DETAILED ACTION
This is in response to the amendment filed on May 8th 2026.
Response to Arguments
Applicant’s arguments, see pg. 9-11, filed 5/8/26, with respect to the rejection(s) of claim(s) 1-20 under 103 have been fully considered and are persuasive. Therefore, the rejection has been withdrawn. However, upon further consideration, a new ground(s) of rejection is made in view of Hutz US 8,786,425 B1.
Claim Rejections - 35 USC § 103
The text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action.
Claim(s) 1, 3-11 and 13-20 are rejected under 35 U.S.C. 103 as being unpatentable over Moulik et al. US 2022/0158894 A1 in view of N. B. V and R. M. R. Guddeti, "Fog-Based Intelligent Machine Malfunction Monitoring System for Industry 4.0," in IEEE Transactions on Industrial Informatics, vol. 17, no. 12, pp. 7923-7932, Dec. 2021, hereinafter “Guddeti” and in further view of Matsubara et al. US 2019/0086909 A1 and Hutz US 8,786,425 B1.
Regarding claim 1, Moulik discloses an electronic device comprising: memory storing instructions, a communication circuit; and a processor connected with the memory and the communication circuit (electronic device – Figs. 1, 2, 8 and paragraphs 37-38, 76);
obtain state information collected by at least one first Internet of Things (IoT) device (receive state information – see Fig, 8, Fig. 10 step 1002, paragraphs 40, 45) and sound information … collected by at least one second IoT device (plurality of IoT devices – Fig. 8, paragraph 41; state information includes actions such as playing media/audio, this is equivalent to “sound information” – see paragraphs 40-43);
perform anomaly monitoring, based on a correlation between the state information and the sound information (detect anomaly based on correlation between data from multiple IoT devices – abstract, Figs. 8, 11, 14, and paragraphs 7-8, 55-56, 61 and 84); and
perform second anomaly monitoring, based on the state information (detect anomaly based on state information – paragraphs 40-45 and 84, Figs. 8-9); and
detect an anomaly based on at least one of the first anomaly monitoring or the second anomaly monitoring (detect anomaly – abstract, paragraphs 7-8, 40-45, 84, Figs. 8-9, 11 and 14).
Moulik does not explicitly disclose the sound information comprises “an amplitude of at least one sound signal” but Moulik discloses monitoring for state information / events using a microphone (paragraphs 63 and 67). One of ordinary skill in the art would understand the function of a microphone is to detect and/or record sound, including the amplitude of a sound signal (all sound waves have an amplitude). 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 microphone of Moulik to explicitly amplitude values. Moulik teaches using a microphone to detect values/events (paragraph 63) and amplitude is merely an inherent feature of a sound signal/wave.
Moulik does not explicitly disclose wherein the at least one sound signal is generated by an operation performed by the first IoT device. It is extremely well-known in the art to diagnose machines/devices by listening. Guddeti explicitly discloses using an IoT-based system to monitor device operation for sound, and then detecting an anomaly based on the analysis (abstract, Sections I-III and Fig. 1). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Moulik to use at least one second IoT device to obtain sound information wherein the sound is generated by an operation performed by another IoT device as taught by Guddeti for the purpose of anomaly detection. Guddeti teaches that failures may be delay sensitive, so real-time monitoring for abnormal or anomalous device behavior is beneficial to avoid significant costs/failures and increase reliability (Sections I, V).
The combination of Moulik and Guddeti discloses performing of the first anomaly monitoring comprises: determine … the at least one sound signal is within each designated range corresponding to each of the at least one second IoT device; and determine that an abnormal situation is detected. As explained above, Moulik teaches using microphones to collect sound and Guddeti analyzes sound signals to detect anomalies in devices. The combination does not explicitly disclose determine whether each of the amplitude of the sound signal is within a range, or when the at least one amplitude is out of the range, determine that an abnormal situation is detected. Matsubara explicitly discloses sound sensors to detect volume, and processing the volume to determine fault or absence of fault by comparing the values to “a normal range of volume” (abstract, paragraph 32, Fig. 1).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the combination of Moulik and Guddeti to use a range of volume/amplitude to diagnose normal vs. abnormal conditions as taught by Matsubara. This is an abstract fundamental diagnostic process. Humans have adapted hearing over thousands of years to detect whether sounds are normal or abnormal based on volume and frequency. The prior art is replete with teachings regarding determining conditions based on analysis of sound (see pertinent art). Thus, this is merely the combination of an extremely well-known common and routine technique according to its established function in order to yield a predictable result. Matsubara provides additional explicit teaching, suggestion and motivation (see paragraphs 7-10).
Moulik discloses the IoT devices are part of a smart home environment (paragraphs 39 and 43) but does not explicitly disclose determine an in-home state or an out-home state indicating whether at least one person is within an area in which the at least one first IoT device and the at least one second IoT device, based on the state information; or performing different (i.e. first and second) type of anomaly monitoring based on determining the out-home state or the in-home state. But this is taught by Hutz as an in-home alarm system that monitors for abnormal patterns (abstract, Fig. 1). Hutz explicitly discloses the system determines whether the user is home or is away from the property (see col. 2 ln. 55-63, col. 3 ln. 34-36); and then performs anomaly pattern detection based on the in-home or out-of-home state (see col. 4 ln. 25-43 and col. 15 ln. 12-16, and Fig. 3). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the combination of Moulik with the in-home and out-home state determination taught by Hutz for the purpose of monitoring abnormal events. Hutz discloses that by detecting patterns, which includes considering whether a user is home or away from home, abnormal activity can be detected which allows for an alarm or alert to be triggered in response. One of ordinary skill in the art would understand the purpose of an in-home alarm system and changing anomaly monitoring based on whether a user is present or away from home.
Moulik also discloses detect an event based on the state information (detect event – abstract, paragraphs 8-9, Fig. 9); determine whether at least one first sound signal related to the first event exists, based on the sound information (state information includes actions such as playing media/audio, this is equivalent to “sound information” – see paragraphs 40-43); and based on identifying that the at least one first sound signal related to the first event does not exist, determine that the abnormal situation is detected (determine anomaly is equivalent to “abnormal situation” – see paragraphs 40-45 and 84, Figs. 8-9; Figs. 14A-D explain how state information and events are used to train a model to correlate events with abnormal conditions; thus when a noise is expected and does not occur, the model may recognize an abnormal situation). Hutz also teaches detecting patterns by considering historical sensor activity as discussed above. Thus when an event occurs and the pattern suggests another event is likely to follow (i.e. first sound), the alarm system may determine that an abnormal situation or anomaly has occurred when the subsequent event is not detected because that violates the expected pattern.
Regarding claim 3, Moulik discloses determine whether a first [value] of the at least one first sound signal is within a designated range based on the at least one first sound signal related to the first event existing (compare data to threshold – abstract, Figs. 5, 11, paragraphs 12-13; also see paragraph 58 which teaches “a normal range”).
based on identifying that the first [value] of the at least one first sound signal is out of the desired range, determine that an abnormal situation is detected (outlier values trigger anomaly – see Fig. 5D, paragraph 107). This concept is also merely common knowledge in the art, a person of ordinary skill is known to associate noises outside of normal operation with an abnormal situation.
Moulik does not explicitly disclose “amplitude” but this would have been obvious to one of ordinary skill in the art as discussed above. Also, Matsubara explicitly teaches volume as discussed above.
Regarding claim 4, Moulik discloses detecting events based on the state information (detect event – abstract, paragraphs 8-9, Fig. 9); based on the sound information, determine whether at least one second sound signal exists, wherein the at least one second sound signal is not related to the second event (state information includes actions such as playing media/audio, this is equivalent to “sound information” – see paragraphs 40-43; signals come from a plurality of devices, thus there are “second sound signals” – Fig. 8; determine correlation between state information/sound and events – paragraphs 51, 55-56); and based on the at least one second sound signal existing, determine that the abnormal situation is detected (determine anomaly is equivalent to “abnormal situation” - paragraphs 40-45 and 84, Figs. 8-9; Figs. 14A-D explain how state information and events are used to train a model to correlate events with abnormal conditions).
Regarding claim 5, Moulik discloses detect the event related to the at least one first IoT device, based on the state information (detect event based on state information – abstract, paragraphs 8-9, Figs. 8-9); based on the sound information identify … a first sound signal and … a second sound signal, wherein both of the first sound signal and the second sound signal are related to the at least one first IoT device (monitor sound information – paragraphs 39-40; monitor devices using microphones to detect events – paragraphs 63, 67); and based on identifying that the … first sound signal is not greater than the … second sound signal, determine that the abnormal situation is detected (use model to detect abnormality based on event data – Figs. 5, 8-9, 11; identify abnormality by comparing data to threshold - see paragraph 64).
Moulik does not explicitly disclose a first amplitude or a second amplitude, however amplitude would have been obvious in view of Moulik as discussed above. Guddeti also discloses measuring amplitude (see Section IV, dB level). Matsubara also discloses volume as discussed above.
Regarding claim 6, Moulik discloses the sound information is received from an external electronic device through the communication circuit in an out-of-home situation in which there is no registered user in a designated area (IoT environment includes smart home environment – paragraph 39, Figs. 1 and 8 show no registered user in a designated area while state/sound information is transmitted over the network; plurality of IoT devices include home appliances, etc. – see paragraph 41, these are “external” devices).
Regarding claim 7, Moulik discloses perform the first anomaly monitoring based on management data comprising at least one of an event identifier, a related device list, or a sound level per device (anomaly detection considers at least event identifier – paragraphs 45, 51, Fig. 6, and related device list – see Fig. 4).
Regarding claim 8, Moulik discloses wherein the management data is generated based on the state information and the sound information collected in a designated training period (train model using collected state information – Figs. 8, 14A and paragraphs 50, 57, 117).
Regarding claim 9, Moulik discloses the state information comprises at least one event indicating at least one of a bulb on/off, a robot vacuum cleaner on/off, a door lock on/off, or a window on/off (paragraphs 40-41, 45 ;also see paragraph 67 – robot cleaner and “open/close states of doors, windows…” ).
Regarding claim 10, Moulik discloses the sound information comprises a device identification (paragraph 39) and … a sound signal recorded by an IoT device corresponding to the device ID (state information includes actions such as playing media/audio, this is equivalent to “sound information” – see paragraphs 40-43; also the use of microphones – see paragraph 63, teaches a sound signal). Moulik does not explicitly disclose an amplitude but this would have been obvious to one of ordinary skill in the art as discussed above.
Regarding claim 11, it is a method claim that corresponds to the device of claim 1. Therefore, it is rejected for the same reasons.
Regarding claims 13-20, they correspond to previously presented dependent claims 3-10 respectively, so they are also rejected for the same reasons.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Huang US 2023/0058503 A1 discloses IoT architecture that judges the volume of a sound to detect abnormal changes (paragraph 93).
Tanaka et al. US 2016/0327522 A1 discloses an abnormal sound detection device (abstract, Fig. 1).
Nixon et al. US 10,031,490 B2 discloses analyzing sound to detect abnormal conditions (abstract).
Kusserow et al. US 2020/0002127 A1 discloses using a microphone sensor to monitor noise and determine normal state, establish threshold values (e.g. range) base on volume, and when exceed threshold, trigger additional monitoring/sensors (abstract, paragraph 50).
Bednar US 5,521,840 discloses an acoustic sensor to monitor sound amplitude and determine normal operation or alarm condition using a neural network (abstract, Fig. 1, col. 3 ln. 20-35).
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.
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/JASON D RECEK/Primary Examiner, Art Unit 2458
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