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 .
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 5/29/25 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Response to Arguments
Applicant’s arguments with respect to the claim(s) have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
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) 1-11 and 13-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over US 2022/0122735 by Sherkat et al. (Sherkat) in view of US 2024/0256856 by Rao et al. (Rao)
With respect to claim 1, Sherkat teaches an enterprise operations system comprising:
one or more edge supported wearable devices; (Fig. 1, Paragraph 63, 64, 68 – wearable devices are in communication with an edge device) and
an edge enterprise operations device comprising at least one processor and at least one non-transitory memory including computer-coded instructions thereon, wherein the computer-coded instructions, with the at least one processor, cause the edge enterprise operations device to: (Fig. 1, Paragraph 68-69, 264 – example edge ML devices 130, 150)
receive server enterprise operations data from an enterprise operations server; (Paragraph 67-69, 88Fig. 1 – operational models and parameters are received from core network cloud servers)
receive at least a first portion of enterprise implementation data from at least one of the one or more edge supported wearable devices; (Paragraph 63, 64, 68, 70 – sensor data and other captured signals are collected from a variety of devices including wearable devices)
apply the server enterprise operations data and the enterprise implementation data to a composite edge enterprise operations machine learning model to generate edge enterprise operations data; (Paragraph 84-85, 123-129 – example edge device uses model to learn about a patient, predict future conditions and determine problems; Paragraphs 136-138 – example detection of patterns to determine mood and other biofeedback) and
initiate performance of one or more edge enabled enterprise operations actions based at least in part on the edge enterprise operations data. (Paragraphs 9,18, 69, 72, 84-85, 136-139 – actions from determined including ongoing monitoring and reporting of conditions and adverse events to both patients and doctors, further actions can include instructing music/video playback to occur)
Sherkat does not explicitly disclose the composite edge enterprise operations machine learning model comprising a plurality of distinct sub-models. Rao teaches an edge machine learning model can comprise a plurality of distinct sub-models (Paragraph 107-116 – a machine learning model may be partitioned into a plurality of distinct sub-models according to device constraints).
It would have been obvious to one of ordinary skill in the art at before the effective filing date of the claimed invention to have the enterprise model of Sherkat be comprised of a plurality of sub-models as in Rao. One would be motivated to have this for the advantage of being able to run models on constrained devices while still maintain the accuracy of the model (See Rao Paragraphs 16-19).
With respect to claim 2, Sherkat teaches the enterprise operations system of claim 1, wherein the edge enterprise operations device is physically located at an asset. (Paragraph 63, 69 – edge devices can be at a remote asset such as a home, business, office or factory)
With respect to claim 3, Sherkat teaches the enterprise operations system of claim 2, wherein the computer-coded instructions, further with the at least one processor, cause the enterprise operations device to: receive at least a second portion of the enterprise implementation data from one or more image capture devices associated with the asset. (Paragraph 62, 64, 92, - signal information can include camera or video camera information for capturing aspects such as facial expression or actions such as orientation and time in regards to a person)
With respect to claim 4, Sherkat teaches the enterprise operations system of claim 1, wherein the enterprise operations server is physically located at a remote location and in communication with the edge enterprise operations device via at least a network. (Fig. 1, Paragraph 60-63)
With respect to claim 5, Sherkat teaches the enterprise operations system of claim 1, wherein the plurality of distinct sub-models of the composite edge enterprise operations machine learning model comprises an edge audio processing machine learning model. (Paragraph 64, 89 – edge ML device processes audio)
With respect to claim 6, Sherkat teaches the enterprise operations system of claim 1, wherein the plurality of distinct sub-models of the composite edge enterprise operations machine learning model comprises an edge image processing machine learning model. (Paragraph 62, 64, 89, 92 - edge ML device processes at least camera images)
With respect to claim 7, Sherkat teaches the enterprise operations system of claim 1, wherein the plurality of distinct sub-models of the composite edge enterprise operations machine learning model comprises an edge generative machine learning model. (Paragraph 14, 27, 103, 119 – model is rule based and include processing according to trained parameters and coefficients)
With respect to claim 8, Sherkat teaches the enterprise operations system of claim 1, wherein the plurality of distinct sub-models of the composite edge enterprise operations machine learning model comprises an edge impact and event machine learning model. (Paragraph 84-85, 123-129 – example edge device uses model to learn about a patient including adverse events, predict future conditions and determine problems; Paragraphs 136-138 – example detection of patterns to determine mood and other biofeedback)
With respect to claim 9, Sherkat teaches the enterprise operations system of claim 1, wherein at least one of the one or more edge supported wearable devices comprises at least one second processor and at least one second non- transitory memory including second computer-coded instructions thereon, the second computer-coded instructions, with the at least one second processor, cause the at least one of the one or more edge supported wearable devices to: generate the first portion of the enterprise implementation data, wherein generating the first portion of the enterprise implementation data comprises: capturing the first portion of the enterprise implementation data. (Paragraph 63, 64, 68, 70 – sensor data and other captured signals are collected from a variety of devices including wearable devices)
With respect to claim 10, Sherkat teaches the enterprise operations system of claim 1, wherein at least one of the one or more edge supported wearable devices comprises at least one second processor and at least one second non- transitory memory including second computer-coded instructions thereon, the second computer-coded instructions, with the at least one second processor, cause the at least one of the one or more edge supported wearable devices to: generate the first portion of the enterprise implementation data, wherein generating the first portion of the enterprise implementation data comprises: capturing enterprise implementation data; and processing the enterprise implementation data using a wearable device machine learning model. (Paragraph 63, 64, 68-70 – sensor data and other captured signals are collected from a variety of devices including wearable devices, ML system processes such signals from wearable devices as part of decision making and determination such as predictions and reporting)
With respect to claim 11, Sherkat teaches the enterprise operations system of claim 1, wherein at least one of the one or more edge supported wearable devices comprises at least one processor and at least one non- transitory memory including computer-coded instructions thereon, the computer coded instructions, with the at least one processor, cause the at least one of the one or more edge supported wearable devices to: generate the first portion of the enterprise implementation data, wherein generating the first portion of the enterprise implementation data comprises: capturing the first portion of the enterprise implementation data; and generate, based at least in part on applying the first portion of the enterprise implementation data to a wearable device machine learning model, wearable enterprise operations data; and initiating performance of one or more wearable enabled enterprise operations actions based at least in part on the wearable enterprise operations data. (Paragraph 63, 64, 68-70 – sensor data and other captured signals are collected from a variety of devices including wearable devices, ML system processes such signals from wearable devices as part of decision making and determination such as predictions and reporting, a specific example is Paragraph 136 indicating biofeedback, such as heartrate provided by a wearable heart rate sensor, can determine and output decision to command entertainment to play in order to reduce the heartrate)
Claims 13-20 are similar in scope to claims 1-11 and are rejected based on the same rationale.
Claim(s) 12 is/are rejected under 35 U.S.C. 103 as being unpatentable over Sherkat in view of Rao and in further view of US 2025/0068885 by Poupyrev et al (Poupyrev).
With respect to claim 12, Sherkat as modified teaches the enterprise operations system of claim 11, but does not explicitly disclose wherein the wearable device machine learning model comprises a wearable device large language model.
Poupyrev teaches the use of large language models in processing sensor data to assist in predicting and diagnosing events and conditions related to a patient, for example. (Paragraph 133-135, 154 )
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have the wearable device machine learning model of Sherkat comprise a large language model as in Poupyrev. Using a known machine learning model to predictably provide the desired patient management of Sherkat would be obvious.
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 DAVID R LAZARO whose telephone number is (571)272-3986. The examiner can normally be reached M-F 8-4:30.
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/DAVID R LAZARO/Primary Examiner, Art Unit 2455