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 .
Acknowledgements
Claims 2, 3, 5, 15, and 22 are cancelled.
Applicant provided information disclosure statement.
Claims 1, 4, 6-14, 16-21, and 23 are pending.
Applicant made no new amendments from 12/26/2025 claim set.
This is a final office action with respect to Applicant’s response filed 12/26/2025.
Response to Arguments
35 USC 101
Applicant's arguments filed 12/26/2025 have been fully considered but they are not persuasive with respect to 35 USC 101. The 35 USC 101 rejection is maintained.
Applicant argues on page 8-9
This analysis reveals an impermissible generalization of the technology claimed.
Businesses may use technology to support their operations. Improvements to technology may thus-as a consequence of a technological improvement-result in improved business operations. By focusing only on the outcome of applying the claimed technology in one example situation, the Office Action elides the technical contributions that underlie such a use case. Here, regardless of how one might use the technology in different implementations, the claimed invention provides a technical solution to a technical problem. As described in Applicant's Specification, the technology of claim 1 addresses technical problems related challenges in data storage, indexing, query processing, and results retrieval across datasets…
Specifically, the inability to query "unindexed and/or uncategorized ... data" is a
technical constraint. Applicant's Specification, at [0022]. The solutions provided by Applicant's disclosure overcome this constraint "by learning a set of parameters for the machine-learned model framework over a smaller set of known indexed data…
Examiner Respectfully Disagrees.
The claims are not solving a technical problem but a business problem. Applicant’s specification states in para 0002 It may be desired to associate data records of activity in the one avenue with activity in the other avenue for providing an improved cross-platform service. Associating data records of customers is not a technical problem, but a business problem. Given a real-world example, a manager of a store is able to look at different sets of records with respect to customers and make associations. In addition, the improvements cited by the Applicant such as indexing, query processing, and result retrieval are nested in the abstract idea grouping of a mental process. For example, indexing, processing a query, and retrieving a result would fall under analyzing data. In addition, indexing is mere data management which is also a business problem and not a technical problem. A technical problem and solution is seen in the court case of McRO. The patents in McRO were an improvement on 3-D animation wherein the prior art comprised that "for each keyframe, the artist would look at the screen and, relying on her judgment, manipulate the character model until it looked right — a visual and subjective process." Thus, the patents in McRO aimed to automate a 3-D animator's tasks, specifically, determining when to set keyframes and setting those keyframes. The Applicant’s amendments also include the additional elements of machine learning model, but the claim language is merely descriptive and doesn’t provide active steps such as actively training to determine weights and actively learning as stated in Applicant’s arguments seen above.
Applicant argues on page 9
In this manner, "example systems and methods can expand the capability
of database processing systems to determine relations between activity data (e.g., between unindexed online and offiine activity). Id at [0021].
Examiner respectfully disagrees.
Determining relations between data is merely analyzing data. A user does not need a computer to look at pieces of data and determine relations between them. The claimed invention is merely using a general-purpose computer as seen in para 0071 and general-purpose computer configurations as seen in para 0028 to carry out this abstract idea. Limitations that are not indicative of integration into a practical application include, adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f).
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, 4, 6-14, 16-21, and 23 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 than the judicial exception itself.
Regarding Step 1 of the Subject Matter Eligibility Test for Products and Processes (from MPEP 2106.03), claims 1, 4, 6-14, 16-21, and 23 are directed to the statutory category of a method and system.
Regarding step 2A-1, Claims 1, 4, 6-14, 16-21, and 23 recite a Judicial Exception. Exemplary independent claim 1 recites the limitations of
Receiving… source activity data; executing…a query for target activity related to the source activity data, wherein executing the query comprises: determining, by…a model …predicted target activity related to the source activity data; and generating, by a…model …a predicted temporal distribution of target activity; and generating…and in response to the query, query results based at least in part on the predicted target activity and the predicted temporal distribution of target activity…sampling
In addition, claim 14 recites
Receiving…tagged records comprising linked source activity and linked target activity; updating… one or more parameters of the first…model and updating… one or more parameters of the second…model… the second…model different than the…model.
With respect to Applicant’s amendments filed 9/9/2025 to claim 14, the claims further recite
Generating…data descriptive of predicted target activity associated with the linked source activity;
Generating…a predicted temporal distribution of target activity,
With respect to Applicant’s amendments filed 9/9/2025 to claim 20, the claims further recite
receiving source activity data…executing a query for target activity related to the source activity data, wherein executing the query comprises: determining…predicted target activity related to the source activity data…generating…a predicted temporal distribution of target activity, the predicted temporal distribution of target activity comprising a plurality of prediction values corresponding to a plurality of probabilities, the plurality of prediction values associated with a plurality of bins of timing data, the plurality of probabilities associated with occurrence of the predicted target activity in the plurality of bins of timing data… and generating, in response to the query, query results based at least in part on the predicted target activity and the predicted temporal distribution of target activity, the generating of the query results comprising: sampling, for the predicted target activity, a predicted lag time from the plurality of bins of timing data based on the predicted temporal distribution of target activity.
These limitations, as drafted, are a process that, under its broadest reasonable interpretation cover concepts of receiving, executing, determining, generating, sampling and updating data. The claim limitations fall under the abstract idea grouping of mental process, because the limitations can be performed in the human mind, or by a human using a pen and paper. For example, but for the language of a system and processor, the claim language encompasses simply receiving data such as source data, executing a query, determining/generating predicted target activity and predicted temporal distribution of target activity, generating query results that includes sampling data, and updating models. These steps are all data manipulation steps that do not require a computer. Given a real-world example, these steps are done by a manager managing consumers and their behaviors.
The claims deal with user management such as monitoring their activities (i.e. source/target activity) which include what they interact with (See Specification para 0017 and 0036). The claims also recite service providers that provide services to users. (See Specification para 0002). These make the claims fall in the abstract idea grouping of certain methods of organizing human activity (behaviors, business relations, and interactions between people). It is clear the limitations recite these abstract idea groupings, but for the recitations of generic computer components. The mere nominal recitations of generic computer components does not take the limitations out of the mental process and certain methods of organizing human activity grouping. The claims are focused on the combination of these abstract idea processes.
Regarding step 2A-2- This judicial exception is not integrated into a practical application, and the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception.
The claim recites the additional elements of computing system, memory devices, processor, machine learned model, and memory devices. These components are recited at a high level of generality, and merely automate the steps. Each of the additional limitations is no more than mere instructions to apply the exception using a generic computer component.
The combination of these additional elements is no more than mere instructions to apply the exception using a generic computer components or software. Accordingly, even in combination, these 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.
Further, the claims do not provide recite any improvements to the functioning of a computer, or to any other technology or technical field; applying or using a judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition; applying the judicial exception with, or by use of, a particular machine; effecting a transformation or reduction of a particular article to a different state or thing; or applying or using the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception.
The dependent claims have the same deficiencies as their parent claims as being directed towards an abstract idea, as the dependent claims merely narrow the scope of their parent claims. For example, the dependent claims further recite what the query results comprise such as a data structure. In addition, the dependent claims further describe what the source activity and target activity are such as online and offline activity. In addition, the dependent claims describe details about the models such as receiving the same input.
Regarding step 2B the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because claims 1 and 14 recite
Method, however method is not considered an additional element
Claim 1 further recites computer system and machine learned model.
Claim 14 recites processor, computing system, machine learned models
Claims 20 recites system, processor, machine learned models, and memory devices.
When looking at these additional elements individually, the additional elements are purely functional and generic, the Applicant’s specification states a general-purpose computer as seen in para 0071 and general-purpose computer configurations as seen in para 0028.
When looking at the additional elements in combination, the computer components add nothing that is not already present when the steps are considered separately. See MPEP 2106.05
Looking at these limitations as an ordered combination and individually adds nothing additional that is sufficient to amount to significantly more than the recited abstract idea because they simply provide instructions to use generic computer components, recitations of generic computer structure to perform generic computer functions that are used to "apply" the recited abstract idea. Thus, the elements of the claims, considered both individually and as an ordered combination, are not sufficient to ensure that the claim as a whole amounts to significantly more than the abstract idea itself. Since there are no limitations in these claims that transform the exception into a patent eligible application such that these claims amount to significantly more than the exception itself, claims 1, 4, 6-14, 16-21, and 23 are rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.
Conclusion
The prior art made of record and not relied upon considered pertinent to Applicant’s disclosure.
Allbright (US20200211021A1) Discloses a computing system for detecting fraudulent payment card network events includes a ratio striping engine that receives scored payment card transaction authorization requests and generates data structures for a plurality of account ranges.
Banerjee (US12182845B2) Discloses a system can include a database and a computing device. The computing device is configured to receive an item recommendation request corresponding to an asset from an analyst device and select a set of item identifiers of a plurality of item identifiers.
Chauhan (20170300948) Discloses systems and methods are provided for use in predicting purchases in regions based on payment account transaction data from in and around the regions.
Cummins (20160239853) Discloses a method for providing insight metrics for transaction behavior includes: storing account profiles, each including a profile identifier and transaction data entries including transaction data related to a payment transaction. The desired insight metrics are with respect to a plurality of different categories, such as time (e.g., per day, per week, per month, per annum, during a weekday, during a weekend, on a specific day, on a specific weekend, etc.).
Wang (12287819) Discloses a system may generate a prompt based in part on a search query from a customer client device. The prompt instructs a machine learned model to provide item predictions. And the model was trained by: converting structured data describing items of an online catalog to annotated text data (unstructured data), generating training examples based in part on the annotated text data, and training the model using the training examples.
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.
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/MUSTAFA IQBAL/Primary Examiner, Art Unit 3625