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 .
Response to Arguments
Applicant’s arguments, see pages 6-9, filed 9/15/2026, with respect to the rejection under 35 U.S.C. 101 have been fully considered and are persuasive. The prior rejection under 35 U.S.C. 101 of claims 1-15 has been withdrawn.
Applicant’s arguments, see pages 9-14, filed 9/15/2026, with respect to the rejection(s) of claim(s) under corresponding 35 U.S.C. 102 or 35 U.S.C. 103 have been fully considered and are persuasive. Therefore, these rejections have been withdrawn. However, upon further consideration, a new ground(s) of rejection is made in view of newly found prior art to address the amended limitations.
Furthermore, in re to arguments under “A. No Lightweight Neural Network Comprised in the Home User End Device” – the rejection is modified to meet the amended limitations which add details for the processing of the neural network.
Furthermore, in re to arguments under “B. No Causality Supervisor as Claimed” – the causality supervisor is only claimed nominally and is not further defined as to what comprises the causality supervisor. The claim is interpreted broadly to include any call center personnel, caregiver or emergency resources defined in [0042].
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.
Claim(s) 1-2, 5, 7, and 9-11 is/are rejected under 35 U.S.C. 103 as being unpatentable over VAN DEN DUNGEN et al. (US 20210352176 A1) in view of VAISHAMPAYAN et al. (US 20210397957 A1).
Re claim 1. VAN DEN DUNGEN discloses (abstract) a system (FIG.1) for emergency monitoring and acting in domestic environments [0002-0010, 0060-64, 0071-77, 0082], the system characterized by comprising:
a home user end device (4/5) of a user located in a user's environment, the home user end device [0060] configured to:
capture information streams [0060];
detect events [0060] by locally processing the captured information streams, wherein the locally processing uses a lightweight neural network configured to classify the detected events (i.e. claim does not clearly require neural network to be included as part of end device) by performing pattern recognition [0062];
detect whether there is an emergency based on the classified events and information from a knowledge database to which the home user end device has access [0073];
if emergency is detected, trigger a set of actions predefined in the knowledge database for the detected emergency [0027, 0073-75];
the home user end device being communicated through a first communication protocol with a causality supervisor configured (FIG.1 – clearly a communication protocol is used to transmit data among supervisor) to:
receive data through the first communication protocol, the data comprising a log of the detected events and, if emergency detected, of the triggered actions (data must be communicated), [0060-0062]
periodically evaluate the received data to determine whether all the set of triggered actions is correctly taken for the log of the detected events [0060], and
update the knowledge database according to the evaluation of the received data and deliver a feedback based on the evaluation to the lightweight neural network, the lightweight neural network learning from the feedback [0060].
[0060] The voice inflection model 330 may analyze the voice of the monitored person to, for example, determine voice pitch patterns, volume, and tone. This model may also determine how fast or slow the person is talking, whether the voice is a shaky or has unstable or variable speech patterns, whether the person is stuttering or stammering, or whether the person is crying, laughing, shouting, or exhibiting some other form of emotion. In one embodiment, audio of the utterances the monitored person made during the alleged fall may be recorded, for example, by a sensor (e.g., smartphone microphone or other sound-capturing detector) at the scene. These utterances may be analyzed, for example, to determine the authenticity of the fall. All of this information may be considered to be initialization information. During use, the model may be updated based on learning and new keywords used during events, voice inflections, emotions, sensor readings in order to provide a personalized model. In one embodiment, cross-user learning may be performed in order to update the baseline. See, for example, the features of FIG. 4.
[0061] All of this information may be compared to reference patterns to ascertain the intent or mental state of the monitored person. This information may be used as a basis for determining, for example, whether the person is in denial of an actual fall or whether the call is actually a social call disguised as a distress call, for example, through activation of the alarm button on the fall detector. In one embodiment, the voice inflection model may correspond to the voice analyzer or the content/intent analyzer of FIG. 2.
[0062] In one embodiment, the models 310, 320, and 330 may be implemented using artificial intelligence to provide a more accurate analysis of the voice signals of the monitored person during a call. One example of an artificial intelligence application is a machine-learning algorithm, neural network, or other model-based logic which is trained based on personal data that relates to the person being monitored. The training data may initially involve taking voice samples of speech patterns, inflections, speech traits, and other verbal behavior characteristics and idiosyncrasies of the monitored person. This information may provide a baseline or reference for how a person normally talks, which may be contrasted to the voice of the person during calls to provide an indication of emotion, stress, intent, and/or other properties relevant to generating a decision by the decision engine. The processor 30 may then update the training data of the model during subsequent calls, in order to allow the model to learn the specific nuances relating to the person being monitored. This learning process produces a model which is more adept at generating an accurate analysis of the specific person, which, in turn, may produce a decision that can predict exactly what type of care and service the person needs when the fall detector is triggered. The training data and data obtained for each call may be stored in the database for access by the conversation analyzer for performing the operations described herein.
[0073] At 412, when the decision indicates that there is a possible emergency or there is no direct emergency (e.g., as indicated by a corresponding score), then the processor may take the corresponding action indicated in Table 1. For example, this may involve passing the call to a human caregiver, who may then conduct a follow-up conversation with the person, and/or an AI bot may be activated. Such a decision may occur, for example, when the monitored person is determined to be in a confused state based on his voice responses or the fall status is otherwise unclear.
[0074] At 414, if the call is passed to a human caregiver, then the caregiver may determine the appropriate action to take. For example, when the caregiver determines that the monitored person is in a severe state, either because of an actual fall, because of a stroke or other health condition, then the human caregiver can pass the call to emergency resources, at 416, and caregivers and relatives may be notified accordingly. When the caregiver determines that the monitored person is not in a severe state (e.g., there is no emergency), then, at 418, a caregiver may be notified to give help and love to the person and the call may be terminated. As in all cases, records of the conversation(s) and the actions taken may be recorded in the database. These records may be used for training the models for improved management of subsequent calls from the person being monitored. In one embodiment, the processor 30 may listen in and control training of machine-learning algorithms 480 used to implement the artificial intelligence models to generate (e.g., optimize) the models for managing calls.
[0075] At 420, when the decision indicates that there is an emergency (e.g., as indicated by a high score), then the processor may take the corresponding action indicated in Table 1. This may involve the processor 30 generating signals to cause the notification router to dispatch emergency services to the location of the person being monitored. In one embodiment, at 420, the conversation analyzer 35 may continue the conversation with the person until it is confirmed that the emergency resources have arrived, after which the call may be ended.
However, VAN DEN DUNGEN fails to explicitly disclose:
lightweight neural network comprised in the home user end device.
VAISHAMPAYAN teaches (FIG.2) [0024-0047] using a home user end device 110 to processor to train a neural network (using machine learning model) instead of the neural network being processed in a remote device, such as a server. [0015]
It would have been obvious to use a home user end device such as a mobile phone rather than a remote device such as a server to process a neural network, because the modification of using a home user end device to comprise the lightweight neural network instead of using a remote device such as servers, would have constituted the mere arrangement of old elements with each performing the same function it had been known to perform, the combination yielding no more than one would expect from such an arrangement.
Re claim 2 and 11. VAN DEN DUNGEN discloses [0073-74, 0082] the system according to claim 1, wherein the home user end device is further communicated through a second communication protocol with an external user end device of an external user configured to, if emergency is detected:
establish a communication through the second communication protocol with the home user end device to contact the user;
select which actions from the set of triggered actions are taken;
send a validation, through the first communication protocol, to the causality supervisor, the validation being provided by the external user and indicating whether all the set of triggered actions is selected and correctly taken; and
wherein the data received by the causality supervisor further comprises the validation sent by the external user end device.
Re claim 5. VAN DEN DUNGEN discloses [0060] the system according to claim 1, wherein the home user end device is a smartphone, a softphone, a smart speaker, an intelligent assistant, a tablet, a personal computer, a laptop, a TV set or a wearable programmable device.
Re claim 7. VAN DEN DUNGEN discloses [0027] the system according to claim 1, wherein the set of actions triggered if emergency is detected is executed locally in the user's environment or is executed by third-parties external to the user's environment.
Re claim 9. VAN DEN DUNGEN discloses [0027, 0063] the system according to claim 1, wherein the captured information streams are selected between audio streams, video streams, biological data streams and contextual data streams.
Re claim 10. As applied for claim 1, a method for emergency monitoring and acting in domestic environments, the method characterized by comprising the following steps:
capturing information streams by a home user end device located in a user's environment of a user,
detecting events by locally processing the captured information streams by the home user end device, wherein the locally processing uses a lightweight neural network comprised in the home user end device configured to classify the detected events by performing pattern recognition;
detecting by the home user end device whether there is an emergency based on the classified events and information from a knowledge database to which the home user end device has access;
if emergency is detected, triggering a set of actions by the home user end device predefined in the knowledge database for the detected emergency;
receiving data through a first communication protocol from the home user end device to a causality supervisor, the data comprising a log of the detected events and, if emergency detected, of the triggered actions,
evaluating the received data periodically by the causality supervisor to determine whether all the set of triggered actions is correctly taken,
updating the knowledge database by the causality supervisor according to the evaluation of the received data; and
delivering a feedback by the causality supervisor based on the evaluation to the lightweight neural network comprised in the home user end device and the lightweight neural network learning from the feedback.
Claim(s) 3 and 12 is/are rejected under 35 U.S.C. 103 as being unpatentable over VAN DEN DUNGEN et al. (US 20210352176 A1) in view of VAISHAMPAYAN et al. (US 20210397957 A1) further in view of MATHENY (US 20230252880 A1).
However, VAN DEN DUNGEN as modified by VAISHAMPAYAN fails to explicitly disclose:
Re claim 3 and 12. the system according to claim 2, wherein the second communication protocol is VoIP.
MATHENY teaches (abstract) the ability to use VoIP as a communication protocol [0026] for emergency communication networks (FIG.1).
A person of ordinary skill in the art would have had good reason to pursue the known options of using VoIP protocol for communication. It would require no more than "ordinary skill and common sense," to try using a known protocol for communication across devices in a network.
Claim(s) 4 and 13 is/are rejected under 35 U.S.C. 103 as being unpatentable over VAN DEN DUNGEN et al. (US 20210352176 A1) in view of VAISHAMPAYAN et al. (US 20210397957 A1) further in view of HORI et al. (US 20230110476 A1).
However, VAN DEN DUNGEN as modified by VAISHAMPAYAN fails to explicitly disclose:
Re claim 4 and 13. the system according to claim 1, wherein the first communication protocol is Kafka or MQTT.
HORI teaches (abstract) the use of MQTT protocol for mobile devices [0103].
A person of ordinary skill in the art would have had good reason to pursue the known options of using MQTT protocol for communication. It would require no more than "ordinary skill and common sense," to try using a known protocol for communication across devices in a network.
Claim(s) 6 and 14 is/are rejected under 35 U.S.C. 103 as being unpatentable over VAN DEN DUNGEN et al. (US 20210352176 A1) in view of VAISHAMPAYAN et al. (US 20210397957 A1) further in view of DING et al. (US 20200380252 A1).
However, VAN DEN DUNGEN and VAISHAMPAYAN fails to explicitly disclose:
Re claim 6 and 14. the system according to claim 1, wherein the lightweight neural network is a convolutional neural network, a recurrent neural network, a residual network, a lambda network, a performer network, or a broadcasted residual learning network.
DING teaches (abstract) in a similar field of invention, using a lightweight convolutional neural network for instance, used as part of neural network model forming a detecting of motion in a system [0035].
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to try using a lightweight convolutional neural network selected from various possible networks in order to properly classifying detected events.
Claim(s) 8 and 15 is/are rejected under 35 U.S.C. 103 as being unpatentable over VAN DEN DUNGEN et al. (US 20210352176 A1) in view of VAISHAMPAYAN et al. (US 20210397957 A1) further in view of NAYAR et al. (US 20250209280 A1).
However, VAN DEN DUNGEN and VAISHAMPAYAN fails to explicitly disclose:
Re claim 8 and 15. wherein the evaluation of the received data by the causality supervisor uses Large Language Models.
NAYAR teaches (abstract) in a similar field of invention, using Large Language Models, to evaluate received data. [0005, 0015, 0146]
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to try Large Language Models for the purpose of evaluation of the received data.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to CARLOS E GARCIA whose telephone number is (571)270-1354. The examiner can normally be reached M-Th 9-6pm F 9-5pm.
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, Brian Zimmerman can be reached at (571) 272-3059. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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CARLOS E. GARCIA
Primary Examiner
Art Unit 2686
/Carlos Garcia/Primary Examiner, Art Unit 2686 10/1/2026
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