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 .
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 June 24, 2026 has been entered.
Claim Objections
Claims 1 and 17 are objected to because of the following informalities:
Claims 1 and 17 are not notated in the manner required in MPEP 714, the following claim limitations below have not been noted as being new amendments. The requirement is being waived for this occasion only and will be met with an issuance of non-compliance and any subsequent time.
Claims 1 and 17 recite “further configured to select an output based on height confidence or lowest error rate, averaging outputs, or weighting outputs”; and further recites “wherein the multi object tracking software system is configured to assign sensor data to individual vertebrate animals over extended periods”.
Appropriate correction is required.
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.
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-9, and 17-24 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 17 recite “each model comprising a distinct tracking algorithm”; “further configured to select an output based on highest confidence or lowest error rate, averaging outputs, or weighting outputs”; and “wherein the multi object tracking software system is configured to assign sensor data to individual vertebrate animals over extended periods”. However, examiner notes, the originally filed specification never discloses the newly added claim amendments.
Claims 1-9 and 17-24 rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claims 1 and 17 recite “wherein the plurality of models comprises at least two different types selected from said cross-checkable group, further configured to select an output based on height confidence or lowest error rate, averaging output or weighing outputs” which is indefinite, because it is unclear what is being further limited.
Claims 1 and 17 recite “(each model comprising a distinct tracking algorithm)”, which is indefinite for two reasons. First, it is unclear if the newly added limitation is part of the claim limitation since it not part of proper claim construction. Adding parenthesis for general claim language is not in accordance with the MPEP. Second, in light of the 112a rejection above, it is unclear what distinct tracking algorithm is used for either the appearance model, motion model, interaction model, exclusion model, or occlusion model.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claim(s) 1-3, and 17-19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Salem et al. (US 2016/0150758 A1), hereinafter Salem, in view of Saliu et al. (US 2023/0045152 A1), hereinafter Saliu, and Datta et al. (US 2018/0279921 A1), hereinafter Datta.
Regarding claim 1, as best understood in light of the 112 rejections above, Salem discloses a smart cage system for housing and assaying multiple vertebrate animals comprising:
at least one inner housing assembly (cage 20, fig.7) and at least one outer housing assembly (Para. [0038], “[t]he SCORHE 10 is configured and dimensioned to enclose or engage a home cage 20 for monitoring mice living therein”; fig. 1), the housing assemblies each having a top portion (inner housing assembly: as shown in fig. 7, cage 20 has a top surface; outer housing assembly: as shown in figs. 5A-5B & fig. 6, SCHORE enclosure 100 may sit on the shelves 302 of the rack system 300), a bottom portion (inner housing assembly: as shown in fig. 7, cage 20 has a bottom surface 26; outer housing assembly: as shown in figs. 5A-5B & fig. 6, the SCORHE enclosure 100 includes a bottom surface 141), and at least one side portion (inner housing assembly: as shown in fig. 7, cage 20 has one or more walls 24; outer housing assembly: as shown in figs. 5A-5B & fig. 6, the SCORHE enclosure includes panel assemblies 142), the inner housing assemblies adapted to be at least partly disposed within and removable from the outer housing assemblies (Para. [0046], “the door assembly 160 of the SCORHE enclosure 100 includes a hinged door 102 with a magnetic latch 104 to allow for insertion and removal of the home cage 20 from the rack 300 without removing the enclosure from the rack system”);
the smart cage including at least one controller (computing device 202, fig. 1) adapted to monitor and record data from two sensors from a group of: optical sensors, motion sensors, pressure sensors, weight sensors, temperature sensors, humidity sensors, proximity sensors, chemical sensors, volume sensors, level sensors, audio sensors, odor sensors, heartbeat sensors, brainwave sensors, bite force sensors, body mass sensors, color sensors, rotary sensors, light sensors, oscillation sensors, balance sensors, reflex or reaction sensors, waterflow sensors, force meter sensors, load sensors, electrical sensors, and bite strength sensors1 (optical sensor chosen; Para. [0044], “each SCORHE enclosure includes…at least one port and/or wire to transmit the output from the dual-camera system 120 to the computing device 202, as shown in Fig. 1, via a wired or wireline connection. Furthermore, Para. [0068] discloses “the video data is recorded and stored at a video recorder device 208, including digital video and audio for subsequent processing”; as shown in fig. 5A there is a rear camera 122B and a front camera 122A), the sensors adapted to monitor at least one or more of the environment (Para. [0047], “the cameras 122A-B are suitable for performing video analysis when two or more mice are housed in the cage 20”), and multiple vertebrate animals within the inner housing assembly (mice 400A-B; figs. 8A & 8B);
the two or more sensors operationally synchronized at least one or more of before, in real time, and after sensing an action, wherein data captured by each sensor can be synchronized by way of at least one time measuring device (Paras. [0094-95], a time stamp is required to assess the data of the dual camera system);
a multi object tracking software system (Para. [0075], SCORHE Video Processing application (SVPA) 204 includes the segmentation module 218 and the occlusion reasoning module 224) operationally coupled to at least two optical sensors by the at least one controller (Para. [0071], “the SCORHE 10 includes at least one computing device 202 executing a SCORHE Video Processing application (“SVPA) 204 for processing video data”), the multi object tracking software adapted to track individuals of the multiple vertebrate animals by way of at least two or more from a group of: object detection, object reidentification, generating trajectories, and aggregating features2 (object detection chosen; the segmentation module 218 identifies objects in each video output from the cameras 122A-B of the SCORHE enclosure 100. For example, the segmentation module 218 partitions each frame of the video outputs into separate objects, based at least in part on the color, intensity, or texture of pixels in the image within each frame”); and
at least the outer housing assembly including at least one or more from a group of ports, slots, shelves, pockets, hooks, fasteners, and sleeves each adapted to retain at least one sensor3 (slot chosen; as shown in fig. 5A, the dual camera system 120 is retained in a slot of the SCORHE enclosure 100).
However, Salem does not appear to specifically disclose wherein the multi object tracking
software was further configured to use a plurality of models, including at least one or more from a cross-checkable group of: appearance models, motion models, interaction models, exclusion models, and occlusion handling models, wherein the tracking of individual vertebrate animals is time-synchronized across multiple sensors and configured to maintain continuous identification of each animal, animal location data synced with sensor data to identify which individual animals produced what sensor data, the multi object tracking software further configured to cross-check tracking outputs from the plurality of models by comparing outputs of at least two of the appearance models, motion models, interaction models, exclusion models, or occlusion handling models to validate individual animal identification, and to cross-check the animal location data with the synchronized sensor data to enhance tracking accuracy in complex multi-animal scenarios. Furthermore, Salem does not appear to specifically disclose cross checking models.
Saliu is in the field of a system to simultaneously track multiple organisms at high
resolution (Abstract) and teaches wherein the multi object tracking software was further configured to use a plurality of models (as discussed in para. [0182-184], the projection process cross references a plurality of models, or in the alternative, at least the YOLO series discloses a plurality of models) from a cross-checkable group of4: appearance models (as discussed in Para. [0099], “unique identification” reads on appearance models), motion models (as discussed in para. [0102], the tracking of a moving object), interaction models (as shown in Figs. 4c to 4e), exclusion models, and occlusion handling models (as shown in Figs. 4c to 4e), each model comprising a distinct tracking algorithm (Para. [0225], for the motion model, there is motion tracking and for the appearance model there is object analysis. Both models comprise a distinct tracking algorithm), wherein the tracking of individual vertebrate animals is time-synchronized across multiple sensors and configured to maintain continuous identification of each animal (Para. [0108], “[t]he object detection algorithm may also be used to identify multiple objects across multiple image frames acquired as a function of time, to enable object tracking as a function of time”. See also Para. [0102]), animal location data synced with sensor data to identify which individual animals produced what sensor data (Para. [0099], “the objects are classified 457 into categories, for example, by a detection algorithm such as convolutional neural network (CNN). For example, CNN can report a classification score for each object, in addition to the location and bounding box widths and heights. The classification score can be used to categorize each object”), the multi object tracking software further configured to cross-check tracking outputs from the plurality of models by comparing outputs of at least two of the appearance models, motion models, interaction models, exclusion models, or occlusion handling models (fig. 24 represents a continuous tracking of the plurality of models (i.e., detecting the organisms) and, specifically, operation 2415 repeats the steps as a function of time for tracking), and to cross-check the animal location data with the synchronized sensor data to enhance tracking accuracy in complex multi-animal scenarios (Para. [0002], “[o]bserving such unconstrained movement and interaction is helpful for improving our understanding of organism behavior”), and wherein the multi object tracking software is configured to assign sensor data to individual vertebrate animals over extended periods (Para. [0101], “[0101] In FIG. 4I, the process can be repeated, e.g., new images 428* are captured, new objects are detected, and object properties are calculated. For example, new locations of the objects are determined to observe the movements of the objects as a function of time”). Furthermore, Saliu discloses wherein the two or more sensors operationally synchronized at least one or more of before, in real time, and after sensing an action, wherein data captured by each sensor can be synchronized by way of at least one time measuring device (Para. [0184], “[t]he CNN process can be applied to the captured images, such as to each camera image data in parallel, to create bounding box coordinates for detected objects for each camera. The bounding box coordinates can be aggregated, using inter-camera overlaps to reduce double-counting objects and to merge objects having portions in multiple neighbor cameras. The object detection CNN algorithms can additionally report a classification score for each object, in addition to the location and bounding box width/heights. The classification score can be used to categorize each object. Categorizations include unique identification of objects, or for unique identification of object type.”; see also fig. 24).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Salem such that the multi object tracking software was further configured to use a plurality of models from a cross-checkable group, and wherein the tracking of individual organisms is time-synchronized as taught by Saliu, in order to benefit from tracking multiple organisms at the same time.
Datta is in the field of automatically classifying animal behavior (Abstract) and teaches cross-checking at least one model with another model (Para. [0054], “FIG. 12D depicts cross-likelihood analysis depicting the probability that a data instance assigned to a particular module will be effectively modeled by another module”). Datta further teaches weighting outputs based on the data (Para. [0094]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Salem such that software further included cross-checking models as taught by Datta, in order to provide a more reliable set of data.
Regarding claim 2, Salem in view of Saliu discloses the invention in claim 1, and Salem further discloses wherein the two or more sensors are operationally coupled to the outer housing assembly (see dual camera system 120 on SCORHE enclosure 100, fig. 5A) wherein the inner housing assembly may be removed without removing the at least one or more sensors (Para. [0046], “the door assembly 160 of the SCORHE enclosure 100 includes a hinged door 102 with a magnetic latch 104 to allow for insertion and removal of the home cage 20 from the rack 300 without removing the enclosure from the rack system”; as shown in figs. 4 & 5A).
Regarding claim 3, Salem in view of Saliu discloses the invention in claim 1, and Salem further discloses wherein at least one physiological software system is adapted (coordinate module 222, fig. 10), from the data gathered from the two or more sensors and a multi object tracking software system measures of each of the multiple vertebrate animals from at least one or more from a group of: lifespan, frailty index, muscle strength, run endurance, learning and memory, balance and coordination, body weight, food intake, total time spent in sleep and awake, temporal pattern of being asleep and awake, speed of nest building, visual acuity, hearing acuity, water intake, and coat color/density, position tracking, distance traveled, movement speed, sleep time, cardiovascular health, cognition, balance and coordination, tremors, gait deficiencies, vision movement, and speed of nest building5 (distance traveled and movement speed chosen; Para. [0081], “[t]he coordinate module 222 determines the physical coordinates of the objects identified by the segmentation module 218 in each frame. For objects in motion, the coordinate module 222 may use temporal data to determine the distance travelled by the object (i.e. mouse) as well as the speed of the object”).
Regarding claim 17, as best understood in light of the 112 rejections above, Salem discloses a smart cage system for housing and assaying multiple vertebrate animals comprising:
a plurality of inner housing assemblies and outer housing assemblies (fig. 4), the housing assemblies each having a top portion (inner housing assembly: as shown in fig. 7, cage 20 has a top surface; outer housing assembly: as shown in figs. 5A-5B & fig. 6, SCHORE enclosure 100 may sit on the shelves 302 of the rack system 300), a bottom portion (inner housing assembly: as shown in fig. 7, cage 20 has a bottom surface 26; outer housing assembly: as shown in figs. 5A-5B & fig. 6, the SCORHE enclosure 100 includes a bottom surface 141), and at least one side portion (inner housing assembly: as shown in fig. 7, cage 20 has one or more walls 24; outer housing assembly: as shown in figs. 5A-5B & fig. 6, the SCORHE enclosure includes panel assemblies 142), each inner housing assembly adapted to be at least partly disposed within and removable from the respective outer housing assembly (Para. [0046], “the door assembly 160 of the SCORHE enclosure 100 includes a hinged door 102 with a magnetic latch 104 to allow for insertion and removal of the home cage 20 from the rack 300 without removing the enclosure from the rack system”), the housing assemblies further disposed within at least one rack assembly adapted to hold the plurality of at least one housing assemblies wherein housing assemblies may be operationally contained at least one or more of vertically and horizontally from each other (double-bay rack 300, fig. 4), and wherein the housing assemblies may be at least partially removed from the rack independently from other housing assemblies (fig. 4);
the plurality of smart cages including at least one controller (computing device 202, fig. 1) adapted to monitor and record data from two or more sensors from a group of: optical sensors, motion sensors, pressure sensors, weight sensors, temperature sensors, humidity sensors, proximity sensors, chemical sensors, volume sensors, level sensors, audio sensors, odor sensors, heartbeat sensors, brainwave sensors, body mass sensors, color sensors, rotary sensors, light sensors, oscillation sensors, balance sensors, reflex or reaction sensors, waterflow sensors, force meter sensors, load sensors, electrical sensors, and bite strength sensors6 (optical sensor chosen; Para. [0044], “each SCORHE enclosure includes…at least one port and/or wire to transmit the output from the dual-camera system 120 to the computing device 202, as shown in Fig. 1, via a wired or wireline connection. Furthermore, Para. [0068] discloses “the video data is recorded and stored at a video recorder device 208, including digital video and audio for subsequent processing”; as shown in fig. 5A there is a rear camera 122B and a front camera 122A), the sensors adapted to monitor at least one or more of the environment (Para. [0047], “the cameras 122A-B are suitable for performing video analysis when two or more mice are housed in the cage 20”), and at least one vertebrate animal within the inner housing assembly (one mouse 400B; figs. 8A & 8B), wherein each smart cage may have unique configurations of at least one or more sensors (cameras 122A-B, fig. 5A);
the at least two or more sensors operationally synchronized at least one or more of before, in real time, and after sensing an action, wherein data captured by each at least one or more sensors can be synchronized by way of at least one time measuring device (Paras. [0094-95], a time stamp is required to assess the data of the dual camera system);
a multi object tracking software system (Para. [0075], SCORHE Video Processing application (SVPA) 204 includes the segmentation module 218 and the occlusion reasoning module 224) operationally coupled to at least two optical sensors by the at least one controller (Para. [0071], “the SCORHE 10 includes at least one computing device 202 executing a SCORHE Video Processing application (“SVPA) 204 for processing video data”), the multi object tracking software adapted to track individuals of the multiple vertebrate animals by way of two or more from a group of: object detection, object reidentification, generating trajectories, and aggregating features7 (object detection chosen; the segmentation module 218 identifies objects in each video output from the cameras 122A-B of the SCORHE enclosure 100. For example, the segmentation module 218 partitions each frame of the video outputs into separate objects, based at least in part on the color, intensity, or texture of pixels in the image within each frame”); and
at least the outer housing assembly including at least one or more from a group of ports, slots, shelves, pockets, hooks, fasteners, and sleeves each adapted to retain at least one sensor8 (slot chosen; as shown in fig. 5A, the dual camera system 120 is retained in a slot of the SCORHE enclosure 100).
However, Salem does not appear to specifically disclose wherein the multi object tracking
software was further configured to use a plurality of models from a cross-checkable group of: appearance models, motion models, interaction models, exclusion models, and occlusion handling, the tracking of individual vertebrate animals adapted to be time synchronized across multiple sensors and adapted to maintain continuous identification of each animal, animal location data synced with sensor data to identify which individual animals produced what sensor data, the multi object tracking software further configured to cross-check tracking outputs from the plurality of models by comparing outputs of at least two of the appearance models, motion models, interaction models, exclusion models, or occlusion handling models to validate individual animal identification, and to cross-check the animal location data with the synchronized sensor data to enhance tracking accuracy in complex multi-animal scenarios. Furthermore, Salem does not appear to specifically disclose cross checking models.
Saliu is in the field of a system to simultaneously track multiple organisms at high
resolution (Abstract) and teaches wherein the multi object tracking software was further configured to use a plurality of models (as discussed in para. [0182-184], the projection process cross references a plurality of models, or in the alternative, at least the YOLO series discloses a plurality of models) from a cross-checkable group of9: appearance models (as discussed in Para. [0099], “unique identification” reads on appearance models), motion models (as discussed in para. [0102], the tracking of a moving object), interaction models (as shown in Figs. 4c to 4e), exclusion models, and occlusion handling models (as shown in Figs. 4c to 4e), each model comprising a distinct tracking algorithm (Para. [0225], for the motion model, there is motion tracking and for the appearance model there is object analysis. Both models comprise a distinct tracking algorithm), wherein the tracking of individual vertebrate animals is time-synchronized across multiple sensors and configured to maintain continuous identification of each animal (Para. [0108], “[t]he object detection algorithm may also be used to identify multiple objects across multiple image frames acquired as a function of time, to enable object tracking as a function of time”. See also Para. [0102]), animal location data synced with sensor data to identify which individual animals produced what sensor data (Para. [0099], “the objects are classified 457 into categories, for example, by a detection algorithm such as convolutional neural network (CNN). For example, CNN can report a classification score for each object, in addition to the location and bounding box widths and heights. The classification score can be used to categorize each object”), the multi object tracking software further configured to cross-check tracking outputs from the plurality of models by comparing outputs of at least two of the appearance models, motion models, interaction models, exclusion models, or occlusion handling models to validate individual animal identification (fig. 24 represents a continuous tracking of the plurality of models (i.e., detecting the organisms) and, specifically, operation 2415 repeats the steps as a function of time for tracking), and to cross-check the animal location data with the synchronized sensor data to enhance tracking accuracy in complex multi-animal scenarios (Para. [0002], “[o]bserving such unconstrained movement and interaction is helpful for improving our understanding of organism behavior”), and wherein the multi object tracking software is configured to assign sensor data to individual vertebrate animals over extended periods (Para. [0101], “[0101] In FIG. 4I, the process can be repeated, e.g., new images 428* are captured, new objects are detected, and object properties are calculated. For example, new locations of the objects are determined to observe the movements of the objects as a function of time”). Furthermore, Saliu discloses wherein the two or more sensors operationally synchronized at least one or more of before, in real time, and after sensing an action, wherein data captured by each sensor can be synchronized by way of at least one time measuring device (Para. [0184], “[t]he CNN process can be applied to the captured images, such as to each camera image data in parallel, to create bounding box coordinates for detected objects for each camera. The bounding box coordinates can be aggregated, using inter-camera overlaps to reduce double-counting objects and to merge objects having portions in multiple neighbor cameras. The object detection CNN algorithms can additionally report a classification score for each object, in addition to the location and bounding box width/heights. The classification score can be used to categorize each object. Categorizations include unique identification of objects, or for unique identification of object type.”; see also fig. 24).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Salem such that the multi object tracking software was further configured to use a plurality of models from a cross-checkable group, and wherein the tracking of individual organisms is time-synchronized as taught by Saliu, in order to benefit from tracking multiple organisms at the same time.
Datta is in the field of automatically classifying animal behavior (Abstract) and teaches cross-checking models (Para. [0054], “FIG. 12D depicts cross-likelihood analysis depicting the probability that a data instance assigned to a particular module will be effectively modeled by another module”). Datta further teaches weighting outputs based on the data (Para. [0094]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Salem such that software further included cross-checking models as taught by Datta, in order to provide a more reliable set of data.
Regarding claim 18, Salem in view of Saliu discloses the invention in claim 17, and Salem further discloses wherein the two or more sensors is operationally coupled to the outer housing assembly (see dual camera system 120 on SCORHE enclosure 100, fig. 5A) wherein the inner housing assembly may be removed without removing the two or more sensors (Para. [0046], “the door assembly 160 of the SCORHE enclosure 100 includes a hinged door 102 with a magnetic latch 104 to allow for insertion and removal of the home cage 20 from the rack 300 without removing the enclosure from the rack system”; as shown in figs. 4 & 5A).
Regarding claim 19, Salem in view of Saliu discloses the invention in claim 17, and Salem further discloses wherein at least one physiological software system is adapted (coordinate module 222, fig. 10), from the data gathered from the at least one or more sensors and a multi object tracking software system measures of each of the multiple vertebrate animals from at least one or more from a group of: lifespan, frailty index, muscle strength, run endurance, learning and memory, balance and coordination, body weight, food intake, total time spent in sleep and awake, temporal pattern of being asleep and awake, speed of nest building, visual acuity, hearing acuity, water intake, and coat color/density, position tracking, distance traveled, movement speed, sleep time, cardiovascular health, cognition, balance and coordination, tremors, gait deficiencies, vision movement, and speed of nest building10 (distance traveled and movement speed chosen; Para. [0081], “[t]he coordinate module 222 determines the physical coordinates of the objects identified by the segmentation module 218 in each frame. For objects in motion, the coordinate module 222 may use temporal data to determine the distance travelled by the object (i.e. mouse) as well as the speed of the object”).
Claim(s) 4, 7, 20, and 23 is/are rejected under 35 U.S.C. 103 as being unpatentable over Salem in view of Saliu as applied to claims 1 and 17 above, respectively, and further in view of Harada et al. (US 2019/0183089 A1), hereinafter Harada.
Regarding claims 4 and 20, Salem in view of Saliu discloses the invention in claims 1 and 17 above, respectively, but is silent regarding the inner housing assembly including a cage floor on which is disposed at least one run wheel, and a tray disposed within the inner housing assembly to contain animal feed.
Harada is in the field of an animal husbandry equipment (Abstract) and teaches a run wheel disposed (wheel 105, fig. 1), and a tray containing animal feed (food tray 109, fig. 1).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Salem such that there was a wheel disposed in the housing as taught by Harada, in order to monitor the physical activity of the subject in the cage. Furthermore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Salem such that there was a feeding tray as taught by Harada, in order to provide the subject in the cage with continuous food. As a result of the above modification, the wheel and food tray of Harada will be disposed in the inner housing assembly of Salem.
Regarding claims 7 and 23, Salem in view of Saliu discloses the invention in claims 1 and 17 above, respectively, but does not appear to specifically disclose wherein the side portion of the outer housing assembly includes at least one or more of: a control panel adapted for mouse learning and memory assays, at least one air valve, at least one main water dispenser and valve, at least one secondary water dispenser and valve adapted for reward administration, at least one speaker and a microphone adapted for hearing acuity testing, at least one force meter for a grip bar adapted for muscle strength testing, at least one force meter for weight estimation, and at least one force meter for bite strength estimation.
However, Harada is in the field of an animal husbandry equipment (Abstract) and teaches wherein the side portion of the outer housing assembly includes at least one or more of: a control panel adapted for mouse learning and memory assays, at least one air valve, at least one main water dispenser and valve, at least one secondary water dispenser and valve adapted for reward administration, at least one speaker and a microphone adapted for hearing acuity testing, at least one force meter for a grip bar adapted for muscle strength testing, at least one force meter for weight estimation, and at least one force meter for bite strength estimation11 (at least one air valve chosen; Para. [0022], “33 and 34 show optional air in and air out vents or ducts for the cage 35”; as shown in fig. 3).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Salem such that the outer housing had at least one air valve as taught by Harada, in order to provide a modular system with access and control of the animal cage from the outside without disturbing the vertebrate animals in the inner housing. Furthermore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to position the air valve on the side portion of the cage, such that it does not interfere with the top portion when opening and closing the cage.
Claim(s) 5 and 21 is/are rejected under 35 U.S.C. 103 as being unpatentable over Salem in view of Saliu as applied to claims 1 and 17 above, respectively, and further in view of Cheung et al. (US 2018/0055434 A1), hereinafter Cheung, and Brunner et al. (US 2003/0028327 A1), hereinafter Brunner.
Regarding claims 5 and 21, Salem in view of Saliu discloses the invention in claims 1 and 17 above, respectively, and Salem further discloses wherein the outer housing assembly includes at least one or more of: a controller adapted to be a local hub to measure and integrate data associated with smart cage assays conducted using the two or more sensors, the controller operationally coupled at least one or more of wired and wirelessly (Para. [0044], “the SCORHE enclosure 100 includes a dual-camera system 120…each SCORHE enclosure includes a power supply (not shown) and at least one port and/or wire (not shown to transmit the output from the dual-camera system 120 to the computing device 202, as shown in FIG. 1, via a wired or wireline connection. Alternatively, the SCORHE enclosure 100 may include one or more wireless transmitters and receivers to transmit or receive data wirelessly), at least one or more of directly or by way of at least one other computer to report data to a central data processing system (Para. [0068], “[d]ata captured by the dual camera system 120 of the SCORHE enclosure 100 may be transmitted to the computing device 202 for video processing in a number of ways”);
at least one overhead LED [strip] adapted to cover at least a portion of a horizontal dimension of the smart cage (Para. [0062], “in one embodiment, the light source is a pair of high-flux light emitting diode (LED) strips 150A-B”; as shown in figs. 5A & 9 the top of the panel 142 has LED strips 150A);
at least one or more of an infrared camera and a near infrared camera adapted to record video substantially continuously from the cage (Paras. [0059-60], “[t]he Near-Infrared (NIR) Illumination System…the SCORHE dual-camera system 120 can record mouse activity for the entire duration of the diurnal cycle due, at least in part, to the NIR illumination system 140”), the video adapted to be used at least for individual animal position tracking (Para. [0048], “the cameras 122A-B are better suited to capture video that provides sufficient mouse shape detail, improve the accuracy of tracking multiple mice, and facilitate detection of mice in images despite frequent changes in bedding position and appearance”); and
at least one or more of an infrared and a near infrared LED adapted to illuminate an interior portion of the inner smart cage (Para. [0060], NIR illumination system 140).
However, modified Salem does not appear to specifically disclose wherein the top portion of the
outer housing assembly includes the at least one or more sensor (i.e., the camera). Further Salem does not appear to specifically disclose an overhead LED screen, the overhead LED screen used to display at least a looming spot for vision assays.
Cheung is in the field of behavior testing and training of animals (Para. [0002]) and teaches wherein the top portion of the outer housing assembly includes the at least one or more sensor (Para. [0172], “the subject camera 45 is mounted to the top of the enclosure 50”; as shown in fig. 1A).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the camera of Salem such that it was positioned on the top portion of the outer housing as taught by Cheung, in order to record the images of the subject from above and also keep the camera out of the way.
Brunner is in the field of a system and method to assess animal behavior for measuring reaction in a cage (Abstract) and further teaches a visual screen to display at least a looming spot for vision assays (Para. [0258] & Para. [0263], the visual stimuli can be represented on a screen 35; as shown in fig. 3. Examiner notes, the screen is capable of projecting a looming spot).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to further modify Salem such that there was a visual screen in the cage as taught by Brunner, in order to monitor the physical effects of the screen on the test subject. Furthermore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the screen of Brunner such that it was an LED screen, in order to make the device more energy efficient and utilizing known technologies, with the reasonable expectation of success. Furthermore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the screen of Brunner such that it was positioned over the top of the cage such that it does not physically interfere with the subject in the cage.
Claim(s) 6 and 22 is/are rejected under 35 U.S.C. 103 as being unpatentable over modified Salem as applied to claims 5 and 21 above, respectively, and further in view of Kumar et al. (US 2020/0337272 A1), hereinafter Kumar.
Regarding claims 6 and 22 above, modified Salem discloses the invention in claims 5 and 21 above, respectively, but is silent regarding at least one pass filter is disposed at least one or more of in front and behind at least one camera lens.
However, Kumar is in the field of continuous animal behavioral monitoring (Abstract) and teaches wherein at least one pass filter is disposed at least one or more of in front and behind at least one camera lens (Para. [0145], “in order to block approximately all visible light from reaching the camera 210 during video data acquisition, IR long-pass filters can be employed. As an example, a physical IR long-pass filter can be employed with the camera(s) 110. This configuration can provide substantially uniform lighting regardless a light or dark phases in the arena 200”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the camera of Salem such that there was at least one pass filter disposed in front of the camera lens as taught by Kumar, in order to block all visible light from reach the camera during video data acquisition (Kumar: para. [0145]).
Claim(s) 8 is/are rejected under 35 U.S.C. 103 as being unpatentable over Salem in view of Saliu as applied to claim 1 above, and further in view of Ingley, III et al. (US 7,497,187 B2), hereinafter Ingley.
Regarding claim 8, Salem in view of Saliu discloses the invention in claim 1, but does not appear to specifically disclose wherein the outside cage assembly includes at least one RFID reader.
However, Ingley is in the field of an animal containment device (Abstract) and teaches wherein the outer cage assembly includes at least one RFID reader (Col. 6, lines 43-45, “the means for identifying the cage may include a radio frequency identification (RFID) tag that is attached to the cage”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Salem such that the outside cage assembly includes at least one RFID reader as taught by Ingley, in order to identify the cage.
Claim(s) 9 is/are rejected under 35 U.S.C. 103 as being unpatentable over Salem in view of Saliu as applied to claim 1 above, and further in view of Boguszewski et al. (US 2018/0271052 A1), hereinafter Boguszewski.
Regarding claim 9, Salem in view of Saliu discloses the invention in claim 1, but does not appear to specifically disclose wherein more than one smart cage may be operationally coupled and configured to permit passage of animals therebetween by way of the one or more ports.
However, Boguszewski is in the field of a system and method for testing spontaneous social interactions in group-housed mice (Abstract) and teaches wherein more than one smart cage may be operationally coupled and configured to permit passage of animals therebetween by way of the one or more ports (Para. [0041], “[0041] The housing compartments are bridged by a suitable number of tube-shaped corridors (105). These inter-territorial connections enable mice to freely travel between compartments and spend time with their preferred conspecific subgroup or favoured territory areas”; as shown in fig. 1).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the smart cage of Salem such that there was a tube-shaped corridor connecting each cage together as taught by Boguszewski, in order to enable mice to freely travel between compartments (Boguszewski: Para. [0041]).
Claim(s) 24 is/are rejected under 35 U.S.C. 103 as being unpatentable over Salem as applied to claim 17 above, and further in view of Copeland et al. (US 2006/0185611 A1), hereinafter Copeland.
Regarding claim 24, Salem in view of Saliu discloses the invention in claim 17, but does not appear to specifically disclose wherein a robotic arm assembly is operationally coupled to move horizontally and vertically substantially along the entirety of the height and width of the rack assembly and is further adapted to remove housing assemblies at least partly from the rack.
However, Copeland is in the field of a robotic animal handling system (Abstract) and teaches a robotic arm assembly is operationally coupled to move horizontally and vertically substantially along the entirety of the height and width of the rack assembly and is further adapted to remove housing assemblies at least partly from the rack (Para. [0022], “[a]nimal cage 10 may be transported along top track 18 back and forth from animal cage racks 22 and one or more holding areas 50, e.g. 50a or 50b, using robotic arm 40”; see robotic arm 40 in figs. 2 & 5-6).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Salem such that there was a robotic adapted to remove housing assemblies at least partly from the rack as taught by Copeland, in order to automize the transportation of the housing assemblies.
Response to Arguments
Applicant’s arguments with respect to claim(s) 1 and 17 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Applicant argues (remarks, p. 3) that “Saliu does not discuss cross-checking models”. In light of Applicant’s amendment to the claim, this argument is deemed moot as examiner has addressed this limitation as being taught by, Datta (US 2018/0279921) which teaches cross-checking one model with at least another model. See new rejection in view of Applicant’s amendments above.
Conclusion
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/N.A./Examiner, Art Unit 3647
/KIMBERLY S BERONA/Supervisory Patent Examiner, Art Unit 3647
1 Examiner notes: only one sensor is required to meet the claimed limitation.
2 Examiner note: only one of the examples in the Markush grouping is required.
3 Examiner notes: only one of the examples in the Markush grouping is required.
4 Examiner notes: only one of the examples in the Markush grouping is required.
5 Examiner notes: only one of the examples in the Markush grouping is required.
6 Examiner notes: only one sensor is required to meet the claimed limitation.
7 Examiner note: only one of the examples in the Markush grouping is required.
8 Examiner notes: only one of the examples in the Markush grouping is required.
9 Examiner notes: only one of the examples in the Markush grouping is required.
10 Examiner notes: only one of the examples in the Markush grouping is required.
11 Examiner notes: only one of the examples in the Markush grouping is required.
Prosecution Insights