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 .
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicants’ submission filed on 2/12/26 has been entered.
DETAILED ACTION
The instant application having Application No. 17101517 has a total of 31 claims pending in the application, of which claims 3-4, 7, 10-13, 17-18 and 26 have been cancelled.
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 1-2, 5-6, 8-9, 14-16, 19, 21-25, and 27-31 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 machine type claim. Independent claim 15 is a process type claim, and claim 28 is a manufacture claim. Therefore, claims 1-2, 5-6, 8-9, 14-16, 19, 21-25, and 27-31 are directed to either a process, machine, manufacture or composition of matter.
As per claim 1,
2A Prong 1:
“format one or more segments of an input streaming dataset, based on a target format, to yield one or more formatted output datasets corresponding to the target format, wherein the target format is based on one or more target parameters predicted from the one or more segments” The user mentally or with pencil and paper determines the format he wants the input data to be in, labeling the formatted dataset as output, and uses target parameters from the input segments to determine the output format needed.
“generate one or more overlapping dataset using the one or more segments of the input streaming datasets and the one or more corresponding formatted output datasets” The user mentally or with pencil and paper breaks up the input/output datasets into corresponding overlapping segments.
“Format one or more subsequent segments of the input streaming dataset received during the first time interval” The user mentally or with pencil and paper formats further data as it comes in.
2A Prong 2: This judicial exception is not integrated into a practical application.
Additional elements:
A processor circuit, a memory storing instructions which when executed by the processor circuit (mere instructions to apply the exception using a generic computer component);
Autoencoder, “train an autoencoder to approximate the target format, using the one or more overlapping datasets as one or more training samples, until an accuracy of a trained autoencoder exceeds a threshold accuracy” (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: The claim requires nothing more than a generic autoencoder with no additional detail or requirements. It is nothing more than a generic off the shelf autoencoder with no specialized components or detail);
2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Additional elements:
A processor, a memory storing instructions which when executed by the processor circuit (mere instructions to apply the exception using a generic computer component)
Autoencoder, “train an autoencoder to approximate the target format, using the one or more overlapping datasets as one or more training samples, until an accuracy of a trained autoencoder exceeds a threshold accuracy” (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: The claim requires nothing more than a generic autoencoder with no additional detail or requirements. It is nothing more than a generic off the shelf autoencoder with no specialized components or detail);
As per claims 2 and 24-25, this claim contains additional mental steps and is rejected for similar reasons to claim 1.
As per claim 5-6, this claim contains additional discussion of the machine learning elements and mental steps of claim 1, and is rejected for similar reasons to claim 1.
As per claim 8,
2A Prong 1:
“wherein the data stream comprises numeric values, wherein formatting of one or more segments of the input streaming dataset according to the target format during the first time interval, comprises:” The user mentally or with pencil and paper formats numeric values into the appropriate format.
Determining a maximum value and a minimum value of a first subset of the numeric values and wherein formatting the one or more subsequent segments of the input streamlining dataset comprises normalizing … during the first time interval, a second subset of the numeric values based on the determined maximum and minimum values ” The user mentally or with pencil and paper determines the maximum and minimum of the numbers they are looking at, and uses those values to normalize the data.
2A Prong 2: This judicial exception is not integrated into a practical application.
Additional elements:
Trained Autoencoder (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: The claim requires nothing more than a generic autoencoder with no additional detail or requirements. It is nothing more than a generic off the shelf autoencoder with no specialized components or detail);
2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Additional elements:
Trained Autoencoder (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: The claim requires nothing more than a generic autoencoder with no additional detail or requirements. It is nothing more than a generic off the shelf autoencoder with no specialized components or detail);
As per claim 9, this claim contains additional mental steps and is rejected for similar reasons to claim 1.
As per claim 14, this claim contains similar machine learning, generic hardware, and mental steps as to claim 1, and are rejected for similar reasons to claim 1.
As per claim 15,
2A Prong 1:
“formatting one or more segments of an input streaming dataset, received during a first time interval, based on a target format, to yield one or more formatted output datasets corresponding to the target format, wherein the target format is based on one or more target parameters predicted form the one or more segments” The user mentally or with pencil and paper determines the format he wants the input data to be in, labeling the formatted dataset as output, and determines the output format based on parameters of the segments.
“generating one or more overlapping datasets from one or more segments of the input streaming datasets and the one or more corresponding formatted output datasets” The user mentally or with pencil and paper breaks up the input/output datasets into corresponding overlapping segments.
“Formatting one or more subsequent segments of the input streaming dataset received during the first time interval” The user mentally or with pencil and paper formats future segments accordingly.
2A Prong 2: This judicial exception is not integrated into a practical application.
Additional elements:
A compute processor (mere instructions to apply the exception using a generic computer component);
Autoencoder, “training an autoencoder to approximate the target format, using the one or more overlapping datasets as one or more training samples, until an accuracy of a trained autoencoder exceeds a threshold accuracy” (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: The claim requires nothing more than a generic autoencoder with no additional detail or requirements. It is nothing more than a generic off the shelf autoencoder with no specialized components or detail);
2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Additional elements:
A compute processor (mere instructions to apply the exception using a generic computer component)
Autoencoder, “training an autoencoder to approximate the target format, using the one or more overlapping datasets as one or more training samples, until an accuracy of a trained autoencoder exceeds a threshold accuracy” (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: The claim requires nothing more than a generic autoencoder with no additional detail or requirements. It is nothing more than a generic off the shelf autoencoder with no specialized components or detail);
As per claims 16 and 22-23, this claim contains additional mental steps and is rejected for similar reasons to claim 15.
As per claim 19, this claim contains similar machine learning, generic hardware, and mental steps as to claim 15, and are rejected for similar reasons to claim 15.
As per claim 21,
2A Prong 1:
“wherein the data stream comprises numeric values, wherein formatting of one or more segments of the input streaming dataset according to the target format during the first time interval, comprises:” The user mentally or with pencil and paper formats numeric values into the appropriate format.
Determining a maximum value and a minimum value of a first subset of the numeric values and wherein formatting the one or more subsequent segments of the input streamlining dataset comprises normalizing … during the first time interval, a second subset of the numeric values based on the determined maximum and minimum values ” The user mentally or with pencil and paper determines the maximum and minimum of the numbers they are looking at, and uses those values to normalize the data.
2A Prong 2: This judicial exception is not integrated into a practical application.
Additional elements:
Trained Autoencoder (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: The claim requires nothing more than a generic autoencoder with no additional detail or requirements. It is nothing more than a generic off the shelf autoencoder with no specialized components or detail);
2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Additional elements:
Trained Autoencoder (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: The claim requires nothing more than a generic autoencoder with no additional detail or requirements. It is nothing more than a generic off the shelf autoencoder with no specialized components or detail);
As per claim 27, this claim contains similar machine learning and mental steps to claim 15, and is rejected for similar reasons
As per claim 28,
2A Prong 1:
“format one or more segments of an input streaming dataset, received during a first time interval, based on a target format, to yield one or more formatted output datasets corresponding to the target format, wherein the target format is based on one or more target parameters predicted from the one or more segments” The user mentally or with pencil and paper determines the format he wants the input data to be in, labeling the formatted dataset as output, and basing the output format on the parameters of the input segments.
“generate one or more overlapping datasets from one or more segments of the input streaming datasets and the one or more corresponding formatted output datasets” The user mentally or with pencil and paper breaks up the input/output datasets into corresponding overlapping segments.
“format one or more subsequent segments of the input streaming dataset received during the first time interval” The user mentally or with pencil and paper formats future inputs to the appropriate format as needed.
2A Prong 2: This judicial exception is not integrated into a practical application.
Additional elements:
A non-transitory computer readable storage medium, a processor (mere instructions to apply the exception using a generic computer component);
Autoencoder, “train an autoencoder to approximate the target format, using the one or more overlapping datasets as one or more training samples until an accuracy of a trained autoencoder exceeds a threshold accuracy” (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: The claim requires nothing more than a generic autoencoder with no additional detail or requirements. It is nothing more than a generic off the shelf autoencoder with no specialized components or detail);
2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Additional elements:
A non-transitory computer readable storage medium, a processor (mere instructions to apply the exception using a generic computer component)
Autoencoder, “train an autoencoder to approximate the target format, using the one or more overlapping datasets as one or more training samples until an accuracy of a trained autoencoder exceeds a threshold accuracy” (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: The claim requires nothing more than a generic autoencoder with no additional detail or requirements. It is nothing more than a generic off the shelf autoencoder with no specialized components or detail);
As per claim 29, this claim contains additional mental steps similar to claim 28, and is rejected for similar reasons.
As per claim 30,
2A Prong 1:
“wherein the data stream comprises numeric values, wherein formatting of one or more segments of the input streaming dataset according to the target format during the first time interval, comprises:” The user mentally or with pencil and paper formats numeric values into the appropriate format.
Determining a maximum value and a minimum value of a first subset of the numeric values and wherein formatting the one or more subsequent segments of the input streamlining dataset comprises normalizing … during the first time interval, a second subset of the numeric values based on the determined maximum and minimum values ” The user mentally or with pencil and paper determines the maximum and minimum of the numbers they are looking at, and uses those values to normalize the data.
2A Prong 2: This judicial exception is not integrated into a practical application.
Additional elements:
Trained Autoencoder (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: The claim requires nothing more than a generic autoencoder with no additional detail or requirements. It is nothing more than a generic off the shelf autoencoder with no specialized components or detail);
2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Additional elements:
Trained Autoencoder (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: The claim requires nothing more than a generic autoencoder with no additional detail or requirements. It is nothing more than a generic off the shelf autoencoder with no specialized components or detail);
As per claim 31, this claim contains additional generic machine learning and mental steps similar to claim 28 and is rejected for similar reasons.
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 1-2, 5-6, 8-9, 14-16, 19, 21-25, and 27-31 rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention.
As per claims 1, 15, and 28, each of these claims calls for “wherein the target format is based on one or more target parameters predicted from the one or more segments.” However, the specification does not support this limitation. First, the specification at no time discusses “Target parameters”, only “Target format.” The specification also never discusses “parameters”, with the only “parameter” term being related to hyperparameters in paragraph 0018. There is no discussion of predicting in the specification other than in paragraph 0023 which discusses a predictive formatting operation on the streaming data, and normalizing the streaming data in a predictive fashion. At no time does the specification discuss any form of “target parameters predicted from the one or more segments”, let alone using these to determine a target format. This causes the limitation to be new matter, and therefore rejected under U.S.C. 112(a).
As per claims 2, 5-6, 8-9, 14, 16, 19, 21-25, 27, and 28-31, these claims are rejected as being dependent on a claim rejected under U.S.C. 112(a) for new matter.
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.
Claim 1-2, 5, 16-16, 19, 27-29, and 31 are rejected under 35 U.S.C. 103 as being unpatentable over Vanhoucke (US 8484022 B1) in view of Xiao et al (US 20160019884 A1).
As per claims 1, 15, and 28, Vanhoucke discloses, “A system for implementing automated single-pass predictive normalization of a data stream” (C6, particularly L45-68; C7, L1-6; EN: this denotes training an autoencoder to take in unnormalized data and output a normalized version). “the system comprising: (C3, particularly L22-43; EN: this denotes the hardware for the system).
“a processor circuit” (C3, particularly L22-43; EN: this denotes the hardware for the system).
“a memory storing instructions which when executed by the processor circuit cause the processor circuit to:” (C3, particularly L22-43; EN: this denotes the hardware for the system).
“format one or more segments of an input streaming dataset”(C19, particularly L44-60; EN: this denotes the system working with streaming data). “received during a first time interval” (C7, particularly L7-23; EN: this denotes receiving historical data from a previous time interval). “based on a target format, to yield one or more formatted output datasets corresponding to the target format” (C6, particularly L45-68; C7, L1-6; EN: this denotes the training data, including the normalized and unnormalized version). “Wherein the target format is based on one or more target parameters predicted from the one or more segments” (C6, particularly L45-68; C7, L1-6; EN: this denotes processing the training data to create the feature vectors, with the selected features in the feature vectors being the “target parameters.”).
“Train an autoencoder to approximate the target format, using the one or more … datasets, as one or more training samples, until an accuracy of a trained autoencoder exceeds a threshold accuracy” (C27, particularly L41-60; EN: this denotes training the autoencoder until it has a threshold accuracy).
“Format one or more subsequent segments of the input streaming dataset received during the first time interval” (C6, particularly L45-68; C7, L1-6; EN: this denotes the autoencoder taking in unnormalized data and providing normalized versions).
However, Vanhoucke fails to explicitly disclose, “generate one or more overlapping datasets using the one or more segments of the input streaming datasets and the one or more corresponding formatted output datasets” and “one or more overlapping datasets.”
Xiao discloses, “generate one or more overlapping datasets using the one or more segments of the input streaming datasets and the one or more corresponding formatted output datasets” and “one or more overlapping datasets” (Pg.5, particularly paragraph 0049; EN: this denotes using a sliding window over training data to train the system, as the window “slides” the training data in the middle will overlap with previous windows).
Vanhoucke and Xiao 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 Vanhoucke and Xiao in order to have overlapping training data batches.
The motivation for doing so would be to “extract[] desired features from the speech data within the window to form a feature vector for the corresponding portion of the speech data” (Xiao, Pg.5, paragraph 0049) or in the case of Vanhoucke, allow the system to break up the training data into segments (i.e. windows) as needed to examine the particular groups of words and phrases of the speech.
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 Vanhoucke and Xiao in order to have overlapping training data batches.
As per claims 2, 16, and 29, Vanhoucke discloses, “Wherein the target format is associated with a data normalization operation” (C6, particularly L45-68; C7, L1-6; EN: this denotes the training data, including the normalized and unnormalized version).
As per claims 5, 27, and 31, Vanhoucke discloses, “wherein the one or more formatted output datasets are generated by a computing model” (C6, particularly L45-68; C7, L1-6; EN: this denotes processing the duplicate of the unnormalized input signal into a normalized form via computer). “Wherein the accuracy of the trained autoencoder is determined based on an output of the trained autoencoder and the input streaming dataset” (C27, particularly L41-60; EN: this denotes training the autoencoder until it has a threshold accuracy).
As per claim 19, Vanhoucke discloses, “One or more formatted output datasets are generated by a computing model” (C6, particularly L45-68; C7, L1-6; EN: this denotes processing the duplicate of the unnormalized input signal into a normalized form via computer). “wherein the autoencoder comprises a latent vector” (Abstract; EN: this denotes the data being stored in vectors. The vector is latent until data is placed within the vector).
Claim Rejections - 35 USC § 103
Claim 6 is rejected under 35 U.S.C. 103 as being unpatentable over Vanhoucke (US 8484022 B1) in view of Hoffer et al (“Train longer, generalize better: closing the generalization gap in large batch training of neural networks”) and further in view of Brownlow et al (US 5681496).
As per claim 6, Vanhoucke fails to explicitly disclose, “wherein the accuracy of the trained autoencoder is determined by a statistical model.”
Brownlow discloses, “wherein the accuracy of the trained autoencoder is determined by a statistical model” (C7, particularly L21-39; EN: this denotes using mean squared error to determine the error).
Vanhoucke and Brownlow 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 Vanhoucke and Hoffer in order to use statistics for error rates.
The motivation for doing so would be to “minimize the mean squared error E between the desired and actual output for the patterns in a training set” (Brownlow, C7, L21-39) or in the case of Vanhoucke, allow the system to use well known algorithms to determine the error rate during training of neural networks.
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 Vanhoucke and Hoffer in order to use statistics for error rates.
Allowable Subject Matter
Claims 8-9, 14, 21-25, and 30 would be allowable over the prior art. However, due to the rejections under U.S.C. 101, these claims are not deemed allowable at this time.
Claims 8, 21, and 30 all disclose an autoencoder determining the maximum and minimum values of a first subset of numeric values, and normalizing a second subset based off those numeric values. The Examiner was unable to find anything in the prior art about an autoencoder determining a maximum and a minimum for a first subset of values, and using those maximum and minimums to normalize a second subset of different values.
Response to Arguments
In pg.10, the Applicant argues in regards to the rejection under U.S.C. 101,
The recited solution can be used to auto-format a set of data in a continuous dataset for ingestion in a more efficient manner, in terms of time and computing resource/load) and can replace the first step, corresponding to formatting the data for ingestion, in any statistical and ML model based data pipeline. The single-pass predictive normalization of streaming data, avoiding a second pass over the data stream is explicitly supported in the specification.
In response, the Examiner maintains the rejection as shown above. The Applicants argument appears to be that they have made an improved auto-format. However, the formatting of data is an abstract idea that can be performed mentally or with pencil and paper as shown above. Whether or not the abstract idea has been improved has no impact on whether or not the abstract idea is statutory. Since the formatting can be performed mentally or with pencil and paper, it is an abstract idea, and therefore rejected under U.S.C. 101.
In pg.10, the Applicant further argues in regards to the rejection under U.S.C. 101,
Examiner's interpretation further characterizes the recited claim features for training an autoencoder with segmented overlapping data chunks (each segment including both raw and formatted data samples) as a mental step. Applicant respectfully disagrees and submits that training an autoencoder using a specifically recited implementation (whereby training of the auto-encoder is performed using a segmented training dataset where there is an overlap between the segments) until a statistical accuracy threshold is not a mental process and requires several machine operations such as latent vector updates as described in the specification.
In response, the Examiner maintains the rejection as shown above. At no time has the Examiner stated that the training of an autoencoder is a mental step. It is part of step 2A/2B, with the training aspects being the application of a generic machine learning model. As there is no detail provided of the autoencoder beyond inputs and outputs, this is a generic autoencoder and not enough to provide significantly more than the abstract idea, and therefore the rejection is maintained as shown above.
In pg.10-11, the Applicant further argues in regards to the rejection under U.S.C. 101,
Furthermore, Applicant submits that the mental process characterization overlooks claim-required temporal and streaming constraints. For example, determining formatting parameters such as a min/max values, during a first time interval and, accordingly, normalizing streaming data in a single pass, cannot be performed mentally because the data is not fully available at the outset. As further support, Applicant points to Enfish, LLC V. Microsoft Corp., 822 F. 3d 1327 (Fed. Cir. 2016) and McRO, Inc. V. Bandai Namco Games America Inc., 837 F.3d 1299 (Fed. Cir. 2016) wherein court findings maintained that claims that improve computers process data, especially under real-time and streaming constraints are not abstract, even when characterized as manipulating information.
In response, the Examiner maintains the rejection as shown above. “Streaming data” is merely a type of data, and not enough to cause the claimed invention to be significantly more than the abstract idea. For example, data can be written over time onto a piece of paper, with the formatting fixed by another person as each set of words is provided. Further, Applicant has provided no details that compare to Enfish or McRo, and merely stating that other cases have been found statutory does not cause the instant claims to be found statutory. Therefore the rejection is maintained as shown above.
In pg.11, Applicant further argues in regards to the rejection under U.S.C. 101,
Applicant further submits that the claimed subject-matter describes a technical solution to a technical problem and as such, cannot be reasonably characterized as a generically implemented autoencoder. Applicant submits, for the purpose of clarification, that each overlapping chunk is not just raw data, it is a paired training unit consisting of an input data segment and a corresponding formatted output segment which together form a training sample for the autoencoder. Applicant further submits that the specific use of overlapping data chunks to stabilize training across shifting distribution of features in a continuous dataset, amounts to significantly more than generic operations of an autoencoder as it corresponds to an unconventional feature associated with technical improvement.
In response, the Examiner maintains the rejection as shown above. Applicant’s argument appears to be that the formatting of data and putting that data into a generic autoencoder is a “technical problem.” However, nothing technical is described in the claims. It is merely data in a generic computer system being provided to train a generic autoencoder and provide output. The remaining steps relate to formatting data, all of which can be performed mentally as described above, and thus describes an abstract idea. Therefore the rejection is maintained as shown above.
Applicant's remaining arguments with respect to claims 1-2, 5-6, 8-9, 14-16, 19, 21-25, and 27-31 are either repeats of the above rejections or have been considered but are moot in view of the new ground(s) of rejection.
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
