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 .
Status of Claims
This Office Action is in response to the application filed on July 30, 2026. Claims 1-20 are presently pending and are presented for examination.
Response to Amendments
In response to Applicant’s amendments filed July 30, 2026, Examiner withdraws all previous rejections.
Response to Arguments
Applicant’s arguments, see Response, filed July 30, 2026, with respect to the rejection(s) of all pending claims 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 Therefore, the rejection has been withdrawn.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 U.S. Pub. No. 2023/0292325 (hereinafter, “Bai”; newly of record).
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 1-20 are rejected under 35 U.S.C. 101, because the claimed invention is directed to an abstract idea without significantly more. Independent claim 1 is directed toward a method, claim 8 is directed toward an apparatus, and claim 15 is directed toward a non-transitory computer readable medium. Therefore, each of the independent claims 1, 8, and 15 along with the corresponding dependent claims 2-7, 9-14, and 16-20 are directed to a statutory category of invention under Step 1.
Under Step 2A, Prong 1, the claims are analyzed to determine whether one or more of the claims recites subject matter that falls within one of the following groups of abstract ideas: (1) mental processes, (2) certain methods of organizing human activity, and/or (3) mathematical concepts. In this case, the independent claims 1, 8, and 15 are directed to an abstract idea without significantly more. Specifically, the claims, under their broadest reasonable interpretation cover certain mental processes. The language of independent claim 1 is used for illustration:
A method, comprising:
receiving, by a device, terrestrial data and location data at multiple locations within an infrastructure and during different time periods (this limitation amounts to insignificant extra-solution activity, as explained further below);
overlaying, by the device, the terrestrial data and the location data on a floor plan of the infrastructure to generate a terrestrial map for the infrastructure (this limitation amounts to organizing data via generic computer functions which amounts to implementing the abstract idea via a generic computer);
storing, by the device, the terrestrial map in a data structure associated with the device (this limitation amounts to a generic computer function which amounts to implementing the abstract idea via a generic computer);
training, by the device, a machine learning model, with the terrestrial data, the location data, and the terrestrial map, to generate a trained machine learning model (this limitation amounts generic training via a generic computer and is considered insignificant extra-solution activity); and
performing, by the device, one or more actions using the trained machine learning model (see below pertaining to the claimed action),
wherein performing the one or more actions comprises:
determining potential anomalies or issues with the infrastructure when a deviation between a received terrestrial intensity and a predicted terrestrial intensity, using the trained machine learning model, deviates from a predefined threshold (this limitation is considered a mental process as a person is capable of mentally determining potential anomalies or issues with the infrastructure based on comparing observed data to a known threshold).
As explained above, independent claim 1 recites at least one abstract idea. The other independent claims 8 and 15, which are of similar scope to claim 1, likewise recite at least one abstract idea under Step 2A, Prong 1.
Under Step 2A, Prong 2, the claims are analyzed to determine whether the claim, as a whole, integrates the abstract idea into a practical application. As noted in the 2019 PEG, it must be determined whether any additional elements in the claim beyond the abstract idea integrate the exception into a practical application in a manner that imposes a meaningful limit on the judicial exception. The courts have indicated that additional elements such as merely using a computer to implement an abstract idea, adding insignificant extra solution activity, or generally linking use of a judicial exception to a particular technological environment or field of use do not integrate a judicial exception into a “practical application”; see at least MPEP 2106.04(d).
In this case, the mental processes concepts judicial exception is/are not integrated into a practical application. For example, independent claims 1, 8, and 15 recite the additional elements of one or more processors…, a non-transitory computer-readable medium…, receiving, by a device, terrestrial data…, overlaying, by the device, the terrestrial data…, storing, by the device, the terrestrial map…, and training, by the device, a machine learning model…. These limitations amount to implementing the abstract idea on a computer, add insignificant extra solution activity, and/or generally link use of the judicial exception to a particular technological environment or field of use; see at least MPEP 2106.04(d). More specifically,
a. one or more processors… found in independent claims 8 and 15. This limitation amounts to implementing the abstract idea via a generic computer.
b. a non-transitory computer-readable medium… found in independent claim 15. This limitation amounts to implementing the abstract idea via a generic computer.
c. receiving, by a device, terrestrial data… found in independent claims 1, 8, and 15. This limitation amounts to insignificant extra-solution activity, as discussed further below.
d. overlaying, by the device, the terrestrial data… found in independent claim 1, 8, and 15. This limitation amounts to implementing the abstract idea via a generic computer.
e. storing, by the device, the terrestrial map… found in independent claims 1, 8, and 15. This limitation amounts to implementing the abstract idea via a generic computer.
f. training, by the device, a machine learning model… found in independent claims 1, 8, and 15. This limitation amounts to insignificant extra-solution activity, as explained further below.
Therefore, taken alone, the additional elements do not integrate the abstract idea into a practical application. Furthermore, looking at the additional limitation(s) as an ordered combination or as a whole, the limitations add nothing significant that is not already present when looking at the elements taken individually. Because the additional elements, do not integrate the abstract idea into a practical application by imposing meaningful limits on practicing the abstract idea, independent claims 1, 8, and 15 are directed to an abstract idea.
Under Step 2B, the claims do not include any additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application in Step 2A, Prong Two, the additional element of limiting the use of the idea to one particular environment employs generic computer functions to execute an abstract idea and, therefore, does not add significantly more. Limiting the use of the abstract idea to a particular environment or field of use cannot provide an inventive concept. Additionally, as discussed above, the limitations of receiving, by a device, terrestrial data…, and training, by the device, a machine learning model…, as recited above, are considered insignificant extra solution activities.
A conclusion that an additional element is insignificant extra solution activity in Step 2A must be re-evaluated in Step 2B to determine if the element is more than what is well-understood, routine, and conventional in the field. In this case, the additional limitations of receiving, by a device, terrestrial data…, and training, by the device, a machine learning model… are well-understood, routine, and conventional activities, because they have all been deemed insignificant extra solution activity by one or more Courts; see at least MPEP 2106.05(d) and MPEP 2106.05(g):
a. receiving, by a device, terrestrial data… is considered well-understood, routine, and conventional activity under TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 614, 118 USPQ2d 1744, 1748 (Fed. Cir. 2016) (Specification described additional elements as "either performing basic computer functions such as sending and receiving data, or performing functions ‘known’ in the art."); and
b. training, by the device, a machine learning model… is considered well-understood, routine, and conventional activity under Recentive Analytics, Inc. v. Fox Corp., claims that merely apply standard or iterative training of a generic machine learning model to a new data environment.
Because the claims fail to recite anything sufficient to amount to significantly more than the judicial exception, independent claims 1, 8, and 15 are patent ineligible under 35 U.S.C. 101.
Dependent claims 2-7, 9-14, and 16-20 have been given the full two-part analysis, including analyzing the additional limitations, both individually and in combination. Dependent claims 2-7, 9-14, and 16-20, when analyzed both individually and in combination, are also patent ineligible under 35 U.S.C. § 101 based on same analysis as above. The additional limitations recited in the dependent claims fail to establish that the dependent claims are not directed to an abstract idea. The additional limitations of the dependent claims, when considered individually and as an ordered combination, do not amount to significantly more than the abstract idea. Accordingly, claims 2-7, 9-14, and 16-20 are patent ineligible. Therefore, claims 1-20 are patent ineligible under 35 U.S.C. 101.
Examiner encourages Applicant to set an interview to discuss potential amendments for overcoming the above rejections under 35 U.S.C. 101.
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, 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.
Claims 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over U.S. Pat. No. 10,042,038 (hereinafter, “Lord”; previously of record) in view of U.S. Pub. No. 2023/0292325 (hereinafter, “Bai”; newly of record).
Regarding claim 1, Lord discloses A method, comprising:
receiving, by a device, terrestrial data and location data at multiple locations within an infrastructure and during different time periods (see at least Col. 30, Ln. 54-Col. 32, Ln. 18; a mobile device (i.e., a device) may receive data associated with a sound map and locations associated with a given infrastructure during different time periods);
overlaying, by the device, the terrestrial data and the location data on a floor plan of the infrastructure to generate an terrestrial map for the infrastructure (see at least Col. 21, Ln. 10-Ln. 16 and Col. 30, Ln. 63-Col. 31, Ln. 12; sound maps may be generated from different time periods for a particular environment which include sound data (i.e., terrestrial data) and locations within the environment, and the sound maps may be overlayed on maps, floor plans, or another layout associated with the particular environment);
storing, by the device, the terrestrial map in a data structure associated with the device (see at least Col. 50, Ln. 58-Col. 51, Ln. 18 and the publication generally; the sound map may be stored in the data structure of the device);
training, by the device, a machine learning model, with the terrestrial data, the location data, and the terrestrial map, to generate a trained machine learning model (see at least Col. 15, Ln. 11-26 and Col. 16, Ln. 36-60; a CNN (i.e., machine learning model) may be trained using the sound data (i.e., terrestrial data) and localization data based on the map information to generate a trained CNN); and
performing, by the device, one or more actions using the trained machine learning model (see at least Col. 16, Ln. 55-60; one or more actions may be performed responsive to the CNN output). However, Lord fails to explicitly teach
wherein performing the one or more actions comprises:
determining potential anomalies or issues with the infrastructure when a deviation between a received terrestrial intensity and a predicted terrestrial intensity, using the trained machine learning model, deviates from a predefined threshold.
Bai, in the same field of endeavor, teaches wherein performing the one or more actions comprises:
determining potential anomalies or issues with the infrastructure when a deviation between a received terrestrial intensity and a predicted terrestrial intensity, using the trained machine learning model, deviates from a predefined threshold (see at least [0069]-[0070] and [0080]-[0082]; anomalies in sensor data may be detected when, for example, a sound exceeds a sound threshold level (i.e., deviates from a predefined threshold) and pertains to a corresponding object (i.e., infrastructure), and the detection may at least derive from an ML output).
One of ordinary skill in the art, before the effective filing date and with a reasonable likelihood of success, would have been motivated to modify the disclosure of Lord with the teachings of Bai in order to determine which data is usable data and transferrable/usable for future processing; see at least Bai at [0069].
Regarding claim 2, Lord discloses and Bai teaches all of the limitations of claim 1. Additionally, Lord discloses wherein performing the one or more actions comprises:
monitoring the infrastructure in real-time using the trained machine learning model to identify one or more deviations from one or more expected terrestrial patterns (see at least Col. 25, Ln. 62-Col. 26, Ln. 13, Col. 40, Ln. 25-37; monitoring of the acoustic signals in the specific environment (i.e., infrastructure) using a CNN may occur including identifying changes in the sound patterns); and
generating an alert based on the one or more deviations from the one or more expected terrestrial patterns (see at least Col. Col. 28, Ln. 27-44; a user may be informed if the captured acoustics do not correspond with the expected sound pattern).
Regarding claim 3, Lord discloses and Bai teaches all of the limitations of claim 1. Additionally, Lord discloses wherein the device is a first device, and wherein receiving the terrestrial data and the location data comprises:
receiving the terrestrial data and the location data from a user device at the multiple locations within the infrastructure during the different time periods (see at least Col. 30, Ln. 54-Col. 32, Ln. 18; a mobile device (i.e., a device) may receive data associated with a sound map and locations associated with a given infrastructure during different time periods).
Regarding claim 4, Lord discloses and Bai teaches all of the limitations of claim 1. Additionally, Lord discloses further comprising:
utilizing a Fourier transform to generate frequency and amplitude data based on the terrestrial data (see at least Col. 34, Ln. 66-Col. 35, Ln. 18 and Col. 46, Ln. 9-16; the sound data is used to add spectral data via Fourier transform to the sound map).
Regarding claim 5, Lord discloses and Bai teaches all of the limitations of claim 1. Additionally, Lord discloses wherein training the machine learning model comprises:
training a long short-term memory (LSTM) model, with the terrestrial data, the location data, and the terrestrial map, to predict terrestrial intensities at various locations within the infrastructure at various times (see at least Col. 15, Ln. 11-35, Col. 16, Ln. 36-49, and Col. 32, Ln. 6-11; an LSTM model may utilize the sound data (i.e., terrestrial data), map, and location data to determine intensities of sound at different locations and times within the infrastructure).
Regarding claim 6, Lord discloses and Bai teaches all of the limitations of claim 1. Additionally, Lord discloses wherein training the machine learning model comprises:
training a convolutional neural network (CNN) model, with the terrestrial data, the location data, and the terrestrial map, to predict various locations within the infrastructure at various times (see at least Col. 15, Ln. 11-35 and Col. 16, Ln. 36-49; a CNN may utilize the sound data (i.e., terrestrial data), map, and location data to determine intensities of sound at different locations and times within the infrastructure).
Regarding claim 7, Lord discloses and Bai teaches all of the limitations of claim 1. Additionally, Lord discloses further comprising:
providing the terrestrial map to a mobile edge computing device (see at least Col. 33, Ln. 45-50; a smartphone (i.e., mobile edge computing device) may be used).
Regarding claim 8, Lord discloses A device, comprising:
one or more processors (see at least Col. 5, Ln. 18-39) configured to:
receive terrestrial data and location data at multiple locations within an infrastructure and during different time periods (see at least Col. 30, Ln. 54-Col. 32, Ln. 18; a mobile device (i.e., a device) may receive data associated with a sound map and locations associated with a given infrastructure during different time periods);
overlay the terrestrial data and the location data on a floor plan of the infrastructure to generate an terrestrial map for the infrastructure (see at least Col. 21, Ln. 10-Ln. 16 and Col. 30, Ln. 63-Col. 31, Ln. 12; sound maps may be generated from different time periods for a particular environment which include sound data (i.e., terrestrial data) and locations within the environment, and the sound maps may be overlayed on maps, floor plans, or another layout associated with the particular environment);
store the terrestrial map in a data structure associated with the device (see at least Col. 50, Ln. 58-Col. 51, Ln. 18 and the publication generally; the sound map may be stored in the data structure of the device);
provide the terrestrial map to a mobile edge computing device (see at least Col. 33, Ln. 45-50; a smartphone (i.e., mobile edge computing device) may be used);
train a machine learning model, with the terrestrial data, the location data, and the terrestrial map, to generate a trained machine learning model (see at least Col. 15, Ln. 11-26 and Col. 16, Ln. 36-60; a CNN (i.e., machine learning model) may be trained using the sound data (i.e., terrestrial data) and localization data based on the map information to generate a trained CNN); and
perform one or more actions using the trained machine learning model (see at least Col. 16, Ln. 55-60; one or more actions may be performed responsive to the CNN output). However, Lord fails to explicitly teach
wherein performing the one or more actions comprises:
determining potential anomalies or issues with the infrastructure when a deviation between a received terrestrial intensity and a predicted terrestrial intensity, using the trained machine learning model, deviates from a predefined threshold.
Bai, in the same field of endeavor, teaches wherein performing the one or more actions comprises:
determining potential anomalies or issues with the infrastructure when a deviation between a received terrestrial intensity and a predicted terrestrial intensity, using the trained machine learning model, deviates from a predefined threshold (see at least [0069]-[0070] and [0080]-[0082]; anomalies in sensor data may be detected when, for example, a sound exceeds a sound threshold level (i.e., deviates from a predefined threshold) and pertains to a corresponding object (i.e., infrastructure), and the detection may at least derive from an ML output).
One of ordinary skill in the art, before the effective filing date and with a reasonable likelihood of success, would have been motivated to modify the disclosure of Lord with the teachings of Bai in order to determine which data is usable data and transferrable/usable for future processing; see at least Bai at [0069].
Regarding claim 9, Lord discloses and Bai teaches all of the limitations of claim 8. Additionally, Lord discloses wherein the one or more processors, to perform the one or more actions, are configured to:
receive an terrestrial intensity from an terrestrial sensor at a location within the infrastructure and at a time (see at least Col. 29, Ln. 49-Col. 30, Ln. 29 and Col. 30, Ln. 54-Col. 32, Ln. 18; acoustic intensity may be acquired via one or more sensors within the specific environment at a specific time);
utilize the trained machine learning model to generate a predicted terrestrial intensity at the location and the time (see at least Col. 25, Ln. 62-Col. 26, Ln. 13, Col. 40, Ln. 25-37; monitoring of the acoustic signals in the specific environment (i.e., infrastructure) using a CNN may occur including identifying changes in the sound patterns);
determine that the terrestrial intensity sufficiently deviates from the predicted terrestrial intensity (see at least Col. Col. 28, Ln. 27-44; it may be determined that the acoustics do not correspond with the expected sound pattern); and
generate an alert based on determining that the terrestrial intensity sufficiently deviates from the predicted terrestrial intensity (see at least Col. Col. 28, Ln. 27-44; a user may be informed if the captured acoustics do not correspond with the expected sound pattern).
Regarding claim 10, Lord discloses and Bai teaches all of the limitations of claim 9. Additionally, Lord discloses wherein the one or more processors, to generate the alert, are configured to:
send, to a remote system, a notification indicating that the terrestrial intensity sufficiently deviates from the predicted terrestrial intensity.
Regarding claim 11, Lord discloses and Bai teaches all of the limitations of claim 8. Additionally, Lord discloses wherein the one or more processors, to perform the one or more actions, are configured to:
receive, from a user device, a request for a location of the user device in the infrastructure based on an terrestrial intensity captured by the user device (see at least Col. 17, Ln. 43-54, Col. 16, Ln. 36-60, and Col. 28, Ln. 1-25; responsive to user input, the location of the user device within the specific environment may be determined based on the sound intensity captured by the user device);
utilize the trained machine learning model to predict the location of the user device based on the terrestrial intensity (see at least Col. 16, Ln. 36-60; a CNN may be trained to predict the location of the user device based on the sound intensity profile overlayed with the location-based map); and
provide the location to the user device (see at least Col. 17, Ln. 43-54, Col. 16, Ln. 36-60, Col. 28, Ln. 1-25, and Col. 39, Ln. 13-16; responsive to user input, the location of the user device within the specific environment may be determined based on the sound intensity captured by the user device, and the location may be displayed to the user).
Regarding claim 12, Lord discloses and Bai teaches all of the limitations of claim 11. Additionally, Lord discloses wherein the location causes the user device to provide the location and a wireless signal to another user device for determining a location of the other user device within the infrastructure (see at least Col. 51, Ln. 6-18; sharing of data between user devices pertaining to the location data may be performed).
Regarding claim 13, Lord discloses and Bai teaches all of the limitations of claim 11. Additionally, Lord discloses wherein the location causes the user device to broadcast a nearness terrestrial signal to enable other user devices to determine locations within the infrastructure (see at least Col. 1, Ln. 31-35; the information may be shared amongst user devices in the environment).
Regarding claim 14, Lord discloses and Bai teaches all of the limitations of claim 8. Additionally, Lord discloses wherein the infrastructure includes one or more of a data center, a server room, a warehouse, or a factory (see at least Col. 1, Ln. 31-35 and Col. 4, Ln. 36-39; the specific environment may be, for example, a warehouse).
Regarding claim 15, Lord discloses A non-transitory computer-readable medium storing a set of instructions (see at least Col. 5, Ln. 40-65), the set of instructions comprising:
one or more instructions that, when executed by one or more processors of a device (see at least Col. 5, Ln. 18-39), cause the device to:
receive terrestrial data and location data at multiple locations within an infrastructure and during different time periods (see at least Col. 30, Ln. 54-Col. 32, Ln. 18; a mobile device (i.e., a device) may receive data associated with a sound map and locations associated with a given infrastructure during different time periods),
wherein the terrestrial data and the location data is received from a user device at the multiple locations of the infrastructure during the different time periods (see at least Col. 30, Ln. 54-Col. 32, Ln. 18; a mobile device (i.e., a device) may receive data associated with a sound map and locations associated with a given infrastructure during different time periods);
overlay the terrestrial data and the location data on a floor plan of the infrastructure to generate an terrestrial map for the infrastructure (see at least Col. 21, Ln. 10-Ln. 16 and Col. 30, Ln. 63-Col. 31, Ln. 12; sound maps may be generated from different time periods for a particular environment which include sound data (i.e., terrestrial data) and locations within the environment, and the sound maps may be overlayed on maps, floor plans, or another layout associated with the particular environment);
store the terrestrial map in a data structure associated with the device (see at least Col. 50, Ln. 58-Col. 51, Ln. 18 and the publication generally; the sound map may be stored in the data structure of the device);
train a machine learning model, with the terrestrial data, the location data, and the terrestrial map, to generate a trained machine learning model (see at least Col. 15, Ln. 11-26 and Col. 16, Ln. 36-60; a CNN (i.e., machine learning model) may be trained using the sound data (i.e., terrestrial data) and localization data based on the map information to generate a trained CNN); and
perform one or more actions using the trained machine learning model (see at least Col. 16, Ln. 55-60; one or more actions may be performed responsive to the CNN output). However, Lord fails to explicitly teach
wherein performing the one or more actions comprises:
determining potential anomalies or issues with the infrastructure when a deviation between a received terrestrial intensity and a predicted terrestrial intensity, using the trained machine learning model, deviates from a predefined threshold.
Bai, in the same field of endeavor, teaches wherein performing the one or more actions comprises:
determining potential anomalies or issues with the infrastructure when a deviation between a received terrestrial intensity and a predicted terrestrial intensity, using the trained machine learning model, deviates from a predefined threshold (see at least [0069]-[0070] and [0080]-[0082]; anomalies in sensor data may be detected when, for example, a sound exceeds a sound threshold level (i.e., deviates from a predefined threshold) and pertains to a corresponding object (i.e., infrastructure), and the detection may at least derive from an ML output).
One of ordinary skill in the art, before the effective filing date and with a reasonable likelihood of success, would have been motivated to modify the disclosure of Lord with the teachings of Bai in order to determine which data is usable data and transferrable/usable for future processing; see at least Bai at [0069].
Regarding claim 16, Lord discloses and Bai teaches all of the limitations of claim 15. Additionally, Lord discloses wherein the one or more instructions, that cause the device to perform the one or more actions, cause the device to:
monitor the infrastructure in real-time using the trained machine learning model to identify one or more deviations from one or more expected terrestrial patterns (see at least Col. 25, Ln. 62-Col. 26, Ln. 13, Col. 40, Ln. 25-37; monitoring of the acoustic signals in the specific environment (i.e., infrastructure) using a CNN may occur including identifying changes in the sound patterns); and
generate an alert based on the one or more deviations from the one or more expected terrestrial patterns (see at least Col. Col. 28, Ln. 27-44; a user may be informed if the captured acoustics do not correspond with the expected sound pattern).
Regarding claim 17, Lord discloses and Bai teaches all of the limitations of claim 15. Additionally, Lord discloses wherein the one or more instructions further cause the device to:
utilize a Fourier transform to generate frequency and amplitude data based on the terrestrial data (see at least Col. 34, Ln. 66-Col. 35, Ln. 18 and Col. 46, Ln. 9-16; the sound data is used to add spectral data via Fourier transform to the sound map).
Regarding claim 18, Lord discloses and Bai teaches all of the limitations of claim 15. Additionally, Lord discloses wherein the one or more instructions, that cause the device to train the machine learning model, cause the device to:
train a long short-term memory (LSTM) model, with the terrestrial data, the location data, and the terrestrial map, to predict terrestrial intensities at various locations within the infrastructure at various times (see at least Col. 15, Ln. 11-35, Col. 16, Ln. 36-49, and Col. 32, Ln. 6-11; an LSTM model may utilize the sound data (i.e., terrestrial data), map, and location data to determine intensities of sound at different locations and times within the infrastructure).
Regarding claim 19, Lord discloses and Bai teaches all of the limitations of claim 15. Additionally, Lord discloses wherein the one or more instructions, that cause the device to train the machine learning model, cause the device to:
train a convolutional neural network (CNN) model, with the terrestrial data, the location data, and the terrestrial map, to predict various locations within the infrastructure at various times (see at least Col. 15, Ln. 11-35 and Col. 16, Ln. 36-49; a CNN may utilize the sound data (i.e., terrestrial data), map, and location data to determine intensities of sound at different locations and times within the infrastructure).
Regarding claim 20, Lord discloses and Bai teaches all of the limitations of claim 15. Additionally, Lord discloses wherein the one or more instructions, that cause the device to perform the one or more actions, cause the device to:
receive an terrestrial intensity from an terrestrial sensor at a location within the infrastructure and at a time (see at least Col. 29, Ln. 49-Col. 30, Ln. 29 and Col. 30, Ln. 54-Col. 32, Ln. 18; acoustic intensity may be acquired via one or more sensors within the specific environment at a specific time);
utilize the trained machine learning model to generate a predicted terrestrial intensity at the location and the time (see at least Col. 25, Ln. 62-Col. 26, Ln. 13, Col. 40, Ln. 25-37; monitoring of the acoustic signals in the specific environment (i.e., infrastructure) using a CNN may occur including identifying changes in the sound patterns);
determine that the terrestrial intensity sufficiently deviates from the predicted terrestrial intensity (see at least Col. Col. 28, Ln. 27-44; it may be determined that the acoustics do not correspond with the expected sound pattern); and
generate an alert based on determining that the terrestrial intensity sufficiently deviates from the predicted terrestrial intensity (see at least Col. Col. 28, Ln. 27-44; a user may be informed if the captured acoustics do not correspond with the expected sound pattern).
Additional Relevant Art (previously of record)
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure and may be found on the accompanying PTO-892 Notice of References Cited:
U.S. Pat. No. 12,578,205 which relates to the automatic generation of an indoor map using wireless communication strength to create fingerprint map data.
Conclusion
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 TIFFANY P YOUNG whose telephone number is (313)446-6575. The examiner can normally be reached M-R 6:30 AM- 4:30 PM.
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, Erin Bishop can be reached at (571) 270-3713. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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TIFFANY YOUNG
Primary Examiner
Art Unit 3666
/TIFFANY P YOUNG/Primary Examiner, Art Unit 3665
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