DETAILED ACTION
The following is a non-final office action upon examination of application number 17/941550.
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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 5/4/2026 has been entered.
Response to Amendment
The rejection of claims 1-20 under 35 USC 112(a) is withdrawn in view of the claim amendments filed 5/4/2026.
Claims 1-20 are pending in the application and have been examined on the merits discussed below.
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 a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
(Step 1) Claims 1-10 are directed to a method; thus these claims are directed to a process, which is one of the statutory categories of invention. Claims 11-15 are directed to a system comprising electronic devices; thus the system comprises a device or set of devices, and therefore, is directed to a machine which is a statutory category of invention. Claims 16-20 are directed to a device comprising one or more processors; thus the device comprises a device or set of devices, and therefore, is directed to a machine which is a statutory category of invention.
(Step 2A) The claims recite an abstract idea instructing how to monetize corporate data, which is described by claim limitations reciting:
capture the user data associated with a user; and
obtaining corporate data associated with one or more corporations,
analyzing the corporate data … to generate metadata associated with data types, amounts, and update frequencies for the corporate data;
tokenizing the corporate data into one or more tokens,
valuing the corporate data … utilizing a valuation algorithm,
…determining potential monetization strategies for the corporate data …; and
presenting the monetization strategies to the one or more corporations … recommending one or more of the potential monetization strategies to the one or more corporations associated with the corporate data based on at least algorithms, historical results, and trends.
The identified recited limitations in the claims describing the monetizing corporate data (i.e., the abstract idea) fall within the “Certain Methods of Organizing Human Activity” grouping of abstract ideas, which covers sales activities and fundamental economic practices. Dependent claims 4, 5, 6, 7, 10, 12, 14, 17, 19, and 20 recite limitations that further narrow/describe the abstract idea (i.e., monetizing corporate data); therefore, these claims are also found to recite an abstract idea.
This judicial exception is not integrated into a practical application because additional elements such as the data platform network-connected having at least a logic engine, a processor, and a memory, and the data refinery of the data platform in claim 1; the plurality of electronic devices executing a data application and data platform accessible by the plurality of electronic devices executing the data application through one or more networks in claim 11; the processor for executing a set of instructions; memory for storing the set of instructions; and data platform in claim 16, do not add a meaningful limitation to the abstract idea since these elements are only broadly applied to the abstract ideas at a high level of generality; thus, none of recited hardware offers a meaningful limitation beyond generally linking the abstract idea to a particular technological environment, in this case, implementation via a processor/computer. Similarly, reciting that certain steps are performed by/utilizing the logic engine of the data platform… only add computer implementation of the abstract idea.
Additional elements such as the devices executing a data application, the data application is configured to capture the user data…; the data platform configured to receive data from a plurality of networked data sources; wherein the data platform obtains corporate data…the corporate data is received by the data platform and presenting… through a user interface do not yield an improvement in the functioning of the computer itself, nor do they yield improvements to a technical field or technology; further, these additional elements only add extra-solution activities (data gathering/display). Limitations reciting that certain steps are performed by the data platform and that certain step are performed automatically … do not provide and improve and only add computer implementation of the abstract idea. Mere automation of manual processes, such as using a generic computer to process an application for financing a purchase does not show an improvement, Credit Acceptance Corp. v. Westlake Services, 859 F.3d 1044, 1055, 123 USPQ2d 1100, 1108-09 (Fed. Cir. 2017). Additional elements in claim 3 related to receiving corporate data from the plurality of networked data sources… do not provide an improvement and only add extra-solution activities. Additional elements in claims 2, 8, 13, 15, and 18, related to blockchain tokens, digital ledger and a smart contract add additional elements that do not yield an improvement; further, these additional elements are recited at a high level of generality and only generally link the abstract idea to a technological environment. Similarly, additional elements in claim 9 related to associating an inaudible tone with each of the one or more tokens to authenticate do not provide an improvement and only generally links the abstract idea to a technological environment.
Accordingly, these additional element do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea.
(Step 2B) The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because as discussed above with respect to integration of the abstract idea into a practical application, the hardware additional elements amount to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. Additional elements such as the data application is configured to capture the user data associated with a user…; the data platform configured to receive data from a plurality of networked data sources; wherein the data platform obtains corporate data…; and presenting… through a user interface do not yield an improvement in the functioning of the computer itself, nor do they yield improvements to a technical field or technology; further, these additional elements only add extra-solution activities (data gathering/display). Limitations reciting that certain steps are performed by the data platform and the data platform automatically recommends… do not provide and improve and only add computer implementation of the abstract idea. Mere automation of manual processes, such as using a generic computer to process an application for financing a purchase does not show an improvement, Credit Acceptance Corp. v. Westlake Services, 859 F.3d 1044, 1055, 123 USPQ2d 1100, 1108-09 (Fed. Cir. 2017). Additional elements in claim 3 related to receiving corporate data from the plurality of networked data sources … do not provide an improvement and only add extra-solution activities. With respect to data gathering limitations, the courts have recognized the use of computers to receive and transmit data as a well-understood, routine, and conventional, OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network). With respect to data display limitations, the courts have found the presentation of data to be a well-understood, routine, conventional activity, OIP Techs., 788 F.3d at 1362-63, 115 USPQ2d at 1092-93 (see MPEP 2106.05(d)). Additional elements in claims 2, 8, 13, 15, and 18, related to blockchain tokens, digital ledger and a smart contract add additional elements that do not yield an improvement; further, these additional elements are recited at a high level of generality and only generally link the abstract idea to a technological environment. Similarly, additional elements in claim 9 related to associating an inaudible tone with each of the one or more tokens to authenticate do not provide an improvement and only generally links the abstract idea to a technological environment. In addition, when taken as an ordered combination, the ordered combination adds nothing that is not already present as when the elements are taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology.
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 1-3, 5-8, 10-16, and 18-20 are rejected under 35 U.S.C. 103 as being unpatentable over WO 2020/097115A1 (Blaikie); US 2021/0192651 (Groth); in view of US 2022/0353329 (Chan).
As per claim 1, Blaikie teaches: a method for managing corporate data utilizing tokens, comprising: obtaining corporate data associated with one or more corporations, the corporate data is received by a data platform network-connected having at least a logic engine, a processor, and a memory (Page 2 …provides a data platform including a processor for executing a set of instructions and a memory for storing the set of instructions… Page 10 In one example, the system 100 may include a smart phone 102, a tablet 104 displaying graphical user interface 105, a laptop 106 (altogether devices 101), a network 110… Page 17 Companies, entities, or other organizations may also value their consumer data 126 and tie that value to their market capitalization providing public companies the ability to measure and place a valuation on corporate data reserves. The logic engine 122 may process data feeds received to capture data 126 from companies)
the data platform configured to received data from a plurality of networked data sources; (Page 6 any number of computing or communications devices, platforms, applications, or other data sources may be utilized in the capture and the refinement of data objects Page 7 an asset that may be tracked, updated, grown, and expanded through an opt in submission from multiple sources and monetized digitally Page 11 The cloud system 114 may aggregate, manage, analyze, and process data 126 and tokens across the Internet and any number of networks, sources 129, and third-party resources 130).
automatically tokenizing the corporate data into one or more tokens utilizing a data refinery of the data platform; (Page 2 … platform for the valuation, management, and monetization of data. A selection is received from a user to monetize a data object associated with the user. Data associated with data object is compiled. A security token is generated referencing the data… data platform …compiles the data associated with the user, generate a security token associated with the data, and monetizes the data utilizing the security token … Monetization based on open market exchange of data assets that are tokenized and converted into named 25 traded assets of a data source provide compensation for objectified data assets that allows for the direct control, valuation and monetization of data Page 5 A user may objectify, value, tokenize and list 20 upon an exchange data objects and enables conversion of all applicable data into data objects that may be controlled, valued, and monetized in commercial exchange transactions Page 6 …The platform may generate security tokens that are representative of data objects…)
valuing the corporate data performed by the logic engine of the data platform utilizing a valuation algorithm; (Page 5 algorithmic processing may determine how and when online and digital data is 25 utilized and monetized, the price point or fair data valuation based on applicable pricing Page 14 The logic engine 122 may utilize any number of thresholds, parameters, criteria, algorithms, instructions, or feedback to interact with users and interested parties and to perform other automated processes Page 15 different licensing tiers, pricing algorithms Page 17 the logic engine 122 may access sources 128 including data exchanges, markets, consultants, management systems, and so forth to determine the value of the data. For example, current and historical values for data may be determined and utilized in real-time Page 18 The logic engine 122 may also track and value accrual, sales, or transfers of data 126 between one or more companies to provide valuations Page 19 the logic engine 122 may access sources 128 including data exchanges, markets, consultants, management systems, and so forth to determine the value of the data. For example, current and historical values for data may be determined and utilized in real-time.)
automatically determining potential monetization strategies for the corporate data utilizing the logic engine of the data platform; and presenting the monetization strategies to the one or more corporations through a user interface (Page 7 The platform may be further used to secure all rights to any revenue streams associated with the data asset (e.g., any sale, sharing, or monetization of the user 25 profile for a third-party, site, or advertiser). Page 23 …the platform monetizes the data object utilizing the security token in accordance with the selection from the user (step 308). As previously noted, the data may 10 be monetized through sale, license, royalty, rent, lease, exchange, pay-per-use, and other forms of commercialization Page 26 The user interface 700, 800 may also allow the user to view tokens held by the user. For example, the user may have sold her data or he company's data through an authorized transaction managed by the platform accessed through the user interface 700, 800. The user interface 700, 800 may be utilized to set any number of alerts for notifying the user regarding … trades (e.g., real-time, limit, margin, shorts, options, futures, etc.), 20 and so forth).
Although not explicitly taught by Blaikie, Groth teaches: …through a user interface, the data platform automatically recommends one or more of the potential monetization strategies to the one or more corporations associated with the corporate data… ([0462] 6. Individualized valuation: The price users attribute to their “loss of privacy” when sharing/selling data. Companies might attribute different values to the perceived loss of competitive advantage or intellectual property when sharing data [0468] … a dynamic pricing model similarly is transferable and preferably used to develop price recommendations in a data marketplace for different datasets [0471] A pricing strategy model, which uses the results of the probability model as input to predict or estimate the optimal price recommendation for a given dataset. [0472] Personalization: This third layer adjusts price to incorporate “loss of privacy” price set by the data creator, to generate the final price suggestion. [0477] A dynamic pricing model 938 which in the first stage 934 operates as an auction and in the second stage 936 predicts prices (probability model) based on historical auction data and other variables. This provides data buyers a higher degree of reliability in planning (strategy and privacy models) [0212] As acceptance among DSPs and users reaches a critical mass, users creating online data may enter into transactions with the DSPs who would like to use that data for commercial purposes. The user preferably creates a profile of the desired transactions that she or he is willing to enter with regard to private data. The user controls which categories of data information they are willing to “license,” the price they will accept, and whether third party dissemination is allowed) based on at least algorithms, historical results, and trends ([0050] [0036] … generating recommendations with an automated recommendation engine to the user; recommendation based on algorithms.[0058] … generating market metadata with the computing system identifying the data type, the data price, data restrictions, and creator identification for each of the data units; generating a license agreement with the computing system based on market metadata specifying a transaction requirement for the alienable data units, including an associated price and associated permissible uses… [0085] … recommends appropriate changes to the PPC (and, if needed, to the CPC), based on how other similar users tend to set their charter parameters; recommendation based on trends. [0352] … A recommendation engine (i.e. a machine-learning algorithm) is preferably employed to rank the available alternatives… [0353] … recommendation engine to rank-order service alternatives at 736 according to choices made by users… [0369] … A machine learning algorithm 844 then studies and classifies the behaviors of the various DSPs to identify particular request types, formats, etc. that are optimized for each site. [0222] … To give the user the benefit of insights gleaned by other members in the privacy community, the behavior of other PA users is also preferably collectively analyzed and aggregated at step 245. From these observations the system 100 preferably can provide recommendations at step 250 to the user…; recommendation based on historical results).
Further, in addition to Blaikie, Groth teaches: …determining potential monetization strategies for the corporate data utilizing the logic engine of the data platform… ([0462] 6. Individualized valuation: The price users attribute to their “loss of privacy” when sharing/selling data. Companies might attribute different values to the perceived loss of competitive advantage or intellectual property when sharing data [0468] … a dynamic pricing model similarly is transferable and preferably used to develop price recommendations in a data marketplace for different datasets [0471] A pricing strategy model, which uses the results of the probability model as input to predict or estimate the optimal price recommendation for a given dataset. [0050] [0036] … generating recommendations with an automated recommendation engine to the user; recommendation based on algorithms. [0085] … recommends appropriate changes to the PPC (and, if needed, to the CPC), based on how other similar users tend to set their charter parameters; recommendation based on trends. [0352] … A recommendation engine (i.e. a machine-learning algorithm) is preferably employed to rank the available alternatives… [0353] … recommendation engine to rank-order service alternatives at 736 according to choices made by users… [0369] … A machine learning algorithm 844 then studies and classifies the behaviors of the various DSPs to identify particular request types, formats, etc. that are optimized for each site. [0222] … To give the user the benefit of insights gleaned by other members in the privacy community, the behavior of other PA users is also preferably collectively analyzed and aggregated at step 245. From these observations the system 100 preferably can provide recommendations at step 250 to the user…; recommendation based on historical results).
It would have been obvious, before the effective filing date of the claimed invention, for one of ordinary skill in the art to have modified the teachings of Blaikie with the aforementioned teachings of Groth with the motivation of developing pricing recommendations (Groth [0468]). Further, one of ordinary skill in the art would have recognized that applying the teachings of Groth to the system of Blaikie would have yielded predictable results and doing so would have been recognized by those of ordinary skill in the art as resulting in an improved system that would allow for recommendations to be provided.
Although not explicitly taught by Blaikie, Chan teaches: analyzing the corporate data utilizing the logic engine of the data platform to generate metadata associated with data types, amounts, and update frequencies for the corporate data; ([0033] … metadata describing the shared data. [0049] A listing 202 may include metadata 204 describing the shared data. The metadata 204 may include some or all of the following information: an identifier of the sharer of the shared data, a URL associated with the sharer, a name of the share, a name of tables, a category to which the shared data belongs, an update frequency of the shared data, a catalog of the tables, a number of columns and a number of rows in each table, as well as name for the columns… metadata 204 may be metadata for use by business intelligence tools, text description of data contained in the table, keywords associated with the table to facilitate searching, a link (e.g., URL) to documentation related to the shared data, and a refresh interval indicating how frequently the shared data is updated along with the date the data was last updated. [0067] … these actions are performed automatically…These functions may include verifying that the metadata 204 is consistent with the shared data to which it references).
It would have been obvious, before the effective filing date of the claimed invention, for one of ordinary skill in the art to have modified the teachings of Blaikie with the aforementioned teachings of Chan with the motivation of facilitating browsing an searching data (Chan [0063]). Further, one of ordinary skill in the art would have recognized that applying the teachings of Chan to the system of Blaikie would have yielded predictable results and doing so would have been recognized by those of ordinary skill in the art as resulting in an improved system that would allow for generation of metadata.
As per claim 2, Blaikie teaches: wherein the one or more tokens are blockchain tokens, and wherein the one or more tokens and associated corporate data are saved in a digital ledger (Page 2 Monetization based on open market exchange of data assets that are tokenized and converted into named 25 traded assets of a data source provide compensation for objectified data assets that allows for the direct control, valuation and monetization of data Page 5 A user may objectify, value, tokenize and list 20 upon an exchange data objects and enables conversion of all applicable data into data objects that may be controlled, valued, and monetized in commercial exchange transactions Page 13 the databases 118 may store a digital ledger for updating information relating to the user's data 126 as well as utilization of that data 126.)
As per claim 3, Blaikie teaches: receiving the corporate data from the plurality of networked data sources associated with the data platform by the one or more corporations (Page 8 The user may be incentivized to provide additional data, such as pictures, audio content, videos, location (e.g., real-time, GPS, beacon, triangulation, delayed for safety, 20 historical, etc.), Internet protocol address, identification of friends from each social network, sharing access to third-party applications, search data, views, likes, shares, comments, and so forth. Page 19 the data platform 120 may extract data from third-party platforms by opting in and providing user credentials to various applications (e.g., Facebook, Twitter, Reddit, News Sites, Amazon, Google, etc.) the data platform 120 may extract data from the sources 129).
As per claim 5, Blaikie teaches: wherein the corporate data is valued utilizing the data types, the amounts of corporate data, and updates frequency for the corporate data, data completeness of the corporate data, and users associated with the corporate data (Page 5 the price point or fair data valuation based on applicable pricing (e.g., demographic, global, location, utilization, etc.) based on going rates, principles of supply and demand, market economics, market analysis, machine learning, exchanges, auctions, real-time bidding, artificial intelligence, and so forth. Relevant information regarding data utilization may be acquired in real-time, based on historical 30 transactions/archives, a data futures market, buying and selling prices, or other applicable information or data that informs the value of a data sale or transaction. Page 16 The logic engine 122 may perform valuation of the data 126. For example, all the of the global resources and information, such as the price paid for data of all types and transactional data (e.g., micro transactions, cost per thousand, bulk sales, etc.) may be 30 utilized to perform valuations. The logic engine 122 may also track and value accrual, sales, or transfers of data 126 between one or more companies to provide valuations as included in corporate transactions Page 20 The data platform 120 performs valuation of the data 154 based on information from any number of sources including current rates, contracts, indices, exchanges, and other applicable information. For example, current targeted advertisement rates may be utilized to value the data. The tokens paid to the consumers 152 in exchange for the data 154 may vary based on the volume, quantity, verification, and types of information 25 included in the data 154; valuation uses data type and amount of data. Page 3 Users may be rewarded for additional data uploads, updates… rewarding the user 15 with additional tokens for providing additional data or updated data. Page 8 …a user may be 15 prompted, incentivized and rewarded with additional tokens for keeping their data/profile updated… The user data that is recorded and stored may 25 reside permanently on the blockchain, but typically only has a three-year lifecycle to be relevant. Thus, the user is incentivized to maintain, share, and update their data and associated profiles. Page 15 …The blockchain may cross-reference updates to the data 126 with the original record for the data platform 120 to 20 ensure proper maintenance, control, licensing, management, and transactions … receive a small fee or 30 percentage per transaction, data uploaded/updated… Page 27 … The data vault 904 may also determine the pace at 15 which new data objects ae added or updated as well as the types of data. For example, the data vault 904 may determine that information relevant to two of the user's clients including company preferences for wireless services and legal services are added to the data vault 904 each day … The data vault 904 may also assign an initial value for the data object. The value may be associated with similar data, going rates, completeness of the data, the type of data, the user supplying the data, historical information, and so forth; valuation based on updates, completeness and user).
As per claim 6, Blaikie teaches: wherein a token is created for each unique data set associated with the corporate data (Page 6 The platform may generate security tokens that are representative of data objects and listed on private or public exchange and that are used as a device for the purpose of secure access and of monetization. Tokenization of data objects may represent any number of existing, custom/proprietary, and other data objects and are listed in ticker or short name 25 form upon the exchange. Page 28 During step 1008, the data assets may be tokenized and converted into named trading assets of a data source provider. The objectified data assets may allow of the direct control, valuation, and monetization of the data).
As per claim 7, Blaikie teaches: wherein the monetization strategies include at least selling the corporate data, leasing the corporate data, and providing the corporate data without identifying information regarding individuals or corporations (Page 20 The agreement or contract may specify how, when, and what portions of the data 154 may be used as well as the associated compensation terms. The agreement may specify that the data 154 may be purchased, licensed, rented, leased Page 27 The data vault 904 may be a physical or virtual storage and vault that securely stores information. In one embodiment, the data objects may be deidentified 20 to remove identifying information to prevent hacking, identity theft, and other unwanted or prohibited utilization of data).
As per claim 8, Blaikie teaches: creating a smart contract controlling utilization of the corporate data if and when monetized (Page 8 For example, a value-based reward system tracked utilizing blockchain may be implemented. Smart contracts may be utilized with blockchain to ensure proper utilization and monetization of the data for verification 10 purposes).
As per claim 10, Blaikie teaches: advertising the corporate data to authorized parties; and (Page 10 system 100 may be utilized by any number of users, organizations, or providers to aggregate, manage, review, analyze, process, tokenize, distribute, advertise, market, display, and/or monetize user data 126)
performing transactions for the one or more tokens associated with the corporate data (Page 28 Next, the platform lists the data objects on an exchange for secured transactions 20 (step 1008). The platform may utilize a ticker, such as GCO.lw or DDT.TCHl, to designate the user or type of data. The ticker may be utilized to perform any number of real-time, market, limit, short, option, or other transactions. Tokens traded may utilized the ticker to identify the data and access the data).
As per claim 11, Blaikie teaches: a system for managing corporate data, comprising:
a plurality of electronic devices executing a data application, the data application is configured to capture the user data associated with a user; and (Page 19 The illustrative embodiments may allow data management to be outsourced from any number of users, businesses, or organizations to the system 100 and/or the data platform 120. For example, the data platform 120 may manage bulk data for a small business without the resources to fully analyze and monetize the data 126)
a data platform accessible by the plurality of electronic devices executing the data application through one or more networks, wherein the data platform obtains corporate data associated with one or more corporations from a plurality of networked data sources, (Page 17 Companies, entities, or other organizations may also value their consumer data 126 and tie that value to their market capitalization providing public companies the ability to measure and place a valuation on corporate data reserves. The logic engine 122 may process data feeds received to capture data 126 from companies Page 19 The illustrative embodiments may allow data management to be outsourced from any number of users, businesses, or organizations to the system 100 and/or the data platform 120. For example, the data platform 120 may manage bulk data for a small business without the resources to fully analyze and monetize the data 126)
tokenizes the corporate data into one or more tokens, (Page 2 Monetization based on open market exchange of data assets that are tokenized and converted into named 25 traded assets of a data source provide compensation for objectified data assets that allows for the direct control, valuation and monetization of data Page 5 A user may objectify, value, tokenize and list 20 upon an exchange data objects and enables conversion of all applicable data into data objects that may be controlled, valued, and monetized in commercial exchange transactions)
values the corporate data, (Page 6 The valuation of tokenized data objects are priced by the seller using clear compensation and renumeration guidelines and 15 then listed to the exchange via short name or ticker that is made available for purchase to registered buyers. Page 17 the logic engine 122 may access sources 128 including data exchanges, markets, consultants, management systems, and so forth to determine the value of the data. For example, current and historical values for data may be determined and utilized in real-time)
automatically determines potential monetization strategies for the corporate data, and presents the monetization strategies to the one or more corporations (Page 7 The platform may be further used to secure all rights to any revenue streams associated with the data asset (e.g., any sale, sharing, or monetization of the user 25 profile for a third-party, site, or advertiser). Page 23 …the platform monetizes the data object utilizing the security token in accordance with the selection from the user (step 308). As previously noted, the data may 10 be monetized through sale, license, royalty, rent, lease, exchange, pay-per-use, and other forms of commercialization Page 26 The user interface 700, 800 may also allow the user to view tokens held by the user. For example, the user may have sold her data or he company's data through an authorized transaction managed by the platform accessed through the user interface 700, 800. The user interface 700, 800 may be utilized to set any number of alerts for notifying the user regarding … trades (e.g., real-time, limit, margin, shorts, options, futures, etc.), 20 and so forth).
Although not explicitly taught by Blaikie, Groth teaches: the data platform automatically recommends one or more of the potential monetization strategies to the one or more corporations associated with the corporate data ([0462] 6. Individualized valuation: The price users attribute to their “loss of privacy” when sharing/selling data. Companies might attribute different values to the perceived loss of competitive advantage or intellectual property when sharing data [0468] … a dynamic pricing model similarly is transferable and preferably used to develop price recommendations in a data marketplace for different datasets [0471] A pricing strategy model, which uses the results of the probability model as input to predict or estimate the optimal price recommendation for a given dataset. [0472] Personalization: This third layer adjusts price to incorporate “loss of privacy” price set by the data creator, to generate the final price suggestion. [0477] A dynamic pricing model 938 which in the first stage 934 operates as an auction and in the second stage 936 predicts prices (probability model) based on historical auction data and other variables. This provides data buyers a higher degree of reliability in planning (strategy and privacy models) [0212] As acceptance among DSPs and users reaches a critical mass, users creating online data may enter into transactions with the DSPs who would like to use that data for commercial purposes. The user preferably creates a profile of the desired transactions that she or he is willing to enter with regard to private data. The user controls which categories of data information they are willing to “license,” the price they will accept, and whether third party dissemination is allowed) based on the metadata and at least algorithms, historical results, and trends ([0050] [0036] … generating recommendations with an automated recommendation engine to the user; recommendation based on algorithms. .[0058] … generating market metadata with the computing system identifying the data type, the data price, data restrictions, and creator identification for each of the data units; generating a license agreement with the computing system based on market metadata specifying a transaction requirement for the alienable data units, including an associated price and associated permissible uses… [0085] … recommends appropriate changes to the PPC (and, if needed, to the CPC), based on how other similar users tend to set their charter parameters; recommendation based on trends. [0352] … A recommendation engine (i.e. a machine-learning algorithm) is preferably employed to rank the available alternatives… [0353] … recommendation engine to rank-order service alternatives at 736 according to choices made by users… [0369] … A machine learning algorithm 844 then studies and classifies the behaviors of the various DSPs to identify particular request types, formats, etc. that are optimized for each site. [0222] … To give the user the benefit of insights gleaned by other members in the privacy community, the behavior of other PA users is also preferably collectively analyzed and aggregated at step 245. From these observations the system 100 preferably can provide recommendations at step 250 to the user…; recommendation based on historical results).
It would have been obvious, before the effective filing date of the claimed invention, for one of ordinary skill in the art to have modified the teachings of Blaikie with the aforementioned teachings of Groth with the motivation of developing pricing recommendations (Groth [0468]). Further, one of ordinary skill in the art would have recognized that applying the teachings of Groth to the system of Blaikie would have yielded predictable results and doing so would have been recognized by those of ordinary skill in the art as resulting in an improved system that would allow for recommendations to be provided.
Although not explicitly taught by Blaikie, Chan teaches: analyzes the corporate data to generate metadata associated with data types, amounts, and update frequencies for the corporate data; ([0033] … metadata describing the shared data. [0049] A listing 202 may include metadata 204 describing the shared data. The metadata 204 may include some or all of the following information: an identifier of the sharer of the shared data, a URL associated with the sharer, a name of the share, a name of tables, a category to which the shared data belongs, an update frequency of the shared data, a catalog of the tables, a number of columns and a number of rows in each table, as well as name for the columns… metadata 204 may be metadata for use by business intelligence tools, text description of data contained in the table, keywords associated with the table to facilitate searching, a link (e.g., URL) to documentation related to the shared data, and a refresh interval indicating how frequently the shared data is updated along with the date the data was last updated. [0067] … these actions are performed automatically…These functions may include verifying that the metadata 204 is consistent with the shared data to which it references).
It would have been obvious, before the effective filing date of the claimed invention, for one of ordinary skill in the art to have modified the teachings of Blaikie with the aforementioned teachings of Chan with the motivation of facilitating browsing an searching data (Chan [0063]). Further, one of ordinary skill in the art would have recognized that applying the teachings of Chan to the system of Blaikie would have yielded predictable results and doing so would have been recognized by those of ordinary skill in the art as resulting in an improved system that would allow for generation of metadata.
As per claim 12, this claim recites limitations substantially similar as those addressed by the rejection of claim 5, above; therefore, the same rejection applies.
As per claim 13, this claim recites limitations substantially similar as those addressed by the rejection of claim 2, above; therefore, the same rejection applies.
As per claim 14, this claim recites limitations substantially similar as those addressed by the rejection of claims 9 and 10, above; therefore, the same rejection applies.
As per claim 15, Blaikie teaches: wherein the monetization strategies include at least selling the corporate data, leasing the corporate data, and providing the corporate data without identifying information regarding individuals or corporations, (Page 20 The agreement or contract may specify how, when, and what portions of the data 154 may be used as well as the associated compensation terms. The agreement may specify that the data 154 may be purchased, licensed, rented, leased Page 27 The data vault 904 may be a physical or virtual storage and vault that securely stores information. In one embodiment, the data objects may be deidentified 20 to remove identifying information to prevent hacking, identity theft, and other unwanted or prohibited utilization of data)
wherein one or more smart contracts govern how the one or more tokens associated with the corporate data are utilized. (Page 8 For example, a value-based reward system tracked utilizing blockchain may be implemented. Smart contracts may be utilized with blockchain to ensure proper utilization and monetization of the data for verification 10 purposes).
As per claim 16, this claim recites limitations substantially similar as those addressed by the rejection of claim 1, above; therefore, the same rejection applies.
As per claim 18, this claim recites limitations substantially similar as those addressed by the rejection of claim 2, above; therefore, the same rejection applies.
As per claim 19, this claim recites limitations substantially similar as those addressed by the rejection of claims 9 and 10, above; therefore, the same rejection applies.
As per claim 20, this claim recites limitations substantially similar as those addressed by the rejection of claim 10, above; therefore, the same rejection applies.
Claims 4 and 17 are rejected under 35 U.S.C. 103 as being unpatentable over WO 2020/097115A1 (Blaikie); US 2021/0192651 (Groth); in view of US 2022/0353329 (Chan); in view of US 2016/0300234 (Moss-Pultz).
As per claim 4, Blaikie teaches: categorizing the types of data received from the plurality of networked data sources as received, wherein the corporate data is categorized… (Page 22 The data may include any number of categories, fields, or values that may be expanded over time to capture relevant information about the user in any number of fields, categories Page 24-25 The hierarchy utilized may allow the user to understand the meaning of the data asset and its relationship to other data. For example, relationships between data may be displayed by the user interface. The hierarchy may include any number of categories, subcategories, links, graphics, associations, and so forth that may be navigated, expanded, contracted, or so forth. FIG. 6 is a pictorial representation of a physical data model 600 in accordance with an illustrative embodiment Page 28 The data object may be associated with particular users and may include many distinct categories and types of data).
Although not explicitly taught by Blaikie, Moss-Pultz teaches: …data is categorized as at least one of physical asset data or intangible asset data ([0017] … Digital assets can be uniquely identified by digital fingerprints using cryptographically-safe hash functions. Fingerprints computed from images of the asset may be used in a method to uniquely identify physical assets. [0022] … the asset is digital property selected from the group consisting of music, video, electronic books, digital photographs, digital images, and personal data. In another embodiment, the asset is physical property [0058] … Asset Record 106 contains metadata for a physical or digital asset as well as the unique asset fingerprint used to identify it [0086] … the user's Personal Data gives the creator/issuer/owner control over usage of his or her asset (Data), and also provides a tool for monetization of the asset).
It would have been obvious, before the effective filing date of the claimed invention, for one of ordinary skill in the art to have modified the teachings of Blaikie with the aforementioned teachings of Moss-Pultz with the motivation of monetizing assets (Moss-Pultz [0086]). Further, one of ordinary skill in the art would have recognized that applying the teachings of Chan to the system of Blaikie would have yielded predictable results and doing so would have been recognized by those of ordinary skill in the art as resulting in an improved system that would allow for monetization of physical and digital asset data .
As per claim 17, this claim recites limitations substantially similar as those addressed by the rejection of claim 4, above; therefore, the same rejection applies.
Claims 9 are rejected under 35 U.S.C. 103 as being unpatentable over WO 2020/097115A1 (Blaikie); US 2021/0192651 (Groth); in view of US 2022/0353329 (Chan); in view of US 2021/0328800 (Lee).
As per claim 9, Blaikie teaches: generating each of the one or more tokens associated with the corporate data; and (Page 2 Monetization based on open market exchange of data assets that are tokenized and converted into named 25 traded assets of a data source provide compensation for objectified data assets that allows for the direct control, valuation and monetization of data Page 5 A user may objectify, value, tokenize and list 20 upon an exchange data objects and enables conversion of all applicable data into data objects that may be controlled, valued, and monetized in commercial exchange transactions Page 28 During step 1008, the data assets may be tokenized and converted into named trading assets of a data source provider. The objectified data assets may allow of the direct control, valuation, and monetization of the data.)
associating [a key] with each of the one or more tokens to authenticate the one or more tokens and the corporate data (Page 3 The security token may be a blockchain token that includes an encryption key for accessing the key, and wherein the data is stored in a blockchain ledger in communication with the data platform. Page 21 maintain digital ledgers that track the transactions within the system 100 to verify and authenticate the data and associated transactions Page 28 tokens may include an encryption key, password, biometric, or other secure identifier for accessing the data object from the data vault or other stored location Page 29 The data may be associated with a secure key accessed through a blockchain token to securely monetize the data for the benefit of the user.)
Although not explicitly taught by Blaikie, Lee teaches: .. associating an inaudible tone with each of the one or more tokens to authenticate… ([0052] … authentication include secure login and user authentication for tokenized or other encrypted payloads for content [0065] … a token vault 235 for tokenized payload or key storage 236 for single key or dual key encryption. The encoder/decoder 232 is also configured to compress a payload into an inaudible soundwave and encrypt the compressed payload for transmission to a downstream device, as well as decrypt the same. [0085] … the system is configured to decrypt the encrypted inaudible soundwave, then decrypt or detokenize the authorization information. For example, in an embodiment, as shown in FIG. 3B, after block 24a, at block 25a the VED 110 receives the inaudible soundwave via VED microphone 238 decrypts the encrypted inaudible soundwave including the encrypted or tokenized authorization information. For example, the VED 110 can be provided with a key to decrypt the inaudible soundwave. [0091] … the VED receives the inaudible soundwave via VED microphone 238 decrypts the encrypted inaudible soundwave including the tokenized payment information. For example, the VED can be provided with a key to decrypt the inaudible soundwave… [0095] … the VED receives the inaudible soundwave, then converts the inaudible soundwave to an encrypted data payload including the tokenized payment information and transmits the encrypted data payload including tokenized payment information).
It would have been obvious, before the effective filing date of the claimed invention, for one of ordinary skill in the art to have modified the teachings of Blaikie with the aforementioned teachings of Lee with the motivation of authenticating tokenized content (Lee [0052]). Further, one of ordinary skill in the art would have recognized that applying the teachings of Lee to the system of Blaikie would have yielded predictable results and doing so would have been recognized by those of ordinary skill in the art as resulting in an improved system that would allow for the use of inaudible soundwaves in the authentication of tokens.
Response to Arguments
Applicant's arguments filed 5/4/2026 have been fully considered but they are not persuasive.
With respect to the rejection under 35 USC 101, Applicant argues the claims are integrated into a practical application.
Examiner respectfully disagrees. Examiner maintains that the recited limitations in the claims describing monetizing corporate data (i.e., the abstract idea) fall within the “Certain Methods of Organizing Human Activity” grouping of abstract ideas, which covers sales activities and fundamental economic practices.
This judicial exception is not integrated into a practical application because additional elements such as the data platform network-connected having at least a logic engine, a processor, and a memory, and the data refinery of the data platform do not add a meaningful limitation to the abstract idea since these elements are only broadly applied to the abstract ideas at a high level of generality; thus, none of recited hardware offers a meaningful limitation beyond generally linking the abstract idea to a particular technological environment, in this case, implementation via a processor/computer. Additionally, reciting that certain steps are performed by/utilizing the logic engine of the data platform… only add computer implementation of the abstract steps (abstract idea). Furthermore, the Specification describes the platform 800 and data refinery 802 as computing devices implementing software ([Page 38] The platform 800 may include a data refinery 802, a data vault 804, and a data exchange 806. The platform 800 of FIG. 8 may be representative of one or more devices, such as the servers 116, data platform of FIG. 1, or other smart networked device implementing specific hardware, software, firmware, and/or sets of instructions). A general purpose computer that applies a judicial exception, such as an abstract idea, by use of conventional computer functions does not qualify as a particular machine. Ultramercial, Inc. v. Hulu, LLC, 772 F.3d 709, 716-17, 112 USPQ2d 1750, 1755-56 (Fed. Cir. 2014). See also TLI Communications LLC v. AV Automotive LLC, 823 F.3d 607, 613, 118 USPQ2d 1744, 1748 (Fed. Cir. 2016) (mere recitation of concrete or tangible components is not an inventive concept). Merely adding a generic computer, generic computer components, or a programmed computer to perform generic computer functions does not automatically overcome an eligibility rejection. Alice Corp. Pty. Ltd. v. CLS Bank Int’l, 573 U.S. 208, 223-24, 110 USPQ2d 1976, 1983-84 (2014).
Additional elements such as the devices executing a data application, the data application is configured to capture the user data…; the data platform configured to receive data from a plurality of networked data sources; wherein the data platform obtains corporate data…the corporate data is received by the data platform and presenting… through a user interface do not yield an improvement in the functioning of the computer itself, nor do they yield improvements to a technical field or technology; further, these additional elements only add extra-solution activities (data gathering/display). Limitations reciting that certain steps are performed by the data platform and that certain step are performed automatically … do not provide and improve and only add computer implementation of the abstract idea.
With respect to the rejection under 35 USC 103, Applicant argues the art of record does not disclose the claimed limitations.
Examiner respectfully disagrees. In response to applicant’s argument that there is no teaching, suggestion, or motivation to combine the references, the examiner recognizes that obviousness may be established by combining or modifying the teachings of the prior art to produce the claimed invention where there is some teaching, suggestion, or motivation to do so found either in the references themselves or in the knowledge generally available to one of ordinary skill in the art. See In re Fine, 837 F.2d 1071, 5 USPQ2d 1596 (Fed. Cir. 1988), In re Jones, 958 F.2d 347, 21 USPQ2d 1941 (Fed. Cir. 1992), and KSR International Co. v. Teleflex, Inc., 550 U.S. 398, 82 USPQ2d 1385 (2007). In this case, Groth is pertinent to monetization of data (Groth [0462][0471]0477]) and provides a motivation to combine the disclose features related to monetization strategies ([0468] … a dynamic pricing model similarly is transferable and preferably used to develop price recommendations in a data marketplace for different datasets) into the system disclosed by Blaikie.
In response to applicant's argument that the examiner's conclusion of obviousness is based upon improper hindsight reasoning, it must be recognized that any judgment on obviousness is in a sense necessarily a reconstruction based upon hindsight reasoning. But so long as it takes into account only knowledge which was within the level of ordinary skill at the time the claimed invention was made, and does not include knowledge gleaned only from the applicant's disclosure, such a reconstruction is proper. See In re McLaughlin, 443 F.2d 1392, 170 USPQ 209 (CCPA 1971).
Examiner maintains Blaikie discloses valuing the corporate data performed by the logic engine of the data platform utilizing a valuation algorithm (Page 5 algorithmic processing may determine how and when online and digital data is 25 utilized and monetized, the price point or fair data valuation based on applicable pricing Page 14 The logic engine 122 may utilize any number of thresholds, parameters, criteria, algorithms, instructions, or feedback to interact with users and interested parties and to perform other automated processes Page 15 different licensing tiers, pricing algorithms Page 17 the logic engine 122 may access sources 128 including data exchanges, markets, consultants, management systems, and so forth to determine the value of the data. For example, current and historical values for data may be determined and utilized in real-time Page 18 The logic engine 122 may also track and value accrual, sales, or transfers of data 126 between one or more companies to provide valuations Page 19 the logic engine 122 may access sources 128 including data exchanges, markets, consultants, management systems, and so forth to determine the value of the data. For example, current and historical values for data may be determined and utilized in real-time.) Examiner notes that the valuing… limitation does not set forth any specific requirements regarding a type valuation algorithm used; therefore, Examiner finds that the algorithmic processing to determine data valuation disclosed by Blaikie satisfies the claimed features.
With respect to claim 9, Examiner maintains that Lee discloses associating an inaudible tone with each of the one or more tokens to authenticate ([0052] … authentication include secure login and user authentication for tokenized or other encrypted payloads for content [0065] … a token vault 235 for tokenized payload or key storage 236 for single key or dual key encryption. The encoder/decoder 232 is also configured to compress a payload into an inaudible soundwave and encrypt the compressed payload for transmission to a downstream device, as well as decrypt the same. [0085] … the system is configured to decrypt the encrypted inaudible soundwave, then decrypt or detokenize the authorization information. For example, in an embodiment, as shown in FIG. 3B, after block 24a, at block 25a the VED 110 receives the inaudible soundwave via VED microphone 238 decrypts the encrypted inaudible soundwave including the encrypted or tokenized authorization information. For example, the VED 110 can be provided with a key to decrypt the inaudible soundwave. [0091] … the VED receives the inaudible soundwave via VED microphone 238 decrypts the encrypted inaudible soundwave including the tokenized payment information. For example, the VED can be provided with a key to decrypt the inaudible soundwave… [0095] … the VED receives the inaudible soundwave, then converts the inaudible soundwave to an encrypted data payload including the tokenized payment information and transmits the encrypted data payload including tokenized payment information).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
US 2022/0076256 (Anderson) – discloses the monetization of physical and digital assets ([0012][0024][0045]).
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ALAN TORRICO-LOPEZ whose telephone number is (571)272-3247. The examiner can normally be reached M-F 10AM-5PM.
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, Beth Boswell can be reached at (571)272-6737. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/ALAN TORRICO-LOPEZ/ Primary Examiner, Art Unit 3625
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