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 .
In a Preliminary Amendment filed on December 29, 2023, the Abstract and claims 1-11, 13, and 14 were amended.
Claims 1-14 are pending, of which claim 1 is an independent claim.
Priority
Acknowledgment is made of applicant's claim for foreign priority based on an application filed in Sweden as 2130194-1 on July 1, 2021.
Information Disclosure Statement
The references cited in the information disclosure statements (IDS) submitted on 12/29/2023 and 12/03/2024 have been considered by the examiner.
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.
Claims 1, 2, 6, 8, 9, 10, 13, and 14 are rejected under 35 U.S.C. 103 as being unpatentable over Rajkondawar et al. (US Patent Publication No. 2015/0109130 A1) (“Rajkondawar”) in view of Simon (US Patent Publication No. 2009/0009289 A1) (“Simon”).
Regarding independent claim 1, Rajkondawar teaches:
A system (100) for determining a distribution of time that any one of a plurality of animals (101, 102, 103) has spent in different zones (210, 220, 230) of a barn (200) during a predetermined time period, wherein the system (100) comprises: Rajkondawar: Paragraph [0006] (“For example, certain embodiments of the present disclosure may facilitate an automated determination regarding whether each of the dairy livestock in a herd is likely unhealthy based on the movement of the dairy livestock within a free stall pen. Because a healthy herd of dairy livestock may have a greater quantity and/or quality of milk production, certain embodiments of the present disclosure may facilitate an increase in the overall milk output and/or quality for a herd of dairy livestock.”) Rajkondawar: Paragraph [0012] (“FIG. 1 illustrates a logical view of an example system 100 for estrus detection and health monitoring using real-time location, according to certain embodiments of the present disclosure. System 100 includes a free stall pen 102 housing a number of dairy livestock 104. Free stall pen 102 may be configured to include a number of stalls 106, walking lanes 108, and water troughs 110, and may be positioned adjacent to a feed lane 112. System 100 further includes a number of identification devices 114 positioned throughout free stall pen 102, each identification device 114 being configured to (1) read tags affixed to each of the dairy livestock 104, and (2) communicate with a controller 116.”) [The dairy livestock illustrated in FIG. 1 reads on “a plurality of animals”. The stalls 106, walking lanes 108, and water troughs 110 read on “different zones”.]
a processing controller (150); a database (140), comprising position coordinates of each of the zones (210, 220, 230); a positioning controller (130); Rajkondawar: Paragraph [0018] (“Controller 116 may additionally include one or more processing modules 118 and one or more memory modules 120 (each referred to in the singular throughout the remainder of this description). Processing module 118 may include one or more microprocessors, controllers, or any other suitable computing devices or resources and may work, either alone or with other components of system 100, to provide a portion or all of the functionality of system 100 described herein. Memory module 120 may take the form of volatile or non-volatile memory including, without limitation, magnetic media, optical media, random access memory (RAM), read-only memory (ROM), removable media, or any other suitable memory component.”) Rajkondawar: Paragraph [0019] (“Controller 116 may additionally include real-time location (RTL) logic 122, estrus detection logic 124, and health monitoring logic 126 (e.g., each stored memory module 120). RTL logic 122 (which, in combination with identification devices 114 and the plurality of tags affixed to the plurality of dairy cows 104, may be referred to as a real-time location system (RTLS)) may include any information, logic, and/or instructions stored and/or executed by controller 116 to determine location information 128 associated with dairy cows 104 in free stall pen 102 based on signals received from identification devices 114 (as described in further detail below). Estrus detection logic 124 may include any information, logic, and/or instructions stored and/or executed by controller 116 to determine, based on the location information 128 associated with a particular dairy cow 104 generated by RTL logic 122, whether the particular dairy cow 104 is likely to be in estrus (as described in further detail below). Health monitoring logic 126 may include any information, logic, and/or instructions stored and/or executed by controller 116 to determine, based on the location information 128 associated with a particular dairy cow 104 generated by RTL logic 122, whether the particular dairy cow 104 is likely unhealthy (as described in further detail below).”) Rajkondawar: Paragraph [0021] (“In certain embodiments, RTL logic 122 may be operable to process signals received from identification devices 114 in order to determine location information 128 associated with dairy cows 104 in free stall pen 102. As described above, a particular subset of the received signals may be generated by the identification devices 114 at approximately the same time and may identify the same particular dairy cow 104. Each of the signals of the particular subset may additionally include information about the position of the particular dairy cow 104 relative to the corresponding identification device 114 (e.g., distance, angle, etc.). Based on one or more of the subset of received signals, RTL logic 122 may determine a coordinate location of the particular dairy cow 104 within free stall pen 102. In certain embodiments, the determined coordinate location may be an (X,Y) location within the free stall pen 102. In certain other embodiments, the determined coordinate location may be an (X,Y,Z) location within the free stall pen 102.”) [The controller including processing modules read on “a processing controller”, the memory module reads on “a database”, and the controller including RTL logic, estrus detection logic, and health monitoring logic reads on “a positioning controller”.]
a set of tags (110) associated with respective said animals (101, 102, 103), … configured to transmit a radio signal …, repeatedly at a regular time interval; wherein each radio signal comprises the tag identity of the tag …, and Rajkondawar: Paragraphs [0018], [0019], and [0021] [As described above.] Rajkondawar: Paragraph [0016] (“In certain embodiments, a generated signal corresponding to a tag affixed to a dairy cow 104 may include (1) an identification number related to the dairy cow 104, (2) the distance, angle, and/or other information concerning the location of the dairy cow 104 relative to the identification device 114 generating the signal, (3) an identification of the identification device 114 generating the signal, and/or (4) a timestamp.”) Rajkondawar: Paragraph [0097] (“As an additional example, controller 116 may initiate the generation of a signal to be communicated to identification devices 114, the signal causing identification devices 114 to increase the frequency with which the tag of the particular dairy cow 104 is read (e.g., from fifteen seconds to five seconds). As a result, updated location information 128 may be generated, movement parameters 130 may be recalculated based on that updated location information 128, and a subsequent determination regarding whether the particular dairy cow 104 is likely to be in estrus may be made (in a manner substantially similar to that described above). As yet another example, controller 116 may access location information for other dairy cows 104 in free stall pen 102 to identify those other dairy cows 104 within a predefined distance of the particular dairy cow 104…”) [The signals received at the controller from the tag of the particular dairy cow being read from fifteen seconds to five seconds reads on “transmit a radio signal …, repeatedly at a regular time interval”. The signal corresponding to the tag affixed to the dairy cow reads on “wherein each radio signal comprises the tag identity of the tag”.]
at least three receivers (120a, 120b, 120 c), positioned at respective predetermined positions in the barn (200), each of the receivers configured to: receive the transmitted radio signal; and communicate information related to the received radio signal to the positioning controller (130); Rajkondawar: Paragraph [0015] (“A number of identification devices 114 may be positioned at various locations within and/or adjacent to free stall pen 102. Although a particular number of identification devices 114 are illustrated as being positioned at particular locations within free stall pen 102, the present disclosure contemplates any suitable number of identification devices 114 located at any suitable positions within and/or adjacent to free stall pen 102, according to particular needs.”) Rajkondawar: Paragraph [0016] (“Identification devices 114 may each include any suitable device operable to receive a signal from a tag affixed to a dairy cow 104 (e.g., an ear tag) located in free stall pen 102. In response to a signal received from a tag affixed to a dairy cow 104, identification devices 114 may generate a signal corresponding to that tag for communication to controller 116 (described below). Communication between identification devices 114 and tags affixed to dairy cows 104 may be facilitated by any suitable technology, including, for example, passive radio-frequency identification (RFID), active RFID, Wi-Fi, Bluetooth, ultra-wide band (UWB), ZigBee, acoustic locating, and computer vision.”) Rajkondawar: Paragraph [0017] (“Identification devices 114 may be communicatively coupled (e.g., via a network facilitating wireless or wireline communication) to controller 116.”) [As shown in FIG. 1, there are at least three identification devices which read on “at least three receivers”. The receiving of a signal from a tag affixed to a dairy cow reads on “receive the transmitted radio signal”. The identification devices receiving the RFID signals from each tag for communication to controller reads on “communicate information related to the received radio signal to the positioning controller”.]
wherein the positioning controller (130) is configured to repeatedly: receive the information related to the received radio signals, received from the respective receivers (120a, 120b, 120c), for each said tag identity; calculate a set of position coordinates of the tag (110) comprising the respective tag identity based on the information related to the received radio signals ... received by the receivers (120a, 120b, 120c); Rajkondawar: Paragraphs [0015]-[0019], and [0021] [As described above.] Rajkondawar: Paragraph [0020] (“Controller 116 may be operable to receive signals generated by identification devices 114. In certain embodiments, controller 116 may receive signals corresponding to each dairy cow 104 in free stall pen 102 from each identification device 114. Furthermore, for a particular dairy cow 104, controller 116 may receive signals from each identification device 114 at or about the same time and at regular intervals (e.g., every fifteen seconds). For example, for a particular dairy cow 104 at a particular time, controller 116 may receive signals generated by a number of identification devices 114 (e.g., each signal identifying the particular dairy cow 104 and the distance the particular dairy cow 104 is located from the corresponding identification device 114). Based on the received signals, controller 116 may determine location information 128 associated with the particular dairy cow 104 at the particular time (e.g., using RTL logic 122, as described below).”) Rajkondawar: Paragraph [0022] (“As just one example, RTL logic 122 may determine a coordinate location of the particular dairy cow 104 within free stall pen 102 using triangulation (based on at least three of the subset of received signals). Because each of the at least three signals may include information about the position of the particular dairy cow 104 (e.g., distance and/or angle) relative to the corresponding identification device 114 and the location of each corresponding identification device 114 within free stall pen 102 may be known, the coordinate location (e.g., an (X,Y) location, an (X,Y,Z) location, or any other suitable coordinate location) of the particular dairy cow 104 within free stall pen 102 may be determined.”) Rajkondawar: Paragraph [0023] (“Furthermore, because controller 116 may receive signals from identification devices 114 for each dairy cow 104 on a periodic basis (e.g., every fifteen seconds), location information 128 may be generated for each dairy cow 104 in free stall pen 102 at each of a number of times during a particular time period (e.g., every fifteen seconds over a one hour period). The generated location information 128 associated with each dairy cow 104 may then be stored (e.g., in memory module 120 or any other suitable location in system 100) such that the location information 128 may be later accessed (e.g., by estrus detection logic 124 and health monitoring logic 126, as described in further detail below).”) [The signal transmission at regular intervals reads on “repeatedly”. The identification devices receiving the RFID signals from each tag identifying each cow reads on “receive the information related to the received radio signals, received from the respective receivers (120a, 120b, 120 c), for each said tag identity”. The determined coordinate location being an (X,Y) location within the free stall pen 102 or the determined coordinate location being an (X,Y,Z) location within the free stall pen reads on “calculate a set of position coordinates of the tag”.]
provide data entities (301), each of the data entities (301) comprising the calculated position coordinates of the tag (110) associated with a timestamp and/or an index number to the processing controller (150); and Rajkondawar: Paragraphs [0015], [0017]-[0019] and [0020]-[0023] [As described above.] Rajkondawar: Paragraph [0016] (“In certain embodiments, a generated signal corresponding to a tag affixed to a dairy cow 104 may include (1) an identification number related to the dairy cow 104, (2) the distance, angle, and/or other information concerning the location of the dairy cow 104 relative to the identification device 114 generating the signal, (3) an identification of the identification device 114 generating the signal, and/or (4) a timestamp.”) [The determined coordinate location about the position of a particular dairy cow along with the timestamp reads on “provide data entities (301), each of the data entities (301) comprising the calculated position coordinates of the tag (110) associated with a timestamp”.]
wherein the processing controller (150) is configured to: determine the distribution of time that the animal (101) associated with the tag (110) comprising the tag identity has spent in different said zones (210, 220, 230) of the barn (200) by associating each of the obtained data entities (301) with a respective one of the zones (210, 220, 230) based on the position coordinates of the respective data entity (301) and the position coordinates of the zones (210, 220, 230); Rajkondawar: Paragraphs [0015]-[0019] and [0020]-[0023] [As described above.] Rajkondawar: Paragraph [0024] (“In certain embodiments, estrus detection logic 124 may be operable to access location information 128 associated with each dairy cow 104 in free stall pen 102 and determine, based on at least a portion of that location information 128, one or more movement parameters 130 associated with each dairy cow 104. The determined movement parameters 130 for dairy cows 104 may be stored (e.g., in memory module 120) such that changes in the movement parameters 130 may be assessed over time.”) Rajkondawar: Paragraph [0025] (“…the movement parameters 130 for a particular dairy cow 104 may include a percentage of a particular time period (e.g., one hour) the particular dairy cow 104 spent in each of a number of areas of free stall pen 102. For example, the movement parameters 130 may include (1) a percentage of time the particular dairy cow 104 spent standing in a stall 106 of free stall pen 102 (stall standing parameter 134 , (2) a percentage of time the particular dairy cow 104 spent lying in a stall 106 of free stall pen 102 (stall lying parameter 136), (3) a percentage of time the particular dairy cow 104 spent walking in a walking alley 108 of free stall pen 102 (alley walking parameter 138), (4) a percentage of time the particular dairy cow 104 spent lying in a walking alley 108 of free stall pen 102 (alley lying parameter 140), (5) a percentage of time the particular dairy cow 104 spent standing in a walking alley 108 of free stall pen 102 (alley standing parameter 142 ) (6) a percentage of time the particular dairy cow 104 spent near a water trough 110 (watering parameter 144), and/or (7) a percentage of time the particular dairy cow 104 spent near feed lane 112 (feeding parameter 146).”)
counting a number of the data entities (301) in each of the zones (210, 220, 230); and Rajkondawar: Paragraphs [0024] and [0025] [As described above.] Rajkondawar: Paragraph [0026] (“The above-described movement parameters 130 for a particular dairy cow 104 may be determined by comparing the location information 128 for the particular dairy cow 104 collected during a particular time period (including a number of coordinate locations for the particular dairy cow 104 at a number of discrete times during the particular time period, as described above) with layout information 132 for free stall pen 102 (e.g., stored in memory module 120). In certain embodiments, layout information 132 for free stall pen 102 may include coordinate locations defining the corners of each stall 106 (and thus defining the area within each stall 106), coordinate locations defining the corners of each walking lane 108 (and thus defining the area within each walking lane 108), coordinate locations defining the corners of the area around each water trough 110 (and thus defining a watering area), and the corners of the area near feed lane 112 (and thus defining a feeding area). For each coordinate location for the particular dairy cow 104 within these defined areas, the area in which the particular dairy cow 104 is located at each discrete time during the time period may be determined.”)
calculating an amount of time the animal (101) has spent in each of the zones (210, 220, 230) by multiplying the number of the data entities (301) associated with each of the zones (210, 220, 230) with the regular time interval. Rajkondawar: Paragraphs [0016] and [0026] [As described above.] Rajkondawar: Paragraph [0027] (“For example, location information 128 for a particular dairy cow 104 may include coordinate locations of the particular dairy cow 104 at discrete times (e.g., every fifteen seconds) during a particular time period (e.g., one hour). By comparing each (X,Y) coordinate location of the particular dairy cow 104 with layout information 132 for free stall pen 102, the position of the particular dairy cow 104 within free stall pen 102 may be determined at each discrete time during the time period. Moreover, if it is assumed that the position of the particular dairy cow 104 remains constant from one discrete time to the next (e.g., for the fifteen second time period until a new coordinate location for the particular dairy cow 104 is available), a percentage of the particular time period that the particular dairy cow 104 spent in various locations within the free stall pen 102 may be determined. Furthermore, in embodiments in which the coordinate location for the particular dairy cow 104 includes a (Z) location, a percentage of the particular time period that the particular dairy cow 104 spent standing versus lying at each location may additionally be determined (as a (Z) location for the particular dairy cow 104 in both a standing and lying position may be known). From this information, the above-described movement parameters 130 (e.g., a stall standing parameter 134, a stall lying parameter 136, an alley walking parameter 138, an alley lying parameter 140, an alley standing parameter 142, a watering parameter 144, and a feeding parameter 146) may be determined.”) [The determined percentage of the particular time period that the particular dairy cow spent in various locations within the free stall pen reads on “calculating an amount of time the animal (101) has spent in each of the zones”.]
Although implicitly, Rajkondawar does not expressly teach that the each tag comprises a processing device (510), a radio transmitter (520) and a memory (530) storing a tag identity. However, Simon describes a method of identifying an animal within a group. Simon teaches:
…wherein each of the tags (110) comprises a processing device (510), a radio transmitter (520) and a memory (530) storing a tag identity, wherein the processing device (510) is configured to transmit a radio signal via the radio transmitter (520)…;…tag at which the radio transmitter is disposed, and… calculate a set of position coordinates of the tag (110) comprising the respective tag identity based on the information related to the received radio signals of the radio transmitter (520) received by the receivers (120a, 120b, 120c); … Simon: Paragraph [0015] (“The system consists of the miniature optical ear tag 1 and receiver (reader) 2. The tag 1 contains transmitter 3 emitting IR signal, IR receiver 4, electronic chip (processor) 6, memory 6 and lithium battery 7.”) Simon: Paragraph [0018] (“The tag reader 2 is directed to the monitored animal by the optical sight 16. Being activated, the reader 2 sends short optical pulse emitted by the transmitter 10 that is received by the receiver 4 of the tag 1. It activates electronics (processor 6) of the tag 1, which start transmitting the data stored in the memory 6 via optical line 8 provided by the transmitter 3 of the tag 1 and the receiver 9 of the reader 2.”)
Accordingly, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, having the teachings of Rajkondawar and Simon before them, for each of the tags to comprise a processing device, a radio transmitter and a memory storing a tag identity, wherein the processing device is configured to transmit a radio signal via the radio transmitter…;…tag at which the radio transmitter is disposed because the references are in the same field of endeavor as the claimed invention and they are focused on cattle identification and monitoring.
One of ordinary skill in the art before the effective filing date of the claimed invention would have been motivated to do this modification because it would automate the identification and data entry in order to reduce expense and to improve accuracy of the data. Simon Paragraph [0003]
Regarding claim 2, Rajkondawar and Simon teach all the claimed features of claim 1, from which claim 2 depends. Rajkondawar further teaches:
The system (100) according to claim 1, wherein the database (140) comprises a historical distribution of time that each of the animals (101, 102, 103) has spent in different said zones (210, 220, 230) of the barn (200); and Rajkondawar: Paragraph [0026] [As described in claim 1.] Rajkondawar: Paragraph [0005] (“In certain embodiments, a method includes storing location information associated with a dairy livestock. The stored location information includes a location of the dairy livestock within a free stall pen at each of a plurality of times during a time period.”)
wherein the processing controller (150) is configured to: compare the determined distribution of time that the animal (101) has spent in different said zones (210, 220, 230) with the historical distribution of time that the respective animal (101, 102, 103) has spent in different said zones (210, 220, 230), extracted from the database (140); Rajkondawar: Paragraph [0041] (“For example, estrus detection logic 124 may determine if a particular dairy cow 104 is likely to be in estrus by comparing a “heat index” for the particular dairy cow 104 (described below) with a baseline heat index. In certain embodiments, the heat index for a particular dairy cow 104 may correspond to the product of an alley walking parameter 138 and a normalized mobility count 154 (e.g., heat index=(alley walking parameter)×(mobility count/hour)). In such embodiments, the heat index may be indicative of the amount of movement of the particular dairy cow 104, with a certain amount of increase in movement of the particular dairy cow 104 (e.g., 250% over a baseline, as described below) being an indicator that the particular dairy cow 104 is in estrus.”) Rajkondawar: Paragraph [0042] (“In certain other embodiments, the heat index for a particular dairy cow 104 may correspond to a function of stall standing parameter 134, stall lying parameter 136, alley walking parameter 138, alley lying parameter 140, ally standing parameter 142, watering parameter 144, feeding parameter 146, watering count 148, feeding count 150, mounting count 152, mobility count 154, distance traveled 156, turn count 158, and/or sign change count 160.”) Rajkondawar: Paragraph [0058] (“In such embodiments, the heat index may be indicative of an increase in certain activity of the particular dairy cow 104 (e.g., walking in walking lanes 108, pacing, and/or mounting other dairy cows 104), with an increase in such activity being an indicator that the particular dairy cow 104 is in estrus.”) Rajkondawar: Paragraph [0070] (“Health monitoring logic 126 may additionally be operable to determine, based on one or more of the above-described movement parameters 130 associated with dairy cows 104 (determined based coordinate locations for the particular dairy cow 104 during a particular time period, as described above), which of the dairy cows 104 are unhealthy at a given time. For example, health monitoring logic 126 may compare a “health index” for a particular dairy cow 104 (described below) with a baseline health index. In certain embodiments, the health index for the particular dairy cow 104 may correspond to a function of a stall standing parameter 134, a stall lying parameter 136, an alley walking parameter 138, an alley lying parameter 140, an alley standing parameter 142 a watering parameter 144, a feeding parameter 146, a watering count 148, and/or a feeding count 150.”)
store the determined distribution of time that the animal (101) has spent in different said zones (210, 220, 230) in the database (140); and Rajkondawar: Paragraph [0025] [As described in claim 1.] Rajkondawar: Paragraph [0067] (“Having determined that one or more dairy cows 104 are likely to be in estrus (and possibly subsequent to confirming those determinations), estrus detection logic 124 may create exception reports to be stored in association with the one or more dairy cows 104 (e.g., in memory module 120).”) Rajkondawar: Paragraph [0068] (“In certain embodiments, health monitoring logic 126 may be operable to access location information 128 associated with each dairy cow 104 in free stall pen 102 and determine, based on at least a portion of that location information, one or more movement parameters 130 associated with each dairy cow 104. For example, health monitoring logic 126 may determine movement parameters 130 including a percentage of a particular time period (e.g., one hour) the dairy cows 104 spent in each of a number of areas of free stall pen 102. For example, health monitoring logic 126 may determine a stall standing parameter 134, a stall lying parameter 136, an alley walking parameter 138, an alley lying parameter 140, an alley standing parameter 142, a watering parameter 144, a feeding parameter 146, a watering count 148, and/or a feeding count 150 in a manner substantially similar to that described above. The determined movement parameters 130 for dairy cows 104 may be stored (e.g., in memory module 120) such that changes in the movement parameters 130 may be assessed over time. Because the movement of a dairy cow 104 within free stall pen 102 may be indicative of whether the dairy cow 104 is likely unhealthy, the movement parameters may be used by health monitoring logic 126 to determine if one or more dairy cows 104 are unhealthy (as described below).”)
generate an alert when a deviation between the determined distribution of time and the historical distribution of time of the animal (101), based on the made comparison, exceeds a threshold limit. Rajkondawar: Paragraph [0067] (“Additionally or alternatively, estrus detection logic 124 may initiate the communication of reports (e.g., emails) to the farmer for each of the one or more dairy cows 104 such that the farmer may further monitor the one or more dairy cows 104 and/or remove the one or more dairy cows 104 from the free stall pen 102 for breeding. In certain embodiments, a report communicated to a farmer may indicate the relative strength of the estrus determination.”) Rajkondawar: Paragraph [0088] (“Having determined that one or more dairy cows 104 are unhealthy (and possibly subsequent to confirming those determinations), health monitoring logic 126 may create exception reports to be stored in association with the one or more dairy cows 104 (e.g., in memory module 120). Additionally or alternatively, health monitoring logic 126 may initiate the communication of reports (e.g., emails) to the farmer for each of the one or more dairy cows 104 such that the farmer may further monitor the one or more dairy cows 104 and/or remove the one or more dairy cows 104 from the free stall pen 102 for medical attention. In certain embodiments, a report communicated to a farmer may indicate the relative strength of the determination that a dairy cow 104 is likely unhealthy.”) [The communication of the reports read on “generate an alarm”.]
Regarding claim 6, Rajkondawar and Simon teach all the claimed features of claim 1, from which claim 6 depends. Rajkondawar further teaches:
The system (100) according claim 1, wherein the zones (210, 220, 230) comprises a feeding zone (210) dedicated for eating, a resting zone (220) dedicated for resting and a transportation zone (230) dedicated for walking. Rajkondawar: Paragraph [0025] and FIG. 1 (“… (2) a percentage of time the particular dairy cow 104 spent lying in a stall 106 of free stall pen 102 (stall lying parameter 136), (3) a percentage of time the particular dairy cow 104 spent walking in a walking alley 108 of free stall pen 102 (alley walking parameter 138), (4) a percentage of time the particular dairy cow 104 spent lying in a walking alley 108 of free stall pen 102 (alley lying parameter 140), (5) a percentage of time the particular dairy cow 104 spent standing in a walking alley 108 of free stall pen 102 (alley standing parameter 142 ) (6) a percentage of time the particular dairy cow 104 spent near a water trough 110 (watering parameter 144), and/or (7) a percentage of time the particular dairy cow 104 spent near feed lane 112 (feeding parameter 146).”) [The feed lane reads on “a feeding zone”, the free stall pen reads on “a resting zone”, and the walking alley reads on “a transportation zone”.]
Regarding claim 8, Rajkondawar and Simon teach all the claimed features of claim 6, from which claim 8 depends. Rajkondawar further teaches:
The system (100) according to claim 6, wherein the processing controller (150) is configured to: compare a calculated time the animal (101) has spent in the transportation zone (230) with a heat time threshold limit; and Rajkondawar: Paragraph [0041] (“For example, estrus detection logic 124 may determine if a particular dairy cow 104 is likely to be in estrus by comparing a “heat index” for the particular dairy cow 104 (described below) with a baseline heat index. In certain embodiments, the heat index for a particular dairy cow 104 may correspond to the product of an alley walking parameter 138 and a normalized mobility count 154 (e.g., heat index=(alley walking parameter)×(mobility count/hour)). In such embodiments, the heat index may be indicative of the amount of movement of the particular dairy cow 104, with a certain amount of increase in movement of the particular dairy cow 104 (e.g., 250% over a baseline, as described below) being an indicator that the particular dairy cow 104 is in estrus.”) Rajkondawar: Paragraph [0042] (“In certain other embodiments, the heat index for a particular dairy cow 104 may correspond to a function of stall standing parameter 134, stall lying parameter 136, alley walking parameter 138, alley lying parameter 140, ally standing parameter 142, watering parameter 144, feeding parameter 146, watering count 148, feeding count 150, mounting count 152, mobility count 154, distance traveled 156, turn count 158, and/or sign change count 160.”) Rajkondawar: Paragraph [0059] (“If the heat index for particular dairy cow 104 is greater than the baseline heat index by more than a predefined amount (e.g., 250%), estrus detection logic 124 may determine that the particular dairy cow 104 is likely to be in estrus (as such an increase may indicate either that … (2) the particular dairy cow 104 is spending time in those portions of the free stall pen 102 in which a dairy cow 104 likely to be in estrus is likely to be located, such as walking lanes 108). The baseline heat index may be (1) a heat index for the particular dairy cow 104 during a previous time period (e.g., the previous twenty-four hours), (2) an average heat index for one or more other dairy cows 104 in free stall pen 102 during a previous time period (e.g., the previous twenty-four hours), (3) a user defined baseline heat index, or (4) any other suitable baseline heat index, according to particular needs.”) Rajkondawar: Paragraph [0060] (“As another example, estrus detection logic 124 may compare a distance traveled 156 by the particular dairy cow 104 (e.g., a distance traveled per hour during a particular time period, as described above) with a baseline distance traveled. If the distance traveled 156 by the particular dairy cow 104 is greater than the baseline distance traveled by more than a predefined amount (e.g., 250%), estrus detection logic 124 may determine that the particular dairy cow 104 is likely to be in estrus (as an increase in movement of the particular dairy cow 104 may be an indicator that the particular dairy cow 104 is in estrus). [Exceeding the heat index during a particular time period reads on “a heat time threshold limit”.]
generate a heat alert for the animal (101) when the heat time threshold limit is exceeded. Rajkondawar: Paragraph [0058] (“In such embodiments, the heat index may be indicative of an increase in certain activity of the particular dairy cow 104 (e.g., walking in walking lanes 108, pacing, and/or mounting other dairy cows 104), with an increase in such activity being an indicator that the particular dairy cow 104 is in estrus.”) Rajkondawar: Paragraph [0067] (“Having determined that one or more dairy cows 104 are likely to be in estrus (and possibly subsequent to confirming those determinations), estrus detection logic 124 may create exception reports to be stored in association with the one or more dairy cows 104 (e.g., in memory module 120). Additionally or alternatively, estrus detection logic 124 may initiate the communication of reports (e.g., emails) to the farmer for each of the one or more dairy cows 104 such that the farmer may further monitor the one or more dairy cows 104 and/or remove the one or more dairy cows 104 from the free stall pen 102 for breeding. In certain embodiments, a report communicated to a farmer may indicate the relative strength of the estrus determination.”)
Regarding claim 9, Rajkondawar and Simon teach all the claimed features of claim 6, from which claim 9 depends. Rajkondawar further teaches:
The system (100) according to claim 6, wherein the processing controller (150) is configured to:
compare a time the animal (101) has spent in the resting zone (220) with a passivity time threshold limit; and Rajkondawar: Paragraph [0068] (“In certain embodiments, health monitoring logic 126 may be operable to access location information 128 associated with each dairy cow 104 in free stall pen 102 and determine, based on at least a portion of that location information, one or more movement parameters 130 associated with each dairy cow 104. For example, health monitoring logic 126 may determine movement parameters 130 including a percentage of a particular time period (e.g., one hour) the dairy cows 104 spent in each of a number of areas of free stall pen 102. For example, health monitoring logic 126 may determine … a stall lying parameter 136, … an alley lying parameter 140, an alley standing parameter 142, a watering parameter 144, a feeding parameter 146, a watering count 148, and/or a feeding count 150 in a manner substantially similar to that described above. The determined movement parameters 130 for dairy cows 104 may be stored (e.g., in memory module 120) such that changes in the movement parameters 130 may be assessed over time. Because the movement of a dairy cow 104 within free stall pen 102 may be indicative of whether the dairy cow 104 is likely unhealthy, the movement parameters may be used by health monitoring logic 126 to determine if one or more dairy cows 104 are unhealthy (as described below).”) Rajkondawar: Paragraph [0086] (“In such embodiments, the health index may be indicative of an amount of time the particular dairy cow 104 spent in each of the various portions of free stall pen 102, with an increase in activity in certain portions of free stall pen 102 (e.g., an increase in the amount of time spent lying in walking alleys 108) being an indicator that the particular dairy cow 104 is unhealthy.”) Rajkondawar: Paragraph [0087] (“If the health index for particular dairy cow 104 is greater than the baseline health index by more than a predefined amount (e.g., 250%), health monitoring logic 126 may determine that the particular dairy cow 104 is likely unhealthy. The baseline health index may be (1) a health index for the particular dairy cow 104 during a previous time period (e.g., the previous twenty-four hours), (2) an average health index for one or more other dairy cows 104 in free stall pen 102 during a previous time period (e.g., the previous twenty-four hours), (3) a user defined baseline health index, or (4) any other suitable baseline health index, according to particular needs.”)
generate an anomaly alert for the animal (101) when the passivity time threshold limit is exceeded. Rajkondawar: Paragraph [0088] (“Having determined that one or more dairy cows 104 are unhealthy (and possibly subsequent to confirming those determinations), health monitoring logic 126 may create exception reports to be stored in association with the one or more dairy cows 104 (e.g., in memory module 120). Additionally or alternatively, health monitoring logic 126 may initiate the communication of reports (e.g., emails) to the farmer for each of the one or more dairy cows 104 such that the farmer may further monitor the one or more dairy cows 104 and/or remove the one or more dairy cows 104 from the free stall pen 102 for medical attention. In certain embodiments, a report communicated to a farmer may indicate the relative strength of the determination that a dairy cow 104 is likely unhealthy.”)
Regarding claim 10, Rajkondawar and Simon teach all the claimed features of claim 1, from which claim 10 depends. Rajkondawar further teaches:
The system (100) according to claim 1, wherein the radio transmitter (520) is configured to transmit the radio signal in an Ultra-Wide Band. Rajkondawar: Paragraph [0016] (“Communication between identification devices 114 and tags affixed to dairy cows 104 may be facilitated by any suitable technology, including, for example, passive radio-frequency identification (RFID), active RFID, Wi-Fi, Bluetooth, ultra-wide band (UWB),…”)
Regarding claim 10, Rajkondawar and Simon teach all the claimed features of claim 1, from which claim 10 depends. Rajkondawar further teaches:
The system (100) according to claim 1, wherein the radio transmitter (520) is configured to transmit the radio signal in an Ultra-Wide Band. Rajkondawar: Paragraph [0016] (“Communication between identification devices 114 and tags affixed to dairy cows 104 may be facilitated by any suitable technology, including, for example, passive radio-frequency identification (RFID), active RFID, Wi-Fi, Bluetooth, ultra-wide band (UWB),…”)
Regarding claim 13, Rajkondawar and Simon teach all the claimed features of claim 2, from which claim 13 depends. Rajkondawar further teaches:
The system (100) according to claim 2, further comprising an output device (160); and wherein the processing controller (150) is configured to: output, on the output device, information concerning a distribution of time that the animal (101) has spent in different said zones (210, 220, 230) of the barn (200). Rajkondawar: Paragraph [0025] [As described in claim 1.] Rajkondawar: Paragraph [0067], [0068], and [0088] [As described in claim 2.] [The monitoring logic of the controller creating exception reports reads on “an output device”.]
Regarding claim 14, Rajkondawar and Simon teach all the claimed features of claim 13, from which claim 14 depends. Rajkondawar further teaches:
The system (100) according to claim 13, wherein the processing controller (150) is configured to: output the generated alert on the output device (160). Rajkondawar: Paragraph [0025] [As described in claim 1.] Rajkondawar: Paragraph [0067], [0068], and [0088] [As described in claim 2.]
Claim 3 is rejected under 35 U.S.C. 103 as being unpatentable over Rajkondawar in view of Simon and further in view of WO 2009/011641 A1 to Lind et al. submitted in IDS dated 12/29/2023 (“Lind”).
Regarding claim 3, Rajkondawar and Simon teach all the claimed features of claim 2, from which claim 3 depends. Rajkondawar and Simon fails to teach the recitations of claim 3. However, Lind describes a method for detecting oestrus behaviour of a milking animal. Lind teaches:
The system (100) according to claim 2, wherein the historical distribution of time that the respective animal (101, 102, 103) has spent in different said zones (210, 220, 230) of the barn (200) is stored associated with a time of the day when the distribution was determined; and Lind: Page 3, line 15-21 (“The method comprises the steps of: monitoring an activity level increase of the milking animal by the sensor means; and detecting the oestrus behaviour in dependence on a set threshold activity level increase, wherein the threshold activity level increase is indicative 20 of an oestrus behaviour of the milking animal and set in dependence on time of day.”)
wherein the processing controller (150) is configured to determine the time of the day when the distribution of time that the animal (101) has spent in different said zones (210, 220, 230) is determined; and Lind: Page 6, line 28, to Page 7, line 5 (“An adjustable sensitivity for the water buffalo's activity level increase regarding heat detection is therefore used m accordance with the present invention. That is, the activity level increase ΔLTOD that is indicative of a water buffalo in heat is set differently depending on time of day, and especially differently during day and night. During daytime the activity level increase that is indicative for an oestrus behaviour, ΔLToD, is smaller than during nighttime. A higher sensitivity for such heat related activity level increase is thus required during daytime compared to nighttime.”)
make the comparison with historical distribution of time the animal (101, 102, 103) has spent in different zones (210, 220, 230) at the corresponding time of the day. Lind: Page 5, line 24, to Page 6, line 5 (“For example, a particular first milking animal may show her oestrus behaviour (as measured by an activity increase) most clearly during morning, while a second milking animal may show her oestrus behaviour most obviously between noon and 6 p.m. Further, the first milking animal may have small differences in activity level when in heat compared to when not in heat, while the second milking animal may have great differences in activity level when in heat and when not in heat. In short, a certain activity level increase of the first milking animal may be indicative for her being in heat, while the same activity level increase for the second milking animal need not be an indication of her being in heat.”) Lind: Page 8, line 27 to Page 9, line 23 (“In accordance with the invention, the increase in activity level used for determining oestrus behaviour can therefore be adjustable in dependence on the time of day. In general, ΔLN > ΔLD but there may be individual differences among the water buffaloes. The activity level increase may therefore be set on an individual basis suitable for the specific buffalo in question. The times during which the different activity level increases are to be used may also differ between the water buffaloes and could also be taken into account and be set accordingly… although describing the differences in activity level increases during day and night for water buffaloes, there are other milking animals amongst which the oestrus behaviour differs depending on time of day… cows housed in a tied-up system have only a small increase in activity level when in heat. In particular, the limited space available for the milking animal affects her activity level, both her "normal" behaviour and her high and low levels. Therefore, for a tied-up milking animal the ratio between normal and high activity level decreases compared to milking animals in loose housing…The environmental influence on the cow' s behaviour is the lowest at night, and her oestrus behaviour may therefore, like for the buffalos, be detected more easily at night hours.”)
Accordingly, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, having the teachings of Rajkondawar, Simon, and Lind before them, for the historical distribution of time that the respective animal (101, 102, 103) has spent in different said zones (210, 220, 230) of the barn (200) is stored associated with a time of the day when the distribution was determined; and wherein the processing controller (150) is configured to determine the time of the day when the distribution of time that the animal (101) has spent in different said zones (210, 220, 230) is determined; and make the comparison with historical distribution of time the animal (101, 102, 103) has spent in different zones (210, 220, 230) at the corresponding time of the day because the references are in the same field of endeavor as the claimed invention and they are focused on cattle identification and monitoring.
One of ordinary skill in the art before the effective filing date of the claimed invention would have been motivated to do this modification because it would to determine the optimum time for insemination or mounting by a bull in order for the farmer to plan his breeding in an optimized way. Simon Page 3, lines 5-8.
Claim 4 is rejected under 35 U.S.C. 103 as being unpatentable over Rajkondawar in view of Simon and further in view of US Patent Publication No. 2015/0293205 A1 to Sloth submitted in IDS dated 12/29/2023 (“Sloth”).
Regarding claim 4, Rajkondawar and Simon teach all the claimed features of claim 1, from which claim 4 depends. Rajkondawar and Simon do not expressly teach the features of claim 4. However, Sloth describes a computer system for measuring real time position of a plurality of animals. Sloth teaches:
The system (100) according to claim 1, wherein the processing controller (150) is configured to: define a cluster (470) of obtained data entities (301) having position coordinates within a distance limit and a timestamp and/or index number within a time limit; calculate position coordinates of a gravity point (480) of the defined cluster (470); determine an associated said zone of (210, 220, 230) the position coordinates of the cluster gravity point (480); and determine that all of the data entities (301) of the cluster (470) are associated with the associated zone (210, 220, 230) of the cluster gravity point (480). Sloth: Paragraph [0014] (“…a cluster module, where a cluster is defined by an aggregated set of location points where the points calculated by the Movement Module are aggregated into a cluster if they are at the approximate same position at the approximate same time. The cluster has one location position, a zone attribute and a trip count. The trip count is the number of points that are represented by the cluster. If an animal stands still or moves very slowly, there will only be a limited number of clusters with a high trip count. If an animal moves at a higher speed, there will be many clusters with a low trip count. The Cluster Module has an algorithm for determining the validity of the data. If the animal appears to have jumped, say, 10 meters in a few seconds, the Cluster Module applies an algorithm to determine whether the current cluster or the previous cluster is erroneous. More precisely, a maximum speed is applied to discard points that are too far away from the current cluster. The cluster calculation has two main parts, Calculation of clusters and Calculation of “confirmed” clusters. By calculation of clusters a cluster has a time, ctime and a centre c. When a new point arrives, one of the following things happens, The point is taken into the cluster, and the cluster centre and time are be adjusted or a new cluster is started or the point is discarded. Furthermore, the Cluster Module has an algorithm for further processing of the clusters. This algorithm merges neighbouring clusters if one of the clusters contains only one point, and the distance between cluster centers is low. The cluster is merged by at least the following operations; the center c is calculated as the weighted average of the centre coordinates in the two clusters. The weights are the number of points in the clusters. The time period is from the start of the first cluster to the end of the last cluster. The count of data in the merged cluster is the sum of the two data counts. A cluster is final if it cannot be merged with a neighboring cluster. The Cluster Module uses the definition of zone boundaries to determine a list of zones that the cluster belongs to. Finally, the cluster is classified as walking or not walking. The cluster is classified as walking if the following requirements are all fulfilled. The cluster centre is inside a walking zone. The cluster time interval is less than a certain threshold, e.g. 1 second. If the cluster is classified as walking, the animal is classified as walking in the corresponding time interval. The distance is calculated as the distance between the cluster centre and the previous cluster centre. Hereby is achieved that a very effective computer analysis can be performed, because with a high probability the animal will remain in the cluster for a period.”) Sloth: paragraph [0080] (“FIG. 6 discloses calculation of clusters. A cluster has a time, ctime and a center c.”) Sloth: paragraph [0081] (“The first part of the cluster calculation considers a new data point p. It is first decided whether p can be considered as belonging to the current cluster. This is done by comparing the distance between the current cluster center and the point. The distance must be below the radius of the cluster, maxDist. Note that the radius depends on whether the point p is pre-classified as “moving” or “not moving”. If the animal is pre-classified as “moving”, the radius of the cluster is set to a pre-defined low value, e.g. 20 centimeters. If the animal is pre-classified as “not moving”, the radius of the cluster is set to a pre-defined high value, e.g. 40 centimeters. Furthermore, the time of the new data point is compared to the time of the cluster (latest time of a point in the cluster). If p is both within the time and distance limits, then p is considered as part of the cluster.”) Sloth: paragraph [0082] (“Furthermore, the Cluster Module has an algorithm for further processing of the clusters. This algorithm merges neighboring clusters if one of the clusters contains only one point, and the distance between cluster centres is low. The cluster is merged by the following operations: The centre c is calculated as the weighted average of the centre coordinates in the two clusters. The weights are the number of points in the clusters. The time period is from the start of the first cluster to the end of the last cluster. The count of data in the merged cluster is the sum of the two data counts. A cluster is final if it cannot be merged with a neighbouring cluster. The Cluster Module uses the definition of zone boundaries to determine a list of zones that the cluster belongs to. Finally, the cluster is classified as walking or not walking. The cluster is classified as walking if the following requirements are all fulfilled: The cluster center is inside a walking zone. The cluster time interval is less than a certain threshold, e.g. 1 second. If the cluster is classified as walking, the animal is classified as walking in the corresponding time interval. The distance is calculated as the distance between the cluster center and the previous cluster centre.”) See also Paragraphs [0083]-[0088] of Sloth.
Accordingly, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, having the teachings of Rajkondawar, Simon, and Sloth before them, to define a cluster (470) of obtained data entities (301) having position coordinates within a distance limit and a timestamp and/or index number within a time limit; calculate position coordinates of a gravity point (480) of the defined cluster (470); determine an associated said zone of (210, 220, 230) the position coordinates of the cluster gravity point (480); and determine that all of the data entities (301) of the cluster (470) are associated with the associated zone (210, 220, 230) of the cluster gravity point (480)because the references are in the same field of endeavor as the claimed invention and they are focused on cattle identification and monitoring.
One of ordinary skill in the art before the effective filing date of the claimed invention would have been motivated to do this modification to achieve a very effective computer analysis to be performed, because with a high probability the animal will remain in the cluster for a period and achieve real time position of animals in a limited area. Sloth Paragraphs [0003] and [0014]
Claim 5 is rejected under 35 U.S.C. 103 as being unpatentable over Rajkondawar in view of Simon and further in view of Melzer, N., Foris, B. and Langbein, J., 2021. Validation of a real-time location system for zone assignment and neighbor detection in dairy cow groups. Computers and Electronics in Agriculture, 187, p.106280. submitted in IDS dated 12/29/2023 (“Melzer”).
Regarding claim 5, Rajkondawar and Simon teach all the claimed features of claim 1, from which claim 5 depends. Rajkondawar and Simon do not expressly teach the features of claim 5. However, Melzer describes accurate RTLS calibration setup and the quality of tracking data when inferring dairy cow behavior based on it. Melzer teaches:
The system (100) according to claim 1, wherein the processing controller (150) is configured to determine the associated zone of the respective data entity (301) by applying a rolling average of the position coordinates of the preceding data entities (301) within a predetermined window length and associating all of the data entities (301) within the predetermined window length with the same said zone (210, 220, 230). Melzer: Section 2.3 Tracking data preparation (“Zone assignment: We assigned the corresponding specific area (i.e., walking alley, brush area, and each specific bin and lying stall) to each measurement for the X-Y coordinates and performed the following two steps for each animal to improve the zone assignments (visualized in Fig. A.3). First, we aimed to eliminate short jumping measurements (e.g., between lying stalls, lying stall and brush area, or walking area). We iterated over the specific areas and determined the time points when the cow was detected.”) Melzer: Section 2.5.2 Evaluation of zone assignments (“Using the data set obtained for the test day in both periods, we considered the zone assignments determined based on the video observations as the gold standard to validate the zones assigned based on the X-Y coordinates with the different sets of prepared tracking data. The following analysis was performed for: (a) the main zones (i.e., feed bunk, walking alley, and lying stalls), and (b) specific areas (i.e., walking alley, brush area, and each specific bin and lying stall). Within each zone, true positives (TPs) comprised measurements where the same zone was assigned by both the video observer and tracking data, false positives (FPs) comprised measurements where the zone was only assigned by the tracking data, and false negatives (FNs) comprised measurements where the zone was only assigned by the video observer. We used the sensitivity (i.e., TP/(TP + FN)) to measure the quantity of measurements correctly assigned to specific zones and the precision (i.e., TP/(TP + FP)) to measure the quality of the zone assignments. In addition, on the validation days (no video observations), we evaluated the zone assignments at the feed bunk using the electronic bin data as the gold standard based on the same measurements.”) Melzer: Section 3.2 Zone Assignment Evaluation (“Fig. 3 shows the sensitivity and precision of the main zone assignments for the different sets of prepared tracking data for the main zones. In both periods, the main zone lying stalls had the highest sensitivity and precision, where both criteria were higher than 0.97 for all approaches employed, thereby agreeing with previously reported results … These findings are not surprising because cows spent most of the day within this zone (i.e., about 12 h (Tucker et al., 2020)) and this was generally the largest area within a pen. The size and time spent in an area appeared to be especially relevant for the feed bunk (smallest area), where visits usually known to range from seconds to several minutes (e.g., Chapinal et al., 2007, Chizzotti et al., 2015). For all of the prepared tracking data sets, in period 1, many measurements that actually occurred at the feed bunk were assigned to the walking alley (especially on the right-hand side of the pen; Fig. 2A), which led to reduced precision (<0.46) in the walking alley due to many FPs and decreased sensitivity (<0.23) at the feed bunk because of many FNs. By contrast, in period 2, we obtained good sensitivity (range: 0.89–0.90) and precision (range: 0.86–0.88) in the walking alley for all of the prepared tracking data sets. The performance obtained in the present study is better than that found in a previous study (Tullo et al., 2016; sensitivity = 0.78 and precision = 0.85). Similarly, we obtained high sensitivity (range: 0.84–0.88) and precision (range: 0.93–0.94) at the feed bunk. These results are comparable to the values obtained using the GEA system (Tullo et al., 2016, Meunier et al., 2018) but higher compared with those reported by Pastell et al. (2018), although comparisons of these data are limited due to differences in the data preparation processes employed.
Accordingly, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, having the teachings of Rajkondawar, Simon, and Melzer before them, to define a cluster (470) of obtained data entities (301) having position coordinates within a distance limit and a timestamp and/or index number within a time limit; calculate position coordinates of a gravity point (480) of the defined cluster (470); determine an associated said zone of (210, 220, 230) the position coordinates of the cluster gravity point (480); and determine that all of the data entities (301) of the cluster (470) are associated with the associated zone (210, 220, 230) of the cluster gravity point (480) because the references are in the same field of endeavor as the claimed invention and they are focused on cattle identification and monitoring.
One of ordinary skill in the art before the effective filing date of the claimed invention would have been motivated to do this modification to using an ultra-wideband real-time location system (RTLS) to assess the health status, welfare, and comfort of cattle at individual and farm levels. Melzer Conclusion
Claim 7 is rejected under 35 U.S.C. 103 as being unpatentable over Rajkondawar in view of Simon and further in view of Biffert et al. (US Patent Publication No. 2022/0200519 A1) (“Biffert”).
Regarding claim 7, Rajkondawar and Simon teach all the claimed features of claim 6, from which claim 7 depends. Rajkondawar and Simon do not expressly teach the features of claim 7. However, Biffert describes livestock management system for detecting, tracking, and responding to livestock location and physical parameters, and for determining livestock behavior and physical conditions correlated thereto. Biffert teaches:
The system (100) according to claims 6, wherein each said tag (110) comprises an accelerometer (540); and wherein each said data entity (301) comprises accelerometer data of the accelerometer (540); and Biffert: Paragraph [0006] (“Each tag locally and autonomously receives and/or acquires data regarding the location, orientation, movement of the livestock and other data about the livestock from embedded receivers and sensors, e.g., an embedded global positioning receiver, gyroscope, and accelerometer. Each tag locally and autonomously receives and/or acquires physical parameters of the livestock from one or more sensors, e.g., an internal body temperature sensor. The sensors may be implanted in and/or attached to the livestock separate from the tag and/or incorporated in the tag. Each tag locally and autonomously processes the data and physical parameters and determines certain activities and behaviors of the livestock, e.g., eating, ruminating, ambulating, and determines certain physical conditions of the livestock correlated thereto, e.g., illness, injury, estrus, breeding, and calving.”)
wherein the processing controller (150) is configured to detect that the animal (100) is ruminating at the position coordinates of any one of the obtained data entities (301), based on analysis of the accelerometer data of the data entity (301); and Biffert: Paragraph [0085] (“The accelerometer 52 provides data indicative of the movement of the tag 20 corresponding to movement of the livestock 12 to which the tag 20 is attached in three axes which may be referred to as longitudinal, lateral, and vertical or pitch roll, and yaw axes. The data provided by the accelerometer 52 is used to determine when the livestock 12 is moving and stationary, as well as the linear direction and perhaps the rate of movement. This information can in turn be used to determine certain activities, behaviors, and physical conditions of the livestock 12. The accelerometer 52 may be a commercially available MEMs, piezoelectric, or other type of accelerometer that is suitable for carrying out and is consistent with the objectives and functionality described herein. The accelerometer 52 may be combined with one or more of the other data collection devices described herein on the same chip or in the same package, or may be a separate device on a separate chip or in a separate package. The accelerometer data may be generated, communicated to, and/or received by the processor 50 continuously or periodically. The accelerometer data also may be generated and communicated to the processor 50 automatically or on demand.”) Biffert: Paragraph [0162] (“The tag 20 can autonomously and automatically determine the above-described physical parameters, activities and behaviors of the livestock 12 from data acquired from the sensor(s) 32, accelerometer 52, gyroscope 54, compass 56, altimeter 58, barometer 59, etc. For example, as illustrated in FIGS. 12A, 12B, and 12C, the tag 20 can determine when and whether a livestock 12 is ruminating, eating and/or ambulating at least in part from orientation data acquired from the gyroscope 54, alone or in combination with other sensors, such as the accelerometer 52 and barometer 59. Inputs from the gyroscope 54 and the accelerometer 52, in combination, may be referred to as inertial measurements. The Inertial Measurement Unit (IMU) 55 can measure the motion of a livestock 12, which may include measuring step count, high acceleration motion, reduction in motion, and average motion, for example.”)
in case the position coordinates of the data entity (301) are situated close to a limitation of the resting zone (220): associating the data entity (301) of the animal (101) with the resting zone (220) when rumination is detected. Biffert: Paragraph [0006] [As described above.] Biffert: Paragraph [0130] (“The tag 20 can also be configured and adapted to detect the presence of the livestock 12 at a particular feed or water station or other point of interest by communication with a local sensor and transceiver 34 located at or near the point of interest. For example, an RFID reader or scanner, a photocell, or another sensor may detect the presence of the tag 20 and/or the livestock 12 at or near a particular feed or water station, a bog, barn, etc. and communicate that data and the identity of the point of interest to the tag 20.”) Biffert: Paragraph [0312] (“A field labeled “tag data” in FIG. 14A can include data regarding livestock location, orientation, heading, movement, elevation, and body temperature; external data such as weather and meteorological data; audio and video data; tag operational data and conditions; and any other data a tag 20 can receive or acquire and communicate. The field labeled ““other sensor data” can include any data a local sensor/transceiver 34 can communicate, including but not limited to weight of a livestock 12. A field labeled “determined activity” can include determinations of eating, drinking, ruminating, resting, breeding and any other determinations a tag can make and communicate. A field labeled “determined conditions” can include determinations of illness, injury, estrus, pregnancy, calving, and any other physical conditions of a livestock 12 a tag 20 can make and communicate. A field labeled “nearby tags” can include data regarding the relative position and angle of the tag 20 that generated the data, information, and/or determinations when they were generated. Alternatively, this data could also be included in the “tag data” field.”) Biffert: Paragraph [0327] (“As one example, one or more models or other detection algorithms can be created to predict and/or determine from selected data (e.g., internal body temperature over time, weight, location, relative position and angle to nearby herd members, orientation, elevation, and movement) and from selected activity determinations (e.g., eating, drinking, ambulation) that the livestock 12 is ill, injured, in estrus, ovulating, breeding, pregnant, or calving.”)
Accordingly, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, having the teachings of Rajkondawar, Simon, and Biffert before them, for each said tag (110) comprises an accelerometer (540); and wherein each said data entity (301) comprises accelerometer data of the accelerometer (540); and wherein the processing controller (150) is configured to detect that the animal (100) is ruminating at the position coordinates of any one of the obtained data entities (301), based on analysis of the accelerometer data of the data entity (301); and in case the position coordinates of the data entity (301) are situated close to a limitation of the resting zone (220): associating the data entity (301) of the animal (101) with the resting zone (220) when rumination is detected because the references are in the same field of endeavor as the claimed invention and they are focused on cattle identification and monitoring.
One of ordinary skill in the art before the effective filing date of the claimed invention would have been motivated to do this modification so that the tag can make predictions and determinations about livestock activity and physical condition by applying one or more AI models and/or other detection algorithms to the received and acquired data. Each tag also locally and autonomously receives or acquires data about events and conditions external to the livestock, processes the data, and determines whether a potential risk to the livestock is present, e.g., a nearby predator or vehicle. Biffert Paragraph [0006]
Claims 11 and 12 are rejected under 35 U.S.C. 103 as being unpatentable over Rajkondawar in view of Simon and further in view of US Patent Publication No. 2024/0130265 A1 to Nilsson (“Nilsson”).
Regarding claim 11, Rajkondawar and Simon teach all the claimed features of claim 1, from which claim 11 depends. Rajkondawar further teaches:
The system (100) according to claim 1, wherein at least one of the zones (210, 220, 230) is divided into sub-zones (310a, 310b, 320a, 320b, 320c, 320d, 320e, 320f, 320g, 330a, 330b), Rajkondawar: Paragraph [0026] (“In certain embodiments, layout information 132 for free stall pen 102 may include coordinate locations defining the corners of each stall 106 (and thus defining the area within each stall 106), coordinate locations defining the corners of each walking lane 108 (and thus defining the area within each walking lane 108), coordinate locations defining the corners of the area around each water trough 110 (and thus defining a watering area), and the corners of the area near feed lane 112 (and thus defining a feeding area). For each coordinate location for the particular dairy cow 104 within these defined areas, the area in which the particular dairy cow 104 is located at each discrete time during the time period may be determined.”)
Rajkondawar and Simon do not expressly teach “compile a calculated amount of time each said animal (101, 102, 103) has spent in each of the sub-zones (310a, 310b, 320a, 320b, 320c, 320d, 320e, 320f, 320g, 330a, 330b); and detect a sub zone any of the sub-zones (310a, 310b, 320a, 320b, 320c, 320d, 320e, 320f, 320g, 330a, 330b) which is used less than a threshold time, based on the compilation”. However, Nilsson describes a method of controlling operation of agricultural devices. Nilsson teaches:
wherein the processing controller (150) is configured to: compile a calculated amount of time each said animal (101, 102, 103) has spent in each of the sub-zones (310a, 310b, 320a, 320b, 320c, 320d, 320e, 320f, 320g, 330a, 330b); and detect a sub zone any of the sub-zones (310a, 310b, 320a, 320b, 320c, 320d, 320e, 320f, 320g, 330a, 330b) which is used less than a threshold time, based on the compilation. Nilsson: Paragraph [0011] (“In some embodiments, the control arrangement is configured to calculate, based on the obtained positions of individual animals, a density of animals in a sub-area of the livestock area and to control the operation of the one or more of the mobile agricultural devices based on the calculated density.”) Nilsson: Paragraph [0069] (“One way to increase efficiency in livestock management is to use the obtained positions to determine when and where tasks need to be performed. For example, historical presence of many animals may indicate a need for cleaning. In other words, in some embodiments the controlling S4 operation comprises determining S4A based on the obtained positions of the individual animals 10 and/or individual mobile agricultural devices 20, a need to operate the mobile agricultural devices 20 at certain times and/or in certain places in the livestock area. For example, if more than a predefined number of animals 10 has been present in a certain sub-area or zone of the livestock area 30, then “need” to perform a task (e.g. cleaning) is triggered. Alternatively, if the density of animals 10 has exceeded a threshold for a certain time then “need” to perform the task is valid. In these embodiments the controlling S4 of the operation is then performed in accordance with the determined need.”) Nilsson: Paragraph [0071] (“Density of animals in different sub-areas of the livestock area is one parameter that can be used to detect a need or appropriate times and/or places. The sub-areas may be predefined zones, such as alleys, rooms, feeding places etc. Alternatively, sub-areas may be dynamically determined to represent different densities of animals, i.e. a count of animals per area unit (e.g. no. animals per m2). In other words, in some embodiments the controlling S4 operation comprises calculating S4C, based on the obtained positions of individual animals, a density of animals 10 in a sub-area of the livestock area 30 and controlling the operation of the one or more of the mobile agricultural devices 20 based on the calculated density. For example, if a feed wagon 20 (a) is automatically operated, the system can increase the number of deliveries when many animals are present at a certain feeding place 32 and a feed pusher 20 (b) may in addition be controlled to pass more times at that particular feeding place 32. In this way (a large farm that has) several different feeding places 32 that are served by one feed wagon 20 (a) and one feed pusher (b), each feeding place 32 is automatically served differently depending on the number of animals 10 that are present at each feeding place 32 (e.g. in a zone associated with the feeding place 32). In this way the system can be controlled to automatically adapt (in real-time) to the number of animals 10 present in the various sub-areas.”)
Accordingly, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, having the teachings of Rajkondawar, Simon, and Nilsson before them, compile a calculated amount of time each said animal (101, 102, 103) has spent in each of the sub-zones (310a, 310b, 320a, 320b, 320c, 320d, 320e, 320f, 320g, 330a, 330b); and detect a sub zone any of the sub-zones (310a, 310b, 320a, 320b, 320c, 320d, 320e, 320f, 320g, 330a, 330b) which is used less than a threshold time, based on the compilation because the references are in the same field of endeavor as the claimed invention and they are focused on cattle monitoring.
One of ordinary skill in the art before the effective filing date of the claimed invention would have been motivated to do this modification because by considering animal density the efficiency and safety in performing tasks (such as feeding or cleaning tasks) is further improved, since the density (number) of animals in the sub-area typically indicates an increased need for a specific task in that sub-area. Animal density can hereby be used to determine when and/or how often the task is executed by the mobile agricultural device in the sub-area. Nilsson Paragraph [0011].
Regarding claim 12, Rajkondawar, Simon, and Nilsson teach all the claimed features of claim 11, from which claim 12 depends. Rajkondawar further teaches:
The system (100) according to claim 11, wherein the sub-zones (310a, 310b, 320a, 320b, 320c, 320d, 320e, 320f, 320g, 330a, 330b) comprises cubicles and/or feeding stations. Rajkondawar: Paragraph [0012] (“System 100 includes a free stall pen 102 housing a number of dairy livestock 104. Free stall pen 102 may be configured to include a number of stalls 106, walking lanes 108, and water troughs 110, and may be positioned adjacent to a feed lane 112. System 100 further includes a number of identification devices 114 positioned throughout free stall pen 102, each identification device 114 being configured to (1) read tags affixed to each of the dairy livestock 104, and (2) communicate with a controller 116. Although this particular implementation of system 100 is illustrated and primarily described, the present disclosure contemplates any suitable implementation of system 100 according to particular needs. Additionally, although the present disclosure contemplates free stall pen 102 housing any suitable dairy livestock 104 (e.g., cows, goats, sheep, water buffalo, etc.), the remainder of this description is detailed with respect to dairy cows.”)
It is noted that any citations to specific paragraphs or figures in the prior art references and any interpretation of the reference should not be considered to be limiting in any way. A reference is relevant for all it contains and may be relied upon for all that it would have reasonably suggested to one having ordinary skill in the art. See MPEP 2123.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
US Patent Publication No. 2019/0059337 A1 to Robbins describes a remote intelligent health monitoring device is attached to the ear of the animal, and the temperature data is based on heat sensed within the ear canal of the animal. In some embodiments, the health condition criteria comprises a temperature threshold. In some embodiments, determining whether temperature data satisfies a health condition criteria is based upon one or more of the ambient temperature near the animal's location, the length of time during which the temperature data satisfied the health condition criteria.
US Patent Publication No. 2021/0144972 A1 to Hicks et al. describes that being able to detect the presence of animals allows the treated water to be provided when it is needed. For example, if no animal approaches the water trough for an extended period of time, the water properties do not have to be maintained at their highest levels. But when an animal approaches, the water may be quickly rejuvenated. Additionally, being able to detect the presence of animals also allows potentially unhealthy animals to receive highly preferred water (e.g., −800 mV).
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/ALICIA M. CHOI/Primary Patent Examiner, Art Unit 2117
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