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 .
Applicant’s Reply
Applicant's response of 02/19/26 has been entered. The examiner will address applicant's remarks at the end of this office action. Currently claims 1, 3-8, 10-21 are pending.
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, 3-8, 10-21, are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Claims 1, 3-8, 10-12, 21 recite a system, claims 13-19 recite a method, and claim 20 recites a non-transitory computer readable medium; therefore, the claims pass step 1 of the eligibility analysis.
For step 2A, the claim(s) recite(s) an abstract idea of resource allocation management such as occurs was a person is deceased and an executor is handing the settlement of the estate according to the will of the deceased and/or in view of applicable governmental regulations. This represents a certain method of organizing human activities as is set forth below.
For claim 13 as a representative example that is applicable to claims 1 and 20, the abstract idea is defined by the elements of:
representing a questionnaire configured to elicit a series of user inputs corresponding to one or more attributes associated with one or more resources of a resource pool;
obtain said series of user inputs corresponding to one or more attributes associated with said one or more resources of said resource pool,
wherein the attributes include data indicating types of resource assets forming part of an estate of a deceased user, data associated with one or more instruments defining the estate of the deceased user, and data associated with an executor user;
retrieving historical data sets representing prior allocations of resources, said historical data including at least one of said one or more attributes;
a machine learning allocation model defining complexity scores associated with respective resource attributes;
generating a complexity prediction associated with resource allocation based on the one or more attributes,
generating, based on the generated complexity predication and said user inputs, a plurality of graphical elements identifying complexity for the one or more attributes, targeted interface elements associated with required attributes of the one or more attributes, said required attributes being required for determining downstream operations, said required attributes having a contribution above a threshold level on the complexity prediction of said resource allocation,
a series granular tasks for allocating resources of said resource pool, wherein said granular tasks are arranged based on an estimated time required to complete said granular tasks and
displaying progressive action status for allocating the one or more resources
The claim is reciting the act of predicting the complexity involved in resource allocation (reads on estate settlement) by providing information regarding the allocation of the resources (the resources being the assets of the deceased). This represents a fundamental economic practice that is part of the field of estate settlement. Various laws govern the passing of assets from a deceased to a beneficiary or next of kin. This is traditionally handled by human beings when one considers that resource allocation for estates was something practiced by people well before the advent of modern computers. Keeping progress of and tracking the execution of the will for a deceased person is something done by an executor and is considered to be reciting a certain method of organizing human activity in the form of a legal interaction or a legal obligation of the executor.
For claims 1, 13, the additional elements are the processor and memory that stores instructions to perform the recited functions that serve to define the abstract idea, the transmission of the signals to a display device or client device, the use of the interface to display data (the data is part of the abstract idea), and the recitation to training and using the machine learning model. The machine learning model itself is considered to be part of the abstract idea because it is just a model, where the training of the model and the use of the model is considered to be an additional element.
For claim 20, the additional element is the recitation to the non-transitory computer readable medium that causes a processor to perform the recited method.
This judicial exception is not integrated into a practical application (2nd prong of eligibility test for step 2A) because the additional elements of the claims when considered individually and in combination, do not amount to more than a mere instruction for one to use a computer and the use of a trained machine learning model, where the computer has an interface and a processor with memory that performs the steps that define the abstract idea, and where the interface is used to display information (where the information is part of the abstract idea). The processor of the claimed system is being used as a tool to perform the abstract idea (see MPEP 2106.05(f)). The claimed transmitting of the signals to a client device and the communicating (claimed signals) with the display device to cause the display of data/information is the use of a computer to send data to another computer so that the information can be displayed on an interface/display, and is the equivalent of reciting “apply it with a computer” for the abstract idea. The claimed interface and its use for display of data is something that all interfaces provide for and is the same as reciting “apply it” using a computer that has an interface display. All computers have an interface that displays data to a user. The claimed training of the machine learning model and the use of the machine learning model to generate the complexity prediction (the complexity predication is part of the abstract idea, the use of machine learning is an additional element) is taken as a general link to the particular technological field of machine learning. The claimed training of the model is reciting what occurs in machine learning by definition, namely that models are trained using datasets so they can learn and work correctly. The claimed training of the model that is inherent to all machine learning models and using machine learning (recited at a high level of generality) is not sufficient to amount to more than a link to a particular technological environment, which is using a computer and machine learning to perform a step(s) that is part of the abstract idea. See MPEP2106.05(h) in this regard in addition to 2106.05(f). The claimed additional elements when taken with the claim as a whole, is simply instructing one to practice the abstract idea by using a generically recited computing device with a processor and memory, training and using a machine learning model, and using a generically recited user interface to perform steps that define the abstract idea. The claim is simply linking the execution of the abstract idea to computer implementation with a general link to the field of machine learning for the complexity prediction. The above indicative of the fact that the claim has not integrated the abstract idea into a practical application and therefore the claim is found to be directed to the abstract idea identified by the examiner. See MPEP 2106.05(f).
For step 2B, the claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because they do not amount to more than simply instructing one to practice the abstract idea by using a generically recited computing device with a processor and memory, training and using a machine learning model, and using a generically recited user interface of a display device to perform steps that define the abstract idea. The use of a computing device with a processor and memory and that has an interface, the use of machine learning, all of which are being used as a tool to execute the abstract idea, does not provide for significantly more, see MPEP 2106.05(f), (h). The rationale set forth for the 2nd prong of the eligibility test above is also applicable to step 2B in this regard so no further comments are necessary. This is consistent with the PEG found in the MPEP 2106.
In addition to that above, with respect to the claimed transmission of signals to cause the display of data, the examiner notes that a mere data transmission is something that is considered to be an insignificant extra solution activity that is also well understood in the computing field. MPEP 2106.05(d)(II), and the cases cited therein, including Intellectual Ventures I, LLC v. Symantec Corp., 838 F.3d 1307, 1321 (Fed. Cir. 2016), TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610 (Fed. Cir. 2016),
and OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363 (Fed. Cir. 2015), indicate that mere collection or receipt of data over a network is a well‐understood, routine, and conventional function when it is claimed in a merely generic manner (as it is here). Using a computer and a network to send a signal to another computer to display data does not provide for integration or significantly more. The examiner makes this comment in response to the applicant arguing that the ending of signals using a computer integrated the invention into a practical application and/or recites significantly more. Transmitting a signal using a computer can be interpreted to be a general instruction for one to use a computer (“apply it”) and can be considered as an insignificant extra solution activity that is also well understood routine and conventional in the computing field. Either way, the user of a computer and sending signals to another computer to display an interface does not render the claims eligible.
For claims 3, 14, the representation that is a predicted time duration for the allocated resources is a further embellishment of the same abstract idea set forth for claims 1, 13 The use of the interface element (an additional element) as far as computer implementation is concerned has been treated in the same manner as was set forth for claims 1, 13. The claims do not recite any additional elements that provide for integration at the 2nd prong or that provide significantly more at step 2B. Therefore the claims are not considered to be eligible.
For claims 4, 15, the identification of the allocation complexity for respective resources as claimed is part of the abstract idea. This is the result of the prediction that is part of the abstract idea. The use of interface elements (additional element) has been treated in the manner set forth for claim 1, 13. The claims do not recite any additional elements that provide for integration at the 2nd prong or that provide significantly more at step 2B. Therefore the claims are not considered to be eligible.
For claims 5, 16, the claimed determining that a proposed resource application is associated with a complexity that exceeds a threshold, providing guidance operations for allocating the resources is a further recitation to the same abstract idea of claims 1, 13. Determining the complexity and if it meets a threshold is part of the abstract idea and is also something that can be done mentally. The providing of guiding operations is claiming that recommendations are being made, and this is also part of the abstract idea. The additional elements of the processor and memory has been treated with claim 1 to which applicant is referred. The claims do not recite any additional elements that provide for integration at the 2nd prong or that provide significantly more at step 2B. Therefore the claims are not considered to be eligible.
For claims 6, 17, similar to claims 5 and 16, the determination if the complexity does not meet a threshold and generating a set of operations for allocating the resources, is a further defining of the same abstract idea that was set forth for claims 1, 13. The claims do not recite any additional elements that provide for integration at the 2nd prong or that provide significantly more at step 2B. Therefore the claims are not considered to be eligible.
For claims 7, 18, the recited transmitting of the signal to a service provider is being done so that a service provider can assist in the estate settlement, according to the disclosure in the specification. With respect to the use of a signal and electronic transmission by the processor, this is simply using a computer as a tool to provide a notification to the service provider and does not provide for integration or significantly more, see MPEP 2106.05(f). Additionally or alternatively, a mere data transmission is something that is considered to be an insignificant extra solution activity that is also well understood in the computing field. MPEP 2106.05(d)(II), and the cases cited therein, including Intellectual Ventures I, LLC v. Symantec Corp., 838 F.3d 1307, 1321 (Fed. Cir. 2016), TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610 (Fed. Cir. 2016),
and OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363 (Fed. Cir. 2015), indicate that mere collection or receipt of data over a network is a well‐understood, routine, and conventional function when it is claimed in a merely generic manner (as it is here). Using a computer and a network to send a signal to another computer to display data does not provide for integration or significantly more for the above reasons. The claims do not recite any additional elements that provide for integration at the 2nd prong or that provide significantly more at step 2B. Therefore the claims are not considered to be eligible.
For claims 8, 19, the claimed pre-population based on user inputs is taken as being part of the abstract idea of the claims. The recitation to using the processor is an instruction for one to use a computer to perform a step that is part of the abstract idea and does not provide for integration or significantly more, see MPEP2106.05(f). Therefore the claims are not considered to be eligible.
For claim 10, the recited types of resources are part of the abstract idea because that is what is being allocated, money or assets, etc... The claims do not recite any additional elements that provide for integration at the 2nd prong or that provide significantly more at step 2B. Therefore the claims are not considered to be eligible.
For claims 11, 12, the applicant claims that the model is trained based on data. This is also taken as a high level recitation to the use of machine learning and trained models. This is not sufficient to amount to more than a link to a particular technological environment that is using a computer and machine learning, as was set forth for claim 1. See MPEP2106.05(h) in this regard. The claimed training is reciting what occurs in machine learning by definition, meanly that models are trained. This does not provide for integration or significantly more. Therefore the claims are not considered to be eligible.
For claim 21, the updating of the machine learning model is considered to be a further embellishment of the abstract idea. A human can update a model by changing weights used by the model or by providing a new data training set to the model. Claim 21 is not reciting the use of a machine learning model as the updating of the model is not use of the model. The use of the machine learning model for the complexity predication has been treated in the same manner that was set forth for claim 1 and does not provide for integration into a practical application or significantly more.
Therefore, for the above reasons, claims 1, 3-8, 10-21, are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Response to arguments
The rejections of claims 1, 13, 20 under 35 USC 112(b) has been overcome by the amendments to the claims. The rejection(s) have been withdrawn.
The traversal of the 35 USC 101 rejection is not persuasive for the following reasons. The 101 rejection is being maintained.
The comments regarding the Desjardins decision on pages 7-8 are noted. The examiner notes that the claims are not reciting training of a model in an particular way and are not improving the machine learning model of the claims so Desjardins is not considered to be particularly relevant to the claimed invention.
The comments that the eligibility analysis looks to see if there is an improvement to technology or to a technical field on page 8 is noted by the examiner. The examiner agrees with this comment.
The comments about reminding examiners to be careful to not overgeneralize a claimed invention on page 8 is also noted and is not disagreed with by the examiner.
On page 8 the applicant argues that the claims are integrated into a practical application because they provide for an improvement to a technical field. The applicant is arguing that resource allocation is a technical field that is being improved. This is not persuasive. There is nothing technical or technological about asset distribution upon the death of an individual such that it would be considered as a technical field or technology. The applicant argues that because people can perform resource allocation in a different way than claimed, that fact should not bar the claims from being patentable. This is not persuasive because the applicant is more or less generally alleging that the claims are eligible with no real reliance upon any additional elements for the eligibility. Even if resource allocation can operate in a different way, that is not something that equates to eligibility. Even novel and/or non-obvious abstract ideas are still abstract ideas. The technology of the claim is not being improved by performing the functions/steps that defines the abstract idea. While the asset distribution process may be improved, any improvement lies in the abstract idea itself and not in technology or a technical field. Asset distribution and complexity predictions for the allocation process is not a technical field, that is an abstract idea that is a certain method of organizing human activities. The argument is not persuasive.
The applicant appears to also generally allege on page 8 that the training and use of the machine learning model to determine the complexity prediction (which is the only use of machine learning in the claims) integrates the abstract idea into a practical application. This is not persuasive because the use of the trained machine learning model, and the training of the model itself, is nothing more than a high level general link to the field of machine learning (MPEP 2106.05(h)). The machine learning model is not being improved in any manner and the training of the machine learning model is claimed so broadly that is can be trained in any manner. The claims are not training the machine learning model in a specific manner such that it would found to be improving the model. The recitation to the training of the machine learning model and the use of the model are recited at a high level of generality and amount to a link to a particular technological environment that is the field of machine learning. This does not provide for integration into a practical application or significantly more.
The applicant argues that the use of a computer for the invention is intentional because it allows a user to be provided with guidance in the form of graphical interface elements that represent granular tasks for allocating resources and that are based on an estimated time. The fact that the invention uses a computer to display data to a user is not something that provides for integration into a practical application. That is simply linking the execution of the abstract idea to computer implementation, where the computer is simply being used as a tool to execute the steps that defines the abstract idea, which does not amount to integration, see MPEP 2106.05(f). There is nothing claimed that would amount to an improvement in the interface and how data is being displayed. The claim recites that an interface is generated and the interface includes elements that represent data such as tasks arranged based on time. The generation of lists of tasks and the associated complexity prediction is part of the abstract idea. The fact that this information is displayed on an interface is a link to using a computer for data display and does not provide for integration into a practical application or significantly more. See MPEP 2106.05(f) in this regard.
Conclusion
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 concerning this communication or earlier communications from the examiner should be directed to DENNIS WILLIAM RUHL whose telephone number is (571)272-6808. The examiner can normally be reached M-F 7am-3:30pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Jessica Lemieux can be reached at 5712703445. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/DENNIS W RUHL/ Primary Examiner, Art Unit 3626