DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Status of Claims
The present application is being examined under the claims filed 08/13/2025.
Claims 1 and 3-10 are pending.
Response to Amendment
This Office Action is in response to Applicant’s communication filed 08/13/2025 in response to office action mailed 04/23/2025. The Applicant’s remarks and any amendments to the claims or specification have been considered with the results that follow.
Response to Arguments
Regarding 35 U.S.C. 112(f)
In Remarks page 8, Argument 1
The Examiner indicates that the claims as presented are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. Applicant respectfully submits amended claims 1, 3-6, and 8 to avoid interpretation under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. Reconsideration is respectfully requested.
Examiner’s response to Argument 1
Examiner agrees that the proposed claim amendments do not invoke 35 U.S.C. 112(f). Corresponding rejections under 35 U.S.C. 112(b) have been withdrawn accordingly.
Regarding 35 U.S.C. 101
In Remarks page 9, Argument 2
(Examiner summarizes Applicant’s arguments) Applicant argues that the claimed invention does not recite an abstract idea because the limitation of “generating a model of high-speed expansion AI” is a limitation of training a model.
Examiner’s response to Argument 2
Examiner disagrees. “generating a model of high-speed expansion AI” is not directed to training a model alone as the broadest reasonable interpretation includes selecting a model architecture and hyperparameters, which can be performed in the human mind. For example, a data scientist could generate a model of high-speed expansion AI by mentally deciding that a convolutional autoencoder and a learning rate of 0.1 is most fit for the task at hand. Moreover, other limitations were identified as mental processes. See rejections under 35 U.S.C. 101 for a complete analysis.
In Remarks pages 9-10, Argument 3
(Examiner summarizes Applicant’s arguments) Applicant argues that the claimed invention should be eligible because it is similar to other claims that have been eligible in the past and it is directed to a technical improvement of a particularized method of digital data compression, citing relevant MPEP sections and case law.
Examiner’s response to Argument 3
Examiner disagrees. The method of data compression is claimed at a highly generic manner, not a “particularized method” as alleged by applicant. In fact, many of the limitations of the claims are broadly recited such that they encompass mental process steps. For example, one of the limitations identified as a mental process below recites
a compressor which compresses data, and an expander which expands the data compressed by the compressor
As a simple example, the string “1111333” could be compressed to “41,33” and then expanded back to it’s original form. As recited currently, the claim is not directed to a particular method of data compression, but rather is recited broadly and could encompass any type of data compression. That is, the claims do not provide a technical solution to a technical problem but rather are open ended to include any kind of compression including those that could be performed mentally (see MPEP 2106.05(a)). Even appending generic words such as “AI” does not substantially limit the compressor/expander to any particular form of data compression. See rejections under 35 U.S.C. 101 for a complete analysis.
In Remarks page 10-11, Argument 4
(Examiner summarizes Applicant’s arguments) Applicant argues that the claimed invention is an improvement to a computer or other technology. Applicant cites to many features of the independent claims and argues that the claims provide substantial improvements to inputting data, generating a model, and analysis of compressed data as well as data compression and training AI. Applicant argues that the independent claims are thus eligible.
Examiner’s response to Argument 4,
Examiner disagrees. MPEP 2106.05(a) requires “It is important to note, the judicial exception alone cannot provide the improvement. The improvement can be provided by one or more additional elements” and “the claim must include the components or steps of the invention that provide the improvement described in the specification.” As noted in the rejections below, the additional elements are recited in a highly generic manner and amount to merely ordinary data inputting/outputting and machine learning recited at a high level. Additionally, it is not clear how the claims would provide any improvement to data compression, AI training, data input, or model generating.
In Remarks page 12 Argument 5
For at least the foregoing reasons, applicant believes that independent claims 1 and 8, and claims 3-7, which depend, directly or indirectly from claims 1 or 8 are also in condition for allowance.
Examiner’s response to Argument 5
For the reasons provided in responses above and in the rejections under 35 U.S.C. 101 below, the independent claims are not deemed to be eligible. Therefore, the rejections of all dependent claims are maintained for similar reasons.
Regarding 35 U.S.C. 112
In Remarks page 12, Argument 6
(Examiner summarizes Applicant’s arguments) Applicant argues that the term “high-speed expansion AI” is a single term and should be interpreted as described throughout the specification. Applicant cites paragraph 4 of the specification (and NPTL 1) as an example and concludes that the claims are definite.
In response to Argument 6
Examiner disagrees. MPEP 2111.01 IV recites “To act as their own lexicographer, the applicant must clearly set forth a special definition of a claim term in the specification that differs from the plain and ordinary meaning it would otherwise possess.” Neither the specification nor NPTL 1 cited by applicant clearly sets forth any special definition for the claimed term “high-speed expansion AI”. The claim term was given its plain meaning in the non-final office action for this reason. Based on the plain meaning of “high-speed expansion AI”, the claim remains indefinite by incorporation of the words “high-speed” and thus the rejections under 35 U.S.C. 112(b) are maintained.
Regarding prior art rejections
In Remarks page 13, Argument 7
On page 27 of the Office Action, the Examiner concedes that Nakanishi does not teach "and a model of prescribed AI" as recited in amended claims I and claim 8. Therefore, the prior art Nakanishi fails to disclose, teach, or suggest each and every element of claim I and claim 8, and does not anticipate the invention as claimed. Claims I and claim 8, as amended, are therefore in condition for allowance. Claim 6 depends directly from claim I and is therefore also in condition for allowance. For at least the foregoing reasons the Applicant submits that claims 1, 6, and 8 are allowable. Reconsideration is respectfully requested.
Examiner’s response to Argument 7
Examiner agrees that Nakanishi does not teach every limitation of claim 1 nor claim 6 nor claim 8. Accordingly, the rejections under 35 U.S.C. 102 have been withdrawn. However, new rejections are necessitated by applicants amendments. Thus claim 1 is now rejected under 35 U.S.C. 103 with Nakanishi in view of Jo and new art reference Isshiki. See updated rejections under 35 U.S.C. 103 below.
In Remarks page 13, Argument 7
On page 27 of the Office Action, the Examiner concedes that Nakanishi does not teach "a prescribed AI model" and cites Jo to allegedly remedy this deficiency. In particular, the Examiner suggests that the compression and decompression of Nakanishi could be performed by the AI model of Jo. Jo, however, teaches an AI model trained with compressed images (input) with different compression ratios (CRs) and concludes that training models on images increased the robustness of the models when tested on compressed data. See Jo at Abstract. There is no teaching, suggestion, or disclosure in Jo for "wherein the AI processor comprises: a generator that executes, after acquiring the configuration information of the compressor by using the first interface: processing for generating a model of high-speed expansion AI from the configuration information
of the compressor and a model of prescribed AI with the data compressed by the compressor as an input;" as recited in amended claim 1. Therefore, a skilled artisan could not have combined Nakanishi which does not teach a model of prescribed AI and Jo, which teaches training AI with images with different compression ratios, to arrive at the model of prescribed AI as claimed. The Applicant therefore submits that the Nakanishi-Jo combination does not render independent claim 1 obvious. Claim 1, as amended, is therefore in condition for allowance. Claims 3-5 depend, directly or indirectly, from claim 1 and are therefore also in condition for allowance. For at least the foregoing reasons the Applicant submits that claims 3-5 are allowable. Reconsideration is respectfully requested.
Examiner’s response to Argument 7
Examiner disagrees. The test for obviousness is not whether the features of a secondary reference may be bodily incorporated into the structure of the primary reference; nor is it that the claimed invention must be expressly suggested in any one or all of the references. Rather, the test is what the combined teachings of the references would have suggested to those of ordinary skill in the art. See In re Keller, 642 F.2d 413, 208 USPQ 871 (CCPA 1981). That is, although the limitation (and claim as a whole) is not taught entirely by one reference, the teachings of Jo suggest that performing compression before AI prediction can increase model robustness. Therefore, a person having ordinary skill in the art would find it obvious to combine the compression/expansion model (high-speed expansion AI) of Nakanishi with the prescribed AI and that doing so could achieve the benefits disclosed by Jo. Therefore, the rejections of claim 1 along with all dependent claims are maintained.
In Remarks pages 14-15, Argument 8
(Examiner summarizes Applicant’s arguments) Applicant argues that Cheng fails to teach or suggest the limitation:
wherein the compressor comprises a padder which pads the data for causing the input data to be a data size to be received by an encoder part of the compressor; and the coder comprises a padder which pads the data for causing the data compressed by the compressor to be a data size to be received by a hyper encoder part of the coder
because Cheng teaches “a convolutional autoencoder based lossy image compression architecture. As such, Cheng teaches a pair of convolution/deconvolution filters for upsampling/downsampling operations in the encoding/decoding process. In this context, Cheng sets padding size to 1 to maintain the same size as the input. See pg. 2 of Cheng.” Applicant argues that therefore claim 7 is allowable for the reason provided and by virtue of claim 6 which depends upon claim 1.
In response to Argument 8,
Examiner disagrees. As discussed in the rejection of claim 7 below, Cheng teaches: (page 254 column 2 first paragraph) “Therefore, we propose a pair of convolution/deconvolution filters for upsampling or downsampling, as shown in Fig. 2 , where Ni denotes the number of filters in the convolution or deconvolution block. By setting the stride as 2, we can get downsampled feature maps. The padding size is set as one to maintain the same size as the input.”; Figure 2
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The downsampling and upsampling units can readily be interpreted as a compressor and expander respectively. The first block conv1 of the downsampling unit [compressor] has a padding of 1 (thus Cheng teaches “wherein the compressor comprises a padder which pads the data for causing the input data to be a data size to be received by an encoder part of the compressor”). The second block conv2 [hyper encoder] of the downsampling unit also has padding 1 (thus Cheng also teaches “and the coder comprises a padder which pads the data for causing the data compressed by the compressor to be a data size to be received by a hyper encoder part of the coder”). As explained above, in the prior office action, and in the rejections below the rejection of claim 7 is therefore maintained. The rejections to claims 1 and 6 are also maintained as noted 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 1 and 3-10 are rejected under 35 U.S.C. 112(b) as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor regards as the invention.
Regarding Claim 1, 3-5, and 8
The term “high-speed” in claims 1, 3-5, and 8 is a relative term which renders the claims indefinite. The term “high-speed” is not defined by the claim, the specification does not provide a standard for ascertaining the requisite degree, and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention. It would not be clear to a person having ordinary skill in the art the degree of speed that would be required for an AI model to be considered “high-speed” or not. Additionally, it is not clear whether speed may refer to model learning, inference, or something else entirely.
Regarding Claim 9
Claim 9 recites the limitation " wherein the compressor is a neural network include at least layers other than all combined layers." In line 2 of the claim. There is insufficient antecedent basis for this limitation in the claim. In particular, there is insufficient antecedent basis for the term “all combined layers”. Examiner believes Applicant is referring to specification paragraph [0065] “The compressor 110 is configured from a padder 1301, an encoder 1303, and a quantizer 1304. The encoder 1303 is, for example, an encoder part of an auto encoder configured from a convolution neural network.” Therefore, for purposes of examination, the examiner interprets the limitation as though it said "wherein the compressor is a neural network include at least some layers but not all combined layers of a plurality of layers of a neural network." Examiner suggests amending the claim accordingly.
Regarding Claim 10
Claim 10 recites the limitation "wherein the neural network includes at least a convolution layer" in line 2 of the claims. There is insufficient antecedent basis for this limitation in the claim. In particular, there is insufficient antecedent basis for the term “the neural network”. Examiner believes that the claim is intended to depend from claim 9 (it is currently dependent upon claim 3). Examiner suggests amending the claim accordingly.
Claims 3-7 and 9-10 are dependent upon claim 1, 10 is dependent upon claim 3, and 5 is dependent upon claim 4 and are therefore similarly rejected for including the deficiencies of claims 1, 3, and 4 respectively.
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 and 3-10 are rejected under 35 U.S.C. 101 for containing an abstract idea without significantly more.
Regarding Claim 1:
Step 1 – Is the claim to a process, machine, manufacture, or composition of matter?
Yes, the claim is to a machine.
Step 2A – Prong 1 – Does the claim recite an abstract idea, law of nature, or natural phenomenon?
Yes, the claim recites the abstract ideas of:
a compressor which compresses data, and an expander which expands the data compressed by the compressor — This limitation is directed to the abstract idea of a mental process (including an observation, evaluation, judgement, opinion) which can be performed by the human mind, or by a human using pen and paper (see MPEP 2106.04(a)(2) III. C.). The limitation is recited in a highly generic manner and may include basic operations which can be performed in the human mind or by a human using pencil and paper.
and the compressor/expander executes: processing for compressing data to be written — This limitation is directed to the abstract idea of a mental process (including an observation, evaluation, judgement, opinion) which can be performed by the human mind, or by a human using pen and paper (see MPEP 2106.04(a)(2) III. C.). The limitation is directed to a mental process because it amounts to performing an evaluation on given data to determine a more succinct way of representing the data.
and processing for expanding the compressed data to be written by using the expander— This limitation is directed to the abstract idea of a mental process (including an observation, evaluation, judgement, opinion) which can be performed by the human mind, or by a human using pen and paper (see MPEP 2106.04(a)(2) III. C.). The limitation is directed to a mental process because it amounts to performing an evaluation on given data to determine a more expanded or comprehensible way of representing the data.
wherein the Al processor comprises: a generator that executes, after acquiring the configuration information of the compressor by using the first interface: processing for generating a model of a high-speed expansion Al from the configuration information of the compressor and a model of prescribed Al with the data compressed by the compressor as an input — This limitation is directed to the abstract idea of a mental process (including an observation, evaluation, judgement, opinion) which can be performed by the human mind, or by a human using pen and paper (see MPEP 2106.04(a)(2) III. C.). The limitation is directed to a mental process because it amounts to performing a judgement or opinion of a model to determine which parameters to use (for example, selecting a model architecture and a set of hyperparameters).
processing for generating a model of the compressor inside the Al processor from the configuration information of the compressor — This limitation is directed to the abstract idea of a mental process (including an observation, evaluation, judgement, opinion) which can be performed by the human mind, or by a human using pen and paper (see MPEP 2106.04(a)(2) III. C.). The limitation is directed to a mental process because it amounts to performing a judgement or opinion of a model to determine which parameters to use (for example, selecting a model architecture and a set of hyperparameters).
processing for compressing learning input data by using the model of the compressor, which is generated inside the AI processor — This limitation is directed to the abstract idea of a mental process (including an observation, evaluation, judgement, opinion) which can be performed by the human mind, or by a human using pen and paper (see MPEP 2106.04(a)(2) III. C.). The limitation is directed to a mental process because it amounts to performing an evaluation on given data to determine a more succinct way of representing the data.
Step 2A – Prong 2 – Does the claim recite additional elements that integrate the judicial exception into a practical application?
No, the claim does not recite additional elements that integrate the judicial exception into a practical application. The additional elements:
A data processing system comprising an Al (Artificial Intelligence) processor which performs analysis by using AI, and a storage subsystem including a compressor/expander — This limitation is directed to merely applying an abstract idea using a generic computer as a tool (see MPEP 2106.05(f)(2), 2106.04(d)).
wherein compressor/expander in the storage subsystem is a computer including — This limitation is directed to merely applying an abstract idea using a generic computer as a tool (see MPEP 2106.05(f)(2), 2106.04(d)).
which has been sent from a data generation source, by using the compressor, and storing the compressed data in a storage — This limitation is directed to mere data gathering and outputting which has been recognized by the courts (as per Ultramercial, 772 F.3d at 715, 112 USPQ2d at 1754) as insignificant extra-solution activity (see MPEP 2106.05(g)).
and sending the expanded data to a request source device outside the storage subsystem — This limitation is directed to mere data gathering and outputting which has been recognized by the courts (as per Ultramercial, 772 F.3d at 715, 112 USPQ2d at 1754) as insignificant extra-solution activity (see MPEP 2106.05(g)).
wherein the compressor/expander further includes: a first interface capable of outputting configuration information of the compressor — This limitation is directed to mere data gathering and outputting which has been recognized by the courts (as per Ultramercial, 772 F.3d at 715, 112 USPQ2d at 1754) as insignificant extra-solution activity (see MPEP 2106.05(g)).
and a second interface capable of outputting the data compressed by the compressor — This limitation is directed to mere data gathering and outputting which has been recognized by the courts (as per Ultramercial, 772 F.3d at 715, 112 USPQ2d at 1754) as insignificant extra-solution activity (see MPEP 2106.05(g)).
and processing for causing the high-speed expansion AI to be learned by using the compressed learning input data and correct label data corresponding to the learning input data — This limitation is directed to mere instructions to apply a judicial exception. Using a learning algorithm to apply a judicial exception (see MPEP 2106.05(f)) is insufficient to integrate the judicial exception into a practical application. Even if the learning algorithm is implemented on a generic computer (see MPEP 2106.05(f)(2), 2106.04(d)), the limitation does not integrate the judicial exception into a practical application.
Step 2B – Does the claim recite additional elements that amount to significantly more than the abstract idea itself?
No, the claim does not recite additional elements which amount to significantly more than the abstract idea itself. The additional elements as identified in step 2A prong 2:
A data processing system comprising an Al (Artificial Intelligence) processor which performs analysis by using AI, and a storage subsystem including a compressor/expander — Using a generic computer as a tool (see MPEP 2106.05(f)(2), 2106.05(d)) cannot amount to significantly more than the judicial exception itself.
wherein compressor/expander in the storage subsystem is a computer including — Using a generic computer as a tool (see MPEP 2106.05(f)(2), 2106.05(d)) cannot amount to significantly more than the judicial exception itself.
which has been sent from a data generation source, by using the compressor, and storing the compressed data in a storage — This limitation is recited at a high level of generality and amounts to mere data gathering of storing and retrieving information in memory, which is well-understood, routine, and conventional activity (see MPEP 2106.05(d) II.), which cannot amount to significantly more than the judicial exception.
and sending the expanded data to a request source device outside the storage subsystem — This limitation is recited at a high level of generality and amounts to mere data gathering of transmitting and receiving data over a network, which is well-understood, routine, and conventional activity (see MPEP 2106.05(d) II.), which cannot amount to significantly more than the judicial exception.
wherein the compressor/expander further includes: a first interface capable of outputting configuration information of the compressor — This limitation is recited at a high level of generality and amounts to mere data gathering of presenting offers and gathering statistics, which is well-understood, routine, and conventional activity (see MPEP 2106.05(d) II.), which cannot amount to significantly more than the judicial exception.
and a second interface capable of outputting the data compressed by the compressor — This limitation is recited at a high level of generality and amounts to mere data gathering of presenting offers and gathering statistics, which is well-understood, routine, and conventional activity (see MPEP 2106.05(d) II.), which cannot amount to significantly more than the judicial exception.
and processing for causing the high-speed expansion AI to be learned by using the compressed learning input data and correct label data corresponding to the learning input data — Mere instructions to apply a judicial exception (see MPEP 2106.05(f)) and using a generic computer as a tool (see MPEP 2106.05(f)(2), 2106.05(d)) cannot amount to significantly more than the judicial exception itself.
Regarding Claim 3
Claim 3 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claim is dependent on claim 1 which included an abstract idea (see rejection for claim 1). The claim recites the additional limitations:
Step 2A Prong 1:
wherein when executing the processing for generating the model of the high-speed expansion AI, the generator executes: processing for generating a preprocessor for converting the data compressed by the compressor into a data format to be input to the prescribed AI on the basis of the configuration information — This limitation is directed to the abstract idea of a mental process (including an observation, evaluation, judgement, opinion) which can be performed by the human mind, or by a human using pen and paper (see MPEP 2106.04(a)(2) III. C.). The limitation is directed to a mental process because it amounts to performing a judgement or opinion of a model to determine which parameters to use (for example, selecting a model architecture and a set of hyperparameters) and an evaluation to determine a more appropriate format for given data.
and processing for combining the generated preprocessor and the model of the prescribed AI model, and generating the model of the high-speed expansion AI — This limitation is directed to the abstract idea of a mental process (including an observation, evaluation, judgement, opinion) which can be performed by the human mind, or by a human using pen and paper (see MPEP 2106.04(a)(2) III. C.). The limitation is directed to a mental process because it amounts to performing a judgement or opinion of a model to determine which parameters to use (for example, selecting a model architecture and a set of hyperparameters).
Thus, the judicial exception is not integrated into a practical application (see MPEP 2106.04(d) I.), failing step 2A prong 2. The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception under step 2B.
Regarding Claim 4
Claim 4 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claim is dependent on claim 1 which included an abstract idea (see rejection for claim 1). The claim recites the additional limitations:
Step 2A Prong 2:
further comprising: a learner which learns high-speed expansion Al, in which the high-speed expansion Al model has been operably read therein, with data in which learning data has been compressed by a compressor, in which the compressor model has been operably read therein, as an input — This limitation is directed to mere instructions to apply a judicial exception. Using learning with a learning unit to apply a judicial exception (see MPEP 2106.05(f)) is insufficient to integrate the judicial exception into a practical application. Even if the is implemented on a generic computer (see MPEP 2106.05(f)(2), 2106.04(d)), the limitation does not integrate the judicial exception into a practical application.
Thus, the judicial exception is not integrated into a practical application (see MPEP 2106.04(d) I.), failing step 2A prong 2.
Step 2B:
The additional elements as identified in step 2A prong 2:
further comprising: a learner which learns high-speed expansion Al, in which the high-speed expansion Al model has been operably read therein, with data in which learning data has been compressed by a compressor, in which the compressor model has been operably read therein, as an input — Mere instructions to apply a judicial exception (see MPEP 2106.05(f)) and using a generic computer as a tool (see MPEP 2106.05(f)(2), 2106.05(d)) cannot amount to significantly more than the judicial exception itself.
Thus, the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception under step 2B.
Regarding Claim 5
Claim 5 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claim is dependent on claim 4 which included an abstract idea (see rejection for claim 4). The claim recites the additional limitations:
Step 2A Prong 1:
and the data processing system further comprises: an analyzer which performs analytical processing of the data by using the data compressed by the compressor and output by the second interface, and the high- speed expansion Al learned by the learner — This limitation is directed to the abstract idea of a mental process (including an observation, evaluation, judgement, opinion) which can be performed by the human mind, or by a human using pen and paper (see MPEP 2106.04(a)(2) III. C.).
Step 2A Prong 2:
wherein: the prescribed Al is Al that performs analytical processing of data — This limitation is directed to merely limiting a judicial exception to a particular field of use (see MPEP 2106.05(h)) as it merely limits the field of the prescribed AI.
Thus, the judicial exception is not integrated into a practical application (see MPEP 2106.04(d) I.), failing step 2A prong 2.
Step 2B:
The additional elements as identified in step 2A prong 2:
wherein: the prescribed Al is Al that performs analytical processing of data — Merely limiting a judicial exception to a particular field of use (see MPEP 2106.05(h)) cannot amount to significantly more than the judicial exception.
Thus, the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception under step 2B.
Regarding Claim 6
Claim 6 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claim is dependent on claim 1 which included an abstract idea (see rejection for claim 1). The claim recites the additional limitations:
Step 2A Prong 1:
wherein: the compressor/expander is configured by including a coder which encodes the data compressed by the compressor, and a decoder which decodes the data encoded with the coder — This limitation is directed to the abstract idea of a mental process (including an observation, evaluation, judgement, opinion) which can be performed by the human mind, or by a human using pen and paper (see MPEP 2106.04(a)(2) III. C.).
Step 2A Prong 2:
and the compressor/expander further comprises: a third interface which stores, in a storage, the data in which the data compressed by the compressor has been encoded using the coder — This limitation is directed to mere data gathering and outputting which has been recognized by the courts (as per Ultramercial, 772 F.3d at 715, 112 USPQ2d at 1754) as insignificant extra-solution activity (see MPEP 2106.05(g)).
and a fourth interface which reads the data from the storage, decodes the read data with the decoder, expands the decoded data with the expander, and outputs the expanded data — This limitation is directed to mere data gathering and outputting which has been recognized by the courts (as per Ultramercial, 772 F.3d at 715, 112 USPQ2d at 1754) as insignificant extra-solution activity (see MPEP 2106.05(g)).
and the second interface reads the data from the storage, decodes the read data with the decoder, and outputs the decoded data — This limitation is directed to mere data gathering and outputting which has been recognized by the courts (as per Ultramercial, 772 F.3d at 715, 112 USPQ2d at 1754) as insignificant extra-solution activity (see MPEP 2106.05(g)).
Thus, the judicial exception is not integrated into a practical application (see MPEP 2106.04(d) I.), failing step 2A prong 2.
Step 2B:
The additional elements as identified in step 2A prong 2:
and the compression/expansion unit further comprises: a third interface which stores, in a storage, the data in which the data compressed by the compressor has been encoded using the coder — This limitation is recited at a high level of generality and amounts to mere data gathering of storing and retrieving information in memory, which is well-understood, routine, and conventional activity (see MPEP 2106.05(d) II.), which cannot amount to significantly more than the judicial exception.
and a fourth interface which reads the data from the storage, decodes the read data with the decoder, expands the decoded data with the expander, and outputs the expanded data —This limitation is recited at a high level of generality and amounts to mere data gathering of storing and retrieving information in memory, which is well-understood, routine, and conventional activity (see MPEP 2106.05(d) II.), which cannot amount to significantly more than the judicial exception.
and the second interface reads the data from the storage, decodes the read data with the decoder, and outputs the decoded data —This limitation is recited at a high level of generality and amounts to mere data gathering of storing and retrieving information in memory, which is well-understood, routine, and conventional activity (see MPEP 2106.05(d) II.), which cannot amount to significantly more than the judicial exception.
Thus, the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception under step 2B.
Regarding Claim 7
Claim 7 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claim is dependent on claim 6 which included an abstract idea (see rejection for claim 6). The claim recites the additional limitations:
Step 2A Prong 2:
wherein: the compressor comprises a padder which pads the data for causing the input data to be a data size to be received by an encoder part of the compressor — This limitation is directed to mere data gathering and outputting which has been recognized by the courts (as per Ultramercial, 772 F.3d at 715, 112 USPQ2d at 1754) as insignificant extra-solution activity (see MPEP 2106.05(g)).
and the coder comprises a padder which pads the data for causing the data compressed by the compressor to be a data size to be received by a hyper encoder part of the coder —This limitation is directed to mere data gathering and outputting which has been recognized by the courts (as per Ultramercial, 772 F.3d at 715, 112 USPQ2d at 1754) as insignificant extra-solution activity (see MPEP 2106.05(g)).
Thus, the judicial exception is not integrated into a practical application (see MPEP 2106.04(d) I.), failing step 2A prong 2.
Step 2B:
The additional elements as identified in step 2A prong 2:
wherein: the compressor comprises a padder which pads the data for causing the input data to be a data size to be received by an encoder part of the compressor — This limitation is recited at a high level of generality and amounts to mere data gathering of storing and retrieving information in memory, which is well-understood, routine, and conventional activity (see MPEP 2106.05(d) II.), which cannot amount to significantly more than the judicial exception.
and the coder comprises a padder which pads the data for causing the data compressed by the compressor to be a data size to be received by a hyper encoder part of the coder — This limitation is recited at a high level of generality and amounts to mere data gathering of storing and retrieving information in memory, which is well-understood, routine, and conventional activity (see MPEP 2106.05(d) II.), which cannot amount to significantly more than the judicial exception.
Thus, the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception under step 2B.
Regarding Claim 8:
Step 1 – Is the claim to a process, machine, manufacture, or composition of matter?
Yes, the claim is to a process.
Step 2A – Prong 1 – Does the claim recite an abstract idea, law of nature, or natural phenomenon?
Yes, the claim recites the abstract ideas of:
A data processing method in a data processing system comprising a compressor which compresses data, and an expander which expands the data compressed by the compressor — This limitation is directed to the abstract idea of a mental process (including an observation, evaluation, judgement, opinion) which can be performed by the human mind, or by a human using pen and paper (see MPEP 2106.04(a)(2) III. C.). The limitation is recited in a highly generic manner and may include basic operations which can be performed in the human mind or by a human using pencil and paper.
and the compressor/expander executes: processing for compressing data to be written — This limitation is directed to the abstract idea of a mental process (including an observation, evaluation, judgement, opinion) which can be performed by the human mind, or by a human using pen and paper (see MPEP 2106.04(a)(2) III. C.). The limitation is directed to a mental process because it amounts to performing an evaluation on given data to determine a more succinct way of representing the data.
and processing for expanding the compressed data to be written by using the expander— This limitation is directed to the abstract idea of a mental process (including an observation, evaluation, judgement, opinion) which can be performed by the human mind, or by a human using pen and paper (see MPEP 2106.04(a)(2) III. C.). The limitation is directed to a mental process because it amounts to performing an evaluation on given data to determine a more expanded or comprehensible way of representing the data.
wherein the Al processor comprises: a generator that executes, after acquiring the configuration information of the compressor by using the first interface: processing for generating a model of a high-speed expansion Al from the configuration information of the compressor and a model of prescribed Al with the data compressed by the compressor as an input — This limitation is directed to the abstract idea of a mental process (including an observation, evaluation, judgement, opinion) which can be performed by the human mind, or by a human using pen and paper (see MPEP 2106.04(a)(2) III. C.). The limitation is directed to a mental process because it amounts to performing a judgement or opinion of a model to determine which parameters to use (for example, selecting a model architecture and a set of hyperparameters).
processing for generating a model of the compressor inside the Al processor from the configuration information of the compressor — This limitation is directed to the abstract idea of a mental process (including an observation, evaluation, judgement, opinion) which can be performed by the human mind, or by a human using pen and paper (see MPEP 2106.04(a)(2) III. C.). The limitation is directed to a mental process because it amounts to performing a judgement or opinion of a model to determine which parameters to use (for example, selecting a model architecture and a set of hyperparameters).
processing for compressing learning input data by using the model of the compressor, which is generated inside the AI processor — This limitation is directed to the abstract idea of a mental process (including an observation, evaluation, judgement, opinion) which can be performed by the human mind, or by a human using pen and paper (see MPEP 2106.04(a)(2) III. C.). The limitation is directed to a mental process because it amounts to performing an evaluation on given data to determine a more succinct way of representing the data.
Step 2A – Prong 2 – Does the claim recite additional elements that integrate the judicial exception into a practical application?
No, the claim does not recite additional elements that integrate the judicial exception into a practical application. The additional elements:
A data processing system comprising an Al (Artificial Intelligence) processor which performs analysis by using AI, and a storage subsystem including a compressor/expander — This limitation is directed to merely applying an abstract idea using a generic computer as a tool (see MPEP 2106.05(f)(2), 2106.04(d)).
wherein compressor/expander in the storage subsystem is a computer including — This limitation is directed to merely applying an abstract idea using a generic computer as a tool (see MPEP 2106.05(f)(2), 2106.04(d)).
which has been sent from a data generation source, by using the compressor, and storing the compressed data in a storage — This limitation is directed to mere data gathering and outputting which has been recognized by the courts (as per Ultramercial, 772 F.3d at 715, 112 USPQ2d at 1754) as insignificant extra-solution activity (see MPEP 2106.05(g)).
and sending the expanded data to a request source device outside the storage subsystem — This limitation is directed to mere data gathering and outputting which has been recognized by the courts (as per Ultramercial, 772 F.3d at 715, 112 USPQ2d at 1754) as insignificant extra-solution activity (see MPEP 2106.05(g)).
wherein the compressor/expander further includes: a first interface capable of outputting configuration information of the compressor — This limitation is directed to mere data gathering and outputting which has been recognized by the courts (as per Ultramercial, 772 F.3d at 715, 112 USPQ2d at 1754) as insignificant extra-solution activity (see MPEP 2106.05(g)).
and a second interface capable of outputting the data compressed by the compressor — This limitation is directed to mere data gathering and outputting which has been recognized by the courts (as per Ultramercial, 772 F.3d at 715, 112 USPQ2d at 1754) as insignificant extra-solution activity (see MPEP 2106.05(g)).
and processing for causing the high-speed expansion AI to be learned by using the compressed learning input data and correct label data corresponding to the learning input data — This limitation is directed to mere instructions to apply a judicial exception. Using a learning algorithm to apply a judicial exception (see MPEP 2106.05(f)) is insufficient to integrate the judicial exception into a practical application. Even if the learning algorithm is implemented on a generic computer (see MPEP 2106.05(f)(2), 2106.04(d)), the limitation does not integrate the judicial exception into a practical application.
wherein the method includes the steps of the compression/expansion unit: outputting configuration information of the compressor; and outputting the data compressed by the compressor — This limitation is directed to mere data gathering and outputting which has been recognized by the courts (as per Ultramercial, 772 F.3d at 715, 112 USPQ2d at 1754) as insignificant extra-solution activity (see MPEP 2106.05(g)).
Step 2B – Does the claim recite additional elements that amount to significantly more than the abstract idea itself?
No, the claim does not recite additional elements which amount to significantly more than the abstract idea itself. The additional elements as identified in step 2A prong 2:
A data processing system comprising an Al (Artificial Intelligence) processor which performs analysis by using AI, and a storage subsystem including a compressor/expander — Using a generic computer as a tool (see MPEP 2106.05(f)(2), 2106.05(d)) cannot amount to significantly more than the judicial exception itself.
wherein compressor/expander in the storage subsystem is a computer including — Using a generic computer as a tool (see MPEP 2106.05(f)(2), 2106.05(d)) cannot amount to significantly more than the judicial exception itself.
which has been sent from a data generation source, by using the compressor, and storing the compressed data in a storage — This limitation is recited at a high level of generality and amounts to mere data gathering of storing and retrieving information in memory, which is well-understood, routine, and conventional activity (see MPEP 2106.05(d) II.), which cannot amount to significantly more than the judicial exception.
and sending the expanded data to a request source device outside the storage subsystem — This limitation is recited at a high level of generality and amounts to mere data gathering of transmitting and receiving data over a network, which is well-understood, routine, and conventional activity (see MPEP 2106.05(d) II.), which cannot amount to significantly more than the judicial exception.
wherein the compressor/expander further includes: a first interface capable of outputting configuration information of the compressor — This limitation is recited at a high level of generality and amounts to mere data gathering of presenting offers and gathering statistics, which is well-understood, routine, and conventional activity (see MPEP 2106.05(d) II.), which cannot amount to significantly more than the judicial exception.
and a second interface capable of outputting the data compressed by the compressor — This limitation is recited at a high level of generality and amounts to mere data gathering of presenting offers and gathering statistics, which is well-understood, routine, and conventional activity (see MPEP 2106.05(d) II.), which cannot amount to significantly more than the judicial exception.
and processing for causing the high-speed expansion AI to be learned by using the compressed learning input data and correct label data corresponding to the learning input data — Mere instructions to apply a judicial exception (see MPEP 2106.05(f)) and using a generic computer as a tool (see MPEP 2106.05(f)(2), 2106.05(d)) cannot amount to significantly more than the judicial exception itself.
wherein the method includes the steps of the compression/expansion unit: outputting configuration information of the compressor; and outputting the data compressed by the compressor — This limitation is recited at a high level of generality and amounts to mere data gathering of transmitting and receiving data over a network, which is well-understood, routine, and conventional activity (see MPEP 2106.05(d) II.), which cannot amount to significantly more than the judicial exception.
Regarding Claim 9
Claim 9 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claim is dependent on claim 1 which included an abstract idea (see rejection for claim 1). The claim recites the additional limitations:
Step 2A Prong 2:
wherein the compressor is a neural network include at least layers other than all combined layers — This limitation is directed to merely limiting a judicial exception to a particular field of use (see MPEP 2106.05(h)) as it merely limits the field of the compressor.
Thus, the judicial exception is not integrated into a practical application (see MPEP 2106.04(d) I.), failing step 2A prong 2.
Step 2B:
The additional elements as identified in step 2A prong 2:
wherein the compressor is a neural network include at least layers other than all combined layers — Merely limiting a judicial exception to a particular field of use (see MPEP 2106.05(h)) cannot amount to significantly more than the judicial exception.
Thus, the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception under step 2B.
Regarding Claim 10
Claim 10 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claim is dependent on claim 3 which included an abstract idea (see rejection for claim 3). The claim recites the additional limitations:
Step 2A Prong 2:
wherein the neural network includes at least a convolution layer — This limitation is directed to merely limiting a judicial exception to a particular field of use (see MPEP 2106.05(h)) as it merely limits the field of the neural network.
Thus, the judicial exception is not integrated into a practical application (see MPEP 2106.04(d) I.), failing step 2A prong 2.
Step 2B:
The additional elements as identified in step 2A prong 2:
wherein the neural network includes at least a convolution layer — Merely limiting a judicial exception to a particular field of use (see MPEP 2106.05(h)) cannot amount to significantly more than the judicial exception.
Thus, the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception under step 2B.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1, 3-6, and 8-10 are rejected under 35 U.S.C. 103 as being unpatentable over Nakanishi et al. (PGPUB no. US20190251418A1) herein referred to as Nakanishi in view of NPL reference Jo et al. “Impact of image compression on deep learning-based mammogram classification” herein referred to as Jo and Isshiki (PGPUB no. US20200177879A1).
Regarding Claim 1
Nakanishi teaches:
A data processing system comprising an Al (Artificial Intelligence) processor which performs analysis by using AI, and a storage subsystem including a compressor/expander
(paragraph [0049]) “In some embodiments, at least one or more of the first data input acceptor 120, the first compression model generator 122, the first data compressor 123, and the first compressed data outputter 124 may be implemented with a special circuit (e.g., circuitry of a FPGA, CPU, GPU or other processing circuits implemented using electronic circuits), a subroutine in a program stored in memory (e.g., EPROM, EEPROM, SDRAM, and flash memory devices, CD ROM, DVD-ROM, or Blu-Ray® discs and the like) and executable by a processor (e.g., CPU, GPU and the like), or the like.”
wherein compressor/expander in the storage subsystem is a computer including a compressor which compresses data, and an expander which expands the data compressed by the compressor
(paragraph [0224]) “That is, the CPU of the computer operates so as to perform computation based on the model stored in the storage part and output the result[*Examiner notes: storage subsystem].”; (paragraph [0027]) “. A data processing system 1 may include a compression apparatus 10 which compresses data[*Examiner notes: compressor], and a decompression apparatus 20 which decompresses the data compressed by the compression apparatus[*Examiner notes: Expander] 10. The compression apparatus 10 and the decompression apparatus 20 may be included in the same apparatus or may be included in different apparatuses connected with each other over a network or the like.” ; (figure 1)
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and the compressor/expander executes: processing for compressing data to be written, which has been sent from a data generation source, by using the compressor, and storing the compressed data in a storage
(paragraph [0003]) “Further, compression of data is a useful technique not only for data transfer but also for data storage because its size can be reduced.”
wherein the compressor/expander further includes: a first interface capable of outputting configuration information of the compressor
(paragraph [0043]) “The first compression model memory 121 may store a form of a model for performing compression, and may additionally store programs and data for the processing. In some embodiments, the first compression model memory 121 may be implemented with EPROM, EEPROM, SDRAM, and flash memory devices, CD ROM, DVD-ROM, or Blu-Ray® discs and the like.”
and a second interface capable of outputting the data compressed by the compressor
(paragraph [0048]) “The first compressed data outputter 124 may output the data subjected to lossy compression. Not only the data compressed by the first data compressor 123 but also parameters of layers to reproduce the first compression model for decoding may be outputted.”
wherein the Al processor comprises: a generator that executes, after acquiring the configuration information of the compressor by using the first interface: processing for generating a model of a high-speed expansion Al from the configuration information of the compressor
(paragraph [0053]) The first decompression model generator 222 may extract a first parameter to generate the first decompression model, of the lossy-compressed data inputted into the first compressed data input acceptor, and generate the first decompression model based on the data and on the model stored in the first decompression model memory 221.”; (paragraph [0057]) “FIG. 4 is a chart illustrating the outline of a neural network configuration for
lossy[*Examiner notes: high-speed expansion AI] compression and decompression according to some embodiments.”
processing for generating a model of the compressor inside the Al processor from the configuration information of the compressor
(paragraph [0044]) “The first compression model generator 122 may be a module which performs lossy compression, and may select or generate the model for compression using a neural network model of the form stored in the first compression model memory 121.”
processing for compressing learning input data by using the model of the compressor, which is generated inside the AI processor; and processing for causing the high-speed expansion AI to be learned by using the compressed learning input data and correct label data corresponding to the learning input data;
(paragraph [0033]) “Assuming that a signal to be compressed is x[*Examiner notes: Correct label data] and a signal after reconstruction of the compressed code is x̂[*Examiner notes: compressed learning input data corresponding to learning input data], x̂ may be a discrete value, whereas x may be a continuous value or a discrete value. Reconstruction with distortion (i.e., lossy compression) may be performed for the purpose of decreasing the expectation of a loss L(x, x̂) which is distortion between the signal after compression x̂ and the signal before compression x, while minimizing a code length l(x̂) of x̂[*Examiner notes: learning high-speed expansion AI].”
and a high-speed expansion AI analyzer that executes, after acquiring the compressed data to be written by using the second interface: processing for inputting the compressed data to be written to the learned high-speed expansion AI; and processing for acquiring an analysis result from the learned high-speed expansion AI.
[*Examiner notes: The broadest reasonable interpretation of the term “analyzer” and “analysis” includes deriving a decompressed image from a compressed image using the expansion AI. (specification paragraph [0028]) “Meanwhile, for example, when there is a request for reading the compressed data for performing analysis with the high-speed expansion Al 161, the compression/expansion unit 101 reads the bit sequence of the target data from the storage 112, and thereafter returns the compressed data that was converted (decoded) by the decoder 115 to the request source.”]; () “The data may be decompressed to data 322 of 128 channels by the processing in the third layer to data 323 of 64 channels by the processing in the second layer, and to data 324 of 3 channels by the processing in the first layer[*Examiner notes: acquiring analysis result from learned high-speed expansion AI].”; Figure 4
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Nakanishi does not explicitly teach:
processing for generating […] a model of prescribed Al with the data compressed by the compressor as an input;
and processing for expanding the compressed data to be written by using the expander and sending the expanded data to a request source device outside the storage subsystem
However, Jo teaches:
processing for generating […] a model of prescribed Al with the data compressed by the compressor as an input;
(page 1 abstract) “This study aimed to analyze the impact of image compression on the performance of deep learning-based models in classifying mammograms[*Examiner notes: mapped to prescribed AI] as “malignant”—cases that lead to a cancer diagnosis and treatment—or “normal” and “benign,” non-malignant cases that do not require immediate medical intervention.”
Nakanishi, Jo, and the instant application are analogous because they are all directed to machine learning.
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the present invention to modify the compression and decompression of Nakanishi with the prescribed AI as taught by Jo because (Jo page 1 abstract) “This paper finds that while training models on images with increased the robustness of the models when tested on compressed data”
And Isshiki teaches:
and processing for expanding the compressed data to be written by using the expander and sending the expanded data to a request source device outside the storage subsystem
(paragraph [0106]) “When the restoration of compressive sensing[*Examiner notes: mapped to expanding ] by the compressive sensing restoring portion 270 is successful (step S943: Yes), the restored image is transmitted from the transmitting portion 202 to the requesting terminals 301 through 303 (step S944).”
Nakanishi, Jo, Isshiki and the instant application are analogous because they are all directed to machine learning.
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the present invention to modify the compression and decompression of Nakanishi in view of Jo with the sending of decompressed data to a requester taught by Isshiki (paragraphs 105 and 106) “As described above, according to the second embodiment of the present technology, setting different sampling matrices for each identifier prevents transmission of an image for an unauthorized image request, thereby enhancing security. More specifically, changing the sampling matrices for each of customers or installation places of the imaging apparatuses 101 through 103 can prevent the viewing and interception of other image data. Further, since compressive sensing is executed on some randomly selected pixels in the imaging apparatuses 101 through 103, leaving of the entire image in the imaging apparatuses 101 through 103 can be avoided. It should be noted that storing a sampling matrix as related with the identifier of the server 200 allows the execution of restoring processing without increasing the data to be transmitted from the imaging apparatuses 101 through 103 to the server 200.”
Regarding Claim 3
Nakanishi in view of Jo and Isshiki teaches:
The data processing system according to claim 1
(see rejection of claim 1)
Nakanishi further teaches:
wherein when executing the processing for generating the model of the high-speed expansion AI, the generator executes: processing for generating a preprocessor for converting the data compressed by the compressor into a data format to be input to the prescribed AI on the basis of the configuration information;
(paragraph [0124]) “As described above, the first decompression apparatus 22 may perform decompression of data subjected to lossy compression by the first compression apparatus 12.”
and generating the model of the high-speed expansion AI
(paragraph [0053]) The first decompression model generator 222 may extract a first parameter to generate the first decompression model, of the lossy-compressed data inputted into the first compressed data input acceptor, and generate the first decompression model based on the data and on the model stored in the first decompression model memory 221.”; (paragraph [0057]) “FIG. 4 is a chart illustrating the outline of a neural network configuration for lossy[*Examiner notes: high-speed expansion AI] compression and decompression according to some embodiments.”
Jo further teaches:
and processing for combining the generated preprocessor and the model of the prescribed AI model
(page 6 paragraph 3) “All images used in this study were sourced from a single clinical center to ensure that diagnoses were confirmed using pathology and patient outcomes. Each DICOM file was originally compressed using JPEG 2000 lossless compression, and all pixel values were decompressed before processing[*Examiner notes: combines the generated preprocessing unit and the prescribed AI model].”
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the present invention to combine Nakanishi and Isshiki with Jo for the same reasons given in claim 2 above.
Regarding Claim 4
Nakanishi in view of Jo and Isshiki teaches:
The data processing system according to claim 1
(see rejection of claim 1)
Nakanishi further teaches:
further comprising: a learner which learns high-speed expansion Al, in which the high-speed expansion Al model has been operably read therein,
(paragraph [0132]) “Then, after the compression model is generated by learning, the encoder layer of the compression model may be stored in the first compression model memory 121 and the decoder layer may be stored in the first decompression model memory 221.”
with data in which learning data has been compressed by a compressor, in which the compressor model has been operably read therein, as an input
(paragraph [0053]) The first decompression model generator 222 may extract a first parameter to generate the first decompression model, of the lossy-compressed data inputted into the first compressed data input acceptor[*Examiner notes: compressor model operably read as input], and generate the first decompression model based on the data and on the model stored in the first decompression model memory 221.”
Regarding Claim 5
Nakanishi in view of Jo and Isshiki teaches:
The data processing system according to claim 4
(see rejection of claim 4)
And Jo further teaches:
wherein: the prescribed Al is Al that performs analytical processing of data; and the data processing system further comprises: an analyzer which performs analytical processing of the data by using the data compressed by the compressor and output by the second interface, and the high- speed expansion Al learned by the learner.
(page 1 abstract) “This study aimed to analyze the impact of image compression on the performance of deep learning-based models in classifying mammograms[*Examiner notes: mapped to prescribed AI] as “malignant”—cases that lead to a cancer diagnosis and treatment—or “normal” and “benign,” non-malignant cases that do not require immediate medical intervention.”; (page 6 paragraph 3) “All images used in this study were sourced from a single clinical center to ensure that diagnoses were confirmed using pathology and patient outcomes. Each DICOM file was originally compressed using JPEG 2000 lossless compression, and all pixel values were
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the present invention to combine Nakanishi and Isshiki with Jo for the same reasons given in claim 2 above.
Regarding Claim 6
Nakanishi in view of Jo and Isshiki teaches:
The data processing system according to claim 1
(see rejection of claim 1)
wherein: the compressor/expander is configured by including a coder which encodes the data compressed by the compressor, and a decoder which decodes the data encoded with the coder;
(paragraph [0044]) “The compression of data may be executed using an encoder portion (or encode network part) of the generated autoencoder.”; (paragraph [0058]) “The first compression apparatus 12 may learn to optimize an objective function to generate an autoencoder including an encode network part illustrated at a left half and a decode network part illustrated at a right half in FIG. 4.”
and the compressor/expander further comprises: a third interface which stores, in a storage, the data in which the data compressed by the compressor has been encoded using the coder;
(paragraph [0067]) “In such a case, information on which model has been used may be stored in the compressed data 317, for example, a header part of the compressed data.”
and a fourth interface which reads the data from the storage, decodes the read data with the decoder, expands the decoded data with the expander, and outputs the expanded data, and the second interface reads the data from the storage, decodes the read data with the decoder, and outputs the decoded data.
(paragraph [0223]) “In the above-described entire description, at least a part of the devices or apparatus may be configured by hardware, or may be configured by software and a CPU and the like perform the operation based on information processing of the software. When it is configured by the software, a program which achieves above mentioned functions and at least a partial function thereof may be stored in a storage medium such as a flexible disk or a CD-ROM, and executed by making a computer read it. The storage medium is not limited to a detachable one such as a magnetic disk or an optical disk, but it may be a fixed-type storage medium such as a hard disk device or a memory.”
Regarding Claim 8
Nakanishi teaches:
A data processing method in a a data processing system comprising an Al (Artificial Intelligence) processor which performs analysis by using AI, and a storage subsystem including a compressor/expander
(paragraph [0049]) “In some embodiments, at least one or more of the first data input acceptor 120, the first compression model generator 122, the first data compressor 123, and the first compressed data outputter 124 may be implemented with a special circuit (e.g., circuitry of a FPGA, CPU, GPU or other processing circuits implemented using electronic circuits), a subroutine in a program stored in memory (e.g., EPROM, EEPROM, SDRAM, and flash memory devices, CD ROM, DVD-ROM, or Blu-Ray® discs and the like) and executable by a processor (e.g., CPU, GPU and the like), or the like.”
wherein compressor/expander in the storage subsystem is a computer including a compressor which compresses data, and an expander which expands the data compressed by the compressor
(paragraph [0224]) “. That is, the CPU of the computer operates so as to perform computation based on the model stored in the storage part and output the result[*Examiner notes: storage subsystem].”; (paragraph [0027]) “. A data processing system 1 may include a compression apparatus 10 which compresses data[*Examiner notes: compressor], and a decompression apparatus 20 which decompresses the data compressed by the compression apparatus[*Examiner notes: Expander] 10. The compression apparatus 10 and the decompression apparatus 20 may be included in the same apparatus or may be included in different apparatuses connected with each other over a network or the like.” ; (figure 1)
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and the compressor/expander executes: processing for compressing data to be written, which has been sent from a data generation source, by using the compressor, and storing the compressed data in a storage
(paragraph [0003]) “Further, compression of data is a useful technique not only for data transfer but also for data storage because its size can be reduced.”
wherein the compressor/expander further includes: a first interface capable of outputting configuration information of the compressor
(paragraph [0043]) “The first compression model memory 121 may store a form of a model for performing compression, and may additionally store programs and data for the processing. In some embodiments, the first compression model memory 121 may be implemented with EPROM, EEPROM, SDRAM, and flash memory devices, CD ROM, DVD-ROM, or Blu-Ray® discs and the like.”
and a second interface capable of outputting the data compressed by the compressor
(paragraph [0048]) “The first compressed data outputter 124 may output the data subjected to lossy compression. Not only the data compressed by the first data compressor 123 but also parameters of layers to reproduce the first compression model for decoding may be outputted.”
wherein the Al processor comprises: a generator that executes, after acquiring the configuration information of the compressor by using the first interface: processing for generating a model of a high-speed expansion Al from the configuration information of the compressor
(paragraph [0053]) The first decompression model generator 222 may extract a first parameter to generate the first decompression model, of the lossy-compressed data inputted into the first compressed data input acceptor, and generate the first decompression model based on the data and on the model stored in the first decompression model memory 221.”; (paragraph [0057]) “FIG. 4 is a chart illustrating the outline of a neural network configuration for
lossy[*Examiner notes: high-speed expansion AI] compression and decompression according to some embodiments.”
processing for generating a model of the compressor inside the Al processor from the configuration information of the compressor
(paragraph [0044]) “The first compression model generator 122 may be a module which performs lossy compression, and may select or generate the model for compression using a neural network model of the form stored in the first compression model memory 121.”
processing for compressing learning input data by using the model of the compressor, which is generated inside the AI processor; and processing for causing the high-speed expansion AI to be learned by using the compressed learning input data and correct label data corresponding to the learning input data;
(paragraph [0033]) “Assuming that a signal to be compressed is x[*Examiner notes: Correct label data] and a signal after reconstruction of the compressed code is x̂[*Examiner notes: compressed learning input data corresponding to learning input data], x̂ may be a discrete value, whereas x may be a continuous value or a discrete value. Reconstruction with distortion (i.e., lossy compression) may be performed for the purpose of decreasing the expectation of a loss L(x, x̂) which is distortion between the signal after compression x̂ and the signal before compression x, while minimizing a code length l(x̂) of x̂[*Examiner notes: learning high-speed expansion AI].”
and a high-speed expansion AI analyzer that executes, after acquiring the compressed data to be written by using the second interface: processing for inputting the compressed data to be written to the learned high-speed expansion AI; and processing for acquiring an analysis result from the learned high-speed expansion AI.
[*Examiner notes: The broadest reasonable interpretation of the term “analyzer” and “analysis” includes deriving a decompressed image from a compressed image using the expansion AI. (specification paragraph [0028]) “Meanwhile, for example, when there is a request for reading the compressed data for performing analysis with the high-speed expansion Al 161, the compression/expansion unit 101 reads the bit sequence of the target data from the storage 112, and thereafter returns the compressed data that was converted (decoded) by the decoder 115 to the request source.”]; () “The data may be decompressed to data 322 of 128 channels by the processing in the third layer to data 323 of 64 channels by the processing in the second layer, and to data 324 of 3 channels by the processing in the first layer[*Examiner notes: acquiring analysis result from learned high-speed expansion AI].”; Figure 4
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wherein the method includes the steps of the compressor/expander: outputting configuration information of the compressor;
(paragraph [0043]) “The first compression model memory 121 may store a form of a model for performing compression, and may additionally store programs and data for the processing. In some embodiments, the first compression model memory 121 may be implemented with EPROM, EEPROM, SDRAM, and flash memory devices, CD ROM, DVD-ROM, or Blu-Ray® discs and the like.”
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and outputting the data compressed by the compressor.
(paragraph [0048]) “The first compressed data outputter 124 may output the data subjected to lossy compression. Not only the data compressed by the first data compressor 123 but also parameters of layers to reproduce the first compression model for decoding may be outputted.”
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Nakanishi does not explicitly teach:
processing for generating […] a model of prescribed Al with the data compressed by the compressor as an input;
and processing for expanding the compressed data to be written by using the expander and sending the expanded data to a request source device outside the storage subsystem
However, Jo teaches:
processing for generating […] a model of prescribed Al with the data compressed by the compressor as an input;
(page 1 abstract) “This study aimed to analyze the impact of image compression on the performance of deep learning-based models in classifying mammograms[*Examiner notes: mapped to prescribed AI] as “malignant”—cases that lead to a cancer diagnosis and treatment—or “normal” and “benign,” non-malignant cases that do not require immediate medical intervention.”
Nakanishi, Jo, and the instant application are analogous because they are all directed to machine learning.
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the present invention to modify the compression and decompression of Nakanishi with the prescribed AI as taught by Jo because (Jo page 1 abstract) “This paper finds that while training models on images with increased the robustness of the models when tested on compressed data”
And Isshiki teaches:
and processing for expanding the compressed data to be written by using the expander and sending the expanded data to a request source device outside the storage subsystem
(paragraph [0106]) “When the restoration of compressive sensing[*Examiner notes: mapped to expanding ] by the compressive sensing restoring portion 270 is successful (step S943: Yes), the restored image is transmitted from the transmitting portion 202 to the requesting terminals 301 through 303 (step S944).”
Nakanishi, Jo, Isshiki and the instant application are analogous because they are all directed to machine learning.
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the present invention to modify the compression and decompression of Nakanishi in view of Jo with the sending of decompressed data to a requester taught by Isshiki (paragraphs 105 and 106) “As described above, according to the second embodiment of the present technology, setting different sampling matrices for each identifier prevents transmission of an image for an unauthorized image request, thereby enhancing security. More specifically, changing the sampling matrices for each of customers or installation places of the imaging apparatuses 101 through 103 can prevent the viewing and interception of other image data. Further, since compressive sensing is executed on some randomly selected pixels in the imaging apparatuses 101 through 103, leaving of the entire image in the imaging apparatuses 101 through 103 can be avoided. It should be noted that storing a sampling matrix as related with the identifier of the server 200 allows the execution of restoring processing without increasing the data to be transmitted from the imaging apparatuses 101 through 103 to the server 200.”
Regarding Claim 9
Nakanishi in view of Jo and Isshiki teaches:
The data processing system according to claim 1
(see rejection of claim 1)
Nakanishi further teaches:
wherein the compressor is a neural network include at least layers other than all combined layers.
(paragraph [0044]) “The compression of data may be executed using an encoder portion (or encode network part) of the generated autoencoder[*Examiner notes: at least layers other than all combined layers].”
Regarding Claim 10
Nakanishi in view of Jo and Isshiki teaches:The data processing system according to claim 3
(see rejection of claim 3)
Nakanishi further teaches:
wherein the neural network includes at least a convolution layer
(paragraph [0061]) “In FIG. 4, the connection between the layers may be subjected to operation for each 2×2 pixels by a convolution kernel (or filter) of 3×3 pixels[*Examiner notes: convolution layer].”
Claim 7 is rejected under 35 U.S.C. 103 as being unpatentable over Nakanishi in view of Jo, Isshiki, and further in view of NPL reference Cheng et al. “Deep Convolutional AutoEncoder-based Lossy Image Compression” herein referred to as Cheng.
Regarding Claim 7
Nakanishi in view of Jo and Isshiki teaches:
The data processing system according to claim 6
(see rejection of claim 6)
Nakanishi does not explicitly teach:
wherein: the compressor comprises a padder which pads the data for causing the input data to be a data size to be received by an encoder part of the compressor; and the coder comprises a padder which pads the data for causing the data compressed by the compressor to be a data size to be received by a hyper encoder part of the coder.
However Cheng teaches:
wherein: the compressor comprises a padder which pads the data for causing the input data to be a data size to be received by an encoder part of the compressor; and the coder comprises a padder which pads the data for causing the data compressed by the compressor to be a data size to be received by a hyper encoder part of the coder.
(page 254 column 2 first paragraph) “Therefore, we propose a pair of convolution/deconvolution filters for upsampling or downsampling, as shown in Fig. 2 , where Ni denotes the number of filters in the convolution or deconvolution block. By setting the stride as 2, we can get downsampled feature maps. The padding size is set as one to maintain the same size as the input.”; Figure 2
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Nakanishi, Jo, Isshiki, Cheng, and the instant application are analogous because they are all directed to machine learning.
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the present invention to modify the compression and decompression as taught by Nakanishi in view of Jo and Isshiki with the padding of Cheng because (Cheng page 254 column 2 first paragraph) “The padding size is set as one to maintain the same size as the input”.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/E.J.B./Examiner, Art Unit 2126
/DAVID YI/Supervisory Patent Examiner, Art Unit 2126
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