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 .
DETAILED ACTION
The instant application having Application No. 17734510 has a total of 19 claims pending in the application, of which claims 2, 5, and 17 have been cancelled.
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.
Claim 12 is 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 claim(s) contains 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.
As per claim 12, this claim calls for “provide the reliability value for the training data set to the federated learning server, wherein the reliability value (i) is based at least in part on the requested metric, and (ii) indicates whether the apparatus is associated with anomalous behavior.” However, the specification does not support these limitations. The claim here requires a single reliability value related to the training data set based both on the requested metric and whether or not the apparatus is associated with anomalous behavior. There is no such reliability value in the specification. The specification specifically spells this out in their own summary, stating “Cause the apparatus at least to obtain reliability values for each user equipment in a group of user equipments, obtain, for each user equipment in the group, a reliability value for a training data stored in the user equipment.” (Instant specification, Pg.1, paragraph 0004). This clearly shows that this is represented in two separate values, one for the equipment, and one for the training data. This causes the limitation be new matter, and therefore rejected under U.S.C. 112(a).
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.
Claims 1, 3-4, 6-8, 12-16, and 18-19 are rejected under 35 U.S.C. 103 as being unpatentable over Jha et al (US 20200027022 A1) in view of Munoz-Gonzalez et al (“Byzantine-Robust Federated Learning through Adaptive Model Averaging”), hereinafter “Munoz.”
As per claim 1, Jha discloses, “An apparatus comprising at least one processing core, and at least one memory including computer program code, the at least one memory and the computer program code being configured to, with the at least one processing core, cause the apparatus at least to” (Pg.13-14, particularly paragraph 0139; EN: this denotes the hardware for the system).
“obtain reliability values for each user equipment” (pg.9, particularly paragraph 0096; EN: this denotes using quality of service (i.e. reliability) to determine which participants to select for learning). “in a group of user equipments” (pg.2, particularly paragraph 0020; EN: this denotes the various participant devices that will be part of the system).
“Send, to each user equipment in the group of use equipments, a request for a reliability value for a training data set stored local in the respective user equipments” (Pg.9, particularly paragraph 0096; EN: this denotes various metrics from the local participant and a query (i.e. request) for the data, this data includes reliability like training data quality, Age of information, etc). “Wherein the request comprises a requested metric concerning what is valued in the training data for the distributed training process” (Pg.9, particularly paragraph 0096; EN: this denotes various metrics from the local participant and a query (i.e. request) for the data, this data includes reliability like training data quality, Age of information, etc, all of which meet the broadest reasonable interpretation of “valued” in the training data).
“obtain, for each user equipment in the group of use equipments, a reliability value for the training data set stored locally in the user equipment” (Pg.9, particularly paragraph 0096; EN: this denotes various metrics from the local participant and a query (i.e. request) for the data, this data includes reliability like training data quality, Age of information, etc). “Wherein each user equipment stores a distinct training data set” (Pg.1, particularly paragraph 0017; EN: this denotes each participant device having its own local data to be trained on). “and wherein the reliability value for the training data set is based at least in part on application of the requested metric to the training data set by the user equipment” (Pg.9, particularly paragraph 0096; EN: this denotes various metrics from the local participant and a query (i.e. request) for the data, this data includes reliability like training data quality, Age of information, etc, all of which meet the broadest reasonable interpretation of “valued” in the training data).
“select, for a federated learning training round” (Pg.9, particularly paragraph 0092; EN: this denotes the system performing hierarchical federated learning). “A subset of the group of user equipments by applying both (i) a user-equipment reliability criterion to the reliability values for the user equipments and (ii) a separate training data reliability criterion to the reliability values for the training data sets” (Pg.9, particularly paragraph 0096; EN: this denotes various metrics from the local participant and a query (i.e. request) for the data, this data includes reliability like training data quality, Age of information, etc as well as device information such as Compute-storage capability and availability windows).
“direct the subset of the group of user equipment’s to separately perform local portions of the federated learning training round” (pg.1, particularly paragraph 0017; EN: this denotes the participant devices training on their local data). “in the subset of the user equipments using the distinct training data sets stored locally in the user equipments in the subset” (Pg.2, particularly paragraph 0022; EN: this denotes the system selecting participants for each training round).
However, Jha fails to explicitly disclose, “Wherein the reliability values for the user equipments are based at least in part on whether respective user equipments are associated with reports of anomalous behavior”
Munoz discloses, “Wherein the reliability values for the user equipments are based at least in part on whether respective user equipments are associated with reports of anomalous behavior” (Pg.3, particularly the Model Aggregation section and Algorithm 1; EN: this denotes monitoring clients in the federated learning system for malicious clients).
Jha and Munoz are analogous art because both involve distributed learning.
Before the effective filing date it would have been obvious to one skilled in the art of distributed learning to combine the work of Jha and Munoz in order to consider potential anomalous behavior of the devices in a distributed learning system.
The motivation for doing so would be to allow the system to “detect and discard[] bad or malicious local model updates at each training iteration. This includes a mechanism that blocks unwanted participants, which also increases the computational and communication efficiency” (Munoz, Abstract) or in the case of Jha, allow the system to consider anomalous behavior such as malicious data from malicious clients when choosing which clients to use for federated learning.
Therefore before the effective filing date it would have been obvious to one skilled in the art of distributed learning to combine the work of Jha and Munoz in order to consider potential anomalous behavior of the devices in a distributed learning system.
As per claims 3 and 15, Jha discloses, “wherein the at least one memory and the computer program code are configured to, with the at least one processing core, cause the apparatus to aggregate the results of the machine learning processes received from the user equipments of the subset to obtain an aggregate machine learning result” (Pg.4, particularly paragraph 0047; EN: this denotes the various participants performing their machine learning process and returning the data to the global model to be used).
As per claims 4 and 16, Jha discloses, “wherein the at least one memory and the computer program code are configured to, with the at least one processing core, cause the apparatus to obtain the reliability values for the user equipments in the group from a network data analytics function” (pg.1-2, particularly paragraph 0019; EN: this denotes looking at quality of service such as latency).
As per claims 6 and 18, Jha discloses, “wherein the apparatus is configured to obtain, for each user equipment in the group, from the reliability value of the user equipment and the reliability values of the training data value of the training data set stored in the user equipment a compound reliability value of the user equipment” (Pg.9, particularly paragraph 0096; EN: this denotes various metrics from the local participant and a query (i.e. request) for the data, this data includes reliability like training data quality, Age of information, etc, all of which meet the broadest reasonable interpretation of “valued” in the training data. Both the training data information (quality and AOI) along with equipment information (compute-storage and availability windows) are included in the calculation). “and to select the subset from among the group based on the compound reliability values of the user equipments of the group” (pg.9-10, particularly paragraphs 0099-0100; EN: this denotes combining the various data in order to select participants).
As per claim 7, Jha discloses, “Wherein the apparatus is further configured to store at least one of the compound reliability values of the user equipments of the group in a network node” (pg.14, particularly paragraph 0140; EN: This denotes storing the various code and data being worked with and manipulated).
As per claim 8, Jha discloses, “wherein the network node the apparatus is configured to store the at least one of the compound reliability values of the user equipment of the group in an analytics data repository function” (pg.14, particularly paragraph 0140; EN: This denotes storing the various code and data being worked with and manipulated. This includes network analytics, which meets the broadest reasonable interpretation of an analytics data repository function).
As per claim 12, Jha discloses, “An apparatus comprising at least one processing core, at least one memory including computer program code, the at least one memory and the computer program code being configured to, with the at least one processing core, cause the apparatus at least to:” (Pg.13-14, particularly paragraph 0139; EN: this denotes the hardware for the system).
“store a training data set locally in the apparatus” (Pg.9, particularly paragraph 0096; EN: this denotes looking at the training data for quality values and other information associated with local devices).
“Receive, from a federated learning server” (Pg.9, particularly paragraph 0092; EN: this denotes the system performing hierarchical federated learning). “A request for a reliability value for the training dataset” (Pg.9, particularly paragraph 0096; EN: this denotes various metrics from the local participant and a query (i.e. request) for the data, this data includes reliability like training data quality, Age of information, etc). “Wherein the request comprises a requested metric concerning what is valued in the training data for a distributed training process” (Pg.9, particularly paragraph 0096; EN: this denotes various metrics from the local participant and a query (i.e. request) for the data, this data includes reliability like training data quality, Age of information, etc, all of which meet the broadest reasonable interpretation of “valued” in the training data).
“Determine the reliability value for the training data set based at least in part on application of the requested metric to the training data set by the apparatus” (Pg.9, particularly paragraph 0096; EN: this denotes various metrics from the local participant and a query (i.e. request) for the data, this data includes reliability like training data quality, Age of information, etc, all of which meet the broadest reasonable interpretation of “valued” in the training data).
“Provide, the reliability value for the training data set to the federated learning server” (Pg.9, particularly paragraph 0096; EN: this denotes various metrics from the local participant and a query (i.e. request) for the data, this data includes reliability like training data quality, Age of information, etc). “Wherein the reliability value (i) is based at least in part on the requested metric…” (Pg.9, particularly paragraph 0096; EN: this denotes various metrics from the local participant and a query (i.e. request) for the data, this data includes reliability like training data quality, Age of information, etc, all of which meet the broadest reasonable interpretation of “valued” in the training data).
“Receive, from the federated learning server, an instruction indicating that the apparatus is included in a subset selected for a federated learning training round” (pg.1, particularly paragraph 0017; EN: this denotes the participant devices training on their local data). “based on application of (i) a user-equipment reliability criterion to reliability values for the user equipments, wherein the reliability values for the user equipments are based at least in part on whether respective user equipments… and (ii) a separate training-data-reliability criterion to reliability values for training data sets” (Pg.9, particularly paragraph 0096; EN: this denotes various metrics from the local participant and a query (i.e. request) for the data, this data includes reliability like training data quality, Age of information, etc as well as device information such as Compute-storage capability and availability windows).
“perform a local portion of the federated training round using the set of training data as a response to the instruction from the federated learning server” (pg.1, particularly paragraph 0017; EN: this denotes the participant devices training on their local data).
However, Jha fails to explicitly disclose, ”(ii) indicates whether the apparatus is associated with anomalous behavior”, “… whether respective user equipments are associated with reports of anomalous behavior”
Munoz discloses, ”(ii) indicates whether the apparatus is associated with anomalous behavior”, “… whether respective user equipments are associated with reports of anomalous behavior” (Pg.3, particularly the Model Aggregation section and Algorithm 1; EN: this denotes monitoring clients in the federated learning system for malicious clients).
Jha and Munoz are analogous art because both involve distributed learning.
Before the effective filing date it would have been obvious to one skilled in the art of distributed learning to combine the work of Jha and Munoz in order to consider potential anomalous behavior of the devices in a distributed learning system.
The motivation for doing so would be to allow the system to “detect and discard[] bad or malicious local model updates at each training iteration. This includes a mechanism that blocks unwanted participants, which also increases the computational and communication efficiency” (Munoz, Abstract) or in the case of Jha, allow the system to consider anomalous behavior such as malicious data from malicious clients when choosing which clients to use for federated learning.
Therefore before the effective filing date it would have been obvious to one skilled in the art of distributed learning to combine the work of Jha and Munoz in order to consider potential anomalous behavior of the devices in a distributed learning system.
As per claim 13, Jha discloses, “A method comprising obtaining, in an apparatus, reliability values for each user equipment” (pg.9, particularly paragraph 0096; EN: this denotes using quality of service (i.e. reliability) to determine which participants to select for learning). “in a group of user equipments” (pg.2, particularly paragraph 0020; EN: this denotes the various participant devices that will be part of the system).
“for each user equipment in the group of use equipments, sending a request for a reliability value for a training data set stored local in the respective user equipments” (Pg.9, particularly paragraph 0096; EN: this denotes various metrics from the local participant and a query (i.e. request) for the data, this data includes reliability like training data quality, Age of information, etc). “Wherein the request comprises a requested metric concerning what is valued in the training data for a distributed machine learning training process” (Pg.9, particularly paragraph 0096; EN: this denotes various metrics from the local participant and a query (i.e. request) for the data, this data includes reliability like training data quality, Age of information, etc, all of which meet the broadest reasonable interpretation of “valued” in the training data).
“obtaining, for each user equipment in the group of use equipments, a reliability value for the training data set based at least in part on the requested metric” (Pg.9, particularly paragraph 0096; EN: this denotes various metrics from the local participant and a query (i.e. request) for the data, this data includes reliability like training data quality, Age of information, etc). “Wherein (i) each user equipment stores a distinct training data set” (Pg.1, particularly paragraph 0017; EN: this denotes each participant device having its own local data to be trained on). “and wherein the reliability value for the training data set is based at least in part on application of the requested metric to the training data set by the user equipment” (Pg.9, particularly paragraph 0096; EN: this denotes various metrics from the local participant and a query (i.e. request) for the data, this data includes reliability like training data quality, Age of information, etc, all of which meet the broadest reasonable interpretation of “valued” in the training data).
“selecting a subset of the group of user equipments by applying both (i) a user-equipment reliability criterion to the reliability values for the user equipments and (ii) a separate training data reliability criterion to the reliability values for the training data sets” (Pg.9, particularly paragraph 0096; EN: this denotes various metrics from the local participant and a query (i.e. request) for the data, this data includes reliability like training data quality, Age of information, etc as well as device information such as Compute-storage capability and availability windows).
“direct the subset of the group of user equipment’s to separately perform local portions of the distributed machine learning training process” (pg.1, particularly paragraph 0017; EN: this denotes the participant devices training on their local data). “in the user equipments in the subset of the group of user equipments using the distinct training data sets stored locally in the user equipments in the subset of the group of user equipments” (Pg.2, particularly paragraph 0022; EN: this denotes the system selecting participants for each training round).
However, Jha fails to explicitly disclose, “Wherein the reliability values for the user equipments are based at least in part on whether respective user equipments are associated with reports of anomalous behavior”
Munoz discloses, “Wherein the reliability values for the user equipments are based at least in part on whether respective user equipments are associated with reports of anomalous behavior” (Pg.3, particularly the Model Aggregation section and Algorithm 1; EN: this denotes monitoring clients in the federated learning system for malicious clients).
Jha and Munoz are analogous art because both involve distributed learning.
Before the effective filing date it would have been obvious to one skilled in the art of distributed learning to combine the work of Jha and Munoz in order to consider potential anomalous behavior of the devices in a distributed learning system.
The motivation for doing so would be to allow the system to “detect and discard[] bad or malicious local model updates at each training iteration. This includes a mechanism that blocks unwanted participants, which also increases the computational and communication efficiency” (Munoz, Abstract) or in the case of Jha, allow the system to consider anomalous behavior such as malicious data from malicious clients when choosing which clients to use for federated learning.
Therefore before the effective filing date it would have been obvious to one skilled in the art of distributed learning to combine the work of Jha and Munoz in order to consider potential anomalous behavior of the devices in a distributed learning system.
As per claims 14, Jha discloses, “receiving, from each user equipment in the subset, a result of the machine learning training process performed by the user equipment” (Pg.4, particularly paragraph 0047; EN: this denotes the various participants performing their machine learning process).
As per claim 19, Jha discloses, “A method performed by a user equipment, the method comprising” (Pg.13-14, particularly paragraph 0139; EN: this denotes the hardware for the system).
“storing a training data set locally in the user equipment” (Pg.9, particularly paragraph 0096; EN: this denotes looking at the training data for quality values and other information associated with local devices).
“Receiving, from a distributed learning server” (Pg.9, particularly paragraph 0092; EN: this denotes the system performing hierarchical federated learning). “A request for a reliability value for the training dataset stored locally in the user equipment” (Pg.9, particularly paragraph 0096; EN: this denotes various metrics from the local participant and a query (i.e. request) for the data, this data includes reliability like training data quality, Age of information, etc). “Wherein the request comprises a requested metric concerning what is valued in the training data for a distributed machine learning training process” (Pg.9, particularly paragraph 0096; EN: this denotes various metrics from the local participant and a query (i.e. request) for the data, this data includes reliability like training data quality, Age of information, etc, all of which meet the broadest reasonable interpretation of “valued” in the training data).
“Determine the reliability value for the training data set based at least in part on application of the requested metric to the training data set by the user equipment” (Pg.9, particularly paragraph 0096; EN: this denotes various metrics from the local participant and a query (i.e. request) for the data, this data includes reliability like training data quality, Age of information, etc, all of which meet the broadest reasonable interpretation of “valued” in the training data).
“Providing, the reliability value for the training data set to the distributed learning server” (Pg.9, particularly paragraph 0096; EN: this denotes various metrics from the local participant and a query (i.e. request) for the data, this data includes reliability like training data quality, Age of information, etc).
“Receiving, from the distributed learning server, an instruction indicating that the user equipment is included in a subset selected for the distributed machine learning training process” (pg.1, particularly paragraph 0017; EN: this denotes the participant devices training on their local data). “based on application of both (i) a user-equipment reliability criterion to reliability values for user equipments, wherein the reliability values for the user equipments are based at least in part on whether respective user equipments… and (ii) a separate training-data-reliability criterion to reliability values for training data sets” (Pg.9, particularly paragraph 0096; EN: this denotes various metrics from the local participant and a query (i.e. request) for the data, this data includes reliability like training data quality, Age of information, etc as well as device information such as Compute-storage capability and availability windows).
“performing a local portion of the distributed machine learning training process using the training data set as a response to an instruction” (pg.1, particularly paragraph 0017; EN: this denotes the participant devices training on their local data).
However, Jha fails to explicitly disclose, ”(ii) indicates whether the apparatus is associated with anomalous behavior”, “… whether respective user equipments are associated with reports of anomalous behavior”
Munoz discloses, ”(ii) indicates whether the apparatus is associated with anomalous behavior”, “… whether respective user equipments are associated with reports of anomalous behavior” (Pg.3, particularly the Model Aggregation section and Algorithm 1; EN: this denotes monitoring clients in the federated learning system for malicious clients).
Jha and Munoz are analogous art because both involve distributed learning.
Before the effective filing date it would have been obvious to one skilled in the art of distributed learning to combine the work of Jha and Munoz in order to consider potential anomalous behavior of the devices in a distributed learning system.
The motivation for doing so would be to allow the system to “detect and discard[] bad or malicious local model updates at each training iteration. This includes a mechanism that blocks unwanted participants, which also increases the computational and communication efficiency” (Munoz, Abstract) or in the case of Jha, allow the system to consider anomalous behavior such as malicious data from malicious clients when choosing which clients to use for federated learning.
Therefore before the effective filing date it would have been obvious to one skilled in the art of distributed learning to combine the work of Jha and Munoz in order to consider potential anomalous behavior of the devices in a distributed learning system.
Claim Rejections - 35 USC § 103
Claims 9-10 are rejected under 35 U.S.C. 103 as being unpatentable over Jha et al (US 20200027022 A1) in view of J Munoz-Gonzalez et al (“Byzantine-Robust Federated Learning through Adaptive Model Averaging”), hereinafter “Munoz” and further in view of Toy et al (“Overall Network and Service Architecture”).
As per claim 9, Jha discloses, “Wherein the apparatus is configured to receive information from the user equipments comprised in the subset…” (Pg.9, particularly paragraph 0096; EN: this denotes looking at the training data for quality values and other information).
However, Jha fails to explicitly disclose, “using user plane traffic.”
Toy discloses, “using user plane traffic” (pg.107, Sixth paragraph; EN: this denotes using user plane functions for communication over wireless).
Jha and Toy are analogous art because both involve data transmission.
Before the effective filing date it would have been obvious to one skilled in the art of data transmission to combine the work of Jha and Toy in order to make use of user plane traffic.
The motivation for doing so would be because “The 5G system architecture separates the user plane (UP) functions from the control plane (CP) functions, allowing independent scalability, evolution and flexible deployments” (Toy, Pg.108, second to last paragraph) or in the case of Jha, allow the system to use modern 5G components to transfer data effectively and flexibly as needed.
Therefore before the effective filing date it would have been obvious to one skilled in the art of data transmission to combine the work of Jha and Toy in order to make use of user plane traffic.
As per claim 10, Jha discloses, “wherein the apparatus is configured to receive information from the user equipments comprised in the subset …”
However, Jha fails to explicitly disclose, “using service based architecture signaling or non-access stratum signaling.”
Toy discloses, “using service based architecture signaling or non-access stratum signaling” (pg.107, particularly the fourth paragraph; EN: this denotes 5G being a service-based architecture).
Jha and Toy are analogous art because both involve data transmission.
Before the effective filing date it would have been obvious to one skilled in the art of data transmission to combine the work of Jha and Toy in order to make use of wireless for service based architecture.
The motivation for doing so would be because “The 5G core, as defined by 3GPP, utilized cloud-aligned virtualized functions, service-based architecture (SBA) that spans across all 5 G functions and interactions, including authentication, security, session management, and aggregation of traffic from end devices” (Toy, Pg.107, fourth paragraph) or in the case of Jha, allow the system to use modern 5G components to provide the security, session management and traffic aggregation as needed by the system.
Therefore before the effective filing date it would have been obvious to one skilled in the art of data transmission to combine the work of Jha and Toy in order to make use of wireless for service based architecture.
Claim Rejections - 35 USC § 103
Claim 11 is rejected under 35 U.S.C. 103 as being unpatentable over Jha et al (US 20200027022 A1) in view of Munoz-Gonzalez et al (“Byzantine-Robust Federated Learning through Adaptive Model Averaging”), hereinafter “Munoz and further in view of Hirose (US 20070189322 A1).
As per claim 11, Jha fails to explicitly disclose, “wherein the apparatus is configured to notify user equipments comprised in the group but not comprised in the subset, that they have been excluded from the machine learning training process.”
Hirose discloses, “wherein the apparatus is configured to notify user equipments comprised in the group but not comprised in the subset, that they have been excluded from the machine learning training process” (abstract; EN: this denotes notifying devices that they have not been selected so they don’t have to take further action of setting parameters).
Jha and Hirose are analogous art because both involve data transmission.
Before the effective filing date it would have been obvious to one skilled in the art of data transmission to combine the work of Jha and Hirose in order to notify devices they have not been selected.
The motivation for doing so would be so that “the communication parameters are not set in unintended pair of devices” or in the case of Jha, allow the system to notify client devices that they don’t need to perform any further actions when they are not selected for processing.
Therefore before the effective filing date it would have been obvious to one skilled in the art of data transmission to combine the work of Jha and Hirose in order to notify devices they have not been selected.
Response to Arguments
Applicant's arguments with respect to claims 1, 3-4, 6-16, and 18-19 have been considered but are moot in view of the new ground(s) of rejection.
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.
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/BEN M RIFKIN/Primary Examiner, Art Unit 2123
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