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 .
Claims 1-20 are presented for examination.
Specification
The abstract of the disclosure is objected to because “data shares” should be “data share”. A corrected abstract of the disclosure is required and must be presented on a separate sheet, apart from any other text. See MPEP § 608.01(b).
The use of the term BLUETOOTH (paragraph 33), which is a trade name or a mark used in commerce, has been noted in this application. The term should be accompanied by the generic terminology; furthermore, the term should be capitalized wherever it appears or, where appropriate, include a proper symbol indicating use in commerce such as ™, SM , or ® following the term.
Although the use of trade names and marks used in commerce (i.e., trademarks, service marks, certification marks, and collective marks) is permissible in patent applications, the proprietary nature of the marks should be respected and every effort made to prevent their use in any manner which might adversely affect their validity as commercial marks.
Claim Objections
Claim 9 is objected to because of the following informalities: “data shares” should be “data share”. Claims 10-13 are objected to for dependency on claim 9. Appropriate correction is required.
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 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The analysis of the claims will follow the 2019 Revised Patent Subject Matter Eligibility Guidance, 84 Fed. Reg. 50 (“2019 PEG”).
Claim 1
Step 1: The claim recites a system comprising memories and processors; therefore, the claim is directed to the statutory category of machines.
Step 2A Prong 1: The claim recites, inter alia:
[G]enerat[ing] a first set of statistical metrics associated with the first data set based on values of the first data set: This limitation could encompass mentally generating the metrics.
[G]enerat[ing] a second set of statistical metrics associated with the first set of statistical metrics and the values of the first data set, wherein the first set of statistical metrics and the second set of statistical metrics comprise a second data set that is not subject to the usage restriction, wherein the second set of statistical metrics represents a set of correlations between the values of the first data set and the first set of statistical metrics: This limitation could encompass mentally generating the metrics subject to the claimed conditions.
[G]enerat[ing] a set of embeddings with artificial noise based on the second data set: This limitation could encompass mentally generating the embeddings by mentally adding noise to the set.
Step 2A Prong 2: This judicial exception is not integrated into a practical application. The claim further recites a “system for synthetic data generation, the system comprising: one or more memories; and one or more processors, communicatively coupled to the one or more memories”. However, this limitation amounts to a mere instruction to apply the judicial exception using a generic computer. MPEP § 2106.05(f). The claim also recites “receiv[ing] a first data set, wherein the first data set is subject to a usage restriction” and “output[ting] information associated with the set of embeddings.” These limitations are directed to the insignificant extra-solution activity of mere data gathering and output. MPEP § 2106.05(g).
Step 2B: The claim does not contain significantly more than the judicial exception. The receiving and outputting limitations, in addition to being insignificant extra-solution activity, are also directed to the well-understood, routine, and conventional activity of receiving or transmitting data over a network. MPEP § 2106.05(d)(II); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network). Otherwise, the analysis at this step mirrors that of step 2A, prong 2. As an ordered whole, the claim is directed to a mentally performable process of generating embeddings for a dataset. Nothing in the claim provides significantly more than this. As such, the claim is not patent eligible.
Claim 2
Step 1: A machine, as above.
Step 2A Prong 1: The claim recites, inter alia, “generat[ing] a set of edge cases associated with the set of embeddings, wherein the set of edge cases represent one or more statistically outlying values based on the second set of statistical metrics”. This limitation could encompass mentally generating the edge cases.
Step 2A Prong 2: This judicial exception is not integrated into a practical application. The claim further recites that certain operations are performed with “the one or more processors”. However, this limitation amounts to a mere instruction to apply the judicial exception using a generic computer. MPEP § 2106.05(f). The claim also recites “output[ting] information identifying the set of edge cases.” This limitation is directed to the insignificant extra-solution activity of mere data gathering and output. MPEP § 2106.05(g).
Step 2B: The claim does not contain significantly more than the judicial exception. The claim further recites that certain operations are performed with “the one or more processors”. However, this limitation amounts to a mere instruction to apply the judicial exception using a generic computer. MPEP § 2106.05(f). The claim also recites “output[ting] information identifying the set of edge cases.” This limitation is directed to the well-understood, routine, and conventional activity of receiving or transmitting data over a network. MPEP § 2106.05(d)(II); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network).
Claim 3
Step 1: A machine, as above.
Step 2A Prong 1: The claim recites, inter alia, “execut[ing] a set of test cases”. This limitation could encompass mentally executing the test cases.
Step 2A Prong 2: This judicial exception is not integrated into a practical application. The claim further recites that certain operations are performed with “the one or more processors” and “train[ing] a machine learning model using the second data set; and [using] … the machine learning model based on training the machine learning model”. However, these limitations amount to mere instructions to apply the judicial exception using a generic computer. MPEP § 2106.05(f). The claim also recites “output[ting] information associated with the machine learning model.” This limitation is directed to the insignificant extra-solution activity of mere data gathering and output. MPEP § 2106.05(g).
Step 2B: The claim does not contain significantly more than the judicial exception. The claim further recites that certain operations are performed with “the one or more processors” and “train[ing] a machine learning model using the second data set; and [using] … the machine learning model based on training the machine learning model”. However, these limitations amount to mere instructions to apply the judicial exception using a generic computer. MPEP § 2106.05(f). The claim also recites “output[ting] information associated with the machine learning model.” This limitation is directed to the well-understood, routine, and conventional activity of receiving or transmitting data over a network. MPEP § 2106.05(d)(II); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network).
Claim 4
Step 1: A machine, as above.
Step 2A Prong 1: The claim recites, inter alia, “generat[ing], using the second data set, a set of values …, the set of values having a correlation to the values of the first data set; and … generat[ing] the set of embeddings based on the set of values generated”. These limitations could encompass mentally generating the values and mentally generating the embeddings based on the values.
Step 2A Prong 2: This judicial exception is not integrated into a practical application. The claim further recites “using a generative adversarial network” and that “the one or more processors … generate the set of embeddings”. This amounts to a mere instruction to apply the judicial exception using a generic computer programmed with a generic class of computer algorithm. MPEP § 2106.05(f).
Step 2B: The claim does not contain significantly more than the judicial exception. The claim further recites “using a generative adversarial network” and that “the one or more processors … generate the set of embeddings”. This amounts to a mere instruction to apply the judicial exception using a generic computer programmed with a generic class of computer algorithm. MPEP § 2106.05(f).
Claim 5
Step 1: A machine, as above.
Step 2A Prong 1: The claim recites that “the first set of statistical metrics includes a set of statistical moments relating to the values of the first data set.” Generating the metrics remains mentally performable under these further assumptions; the generation of the metrics also represents a mathematical concept.
Step 2A Prong 2: This judicial exception is not integrated into a practical application. See claim 1 analysis.
Step 2B: The claim does not contain significantly more than the judicial exception. See claim 1 analysis.
Claim 6
Step 1: A machine, as above.
Step 2A Prong 1: The claim recites that “the first set of statistical metrics includes one or more distributions relating to the values of the first data set.” Generating the metrics remains mentally performable under these further assumptions; the generation of the metrics also represents a mathematical concept.
Step 2A Prong 2: This judicial exception is not integrated into a practical application. See claim 1 analysis.
Step 2B: The claim does not contain significantly more than the judicial exception. See claim 1 analysis.
Claim 7
Step 1: A machine, as above.
Step 2A Prong 1: The claim recites that “the second set of statistical metrics includes a copula between the values of the first data set and the first set of statistical metrics.” Generating the metrics remains mentally performable under these further assumptions; the generation of the metrics also represents a mathematical concept.
Step 2A Prong 2: This judicial exception is not integrated into a practical application. See claim 1 analysis.
Step 2B: The claim does not contain significantly more than the judicial exception. See claim 1 analysis.
Claim 8
Step 1: A machine, as above.
Step 2A Prong 1: The claim recites the same judicial exceptions as in claim 1.
Step 2A Prong 2: This judicial exception is not integrated into a practical application. The claim further recites that “the one or more processors, when configured to generate the set of embeddings, are configured to: generate the set of embeddings using a neural network training technique.” This amounts to a mere instruction to apply the judicial exception using a generic computer programmed with a generic class of computer algorithm. MPEP § 2106.05(f).
Step 2B: The claim does not contain significantly more than the judicial exception. The claim further recites that “the one or more processors, when configured to generate the set of embeddings, are configured to: generate the set of embeddings using a neural network training technique.” This amounts to a mere instruction to apply the judicial exception using a generic computer programmed with a generic class of computer algorithm. MPEP § 2106.05(f).
Claim 9
Step 1: The claim recites a method; therefore, it is directed to the statutory category of processes.
Step 2A Prong 1: The claim recites, inter alia, “generating, … based on the input data, artificial data …, … [and] generat[ing] the artificial data such that the artificial data share[] a set of common characteristics with the input data”. This limitation could encompass mentally generating the artificial data that share characteristics with another set of input data.
Step 2A Prong 2: This judicial exception is not integrated into a practical application. The claim further recites that the generation is performed “by the device and based on the input data … using one or more machine learning models operating on one or more servers, wherein the one or more machine learning models includes a generative artificial intelligence model”. This amounts to a mere instruction to apply the judicial exception using a generic computer programmed with a generic class of computer algorithm. MPEP § 2106.05(f). The claim further recites “receiving, by a device, input data for machine learning model training” and “transmitting, by the device, an output associated with the particular machine learning model.” These limitations are directed to the insignificant extra-solution activity of mere data gathering and output. MPEP § 2106.05(g). Finally, the claim recites “training, by the device and using the artificial data and metadata associated with the artificial data, a particular machine learning model”. This limitation merely restricts the field of use of the judicial exception to model training. MPEP § 2106.05(h).
Step 2B: The claim does not contain significantly more than the judicial exception. The receiving and transmitting limitations, in addition to being insignificant extra-solution activity, are also directed to the well-understood, routine, and conventional activity of receiving or transmitting data over a network. MPEP § 2106.05(d)(II); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network). Otherwise, the analysis at this step mirrors that of step 2A, prong 2. As an ordered whole, the claim is directed to a mentally performable process of generating an artificial dataset. Nothing in the claim provides significantly more than this. As such, the claim is not patent eligible.
Claim 10
Step 1: A process, as above.
Step 2A Prong 1: The claim recites, inter alia, “ generating a set of copies of the input data”. This limitation could encompass mentally generating the copies by writing them down.
Step 2A Prong 2: This judicial exception is not integrated into a practical application. The claim further recites “transmitting the set of copies of the input data to the set of servers …; and receiving, as a response to transmitting the set of copies of the input data, a set of portions of the artificial data.” These limitations are directed to the insignificant extra-solution activity of mere data gathering and output. MPEP § 2106.05(g). The claim also recites that “a server, of the set of servers, implements a machine learning model of the one or more machine learning models”. This amounts to a mere instruction to apply the judicial exception using a generic computer programmed with a generic class of computer algorithm. MPEP § 2106.05(f).
Step 2B: The claim does not contain significantly more than the judicial exception. The claim further recites “transmitting the set of copies of the input data to the set of servers …; and receiving, as a response to transmitting the set of copies of the input data, a set of portions of the artificial data.” These limitations are directed to the well-understood, routine, and conventional activity of receiving or transmitting data over a network. MPEP § 2106.05(d)(II); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network). The claim also recites that “a server, of the set of servers, implements a machine learning model of the one or more machine learning models”. This amounts to a mere instruction to apply the judicial exception using a generic computer programmed with a generic class of computer algorithm. MPEP § 2106.05(f).
Claim 11
Step 1: A process, as above.
Step 2A Prong 1: The claim recites, inter alia, “exposing the set of copies of the artificial data stored at the one or more servers”. This limitation could encompass mentally exposing the data by divulging it orally or in writing.
Step 2A Prong 2: This judicial exception is not integrated into a practical application. The claim further recites that the exposure is performed “via one or more protocol functions”. This amounts to a mere instruction to apply the judicial exception using a generic computer programmed with a generic class of computer algorithm. MPEP § 2106.05(f). The claim further recites “storing a set of copies of the artificial data at the one or more servers”. This limitation is directed to the insignificant extra-solution activity of mere data gathering and output. MPEP § 2106.05(g).
Step 2B: The claim does not contain significantly more than the judicial exception. The claim further recites that the exposure is performed “via one or more protocol functions”. This amounts to a mere instruction to apply the judicial exception using a generic computer programmed with a generic class of computer algorithm. MPEP § 2106.05(f). The claim further recites “storing a set of copies of the artificial data at the one or more servers”. This limitation is directed to the well-understood, routine, and conventional activity of storing and retrieving information in memory. MPEP § 2106.05(d)(II); Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015).
Claim 12
Step 1: A process, as above.
Step 2A Prong 1: The claim recites the same judicial exceptions as in claim 9.
Step 2A Prong 2: This judicial exception is not integrated into a practical application. The claim further recites that “transmitting the output associated with the particular machine learning model comprises: transmitting a prediction associated with the machine learning model.” This limitation is directed to the insignificant extra-solution activity of mere data gathering and output. MPEP § 2106.05(g).
Step 2B: The claim does not contain significantly more than the judicial exception. The claim further recites that “transmitting the output associated with the particular machine learning model comprises: transmitting a prediction associated with the machine learning model.” This limitation is directed to the well-understood, routine, and conventional activity of receiving or transmitting data over a network. MPEP § 2106.05(d)(II); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network).
Claim 13
Step 1: A process, as above.
Step 2A Prong 1: The claim recites the same judicial exceptions as in claim 9.
Step 2A Prong 2: This judicial exception is not integrated into a practical application. The claim further recites that “transmitting an output associated with the particular machine learning model comprises: transmitting information for storage in a data structure of a synthetic resource group, the synthetic resource group being configured to persist data of the data structure across one or more other synthetic resource groups.” This limitation is directed to the insignificant extra-solution activity of mere data gathering and output. MPEP § 2106.05(g).
Step 2B: The claim does not contain significantly more than the judicial exception. The claim further recites that “transmitting an output associated with the particular machine learning model comprises: transmitting information for storage in a data structure of a synthetic resource group, the synthetic resource group being configured to persist data of the data structure across one or more other synthetic resource groups.” This limitation is directed to the well-understood, routine, and conventional activity of receiving or transmitting data over a network. MPEP § 2106.05(d)(II); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network).
Claim 14
Step 1: The claims recite a non-transitory computer-readable medium; therefore, the claim is directed to the statutory category of articles of manufacture.
Step 2A Prong 1: The claim recites, inter alia:
[G]enerat[ing] a first set of statistical metrics associated with the first data set based on values of the first data set: This limitation could encompass mentally generating the metrics.
[G]enerat[ing] a second set of statistical metrics associated with the first set of statistical metrics and the values of the first data set, wherein the first set of statistical metrics and the second set of statistical metrics comprise a second data set that is not subject to [a] usage restriction, and wherein the second data set includes artificial data and metadata for the artificial data, wherein the second set of statistical metrics represents a set of correlations between the values of the first data set and the first set of statistical metrics: This limitation could encompass mentally generating the second set of statistical metrics and the second data subject to the claimed constraints.
[G]enerat[ing] a set of embeddings with artificial noise based on the second data set: This limitation could encompass mentally generating the embeddings by randomly perturbing the data entries.
Step 2A Prong 2: This judicial exception is not integrated into a practical application. The claim further recites a “non-transitory computer-readable medium storing a set of instructions, the set of instructions comprising: one or more instructions that, when executed by one or more processors of a system, cause the system to [perform the method]”. This amounts to a mere instruction to apply the judicial exception using a generic computer. MPEP § 2106.05(f). The claim further recites “receiv[ing] a first data set, wherein the first data set is subject to a usage restriction” and “output[ting] information associated with the set of embeddings”. These limitations are directed to the insignificant extra-solution activity of mere data gathering and output. MPEP § 2106.05(g).
Step 2B: The claim does not contain significantly more than the judicial exception. The receiving and outputting limitations, in addition to being insignificant extra-solution activity, are also directed to the well-understood, routine, and conventional activity of receiving or transmitting data over a network. MPEP § 2106.05(d)(II); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network). Otherwise, the analysis at this step mirrors that of step 2A, prong 2. As an ordered whole, the claim is directed to a mentally performable process of generating a set of embeddings using an artificial dataset. Nothing in the claim provides significantly more than this. As such, the claim is not patent eligible.
Claim 15
Step 1: An article of manufacture, as above.
Step 2A Prong 1: The claim recites, inter alia, “generat[ing] a set of edge cases associated with the set of embeddings, wherein the set of edge cases represent one or more statistically outlying values based on the second set of statistical metrics”. This limitation could encompass mentally generating the edge cases.
Step 2A Prong 2: This judicial exception is not integrated into a practical application. The claim further recites that certain operations are performed with “the one or more instructions, that cause the system to [perform the functions]”. However, this limitation amounts to a mere instruction to apply the judicial exception using a generic computer. MPEP § 2106.05(f). The claim also recites “output[ting] information identifying the set of edge cases.” This limitation is directed to the insignificant extra-solution activity of mere data gathering and output. MPEP § 2106.05(g).
Step 2B: The claim does not contain significantly more than the judicial exception. The claim further recites that certain operations are performed with “the one or more instructions, that cause the system to [perform the functions]”. However, this limitation amounts to a mere instruction to apply the judicial exception using a generic computer. MPEP § 2106.05(f). The claim also recites “output[ting] information identifying the set of edge cases.” This limitation is directed to the well-understood, routine, and conventional activity of receiving or transmitting data over a network. MPEP § 2106.05(d)(II); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network).
Claim 16
Step 1: An article of manufacture, as above.
Step 2A Prong 1: The claim recites, inter alia, “execut[ing] a set of test cases”. This limitation could encompass mentally executing the test cases.
Step 2A Prong 2: This judicial exception is not integrated into a practical application. The claim further recites that certain operations are performed with “the one or more instructions” and “train[ing] a machine learning model using the second data set; and [using] … the machine learning model based on training the machine learning model”. However, these limitations amount to mere instructions to apply the judicial exception using a generic computer. MPEP § 2106.05(f). The claim also recites “output[ting] information associated with the machine learning model.” This limitation is directed to the insignificant extra-solution activity of mere data gathering and output. MPEP § 2106.05(g).
Step 2B: The claim does not contain significantly more than the judicial exception. The claim further recites that certain operations are performed with “the one or more instructions” and “train[ing] a machine learning model using the second data set; and [using] … the machine learning model based on training the machine learning model”. However, these limitations amount to mere instructions to apply the judicial exception using a generic computer. MPEP § 2106.05(f). The claim also recites “output[ting] information associated with the machine learning model.” This limitation is directed to the well-understood, routine, and conventional activity of receiving or transmitting data over a network. MPEP § 2106.05(d)(II); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network).
Claim 17
Step 1: An article of manufacture, as above.
Step 2A Prong 1: The claim recites, inter alia, “generat[ing], using the second data set, a set of values …, the set of values … hav[ing] a correlation to the values of the first data set; and … generat[ing] the set of embeddings based on the set of values generated”. These limitations could encompass mentally generating the values and mentally generating the embeddings based on the values.
Step 2A Prong 2: This judicial exception is not integrated into a practical application. The claim further recites “using a generative adversarial network” and that “the one or more instructions … generate the set of embeddings”. This amounts to a mere instruction to apply the judicial exception using a generic computer programmed with a generic class of computer algorithm. MPEP § 2106.05(f).
Step 2B: The claim does not contain significantly more than the judicial exception. The claim further recites “using a generative adversarial network” and that “the one or more instructions … generate the set of embeddings”. This amounts to a mere instruction to apply the judicial exception using a generic computer programmed with a generic class of computer algorithm. MPEP § 2106.05(f).
Claim 18
Step 1: An article of manufacture, as above.
Step 2A Prong 1: The claim recites that “the first set of statistical metrics includes a set of statistical moments relating to the values of the first data set.” Generating the metrics remains mentally performable under these further assumptions; the generation of the metrics also represents a mathematical concept.
Step 2A Prong 2: This judicial exception is not integrated into a practical application. See claim 14 analysis.
Step 2B: The claim does not contain significantly more than the judicial exception. See claim 14 analysis.
Claim 19
Step 1: An article of manufacture, as above.
Step 2A Prong 1: The claim recites that “the first set of statistical metrics includes one or more distributions relating to the values of the first data set.” Generating the metrics remains mentally performable under these further assumptions; the generation of the metrics also represents a mathematical concept.
Step 2A Prong 2: This judicial exception is not integrated into a practical application. See claim 14 analysis.
Step 2B: The claim does not contain significantly more than the judicial exception. See claim 14 analysis.
Claim 20
Step 1: An article of manufacture, as above.
Step 2A Prong 1: The claim recites that “the second set of statistical metrics includes a copula between the values of the first data set and the first set of statistical metrics.” Generating the metrics remains mentally performable under these further assumptions; the generation of the metrics also represents a mathematical concept.
Step 2A Prong 2: This judicial exception is not integrated into a practical application. See claim 14 analysis.
Step 2B: The claim does not contain significantly more than the judicial exception. See claim 14 analysis.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1, 3-6, 8-10, 12, 14, and 16-19 are rejected under 35 U.S.C. 103 as being unpatentable over Nandi et al. (US 20240346367) (“Nandi”) in view of Walters et al. (US 20210397972) (“Walters”).
Regarding claim 1, Nandi discloses [a] system for synthetic data generation, the system comprising:
one or more memories (systems include program files stored on a storage device, loaded into a memory and executed by one or more processors – Nandi, paragraph 90); and
one or more processors, communicatively coupled to the one or more memories (systems include program files stored on a storage device, loaded into a memory and executed by one or more processors – Nandi, paragraph 90), configured to:
receive a first data set,
wherein the first data set is subject to a usage restriction (in an example setting containing complete entity-space fragmentation and zero feature-space fragmentation, data silo A contains data examples [first data set] for entities E1, E2, and E3, each containing data values for features F1, F2, and F3 – Nandi, paragraph 65; embeddings in shared embedding space are generated without providing access to the underlying raw feature data (i.e., the data in the data silos remain separate), thereby preserving the privacy of the data examples [usage restriction = siloed raw data cannot be directly accessed] – id. at paragraph 32);
generate a first set of statistical metrics associated with the first data set based on values of the first data set (each of the data silos can provide distribution data to the central computing system [distribution of data silo A = first set of statistical metrics based on the distribution of values of data points of silo A] – Nandi, paragraph 66);
generate a second set of statistical metrics associated with the first set of statistical metrics and the values of the first data set (Nandi Fig. 1C shows that a central computing system computes aggregate distribution data [second set of statistical metrics] based on the distribution data from data silos A and B [so the aggregate data are based both on the distribution of silo A’s data and on the underlying values of silo A’s raw data]),
wherein the first set of statistical metrics and the second set of statistical metrics comprise a second data set that is not subject to the usage restriction (embeddings in shared embedding space are generated without providing access to the underlying raw feature data (i.e., the data in the data silos remain separate), thereby preserving the privacy of the data examples [note that, since neither silo A’s distribution nor the aggregate distribution contains raw data, they can be used without regard to the rule against sharing raw data] – Nandi, paragraph 32 and Fig. 1C; paragraph 28 also discloses the generation of silo-specific synthetic data examples at each silo, which may also be regarded as part of the second data set), …
generate a set of embeddings with artificial noise based on the second data set (embedding generation system transmits embeddings to the embedding re-orientation system; the embeddings can be subjected to a differential privacy approach (e.g., noise can be added) prior to transmission from each embedding generation system to the embedding re-orientation system – Nandi, paragraph 82; embedding generation system can generate embeddings for items represented by data examples stored in each data silo – id. at paragraph 81 [so that the noise-added embeddings are based on silo A’s data, including its distribution which is part of the second data set]); and
output information associated with the set of embeddings (embedding generation system transmits embeddings to the embedding re-orientation system; the embeddings can be subjected to a differential privacy approach (e.g., noise can be added) prior to transmission [output] from each embedding generation system to the embedding re-orientation system – Nandi, paragraph 82).”
Nandi appears not to disclose explicitly the further limitations of the claim. However, Walters discloses that “the second set of statistical metrics represents a set of correlations between the values of the first data set and the first set of statistical metrics (system may generate a similarity metric value according to a similarity metric using a normalized reference dataset [first set of statistical metrics] and a synthetic dataset [first data set]; the similarity metric value may include a statistical correlation score [second set of statistical metrics representing a set of correlations] – Walters, paragraph 104) ….”
Walters and the instant application both relate to synthetic data generation for machine learning and are analogous. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Nandi to include correlation values between a data set and statistical metrics, as disclosed by Walters, and an ordinary artisan could reasonably expect to have done so successfully. Doing so would allow the system to determine how similar the two datasets are, thereby ensuring that the derived data set is faithful to the original. See Walters, paragraph 104.
Regarding claim 3, Nandi, as modified by Walters, discloses that “the one or more processors, to generate the set of embeddings, are configured to:
train a machine learning model using the second data set (additional generative model [machine learning model] can be trained on an aggregated set of all the silo-specific synthetic data examples [part of the second data set] – Nandi, paragraph 28); and
execute a set of test cases on the machine learning model based on training the machine learning model (additional generative model [machine learning model] can be trained on an aggregated set of all the silo-specific synthetic data examples and used to generate the plurality of synthetic data examples – Nandi, paragraph 28 [test cases = inputs to trained additional model that cause it to generate the synthetic data]); and
wherein the one or more instructions, that cause the system to output information associated with the set of embeddings, cause the system to:
output information associated with the machine learning model (additional generative model [machine learning model] can be trained on an aggregated set of all the silo-specific synthetic data examples [information associated with the machine learning model] – Nandi, paragraph 28).”
Regarding claim 4, Nandi, as modified by Walters, discloses that “the one or more processors are further configured to:
generate, using the second data set, a set of values using a generative adversarial network, the set of values having a correlation to the values of the first data set (additional generative model [generative network] can be trained on an aggregated set of all the silo-specific synthetic data examples [part of the second data set] and used to generate the plurality of synthetic data examples [set of values]; synthetic data samples have respective feature data within the aggregate feature space [i.e., are correlated with the feature spaces of data of the individual silos in the aggregate] – Nandi, paragraph 28; see also paragraph 47 (indicating that a GAN can be used to generate the data)); …
wherein the one or more processors, to generate the set of embeddings, are configured to:
generate the set of embeddings based on the set of values generated using the generative adversarial network (silo computing systems can use received synthetic data examples [set of values generated with the generative network] to train embedding generation models and generate embeddings within a shared embedding space – Nandi, paragraph 20).”
Regarding claim 5, Nandi, as modified by Walters, discloses that “the first set of statistical metrics includes a set of statistical moments relating to the values of the first data set (aggregating the different component feature-spaces can include imputing feature values (e.g., null values or average values) for data examples [from each data silo] that did not previously have feature values for a particular feature – Nandi, paragraph 68 [average = first moment]).”
Regarding claim 6, Nandi, as modified by Walters, discloses that “the first set of statistical metrics includes one or more distributions relating to the values of the first data set (each of the data silos can provide distribution data to the central computing system – Nandi, paragraph 66).”
Regarding claim 8, Nandi, as modified by Walters, discloses that “the one or more processors, when configured to generate the set of embeddings, are configured to:
generate the set of embeddings using a neural network training technique (central computing system can transmit synthetic data examples to silo computing systems and silo computing systems can use the received synthetic data examples to train embedding generation models – Nandi, paragraph 20; see also paragraph 47 (indicating that a GAN, i.e., a type of neural network, can be used to generate the data)).”
Regarding claim 9, Nandi discloses “[a] method of generating testing data using a generative artificial intelligence model, comprising:
receiving, by a device, input data for machine learning model training (each silo computing system associated with one of the data silos can train a differentially-private generative model [machine learning model] on the data [input data] contained in the corresponding silo – Nandi, paragraph 26);
generating, by the device and based on the input data, artificial data using one or more machine learning models operating on one or more servers (silo computing system can then use the differentially-private generative model [machine learning model] to generate a respective plurality of silo-specific synthetic data examples [artificial data] that are representative of the respective data distribution in the corresponding data silo – Nandi, paragraph 26; see also paragraph 60 (indicating that the computing devices may include servers)),
wherein the one or more machine learning models includes a generative artificial intelligence model configured to generate the artificial data such that the artificial data shares a set of common characteristics with the input data (silo computing system can then use the differentially-private generative model [machine learning model] to generate a respective plurality of silo-specific synthetic data examples [artificial data] that are representative of [share a set of common characteristics with] the respective data distribution in the corresponding data silo – Nandi, paragraph 26);
training, by the device and using the artificial data …, a particular machine learning model (additional generative model [particular machine learning model] can be trained on the aggregated set of all silo-specific synthetic data examples [artificial data] and used to generate the plurality of synthetic data examples – Nandi, paragraph 28); and
transmitting, by the device, an output associated with the particular machine learning model (additional generative model [particular machine learning model] can be trained on the aggregated set of all silo-specific synthetic data examples and used to generate the plurality of synthetic data examples – Nandi, paragraph 28; central computing system can then provide [transmit] the plurality of synthetic data examples [output associated with the machine learning model] to silo computing systems – id. at paragraph 29).”
Nandi appears not to disclose explicitly the further limitations of the claim. However, Walters discloses “metadata associated with the artificial data (model optimizer may store a synthetic data model and metadata of the synthetic data model – Walters, paragraph 154) ….” It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Nandi to produce metadata associated with artificial data, as disclosed by Walters, and an ordinary artisan could reasonably expect to have done so successfully. Doing so would increase data transparency by allowing the system to store data about the origin and other characteristics of the data. See Walters, paragraph 154.
Regarding claim 10, Nandi, as modified by Walters, discloses “generating a set of copies of the input data (each silo computing system associated with one of the data silos can train a differentially-private generative model on the data [input data] contained in the corresponding silo – Nandi, paragraph 26 [i.e., at least one copy of the input data is generated]); …
wherein generating the artificial data using the one or more machine learning models operating on the one or more servers comprises:
transmitting the set of copies of the input data to the set of servers (each silo computing system associated with one of the data silos can train a differentially-private generative model on the data [input data] contained in the corresponding silo – Nandi, paragraph 26 [note that the existence of the data in the silo implies its previous transmission to the silo]; see also paragraph 60 (indicating that the computing devices may include servers)),
wherein a server, of the set of servers, implements a machine learning model of the one or more machine learning models (silo computing system can then use the differentially-private generative model [machine learning model] to generate a respective plurality of silo-specific synthetic data examples that are representative of the respective data distribution in the corresponding data silo – Nandi, paragraph 26; see also paragraph 60 (indicating that the computing devices may include servers)); and
receiving, as a response to transmitting the set of copies of the input data, a set of portions of the artificial data (silo computing system can then use the differentially-private generative model to generate a respective plurality of silo-specific synthetic data examples [set of portions of the artificial data] that are representative of the respective data distribution in the corresponding data silo; these silo-specific synthetic data examples can be sent to protocothe central computing system [i.e., received thereby] – Nandi, paragraph 26).”
Regarding claim 12, Nandi, as modified by Walters, discloses that “transmitting the output associated with the particular machine learning model comprises:
transmitting a prediction associated with the machine learning model (additional generative model [particular machine learning model] can be trained on the aggregated set of all silo-specific synthetic data examples and used to generate the plurality of synthetic data examples – Nandi, paragraph 28; central computing system can then provide [transmit] the plurality of synthetic data examples [output associated with the machine learning model] to silo computing systems – id. at paragraph 29 [note that the synthetic data constitute a prediction of what representative data would fit within the existing data distribution]).”
Regarding claim 14, Nandi discloses “[a] non-transitory computer-readable medium storing a set of instructions, the set of instructions comprising:
one or more instructions that, when executed by one or more processors of a system (systems include program files [instructions] stored on a storage device [non-transitory computer-readable medium], loaded into a memory and executed by one or more processors – Nandi, paragraph 90), cause the system to:
receive a first data set,
wherein the first data set is subject to a usage restriction (in an example setting containing complete entity-space fragmentation and zero feature-space fragmentation, data silo A contains data examples [first data set] for entities E1, E2, and E3, each containing data values for features F1, F2, and F3 – Nandi, paragraph 65; embeddings in shared embedding space are generated without providing access to the underlying raw feature data (i.e., the data in the data silos remain separate), thereby preserving the privacy of the data examples [usage restriction = siloed raw data cannot be directly accessed] – id. at paragraph 32);
generate a first set of statistical metrics associated with the first data set based on values of the first data set (each of the data silos can provide distribution data to the central computing system [distribution of data silo A = first set of statistical metrics based on the distribution of values of data points of silo A] – Nandi, paragraph 66);
generate a second set of statistical metrics associated with the first set of statistical metrics and the values of the first data set (Nandi Fig. 1C shows that a central computing system computes aggregate distribution data [second set of statistical metrics] based on the distribution data from data silos A and B [so the aggregate data are based both on the distribution of silo A’s data and on the underlying values of silo A’s raw data]),
wherein the first set of statistical metrics and the second set of statistical metrics comprise a second data set that is not subject to the usage restriction (embeddings in shared embedding space are generated without providing access to the underlying raw feature data (i.e., the data in the data silos remain separate), thereby preserving the privacy of the data examples [note that, since neither silo A’s distribution nor the aggregate distribution contains raw data, they can be used without regard to the rule against sharing raw data] – Nandi, paragraph 32 and Fig. 1C; paragraph 28 also discloses the generation of silo-specific synthetic data examples at each silo, which may also be regarded as part of the second data set), and
wherein the second data set includes artificial data (Nandi paragraph 28 also discloses the generation of silo-specific synthetic [artificial] data examples at each silo, which may be regarded as part of the second data set) …, …
generate a set of embeddings with artificial noise based on the second data set (embedding generation system transmits embeddings to the embedding re-orientation system; the embeddings can be subjected to a differential privacy approach (e.g., noise can be added) prior to transmission from each embedding generation system to the embedding re-orientation system – Nandi, paragraph 82; embedding generation system can generate embeddings for items represented by data examples stored in each data silo – id. at paragraph 81 [so that the noise-added embeddings are based on silo A’s data, including its distribution which is part of the second data set]); and
output information associated with the set of embeddings (embedding generation system transmits embeddings to the embedding re-orientation system; the embeddings can be subjected to a differential privacy approach (e.g., noise can be added) prior to transmission [output] from each embedding generation system to the embedding re-orientation system – Nandi, paragraph 82).”
Nandi appears not to disclose explicitly the further limitations of the claim. However, Walters discloses that “the second data set includes … metadata for the artificial data (model optimizer may be configured to store a trained synthetic data model and metadata of the synthetic data model such as values of the prediction metrics of the data – Walters, paragraph 154; prediction metrics enable a user to determine whether data models perform similarly for synthetic data and actual data [i.e., they are metadata of the synthetic/artificial data] – id. at paragraph 137),
wherein the second set of statistical metrics represents a set of correlations between the values of the first data set and the first set of statistical metrics (system may generate a similarity metric value according to a similarity metric using a normalized reference dataset [first set of statistical metrics] and a synthetic dataset [first data set]; the similarity metric value may include a statistical correlation score [second set of statistical metrics representing a set of correlations] – Walters, paragraph 104) ….” It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Nandi to include correlation values between a data set and statistical metrics, as disclosed by Walters, and an ordinary artisan could reasonably expect to have done so successfully. Doing so would allow the system to determine how similar the two datasets are, thereby ensuring that the derived data set is faithful to the original. See Walters, paragraph 104.
Regarding claim 16, Nandi, as modified by Walters, discloses that “the one or more instructions, that cause the system to configure to generate the set of embeddings, cause the system to:
train a machine learning model using the second data set (additional generative model [machine learning model] can be trained on an aggregated set of all the silo-specific synthetic data examples [part of the second data set] – Nandi, paragraph 28); and
execute a set of test cases on the machine learning model based on training the machine learning model (additional generative model [machine learning model] can be trained on an aggregated set of all the silo-specific synthetic data examples and used to generate the plurality of synthetic data examples – Nandi, paragraph 28 [test cases = inputs to trained additional model that cause it to generate the synthetic data]); and
wherein the one or more instructions, that cause the system to output information associated with the set of embeddings, cause the system to:
output information associated with the machine learning model (additional generative model [machine learning model] can be trained on an aggregated set of all the silo-specific synthetic data examples [information associated with the machine learning model] – Nandi, paragraph 28).”
Regarding claim 17, Nandi, as modified by Walters, discloses that “the one or more instructions further cause the system to:
generate, using the second data set, a set of values using a generative adversarial network, wherein the set of values have a correlation to the values of the first data set (additional generative model [generative network] can be trained on an aggregated set of all the silo-specific synthetic data examples [part of the second data set] and used to generate the plurality of synthetic data examples [set of values]; synthetic data samples have respective feature data within the aggregate feature space [i.e., are correlated with the feature spaces of data of the individual silos in the aggregate] – Nandi, paragraph 28; see also paragraph 47 (indicating that a GAN can be used to generate the data)); …
wherein the one or more instructions, that cause the system to generate the set of embeddings, cause the system to:
generate the set of embeddings based on the set of values generated using the generative adversarial network (silo computing systems can use received synthetic data examples [set of values generated with the generative network] to train embedding generation models and generate embeddings within a shared embedding space – Nandi, paragraph 20).”
Regarding claim 18, Nandi, as modified by Walters, discloses that “the first set of statistical metrics includes a set of statistical moments relating to the values of the first data set (aggregating the different component feature-spaces can include imputing feature values (e.g., null values or average values) for data examples [from each data silo] that did not previously have feature values for a particular feature – Nandi, paragraph 68 [average = first moment]).”
Regarding claim 19, Nandi, as modified by Walters, discloses that “the first set of statistical metrics includes one or more distributions relating to the values of the first data set (each of the data silos can provide distribution data to the central computing system – Nandi, paragraph 66).”
Claims 2 and 15 are rejected under 35 U.S.C. 103 as being unpatentable over Nandi in view of Walters and further in view of Inzelberg (US 20240330746) (“Inzelberg”).
Regarding claim 2, neither Nandi nor Walters appears to disclose explicitly the further limitations of the claim. However, Inzelberg discloses that “the one or more processors are further configured to: generate a set of edge cases associated with the set of embeddings (synthetic outlier data [set of edge cases] are generated; the synthetic outlier events may be similar, but not identical to, historical outlier events that have actually occurred; these historical outlier events may be embedded in a vector space – Inzelberg, paragraph 54), …
the set of edge cases represent one or more statistically outlying values based on the second set of statistical metrics (synthetic outlier data are generated; the synthetic outlier events may be similar, but not identical to, historical outlier events that have actually occurred; to generate the synthetic outlier events, the values of the parameters of the historical outlier events [second set of statistical metrics] may be varied [i.e., the synthetic data are statistically based on the real data] – Inzelberg, paragraph 54); and …
the one or more processors, to output the information associated with the set of embeddings, are configured to:
output information identifying the set of edge cases (historical outlier data and the synthetically-generated outlier data are combined into a unified dataset [i.e., the synthetic outlier data are output to this unified dataset] – Inzelberg, paragraph 56).”
Inzelberg and the instant application both relate to machine learning with synthetic outlier data and are analogous. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Nandi/Walters to generate outlying values and output them, as disclosed by Inzelberg, and an ordinary artisan could reasonably expect to have done so successfully. Doing so would compensate for a paucity of real outlier data that may be present in the training set, thereby making the dataset more balanced. See Inzelberg, paragraph 54.
Regarding claim 15, neither Nandi nor Walters appears to disclose explicitly the further limitations of the claim. However, Inzelberg discloses that “the one or more instructions further cause the system to:
generate a set of edge cases associated with the set of embeddings (synthetic outlier data [set of edge cases] are generated; the synthetic outlier events may be similar, but not identical to, historical outlier events that have actually occurred; these historical outlier events may be embedded in a vector space – Inzelberg, paragraph 54), …
the set of edge cases represent one or more statistically outlying values based on the second set of statistical metrics (synthetic outlier data are generated; the synthetic outlier events may be similar, but not identical to, historical outlier events that have actually occurred; to generate the synthetic outlier events, the values of the parameters of the historical outlier events [second set of statistical metrics] may be varied [i.e., the synthetic data are statistically based on the real data] – Inzelberg, paragraph 54); and …
wherein the one or more instructions, that cause the system to configure to output the information associated with the set of embeddings, cause the system to:
output information identifying the set of edge cases (historical outlier data and the synthetically-generated outlier data are combined into a unified dataset [i.e., the synthetic outlier data are output to this unified dataset] – Inzelberg, paragraph 56).” It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Nandi/Walters to generate outlying values and output them, as disclosed by Inzelberg, and an ordinary artisan could reasonably expect to have done so successfully. Doing so would compensate for a paucity of real outlier data that may be present in the training set, thereby making the dataset more balanced. See Inzelberg, paragraph 54.
Claims 7 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Nandi in view of Walters and further in view of Johri et al. (WO 2023167707) (“Johri”).
Regarding claim 7, neither Nandi nor Walters appears to disclose explicitly the further limitations of the claim. However, Johri discloses that “the second set of statistical metrics includes a copula between the values of the first data set and the first set of statistical metrics (Sklar’s theorem states that any multivariate joint distribution can be written or expressed in terms of univariate marginal distributions and a copula that describes the dependence structure between the variables [i.e., between one marginal distribution/set of statistical metrics and another marginal distribution/data set] – Johri, paragraph 17).”
Johri and the instant application both relate to statistics and are analogous. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Nandi/Walters to include a copula among the statistical metrics, as disclosed by Johri, and an ordinary artisan could reasonably expect to have done so successfully. Doing so would provide insights into the relationships among variables in a multivariate distribution. See Johri, paragraph 17.
Regarding claim 20, neither Nandi nor Walters appears to disclose explicitly the further limitations of the claim. However, Johri discloses that “the second set of statistical metrics includes a copula between the values of the first data set and the first set of statistical metrics (Sklar’s theorem states that any multivariate joint distribution can be written or expressed in terms of univariate marginal distributions and a copula that describes the dependence structure between the variables [i.e., between one marginal distribution/set of statistical metrics and another marginal distribution/data set] – Johri, paragraph 17).” It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Nandi/Walters to include a copula among the statistical metrics, as disclosed by Johri, and an ordinary artisan could reasonably expect to have done so successfully. Doing so would provide insights into the relationships among variables in a multivariate distribution. See Johri, paragraph 17.
Claim 11 is rejected under 35 U.S.C. 103 as being unpatentable over Nandi in view of Walters and further in view of Estep et al. (US 11843624) (“Estep”).
Regarding claim 11, Nandi, as modified by Walters, discloses “storing a set of copies of the artificial data at the one or more servers (additional generative model [particular machine learning model] can be trained on the aggregated set of all silo-specific synthetic data examples and used to generate the plurality of synthetic data examples – Nandi, paragraph 28; central computing system can then provide the plurality of synthetic data examples to silo computing systems [where they are stored] – id. at paragraph 29; see also paragraph 60 (indicating that the computing devices may include servers)) ….”
Neither Nandi nor Walters appears to disclose explicitly the further limitations of the claim. However, Estep discloses “exposing the set of copies of the artificial data stored at the one or more servers via one or more protocol functions (like file transfer protocols, email protocols [protocol functions] can be used by a network security system to transmit [expose] the synthetic requests [artificial data] – col. 14, ll. 56-67; see also col. 14, ll. 3-17 (disclosing the transfer of files to and from a server)).”
Estep and the instant application both relate to transmission of data via protocol functions and are analogous. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the combination of Nandi and Walters to expose the artificial data via protocol functions, as disclosed by Estep, and an ordinary artisan could reasonably expect to have done so successfully. Doing so would allow for the efficient transmission of data that need to be transmitted while refraining from transmitting data that do not. See Estep, col. 14, ll. 56-67.
Claim 13 is rejected under 35 U.S.C. 103 as being unpatentable over Nandi in view of Walters and further in view of Nakamura Sakai et al. (US 20250124332) (“Nakamura”).
Regarding claim 13, neither Nandi nor Walters appears to disclose explicitly the further limitations of the claim. However, Nakamura discloses that “transmitting an output associated with the particular machine learning model comprises:
transmitting information for storage in a data structure of a synthetic resource group, the synthetic resource group being configured to persist data of the data structure across one or more other synthetic resource groups (server devices [synthetic resource groups] may represent multiple servers in a pool; the server devices host the databases [data structures] that are configured to store [persist], inter alia, synthetic data – Nakamura, paragraph 56; see also Fig. 2 (showing the bidirectional transmission and receipt of data to and from these server devices, i.e., each server is configured to distribute the data across other servers)).”
Nakamura and the instant application both relate to machine learning with synthetic data and are analogous. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Nandi/Walters to persist synthetic data across several resources, as disclosed by Nakamura, and an ordinary artisan could reasonably expect to have done so successfully. Doing so would keep the data secure by ensuring that they are distributed across multiple nodes, thereby mitigating the impact of failure of any one node. See Nakamura, paragraph 56.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to RYAN C VAUGHN whose telephone number is (571)272-4849. The examiner can normally be reached M-R 7:00a-5:00p ET.
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/RYAN C VAUGHN/ Primary Examiner, Art Unit 2125
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