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 .
Response to Arguments
Applicant's arguments filed 7/23/2026 have been fully considered but they are not persuasive. After reviewing the applicant’s disclosure, the claims do not appear to cover novel subject matter based on how the applicant has described the claimed subject matter. Paragraph 36 of the applicant’s disclosure states that a technique for determining NRP metrics was described in U.S. Publication Number 2017/0195171, which was published more than a year prior to the applicant’s filing date. Paragraph 31 references the term “geometric deep learning” but does not provide any description of algorithms for implementing such learning. It can be assumed that the applicant did not disclose any details about such “geometric deep learning” algorithms because they are well known and thus did not need to be described in detail in order to meet the written description requirement. Otherwise, the applicant would not have complied with the written description requirement regarding disclosing details of algorithms (see section 2161.01(I) of the MPEP). Therefore, the Examiner found the claimed details of determining NRP metrics to be obvious in view of the prior art referenced in the applicant’s disclosure.
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) 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over U.S. Patent Application Publication Number 2018/0041256 by Schmidt et al. in view of U.S. Patent Application Publication Number 2017/0195171 by Wohlert et al. and the article entitled “Geometric Deep Learning” by Bronstein et al.
As to claim 20, Schmidt teaches a method comprising: identifying, by a processing system including a processor, a group of communication devices (paragraphs 35 and 103) within a proximity threshold of a first mobile edge compute (MEC) (paragraphs 52 and 54, the invention detects a “close” proximity and thus uses a threshold), wherein each communication device of the group of communication devices implements an application (paragraph 34, user equipment operates wireless communication applications), wherein the first MEC is provisioned with an MEC agent for the application (paragraphs 20 and 54, the base station operates SFN agent for wireless communication); determining, by the processing system, an application parameter for the application associated with each communication device of the group of communication devices resulting in a group of application parameters (paragraph 150, reference device manages parameters for further mobile devices), wherein each of the group of application parameters is associated with a first time period (paragraphs 23, 28, 54); determining, by the processing system, that each of the group of application parameters is below a threshold during the first time period, resulting in a first determination (paragraphs 28, 52, and 54 and Figure 1, the train is below the distance of “close” to the first base station at t1); determining, by the processing system and based on the first determination, that each application parameter associated with the application during a second time period is above the threshold, resulting in a second determination (paragraphs 28, 52, and 54 and Figure 1, the train is above the distance of “close” to the first base station at t3); identifying, by the processing system, that the group of communication devices is within the proximity threshold of a second MEC, resulting in a first identification (Figure 1, the group of devices on the train approaches BS n+3 at t3); and provisioning, by the processing system and based on the second determination and the first identification, the MEC agent on the second MEC (Figure 1, SFN agent on BS n+3 is activated as the train nears at t3); however Schmidt does not explicitly teach the claimed manner of determining the claimed NRP metric.
Wohlert teaches determining a first network relative performance (NRP) metric for a first network comprising a group of communication devices (components 110) and a first MEC (controller 130), wherein the determining of the first NRP metric comprises: (i) obtaining a graph representative of a topology or geometry of a first network (paragraphs 24, 38 and 39, step 220 in Figure 2); (ii) obtaining one or more performance metrics for links and network components of the first network (paragraph 28 and 29, step 210 in Figure 2); and (iii) applying geometric deep learning to the one or more performance metrics in view of the graph representative of the topology or geometry of the first network to generate the first NRP metric (paragraphs 24, 38, 39, 45 and 46, the network uses past information to evaluate new configurations and thus learns); however, Wohlert does not explicitly describe its learning technique as a “geometric deep learning” technique”.
Bronstein shows that the concept of “geometric deep learning”, referenced only in paragraph 31 of the applicant’s disclosure but not otherwise described, was a well-known concept for applying machine learning.
It would have been obvious to one of ordinary skill in the device mobility art at the time of the applicant’s filing to combine the teachings of Wohlert regarding using a learning technique to determine a NRP metric with the teachings of Bronstein regarding geometric deep learning because such techniques provide a powerful tool for a broad range of problems (see Bronstein) using learning algorithms.
It would have been obvious to one of ordinary skill in the device mobility art at the time of the applicant’s filing to combine the teachings of Schmidt regarding managing services for groups of devices with a movement pattern with the teachings of the Wohlert-Bronstein combination regarding determining NRP metrics because such metrics would aid the system of Schmidt in deciding on how to deploy agents to service the multi-node network of Schmidt.
Claim(s) 1, 3-5, 8, 9, 11, 13-15, 18, and 19 is/are rejected under 35 U.S.C. 103 as being unpatentable over U.S. Patent Application Publication Number 2018/0041256 by Schmidt et al. in view of U.S. Patent Application Publication Number 2017/0195171 by Wohlert et al. and the article entitled “Geometric Deep Learning” by Bronstein et al. in further view of U.S. Patent Application Publication Number 2015/0063300 by Wenger et al.
As to claim 1, Schmidt teaches a device, comprising: a processing system including a processor; and a memory that stores executable instructions that, when executed by the processing system, facilitate performance of operations, the operations comprising: identifying a group of communication devices in a first movement pattern (paragraphs 35, 74, and 103) within a first proximity threshold of a first mobile edge compute (MEC) (paragraphs 52 and 54, the invention detects a “close” proximity and thus uses a threshold), wherein each communication device of the group of communication devices implements an application (paragraph 34, user equipment operates wireless communication applications), wherein the first MEC is provisioned with an MEC agent for the application (paragraphs 20 and 54, the base station operates SFN agent for wireless communication); determining an application parameter for the application associated with each communication device of the group of communication devices resulting in a group of application parameters (paragraph 150, reference device manages parameters for further mobile devices), wherein each of the group of application parameters is associated with a first time period (paragraphs 23, 28, 54); determining that each of the group of application parameters is below a threshold (paragraphs 28, 52, and 54 and Figure 1, the train is below the distance of “close” to the first base station at t1); determining that each application parameter associated with the application between the first MEC and each communication device of the group of communication devices during a second time period is above the threshold resulting in a first determination (paragraphs 28, 52, and 54 and Figure 1, the train is above the distance of “close” to the first base station at t3); identifying that the group of communication devices is within a second proximity threshold of a second MEC (Figure 1, the group of devices on the train approaches BS n+3 at t3); and provisioning, based on the first determination and the identifying that the group of communication devices is within the second proximity threshold of the second ME, the MEC agent for the application on the second MEC (Figure 1, SFN agent on BS n+3 is activated as the train nears at t3), however, Schmidt does not explicitly teach identifying that the group of communication devices has moved in a second movement pattern and Schmidt does not explicitly teach the claimed manner of determining the claimed NRP metric.
Wenger teaches a device (device 9) that performs operations of identifying a group of communication devices in a first movement pattern (paragraph 60) within a first proximity of a MEC (base station) and subsequently identifying that the group of communication devices has moved into a second movement pattern (paragraph 63, the divergent subgroups form a “second” movement pattern).
It would have been obvious to one of ordinary skill in the device mobility art at the time of the applicant’s filing to combine the teachings of Schmidt regarding managing services for a groups of devices with a movement pattern with the teachings of Wenger regarding detecting a second movement pattern because both reference deal with providing wireless services on trains to groups of a mobile devices and Wenger shows that movement patterns of train passengers and their devices change. The Examiner notes that the claims do not do anything in response to the identification of the second movement pattern.
Wohlert teaches determining a first network relative performance (NRP) metric for a first network comprising a group of communication devices (components 110) and a first MEC (controller 130), wherein the determining of the first NRP metric comprises: (i) obtaining a graph representative of a topology or geometry of a first network (paragraphs 24, 38 and 39, step 220 in Figure 2); (ii) obtaining one or more performance metrics for links and network components of the first network (paragraph 28 and 29, step 210 in Figure 2); and (iii) applying geometric deep learning to the one or more performance metrics in view of the graph representative of the topology or geometry of the first network to generate the first NRP metric (paragraphs 24, 38, 39, 45 and 46, the network uses past information to evaluate new configurations and thus learns); however, Wohlert does not explicitly describe its learning technique as a “geometric deep learning” technique”.
Bronstein shows that the concept of “geometric deep learning”, referenced only in paragraph 31 of the applicant’s disclosure but not otherwise described, was a well-known concept for applying machine learning.
It would have been obvious to one of ordinary skill in the device mobility art at the time of the applicant’s filing to combine the teachings of Wohlert regarding using a learning technique to determine a NRP metric with the teachings of Bronstein regarding geometric deep learning because such techniques provide a powerful tool for a broad range of problems (see Bronstein) using learning algorithms.
It would have been obvious to one of ordinary skill in the device mobility art at the time of the applicant’s filing to combine the teachings of Schmidt regarding managing services for groups of devices with a movement pattern with the teachings of the Wohlert-Bronstein combination regarding determining NRP metrics because such metrics would aid the system of Schmidt in deciding on how to deploy agents to service the multi-node network of Schmidt.
As to claim 11, it is rejected for the same reasoning as claim 1.
As to claims 3 and 13, in both references the devices are in a vehicle. The applicant does not define the term “vehicular communication device”. See also paragraph 167 of Schmidt.
As to claims 4, 5, 14, and 15, see paragraphs 23 and 24 of Schmidt. The transmission power is a NRP that satisfies a metric. Paragraph 24 describes how the transmission power changes relative to location.
As to claims 8 and 18, see paragraph 167 of Schmidt.
As to claims 9 and 19, see paragraphs 167 and 168 of Schmidt.
Claim(s) 2, 6, 7, 10, 12, 16, and 17 is/are rejected under 35 U.S.C. 103 as being unpatentable over U.S. Patent Application Publication Number 2018/0041256 by Schmidt et al. in view of U.S. Patent Application Publication Number 2017/0195171 by Wohlert et al. and the article entitled “Geometric Deep Learning” by Bronstein et al. in further view of U.S. Patent Application Publication Number 2015/0063300 by Wenger et al. in view of U.S. Patent Application Publication Number 2019/0104030 by Giust et al.
As to claims 2 and 12, the Schmidt-Wenger-Wohlert-Bronstein combination teaches the subject matter of claims 1 and 11 however the Schmidt-Wenger-Wohlert-Bronstein combination does not explicitly teach the use of the specifically claimed application parameters in claims 2 and 12.
Giust shows that it would be obvious to use application parameters such a delay when considering serving an application from a first MEC to communication devices in a group of communication devices (see paragraphs 32 and 56).
It would have been obvious to one of ordinary skill in the device mobility art at the time of the applicant’s filing to combine the teachings of Schmidt regarding determining whether to serve an application from a base station with the teachings of Giust regarding considering delay when determining whether to serve an application from a base station because such a consideration would improve the efficiency of the system of Schmidt for provisioning base stations with SFN agents.
As to claims 6 and 16, the Schmidt-Wenger-Wohlert-Bronstein combination teaches the subject matter of claims 1 and 11 however the Schmidt-Wenger-Wohlert-Bronstein combination does not explicitly teach provisioning the agent based on the second determination (in addition to the determination covered by claims 1 and 11).
Giust shows that it would be obvious to use additional parameters such a delay and throughput when considering serving an application from a first MEC to communication devices in a group of communication devices (paragraphs 39 and 56).
It would have been obvious to one of ordinary skill in the device mobility art at the time of the applicant’s filing to combine the teachings of Schmidt regarding determining whether to serve an application from a base station with the teachings of Giust regarding considering additional parameters when determining whether to serve an application from a base station because such a consideration would improve the efficiency of the system of Schmidt for provisioning base stations with SFN agents.
As to claims 7, 10 and 17, the Schmidt-Wenger-Wohlert-Bronstein combination teaches the subject matter of claims 1 and 11 however the Schmidt-Wenger-Wohlert-Bronstein combination does not explicitly teach the use of the specifically claimed metrics of claims 7, 10 and 17.
Giust shows that it would be obvious to use metrics such as throughput when considering the performance of a base station (paragraph 39).
It would have been obvious to one of ordinary skill in the device mobility art at the time of the applicant’s filing to combine the teachings of Schmidt regarding determining whether to serve an application from a base station with the teachings of Giust regarding considering throughput when determining whether to serve an application from a base station because such a consideration would improve the efficiency of the system of Schmidt for provisioning base stations with SFN agents.
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 DOUGLAS B BLAIR whose telephone number is (571)272-3893. The examiner can normally be reached Monday-Friday 9am-5pm.
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/DOUGLAS B BLAIR/Primary Examiner, Art Unit 2454
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