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 office action is in response to the amendment filed on 7/07/2026. Claims 1-19 are currently pending in the filing of 7/07/2026, claims 1-19 were pending in the previous filing of 02/11/2026.
Response to Applicant’s Amendments / Arguments Regarding 35 U.S.C. § 103
The applicant’s remarks, on pages 7-14 of the response / amendment, the applicant argues the features which allegedly distinguish over the previously cited references cited in the 35 U.S.C. § 103 rejections.
The examiner invites the applicant’s representative to schedule a telephone interview with the examiner, in order to discuss potential amendments to the application.
Applicant’s arguments have been considered but are moot in view of the new ground(s) of rejection.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-3, 10-11, and 13-15 are rejected under 35 U.S.C. 103 as being unpatentable over US 20190155643 to Bhageria et al. (hereinafter Bhageria), in view of US 20230421350 to Soceanu et al. (hereinafter Soceanu), in view of US 20200244719 to Morris (hereinafter Morris).
Regarding claim 1, Bhageria teaches,
A computing device providing method performed by a computer device having at least one processor and implementing at least one node in a cloud environment, the method comprising:
(Bhageria, fig. 3, teaches the cloud environment. [0029-30] teaches the use of nodes.) (The printed publication of the application at fig. 3 and [0060-61] describes the node 320 being implemented by a plurality of “computer devices” that may correspond to “computer device” 200 of fig. 2; and [0072] describes the “computer device” (nodes) recommending the “computing device” that processes the homomorphic encryption operation requested through the management tool 330 of the “client”.)
providing, to a client device, (fig. 1, devices 54.) a management tool (Bhageria, fig. 3, analyzer 116) including an application function of a at least one computing device for processing a homomorphic encryption operation; (Bhageria, figs. 3, Abstract, [0045-47] teach processing homomorphic operations using analyzer 116. See also fig. 4 and [0060-61] teach processing homomorphic operations and selecting based on cost.)
wherein the management tool:
enables the client device to select the homomorphic encryption operation that includes searching for information on the homomorphic encryption operation or information on encrypted data to be processed through the homomorphic encryption operation; (Bhageria, fig. 4, teaches searching through options, applying encryption in 146 and 150 vs 148 and 152 where the private cloud is used, and then processing at 138. [0046] teaches using a combination of public and private cloud options.)
recommends, to the client device, the at least one computing device having the predicted computing (Bhageria, fig. 3 and [0048-51] teaches outsourcing jobs to public and private clouds based on conditions. [0051-52] teaches evaluating cost and security and determining which cloud is best suited for task. Fig. 4, Abstract, and [0060-61] teach, at 124 storage only option regarding 126 to 130 and storage and processing option at 132 and after as discussed in [0060-61] which takes cost of different homomorphic encryptions shown in 146 and 150 including cost of using resources and computation costs, including comparison of SWHE and FHE encryption with different security markers, and the selection of the appropriate resources based on security needs and cost. [0046] teaches using a combination of public and private cloud options.)
Bhageria fails to explicitly teach performing the resource intensive computation based on power / computational efficiency,
However, Soceanu teaches,
wherein the management tool:
enables the client device to select the homomorphic encryption operation that includes searching for information on the homomorphic encryption operation or information on encrypted data to be processed through the homomorphic encryption operation; (Soceanu, Abstract teaches a system for evaluating and selecting an optimal solution for data run through fully homographic encryption (FHE). Fig. 2 teaches receive user requirements data set. [0003] teaches the user requirement that are used to evaluate the details of configurations for FHE. See also at least [0033-34] and fig. 4. [0036] teaches that the real computation time would be an hour while the evaluation takes one second to perform, to select the most efficient homomorphic parameters and operations.)
predicts computing power required for the selected homomorphic encryption operation; (Soceanu, Abstract, [0003], and [0077] as discussed below. Also, [0037] teaches ranking based on high to low cost.)
recommends, to the client device, the at least one computing device having the predicted computing power capable for processing of the homomorphic encryption operation selected by the client device through the (Soceanu, Abstract and [0003] teach multiple configurations being simulated, and a cost evaluation module (see fig. 4) to consider the different configurations, where the Abstract specifically teaches system for evaluating and selecting an optimal packing solution for data that is run through a FHE simulation / “recommends, … . [0003] includes the modules of fig. 4. [0077] teaches selecting computing power supply to perform complex processing that is resource intensive.)
enables the client device to select the at least one computing device recommended by the to process the homomorphic encryption operation selected by the client device; and (Soceanu, Abstract, teaches user selected model. fig. 2 & [0038] teaches S255-S270 simulation produces cost value score acceptable to user, producing optimal configuration S275 for selection by the user. [0052] teaches the use of deep neural networks for complex models, where the neural network include the “computing device”. Examiner asserts that fig. 4 teaches a management tool provided outside of the client, which performs the steps of fig. 2.)
Before the effective filing date of the invention, it would have been obvious to one of ordinary skill in the art to combine the teachings of Bhageria, which teaches performing evaluation of different homomorphic encryption operations (Abstract) and evaluating the cost of the operations to select the most efficient operations (fig. 4 & [0060-61]) based on an analyzer performing cost analysis ([0045-47]), with Soceanu, which also teaches homomorphic encryption (Abstract), and additionally teaches evaluation using a simulator of different techniques to use homomorphic encryption, where the simulation determines the different costs of the different techniques / configuration (figs. 2 & 4) and the use of power ([0077]). One of ordinary skill in the art would have been motivated to perform such an addition to provide Bhageria with the added ability determine, using different simulations, the costs of the simulations and optimal solutions based on costs, where the user may select the solution, as taught by Soceanu, to increase computational efficiency by selecting the optimal profile / solution.
Bhageria and Soceanu fail to explicitly teach the selectable virtual image,
However, Morris teaches,
predicts computing power required for the selected (Morris, Title & Abstract teach processing cost computation. [0068] teaches identifying resources based on file type. [0094] teaches predicting a power condition is not met and providing a warning.)
recommends, to the client device, the at least one computing device having the predicted computing power capable for processing of the device through the management tool; and (Morris, Fig. 6 & [0089-90] teach selectable icons 608 and non-selectable icon, such as 608mb, and scoring 1-5 based on processing cost.)
enables the client device to select the at least one computing device recommended by the management tool to process the (Morris, fig. 6 & [0089-94]. See also cost management 407c of fig. 4c.)
forwarding a virtual image of the at least one computing device selected . (Morris, Fig. 6 & [0089-91] teach selectable icons 608 and non-selectable icon 608mb.)
Before the effective filing date of the invention, it would have been obvious to one of ordinary skill in the art to combine the teachings of Bhageria, which teaches performing evaluation of different homomorphic encryption operations (Abstract) and evaluating the cost of the operations to select the most efficient operations (fig. 4 & [0060-61]) based on an analyzer performing cost analysis ([0045-47]), with Soceanu, which also teaches homomorphic encryption (Abstract), and additionally teaches evaluation using a simulator of different techniques to use homomorphic encryption, where the simulation determines the different costs of the different techniques / configuration (figs. 2 & 4) and the use of power ([0077]), with Morris, which also teaches resource recommendation and selection base on processing cost (Title & Abstract), and additionally teaches user selecting the resource based on a presented graphics indicating processing cost (Abstract & fig. 6). One of ordinary skill in the art would have been motivated to perform such an addition to provide Bhageria and Soceanu with the added ability to perform user selection, using graphics interface, based on processing cost, as taught by Morris, for the purpose of increasing computational efficiency and user convenience.
Regarding claim 2, Bhageria, Soceanu, and Morris teach,
The computing device providing method of claim 1,
wherein the computing device includes a virtual device provisioned with an environment for processing of the homomorphic encryption operation. (Bhageria, fig. 3 teaches the cloud network. [0013] teaches use of virtual machines.)
Regarding claim 3, Bhageria, Soceanu, and Morristeach teach,
The computing device providing method of claim 2,
wherein the environment for processing of the homomorphic encryption operation includes an environment in which a library for the homomorphic encryption is installed. (Soceanu, [0041] teaches a homomorphic encryption (HE) library of parameters.)
Regarding claim 10, Bhageria, Soceanu, and Morris teach,
The computing device providing method of claim 1,
wherein the management tool further includes a key generation function for generation of a key. (Bhageria, [0036] teaches encrypted data is encrypted with a public key, which would inherently have to be generated.)
Regarding claim 11, Bhageria, Soceanu, and Morris teach,
The computing device providing method of claim 1,
wherein the management tool further includes encryption and decryption functions for encryption and decryption of data for the homomorphic encryption operation.. (Soceanu, [0035-36] teaches encrypting the model, and [0035] teaches decrypting the partial results. [0036] teaches the evaluations estimates that the encrypted model would take an hour to run, while the evaluation only takes one second to run.) (Bhageria, Abstract, teaches homomorphic encryption.)
Regarding claim 13, Bhageria, Soceanu, and Morris teach,
A non-transitory computer-readable recording medium storing instructions that, when executed by a processor, cause the processor to perform the computing device providing method of claim 1. (Soceanu, [0011] teaches a computer readable medium that is non-transitory.)
Regarding claim 14, Bhageria, Soceanu, and Morris teach,
A computer device comprising:
at least one processor configured to execute computer-readable instructions in the computer device that implements at least one node included in a cloud environment, (Soceanu, [0011] teaches a computer readable medium that is non-transitory.)
wherein the at least one processor is configured to:
provide, to a client device, a management tool including an application function of at least one computing device that processes a homomorphic encryption operation,
enable the client device to select the homomorphic encryption operation that includes searching for information on the homomorphic encryption operation or information on encrypted data to be processed through the homomorphic encryption operation;
predict computing power required for the selected homomorphic encryption operation;
recommend, to the client device, the at least one computing device having the predicted computing power capable of processing the homomorphic encryption operation selected through the management tool; and
enable the client device to select the at least one computing device recommended by the management tool to process the homomorphic encryption operation selected by the client device; and
forwarding a virtual image of the at least one computing device selected by the user to the client device.
Claim 14 is rejected using the same basis of arguments used to reject claim 1 above.
Regarding claim 15, Bhageria, Soceanu, and Morris teach,
The computer device of claim 14,
wherein the computing device includes a virtual device provisioned with an environment for processing of the homomorphic encryption operation.
Claim 15 is rejected using the same basis of arguments used to reject claim 2 above.
Claims 4-6, 8, 16, and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Bhageria, in view of Soceanu, in view of Morris, in view of US 20220116198 to Na et al. (hereinafter Na).
Regarding claim 4, Bhageria, Soceanu, and Morris teach,
The computing device providing method of claim 1,
Bhageria, Soceanu, and Morris fail to explicitly teach the specifics of selecting different types of homomorphic operations,
However, Na teaches,
wherein the recommending of the computing device is based on at least one of a type of the homomorphic encryption operation, a number of homomorphic encryption operations, a homomorphic encryption scheme, and a type of a parameter of homomorphic encryption. ([0007] teaches selecting the appropriate circuit based on the type of parameter (e.g., multiplication) that is being performed by the homomorphic circuit, based on ciphertext data, encryption information, and homomorphic operation information received. Fig. 2 teaches a homomorphic encryption processing server 100. This may be interpreted as the number of multiplications being 0 or 1. [0039] teaches the server performing the selection of the homomorphic operations to increase efficiency.) (The applicant’s printed publication at [0013] teaches a parameter may be number of multiplications allowed, speed, accuracy of operation, and capacity of homomorphic ciphertext, and rebooting)
Before the effective filing date of the invention, it would have been obvious to one of ordinary skill in the art to combine the teachings of Bhageria, which teaches performing evaluation of different homomorphic encryption operations (Abstract) and evaluating the cost of the operations to select the most efficient operations (fig. 4 & [0060-61]) based on an analyzer performing cost analysis ([0045-47]), with Soceanu, which also teaches homomorphic encryption (Abstract), and additionally teaches evaluation using a simulator of different techniques to use homomorphic encryption, where the simulation determines the different costs of the different techniques / configuration (figs. 2 & 4) and the use of power ([0077]), with Morris, which also teaches resource recommendation and selection base on processing cost (Title & Abstract), and additionally teaches user selecting the resource based on a presented graphics indicating processing cost (Abstract & fig. 6), with Na, which also teaches homomorphic encryption / operations (Abstract), and additionally teaches the selection of the appropriate circuits to perform particular homomorphic operations such as homomorphic multiplication ([0007]). One of ordinary skill in the art would have been motivated to perform such an addition to provide Bhageria, Soceanu, and Morris with the added ability to select specific circuits to perform specific homomorphic operations, as taught by Na, for the purpose of increasing security by utilizing homomorphic encryption to perform analysis on data that is not decrypted, and increasing computational efficiency by selecting the most efficient resources to perform different types of homomorphic operations on the encrypted data.
Regarding claim 5, Bhageria, Soceanu, and Na teach,
The computing device providing method of claim 4,
Na teaches,
wherein the type of the parameter is classified according to at least one of a number of multiplication operations allowed, a speed of the homomorphic encryption operation, an accuracy of the homomorphic encryption operation, a capacity of homomorphic ciphertext, and an availability of bootstrap. ([0007] teaches selection of multiplication circuits, as discussed above in the rejection of claim 4. The examiner notes that the selection of 0 or 1 multiplication step is taught in [0007]. Also, [0007] teaches that bootstrapping is available.)
Regarding claim 6, Bhageria, Soceanu, and Na teach,
The computing device providing method of claim 4, wherein the recommending of the computing device further comprises
Na teaches,
recommending the computing device for processing of the requested homomorphic encryption operation based on at least one of a size of the encrypted data for the homomorphic encryption operation and an execution time expected for processing the homomorphic encryption operation. (Regarding time: [0056] teaches the homomorphic operation information including time / timing information of each operation, and [0057] teaches a homomorphic encryption processing server that uses the homomorphic operation information to enable / select the circuits that perform specific homomorphic operations, such as those shown in fig. 7. Regarding size: [0118] describe figs. 14-16 teaching learning using homomorphic operations. [0125-126] teaches selecting the structure of the network based on the size / type of data, with fig. 14 general neural network being used for audio data, where fig. 14 general neural network is not appropriate for image data), and thus, fig. 15 is applied to image data having multiple dimensions using a convoluted neural network (CNN). [0125] teaches the connections between the nodes are based on the class of data being input.) (Additionally, Fig. 15 and [0128] teach processing data having a size / volume of 32 pixels by 32 pixels with each pixel having 3 different colors. [0132] teaches applying different filters based on the size to create four 16 X 16 matrices based on using a 2 X 2 matrix on 32 X 32 data.)
Regarding claim 8, Bhageria, Soceanu, and Na teach,
The computing device providing method of claim 1,
Na teaches,
wherein the homomorphic encryption operation processed by the computing device includes at least one operation among a constant operation for constant data, a column operation for matrix-type data, a statistical operation using the matrix-type data for statistics of a target, a categorical operation for processing an operation on values that meet a condition among values of a specific column, and a machine learning operation for learning and inference of machine learning. (Regarding, “a column operation for matrix-type data”, [0128] teaches matrix operations, where a matrix teaches columns and rows. Regarding “a categorical operation for processing an operation on values that meet a condition among values of a specific column”, [0128] where the 32 X 32 matrix also includes three colors, which teaches categories that are conditions / colors. Regarding, “machine learning operation for learning and inference of machine learning”, [0118] teaches figs. 14-16 teach network structure used to perform deep learning by a homomorphic operation performing device according to example embodiments.)
Before the effective filing date of the invention, it would have been obvious to one of ordinary skill in the art to combine the teachings of Bhageria, which teaches performing evaluation of different homomorphic encryption operations (Abstract) and evaluating the cost of the operations to select the most efficient operations (fig. 4 & [0060-61]) based on an analyzer performing cost analysis ([0045-47]), with Soceanu, which also teaches homomorphic encryption (Abstract), and additionally teaches evaluation using a simulator of different techniques to use homomorphic encryption, where the simulation determines the different costs of the different techniques / configuration (figs. 2 & 4) and the use of power ([0077]), with Morris, which also teaches resource recommendation and selection base on processing cost (Title & Abstract), and additionally teaches user selecting the resource based on a presented graphics indicating processing cost (Abstract & fig. 6), with Na, which also teaches homomorphic encryption / operations (Abstract), and additionally teaches the use of matrix operations in deep learning using homomorphic encrypted data to preserve privacy of the original data ([0128]). One of ordinary skill in the art would have been motivated to perform such an addition to provide Bhageria, Soceanu, and Morris with the added ability to select specific circuits to perform specific homomorphic operations, as taught by Na, for the purpose of increasing security by utilizing homomorphic encryption to perform analysis and learning on data the data that is not decrypted, and increasing computational efficiency by selecting the most efficient resources to perform different types of homomorphic operations on the encrypted data.
Regarding claim 16, Bhageria, Soceanu, and Na teach,
The computer device of claim 14,
wherein, the requested homomorphic encryption operation is recommended based on at least one of a size of the encrypted data for the homomorphic encryption operation, (See rejection of claim 6) a type of the homomorphic encryption operation, (See rejection of claim 4) a number of homomorphic encryption operations, (See rejection of claim 4) an execution time expected for processing the homomorphic encryption operation, (See rejection of claim 6) a homomorphic encryption scheme, and a type of a parameter of homomorphic encryption. (See rejection of claim 4)
Claim 16 is rejected using the same basis of arguments used to reject claims 4 and 6 above.
Regarding claim 18, Bhageria, Soceanu, and Na teach,
The computer device of claim 14,
wherein the homomorphic encryption operation processed by the computing device includes at least one operation among a constant operation for constant data, a column operation for matrix-type data, a statistical operation using the matrix-type data for statistics of a target, a categorical operation for processing an operation on values that meet a condition among values of a specific column, and a machine learning operation for learning and inference of machine learning.
Claim 18 is rejected using the same basis of arguments used to reject claim 8 above.
Claims 7 and 17 are rejected under 35 U.S.C. 103 as being unpatentable over Bhageria, in view of Soceanu, in view of Morris, in view of US 20230119749 to Micciancio et al. (hereinafter Micciancio).
Regarding claim 7, Bhageria, Soceanu, and Morris teach,
The computing device providing method of claim 1,
Bhageria, Soceanu, and Morris fail to explicitly teach bootstrapping,
However, Micciancio teaches,
wherein the homomorphic encryption operation processed by the computing device includes an operation that requires bootstrap and an operation that does not require rebooting. (Abstract, teaches the use of bootstrapping when evaluating homomorphic operations.) (Applicant’s printed publication at [0006] describes rebooting as bootstrapping.)
Before the effective filing date of the invention, it would have been obvious to one of ordinary skill in the art to combine the teachings of Bhageria, which teaches performing evaluation of different homomorphic encryption operations (Abstract) and evaluating the cost of the operations to select the most efficient operations (fig. 4 & [0060-61]) based on an analyzer performing cost analysis ([0045-47]), with Soceanu, which also teaches homomorphic encryption (Abstract), and additionally teaches evaluation using a simulator of different techniques to use homomorphic encryption, where the simulation determines the different costs of the different techniques / configuration (figs. 2 & 4) and the use of power ([0077]), with Morris, which also teaches resource recommendation and selection base on processing cost (Title & Abstract), and additionally teaches user selecting the resource based on a presented graphics indicating processing cost (Abstract & fig. 6), with Micciancio, which also teaches performing homomorphic evaluation functions (Abstract), and additionally teaches the use of bootstrapping in the homomorphic computation and evaluation (Abstract). One of ordinary skill in the art would have been motivated to perform such an addition to provide Bhageria, Soceanu, and Morris with the added ability to utilize bootstrapping when performing homomorphic computations, as taught by Micciancio, for the purpose of increasing security and computational efficiency.
Regarding claim 17, Bhageria, Soceanu, Morris, and Micciancio teach,
The computer device of claim 14, wherein the homomorphic encryption operation processed by the computing device includes an operation that requires bootstrap and an operation that does not require rebooting.
Claim 17 is rejected using the same basis of arguments used to reject claim 7 above.
Claims 9 and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Bhageria, in view of Soceanu, in view of Morris, in view of US 20170013160 to Yamano et al. (hereinafter Yamano).
Regarding claim 9, Bhageria, Soceanu, and Morris teach,
The computing device providing method of claim 1,
wherein the computing device providing method further comprises,
in response to a selection on the recommended computing device through the management tool, . (see rejection of claim 1.)
Bhageria, Soceanu, and Morris fail to teach displaying the selected server,
However, Yamano teaches,
in response to a selection on the recommended computing device through the management tool, providing a virtual image of the selected computing device to the client device. ([0030] teach a management server that selects one or more servers for information processing. [0031-32] teach displaying information regarding the selected server.)
Before the effective filing date of the invention, it would have been obvious to one of ordinary skill in the art to combine the teachings of Bhageria, which teaches performing evaluation of different homomorphic encryption operations (Abstract) and evaluating the cost of the operations to select the most efficient operations (fig. 4 & [0060-61]) based on an analyzer performing cost analysis ([0045-47]), with Soceanu, which also teaches homomorphic encryption (Abstract), and additionally teaches evaluation using a simulator of different techniques to use homomorphic encryption, where the simulation determines the different costs of the different techniques / configuration (figs. 2 & 4) and the use of power ([0077]), with Morris, which also teaches resource recommendation and selection base on processing cost (Title & Abstract), and additionally teaches user selecting the resource based on a presented graphics indicating processing cost (Abstract & fig. 6), with Yamano, which also teaches external devices / servers processing information (fig. 1, multifunction machine 10), and additionally teaches displaying the selected server / device to the user to allow the user to confirm the selection ([0030-32]). One of ordinary skill in the art would have been motivated to perform such an addition to provide Bhageria, Soceanu, and Morris with the added ability to have a user visually confirm a selection of an external device / server, as taught by Yamano, for the purpose of increasing security by allowing the user to confirm the external device that is used to process data.
Regarding claim 19, Bhageria, Soceanu, and Yamano teach,
The computer device of claim 14, wherein the at least one processor is configured to provide a virtual image of the selected computing device in response to a selection on the recommended computing device through the management tool.
Claim 19 is rejected using the same basis of arguments used to reject claim 9 above.
Claim 12 is rejected under 35 U.S.C. 103 as being unpatentable over Bhageria, in view of Soceanu, in view of Morris, in view of US 20200327250 to Wang et al. (hereinafter Wang).
Regarding claim 12, Bhageria, Soceanu, and Morris teach,
The computing device providing method of claim 1,
Bhageria, Soceanu, and Morris fail to explicitly teach the return function of the computing device and the history of the function,
However, Wang teaches,
wherein the management tool further includes a return function of the computing device and a history management function according to application and return of the computing device. ([0399] teaches a return of results from the analysis device. [0397-398] and [0447] teach history of data / results being provided to devices.)
Before the effective filing date of the invention, it would have been obvious to one of ordinary skill in the art to combine the teachings of Bhageria, which teaches performing evaluation of different homomorphic encryption operations (Abstract) and evaluating the cost of the operations to select the most efficient operations (fig. 4 & [0060-61]) based on an analyzer performing cost analysis ([0045-47]), with Soceanu, which also teaches homomorphic encryption (Abstract), and additionally teaches evaluation using a simulator of different techniques to use homomorphic encryption, where the simulation determines the different costs of the different techniques / configuration (figs. 2 & 4) and the use of power ([0077]), with Morris, which also teaches resource recommendation and selection base on processing cost (Title & Abstract), and additionally teaches user selecting the resource based on a presented graphics indicating processing cost (Abstract & fig. 6), with Wang, which also teaches homomorphic encryption / operations ([0024-25]), and additionally teaches returning results to another device from analysis device and histories of data / results also being provided ([0399]). One of ordinary skill in the art would have been motivated to perform such an addition to provide Bhageria, Soceanu, and Morris with the added ability to use histories and returning performed by the computing device (i.e., intermediary), as taught by Wang, for the purpose of increasing security by utilizing homomorphic encryption to perform analysis on data the is not decrypted, and increasing computational efficiency by selecting the most efficient resources to perform different types of homomorphic operations on the encrypted data while returning data from the intermediary device.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. 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 BRIAN WILLIAM AVERY whose telephone number is (571)272-3942. The examiner can normally be reached on 9AM-5PM.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Farid Homayounmehr can be reached on (571)272-3739.
Information regarding the status of an application may be obtained from the Patent Application Information Retrieval (PAIR) system. Status information for published applications may be obtained from either Private PAIR or Public PAIR. Status information for unpublished applications is available through Private PAIR only. For more information about the PAIR system, see https://ppair-my.uspto.gov/pair/PrivatePair. Should you have questions on access to the Private PAIR system, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative or access to the automated information system, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/B.W.A./
/JASON K GEE/ Primary Examiner, Art Unit 2495
Prosecution Insights