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 Amendment
The amendments filed 06/04/2026 have been entered.
Claims 1-7 and 10-13 remain pending within the application.
The amendments filed 06/04/2026 are sufficient to overcome each and every objection previously set forth in the Non-Final Office Action mailed 02/04/2026. The objections have been withdrawn.
The amendments filed 06/04/2026 overcome the 112 rejections previously set forth in the Non-Final Office Action mailed 02/04/2026. The rejections have been withdrawn. Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action.
The amendments filed 06/04/2026 overcome the 101 rejections previously set forth in the Non-Final Office Action mailed 02/04/2026. The rejections have been withdrawn. Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action.
The applicant's amendment and remarks filed 06/04/2026 have prompted the updated ground(s) of 103 rejection presented in this Office action.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 5, 6, and 10 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 5 recites the limitation "… using other partial model…". It is unclear whether this other partial model refers to the “first partial model”, “second partial model”, “third partial model”, or a subset of partial models different from the aforementioned three recited in claim 1. There is insufficient antecedent basis for this limitation. For examination purposes, the examiner is interpreting the BRI of "… using other partial model” to be using any other partial model not including the predetermined second partial model. Dependent claim 6 inherits the deficiency from claim 5 and therefore is rejected on the same basis.
Claim 10 recites the limitation "… the other second partial model…". There is insufficient antecedent basis for this limitation. For examination purposes, the examiner is interpreting the BRI of "… the other second partial model” to be any other second partial models not including the predetermined second partial model.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
The claims 1-7, and 10-13 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 2A Prong 1: Does the claim recite an abstract idea, law of nature, or natural phenomenon?
Claim 1 includes the steps of:
An inference information processing system, which performs inference processing on inference target data acquired by a first information processing apparatus using a trained inference model based on a multilayer neural network, comprising:
the first information processing apparatus; and
a second information processing apparatus configured to communicate with the first information processing apparatus via a network,
wherein the trained inference model includes a first partial model, a plurality of second partial models, and a plurality of third partial models respectively corresponding to the plurality of second partial models, wherein:
the first partial model comprises an input layer of the trained inference model and intermediate layers of the trained inference model;
each of the plurality of second partial models comprises, among intermediate layers of the trained inference model, intermediate layers different from the intermediate layers included in the first partial model; and
each of the plurality of third partial models comprises an output layer of the trained inference model;
wherein the first information processing apparatus comprises:
a memory storing instructions, and at least one processor configured to execute the instructions to:
acquire inference target medical data and selection information indicating a partial model to be applied to the inference target medical data;
perform first inference processing on the inference target medical data using the first partial model corresponding to the plurality of second partial models,
output a result of the first inference processing and the selection information to the second information processing apparatus,
receive a second inference result from the second information processing apparatus, wherein the second inference result is generated by second inference processing performed by the second information processing apparatus, and
perform third partial inference processing by inputting the second inference result to a third partial model corresponding to the second partial model indicated by the selection information; and
wherein the second information processing apparatus comprises: a memory storing instructions, and at least one processor configured to execute the instructions to:
acquire the result of the first inference processing and the selection information from the first information processing apparatus;
perform second inference processing by inputting the result of the first inference processing to a second partial model selected from among the plurality of second partial models based on the selection and
output the result of the second inference processing to the first information processing apparatus.
The broadest reasonable interpretation of the bolded limitations above are directed to a mental process able to be performed in the human mind through the use of a physical aid, like a pen and paper. A human can:
perform inference processing on inference target medical data,
generate second inference results by performing second inference processing,
perform third partial inference processing by using the second inference result, and
perform second inference processing by using the result of the first inference processing.
Step 2A Prong 2: Does the claim recite additional elements that integrate the judicial exception into a practical application?
As drafted and under their broadest reasonable interpretation, the following limitations recite additional elements which amount to generic computer components recited at a high level of generality, with merely the words “apply it” or an equivalent with the judicial exception, merely including instructions to implement an abstract idea on the additional elements, or merely using the additional elements as a tool to perform an abstract idea, as discussed in MPEP 2106.05(f).
An inference information processing system, which performs inference processing on inference target data acquired by a first information processing apparatus using a trained inference model based on a multilayer neural network, comprising:
the first information processing apparatus; and
a second information processing apparatus configured to communicate with the first information processing apparatus via a network,
wherein the trained inference model includes a first partial model, a plurality of second partial models, and a plurality of third partial models respectively corresponding to the plurality of second partial models, wherein:
the first partial model comprises an input layer of the trained inference model and intermediate layers of the trained inference model;
each of the plurality of second partial models comprises, among intermediate layers of the trained inference model, intermediate layers different from the intermediate layers included in the first partial model; and
each of the plurality of third partial models comprises an output layer of the trained inference model;
wherein the first information processing apparatus comprises:
a memory storing instructions, and at least one processor configured to execute the instructions to:
wherein the second information processing apparatus comprises: a memory storing instructions, and at least one processor configured to execute the instructions to:
…using the first partial model corresponding to the plurality of second partial models,
…processing performed by the second information processing apparatus, and
… to a second partial model selected from among the plurality of second partial models…
As drafted and under their broadest reasonable interpretation, the following limitations recite additional elements which amount to mere data gathering and output recited at a high level of generality, and thus are insignificant extra-solution activity. See MPEP 2106.05(g) (“whether the limitation is significant”). In addition, all uses of the recited judicial exceptions require such data gathering and output, and, as such, these limitations do not impose any meaningful limits on the claim. These limitations amount to necessary data gathering and outputting. See MPEP 2106.05.
acquire inference target medical data and selection information indicating a partial model to be applied to the inference target medical data;
output a result of the first inference processing and the selection information to the second information processing apparatus,
receive a second inference result from the second information processing apparatus,
…inputting the second inference result to a third partial model…
acquire the result of the first inference processing and the selection information from the first information processing apparatus;
output the result of the second inference processing to the first information processing apparatus.
The additional elements have been considered both individually and as an ordered combination in order to determine whether they integrates the exception into a practical application. Therefore, no meaningful claim limits are imposed practicing the abstract idea. Accordingly, at Step 2A, prong two, the additional elements do not integrate the judicial exception into a practical application.
Step 2B: Does the claim recite additional elements that amount to significantly more than the judicial exception?
In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception.
The claim limitation(s) reciting generic computer elements amounts to no more than mere instructions to apply the exception using a generic computer.
The claim reciting the additional element(s) of “acquiring”, “receiving”, “inputting”, and/or “outputting” amount to necessary data gathering and output.
The additional elements have been considered both individually and as an ordered combination in order to determine whether they warrant significantly more consideration. Thus, the claim does not provide an inventive concept.
The claim is ineligible.
Claims 2-7 and 10 further recite limitations that encompass mental evaluations that are practically performed in the human mind, but for the recitation of generic computer components. The claims do not integrate the judicial exception into practical application. The claims do not recite additional elements that are sufficient to amount to significantly more than the judicial exception.
Claims 11-13 are substantially similar to claim 1, but for the recitation of generic computer components, and thus are rejected on the same basis as claim 1.
Claims 2-7 and 10-13 are ineligible.
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 1-2, 4-7, and 11-13 are rejected under 35 U.S.C. 103 as being unpatentable over Chang et al. (Pub. No.: US 2019/0108442 A1), hereafter Chang, in view of Sheller et al. (Pub. No.: US 2019/0042878 A1), hereafter Sheller.
Regarding claim 1, Chang discloses:
An inference information processing system, which performs inference processing on inference target data acquired by a first information processing apparatus using a trained inference model based on a multilayer neural network, comprising: the first information processing apparatus; and a second information processing apparatus configured to communicate with the first information processing apparatus via a network (Fig. 1, Fig. 4B and ¶[0030] teaches communicable local and remote ends as first and second information processing apparatus respectively),
wherein the trained inference model includes a first partial model, a plurality of second partial models… wherein: (Fig. 4B elements PT1 and PT2, and ¶[0079-0080] teaches PT1 as the first partial model and PT2 as second partial models, where the remote end includes different partial models),
the first partial model comprises an input layer of the trained inference model and intermediate layers of the trained inference mode (Fig. 4B and ¶[0076-0078] teaches a partial neural network PT1 comprising input and intermediate layers of the inference model)
each of the plurality of second partial models comprises, among intermediate layers of the trained inference model, intermediate layers different from the intermediate layers included in the first partial model (Fig. 4B element PT2 and ¶[0079-0080] teaches the remote end to include partial models, i.e. different numbers of computation layers arranged in different orders, not included in the local end),
wherein the first information processing apparatus comprises: a memory storing instructions, and at least one processor configured to execute the instructions to: (¶[0004] and ¶[0026]),
acquire inference target medical data and … a partial model to be applied to the inference target medical data (¶[0035] teaches acquiring target medical data from hospitals),
perform first inference processing on the inference target medical data using the first partial model corresponding to the plurality of second partial models (Fig. 4B and ¶[0076-0078] teaches performing first partial inference on target medical data at local end using a partial neural network PT1 corresponding to second partial models at remote end),
output a result of the first inference processing … to the second information processing apparatus (Fig. 4B and [0079] teaches transmitting the output of the local end partition to the remote end),
… a second inference result from the second information processing apparatus, wherein the second inference result is generated by second inference processing performed by the second information processing apparatus, and (4B and ¶[0071) teaches computing the output of a second inference result from the second information processing apparatus),
wherein the second information processing apparatus comprises: a memory storing instructions, and at least one processor configured to execute the instructions t0 (¶[0066]),
acquire the result of the first inference processing … from the first information processing apparatus (Fig. 4B and ¶[0079] teaches the remote end to acquire the result of the local end inference processing),
perform second inference processing by inputting the result of the first inference processing to a second partial model … (Fig. 4B and ¶[0079] teaches obtaining the result of the neural network as performing second inference processing by inputting the result of the first inference processing to a second partial model PT2),
output the result of the second inference processing … (¶[0071] teaches outputting the result of the second inference processing).
While Chang discloses wherein the trained inference model includes a first partial model, a plurality of second partial models, they do not disclose a plurality of third partial models respectively corresponding to the plurality of second partial models and each of the plurality of third partial models comprises an output layer of the trained inference model.
Sheller teaches:
a plurality of third partial models respectively corresponding to the plurality of second partial models (Fig. 4 and ¶[0062] teaches a plurality of third partial models in different edge devices that correspond to second partial models),
each of the plurality of third partial models comprises an output layer of the trained inference model (Fig. 2, Fig. 4 and ¶[0062] teaches the private layers of the third partial models at edge devices to comprise output layers of the inference model).
Chang and Sheller are analogous art because they are from the same field of endeavor, distributed learning and neural networks.
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of Chang to include a plurality of third partial models respectively corresponding to the plurality of second partial models, and each of the plurality of third partial models comprises an output layer of the trained inference model based on the teachings of Sheller. One of ordinary skill in the art would have been motivated to make this modification in order to implement classification layers in a trusted environment, as suggested by Sheller (¶[0016]).
While Chang discloses acquire inference target medical data and … a partial model to be applied to the inference target medical data, output a result of the first inference processing … to the second information processing apparatus, and …a second inference result from the second information processing apparatus, wherein the second inference result is generated by second inference processing performed by the second information processing apparatus, they do not disclose:
acquire … selection information indicating a partial model to be applied to the inference target medical data,
output … selection information to the second information processing apparatus,
receive a second inference result.
Sheller discloses:
acquire … selection information indicating a partial model to be applied to the inference target medical data (Fig. 2, Fig. 5, ¶[0066] and ¶[0031] teaches receiving models and their public/private layer information as selection information indicating a partial model to be applied to target medical records),
output … selection information to the second information processing apparatus, (Figs. 3, 5, and 6 teaches outputting the selection information to data stores and edge devices),
receive a second inference result (Fig. 4 and ¶[0063] teaches receiving results provided by edge devices),
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of Chang to include acquire … selection information indicating a partial model to be applied to the inference target medical data, output … selection information to the second information processing apparatus, and receive a second inference result, based on the teachings of Sheller. One of ordinary skill in the art would have been motivated to make this modification in order to implement classification layers in a trusted environment, as suggested by Sheller (¶[0016]).
While Chang discloses acquire the result of the first inference processing …from the first information processing apparatus , perform second inference processing by inputting the result of the first inference processing to a second partial model …, output the result of the second inference processing …, they do not disclose:
perform third partial inference processing by inputting the second inference result to a third partial model corresponding to the second partial model indicated by the selection information,
acquire … the selection information from the first information processing apparatus,
… a second partial model selected from among the plurality of second partial models based on the selection,
output the result of the second inference processing to the first information processing apparatus.
Sheller discloses:
perform third partial inference processing by inputting the second inference result to a third partial model corresponding to the second partial model indicated by the selection information (Fig. 4 and Fig. 5 teaches performing third partial inference processing by a third edge device by inputting the aggregated second inference result from a previous round to a third partial model in an edge device corresponding to the second partial model indicated by the selection information of public/private layer identification),
acquire … the selection information from the first information processing apparatus (Fig. 2, Fig. 5, ¶[0066] and ¶[0031] teaches receiving models and their public/private layer information as selection information indicating a partial model to be applied from the first information processing apparatus),
… a second partial model selected from among the plurality of second partial models based on the selection (Fig. 5, ¶[0066-0068] teaches selecting the partial model from a plurality of options, i.e. different types of model layers, based on the selection information of whether the layer is public or private),
output the result of the second inference processing to the first information processing apparatus (¶[0063] teaches outputting the results of the edge devices to the aggregator, i.e. first apparatus).
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of Chang to include perform third partial inference processing by inputting the second inference result to a third partial model corresponding to the second partial model indicated by the selection information, acquire … the selection information from the first information processing apparatus, … a second partial model selected from among the plurality of second partial models based on the selection, and output the result of the second inference processing to the first information processing apparatus, based on the teachings of Sheller. One of ordinary skill in the art would have been motivated to make this modification in order to implement classification layers in a trusted environment, as suggested by Sheller (¶[0016]).
Regarding claim 2, Chang, in view of Sheller, discloses the inference information processing system according to claim 1. Chang further discloses:
wherein each of the plurality of second partial models is a partial model generated by additional training with a fixed parameter for the first partial model (¶[0071] and ¶[0080] teaches the computation layers of the first partial model being trained with a fixed parameter to generate the trained second partial models).
Regarding claim 4, Chang, in view of Sheller, discloses the inference information processing system according to claim 1. Sheller further discloses:
wherein the selection information is information including an application order of the plurality of second partial models (¶[0033] teaches the selection information to include an order of the processing of layers).
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of Chang to include wherein the selection information is information including an application order of the plurality of second partial models, based on the teachings of Sheller. One of ordinary skill in the art would have been motivated to make this modification in order to implement classification layers in a trusted environment, as suggested by Sheller (¶[0016]).
Regarding claim 5, Chang, in view of Sheller, discloses the inference information processing system according to claim 4, wherein the at least one processor of the second information processing apparatus is configured. Sheller further discloses:
perform the second inference processing using a predetermined second partial model of the plurality of second partial models in accordance with the application order and determines whether to perform inference processing using other partial model depending on a result of the inference processing (Fig. 4, Fig. 5, and ¶[0069] teaches performing inference processing using a predetermined second partial model at the edge device in the plurality of second partial models in edge devices based on the application order and determines whether to perform inference processing using other partial model depending on a result of the inference processing, i.e. if the result of the partial model is from a final layer or not).
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of Chang to include perform the second inference processing using a predetermined second partial model of the plurality of second partial models in accordance with the application order and determines whether to perform inference processing using other partial model depending on a result of the inference processing, based on the teachings of Sheller. One of ordinary skill in the art would have been motivated to make this modification in order to implement classification layers in a trusted environment, as suggested by Sheller (¶[0016]).
Regarding claim 6, Chang, in view of Sheller, discloses the inference information processing system according to claim 5. Sheller further discloses:
wherein the(Fig. 4, Fig. 5, and ¶[0069] teaches performing the second inference processing so as to perform inference processing using the predetermined second partial model of the plurality of second partial models),
depending on the result of the inference processing using the one of the plurality of second partial models, not to perform inference processing using the other partial model of the plurality of second partial models having a lower application order (Fig. 4, Fig. 5, and ¶[0069] teaches not to perform inference processing using the other partial model of the plurality of second partial models having a lower application order).
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of Chang to include wherein the at least one processor of the second information processing apparatus is configured to perform the second inference processing so as to perform inference processing using the predetermined second partial model of the plurality of second partial models, and depending on the result of the inference processing using the one of the plurality of second partial models, not to perform inference processing using the other partial model of the plurality of second partial models having a lower application order, based on the teachings of Sheller. One of ordinary skill in the art would have been motivated to make this modification in order to implement classification layers in a trusted environment, as suggested by Sheller (¶[0016]).
Regarding claim 7, Chang, in view of Sheller, discloses the inference information processing system according to claim 1. Chang further discloses:
at least one processor of the first information processing apparatus is configured to output an output from the intermediate layers forming the first partial model to the second information processing apparatus (Fig. 4B and [0079]).
Claims 11-13 are substantially similar to claim 1 and thus are rejected on the same basis as claim 1.
Claims 3 and 10 are rejected under 35 U.S.C. 103 as being unpatentable over Chang et al. (Pub. No.: US 2019/0108442 A1), hereafter Chang, in view of Sheller et al. (Pub. No.: US 2019/0042878 A1), hereafter Sheller, in further view of Vepakomma et al. ("Split learning for health: Distributed deep learning without sharing raw patient data"), hereafter Vepakomma.
Regarding claim 3, Chang, in view of Sheller, discloses the inference information processing system according to claim 1. They do not discloses:
wherein the second partial models are partial models in which at least one of an inference task to be performed by each second partial model and an inference class of each second partial model is different from that of other second partial model.
Vepakomma discloses:
wherein the second partial models are partial models in which at least one of an inference task to be performed by each second partial model and an inference class of each second partial model is different from that of other second partial model (Figure 4 (b) teaches partial models where at least one of an inference task to be performed by each second partial model and an inference class, i.e. label, of each second partial model is different from that of other second partial model).
Chang, Sheller, and Vepakomma are analogous art because they are from the same field of endeavor, distributed learning and neural networks.
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of Chang, in view of Sheller, to include wherein the second partial models are partial models in which at least one of an inference task to be performed by each second partial model and an inference class of each second partial model is different from that of other second partial model, based on the teachings of Vepakomma. One of ordinary skill in the art would have been motivated to make this modification in order to train multiple models that solve different supervised learning tasks, as suggested by Vepakomma (page 5, paragraph 4, lines 4-5).
Regarding claim 10, Chang, in view of Sheller, discloses the inference information processing system according to claim 5 and at least one processor of the second information processing apparatus is configured to perform the second inference processing. They do not disclose:
inputting the result of the inference processing using the predetermined second partial model to the other second partial model.
Vepakomma discloses:
inputting the result of the inference processing using the predetermined second partial model to the other second partial model (Figure 4 (c) teaches further inputting the result of the inference processing of partial models to other second partial models).
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of Chang, in view of Sheller, to include inputting the result of the inference processing using the predetermined second partial model to the other second partial model, based on the teachings of Vepakomma. One of ordinary skill in the art would have been motivated to make this modification in order to train multiple models that solve different supervised learning tasks, as suggested by Vepakomma (page 5, paragraph 4, lines 4-5).
Response to Arguments
Applicant's arguments filed 06/04/2026 have been fully considered with regards to the 35 U.S.C. 101 rejection, but they are not persuasive.
The applicant asserts on page 12 of the remarks “Through this specific arrangement, the claimed invention enables inference processing to be performed without transmitting the inference target medical data to the second information processing apparatus. As a result, the claimed invention provides a technical improvement in protecting the privacy of the inference target medical data. The claimed invention further provides a technical improvement in preserving the confidentiality of the inference model by distributing and arranging portions of the inference model between the first information processing apparatus and the second information processing apparatus.”.
The Examiner respectfully disagrees, as the claims do not show an improvement. The MPEP 2106.04(d)(1) discloses the evaluation of claimed improvements in the functioning of a computer or improvement to a technical field in step 2A prong two. The MPEP section discloses — "if the specification explicitly sets forth an improvement but in a conclusory manner (i.e., a bare assertion of an improvement without the detail necessary to be apparent to a person of ordinary skill in the art), the examiner should not determine the claim improves technology. Second, if the specification sets forth an improvement in technology, the claim must be evaluated to ensure that the claim itself reflects the disclosed improvement. That is, the claim includes the components or steps of the invention that provide the improvement described in the specification ... ". The claims are directed to various partial inference processing evaluations reasonably performable in the human mind through the use of a physical aid, like a pen and paper, and do not describe a technical improvement in protecting the privacy of the inference target medical data.
Applicant's arguments filed 06/04/2026 have been fully considered with regards to the 35 U.S.C. 102/103 rejection, but they are not persuasive.
The applicant asserts on page 16 of the remarks “Although Sheller discloses inputting an output of the public layer to the private layer, Sheller neither discloses nor suggests further inputting the output of the private layer to another model for additional inference processing. Therefore, Sheller likewise neither discloses nor suggests the claimed feature of inputting the second inference result to a third partial model to perform third inference processing.”.
The Examiner respectfully disagrees, Fig. 4 and Fig. 5 of Sheller teaches performing third partial inference processing by a third edge device by inputting the aggregated second inference result from a previous training round to a third partial model in an edge device corresponding to the second partial model indicated by the selection information of public/private layer identification.
Claims 11-13 are substantially similar to claim 1, and thus are rejected on the same basis.
Claims dependent on independent claims do not overcome the deficiencies of the rejected independent claims.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's
disclosure.
U.S. Pub No. 20200125933 A1: Aldea Lopez et al. teaches neural networks and partial models.
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 extension fee 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 date of this final action.
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/H.Z.M./Examiner, Art Unit 2141
/ANDREW L TANK/Primary Examiner, Art Unit 2141
Prosecution Insights