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 .
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-15 rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claim(s) recite(s) a combination of mental processes concepts performed in the human mind (including an observation, evaluation, judgment, opinion) and data processing/gathering using generic computer components. This judicial exception is not integrated into a practical application because the claim is directed to an abstract idea with additional generic computer elements, wherein the generically recited computer elements do not add a meaningful limitation to the abstract idea because they amount to simply implementing the abstract idea on a computer. The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional limitations only store and retrieve information in memory, explain that these are well-understood, routine, conventional computer functions as recognized by the court decisions listed in MPEP § 2106.05(d).
For claim 1 and similarly claim 10, a system for emergency monitoring and acting in domestic environments, the system characterized by comprising:
- a home user end device (generic computer element) of a user located in a user's environment, the home user end device configured to:
- capture information streams (data gathering);
- detect events by locally processing (data processing) the captured information streams, wherein the locally processing uses a lightweight neural network configured to classify the detected events by performing pattern recognition (data gathering processing using generic computer element);
- 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; (mental processes concepts performed in the human mind including an observation, evaluation, judgment and opinion)
- if emergency is detected, trigger a set of actions predefined in the knowledge database for the detected emergency; (mental processes concepts performed in the human mind including an opinion and data processing/outputting)
the home user end device being communicated through a first communication protocol with a causality supervisor (data processing/transmission using generic computer components) configured 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 gathering)
- periodically evaluate the received data to determine whether all the set of triggered actions is correctly taken for the log of the detected events, and (mental processes concepts performed in the human mind including an observation, evaluation, judgment and opinion)
- 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 (data processing/outputting using generic computer components).
Re claim 2, 9 and 11 is directed to data processing/gathering using generic computer components.
Re claim 3-5 and 12-13 is directed to data processing/outputting using generic computer components.
Re claim 6-8 and 14-15 is directed to data processing.
Claim Rejections - 35 USC § 102
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 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.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claim(s) 1-2, 5, 7, and 9-11 is/are rejected under 35 U.S.C. 102(a)(2) as being anticipated by VAN DEN DUNGEN et al. (US 20210352176 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.
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 (200, 310) by a home user end device (300) located in a user's environment of a user (10),- detecting events (210) by locally processing (320) the captured information streams by the home user end device (300), wherein the locally processing (320) uses a lightweight neural network configured to classify the detected events by performing pattern recognition;- detecting by the home user end device (300) whether there is an emergency (220) based on the classified events and information from a knowledge database (240) to which the home user end device (300) has access;- if emergency is detected, triggering (230) a set of actions by the home user end device (300) predefined in the knowledge database (240, 380) for the detected emergency;- receiving data through a first communication protocol from the home user end device (300) to a causality supervisor (130, 340), the data comprising a log (140,360) of the detected events and, if emergency detected, of the triggered actions,- evaluating the received data periodically by the causality supervisor (130, 340) to determine whether all the set of triggered actions is correctly taken,- updating the knowledge database (240, 380) by the causality supervisor (130, 340) according to the evaluation of the received data,- delivering a feedback (150) by the causality supervisor (130, 340) based on the evaluation to the lightweight neural network and the lightweight neural network learning from the feedback (150).
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) 3-4, 6 and 12-14 is/are rejected under 35 U.S.C. 103 as being unpatentable over VAN DEN DUNGEN et al. (US 20210352176 A1).
However, VAN DEN DUNGEN fails to explicitly disclose:
Re claim 3 and 12. the system according to claim 2, wherein the second communication protocol is VoIP.
Re claim 4 and 13. the system according to claim 1wherein the first communication protocol is Kafka or MQTT.
Official notice is taken that one of ordinary skill in the art understands that various communication protocols exist, such as those claimed.
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 an appropriate communication protocol in order to securely communicate data across a system network.
However, VAN DEN DUNGEN fails to explicitly disclose:
Re claim 6 and 14. the system according to claim 1, wherein the lightweight neural network is a convolutions neural network, a recurrent neural network, a residual network, a lambda network, a performer network, or a broadcasted residual learning network.
Official notice is taken that one of ordinary skill in the art understands that various known neural network types exist with similar capabilities.
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 an appropriate neural network selected from various possible networks in order to properly processing classification 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 SINGH et al. (US 20240370584 A1).
However, VAN DEN DUNGEN fails to explicitly disclose:
Re claim 8 and 15. the system according to claim 1, wherein the evaluation of the received data by the causality supervisor uses Large Language Models.
SINGH teaches (abstract) in a similar field of invention, using Large Language Models, as an artificial intelligence tool to evaluate data.
[0063] The data loss prevention service 114 can detect communication through intercepting communications between a user or any other entity on the network 104, or that is part of the enterprise and the generative large language model (or other artificial intelligence tool) by a data loss prevention policy proxy. This includes communications being received through interacting with an API that may itself interact with the artificial intelligence tool. For example, all communications to and from the network 104 can pass through a firewall 118, gateway 142, or virtualized service, configured as a proxy to receive and evaluate all data, including data received from an artificial intelligence tool, such as a generative large language model. The proxy can be an extension of the data loss prevention service 114 and evaluate this data for compliance with the data loss prevention policy-which in this example pertains to tracking the propagation of the output of the generative large language model for use or occurrence in documents and products within the enterprise.
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 improving evaluation of data.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
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.
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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 6/10/2026
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