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 .
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 2/17/2026 has been entered.
Response to Arguments
Applicant's arguments filed 2/17/2026 have been fully considered but they are not persuasive.
Applicants representative has amended the independent claims to recite a trained machine learning model and argued that the 35 USC 101 rejection should be withdrawn.
In response, the Examiner’s response is incorporated into the rejection found below.
`The 35 USC 102 and 103 rejections have been withdrawn in light of the applicant’s amendment.
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.
Subject Matter Eligibility Standard
When considering subject matter eligibility under 35 U.S.C. 101, it must be determined whether the claim is directed to one of the four statutory categories of invention, i.e., process, machine, manufacture, or composition of matter.
Specifically, claim 1 is directed to a method. Claims 11 is directed to a system. Each of the claims falls under one of the four statutory classes of invention.
If the claim does fall within one of the statutory categories, it must then be determined whether the claim is directed to a judicial exception (i.e., law of nature, natural phenomenon, and abstract idea).
Step 2A, Prong One: For example, claim 1 recites the following limitations absent the bolded elements and understood to recite an abstract idea:
Claim 1 recites:
receiving, utilizing a data acquisition system, information about products;
identifying, utilizing the data acquisition system, intellectual-property (IP) assets;
generating a machine learning model configured to analyze features of example products from the information and features of example IP assets to determine relationships;
generating feedback data indicating performance of the machine learning model over time;
transforming the feedback data into a training dataset configured to be utilized for training the machine learning model;
training the machine learning model utilizing the training dataset such that a trained machine learning model is generated, the training including optimizing future performance of the machine learning model while maintaining past performance of the machine learning model;
selecting utilizing an IP mapping and learning system, a computer-centric framework for representing one or more relationships between individual ones of the products and individual ones of the IP assets, wherein determining the computer-centric framework for representing the one or more relationships is performed utilizing a trained machine learning model configured to analyze features of example products from the information and features of example IP assets to determine relationships;
generating, based at least in part on the one or more relationships, association data indicating the one or more relationships between the individual ones of the products and the individual ones of the IP assets the association data indicating nodal relationships in the computer-centric framework;
receiving a first request to identify an IP asset of the IP assets that corresponds to a product of the products;
identifying, utilizing the IP mapping and learning system and based at least in part on the computer-centric framework and the association data, a portion of the IP asset that corresponds to the product;
determining, utilizing the IP mapping and learning system, and the computer-centric framework a portion of the product that corresponds to the portion of the IP asset;
determining a valuation of the IP asset based at least in part on revenue data associated with the portion of the product; and
generating, utilizing the IP mapping and learning system, a response to the first request, the response indicating at least the portion of the IP asset and the valuation.
Claim 2 recites:
determining a keyword associated with the product;
identifying, based at least in part on a publicly-accessible data source, data corresponding to the keyword; and
extracting the data that corresponds to the keyword from the publicly-accessible data source.
Claim 3 recites:
determining, in association with a first organization, a keyword associated with the product;
identifying, in association with a second organization and from a data store of the second organization, data that corresponds to the keyword; and
extracting, in association with the second organization, the data that corresponds to the keyword.
Claim 4 recites:
identifying, utilizing the data store, data indicating a relationship between the IP asset and the product; and
wherein generating the association data comprises generating the association data based at least partly on the data indicating the relationship between the IP asset and the product.
Claim 5 recites:
generating a user interface including a user-interface element configured to receive input representing information about the IP asset;
receiving, utilizing the user-interface element, the input; and
wherein generating the association data comprises generating the association data based at least in part on the input.
Claim 6 recites:. determining a metric associated the IP asset, the metric including at least one of:
a measure of breadth of at least a portion of the IP asset;
a measure of exposure associated with the at least the portion of the IP asset; or
a measure of coverage of the at least the portion of the IP asset.
Claim 7 recites: wherein the information includes a description of the products, and the method further comprises determining a feature of the products based at least partly on the description.
Claim 8 recites:
generating a user interface including one or more user-interface elements configured to capture input about the IP assets, the one or more user-interface elements including at least one of:
a first element configured to receive first information associated with trade secret documents;
a second element configured to receive second information associated with trademark documents; or a third element configured to receive third information associated with copyright documents.
Claim 9 recites: a patent document, and the method further comprises: receiving a description of the products, the description including words related to the products;
determining that at least a portion of the words are included in a claim of the patent document; and wherein the association data indicates that the claim corresponds to the product based at least partly on the at least the portion of the words being included in the claim.
Claim 10 recites: receiving input data indicating that the product does not correspond to the IP asset; and causing the association data to indicate that the product does not correspond to the IP asset.
Claim 11 recites: A system comprising:
one or more processors; and
non-transitory computer-readable media storing instructions that, when executed by the one or more processors, cause the one or more processors to perform operations comprising:
receiving, utilizing a data acquisition system, information about products;
identifying, utilizing the data acquisition system, intellectual-property (IP) assets;
generating a machine learning model configured to analyze features of example products from the information and features of example IP assets to determine relationships;
generating feedback data indicating performance of the machine learning model over time;
transforming the feedback data into a training dataset configured to be utilized for training the machine learning model;
training the machine learning model utilizing the training dataset such that a trained machine learning model is generated, the training including optimizing future performance of the machine learning model while maintaining past performance of the machine learning model;
selecting utilizing an IP mapping and learning system, a computer-centric framework for representing one or more relationships between individual ones of the products and individual ones of the IP assets, wherein determining the computer-centric framework for representing the one or more relationships is performed utilizing a trained machine learning model configured to analyze features of example products from the information and features of example IP assets to determine relationships;
generating, based on the computer-centric framework and the one at least in part on the one or more relationships, association data indicating the one or more relationships between the individual ones of the products and the individual ones of the IP assets;
receiving a first request to identify an IP asset of the IP assets that corresponds to a product of the products;
identifying, utilizing the IP mapping and learning system and based at least in part on the computer-centric framework and association data, a portion of the IP asset that corresponds to the product;
determining, utilizing the IP mapping and learning system, and the computer-centric framework a portion of the product that corresponds to the portion of the IP asset;
determining a valuation of the IP asset based at least in part on revenue data associated with the portion of the product;
generating, utilizing the IP mapping and learning system, a response to the first request, the response indicating at least the portion of the IP asset and the valuation.
Claim 12 recites: determining a keyword associated with the product;
identifying, based at least in part on a publicly-accessible data source, data corresponding to the keyword; and extracting the data that corresponds to the keyword from the publicly-accessible data source.
Claim 13 recites:
determining, in association with a first organization, a keyword associated with the product;
identifying, in association with a second organization and from a data store of the second organization, data that corresponds to the keyword; and
extracting, in association with the second organization, the data that corresponds to the keyword.
Claim 14 recites:
identifying, utilizing the data store, data indicating a relationship between the IP asset and the product; and
wherein generating the association data comprises generating the association data based at least partly on the data indicating the relationship between the IP asset and the product.
Claim 15 recites comprising:
generating a user interface including a user-interface element configured to receive input representing information about the IP asset;
receiving, utilizing the user-interface element, the input; and
wherein generating the association data comprises generating the association data based at least in part on the input.
Claim 16 recites the operations further comprising determining a metric associated the IP asset, the metric including at least one of:
a measure of breadth of at least a portion of the IP asset;
a measure of exposure associated with the at least the portion of the IP asset; or
a measure of coverage of the at least the portion of the IP asset.
Claim 17 recites wherein the information includes a description of the products, and the operations further comprise determining a feature of the products based at least partly on the description.
Claim 18 recites the operations further comprising:
generating a user interface including one or more user-interface elements configured to capture input about the IP assets, the one or more user-interface elements including at least one of:
a first element configured to receive first information associated with trade secret documents;
a second element configured to receive second information associated with trademark documents; or
a third element configured to receive third information associated with copyright documents.
Claim 19 recites wherein the IP asset comprises a patent document, and the operations further comprise:
receiving a description of the products, the description including words related to the products;
determining that at least a portion of the words are included in a claim of the patent document; and
wherein the association data indicates that the claim corresponds to the product based at least partly on the at least the portion of the words being included in the claim.
Claim 20 recites the operations further comprising:
receiving input data indicating that the product does not correspond to the IP asset; and
causing the association data to indicate that the product does not correspond to the IP asset.
The above limitations, under their broadest reasonable interpretation, still fall within the “Certain Methods of Organizing Human Activity” grouping of abstract ideas, enumerated in MPEP 2106. 04(a}2)UD, because they amount to limitations such as mental steps and also specifying steps for managing commercial or legal interactions and managing personal behavior or relationships or interactions between people by describing steps or functions of identifying, a portion of the IP asset that corresponds to the product, determining, a portion of the product that corresponds to the portion of the IP asset, determining a valuation of the IP asset based at least in part on revenue data associated with the portion of the product, and generating, a response to the first request, the response indicating at least the portion of the IP asset and the valuation.
The BRI of these limitations describes functions or steps for identifying a portion of the IP asset that corresponds to the product, determining, a portion of the product that corresponds to the portion of the IP asset, determining a valuation of the IP asset based at least in part on revenue data associated with the portion of the product, and generating, a response to the first request, the response indicating at least the portion of the IP asset and the valuation.
Step 2A, Prong two: This judicial exception is not integrated into a practical application, In particular, the clams recite the bolded limitations found above understood to be the additional limitations:
These limitations recite performing steps or functions of utilizing a data acquisition system, a computer-centric computer, machine learning model and mapping and learning system for determining one or more relationships between individual ones of products and individual ones of intellectual property (IP) assets, then determining a valuation of the IP asset based at least on revenue data associated with portion of the product and generating using the IP mapping and learning system, and a computer-centric framework a response to a first request indicating at least a portion of the IP asset and the valuation via a user interface having one or more user interface elements.
Utilizing one or more processors, a computer-centric framework, machine learning model, and learning system, and generating a user interface including user interface elements for receiving input data and processing and determining data merely amount to instructions to implement an abstract idea on a computer or merely using or utilizing a computer as a tool to perform an abstract idea. See MPEP 2106.05(f). Also see applicant's specification for guiding interpretation of these claim features, describing implementation with generic commercially available devices or any machine capable of executing a set of instructions, similarly describing usage of general and special purpose computer and “any kind of digital computer” including generic commercially available devices. The electronic communications system is similarly understood in light of applicant's specification as mere usage of any arrangement of computer software or hardware intermediate components potentially using networks to communicate between systems which are properly understood to be mere instructions to apply the abstraction using a computer.
Performance of a receiving step or function by a computer processor amounts to performing functions or steps which amounts to insignificant extra-solution activity of data gathering - see MPEP 2106.05(g).
Performing steps by computer processor hardware with electronic messages and an electronic marketplace merely limit the abstraction to computer field by execution by generic computers - see MPEP 2106.05(h).
As noted in MPEP 2106.04(d), limitations which amount to instructions to implement an abstract idea on a computer or merely using a computer as a tool, limitations which amount to insignificant extra-solution activity, and limitations which amount generally linking to a particular technological environment do not integrate a judicial exception into a practical application. While the claims do not specify any particular manner of receiving, identifying, determining, extracting and generating, the breadth of the limitations reasonably includes generating a response to a request.
Reciting one or more processors, a computer-centric framework, machine learning model, and learning system, and generating a user interface including user interface elements is understood to be similar to Alappat, which as noted in MPEP 2106. 05(b)(I), and is superseded, and the correct analysis is to look whether the added elements integrate the exception into a practical application or provide significantly more than the judicial exception. The claims in the instant application are performed by one or more processors with a data acquisition system, and a mapping and learning system and generate a response to a request.
Consideration of these steps or functions noted above as a combination does not change the analysis as they do not add anything compared to when the steps are considered separately. The claims recite a particular sequence of operations arriving at a step or function of generating a response to a first request, the request indicating at least a portion of the intellectual property asset and its valuation.
Step 2B: The elements discussed above with respect to the practical application in Step 2A, prong 2 are equally applicable to consideration of whether the claims amount to significantly more. Accordingly, the clams fail to recite additional elements which, when considered individually and in combination, amount to significantly more. Reconsideration of these elements identified as insignificant extra-solution activity as part of Step 2B does not change the analysis. Requesting and receiving information by computer hardware amounts to receiving and transmitting information over a network has been recognized by the courts as well- understood, routine, and conventional (See MPEP 2106.05(d)(II), citing Symantec, 835 F.3d at 1321, 120 OSPQ2d at 1362 (utilizing an intermediary computer to forward information); TL Communications LEC v. AV Auto. LLC, 823 F.3d 607, G10, L18 USPO2d 1744, 1748 (ed. Cir. 2016) Casing a telephone for image transmission); OFF Techs., fac. v. Amazon.com, fic., 788 F.3d 1359, 1363, 115 USPO2d 1090, 1093 (ed, Cir. 2015) (sending messages over a network), buySAFE fic. v. Google, Inc.. 768 F.3d 1350, 1355, 112 USPQ2d 1093, 1996 (Pod, Cyr. 2014) (computer receives and sends information over a network)).
Independent claims 11 and 1: Independent claim 11 recites the same limitations as claim 1 but instead claims a system for performing the steps of process or method claim 1. The same reasons discussed above with respect to claim 1 are equally applicable to claim 10.
Positively a computer-centric framework, machine learning model, and learning system, and generating a user interface including user interface elements and one or more processors with a readable media storing software instructions docs not change the analysis as these aspects are properly considered as additional elements which amount to instructions to apply it with a computer.
Positively reciting the logic stored in memory which when executed by one or more processor causes performance of the steps or functions does not change the analysis as these aspects are properly considered as additional elements which amount to instructions to apply it with a computer. These claimed elements also as found in the dependent claims are also recited at a high level of generality such that they amount to no more than mere instructions to apply the exception using a generic component.
In processing the claims, it is noted that the recitation of these additional elements does not impact the analysis of the claims because these elements in combination are noted only to be a general purpose computer for performing basic or routine computer functions. These claimed elements are noted to a be a generic computer for receiving data, storing data, determining data, receiving data, identifying data and performing data which are expected functions of a generic computer.
These additional elements do not overcome the analysis as these elements are merely considered as additional elements which amount to instructions to be applied to the generic computer or processors.
Accordingly, the additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claimed elements are also seen as generic computer components for receiving data, storing data performing generic functions without an inventive concept as they do not amount to significantly more than the abstract idea. The claimed additional elements are interpreted as being recited at a high level of generality and even if the claims recited in the affirmative. The type of data being manipulated does not impose meaningful limitations or renders the idea less abstract. Looking at the elements as a combination, the elements do not add anything more than the elements analyzed individually. Therefore, the claims do not amount to significantly more than the abstract idea itself.
Applicant is reminded that a statutory claim would recite an automated machine implemented method or system with specific structures for performing the claimed invention so as to provide an improvement to another technology or technical field, an improvement to the functioning of the computer itself, or meaningful limitations beyond generally linking the use of an abstract idea to a particular technological environment. The claims as a whole, do not amount to significantly more than the abstract idea itself. This is because the claims do not effect an improvement to another technology or technical field; the claims do not amount to an improvement to the functioning of a computer itself; and the claims do not move beyond a general link of the use of an abstract idea to a particular technological environment.
Accordingly, claims 1-20 remain directed to an abstract idea.
The prior art taken alone or in combination failed to teach or suggest:
“training the machine learning model utilizing the training dataset such that a trained machine learning model is generated, the training including optimizing future performance of the machine learning model while maintaining past performance of the machine learning model, identifying, utilizing the IP mapping and learning system and based at least in part on the computer-centric framework and the association data, a portion of the IP asset that corresponds to the product; determining, utilizing the IP mapping and learning system and the computer-centric framework, a portion of the product that corresponds to the portion of the IP asset, determining a valuation of the IP asset based at least in part on revenue data associated with the portion of the product, and generating, utilizing the IP mapping and learning system, a response to the first request, the response indicating at least the portion of the IP asset and the valuation”, as recited in independent claim 1 and as similarly recited in independent claim 11.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to FRANTZY POINVIL whose telephone number is (571)272-6797. The examiner can normally be reached M-Th 7:00AM to 5:30PM.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Michael Anderson can be reached on 571-270-0508. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/FRANTZY POINVIL/Primary Examiner, Art Unit 3693
March 12, 2026