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 action is made final.
This office action is in response to the amendments filed May 29, 2026.
Claims 1, 12, 23, and 27 have been amended.
Response to Amendment
The amendments filed have been entered. Claims 1-30 remain pending in the case and have been rejected.
Response to Arguments
Regarding the 112 arguments
Applicant’s arguments with respect to the claims have been considered but the interpretation is maintained.
Regarding the 101 arguments
Applicant’s arguments, see pages 10-17, filed May 29, 2026, with respect to Step 2A Prong Two have been fully considered and are persuasive. The rejection of March 3, 2026 has been withdrawn.
Regarding the 103 arguments
Applicant’s arguments with respect to the claims have been considered but are moot in view of the new grounds of rejection necessitated by the amendment.
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
This application includes one or more claim limitations that use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AlA35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitation(s) is/are:
means for determining a first heterogeneity level as recited in claim 27 and invokes 112(f), is interpreted as a variance, dispersion algorithm, or equivalent thereof performing the claimed functions as supported in paragraph(s) [0116] of Applicant’ s Specification.
means for determining... a first aggregation period as recited in claim 27 and invokes 112(f), is interpreted as a period selection, or equivalent thereof performing the claimed functions as supported in paragraph(s) [0117-0119] of Applicant’s Specification.
means for obtaining the first and second set of updated model parameters as recited in claim 27 and invokes 112(f), is interpreted as a antenna, demodulator, or equivalent thereof performing the claimed functions as supported in paragraph(s) [0136] of Applicant’s Specification.
means for combining the first and second set of updated model parameters as recited in claim 27 and invokes 112(f), is interpreted as a averaging algorithm, or equivalent thereof performing the claimed functions as supported in paragraph(s) [0128-0138] of Applicant’s Specification.
means for determining a second heterogeneity level as recited in claim 28 and invokes 112(f), is interpreted as a variance, dispersion algorithm, or equivalent thereof performing the claimed functions as supported in paragraph(s) [0139] of Applicant’s Specification.
means for determining...a second data aggregation period as recited in claim 28 and invokes 112(f), is interpreted as a period selection, or equivalent thereof performing the claimed functions as supported in paragraph(s) [139-0140] of Applicant’s Specification.
means for sending the first and second set of updated model parameters as recited in claim 29 and invokes 112(f), is interpreted as a transmitter, antenna, or equivalent thereof performing the claimed functions as supported in paragraph(s) [0140-0142] of Applicant’s Specification.
means for receiving a first and second decoded set of updated model parameters as recited in claim 29 and invokes 112(f), is interpreted as a demodulator, or equivalent thereof performing the claimed functions as supported in paragraph(s) [0140-0142] of Applicant’s Specification.
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AlA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AlA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AlA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AlA 35 U.S.C. 112, sixth paragraph.
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.
The text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action.
Claim(s) 1, 5-12, 16-23, 26-27, and 30 is/are rejected under 35 U.S.C. 103 as being unpatentable over Akdeniz et al. (US 20230068386 A1, referred to as Akdeniz) in view of Li et al. ("Federated optimization in heterogeneous networks.", referred to as Li), in view of Zeng et al. (“Local Epochs Inefficiency Caused by Device Heterogeneity in Federated Learning”, referred to as Zeng), in view of Wang et al. ("When edge meets learning: Adaptive control for resource-constrained distributed machine learning." , referred to as Wang).
Regarding claim 1 , Akdeniz teaches an apparatus for performing federated learning, comprising:
at least one memory comprising instructions ([0020-0030], and [0040-0045]: Describes a master node (central server/MEC system 200) coordinating distributed or federated model training among heterogenous edge devices (UEs 01, MEC servers 201). The process is called distributed gradient descent corresponding to federated learning.; FIG. 6, and [0077-0078]: Describes a system that comprises “ edge provisioning node 644 includes one or more servers and one or more storage devices” and “the processor platform(s) that execute the computer readable instructions “.) ; and
at least one processor configured to execute the instructions and cause the apparatus to (FIG. 8, and [0083-0088]: Describes an edge computing node 850 and wireless transceiver 866 to implement radio functions, “ edge computing device 850 may include processing circuitry in the form of a processor 852,” and the device may store instructions to be executed in the system/apparatus “The processor 852 may communicate with a system memory 854 over an interconnect 856 (e.g., a bus) through an interconnect interface 853 of the processor.”):
obtain, from a first client device and a second client device, respective updated model parameters for a machine learning model ([0127-0128], and [0186]: Describes that the federated learning system where multiple clients (edge devise) perform local training and provides “updates”/”weight values”/”locally updated models” to a server (MEC/central server), which corresponds to obtaining those client-provided updates in order to aggregate them into a global model. Each client (multiple client computing node/edge devices) computing node obtains a global model, updates aspects of the global model (model parameters or neural network weights) using its local data, and communicates the updates to the central server, which aggregates the received updates and updates model weight values based on an average of the weight values received from the clients. The model updates include updated values for neural network nodes and that the aggregated node weight values are used for subsequent implementations of the model. The clients perform multiple local epochs of training on their respective local raw data, and that the MEC receives the locally updated models. The data across edge devices is non-IID, and that it is used to account for such differences across clients during training):
Although Akdeniz teaches obtain, from a first client device and a second client device, respective updated model parameters for a machine learning model and receiving updated model parameters from client devices in a federated learning environment. It does not teach to determining a data heterogeneity level and that such heterogeneity level is based on the respective updated model parameters received form the client devices.
Li teaches, determine a first data heterogeneity level associated with input data for training the machine learning model at the first client device and the second client device, (Pages 5-6, Section 4.1, and Page 10 Section 5.3.3, Figure 2: Describes that statistical heterogeneity (non-IID data) can be characterized by a scalar dissimilarity metric derived from local gradient information and tracks gradient variance to measure divergence among client updates. Local gradients are computed from client training data, divergence among local gradients reflects differences in client data distributions (statistical heterogeneity).)
It would have been obvious to one of ordinary skill in the art at the time of the claimed invention to have combine Akdeniz’s federated learning MEC system with Li’s data heterogeneity metrics based on gradients. Doing so would have enabled the system to quantify statistical heterogeneity among client devices. This would improve convergence, stability, and adaptive scheduling in the federated leaning process.
Akdeniz in view of Li further teaches, wherein the first data heterogeneity level is based on the respective updated model parameters received from the first client device and the second client device (The scalar dissimilarity metric B in Li represents a data heterogeneity level derived from client model update information. In the federated learning system of Akdeniz, client devices transmit updated model parameters (gradients or weight updates) to the server.)
Akdeniz in view of Li teaches, configure, using on the first data heterogeneity level, the first client device and the second client device with a first data aggregation period associated with a number of training cycles, within a communication round between the apparatus and the first client device and a communication round between the apparatus and the second client device(Akdeniz [0186]: Describes a controller that communicates global model, and clients carry out multiple local epochs. The MEC receives locally updated models. Showing that the controller/server orchestrates how clients train per global epoch/round and they configure client behaviors at least at the level of training/round structure (global epoch, to local epoch, to return updates).)
Although Akdeniz in view of Li, teaches to configure… the first client device and the second client device. It does not teach using on the first data heterogeneity level, … with a first data aggregation period associated with a number of training cycles, within a communication round between the apparatus and the first client device and a communication round between the apparatus and the second client device.
Zeng teaches using on the first data heterogeneity level, … with a first data aggregation period associated with a number of training cycles, within a communication round between the apparatus and the first client device and a communication round between the apparatus and the second client device ( Page 1 Abstract: Describes client heterogeneity causes inconsistent training speeds and that the time cost of clients’ local training reflects such heterogeneity and can be used to guide dynamic setting of local epochs (training cycles). ; Page 6, Algorithm 2: Describes that a client predicts the training time of one local epoch Ttrain and computes the number of local epochs as Ei = T/Ttrain, and then performs local training for epochs 1 through Ei before returning updates to the server. ; Pages 10-11 Section 3.4: Describes dynamically setting the number of local epochs (training cycles) for a client within a given communication round/time window between the client and the server. Specifically, it shows that the number of local epochs may be calculated according to a preset communication time window T between the client and server. The correspond to configuring a client with a data aggregation period (communication window T / per-round sync window) that is associated with a number of training cycles (local epochs Ei) within a communication round between server and client.)
It would have been obvious to one of ordinary skill in the art at the time of the claimed invention to have combine Akdeniz’s, in view of Li’s federated learning MEC system with Zeng’s technique for setting client’s number of epochs within a communication time window. Doing so would have enabled the system to reduce inefficiency caused by heterogeneous client training speeds, to improve round completion efficiency by configuring clients with an appropriate number of local training cycles per round.
Akdeniz in view of Li, in view of Zeng, further teaches, for training the machine learning model at the first client device and the second client device (Akdeniz [0186]: Describes that during training for a given global epoch, the controller communicates the global model to participating client devices and the clients perform multiple local epochs of training on their local raw data, producing locally updated models that are received by the MEC, corresponding to training the machine learning model at the client devices.)
Although Akdeniz in view of Li, in view of Zeng, teaches to for training the machine learning model at the first client device and the second client device. They do not teach wherein the number of training cycles associated with the first data aggregation period decreases as the first data heterogeneity level increases.
Wang teaches wherein the number of training cycles associated with the first data aggregation period decreases as the first data heterogeneity level increases (Pages 67-69, Section VI: Describes the number of local training interactions (τ) performed between successive global aggregation operations and determines an updated value of τ based on the measured gradient divergence (δ), which represents the degree of data heterogeneity among distributed nodes. Greater gradient divergence (higher data heterogeneity) results in selecting a smaller value of τ so that global aggregation occurs more frequently, which decreases the number of local training cycles performed during each aggregation period.)
It would have been obvious to one of ordinary skill in the art at the time of the claimed invention to have combined the federated learning apparatus of Akdeniz in view of Li, in view of Zeng, with the determined heterogeneity measurement of Wang. Doing so would have enabled the apparatus to limit divergence between local and global training, to improve convergence, and efficiently balance local computation with communication.
Akdeniz in view of Li, in view of Zeng, in view of Wang teaches obtain a first set of updated model parameters from a first client device and a second set of updated model parameters from a second client device, wherein the first set of updated model parameters and the second set of updated model parameters are based on the first data aggregation period (Akdeniz FIG. 15, and [0194-0195]: Describes “At operation 1530, the client computing nodes 1591 transmit the locally updated models to the controller node 1593.” Wherein each client sends its own updated model parameters, and further details that the operations are repeated until the model converges. These updates are obtained after the clients conduct their distance calculations.; [0328], [0345], and [0347]: Describes receiving partial/updated gradients or model parameters from each client after the locally determined training cycles (aggregation period) and before the next communication round.) wherein the first data aggregation period defines the number of training cycles for the first client device and the second client device to generate the first set of updated model parameters and the second set of updated model parameters (Wang Pages 67-69, Section VI: Describes determining a communication (aggregation) interval by selecting a number of local training iterations (τ) to be performed between successive aggregation operations. Each participating client performs the selected number of local training iterations during the aggregation period to generate updated local model parameters, which are then transmitted for aggregation at the server. The aggregation period defines the number of training cycles performed by each client before generating and providing its respective updated model parameters.); and
combine the first set of updated model parameters and the second set of updated model parameters to yield a first combined set of updated model parameters (Akdeniz FIG. 13, and [0150]: Describes that multiple clients compute and send model updates to the server. This server aggregates the different models from the clients to update the global machine learning model. This aggregation may include averaging, such as straight or weighted averaging of the model updates. This corresponds to combining sets of updated parameters from multiple clients to combine a set of updated parameters.).
Regarding claim 5, Akdeniz in view of Li, in view of Zeng, in view of Wang further teaches the apparatus of claim 1, wherein the at least one processor is further configured to: send the first combined set of updated model parameters to a network entity for aggregation with a second combined set of updated model parameters, wherein the network entity is upstream from the apparatus (Akdeniz, FIG. 13, and [0150]: Describes that multiple clients compute and send model updates to the server. This server receives these updates as the head device in the system, to execute the steps of combining the parameters. This corresponds to combining sets of updated parameters in a networked entity that is upstream.).
Regarding claim 6, Akdeniz in view of Li, in view of Zeng, in view of Wang further teaches the apparatus of claim 1, wherein to combine the first set of updated model parameters and the second set of updated model parameters the at least one processor is further configured to:
average the first set of updated model parameters and the second set of updated model parameters (Akdeniz FIG. 13, and [0150]: Describes that “The central server 1360 uses the coded training data and label data to compute a model update (e.g., partial gradients) at 1312, and then aggregates the different model updates at 1314 to update the global ML model. The aggregation may include an averaging, e.g., a straight or weighted averaging, of the model updates.” Which corresponds to averaging a first and second set of client provided parameters.) .
Regarding claim 7, Akdeniz in view of Li, in view of Zeng, in view of Wang further teaches the apparatus of claim 1, wherein the first set of updated model parameters and the second set of updated model parameters are combined using a same shared channel (Akdeniz [0036]: Describes “exploit wireless for computation including over the air combining, and to promote multi-stage learning” to transmit model updates over the air on a common channel so the server can receive the parameter sets before further processing.).
Regarding claim 8, Akdeniz in view of Li, in view of Zeng, in view of Wang further teaches the apparatus of claim 1, wherein the input data corresponds to data that is not independently and identically distributed (Akdeniz [0388-0392]: Describes that “ clients may have statistically differing datasets, i.e., not independent and identically distributed” and scheduling methods that cope with those non-IID client data distributions.).
Regarding claim 9, Akdeniz in view of Li, in view of Zeng, in view of Wang further teaches the apparatus of claim 1, wherein the first set of updated model parameters and the second set of updated model parameters correspond to a single layer of a plurality of layers of the machine learning model (Akdeniz [0392-0397]: Describes that “based on the norm of the local gradients computed with respect to the last layer” the server selects uploads of layer specific gradients, each client transmits only the parameter updates that one later of the first and second sets obtained by the server.).
Regarding claim 10, Akdeniz in view of Li, in view of Zeng, in view of Wang further teaches the apparatus of claim 1, wherein the at least one processor is further configured to:
update the machine learning model based on the first combined set of updated model parameters to yield a modified machine learning model (Akdeniz [0167]: Describes that after averaging client updates, the central server “aggregates the received weights to obtain a new global weight” and then shares the aggregated weights with the client nodes, so both server and clients replace previous model weights with the newly combined sets, corresponding to a modified machine learning model.).
Regarding claim 11 Akdeniz in view of Li, in view of Zeng, in view of Wang further teaches the apparatus of claim 1, wherein the first data heterogeneity level is based on at least one of a variance and a dispersion among the respective updated model parameters received from the first client device and the second client device (Akdeniz [0343-0347]: Describes that the heterogeneity metric as a variance/dispersion of the updated vectors received from different clients, corresponding to the heterogeneity level based on the variance determined from the system.).
Regarding claims 12, 16-22, which recites substantially the same limitations as claims 1, 5-11. Claims 12, 16-22 further recite a method (Akdeniz, [0082]: Describes a method to execute instructions.) to perform the system steps of claims 1, 5-11, respectively, and are therefore rejected on the same premise.
Regarding claims 23 and 26, which recites substantially the same limitations as claims 1 and 5. Claims 23 and 26 further recite a computer-readable medium (Akdeniz, [0100]: Describes a computer readable medium storing instructions to be executed.) to perform the system steps of claims 1 and 5, respectively, and are therefore rejected on the same premise.
Regarding claims 27 and 30, which recites substantially the same limitations as claims 1 and 7. Claims 27 and 30 further recite a apparatus for wireless communication (Akdeniz, [0036]: Describes wireless devices to transmit data.) to perform the system steps of claims 1 and 7, respectively, and are therefore rejected on the same premise.
Claim(s) 2-4, 13-15, 24-25, and 28-29 is/are rejected under 35 U.S.C. 103 as being unpatentable over Akdeniz et al. (US 20230068386 A1, referred to as Akdeniz)in view of Li et al. ("Federated optimization in heterogeneous networks.", referred to as Li), Zeng et al. (“Local Epochs Inefficiency Caused by Device Heterogeneity in Federated Learning”, referred to as Zeng) in view of Wang et al. ("When edge meets learning: Adaptive control for resource-constrained distributed machine learning." , referred to as Wang), in view of Prakash et al. (US 20190220703 A1, referred to as Parkash).
Regarding claim 2, Akdeniz in view of Li, in view of Zeng, in view of Wang teaches the apparatus of claim 1, wherein the at least one processor is further configured to cause the apparatus to: determine that a second data heterogeneity level associated with input data for training the machine learning model is less than the first data heterogeneity level (Akdeniz[0217]: Describes measuring heterogeneous compute/communication characteristics and model them per client.; [0320-0328] Describes that the server classifies clients into sets and compares divergence(heterogeneity) values, selecting sets where divergence is lower than a previous (higher) level to decide scheduling.);
Although Akdeniz teaches determine that a second data heterogeneity level associated with input data for training the machine learning model is less than the first data heterogeneity level, it does not teach determine, based on the second data heterogeneity level, a second data aggregation period for training the machine learning model, wherein the second data aggregation period is greater than the first data aggregation period.
Prakash teaches determine, based on the second data heterogeneity level, a second data aggregation period for training the machine learning model, wherein the second data aggregation period is greater than the first data aggregation period ([0084]: Describes a predetermined epoch/aggregation time and transmits a probability value tied to that epoch; [0085]: Describes load-allocation criterion uses the heterogeneity of compute/comm parameters to adjust epoch time, which lengthens the epoch when heterogeneity is lower so that more data can be aggregated.).
It would have been obvious to one of ordinary skill in the art at the time of the claimed invention to modify the system of Akdeniz with Prakash’s time selection logic. Doing so would all the system to automatically lengthen the aggregation epoch whenever client heterogeneity is low, lowering communication rounds and idle wait times and speed convergence without loss in accuracy.
Regarding claim 3, Akdeniz in view of Li, in view of Zeng, in view of Wang teaches the apparatus of claim 1, wherein the at least one processor is further configured to cause the apparatus to: send the first set of updated model parameters and the second set of updated model parameters to a network entity (Akdeniz [0216]: Describes that clients send partial/updated gradients (model parameters) to the controller for aggregation.);
Although Akdeniz teaches send the first set of updated model parameters and the second set of updated model parameters to a network entity, it does not teach determine, receive a first decoded set of updated model parameters and a second decoded set of updated model parameters from the network entity.
Prakash teaches receive a first decoded set of updated model parameters and a second decoded set of updated model parameters from the network entity ([0221-0222]: Describes that a central server decodes coded updates then distributes the resulting decoded model parameters back to the clients.).
It would have been obvious to one of ordinary skill in the art at the time of the claimed invention to modify the system of Akdeniz with Prakash’s time selection logic. Doing so would apply probability weighting framework to send and receive data, boosting reliability over variable links and aligning parameter flow within standard 5G RAN splits with predictable performance gains.
Regarding claim 4, Akdeniz in view of Li, in view of Zeng, in view of Wang, in view of Prakash teaches the apparatus of claim 3, Prakash further teaches wherein the apparatus corresponds to a radio unit (RU) and the network entity corresponds to a distributed unit (DU) ([0087-0088]: Describes edge compute nodes acting as radio units and identifies distributed units as upstream aggregation points in a RAN split.).
Regarding claims 13-15, which recites substantially the same limitations as claims 2-4. Claims 13-15 further recite a method (Akdeniz, [0082], and Prakash, [0020]: Both describe a method to execute instructions.) to perform the system steps of claims 2-4, respectively, and are therefore rejected on the same premise.
Regarding claims 24 and 25, which recites substantially the same limitations as claims 2 and 3. Claims 24 and 25 further recite a computer-readable medium (Akdeniz, [0100], and Prakash, [0076]: Both describe a computer readable medium storing instructions to be executed.) to perform the system steps of claims 2 and 3, respectively, and are therefore rejected on the same premise.
Regarding claims 28 and 29, which recites substantially the same limitations as claims 2 and 3. Claims 24 and 25 further recite a apparatus for wireless communication (Akdeniz, [0036], and Prakash, [0019]: Both describe wireless devices to transmit data.) to perform the system steps of claims 2 and 3, respectively, and are therefore rejected on the same premise.
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 DONALD T RODEN whose telephone number is (571)272-6441. The examiner can normally be reached Mon-Thur 8:00-5:00 EST.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Omar Fernandez Rivas can be reached at (571) 272-2589. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/D.T.R./Examiner, Art Unit 2128
/MICHAEL H HOANG/PRIMARY EXAMINER, Art Unit 2122
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