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 .
Election/Restrictions
Claims 17-31 are withdrawn from further consideration pursuant to 37 CFR 1.142(b) as being drawn to a nonelected Group, there being no allowable generic or linking claim. Election was made without traverse in the reply filed on May 15, 2025.
Drawings
The drawings are objected to as failing to comply with 37 CFR 1.84(p)(5) because they include the following reference character(s) not mentioned in the description: element ‘114’ in Fig. 2C. Corrected drawing sheets in compliance with 37 CFR 1.121(d), or amendment to the specification to add the reference character(s) in the description in compliance with 37 CFR 1.121(b) are required in reply to the Office action to avoid abandonment of the application. Any amended replacement drawing sheet should include all of the figures appearing on the immediate prior version of the sheet, even if only one figure is being amended. Each drawing sheet submitted after the filing date of an application must be labeled in the top margin as either “Replacement Sheet” or “New Sheet” pursuant to 37 CFR 1.121(d). If the changes are not accepted by the examiner, the applicant will be notified and informed of any required corrective action in the next Office action. The objection to the drawings will not be held in abeyance.
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 Vienna, Virginia 22180not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claim(s) 1-5, 7, 9-14, and 16 is/are rejected under 35 U.S.C. 103 as being unpatentable over Seo et al. (US 2022/0211010 A1), hereinafter Seo, in view of Piggott et al. (US 2024/0156061 A1), hereinafter Piggott.
Regarding claim 1, Seo discloses a system for autonomously training an animal comprising:
an input device (wearable device 10, fig. 2) comprising one or more sensors (sensor 111, fig. 2), wherein the input device is configured to communicate with a processing system (Para. [0047], “[t]he wearable device 10, the mobile robot 20 and the station 30 may communicate with each other”; see station 30 and pet care robot 20 in figs. 6 & 7),
wherein the system is configured to:
receive a first set of data from the input device (Para. [0047], “the wearable device 10 and the station 30 may communicate with each other…[f]or example, the wearable device 10, the mobile robot 20 and the station 30 may communicate with each other through an ultra-wideband (UWB) communication technology. At least one of the mobile robot 20 or the station 30 may determine a position of the wearable device 10 based on a communication signal received from the wearable device 10”), the first set of data comprising one or more of a pose, a linear acceleration, an angular velocity, a location, a sound, an image, a video, or magnetic field information of the animal1 (Para. [0055], “[t]he sensor 111 may detect a movement of the pet and may transmit a movement signal. For example, the sensor 111 may further include an acceleration sensor, a magnetic sensor, a gravity sensor, and/or a gyroscope. The sensor 111 may include an inertial measurement unit (IMU). The IMU may be composed of an acceleration sensor, a magnetic sensor, and a gyroscope. The sensor 111 may measure a force, acceleration, and angular velocity generated by the movement of the pet, and may measure a magnetic field surrounding the pet”);
transmit the first set of data to the processing system (Para. [0083], “the controller 350 of the station 30 may determine the position of the wearable device 10 based on a communication signal received from the wearable device 10, and determine whether to start the operation of the pet care robot 20 based on the position of the wearable device 10”);
receive a signal from the processing system that the first behavior has been performed by the animal based on the first set of data (when the sensor 111 on collar 10 sends the signal to the processor of device 30 indicating that a force, acceleration, and angular velocity generated by the movement of the pet, [0047 & 55]); and
upon receipt of the signal from the processing system (Para. [0065], “[t]he second communication circuit 230 may transmit an event occurrence signal to the controller 280 of the pet care robot 20, and the controller 280 may analyze the event occurrence signal to determine the event occurrence caused by an external force), cause a reward device to provide one or more of audible feedback or a reward for the animal2 (audible feedback; Para. [0068] discloses “[t]he pet care robot 20 may output a sound in response to the pet’s touch to the mobile robot 20 with its mouth or foot”. A reward for the animal; Para., [0096] discloses “[t]he station 30 may launch a snack to the position of the pet care robot 20 in response to the reception of the event occurrence information.” Examiner notes, as set forth in Para. [0065], the event occurrence may include the movement of the pet care robot 20 by an external force, for example, when the pet touches the pet care robot 20 with its foot or mouth).
Seo does not appear to specifically disclose that the processing system is configured with a first machine learning model trained to detect the performance of a first behavior in the first set of data, wherein the machine learning model is trained from training data comprising one or more of an animal pose, an animal linear acceleration, an animal angular velocity, an animal location, an animal sound, an animal image, or animal magnetic field information corresponding to the first behavior. Furthermore, Seo does not appear to specifically disclose that the signal originated form the first machine learning model.
However, Piggott is in the field of a wearable device for moving animals (Abstract) and teaches that the processing system (Para. [0505], “the term “computer” may refer to any computing device that includes the necessary components to receive, process, and output data, and normally includes a display, a processor, a memory, an input device, and a network interface”) is configured with a first machine learning model trained to detect the performance of a first behavior in the first set of data (Para. [0637], “[a] routine of the animal may be determined to predict voiding events. The routine may be determined from one or more of [0638] 1. current location information of the animal; [0639] 2. current activity information of the animal; [0640] 3. historical location information of the animal; [0641] 4. historical activity information of the animal; and [0642] 5. other inputs as described above” Furthermore, Para. [0643], “[t]he routine may be determined by the controller. The routine, target locations or other determinations may be calculated using machine learning”). Furthermore, Piggott discloses wherein the machine learning model is trained from training data comprising one or more of an animal pose, an animal linear acceleration, an animal angular velocity, an animal location, an animal sound, an animal image, or animal magnetic field information corresponding to the first behavior (one of pose, animal linear acceleration, and animal location chosen; Para. [0650], “lying, running, sleeping, standing, defecating or urinating. Each activity type has a unique set of movement patterns that can be identified by machine learning algorithms”. See also Para. [0643]).
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 processor of Seo such that it was configured with a first machine learning model as taught by Piggott, in order to increase the overall efficiency of the system by detecting patterns and predicting outcomes of the animal.
Regarding claim 2, Seo in view of Piggott discloses the invention in claim 1, and Seo further discloses wherein the input device comprises a wearable device (as set forth above in claim 1, wearable device 10, fig. 2).
Regarding claim 3, Seo in view of Piggott discloses the invention in claim 1, and Seo further discloses wherein the system (station 30 and pet care robot 20, figs. 6 & 7), comprises the reward device (Para. [0050], “[t]he station 30 may accommodate and store the mobile robot 20, perform charging of the mobile robot 20, and provide snacks to pets”).
Regarding claim 4, Seo in view of Piggott discloses the invention in claim 1, and Seo further discloses wherein the reward comprises one or more of a food reward or a toy reward (food reward chosen; Para. [0050], station 30 has an outlet 321 though which snacks are discharged may be provided on an upper surface of the station 30”).
Regarding claim 5, Seo in view of Piggott discloses the invention in claim 1, and Seo further discloses wherein the audible feedback comprises one or more instructions to the animal to perform one or more additional behaviors (Para. [0084], “in response to the pet not moving for a predetermined period of time, the station 30 may output a sound to attract the pet’s interest, and induce the pet to move near the station 30”).
Regarding claim 7, Seo in view of Piggott discloses the invention in claim 1, and Seo further discloses wherein the one or more sensors of the input device (wearable device 10 comprises a sensor 111, fig. 2) comprise at least one of a gyroscope, an accelerometer, a magnetometer, an inertial measurement unit (IMU), a microphone, a speaker, a camera, a time-of-flight (ToF) sensor, or a global positioning system (GPS)3 (a gyroscope, an accelerometer, a magnetometer, and an IMU chosen; Para. [0055], “[f]or example, the sensor 111 may further include an acceleration sensor, a magnetic sensor, a gravity sensor, and/or a gyroscope. The sensor 111 may include an inertial measurement unit (IMU). The IMU may be composed of an acceleration sensor, a magnetic sensor, and a gyroscope”).
Regarding claim 9, Seo in view of Piggott discloses the system of claim 1, wherein system is further configured to:
receive a second set of data from the input device (see Para. [0047] in claim 1 above), the second set of data comprising one or more of a second pose, a second linear acceleration, a second angular velocity, a second location, a second sound, a second image, a second video, or second magnetic field information of the animal (another one of linear acceleration, angular velocity, location, sound, or magnetic field chosen; Para. [0055], “[t]he sensor 111 may detect a movement of the pet and may transmit a movement signal. For example, the sensor 111 may further include an acceleration sensor, a magnetic sensor, a gravity sensor, and/or a gyroscope. The sensor 111 may include an inertial measurement unit (IMU). The IMU may be composed of an acceleration sensor, a magnetic sensor, and a gyroscope. The sensor 111 may measure a force, acceleration, and angular velocity generated by the movement of the pet, and may measure a magnetic field surrounding the pet”);
transmit the second set of data to the processing system (Para. [0083], “the controller 350 of the station 30 may determine the position of the wearable device 10 based on a communication signal received from the wearable device 10, and determine whether to start the operation of the pet care robot 20 based on the position of the wearable device 10”. Examiner notes, the second set of data listed out above can be transmitted to the processing system).
receive a signal from the processing system that the second behavior has been performed by the animal based on the first set of data (when the sensor 111 on collar 10 sends the signal to the processor of device 30 indicating that a force, acceleration, and angular velocity generated by the movement of the pet, [0047 & 55]. Examiner notes, the second behavior is another of force, acceleration or angular velocity generated by the movement of the pet); and
upon receipt of the signal from the processing system (Para. [0065], “[t]he second communication circuit 230 may transmit an event occurrence signal to the controller 280 of the pet care robot 20, and the controller 280 may analyze the event occurrence signal to determine the event occurrence caused by an external force), cause the reward device to provide an output comprising one or more of audible feedback or a reward4 (audible feedback; Para. [0068] discloses “[t]he pet care robot 20 may output a sound in response to the pet’s touch to the mobile robot 20 with its mouth or foot”. A reward for the animal; Para., [0096] discloses “[t]he station 30 may launch a snack to the position of the pet care robot 20 in response to the reception of the event occurrence information.” Examiner notes, as set forth in Para. [0065], the event occurrence may include the movement of the pet care robot 20 by an external force, for example, when the pet touches the pet care robot 20 with its foot or mouth).
Seo does not appear to specifically disclose that the processing system is configured with a second machine learning model trained to detect the performance of a second behavior in the second set of data, wherein the machine learning model is trained from training data comprising one or more of an animal pose, an animal linear acceleration, an animal angular velocity, an animal location, an animal sound, an animal image, or animal magnetic field information corresponding to the second behavior;
Furthermore, Seo does not appear to specifically disclose that the signal originated form the second machine learning model.
However, Piggott is in the field of a wearable device for moving animals (Abstract) and teaches that the processing system (Para. [0505], “the term “computer” may refer to any computing device that includes the necessary components to receive, process, and output data, and normally includes a display, a processor, a memory, an input device, and a network interface”) is configured with a [second] machine learning model trained to detect the performance of a second behavior in the second set of data (Para. [0637], “[a] routine of the animal may be determined to predict voiding events. The routine may be determined from one or more of [0638] 1. current location information of the animal; [0639] 2. current activity information of the animal; [0640] 3. historical location information of the animal; [0641] 4. historical activity information of the animal; and [0642] 5. other inputs as described above” Furthermore, Para. [0643], “[t]he routine may be determined by the controller. The routine, target locations or other determinations may be calculated using machine learning”). Furthermore, Piggott discloses wherein the second machine learning model is trained from training data comprising one or more of an animal pose, an animal linear acceleration, an animal angular velocity, an animal location, an animal sound, an animal image, or animal magnetic field information corresponding to the second behavior (another of pose, animal linear acceleration, and animal location chosen; Para. [0650], “lying, running, sleeping, standing, defecating or urinating. Each activity type has a unique set of movement patterns that can be identified by machine learning algorithms”. See also Para. [0643]).
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 the processing system of modified Seo such that it was configured with a second machine learning model trained to detect the performance of a second behavior in the second set of date as taught by Piggott, in to provide distinct machine learning systems responsible for understanding only one pattern, and further provide a perturbative approach to animal training, yielding predictable results.
Regarding claim 10, Seo in view of Piggott discloses the invention in claim 1, and Seo further discloses wherein the system comprises the processing system (Para. [0047], “[t]he wearable device 10, the mobile robot 20 and the station 30 may communicate with each other”; see station 30 and pet care robot 20 in figs. 6 & 7).
Regarding claim 11, Seo in view of Piggott discloses the invention in claim 1, and Seo further discloses wherein the input device (wearable device 10, fig. 2) comprises the processing system (similar to the instant invention, the wearable device 10 can communicate with the processing system [i.e., station 30 and pet care robot 20, Para. [0047]).
Regarding claim 12, Seo in view of Piggott discloses the invention in claim 1, and Seo further discloses wherein one or more of the input device or the processing system are configured to communicate over a wireless network (input device; Para. [0054], “the sensor 111 may be implemented using various wireless communication technologies (for example, radio frequency (RF) communication, infrared communication, Wi-Fi™, Bluetooth™, or Zigbee™)”. The sensor 111 is part of the wearable device 10, fig. 2. Furthermore, the processing system is configured to communicate over a wireless network, Para. [0062] discloses “[t]he first communication circuit 220 may be implemented using various wireless communication technologies. For example, the first communication circuit 220 may communicate with the wearable device 10 and the station 30 by using Ultra-wideband (UWB) communication, Radio Frequency (RF) communication, infrared communication, Wi-Fi™ communication, Bluetooth™ communication, and/or Zigbee™ communication”).
Regarding claim 13, Seo in view of Piggott discloses the invention in claim 12, and Seo further discloses wherein the wireless network being a mesh-based wireless network (as set forth above in claim 12, the sensor 111 may be implement using various wireless communication technologies, for example, Zigbee. Furthermore, Examiner notes that Zigbee is defined as a mesh network).
Regarding claim 14, Seo in view of Piggott discloses the invention in claim 1, but is silent regarding the processing system comprising a cloud-based processor.
However, Piggott discloses wherein the processing system comprises a cloud-based processor (Para. [0163], “the process is located on…the cloud”).
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 processor of Seo such that the processor system comprises a cloud-based processor as taught by Piggott, due to their accessibility and enhanced security capabilities.
Regarding claim 16, Seo in view of Piggott discloses the invention in claim 1, and Piggott further discloses wherein the processing system is configured to store one or more of the first set of data or data comprising the determination by the machine learning model that the first behavior has been performed by the animal to a profile associated with the animal (wherein the processing system is configured to store data comprising the determination by the machine learning model that the first behavior has been performed by the animal to a profile associated with the animal chosen; Piggott: Para. [0586], the need for collars is to record and store the events of the respective animal the collar is on).
Claim(s) 6 and 15 is/are rejected under 35 U.S.C. 103 as being unpatentable over Seo in view of Piggott as applied to claim 1 above, and further in view of Lewis et al. (US 2021/0251191 A1), hereinafter Lewis.
Regarding claim 6, Seo in view of Piggott discloses the invention in claim 1, and further discloses an audible feedback (as set forth above in claim 1), but is silent regarding wherein the audible feedback comprises praise for the animal.
However, Lewis is in the field of an animal communication device (Abstract) and teaches wherein the audible feedback comprises praise for the animal (Para. [0052], “the collar 225 may generate an auditory message 820 for the benefit of the animal (e.g., congratulatory feedback or praise) and/or the human user(s) (e.g., a spoken pronouncement of the determined communication), among other example alerts and results”).
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 audible feedback of Seo such that the audible feedback comprises praise for the animal as taught by Lewis, in order to encourage the desired behavior by the animal.
Regarding claim 15, Seo in view of Piggott discloses the invention in claim 1, and Seo further discloses wherein the processing system comprises a computer (Para. [0027]), but is silent regarding wherein the computer is connected to the wireless network.
However, Lewis is in the field of an animal communication device (Abstract) and teaches wherein the computer is connected to the wireless network (Para. [0026], “[p]rogram code embodied on a computer readable signal medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing”).
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 computer of Seo was connected to the wireless network as taught by Lewis, in order to access the internet and other network resources without being physically tethered by a cable.
Claim(s) 8 is/are rejected under 35 U.S.C. 103 as being unpatentable over Seo in view of Piggott as applied to claim 1 above, and further in view of Vivathana (US 2017/0372580 A1).
Regarding claim 8, Seo in view of Piggott discloses the invention in claim 1, but is silent regarding the one or more sensors of the input device comprise an attitude and heading reference system (AHRS).
However, Vivathana is in the field of containing and tracking a subject (Abstract) and teaches wherein the sensors use an attitude and heading reference system (Para. [0019], “[t]he positioning unit may include a global navigation satellite system receiver in communication with a plurality of satellite constellations to produce the satellite positioning data and wherein the positioning unit further includes an attitude and heading reference system configured to provide to the processor unit attitude and reference data regarding the subject; wherein the position data includes the attitude and reference data”. Furthermore, see also Para. [0108]).
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 modified Seo such that the sensor of the input device comprises an attitude and heading reference system as taught by Vivathana, in order to provide accurate and reliable orientation information of the animal.
Claim(s) 32-34 is/are rejected under 35 U.S.C. 103 as being unpatentable over Seo in view of Piggott as applied to claim 1 above, and further in view of Kelly et al. (2015/0373951 A1), hereinafter Kelly.
Regarding claim 32, Seo in view of Piggott discloses the invention in claim 1, but is silent regarding wherein the processing system is configured to generate, based on the first set of data, one or more training plans for the animal using a third machine learning model, wherein the third machine learning model is trained to generate a training plan from training data comprising one or more of animal training plans or published behavioral science knowledge, and wherein the system is configured to provide an output comprising the one or more training plans.
However, Piggott is in the field of a wearable device for moving animals (Abstract) and teaches that the processing system (Para. [0505]) is configured with a [third] machine learning model (Para. [0637]). Furthermore, Piggott discloses wherein the third machine learning model is trained from training data (a third of, animal linear acceleration, and animal location chosen; Para. [0650], “lying, running, sleeping, standing, defecating or urinating. Each activity type has a unique set of movement patterns that can be identified by machine learning algorithms”. See also Para. [0643]).
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 the processing system of modified Seo such that it was configured with a third machine learning model as taught by Piggott, in to provide distinct machine learning systems responsible for understanding only one pattern, and further provide a perturbative approach to animal training, yielding predictable results.
Kelly is in the field of systems and devices for acquiring data from and communicating with a wearable device (e.g., a collar) affixed to animal (Abstract) and teaches generating a training plan from training data comprising one or more of animal training plans or published behavioral science knowledge, and wherein the system is configured to provide an output comprising the one or more training plans (Para. [0034], “[i]n various examples, an application residing on the server 170 or mobile device 125, or both, may analyze the received data and develop content or programming for a user. Additionally, the server 170 or the mobile device 125 may access third-party data through the Internet 135, and may incorporate the data into the analysis or user-specific content. For instance, the application may generate feeding schedules, training programs or methods exercise regimens, activity recommendations, recommended outings, and the like, based on data acquired from a wearable device 105 and based on time of day, time or year, current weather, present location, current events, and the like. In some cases, the application may develop and make recommendations by learning from historic data”).
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 Seo in view of Piggott such that all position data (first, second, and third) is input into each respective of the above-modified machine learning model in order to produce at least a user-specific training plan as taught by Kelly, in order to gain the advantage of a more in-depth individualized approach to animal user well-being, yielding predictable results.
Regarding claim 33, Seo in view of Piggott discloses the invention in claim 1, and Seo further discloses wherein the processing system is configured to select, based on the first set of data, one or more pre-programmed training plans for the animal (Para. [0074], “[t]he controller 280 may be provided inside the housing 201 and may control the overall operation of the pet care robot 20. The controller 280 may include a processor 281 and a memory 282. The memory 282 may store programs, instructions, and data for controlling the operation of the pet care robot 20. The processor 281 may generate a control signal for controlling the operation of the pet care robot 20 based on the program, instructions, and data memorized and/or stored in the memory 282”. Since the pet care robot 20 is used to communicate and interact with the pet and play with the pet, then the processor is capable of selecting one training program for the pet).
However, Seo is silent regarding wherein the processing system is configured with a third machine learning model, and wherein the wherein the third machine learning model is trained from training data comprising one or more of animal training plans or published behavioral science knowledge, and wherein the system is configured to provide an output comprising the one or more pre-programmed training plans.
Piggott is in the field of a wearable device for moving animals (Abstract) and teaches that the processing system (Para. [0505]) is configured with a [third] machine learning model (Para. [0637]). Furthermore, Piggott discloses wherein the third machine learning model is trained from training data (a third of, animal linear acceleration, and animal location chosen; Para. [0650], “lying, running, sleeping, standing, defecating or urinating. Each activity type has a unique set of movement patterns that can be identified by machine learning algorithms”. See also Para. [0643]).
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 the processing system of modified Seo such that it was configured with a third machine learning model as taught by Piggott, in to provide distinct machine learning systems responsible for understanding only one pattern, and further provide a perturbative approach to animal training, yielding predictable results.
Kelly is in the field of systems and devices for acquiring data from and communicating with a wearable device (e.g., a collar) affixed to animal (Abstract) and teaches wherein the [third machine learning model] is trained from training data comprising one or more of animal training plans or published behavioral science knowledge (Para. [0093], “and the recommended exercise schedules may be based on historic movement patterns, recent amounts of movement, or the like”). Furthermore, Kelly teaches wherein the processing system is configured to select one or more pre-programmed training plans for the animal and wherein the system is configured to provide an output comprising the one or more pre-programmed training plans (Para. [0057], “the controller module 305 may acquire data from an animal wearing the collar 105-h, and the controller module 305 may report the acquired data to a server 170-a or via the Internet 135-a. The reported data may be stored, analyzed, or utilized to develop various training programs or feedback to be implemented via the collar 105-h”. Furthermore, Para. [0088] discloses “the training module 815 may be configured to store or record user preferences for various training regimens. The training module 815 may also be configured to implement training regimens on behalf of the user. Training regimens may be uploaded from the mobile device 125 and stored within the training module 815, or a user may select from a variety of training regimens hosted within the training module 815”).
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 Seo in view of Piggott such that all position data (first, second, and third) is input into each respective of the above-modified machine learning model in order to produce at least a user-specific training plan as taught by Kelly, in order to gain the advantage of a more in-depth individualized approach to animal user well-being, yielding predictable results. Furthermore, it would have been obvious to one of ordinary skill in the art to modify the processor of Seo in view of Piggott such that it was configured to output one or more pre-programmed training plans as taught by Kelly, in order to provide a more consisting training outcome for the animal.
Regarding claim 34, Seo in view of Piggott discloses the invention in claim 1, and Seo further discloses wherein the processing system is configured to: determine a first routine score for the animal based on the first set of data (represented by the data associated with the first set of data from Seo in view of Piggott);
Seo in view Piggott is silent to the processing system is configured to: determine a second routine score for the animal based on an external set of data, wherein the external set of data comprises one or more of lab reports, veterinary reports, or trainer reports; aggregate the first and second routine scores into an aggregate routine score; and select, based on the aggregate routine score, one or more training plans for the animal (historic movement patterns).
Kelly is in the field of systems and devices for acquiring data from and communicating with a wearable device (e.g., a collar) affixed to animal (Abstract) and teaches wherein the processing system is configured to: determine a second routine score for the animal based on an external set of data, wherein the external set of data comprises one or more of lab reports, veterinary reports, or trainer reports (Para. [0095], “third-party content module 925 may be configured to request and receive data related to veterinary practices”); select, based on an aggregate routine score, one or more training plans for the animal (Para. [0093], “recommended exercise schedules may be based on historic movement patterns”).
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 Seo in view of Piggott such that the processing system is configured to determine a second routine score for the animal based on an external set of data as taught by Kelly, in order to gain the advantage of a more in-depth individualized approach to animal user well-being. The resulting device renders obvious wherein the processing system is configured to: aggregate the first and second routine scores into an aggregate routine score (by incorporating the first set of data, Seo in view of Piggott); and select, based on the aggregate routine score, one or more training plans for the animal (historic movement patterns, Kelly).
Conclusion
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/N.A./Examiner, Art Unit 3647
/KIMBERLY S BERONA/Supervisory Patent Examiner, Art Unit 3647
1 Interpretation note: only one aspect from the Markush grouping is required to satisfy the requirements of the claim.
2 Examiner notes: only one of the audible feedback or a reward for the animal is required to meet the claimed limitation.
3 Examiner note: only one of the examples in the Markush grouping is required.
4 Examiner notes: only one of the audible feedback or a reward for the animal is required to meet the claimed limitation.
Prosecution Insights