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 .
Response to Arguments
2. The Amendment filed on April 21, 2026 has been entered. The examiner acknowledges the amendments to claims 1, 4, 11, and 14, and the cancellation of claims 6 and 16.
Rejections under 35 U.S.C. § 101: Applicant argues that only certain methods of organizing human activity are abstract ideas. Examiner notes that the valuation of an entity, a dataset, involves exercising fundamental economic principles, an abstract idea. The action of transmitting data from a client to the entity performing analysis is an interaction, that at its roots, involves people. That is an abstract idea. Managing personal behavior, such as following rules; rules to define similarities between attributes, high or low frequency attributes, or even choices of inputs and datasets, rules for a commercial interaction- performing services to enable a valuation of data, and following the steps to properly execute a model are each abstract ideas underlying the claims presented in the application.
Applicant further argues that the claim cannot practically be performed in the human mind. Examiner disagrees noting that determining metadata, setting prices and making determinations about attributes and identification of similarities and differences are fundamental human processes, performed to this day by human analysts. Where a computer provides an essential element of the invention, the argument that actions “cannot be performed in the human mind” reasoning can be applied. Real time monitoring, response, and control applications fit into this category. These essential elements do not appear to be in play in the present application.
Arguments that recite the computer’s ability to address technical difficulties
associated with very large datasets, including the burden of transmitting full datasets, the need to update changing datasets, and the security risks associated with sharing raw dataset contents with a remote platform, do reflect the practicality of employing automation to problem solving, but these routinely fall into the category of “Apply it,” performing abstract ideas on a processor. To demonstrate patentability, an enumeration of the functions performed, demonstrating a practical application, in a significant level of detail would be required. This is not apparent in the claims or the application.
Arguments for a practical application are not compelling, as the specification repeats anticipated benefits in the absence of essential structure and process such that one of ordinary skill in the art would need to build upon these concepts. Of note, machine learning (ML) is mentioned in the independent claims, but without support that could lead to a practical application. Examiner also notes [0014], and [0023] suggest elements of training a MLM, but do not indicate how the training occurs. Procedures that demonstrate the use of MLM output evaluated in the real world setting and fed back into the training cycle can be used to form a basis for training a MLM and are on the path to a practical application. In the absence of this, the software operating on a processor presents a case of “Apply it,” where the additional elements are routine components of an information system applied in conventional ways without evidence of an innovative application of the additional elements.
In view of the above, the request to withdraw the rejections under 35 U.S.C. § 101 is denied.
Rejections under 35 U.S.C. § 103: Applicant’s amendments to the claims overcome the previously cited prior art and the rejections to claims 1 and 11 are withdrawn. Rejections to dependent claims, 2-5, 7-10, 12-15, and 17-20 are withdrawn due to their inherent dependency on claims 1 and 11.
Claim Rejections – 35 U.S.C. § 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 1-5, 7-15, 17-20 are rejected under 35 U.S.C. § 101 because the claimed
invention is directed to non-statutory subject matter. The claims, 1-5, 7-15, 17-20 are directed to a judicial exception (i.e., law of nature, natural phenomenon, abstract idea) without providing significantly more.
Step 1
Step 1 of the subject matter eligibility analysis per MPEP § 2106.03, required the claims to be a process, machine, manufacture or a composition of matter. Claims 1-5, 7-15, 17-20 are directed to a process (method), and a machine (system), which are statutory categories of invention.
Step 2A
Claims 1-5, 7-15, 17-20 are directed to abstract ideas, as explained below.
Prong one of the Step 2A analysis requires identifying the specific limitation(s) in the claim under examination that the examiner believes recites an abstract idea, and determining whether the identified limitation(s) falls within at least one of the groupings of abstract ideas of mathematical concepts, mental processes, and certain methods of organizing human activity.
Step 2A-Prong 1
The claims recite the following limitations that are directed to abstract ideas, which can be summarized as being directed to a method, the abstract idea, of determining the value of a dataset of an entity.
Claim 1 discloses: A method comprising:
Receiving metadata related to the first dataset to access the first dataset to
generate the metadata related to the first dataset, (following rules or instructions, observation, evaluation, judgment, opinion), the metadata comprising a plurality of attributes of the first dataset and a summary of the first dataset, wherein the metadata is generated from the first dataset,
applying a valuation model to the received metadata, wherein the valuation model comprises marketplace data, the marketplace data comprising sales prices of one or more datasets and attributes of one or more datasets, wherein the attributes are used to provide inputs to the model and output the sales prices of the one or more datasets; (following rules or instructions, observation, evaluation, judgment, opinion),
determining, based on the applying the valuation model, an estimated value of the first dataset, (economic principles or practices, following rules or instructions, observation, evaluation, judgment, opinion),
receiving, a plurality of attributes from a marketplace associated with a plurality of dataset sales; (following rules or instructions, observation, evaluation, judgment, opinion),
determining, one or more similar attributes of the first dataset and the plurality of attributes from the marketplace associated with the plurality of dataset sales; (following rules or instructions, observation, evaluation, judgment, opinion),
grouping, the one or more similar attributes into one or more categories; (following rules or instructions, observation, evaluation, judgment, opinion),
determining one or more high significance categories; (following rules or instructions, observation, evaluation, judgment, opinion),
determining, one or more frequencies of one or more similar attributes; (following rules or instructions, observation, evaluation, judgment, opinion),
removing, one or more high frequency attributes of the one or more similar attributes; (following rules or instructions, observation, evaluation, judgment, opinion),
removing, one or more zero frequency attributes of the one or more similar attributes; (following rules or instructions, observation, evaluation, judgment, opinion), and
identifying, one or more scarce attributes, wherein the one or more scarce attributes having greater than zero frequency and less than high frequency, (following rules or instructions, observation, evaluation, judgment, opinion).
Additional limitations employ the method to summarize the number of records in the dataset, completeness of the dataset, uniqueness of records in the dataset, a growth rate of the dataset and average number of records with each primary key identified in the dataset or an age of the dataset, (following rules or instructions, observation, evaluation, judgement, opinion – claim 2), receiving product ideas from the marketplace, each idea comprising product attributes, (following rules or instructions, observation, evaluation, judgement, opinion – claim 3), determining similar attributes in the first dataset with the attributes of the marketplace, correlating attributes, recommending attributes to add to the dataset, estimating values of adding attributes to the dataset, and providing recommended attributes and values to a client, (following rules or instructions, observation, evaluation, judgement, opinion, mitigating risk – claim 4), receiving attributes from a marketplace associated with dataset sales, receiving buyer identifiers associated with the dataset sales, determining similar attributes of the dataset and the attributes of the marketplace associated with the dataset sales, determining based on similar attributes and buyer identifiers one or more recommended buyers of the dataset and providing the buyers to a client, (following rules or instructions, observation, evaluation, judgement, opinion, mitigating risk – claim 5), using the attributes of the first dataset and generating a description for each attribute in the dataset, sorting the attributes and descriptions, to enable a buyer to be able to answer questions, (following rules or instructions, observation, evaluation, judgement, opinion – claim 7), receiving and storing a copy of the dataset, (following rules or instructions, observation, evaluation, judgement, opinion – claim 8), receiving an update to the dataset including a delta update, (following rules or instructions, observation, evaluation, judgement, opinion, mitigating risk – claim 9), receiving a copy of the dataset, storing the dataset, and an encryption key stored in a different location, (following rules or instructions, observation, evaluation, judgement, opinion, mitigating risk – claim 10).
Each of these claimed limitations employ processes involving organizing human activity, mitigating risk, following rules or instructions, and observation, and mental processes including observation, evaluation, judgement, and opinion.
Claims 11-15, 17-20 recite similar abstract ideas as those identified with respect to claims 1-10.
Thus, the concepts set forth in claims 1-5, 7-15, 17-20 recite abstract ideas.
Step 2A-Prong 2
As per MPEP § 2106.04, while the claims 1-5, 7-15, 17-20 recite additional limitations which are hardware or software elements such as a computer, establishing, by a computing system, a secure electronic network connection, encryption technology, an electronic agent running on the client computing system; a machine learning model, these limitations are not sufficient to qualify as a practical application being recited in the claims along with the abstract ideas since these elements are invoked as tools to apply the instructions of the abstract ideas in a specific technological environment. The mere application of an abstract idea in a particular technological environment and merely limiting the use of an abstract idea to a particular technological field do not integrate an abstract idea into a practical application (MPEP § 2106.05 (f) & (h)).
Evaluated individually, the additional elements do not integrate the identified abstract ideas into a practical application. Evaluating the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually.
The claims do not amount to a “practical application” of the abstract idea because they neither (1) recite any improvements to another technology or technical field; (2) recite any improvements to the functioning of the computer itself; (3) apply the judicial exception with, or by use of, a particular machine; (4) effect a transformation or reduction of a particular article to a different state or thing; (5) provide other meaningful limitations beyond generally linking the use of the judicial exception to a particular technological environment.
Accordingly, claims 1-5, 7-15, 17-20 are directed to abstract ideas.
Step 2B
Claims 1-5, 7-15, 17-20 do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements when considered both individually and as an ordered combination, do not amount to significantly more than the abstract idea.
The analysis above describes how the claims recite the additional elements beyond those identified above as being directed to an abstract idea, as well as why identified judicial exception(s) are not integrated into a practical application. These findings are hereby incorporated into the analysis of the additional elements when considered both individually and in combination.
For the reasons provided in the analysis in Step 2A, Prong 1, evaluated individually, the additional elements do not amount to significantly more than a judicial exception. Thus, taken alone, the additional elements do not amount to significantly more than a judicial exception.
Evaluating the claim limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. In addition to the factors discussed regarding Step 2A, prong two, there is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely amount to instructions to implement the identified abstract ideas on a computer.
Therefore, since there are no limitations in the claims 1-5, 7-15, 17-20 that transform the exception into a patent eligible application such that the claims amount to significantly more than the exception itself, the claims are directed to non-statutory subject matter and are rejected under 35 U.S.C. § 101.
Conclusion
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as
set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Claims 1 and 11 are not rejected by prior art under 35 U.S.C. § 103. Rejections to
dependent claims, 2-5, 7-10, 12-15, and 17-20 are withdrawn due to their inherent dependency on claims 1 and 11.
The closest prior art to the invention includes Sodhi, (US 20220197914 A1), “Ranking
Datasets Based on Data Attributes,” Wang, (US 20230015813 A1), “Data-Sharing Systems and Methods which use Multi-Angle Incentive Allocation,” and Crabtree, (US 20210112101 A1), “Data Set and Algorithm Validation, Bias Characterization, and Valuation.”
None of the prior art alone or in combination teach the claimed invention as recited in this claim wherein the novelty is in the combination of all the limitations and not in a single limitation.
Regarding claim 1, Sodhi teaches, A computer implemented method comprising:
establishing, by a computing system a connection to the electronic agent running on the client computing system;
receiving, by the computing system, from the electronic agent operating on the
client computing system via the secure electronic network connection, metadata related to the first dataset, wherein the electronic agent is configured to access the first dataset to dynamically generate the metadata related to the first dataset, the metadata comprising a plurality of attributes of the first dataset and a summary of the first dataset, and determining, by the computing system based on the applying the valuation model, an estimated value of the first dataset, (the computer assesses the associated metadata set for each candidate dataset, with regard to the target attributes, [0004], the computer generates a group of metadata sets for an associated plurality of datasets. The computer determines candidate datasets having a field suitability value that exceeds a predetermined suitability threshold value, and the field suitability value represents a degree of similarity between a set of fields associated with said dataset and the set of target data fields. The computer assesses the associated metadata set for each candidate dataset, with regard to the target attributes. The computer generates a compared attribute score for each candidate dataset that indicates a degree of likelihood that an associated dataset will have content exhibiting said target dataset attributes, [0004], the compared attribute scores are based, at least in part on an associated desirability value associated with each of said target dataset attributes, [0005]).
Sodhi also teaches the plurality of attributes from a marketplace, determining similar attributes, grouping similar attributes, and high significance categories.
Sodhi does not teach, but Wang teaches, establishing, by a computing system, a secure electronic network connection to the electronic agent running on the client computing system; applying, by the computing system, a valuation model to the received metadata, wherein the valuation model comprises a machine learning model, that is trained using marketplace data, the marketplace data comprising sales prices of one or more datasets and attributes of one or more datasets, wherein the attributes are used to provide inputs to the machine learning model (to facilitate data sharing, a computing system is used to link data providers and data consumers and to provide a mechanism such as a platform using necessary technologies for smoothly and securely transferring data received from data providers to data consumers, [0005]), the data-sharing system disclosed herein comprises a system-level software structure and method for incentive allocation with a number of functional modules, which surpasses the existing solutions in the ability to include more advanced and comprehensive aspects of data valuation of data provided by data providers, and smartly makes use of machine learning or Artificial Intelligence (AI) to reflect the value of the data of data providers for specific real-world tasks, [0008], with the selection of the AI model performance comparison route from the routing function (described later), the AI engine 214 (see FIG. 4) uses a training function 380 to train the AI model 228 using each of the filtered training datasets 332 obtained from the data-quality assurance module 322 and a machine learning algorithm, to generate a plurality of trained AI models each corresponding to a filtered training dataset 332. Then, an inference function 384 of the data-valuation module 324 starts the valuation computation process for testing each of the trained AI models using the test datasets 342 and computing an evaluation value 368 for each trained AI model(which represents the value of the corresponding filtered training dataset 332). The obtained evaluation values 368 are combined by the overall evaluation function 366 for generating data value ranking results for the datasets 332 which are then output from the data-valuation module 324 to the rank-to-unit-value mapping module 326 for generating rank results 386 for the filtered training datasets 332, which are the used by the unit-value mapping function 388 to generate an incentive-split scheme (such as the estimated unit values of the datasets 332, that is, the estimated value of a unit of data of each dataset [0153]).
Neither Sodhi or Wang teach, and wherein the machine learning model is trained to output the sales prices of the one or more datasets; but Crabtree teaches, (the value score is used as a pricing schedule for data set monetization, [0008]).
None of the prior art teaches wherein the metadata is generated at the client computing system from the first dataset and transmitted to the computing system without transmitting the first dataset to the computing system, or the frequency of similar attributes, removing high frequency and zero frequency attributes to identify “scarce” attributes.
Claim 11 recites substantially similar limitations as claim 1 and therefore is not rejected with a similar rationale, reasoning and motivation as claim 1.
The prior art made of record and not relied upon is considered pertinent to
applicant's disclosure or directed to the state of the art is listed on the enclosed PTO-892.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MICHAEL BOROWSKI whose telephone number is (703) 756-1822. The examiner can normally be reached M-F 8-4:30.
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, Jerry O’Connor can be reached on (571) 272-6787. The fax phone number for the organization where this application or proceeding is assigned is (571) 273-8300.
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/MB/
Patent Examiner, Art Unit 3624
/MEHMET YESILDAG/Primary Examiner, Art Unit 3624
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