk production quantity for the animal group based on a purchase price of the animal, calves birthed by the animal, an time to first calving by the animal, the current market price for milk, manure, and calves, and the cost of feed, labor, land, capital, etc. support the animal and to produce a volume and/or quality of milk. Block S130 can also identify milk production quantity corresponding to a peak revenue, a peak cost per animal, and a peak profit for the group. Block can further generate cost, revenue, and/or profit trend lines for various milk production quantities within the group for a specific milking event or time period
Kopic does not teach wherein the IoF is calculated according to the following formula:IoF= MP*(price of milk) - DMI*(cost of feed) and wherein the NIMP is calculated according to the following formula: NIMP =IoF - [additional cost parameters]. However, Gross para. 92 teaches According to some embodiments of the above system, the output generated by the operation center 500 includes, without limitation, at least one of the following parameters: daily feed intake per animal, feed intake per animal, feed intake per meal, meal duration, feeding rate, milk costs, daily milk yield, number of milking per animal per day, number of meals per day, Residual feed intake (RFI), food cost, food composition, feed efficiency, animal destined for selection, and income over feed cost (IOFC). As used herein, the term “RFI” refers, without limitation, to animal's feed efficiency independent of growth performance. The RFI is calculated actual feed intake minus the expected feed intake. As used herein, the term “IOFC” refers, without limitation, to the portion of income from milk sold that remains after paying for purchased and farm-raised feed used to produce the milk. Both Kopic and Gross are directed to managing livestock. Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the Applicant’s invention to modify the teachings of Kopic to include wherein the IoF is calculated according to the following formula:IoF= MP*(price of milk) - DMI*(cost of feed) and wherein the NIMP is calculated according to the following formula: NIMP =IoF - [additional cost parameters] as taught by Gross to allow for more accurate feeding of the herd, thus increasing the productivity of the herd and the food efficiency of the livestock farm (see para. 105).
As per Claim 18 Kopic does not teach The system according to claim 17, wherein the monetary data includes at least the price of milk and the cost of feed, wherein the IoF is calculated according to the following formula:IoF = MP*(price of milk) - DMI*(cost of feed); and wherein the NIMP is calculated according to the following formula: NIMP = IoF - [additional cost parameters]. However, Gross para. 92 teaches According to some embodiments of the above system, the output generated by the operation center 500 includes, without limitation, at least one of the following parameters: daily feed intake per animal, feed intake per animal, feed intake per meal, meal duration, feeding rate, milk costs, daily milk yield, number of milking per animal per day, number of meals per day, Residual feed intake (RFI), food cost, food composition, feed efficiency, animal destined for selection, and income over feed cost (IOFC). As used herein, the term “RFI” refers, without limitation, to animal's feed efficiency independent of growth performance. The RFI is calculated actual feed intake minus the expected feed intake. As used herein, the term “IOFC” refers, without limitation, to the portion of income from milk sold that remains after paying for purchased and farm-raised feed used to produce the milk. Both Kopic and Gross are directed to managing livestock. Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the Applicant’s invention to modify the teachings of Kopic to include wherein the monetary data includes at least the price of milk and the cost of feed, wherein the IoF is calculated according to the following formula:IoF = MP*(price of milk) - DMI*(cost of feed); and wherein the NIMP is calculated according to the following formula: NIMP = IoF - [additional cost parameters] as taught by Gross to allow for more accurate feeding of the herd, thus increasing the productivity of the herd and the food efficiency of the livestock farm (see para. 105).
As per Claim 28 Kopic does not teach The method according to claim 27, wherein the monetary data includes at least the price of milk and the cost of feed, and wherein the IoF is calculated according to the following formula:IoF = MP*(price of milk) - DMI*(cost of feed); and wherein the NIMP is calculated according to the following formula: NIMP = IoF - [additional cost parameters]. However, Gross para. 92 teaches According to some embodiments of the above system, the output generated by the operation center 500 includes, without limitation, at least one of the following parameters: daily feed intake per animal, feed intake per animal, feed intake per meal, meal duration, feeding rate, milk costs, daily milk yield, number of milking per animal per day, number of meals per day, Residual feed intake (RFI), food cost, food composition, feed efficiency, animal destined for selection, and income over feed cost (IOFC). As used herein, the term “RFI” refers, without limitation, to animal's feed efficiency independent of growth performance. The RFI is calculated actual feed intake minus the expected feed intake. As used herein, the term “IOFC” refers, without limitation, to the portion of income from milk sold that remains after paying for purchased and farm-raised feed used to produce the milk. Both Kopic and Gross are directed to managing livestock. Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the Applicant’s invention to modify the teachings of Kopic to include wherein the monetary data includes at least the price of milk and the cost of feed, and wherein the IoF is calculated according to the following formula:IoF = MP*(price of milk) - DMI*(cost of feed); and wherein the NIMP is calculated according to the following formula: NIMP = IoF - [additional cost parameters] as taught by Gross to allow for more accurate feeding of the herd, thus increasing the productivity of the herd and the food efficiency of the livestock farm (see para. 105).
As per Claim 37 Kopic does not teach The method according to claim 36, wherein the monetary data includes at least the price of milk and the cost of feed, and wherein the IoF is calculated according to the following formula:IoF = MP*(price of milk) - DMI*(cost of feed); and wherein the NIMP is calculated according to the following formula: NIMP = IoF - [additional cost parameters]. However, Gross para. 92 teaches According to some embodiments of the above system, the output generated by the operation center 500 includes, without limitation, at least one of the following parameters: daily feed intake per animal, feed intake per animal, feed intake per meal, meal duration, feeding rate, milk costs, daily milk yield, number of milking per animal per day, number of meals per day, Residual feed intake (RFI), food cost, food composition, feed efficiency, animal destined for selection, and income over feed cost (IOFC). As used herein, the term “RFI” refers, without limitation, to animal's feed efficiency independent of growth performance. The RFI is calculated actual feed intake minus the expected feed intake. As used herein, the term “IOFC” refers, without limitation, to the portion of income from milk sold that remains after paying for purchased and farm-raised feed used to produce the milk. Both Kopic and Gross are directed to managing livestock. Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the Applicant’s invention to modify the teachings of Kopic to include wherein the monetary data includes at least the price of milk and the cost of feed, and wherein the IoF is calculated according to the following formula:IoF = MP*(price of milk) - DMI*(cost of feed); and wherein the NIMP is calculated according to the following formula: NIMP = IoF - [additional cost parameters] as taught by Gross to allow for more accurate feeding of the herd, thus increasing the productivity of the herd and the food efficiency of the livestock farm (see para. 105).
As per Claim 38 Kopic does not teach The method according to claim 37,wherein the method further comprises classifying the individual herd member according to the NIMP or the IoF, wherein the classifying according to the NIMP or the IoF is performed before, at the same time, or after classifying according to the FE and/or the MP over the first pre-determined time period, if the individual herd member is further classified according to the MP. Kopic para. 41 teaches In one example implementation, the method S100 can generate a feed schedule for a group of milking animals to achieve a target milk production value (i.e., milk output quantity and/or quality), such as based on life events, milking records, and other animal group data collected in Block S110 and electronic feed data (as described below). However, Block S140 can collect subsequent milk records for the group of animals (as described below), and Block S102 can detect deviation from the target milk production value by a particular milking animal in the group of milking animals. Block S102 can respond to this detected deviation by identifying a more suitable animal group for the particular milking animal, such as by selecting an alternative group with characteristics more compatible with the particular milking animal. For example, Block S102 can identify milk production volume output by the particular milking animal that falls short of a target milk production quantity (e.g., volume, mass, weight) by a preset quantity threshold (e.g., more than 8%) over a set of milking periods (e.g., three consecutive days) and selecting the alternative group of milking animals that produces similar milk quantities for similar feed schedules. Alternatively, in this example, Block S102 can specify the particular milking animal for culling (i.e., removal from the herd) based on the deviation from the target milk production by the herd. In this example, Block S102 can also account for the age of the particular milking animal, the genetic potential of the particular milking animal to continue profitable milk production, etc. to determine if the particular milking animal should be culled or moved to an alternative group characterized by lower milk production than the particular milking animal's current group. Block S102 can then prompt placement of the particular milking animal into the alternatively group of milking animals, such as by issuing a notification within a farmer's dashboard. Alternatively, Block S102 can generate a notification (or work order) to move or cull the particular milking animal based on a unique animal identifier associated with the particular milking animal (and then transmit the work order to a farmhand for implementation). Block S102 can implement similar functionality to move a particular milking animal to another group characterized by higher milk production, to a group awaiting insemination, to a group pending or recently completing a birth, etc. based on the milk production figures, age, weight, and/or other data amassed in Blocks S110, S140, etc.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claim(s) 41 is/are rejected under 35 U.S.C. 102(a)(1) and (a)(2) as being anticipated by Kopic US 2014/0116341 A1.
As per Claim 41 Kopic teaches A method for timing the removal of at least one individual herd member from a herd, or group within the herd, said method comprising:
selecting at least one individual herd member that is to be removed from the herd, or the group within the herd; Kopic para. 41 teaches bock S102 can specify the particular milking animal for culling (i.e., removal from the herd) based on the deviation from the target milk production by the herd.
obtaining a dry matter intake (DMI) of the individual herd member over a pre- determined period of time;
Kopic para. 24 teaches For example, for one or more individual animal within the animal group, Block S110 can collect information relating to any one or more of birth date, auction or purchase date, feed history (e.g., schedule, content, quantity), nutritional demand, reproductive history (e.g., inseminated, expected pregnant, diagnosed pregnant, birth pending, recent birth), immunological history, exposure to weather (e.g., drought, rain, indoor and outdoor temperature, indoor and outdoor humidity, indoor and outdoor oxygen level, light, heat stress index, moon phase), weight, age, number of days in milk, days carrying calf, growth rate, maturity rate, life stage (e.g., milk heifer, dry cow, dry heifer, calf, etc.), lactation stage, size, weight, type, housing need, or any other relevant life data for one or more individual animal within the group. Further, para. 84 teaches once the target feed volume and nutritional content is set for the group, Block S150 can select a particular feed material or a combination of feed materials--from the list of available feed materials of known nutritional content--to achieve the target feed volume and nutritional content. For example, Block S150 can select a volume or mass of each of a subset of the available feed materials to approximate each of the dry matter, energy, ash, starch, sugar, soluble fiber, beta glucan, crude protein, rumen digestible protein, rumen indigestible protein, usable protein, crude fat, ADF, NDF, lignin, and/or micro element content targets for the feed. Block S150 can also select quantities (e.g., volumes, masses, weights) of various supplements to add to the feed, such as vitamin, mineral, and antibiotic supplements for the feed.
obtaining a milk production (MP) of the individual herd member over the pre- determined period of time;
Kopic para. 53 teaches Block S140 can collect milking data on a daily, weekly, or other timed schedule, or in real-time during each scheduled milking event for the animal group. Block S140 can further assemble these milking data for various milking events into a timeline of milk production for the animal group. For example, for a particular milking event defining a milking period for animals within the animal group, Block S140 can receive, for each individual animal within the animal group, a milk file corresponding to a singular milking animal within the animal group and define a quality and quantity of milk output by the singular animal. In this example, Block S140 can combine the quantity values and average the quality values of milk output by all individual animal within the animal group to generate a milk quality and quantity metric for the animal group for the particular milking event. Block S140 can repeat this process for each milking event and aggregate milk quality and quantity metrics for each milking period into a milk production timeline. Finally, Block S140 can pass this milk production timeline to Block S130, and Block S130 can apply feed, environment, gynecological, and/or other timelines described above to extrapolate trends in milk production with respect to one or more variables and thus identify effects of the one or more variables on milk production within the animal group.
receiving monetary data; Kopic 62 Block S130 can therefore function to generate a milk production model that defines a group of milking animals as a profit center with costs associated with milk production and incomes associated with milk, manure, calf, and salvage revenues. Block S130 can similarly generate a milk production model that defines a single milking animal as a profit center and then group multiple milk production models for various unique milking animals to simulate production, costs, and incomes for a set, group, farm, region, etc. of milking animals.
calculating the net income from milk production (NIMP) or the income over feed (IoF) for the individual herd member over the pre-determined period of time, based on the DMI, MP, and the monetary data; and
Kopic 62 Block S130 can therefore function to generate a milk production model that defines a group of milking animals as a profit center with costs associated with milk production and incomes associated with milk, manure, calf, and salvage revenues. Block S130 can similarly generate a milk production model that defines a single milking animal as a profit center and then group multiple milk production models for various unique milking animals to simulate production, costs, and incomes for a set, group, farm, region, etc. of milking animals.
removing the individual herd member from the herd, or group within the herd, at a time when the NIMP becomes lower than a pre-determined NIMP value or at a time when the IoF becomes lower than a pre-determined IoF value. Para. 41 teaches For example, Block S102 can identify milk production volume output by the particular milking animal that falls short of a target milk production quantity (e.g., volume, mass, weight) by a preset quantity threshold (e.g., more than 8%) over a set of milking periods (e.g., three consecutive days) and selecting the alternative group of milking animals that produces similar milk quantities for similar feed schedules. Alternatively, in this example, Block S102 can specify the particular milking animal for culling (i.e., removal from the herd) based on the deviation from the target milk production by the herd. In this example, Block S102 can also account for the age of the particular milking animal, the genetic potential of the particular milking animal to continue profitable milk production, etc. to determine if the particular milking animal should be culled or moved to an alternative group characterized by lower milk production than the particular milking animal's current group. Block S102 can then prompt placement of the particular milking animal into the alternatively group of milking animals, such as by issuing a notification within a farmer's dashboard. Alternatively, Block S102 can generate a notification (or work order) to move or cull the particular milking animal based on a unique animal identifier associated with the particular milking animal (and then transmit the work order to a farmhand for implementation). Block S102 can implement similar functionality to move a particular milking animal to another group characterized by higher milk production, to a group awaiting insemination, to a group pending or recently completing a birth, etc. based on the milk production figures, age, weight, and/or other data amassed in Blocks S110, S140, etc.
Relevant Art Not Relied Upon in the Rejection
Attwood US 20240334952 A1- Feed efficiency can be calculated by dividing the weight of milk produced by an animal, or the liveweight of an animal, by the weight of dry matter consumed by that animal. Thus, an animal with a higher feed efficiency will produce more milk, milk with a higher content of milk components such as, but not limited to, fat and protein, and/or will show increased weight gain compared to an animal with a lower feed efficiency when given the same nutrient input. Feed efficiency can be measured by differences in the growth of an animal by any of the following parameters: average daily weight gain, total weight gain, feed conversion, which includes both feed:gain and gain:feed, feed efficiency, mortality, and feed intake. That is to say, improved feed efficiency can mean that the ratio of feed intake/muscle weight gain is decreased. Improved feed efficiency can also mean that the ratio of muscle weight gain/feed intake is increased. The term feed efficiency may also refer to the feed intake/weight gain or weight gain/feed intake.
Schaffer US 20230225294 A1 As will be described below, the foregoing thermal coat patterns to detect metabolic efficiency in animals were confirmed using conventional methods, such as Feed Conversion Efficiency (FCE), wherein the metabolically efficient animal had an FCE of 1.7 kg feed required per kg of weight gain (right), and the metabolically inefficient animal had an FCE of 3.2 kg feed required per kg of weight gain, wherein such conventional methods being further accounted for in order to generate an overall thermal efficiency index, ‘TEI’ (such that animals can be ranked by their mean temperature and corrected for their TEI). By way of explanation, the metabolically efficient animal shown on the right may, during a typical 100-day performance period, require some $35-$50 USD less feed and produce some 700 kg less CO.sub.2. Accordingly, the present methods provide animal caregiver’s an accurate and efficient means for determining an animal’s metabolic efficiency, allowing for the ranking of animals according to their efficiency and the possible culling of those less efficient from the herd.
Johnston US 20160324126 A1 The present application includes a method for estimating impact of a milk producing animal on carbon footprint, comprising providing one or more primary parameters associated with one or more of: a) a measure of microbial protein for a selected feed sample from a digestion model associated with the milk producing animal; b) a measure of total digestible nutrients for the selected feed sample from the digestion model associated with the milk producing animal; and c) an amount or a percent of components in the selected feed sample; producing with a computing device a baseline performance comprising milk production efficiency using at least one or more of the primary parameters and one or more secondary parameters for the milk producing animal, wherein the one or more secondary parameters are associated with one or more of: a measure of animal weight, a measure of animal milk production, a measure of animal milk protein, a measure of animal dry matter intake, a measure of animal milk price, and a measure of animal dietary protein; and producing with the computing device a carbon footprint for the milk producing animal using the baseline performance. Secondary parameters include but are not limited to animal weight (kg), milk production (L), milk protein (%), dry matter intake (kg), milk price ($/L), or dietary protein (%).
Conclusion
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/DEIRDRE D HATCHER/Primary Examiner, Art Unit 3625