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 .
1. This is in response to communication filed on 9/08/25 in which claims 1-2, 4-8, 10-12, 14-21 are pending.
Response to Arguments
2. Applicant’s arguments with respect to claims 1, 11 and 21 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 § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
3. Claims 1, 11 and 21 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claims contain subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention. The claims recite “operation adjustment is stored in association with the evaluation of performance and the environment context as an element of an operation adjustment history, the operation adjustment history being used to allow learning of operation adjustment over time”, those element as recited are not part of the disclosure as originally filed. The original disclosure does not recite an operation adjustment association with the evaluation of performance and the environment context as an element of an operation adjustment history and affects the scope of the claims.
Claim Rejections - 35 USC § 102
4. 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.
5. Claims 1-5, 10-15, 19-21 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by U.S Publication No. 2016/0197800 to Hui et al.
a. As per claim 1, Hui et al teaches a method for adjusting a network of sensor devices, the network also comprising an aggregation node for aggregating sensor data from at least two of the sensor devices to processed data (See paragraph [0066], the supervisory device may aggregate sensor data from multiple devices in the network ) and an application node configured to approach an application goal based on the processed data (See paragraph [0049-0050]), the method being performed in a network device, the method comprising repeatedly performing: receiving an evaluation of performance of the application node in relation to the application goal (See paragraph [0046]) and an environment context not known by the application goal (See paragraph [0062-0064, and 0073], decreased network performance due to environmental/weather conditions may not necessitate an adjustment to the operation of the network, if the traffic profile indicates low use of the network and the actual traffic is of low priority (e.g., the building is effectively vacant during a holiday and, consequently, the network traffic is extremely low, etc. ); determining that the evaluation indicates that an adjustment is needed (See paragraph [0046, 0062-0063]); determining an operation adjustment of at least one of the aggregation node and the sensor devices, wherein the determining the operation adjustment comprises determining an adjustment action to adjust how at least one sensor device provides sensor data (See paragraph [0067 and 0081-0083]); and wherein the operation adjustment is stored in association with the evaluation of performance and the environment context as an element of an operation adjustment history, the operation adjustment history being used to allow learning of operation adjustment over time, by comparing with subsequent evaluations of performance and corresponding environment contexts (See paragraph [0049, 0054, 0058-0059, 0063, 0083], Both the sensor data and the network traffic profile information may be used as inputs to a machine learning model); creating, based on the operation adjustment history a feedback loop to allow the network device to further adjust the operation adjustment based on the learning (See paragraph [0081-0083], the device may change which parameters are adjusted, the parameter values used during an operation adjustment, an adjustment policy sent to another device, or the like, if the resulting performance after the previous operation adjustment was not acceptable. Procedure then continues on to step 1010. In various embodiments, procedure 1000 may be repeated iteratively any number of times until acceptable network performance is achieved or, in some cases, until a timeout event occurs (e.g., after a set number of failed adjustments, after receiving a stop command from a user interface, etc.). Thus, procedure 1000 may enable the network to use a feedback loop to control when and how operation adjustments are made); and triggering the adjustment (See paragraph [0054 and 0061]).
b. As per claim 11, Hui et al teaches a network device for adjusting a network of sensor devices, the network also comprising an aggregation node for aggregating sensor data from at least two of the sensor devices to processed data (See paragraph [0066], the supervisory device may aggregate sensor data from multiple devices in the network ) and an application node configured to approach an application goal based on the processed data (See paragraph [0049-0050]), the network device comprising: a processor; and a memory storing instructions that, when executed by the processor, cause the network device to repeatedly perform: receiving an evaluation of performance of the application node in relation to the application goal (See paragraph [0046]) and an environment context not known by the application goal (See paragraph [0062-0064, 0073], decreased network performance due to environmental/weather conditions may not necessitate an adjustment to the operation of the network, if the traffic profile indicates low use of the network and the actual traffic is of low priority (e.g., the building is effectively vacant during a holiday and, consequently, the network traffic is extremely low, etc.); determining that the evaluation indicates that an adjustment is needed (See paragraph [0046]); determining an operation adjustment of at least one of the aggregation node and sensor devices, wherein the instructions to determine an operation adjustment comprise instructions that, when executed by the processor, cause the network device to determine an adjustment action to adjust how at least one sensor device provides sensor data (See paragraph [0067 and 0083]), and wherein the operation adjustment is stored in association with the evaluation of performance and the environment context as an element of an operation adjustment history, the operation adjustment history being used to allow learning of operation adjustment over time, by comparing with subsequent evaluations of performance and corresponding environment context (See paragraph [0049, 0054, 0058-0059, 0083], Both the sensor data and the network traffic profile information may be used as inputs to a machine learning model); creating, based on the operation adjustment history, a feedback loop to allow the network device to further adjust the operation adjustment based on the learning (See paragraph [0081-0083], the device may change which parameters are adjusted, the parameter values used during an operation adjustment, an adjustment policy sent to another device, or the like, if the resulting performance after the previous operation adjustment was not acceptable. Procedure then continues on to step 1010. In various embodiments, procedure 1000 may be repeated iteratively any number of times until acceptable network performance is achieved or, in some cases, until a timeout event occurs (e.g., after a set number of failed adjustments, after receiving a stop command from a user interface, etc.). Thus, procedure 1000 may enable the network to use a feedback loop to control when and how operation adjustments are made); and triggering the adjustment (See paragraph [0054 and 0061]).
c. As per claims 2 and 12, Hui et al teaches the claimed invention as described above. Furthermore, Hui et al teaches wherein the step of determining the operation adjustment comprises determining an adjustment of a processing function of the aggregation node, and wherein the processing function combines sensor data from the at least two of the sensor devices to processed data (See paragraph [0049 0054], a supervisory device may receive sensor data from one or more other devices and initiate a network operation change by providing instructions to the one or more other devices based on the sensor data).
d. As per claims 3 and 13, Hui et al teaches the claimed invention as described above. Furthermore, Hui et al teaches wherein the determining the operation adjustment comprises determining an adjustment action to adjust how at least one sensor device provides sensor data (See paragraph [0056 and 0076]).
e. As per claims 4 and 14, Hui et al teaches the claimed invention as described above. Furthermore, Hui et al teaches wherein the determining an operation adjustment is performed using a machine learning engine (See paragraph [0058]).
f. As per claims 5 and 15, Hui et al teaches the claimed invention as described above. Furthermore, Hui et al teaches further comprising: obtaining updated processed data (See paragraph [0049 and 0062]); wherein the determining an operation adjustment is based at least partly on the updated processed data (See paragraph [0059, 0062-0063]).
g. As per claims 10 and 20, Hui et al teaches the claimed invention as described above. Furthermore, Hui et al teaches wherein the sensor devices are telecommunication network sensors (See paragraph [0019]).
h. As per claim 19, Hui et al teaches the claimed invention as described above. Furthermore, Hui et al teaches wherein the sensor devices are internet-of-things (IoT), sensors (See paragraph [0035]).
i. As per claim 21, Hui et al teaches a computer program product for adjusting a network of sensor device, the network also comprising an aggregation node for aggregating sensor data from at least two of the sensor devices to processed data and an application node configured to approach an application goal based on the processed data (See paragraph [0066], the supervisory device may aggregate sensor data from multiple devices in the network ), the computer program comprising a non-transitory computer readable medium storing computer program code which, when executed by at least one processor on a network device causes the network device to perform: receiving an evaluation of performance of the application node in relation to the application goal (See paragraph [0046]) and an environment context not known by the application goal (See paragraph [0062-0064, 0073], decreased network performance due to environmental/weather conditions may not necessitate an adjustment to the operation of the network, if the traffic profile indicates low use of the network and the actual traffic is of low priority (e.g., the building is effectively vacant during a holiday and, consequently, the network traffic is extremely low, etc.); determining that the evaluation indicates that an adjustment is needed (See paragraph [0026]); determining an operation adjustment of at least one of the aggregation node and the sensor devices; wherein the determining the operation adjustment comprises determining adjustment action to adjust how at least one sensor device provides sensor data (See paragraph [0067 and 0081-0083]) and wherein the operation adjustment is stored in association with the evaluation of performance and the environment context as an element of an operation adjustment history, the operation adjustment history being used to allow learning of operation adjustment over time, by comparing with subsequent evaluation s of performance and corresponding environment contexts(See paragraph [0049, 0054, 0058-0059, 0063 and 0083], Both the sensor data and the network traffic profile information may be used as inputs to a machine learning model); creating, based on the operation adjustment history a feedback loop to allow the network device to further adjust the operation adjustment based on the learning (See paragraph [0081-0083], the device may change which parameters are adjusted, the parameter values used during an operation adjustment, an adjustment policy sent to another device, or the like, if the resulting performance after the previous operation adjustment was not acceptable. Procedure then continues on to step 1010. In various embodiments, procedure 1000 may be repeated iteratively any number of times until acceptable network performance is achieved or, in some cases, until a timeout event occurs (e.g., after a set number of failed adjustments, after receiving a stop command from a user interface, etc.). Thus, procedure 1000 may enable the network to use a feedback loop to control when and how operation adjustments are made) and triggering the adjustment (See paragraph [0054 and 0061]).
Claim Rejections - 35 USC § 103
6. 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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
7. Claims 6-8 and 16-18 are rejected under 35 U.S.C. 103 as being unpatentable over U.S Publication No. 2016/0197800 to Hui et al in view of U.S. Publication No. 2019/0377305 to Petrus et al.
a. As per claims 6 and 16, Hui et al teaches the claimed invention as described above. However, Hui et al fails to teach wherein the method is performed in an agent module executing in the network device, the agent module forming part of a multi-agent programming framework.
Petrus et al teaches wherein the method is performed in an agent module executing in the network device, the agent module forming part of a multi-agent programming framework (See paragraph [0164]).
It would have been obvious to one with ordinary skill in the art to incorporate the teaching of Petrus et al in the claimed invention of Hui et al in order to monitor and control all sensors.
b. As per claims 7 and 17, Hui et al teaches the claimed invention as described above. However, Hui et al fails to teach comprising the step of: transmitting the updated processed data to another agent module.
Petrus et al teaches comprising the step of: transmitting the updated processed data to another agent module (See paragraph [0164]).
It would have been obvious to one with ordinary skill in the art to incorporate the teaching of Petrus et al in the claimed invention of Hui et al in order to monitor and control all sensors.
c. As per claims 8 and 18, Hui et al teaches the claimed invention as described above. However, Hui et al fails to teach wherein the agent modules are arranged hierarchically.
Petrus et al teaches wherein the agent modules are arranged hierarchically (See paragraph [0005]).
It would have been obvious to one with ordinary skill in the art to incorporate the teaching of Petrus et al in the claimed invention of Hui et al in order to monitor and control all sensors.
Conclusion
8. The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
U.S. Publication No. 2022/0327204 to Abbaszadeh et al teaches an Unified Multi-Agent System for Abnormality Detection and Isolation.
9. 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.
10. Any inquiry concerning this communication or earlier communications from the examiner should be directed to DJENANE BAYARD whose telephone number is (571)272-3878. The examiner can normally be reached 9-5.
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, John Follansbee can be reached on (571)272-3964. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/DJENANE M BAYARD/Primary Examiner, Art Unit 2444
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