DETAILED ACTION
Claims 1-20 are presented for examination.
This office action is in response to submission of application on 02-JULY-2024.
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 .
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 21-OCTOBER-2024 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
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 1-20 rejected under 35 U.S.C. 101 because the claimed invention is direction to an abstract idea without significantly more.
MPEP 2106.04(a)(2)(Ill) “Accordingly, the "mental processes" abstract idea grouping is defined as concepts performed in the human mind, and examples of mental processes include observations, evaluations, Judgments, and opinions.
Further, the MPEP recites “The courts do not distinguish between mental processes that are performed entirely in the human mind and mental processes that require a human to use a physical aid (e.g., pen and paper or a slide run) to perform the claim limitation.
Regarding claim 1:
Step 2A, Prong 1 will now be evaluated for this claim:
A judicial exception is recited in this claim as it recites a mental process:
selectively identify informative portions of one or more training data files for improving the ML model, the method comprising: automatically selectively identifying, by a computing system, one or more informative portions of one or more training data files
Identification of a training data which may improve a machine learning model is performable in the human mind, i.e. curation of a database.
A judicial exception is recited in this claim as it recites a mathematical concept:
calculating, by the computing system, gradients for the identified one or more informative portions
Calculation of gradients describes a particular mathematical calculation that takes place within a neural network.
Step 2A, Prong 2 will now be evaluated for this claim:
Furthermore, the additional elements:
training a Machine Learning (ML) model using arbitrarily sized training data files
The model in question is a generic machine learning model, wherein training it would be a generic computer function.
updating, by the computing system, weights of a ML model using the calculated gradients
This claim describes a general function of a neural network, and hence a function of a general computer.
are interpreted as a general purpose computer under MPEP 2106.05(f)
The additional elements have been considered both individually and as an ordered combination in order to determine whether they integrate the exception into a practical application. Therefore, no meaningful limits are imposed practicing the abstract idea.
Therefore, the claim is related to an abstract idea.
Step 2B will now be discussed with regards to this claim:
The claim does not provide an inventive concept. There is no additional Insignificant Extra- Solution Activity, as identified in Step 2A Prong Two, that provides an inventive concept.
Generally linking the use of the judicial exception to computer environments, e.g., a claim describing how the abstract idea of creating a contractual relationship that guarantees performance of a transaction be performed using a computer that receives and sends information over a network, as discussed in buySAFE Inc. v. Google, Inc., 765 F.3d 1350, 1354, 112 USPQ2d 1093, 1095-96 (Fed. Cir. 2014). (MPEP § 2106.05(h)) does not overcome a rejection.
The additional elements have been considered both individually and as an ordered combination as to whether they whether they warrant significantly more consideration.
The claim is ineligible.
Regarding claim 2, which depends upon claim 1:
This claim recites a mathematical concept:
analyzing, by the computing system, exclusively the identified one or more informative portions to calculate the gradients
Calculation of gradients describes a particular mathematical calculation that takes place within a neural network.
This claim is rejected for incorporating the parent claim in full.
This claim is ineligible.
Regarding claim 3, which depends upon claim 1:
This claim recites a mental process:
iteratively conditioning each selection on one or more previously chosen informative portions
‘Conditioning’ in this context effectively means ‘using as a factor in selection’, which would be a form of evaluation for the selection.
This claim is rejected for incorporating the parent claim in full.
This claim is ineligible.
Regarding claim 4, which depends upon claim 1:
This claim further limits the training data of claim 1. Further specifying the training data in this manner does not overcome the parent claim’s rejection.
This claim is rejected for incorporating the parent claim in full.
This claim is ineligible.
Regarding claim 5, which depends upon claim 1:
This claim recites a mathematical concept:
calculating the gradients based on a learning error of the classification model and based on an output of the classification model for the identified one or more informative portions
Calculation of gradients describes a particular mathematical calculation that takes place within a neural network. This limitation further identifies the inputs that would be used for this calculation.
This claim is rejected for incorporating the parent claim in full.
This claim is ineligible.
Regarding claim 6, which depends upon claim 1:
This claim further limits the training data file and classification model of claim 1. Further specifying the training data file and classification model in this manner does not overcome the parent claim’s rejection.
This claim is rejected for incorporating the parent claim in full.
This claim is ineligible.
Regarding claim 7, which depends upon claim 6:
This claim further limits the identified one or more information portions of claim 1. Further specifying the identified one or more information portions in this manner does not overcome the parent claim’s rejection.
This claim is rejected for incorporating the parent claim in full.
This claim is ineligible.
Regarding claim 8, which depends upon claim 1:
This claim recites a mental process:
determine relationships and context between informative portions of training data files.
Determining relationships and context between pieces of data is performable in the human mind.
Furthermore, the transformer layer, and wherein the transformer layer is trained to perform the mental process would be considered generally linking the judicial exception to a technical environment.
This claim is rejected for incorporating the parent claim in full.
This claim is ineligible.
Regarding claim 9, which depends upon claim 8:
This claim further limits the transformer layer of claim 8. Further specifying the transformer layer in this manner does not overcome the parent claim’s rejection.
This claim is rejected for incorporating the parent claim in full.
This claim is ineligible.
Regarding claim 10, which depends upon claim 1:
MPEP 2106.05(g) Insignificant Extra-Solution Activity has found mere data gathering and post-solution activity to be insignificant extra-solution activity.
This claim recites post-solution activity:
wherein the one or more informative portions are stored and processed by the classification model using a portion of a memory according to a memory size constraint
Storing and processing with no further usage of the results of the classification model would be post-solution activity.
Adding insignificant extra-solution activity to the judicial exception, e.g., mere data gathering in conjunction with a law of nature or abstract idea such as a step of obtaining information about credit card transactions so that the information can be analyzed by an abstract mental process, as discussed in CyberSource v. Retail Decisions, Inc., 654 F.3d 1366, 1375, 99 USPQ2d 1690, 1694 (Fed. Cir. 2011) (see MPEP § 2106.05(g)) does not overcome a rejection.
This claim is rejected for incorporating the parent claim in full.
This claim is ineligible.
Regarding claim 11, which depends upon claim 10:
This claim further limits the size of the training data files of claim 1. Further specifying the size of the training data files in this manner does not overcome the parent claim’s rejection.
This claim is rejected for incorporating the parent claim in full.
This claim is ineligible.
Regarding claim 12, which depends upon claim 1:
This claim further limits the one or more informative portions of claim 1. Further specifying the one or more informative portions in this manner does not overcome the parent claim’s rejection.
This claim is rejected for incorporating the parent claim in full.
This claim is ineligible.
Regarding claim 13, which depends upon claim 1:
This claim recites a mental process:
generating a matrix based on one or more features of the training data files:
Generating a matrix is a form of organizing data that can be performed in a human mind with the aid of a pen and paper, i.e. drawing a table.
wherein automatically selectively identifying the one or more informative portions of one or more training data files further comprises automatically selectively identifying the one or more informative portions using the generated matrix:
Using a matrix or a table in order to select informative portions can be performed in a human mind, as it is factoring in further information in order to make a particular selection.
This claim is rejected for incorporating the parent claim in full.
This claim is ineligible.
Regarding claim 14, which depends upon claim 13:
This claim further limits the generated matrix of claim 13. Further specifying the generated matrix in this manner does not overcome the parent claim’s rejection.
This claim is rejected for incorporating the parent claim in full.
This claim is ineligible.
Regarding claim 15:
This claim recites data gathering:
obtaining one or more arbitrarily sized data files
Obtaining data is a form of gathering data.
This claim recites a mental process:
classifying the one or more arbitrarily sized data files using a classification model trained to classify arbitrarily sized data files according to a memory size constraint
Classification would be sorting the data files according to a memory size constraints, for example determining if they had met a size threshold, which would be performable in the human mind. ‘Using a classification model’ would be considered generally linking the judicial exception to a technical environment.
Regarding claim 16, which depends upon claim 15:
This claim further limits the data files of claim 15. Further specifying the data files in this manner does not overcome the parent claim’s rejection.
This claim is rejected for incorporating the parent claim in full.
This claim is ineligible.
Claims 17-19 recite a system that parallels the method of claims 1-3 respectively. Therefore, the analysis discussed above with respect to claims 1-3 also applies to claims 17-19 respectively. Accordingly, claims 17-19 are rejected based on substantially the same rationale as set forth above with respect to claims 1-3 respectively.
Claim 20 recites a non-transitory computer readable storage media that parallels the method of claim 1. Therefore, the analysis discussed above with respect to claim 1 also applies to claim 20. Accordingly, claim 20 is rejected based on substantially the same rationale as set forth above with respect to claim 1.
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 1-8, 12-14, and 17-20 are rejected under 35 U.S.C. 103 as being unpatentable over Lee et al. (Pub. No. US 20190325275 A1, filed April 19th 2018, hereinafter Lee) in view of Manolache et al. (Pub. No. US 20220327108 A1, filed April 9th 2021, hereinafter Manolache).
Regarding claim 1:
Claim 1 recites:
A method for training a Machine Learning (ML) model using arbitrarily sized training data files, to selectively identify informative portions of one or more training data files for improving the ML model, the method comprising: automatically selectively identifying, by a computing system, one or more informative portions of one or more training data files; calculating, by the computing system, gradients for the identified one or more informative portions; and updating, by the computing system, weights of a ML model using the calculated gradients.
Lee discloses training a Machine Learning (ML) model using arbitrarily sized training data files, to selectively identify informative [portions of one or more] training data files for improving the ML model, the method comprising:
Lee teaches the selection i.e. selective identification of one or more data samples that would improve the performance of a model the most when the model is trained on them (Paragraph 4). The data samples would be informative training data files for improving the model as the increase model performance. Since the size of the sample is not specified, they would be arbitrarily sized.
However, Lee does not disclose that it identified informative portions for improving the ML model of those data files.
Lee discloses automatically selectively identifying, by a computing system, one or more informative portions of one or more training data files:
Lee teaches identifying, within a video, particular moments of actions and the start and end time of the respective video snippet containing the action (Paragraph 4). This would be automatically selectively identifying, as Lee teaches that it may be a specific action that is identified, wherein the action would be an informative portion of the video.
Lee discloses calculating, by the computing system, gradients for the identified one or more informative portions; and updating, by the computing system, weights of a ML model using the calculated gradients:
Lee teaches the use of backpropagation (Paragraph 32), which would consist of calculating gradients for new training portions such as the one or more informative portions, and updating weights of a ML model using the calculated gradients.
Lee does not discloses portions of one or more training data files. Instead, this limitation is disclosed below by Manolache.
Manolache in the same field of endeavor of machine learning discloses portions of one or more training data files:
Manolache teaches selecting a training token sequence from a training corpus, wherein the token sequence would be an example of a portion of one or more training data files (Paragraph 4). This may be used alongside the disclosed methodology of Lee for reasons of the advantage provided below.
Manolache further teaches separate modification of this selected token sequence (Paragraph 31), distinguishing it from the identification of video action snippets of Lee, which are not separated from their initial data file.
Manolache and the present application are analogous art because they are in the same field of endeavor.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the present application to implement a method that utilized the teachings of Lee and the teachings of Manolache. This would have provided the improvement of preventing high computational cost or requiring large training corpora (Manolache, Paragraph 3).
Regarding claim 2, which depends upon claim 1:
Claim 2 recites:
The method of claim 1, further comprising: analyzing, by the computing system, exclusively the identified one or more informative portions to calculate the gradients.
Lee in view of Manolache disclose the method of claim 1 upon which claim 2 depends. Furthermore, Lee discloses analyzing, by the computing system, exclusively the [identified one or more informative portions] to calculate the gradients:
Lee teaches that a portion of the most informative video, i.e. exclusively the informative training samples may be analyzed in order to train an updated localization model, wherein training would include calculation of the gradients (Paragraph 20).
However, Lee does not disclose that this exclusive analysis happens to individual portions. Rather, these individual separated portions are taught by Manolache.
Manolache discloses identified one or more informative portions:
Manolache teaches separate modification of its selected token sequence (Paragraph 31), distinguishing it from the identification of video action snippets of Lee, which are not separated from their initial data file. Manolache’s separately modifiable token sequence selected from a training corpus would be an identified one or more informative portions that is analyzed by prediction indicator (Paragraph 4).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the present application to implement a method that utilized the teachings of Lee and the teachings of Manolache. This would have provided the improvement of preventing high computational cost or requiring large training corpora (Manolache, Paragraph 3).
Regarding claim 3, which depends upon claim 1:
Claim 3 recites:
The method of claim 1, wherein automatically selectively identifying the one or more portions further comprises: iteratively conditioning each selection on one or more previously chosen informative portions.
Lee in view of Manolache disclose the method of claim 1 upon which claim 3 depends. Manolache has previously disclosed informative portions. Furthermore, Lee discloses the limitations of claim 3:
Lee teaches iteratively retraining the model until all samples in the subset are used for training (Paragraph 69). This means that subsequent selected samples would be iteratively conditioned on one or more previously chosen informative samples already integrated into the training of the model.
Regarding claim 4, which depends upon claim 1:
Claim 4 recites:
The method of claim 1, wherein the training data file comprises a video file, and wherein the identified one or more informative portions comprise one or more video clips.
Lee in view of Manolache disclose the method of claim 1 upon which claim 4 depends. Furthermore, Lee discloses the limitations of claim 4:
Lee teaches identifying, within a video, particular moments of actions and the start and end time of the respective video clip containing the action (Paragraph 4). This would be automatically selectively identifying, as Lee teaches that it may be a specific action that is identified, wherein the action would be an informative portion of the video.
Regarding claim 5, which depends upon claim 1:
Claim 5 recites:
The method of claim 1, wherein calculating gradients for the identified one or more informative portions comprises: calculating the gradients based on a learning error of the classification model and based on an output of the classification model for the identified one or more informative portions.
Lee in view of Manolache disclose the method of claim 1 upon which claim 5 depends. Manolache has previously disclosed informative portions. Furthermore, Lee discloses the limitations of claim 5:
Lee teaches backpropagation which includes calculating the gradients, which uses the calculated learning error of the classification model. In turn, this error would incorporate the output of the classification model for the identified one or more portions as it considers the results of the model compared to the ground truth labels of the training videos (Paragraph 32).
Regarding claim 6, which depends upon claim 1:
Claim 6 recites:
The method of claim 1, wherein the classification model is trained to identify an identity of an author and wherein the training data file is a document.
Lee in view of Manolache disclose the method of claim 1 upon which claim 6 depends. However, Lee does not disclose the limitations of claim 6. Instead, these limitations are taught by Manolache:
Manolache teaches an anomaly detector wherein an anomaly may be that the identity of an author of a text is a different author from a reference text. This would be identification of the identity of an author wherein the training data file is a document i.e. a text (Paragraph 20).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the present application to implement a method that utilized the teachings of Lee and the teachings of Manolache. This would have provided the improvement of preventing high computational cost or requiring large training corpora (Manolache, Paragraph 3).
Regarding claim 7, which depends upon claim 6:
Claim 7 recites:
The method of claim 6, wherein the identified one or more informative portions are selected so that the identified one or more informative portions are representative of a writing style of the author.
Lee in view of Manolache disclose the method of claim 6 upon which claim 7 depends. However, Lee does not disclose the limitations of claim 7. Instead, these limitations are taught by Manolache:
Manolache teaches that a training corpus is arranged such that the texts are written by a selected author, which would make the corpus representative of a writing style of the author (Paragraph 70). Therefore, the selected identified one or more informative portions would be representative of the author.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the present application to implement a method that utilized the teachings of Lee and the teachings of Manolache. This would have provided the improvement of preventing high computational cost or requiring large training corpora (Manolache, Paragraph 3).
Regarding claim 8, which depends upon claim 1:
Claim 8 recites:
The method of claim 1, wherein the ML model includes a transformer layer, and wherein the transformer layer is trained to determine relationships and context between informative portions of training data files.
Lee in view of Manolache disclose the method of claim 1 upon which claim 8 depends. However, Lee does not disclose the limitations of claim 8. Instead, these limitations are taught by Manolache:
Manolache teaches a transformer layer that is used to produce a sequence of token embedding vectors (Paragraph 48). These token embedding vectors are used to determine relationships and context between the tokens i.e. informative portions of training data files in the form of creating an embedding space between them (Paragraph 38).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the present application to implement a method that utilized the teachings of Lee and the teachings of Manolache. This would have provided the improvement of preventing high computational cost or requiring large training corpora (Manolache, Paragraph 3).
Regarding claim 12, which depends upon claim 1:
Claim 12 recites:
The method of claim 1, wherein the one or more informative portions are unlabeled.
Lee in view of Manolache disclose the method of claim 1 upon which claim 12 depends. Manolache has previously disclosed informative portions. Furthermore, Lee discloses the further limitations of claim 12:
Lee teaches that the initial training data is unlabeled (Paragraph 20).
Regarding claim 13, which depends upon claim 1:
Claim 13 recites:
The method of claim 1, further comprising: generating a matrix based on one or more features of the training data files; and wherein automatically selectively identifying the one or more informative portions of one or more training data files further comprises automatically selectively identifying the one or more informative portions using the generated matrix.
Lee in view of Manolache disclose the method of claim 1 upon which claim 13 depends. Manolache has previously disclosed informative portions. Furthermore, Lee discloses the further limitations of claim 13:
Lee teaches a generating a feature matrix which would be based on one or more features of the samples (e.g., the sample description mentioned by Lee). Furthermore, the automatic selective identification of the one or more informative samples is performed using the generated matrix, as the matrix may contain confidence scores corresponding to matrix X (Paragraph 49) which is used to select the samples (Paragraph 70).
Regarding claim 14, which depends upon claim 13:
Claim 14 recites:
The method of claim 13, wherein the generated matrix comprises one of: a static matrix, conditionally generated matrix, or conditionally executed matrix.
Lee in view of Manolache disclose the method of claim 13 upon which claim 14 depends. Furthermore, Lee discloses the limitations of claim 14:
The applicant’s specification describes a conditionally generated matrix as one that is built based on some criteria (Present specification, Paragraph 79). Therefore, Lee’s matrix which generates a matrix based on present visual observations (Paragraph 37) would be based on the criteria of what was observed, classifying it as a conditionally generated matrix.
Claims 17-19 recite a system that parallels the method of claims 1-3 respectively. Therefore, the analysis discussed above with respect to claims 1-3 also applies to claims 17-19 respectively. Accordingly, claims 17-19 are rejected based on substantially the same rationale as set forth above with respect to claims 1-3 respectively.
Claim 20 recites a non-transitory computer readable storage media that parallels the method of claim 1. Therefore, the analysis discussed above with respect to claim 1 also applies to claim 20. Accordingly, claim 20 is rejected based on substantially the same rationale as set forth above with respect to claim 1.
Claims 9-11 are rejected under 35 U.S.C. 103 as being unpatentable over Lee in view of Manolache, further in view of Yan et al. (Pub. No. WO 2022072003 A1, filed May 28th 2021, hereinafter Yan).
Regarding claim 9, which depends upon claim 8:
Claim 9 recites:
The method of claim 8, wherein the transformer layer comprises an autoregressive transformer.
Lee in view of Manolache disclose the method of claim 8 upon which claim 9 depends. However, neither Lee nor Manolache discloses the limitations of claim 9. Instead, these limitations are disclosed by Yan in the same field of endeavor of machine learning:
Yan teaches the use of an autoregressive transformer (Paragraph 48).
Yan and the present application are analogous art because they are in the same field of endeavor.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the present application to implement a method that utilized the teachings of Lee, the teachings of Manolache, and the teachings of Yan. This would have provided the improvement of increasing search speed without losing accuracy (Yan, Paragraph 3).
Regarding claim 10, which depends upon claim 1:
Claim 10 recites:
The method of claim 1, wherein the one or more informative portions are stored and processed by the classification model using a portion of a memory according to a memory size constraint.
Lee in view of Manolache disclose the method of claim 1 upon which claim 10 depends. Lee in view of Manolache have therefore previously disclosed the one or more informative portions processed by a classification model. However, neither Lee nor Manolache discloses the further limitations of claim 10. Instead, these limitations are disclosed by Yan:
Yan teaches determining that an amount of memory used by a cache is above a particular threshold memory size, then modifying the cache in order for it to fit the memory size constraint (Paragraph 6). Therefore, the cache is data which uses a portion of memory according to a memory size constraint for storage, wherein the data may be processed by Lee in view of Manolache’s classification model for purposes of the improvement provided below.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the present application to implement a method that utilized the teachings of Lee, the teachings of Manolache, and the teachings of Yan. This would have provided the improvement of increasing search speed without losing accuracy (Yan, Paragraph 3).
Regarding claim 11, which depends upon claim 10:
Claim 11 recites:
The method of claim 10, wherein the size of the training data files exceeds the size of the portion of the memory.
Lee in view of Manolache further in view of Yan disclose the method of claim 10 upon which claim 11 depends. Therefore, Lee in view of Manolache have previously disclosed training data files However, neither Lee nor Manolache discloses the limitations of claim 11. Instead, these limitations are disclosed by Yan:
Yan teaches determining that an amount of memory used by a cache is above a particular threshold memory size, then modifying the cache in order for it to fit the memory size constraint (Paragraph 6). Therefore, Yan teaches data that exceeds the size of the portion of memory, wherein the data may be the training data files of Lee in view of Manolache for purposes of the improvement provided below.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the present application to implement a method that utilized the teachings of Lee, the teachings of Manolache, and the teachings of Yan. This would have provided the improvement of increasing search speed without losing accuracy (Yan, Paragraph 3).
Claim 15 is rejected under 35 U.S.C. 103 as being unpatentable over Lee in view of Yan.
Regarding claim 15:
Claim 15 recites:
A method for classifying data files, the method comprising: obtaining one or more arbitrarily sized data files; and classifying the one or more arbitrarily sized data files using a classification model trained to classify arbitrarily sized data files according to a memory size constraint.
Lee discloses obtaining one or more [arbitrarily sized] data files; and classifying the one or more arbitrarily sized data files using a classification model trained to classify:
Lee teaches obtaining a large set of videos or clips, such as YouTube videos, which would be data files. The videos are then classified by a classification model (Paragraph 84).
However, Lee does not disclose that these specific obtained files are arbitrarily sized unlike some of its other teaching. Furthermore, Lee does not disclose that the classification is according to a memory size constraint.
Instead, arbitrarily sized data files and a memory size constraint are disclosed by Yan:
Yan teaches determining that an amount of memory used by a cache is above a particular threshold memory size, then modifying the cache in order for it to fit the memory size constraint (Paragraph 6). Therefore, the cache is data which uses a portion of memory according to a memory size constraint, wherein the determination of a whether the size is above a threshold may be combined with the classification of Lee for purposes of the improvement presented below. Furthermore, since the size may be variable, the size of the data is arbitrary.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the present application to implement a method that utilized the teachings of Lee and the teachings of Yan. This would have provided the improvement of increasing search speed without losing accuracy (Yan, Paragraph 3).
Claim 16 is rejected under 35 U.S.C. 103 as being unpatentable over Lee in view of Yan further in view of Manolache.
Regarding claim 16, which depends upon claim 15:
Claim 16 recites:
The method of claim 15, wherein the one or more arbitrarily sized data files comprise one or more collections of data files.
Lee in view of Yan discloses the method of claim 15 upon which claim 16 depends. However, neither Lee nor Yan disclose the limitations of claim 16. Instead, these limitations are disclosed by Manolache:
Manolache teaches that a corpus, the arbitrarily sized data file, may consist of a collection of data files in the form of various texts (Paragraph 27).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the present application to implement a method that utilized the teachings of Lee and the teachings of Manolache. This would have provided the improvement of preventing high computational cost or requiring large training corpora (Manolache, Paragraph 3).
Conclusion
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/A.J.M./Examiner, Art Unit 2142
/HAIMEI JIANG/Primary Examiner, Art Unit 2142
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