DETAILED ACTION
This communication is a Final Rejection Office Action in response to the 4/19/2026 communication filed in Application 18/751,665.
Claims 1, 11, 22, 28, 29, 31, and 41 have been amended. Claims 2-3 and 23-25 have been cancelled
Claims 1,5-8, 11, 16-18, 22, 26-29, 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 .
Response to Arguments
Applicant’s arguments filed 4/19/2026 with respect to the prior art rejections have been considered but are moot because the arguments do not apply to the new grounds of rejection that was necessitated by amendment.
Applicant’s arguments, with respect to the rejection under 112(b) have been fully considered and are persuasive. The 112(b) rejections of claim 28 and 29 have been withdrawn.
Applicant's remaining arguments have been fully considered but they are not persuasive.
Regarding the interpretation of the claims under 112(f), the Applicant argues “Applicant has amended both claims 1 and 11 to include the following elements:
"wherein the DMI module comprises a sensor comprising an accelerometer, wherein the sensor is positioned on, or in, a collar or ear tag of the individual herd member;
wherein the sensor is configured to collect at least one type of behavioral data of the individual herd member, including at least head movements, and is further configured to transmit signals related to the at least one type of behavioral data to a central processing unit (CPU); "
These elements are supported in the specification, as well as in original claims 2 and 3, now canceled, and further define the DMI module, such that is should be interpreted according to the specifics in the amended claims, and not according to 35 U.S.C. § 112(f). As for the milk production (MP) module it is respectfully asserted that various types of such modules, such as milk meters and the like, are known in the art and therefore, do not require specific definitions in the claims.”
The Examiner respectfully disagrees. The (DMI) module configured to obtain the DMI or DMI-related data; the milk production (MP) module, configured to obtain the MP or MP-related data recite functions without reciting sufficient structure. For example, the clams do not recite any hardware (I.e. a processor) that executed the recited modules As such, the modules are still interpreted under 112(f).
Regarding the rejection under 101, the Applicant argues “it is respectfully submitted that such a particular type of sensor, that includes an accelerometer, and that is positioned in a particular location (collar/ear tag) in order to collect data regarding head movements, cannot be considered an abstract idea (or any other type of judicial exception). Further, such a specific type of sensor, positioned in such a specific location, cannot be considered a "generic computer component performing generic computer functions". In this respect it is noted that there are many types of sensors known in the art, not all include accelerometers and certainly not all are purposely positioned in the vicinity of the head of a herd member (on the collar or ear tag) in order to particularly collect data related to the head movements of that herd member. This collection of data would not be possible if the sensor did not comprise an accelerometer, and further, it would not be possible if the sensor was not placed in the vicinity of the head of the herd member, as recited in the claims, particularly on/in the collar/ear tag of the herd member. This could not be performed by any type of sensor positioned in any location and therefore, cannot be considered "conventional data gathering".
The Examiner respectfully disagrees. The recited sensors the comprise accelerometers are well-known in the art. There is no improvement of unconventional arrangement. The recited sensor function in a conventional way to perform data gathering. As such, the Examiner is unpersuaded that the recited sensor cannot be considered "conventional data gathering
Regarding the rejection under 101, the Applicant further argues “In addition to the above, it is respectfully noted that claims 31 and 41 specifically recite:
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."
This "removing" of the herd member from the herd is a clear physical act, requiring a human operator, e.g., a farmer, to locate the particular herd member to be removed within the herd, and to physically remove it therefrom, e.g., by moving that herd member into a separate pen. It is respectfully submitted that such a physical act cannot be considered "abstract".
The Examiner respectfully disagrees. Removing an individual member from the group amounts to a human following output rules or instructions which is an abstract method of organizing human activity.
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 § 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 1,5-8, 11, 16-18, 22, 26-29, 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 1,5-8, 11, 16-18 are directed toward systems for classifying an individual herd member. Claims 22, 26-29, 31, 35-38, 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;
wherein the DMI module comprises a sensor comprising an accelerometer, wherein the sensor is positioned on, or in, a collar or ear tag of the individual herd member;
wherein the sensor is configured to collect at least one type of behavioral data of the individual herd member, including at least head movements, and is further configured to transmit signals related to the at least one type of behavioral data to a central processing unit (CPU)
;wherein the first pre-determined period of time is between about 5-25 days from calving or lactation to about 45-300 days from calving or lactation;
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;
the 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, the sensor comprising an accelerometer positioned on, or in, a collar or ear tag of the individual herd member to collect at least one type of behavioral data amounts to data gathering which is insignificant pre-solution activity.
Further, obtaining DMI or DMO related data and MP or MP related data also 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 DMI or DMO related data and MP or MP related data is recited broadly and such does not provide an inventive concept.
Further, the sensors comprising accelerometers to gather data are well-known and conventional. Nothings in the claim or specification describes the sensor as anything other than a well-known and widely used accelerometer to gather data and transm8it data.
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.
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, 6, 7, 11, 16, 17, 22, 26, 27, 29, 31, 35, 36, 41 is/are rejected under 35 U.S.C. 103 as being unpatentable over Kopic US 2014/0116341 A1 in view of Biffert US 20240407338 A1 in view of Kerley US 20200260759 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 does not teach wherein the DMI module comprises a sensor comprising an accelerometer, wherein the sensor is positioned on, or in, a collar or ear tag of the individual herd member;
wherein the sensor is configured to collect at least one type of behavioral data of the individual herd member, including at least head movements, and is further configured to transmit signals related to the at least one type of behavioral data to a central processing unit (CPU); and However, Biffert para. 42 teaches 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). Further, para. 61 teaches The method can include a step 43 of measuring a weight of each of the livestock, as well a step 44 of tracking a change in the weight over time. In some embodiments, the method includes a step 45 of associating the weight with a unique identifier for each of the livestock. Again, the unique identifier is transmitted each time a sensor transmits data to the service provider so that the data can be associated with a unique animal. 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, a collar or ear tag of the individual herd member; wherein the sensor is configured to collect at least one type of behavioral data of the individual herd member, including at least head movements, and is further configured to transmit signals related to the at least one type of behavioral data to a central processing unit (CPU) as taught by Biffert to help better identify feed efficiencies of the animals (see para. 42).
Kopic does not teach wherein the pre-determined period of time is between about 5-25 days from calving or lactation to about 45-300 days from calving or lactation; However, Kerley para 144-145 teach The performance data were separated into two periods: day 1 to day 58 and day 1 to finish. As shown in Table 6, during the first 58 days, animals fed the adjusted diet had an ADG of 4.62 lbs which was 7% greater than animals fed the unadjusted diet. Animals fed the adjusted diet further showed a 7% improvement in feed efficiency compared to animals fed the unadjusted diet. Dry matter intake did not differ between treatments. The adjusted diet was more costly per unit of dry weight; however, the cost of gain was lower for animals fed the adjusted diet because daily gain and feed efficiency both were improved by feeding the adjusted diet. Improved ADG and feed efficiency were statistically significant.
Overall performance is presented in Table 7 and carcass measurements are presented in Table 8. Over the entire 142-day study, animals fed the adjusted diet had an improved feed efficiency of 2.5% and ADG was numerically improved. Yield of carcass, based on 4% shrink of final live weight, tended P<0.18) to be greater for animals fed the adjusted vs. unadjusted diet (904 lbs vs. 892 lbs). Marbling score was greater for animals fed the unadjusted diet but animals fed the adjusted diet averaged 5% prime carcasses compared to 1% prime for animals fed the unadjusted diet. Subcutaneous fat and yield grade were lower and ribeye area was greater for animals fed the adjusted diet. Further table 6 discloses tracking the performance of cattle from days 1-58 and table 7 teaches tracking performance of cattle from, days 1-142.
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, Kerley para. 39 teaches Feed efficiency: A measure of an animal's efficiency in converting feed mass into the desired output, e.g., weight gain, milk production. Feed efficiency also may be referred to as feed conversion ratio, feed conversion rate, or feed conversion efficiency. Further, para. 144 teaches The performance data were separated into two periods: day 1 to day 58 and day 1 to finish. As shown in Table 6, during the first 58 days, animals fed the adjusted diet had an ADG of 4.62 lbs which was 7% greater than animals fed the unadjusted diet. Animals fed the adjusted diet further showed a 7% improvement in feed efficiency compared to animals fed the unadjusted diet. Dry matter intake did not differ between treatments. The adjusted diet was more costly per unit of dry weight; however, the cost of gain was lower for animals fed the adjusted diet because daily gain and feed efficiency both were improved by feeding the adjusted diet. Improved ADG and feed efficiency were statistically significant. Both Kopic and Kerley 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 pre-determined period of time is between about 5-25 days from calving or lactation to about 45-300 days from calving or lactation and 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 Kerley to prepare ruminant diets more accurately for provision of amino acids (AA) relative to ruminant intake of usable energy. Ruminant animals fed more precisely formulated diets demonstrate improved feed efficiency, improved output of usable products, or both. A further benefit is the reduction in manure and greenhouse gases produced per unit of usable product produced. And still further, the nutritional value of edible products is improved by provision of a more balanced and nourishing diet to the growing or lactating ruminant (see para. 4)
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.
the 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 wherein the DMI module comprises a sensor comprising an accelerometer, wherein the sensor is positioned on, or in, a collar or ear tag of the individual herd member; wherein the sensor is configured to collect at least one type of behavioral data of the individual herd member, including at least head movements, and is further configured to transmit signals related to the at least one type of behavioral data to a central processing unit (CPU); and However, Biffert para. 42 teaches 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). Further, para. 61 teaches The method can include a step 43 of measuring a weight of each of the livestock, as well a step 44 of tracking a change in the weight over time. In some embodiments, the method includes a step 45 of associating the weight with a unique identifier for each of the livestock. Again, the unique identifier is transmitted each time a sensor transmits data to the service provider so that the data can be associated with a unique animal. 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, a collar or ear tag of the individual herd member; wherein the sensor is configured to collect at least one type of behavioral data of the individual herd member, including at least head movements, and is further configured to transmit signals related to the at least one type of behavioral data to a central processing unit (CPU) as taught by Biffert to help better identify feed efficiencies of the animals (see para. 42).
Kopic does not teach wherein the first pre-determined period of time is between about 5-25 days from calving or lactation to about 45-300 days from calving or lactation; However, Kerley para 144-145 teach The performance data were separated into two periods: day 1 to day 58 and day 1 to finish. As shown in Table 6, during the first 58 days, animals fed the adjusted diet had an ADG of 4.62 lbs which was 7% greater than animals fed the unadjusted diet. Animals fed the adjusted diet further showed a 7% improvement in feed efficiency compared to animals fed the unadjusted diet. Dry matter intake did not differ between treatments. The adjusted diet was more costly per unit of dry weight; however, the cost of gain was lower for animals fed the adjusted diet because daily gain and feed efficiency both were improved by feeding the adjusted diet. Improved ADG and feed efficiency were statistically significant.
Overall performance is presented in Table 7 and carcass measurements are presented in Table 8. Over the entire 142-day study, animals fed the adjusted diet had an improved feed efficiency of 2.5% and ADG was numerically improved. Yield of carcass, based on 4% shrink of final live weight, tended P<0.18) to be greater for animals fed the adjusted vs. unadjusted diet (904 lbs vs. 892 lbs). Marbling score was greater for animals fed the unadjusted diet but animals fed the adjusted diet averaged 5% prime carcasses compared to 1% prime for animals fed the unadjusted diet. Subcutaneous fat and yield grade were lower and ribeye area was greater for animals fed the adjusted diet. Further table 6 discloses tracking the performance of cattle from days 1-58 and table 7 teaches tracking performance of cattle from, days 1-142.
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, Kerley para. 39 teaches Feed efficiency: A measure of an animal's efficiency in converting feed mass into the desired output, e.g., weight gain, milk production. Feed efficiency also may be referred to as feed conversion ratio, feed conversion rate, or feed conversion efficiency. Further, para. 144 teaches The performance data were separated into two periods: day 1 to day 58 and day 1 to finish. As shown in Table 6, during the first 58 days, animals fed the adjusted diet had an ADG of 4.62 lbs which was 7% greater than animals fed the unadjusted diet. Animals fed the adjusted diet further showed a 7% improvement in feed efficiency compared to animals fed the unadjusted diet. Dry matter intake did not differ between treatments. The adjusted diet was more costly per unit of dry weight; however, the cost of gain was lower for animals fed the adjusted diet because daily gain and feed efficiency both were improved by feeding the adjusted diet. Improved ADG and feed efficiency were statistically significant. Both Kopic and Kerley 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 pre-determined period of time is between about 5-25 days from calving or lactation to about 45-300 days from calving or lactation and 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 Kerley to prepare ruminant diets more accurately for provision of amino acids (AA) relative to ruminant intake of usable energy. Ruminant animals fed more precisely formulated diets demonstrate improved feed efficiency, improved output of usable products, or both. A further benefit is the reduction in manure and greenhouse gases produced per unit of usable product produced. And still further, the nutritional value of edible products is improved by provision of a more balanced and nourishing diet to the growing or lactating ruminant (see para. 4)
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 computer implemented 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 obtaining, by way of signals received from a sensor comprising an accelerometer, and wherein the sensor is positioned on, or in, a collar or ear tag of the individual herd member and is configured to collect at least one type of behavioral data of the individual herd member, including at least head movements, and is further configured to transmit signals related to the at least one type of behavioral data to a central processing unit (CPU) However, Biffert para. 42 teaches 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). Further, para. 61 teaches The method can include a step 43 of measuring a weight of each of the livestock, as well a step 44 of tracking a change in the weight over time. In some embodiments, the method includes a step 45 of associating the weight with a unique identifier for each of the livestock. Again, the unique identifier is transmitted each time a sensor transmits data to the service provider so that the data can be associated with a unique animal. 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 obtaining, by way of signals received from a sensor comprising an accelerometer, and wherein the sensor is positioned on, or in, a collar or ear tag of the individual herd member and is configured to collect at least one type of behavioral data of the individual herd member, including at least head movements, and is further configured to transmit signals related to the at least one type of behavioral data to a central processing unit (CPU) as taught by Biffert to help better identify feed efficiencies of the animals (see para. 42).
Kopic does not teach wherein the pre-determined period of time is between about 5-25 days from calving or lactation to about 45-300 days from calving or lactation However, Kerley para 144-145 teach The performance data were separated into two periods: day 1 to day 58 and day 1 to finish. As shown in Table 6, during the first 58 days, animals fed the adjusted diet had an ADG of 4.62 lbs which was 7% greater than animals fed the unadjusted diet. Animals fed the adjusted diet further showed a 7% improvement in feed efficiency compared to animals fed the unadjusted diet. Dry matter intake did not differ between treatments. The adjusted diet was more costly per unit of dry weight; however, the cost of gain was lower for animals fed the adjusted diet because daily gain and feed efficiency both were improved by feeding the adjusted diet. Improved ADG and feed efficiency were statistically significant.
Overall performance is presented in Table 7 and carcass measurements are presented in Table 8. Over the entire 142-day study, animals fed the adjusted diet had an improved feed efficiency of 2.5% and ADG was numerically improved. Yield of carcass, based on 4% shrink of final live weight, tended P<0.18) to be greater for animals fed the adjusted vs. unadjusted diet (904 lbs vs. 892 lbs). Marbling score was greater for animals fed the unadjusted diet but animals fed the adjusted diet averaged 5% prime carcasses compared to 1% prime for animals fed the unadjusted diet. Subcutaneous fat and yield grade were lower and ribeye area was greater for animals fed the adjusted diet. Further table 6 discloses tracking the performance of cattle from days 1-58 and table 7 teaches tracking performance of cattle from, days 1-142.
calculating, by way of the CPU, 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, Kerley para. 39 teaches Feed efficiency: A measure of an animal's efficiency in converting feed mass into the desired output, e.g., weight gain, milk production. Feed efficiency also may be referred to as feed conversion ratio, feed conversion rate, or feed conversion efficiency. Further, para. 144 teaches The performance data were separated into two periods: day 1 to day 58 and day 1 to finish. As shown in Table 6, during the first 58 days, animals fed the adjusted diet had an ADG of 4.62 lbs which was 7% greater than animals fed the unadjusted diet. Animals fed the adjusted diet further showed a 7% improvement in feed efficiency compared to animals fed the unadjusted diet. Dry matter intake did not differ between treatments. The adjusted diet was more costly per unit of dry weight; however, the cost of gain was lower for animals fed the adjusted diet because daily gain and feed efficiency both were improved by feeding the adjusted diet. Improved ADG and feed efficiency were statistically significant. Both Kopic and Kerley 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 pre-determined period of time is between about 5-25 days from calving or lactation to about 45-300 days from calving or lactation and calculating, by way of the CPU, 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 as taught by Kerley to prepare ruminant diets more accurately for provision of amino acids (AA) relative to ruminant intake of usable energy. Ruminant animals fed more precisely formulated diets demonstrate improved feed efficiency, improved output of usable products, or both. A further benefit is the reduction in manure and greenhouse gases produced per unit of usable product produced. And still further, the nutritional value of edible products is improved by provision of a more balanced and nourishing diet to the growing or lactating ruminant (see para. 4)
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 28 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 computer implemented 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 by way of signals received from a sensor comprising an accelerometer, and wherein the sensor is positioned on, or in, a collar or ear tag of the individual herd member and is configured to collect at least one type of behavioral data of the individual herd member, including at least head movements, and is further configured to transmit signals related to the at least one type of behavioral data to a central processing unit (CPU); However, Biffert para. 42 teaches 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). Further, para. 61 teaches The method can include a step 43 of measuring a weight of each of the livestock, as well a step 44 of tracking a change in the weight over time. In some embodiments, the method includes a step 45 of associating the weight with a unique identifier for each of the livestock. Again, the unique identifier is transmitted each time a sensor transmits data to the service provider so that the data can be associated with a unique animal. 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 by way of signals received from a sensor comprising an accelerometer, and wherein the sensor is positioned on, or in, a collar or ear tag of the individual herd member and is configured to collect at least one type of behavioral data of the individual herd member, including at least head movements, and is further configured to transmit signals related to the at least one type of behavioral data to a central processing unit (CPU) as taught by Biffert to help better identify feed efficiencies of the animals (see para. 42).
Kopic does not teach wherein the first pre-determined period of time is between about 5-25 days from calving or lactation to about 45-300 days from calving or lactation, However, Kerley para 144-145 teach The performance data were separated into two periods: day 1 to day 58 and day 1 to finish. As shown in Table 6, during the first 58 days, animals fed the adjusted diet had an ADG of 4.62 lbs which was 7% greater than animals fed the unadjusted diet. Animals fed the adjusted diet further showed a 7% improvement in feed efficiency compared to animals fed the unadjusted diet. Dry matter intake did not differ between treatments. The adjusted diet was more costly per unit of dry weight; however, the cost of gain was lower for animals fed the adjusted diet because daily gain and feed efficiency both were improved by feeding the adjusted diet. Improved ADG and feed efficiency were statistically significant.
Overall performance is presented in Table 7 and carcass measurements are presented in Table 8. Over the entire 142-day study, animals fed the adjusted diet had an improved feed efficiency of 2.5% and ADG was numerically improved. Yield of carcass, based on 4% shrink of final live weight, tended P<0.18) to be greater for animals fed the adjusted vs. unadjusted diet (904 lbs vs. 892 lbs). Marbling score was greater for animals fed the unadjusted diet but animals fed the adjusted diet averaged 5% prime carcasses compared to 1% prime for animals fed the unadjusted diet. Subcutaneous fat and yield grade were lower and ribeye area was greater for animals fed the adjusted diet. Further table 6 discloses tracking the performance of cattle from days 1-58 and table 7 teaches tracking performance of cattle from, days 1-142.
calculating by way of the CPU 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, Kerley para. 39 teaches Feed efficiency: A measure of an animal's efficiency in converting feed mass into the desired output, e.g., weight gain, milk production. Feed efficiency also may be referred to as feed conversion ratio, feed conversion rate, or feed conversion efficiency. Further, para. 144 teaches The performance data were separated into two periods: day 1 to day 58 and day 1 to finish. As shown in Table 6, during the first 58 days, animals fed the adjusted diet had an ADG of 4.62 lbs which was 7% greater than animals fed the unadjusted diet. Animals fed the adjusted diet further showed a 7% improvement in feed efficiency compared to animals fed the unadjusted diet. Dry matter intake did not differ between treatments. The adjusted diet was more costly per unit of dry weight; however, the cost of gain was lower for animals fed the adjusted diet because daily gain and feed efficiency both were improved by feeding the adjusted diet. Improved ADG and feed efficiency were statistically significant. Both Kopic and Kerley 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 pre-determined period of time is between about 5-25 days from calving or lactation to about 45-300 days from calving or lactation and calculating, by way of the CPU, 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 as taught by Kerley to prepare ruminant diets more accurately for provision of amino acids (AA) relative to ruminant intake of usable energy. Ruminant animals fed more precisely formulated diets demonstrate improved feed efficiency, improved output of usable products, or both. A further benefit is the reduction in manure and greenhouse gases produced per unit of usable product produced. And still further, the nutritional value of edible products is improved by provision of a more balanced and nourishing diet to the growing or lactating ruminant (see para. 4)
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.
As per Claim 41 Kopic teaches A computer implemented 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, by way of the CPU, 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.
Kopic does not teach obtaining, by way of signals received from a sensor comprising an accelerometer and wherein the sensor is positioned on, or in, a collar or ear tag of the individual herd member and is configured to collect at least one type of behavioral data of the individual herd member, including at least head movements, and is further configured to transmit signals related to the at least one type of behavioral data to a central processing unit (CPU) However, Biffert para. 42 teaches 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). Further, para. 61 teaches The method can include a step 43 of measuring a weight of each of the livestock, as well a step 44 of tracking a change in the weight over time. In some embodiments, the method includes a step 45 of associating the weight with a unique identifier for each of the livestock. Again, the unique identifier is transmitted each time a sensor transmits data to the service provider so that the data can be associated with a unique animal. 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 obtaining, by way of signals received from a sensor comprising an accelerometer and wherein the sensor is positioned on, or in, a collar or ear tag of the individual herd member and is configured to collect at least one type of behavioral data of the individual herd member, including at least head movements, and is further configured to transmit signals related to the at least one type of behavioral data to a central processing unit (CPU) as taught by Biffert to help better identify feed efficiencies of the animals (see para. 42).
Kopic does not teach wherein the pre-determined period of time is between about 5-25 days from calving or lactation to about 45-300 days from calving or lactation, However, Kerley para 144-145 teach The performance data were separated into two periods: day 1 to day 58 and day 1 to finish. As shown in Table 6, during the first 58 days, animals fed the adjusted diet had an ADG of 4.62 lbs which was 7% greater than animals fed the unadjusted diet. Animals fed the adjusted diet further showed a 7% improvement in feed efficiency compared to animals fed the unadjusted diet. Dry matter intake did not differ between treatments. The adjusted diet was more costly per unit of dry weight; however, the cost of gain was lower for animals fed the adjusted diet because daily gain and feed efficiency both were improved by feeding the adjusted diet. Improved ADG and feed efficiency were statistically significant.
Overall performance is presented in Table 7 and carcass measurements are presented in Table 8. Over the entire 142-day study, animals fed the adjusted diet had an improved feed efficiency of 2.5% and ADG was numerically improved. Yield of carcass, based on 4% shrink of final live weight, tended P<0.18) to be greater for animals fed the adjusted vs. unadjusted diet (904 lbs vs. 892 lbs). Marbling score was greater for animals fed the unadjusted diet but animals fed the adjusted diet averaged 5% prime carcasses compared to 1% prime for animals fed the unadjusted diet. Subcutaneous fat and yield grade were lower and ribeye area was greater for animals fed the adjusted diet. Further table 6 discloses tracking the performance of cattle from days 1-58 and table 7 teaches tracking performance of cattle from, days 1-142. Both Kopic and Kerley 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 pre-determined period of time is between about 5-25 days from calving or lactation to about 45-300 days from calving or lactation as taught by Kerley to prepare ruminant diets more accurately for provision of amino acids (AA) relative to ruminant intake of usable energy. Ruminant animals fed more precisely formulated diets demonstrate improved feed efficiency, improved output of usable products, or both. A further benefit is the reduction in manure and greenhouse gases produced per unit of usable product produced. And still further, the nutritional value of edible products is improved by provision of a more balanced and nourishing diet to the growing or lactating ruminant (see para. 4)
Claim(s) 5 is/are rejected under 35 U.S.C. 103 as being unpatentable over Kopic US 2014/0116341 A1 in view of Biffert US 20240407338 A1 in view of Kerley US 20200260759 A1 as applied to claim 1 and in further view of Huisma US 20200323172 A1.
As per Claim 5 Kopic in view of Kerley does not explicitly disclose 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 in view of Kerley 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).
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 Biffert US 20240407338 A1 in view of Kerley US 20200260759 A1 as applied to claim 7, 17, 27, 36 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 an income over feed (IoF) is calculated according to the following formula:IoF = MP*(price of milk) - DMI*(cost of feed); and wherein a net income from milk production (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 an income over feed (IoF) is calculated according to the following formula:IoF = MP*(price of milk) - DMI*(cost of feed); and wherein a net income from milk production (NIMP) is calculated according to the following formula: NIMP = IoF - [additional cost parameter 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.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to DEIRDRE D HATCHER whose telephone number is (571)270-5321. The examiner can normally be reached Monday-Friday 8-4:30.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Brian Epstein can be reached at 571-270-5389. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/DEIRDRE D HATCHER/Primary Examiner, Art Unit 3625
Prosecution Insights