DETAILED ACTION
The present application is being examined under the first inventor to file provisions of the AIA . In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
This Office Action is in response Applicant communication filed on 2/17/2026.
Claims
Claims 1-20 have been presented and are currently pending in the application.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
In the instant case, claims 1-7 are directed to a method, claims 8-13 are directed to a non-transitory computer readable medium, and claims 14-20 are directed to a device. Therefore, these claims fall within the four statutory categories of invention.
Claim 1 recites searching for requested data and providing access to the data if it exists. Specifically, the claim recites “receiving… a request from a first user for a data model, the request comprising information identifying a type of data and information associated with the first user; analyzing… the request to identify event data, the event data corresponding to one or more factors of a decision intelligence (DI) configuration; searching… to determine whether an existing data model corresponding to the event data is stored…; performing… a similarity analysis between the event data and data associated with the existing data model, the similarity analysis comprising comparing a computed similarity value against a similarity threshold; based on the similarity value satisfying the similarity threshold, requesting… access to the existing data model, the access request being processed in accordance with a… contract associated with the existing data model; and upon approval of the access request, providing… a token to the first user…, the token enabling the first user to access the existing data model”, which is grouped within the “certain methods of organizing human activity” and “Mental Processes” groupings of abstract ideas in prong one of step 2A of the Alice/Mayo test because the claims involve searching for requested data and providing access to the data if it exists which falls under the category of “commercial or legal interactions (including agreements in the form of contracts; legal obligations; advertising, marketing or sales activities or behaviors; business relations)” and “concepts performed in the human mind (including an observation, evaluation, judgement, opinion)”. Accordingly, the claims recite an abstract idea (See MPEP § 2106.04(a)). Claim 8 is directed to a non-transitory computer-readable medium that stores instructions that causes a device to perform the same functions of claim 1 and claim 14 recites a device that performs the same functions of claim 1. Therefore Claims 8 and 14 are also directed to the abstract idea of searching for requested data and providing access to the data if it exists.
This judicial exception is not integrated into a practical application because, when analyzed under prong two of step 2A of the Alice/Mayo test, the additional element(s) of claims 1, 8, and 14, such as the use of the device, network, processor, computer readable storage medium, merely use(s) a computer as a tool to perform an abstract idea. Specifically, the device, network, processor, computer readable storage medium perform(s) the steps or functions of searching for requested data and providing access to the data if it exists. The use of a processor/computer as a tool to implement the abstract idea 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. Further, the use of a distributed ledger, smart contract, and wallet is generally linking the use of the judicial exception to a particular technological environment (e.g. blockchain network) or field of use. 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 the abstract idea with, or by use of, a particular machine (MPEP § 2106.05(b)), 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.
Claims 1, 8, and 14 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 MPEP § 2106.05), the additional element(s) of using a device, network, processor, 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 searching for requested data and providing access to the data if it exists. As discussed above, taking the claim elements separately, the device, network, processor, computer readable storage medium perform(s) the steps or functions of the abstract idea. Viewed as a whole, the combination of elements recited in the claims merely recite the concept of searching for requested data and providing access to the data if it exists. 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)). Further, the use of the distributed ledger, smart contract, and wallet are recited at a high level and are used for generally linking the use of the judicial exception (e.g. searching for requested data and providing access to the data if it exists) to a particular technological environment (e.g. blockchain network) or field of use and is not indicative of an inventive concept. Therefore, the claim is not patent eligible.
The dependent claims 2, 4-7, 9, 11-13, 15, and 17-20 further describe the abstract idea.
Claims 2, 9, and 15 recite the abstract idea of determining whether the data was created within a predetermined time period. The claims do not include any additional elements that integrate the abstract idea into a practical application or provide significantly more than the abstract idea.
Claims 4, 11, and 17 recite the abstract idea of determining a type of processing to perform on the existing data based on the request and performing factorization of the existing data and associated data to generate a second data. The claims do not include any additional elements that integrate the abstract idea into a practical application or provide significantly more than the abstract idea.
Claims 5, 12, and 18 recites the abstract idea of limiting the usage of the token to access the data based on time. The claims do not include any additional elements that integrate the abstract idea into a practical application or provide significantly more than the abstract idea.
Claims 6, 13, and 19 recite the abstract idea of determining a type of access, time frame for use, geography of use, type of data, or a type of entity associated with the first user. The claims do not include any additional elements that integrate the abstract idea into a practical application or provide significantly more than the abstract idea.
Claims 7 and 20 recite the abstract idea of verifying the identity of the user prior to analyzing the user request and using an identifier of the user to store the token. The additional elements of a wallet on a network for storage of the token is generally linking the abstract idea to a particular technological environment (e.g. blockchain network).
The dependent claims 2, 4-7, 9, 11-13, 15, and 17-20 do not include additional elements that integrate the abstract idea into a practical application or that provide significantly more than the abstract idea. Therefore, the dependent claims are also not patent eligible.
Note: Dependent claims 3, 10, and 16 contain subject matter that is eligible under 35 U.S.C. 101. Claims 3, 10, and 16 recite the generation of a new data model by performing factorization of a set of data via a set of application program interfaces (APIs) providing executable functionality for executing DI software. These additional elements are indicative of integration into a practical application and are indicative of an inventive concept (aka “significantly more”) than the abstract idea.
Rejections under 35 § U.S.C. 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 of this title, 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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1, 4, 7, 8, 11, 14, 17, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over US 20200019938 A1 (“Wang”) and US 20210390201 A1 (“Adhikari”) and US 20180060496 A1 (“Bulleit”).
Per claims 1, 8, and 14, Wang discloses:
receiving, by a device, over a network, a request from a first user for a data model, the request comprising information identifying a type of data and information associated with the first user (e.g. As illustrated in FIG. 3, at step 302, one or more of the systems described herein can receive customer data related to surface anomalies of objects in a first industry from a third-party entity and can receive a request for a targeted model built from a pre-trained model and the data. For example, receiving module 104 may, as part of server 204 in FIG. 2, receive customer data 124 and a targeted model request 132 from the third-party entity computing device 202) (Section [0041]);
analyzing, by the device, the request to identify event data, the event data corresponding to one or more factors of a decision intelligence (DI) configuration (e.g. At step 304, one or more of the systems described herein may retrieve a pre-trained model 128 from a pre-trained model pool 126. A pre-trained model 128 is a model that has undergone some training, e.g., has been fed some training data or has undergone some other form of parameter adjustment to improve the accuracy of the model without yet achieving a level of accuracy desired) and (At step 306, one or more of the systems described herein may generate the targeted model 134 from the pre-trained model 128 and the customer data 124. In some embodiments, the targeted model 134 can be related to mapping sensor data 136 to surface anomalies. In some embodiments, the received customer data 124 and the pre-trained model 128 are transmitted to one or more of a plurality of network 402 of nodes 408, where the training of the pre-trained model 128 is completed by one or more of the nodes 408) (Section [0057]-[0059]).
Although Wang discloses receiving a request for a data model stored on the blockchain and identifying event data corresponding to one or more factors of a decision intelligence (DI) configuration, Wang does not specifically disclose:
searching, by the device, a distributed ledger to determine whether an existing data model corresponding to the event data is stored on the distributed ledger;
performing, by the device, a similarity analysis between the event data and data associated with the existing data model, the similarity analysis comprising comparing a computed similarity value against a similarity threshold;
based on the similarity value satisfying the similarity threshold, requesting, by the device, access to the existing data model, the access request being processed in accordance with a smart contract associated with the existing data model;
upon approval of the access request, providing, by the device, a token to the first user via a wallet transfer, the token enabling the first user to access the existing data model.
However Adhikari, in analogous art requesting data on the blockchain, discloses:
searching, by the device, a distributed ledger to determine whether an existing data model corresponding to the event data is stored on the distributed ledger (e.g. The distributed ledger interface server 270 may query the determined data value source system 240 for records pertaining to the individual (e.g., as in step 404). In some aspects metadata in the data value that identified the data value source system 240 may also include an identifier of the individual used by the data value source system 240. The identifier may be presented in the query) and (e.g. The data value source system 240 may indicate, and the distributed ledger interface server 270 may receive the indication of, whether there are records pertaining to the individual in the data value source system (e.g., as in step 406)) (Section [0075] and [0076]);
performing, by the device, a similarity analysis between the event data and data associated with the existing data model, the similarity analysis comprising comparing a computed similarity value against a similarity threshold (e.g. The distributed ledger interface server, at step 414 may compare the dates retrieved to the dates stored in the individual-specific data structure 291 to see if they match (e.g., satisfy a similarity threshold). If the dates do match, the secondary verification of the data value may be deemed successful by the distributed ledger interface server 270) (Section [0077]);
based on the similarity value satisfying the similarity threshold, requesting, by the device, access to the existing data model… (e.g. If the data value is a job description to be verified, the distributed ledger interface server 270 may retrieve (e.g., after sending a request to the data value source system 240), records associated with the job duties of the individual (e.g., as in step 417)) (Section [0077] and [0078]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the data model request of Wang to include the similarity analysis of the data, as taught by Adhikari, in order to achieve the predictable result of improving reliability by validating the data model being requested.
Although Wang/Adhikari discloses receiving a data model request and performing a similarity analysis by comparing the data in the request with existing data stored on the blockchain, Wang/Adhikari does not specifically disclose:
…the access request being processed in accordance with a smart contract associated with the existing data model;
upon approval of the access request, providing, by the device, a token to the first user via a wallet transfer, the token enabling the first user to access the existing data model.
However Bulleit, in analogous art of requesting data on the blockchain, discloses:
…the access request being processed in accordance with a smart contract associated with the existing data model (e.g. responsive to receiving the request for verification of permissions associated with the CSI, the blockchain system(s) 180 may execute the smart contract associated with the healthcare data of the request and the CSI of the request. If the particular healthcare data has more than one permission pertaining to specific resource for the CSI to a specific application, then the most recent permission, and the smart contract(s) therein, may control access. If the CSI of the request is indeed authorized, then the execution of the smart contract may result in the issuance of an access token for the healthcare data and the CSI of the request) (Section [0110]);
upon approval of the access request, providing, by the device, a token to the first user via a wallet transfer, the token enabling the first user to access the existing data model (e.g. If the CSI of the request is indeed authorized, then the execution of the smart contract may result in the issuance of an access token for the healthcare data and the CSI of the request) and (e.g. In example embodiments, the digital wallet token module 722 may include instructions executable by the processor(s) 700 to cooperate with the blockchain system(s) 180 to acquire and store access tokens associated with healthcare records. The instructions, therefore may enable the processor(s) 700 to receive the access token and store the same until a request for healthcare records is sent, to which the access token may be appended) (Section [0110] and [0128]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the data model access of Wang/Adhikari to include the use of a smart contract and token to enable access to the data model, as taught by Bulleit, in order to achieve the predictable result of improving the security of the system by requiring a user to hold a token in order to access the data model.
Per claims 4, 11, and 17, Wang/Adhikari/Bulleit discloses all the limitations of claims 1, 8, and 14 above. Wang further discloses:
upon the first user receiving access to the existing data model, determining, by the device, a type of processing to perform on the existing data model based on the request (e.g. In some embodiments, automated surface inspection can include using automated AI technology to analyze images of items being examined for various types of irregularities, such as damage or flaws. Such items can include, without limitation, various types of products, merchandise, raw materials, and articles of manufacturing. The image analysis can include classifying each image according to a detected type of anomaly or as being free from defects or damage) and (e.g. At step 306, one or more of the systems described herein may generate the targeted model 134 from the pre-trained model 128 and the customer data 124. In some embodiments, the targeted model 134 can be related to mapping sensor data 136 to surface anomalies. In some embodiments, the received customer data 124 and the pre-trained model 128 are transmitted to one or more of a plurality of network 402 of nodes 408, where the training of the pre-trained model 128 is completed by one or more of the nodes 408. Once the training is complete, the targeted model 134 is received from the one or more of the plurality of network 402 of nodes 408) (Section [0036], [0058], and [0059]);
based on the type of processing indicating generation of a new model, performing, by the device, factorization of the existing data model and associated data to generate a second data model (e.g. (e.g. At step 306, one or more of the systems described herein may generate the targeted model 134 from the pre-trained model 128 and the customer data 124. In some embodiments, the targeted model 134 can be related to mapping sensor data 136 to surface anomalies. In some embodiments, the received customer data 124 and the pre-trained model 128 are transmitted to one or more of a plurality of network 402 of nodes 408, where the training of the pre-trained model 128 is completed by one or more of the nodes 408. Once the training is complete, the targeted model 134 is received from the one or more of the plurality of network 402 of nodes 408)) (Section [0036], [0058], and [0059]).
Per claims 7 and 20, Wang/Adhikari/Bulleit discloses all the limitations of claims 1 and 14 above. Bulleit further discloses:
prior to analyzing the request, performing, by the device, an identity verification of the first user, the identity verification comprising one or more of a biometric verification, a two-factor authorization, or a credential-based verification (e.g. In some example embodiments, this process may involve various biometric and/or other multi-factor identification techniques and may result in the user being bound to the client device, and the user, with his or her CSI, bound to the healthcare blockchain) (Section [0018]);
upon verifying the identity of the first user, utilizing an identifier of the first user to locate a wallet on the network for storage of the token (e.g. The healthcare application may be installed on the client device and bound to the CSI of the user. This may entail storing the CSI key pair, hash thereof, and/or other identifying information about the user in a digital wallet and/or other modules on the client device) and (e.g. If the CSI of the request is indeed authorized, then the execution of the smart contract may result in the issuance of an access token for the healthcare data and the CSI of the request) and (e.g. the digital wallet token module 722 may include instructions executable by the processor(s) 700 to cooperate with the blockchain system(s) 180 to acquire and store access tokens associated with healthcare records. The instructions, therefore may enable the processor(s) 700 to receive the access token and store the same until a request for healthcare records is sent, to which the access token may be appended) (Section [0019], [0110], and [0128]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the data model access system of Wang/Adhikari to include the authentication of the user, as taught by Bulleit, in order to achieve the predictable result of improving the security of the system by ensuring that the requesting user is authorized.
Claims 2, 9, and 15 are rejected under 35 U.S.C. 103 as being unpatentable over Wang/Adhikari/Bulleit, as applied to claims 1, 8, and 14 above, in further view of US 11562007 B1 (“Tang”).
Per claims 2, 9, and 15, Although Wang/Adhikari/Bulleit disclose receiving a request for a data model on the blockchain, Wang/Adhikari/Bulleit do not specifically disclose:
determining, by the device, that the existing data model was created within a predetermined time period, the determination being performed prior to requesting access to the existing data model.
However Tang, in analogous art of machine learning data models, discloses:
determining, by the device, that the existing data model was created within a predetermined time period, the determination being performed prior to requesting access to the existing data model (e.g. In some implementations, the method 700 begins with feature analytics engine 144 determining whether a trained data model 122 is up-to-date (702), which in some examples, is based on whether training data 129 for a respective trained data model 122 has been updated since the model was last trained. In one example, the feature analytics engine 144 determines whether the trained data model incorporates the most recent training data by comparing a date associated with a trained data model 122 to an update date for applicable data received from external data sources 106) (Column 21, Ln 6-23).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the data model identification of Wang/Adhikari/Bulleit to ensure that the existing data model was created within a predetermined time period, as taught by Tang, in order to achieve the predictable result of improving reliability of the data model by ensuring that the data model is up-to-date and relevant for the requesting user’s needs.
Claims 3, 10, and 16 are rejected under 35 U.S.C. 103 as being unpatentable over Wang/Adhikari/Bulleit, as applied to claims 1, 8, and 14 above, in further view of US 20230267136 A1 (“Vasudevan”).
Per claims 3, 10, and 16, Wang/Adhikari/Bulleit discloses all the limitations of claims 1, 8, and 14 above. Adhikari further discloses:
based on the similarity value not satisfying the similarity threshold… (e.g. The distributed ledger interface server, at step 414 may compare the dates retrieved to the dates stored in the individual-specific data structure 291 to see if they match (e.g., satisfy a similarity threshold). If the dates do match, the secondary verification of the data value may be deemed successful by the distributed ledger interface server 270. If the dates do not match, the distributed ledger interface server 270 may indicate this (e.g., a failure to meet secondary verification)) (Section [0077]).
The motivation to combine Adhikari with Wang/Bulleit is disclosed above with reference to claims 1, 8, and 14.
Although Wang/Adhikari/Bulleit disclose performing a similarity analysis between the request and data and the data model, Wang/Adhikari/Bulleit do not specifically disclose:
…initiating, by the device, generation of a new data model based on the request, the generation comprising performing factorization of a set of data via a set of application program interfaces (APIs) providing executable functionality for executing DI software.
However Vasudevan, in analogous art of data models, discloses:
…initiating, by the device, generation of a new data model based on the request, the generation comprising performing factorization of a set of data via a set of application program interfaces (APIs) providing executable functionality for executing DI software (e.g. The topic model is generated using an application programming interface (API) (e.g., gensim, and sklearn) with one or more algorithms but not limited to a probability based statistical model (e.g., Latent Dirichlet allocation (LDA)), a matrix factorization model 1 (e.g., Non-negative Matrix Factorization (NMF)), a matrix factorization model 2 (e.g., Singular value decomposition (SVD)), and a semantic analysis model (e.g., Latent Semantic Indexing (LSI)) etc.) (Section [0020]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the data model generation of Wang/Adhikari/Bulleit to include factorization of the data via a set of APIs, as taught by Vasudevan, in order to achieve the predictable result of improving scalability and reducing costs of the system by offloading the heavy mathematical computations.
Claims 5, 6, 12, 13, 18, and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Wang/Adhikari/Bulleit, as applied to claims 1, 8, and 14 above, in further view of US 20190097812 A1 (“Toth”).
Per claims 5, 12, and 18, Although Wang/Adhikari/Bulleit disclose the use of tokens to access data models, Wang/Adhikari/Bulleit do not specifically disclose:
wherein the token comprises a time-decay function that limits a duration of usage of the token by the first user.
However Toth, in analogous art of data model access via tokens, discloses:
wherein the token comprises a time-decay function that limits a duration of usage of the token by the first user (e.g. FIG. 20 provides a data model for depicted consent tokens 2001 with fields in the token identifying the resource owner, the custodian service holding the owners' resources, the relying party seeking to access resources, permissions to access the resources, and the expiry date/time for the consent token. Valid access permissions may include read, write, append, delete and other such privileged operations. To simplify the presentation, illustrative resource names and identifiers are not depicted) (Section [0583]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the token of Wang/Adhikari/Bulleit to include an expiration time, as taught by Toth, in order to achieve the predictable result of improving control of the data model by not allowing a token holder to have access to the data model indefinitely.
Per claims 6, 13, and 19, Although Wang/Adhikari/Bulleit disclose the use of tokens to access data models, Wang/Adhikari/Bulleit do not specifically disclose:
determining, by the device, a structure for the access to the existing data model, the structure being based on one or more of a type of access, a time frame for use, a geography of use, a type of data, or a type of entity associated with the first user.
However Toth, in analogous art of data model access via tokens, discloses:
determining, by the device, a structure for the access to the existing data model, the structure being based on one or more of a type of access, a time frame for use, a geography of use, a type of data, or a type of entity associated with the first user (e.g. FIG. 20 provides a data model for depicted consent tokens 2001 with fields in the token identifying the resource owner, the custodian service holding the owners' resources, the relying party seeking to access resources, permissions to access the resources, and the expiry date/time for the consent token. Valid access permissions may include read, write, append, delete and other such privileged operations. To simplify the presentation, illustrative resource names and identifiers are not depicted) (Section [0583]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the token of Wang/Adhikari/Bulleit to include valid access permissions, as taught by Toth, in order to achieve the predictable result of improving control of the data model by only giving the user access to certain types of permissions.
Conclusion
The following prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
US Publication Number 20120226658 A1 to Connor teaches a system and method maintains versioning of data models to ensure that the proper data model is being used. US Patent Number 12170694 B2 to Hu teaches a system and method that generates an authentication token using a smart contract which allows users to access data. US Patent Number 10706141 B2 to Costa Faidella teaches a system and method that uses smart contracts to generate an identity token that is used to verify the user and authorize access. Non Patent Literature “Ocean Protocol: Tools for the Web3 Data Economy” is a white paper by the Ocean Protocol Foundation that teaches a marketplace for data where datatokens are used to access a data service.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to TIMOTHY P SAX whose telephone number is (571) 272-2935. The examiner can normally be reached on M-F 8-4:30.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Patrick McAtee can be reached at (571) 272-7575. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/TIMOTHY PAUL SAX/Examiner, Art Unit 3698
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