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
1. This is a first non-final Office Action on the merits for application 18637866. Claim 1 is canceled. Claims 2-21 are pending examination.
Withdrawing Restriction
2. Applicant arguments submitted on 01/21/2026 regarding withdrawing restriction is persuasive, the restriction is withdrawn.
Claim Rejections - 35 USC § 101
3. 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 2-21 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Claim(s) 2 is/are drawn to method (i.e., a process), claim(s) 10 is/are drawn to a system (i.e., a machine/manufacture), and claim(s) 17 is/are drawn to non-transitory computer readable medium (i.e., a machine/manufacture). As such, claims 2, 10, and 17 is/are drawn to one of the statutory categories of invention.
Claims 2-21 are directed to receiving examples of data and classifying and predicting status of the example data and updating model based on status. Specifically, claim(s) 2, 10, and 17 recite(s) receiving, one or more first examples of data, each one or more of the first examples of data has a corresponding first status; classifying, using a learning model, each one of the one or more first examples of data to predict a corresponding first status for each of the one or more first examples of data; identifying, one or more second examples of data stored in a distributed ledger, each one of the one or more second examples has a corresponding second status; comparing, the first status and the second status; and updating, the learning model based on the comparing of the first status and the second status, and generating an updated learning model, which is grouped within the Methods Of Organizing Human Activity and is similar to the concept of (commercial or legal interactions including agreements in the form of contracts, legal obligations, advertising, marketing or sales activities or behaviors business relations) (fundamental economic principles or practices including hedging insurance, mitigating risk) grouping of abstract ideas in prong one of step 2A of the Alice/Mayo test (See 2019 Revised Patent Subject Matter Eligibility Guidance, 84 Fed. Reg. 50, 52, 54 (January 7, 2019)). Accordingly, the claims recite an abstract idea (See pages 7, 10, Alice Corporation Pty. Ltd. v. CLS Bank International, et al., US Supreme Court, No. 13-298, June 19, 2014; 2019 Revised Patent Subject Matter Eligibility Guidance, 84 Fed. Reg. 50, 53-54 (January 7, 2019)).
The Claim limitations are listed under Methods Of Organizing Human Activity, and grouped as following:
receiving, one or more first examples of data, each one or more of the first examples of data has a corresponding first status; which is similar to the concept of (advertising, marketing or sales activities or behaviors business relations),
classifying, using a learning model, each one of the one or more first examples of data to predict a corresponding first status for each of the one or more first examples of data; which is similar to the concept of (advertising, marketing or sales activities or behaviors business relations),
identifying, one or more second examples of data stored in a distributed ledger, each one of the one or more second examples has a corresponding second status; which is similar to the concept of (advertising, marketing or sales activities or behaviors business relations),
comparing, the first status and the second status; and which is similar to the concept of (advertising, marketing or sales activities or behaviors business relations),
updating, the learning model based on the comparing of the first status and the second status, and generating an updated learning model; which is similar to the concept of (advertising, marketing or sales activities or behaviors business relations).
This judicial exception is not integrated into a practical application because, when analyzed under prong two of step 2A of the Alice/Mayo test (See 2019 Revised Patent Subject Matter Eligibility Guidance, 84 Fed. Reg. 50, 54-55 (January 7, 2019)), the additional element(s) of the claim(s) such as processor, machine, system, memory, non transitory computer readable storage medium merely use(s) a computer as a tool to perform an abstract idea and/or generally link(s) the use of a judicial exception to a particular technological environment. Specifically, the processor, machine, system, memory, non transitory computer readable storage medium perform(s) the steps or functions of receiving, one or more first examples of data, each one or more of the first examples of data has a corresponding first status; classifying, using a learning model, each one of the one or more first examples of data to predict a corresponding first status for each of the one or more first examples of data; identifying, one or more second examples of data stored in a distributed ledger, each one of the one or more second examples has a corresponding second status; comparing, the first status and the second status; and updating, the learning model based on the comparing of the first status and the second status, and generating an updated learning model. The use of a processor/computer as a tool to implement the abstract idea and/or generally linking the use of the abstract idea to a particular technological environment does not integrate the abstract idea into a practical application because it requires no more than a computer performing functions that correspond to acts required to carry out the abstract idea. The additional elements do not involve improvements to the functioning of a computer, or to any other technology or technical field (MPEP 2106.05(a)), the claims do not apply or use the abstract idea to effect a particular treatment or prophylaxis for a disease or medical condition (Vanda Memo), the claims do not apply the abstract idea with, or by use of, a particular machine (MPEP 2106.05(b)), the claims do not effect a transformation or reduction of a particular article to a different state or thing (MPEP 2106.05(c)), and the claims do not apply or use the abstract idea in some other meaningful way beyond generally linking the use of the abstract idea to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception (MPEP 2106.05(e) and Vanda Memo). Therefore, the claims do not, for example, purport to improve the functioning of a computer. Nor do they effect an improvement in any other technology or technical field. Accordingly, the additional elements do not impose any meaningful limits on practicing the abstract idea, and the claims are directed to an abstract idea.
The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because, when analyzed under step 2B of the Alice/Mayo test (See 2019 Revised Patent Subject Matter Eligibility Guidance, 84 Fed. Reg. 50, 52, 56 (January 7, 2019)), the additional element(s) of using a processor, machine, system, memory, non transitory computer readable storage medium to perform the steps amounts to no more than using a computer or processor to automate and/or implement the abstract idea of receiving examples of data and classifying and predicting status of the example data and updating model based on status. As discussed above, taking the claim elements separately, the processor, machine, system, memory, non transitory computer readable storage medium perform(s) the steps or functions of receiving, one or more first examples of data, each one or more of the first examples of data has a corresponding first status; classifying, using a learning model, each one of the one or more first examples of data to predict a corresponding first status for each of the one or more first examples of data; identifying, one or more second examples of data stored in a distributed ledger, each one of the one or more second examples has a corresponding second status; comparing, the first status and the second status; and updating, the learning model based on the comparing of the first status and the second status, and generating an updated learning model. These functions correspond to the actions required to perform the abstract idea. Viewed as a whole, the combination of elements recited in the claims merely recite the concept of receiving examples of data and classifying and predicting status of the example data and updating model based on status. Therefore, the use of these additional elements does no more than employ the computer as a tool to automate and/or implement the abstract idea. The use of a computer or processor to merely automate and/or implement the abstract idea cannot provide significantly more than the abstract idea itself (MPEP 2106.05(I)(A)(f) & (h)). Therefore, the claim is not patent eligible.
As for dependent claims 3-9, 11-16, and 18-21 further describe the abstract idea of receiving examples of data and classifying and predicting status of the example data and updating model based on status. Claim(s) 3-9, 11-16, and 18-21 does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because, when analyzed under step 2B of the Alice/Mayo test (See 2019 Revised Patent Subject Matter Eligibility Guidance, 84 Fed. Reg. 50, 52, 56 (January 7, 2019)), the additional element(s) of using a machine, system, non transitory computer readable storage medium to perform the steps amounts to no more than using a computer or processor to automate and/or implement the abstract idea of receiving examples of data and classifying and predicting status of the example data and updating model based on status. As discussed above, taking the claim elements separately, the machine, system, non transitory computer readable storage medium perform(s) the steps or functions of wherein at least one of the first status and the second status include at least one of the following: a valid example of data type, a not valid example of data type, and any combination thereof; wherein each one of the one or more first examples of data has a corresponding predetermined first type, and each of the one or more second examples of data has a corresponding predetermined second type; wherein the comparing includes comparing a first status of at least one first example in the one or more first examples of data and a second status of at least one second example in the one or more second examples of data upon the predetermined first type of the at least one first example matching the predetermined second type of the at least one second example; wherein a result of the comparing is stored in the distributed ledger; wherein at least one of the one or more first examples of data and the one or more second examples of data include at least one of the following: a nonsensitive example data, a non-confidential example of data, and any combination thereof; storing the corresponding first status for each of the one or more first examples of data in the distributed ledger; wherein at least one of the one or more first examples of data and the one or more second examples of data include at least one of the following: a clause in an agreement document, a sentence in an agreement document, a text in an agreement document, and any combination thereof; wherein at least one of the one or more first examples of data is stored on one or more first nodes in the distributed ledger; at least one of the one or more second examples of data is stored on one or more second nodes in the distributed ledger; and at least one of the first status and the second status is stored on one or more third nodes in the distributed ledger. These functions correspond to the actions required to perform the abstract idea. Viewed as a whole, the combination of elements recited in the claims merely recite the concept of receiving examples of data and classifying and predicting status of the example data and updating model based on status. Therefore, the use of these additional elements does no more than employ the computer as a tool to automate and/or implement the abstract idea. The use of a computer or processor to merely automate and/or implement the abstract idea cannot provide significantly more than the abstract idea itself (MPEP 2106.05(I)(A)(f) & (h)). Therefore, the claim is not patent eligible.
Claim Rejections - 35 USC § 103
4. 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.
A. Claim(s) 2, 7, 9, 10, 14, and 16 is/are rejected under 35 U.S.C. 103 as being unpatentable over Park et al., (U.S. Patent Application Publication No. 20150324701) in view of Johnsrud et al. (U.S. Patent No. US10607285B2).
As to Claim 2, Park teaches a computer-implemented method, comprising: receiving, using at least one processor, one or more first examples of data, each one or more of the first examples of data has a corresponding first status; (0024: apparatus including: a collection unit configured to collect first user information, collect first user status information), (Examiner notes: Example data can be any user data such as user information, and it is well known in the art that apparatus has a processor),classifying, using the at least one processor, using a machine learning model each one of the one or more first examples of data to predict a corresponding first status for each of the one or more first examples of data; (0066: The training set is transmitted to the learning server 310… receives a classifier corresponding to the user status prediction criterion from the learning server 310 and applies the classifier to the first user status information, apparatus including: receives a classifier corresponding to the user status prediction criterion from the learning server 310 and applies the classifier to the first user status information, and generate a first user status prediction pattern 413),identifying, using the at least one processor, one or more second examples of data stored, each one of the one or more second examples has a corresponding second status; (0024: apparatus including: a collection unit configured to collect second user information, collect second user status information, a storage unit configured to store sensory effects and prediction patterns), (Examiner notes: Example data can be any user data such as user information, and it is well known in the art that apparatus has a processor),comparing, using the at least one processor, the first status and the second status; and (0066: compares the first user status and 0067: compares the second user status information),updating, using the at least one processor, the machine learning model based on the comparing of the first status and the second status, and generating an updated machine learning model; (0066: compares the first user status information with a first user preference pattern to generate the first user status prediction pattern 413, 0067: compares the second user status information with a second user preference pattern to generate the second user status prediction pattern 423, generate a training set corresponding to the user status pattern. The training set is transmitted to the learning server 310, 0068: The composite training set is transmitted to the learning server 310 and 0066: generate a training set corresponding to the user), (Examiner notes: the processor compares first user status and second user status to generate the status prediction then generates a training sets corresponding to status pattern and that composite training sets are then transmitted to the learning server 310 to be stored for future determinations).
Park does not teach information stored in a distributed ledger.
However Johnsrud teaches information stored in a distributed ledger (claim 1: storing on the block chain distributed ledger, the information associated with the user).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Park to include information stored in a distributed ledger of Johnsrud. Motivation to do so comes from the knowledge well known in the art that information stored in a distributed ledger would help store information for future use which would help in future determination of information data and would therefore make the method/system more accurate.
As to Claim 7, Park and Johnsrud teach the method of claim 2.
Johnsrud further teaches storing the corresponding first status for each of the one or more first examples of data in the distributed ledger; (claim 1: storing on the block chain distributed ledger, the information associated with the user and 45: A block chain or blockchain is a distributed database that maintains a list of data records, the security of which is enhanced by the distributed nature of the block chain. A block chain typically includes several nodes, which may be one or more systems, machines, computers, databases, data stores or the like operably connected with one another. In some cases, each of the nodes or multiple nodes are maintained by different entities. A block chain typically works without a central repository or single administrator. One well-known application of a block chain is the public ledger of transactions for cryptocurrencies such as used in bitcoin. The data records recorded in the block chain are enforced cryptographically and stored on the nodes of the block chain.).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Park to include storing the corresponding first status for each of the one or more first examples of data in the distributed ledger of Johnsrud. Motivation to do so comes from the knowledge well known in the art that storing the corresponding first status for each of the one or more first examples of data in the distributed ledger would help store information for future use which would help in future determination of information data and would therefore make the method/system more accurate.
As to Claim 9, Park and Johnsrud teach the method of claim 2.
Johnsrud further teaches wherein at least one of the one or more first examples of data is stored on one or more first nodes in the distributed ledger; at least one of the one or more second examples of data is stored on one or more second nodes in the distributed ledger; and at least one of the first status and the second status is stored on one or more third nodes in the distributed ledger; (claim 1: storing on the block chain distributed ledger, the information associated with the user and 45: A block chain or blockchain is a distributed database that maintains a list of data records, the security of which is enhanced by the distributed nature of the block chain. A block chain typically includes several nodes, which may be one or more systems, machines, computers, databases, data stores or the like operably connected with one another. In some cases, each of the nodes or multiple nodes are maintained by different entities. A block chain typically works without a central repository or single administrator. One well-known application of a block chain is the public ledger of transactions for cryptocurrencies such as used in bitcoin. The data records recorded in the block chain are enforced cryptographically and stored on the nodes of the block chain.).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Park to include storing the corresponding first status for each of the one or more first examples of data in the distributed led wherein at least one of the one or more first examples of data is stored on one or more first nodes in the distributed ledger; at least one of the one or more second examples of data is stored on one or more second nodes in the distributed ledger; and at least one of the first status and the second status is stored on one or more third nodes in the distributed ledger ger of Johnsrud. Motivation to do so comes from the knowledge well known in the art that wherein at least one of the one or more first examples of data is stored on one or more first nodes in the distributed ledger; at least one of the one or more second examples of data is stored on one or more second nodes in the distributed ledger; and at least one of the first status and the second status is stored on one or more third nodes in the distributed ledger would help store information for future use which would help in future determination of information data and would therefore make the method/system more accurate.
As to Claim 10, Park teaches a system, comprising: at least one processor; and at least one memory storing instructions (0024: apparatus), that, when executed by the at least one processor, cause the at least one processor to:classify, using a machine learning model, each one of one or more first examples of data to predict a corresponding first status for each of the one or more first examples of data, wherein each one or more of the first examples of data has a corresponding first status; (0066: The training set is transmitted to the learning server 310… receives a classifier corresponding to the user status prediction criterion from the learning server 310 and applies the classifier to the first user status information, apparatus including: receives a classifier corresponding to the user status prediction criterion from the learning server 310 and applies the classifier to the first user status information, and generate a first user status prediction pattern 413),select one or more second examples of data stored, each one of the one or more second examples has a corresponding second status; (0024: apparatus including: a collection unit configured to collect second user information, collect second user status information, a storage unit configured to store sensory effects and prediction patterns), (Examiner notes: Example data can be any user data such as user information, and it is well known in the art that apparatus has a processor),compare the first status and the second status; and (0066: compares the first user status and 0067: compares the second user status information),update the machine learning model based on the comparing of the first status and the second status, and generate an updated machine learning model; (0066: compares the first user status information with a first user preference pattern to generate the first user status prediction pattern 413, 0067: compares the second user status information with a second user preference pattern to generate the second user status prediction pattern 423, generate a training set corresponding to the user status pattern. The training set is transmitted to the learning server 310, 0068: The composite training set is transmitted to the learning server 310 and 0066: generate a training set corresponding to the user), (Examiner notes: the processor compares first user status and second user status to generate the status prediction then generates a training sets corresponding to status pattern and that composite training sets are then transmitted to the learning server 310 to be stored for future determinations).
are then transmitted to the learning server 310 to be stored for future determinations).
Park does not teach information stored in a distributed ledger.
However Johnsrud teaches information stored in a distributed ledger (claim 1: storing on the block chain distributed ledger, the information associated with the user).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Park to include information stored in a distributed ledger of Johnsrud. Motivation to do so comes from the knowledge well known in the art that information stored in a distributed ledger would help store information for future use which would help in future determination of information data and would therefore make the method/system more accurate.
As to Claim 14, Park and Johnsrud teach the system of claim 14.
Johnsrud further teaches wherein the at least one processor is configured to store the corresponding first status for each of the one or more first examples of data in the distributed ledger; (claim 1: storing on the block chain distributed ledger, the information associated with the user and 45: A block chain or blockchain is a distributed database that maintains a list of data records, the security of which is enhanced by the distributed nature of the block chain. A block chain typically includes several nodes, which may be one or more systems, machines, computers, databases, data stores or the like operably connected with one another. In some cases, each of the nodes or multiple nodes are maintained by different entities. A block chain typically works without a central repository or single administrator. One well-known application of a block chain is the public ledger of transactions for cryptocurrencies such as used in bitcoin. The data records recorded in the block chain are enforced cryptographically and stored on the nodes of the block chain.).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Park to include wherein the at least one processor is configured to store the corresponding first status for each of the one or more first examples of data in the distributed ledger of Johnsrud. Motivation to do so comes from the knowledge well known in the art that wherein the at least one processor is configured to store the corresponding first status for each of the one or more first examples of data in the distributed ledger would help store information for future use which would help in future determination of information data and would therefore make the method/system more accurate.
As to Claim 16, Park and Johnsrud teach the system of claim 10.
Johnsrud further teaches wherein at least one of the one or more first examples of data is stored on one or more first nodes in the distributed ledger; at least one of the one or more second examples of data is stored on one or more second nodes in the distributed ledger; and at least one of the first status and the second status is stored on one or more third nodes in the distributed ledger; (claim 1: storing on the block chain distributed ledger, the information associated with the user and 45: A block chain or blockchain is a distributed database that maintains a list of data records, the security of which is enhanced by the distributed nature of the block chain. A block chain typically includes several nodes, which may be one or more systems, machines, computers, databases, data stores or the like operably connected with one another. In some cases, each of the nodes or multiple nodes are maintained by different entities. A block chain typically works without a central repository or single administrator. One well-known application of a block chain is the public ledger of transactions for cryptocurrencies such as used in bitcoin. The data records recorded in the block chain are enforced cryptographically and stored on the nodes of the block chain.).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Park to include storing the corresponding first status for each of the one or more first examples of data in the distributed led wherein at least one of the one or more first examples of data is stored on one or more first nodes in the distributed ledger; at least one of the one or more second examples of data is stored on one or more second nodes in the distributed ledger; and at least one of the first status and the second status is stored on one or more third nodes in the distributed ledger ger of Johnsrud. Motivation to do so comes from the knowledge well known in the art that wherein at least one of the one or more first examples of data is stored on one or more first nodes in the distributed ledger; at least one of the one or more second examples of data is stored on one or more second nodes in the distributed ledger; and at least one of the first status and the second status is stored on one or more third nodes in the distributed ledger would help store information for future use which would help in future determination of information data and would therefore make the method/system more accurate.
B. Claim(s) 3, 4, 5, 11, 12, 17, 20, and 21 is/are rejected under 35 U.S.C. 103 as being unpatentable over Park et al., (U.S. Patent Application Publication No. 20150324701) in view of Johnsrud et al. (U.S. Patent No. US10607285B2) in view of Adams, (U.S. Patent Application Publication No. 20070058530).
As to Claim 3, Park, and Johnsrud teach method of claim 2.
Park, and Johnsrud do not teach wherein at least one of the first status and the second status include at least one of the following: a valid example of data type, a not valid example of data type, and any combination thereof.
However Adams teaches wherein at least one of the first status and the second status include at least one of the following: a valid example of data type, a not valid example of data type, and any combination thereof; (abstract: first status and the second status. The first parameter is based at least in part on the first data and the second parameter is based at least in part on the second data… 0015: Receive the parameter from each of the data, the first status has valid or invalid data and the second status has valid data or invalid data).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include wherein at least one of the first status and the second status include at least one of the following: a valid example of data type, a not valid example of data type, and any combination thereof. Motivation to do so comes from the knowledge well known in the art that wherein at least one of the first status and the second status include at least one of the following: a valid example of data type, a not valid example of data type, and any combination thereof would help provide more information for future use which would help in future determination of information data and would therefore make the method/system more accurate.
As to Claim 4, Park, and Johnsrud teach method of claim 2.
Park, and Johnsrud do not teach wherein each one of the one or more first examples of data has a corresponding predetermined first type, and each of the one or more second examples of data has a corresponding predetermined second type; wherein the comparing includes comparing a first status of at least one first example in the one or more first examples of data and a second status of at least one second example in the one or more second examples of data upon the predetermined first type of the at least one first example matching the predetermined second type of the at least one second example.
However Adams teaches wherein at least one of the first status and the second status include at least one of the following: a valid example of data type, a not valid example of data type, and any combination thereof; (abstract: The computer readable medium may include a first evaluation code segment for determining a first status of a first redundant data stream based at least in part on a first parameter and a predetermined policy, a second evaluation code segment for determining a second status of a second redundant data stream based on a second parameter and the predetermined policy, and an output code segment for transmitting a signal based at least in part on at least one of the first status and the second status. The first parameter is based at least in part on the first redundant data stream and the second parameter is based at least in part on the second redundant data stream. The first status is valid or invalid and the second status is valid or invalid.).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include wherein each one of the one or more first examples of data has a corresponding predetermined first type, and each of the one or more second examples of data has a corresponding predetermined second type; wherein the comparing includes comparing a first status of at least one first example in the one or more first examples of data and a second status of at least one second example in the one or more second examples of data upon the predetermined first type of the at least one first example matching the predetermined second type of the at least one second example. Motivation to do so comes from the knowledge well known in the art that wherein each one of the one or more first examples of data has a corresponding predetermined first type, and each of the one or more second examples of data has a corresponding predetermined second type; wherein the comparing includes comparing a first status of at least one first example in the one or more first examples of data and a second status of at least one second example in the one or more second examples of data upon the predetermined first type of the at least one first example matching the predetermined second type of the at least one second example would help provide more information for future use which would help in future determination of information data and would therefore make the method/system more accurate.
As to Claim 5, Park, Johnsrud, and Adams teach method of claim 4.
Adams further teaches wherein a result of the comparing is stored in the distributed ledger; (claim 1: storing on the block chain distributed ledger, the information associated with the user… claim 1: storing on the block chain distributed ledger, the information associated with the first user).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include wherein a result of the comparing is stored in the distributed ledger. Motivation to do so comes from the knowledge well known in the art that wherein a result of the comparing is stored in the distributed ledger would help store information for future use which would help in future determination of information data and would therefore make the method/system more accurate.
As to Claim 11, Park, and Johnsrud teach system of claim 10.
Park, and Johnsrud do not teach wherein at least one of the first status and the second status include at least one of the following: a valid example of data type, a not valid example of data type, and any combination thereof.
However Adams teaches wherein at least one of the first status and the second status include at least one of the following: a valid example of data type, a not valid example of data type, and any combination thereof; (abstract: first status and the second status. The first parameter is based at least in part on the first data and the second parameter is based at least in part on the second data… 0015: Receive the parameter from each of the data, the first status has valid or invalid data and the second status has valid data or invalid data).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include wherein at least one of the first status and the second status include at least one of the following: a valid example of data type, a not valid example of data type, and any combination thereof. Motivation to do so comes from the knowledge well known in the art that wherein at least one of the first status and the second status include at least one of the following: a valid example of data type, a not valid example of data type, and any combination thereof would help provide more information for future use which would help in future determination of information data and would therefore make the method/system more accurate.
As to Claim 12, Park, and Johnsrud teach system of claim 10.
Park, and Johnsrud do not teach wherein each one of the one or more first examples of data has a corresponding predetermined first type, and each of the one or more second examples of data has a corresponding predetermined second type; wherein the comparing includes comparing a first status of at least one first example in the one or more first examples of data and a second status of at least one second example in the one or more second examples of data upon the predetermined first type of the at least one first example matching the predetermined second type of the at least one second example; wherein a result of the comparing is stored in the distributed ledger.
However Adams teaches wherein each one of the one or more first examples of data has a corresponding predetermined first type, and each of the one or more second examples of data has a corresponding predetermined second type; wherein the comparing includes comparing a first status of at least one first example in the one or more first examples of data and a second status of at least one second example in the one or more second examples of data upon the predetermined first type of the at least one first example matching the predetermined second type of the at least one second example; wherein a result of the comparing is stored in the distributed ledger; (abstract: The computer readable medium may include a first evaluation code segment for determining a first status of a first redundant data stream based at least in part on a first parameter and a predetermined policy, a second evaluation code segment for determining a second status of a second redundant data stream based on a second parameter and the predetermined policy, and an output code segment for transmitting a signal based at least in part on at least one of the first status and the second status. The first parameter is based at least in part on the first redundant data stream and the second parameter is based at least in part on the second redundant data stream. The first status is valid or invalid and the second status is valid or invalid.).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include wherein each one of the one or more first examples of data has a corresponding predetermined first type, and each of the one or more second examples of data has a corresponding predetermined second type; wherein the comparing includes comparing a first status of at least one first example in the one or more first examples of data and a second status of at least one second example in the one or more second examples of data upon the predetermined first type of the at least one first example matching the predetermined second type of the at least one second example; wherein a result of the comparing is stored in the distributed ledger. Motivation to do so comes from the knowledge well known in the art that wherein each one of the one or more first examples of data has a corresponding predetermined first type, and each of the one or more second examples of data has a corresponding predetermined second type; wherein the comparing includes comparing a first status of at least one first example in the one or more first examples of data and a second status of at least one second example in the one or more second examples of data upon the predetermined first type of the at least one first example matching the predetermined second type of the at least one second example; wherein a result of the comparing is stored in the distributed ledger would help provide more information for future use which would help in future determination of information data and would therefore make the method/system more accurate.
As to Claim 17, Park teaches a non-transitory computer-readable storage medium, the computer- readable storage medium including instructions that when executed by at least one processor, cause the at least one processor to: (0024: apparatus),classify, using a machine learning model, each one of one or more first examples of data to predict a corresponding first status for each of the one or more first examples of data, wherein each one or more of the first examples of data has a corresponding first status; (0024: apparatus including: a collection unit configured to collect first user information, collect first user status information), (Examiner notes: Example data can be any user data such as user information, and it is well known in the art that apparatus has a processor),select one or more second examples of data stored, each one of the one or more second examples has a corresponding second status (0024: apparatus including: a collection unit configured to collect second user information, collect second user status information, a storage unit configured to store sensory effects and prediction patterns), (Examiner notes: Example data can be any user data such as user information, and it is well known in the art that apparatus has a processor), update the machine learning model based on the comparing of the first status and the second status, and generate an updated machine learning model; (0066: compares the first user status information with a first user preference pattern to generate the first user status prediction pattern 413, 0067: compares the second user status information with a second user preference pattern to generate the second user status prediction pattern 423, generate a training set corresponding to the user status pattern. The training set is transmitted to the learning server 310, 0068: The composite training set is transmitted to the learning server 310 and 0066: generate a training set corresponding to the user), (Examiner notes: the processor compares first user status and second user status to generate the status prediction then generates a training sets corresponding to status pattern and that composite training sets are then transmitted to the learning server 310 to be stored for future determinations).
Park does not teach information stored in a distributed ledger;
compare a first status of at least one first example in the one or more first examples of data and a second status of at least one second example in the one or more second examples of data upon the predetermined first type of the at least one first example matching the predetermined second type of the at least one second example;
store a result of comparison of the first status and the second status in the distributed ledger; and.
However Johnsrud teaches information stored in a distributed ledger (claim 1: storing on the block chain distributed ledger, the information associated with the user),
compare a first status of at least one first example in the one or more first examples of data and a second status of at least one second example in the one or more second examples of data upon the predetermined first type of the at least one first example matching the predetermined second type of the at least one second example; (claim 1: storing on the block chain distributed ledger, the information associated with the user… claim 1: storing on the block chain distributed ledger, the information associated with the first user), (claim 1: storing on the block chain distributed ledger, the information associated with the user and 45: A block chain or blockchain is a distributed database that maintains a list of data records, the security of which is enhanced by the distributed nature of the block chain. A block chain typically includes several nodes, which may be one or more systems, machines, computers, databases, data stores or the like operably connected with one another. In some cases, each of the nodes or multiple nodes are maintained by different entities. A block chain typically works without a central repository or single administrator. One well-known application of a block chain is the public ledger of transactions for cryptocurrencies such as used in bitcoin. The data records recorded in the block chain are enforced cryptographically and stored on the nodes of the block chain.),
store a result of comparison of the first status and the second status in the distributed ledger; and (claim 1: storing on the block chain distributed ledger, the information associated with the user… claim 1: storing on the block chain distributed ledger, the information associated with the first user), (claim 1: storing on the block chain distributed ledger, the information associated with the user and 45: A block chain or blockchain is a distributed database that maintains a list of data records, the security of which is enhanced by the distributed nature of the block chain. A block chain typically includes several nodes, which may be one or more systems, machines, computers, databases, data stores or the like operably connected with one another. In some cases, each of the nodes or multiple nodes are maintained by different entities. A block chain typically works without a central repository or single administrator. One well-known application of a block chain is the public ledger of transactions for cryptocurrencies such as used in bitcoin. The data records recorded in the block chain are enforced cryptographically and stored on the nodes of the block chain.).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Park to include store a result of comparison of the first status and the second status in the distributed ledger of Johnsrud. Motivation to do so comes from the knowledge well known in the art that store a result of comparison of the first status and the second status in the distributed ledger would help store information for future use which would help in future determination of information data and would therefore make the method/system more accurate.
Park does not teach wherein each one of the one or more first examples of data has a corresponding predetermined first type, and each of the one or more second examples of data has a corresponding predetermined second type.
However Adams teaches wherein each one of the one or more first examples of data has a corresponding predetermined first type, and each of the one or more second examples of data has a corresponding predetermined second type; (abstract: The computer readable medium may include a first evaluation code segment for determining a first status of a first redundant data stream based at least in part on a first parameter and a predetermined policy, a second evaluation code segment for determining a second status of a second redundant data stream based on a second parameter and the predetermined policy, and an output code segment for transmitting a signal based at least in part on at least one of the first status and the second status. The first parameter is based at least in part on the first redundant data stream and the second parameter is based at least in part on the second redundant data stream. The first status is valid or invalid and the second status is valid or invalid.).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Park to include wherein each one of the one or more first examples of data has a corresponding predetermined first type, and each of the one or more second examples of data has a corresponding predetermined second type of Adams. Motivation to do so comes from the knowledge well known in the art that wherein each one of the one or more first examples of data has a corresponding predetermined first type, and each of the one or more second examples of data has a corresponding predetermined second type would help determine types of information ahead of time that would be used is future determination which would therefore make the method/system more efficient.
As to Claim 20, Park, Johnsrud, and Adams teach non-transitory computer-readable storage medium of claim 17.
Park, Johnsrud, and Adams further teaches wherein at least one of the one or more first examples of data is stored on one or more first nodes in the distributed ledger; at least one of the one or more second examples of data is stored on one or more second nodes in the distributed ledger; and at least one of the first status and the second status is stored on one or more third nodes in the distributed ledger; (claim 1: storing on the block chain distributed ledger, the information associated with the user and 45: A block chain or blockchain is a distributed database that maintains a list of data records, the security of which is enhanced by the distributed nature of the block chain. A block chain typically includes several nodes, which may be one or more systems, machines, computers, databases, data stores or the like operably connected with one another. In some cases, each of the nodes or multiple nodes are maintained by different entities. A block chain typically works without a central repository or single administrator. One well-known application of a block chain is the public ledger of transactions for cryptocurrencies such as used in bitcoin. The data records recorded in the block chain are enforced cryptographically and stored on the nodes of the block chain.).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include wherein at least one of the one or more first examples of data is stored on one or more first nodes in the distributed ledger; at least one of the one or more second examples of data is stored on one or more second nodes in the distributed ledger; and at least one of the first status and the second status is stored on one or more third nodes in the distributed ledger. Motivation to do so comes from the knowledge well known in the art that wherein at least one of the one or more first examples of data is stored on one or more first nodes in the distributed ledger; at least one of the one or more second examples of data is stored on one or more second nodes in the distributed ledger; and at least one of the first status and the second status is stored on one or more third nodes in the distributed ledger would help store information for future use which would help in future determination of information data and would therefore make the method/system more accurate.
As to Claim 21, Park, Johnsrud, and Adams teach non-transitory computer-readable storage medium of claim 17.
Park, Johnsrud, and Adams further teaches wherein at least one of the first status and the second status include at least one of the following: a valid example of data type, a not valid example of data type, and any combination thereof; (claim 1: storing on the block chain distributed ledger, the information associated with the user and 45: A block chain or blockchain is a distributed database that maintains a list of data records, the security of which is enhanced by the distributed nature of the block chain. A block chain typically includes several nodes, which may be one or more systems, machines, computers, databases, data stores or the like operably connected with one another. In some cases, each of the nodes or multiple nodes are maintained by different entities. A block chain typically works without a central repository or single administrator. One well-known application of a block chain is the public ledger of transactions for cryptocurrencies such as used in bitcoin. The data records recorded in the block chain are enforced cryptographically and stored on the nodes of the block chain.).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include wherein at least one of the first status and the second status include at least one of the following: a valid example of data type, a not valid example of data type, and any combination thereof. Motivation to do so comes from the knowledge well known in the art that wherein at least one of the first status and the second status include at least one of the following: a valid example of data type, a not valid example of data type, and any combination thereof would help store information for future use which would help in future determination of information data and would therefore make the method/system more accurate.
C. Claim(s) 6, and 13 is/are rejected under 35 U.S.C. 103 as being unpatentable over Park et al., (U.S. Patent Application Publication No. 20150324701) in view of Johnsrud et al. (U.S. Patent No. US10607285B2) in view of Gvili, (U.S. Patent Application Publication No. 20170149796).
As to Claim 6, Park and Johnsrud teach the method of claim 2.
Park and Johnsrud do not teach wherein at least one of the one or more first examples of data and the one or more second examples of data include at least one of the following: a nonsensitive example data, a non-confidential example of data, and any combination thereof.
However Gvili teaches wherein at least one of the one or more first examples of data and the one or more second examples of data include at least one of the following: a nonsensitive example data, a non-confidential example of data, and any combination thereof; (claim 23: a first characteristic of the confidential data; and derive additional information relevant to a second characteristic of the confidential data using available information.).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include wherein at least one of the one or more first examples of data and the one or more second examples of data include at least one of the following: a nonsensitive example data, a non-confidential example of data, and any combination thereof. Motivation to do so comes from the knowledge well known in the art that wherein at least one of the one or more first examples of data and the one or more second examples of data include at least one of the following: a nonsensitive example data, a non-confidential example of data, and any combination thereof would help provide more examples of what would be found in a document and would help users in future determinations which would therefore make the method/system more efficient and more accurate.
As to Claim 13, Park and Johnsrud teach the system of claim 10.
Park and Johnsrud do not teach wherein at least one of the one or more first examples of data and the one or more second examples of data include at least one of the following: a nonsensitive example data, a non-confidential example of data, and any combination thereof.
However Gvili teaches wherein at least one of the one or more first examples of data and the one or more second examples of data include at least one of the following: a nonsensitive example data, a non-confidential example of data, and any combination thereof; (claim 23: a first characteristic of the confidential data; and derive additional information relevant to a second characteristic of the confidential data using available information.).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include wherein at least one of the one or more first examples of data and the one or more second examples of data include at least one of the following: a nonsensitive example data, a non-confidential example of data, and any combination thereof. Motivation to do so comes from the knowledge well known in the art that wherein at least one of the one or more first examples of data and the one or more second examples of data include at least one of the following: a nonsensitive example data, a non-confidential example of data, and any combination thereof would help provide more examples of what would be found in a document and would help users in future determinations which would therefore make the method/system more efficient and more accurate.
D. Claim(s) 8, and 15 is/are rejected under 35 U.S.C. 103 as being unpatentable over Park et al., (U.S. Patent Application Publication No. 20150324701) in view of Johnsrud et al. (U.S. Patent No. US10607285B2) in view of Martin et al., (U.S. Patent Application Publication No. 20110055206).
As to Claim 8, Park and Johnsrud teach the method of claim 2.
Park and Johnsrud do not teach wherein at least one of the one or more first examples of data and the one or more second examples of data include at least one of the following: a clause in an agreement document, a sentence in an agreement document, a text in an agreement document, and any combination thereof.
However Martin wherein at least one of the one or more first examples of data and the one or more second examples of data include at least one of the following: a clause in an agreement document, a sentence in an agreement document, a text in an agreement document, and any combination thereof; (0037: When block 350 finishes, an index of phrases, phrase clusters, groups of phrase clusters, and superphrases (or clauses) is available for use. In this index or data structure, each of the phrases, groups, and superphrases is associated not only with a document identifier and document positional information for the associated text, but also metadata regarding the origin, authors, law firms, dates, jurisdictions, type of document (will, real estate agreement, mergers and acquisition agreement, confidentiality agreement, license agreement, etc.) (Some embodiments tag one or more of the phrases or clauses from the document corpus as good or bad (valid or invalid). This tagging may be done automatically based on a classifier (for example, an support vector machine) trained with known good or bad phrases or clauses, or manually by legal experts.).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include wherein at least one of the one or more first examples of data and the one or more second examples of data include at least one of the following: a clause in an agreement document, a sentence in an agreement document, a text in an agreement document, and any combination thereof. Motivation to do so comes from the knowledge well known in the art that wherein at least one of the one or more first examples of data and the one or more second examples of data include at least one of the following: a clause in an agreement document, a sentence in an agreement document, a text in an agreement document, and any combination thereof would help provide more examples of what would be found in a document and would help users in future determinations which would therefore make the method/system more efficient and more accurate.
As to Claim 15, Park and Johnsrud teach the system of claim 10.
Park and Johnsrud do not teach wherein at least one of the one or more first examples of data and the one or more second examples of data include at least one of the following: a clause in an agreement document, a sentence in an agreement document, a text in an agreement document, and any combination thereof.
However Martin wherein at least one of the one or more first examples of data and the one or more second examples of data include at least one of the following: a clause in an agreement document, a sentence in an agreement document, a text in an agreement document, and any combination thereof; (0037: When block 350 finishes, an index of phrases, phrase clusters, groups of phrase clusters, and superphrases (or clauses) is available for use. In this index or data structure, each of the phrases, groups, and superphrases is associated not only with a document identifier and document positional information for the associated text, but also metadata regarding the origin, authors, law firms, dates, jurisdictions, type of document (will, real estate agreement, mergers and acquisition agreement, confidentiality agreement, license agreement, etc.) (Some embodiments tag one or more of the phrases or clauses from the document corpus as good or bad (valid or invalid). This tagging may be done automatically based on a classifier (for example, an support vector machine) trained with known good or bad phrases or clauses, or manually by legal experts.).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include wherein at least one of the one or more first examples of data and the one or more second examples of data include at least one of the following: a clause in an agreement document, a sentence in an agreement document, a text in an agreement document, and any combination thereof. Motivation to do so comes from the knowledge well known in the art that wherein at least one of the one or more first examples of data and the one or more second examples of data include at least one of the following: a clause in an agreement document, a sentence in an agreement document, a text in an agreement document, and any combination thereof would help provide more examples of what would be found in a document and would help users in future determinations which would therefore make the method/system more efficient and more accurate.
E. Claim(s) 18 is/are rejected under 35 U.S.C. 103 as being unpatentable over Park et al., (U.S. Patent Application Publication No. 20150324701) in view of Johnsrud et al. (U.S. Patent No. US10607285B2) in view of Adams, (U.S. Patent Application Publication No. 20070058530) in view of Gvili, (U.S. Patent Application Publication No. 20170149796).
As to Claim 18, Park Johnsrud, and Adams teach the non-transitory computer-readable storage medium of claim 17.
Park Johnsrud, and Adams do not teach wherein at least one of the one or more first examples of data and the one or more second examples of data include at least one of the following: a nonsensitive example data, a non-confidential example of data, and any combination thereof.
However Gvili teaches wherein at least one of the one or more first examples of data and the one or more second examples of data include at least one of the following: a nonsensitive example data, a non-confidential example of data, and any combination thereof; (claim 23: a first characteristic of the confidential data; and derive additional information relevant to a second characteristic of the confidential data using available information.).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include wherein at least one of the one or more first examples of data and the one or more second examples of data include at least one of the following: a nonsensitive example data, a non-confidential example of data, and any combination thereof. Motivation to do so comes from the knowledge well known in the art that wherein at least one of the one or more first examples of data and the one or more second examples of data include at least one of the following: a nonsensitive example data, a non-confidential example of data, and any combination thereof would help provide more examples of what would be found in a document and would help users in future determinations which would therefore make the method/system more efficient and more accurate.
F. Claim(s) 19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Park et al., (U.S. Patent Application Publication No. 20150324701) in view of Johnsrud et al. (U.S. Patent No. US10607285B2) in view of Adams, (U.S. Patent Application Publication No. 20070058530) in view of Martin et al., (U.S. Patent Application Publication No. 20110055206).
As to Claim 19, Park Johnsrud, and Adams teach the non-transitory computer-readable storage medium of claim 17.
Park Johnsrud, and Adams do not teach wherein at least one of the one or more first examples of data and the one or more second examples of data include at least one of the following: a clause in an agreement document, a sentence in an agreement document, a text in an agreement document, and any combination thereof.
However Martin wherein at least one of the one or more first examples of data and the one or more second examples of data include at least one of the following: a clause in an agreement document, a sentence in an agreement document, a text in an agreement document, and any combination thereof; (0037: When block 350 finishes, an index of phrases, phrase clusters, groups of phrase clusters, and superphrases (or clauses) is available for use. In this index or data structure, each of the phrases, groups, and superphrases is associated not only with a document identifier and document positional information for the associated text, but also metadata regarding the origin, authors, law firms, dates, jurisdictions, type of document (will, real estate agreement, mergers and acquisition agreement, confidentiality agreement, license agreement, etc.) (Some embodiments tag one or more of the phrases or clauses from the document corpus as good or bad (valid or invalid). This tagging may be done automatically based on a classifier (for example, an support vector machine) trained with known good or bad phrases or clauses, or manually by legal experts.).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include wherein at least one of the one or more first examples of data and the one or more second examples of data include at least one of the following: a clause in an agreement document, a sentence in an agreement document, a text in an agreement document, and any combination thereof. Motivation to do so comes from the knowledge well known in the art that wherein at least one of the one or more first examples of data and the one or more second examples of data include at least one of the following: a clause in an agreement document, a sentence in an agreement document, a text in an agreement document, and any combination thereof would help provide more examples of what would be found in a document and would help users in future determinations which would therefore make the method/system more efficient and more accurate.
NPL Reference
5. The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. The NPL “Decision tree methods: applications for classification and prediction” describes “Decision tree methodology is a commonly used data mining method for establishing classification systems based on multiple covariates or for developing prediction algorithms for a target variable. This method classifies a population into branch-like segments that construct an inverted tree with a root node, internal nodes, and leaf nodes. The algorithm is non-parametric and can efficiently deal with large, complicated datasets without imposing a complicated parametric structure. When the sample size is large enough, study data can be divided into training and validation datasets. Using the training dataset to build a decision tree model and a validation dataset to decide on the appropriate tree size needed to achieve the optimal final model. This paper introduces frequently used algorithms used to develop decision trees (including CART, C4.5, CHAID, and QUEST) and describes the SPSS and SAS programs that can be used to visualize tree structure.”.
Pertinent Art
6. The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Reference#20170153864 teaches similar invention which describes a synchronization object determining method is provided, where the method includes obtaining, by a synchronization device, status information of a first terminal and status information of a second terminal, where the status information includes location information, velocity information, and acceleration information, the location information is used to describe a coordinate location of the terminal in a display picture, the velocity information is used to describe a velocity and a velocity direction of the terminal, and the acceleration information is used to describe an acceleration and an acceleration direction of the terminal, determining, by the synchronization device, a first distance between the first terminal and the second terminal in the display picture according to the location information of the first terminal and the location information of the second terminal, and predicting a second distance between the first terminal and the second terminal after specified duration in the display picture according to the location information, the velocity information, and the acceleration information of the first terminal and the location information, the velocity information, and the acceleration information of the second terminal, and classifying, by the synchronization device, the second terminal as a synchronization object of the first terminal if the first distance is greater than the second distance.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to TAREK ELCHANTI whose telephone number is (571) 272-9638. The examiner can normally be reached on Flex Mon - Thur 7-7:00 and Fri 7-4:00.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Waseem Ashraf can be reached on (571) 270-3948. The fax phone number for the organization where this application or proceeding is assigned is (571) 273-8300.
Information regarding the status of an application may be obtained from the Patent Application Information Retrieval (PAIR) system. Status information for published applications may be obtained from either Private PAIR or Public PAIR. Status information for unpublished applications is available through Private PAIR only. For more information about the PAIR system, see http://pair-direct.uspto.gov. Should you have questions on access to the Private PAIR system, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative or access to the automated information system, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/TAREK ELCHANTI/Primary Examiner, Art Unit 3621B