DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Double Patenting
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.
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Claims 1-20 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1, 3-6, 8, 10-13 and 15-19 of U.S. Patent No. US 12,182,203 B2. Although the claims at issue are not identical, they are not patentably distinct from each other. See the table below.
App. No. 19/003,048
Pat. No. US 12,182,203 B2
Claim 1: A method for providing a prediction service via a blockchain, comprising:
creating a blockchain comprising a plurality of blockchain units, each of which operates independently to provide a respective different operation associated with a prediction service;
in response to a first request for one of the plurality of operations provided by the plurality of blockchain units, directing the first request to the corresponding blockchain unit; and
in response to a second request for a new operation, adding a new blockchain unit to the blockchain, and directing the second request to the new blockchain unit.
Similarly claims 8 and 15.
Claim 1: A method implemented on at least one processor, a memory, and a communication platform for communication, comprising:
creating a communication chain comprising a plurality of chain units, each of which operates independently to provide a different prediction service associated with a prediction generated for a prompt, wherein the plurality of chain units form the communication chain, providing prediction services for different aspects of a prediction, including soliciting a prediction with respect to a prompt, providing a prediction for a prompt, verifying a prediction, and facilitating access to a prediction;
receiving a request for a requested prediction operation relating to one of the aspects of the prediction services associated with a query prompt, wherein the requested operation is directed to one of soliciting a prediction, providing a prediction, verifying a prediction, and accessing a prediction;
if one of the plurality of chain units corresponds to the query prompt, directing the request to the chain unit for carrying out the requested operation; and
if none of the plurality of chain units is associated with query prompt, creating an additional chain unit for the query prompt, adding the additional chain unit to the communication chain to create an updated communication chain, and directing the request to the additional chain unit in the updated communication chain for carrying out the requested operation.
Similarly claims 8 and 15.
Claim 2: The method of claim 1, wherein the requested operation relates to an aspect of the prediction services associated with a query prompt.
Similarly claims 9 and 16.
(Claim 1) …wherein the plurality of chain units form the communication chain, providing prediction services for different aspects of a prediction, including soliciting a prediction with respect to a prompt, providing a prediction for a prompt, verifying a prediction, and facilitating access to a prediction…
Similarly claims 8 and 15.
Claim 3: The method of claim 1, wherein the requested operation is directed to one of soliciting a prediction, providing a prediction, verifying a prediction, and accessing a prediction.
Similarly claims 10.
(Claim 1) …wherein the plurality of chain units form the communication chain, providing prediction services for different aspects of a prediction, including soliciting a prediction with respect to a prompt, providing a prediction for a prompt, verifying a prediction, and facilitating access to a prediction…
Similarly claim 8.
Claim 4: The method of claim 3, wherein soliciting a prediction comprises:
receiving a query prompt from a prediction soliciting user;
storing the query prompt received in one of the blockchain units; and
presenting the query prompt on an interface of the blockchain unit associated with the query prompt in order to solicit a prediction directed to the query prompt.
Similarly claims 11 and 17.
Claim 3: The method of claim 1, wherein the soliciting a prediction with respect to a prompt involves:
receiving the query prompt from a prediction soliciting user;
storing the query prompt received in the chain unit; and
presenting the query prompt on an interface of the chain unit associated with the query prompt in order to solicit a prediction directed to the query prompt.
Similarly claims 10 and 16.
Claim 5: The method of claim 3, wherein each of the plurality of blockchain units includes embedded information associated with one or more predictions to facilitate the prediction services, and providing a prediction comprises:
receiving a prediction from a predicting user directed to a query prompt;
storing the received prediction with the embedded information; and
presenting the received prediction on an interface of the blockchain unit associated with the query prompt in order to solicit verification on the prediction.
Similarly claims 12 and 18.
Claim 4: The method of claim 1, wherein each of the plurality of chain units includes embedded information associated with one or more predictions to facilitate the prediction services, and the providing a prediction involves:
receiving a prediction from a predicting user directed to the query prompt;
storing the received prediction with the embedded information; and
presenting the received prediction on an interface of the chain unit associated with the query prompt in order to solicit verification on the prediction, wherein the received prediction includes content of the prediction and an identifier of the predicting user.
Similarly claims 11 and 17.
Claim 6: The method of claim 3, wherein each of the plurality of blockchain units includes embedded information associated with one or more predictions to facilitate the prediction services, and verifying a prediction comprises:
receiving verification information from a verifying user directed to one of the one or more predictions associated with a query prompt, wherein the verification information includes an indication of the prediction for which the verification is provided, a verification result, and an identifier of the verifying user; and storing the verification information in the embedded information in connection with the prediction.
Similarly claims 13 and 19.
Claim 5: The method of claim 1, wherein each of the plurality of chain units includes embedded information associated with one or more predictions to facilitate the prediction services, and the verifying a prediction involves:
receiving verification information from a verifying user directed to one of the one or more predictions associated with the query prompt, wherein the verification information includes an indication of the prediction for which the verification is provided, a verification result, and an identifier of the verifying user; and storing the verification information in the embedded information in connection with the prediction.
Similarly claims 12 and 18.
Claim 7: The method of claim 3, wherein each of the plurality of blockchain units includes embedded information associated with one or more predictions to facilitate the prediction services, and facilitating access to a prediction comprises:
receiving consumption specification from a consumer user, wherein the consumption specification is indicative of an interested prediction service;
retrieving, from the embedded information in the blockchain unit, service information consistent with the interested prediction service; and
presenting the retrieved service information to the consumer user to deliver the requested interested prediction service.
Similarly claims 14 and 20.
Claim 6: The method of claim 1, wherein the facilitating access to a prediction involves:
receiving consumption specification from a consumer user wherein the consumption specification is indicative of an interested prediction service;
retrieving, from the embedded information in the chain unit, service information consistent with the interested prediction service; and
presenting the retrieved service information to the consumer user to deliver the requested interested prediction service.
Similarly claims 13 and 19.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over SINGH et al., US 2021/0334836 A1 (hereinafter “Singh” – as cited in the IDS filed 27 December 2024) in view of Johnson et al., US 2021/0272040 A1 (hereinafter “Johnson”).
Claim 1: Singh teaches a method for providing a prediction service via a blockchain, comprising:
creating a blockchain comprising a plurality of blockchain units, each of which operates independently to provide a respective different operation associated with a service (Singh, [0028] note a recommendation system, [0037] note the verification and reward issuance engine 104 performs the verification process using a blockchain networking system 204 comprising a plurality of computing nodes 204a and a blockchain 204b (as depicted in FIG. 2c));
in response to a first request for one of the plurality of operations provided by the plurality of blockchain units, directing the first request to the corresponding blockchain unit (Singh, [0024] note receiving at least one input from at least one registered user, [0046] note If the received verified input matches with the recommendation present in any of the tiers of recommendations, the recommendation engine 106 identifies the tier associated with the matched recommendation and determines the identified tier as the current tier for the received verified input,, [0047] note Once the positions of the recommendations are updated, the recommendation engine 106 selects the recommendation(s) from the tier of recommendations with the highest priority); and
in response to a second request for a new operation, adding a new blockchain unit to the blockchain, and directing the second request to the new blockchain unit (Singh, [0024] note receiving at least one input from at least one registered user, [0045] note The recommendation engine 106 checks if the received verified input matches with the recommendations present in any of the multiple tiers of recommendations. If the received verified input does not match with the recommendations present in any of the multiple tiers of recommendations, then the recommendation engine 106 adds the received verified input as the latest recommendation, [0047] note Once the positions of the recommendations are updated, the recommendation engine 106 selects the recommendation(s) from the tier of recommendations).
Singh does not explicitly teach a prediction service.
However, Johnson teaches this (Johnson, [0216] note Referring now to FIG. 17, a block diagram of a decentralized AI system 1700 is shown, according to some embodiments. Decentralized AI system 1700 can be utilized to validate data with analysts (or other sources of verification/validation). Decentralized AI system 1700 can be particularly helpful in validation of prediction scores, [0217] note Decentralized AI system 1700 is shown to include providing data to analysts. For example, the data provided to analysts may include patterns for validation/verification. Based on the data, the analysts can bet tokens and provide predictions (or other analyses) on historical and/or future patterns, [0219] note Each time an analyst bets tokens, the “transaction” can be recorded in a blockchain-based smart contract, [0220] note Decentralized AI system 1700 can provide a meaningful system for obtain feedback from analysts on trends and/or other patterns).
It would have been obvious to one of ordinary skill in the art at the effective filing date of the application to combine the system for providing reward based verified recommendations of Singh with the decentralized system of Johnson to according to known methods (i.e. utilizing a decentralized system to receive predictions). Motivation for doing so is that this can be particularly helpful in validation of prediction scores, making changes to an environment as per the credibility of sources, and in assigning a weightage score to the sources (Johnson, [0216]).
Claim 2: Singh and Johnson teach the method of claim 1, wherein the requested operation relates to an aspect of the prediction services associated with a query prompt (Singh, [0059] note In response to the broadcasted recommendation request, the controller 202c may receive the input(s) from the registered at least one user. The input can be related to the query received from the requested user).
Claim 3: Singh and Johnson teach the method of claim 1, wherein the requested operation is directed to one of soliciting a prediction, providing a prediction, verifying a prediction, and accessing a prediction (Singh, [0059] note On verifying, that the user requested for the recommendations is a registered user, the controller 202c broadcasts the received recommendation request to the registered users of the recommendation system 100. In response to the broadcasted recommendation request, the controller 202c may receive the input(s) from the registered at least one user).
Claim 4: Singh and Johnson teach the method of claim 3, wherein soliciting a prediction comprises:
receiving a query prompt from a prediction soliciting user; storing the query prompt received in one of the blockchain units; and presenting the query prompt on an interface of the blockchain unit associated with the query prompt in order to solicit a prediction directed to the query prompt (Singh, [0044] note The recommendation engine 106 may maintain the recommendations provided for the previous recommendation requests of the users and associated recommendation details, [0047] note The recommendation engine 106 can select the recommendation based on the received recommendation request, the recommendation details associated with the recommendations, and so on. The recommendation engine 106 provides the selected recommendation to the requested user. Thus, the recommendations provided to the user can be reliable, relevant, and accurate).
Claim 5: Singh and Johnson teach the method of claim 3, wherein each of the plurality of blockchain units includes embedded information associated with one or more predictions to facilitate the prediction services, and providing a prediction comprises:
receiving a prediction from a predicting user directed to a query prompt (Sing, [0030] note Examples of the inputs can be, but not limited to, feedbacks, ratings, reviews, recommendations, likes, shares, subscriptions, suggestions, discussions, and so on);
storing the received prediction with the embedded information (Singh, [0039] note the verification and reward issuance engine 204 accepts the input as the verified input (newest/latest recommendation) for the received recommendation request and stores the verified input in the blockchain 204b); and
presenting the received prediction on an interface of the blockchain unit associated with the query prompt in order to solicit verification on the prediction (Singh, [0036] note the verification and reward issuance engine 104 broadcasts the recommendation request of the requested user to the other users registered with the verification and reward issuance engine 104. In response to the broadcasted recommendation request, the at least one registered user may provide the input(s) to the verification and reward issuance engine 104).
Claim 6: Singh and Johnson teach the method of claim 3, wherein each of the plurality of blockchain units includes embedded information associated with one or more predictions to facilitate the prediction services, and verifying a prediction comprises:
receiving verification information from a verifying user directed to one of the one or more predictions associated with a query prompt, wherein the verification information includes an indication of the prediction for which the verification is provided, a verification result, and an identifier of the verifying user; and storing the verification information in the embedded information in connection with the prediction (Singh, [0030] note Examples of the inputs can be, but not limited to, feedbacks, [0039] note the verification and reward issuance engine 204 accepts the input as the verified input (newest/latest recommendation) for the received recommendation request and stores the verified input in the blockchain 204b, [0035] note The verification and reward issuance engine 104 also maps and stores the generated RP-ID with the user details and/or the service details of the user).
Claim 7: Singh and Johnson teach the method of claim 3, wherein each of the plurality of blockchain units includes embedded information associated with one or more predictions to facilitate the prediction services, and facilitating access to a prediction comprises:
receiving consumption specification from a consumer user, wherein the consumption specification is indicative of an interested prediction service; retrieving, from the embedded information in the blockchain unit, service information consistent with the interested prediction service; and presenting the retrieved service information to the consumer user to deliver the requested interested prediction service (Singh, [Fig. 10], [0114] note At step 1004, the recommendation engine 106 receives the recommendation request of the requested user, [0118] note At step 1014, the recommendation engine 106 selects the recommendation from the tier of recommendations having the highest priority and provides the selected recommendation to the requested user).
Claim 8: Singh teaches a non-transitory, computer-readable medium having information recorded thereon for providing a prediction service via a blockchain, wherein the information, when read by a machine, causes the machine to perform operations comprising:
creating a blockchain comprising a plurality of blockchain units, each of which operates independently to provide a respective different operation associated with a service (Singh, [0028] note a recommendation system, [0037] note the verification and reward issuance engine 104 performs the verification process using a blockchain networking system 204 comprising a plurality of computing nodes 204a and a blockchain 204b (as depicted in FIG. 2c));
in response to a first request for one of the plurality of operations provided by the plurality of blockchain units, directing the first request to the corresponding blockchain unit (Singh, [0024] note receiving at least one input from at least one registered user, [0046] note If the received verified input matches with the recommendation present in any of the tiers of recommendations, the recommendation engine 106 identifies the tier associated with the matched recommendation and determines the identified tier as the current tier for the received verified input,, [0047] note Once the positions of the recommendations are updated, the recommendation engine 106 selects the recommendation(s) from the tier of recommendations with the highest priority); and
in response to a second request for a new operation, adding a new blockchain unit to the blockchain, and directing the second request to the new blockchain unit (Singh, [0024] note receiving at least one input from at least one registered user, [0045] note The recommendation engine 106 checks if the received verified input matches with the recommendations present in any of the multiple tiers of recommendations. If the received verified input does not match with the recommendations present in any of the multiple tiers of recommendations, then the recommendation engine 106 adds the received verified input as the latest recommendation, [0047] note Once the positions of the recommendations are updated, the recommendation engine 106 selects the recommendation(s) from the tier of recommendations).
Singh does not explicitly teach a prediction service
However, Johnson teaches this (Johnson, [0216] note Referring now to FIG. 17, a block diagram of a decentralized AI system 1700 is shown, according to some embodiments. Decentralized AI system 1700 can be utilized to validate data with analysts (or other sources of verification/validation). Decentralized AI system 1700 can be particularly helpful in validation of prediction scores, [0217] note Decentralized AI system 1700 is shown to include providing data to analysts. For example, the data provided to analysts may include patterns for validation/verification. Based on the data, the analysts can bet tokens and provide predictions (or other analyses) on historical and/or future patterns, [0219] note Each time an analyst bets tokens, the “transaction” can be recorded in a blockchain-based smart contract, [0220] note Decentralized AI system 1700 can provide a meaningful system for obtain feedback from analysts on trends and/or other patterns).
It would have been obvious to one of ordinary skill in the art at the effective filing date of the application to combine the system for providing reward based verified recommendations of Singh with the decentralized system of Johnson to according to known methods (i.e. utilizing a decentralized system to receive predictions). Motivation for doing so is that this can be particularly helpful in validation of prediction scores, making changes to an environment as per the credibility of sources, and in assigning a weightage score to the sources (Johnson, [0216]).
Claim 9: Singh and Johnson teach the medium of claim 8, wherein the requested operation relates to an aspect of the prediction services associated with a query prompt (Singh, [0059] note In response to the broadcasted recommendation request, the controller 202c may receive the input(s) from the registered at least one user. The input can be related to the query received from the requested user).
Claim 10: Singh and Johnson teach the medium of claim 8, wherein the requested operation is directed to one of soliciting a prediction, providing a prediction, verifying a prediction, and accessing a prediction (Singh, [0059] note On verifying, that the user requested for the recommendations is a registered user, the controller 202c broadcasts the received recommendation request to the registered users of the recommendation system 100. In response to the broadcasted recommendation request, the controller 202c may receive the input(s) from the registered at least one user).
Claim 11: Singh and Johnson teach the medium of claim 10, wherein soliciting a prediction comprises:
receiving a query prompt from a prediction soliciting user; storing the query prompt received in one of the blockchain units; and presenting the query prompt on an interface of the blockchain unit associated with the query prompt in order to solicit a prediction directed to the query prompt (Singh, [0044] note The recommendation engine 106 may maintain the recommendations provided for the previous recommendation requests of the users and associated recommendation details, [0047] note The recommendation engine 106 can select the recommendation based on the received recommendation request, the recommendation details associated with the recommendations, and so on. The recommendation engine 106 provides the selected recommendation to the requested user. Thus, the recommendations provided to the user can be reliable, relevant, and accurate).
Claim 12: Singh and Johnson teach the medium of claim 10, wherein each of the plurality of blockchain units includes embedded information associated with one or more predictions to facilitate the prediction services, and providing a prediction comprises:
receiving a prediction from a predicting user directed to a query prompt (Sing, [0030] note Examples of the inputs can be, but not limited to, feedbacks, ratings, reviews, recommendations, likes, shares, subscriptions, suggestions, discussions, and so on);
storing the received prediction with the embedded information (Singh, [0039] note the verification and reward issuance engine 204 accepts the input as the verified input (newest/latest recommendation) for the received recommendation request and stores the verified input in the blockchain 204b); and
presenting the received prediction on an interface of the blockchain unit associated with the query prompt in order to solicit verification on the prediction (Singh, [0036] note the verification and reward issuance engine 104 broadcasts the recommendation request of the requested user to the other users registered with the verification and reward issuance engine 104. In response to the broadcasted recommendation request, the at least one registered user may provide the input(s) to the verification and reward issuance engine 104).
Claim 13: Singh and Johnson teach the medium of claim 10, wherein each of the plurality of blockchain units includes embedded information associated with one or more predictions to facilitate the prediction services, and verifying a prediction comprises:
receiving verification information from a verifying user directed to one of the one or more predictions associated with a query prompt, wherein the verification information includes an indication of the prediction for which the verification is provided, a verification result, and an identifier of the verifying user; and storing the verification information in the embedded information in connection with the prediction (Singh, [0030] note Examples of the inputs can be, but not limited to, feedbacks, [0039] note the verification and reward issuance engine 204 accepts the input as the verified input (newest/latest recommendation) for the received recommendation request and stores the verified input in the blockchain 204b, [0035] note The verification and reward issuance engine 104 also maps and stores the generated RP-ID with the user details and/or the service details of the user).
Claim 14: Singh and Johnson teach the medium of claim 10, wherein each of the plurality of blockchain units includes embedded information associated with one or more predictions to facilitate the prediction services, and facilitating access to a prediction comprises:
receiving consumption specification from a consumer user, wherein the consumption specification is indicative of an interested prediction service; retrieving, from the embedded information in the blockchain unit, service information consistent with the interested prediction service; and presenting the retrieved service information to the consumer user to deliver the requested interested prediction service (Singh, [Fig. 10], [0114] note At step 1004, the recommendation engine 106 receives the recommendation request of the requested user, [0118] note At step 1014, the recommendation engine 106 selects the recommendation from the tier of recommendations having the highest priority and provides the selected recommendation to the requested user).
Claim 15: Singh teaches a system for providing a prediction service via a blockchain, the system comprising: memory storing computer program instructions; and one or more processors that, in response to executing the computer program instructions, effectuate operations comprising:
creating a blockchain comprising a plurality of blockchain units, each of which operates independently to provide a respective different operation associated with a prediction service (Singh, [0028] note a recommendation system, [0037] note the verification and reward issuance engine 104 performs the verification process using a blockchain networking system 204 comprising a plurality of computing nodes 204a and a blockchain 204b (as depicted in FIG. 2c));
in response to a first request for one of the plurality of operations provided by the plurality of blockchain units, directing the first request to the corresponding blockchain unit (Singh, [0024] note receiving at least one input from at least one registered user, [0046] note If the received verified input matches with the recommendation present in any of the tiers of recommendations, the recommendation engine 106 identifies the tier associated with the matched recommendation and determines the identified tier as the current tier for the received verified input,, [0047] note Once the positions of the recommendations are updated, the recommendation engine 106 selects the recommendation(s) from the tier of recommendations with the highest priority); and
in response to a second request for a new operation, adding a new blockchain unit to the blockchain, and directing the second request to the new blockchain unit (Singh, [0024] note receiving at least one input from at least one registered user, [0045] note The recommendation engine 106 checks if the received verified input matches with the recommendations present in any of the multiple tiers of recommendations. If the received verified input does not match with the recommendations present in any of the multiple tiers of recommendations, then the recommendation engine 106 adds the received verified input as the latest recommendation, [0047] note Once the positions of the recommendations are updated, the recommendation engine 106 selects the recommendation(s) from the tier of recommendations).
Singh does not explicitly teach a prediction service
However, Johnson teaches this (Johnson, [0216] note Referring now to FIG. 17, a block diagram of a decentralized AI system 1700 is shown, according to some embodiments. Decentralized AI system 1700 can be utilized to validate data with analysts (or other sources of verification/validation). Decentralized AI system 1700 can be particularly helpful in validation of prediction scores, [0217] note Decentralized AI system 1700 is shown to include providing data to analysts. For example, the data provided to analysts may include patterns for validation/verification. Based on the data, the analysts can bet tokens and provide predictions (or other analyses) on historical and/or future patterns, [0219] note Each time an analyst bets tokens, the “transaction” can be recorded in a blockchain-based smart contract, [0220] note Decentralized AI system 1700 can provide a meaningful system for obtain feedback from analysts on trends and/or other patterns).
It would have been obvious to one of ordinary skill in the art at the effective filing date of the application to combine the system for providing reward based verified recommendations of Singh with the decentralized system of Johnson to according to known methods (i.e. utilizing a decentralized system to receive predictions). Motivation for doing so is that this can be particularly helpful in validation of prediction scores, making changes to an environment as per the credibility of sources, and in assigning a weightage score to the sources (Johnson, [0216]).
Claim 16: Singh and Johnson teach the system of claim 15, wherein the requested operation is directed to one of soliciting a prediction, providing a prediction, verifying a prediction, and accessing a prediction (Singh, [0059] note On verifying, that the user requested for the recommendations is a registered user, the controller 202c broadcasts the received recommendation request to the registered users of the recommendation system 100. In response to the broadcasted recommendation request, the controller 202c may receive the input(s) from the registered at least one user).
Claim 17: Singh and Johnson teach the system of claim 16, wherein soliciting a prediction comprises:
receiving a query prompt from a prediction soliciting user; storing the query prompt received in one of the blockchain units; and presenting the query prompt on an interface of the blockchain unit associated with the query prompt in order to solicit a prediction directed to the query prompt (Singh, [0044] note The recommendation engine 106 may maintain the recommendations provided for the previous recommendation requests of the users and associated recommendation details, [0047] note The recommendation engine 106 can select the recommendation based on the received recommendation request, the recommendation details associated with the recommendations, and so on. The recommendation engine 106 provides the selected recommendation to the requested user. Thus, the recommendations provided to the user can be reliable, relevant, and accurate).
Claim 18: Singh and Johnson teach the system of claim 16, wherein each of the plurality of blockchain units includes embedded information associated with one or more predictions to facilitate the prediction services, and providing a prediction comprises:
receiving a prediction from a predicting user directed to a query prompt (Sing, [0030] note Examples of the inputs can be, but not limited to, feedbacks, ratings, reviews, recommendations, likes, shares, subscriptions, suggestions, discussions, and so on);
storing the received prediction with the embedded information (Singh, [0039] note the verification and reward issuance engine 204 accepts the input as the verified input (newest/latest recommendation) for the received recommendation request and stores the verified input in the blockchain 204b); and
presenting the received prediction on an interface of the blockchain unit associated with the query prompt in order to solicit verification on the prediction (Singh, [0036] note the verification and reward issuance engine 104 broadcasts the recommendation request of the requested user to the other users registered with the verification and reward issuance engine 104. In response to the broadcasted recommendation request, the at least one registered user may provide the input(s) to the verification and reward issuance engine 104).
Claim 19: Singh and Johnson teach the system of claim 16, wherein each of the plurality of blockchain units includes embedded information associated with one or more predictions to facilitate the prediction services, and verifying a prediction comprises:
receiving verification information from a verifying user directed to one of the one or more predictions associated with a query prompt, wherein the verification information includes an indication of the prediction for which the verification is provided, a verification result, and an identifier of the verifying user; and storing the verification information in the embedded information in connection with the prediction (Singh, [0030] note Examples of the inputs can be, but not limited to, feedbacks, [0039] note the verification and reward issuance engine 204 accepts the input as the verified input (newest/latest recommendation) for the received recommendation request and stores the verified input in the blockchain 204b, [0035] note The verification and reward issuance engine 104 also maps and stores the generated RP-ID with the user details and/or the service details of the user).
Claim 20: Singh and Johnson teach the system of claim 16, wherein each of the plurality of blockchain units includes embedded information associated with one or more predictions to facilitate the prediction services, and facilitating access to a prediction comprises:
receiving consumption specification from a consumer user, wherein the consumption specification is indicative of an interested prediction service; retrieving, from the embedded information in the blockchain unit, service information consistent with the interested prediction service; and presenting the retrieved service information to the consumer user to deliver the requested interested prediction service (Singh, [Fig. 10], [0114] note At step 1004, the recommendation engine 106 receives the recommendation request of the requested user, [0118] note At step 1014, the recommendation engine 106 selects the recommendation from the tier of recommendations having the highest priority and provides the selected recommendation to the requested user).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Basch et al., US 2022/0188672 A1 – Systems and methods for automatic event outcome prediction, confirmation, and validation using unique data sources and machine learning.
Padmanabhan et al., US 2020/0250747 A1 – Independent blockchains made to be flexible enough to support many assets, including assets that did not exist when the chain was first created.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Giuseppi Giuliani whose telephone number is (571)270-7128. The examiner can normally be reached Monday-Friday.
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/GIUSEPPI GIULIANI/Primary Examiner, Art Unit 2153