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 .
DETAILED ACTION
Status of Claims
This action is a Final action based on communications filed on 10/28/2025.
Claims 1, 11-13 and 18-20 have been amended. Claims 5 is cancelled. Claim 22 has been added. Therefore Claims 1-4 and 6 - 22 are currently pending and have been examined in this application.
Response to Amendment
Applicant’s amendment has been considered.
Response to Arguments
Applicant’s arguments have been considered.
In the remarks Applicant argues, “ Here, the Office fails to provide reasoning sufficient to establish that Applicant's independent claims recite a patent-ineligible mental process, or any other patent-ineligible abstract idea or judicial.” (pgs. 16-17)
Examiner respectfully disagrees. Application of a trained artificial intelligence process to an input data set is applying a machine learning process/algorithm to a set of data which is considered complex mathematics that could potentially be performed using a pen/paper. Further, based on MPEP 2106.04(a)(2)(III)(C), “Claims can recite a mental process even if they are claimed as being performed on a computer.” Here, generic computer components (e.g. a processor and memory) are performing generic computer functions such training an AI process using training datasets that generate values of process parameters (complex mathematics), applying the trained AI process to validation data sets (complex mathematics), generating elements of validation outputs, generating an input dataset based on interaction data (e.g. data manipulation), applying trained artificial intelligence process to the input dataset to generate output data (e.g. complex mathematics is analyzing data) (see Spec ¶0053, e.g. gradient-boosted, decision-tree process) and transmitting the output (e.g. sending/receiving data).
Applicant argues, “…Applicant's claims are nevertheless integrate allegedly recited abstract idea into a patent-eligible, practical application and as such, are not directed to any patent-ineligible abstract idea, even in unamended form.” (pgs.17-18)
Examiner respectfully disagrees. The judicial exception is not integrated into a practical application. The claims recite the additional elements of a plurality of distributed computing components, a memory, a communication interface, a processor, a tangible non-transitory computer readable medium These are generic computer components recited at a high level of generality as performing generic computer components (Spec see ¶0164), general purpose processor).
For instance, the steps of training an AI process using a plurality of training datasets and generate values of process parameters, applying the trained AI process to a plurality of validation datasets that generate elements of validation output data and computing a value of a validation metric based on the elements of validation output data involve analyzing data using complex mathematics (see Spec ¶0053). The step of generating an input data set based on interaction data is considered data manipulation for representation of data. The step of applying a trained artificial intelligence process to the input dataset to generate output data comprising numerical values indicative of (i) a predicted likelihood of an occurrence of a first targeted event during a second temporal interval, (ii) a predicted likelihood of an occurrence of a second targeted event during the second temporal interval, and (iii) and a predicted likelihood of a non-occurrence of the first and second targeted events during the second temporal interval, the second temporal interval being subsequent to the first temporal interval and being separated from the first temporal interval by a corresponding buffer interval is analyzing data using complex mathematical operations. The step of transmitting the output data to a computing system is generic data transmission functionality (sending/receiving data). Examiner notes that utilizing distributed computing components in parallel to perform tasks are commonly used and well-known functionality.
Each of the additional limitations is no more than mere instructions to apply the exception using a generic computer components (e.g. distributed computing components). The combination of these additional elements is no more than mere instructions to apply the exception using a generic computer component (e.g. distributed computing components). The additional elements do not integrate the abstract idea into a practical application because they do not impose meaningful limits on practicing the abstract idea. Therefore, the claims are directed to an abstract idea.
Applicant argues the claims, “ …when considered as a whole even in unamended form, provide a specific, technological improvement that addresses a problem within, and that improves an operation of, existing computer-implemented solutions that generate multiple elements of output through a repeated application of predictive processes to corresponding input datasets …” (pgs. 19-20)
In DDR Holdings the court found that the claims recite a specific way to automate the creation of composite Web page by an outsource provider that incorporates elements from multiple sources in order to solve a problem faced by web sites on the internet, where the claimed solution is necessarily rooted in computer technology. The
claims specify how interactions with the Internet are manipulated to yield a desired result, that overrides the routine and conventional sequence of events ordinarily triggered by the click of a hyperlink. The claimed system generates and directs the visitor to a hybrid (composite) web page that presents product information from the third-party and visual “look and feel” elements from the host website. When the limitations of the asserted claims are taken together as an ordered combination, the claims recite an invention that is not merely the routine or conventional use of the Internet (DDR Holdings, CAFC 2013-1505).
Here, unlike DDR the claims are not directed to a technical solution to a technical problem but are directed to training an AI processes and generating values of process parameters, apply the trained AI process to validation datasets, computing a value of a validation metric, generating an input data set, applying an artificial intelligence process to the input data set to generate an output data of predictive likelihood and transmitting the output data performed by generic computer components (e.g. distributing computing components). The claimed limitations exemplify the abstract concept of Mathematical Concepts related to mathematical calculations and Mental Processes related to observation and evaluation of data.
The claims appear to provide an improved business process for predicting the likelihood of an occurrence of targeted acquisition events for a customer and utilizes a distributed computing system and parallel processing (commonly used functionality) to implement the claimed limitations. There is no support in the Specification or the claims for an improvement in a technology (e.g. functioning of computer) or a technical field.
Applicant argues, “…by performing operations in parallel that generate output data indicative of the likelihood of each of a plurality of predetermined, targeted events based on a single application of a trained, artificial intelligence process to an input dataset associated with a received identifier, the specific, technological solution provided by Applicant's amended independent claims reduces a number of discrete computational operations, and an amount of computational resources, required to generate the claimed output data when compared to these existing, computer-implemented predictive solutions.” (pgs. 21-22)
The Federal Circuit has found that "merely adding computer functionality to increase the speed or efficiency of the process does not confer patent eligibility on an otherwise abstract idea." Intellectual Ventures I LLC, 792 F.3d at 1369-70; see also Intellectual Ventures I LLC v. Erie Indemnity Co., 711 F. App'x 1012, 1017 (Fed. Cir. 2017) (unpublished) ("Though the claims purport to accelerate the process of finding errant files and to reduce error, we have held that speed and accuracy increases
stemming from the ordinary capabilities of a general-purpose computer 'do[] not materially alter the patent eligibility of the claimed subject matter.").
Examiner notes that distributing computing and parallel processing are commonly used configurations used as a tool to implement the judicial exception. Additionally, the generic computer components generally link the judicial exception to a particular technological environment.
Applicant argues, “ …which train an artificial intelligence process to predict, based on an ingestion of a single input dataset, a likelihood of an occurrence of each of a plurality of predetermined, targeted events during a future temporal interval, provide an improvement to an operation of existing artificial intelligence processes that are trained to predict a likelihood of an occurrence of a single, targeted event based on an ingestion of a corresponding input dataset.” (pgs. 22-23)
There is no indication in the Specification or the claims of an improvement in operation of existing AI processes. Applicant has not specifically shown an improvement in an AI process (model or algorithm). The claims appear to provide an improved business process for predicting the likelihood of an occurrence of targeted acquisition events for a customer and utilizes a distributed computing system and parallel processing (commonly used functionality) to implement the claimed limitations.
Applicant argues, “ …Applicant's claims amount to "significantly more"
than any alleged abstract idea.” (pgs. 23-24)
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As stated above, the additional elements of a processor, a memory, a crm, etc. are considered generic computer components performing generic computer functions, i.e., generating an input data set, generating output data, transmitting output data, that amount to no more than instructions to implement the judicial exception and generally linking the judicial exception to a particular technological environment. Mere, instructions to apply an exception using generic computer components cannot provide an inventive concept.
Further, there is no support in the Specification or the claims for an improvement in a technology (e.g. functioning of computer) or a technical field.
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 and 6 - 22 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. Claim 1 recites:
perform operations, [in parallel across the distributed computing components], that train an artificial intelligence process using a plurality of training datasets and that generate values of process parameters that characterize the trained artificial intelligence process;
perform operations, [in parallel across the distributed computing components,] that apply the trained artificial intelligence process to a plurality of validation datasets in accordance with the process parameter values, and that generate elements of validation output data based on an application of the trained artificial intelligence process to the validation datasets;
compute a value of a validation metric based on the elements of validation output data, and based on a determined inconsistency between the value of the validation metric value and a threshold condition, perform operations that update at least one of the process parameter values characterizing the trained artificial intelligence process;
generate an input dataset based on elements of first interaction data associated with a first temporal interval and with the received identifier;
perform operations, [in parallel across the distributed computing components,] that apply the trained artificial intelligence process to the input dataset, in accordance with the at least one of the updated process parameter values, and based on the application of the trained artificial intelligence process to the input dataset, that generate output data comprising a plurality of numerical values indicative of (i) a predicted likelihood of an occurrence of a first targeted event during a second temporal interval, (ii) a predicted likelihood of an occurrence of a second targeted event during the second temporal interval, and (iii) and a predicted likelihood of a non-occurrence of the first and second targeted events during the second temporal interval, the second temporal interval being subsequent to the first temporal interval and being separated from the first temporal interval by a corresponding buffer interval;
The limitations under the broadest reasonable interpretation covers Mathematical Concepts related mathematical calculations, but for the recitation of generic computer components (e.g. a processor and memory). The Specification indicates artificial intelligence processes may include trained, gradient-boosted or decision-tree process (see Spec Abstract). For example, training an AI process using training data sets and generating values of process parameters and applying the trained AI process to a plurality of validation datasets involves mathematical calculations. Accordingly, the claim recites an abstract idea of Mathematical Processes.
Additionally, the claims encompass Mental Processes related to observation and evaluation ad generating output data for a predicted likelihood of an occurrence of an event based on inputted interaction data involves evaluation of data, which could reasonably be performed with a pen and paper or in the human mind.
Claims 13 and Claim 20 substantially recite the subject matter of Claim 1 and encompass the same abstract concepts. The dependent claims encompass the same abstract concept. Claim 2 is directed to receiving and storing interaction data, Claim 3 is directed to characterizing trained AI process and composition of input data set and applying the trained AI process (analyzing data), Claim 4 is directed to extracting features(analyzing data), Claim 6 is directed to targeted events, Claim 7 is directed to interaction data and targeted acquisition events, Claim 8 is directed to customer identifier and interaction data, Claim 9 is directed to customer interaction data, Claim 10 is directed to explainability data, Claim 11 is directed interaction data and temporal identifiers, Claim 12 is directed to validation data sets, and Claim 22 is directed to training the AI process. Claims 14-19 encompass the same abstract concept.
The judicial exception is not integrated into a practical application. Claim 1 recites the additional elements of a plurality of distributed computing components, a memory, a communication interface, a processor and a computing system for performing the claimed steps. Claim 13 recites the additional elements of a plurality of processors associated with a corresponding one of a plurality of distributed computing components. Claim 20 recites the additional elements of a tangible non-transitory computer readable medium, a processor and distributed computing components. These are generic computer components recited at a high level of generality as performing generic computer components (Spec see ¶0164), general purpose processor).
For instance, the steps of training an AI process using a plurality of training datasets and generate values of process parameters, applying the trained AI process to a plurality of validation datasets that generate elements of validation output data and computing a value of a validation metric based on the elements of validation output data involve analyzing data using complex mathematics (see Spec ¶0053). The step of generating an input data set based on interaction data is considered data manipulation for representation of data. The step of applying a trained artificial intelligence process to the input dataset to generate output data comprising numerical values indicative of (i) a predicted likelihood of an occurrence of a first targeted event during a second temporal interval, (ii) a predicted likelihood of an occurrence of a second targeted event during the second temporal interval, and (iii) and a predicted likelihood of a non-occurrence of the first and second targeted events during the second temporal interval, the second temporal interval being subsequent to the first temporal interval and being separated from the first temporal interval by a corresponding buffer interval is analyzing data using complex mathematical operations. The step of transmitting the output data to a computing system is generic data transmission functionality (sending/receiving data).
Each of the additional limitations is no more than mere instructions to apply the exception using a generic computer components (e.g. distributed computing components). The combination of these additional elements is no more than mere instructions to apply the exception using a generic computer component (e.g. distributed computing components). Additionally, the generic computer components generally link the judicial exception to a particular technological environment. The additional elements do not integrate the abstract idea into a practical application because they do not impose meaningful limits on practicing the abstract idea. Therefore, the claims are directed to an abstract idea.
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As stated above, the additional elements of a processor, a memory, a crm, etc. are considered generic computer components performing generic computer functions, i.e., generating an input data set, generating output data, transmitting output data, that amount to no more than instructions to implement the judicial exception and generally linking the judicial exception to a particular technological environment. Mere, instructions to apply an exception using generic computer components cannot provide an inventive concept.
The dependent claims when analyzed both individually and in combination are also held to be ineligible for the same reason above and the additional recited limitations fail to establish that the claims are not directed to an abstract. The additional limitations of the dependent claims when considered individually and as an ordered combination do not amount to significantly more than the abstract idea.
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, 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. Therefore, Claims 1-20 are not patent eligible.
Conclusion
The prior art made of record and not relied upon is considered relevant but not applied:
Skripkin (US 2022/0114302) discloses training a suite of machine learning models using at least a portion of the model data (e.g., a training portion, a testing portion, etc.) to generate trained ML models (e.g., predictive ML models), selecting one or more of the trained ML models (e.g., using one or more criteria, such as ability to train, test performance, etc.), computing values of one or more defined accuracy metrics for the selected one or more of the trained ML models with respect to an index (e.g., to characterize the selected one or more models as to prediction accuracy, etc.), and determining one or more actions using at least a portion of the computed values of the one or more accuracy metrics.
Goldszmidt et al. (US 2021/0224687) discloses A first training input data set that includes a plurality of first input data elements can be accessed. The first training input data set and a corresponding training label data set may have been used to learn a set of parameter values during training of a machine learning model. A first accuracy metric can be determined that characterizes a first performance of the machine learning model using a first testing data set.
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). 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.
Any inquiry of a general nature or relating to the status of this application or concerning this communication or earlier communications from the Examiner should be directed to Renae Feacher whose telephone number is 571-270-5485. The Examiner can normally be reached Monday-Friday, 9:00 am - 5:00 pm. If attempts to reach the examiner by telephone are unsuccessful, the Examiner's supervisor, Eric Stamber can be reached at 571-272-6724.
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/Renae Feacher/
Primary Examiner, Art Unit 3683
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