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 .
This is a Final Office Action responsive to Applicant’s reply filed 7/6/2026.
Claims 1-12 are pending.
Response to Amendment
Applicant has amended the claims to include new limitations necessitating a new search and further consideration.
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.
Claim(s) ) 1, 3-7, and 10-12 are rejected under 35 U.S.C. 103 as being unpatentable over Li et al. (US 2016/0379107) in view of Nochta (US 2018/0097710).
With respect to claim 1, Li discloses: receiving, by at least one IoT device, the user input from a user of the at least one IoT device to execute at least one task associated with the at least one IoT device in the IoT environment ([0119], [0120]; “please answer at once” corresponds to Applicant’s “user input”, the act of answering the call and providing the video communication is interpreted as the “task”);
acquiring, by the at least one IoT device, multimodal data of the IoT environment based on the user input ([0120], [0141]; the camera identifies the direction of the speaker and turns the camera to the direction of the speaker, the image and sound of the speaker corresponds to Applicant’s “multimodal data”);
predicting, by the at least one IoT device based on the user input and the multimodal data, a task execution intensity for the at least one task associated with the at least one IoT ([0120]; “double camera switch algorithm ensures picture of camera is stable and does not shake” corresponds to Applicant’s “task execution intensity”. The claim does not disclose how the “predicting” is accomplished. The broadest reasonable interpretation of “predicting” is anticipating that the camera will shake and employ the algorithm to ensure the camera is stable and does not shake); and
executing, by the at least one IoT device, the at least one task associated with the at least one IoT device with the predicted task execution intensity ([0120], [0121]).
Li does not specifically disclose: wherein the multimodal data of the IoT environment includes data associated with a plurality of IoT devices of the IoT environment and data of at least one sensor in the IoT environment.
However, Nochta discloses: wherein the multimodal data of the IoT environment includes data associated with a plurality of IoT devices of the IoT environment and data of at least one sensor in the IoT environment ([0014).
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the invention to incorporate the teachings of Nochta to ensure Li’s artificial terminal device is in proper order by predicting when maintenance or repairs are required thereby ensuring continuity of service.
With respect to claim 3, Li discloses: wherein acquiring, by the at least one IoT device, the multimodal data of the IoT environment comprises: determining, by the at least one IoT device, a multimodal context of the IoT environment relevant to the at least one task associated with the at least one IoT device based on the received user input; and acquiring, by the at least one IoT device, the multimodal data of the IoT environment corresponding to the determined multimodal context ([0119]-[0121]; camera activates in response to user input).
With respect to claim 4, Li discloses: wherein the multimodal context comprises at least one of a context of the user, a context of the at least one IoT device and an ambient context, and wherein the context of the user context is determined from one or more derived input from the multimodal data, pertaining to an user activity, and state of connected IoT devices, wherein the ambient context is determined from one or more derived inputs from IoT device data, non-speech scene detection, sensory output, and an external parameter ([0119]-[0121]).
With respect to claim 5, Li discloses: wherein the multimodal data comprises at least one of: a gesture of the user, Ultra-wideband (UWB) position of the at least one IoT device, data associated with the at least one IoT device, at least one sensor input, feed associated with an imaging device, voice assistant information, and non-speech information ([0120]; camera corresponds to “imaging device”).
With respect to claim 6, Li discloses: wherein the task execution intensity comprises at least one of: a functional mode of the at least one IoT device, a position of the at least one IoT device, a movement of the at least one IoT device, and a control function of the at least one IoT device ([0120]).
With respect to claim 7, Li discloses: wherein the multimodal data is acquired at least by: receiving at least one of the user input, a gesture of the user, Ultra-wideband (UWB) position of the at least one IoT device, data associated with the at least one IoT device, at least one sensor input, feed associated with an imaging device, voice assistant information, and non-speech information to generate the multimodal data; and converting and normalizing the generated multimodal data ([0119], [0120], [0140]).
With respect to claim 10, Li discloses: wherein the task execution intensity is determined using at least one of a machine learning (ML) based technique, a Random forest technique, a clustering based technique and a decision tree based classifier, wherein the task execution intensity is determined based on at least one of capability of the at least one IoT device, a state of the at least one IoT device, and an execution control data associated with the at least one IoT device ([0119]-[0121], the decisions made by the IoT device corresponds to “decision tree”).
With respect to claims 11 and 12, they recite similar limitations as claim 1 and is therefore rejected under the same citations and rationale.
Claim(s) 2 is rejected under 35 U.S.C. 103 as being unpatentable over Li et al. (US 2016/0379107) in view of Nochta (US 2018/0097710) further in view of Choi et al. (US 2019/0140856).
With respect to claim 2, Li does not specifically disclose: monitoring, by the at least one IoT device, the task execution intensity for the at least one task as a feedback over a period of time; and executing, by the at least one IoT device, the at least one task associated with the at least one IoT device based on the feedback.
However, Choi discloses: monitoring, by the at least one IoT device, the task execution intensity for the at least one task as feedback over a period of time; and executing, by the at least one IoT device, the at least one task associated with the at least one IoT device based on the feedback ([0127]- [0130]).
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the invention to incorporate feedback as taught by Choi to enable Li’s IoT device to leverage active learning, transfer learning, and reinforcement learning to further enhance machine learning models as taught by Choi, thereby improving Li’s algorithm to reduce camera shaking or provide alerts for emergencies.
Claim(s) 9 is rejected under 35 U.S.C. 103 as being unpatentable over Li et al. (US 2016/0379107) in view of Nochta (US 2018/0097710) further in view of Mueen et al. (US 10853372)
With respect to claim 9, Li does not specifically disclose: wherein the multimodal data is updated over a period of time, using a data driven model, based on at least one of the user behavior, an user usage pattern and the at least one IoT device, wherein the multimodal data is processed using a map reduction technique.
However, Mueen discloses: wherein the multimodal data is updated over a period of time, using a data driven model, based on at least one of the user behavior, an user usage pattern and the at least one IoT device, wherein the multimodal data is processed using a map reduction technique (col. 16, lines 36-54).
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the invention to incorporate the teachings of Mueen’s map reduce technique to allow processing large datasets by dividing them into smaller, manageable tasks that can be processed in parallel and significantly improve performance by distributing the tasks to multiple worker/threads/processors.
Response to Arguments
Applicant’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.
Allowable Subject Matter
Claim 8 is allowed.
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 WISSAM RASHID whose telephone number is (571)270-3758. The examiner can normally be reached Monday-Friday 8:00 am-5:00 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, Aimee Li can be reached at (571)272-4169. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/WISSAM RASHID/Primary Examiner, Art Unit 2195
Prosecution Insights