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 .
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(d):
(d) REFERENCE IN DEPENDENT FORMS.—Subject to subsection (e), a claim in dependent form shall contain a reference to a claim previously set forth and then specify a further limitation of the subject matter claimed. A claim in dependent form shall be construed to incorporate by reference all the limitations of the claim to which it refers.
Claim 20 is rejected under 35 U.S.C. 112(d) as being of improper dependent form for failing to further limit the subject matter of the claim upon which it depends, or for failing to include all the limitations of the claim upon which it depends. Claim 20 depends on claim 13 and it recites the same claimed features as claim 13. Applicant may cancel the claim(s), amend the claim(s) to place the claim(s) in proper dependent form, rewrite the claim(s) in independent form, or present a sufficient showing that the dependent claim(s) complies with the statutory requirements.
Claim Rejections - 35 USC § 102
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 the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1, 6, 7, 12 – 14, 19, and 20 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Anand (Patent No.: US 10,152,349; hereinafter Anand).
Regarding claim 1, Anand discloses a method, comprising: receiving, from a controller, routing information corresponding to a specified calculation task (see col. 7 lines 20 – 23, process 400 may include receiving information that identifies a set of tasks to be executed and precedence constraints associated with the set of tasks (block 410), col. 7 lines 60 – 65, the information may include information that identifies a particular type of task (e.g., a task identifier) (ex: routing information)); performing data calculation based on an algorithm model corresponding to the specified calculation task to obtain data (see col. 7 lines 30 – 35, network device 210 (e.g., a kernel of network device 210) may receive information that identifies tasks (e.g., processes, threads, etc.) to be executed by a processing unit of network device 210 (e.g., a processor, a particular core of a processor, or the like), col. 9 lines 14 – 30, network device 210 may use one or more artificial intelligence and/or machine learning techniques to analyze data (e.g., training data, such as historical execution times of particular tasks, etc.) and create models); and sending the data to a switching device based on the routing information (see col. 8 lines 12 – 18, process 400 may include determining, based on the information that identifies the set of tasks and the precedence constraints associated with the set of tasks, a set of paths (block 430). For example, network device 210 may determine a set of paths based on the information that identifies the set of tasks and the precedence constraints of the set of tasks).
Regarding claim 6, Anand discloses wherein the specified calculation task is a distributed artificial intelligence (AI) training task (col. 9 lines 14 – 30, network device 210 may use one or more artificial intelligence and/or machine learning techniques to analyze data (e.g., training data, such as historical execution times of particular tasks, etc.)).
Regarding claim 7, Anand discloses an apparatus of a computing node and comprising: a memory storing instructions; and a processor coupled to the memory and configured to execute the instructions to cause the apparatus (see Fig. 2, network device 210, Fig. 3, device 300 (ex: network device 210), col. 6 lines 24 – 62, Controller 320 (ex: processor) may perform these processes in response to executing software instructions stored by a non-transitory computer-readable medium) to: receive, from a controller, routing information that corresponds to a specified calculation task (see col. 7 lines 20 – 23, process 400 may include receiving information that identifies a set of tasks to be executed and precedence constraints associated with the set of tasks (block 410), col. 7 lines 60 – 65, the information may include information that identifies a particular type of task (e.g., a task identifier) (ex: routing information)); perform data calculation based on an algorithm model corresponding to the specified calculation task to obtain data (see col. 7 lines 30 – 35, network device 210 (e.g., a kernel of network device 210) may receive information that identifies tasks (e.g., processes, threads, etc.) to be executed by a processing unit of network device 210 (e.g., a processor, a particular core of a processor, or the like), col. 9 lines 14 – 30, network device 210 may use one or more artificial intelligence and/or machine learning techniques to analyze data (e.g., training data, such as historical execution times of particular tasks, etc.) and create models); and send the data to a corresponding switching device based on the routing information (see col. 8 lines 12 – 18, process 400 may include determining, based on the information that identifies the set of tasks and the precedence constraints associated with the set of tasks, a set of paths (block 430). For example, network device 210 may determine a set of paths based on the information that identifies the set of tasks and the precedence constraints of the set of tasks).
Regarding claim 12, Anand discloses wherein the specified calculation task is a distributed artificial intelligence (AI) training task (col. 9 lines 14 – 30, network device 210 may use one or more artificial intelligence and/or machine learning techniques to analyze data (e.g., training data, such as historical execution times of particular tasks, etc.)).
Regarding claim 13, Anand discloses wherein the routing information indicates a data forwarding path to the switching device (see col. 7 lines 60 – 65, col. 8 lines 12 – 19, the information may include information that identifies a particular type of task (e.g., a task identifier) (ex: routing information) and this information is used for the determination of a set of paths).
Regarding claim 14, Anand discloses a network system, comprising: a controller (see Fig. 3, col. 5 lines 39 – 46, col. 6 lines 23 – 25, device 300 with a controller 320 such as a processor) configured to send routing information corresponding to a specified calculation task (see col. 7 lines 20 – 23, process 400 may include receiving information that identifies a set of tasks to be executed and precedence constraints associated with the set of tasks (block 410), col. 7 lines 60 – 65, the information may include information that identifies a particular type of task (e.g., a task identifier) (ex: routing information)); and a computing node coupled to the controller and comprising: a memory storing instructions; and a processor coupled to the memory and configured to execute the instructions that cause the computing node (see Fig. 2, col. 5 lines 4 – 6, col. 5 lines 28 – 32, network device 210 includes one or more devices, Fig. 3, col. 6 lines 24 – 62, Controller 320 (ex: processor) may perform these processes in response to executing software instructions stored by a non-transitory computer-readable medium) to: receive, from the controller, the routing information (see col. 7 lines 20 – 23, process 400 may include receiving information that identifies a set of tasks to be executed…col. 7 lines 60 – 65, the information may include information that identifies a particular type of task (e.g., a task identifier) (ex: routing information)); perform data calculation based on an algorithm model corresponding to the specified calculation task to obtain data (see col. 7 lines 30 – 35, network device 210 (e.g., a kernel of network device 210) may receive information that identifies tasks (e.g., processes, threads, etc.) to be executed by a processing unit of network device 210 (e.g., a processor, a particular core of a processor, or the like), col. 9 lines 14 – 30, network device 210 may use one or more artificial intelligence and/or machine learning techniques to analyze data (e.g., training data, such as historical execution times of particular tasks, etc.) and create models); and send the data to a corresponding switching device based on the routing information (see col. 8 lines 12 – 18, process 400 may include determining, based on the information that identifies the set of tasks and the precedence constraints associated with the set of tasks, a set of paths (block 430). For example, network device 210 may determine a set of paths based on the information that identifies the set of tasks and the precedence constraints of the set of tasks).
Regarding claim 19, Anand discloses wherein the specified calculation task is a distributed artificial intelligence (AI) training task (col. 9 lines 14 – 30, network device 210 may use one or more artificial intelligence and/or machine learning techniques to analyze data (e.g., training data, such as historical execution times of particular tasks, etc.)).
Regarding claim 20, Anand discloses wherein the routing information indicates a data forwarding path to the switching device (see col. 7 lines 60 – 65, col. 8 lines 12 – 19, the information may include information that identifies a particular type of task (e.g., a task identifier) (ex: routing information) and this information is used for the determination of a set of paths).
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 2, 5, 8, 9, 15, and 16 are rejected under 35 U.S.C. 103 as being unpatentable over Anand (Patent No.: US 10,152,349; hereinafter Anand) in view of Jain et al. (Pub. No.: US 2017/0070417; hereinafter Jain).
Anand does not disclose the claimed features as recited in claims 2, 5, 8, 9, 15, and 16.
Regarding claim 2, Jain discloses wherein the routing information comprises an identifier of a switching device that is directly connected to the computing node (see Fig. 3, router 58 is connected to computing device 54, para. 0006, Internet Protocol (IP) address of one or more routers between the computing device).
Regarding claim 5, Jain discloses wherein the identifier is an Internet Protocol (IP) address (see para. 0006, Internet Protocol (IP) address).
Regarding claim 8, Jain discloses wherein the routing information comprises an identifier of a switching device that is directly connected to the computing node (see Fig. 3, router 58 is connected to computing device 54, para. 0006, Internet Protocol (IP) address of one or more routers between the computing device).
Regarding claim 9, Jain discloses wherein the identifier is an Internet Protocol (IP) address (see para. 0006, Internet Protocol (IP) address).
Regarding claim 15, Jain discloses wherein the computing node is directly connected to a switching device, and wherein the routing information comprises an identifier of the switching device (see Fig. 3, router 58 is connected to computing device 54, para. 0006, Internet Protocol (IP) address of one or more routers between the computing device).
Regarding claim 16, Jain discloses wherein the identifier is an Internet Protocol (IP) address (see para. 0006, Internet Protocol (IP) address).
It would have been obvious to one ordinary skilled in the art before the effective filing date of the claimed invention to modify the invention of Anand, and have the features, as taught by Jain, in order to implement Network Address Translation (NAT) protocol which provides additional security by abstracting and isolating devices in the network from direct access from the Internet, as discussed by Jain (para. 0002).
Claims 3, 4, 10, 11, 17, and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Anand (Patent No.: US 10,152,349; hereinafter Anand) in view of Foerster et al. (Patent No.: US 9,798,612; hereinafter Foerster).
Anand does not disclose the claimed features as recited in claims 3, 4, 10, 11, 17, and 18.
Regarding claim 3, Foerster discloses wherein performing the data calculation based on the algorithm model comprises: receiving a plurality of sample pictures (see col. 1 lines 20 – 40, col. 2 lines 55 – 58, data samples include images of panoramic image); performing data calculation on the sample pictures based on a pre-stored neural network model to obtain a gradient of the neural network model; and sending the gradient to an image recognition application (see col. 3 lines 4 – 14, col. 4 lines 18 – 35, the corruption score reduction subsystem 108 performs multiple iterations of gradient descent against the feature representation of the corrupted data input 102 by adjusting the position of the feature representation of the corrupted data input 102 in the feature space to reduce the corruption score, i.e., by optimizing the position of the feature representation of the corrupted data input 102 in the feature space, col. 6 lines 40 – 45, use a single neural network to perform corrections for a range of errors present in the dataset).
Regarding claim 4, Foerster discloses wherein the image recognition application is a deep neural network (DNN)-based image recognition application (see col. 1 lines 35 – 38, col. 2 lines 24 – 25, a trained deep neural network for image processing), and wherein the specified calculation task is a training task of the DNN-based image recognition application (see col. 1 lines 35 – 55, col. 5 lines 23 – 30, where discussed are tasks of a trained deep neural network for performing to correct a data sample).
Regarding claim 10, Foerster discloses wherein the processor is further configured to execute the instructions to: receive a plurality of sample pictures (see col. 1 lines 20 – 40, col. 2 lines 55 – 58, data samples include images of panoramic image); perform data calculation on the sample pictures based on a pre-stored neural network model to obtain a gradient of the neural network model; and send the gradient of the neural network model to an image recognition application (see col. 3 lines 4 – 14, col. 4 lines 18 – 35, the corruption score reduction subsystem 108 performs multiple iterations of gradient descent against the feature representation of the corrupted data input 102 by adjusting the position of the feature representation of the corrupted data input 102 in the feature space to reduce the corruption score, i.e., by optimizing the position of the feature representation of the corrupted data input 102 in the feature space, col. 6 lines 40 – 45, use a single neural network to perform corrections for a range of errors present in the dataset).
Regarding claim 11, Foerster discloses wherein the image recognition application is a deep neural network (DNN)-based image recognition application (see col. 1 lines 35 – 38, col. 2 lines 24 – 25, a trained deep neural network for image processing), and wherein the specified calculation task is a training task of the DNN-based image recognition application (see col. 1 lines 35 – 55, col. 5 lines 23 – 30, where discussed are tasks of a trained deep neural network for performing to correct a data sample).
Regarding claim 17, Foerster discloses wherein the computing node is further configured to: receive a plurality of sample pictures (see col. 1 lines 20 – 40, col. 2 lines 55 – 58, data samples include images of panoramic image); perform data calculation on the sample pictures based on a pre-stored neural network model to obtain a gradient of the neural network model; and send the gradient of the neural network model to an image recognition application (see col. 3 lines 4 – 14, col. 4 lines 18 – 35, the corruption score reduction subsystem 108 performs multiple iterations of gradient descent against the feature representation of the corrupted data input 102 by adjusting the position of the feature representation of the corrupted data input 102 in the feature space to reduce the corruption score, i.e., by optimizing the position of the feature representation of the corrupted data input 102 in the feature space, col. 6 lines 40 – 45, use a single neural network to perform corrections for a range of errors present in the dataset).
Regarding claim 18, Foerster discloses wherein the image recognition application is a deep neural network (DNN)-based image recognition application (see col. 1 lines 35 – 38, col. 2 lines 24 – 25, a trained deep neural network for image processing), and wherein the specified calculation task is a training task of the DNN-based image recognition application (see col. 1 lines 35 – 55, col. 5 lines 23 – 30, where discussed are tasks of a trained deep neural network for performing to correct a data sample).
It would have been obvious to one ordinary skilled in the art before the effective filing date of the claimed invention to modify the invention of Anand, and have the features, as taught by Foerster, by using neural networks to reduce the corruption and improve the quality of data samples, including data samples that have been artificially generated through the merging and modifying of original content, as discussed by Foerster (col. 2 lines 55 - 58).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Anh Ngoc M Nguyen whose telephone number is (571) 270-5139. The examiner can normally be reached on Monday to Friday, from 7:30 am to 4:00 pm.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Kwang Bin Yao can be reached on ((571) 272-3182. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/ANH NGOC M NGUYEN/Primary Examiner, Art Unit 2473