Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
DETAILED ACTION
2. This office action is in response to the original filing of 06/07/2024 and 12/20/2024. Claims 1-20 are canceled and claims 21-40 are pending and have been considered below.
Claim Objections
3. Claim 1 is objected to because of the following informalities: “…configuring the a processor to…” line 5. Appropriate correction is required.
Double Patenting
4. The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/apply/applying-online/eterminal-disclaimer.
Claims 21, 23-26, 30-31, 33-36 and 40 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1, 3-6 and 9 of U.S. Application 12,008,472 in view of Agarwal et al. (US 2021/0405984).
Instant Application 19/701084
US Patent No. 12,008,472
21. An apparatus for generating a compiled artificial intelligence (AI) model the apparatus comprising: a processor; and a memory communicatively coupled to the processer, the memory containing instructions configuring the a processor to: receive a plurality of data sets from a plurality of user devices, each data set associated with a respective data source; receive a serial identifier for each received data set and associate the serial identifier with its respective data source; convert each data set plurality of data sets using a machine-learning model into a cleansed data format; generate and train an accumulated model using at least a portion of the cleansed data sets, associating each serial identifier of the used data sets with the accumulated model; detect an event triggering execution of a smart contract associated with the accumulated model; determine, based on the triggering event, serial identifiers associated with data sets used to train the accumulated model; and execute the smart contract to initiate distribution of payments to corresponding data sources of serial identifiers associated with data sets used to train the accumulated model.
23. The apparatus of claim 21, wherein the converting each data set comprises: parsing each data set of the plurality of data sets and each header of the plurality of data sets into parsed data; and compiling each data set of the plurality of data sets into a cumulative data set, wherein the cumulative data set includes canonical categories of data, wherein the compiling of each data set comprises: classifying the parsed data from each data set of the plurality of data sets into the canonical categories of data; and inputting the parsed data into the canonical categories of data based on the categorization of the parsed data.
24. The apparatus of claim 23, wherein the processor is further configured to input the parsed data each data set of the plurality of data sets into a data set template with canonical headers based on the classification of the parsed data.
25. The apparatus of claim 24, wherein the processor is further configured to: generate the data set template using a template machine-learning model; and modify the data set template using the template machine-learning model.
26. The apparatus of claim 21, wherein converting each data set of the plurality of data sets comprises training a data category classifier using a data category training set, the category classifier configured to classify a data bucket for each data set of the plurality of data sets based on data within each respective data set.
30. The apparatus of claim 29, wherein the generating the accumulated model based on each data set of the plurality of data sets comprises: verifying each data set of the plurality of data sets to entries in an immutable sequential listing as a function of a Merkle Proof; and generating the accumulated model using the verified a data set.
1. An apparatus for generating a compiled artificial intelligence (AI) model the apparatus comprising: a processor; and a memory communicatively coupled to the processer, the memory containing instructions configuring the processor to: receive a plurality of data sets, wherein: each data set of the plurality of data sets has a corresponding source; and receiving each data set includes receiving the data set from a user device of a plurality of user devices, wherein the received data set comprises meta data describing a data bucket associated with the received data set; generate, for each data set of the plurality of data sets, a serial identifier, wherein the serial identifier comprises entries that are verified on an immutable sequential listing, wherein the immutable sequential listing further verifies the corresponding source of each data set of the plurality of data sets which is used to generate an accumulated model; generate a smart contract associated with the plurality of data sets, wherein: generating the smart contract further comprises generating the smart contract as a function of each serial identifier and each user device of the plurality of user devices and wherein_the smart contract is configured to validate each user device as a function of the serial identifier; and the smart contract is configured to make a payment as a function of a conditional trigger; convert each data set of the plurality of data sets using a machine-learning model into a cleansed data format, wherein: the conversion comprises: parsing each data set of the plurality of data sets; correcting the parsed data set, wherein correcting the parsed data set further comprises: determining a selected unit of measurement for the parsed data based on an initial unit of measurement of the parsed data; and calculating an adjusted value of the parsed data set as a function of the selected unit of measurement; relabeling the parsed data set standardizing each data set of the plurality of data sets; and the cleansed data format comprises a plurality of converted data sets; and generate the accumulated model as a function of real time data received from a data well using the plurality of converted data sets as training data and the serial identifier, wherein the conditional trigger is configured to determine that the accumulated model is using a data set of the plurality of converted data sets and to make a payment to the corresponding source of a data set of the plurality of converted data sets as a function of the determination.
3. The apparatus of claim 1, wherein the converting of each data set of the plurality of data sets comprises: parsing each data set of the plurality of data sets and each header of headers of each data set of the plurality of data sets into parsed data; and compiling each data set of the plurality of data sets into a cumulative data set, wherein the cumulative data set includes canonical categories of data, wherein the compiling of each data set comprises: classifying the parsed data from each data set of the plurality of data sets into the canonical categories of data; and inputting the parsed data into the canonical categories of data based on the categorization of the parsed data.
4. The apparatus of claim 3, wherein the processor is further configured to input the parsed data from a data set into a data set template with canonical headers based on the classification of the parsed data.
5. The apparatus of claim 4, wherein the processor is further configured to: generate the data set template using a template machine-learning model; and modify the data set template using the template machine-learning model.
6. The apparatus of claim 1, wherein the converting of each data set of the plurality of data sets comprises: training a data category classifier using a data category training set, wherein the machine learning model is configured to classify a data bucket for each data set of the plurality of data sets based on data within each respective data set.
9. The apparatus of claim 8, wherein the generating the accumulated model based on the plurality of converted data sets comprises: verifying the plurality of converted data sets to entries in an immutable sequential listing as a function of a Merkle Proof; and generating the accumulated model using the verified the plurality of converted data sets.
Claim 21
Claim 1 of the reference US patent recites all of the limitations of claim 21 of the instant application except “receive a serial identifier for each received data set and associate the serial identifier with its respective data source; detect an event triggering execution of a smart contract associated with the accumulated model; determine, based on the triggering event, serial identifiers associated with data sets used to train the accumulated model ” However, Ferreira Moreno discloses receive a serial identifier for each received data set and associate the serial identifier with its respective data source (claimed terminology of a “serial identifier” is interpreted as identifying a data provider/data server and tracking which provider contributed to creation or enhancement of a machine-learning model) (fig. 2)…(maintain a blockchain-transaction registry identifying the input datasets used to train each model and the corresponding data providers (serial identifier))([0029]);
detect an event triggering execution of a smart contract associated with the accumulated model (smart contracts associated with datasets and blockchain-recorded contractual obligations) ([0041]-[0043]);
determine, based on the triggering event, serial identifiers associated with data sets used to train the accumulated model (records which data units from which data providers were actually selected to train the machine-learning models)([0044])…(..further maintains a registry identifying the input datasets used to train each model and corresponding providers) ([0029]). Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Cook further in view of Ferreira Moreno to incorporate the above cited feature. One would have been motivated to do so to generate output in the form of canonical datasets to feed downstream system.
Claim Rejections - 35 USC § 101
5. 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 21-40 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. The claimed invention, when the claims are taken as a whole, is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Claim 21, the claim recites an apparatus which falls into one of the statutory categories.
2A – Prong 1: Claim 21, in part, recites
“generate and train an accumulated model using at least a portion of the cleansed data sets, associating each serial identifier of the used data sets with the accumulated model”;” convert each data set plurality of data sets using a machine-learning model into a cleansed data format”; “determine, based on the triggering event, serial identifiers associated with data sets used to train the accumulated model” under broadest reasonable interpretation covers a mental process including an observation, evaluation, judgment or opinion that could be performed in the human mind or with the aid of pencil and paper.
2A – Prong 2: This judicial exception is not integrated into a practical application. Claim 21 further recites “receive a plurality of data sets from a plurality of user devices, each data set associated with a respective data source” amounts to mere data gathering, which is an insignificant extra-solution activity that does not integrate the judicial exception
into a practical application. See MPEP 2106.05(g); “receive a serial identifier for each received data set and associate the serial identifier with its respective data source” amounts to mere data gathering, which is an insignificant extra-solution activity that does not integrate the judicial exception into a practical application. See MPEP 2106.05(g); “detect an event triggering execution of a smart contract associated with the accumulated model” these are mere instructions to apply the judicial exception using a generic computer programmed with a generic class of computer algorithm. MPEP § 2106.05(f); and “execute the smart contract to initiate distribution of payments to corresponding data sources of serial identifiers associated with data sets used to train the accumulated model” these are mere instructions to apply the judicial exception using a generic computer programmed with a generic class of computer algorithm. MPEP § 2106.05(f).
“a processor, and a memory communicatively coupled to the processer..” amount to elements that have been recognized as well-understood, routine, and conventional activity in particular fields, as demonstrate by: Relevant court decision: the followings are examples of court decisions demonstrating well-understood, routine and conventional activities, see e.g., MPEP 2106.05(d)(II) and MPEP 2106.05(f)(2): Computer readable storage media comprising instructions to implement a method, e.g., see Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015).
The courts have found limitations directed to obtaining information electronically, recited at a high level of generality, to be well-understood routine, and conventional (see MPEP 2106.05(d)(II), “receiving or transmitting data over a network”, "electronic record keeping," and "storing and retrieving information in memory"). As explained by the Supreme Court, a claim directed to a judicial exception cannot be made eligible "simply by having the applicant acquiesce to limiting the reach of the patent for the formula to a particular technological use." Diamond v. Diehr, 450 U.S. 175, 192 n.14, 209 USPQ 1, 10 n. 14 (1981). Thus, limitations that amount to merely indicating a field of use or technological environment in which to apply a judicial exception do not amount to significantly more than the exception itself, and cannot integrate a judicial exception into a practical application. Accordingly, these additional elements do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
Step 2B: The claim does not contain significantly more than the judicial exception. The claim further recites “receive a plurality of data sets from a plurality of user devices, each data set associated with a respective data source” amounts to mere data gathering, which is an insignificant extra-solution activity that does not integrate the judicial exception
into a practical application. See MPEP 2106.05(g); “receive a serial identifier for each received data set and associate the serial identifier with its respective data source” amounts to mere data gathering, which is an insignificant extra-solution activity that does not integrate the judicial exception into a practical application. See MPEP 2106.05(g); “detect an event triggering execution of a smart contract associated with the accumulated model” these are mere instructions to apply the judicial exception using a generic computer programmed with a generic class of computer algorithm. MPEP § 2106.05(f); and “execute the smart contract to initiate distribution of payments to corresponding data sources of serial identifiers associated with data sets used to train the accumulated model” these are mere instructions to apply the judicial exception using a generic computer programmed with a generic class of computer algorithm. MPEP § 2106.05(f).
“a processor, and a memory communicatively coupled to the processer..” amount to elements that have been recognized as well-understood, routine, and conventional activity in particular fields, as demonstrate by: Relevant court decision: the followings are examples of court decisions demonstrating well-understood, routine and conventional activities, see e.g., MPEP 2106.05(d)(II) and MPEP 2106.05(f)(2): Computer readable storage media comprising instructions to implement a method, e.g., see Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015).
The courts have found limitations directed to obtaining information electronically, recited at a high level of generality, to be well-understood routine, and conventional (see MPEP 2106.05(d)(II), “receiving or transmitting data over a network”, "electronic record keeping," and "storing and retrieving information in memory"). As explained by the Supreme Court, a claim directed to a judicial exception cannot be made eligible "simply by having the applicant acquiesce to limiting the reach of the patent for the formula to a particular technological use." Diamond v. Diehr, 450 U.S. 175, 192 n.14, 209 USPQ 1, 10 n. 14 (1981). Thus, limitations that amount to merely indicating a field of use or technological environment in which to apply a judicial exception do not amount to significantly more than the exception itself, and cannot integrate a judicial exception into a practical application. Accordingly, these additional elements do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
Claim 31
2A – Prong 1: Claim 31, in part, recites
“generate and train an accumulated model using at least a portion of the cleansed data sets, associating each serial identifier of the used data sets with the accumulated model”;” convert each data set plurality of data sets using a machine-learning model into a cleansed data format”; “determine, based on the triggering event, serial identifiers associated with data sets used to train the accumulated model” under broadest reasonable interpretation covers a mental process including an observation, evaluation, judgment or opinion that could be performed in the human mind or with the aid of pencil and paper.
2A – Prong 2: This judicial exception is not integrated into a practical application. Claim 21 further recites “receive a plurality of data sets from a plurality of user devices, each data set associated with a respective data source” amounts to mere data gathering, which is an insignificant extra-solution activity that does not integrate the judicial exception
into a practical application. See MPEP 2106.05(g); “receive a serial identifier for each received data set and associate the serial identifier with its respective data source” amounts to mere data gathering, which is an insignificant extra-solution activity that does not integrate the judicial exception into a practical application. See MPEP 2106.05(g); “detect an event triggering execution of a smart contract associated with the accumulated model” these are mere instructions to apply the judicial exception using a generic computer programmed with a generic class of computer algorithm. MPEP § 2106.05(f); and “execute the smart contract to initiate distribution of payments to corresponding data sources of serial identifiers associated with data sets used to train the accumulated model” these are mere instructions to apply the judicial exception using a generic computer programmed with a generic class of computer algorithm. MPEP § 2106.05(f).
“a processor,..” amount to elements that have been recognized as well-understood, routine, and conventional activity in particular fields, as demonstrate by: Relevant court decision: the followings are examples of court decisions demonstrating well-understood, routine and conventional activities, see e.g., MPEP 2106.05(d)(II) and MPEP 2106.05(f)(2): Computer readable storage media comprising instructions to implement a method, e.g., see Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015).
The courts have found limitations directed to obtaining information electronically, recited at a high level of generality, to be well-understood routine, and conventional (see MPEP 2106.05(d)(II), “receiving or transmitting data over a network”, "electronic record keeping," and "storing and retrieving information in memory"). As explained by the Supreme Court, a claim directed to a judicial exception cannot be made eligible "simply by having the applicant acquiesce to limiting the reach of the patent for the formula to a particular technological use." Diamond v. Diehr, 450 U.S. 175, 192 n.14, 209 USPQ 1, 10 n. 14 (1981). Thus, limitations that amount to merely indicating a field of use or technological environment in which to apply a judicial exception do not amount to significantly more than the exception itself, and cannot integrate a judicial exception into a practical application. Accordingly, these additional elements do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
Step 2B: The claim does not contain significantly more than the judicial exception. The claim further recites “receive a plurality of data sets from a plurality of user devices, each data set associated with a respective data source” amounts to mere data gathering, which is an insignificant extra-solution activity that does not integrate the judicial exception
into a practical application. See MPEP 2106.05(g); “receive a serial identifier for each received data set and associate the serial identifier with its respective data source” amounts to mere data gathering, which is an insignificant extra-solution activity that does not integrate the judicial exception into a practical application. See MPEP 2106.05(g); “detect an event triggering execution of a smart contract associated with the accumulated model” these are mere instructions to apply the judicial exception using a generic computer programmed with a generic class of computer algorithm. MPEP § 2106.05(f); and “execute the smart contract to initiate distribution of payments to corresponding data sources of serial identifiers associated with data sets used to train the accumulated model” these are mere instructions to apply the judicial exception using a generic computer programmed with a generic class of computer algorithm. MPEP § 2106.05(f).
“a processor,” amount to elements that have been recognized as well-understood, routine, and conventional activity in particular fields, as demonstrate by: Relevant court decision: the followings are examples of court decisions demonstrating well-understood, routine and conventional activities, see e.g., MPEP 2106.05(d)(II) and MPEP 2106.05(f)(2): Computer readable storage media comprising instructions to implement a method, e.g., see Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015).
The courts have found limitations directed to obtaining information electronically, recited at a high level of generality, to be well-understood routine, and conventional (see MPEP 2106.05(d)(II), “receiving or transmitting data over a network”, "electronic record keeping," and "storing and retrieving information in memory"). As explained by the Supreme Court, a claim directed to a judicial exception cannot be made eligible "simply by having the applicant acquiesce to limiting the reach of the patent for the formula to a particular technological use." Diamond v. Diehr, 450 U.S. 175, 192 n.14, 209 USPQ 1, 10 n. 14 (1981). Thus, limitations that amount to merely indicating a field of use or technological environment in which to apply a judicial exception do not amount to significantly more than the exception itself, and cannot integrate a judicial exception into a practical application. Accordingly, these additional elements do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
Claim 22 recites “store each serial identifier and its associated data source information in a data structure selected from the group consisting of: an immutable sequential listing; a distributed ledger; or a centralized database, thereby enabling verification of each data source's contribution” insignificant extra-solution activity of mere data gathering and output. MPEP § 2106.05(g). 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 23 recites “ parsing each data set of the plurality of data sets and each header of the plurality of data sets into parsed data; and compiling each data set of the plurality of data sets into a cumulative data set, wherein the cumulative data set includes canonical categories of data, wherein the compiling of each data set comprises: classifying the parsed data from each data set of the plurality of data sets into the canonical categories of data; and inputting the parsed data into the canonical categories of data based on the categorization of the parsed data” amounts to insignificant extra-solution activity of mere data gathering and output. MPEP § 2106.05(g).
Claim 24 recites “input the parsed data each data set of the plurality of data sets into a data set template with canonical headers based on the classification of the parsed data” amounts to insignificant extra-solution activity of mere data gathering and output. MPEP § 2106.05(g).
Claim 25 recites” generate the data set template using a template machine-learning model; and modify the data set template using the template machine-learning model” these are mere instructions to apply the judicial exception using a generic computer programmed with a generic class of computer algorithm. MPEP § 2106.05(f).
Claim 26 recites “wherein converting each data set of the plurality of data sets comprises training a data category classifier using a data category training set, the category classifier configured to classify a data bucket for each data set of the plurality of data sets based on data within each respective data set” amounts to insignificant extra-solution activity of mere data gathering and output. MPEP § 2106.05(g).
Claim 27 recites “wherein the triggering event comprises at least one of: a licensing agreement for use of the accumulated model; a subscription-based revenue event; or a transaction initiating payment for access to the accumulated model's outputs” amounts to insignificant extra-solution activity of mere data gathering and output. MPEP § 2106.05(g).
Claim 28 recites “wherein the serial identifier is received by detecting an embedded identifier within the received data set, the embedded identifier comprising a blockchain-based ID included in metadata, a webpage tag, or a resource description file” amounts to insignificant extra-solution activity of mere data gathering and output. MPEP § 2106.05(g).
Claim 29 recites “wherein execution of the smart contract comprises using a blockchain network to distribute payments to digital wallets associated with the data sources” amounts to insignificant extra-solution activity of mere data gathering and output. MPEP § 2106.05(g).
Claim 30 recites “verifying each data set of the plurality of data sets to entries in an immutable sequential listing as a function of a Merkle Proof; and generating the accumulated model using the verified a data set” these are mere instructions to apply the judicial exception using a generic computer programmed with a generic class of computer algorithm. MPEP § 2106.05(f).
Claims 31-40 contains subject matter similar to claims 21-30 and are rejected under the same rationale.
Claim Rejections - 35 USC § 103
6. 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 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.
7. Claims 21-23, 26-27, 29, 31-33, 36-37 and 39 are rejected under 35 U.S.C. 103 as being unpatentable over Ferreira Moreno et al. (US 2019/0370634) in view of Garg et al. (US 2019/0114360).
Claim 21. Ferreira Moreno discloses an apparatus for generating a compiled artificial intelligence (AI) model the apparatus comprising:
a processor ([0039]); and
a memory ([0065]) communicatively coupled to the processer, the memory containing instructions configuring the a processor to:
receive a plurality of data sets from a plurality of user devices, each data set associated with a respective data source (receiving data from a plurality of data providers, with the received data comprising a plurality of datasets, each potentially served by a different data provider) ([0022]);
receive a serial identifier for each received data set and associate the serial identifier with its respective data source (claimed terminology of a “serial identifier” is interpreted as identifying a data provider/data server and tracking which provider contributed to creation or enhancement of a machine-learning model) (fig. 2)…(maintain a blockchain-transaction registry identifying the input datasets used to train each model and the corresponding data providers (serial identifier))([0029]);
generate and train an accumulated model using at least a portion of the cleansed data sets, associating each serial identifier of the used data sets with the accumulated model (using multiple datasets, including different combinations of datasets, to train machine-learning models) ([0020],[0024]-[0025]).. maintain a blockchain-transaction registry identifying the input datasets used to train each model and the corresponding data providers (serial identifier))([0029]);
detect an event triggering execution of a smart contract associated with the accumulated model (smart contracts associated with datasets and blockchain-recorded contractual obligations) ([0041]-[0043]);
determine, based on the triggering event, serial identifiers associated with data sets used to train the accumulated model (records which data units from which data providers were actually selected to train the machine-learning models)([0044])…(..further maintains a registry identifying the input datasets used to train each model and corresponding providers) ([0029]); and execute the smart contract to initiate distribution of payments to corresponding data sources of serial identifiers associated with data sets used to train the accumulated model (determining payback to data providers based on the value provided by their data and executing contract programming routines for exchanging value) ([0041]–[0043])…(additionally the quality of the data unit dictates the percentage of service fees distributed back to the data provider) ([0044]).
Ferreira Moreno does not explicitly disclose convert each data set plurality of data sets using a machine-learning model into a cleansed data format.
However, Garg discloses (processing structured and unstructured input data using ML/NLP and rules-based classifiers and mapping relevant data to standardized output)([0017]-[0019],[0021])..( ML-based transformation of heterogeneous input data into a standardized/canonical representation suitable for downstream processing… converts standardized outputs into canonical datasets) ([0020], [0023]).Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Ferreira Moreno further in view of Garg to incorporate the above cited features. One would have been motivated to do so in order to generate output in the form of canonical datasets to feed downstream system.
Claim 22. Ferreira Moreno and Garg disclose the apparatus of claim 21, Ferreira Moreno further discloses wherein the processor is further configured to store each serial identifier and its associated data source information in a data structure selected from the group consisting of: an immutable sequential listing; a distributed ledger; or a centralized database, thereby enabling verification of each data source's contribution (a blockchain registry identifying datasets used to train models and their corresponding data providers ([0029])..further that blockchain records are immutable and that the chain of records permits secure tracking of data providers and data units ([0041], [0044]).. track which data providers contributed to creation or enhancement of ML models ([0014]).
Claim 23. Ferreira Moreno and Garg disclose the apparatus of claim 21, Garg further discloses wherein the converting each data set comprises: parsing each data set of the plurality of data sets and each header of the plurality of data sets into parsed data (parsing input documents and unstructured content and extracting relevant data ([0020]–[0021]); and compiling each data set of the plurality of data sets into a cumulative data set, wherein the cumulative data set includes canonical categories of data (converting standardized labels into a canonical dataset and joining classified values into canonical data structures across different documents. ([0020], [0023]), wherein the compiling of each data set comprises: classifying the parsed data from each data set of the plurality of data sets into the canonical categories of data (ML/NLP and rules-based classification of extracted data into standardized outputs. KPMG ([0020]–[0021]); and inputting the parsed data into the canonical categories of data based on the categorization of the parsed data (mapping relevant data identified by the parser to standardized outputs and subsequently converting those standardized outputs into canonical data structures ([0020]–[0023]). One would have been motivated to do so in order to generate output in the form of canonical datasets to feed downstream system.
Claim 26. Ferreira Moreno and Garg disclose the apparatus of claim 21, Garg further discloses wherein converting each data set of the plurality of data sets comprises training a data category classifier using a data category training set (ML/NLP and rules-based classifiers trained and refined using subject-matter expertise and feedback) ([0017], [0020]–[0021]), the category classifier configured to classify a data bucket for each data set of the plurality of data sets based on data within each respective data set (classifying data and mapping extracted data to standardized categories) ([0020]–[0023]) [“data bucket,” is interpreted as classification of data into categories based on the data itself]. One would have been motivated to do so to generate output in the form of canonical datasets to feed downstream system.
Claim 27. Ferreira Moreno and Garg disclose the apparatus of claim 21, Ferreira Moreno further discloses wherein the triggering event comprises at least one of: a licensing agreement for use of the accumulated model; a subscription-based revenue event; or a transaction initiating payment for access to the accumulated model's outputs(The smart contract, for example, specifies rules and penalties, and also a functionality that automatically enforces the obligations as specified in the rules and penalties.)([0015])…(service fees and determining payback to providers) ([0044]).. (contracts governing access to and use of datasets and services, including contractual programming routines for exchanging value) ([0041]–[0043]).
Claim 29. Ferreira Moreno and Garg disclose the apparatus of claim 21, Ferreira Moreno further discloses wherein execution of the smart contract comprises using a blockchain network to distribute payments to digital wallets associated with the data sources (blockchain-recorded smart contracts and payment/payback to data providers)([0041]–[0044])...( virtual wallets associated with users and contractual transfer of value)([0043]).
Claims 31-33, 36-37 and 39 contain subject matter similar to claims 21-23, 26-27, 29, respectively and are rejected under the same rationale.
8. Claims 24 and 34 are rejected under 35 U.S.C. 103 as being unpatentable over Ferreira Moreno et al. (US 2019/0370634) in view of Garg et al. (US 2019/0114360) and further in view of Yang et al. (US 10,540,610).
Claim 24. Ferreira Moreno and Garg disclose the apparatus of claim 23 but fail to explicitly disclose wherein the processor is further configured to input the parsed data each data set of the plurality of data sets into a data set template with canonical headers based on the classification of the parsed data.
However, Yang discloses input the parsed data from a data set into a data set template with canonical headers based on the classification of the parsed data (Fig. 2 and Col. 17 lines 3-14: “FIG. 2 depicts an example of how subsequent communications 200 may be analyzed after a plurality of templates 154 a-n have been generated and a plurality of trained structured machine learning models 170 a-n have been generated for corresponding of the templates 154 a-n and assigned to corresponding of the templates 154 a-n. Cluster engine 124 may be configured to employ techniques similar to those described above to determine which cluster structured communications 200 should be associated with. Based on that decision, a data extraction engine 240 may apply the extraction template (e.g., one of 154 a-n) to the structured communication to extract the appropriate data” teach perform clustering on subsequent communication to produce clustered (parsed) data that is then inputted into data set templates based on the cluster (classification) for which the parsed data belongs; Fig. 1B and Col. 11 lines 3-14: “Each of the fixed segments of the template 154 a of FIG. 1B is defined by a tuple that includes the classification of the segment (fixed), a location of the segment (e.g., “location 1” (e.g., an XPath) for “segment 1”), and the fixed text for the segment (e.g., “fixed text 1” for “segment 1”). Each of the transient segments of the template 154 a is defined by a tuple that includes the classification of the segment (transient), a location of the segment (e.g., “location 2” (e.g., an XPath) for “segment 2”), and a semantic label (if known) for the segment (e.g., “tracking #” for “segment 2”). The locations of the segments defines the order of the segments relative to one another” teach data set templates contain information such as classification of the segment, location of the segment, and semantic label (correspond to canonical headers)). Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Ferreira Moreno further in view of Yang to incorporate the above cited features. One would have been motivated to do so to enable extraction of content of one or more transient segments from subsequent communications, while optionally ignoring content of confidential transient segments and/or content of fixed segments that is shared among the structured communications of the cluster (e.g., boilerplate).
Claim 34 represents the method of claim 24 and is rejected along the same rationale.
9. Claims 25 and 35 are rejected under 35 U.S.C. 103 as being unpatentable over Ferreira Moreno et al. (US 2019/0370634) in view of Garg et al. (US 2019/0114360) in view of Yang et al. (US 10,540,610) and further in view of Stoval, III et al. (US 2020/0251218 A1).
Claim 25. Ferreira Moreno Garg and Yang disclose the apparatus of claim 24, but fail to explicitly disclose wherein the processor is further configured to: generate the data set template using a template machine-learning model; and modify the data set template using the template machine-learning model.
However, Stoval, III discloses generate the data set template using a template machine-learning model; and modify the data set template using the template machine-learning model (pg. 3 [0026]: “Template module 160 may generate and modify templates for the creation of personalized clinical summaries. Template module 160 may generate default templates for a particular role and sub-specialty of a user, and may modify default templates based on a user's preferences. A template may be a listing of fields that can be populated with electronic health record information to produce a clinical summary. Each template may contain a patient name field and one or more fields that contain health records of the patient. Template module 160 may generate default templates for one or more roles and sub-specialties by using a machine learning model to analyze crowdsourced feedback data. A new user may first be provided with default templates, and based on the user's feedback, template module 160 will utilize machine learning to create personalized templates for the user” teaches generating and modifying data set templates using a machine learning model, thus rendering the model to be template machine-learning model; Fig. 1 teaches processor).
Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Ferreira Moreno further in view of Stoval, III to incorporate the above cited features. One would have been motivated to do so to create templates for personalized clinical summaries is eliminated. Furthermore, when clinical summaries are personalized to the preferences of clinicians, patients can be treated more quickly and more efficiently.
Claim 35 represents the method of claim 25 and is rejected along the same rationale.
10. Claims 28 and 38 are rejected under 35 U.S.C. 103 as being unpatentable over Ferreira Moreno et al. (US 2019/0370634) in view of Garg et al. (US 2019/0114360) and further in view of MEIROSU et al. (US 2020/0372184).
Claim 28. Ferreira Moreno and Garg disclose the apparatus of claim 21, Ferreira Moreno further discloses wherein the serial identifier is received by detecting an embedded identifier within the received data set (URI/reference to the dataset in the data unit and identifies the provider) ([0035]-[0036],
However, MEIROSU discloses the embedded identifier comprising a blockchain-based ID included in metadata, a webpage tag, or a resource description file ([0096]. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Ferreira Moreno further in view of MEIROSU to incorporate the above cited features. One would have been motivated to do so to enable trusted computing in a decentralized manner.
Claim 38 represents the method of claim 28 and is rejected along the same rationale.
11. Claims 30 and 40 are rejected under 35 U.S.C. 103 as being unpatentable over Ferreira Moreno et al. (US 2019/0370634) in view of Garg et al. (US 2019/0114360) and further in view of MANAMOHAN et al. (US 2021/0233192).
Claim 30. Ferreira Moreno and Garg disclose the apparatus of claim 29, but fail to explicitly disclose wherein the generating the accumulated model based on each data set of the plurality of data sets comprises: verifying each data set of the plurality of data sets to entries in an immutable sequential listing as a function of a Merkle Proof; and generating the accumulated model using the verified a data set.
However, MANAMOHAN discloses a contribution-verification process using Merkle trees and Merkle proofs registered against a blockchain ledger) ([0081]–[0082])…(building a hash tree, registering its root in a distributed ledger, and providing a hash-tree proof to verify the contribution)(claim 1)…(distributed ML in which local data trains a common/global model)([0068], [0071]–[0075]). Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Ferreira Moreno further in view of MANAMOHAN to incorporate the above cited features. One would have been motivated to do so to provide a predictable way to verify that the datasets attributed to model training were authentic contributions before relying on those datasets in the training/reward process.
Claim 40 represents the method of claim 30 and is rejected along the same rationale.
Conclusion
12. The prior art made of record and not relied upon is considered pertinent to applicant's disclosure (See PTO-892).
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Phenuel S. Salomon whose telephone number is (571) 270-1699. The examiner can normally be reached on Mon-Fri 7:00 A.M. to 4:00 P.M. (Alternate Friday Off) EST.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Usmaan Saeed can be reached on (571) 272-4046. The fax phone number for the organization where this application or proceeding is assigned is 571-273-3800.
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/PHENUEL S SALOMON/Primary Examiner, Art Unit 2146
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