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. 18571131 has a total of 22 claims pending in the application, of which claims 11-12 have been cancelled.
I. ACKNOWLEDGEMENT OF REFERENCES CITED BY APPLICANT
Information Disclosure Statement
As required by M.P.E.P 609(c), the applicant’s submissions of the Information Disclosure Statements 3/15/2024 and 8/21/2024 are acknowledged by the examiner and the cited references have been considered in the examination of the claims now pending. As required by M.P.E.P 609 C(2), a copy of the PTOL-1449 initialed and dated by the examiner is attached to the instant office action.
II. REJECTIONS NOT BASED ON PRIOR ART
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.
Claims 1-10, and 13-22 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1: Claim 1 is a process type claim. Claim 13 is a machine type claim, and claim 14 is a manufacture type claim. Therefore, claims 1-10 and 13-222 are directed to either a process, machine, manufacture or composition of matter.
As per claim 1,
2A Prong 1:
“executing the target data service by using … the candidate dataset” A user mentally or with pencil identifies objects within an image of the candidate dataset.
2A Prong 2: This judicial exception is not integrated into a practical application.
Additional elements:
“A neural network graph structure” (Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f) – Examiner’s note: Claims required nothing more than a generic neural network graph structure, with no additional limitations or details beyond a generic off the shelf generic neural network.
“acquiring a candidate dataset that is outside a neural network graph structure and corresponding to a target data service through a custom operator in the neural network graph structure after a process of the target service being started” (Adding insignificant extra-solution activity to the judicial exception - see MPEP 2106.05(g)).
2B: The claim does not include additional elements individually or in combination that are sufficient to amount to significantly more than the judicial exception.
Additional elements:
“A neural network graph structure” (Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f) – Examiner’s note: Claims required nothing more than a generic neural network graph structure, with no additional limitations or details beyond a generic off the shelf generic neural network.
“acquiring a candidate dataset that is outside a neural network graph structure and corresponding to a target data service through a custom operator in the neural network graph structure after a process of the target service being started” (MPEP 2106.05(d)(II) indicate that merely “receiving and transmitting data” is a well‐understood, routine, conventional function when it is claimed in a merely generic manner (as it is in the present claim). Thereby, a conclusion that the claimed acquiring step is well-understood, routine, conventional activity is supported under Berkheimer).
As per claims 2-4, these claims disclose additional mental steps, acquiring, and generic machine learning models, and are rejected for similar reasons to claim 1.
As per claim 5, this claim contains similar mental steps and generic machine learning models, and are rejected for similar reasons to claim 1.
2A Prong 2: This judicial exception is not integrated into a practical application.
Additional elements:
“computing devices” (mere instructions to apply the exception using a generic computer component);
2B: The claim does not include additional elements individually or in combination that are sufficient to amount to significantly more than the judicial exception.
Additional elements:
“computing devices” (mere instructions to apply the exception using a generic computer component)
As per claim 6-7, and 9, this claim contains additional mental steps and generic machine learning similar to claim 1 above, and is rejected for similar reasons.
As per claim 8, this claim contains similar mental steps to claim 1, and is rejected for similar reasons given above.
As per claim 10, this claim contains similar generic computer hardware and machine learning models to claim 5, and is rejected for similar reasons.
As per claim 13,
2A Prong 1:
“executing the target data service by using … the candidate dataset” A user mentally or with pencil identifies objects within an image of the candidate dataset.
2A Prong 2: This judicial exception is not integrated into a practical application.
Additional elements:
“an electronic apparatus”, “one or more processors”, “a memory” (mere instructions to apply the exception using a generic computer component);
“A neural network graph structure” (Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f) – Examiner’s note: Claims required nothing more than a generic neural network graph structure, with no additional limitations or details beyond a generic off the shelf generic neural network.
“acquiring a candidate dataset that is outside a neural network graph structure and corresponding to a target data service through a custom operator in the neural network graph structure after a process of the target service being started” (Adding insignificant extra-solution activity to the judicial exception - see MPEP 2106.05(g)).
2B: The claim does not include additional elements individually or in combination that are sufficient to amount to significantly more than the judicial exception.
Additional elements:
“an electronic apparatus”, “one or more processors”, “a memory” (mere instructions to apply the exception using a generic computer component)
“A neural network graph structure” (Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f) – Examiner’s note: Claims required nothing more than a generic neural network graph structure, with no additional limitations or details beyond a generic off the shelf generic neural network.
“acquiring a candidate dataset that is outside a neural network graph structure and corresponding to a target data service through a custom operator in the neural network graph structure after a process of the target service being started” (MPEP 2106.05(d)(II) indicate that merely “receiving and transmitting data” is a well‐understood, routine, conventional function when it is claimed in a merely generic manner (as it is in the present claim). Thereby, a conclusion that the claimed acquiring step is well-understood, routine, conventional activity is supported under Berkheimer).
As per claim 14,
2A Prong 1:
“executing the target data service by using … the candidate dataset” A user mentally or with pencil identifies objects within an image of the candidate dataset.
2A Prong 2: This judicial exception is not integrated into a practical application.
Additional elements:
“A computer readable storage medium”, “a processor” (mere instructions to apply the exception using a generic computer component);
“A neural network graph structure” (Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f) – Examiner’s note: Claims required nothing more than a generic neural network graph structure, with no additional limitations or details beyond a generic off the shelf generic neural network.
“acquiring a candidate dataset that is outside a neural network graph structure and corresponding to a target data service through a custom operator in the neural network graph structure after a process of the target service being started” (Adding insignificant extra-solution activity to the judicial exception - see MPEP 2106.05(g)).
2B: The claim does not include additional elements individually or in combination that are sufficient to amount to significantly more than the judicial exception.
Additional elements:
“A computer readable storage medium”, “a processor” (mere instructions to apply the exception using a generic computer component)
“A neural network graph structure” (Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f) – Examiner’s note: Claims required nothing more than a generic neural network graph structure, with no additional limitations or details beyond a generic off the shelf generic neural network.
“acquiring a candidate dataset that is outside a neural network graph structure and corresponding to a target data service through a custom operator in the neural network graph structure after a process of the target service being started” (MPEP 2106.05(d)(II) indicate that merely “receiving and transmitting data” is a well‐understood, routine, conventional function when it is claimed in a merely generic manner (as it is in the present claim). Thereby, a conclusion that the claimed acquiring step is well-understood, routine, conventional activity is supported under Berkheimer).
As per claims 15-17, these claims disclose additional mental steps, acquiring, and generic machine learning models, and are rejected for similar reasons to claim 13.
As per claims 18, this claim contains similar mental steps, generic hardware, and generic machine learning models, and are rejected for similar reasons to claim 13.
As per claim 19-20, and 22, this claim contains additional mental steps and generic machine learning similar to claim 13 above, and is rejected for similar reasons.
As per claim 21, this claim contains similar mental steps to claim 13, and is rejected for similar reasons given above.
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 2, 5, 8, 15, 18 and 21 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.
As per claims 2 and 15, this claim calls for “setting a name of the candidate dataset as a solidified parameter of the custom operator.” The term solidified is never defined in the specification, and does not seem to be a term of art related to neural networks. Merriam webster defines “solidified” to mean “to make solid, compact, or hard.” Or “to make secure, substantial, or firmly fixed.” How is a name for data a “solidified parameter.” How can data be made “secure, substantial, or firmly fixed?” This causes the claim to be confusing, and therefore rejected under U.S.C. 112(b) for failing to particularly point out and claim the intended invention.
As per claims 8 and 21, this claim is rejected as being dependent on a claim rejected under U.S.C. 112(b).
As per claims 5 and 18, these claims call for “executing the target data service by the plurality of neural network computing devices based on the plurality of copies loaded therein and the candidate dataset that is a same one, respectively.” That is a same one what? Each of the copies works on the same candidate set of data, or there are multiple candidate data sets that are the same one fed into each copy? In the next response, please indicate what is “is a same one, respectively” here.
III. REJECTIONS BASED ON PRIOR ART
Examiners Note: Some rejections will be followed by an ‘EN’ that will denote an examiners note. This will be placed to further explain a rejection.
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)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claims 1, 3, 6, 9, 13-14, 16, 19, and 22 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Kim et al (US 10579924 B1).
As per claim 1, Kim discloses, “A data service method” (C10, particularly L42-61; EN: this denotes the neural network being used for image segmentation).
“Acquiring a candidate dataset that is outside a neural network graph structure” (Figure 2 and associated paragraphs; C2, particularly L30-64; EN: this denotes acquiring input images to put into the neural network. This is outside the neural network to start with). “and corresponding to a target data service” (C10, particularly L42-61; EN: this denotes the neural network being used for image segmentation). “through a custom operator in the neural network graph structure” (Figure 2 and associated paragraphs; C8, particularly L30-64; EN: this denotes inputting the image to a 1st convolutional layer, which is custom to this particular neural network). “after a process of the target data service being started” (Figure 2 and associated paragraphs; C8, particularly L30-64; EN: No data will be taken until the neural network is activated and the process has begun).
“executing the target data service by using the neural network graph structure and the candidate dataset” (C10, particularly L42-61; EN: this denotes the neural network being used for image segmentation).
As per claims 3 and 16, Kim discloses, “encapsulating candidate data into a data structure outside the neural network graph structure to form the candidate dataset” (Figure 2 and associated paragraphs; C2, particularly L30-64; EN: this denotes acquiring input images to put into the neural network. This is outside the neural network to start with, and the data being in an image format is the encapsulating data structure).
“encapsulating the candidate dataset to obtain an encapsulation class of the candidate dataset” (Figure 2 and associated paragraphs; C2, particularly L30-64; EN: this denotes acquiring input images to put into the neural network. This is outside the neural network to start with, and the data being in an image format is the encapsulating data structure. Here the class is “image”).
“wherein the acquiring the candidate dataset that is outside the neural network graph structure and corresponding to the target data service through the custom operator in the neural network graph structure comprises” (Figure 2 and associated paragraphs; C2, particularly L30-64; EN: this denotes acquiring input images to put into the neural network. This is outside the neural network to start with).
“acquiring the candidate dataset or acquiring a calculation result obtained through performing a preset calculation based on the candidate dataset, by the custom operator through invoking an interface function of the encapsulation class” (Figure 2 and associated paragraphs; C2, particularly L30-64; EN: this denotes acquiring input images to put into the neural network. This is outside the neural network to start with, and the data being in an image format is the encapsulating the data. The interface function is the use of the neural network layer to take in the image).
As per claims 6 and 19, Kim discloses, “updating the candidate dataset independently and/or updating the neural network graph structure independently” (Figure 2 and associated paragraphs; C8, particularly L30-64; EN: This denotes the training image and the neural network being separate, with the input data of the neural network able to be chosen (i.e. updated) as needed, and the neural network being updated based on whatever training data is put into it).
As per claims 9 and 22, Kim discloses, “creating the custom operator” (Figure 2 and associated paragraphs; C8, particularly L30-64; EN: this denotes inputting the image to a 1st convolutional layer, which is custom to this particular neural network).
“acquiring an original graph structure” (Figure 2 and associated paragraphs: this denotes the entire graph structure).
“importing the custom operator into the original graph structure and obtaining the neural network graph structure” (Figure 2 and associated paragraphs: this denotes the entire graph structure, including the first layer taking in the inputs).
As per claims 13-14, Kim discloses, “An electronic apparatus” (C5, particularly L5-34; EN: this denotes the use of processors to execute the system) “for data service, comprising” (C10, particularly L42-61; EN: this denotes the neural network being used for image segmentation).
“One or more processors” (C5, particularly L5-34; EN: this denotes the use of processors to execute the system)
“a memory configured for storing one or more programs” (C11, particularly L50-68; C12, L1-3; EN: this denotes the use of memory for the system).
“Wherein the one or more programs, upon being executed by the one or more processors, are configured to cause the one or more processors to realize the data service method comprising” (C5, particularly L5-34; EN: this denotes the use of processors to execute the system)
“Acquiring a candidate dataset that is outside a neural network graph structure” (Figure 2 and associated paragraphs; C2, particularly L30-64; EN: this denotes acquiring input images to put into the neural network. This is outside the neural network to start with). “and corresponding to a target data service” (C10, particularly L42-61; EN: this denotes the neural network being used for image segmentation). “through a custom operator in the neural network graph structure” (Figure 2 and associated paragraphs; C8, particularly L30-64; EN: this denotes inputting the image to a 1st convolutional layer, which is custom to this particular neural network). “after a process of the target data service being started” (Figure 2 and associated paragraphs; C8, particularly L30-64; EN: No data will be taken until the neural network is activated and the process has begun).
“executing the target data service by using the neural network graph structure and the candidate dataset” (C10, particularly L42-61; EN: this denotes the neural network being used for image segmentation).
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, 8, 15, and 21 are rejected under 35 U.S.C. 103 as being unpatentable over Kim et al (US 10579924 B1) as applied to claims 1 and 14 above, and further in view of Skeirik (US 5167009 A).
As per claims 2 and 15, Kim discloses, “encapsulating candidate data into a data structure outside the neural network graph structure to form the candidate dataset” (Figure 2 and associated paragraphs; C2, particularly L30-64; EN: this denotes acquiring input images to put into the neural network. This is outside the neural network to start with, and the data being in an image format is the encapsulating data structure).
“…wherein the acquiring the candidate dataset that is outside the neural network graph structure and corresponding to the target service through the custom operator in the neural network graph structure comprises…” (Figure 2 and associated paragraphs; C2, particularly L30-64; EN: this denotes acquiring input images to put into the neural network. This is outside the neural network to start with).
However, Kim fails to explicitly disclose, “setting a name of the candidate dataset as a solidified parameter of the custom operator”, “obtaining a preset information carrier according to the solidified parameter through the custom operator”, and “accessing the candidate dataset according to the pointer address”
Skeirik discloses, “setting a name of the candidate dataset as a solidified parameter of the custom operator” (C21, L56-68; C22, L1-3; EN: this denotes using control tag names for desired data for the neural network. The examiner is interpreting this to be the name of the required data, and it has been “solidified” by the custom operator as it denotes a name of the data the system wishes to use and this is how that data is labeled (i.e. solidified)).
“obtaining a preset information carrier according to the solidified parameter through the custom operator” (C6, particularly L54-63; EN: this denotes the system having predefined systems where data will come and go from, including a historical database. The information carrier is the system holding the data).
“obtaining a pointer address of the candidate dataset according to the preset information carrier” (C6, particularly L54-63; EN: this denotes using the data pointer to select the data from the historical database).
“accessing the candidate dataset according to the pointer address” (C6, particularly L54-63; EN: this denotes using the data pointer to select the data from the historical database).
Kim and Skeirik are analogous art because both involve neural networks.
Before the effective filing date it would have been obvious to one skilled in the art of neural networks to combine the work of Kim and Skeirik in order to use pointers to grab the data you want for the neural network.
The motivation for doing so would be to “allow[] the user of the neural network to specify the source or destination of the data” (Skeirik, C6, L64-63) or in the case of Kim, allow the use of data pointers to select data as needed to be routed to the neural network for use.
Therefore before the effective filing date it would have been obvious to one skilled in the art of neural networks to combine the work of Kim and Skeirik in order to use pointers to grab the data you want for the neural network.
As per claims 8 and 21, Skeirik discloses, “wherein the preset information carrier comprises a static variable, a shared variable in a shared memory, or a file” (C13, particularly L62-68; EN: this denotes the historical database storing the training data, which the Examiner is interpreting to be a file).
Claim Rejections - 35 USC § 103
Claims 4 and 17 are rejected under 35 U.S.C. 103 as being unpatentable over Kim et al (US 10579924 B1) as applied to claims 1 and 14 above, and further in view of Poggio et al (US 20150278635 A1).
As per claims 4 and 17, Kim fails to explicitly disclose, “acquiring an identification of target candidate data, wherein the candidate dataset contains the target candidate data”, “the executing the target data service by using the neural network graph structure and the candidate dataset comprises, extracting the target candidate data from the candidate dataset according to the identification of the target candidate data or performing a preset calculation on the target candidate data from an encapsulation class of the candidate dataset according to the identification of the target candidate data and obtaining a calculation result, and executing the target data service by using the neural network graph structure and the calculation result.”
Poggio discloses, “acquiring an identification of target candidate data, wherein the candidate dataset contains the target candidate data” (pg.2, particularly paragraph 0025; EN :this denotes the training set (the candidate data set) having multiple images to be used for training, each individual image is a target candidate data).
“the executing the target data service by using the neural network graph structure and the candidate dataset comprises, extracting the target candidate data from the candidate dataset according to the identification of the target candidate data or performing a preset calculation on the target candidate data from an encapsulation class of the candidate dataset according to the identification of the target candidate data and obtaining a calculation result, and executing the target data service by using the neural network graph structure and the calculation result” (Pg.2, particularly paragraph 0021; EN: this denotes using the images from the training set to train the system, which meets extracting the image from the training set and executing the neural network using the target candidate data).
Kim and Poggio are analogous art because both involve neural networks.
Before the effective filing date it would have been obvious to one skilled in the art of neural networks to combine the work of Kim and Poggio in order to use target data from a candidate data set for the neural network.
The motivation for doing so would be “of the task is to identify a person from an image, the system may be trained using images that are each labeled with the name, or some other suitable identifier, of the person depicted” (Poggio, pg.2, paragraph 0021) or in the case of Kim, allow the system to perform the training process with multiple relevant images to the training set.
Therefore before the effective filing date it would have been obvious to one skilled in the art of neural networks to combine the work of Kim and Poggio in order to use target data from a candidate data set for the neural network.
Claim Rejections - 35 USC § 103
Claims 5, 7, 10, 18 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Kim et al (US 10579924 B1) as applied to claims 1 and 14 above, and further in view of Zhao et al (US 20190312772 A1).
As per claims 5 and 18, Kim fails to explicitly disclose, “wherein the neural network graph structure has a plurality of copies, and the plurality of copies are loaded into a plurality of neural network computing devices in one to one correspondence;”, “the candidate dataset is stored outside the plurality of neural network devices” “the executing the target data service by using the neural network graph structure and the candidate dataset comprises: executing the target data service by the plurality of neural network computing devices base don the plurality of copies loaded therein and the candidate dataset that is a same one respectively”
Zhao discloses, “wherein the neural network graph structure has a plurality of copies, and the plurality of copies are loaded into a plurality of neural network computing devices in one to one correspondence;” (pg.5, particularly paragraph 0039; EN: this denotes placing the neural network on multiple GPU cards in order to parallelize the processing of the data through the neural network). “the candidate dataset is stored outside the plurality of neural network devices” (Pg.2, particularly paragraph 0018; EN: this denotes the training data being stored on server clusters providing data to the GPU)
“the executing the target data service by using the neural network graph structure and the candidate dataset comprises: executing the target data service by the plurality of neural network computing devices based on the plurality of copies loaded therein and the candidate dataset that is a same one respectively” (pg.5, particularly paragraph 0039; EN: this denotes placing the neural network on multiple GPU cards in order to parallelize the processing of the data through the neural network).
Kim and Zhao are analogous art because both involve neural networks.
Before the effective filing date it would have been obvious to one skilled in the art of neural networks to combine the work of Kim and Zhao in order to use multiple copies of the neural network.
The motivation for doing so would be to “Speed up the training using a data parallelism programming model” (Zhao, Pg.5, paragraph 0039) or in the case of Kim, allow multiple copies to be used to speed up the processing and training of the model as needed.
Therefore before the effective filing date it would have been obvious to one skilled in the art of neural networks to combine the work of Kim and Zhao in order to use multiple copies of the neural network.
As per claims 7 and 20, Kim fails to explicitly disclose, “packaging the candidate dataset and the neural network graph structure into one data packet; and updating the one data packet upon at least one of the candidate dataset and the neural network graph structure being required for an update.”
Zhao discloses, “packaging the candidate dataset and the neural network graph structure into one data packet;” (pg.5, particularly paragraph 0039; EN: this denotes placing the neural network on multiple GPU cards in order to parallelize the processing of the data through the neural network).
“updating the one data packet upon at least one of the candidate dataset and the neural network graph structure being required for an update” (pg.5, particularly paragraph 0039; EN: this denotes the individual neural networks being given training data to work with. By being given the training data to process, the device is a “data packet” with both the data and the neural network, and the newly updated training data is used to further update the neural network via training).
Kim and Zhao are analogous art because both involve neural networks.
Before the effective filing date it would have been obvious to one skilled in the art of neural networks to combine the work of Kim and Zhao in order to use multiple copies of the neural network.
The motivation for doing so would be to “Speed up the training using a data parallelism programming model” (Zhao, Pg.5, paragraph 0039) or in the case of Kim, allow multiple copies to be used to speed up the processing and training of the model as needed.
Therefore before the effective filing date it would have been obvious to one skilled in the art of neural networks to combine the work of Kim and Zhao in order to use multiple copies of the neural network.
As per claim 10, Zhao discloses, “wherein the neural network computing devices is a graphics card” (pg.5, particularly paragraph 0039; EN: this denotes placing the neural network on multiple GPU cards in order to parallelize the processing of the data through the neural network).
Conclusion
The examiner requests, in response to this Office action, support be shown for language added to any original claims on amendment and any new claims. That is, indicate support for newly added claim language by specifically pointing to page(s) and line no(s) in the specification and/or drawing figure(s). This will assist the examiner in prosecuting the application.
When responding to this office action, Applicant is advised to clearly point out the patentable novelty which he or she thinks the claims present, in view of the state of the art disclosed by the references cited or the objections made. He or she must also show how the amendments avoid such references or objections See 37 CFR 1.111(c).
Any inquiry concerning this communication or earlier communications from the examiner should be directed to BEN M RIFKIN whose telephone number is (571)272-9768. The examiner can normally be reached Monday-Friday 9 am - 5 pm.
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, Alexey Shmatov can be reached at (571) 270-3428. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/BEN M RIFKIN/Primary Examiner, Art Unit 2123
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