DETAILED ACTION
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
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 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.
Election/Restriction
Applicant’s election without traverse of group I which includes claims 30-43, in the reply filed on 04/29/26 is acknowledged.
Information Disclosure Statement
The references listed in the Information Disclosure Statement filed on 09/26/23 have been considered by the examiner (see attached PTO-1449 form or PTO/SB/08A and 08B).
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale or otherwise available to the public before the effective filing date of the claimed invention.
Claims 30-33, 35, 37-39 and 41-43 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Mundell et al. (U.S. 20230092647).
For claim 30, Mundell et al. disclose a system comprising a contextualization engine configured to:
determine and/or predict one or more context(s) of at least one entity in one or more environment(s) from available and/or generated data including data from a plurality of devices, sensors, other systems, and/or communications network(s) (at least [0020]-[0023]. As shown in FIGS. 1-5 and 7, a method S100 for monitoring health of a dog during autonomous training with a training apparatus 100 includes, during a first period of time: accessing a first video feed of a working field adjacent the training apparatus 100 via an optical sensor integrated into the training apparatus 100 in Block S110; detecting the dog in the first video feed; initiating a health analysis protocol including a series of exercises configured to evaluate movements (e.g., postural movements, body alignment, gait) of the dog; extracting a first series of movement data for the dog based on behaviors of the dog exhibited during the series of exercises in the first video feed in Block S120; and characterizing a first movement profile for the animal based on the first series of movement data in Block S130. The method S100 further includes: during a second period of time succeeding the first period of time: accessing a second video feed of the working field in Block S110; detecting the dog in the second video feed; extracting a second series of movement data for the dog based on movements of the dog exhibited in the second video feed in Block S120; characterizing a second movement profile for the animal based on the second series of movement data in Block S130; and characterizing a first difference between the second movement profile and the first movement profile in Block S140. The method S100 further includes, in response to the first difference exceeding a first threshold difference: flagging the first difference as a first form abnormality (e.g., exercise form abnormality) for the dog in Block S150; accessing a diagnostic model linking form abnormalities to a set of diagnoses for dogs in Block S152; and interpreting a first diagnosis (or “causal pathway”) for the dog based on the first form abnormality and the diagnostic model in Block S160.);
compare the one or more context(s) and underlying and/or associated data/data sets with various other contexts and/or instances of one of more context(s) and underlying and/or associated data/data sets in other environment(s), to determine differences and commonalities among the contexts' underlying and/or associated data/data sets(at least [0020]-[0023]. As shown in FIGS. 1-5 and 7, a method S100 for monitoring health of a dog during autonomous training with a training apparatus 100 includes, during a first period of time: accessing a first video feed of a working field adjacent the training apparatus 100 via an optical sensor integrated into the training apparatus 100 in Block S110; detecting the dog in the first video feed; initiating a health analysis protocol including a series of exercises configured to evaluate movements (e.g., postural movements, body alignment, gait) of the dog; extracting a first series of movement data for the dog based on behaviors of the dog exhibited during the series of exercises in the first video feed in Block S120; and characterizing a first movement profile for the animal based on the first series of movement data in Block S130. The method S100 further includes: during a second period of time succeeding the first period of time: accessing a second video feed of the working field in Block S110; detecting the dog in the second video feed; extracting a second series of movement data for the dog based on movements of the dog exhibited in the second video feed in Block S120; characterizing a second movement profile for the animal based on the second series of movement data in Block S130; and characterizing a first difference between the second movement profile and the first movement profile in Block S140. The method S100 further includes, in response to the first difference exceeding a first threshold difference: flagging the first difference as a first form abnormality (e.g., exercise form abnormality) for the dog in Block S150; accessing a diagnostic model linking form abnormalities to a set of diagnoses for dogs in Block S152; and interpreting a first diagnosis (or “causal pathway”) for the dog based on the first form abnormality and the diagnostic model in Block S160.); and
formulate actions, modifications, and/or adjustments needed to appropriately cross-reference, calibrate, adjust, modify, and/or synchronize the one or more contexts in the one or more environment(s) with other context(s) and/or instances of the one or more context(s) in other environment(s), for the purpose of adjusting, modifying, calibrating, synchronizing, compensating for, or otherwise taking into account the other contexts/instances of contexts' data into the one or more context(s) underlying and/or associated data elements to compensate for, adjust, modify, calibrate, synchronize, compensate for, and/or otherwise take into account the differences between the contexts and their underlying and/or associated data/data sets, for use in analyzing, comparing, diagnosing, treating, modifying, performing, executing, facilitating, discouraging, and/or operating one or more behaviors and/or activities associated with the contexts (at least [0020]-[0023] and [0107]-[0109]. As shown in FIGS. 1-5 and 7, a method S100 for monitoring health of a dog during autonomous training with a training apparatus 100 includes, during a first period of time: accessing a first video feed of a working field adjacent the training apparatus 100 via an optical sensor integrated into the training apparatus 100 in Block S110; detecting the dog in the first video feed; initiating a health analysis protocol including a series of exercises configured to evaluate movements (e.g., postural movements, body alignment, gait) of the dog; extracting a first series of movement data for the dog based on behaviors of the dog exhibited during the series of exercises in the first video feed in Block S120; and characterizing a first movement profile for the animal based on the first series of movement data in Block S130. The method S100 further includes: during a second period of time succeeding the first period of time: accessing a second video feed of the working field in Block S110; detecting the dog in the second video feed; extracting a second series of movement data for the dog based on movements of the dog exhibited in the second video feed in Block S120; characterizing a second movement profile for the animal based on the second series of movement data in Block S130; and characterizing a first difference between the second movement profile and the first movement profile in Block S140. The method S100 further includes, in response to the first difference exceeding a first threshold difference: flagging the first difference as a first form abnormality (e.g., exercise form abnormality) for the dog in Block S150; accessing a diagnostic model linking form abnormalities to a set of diagnoses for dogs in Block S152; and interpreting a first diagnosis (or “causal pathway”) for the dog based on the first form abnormality and the diagnostic model in Block S160. In this variation, the method S100 further includes, in response to the second difference exceeding a second threshold difference: flagging the second difference as a second form abnormality in Block S150; and interpreting a second diagnosis for the dog based on the second difference and the diagnostic model in Block S160. In one variation, the method S100 further includes: generating a notification indicating presence of the first form abnormality and suggesting the first diagnosis for the dog; and transmitting the notification to a user associated with the dog in Block S172.)
For claim 31, Mundell et al. disclose the system of claim 30, wherein the system is configured to compensate for, adjust, modify, calibrate, synchronize, compensate for, and/or otherwise take into account the differences between the contexts and their underlying and/or associated data/data sets, for use in analyzing, comparing, diagnosing, treating, and/or modifying the behavior(s) and/or activity(ies) of the at least one entity, and/or for performing, executing, analyzing, facilitating, discouraging, and/or operating the activity(ies) of and/or associated with the at least one entity (at least [0020]-[0023] and [0107]-[0109]. As shown in FIGS. 1-5 and 7, a method S100 for monitoring health of a dog during autonomous training with a training apparatus 100 includes, during a first period of time: accessing a first video feed of a working field adjacent the training apparatus 100 via an optical sensor integrated into the training apparatus 100 in Block S110; detecting the dog in the first video feed; initiating a health analysis protocol including a series of exercises configured to evaluate movements (e.g., postural movements, body alignment, gait) of the dog; extracting a first series of movement data for the dog based on behaviors of the dog exhibited during the series of exercises in the first video feed in Block S120; and characterizing a first movement profile for the animal based on the first series of movement data in Block S130. The method S100 further includes: during a second period of time succeeding the first period of time: accessing a second video feed of the working field in Block S110; detecting the dog in the second video feed; extracting a second series of movement data for the dog based on movements of the dog exhibited in the second video feed in Block S120; characterizing a second movement profile for the animal based on the second series of movement data in Block S130; and characterizing a first difference between the second movement profile and the first movement profile in Block S140. The method S100 further includes, in response to the first difference exceeding a first threshold difference: flagging the first difference as a first form abnormality (e.g., exercise form abnormality) for the dog in Block S150; accessing a diagnostic model linking form abnormalities to a set of diagnoses for dogs in Block S152; and interpreting a first diagnosis (or “causal pathway”) for the dog based on the first form abnormality and the diagnostic model in Block S160. In this variation, the method S100 further includes, in response to the second difference exceeding a second threshold difference: flagging the second difference as a second form abnormality in Block S150; and interpreting a second diagnosis for the dog based on the second difference and the diagnostic model in Block S160. In one variation, the method S100 further includes: generating a notification indicating presence of the first form abnormality and suggesting the first diagnosis for the dog; and transmitting the notification to a user associated with the dog in Block S172.)
For claim 32, Mundell et al. disclose the system of claim 30, wherein the contextualization engine is configured to capture, generate, and/or predict metadata associated with each data measurement of the one or more contexts to thereby enable the contextualization engine to perform an apples to apples cross-referencing, calibrating, adjusting, modifying, and/or synchronizing of the data obtained for the at least one entity for a particular purpose with data obtained elsewhere under a different instance(s) of the one or more context(s) for the same or similar purpose (at least [0020]-[0021]. As shown in FIGS. 1-5 and 7, a method S100 for monitoring health of a dog during autonomous training with a training apparatus 100 includes, during a first period of time: accessing a first video feed of a working field adjacent the training apparatus 100 via an optical sensor integrated into the training apparatus 100 in Block S110; detecting the dog in the first video feed; initiating a health analysis protocol including a series of exercises configured to evaluate movements (e.g., postural movements, body alignment, gait) of the dog; extracting a first series of movement data for the dog based on behaviors of the dog exhibited during the series of exercises in the first video feed in Block S120; and characterizing a first movement profile for the animal based on the first series of movement data in Block S130.)
For claim 33, Mundell et al. disclose the system of claim 30, wherein the contextualization engine is configured to holistically identify and set up sensors including associated parameters, operate and collect data from the sensors, organize and store the sensor data, process and analyze the sensor data, compare the sensor data with baseline(s) of applicable data and adjust the sensor data accordingly (at least [0090] and [0111]-[0114]. The system can continue monitoring the dog’s health during autonomous training sessions with the dog based on the dog’s exercise form (e.g., body position, body velocity) during various exercises performed by the dog during training sessions. By continuously monitoring the dog’s form (e.g., gait, pose, and/or transition form) before, during, and/or after training sessions, the system can identify new and/or recurring changes to movement data collected over time for this dog. Additionally and/or alternatively, in another implementation, the system can regularly update the baseline movement profile for the dog as the system derives additional movement profiles for the dog over time.)
For claim 35, Mundell et al. disclose the system of claim 30, wherein the contextualization engine is configured to develop a score or set of scores to capture the value of contextual factors of the context(s) associated with the at least one entity (at least [0088]. The system can then calculate a health score for the dog based on the difference. For example, in response to the difference exceeding a threshold difference, the system can calculate a low health score indicating poor health of the dog. Alternatively, in response to the difference exceeding a lower threshold and falling below the threshold, the system can calculate a moderate health score indicating moderate health of the dog. Alternatively, in response to the difference falling below the lower threshold, the system can calculate a high health score indicating good health.)
For claim 37, Mundell et al. disclose the system of claim 30, wherein the contextualization engine is configured to: determine and/or predict, through a plurality of measurements/readings taken by a plurality of different devices, sensors, other systems, and/or communication network(s) and/or through information from and/or about system inputs, the context(s) of the at least one entity within an environment; and determine whether the context(s) of the at least one entity should be adjusted before and/or during a test on the at least one entity for a particular purpose within the environment to thereby reduce or eliminate the need for subsequently cross- referencing, calibrating, adjusting, modifying, and/or synchronizing the medical/medical activity data obtained for the at least one entity within the environment to enable an apples to apples comparison with data obtained elsewhere under a different instance(s) of the context(s) for the same or similar purpose (at least [0088]-[0093]. The system can then calculate a health score for the dog based on the difference. For example, in response to the difference exceeding a threshold difference, the system can calculate a low health score indicating poor health of the dog. Alternatively, in response to the difference exceeding a lower threshold and falling below the threshold, the system can calculate a moderate health score indicating moderate health of the dog. Alternatively, in response to the difference falling below the lower threshold, the system can calculate a high health score indicating good health. In one example, the system can: insert the set of movement data into the series of movement data; and update the baseline movement profile by equally weighting all movement data in the series of movement data. In another example, the system can: weight most-recently collected data — such as data collected within an immediately preceding time period — more heavily when updating the baseline movement profile for the dog. In particular, in this example, the system can: assign a first weight to the set of movement data; assign a second weight — less than the first weight — to the series of movement data; and update the baseline movement profile — according to the first and second weights — based on the set of movement data and the series of movement data. Additionally and/or alternatively, in another example, the system can: discard movement data stored in the baseline movement profile and collected outside of a rolling time window (e.g., one week, one month, three months, one year); and regularly update the baseline movement profile of the dog accordingly.)
For claim 38, Mundell et al. disclose the system of claim 37, wherein the contextualization engine is configured to cross-reference, calibrate, adjust, modify, and/or synchronize measurements of the plurality of different devices, sensors, other systems, and/or communications network(s) to determine, assess, and/or accommodate for different context(s) of the environment under which the plurality of different devices, sensors, other systems, and/or communications network(s) are being used, whereby the cross-referencing, calibrating, adjusting, modifying, and/or synchronizing provides the ability to compare the medical/medical activity test data obtained for the at least one entity for the particular purpose within the environment with the data obtained elsewhere under different context(s) and/or instance(s) of the one or more contexts in a different environment(s) for the same or similar purpose (at least [0088]-[0093]. The system can then calculate a health score for the dog based on the difference. For example, in response to the difference exceeding a threshold difference, the system can calculate a low health score indicating poor health of the dog. Alternatively, in response to the difference exceeding a lower threshold and falling below the threshold, the system can calculate a moderate health score indicating moderate health of the dog. Alternatively, in response to the difference falling below the lower threshold, the system can calculate a high health score indicating good health. In one example, the system can: insert the set of movement data into the series of movement data; and update the baseline movement profile by equally weighting all movement data in the series of movement data. In another example, the system can: weight most-recently collected data — such as data collected within an immediately preceding time period — more heavily when updating the baseline movement profile for the dog. In particular, in this example, the system can: assign a first weight to the set of movement data; assign a second weight — less than the first weight — to the series of movement data; and update the baseline movement profile — according to the first and second weights — based on the set of movement data and the series of movement data. Additionally and/or alternatively, in another example, the system can: discard movement data stored in the baseline movement profile and collected outside of a rolling time window (e.g., one week, one month, three months, one year); and regularly update the baseline movement profile of the dog accordingly.)
For claim 39, Mundell et al. disclose the system of claim 37, wherein: the environment is an off-Earth environment, or the environment is an on-Earth enclosed facility and/or an on-Earth specialty environment having abnormal condition(s); and the at least one entity comprises one or more of a human, an animal, a plant, another system, a machine, a robot, an artificial intelligence, a virtual agent, a corporation, a business entity, a nation, a network, a driverless vehicle, a connected vehicle, a drone, and/or a governmental entity (at least [0014]. As shown in FIGS. 1A, 1B, 2A, 2B, 3A, 3B, 4, 5A, 5B, and 6, a method S100 for monitoring health of an animal during autonomous training with a training apparatus).
For claim 41, Mundell et al. disclose the system of claim 30, wherein the system is configured to solicit and/or collect user feedback and to thereafter interpret the user feedback for use in determining the one or more context(s) of at least one entity in the one or more environment(s) (at least [0132]-[0134]. The system can prompt the user to share all or part of the dog profile for her dog with a third-party user (e.g., a veterinarian, an animal specialist, an animal trainer) running the native application (or a web application) on a different device such that the system can update the third-party user with certain data about the user’s dog (e.g., gait abnormalities, training updates, health trends).)
For claim 42, Mundell et al. disclose the system of claim 30, wherein the system is configured to obtain and interpret human feedback for use in determining the one or more context(s) of at least one entity in the one or more environment(s) (at least [0132]-[0134]. The system can prompt the user to share all or part of the dog profile for her dog with a third-party user (e.g., a veterinarian, an animal specialist, an animal trainer) running the native application (or a web application) on a different device such that the system can update the third-party user with certain data about the user’s dog (e.g., gait abnormalities, training updates, health trends).)
For claim 43, Mundell et al. disclose the system of claim 30, wherein the contextualization engine is configured for ongoing monitoring for useful context(s) that will, may, or could be encountered in the one or more environment(s) and for using data obtained from the ongoing monitoring for predicting future context(s) that the one or more environment(s) have not yet encountered but there is a likelihood the one or more environment(s) will encounter the predicted future context(s) (at least [0095]-[0096]. The system can also predict a future position of the particular feature at a particular time based on the rate of change and notify a user (e.g., a dog owner) of this prediction).
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 of this title, 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.
Claim 34 is rejected under 35 U.S.C. 103 as being unpatentable over Mundell et al. (U.S. 20230092647) in view of Page (U.S. 20030199780).
For claim 34, Mundell et al. do not disclose the system of claim 30, wherein the system is configured to be operable for modifying the context(s) before and/or during a medical activity on the at least one entity by changing one or more of radiation level, gravity level, temperature, humidity, oxygen level, and noise level.
In the same field of endeavor, Page discloses the system is configured to be operable for modifying the context(s) before and/or during a medical activity on the at least one entity by changing one or more of radiation level, gravity level, temperature, humidity, oxygen level, and noise level (at least abstract. A system and method are provided to monitor a patient's respiration during and after medical/surgical procedures, as well as in clinical situations wherein the patient is at increased risk for developing central or obstructive respiratory depression and/or apnea. A sensor attached to the patient's face, which monitors nasal and oral air-flows. An electronic monitor analyzes patient respiratory patterns and initiates bedside and/or remote nursing station alarms in real time if and when a clinically significant respiratory event is detected. Respiration is measured by monitoring changes in the acoustic signature of the patient's breath passing over a corrugated or uneven surface, changes in the pressure of an inflatable closed volume of a sensor caused by the changes in temperature of the interior volume caused by the patient breathing over the sensor, or both.)
Therefore, it would have been obvious to one of ordinary skill in the art at the time the invention was made to modify the invention of Mundell et al. as taught by Page for purpose of monitoring technology for the detection of any and all potentially life threatening respiratory events.
Allowable Subject Matter
Claims36 and 40 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to DAI PHUONG whose telephone number is 571-272-7896. The examiner can normally be reached on Monday-Friday, 8am-5pm.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, Applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Kathy Wang-Hurst can be reached on 571-270-5371. The fax phone number for the organization where this application or proceeding is assigned is 571-273-7687.
Information regarding the status of an application may be obtained from the Patent Application Information Retrieval (PAIR) system. Status information for published applications may be obtained from either Private PAIR or Public PAIR. Status information for unpublished applications is available through Private PAIR only. For more information about the PAIR system, see http://pair-direct.uspto.gov. Should you have questions on access to the Private PAIR system, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free).
/DAI PHUONG/Primary Examiner, Art Unit 2644
Prosecution Insights