DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Status of Claims
This is a nonfinal rejection in response to claims filed on 06/17/2026. Claims 1, 3, 11-13, 15, and 17-20 are amended. Claims 4, 5, and 21 stand cancelled. Thus claims 1-3 and 6-20 remain pending and are considered herein.
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 06/17/2026 has been entered.
Priority
Applicant's claim for the benefit of a prior-filed application under 35 U.S.C. 119(e) or under 35 U.S.C. 120, 121, 365(c), or 386(c) is acknowledged. The present application is a continuation(CON) of US Patent No. 11997988(Application No. 17/070,261) filed on 2020-10-14, which is a CON of US Patent No. 10806130 (Application No. 16/225,740) filed on 2018-12-19, which holds priority to provisional application No. 62/607,500 filed on 2017-12-19. Therefore, the earliest priority date of the present claims is 2017-12-19.
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 1-3 and 6-17 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention. Claims 1 and 12 as amended recite new matter, that lacks support in the original disclosure of the claims, particularly the amendment:
-upon receiving the confirmation, automatically transmitting a wireless signal to livestock feed manufacturing equipment via a livestock feed producer interface, the wireless signal including instructions to manufacture the customized feedstock recipe for the one or more livestock, thereby initiating automated production of the customized feedstock.
However, the written description lacks support for the automatic transmission to livestock feed manufacturing via the livestock feed producer interface. In the closest embodiment in the specification page 9 recites, “According to one embodiment, a livestock health management plan is generated and provided by the methods and systems provided herein... According to one embodiment, the plan is provided to the livestock owner wirelessly via the livestock owner interface. According to one embodiment, the plan is provided to a livestock feed producer wirelessly via the livestock feed manufacturer interface or directly to the livestock feed manufacturing equipment upon confirmation of acceptance by the livestock owner...” In this embodiment, the plan is either provided to a livestock feed producer wirelessly via the livestock feed manufacturer interface, or directly to the livestock feed manufacturing equipment, however, no embodiment teaches the specific step of “automatically transmitting a wireless signal to livestock feed manufacturing equipment via a livestock feed producer interface.”
Furthermore, the written description does not provide support for “thereby, initiating automated production of the customized feedstock.” There are no examples in the claims that provide “automated production of the customized feedstock,” given the broadest reasonable interpretation of “automated production” which would require that the production itself is done without external assistance. While the page 9 excerpt above may support “automatically initiating production of the customized feedstock,” this is not the same as “initiating automated production of the customized feedstock.” For purposes of compact prosecution, the claims are analyzed given their plain language, as if the claims were supported by the specification.
The examiner notes, as a result of compact prosecution, the 101 rejection is withdrawn for claims 1-3 and 6-17, because the combination of additional elements, including the automatic transmission of a wireless signal to livestock feed manufacturing equipment via a livestock feed producer interface” and “thereby, initiating automated production of the customized feedstock” would integrate the abstract idea into a practical application of automatically producing the customized feedstock. However, since the claims lack support in the original disclosure for the automated production, the examiner reserves the right to reintroduce the 101 rejection upon amendment of the claims to address the 112(a) written description issue.
Additionally, the examiner notes that the same limitations above result in the withdrawal of the nonstatutory double patenting rejection for claims 1-3 and 6-17, as the particular interaction between the “automatic transmission of a wireless signal to livestock feed manufacturing equipment via a livestock feed producer interface” is not anticipated by the patented claims nor rendered obvious in in a combination of the patented claims and the prior art of record. However, the examiner reserves the right to reintroduce the double patenting rejection if the applicant amends to address the written description issue. The applicant has expressed intent to file a terminal disclaimer in the remarks filed on 06/17/2026, however, no terminal disclaimer has actually been filed as of the present office action.
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 18-20 rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1: Is the claim to a Process, Machine, Manufacture, or Composition of Matter?
Claim 18-20: A method for manufacturing a customized feed for at least one livestock animal, the method comprising the steps of:
Claims 18-20 recite a method which falls under “process.” Therefore the claims are potentially eligible and are to be further analyzed under step 2.
Step 2a Prong 1: Is the claim reciting a Judicial Exception (A Law of Nature, a Natural Phenomenon (Product of Nature), or An Abstract Idea?)
The claims under the broadest reasonable interpretation in light of the specification are analyzed herein. Representative claim 18 is marked up, isolating the abstract idea from additional elements, wherein the abstract idea is set in bold and the additional elements have been italicized as follows:
Claim 18: A method for manufacturing a customized feed for at least one livestock animal, the method comprising the steps of:
Obtaining, by at least one processor in wireless communication with at least one livestock sensor coupled to a custom electronic board configured to filter a data signal from the sensor, real-time livestock sensor data from at least one sensor located on or around one or more livestock, the data comprising at least one of livestock weight, livestock activity level, livestock ammonia level, body temperature, body weight, water intake, or body pH, the sensor data transmitted via a gateway in wireless communication with at least one processor;
analyzing, by the at least one processor using at least one of a statistical model or a machine learning model, the real-time livestock sensor data to detect at least one anomaly in the livestock weight, livestock activity level, livestock ammonia level, body temperature, body weight, water intake, or body pH, wherein the anomaly indicates a deviation from a predetermined health threshold for a particular species or breed stored in a database in wireless communication with the at least one processor; and
generating, by the processor, a customized livestock health management plan that includes a tailored feedstock recipe adapted to cure the at least one detected anomaly through a change in at least of protein, vitamin, mineral or caloric intake, the tailored feedstock recipe formulated based on nutritional parameters stored in the database for the particular species or breed.
When evaluating the bolded limitations of the claims under the broadest reasonable interpretation in light of the specification, it is clear that representative claim 18 recites the abstract idea category of certain methods of organizing human activity. This abstract idea grouping found in MPEP 2106.04(a)(2)(II) includes claims to “managing personal behavior or relationships or interactions between people.” The invention is directed to this subcategory which includes social activities, teaching, and following rules or instructions, which is supported by the background of the specification,
“Livestock production currently suffers from a fragmented system where an animal may be weighed only a limited number of times during ownership. Any adjustments made to the livestock’s nutritional program to accommodate for changes in weight, health status and other health indicators are not usually made or are made at a time that is too late to improve the animal’s health. Even when changes to a livestock feed is made, a livestock owner is usually relegated to purchasing and feeding livestock from bulk purchased foodstuff. Such a lack of care may result in underweight, overweight or otherwise unhealthy livestock since particular livestock may need regular nutritional adjustments during their lifespan to compensate for changes in various factors. Such lack of nutritional adjustment and feeding conditions may result in profit loss for the livestock owner. Thus, there remains a need for system and method that addresses these and other challenges in real-time and provides a health management plan particularly suited to livestock in immediate need thereof.”
Due to the fact that that the claims merely gather data and result in a recommendation/plan provided to the owner that the owner must execute, the claims are merely “managing personal behavior” by providing teachings or instructions to a person. Therefore, in light of the specification, the claims merely recite methods of organizing activity in the subcategory of “managing personal behavior or interactions between individuals.” Therefore, in view of the claims in bold, the abstract idea is the “obtaining of real-time livestock data, analyzing the real-time livestock data to detect anomalies, and generating a customized livestock health management plan such as a tailored feedstock recipe adapted to cure the at least one detected anomaly being fed to the livestock.” Due to the generality and breadth of these claims which allow for any manner in determining the anomalies and generating a plan, it is no more than a management of personal behavior because it merely provides teachings and instructions to a person. Even when considering the filtering of data signals, transmission via wireless communication, and storing in a database, the steps still recite an abstract idea because the number of people, or the interactions being conducted on a computer is not dispositive as to whether a claim limitation falls within the grouping. MPEP 2106.04(a)(2)(II) states, “Finally, the sub-groupings encompass both activity of a single person (for example, a person following a set of instructions or a person signing a contract online) and activity that involves multiple people (such as a commercial interaction), and thus, certain activity between a person and a computer (for example a method of anonymous loan shopping that a person conducts using a mobile phone) may fall within the "certain methods of organizing human activity" grouping. It is noted that the number of people involved in the activity is not dispositive as to whether a claim limitation falls within this grouping. Instead, the determination should be based on whether the activity itself falls within one of the sub-groupings.”
Therefore, the claims recite an abstract and are to be further analyzed under step 2a prong 2 and step 2b.
Step 2A Prong 2: Does the claim recite additional elements that integrate the judicial exception into a practical application?
Claim 18 recite the following additional elements:
-at least one processor in wireless communication with
-at least one livestock sensor
- coupled to a custom electronic board configured to filter a data signal from the sensor,
-at least one sensor located on or around one or more livestock,
- sensor data transmitted via a gateway in wireless communication with at least one processor;
- machine learning model
- a database in wireless communication with the at least one processor;
The additional elements are no more than a recitation of the words “apply it” (or an equivalent) or mere instructions to implement an abstract idea or other exception on a computer on its ordinary capacity as outlined in MPEP 2106.05(f). In this case, the abstract idea of “obtaining of real-time livestock data, analyzing the real-time livestock data to detect anomalies, and generating a customized livestock health management plan such as a tailored feedstock recipe adapted to cure the at least one detected anomaly being fed to the livestock” is merely instructed to be performed on generic computing devices such as a processor, livestock sensor, custom electronic board, gateway, and machine. These are generic computing components as evident in at least in Page 6 of the specification,
“According to one embodiment, the system as provided herein includes the components shown in FIG. 1. According to one embodiment, the system as provided herein includes at least one livestock owner interface including a data entry system. The livestock owner interface may be in wireless communication with a gateway, at least one server, processor and memory. According to one embodiment, the livestock owner interface includes a livestock owner portal for owner access to the system as provided herein. According to one embodiment, the owner interface includes an application that may be installed on a stationary device such as, for example, a desktop computer. According to one embodiment, the owner interface includes an application that may be installed on a mobile device such as laptop computer or smart device such as a smart phone or tablet. According to either embodiment, a user-friendly dashboard may be provided.”
Despite the volume of additional elements provided in the claims, since they all are considered generic computing components instructed to perform the abstract idea or are ordinary devices operating in their ordinary capacity (such as sensors to sense weight, activity level, ammonia level...or livestock feed manufacturing equipment not specifically utilized in the claims), they fail to integrate the abstract idea into a practical application. The additional elements are merely computing devices not instructed to perform any particular task with specific steps that would constitute an improvement to technology or technical field (MPEP 2106.05(a)). Furthermore, since the sensors are merely used in their ordinary capacity, without specific detail on how they sense and how the anomalies are detected, then they are merely “apply it” level elements. As stated in MPEP 2106.05(f), “Use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., a fundamental economic practice or mathematical equation) does not integrate a judicial exception into a practical application or provide significantly more.”
The limitations are merely claimed in a manner that requires such interactions to be performed on any generic computer or device in its ordinary capacity. For example, using an interface to collect, capture, and display data is merely using computer interfaces in their ordinary capacity to perform an economic task. Furthermore, requiring the processor to be “in wireless communication with at least one livestock sensor” is also merely using generic computers to perform the abstract idea because generic computers are capable of transmitting signals between devices wirelessly. Furthermore, the claimed computer infrastructure claimed does not make it apparent to one of ordinary skill in the art that an improvement to technology is reflected within the present claim scope. See MPEP 2106.05(a) for more information on improvements to technology. Finally, merely limiting the model to be a “machine learning” model is no more than a general link to particular technological environment or technical field, because it does not meaningfully limit the claim other than merely indicating the field of use or technological environment in which to apply a judicial exception. See MPEP 2106.05(h).
Even when considering the additional elements individually or as an ordered combination, and even when viewing the claims as a whole, the claims merely use computers/devices to perform the abstract idea or generate outputs such as instructions to a person, therefore the claims are directed to an abstract idea.
Step 2B: Does the claim recite additional elements that amount to significantly more than the judicial exception?
Claim 18 recite the following additional elements:-at least one processor in wireless communication with
-at least one livestock sensor
- coupled to a custom electronic board configured to filter a data signal from the sensor,
-at least one sensor located on or around one or more livestock,
- sensor data transmitted via a gateway in wireless communication with at least one processor;
- machine learning model
- a database in wireless communication with the at least one processor;
The additional elements have also not been found to include significantly more in order to consider it an inventive concept for the same reasons set forth in Prong 2. The additional elements are no more than a recitation of the words “apply it” (or an equivalent) or mere instructions to implement an abstract idea or other exception on a computer on its ordinary capacity as outlined in MPEP 2106.05(f). More specifically, the use of a processor, livestock sensor, custom electronic board, gateway, and machine to perform the abstract idea of “obtaining of real-time livestock data, analyzing the real-time livestock data to detect anomalies, and generating a customized livestock health management plan such as a tailored feedstock recipe adapted to cure the at least one detected anomaly being fed to the livestock” does not provide significantly more, because they do not meaningfully limit the use of the field on the abstract idea. Furthermore, improvements to the technology or technical field have not been purported. Please review MPEP 2106.05(a) for more information regarding improvements to computing devices(Section I), or technological fields(Section II). Furthermore, the additional element of “machine learning model” is no more than a general link to a particular technological environment or field of use.
Even when viewed as a whole, nothing in the claims meaningfully limits the abstract idea such that it is significantly more. The claims as a whole are merely using generic computing capabilities to perform the certain methods of organizing human activity. Therefore the claims are directed to an abstract idea without integration into a practical application or significantly more and are not patent eligible.
The dependent claims 19-20 are also given the full two-part analysis, individually and in combination with the claims they depend on, in the following analysis:
Claim 19 is merely further limitations of the same abstract idea, as they merely provide steps of outputting the management plan to the owner, and receiving feedback as to whether the acceptance of the plan is accepted or denied. Such interactions are still more of the same abstract idea because they are merely data output steps providing the instructions, which is still categorized under “certain methods of organizing human activity.” Furthermore, even though the providing of feedback is done on a generic computing device, it is still a “managing of personal behavior or interactions,” as stated in MPEP 2106.04(a)(2)(II),
“Finally, the sub-groupings encompass both activity of a single person (for example, a person following a set of instructions or a person signing a contract online) and activity that involves multiple people (such as a commercial interaction), and thus, certain activity between a person and a computer (for example a method of anonymous loan shopping that a person conducts using a mobile phone) may fall within the “certain methods of organizing human activity” grouping. It is noted that the number of people involved in the activity is not dispositive as to whether a claim limitation falls within this grouping. Instead, the determination should be based on whether the activity itself falls within one of the sub-groupings.” Therefore, even when considering the additional elements of transmitting the plan wirelessly to at least one livestock owner interface individually or in combination with the previous additional elements, the claims are still reciting an abstract idea without integration into a practical application. Even when considering the claims as a whole, including any intervening claims, the claims are patent ineligible under 101 because they are directed to an abstract idea without significantly more.
Claim 20 further defines the abstract idea by adding the steps of transmitting the confirmed livestock health management plan from the livestock owner to a feedstock producer; manufacturing the tailored feedstock according to the customized livestock feed recipe; and shipping the customized livestock feed to the livestock owner. The examiner notes that these additional steps require more consideration for whether they integrate the abstract idea into a practical application. The examiner points to the October 2019 PEG, Example 46, regarding livestock management. In this example, claim 1 is directed to an abstract idea without significantly more because it merely collects data and provides the results of the data. However, in claims 2 and 3 the additional elements integrate the abstract idea into a practical application because they effectuate the dispersal of the proper amounts of nutrition through a physical sorting gate. In the present application, the transmission of the plan is merely part of the abstract idea because it is also merely a transfer and output of information. At the manufacturing step, compared to example 46, there is an insufficient amount of detail to meaningfully limit how the customized livestock feed is manufactured according to the recipe. Since this limitation encompasses the scope of instructing a person to perform the weighing and mixing of the various ingredients at the instructed amounts, it is still a “certain method of organizing human activity.” Regarding the shipping step, since this shipping also encompasses the scope of instructing a person to carry a bag of feed to the livestock owner, it is still “certain methods of organizing human activity.” Therefore, even when viewing the claims as a whole, and after a detailed analysis, the claims are still directed to an abstract idea without integration into a practical application or significantly more. Therefore, claim 20 is also patent ineligible under 35 U.S.C. 101.
Claim Rejections – 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claim 18 is rejected under 35 U.S.C. 103 as being unpatentable over Madhusudan et al. (US 20180206448 A1), in view of Cook et al. (US 20110010154 A1) hereinafter Cook.
Regarding Claim 18:
Madhusudan teaches:
A method for manufacturing a customized feed for at least one livestock animal, the method comprising the steps of: (Madhusudan [0014] The system may be further configured to determine a feed composition for each of the plurality of dairy animals, based on the determined amount of milk yielded by corresponding dairy animal and the monitored activities. The system may be further configured to control a loading device that loads a feed container with the determined feed composition for a first dairy animal to manage consumption of the feed composition by the first dairy animal.)
- obtaining, by at least one processor in wireless communication with at least one livestock sensor coupled to a custom electronic board configured to filter data signal from the sensor, (Madhusudan [0020] The plurality of sensing devices 102 may comprise suitable logic, circuitry, interfaces, and/or code that may be configured to track activities of a plurality of entities in a defined area of a dairy farm. The plurality of entities may include a plurality of dairy animals (such as the first dairy animal 106a) and a plurality of care-takers (such as the first care-taker 108a). The plurality of sensing devices 102 may be configured to transmit information pertaining to the tracked activities of the plurality of entities to the electronic device 110 and/or the feed-management server 112. The plurality of sensing devices 102 may comprise a plurality of sensing tags (such as the sensing tag 102a) and a plurality of wearable devices (such as the wearable device 102b). [0045] The electronic device 110 may identify a sensing tag, such as the sensing tag 102a, which has the strongest signal strength with the wearable device 102b. The electronic device 110 may further identify the dairy animal, such as the first dairy animal 106a, associated with the identified sensing tag 102a. ) Circuitry specially configured to track sensing devices satisfies the claim limitation above, and [0045] satisfies the filtering of data.
- real-time livestock sensor data from at least one sensor located on or around one or more livestock, (Madhusudan [0021]The sensing tag 102a may be further configured to store identification information of a dairy animal... configured to determine health information of the first dairy animal 106a... to transmit the determined health information of the first dairy animal 106a to the feed-management server 112. The sensing tag 102a may include the image-capture device 104. The sensing tag 102a may comprise a plurality of sensors, such as a radio frequency identification (RFID) sensor, a health monitoring sensor, a proximity sensor, an infra-red (IR) sensor, or a combination thereof, which may enable the sensing tag 102a to determine the health information of the first dairy animal 106a. The sensing tag 102a may be worn by an individual dairy animal to track activities of the individual dairy animal. [0023] Examples of the image-capture device 104 may include, but are not limited to, at least a camera, a camcorder, and an action cam. The image-capture device 104 may be implemented as an integrated unit of the sensing tag 102a or a separate device. For example, the image-capture device 104 may be positioned at various body portions, such as strapped around the neck portion of a dairy animal, or along the legs or lower portion of stomach, of a dairy animal, such as the first dairy animal 106a... The image-capture device 104 may be positioned at other body portions of the dairy animal to focus at surrounding areas around the dairy animal and/or an udder portion of the dairy animal. [0103] The disclosed system, such as the feed-management server 112, comprises one or more circuits, such as the processor 202 and the feed controller 206. The one or more circuits in the feed-management server 112 monitors the real time activities of the plurality of entities in a dairy farm by the plurality of sensing devices 102.) Madhusudan [0021] teaches a sensing tag located directly on the livestock, and [0023] teaches examples of sensors located around the livestock. [0021] teaches the health information being transmitted to the feed management server, satisfying the “obtaining...” limitation.
-the data comprising at least one of livestock weight, livestock activity level, livestock ammonia level, body temperature, body weight, water intake, or body pH; (Madhusudan [0031] For example, the first dairy animal 106a may graze grass in a grazing area, which is referred to as a free-style grazing activity. The image-capture device 104 of the sensing tag 102a may capture one or more digital images and/or videos during the free-style grazing activity by the first dairy animal 106a. [0040] In accordance with an embodiment, the sensing tag 102a may be further configured to determine health information of the first dairy animal 106a. The health information may include one or more health parameters of the first dairy animal 106a. Examples of the one or more health parameters, may include, but are not limited to, blood count, body temperature, respiration rate, heart beat rate, and/or a combination thereof.) Since Madhusudan teaches “livestock activity level,” in the form of capturing the free-style grazing activity of the animal, and also teaches “body temperature” the limitation above has been satisfied since only at least one of the list is required.
-the sensor data transmitted via a gateway in wireless communication with the at least one processor;(Madhusudan [0021] The sensing tag 102a may be configured to transmit the determined health information of the first dairy animal 106a to the feed-management server 112. The sensing tag 102a may include the image-capture device 104. The sensing tag 102a may comprise a plurality of sensors, such as a radio frequency identification (RFID) sensor, a health monitoring sensor, a proximity sensor, an infra-red (IR) sensor, or a combination thereof, which may enable the sensing tag 102a to determine the health information of the first dairy animal 106a. The sensing tag 102a may be worn by an individual dairy animal to track activities of the individual dairy animal.)
- analyzing, by processor, the real-time livestock sensor data to detect at least one anomaly in the livestock weight, livestock activity level, livestock ammonia level, body temperature, body weight, water intake, or body pH, (Madhusudan [0040] The sensing tag 102a may further transmit the statistics of health information of the first dairy animal 106a to the feed-management server 112. The feed-management server 112 in return may update the determined feed composition based on an anomaly that is detected in the health information of the first dairy animal 106a. The feed-management server 112 may add one or more medicinal ingredients in the determined feed composition to treat the detected anomaly. For example, the feed-management server 112 may detect an increment in body temperature of the first dairy animal 106a, based on the health information received from the sensing tag 102a.) Since Madhusudan detects anomalies in the data, which Madhusudan has been shown to teach including “livestock activity level” and “body temperature,” then the limitation above has been satisfied since it only requires at least one of the items in the list.
-stored in a database in wireless communication with the at least one processor;(Madhusudan [0027] Examples of the feed-management server 112 may include, but are not limited to, an application server, a cloud server, a web server, a database server, a file server, a mainframe server, or a combination thereof.)
- and generating, by the processor, a customized livestock health management plan that includes a tailored feedstock recipe adapted to cure the at least one detected anomaly (Madhusudan [0040] The feed-management server 112 may add a medicinal ingredient (such as an antibiotic) required to treat the increased body temperature. In accordance with an embodiment, the feed-management server 112 may be further configured to transmit the health information of the first dairy animal 106a to a computing device (not shown) of a health analyst, in real time. The health analyst may further prescribe one or more medicinal ingredients to be added to the determined feed composition of the first dairy animal 106a by use of the computing device. The feed-management server 112 may further add the one or more medicinal ingredients prescribed by the health analyst. In accordance with an embodiment, the feed-management server 112 may further update the feed composition based on one or more external parameters, such as temperature conditions, weather conditions, or one or more guidelines by various health agencies.)
However, Madhusudan fails to teach:
-that the analyzing is done by the at least one processor using at least one of a statistical model or a machine learning model,
- wherein the anomaly indicates a deviation from a predetermined health threshold for a particular species or breed stored in a database in wireless communication with the at least one processor;
-the tailored feedstock recipe adapted to cure the at least one detected anomaly through a change in at least one protein, vitamin, mineral, or caloric intake(Madhusudan does not explicitly teach that the tailored feedstock recipe specifically changes a protein, vitamin, mineral, or caloric intake in order to cure at least one detected anomaly.
--the tailored feedstock recipe formulated based on nutritional parameters stored in the database for the particular species or breed.
However, Cook discloses a system and method for optimizing animal production, including generation of diet information for a particular species of animal based on a particular nutritional anomaly. Cook teaches:
-that the analyzing is done by the at least one processor using at least one of a statistical model or a machine learning model,(Cook [0072] Enterprise supervisor 200 may be configured to implement a Monte Carlo method where a specific set of values is drawn from a set of distributions of model parameters to solve for optimized values for the variable inputs. This process may be repeated many times, creating a distribution of optimized solutions. Based on the type of optimization, enterprise supervisor 200 maybe used to select either the value most likely to provide the optimal solution or the value that gives confidence that is sufficient to meet a target. For example, a simple optimization might be selected which provides a net energy level that maximizes the average daily gain for a particular animal. [0085] Empirical testing provides the advantage of verifying the accuracy of predictive models generated by simulator 300. Optimization results generated from imperfect models may different from real world results obtained through empirical testing. System 100 may be configured to provide dynamic control based on the empirical testing feedback, adjusting animal information inputs or generate values, such as an animal's feed formulation, to achieve specified targets based on the difference between model results and empirical testing feedback. Further, simulator 300 may be configured to adjust how models are generated based on the data obtained through the empirical testing to increase the accuracy of future models.)
- wherein the anomaly indicates a deviation from a predetermined health threshold for a particular species or breed stored in a database in wireless communication with the at least one processor;(Cook [0084] Empirical testing may further include monitoring the animals within the animal production system. For example, an animal may be monitored for metabolic indicators. Metabolic indicators may be indicative of metabolic problems such as milk fever, ketosis, imbalances in dietary protein, overheating, etc. Other monitored characteristics may include characteristics that must be tested within a laboratory such as non-esterified fatty acids (NEFA), beta hydroxyl butyrate (BHBA), urine pH, milk urea nitrogen (MUN), blood urea nitrogen (BUN), body temperature, blood AA, manure characteristics, carbon dioxide levels, minerals, fat pad probes for pesticide residue testing, etc... Other physiological measurements may include microbial profile or but histological measurements. [0096] Animal requirements generated by requirements engine 310 may include a listing of nutrient requirements for a specific animal or group of animals. Animal requirements may be a description of the overall diet to be fed to the animal or group of animals. Animal requirements further may be defined in terms of a set of nutritional parameters ("nutrients"). Nutrients and/or nutritional parameters may include those terms commonly referred to as nutrients as well as groups of ingredients, microbial measurements, indices of health, relationships between multiple ingredients, etc. Further, the set of animal requirements may include constraints or limits on the amount of any particular nutrient, combination of nutrients, and/or specific ingredients... The constraints may be minimums or maximums and may be placed on the animal requirement as a whole, any single ingredient, or any combination ingredients. [0097] Additionally, animal requirements may be generated that define ranges of acceptable nutrient levels. [0100] Table 2 below includes an exemplary listing of nutrients that may be included in the animal requirements. According to an exemplary embodiment, within the animal requirements, each listed nutrient may be associated with a value, percentage, range, or other measure of amount. The listing of nutrients may be customized to include more, fewer, or different nutrients based on any of a variety of factors, such as animal type, animal health, nutrient availability, etc. [0030] Information associated with a specific animal or a group or type of animals may include, but is not limited to, a species, ) Cook’s indicators of “metabolic problems” is an example of an anomaly, such as imbalances in dietary protein, overheating, etc. Since [0096-0100] provides examples of predetermined health thresholds (acceptable nutrient levels, constraints/limits), it is clear that an imbalance in dietary protein, or “overheating” falls within the scope of “a deviation from a predetermined health threshold.” Since these nutritional ranges are specific requirements for a specific “animal” or “animal type”, which includes a “species” based on [0030].
- the tailored feedstock recipe adapted to cure the at least one detected anomaly through a change in at least one protein, vitamin, mineral, or caloric intake;(Cook [0009] What is needed is a system and method for maximizing nutritional criteria satisfaction in view of nutrient modification and nutrient utilization factors. Further, there is a need for a such a system and method configured to create a customized animal feed formulated to satisfy a requirement in view of the nutrient modification and nutrient utilization factors. [0102] The requirements engine 310 may further be configured to generate the animal requirements based on one or more dynamic nutrient utilization models. Dynamic nutrient utilization may include a model of the amount of nutrients ingested by an animal feed that are utilized by an animal based on information received in the animal information inputs... Nutrient utilization may further depend on the presence or absence of other nutrient additives, microbes and/or enzymes,... animal production or life stage, previous nutrition level, etc. [0103] Simulator 300 may be configured to account for these effects. For example, simulator 300 may be configured to adjust the level of a particular nutrient, defined in an animal feed formulation input, from the level determined based on the animal requirement to a different level based on the presence or absence of another particular nutrient. [0104] Accordingly, an animal feed formulation input may be modified based on the nutrient utilization model. However, this change in the animal feed formulation may have an effect on the animal feed formulation, including the animal feed formulation that was just modified. Accordingly, compensating for a nutrient utilization model may require an iterative calculation, constantly updating values, to arrival at a final value that is within a predefined tolerance. [0127] Table 3 below includes an exemplary list of ingredients which may be used in generating the animal feed formulation. The listing of ingredients may include more, fewer, or different ingredients depending on a variety of factors, such as ingredient availability, entry price, animal type, etc.... Soy Protein Concentrate... Vitamin A Vitamin B Complex Vitamin B12 Vitamin D3 Vitamin E) As seen above, an optimized feed formulation is generated specifically to address the requirements (including anomalies) by changing the ingredients, which include various proteins, vitamins, and minerals. Therefore, Cook satisfies the limitations above.
-the tailored feedstock recipe formulated based on nutritional parameters stored in the database for the particular species or breed.(Cook [0059] User interface 210 may be customized based upon the animal information inputs and database information. For example, where a user defines a specific species of animal, enterprise supervisor 200 may be configured to customize user interface 210 such that only input fields that are relevant to that specific species of animal are displayed. Further, enterprise supervisor 200 may be configured to automatically populate some of the input fields with information retrieved from a database. The information may include internal information, such as stored population information for the particular user, or external information, such as current market prices that are relevant for the particular species as described above. [0100] Table 2 below includes an exemplary listing of nutrients that may be included in the animal requirements. According to an exemplary embodiment, within the animal requirements, each listed nutrient may be associated with a value, percentage, range, or other measure of amount. The listing of nutrients may be customized to include more, fewer, or different nutrients based on any of a variety of factors, such as animal type, animal health, nutrient availability, etc.)
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date to further modify Madhusudan by adding the teachings of Cook which specify particular health thresholds for a species or breed, and wherein the plan addresses anomalies in the thresholds through a change in proteins, vitamins, or minerals content. One of ordinary skill in the art would have been motivated to perform this combination by the benefit of Cook’s system optimizing for the desired outcome, which leads to maximal quality or amount of product produced by an animal. (Cook [0069] Advantageously, system 100 may optimize across all variable animal information inputs to generate recommendations for producing the output having specified target characteristics at the lowest cost. The recommendation may include a single optimal recommendation or a plurality of recommendations yielding equivalent benefits. [0004] A producer (i.e. a farmer, rancher, aquaculture specialist, etc.) generally benefits from maximizing the amount or quality of the product produced by an animal (e.g. gallons of milk, pounds of meat, quality of meat, amount of eggs, nutritional content of eggs produced, amount of work, hair/coat appearance/health status, etc.) while reducing the cost for the inputs associated with that production.)
Claims 1-3, and 6-13, 17, 19 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Madhusudan et al. (US 20180206448 A1), in view of Case et al. (US 20060085272 A1) hereinafter Case, further in view of Cook (US 20110010154 A1)
Regarding Claim 1:
Madhusudan discloses a system and method for animal feed management for a dairy farm with servers connected to health monitoring sensors to monitor the activities of a plurality of animals and determine a feed composition for each of the plurality based on the monitored activities. Madhusudan teaches:
- A computer-implemented method of managing livestock health in real-time, (Madhusudan [0016] The system may be further configured to update the determined feed composition based on health information of the plurality of dairy animals received from the plurality of sensing devices associated with the plurality of dairy animals. [0101] Various embodiments of the disclosure may provide a non-transitory, computer readable medium and/or storage medium, and/or a non-transitory machine readable medium and/or storage medium stored thereon, a machine code and/or a set of instructions executable by a machine and/or a computer for animal feed management.)
- the method comprising obtaining, by at least one processor, real-time livestock sensor data from at least one sensor located on or around one or more livestock, (Madhusudan [0021] The sensing tag 102a may be further configured to store identification information of a dairy animal... configured to determine health information of the first dairy animal 106a... to transmit the determined health information of the first dairy animal 106a to the feed-management server 112. The sensing tag 102a may include the image-capture device 104. The sensing tag 102a may comprise a plurality of sensors, such as a radio frequency identification (RFID) sensor, a health monitoring sensor, a proximity sensor, an infra-red (IR) sensor, or a combination thereof, which may enable the sensing tag 102a to determine the health information of the first dairy animal 106a. The sensing tag 102a may be worn by an individual dairy animal to track activities of the individual dairy animal. [0023] Examples of the image-capture device 104 may include, but are not limited to, at least a camera, a camcorder, and an action cam. The image-capture device 104 may be implemented as an integrated unit of the sensing tag 102a or a separate device. For example, the image-capture device 104 may be positioned at various body portions, such as strapped around the neck portion of a dairy animal, or along the legs or lower portion of stomach, of a dairy animal, such as the first dairy animal 106a... The image-capture device 104 may be positioned at other body portions of the dairy animal to focus at surrounding areas around the dairy animal and/or an udder portion of the dairy animal. [0103] The disclosed system, such as the feed-management server 112, comprises one or more circuits, such as the processor 202 and the feed controller 206. The one or more circuits in the feed-management server 112 monitors the real time activities of the plurality of entities in a dairy farm by the plurality of sensing devices 102.) Madhusudan [0021] teaches a sensing tag located directly on the livestock, and [0023] teaches examples of sensors located around the livestock. [0021] teaches the health information being transmitted to the feed management server, satisfying the “obtaining...” limitation.
- wherein the at least one sensor is coupled to a custom electronic board configured to filter a data signal from the sensor(Madhusudan [0020] The plurality of sensing devices 102 may comprise suitable logic, circuitry, interfaces, and/or code that may be configured to track activities of a plurality of entities in a defined area of a dairy farm. The plurality of entities may include a plurality of dairy animals (such as the first dairy animal 106a) and a plurality of care-takers (such as the first care-taker 108a). The plurality of sensing devices 102 may be configured to transmit information pertaining to the tracked activities of the plurality of entities to the electronic device 110 and/or the feed-management server 112. The plurality of sensing devices 102 may comprise a plurality of sensing tags (such as the sensing tag 102a) and a plurality of wearable devices (such as the wearable device 102b). [0063] The network interface 310 may include, but is not limited to, an antenna, a radio frequency (RF) transceiver, one or more amplifiers, a tuner, one or more oscillators, a digital signal processor, a coder-decoder (CODEC) chipset, a subscriber identity module (SIM) card, and/or a local buffer. ) Amplifiers, tuners, one or more oscillators, and digital signal processors all fall within the scope of “configured to filter data signal from the sensor.”
- and to transmit the sensor data via a gateway in wireless communication with the at least one processor(Madhusudan [0063] The network interface 310 may communicate via wireless communication with networks, such as the Internet, an Intranet and/or a wireless network, such as a cellular telephone network, a wireless local area network (LAN) and/or a metropolitan area network (MAN). The wireless communication may use any of a plurality of communication standards, protocols and technologies, such as Global System for Mobile Communications (GSM), Enhanced Data GSM Environment (EDGE), wideband code division multiple access (W-CDMA), Long Term Evolution (LTE), code division multiple access (CDMA), time division multiple access (TDMA), Bluetooth, Wireless Fidelity (Wi-Fi) (such as IEEE 802.11a, IEEE 802.11b, IEEE 802.11g and/or IEEE 802.11n), voice over Internet Protocol (VoIP), light fidelity (Li-Fi), Wi-MAX, a protocol for email, instant messaging, and/or Short Message Service (SMS).)
- the data comprising at least one of livestock weight, livestock activity level, livestock ammonia level, body temperature, body weight, water intake, or body pH; (Madhusudan [0031] For example, the first dairy animal 106a may graze grass in a grazing area, which is referred to as a free-style grazing activity. The image-capture device 104 of the sensing tag 102a may capture one or more digital images and/or videos during the free-style grazing activity by the first dairy animal 106a. [0040] In accordance with an embodiment, the sensing tag 102a may be further configured to determine health information of the first dairy animal 106a. The health information may include one or more health parameters of the first dairy animal 106a. Examples of the one or more health parameters, may include, but are not limited to, blood count, body temperature, respiration rate, heart beat rate, and/or a combination thereof.) Since Madhusudan teaches “livestock activity level,” in the form of capturing the free-style grazing activity of the animal, and also teaches “body temperature” the limitation above has been satisfied since only at least one of the list is required.
- analyzing, by the at least one processor the real-time livestock sensor data to detect at least one anomaly in the livestock weight, livestock activity level, livestock ammonia level, body temperature, body weight, water intake, or body pH; and (Madhusudan [0040] The sensing tag 102a may further transmit the statistics of health information of the first dairy animal 106a to the feed-management server 112. The feed-management server 112 in return may update the determined feed composition based on an anomaly that is detected in the health information of the first dairy animal 106a. The feed-management server 112 may add one or more medicinal ingredients in the determined feed composition to treat the detected anomaly. For example, the feed-management server 112 may detect an increment in body temperature of the first dairy animal 106a, based on the health information received from the sensing tag 102a.) Since Madhusudan detects anomalies in the data, which Madhusudan has been shown to teach including “livestock activity level” and “body temperature,” then the limitation above has been satisfied since it only requires at least one of the items in the list.
- generating, by the at least one processor, a customized livestock health management plan in response to the detected anomaly, (Madhusudan [0040] The feed-management server 112 may add a medicinal ingredient (such as an antibiotic) required to treat the increased body temperature. In accordance with an embodiment, the feed-management server 112 may be further configured to transmit the health information of the first dairy animal 106a to a computing device (not shown) of a health analyst, in real time. The health analyst may further prescribe one or more medicinal ingredients to be added to the determined feed composition of the first dairy animal 106a by use of the computing device. The feed-management server 112 may further add the one or more medicinal ingredients prescribed by the health analyst. In accordance with an embodiment, the feed-management server 112 may further update the feed composition based on one or more external parameters, such as temperature conditions, weather conditions, or one or more guidelines by various health agencies. [0092] At 510, a check may be performed to detect any health anomaly in each of the plurality of dairy animals (such as the first dairy animal 106a) and/or a change in the monitored activities. Based on any health anomaly that is detected in the first dairy animal and/or a change that is detected in the monitored activities, control passes to 512 else control passes to 514. At 512, the determined feed composition of the first dairy may be updated by the feed controller 206. An example is described in FIG. 4A, where the feed controller 206 may update the determined feed composition of the first dairy animal 106a based on the detection of the health anomaly in the first dairy animal 106a.) The broadest reasonable interpretation (BRI) of a customized livestock health management plan, in view of the specification, includes customized feed recipes to treat the anomalies.
However, Madhusudan fails to teach:
-that the analyzing is done by the at least one processor using at least one of a statistical model or a machine learning model,
- wherein the anomaly indicates a deviation from a predetermined health threshold for a particular species or breed stored in a database in wireless communication with the at least one processor;
- the plan including a customized feedstock recipe tailored to address the anomaly through a change in at least one of protein, vitamin, mineral or caloric intake;
-the customized feedstock recipe formulated based on nutritional parameters stored in the database for the particular species or breed.
- transmitting the customized livestock health management plan wirelessly to a livestock owner interface configured to receive and display the plan;
- receiving, from the livestock owner interface, a confirmation of acceptance of the customized livestock health management plan; and
- upon receiving the confirmation, automatically transmitting a wireless signal to livestock feed manufacturing equipment via a livestock feed producer interface, the wireless signal including instructions to manufacture the customized feedstock recipe for the one or more livestock, thereby initiating automated production of the customized feedstock.
Alternatively, Case teaches:
- transmitting the customized livestock health management plan wirelessly to a livestock owner interface configured to receive and display the plan; (Case [0010] In preferred embodiments, the system can identify and provide to the user a group of suggested nutrients for each of several possible intended uses; after the user indicates a particular intended use for the nutritional blend formulation, the system presents to the user through the graphical user interface a list of the suggested nutrients for that intended use... the system contains means for notifying the user through the graphical user interface if the desired nutritional activity and/or concentration for one or more of the nutrients are below the suggested minimum or above the suggested maximum concentration or activity for one or more of the nutrients for the intended use; the system contains means to communicate to the user at least a portion of the hierarchy of the available nutritional formulation materials for one or more of the nutrients in the nutritional blend formulation; [0014] Thus, a user may be an individual who on behalf of a company for which he or she works enters specifications on his or her home or office computer or input/output device for a nutritional blend formulation the company ... uses internally in its own operations (e.g., animal husbandry).[0031] Using a communications network, preferably the Internet, a connection between system 15 and a local node 12 of a user is established, preferably over the “World Wide Web” or other computer network, which network is preferably but not necessarily global and which network may or may not be accessible by the public at large (for convenience the term “web” is used herein to refer to a communications network.) The suggest nutrients for the particular nutritional blend formulation falls within the scope of “livestock health management plan.” Since this is presented to the user, which is the owner of the livestock, the limitation is satisfied.
- receiving, from the livestock owner interface, a confirmation of acceptance of the customized livestock health management plan; and(Case [0102] At the bottom (FIG. 7t), the screen provides space for user (customer) notes, which were previously entered by the user (FIG. 7i), as well as space for notes to be added by the formulator. [0103] When the formulator is satisfied with all features of the proposed formulation (including, for example, the estimated price, the particle size distribution, and the nutritional formulation materials to be used), the formulator clicks on the "Approve" icon. That sends the formulation information to a second approval level (e.g., approval by a supervisor). Once the vendor is satisfied with the formulation (i.e., after it has been approved at the second approval level), the status of the formulation is changed to indicate that it has been so approved and the user is so notified (e.g., by email). The user then re-enters the system (e.g., through the graphical user interface and via the Internet), retrieves the formulation information (as modified and approved by vendor), reviews the formulation product information (including estimated price, final specifications, etc.), and makes any further changes the user wishes to make. For example, the formulator may have added a nutrient that the user does not wish to have present in the premix. In that case, the user would communicate with the formulator and explain the basis for the exclusion of that nutrient and resubmit revised specifications omitting that nutrient. [0014] Thus, a user may be an individual who on behalf of a company for which he or she works enters specifications on his or her home or office computer or input/output device for a nutritional blend formulation the company is considering adding to a food product it manufactures for resale (e.g., livestock feed, breakfast cereal for humans) or uses internally in its own operations (e.g., animal husbandry).) In Case, the “customer/user” interface is the “livestock owner interface,” given that a user can refer to a user involved in animal husbandry.
- upon receiving the confirmation, automatically transmitting a wireless signal to livestock feed manufacturing equipment via a livestock feed producer interface, the wireless signal including instructions to manufacture the customized feedstock recipe for the one or more livestock, thereby initiating automated production of the customized feedstock (Case [0010] the system contains means to allow the user to approve the nutritional blend formulation (e.g., based on original or modified specifications) for manufacture.. the system contains means for allowing review of the nutritional blend formulation after it has been approved for manufacture; and/or the system contains means to transmit the nutritional blend formulation and/or its corresponding formulation product information to another system (e.g., a manufacturing control system or an enterprise system) to allow the nutritional blend formulation to be manufactured.; [0079] The user may choose to make modifications because of curiosity as to the effect that one or more modifications would have on the formulation product information (e.g., how would reducing the activity of a nutrient affect the cost) or as a result of something in the formulation product information being unacceptable. [0081] Upon completion of the review of the on-screen information, the user may transmit the formulation product information to another system 50, for instance, the manufacturer's internal computer system, linked on line to system 15. Such transmission to system 50 may be made by any suitable means, for example, on line through the graphical user interface 14 and the reporting module 30. As indicated above, the transmission may be made for any of a number of reasons (e.g., to submit the proposed nutritional blend formulation for review and/or approval by the manufacturer or vendor or by any other entity, e.g., consultants or agents). System 50 may be a simple transmittal system for conveying the information (e.g., to a formulator or a consultant for review and/or approval), or system 50 may be a complex enterprise system that oversees the production systems (e.g., manufacturing plants, raw material ordering systems) and the inventory and billing systems. [0054] After the user makes selections at the hierarchical levels above the manufacturer and/or lot level(s), the system could automatically select a manufacturer or vendor (if there were more than one) and a lot based on, for example, the age of the lot, whether the amount of the lot remaining on hand was sufficient to meet the batch size specification entered by the user, particle size distribution, or on other manufacturing considerations.) The broadest reasonable interpretation of the claims allows for any transmission of a signal to livestock feed manufacturing equipment (which is broad enough to include the “manufacturer’s internal computing system,” or “manufacturing control system). The “automated production of the customized feedstock” is recited broadly enough such that if any step in the process of production is automated, then the limitation is satisfied. Therefore, the automatic selection of a manufacturer or vendor, is automatic production of the feedstock.
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date to modify Madhusudan to add interface features as taught by Case such as transmitting the health plan to receive confirmation or denial of the plan by the owner, transmitting a signal to livestock feed manufacturing equipment, and initiating automated production of the feedstock. By combining these two inventions one would at arrive at the predictable outcome of transmitting the plan to the livestock owner interface and confirming or denying acceptance, because it would merely be a simple substitution wherein Madhusudan’s recipe is transmitted in Case’s system. By implementing Case’s system of automatically transmitting signals between the manufacturers, formulators, and all parties, one would have arrived at the predictable outcome of “transmitting a wireless signal to livestock feed manufacturing equipment via a livestock feed producer interface.” One of ordinary skill in the art would have been motivated to perform the combination due to the benefit of providing up-to-date, accurate and clearly presented information between customer’s and manufacturer’s to improve the business relations. (Case [0006])
However, neither Madhusudan nor Case teach or suggest:
-that the analyzing is done by the at least one processor using at least one of a statistical model or a machine learning model,
- wherein the anomaly indicates a deviation from a predetermined health threshold for a particular species or breed stored in a database in wireless communication with the at least one processor;
- the plan including a customized feedstock recipe tailored to address the anomaly through a change in at least one of protein, vitamin, mineral or caloric intake;
-the customized feedstock recipe formulated based on nutritional parameters stored in the database for the particular species or breed.
- upon receiving the confirmation, automatically transmitting a wireless signal to livestock feed manufacturing equipment via a livestock feed producer interface, the wireless signal including instructions to manufacture the customized feedstock recipe for the one or more livestock.
-thereby initiating automated production of the customized feedstock.
Alternatively, Cook teaches:
-that the analyzing is done by the at least one processor using at least one of a statistical model or a machine learning model,(Cook [0072] Enterprise supervisor 200 may be configured to implement a Monte Carlo method where a specific set of values is drawn from a set of distributions of model parameters to solve for optimized values for the variable inputs. This process may be repeated many times, creating a distribution of optimized solutions. Based on the type of optimization, enterprise supervisor 200 maybe used to select either the value most likely to provide the optimal solution or the value that gives confidence that is sufficient to meet a target. For example, a simple optimization might be selected which provides a net energy level that maximizes the average daily gain for a particular animal. [0085] Empirical testing provides the advantage of verifying the accuracy of predictive models generated by simulator 300. Optimization results generated from imperfect models may different from real world results obtained through empirical testing. System 100 may be configured to provide dynamic control based on the empirical testing feedback, adjusting animal information inputs or generate values, such as an animal's feed formulation, to achieve specified targets based on the difference between model results and empirical testing feedback. Further, simulator 300 may be configured to adjust how models are generated based on the data obtained through the empirical testing to increase the accuracy of future models.)
- wherein the anomaly indicates a deviation from a predetermined health threshold for a particular species or breed stored in a database in wireless communication with the at least one processor;(Cook [0051] Designation of a variable input may require submission of additional information, such as a cost and/or benefit of variation of the variable input, recommended degrees of variation for optimization testing, etc. Alternatively, the additional information may be stored and retrievable from within system 100 or an associated database. [0084] Empirical testing may further include monitoring the animals within the animal production system. For example, an animal may be monitored for metabolic indicators. Metabolic indicators may be indicative of metabolic problems such as milk fever, ketosis, imbalances in dietary protein, overheating, etc. Other monitored characteristics may include characteristics that must be tested within a laboratory such as non-esterified fatty acids (NEFA), beta hydroxyl butyrate (BHBA), urine pH, milk urea nitrogen (MUN), blood urea nitrogen (BUN), body temperature, blood AA, manure characteristics, carbon dioxide levels, minerals, fat pad probes for pesticide residue testing, etc... Other physiological measurements may include microbial profile or but histological measurements. [0096] Animal requirements generated by requirements engine 310 may include a listing of nutrient requirements for a specific animal or group of animals. Animal requirements may be a description of the overall diet to be fed to the animal or group of animals. Animal requirements further may be defined in terms of a set of nutritional parameters ("nutrients"). Nutrients and/or nutritional parameters may include those terms commonly referred to as nutrients as well as groups of ingredients, microbial measurements, indices of health, relationships between multiple ingredients, etc. Further, the set of animal requirements may include constraints or limits on the amount of any particular nutrient, combination of nutrients, and/or specific ingredients... The constraints may be minimums or maximums and may be placed on the animal requirement as a whole, any single ingredient, or any combination ingredients. [0097] Additionally, animal requirements may be generated that define ranges of acceptable nutrient levels. [0100] Table 2 below includes an exemplary listing of nutrients that may be included in the animal requirements. According to an exemplary embodiment, within the animal requirements, each listed nutrient may be associated with a value, percentage, range, or other measure of amount. The listing of nutrients may be customized to include more, fewer, or different nutrients based on any of a variety of factors, such as animal type, animal health, nutrient availability, etc. [0030] Information associated with a specific animal or a group or type of animals may include, but is not limited to, a species, ) Cook’s indicators of “metabolic problems” is an example of an anomaly, such as imbalances in dietary protein, overheating, etc. Since [0096-0100] provides examples of predetermined health thresholds (acceptable nutrient levels, constraints/limits), it is clear that an imbalance in dietary protein, or “overheating” falls within the scope of “a deviation from a predetermined health threshold.” Since these nutritional ranges are specific requirements for a specific “animal” or “animal type”, which includes a “species” based on [0030].
- the plan including a customized feedstock recipe tailored to address the anomaly through a change in at least one of protein, vitamin, mineral or caloric intake;(Cook [0009] What is needed is a system and method for maximizing nutritional criteria satisfaction in view of nutrient modification and nutrient utilization factors. Further, there is a need for a such a system and method configured to create a customized animal feed formulated to satisfy a requirement in view of the nutrient modification and nutrient utilization factors. [0102] The requirements engine 310 may further be configured to generate the animal requirements based on one or more dynamic nutrient utilization models. Dynamic nutrient utilization may include a model of the amount of nutrients ingested by an animal feed that are utilized by an animal based on information received in the animal information inputs... Nutrient utilization may further depend on the presence or absence of other nutrient additives, microbes and/or enzymes,... animal production or life stage, previous nutrition level, etc. [0103] Simulator 300 may be configured to account for these effects. For example, simulator 300 may be configured to adjust the level of a particular nutrient, defined in an animal feed formulation input, from the level determined based on the animal requirement to a different level based on the presence or absence of another particular nutrient. [0104] Accordingly, an animal feed formulation input may be modified based on the nutrient utilization model. However, this change in the animal feed formulation may have an effect on the animal feed formulation, including the animal feed formulation that was just modified. Accordingly, compensating for a nutrient utilization model may require an iterative calculation, constantly updating values, to arrival at a final value that is within a predefined tolerance. [0127] Table 3 below includes an exemplary list of ingredients which may be used in generating the animal feed formulation. The listing of ingredients may include more, fewer, or different ingredients depending on a variety of factors, such as ingredient availability, entry price, animal type, etc.... Soy Protein Concentrate... Vitamin A Vitamin B Complex Vitamin B12 Vitamin D3 Vitamin E) As seen above, an optimized feed formulation is generated specifically to address the requirements (including anomalies) by changing the ingredients, which include various proteins, vitamins, and minerals. Therefore, Cook satisfies the limitations above.
-the customized feedstock recipe formulated based on nutritional parameters stored in the database for the particular species or breed.(Cook [0059] User interface 210 may be customized based upon the animal information inputs and database information. For example, where a user defines a specific species of animal, enterprise supervisor 200 may be configured to customize user interface 210 such that only input fields that are relevant to that specific species of animal are displayed. Further, enterprise supervisor 200 may be configured to automatically populate some of the input fields with information retrieved from a database. The information may include internal information, such as stored population information for the particular user, or external information, such as current market prices that are relevant for the particular species as described above. [0100] Table 2 below includes an exemplary listing of nutrients that may be included in the animal requirements. According to an exemplary embodiment, within the animal requirements, each listed nutrient may be associated with a value, percentage, range, or other measure of amount. The listing of nutrients may be customized to include more, fewer, or different nutrients based on any of a variety of factors, such as animal type, animal health, nutrient availability, etc.)
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date to further modify the combination of Madhusudan and Case by adding the teachings of Cook which specify particular health thresholds for a species or breed, and wherein the plan addresses anomalies in the thresholds through a change in proteins, vitamins, or minerals content. One of ordinary skill in the art would have been motivated to perform this combination by the benefit of Cook’s system optimizing for the desired outcome, which leads to maximal quality or amount of product produced by an animal. (Cook [0069] and [0004].)
Regarding Claim 2:
The combination of Madhusudan, Case, and Cook teach or suggest: The method of claim 1,
Furthermore, Madhusudan teaches:
-wherein the livestock health management plan includes a customized feedstock recipe adapted to cure the at least one detected anomaly upon being fed to the livestock. (Madhusudan [0040] The feed-management server 112 may add a medicinal ingredient (such as an antibiotic) required to treat the increased body temperature. In accordance with an embodiment, the feed-management server 112 may be further configured to transmit the health information of the first dairy animal 106a to a computing device (not shown) of a health analyst, in real time. The health analyst may further prescribe one or more medicinal ingredients to be added to the determined feed composition of the first dairy animal 106a by use of the computing device. The feed-management server 112 may further add the one or more medicinal ingredients prescribed by the health analyst. In accordance with an embodiment, the feed-management server 112 may further update the feed composition based on one or more external parameters, such as temperature conditions, weather conditions, or one or more guidelines by various health agencies.)
Regarding Claim 3:
The combination of Madhusudan, Case, and Cook teach or suggest: The method of claim 2,
Furthermore, Madhusudan teaches:
- wherein the customized feedstock recipe includes a change in mineral to correct the anomaly. (Madhusudan [0036] The feed composition may also depend on the amount of milk yielded by a dairy animal. For example, for a milk yield of “1 liter” from a cow, the feed composition may include “500 g” of balanced cattle feed, “100 g” of mineral mixture, and “10 liters” of water. [0092] At 510, a check may be performed to detect any health anomaly in each of the plurality of dairy animals (such as the first dairy animal 106a) and/or a change in the monitored activities. Based on any health anomaly that is detected in the first dairy animal and/or a change that is detected in the monitored activities, control passes to 512 else control passes to 514. At 512, the determined feed composition of the first dairy may be updated by the feed controller 206. An example is described in FIG. 4A, where the feed controller 206 may update the determined feed composition of the first dairy animal 106a based on the detection of the health anomaly in the first dairy animal 106a.) See Fig. 5, 512, which clearly shows a change in the recipe based on the anomaly. Madhusudan [0036] is included to show that the composition change includes a change in minerals specifically.
However, neither Madhusudan nor Case teach or suggest: -wherein the change is determined based on a comparison of the detected anomaly against the predetermined health threshold for the particular species or breed.
Alternatively, Cook teaches:
-wherein the change is determined based on a comparison of the detected anomaly against the predetermined health threshold for the particular species or breed. (Cook [0096] Animal requirements generated by requirements engine 310 may include a listing of nutrient requirements for a specific animal or group of animals. Animal requirements may be a description of the overall diet to be fed to the animal or group of animals. Animal requirements further may be defined in terms of a set of nutritional parameters ("nutrients"). Nutrients and/or nutritional parameters may include those terms commonly referred to as nutrients as well as groups of ingredients, microbial measurements, indices of health, relationships between multiple ingredients, etc. Further, the set of animal requirements may include constraints or limits on the amount of any particular nutrient, combination of nutrients, and/or specific ingredients... The constraints may be minimums or maximums and may be placed on the animal requirement as a whole, any single ingredient, or any combination ingredients. [0097] Additionally, animal requirements may be generated that define ranges of acceptable nutrient levels. [0100] Table 2 below includes an exemplary listing of nutrients that may be included in the animal requirements. According to an exemplary embodiment, within the animal requirements, each listed nutrient may be associated with a value, percentage, range, or other measure of amount. The listing of nutrients may be customized to include more, fewer, or different nutrients based on any of a variety of factors, such as animal type, animal health, nutrient availability, etc. [0030] Information associated with a specific animal or a group or type of animals may include, but is not limited to, a species, )
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date to further modify the combination of Madhusudan and Case by adding the teachings of Cook which specify particular health thresholds for a species or breed, and wherein the plan addresses anomalies in the thresholds through a change in proteins, vitamins, or minerals content. One of ordinary skill in the art would have been motivated to perform this combination by the benefit of Cook’s system optimizing for the desired outcome, which leads to maximal quality or amount of product produced by an animal. (Cook [0069] and [0004].)
Regarding Claim 6:
The combination of Madhusudan, Case, and Cook teach or suggest: The method of claim 1,
Furthermore, Madhusudan teaches:
However, Madhusudan fails to teach or suggest:
- wherein the livestock owner interface includes a livestock owner portal.
Alternatively, Case teaches:
- wherein the livestock owner interface includes a livestock owner portal. (Case [0034] FIG. 2 illustrates the functionalities encompassed by graphical user interface 14. Graphical user interface 14 encompasses web pages 3, which are accessible to the user on local node 12 using, for instance, a local web browser program, as well as other supporting programs, tools, and software needed to enable communication and interaction between the user and system 15 through local node 12. Thus, graphical user interface 14 is the point of interactivity between local node 12 and the rest of system 15 and its various other components and functionalities, allowing a user to enter, modify, and receive information. Preferably, graphical user interface 14 provides web pages 3 in appropriate graphical formats for the user’s entry and receipt of information, which formats may include one or more templates, pull-down menus, selection fields, input fields, text area fields, dialog boxes, and/or other modes and structures to receive data from or display data to a user.) The BRI of “livestock owner portal” is any web-accessible interface that allows for the user to interact with the platform. Fig. 7 of Case is an example of a portal.
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date to modify Madhusudan to add Case’s livestock owner portal. One of ordinary skill in the art would have been motivated to perform the combination due to the benefit of providing up-to-date, accurate and clearly presented information between customer’s and manufacturer’s to improve the business relations. (Case [0006])
Regarding Claim 7:
The combination of Madhusudan, Case, and Cook teach or suggest: The method of claim 6,
Furthermore, Madhusudan teaches:
-wherein the livestock owner interface is a laptop or smart device. (Madhusudan [0026] Examples of the electronic device 110 may include, but are not limited to, a smartphone, a tablet computer, a computing device, a server, a computer work-station, a mainframe machine, and/or other electronic devices.)
Regarding Claim 8:
The combination of Madhusudan, Case, and Cook teach or suggest: The method of claim 1,
Furthermore, Madhusudan teaches:
-wherein the livestock sensor data is obtained from a plurality of sensors located on a plurality of livestock and (Madhusudan [0021]The sensing tag 102a may be further configured to store identification information of a dairy animal... configured to determine health information of the first dairy animal 106a... to transmit the determined health information of the first dairy animal 106a to the feed-management server 112. The sensing tag 102a may include the image-capture device 104. The sensing tag 102a may comprise a plurality of sensors, such as a radio frequency identification (RFID) sensor, a health monitoring sensor, a proximity sensor, an infra-red (IR) sensor, or a combination thereof, which may enable the sensing tag 102a to determine the health information of the first dairy animal 106a. The sensing tag 102a may be worn by an individual dairy animal to track activities of the individual dairy animal.)
-the customized feedstock recipe may be fed to the plurality of livestock. (Madhusudan [0073] The feed controller 206 may further determine the feed composition for the first dairy animal 106a based on the amount of milk yielded by the first dairy animal 106a and the monitored activities (such as the free style grazing) of the first dairy animal 106a. The feed controller 206 may further determine an amount of feed of the determined feed composition to be fed to the first dairy animal 106a. The feed controller 206 may be further configured to control the loading device 116 that may load a feed container or dispenser (such as the feed container 120), associated with the first dairy animal 106a, with the determined feed composition. The feed controller 206 may load the feed container 120 with the determined feed composition for the first dairy animal 106a to manage consumption of the determined feed composition by the first dairy animal 106a. An example to manage consumption of the determined feed composition by a specific dairy animal is described in FIG. 4C.)
Regarding Claim 9:
The combination of Madhusudan, Case, and Cook teach or suggest: The method of claim 1,
Furthermore, Madhusudan teaches:
-wherein the livestock sensor data is obtained from at least one sensor on or around an individual livestock animal and (Madhusudan [0023] Examples of the image-capture device 104 may include, but are not limited to, at least a camera, a camcorder, and an action cam. The image-capture device 104 may be implemented as an integrated unit of the sensing tag 102a or a separate device. For example, the image-capture device 104 may be positioned at various body portions, such as strapped around the neck portion of a dairy animal, or along the legs or lower portion of stomach, of a dairy animal, such as the first dairy animal 106a... The image-capture device 104 may be positioned at other body portions of the dairy animal to focus at surrounding areas around the dairy animal and/or an udder portion of the dairy animal.)
- the customized feedstock recipe may be fed to the individual livestock animal. (Madhusudan [0073] The feed controller 206 may further determine the feed composition for the first dairy animal 106a based on the amount of milk yielded by the first dairy animal 106a and the monitored activities (such as the free style grazing) of the first dairy animal 106a. The feed controller 206 may further determine an amount of feed of the determined feed composition to be fed to the first dairy animal 106a. The feed controller 206 may be further configured to control the loading device 116 that may load a feed container or dispenser (such as the feed container 120), associated with the first dairy animal 106a, with the determined feed composition. The feed controller 206 may load the feed container 120 with the determined feed composition for the first dairy animal 106a to manage consumption of the determined feed composition by the first dairy animal 106a. An example to manage consumption of the determined feed composition by a specific dairy animal is described in FIG. 4C.)
Regarding Claim 10:
The combination of Madhusudan, Case, and Cook teach or suggest: The method of claim 9,
Furthermore, Madhusudan teaches:
-wherein the at least one livestock sensor transmits a particular code associated with an individual livestock animal. (Madhusudan [0021] The sensing tag 102a may comprise suitable logic, circuitry, interfaces, and/or code that may be configured to track the activities of a dairy animal (such as the first dairy animal 106a). The sensing tag 102a may be further configured to store identification information of a dairy animal (such as the first dairy animal 106a) of the plurality of dairy animals... such as a radio frequency identification (RFID) sensor, [0045] The electronic device 110 may further identify the dairy animal, such as the first dairy animal 106a, associated with the identified sensing tag 102a.) The BRI of “particular code” is any form of signals identifying the individual livestock animal, which is satisfied by the use of code to transmit identification information from an RFID as taught by Madhusudan.
Regarding Claim 11:
The combination of Madhusudan, Case, and Cook teach or suggest: The method of claim 1,
Furthermore, Madhusudan teaches:
-wherein the livestock health management plan further includes at least one livestock medical diagnosis and, optionally, at least one prescribed medicament. (Madhusudan [0040] The feed-management server 112 may add one or more medicinal ingredients in the determined feed composition to treat the detected anomaly. For example, the feed-management server 112 may detect an increment in body temperature of the first dairy animal 106a, based on the health information received from the sensing tag 102a. The feed-management server 112 may add a medicinal ingredient (such as an antibiotic) required to treat the increased body temperature. In accordance with an embodiment, the feed-management server 112 may be further configured to transmit the health information of the first dairy animal 106a to a computing device (not shown) of a health analyst, in real time. The health analyst may further prescribe one or more medicinal ingredients to be added to the determined feed composition of the first dairy animal 106a by use of the computing device. The feed-management server 112 may further add the one or more medicinal ingredients prescribed by the health analyst. In accordance with an embodiment, the feed-management server 112 may further update the feed composition based on one or more external parameters, such as temperature conditions, weather conditions, or one or more guidelines by various health agencies.)
Regarding Claim 12:
A system for livestock health management comprising:
- at least one server; (Madhusudan [0027] The feed-management server 112 may comprise suitable logic, circuitry, interfaces, and/or code that may be configured to determine a feed composition for each of the plurality of dairy animals (such as the first dairy animal 106a)... Examples of the feed-management server 112 may include, but are not limited to, an application server, a cloud server, a web server, a database server, a file server, a mainframe server, or a combination thereof.)
- at least one livestock owner interface including a data entry system, the livestock owner interface in wireless communication with a gateway and the at least one server; (Madhusudan [0049] The training data for training the feed-management server 112 to determine the milking capacity of a care-taker (such as the first care-taker 108a) may include an amount of milk collected by the first care-taker 108a and a time duration of the hand-based milking activity performed by the first care-taker 108a to collect the amount of milk... In accordance with an embodiment, the first care-taker 108a may use a specific application installed in the electronic device 110 to manually feed the information of the amount of milk collected. [0056] The network interface 208 may comprise suitable logic, circuitry, interfaces, and/or code that may be configured to establish communication between the feed-management server 112, the plurality of sensing devices 102, and the electronic device 110, via the communication network 114. The network interface 208 may be implemented by use of various known technologies to support wired or wireless communication of the feed-management server 112 with the communication network 114.) The specific application installed in the electronic device, which is connected to the server, is an example of a livestock owner interface. Madhusudan’s network interface is an example of a gateway.
- at least one livestock sensor coupled to a custom electronic board configured to filter a data signal from the sensor(Madhusudan [0020] The plurality of sensing devices 102 may comprise suitable logic, circuitry, interfaces, and/or code that may be configured to track activities of a plurality of entities in a defined area of a dairy farm. The plurality of entities may include a plurality of dairy animals (such as the first dairy animal 106a) and a plurality of care-takers (such as the first care-taker 108a). The plurality of sensing devices 102 may be configured to transmit information pertaining to the tracked activities of the plurality of entities to the electronic device 110 and/or the feed-management server 112. The plurality of sensing devices 102 may comprise a plurality of sensing tags (such as the sensing tag 102a) and a plurality of wearable devices (such as the wearable device 102b). [0063] The network interface 310 may include, but is not limited to, an antenna, a radio frequency (RF) transceiver, one or more amplifiers, a tuner, one or more oscillators, a digital signal processor, a coder-decoder (CODEC) chipset, a subscriber identity module (SIM) card, and/or a local buffer. ) Amplifiers, tuners, one or more oscillators, and digital signal processors all fall within the scope of “configured to filter data signal from the sensor.”
- the livestock sensor coupled to a gateway that is in wireless communication with the at least one server; (Madhusudan [0021] The sensing tag 102a may be configured to transmit the determined health information of the first dairy animal 106a to the feed-management server 112. The sensing tag 102a may include the image-capture device 104. The sensing tag 102a may comprise a plurality of sensors, such as a radio frequency identification (RFID) sensor, a health monitoring sensor, a proximity sensor, an infra-red (IR) sensor, or a combination thereof, which may enable the sensing tag 102a to determine the health information of the first dairy animal 106a. The sensing tag 102a may be worn by an individual dairy animal to track activities of the individual dairy animal.)
- livestock feed manufacturing equipment; and (Madhusudan [0039] The feed-management server 112 may be further configured to control the loading device 116 that may load the feed container 120, associated with the first dairy animal 106a, with the determined feed composition. The feed-management server 112 may load the feed container 120 with the determined feed composition for the first dairy animal 106a to manage consumption of the feed composition by the first dairy animal 106a. The loading device 116 may refer to a device where the feed of the determined feed composition is prepared. The loading device 116 may comprise one or more feed mixing compartments (such as the feed mixing compartment 118). The one or more ingredients of the feed corresponding to the first dairy animal 106a are mixed in accordance with the determined feed composition in the feed mixing compartment 118 that is related to the first dairy animal 106a.) The loading device having mixing compartment’s is an example of livestock feed manufacturing equipment, which is connected to the server.
- a memory and processor in wireless communication with the server, livestock owner interface, at least one livestock sensor, database, the memory and processor configured to perform the steps of: (Madhusudan [0052] FIG. 2 is a detailed block diagram that illustrates an exemplary feed-management server for management of animal feed in a dairy farm, in accordance with an embodiment of the disclosure. FIG. 2 is explained in conjunction with elements from FIG. 1. With reference to FIG. 2, the exemplary feed-management server (such as the feed-management server 112) may comprise one or more circuits, such as a processor 202, a memory 204, a feed controller 206, and a network interface 208. The memory 204, the feed controller 206, and the network interface 208 may be communicatively connected to the processor 202. The feed-management server 112 may correspond to the animal feed management system.[0027] Examples of the feed-management server 112 may include, but are not limited to, an application server, a cloud server, a web server, a database server, a file server, a mainframe server, or a combination thereof.)
- obtaining, by the at least one processor in wireless communication with at least one livestock sensor, real-time livestock sensor data from at least one sensor located on or around one or more livestock, (Madhusudan [0021]The sensing tag 102a may be further configured to store identification information of a dairy animal... configured to determine health information of the first dairy animal 106a... to transmit the determined health information of the first dairy animal 106a to the feed-management server 112. The sensing tag 102a may include the image-capture device 104. The sensing tag 102a may comprise a plurality of sensors, such as a radio frequency identification (RFID) sensor, a health monitoring sensor, a proximity sensor, an infra-red (IR) sensor, or a combination thereof, which may enable the sensing tag 102a to determine the health information of the first dairy animal 106a. The sensing tag 102a may be worn by an individual dairy animal to track activities of the individual dairy animal. [0023] Examples of the image-capture device 104 may include, but are not limited to, at least a camera, a camcorder, and an action cam. The image-capture device 104 may be implemented as an integrated unit of the sensing tag 102a or a separate device. For example, the image-capture device 104 may be positioned at various body portions, such as strapped around the neck portion of a dairy animal, or along the legs or lower portion of stomach, of a dairy animal, such as the first dairy animal 106a... The image-capture device 104 may be positioned at other body portions of the dairy animal to focus at surrounding areas around the dairy animal and/or an udder portion of the dairy animal.) Madhusudan [0021] teaches a sensing tag located directly on the livestock, and [0023] teaches examples of sensors located around the livestock. [0021] teaches the health information being transmitted to the feed management server, satisfying the “obtaining...” limitation.
-the data comprising at least one of livestock weight, livestock activity level, livestock ammonia level, body temperature, body weight, water intake, or body pH, the sensor data transmitted via the gateway; (Madhusudan [0031] For example, the first dairy animal 106a may graze grass in a grazing area, which is referred to as a free-style grazing activity. The image-capture device 104 of the sensing tag 102a may capture one or more digital images and/or videos during the free-style grazing activity by the first dairy animal 106a. [0040] In accordance with an embodiment, the sensing tag 102a may be further configured to determine health information of the first dairy animal 106a. The health information may include one or more health parameters of the first dairy animal 106a. Examples of the one or more health parameters, may include, but are not limited to, blood count, body temperature, respiration rate, heart beat rate, and/or a combination thereof. [0067] The wearable device 406 may further transmit the information pertaining to the detected presence of the first sensing tag 410a to the processor 302, via a short range communication of the communication network 114.) Since Madhusudan teaches “livestock activity level,” in the form of capturing the free-style grazing activity of the animal, and also teaches “body temperature” the limitation above has been satisfied since only at least one of the list is required.
- analyzing, by the processor, the real-time livestock sensor data to detect at least one anomaly in the livestock weight, livestock activity level, livestock ammonia level, body temperature, body weight, water intake, or body pH, (Madhusudan [0040] The sensing tag 102a may further transmit the statistics of health information of the first dairy animal 106a to the feed-management server 112. The feed-management server 112 in return may update the determined feed composition based on an anomaly that is detected in the health information of the first dairy animal 106a. The feed-management server 112 may add one or more medicinal ingredients in the determined feed composition to treat the detected anomaly. For example, the feed-management server 112 may detect an increment in body temperature of the first dairy animal 106a, based on the health information received from the sensing tag 102a.) Since Madhusudan detects anomalies in the data, which Madhusudan has been shown to teach including “livestock activity level” and “body temperature,” then the limitation above has been satisfied since it only requires at least one of the items in the list.
-
- and generating, by processor, a customized livestock health management plan in response to the detected anomaly, (Madhusudan [0040] The feed-management server 112 may add a medicinal ingredient (such as an antibiotic) required to treat the increased body temperature. In accordance with an embodiment, the feed-management server 112 may be further configured to transmit the health information of the first dairy animal 106a to a computing device (not shown) of a health analyst, in real time. The health analyst may further prescribe one or more medicinal ingredients to be added to the determined feed composition of the first dairy animal 106a by use of the computing device. The feed-management server 112 may further add the one or more medicinal ingredients prescribed by the health analyst. In accordance with an embodiment, the feed-management server 112 may further update the feed composition based on one or more external parameters, such as temperature conditions, weather conditions, or one or more guidelines by various health agencies. [0092] At 510, a check may be performed to detect any health anomaly in each of the plurality of dairy animals (such as the first dairy animal 106a) and/or a change in the monitored activities. Based on any health anomaly that is detected in the first dairy animal and/or a change that is detected in the monitored activities, control passes to 512 else control passes to 514. At 512, the determined feed composition of the first dairy may be updated by the feed controller 206. An example is described in FIG. 4A, where the feed controller 206 may update the determined feed composition of the first dairy animal 106a based on the detection of the health anomaly in the first dairy animal 106a.)
However, Madhusudan fails to teach:
- at least one database coupled to or in wireless communication with a gateway that is in wireless communication with the at least one server, the database storing predetermined health thresholds for particular livestock species or breeds and nutritional parameters for formulating customized feedstock recipes;
-that the analyzing is done by the at least one processor using at least one of a statistical model or a machine learning model,
- wherein the anomaly indicates a deviation from a predetermined health threshold for a particular species or breed retrieved from the database;
- the plan including a customized feedstock recipe tailored to address the anomaly through a change in at least one of protein, vitamin, mineral or caloric intake;
-the customized feedstock recipe formulated based on nutritional parameters retrieved from the database for the particular species or breed;
- a livestock feed producer interface including a data entry system, the livestock manufacturer interface in wireless communication with the at least one server and livestock feed manufacturing equipment; and
-That the memory and processor is also in wireless communication with the livestock producer interface
- transmitting the customized livestock health management plan wirelessly to a livestock owner interface configured to receive and display the plan;
- receiving, from the livestock owner interface, a confirmation of acceptance of the customized livestock health management plan; and
- upon receiving the confirmation, automatically transmitting a wireless signal to the livestock feed manufacturing equipment via the livestock feed producer interface, the wireless signal including instructions to manufacture the customized feedstock recipe for the one or more livestock.
Alternatively, Case teaches:
- a livestock feed producer interface including a data entry system, the livestock manufacturer interface in wireless communication with the at least one server and livestock feed manufacturer; and (Case [0083] The system may be configured to allow the manufacturer or vendor to send back the approved and/or modified nutritional blend formulation (and other pertinent information) to the user via the graphical user interface 14 and/or the reporting module 30 for the user’s further review and approval. The system may further be configured to allow the user after such further review and approval to submit the nutritional blend formulation for a price quotation or for manufacture, for instance, through the on line link (e.g., graphical user interface 14 and reporting module 30) to manufacturer’s system 50. [0032] System 15 may be implemented through one or more servers or groupings of servers or other host computer systems having programs, modules, or other processing units accessible to a user operating local node 12. [0104] If the user accepts (i.e., places an order), the master system (e.g., enterprise system) so indicates in the appropriate systems and the nutritional blend formulation is released for manufacture, billing, etc.) The manufacturer’s system 50 is mapped to the livestock feed producer interface because it is the user interface for the manufacturer or vendor to interact with the data. Though Case does not explicitly disclose the interface being in wireless communication with the livestock feed manufacturing equipment, the memory or the processor, it would have been obvious to one of ordinary skill in the art before the effective filing date of the present disclosure to modify Madhusudan by connecting the livestock feed manufacturer interface of Case with the livestock feed manufacturing equipment of Madhusudan. One of ordinary skill would have been motivated to perform this combination as it would yield the benefit of conveniently and rapidly develop and manufacture the custom feed. (Case [0005])
- transmitting the customized livestock health management plan wirelessly to a livestock owner interface configured to receive and display the plan; (Case [0010] In preferred embodiments, the system can identify and provide to the user a group of suggested nutrients for each of several possible intended uses; after the user indicates a particular intended use for the nutritional blend formulation, the system presents to the user through the graphical user interface a list of the suggested nutrients for that intended use... the system contains means for notifying the user through the graphical user interface if the desired nutritional activity and/or concentration for one or more of the nutrients are below the suggested minimum or above the suggested maximum concentration or activity for one or more of the nutrients for the intended use; the system contains means to communicate to the user at least a portion of the hierarchy of the available nutritional formulation materials for one or more of the nutrients in the nutritional blend formulation; [0014] Thus, a user may be an individual who on behalf of a company for which he or she works enters specifications on his or her home or office computer or input/output device for a nutritional blend formulation the company ... uses internally in its own operations (e.g., animal husbandry).[0031] Using a communications network, preferably the Internet, a connection between system 15 and a local node 12 of a user is established, preferably over the “World Wide Web” or other computer network, which network is preferably but not necessarily global and which network may or may not be accessible by the public at large (for convenience the term “web” is used herein to refer to a communications network.) The suggest nutrients for the particular nutritional blend formulation falls within the scope of “livestock health management plan.” Since this is presented to the user, which is the owner of the livestock, the limitation is satisfied.
- receiving, from the livestock owner interface, a confirmation of acceptance of the customized livestock health management plan; and (Case [0010] the system contains means to allow the user to approve the nutritional blend formulation (e.g., based on original or modified specifications) for manufacture; [0079] The user may choose to make modifications because of curiosity as to the effect that one or more modifications would have on the formulation product information (e.g., how would reducing the activity of a nutrient affect the cost) or as a result of something in the formulation product information being unacceptable)
- upon receiving the confirmation, automatically transmitting a wireless signal to livestock feed manufacturing equipment via a livestock feed producer interface, the wireless signal including instructions to manufacture the customized feedstock recipe for the one or more livestock, thereby initiating automated production of the customized feedstock (Case [0010] the system contains means to allow the user to approve the nutritional blend formulation (e.g., based on original or modified specifications) for manufacture.. the system contains means for allowing review of the nutritional blend formulation after it has been approved for manufacture; and/or the system contains means to transmit the nutritional blend formulation and/or its corresponding formulation product information to another system (e.g., a manufacturing control system or an enterprise system) to allow the nutritional blend formulation to be manufactured.; [0079] The user may choose to make modifications because of curiosity as to the effect that one or more modifications would have on the formulation product information (e.g., how would reducing the activity of a nutrient affect the cost) or as a result of something in the formulation product information being unacceptable. [0081] Upon completion of the review of the on-screen information, the user may transmit the formulation product information to another system 50, for instance, the manufacturer's internal computer system, linked on line to system 15. Such transmission to system 50 may be made by any suitable means, for example, on line through the graphical user interface 14 and the reporting module 30. As indicated above, the transmission may be made for any of a number of reasons (e.g., to submit the proposed nutritional blend formulation for review and/or approval by the manufacturer or vendor or by any other entity, e.g., consultants or agents). System 50 may be a simple transmittal system for conveying the information (e.g., to a formulator or a consultant for review and/or approval), or system 50 may be a complex enterprise system that oversees the production systems (e.g., manufacturing plants, raw material ordering systems) and the inventory and billing systems. [0054] After the user makes selections at the hierarchical levels above the manufacturer and/or lot level(s), the system could automatically select a manufacturer or vendor (if there were more than one) and a lot based on, for example, the age of the lot, whether the amount of the lot remaining on hand was sufficient to meet the batch size specification entered by the user, particle size distribution, or on other manufacturing considerations.) The broadest reasonable interpretation of the claims allows for any transmission of a signal to livestock feed manufacturing equipment (which is broad enough to include the “manufacturer’s internal computing system,” or “manufacturing control system). The “automated production of the customized feedstock” is recited broadly enough such that if any step in the process of production is automated, then the limitation is satisfied. Therefore, the automatic selection of a manufacturer or vendor, is automatic production of the feedstock.
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date to modify Madhusudan to add interface features as taught by Case such as transmitting the health plan to receive confirmation or denial of the plan by the owner, transmitting a signal to livestock feed manufacturing equipment, and initiating automated production of the feedstock. By combining these two inventions one would at arrive at the predictable outcome of transmitting the plan to the livestock owner interface and confirming or denying acceptance, because it would merely be a simple substitution wherein Madhusudan’s recipe is transmitted in Case’s system. By implementing Case’s system of automatically transmitting signals between the manufacturers, formulators, and all parties, one would have arrived at the predictable outcome of “transmitting a wireless signal to livestock feed manufacturing equipment via a livestock feed producer interface.” One of ordinary skill in the art would have been motivated to perform the combination due to the benefit of providing up-to-date, accurate and clearly presented information between customer’s and manufacturer’s to improve the business relations. (Case [0006])
However, neither Madhusudan nor Case teach or suggest:
- at least one database coupled to or in wireless communication with a gateway that is in wireless communication with the at least one server, the database storing predetermined health thresholds for particular livestock species or breeds and nutritional parameters for formulating customized feedstock recipes;
-that the analyzing is done by the at least one processor using at least one of a statistical model or a machine learning model
-That the memory and processor is also in wireless communication with the livestock producer interface.
- wherein the anomaly indicates a deviation from a predetermined health threshold for a particular species or breed retrieved from the database;
- the plan including a customized feedstock recipe tailored to address the anomaly through a change in at least one of protein, vitamin, mineral or caloric intake;
-the customized feedstock recipe formulated based on nutritional parameters retrieved from the database for the particular species or breed;
Alternatively, Cook discloses a system and method for optimizing animal production, including generation of diet information for a particular species of animal based on a particular nutritional anomaly. Cook teaches:
- at least one database coupled to or in wireless communication with a gateway that is in wireless communication with the at least one server, the database storing predetermined health thresholds for particular livestock species or breeds and nutritional parameters for formulating customized feedstock recipes; (Cook [0051] Designation of a variable input may require submission of additional information, such as a cost and/or benefit of variation of the variable input, recommended degrees of variation for optimization testing, etc. Alternatively, the additional information may be stored and retrievable from within system 100 or an associated database. [0084] Empirical testing may further include monitoring the animals within the animal production system. For example, an animal may be monitored for metabolic indicators. Metabolic indicators may be indicative of metabolic problems such as milk fever, ketosis, imbalances in dietary protein, overheating, etc. Other monitored characteristics may include characteristics that must be tested within a laboratory such as non-esterified fatty acids (NEFA), beta hydroxyl butyrate (BHBA), urine pH, milk urea nitrogen (MUN), blood urea nitrogen (BUN), body temperature, blood AA, manure characteristics, carbon dioxide levels, minerals, fat pad probes for pesticide residue testing, etc... Other physiological measurements may include microbial profile or but histological measurements. [0096] Animal requirements generated by requirements engine 310 may include a listing of nutrient requirements for a specific animal or group of animals. Animal requirements may be a description of the overall diet to be fed to the animal or group of animals. Animal requirements further may be defined in terms of a set of nutritional parameters ("nutrients"). Nutrients and/or nutritional parameters may include those terms commonly referred to as nutrients as well as groups of ingredients, microbial measurements, indices of health, relationships between multiple ingredients, etc. Further, the set of animal requirements may include constraints or limits on the amount of any particular nutrient, combination of nutrients, and/or specific ingredients... The constraints may be minimums or maximums and may be placed on the animal requirement as a whole, any single ingredient, or any combination ingredients. [0097] Additionally, animal requirements may be generated that define ranges of acceptable nutrient levels. [0100] Table 2 below includes an exemplary listing of nutrients that may be included in the animal requirements. According to an exemplary embodiment, within the animal requirements, each listed nutrient may be associated with a value, percentage, range, or other measure of amount. The listing of nutrients may be customized to include more, fewer, or different nutrients based on any of a variety of factors, such as animal type, animal health, nutrient availability, etc. [0030] Information associated with a specific animal or a group or type of animals may include, but is not limited to, a species, ) Cook’s indicators of “metabolic problems” is an example of an anomaly, such as imbalances in dietary protein, overheating, etc. Since [0096-0100] provides examples of predetermined health thresholds (acceptable nutrient levels, constraints/limits), it is clear that an imbalance in dietary protein, or “overheating” falls within the scope of “a deviation from a predetermined health threshold.” Since these nutritional ranges are specific requirements for a specific “animal” or “animal type”, which includes a “species” based on [0030].
-that the analyzing is done by the at least one processor using at least one of a statistical model or a machine learning model,(Cook [0072] Enterprise supervisor 200 may be configured to implement a Monte Carlo method where a specific set of values is drawn from a set of distributions of model parameters to solve for optimized values for the variable inputs. This process may be repeated many times, creating a distribution of optimized solutions. Based on the type of optimization, enterprise supervisor 200 maybe used to select either the value most likely to provide the optimal solution or the value that gives confidence that is sufficient to meet a target. For example, a simple optimization might be selected which provides a net energy level that maximizes the average daily gain for a particular animal. [0085] Empirical testing provides the advantage of verifying the accuracy of predictive models generated by simulator 300. Optimization results generated from imperfect models may different from real world results obtained through empirical testing. System 100 may be configured to provide dynamic control based on the empirical testing feedback, adjusting animal information inputs or generate values, such as an animal's feed formulation, to achieve specified targets based on the difference between model results and empirical testing feedback. Further, simulator 300 may be configured to adjust how models are generated based on the data obtained through the empirical testing to increase the accuracy of future models.)
-That the memory and processor is also in wireless communication with the livestock producer interface (Cook [0057] Enterprise supervisor 200 may include or be linked to one or more databases configured to automatically provide animal information inputs or to provide additional information based upon the animal information inputs. [0058] User interface 210 may be any type of interface configured to allow a user to provide input and receive output from system 100... For example, user interface 210 may be implemented as a web page including a plurality of input fields configured to receive animal information input from a user. [0028] System 100 may be implemented utilizing a single or multiple computing systems. For example, where system 100 is implemented using a single computing system, each of enterprise supervisor 200... Each separate computing system may further include hardware configured for communicating with the other components of system 100 over a network. According to yet another embodiment, system 100 may be implemented as a combination of single computing systems implementing multiple processes and distributed systems. [0141] Although specific functions are described herein as being associated with specific components of system 100, functions may alternatively be associated with any other component of system 100. For example, user interface 210 may alternatively be associated with simulator 300 according to an alternative embodiment.)
- wherein the anomaly indicates a deviation from a predetermined health threshold for a particular species or breed retrieved from the database;(Cook [0084] Empirical testing may further include monitoring the animals within the animal production system. For example, an animal may be monitored for metabolic indicators. Metabolic indicators may be indicative of metabolic problems such as milk fever, ketosis, imbalances in dietary protein, overheating, etc. Other monitored characteristics may include characteristics that must be tested within a laboratory such as non-esterified fatty acids (NEFA), beta hydroxyl butyrate (BHBA), urine pH, milk urea nitrogen (MUN), blood urea nitrogen (BUN), body temperature, blood AA, manure characteristics, carbon dioxide levels, minerals, fat pad probes for pesticide residue testing, etc... Other physiological measurements may include microbial profile or but histological measurements. [0096] Animal requirements generated by requirements engine 310 may include a listing of nutrient requirements for a specific animal or group of animals. Animal requirements may be a description of the overall diet to be fed to the animal or group of animals. Animal requirements further may be defined in terms of a set of nutritional parameters ("nutrients"). Nutrients and/or nutritional parameters may include those terms commonly referred to as nutrients as well as groups of ingredients, microbial measurements, indices of health, relationships between multiple ingredients, etc. Further, the set of animal requirements may include constraints or limits on the amount of any particular nutrient, combination of nutrients, and/or specific ingredients... The constraints may be minimums or maximums and may be placed on the animal requirement as a whole, any single ingredient, or any combination ingredients. [0097] Additionally, animal requirements may be generated that define ranges of acceptable nutrient levels. [0100] Table 2 below includes an exemplary listing of nutrients that may be included in the animal requirements. According to an exemplary embodiment, within the animal requirements, each listed nutrient may be associated with a value, percentage, range, or other measure of amount. The listing of nutrients may be customized to include more, fewer, or different nutrients based on any of a variety of factors, such as animal type, animal health, nutrient availability, etc. [0030] Information associated with a specific animal or a group or type of animals may include, but is not limited to, a species, ) Cook’s indicators of “metabolic problems” is an example of an anomaly, such as imbalances in dietary protein, overheating, etc. Since [0096-0100] provides examples of predetermined health thresholds (acceptable nutrient levels, constraints/limits), it is clear that an imbalance in dietary protein, or “overheating” falls within the scope of “a deviation from a predetermined health threshold.” Since these nutritional ranges are specific requirements for a specific “animal” or “animal type”, which includes a “species” based on [0030].
- the plan including a customized feedstock recipe tailored to address the anomaly through a change in at least one of protein, vitamin, mineral or caloric intake;(Cook [0009] What is needed is a system and method for maximizing nutritional criteria satisfaction in view of nutrient modification and nutrient utilization factors. Further, there is a need for a such a system and method configured to create a customized animal feed formulated to satisfy a requirement in view of the nutrient modification and nutrient utilization factors. [0102] The requirements engine 310 may further be configured to generate the animal requirements based on one or more dynamic nutrient utilization models. Dynamic nutrient utilization may include a model of the amount of nutrients ingested by an animal feed that are utilized by an animal based on information received in the animal information inputs... Nutrient utilization may further depend on the presence or absence of other nutrient additives, microbes and/or enzymes,... animal production or life stage, previous nutrition level, etc. [0103] Simulator 300 may be configured to account for these effects. For example, simulator 300 may be configured to adjust the level of a particular nutrient, defined in an animal feed formulation input, from the level determined based on the animal requirement to a different level based on the presence or absence of another particular nutrient. [0104] Accordingly, an animal feed formulation input may be modified based on the nutrient utilization model. However, this change in the animal feed formulation may have an effect on the animal feed formulation, including the animal feed formulation that was just modified. Accordingly, compensating for a nutrient utilization model may require an iterative calculation, constantly updating values, to arrival at a final value that is within a predefined tolerance. [0127] Table 3 below includes an exemplary list of ingredients which may be used in generating the animal feed formulation. The listing of ingredients may include more, fewer, or different ingredients depending on a variety of factors, such as ingredient availability, entry price, animal type, etc.... Soy Protein Concentrate... Vitamin A Vitamin B Complex Vitamin B12 Vitamin D3 Vitamin E) As seen above, an optimized feed formulation is generated specifically to address the requirements (including anomalies) by changing the ingredients, which include various proteins, vitamins, and minerals. Therefore, Cook satisfies the limitations above.
-the customized feedstock recipe formulated based on nutritional parameters retrieved from the database for the particular species or breed; (Cook [0059] User interface 210 may be customized based upon the animal information inputs and database information. For example, where a user defines a specific species of animal, enterprise supervisor 200 may be configured to customize user interface 210 such that only input fields that are relevant to that specific species of animal are displayed. Further, enterprise supervisor 200 may be configured to automatically populate some of the input fields with information retrieved from a database. The information may include internal information, such as stored population information for the particular user, or external information, such as current market prices that are relevant for the particular species as described above. [0100] Table 2 below includes an exemplary listing of nutrients that may be included in the animal requirements. According to an exemplary embodiment, within the animal requirements, each listed nutrient may be associated with a value, percentage, range, or other measure of amount. The listing of nutrients may be customized to include more, fewer, or different nutrients based on any of a variety of factors, such as animal type, animal health, nutrient availability, etc.)
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date to further modify the combination of Madhusudan and Case by adding the teachings of Cook which specify particular health thresholds for a species or breed, and wherein the plan addresses anomalies in the thresholds through a change in proteins, vitamins, or minerals content. One of ordinary skill in the art would have been motivated to perform this combination by the benefit of Cook’s system optimizing for the desired outcome, which leads to maximal quality or amount of product produced by an animal. (Cook [0069] and [0004].)
Regarding Claim 13:
The combination of Madhusudan, Case, and Cook teach or suggest: The system of claim 12,
Furthermore, Madhusudan teaches:
- wherein the memory and processor are further configured to transmit the livestock health management plan wirelessly to a mobile device livestock owner interface.(Madhusudan [0040] The sensing tag 102a may further transmit the statistics of health information of the first dairy animal 106a to the feed-management server 112. The feed-management server 112 in return may update the determined feed composition based on an anomaly that is detected in the health information of the first dairy animal 106a. The feed-management server 112 may add one or more medicinal ingredients in the determined feed composition to treat the detected anomaly. For example, the feed-management server 112 may detect an increment in body temperature of the first dairy animal 106a, based on the health information received from the sensing tag 102a. The feed-management server 112 may add a medicinal ingredient (such as an antibiotic) required to treat the increased body temperature. In accordance with an embodiment, the feed-management server 112 may be further configured to transmit the health information of the first dairy animal 106a to a computing device (not shown) of a health analyst, in real time. [0056] The network interface 208 may comprise suitable logic, circuitry, interfaces, and/or code that may be configured to establish communication between the feed-management server 112, the plurality of sensing devices 102, and the electronic device 110, via the communication network 114. The network interface 208 may be implemented by use of various known technologies to support wired or wireless communication of the feed-management server 112 with the communication network 114. [0026] Examples of the electronic device 110 may include, but are not limited to, a smartphone, a tablet computer, a computing device, a server, a computer work-station, a mainframe machine, and/or other electronic devices.)
Regarding Claim 17:
The combination of Madhusudan, Case, and Cook teaches or suggests The system of claim 12,
Furthermore, Madhusudan teaches:
-wherein the at least one server, memory and processor are configured to process one or more of livestock owner input data received via the livestock owner interface, the real-time livestock sensor data, and normalized data to produce the livestock health management plan that includes the customized feedstock recipe that cures the at least one anomaly. (Madhusudan [0040] In accordance with an embodiment, the sensing tag 102a may be further configured to determine health information of the first dairy animal 106a. The health information may include one or more health parameters of the first dairy animal 106a. Examples of the one or more health parameters, may include, but are not limited to, blood count, body temperature, respiration rate, heart beat rate, and/or a combination thereof. The sensing tag 102a may further transmit the statistics of health information of the first dairy animal 106a to the feed-management server 112. The feed-management server 112 in return may update the determined feed composition based on an anomaly that is detected in the health information of the first dairy animal 106a. The feed-management server 112 may add one or more medicinal ingredients in the determined feed composition to treat the detected anomaly. For example, the feed-management server 112 may detect an increment in body temperature of the first dairy animal 106a, based on the health information received from the sensing tag 102a. The feed-management server 112 may add a medicinal ingredient (such as an antibiotic) required to treat the increased body temperature. In accordance with an embodiment, the feed-management server 112 may be further configured to transmit the health information of the first dairy animal 106a to a computing device (not shown) of a health analyst, in real time.) Since the claims only require “one or more of” and Madhusudan satisfies the “real-time livestock sensor data” then the claims are satisfied.
Regarding Claim 19:
The combination of Madhusudan, and Cook teaches or suggests The method of claim 18,
However, neither Madhusudan nor Cook teach or disclose:
- further comprising the steps of: transmitting the livestock health management plan wirelessly to at least one livestock owner interface associated with a livestock owner;
- and confirming acceptance of the livestock health management plan by the livestock owner.
Alternatively, Case discloses a customized feed ordering platform with interfaces providing the features of:
- transmitting the livestock health management plan wirelessly to at least one livestock owner interface associated with a livestock owner; (Case [0010] In preferred embodiments, the system can identify and provide to the user a group of suggested nutrients for each of several possible intended uses; after the user indicates a particular intended use for the nutritional blend formulation, the system presents to the user through the graphical user interface a list of the suggested nutrients for that intended use... the system contains means for notifying the user through the graphical user interface if the desired nutritional activity and/or concentration for one or more of the nutrients are below the suggested minimum or above the suggested maximum concentration or activity for one or more of the nutrients for the intended use; the system contains means to communicate to the user at least a portion of the hierarchy of the available nutritional formulation materials for one or more of the nutrients in the nutritional blend formulation; [0014] Thus, a user may be an individual who on behalf of a company for which he or she works enters specifications on his or her home or office computer or input/output device for a nutritional blend formulation the company ... uses internally in its own operations (e.g., animal husbandry).[0031] Using a communications network, preferably the Internet, a connection between system 15 and a local node 12 of a user is established, preferably over the “World Wide Web” or other computer network, which network is preferably but not necessarily global and which network may or may not be accessible by the public at large (for convenience the term “web” is used herein to refer to a communications network.)
- and confirming acceptance of the livestock health management plan by the livestock owner. (Case [0010] the system contains means to allow the user to approve the nutritional blend formulation (e.g., based on original or modified specifications) for manufacture; [0079] The user may choose to make modifications because of curiosity as to the effect that one or more modifications would have on the formulation product information (e.g., how would reducing the activity of a nutrient affect the cost) or as a result of something in the formulation product information being unacceptable)
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date to further modify the combination of Madhusudan and Cook to add Case’s livestock owner portal. One of ordinary skill in the art would have been motivated to perform the combination due to the benefit of providing up-to-date, accurate and clearly presented information between customer’s and manufacturer’s to improve the business relations. (Case [0006])
Regarding Claim 20:
The combination of Madhusudan, Case, and Cook teaches or suggests The method of claim 19,
However, neither Madhusudan nor Cook teach or disclose:
further comprising the steps of:
-transmitting the confirmed livestock health management plan from the livestock owner to a feedstock producer;
-manufacturing the customized livestock feed according to the tailored feedstock recipe; and shipping the customized livestock feed to the livestock owner.
Alternatively, Case teaches:
-transmitting the confirmed livestock health management plan from the livestock owner to a feedstock producer; (Case [0081] Upon completion of the review of the on-screen information, the user may transmit the formulation product information to another system 50, for instance, the manufacturer’s internal computer system, linked on line to system 15. Such transmission to system 50 may be made by any suitable means, for example, on line through the graphical user interface 14 and the reporting module 30. [0010] the system contains means to transmit the nutritional blend formulation and/or its corresponding formulation product information to another system (e.g., a manufacturing control system or an enterprise system) to allow the nutritional blend formulation to be manufactured.)
-manufacturing the customized livestock feed according to the tailored feedstock recipe; (Case[0104] When the user and vendor (at all approval levels) are satisfied, the proposed formulation can be entered into the vendor’s or manufacturer’s records. That might involve entering the formulation into a master system such as an enterprise system that contains tracking, raw material ordering, accounting, billing, production scheduling, and other systems. At this point, a final quotation (including a firm offer price) can be given to user (e.g., by email, by telephone, and/or through the graphical user interface of the system). If the user accepts (i.e., places an order), the master system (e.g., enterprise system) so indicates in the appropriate systems and the nutritional blend formulation is released for manufacture, billing, etc.)
- and shipping the customized livestock feed to the livestock owner. (Case [0017] The term “formulation product information” should be understood broadly and will typically include an identification of the nutrients and/or nutritional formulation materials in the nutritional blend formulation and their quantities and/or activities and may also include the name or other identification assigned to the nutritional blend formulation (e.g., by the user) as well as an identification of the physical form of the nutritional blend formulation, estimates of various physical properties of the formulation, and/or information concerning its packaging, estimated delivery time, and/or estimated cost.)
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date to modify the combination of Madhusudan, Cook and Case by adding the features of transmitting the custom recipes to the feedstock producer, manufacturing the feed, and delivering the feed to the producer as taught by Case. One of ordinary skill would have been motivated to perform the combination as it would provide the benefit of providing user convenience by promptly facilitating the ordering of such custom feeds. (Case [0005])
Claims 14-16 are rejected under 35 U.S.C. 103 as being unpatentable over Madhusudan et al. (US 20180206448 A1), in view of Case et al. (US 20060085272 A1) hereinafter Case, further in view of Cook (US 20110010154 A1), further in view of Albornoz et al. (US 20190075756 A1) hereinafter Albornoz.
Regarding Claim 14:
The combination of Madhusudan, Case, and Cook teach The system of claim 12,
Furthermore, Madhusudan teaches:
- further comprising at least one livestock sensor, the livestock sensor coupled to a gateway that is in wireless communication with the at least one server, memory and processor.(See Madhusudan [0067] The wearable device 406 may include, and/or may be communicatively coupled to one or more sensors that may track the activities of the first care-taker 108a. The wearable device 406 may be configured to detect a presence of the first sensing tag 410a, based on the first sensing tag 410a that is in the proximity range of the wearable device 406. The wearable device 406 may further transmit the information pertaining to the detected presence of the first sensing tag 410a to the processor 302, via a short range communication of the communication network 114.)
However, neither Madhusudan, Case, nor Cook teach or suggest:
-The livestock sensor is a livestock scale
- the livestock scale coupled to a gateway that is in wireless communication with the at least one server, memory and processor.
Alternatively, Albornoz discloses
- further comprising at least one livestock scale, the livestock scale coupled to a gateway that is in wireless communication with the at least one server, memory and processor. (Albornoz [0017] This disclosure is directed to systems, methods, and/or apparatuses for improved animal weight monitoring and management. In some examples, a system, method, and/or apparatus may comprise one or more weight sensors fixed to a platform, an identification tag detector for detecting one or more identification tags and determining one or more identifiers associated with the one or more identification tags, a localized computing device and/or a remote server, an animal profile database, an analysis module, and/or a graphical user interface. [0032] The localized computing device 122 may comprise a memory (400 of FIG. 4) coupled to a processor (402 of FIG. 4), e.g., via a PCB board or mini-board, and may be situated in proximity to other components of the system 100 (e.g., the one or more weight sensors 102, the identification tag detector 112, and the power supply 120).)
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date to modify the combination of Madhusudan, Case, and Cook by adding the features of allowing weight sensors such as a scale coupled to the server to monitor the weight of the animals. One of ordinary skill would have been motivated to perform the combination as it would provide the benefit of storing a reliable profile of information to help manage the animal’s weight. (Albornoz [0018])
Regarding Claim 15:
The combination of Madhusudan, Case, and Cook teaches or suggests The system of claim 12,
Furthermore, neither Madhusudan, Case nor Cook teach or suggest:
-wherein the at least one database comprises a NoSQL database
Alternatively, Albornoz teaches:
-wherein the at least one database comprises a NoSQL database (Albornoz [0059] The identifier 118 may be added to the identifier database 416 in addition to one or more previously-stored identifiers 506. In some examples, the analysis module 422 may use the identifier 118 and a collection of previously-stored identifiers 506 in making various determinations or associations, often to generate historical and/or up-to-date, accurate statistics about animal weight, animal health, animal locations, herd patterns, supplier performance, and breed performance, as discussed in greater detail below with respect to FIG. 7. [0060] Although the spreadsheets 512 and 522 are discussed herein, the identifier database 416 and/or the weight value database 418 may, additionally or alternatively, be stored as a comma delimited list, a NoSQL data structure, or any other data type, data structure, and/or data system.)
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date to modify the combination of Madhusudan, Case, and Cook by adding the features of Albornoz, particularly the use of a NoSQL database, as it is an alternative type of format that can be used to store the spreadsheet information. One of ordinary skill in the art would have been motivated to use the NoSQL database as it enables an easier way for the animal profiles to be combinable. (Albornoz 0066] In some examples, the animal profile 426 may comprise information associated with a group or sub-group of animals. For instance a first animal profile 606 associated with a first particular animal may be combined with a second animal profile 608 associated with a second particular animal to create a third animal profile 610 that is an animal group profile associated with the first particular animal and the second particular animal. In some instances, the animal group profile may be associated with a collection of animals based having a same supplier, a same breed, a same purchase date (or range of purchase dates), a same weight value (or range of weight values), or a same location. Information associated with the animal group profile (e.g., average weight values, trends in weight value changes, location(s), or other additional information associated with the animal group profile may) be presented on the graphical user interface 410 in response to a receiving a request, for instance, from an interactive element of the graphical user interface 410.)
Regarding Claim 16:
The combination of Madhusudan, Case, and Cook teach The system of claim 12,
Furthermore, Madhusudan teaches:
-wherein the livestock owner interface allows the livestock owner to enter individual livestock input data (Madhusudan [0049] Alternatively, the amount of milk collected may be fed manually by the first care-taker 108a by use of the electronic device 110. In accordance with an embodiment, the first care-taker 108a may use a specific application installed in the electronic device 110 to manually feed the information of the amount of milk collected. [0050] The first care-taker 108a may transmit the training data (i.e., the determined amount of milk consumed by the off-spring from the feeder bottle and the time duration of the milk consumption activity from the feeder bottle) to the feed-management server 112 by use of the specific application installed in the electronic device 110.)
However, neither Madhusudan, Case, nor Cook teach or suggest:
- regarding at least one of feeding method, feeding schedule, medical history, breed, gender, breeding status, age, and body condition.
Alternatively, Albornoz teaches:
- regarding at least one of medical history, breed, and body condition. (Albornoz [0023] In some examples, the systems, methods, and/or apparatuses may provide a user interface to present information collected or generated by the systems, methods, and/or apparatuses. The user interface may present statistical information on the weight(s) of a herd, and may present the results of analyses that calculate metrics of a whole herd (e.g., the weight distribution of the stock, weight changes over time of the herd, etc.), weight gain performances of different sub-groups (e.g., breeds of cattle, cattle from different suppliers, etc.), or weight change histories of individual animals. [0065] In some embodiments, the animal profile 426 may include additional information 604 corresponding to the animal 106 associated with the animal profile 426. For instance, the additional information may include a supplier identity of the animal 106, a breed of the animal 106, a purchase date of the animal 106, and/or a birthdate of the animal 106. The additional information 604 may be accessed and processed by the analysis module 422 to generate output data 408 for presentation on the graphical user interface 410, and/or to set alarms 602, as discussed in greater detail below with respect to FIGS. 8-10.)
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date to modify the combination of Madhusudan, Case, and Cook by adding the features of allowing the user to input medical history (such as weight), breed, and body condition. One of ordinary skill would have been motivated to perform the combination as it would provide the benefit of storing a reliable profile of information for cases such as sales, auditing, statistical analysis, or regulatory reasons. (Albornoz [0020])
Response to Arguments
Applicant’s arguments have been fully considered but are not persuasive for the following reasons. Regarding arguments over nonstatutory double patenting rejections, the remarks state “Applicant submits herewith a terminal disclaimer to overcome the rejection, without conceding its propriety,” however no terminal disclaimer has been filed as of the mailing date of this office action. However, this issue is moot, as the amendments overcome the nonstatutory double patenting rejection by distinguishing from the patented claims even when considering obviousness. However, the examiner reserves the right to reintroduce the nonstatutory double patenting rejection, based on the amendments to the claims.
Regarding arguments over the rejection of claims 1-3 and 6-20 over 101, the applicant’s arguments in regards to claims 1-3 and 6-17 have been fully considered and are persuasive. Claims 1-3 and 6-17 overcome the 101 rejection due to the combination of elements of (1) filter sensor signals at the hardware level before transmission, (2) analyze physiological data using machine learning models, and (4) automatically initiate manufacturing of the customized feed. In fact, the automatic manufacturing of the customized feed would represent an integration of a practical application to the abstract idea. However, the examiner notes that because there is no support in the specification for “transmission of a wireless signal to livestock feed manufacturing equipment via a livestock feed producer interface” and “thereby, initiating automated production of the customized feedstock” the claims remain rejected under 112(a). If the claim is amended to address the 112(a) issue, resulting in removal of additional elements that integrate the abstract idea into a practical application, the examiner reserves the right to reintroduce the 101 rejection to claims 1-3 and 6-17.
The applicant’s arguments have been fully considered but are not persuasive in regards to claims 18-20, because the applicant’s arguments are based on limitations that are not found in claim 18. For example, section A. “The Claims are Not Directed to An Abstract idea Under Step 2A, Prong 1” is not persuasive because the claims argue based on limitations such as “The claims require: (1) signal filtering by a custom electronic board, (2) anomaly detection using machine learning or statistical models, (3) comparison against database-stored species-specific thresholds, (4) generation of a customized feedstock recipe based on nutritional parameters retrieved from the database, and (5) automated
transmission of manufacturing instructions to feed production equipment. This closed-loop system that extends from sensor signal processing through automated manufacturing initiation is fundamentally different from the mere data collection and display rejected in Example 46, Claim 1.” However this argument is not persuasive in regards to claim 18 because it does not reflect the actual scope of claim 18, which does not include (5). Furthermore, as stated in the rejection, the claims are recited at such a high level of generality that “filtering data signals” is merely an idea of an outcome without a specific mechanism that limits how the filtering occurs. In fact, any time any signal passes through a board, it is inherently “filtered” (by the broadest reasonable interpretation). Furthermore, a “custom electronic board” is broad enough to satisfy any circuit board, as they are all “customized” to perform the task at hand, in some manner. Without specifying what is limited by the “filtering” or “customization” the claims are still equivalent to “apply it” or mere instructions.
Similarly, arguments over section B. of the 101 argument are not persuasive because the alleged “claimed solution” is not reflected within the scope of claim 18, (a closed-loop system integrating custom signal-filtering hardware, machine learning analysis, database-driven threshold comparison, and automated manufacturing control). Claim 18 does not specifically limit to “machine-learning analysis” because it also allows for “statistical models,” and the steps do not actually provide the alleged “automated manufacturing control.” The claims do not satisfy the “practical application” standard because no “physical transformation” (as alleged by the applicant) occurs in claim 18.
Similarly, arguments over section C, (Step 2B) are not persuasive because the arguments are based on a combination of elements that is not reflected within the scope of claim 18. More specifically, the alleged “automatically transmitting a wireless signal to livestock feed manufacturing equipment...thereby initiating automated production” is not reflected in scope claims. Furthermore, the argument that “the examiner’s assertion that “automated manufacturing is not reflected within the scope of the claims is directly contradicted by the amended claim language.” However, this argument is not persuasive because the amended claim 18 language does not reflect the arguments, and even assuming arguendo that it did, it would be rejected under 112(a) for lack of written description, as claims 1-3 and 6-17 are rejected. Therefore, claims 18-20 remain rejected under 35 U.S.C. 101. The applicant’s argument in section D. (The claims are distinguishable from unfavorable precedent) are not persuasive because the rejection does not allege that veterinarians are capable of performing machine learning analysis, querying structured databased of breed-specific thresholds, or transmits automated manufacturing instructions to production equipment. However, the claims are rejected for at least reciting the abstract idea of certain methods of organizing human activity, then merely “apply it” to technology or generally linking it to a particular technological environment (machine learning).
In regards to the arguments over the prior art, the applicant’s remarks over the rejection of claim 18 in view of Madhusudan/Cook has been fully considered but is not persuasive in view of the following reasons. In response to applicant argument A in page 13 of 22 of the applicant’s response, the “custom electronic board aspect” is taught by the prior art of record, because the broadest reasonable interpretation of the “custom electronic board configured to filter a data signal from the sensor,” is broad enough to include any circuitry specially configured to perform the task, since the scope of “custom” is meaningfully limited, and any manner of “filter a data signal” satisfies the claims, as seen in Madhusudan in at least paragraphs [0056], and [0063]. Argument B, “the statistical model/machine learning aspect is not taught” is not persuasive because at least one of the prior art references suggests using “monte Carlo analysis” which satisfies the “statistical model” limitation. The applicant’s arguments that Cook’s model’s don’t apply to health anomaly detection are not persuasive in view of Cook [0084-0085], which include using empirical testing to determine “metabolic problems,” which integrates the use of “predictive models.” Therefore, the limitation is satisfied, because the BRI of the analyzing step, enables any manner of using statistical models in the analyzation.
Argument C is not persuasive, because though Cook does disclose nutrient constraints for feed formulation, in response to applicant's arguments against the references individually, one cannot show nonobviousness by attacking references individually where the rejections are based on combinations of references. See In re Keller, 642 F.2d 413, 208 USPQ 871 (CCPA 1981); In re Merck & Co., 800 F.2d 1091, 231 USPQ 375 (Fed. Cir. 1986). Furthermore, Cook [0084] does teach “[0084] Empirical testing may further include monitoring the animals within the animal production system. For example, an animal may be monitored for metabolic indicators. Metabolic indicators may be indicative of metabolic problems such as milk fever, ketosis, imbalances in dietary protein, overheating, etc. Other monitored characteristics may include characteristics that must be tested within a laboratory such as non-esterified fatty acids (NEFA), beta hydroxyl butyrate (BHBA), urine pH, milk urea nitrogen (MUN), blood urea nitrogen (BUN), body temperature, blood AA, manure characteristics, carbon dioxide levels, minerals, fat pad probes for pesticide residue testing, etc. Other characteristics may be monitored through observation, such as animals in heat, limping animals, sick animal, pregnancy, etc. that may not eat and produce as well as normal. Yet other characteristics may be a combination of these categories. Other physiological measurements may include microbial profile or but histological measurements.” Thus the combination of Cook’s database with Madhusudan’s sensors, in view of the paragraph above render the claim limitation obvious because of one of ordinary skill in the art would have arrived at the predictable outcome of “storing breed-specific health thresholds for detecting physiological anomalies.”
Argument D is not persuasive for similar reasons, in response to applicant's arguments against the references individually, one cannot show nonobviousness by attacking references individually where the rejections are based on combinations of references. See In re Keller, 642 F.2d 413, 208 USPQ 871 (CCPA 1981); In re Merck & Co., 800 F.2d 1091, 231 USPQ 375 (Fed. Cir. 1986). The applicant alleges that “Cook’s formulations are generated based on manual data entry by a user regarding...but not as an automated response to real-time physiological anomalies detected by sensors.” However the combination of Madhusudan and Cook arrive at the claimed limitations because Madhusudan satisfies the sensor-level physiological anomaly detection, tied to Cook’s database.
Argument’s E and F are not persuasive, In response to applicant’s argument that there is no teaching, suggestion, or motivation to combine the references, the examiner recognizes that obviousness may be established by combining or modifying the teachings of the prior art to produce the claimed invention where there is some teaching, suggestion, or motivation to do so found either in the references themselves or in the knowledge generally available to one of ordinary skill in the art. See In re Fine, 837 F.2d 1071, 5 USPQ2d 1596 (Fed. Cir. 1988), In re Jones, 958 F.2d 347, 21 USPQ2d 1941 (Fed. Cir. 1992), and KSR International Co. v. Teleflex, Inc., 550 U.S. 398, 82 USPQ2d 1385 (2007). In this case, the applicant argues that the claims “generally relate to livestock feed, does not provide a motivation to combine them in a specific manner required to arrive at the claimed invention.” However, the motivation to combine has been given at each step of the analysis, particularly, that one of ordinary skill would have been motivated by the suggested benefits of the prior art, and would have arrived at the limitations. In response to applicant's argument that the office action has not explained why one would modify Madhusudan’s sensor-based monitoring system to include...(1), (2), (3), and (4), the test for obviousness is not whether the features of a secondary reference may be bodily incorporated into the structure of the primary reference; nor is it that the claimed invention must be expressly suggested in any one or all of the references. Rather, the test is what the combined teachings of the references would have suggested to those of ordinary skill in the art. See In re Keller, 642 F.2d 413, 208 USPQ 871 (CCPA 1981).
Regarding arguments over the rejection of claims 1-3, 6-13, 15, 17 and 19 over 103 in view of Madhusudan/Case/Cook, the applicant’s arguments have been fully considered but are not persuasive for the reasons set forth herein. Argument A is not persuasive because in response to applicant's arguments against the references individually, one cannot show nonobviousness by attacking references individually where the rejections are based on combinations of references. See In re Keller, 642 F.2d 413, 208 USPQ 871 (CCPA 1981); In re Merck & Co., 800 F.2d 1091, 231 USPQ 375 (Fed. Cir. 1986). Case is not relied upon alone to teach (1) a custom electronic board configured to filter sensor data signals, (2) statistical or machine learning models for anomaly detection (3) a database of predetermined breed-specific or species-specific health thresholds and (4) automated generation of feedstock recipes based on database-stored nutritional parameters. In response to applicant's argument that Case is fundamentally different from the claimed system, it has been held that a prior art reference must either be in the field of the inventor’s endeavor or, if not, then be reasonably pertinent to the particular problem with which the inventor was concerned, in order to be relied upon as a basis for rejection of the claimed invention. See In re Oetiker, 977 F.2d 1443, 24 USPQ2d 1443 (Fed. Cir. 1992). In this case, Case’s RFID-based identification and data logging system covers the owner and producer interface, feedstock formulation recipe, and approval mechanisms, that when in combination with Madhusudan and Cook satisfy each and every limitation.
Argument B is not persuasive Applicant's arguments fail to comply with 37 CFR 1.111(b) because they amount to a general allegation that the claims define a patentable invention without specifically pointing out how the language of the claims patentably distinguishes them from the references. More specifically, that arguments state “Cook discloses feed formulation but not automated transmission to manufacturing equipment upon owner confirmation,” however, the transmission to manufacturing equipment (given the BRI which includes manufacturing control systems) is satisfied by Case [0010], and not relied upon by Cook. In response to applicant's arguments against the references individually, one cannot show nonobviousness by attacking references individually where the rejections are based on combinations of references. See In re Keller, 642 F.2d 413, 208 USPQ 871 (CCPA 1981); In re Merck & Co., 800 F.2d 1091, 231 USPQ 375 (Fed. Cir. 1986).
Argument C is not persuasive because the independent claims remain rejected under 35 U.S.C. 103, and the updated rejection finds all of the elements of the dependent claims taught or suggested by the prior art.
Argument D is not persuasive, in response to applicant’s argument that there is no teaching, suggestion, or motivation to combine the references, the examiner recognizes that obviousness may be established by combining or modifying the teachings of the prior art to produce the claimed invention where there is some teaching, suggestion, or motivation to do so found either in the references themselves or in the knowledge generally available to one of ordinary skill in the art. See In re Fine, 837 F.2d 1071, 5 USPQ2d 1596 (Fed. Cir. 1988), In re Jones, 958 F.2d 347, 21 USPQ2d 1941 (Fed. Cir. 1992), and KSR International Co. v. Teleflex, Inc., 550 U.S. 398, 82 USPQ2d 1385 (2007). In this case, the combination of Madhusudan, Case, and Cook is properly motivated by the benefit that each individual system provides to the other, particularly, that Madhusudan covers the sensing tag system, Case covers the interface system that leads to approval and manufacturing, and Cook provides a simulator to determine the health problems and determine the related formulations to address the anomalies. As a result, the combination of these teachings predictably arrive at the claimed invention and would have been obvious to one of ordinary skill in the art to combine at the time of the earliest filing date.
In regards to the rejection of claims 14-16 under 35 U.S.C. 103 in view of Madhusudan/Case/Cook/Albornoz, the applicant’s arguments have been fully considered but are not persuasive for many of the same reasons above. Argument A is not persuasive because claim 12 is not patentable over the prior art. Argument B is not persuasive because the updated rejection above cites the appropriate sections of Albornoz that remedy the deficiencies of the prior references. Regarding claim 14, the applicant argues that Ablornoz does not disclose integrate such scales into the specific closed-loop system architecture. In response to applicant's argument, the test for obviousness is not whether the features of a secondary reference may be bodily incorporated into the structure of the primary reference; nor is it that the claimed invention must be expressly suggested in any one or all of the references. Rather, the test is what the combined teachings of the references would have suggested to those of ordinary skill in the art. See In re Keller, 642 F.2d 413, 208 USPQ 871 (CCPA 1981). The applicant alleges claim 15 is not taught by Albornoz, however, Albornoz satisfies the limitation in [0060], and the use of NoSQL databased, can also be applied to the storing or retrieving breed-specific health data when considering the combination of Madhusudan/Case/Cook/Albornoz. In response to the applicant’s argument in claim 16 that Albornoz does not disclose such interfaces in the context of the specific-closed loop system, one cannot show nonobviousness by attacking references individually where the rejections are based on combinations of references. See In re Keller, 642 F.2d 413, 208 USPQ 871 (CCPA 1981); In re Merck & Co., 800 F.2d 1091, 231 USPQ 375 (Fed. Cir. 1986).
Argument C is not persuasive, in response to applicant’s argument that there is no teaching, suggestion, or motivation to combine the references, the examiner recognizes that obviousness may be established by combining or modifying the teachings of the prior art to produce the claimed invention where there is some teaching, suggestion, or motivation to do so found either in the references themselves or in the knowledge generally available to one of ordinary skill in the art. See In re Fine, 837 F.2d 1071, 5 USPQ2d 1596 (Fed. Cir. 1988), In re Jones, 958 F.2d 347, 21 USPQ2d 1941 (Fed. Cir. 1992), and KSR International Co. v. Teleflex, Inc., 550 U.S. 398, 82 USPQ2d 1385 (2007). The motivations to combine each and every prior art reference has been fully rationalized in the office action.
Therefore, in view of all of the applicant’s arguments over the prior art, none of the arguments are considered persuasive, therefore, the rejection to claims 1-3 and 6-20 under 35 U.S.C. 103 stand.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
-Jung et al. (US 20140122169 A1) discloses a food supply chain automation interface that transmits instructions to automatically operate feed manufacturing equipment. See [0120-0453] Food Supply Chain Automation.
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/NICO L PADUA/Junior Patent Examiner, Art Unit 3626
/SANGEETA BAHL/Primary Examiner, Art Unit 3626
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