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 .
Status of Claims
Claims 1-20 are pending in this application.
Examiner’s Comments Relating to Prior Art
In the office action dated 1/30/26, the examiner rejected claims 3, 5-6, 8-13, and 15-20 under AIA 35 U.S.C. 103 as being unpatentable over Dolan (20220230236) in view of Shin (KR20100003154A). Claims 2, 4, 7, and 14 were rejected under AIA 35 U.S.C. 103 as being unpatentable over Dolan in view of Shin, further in view of and Ezov (20210042629). In response, the applicant substantially narrowed down the claim scope in the amendments dated 4/30/26. Specifically, the independent claims 1, 12 and 17 now recite the new limitations of: “generating groupings of training data; determining, via a front-end algorithm, relationships between individual data included in the training data and the training data; identifying, based on the relationships, that the individual data falls outside of a normal pattern of the training data; and reducing, based on the individual data, dimensionality of the training data” (emphasis examiner’s). This is in addition to the other disclosed elements for processing user inputs to determine user’s assets/savings as compared to others, previously disclosed and currently existing in the claim language. The claims now consist of specific steps of:
obtaining investment data of users;
training and deploying ML model to process investment data and predict users investment percentage;
generating training data groupings;
determining relationships between individual data and the training data;
identifying the individual data falling outside of a normal pattern of the training data; and reducing dimensionality of the training data;
determining an attribute level;
comparing user’s savings to other users; and
displaying the results.
The newly added elements – in combination with the other claim elements – overcome the prior art previously found and currently searched. The prior art rejections are withdrawn.
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 an abstract idea without significantly more.
Claims 1-20 are directed to a system or method, which are/is one of the statutory categories of invention. (Step 1: YES”).
The Examiner has identified independent method claim 17 as the claim that represents the claimed invention for analysis and is similar to independent system claims 1 and 12. Claim 17 recites the limitations of processing user inputs to determine user’s assets/savings as compared to others.
These limitations, under their broadest reasonable interpretation, cover performance of the limitation as certain methods of organizing human activity. Obtaining investment data of users; training and deploying ML model to process investment data and predict users investment percentage; generating training data groupings; determining relationships between individual data and the training data; identifying the individual data falling outside of a normal pattern of the training data; and reducing dimensionality of the training data; determining an attribute level (note, “attribute level” may be savings, see para 117; comparing user’s savings to other users; and displaying the results, – specifically, the claim recites “train and deploy a machine learning model, the machine learning model being trained to process input data of a plurality of users to determine how the input data is related, the training including: tuning parameters of the input data to correlate ascertained numerical levels to ascertained stored quantities; generating groupings of training data; determining, via a front-end algorithm, relationships between individual data included in the training data and the training data; identifying, based on the relationships, that the individual data falls outside of a normal pattern of the training data; and reducing, based on the individual data, dimensionality of the training data, access user data of one or more user registers of a user to determine a user quantity stored in the one or more user registers; process at least one user input associated with a numerical level; apply the deployed machine learning model to process at least the accessed user data and the at least one user input, the applying generating an output comprising analysis of the user quantity and the at least one user input relative to ascertained numerical levels of multiple users of the plurality of users, the multiple users having an associated numerical level that is determined to be similar to the numerical level of the at least one user input; and display… the generated output comprising results of the analysis”, recites a fundamental economic practice, directed to mitigating risk.
If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation as a fundamental economic practice or commercial or legal interactions, then it falls within the “Certain Methods of Organizing Human Activity” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
The “a computing system”, “a memory”, “one or more processors”, “program instructions”, “a machine learning model”, “a user interface”, “a front-end algorithm”, “”, and “a user device”, in claim 1, are just applying generic computer components to the recited abstract limitations. The recitation of generic computer components in a claim does not necessarily preclude that claim from reciting an abstract idea. Claims 12 and 17 are also abstract for similar reasons. (Step 2A-Prong 1: YES. The claims recite an abstract idea)
This judicial exception is not integrated into a practical application. In particular, the claims recite the additional elements of: a computer such as a computing system, one or more processors, and a user device; a communication device such as a user interface; a storage unit such as a memory; and software module and algorithm such as program instructions, a front-end algorithm, and a machine learning model. The computer hardware/software is/are recited at a high-level of generality (i.e., as a generic processor performing a generic computer function) such that it amounts no more than mere instructions to apply the exception using a generic computer component.
The examiner notes that although the claim recites “a machine learning model”, it is recited at a high level. See claims 1, 12 and 17. For example, the claims simply state what the “a machine learning model” will do in the claimed business process – i.e. “to process input data of a plurality of users to determine how the input data is related”. See claim 1 and 17. Similarly the specification recites “a machine learning model” at a high level – see, for examples, paragraphs 111, 118, and 119. These are nominal recitations. The newly added language are merely adding machine learning training step of (1) establishing data validity, and (2) identifying and eliminating data outliers (from training data). The examiner notes that the applicant is not improving “a machine learning model”. Rather the applicant is using “a machine learning model”, in a business process. Accordingly, these additional elements, when considered separately and as an ordered combination, do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea and are at a high level of generality. Therefore, claims 1, 12, and 17 are directed to an abstract idea without a practical application. (Step 2A-Prong 2: NO. The additional claimed elements are not integrated into a practical application)
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because, when considered separately and as an ordered combination, they do not add significantly more (also known as an “inventive concept”) to the exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional element of using a computer hardware amounts to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. Accordingly, these additional elements, do not change the outcome of the analysis, when considered separately and as an ordered combination. Thus, claims 1, 12, and 17 are not patent eligible. (Step 2B: NO. The claims do not provide significantly more)
Dependent claims further define the abstract idea that is present in their respective independent claims 1, 12, and 17 and thus correspond to Certain Methods of Organizing Human Activity, and hence are abstract for the reasons presented above.
Dependent claim 2 discloses the limitation of the ascertained numerical levels include remuneration levels of the plurality of users, and wherein the ascertained stored quantities include saved financial assets, which further narrows the abstract idea.
Dependent claim 3 discloses the limitation of the one or more user registers include one or more financial accounts, and wherein the user quantity includes one or more financial assets, which further narrows the abstract idea.
Dependent claim 4 discloses the limitation of the at least one user input that is received includes a remuneration amount of the user, which further narrows the abstract idea.
Dependent claim 5 discloses the limitation of the program instructions further receive the at least one user input from the user via the user device, which further narrows the abstract idea. Note that the technical element “the user device” is recited at a high level of generality. It does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
Dependent claim 6 discloses the limitation of the program instructions further ascertain the at least one user input from deposits made to the one or more user registers, the at least one user input including a culmination of the deposits over a designated period of time, which further narrows the abstract idea.
Dependent claim 7 discloses the limitation of the deposits include regular financial deposits determined to be associated with a remuneration received by the user, which further narrows the abstract idea.
Dependent claim 8 discloses the limitation of the analysis comprises a comparative analysis, and where the generated output displayed provides the user with a comparison of how the user quantity stored in the one or more user registers compares to ascertained stored quantities of the multiple users, which further narrows the abstract idea.
Dependent claim 9 discloses the limitation of based on the analysis determining that the user quantity stored in the one or more user registers is below an average of the ascertained stored quantities of the multiple users, the generated output includes a recommendation to increase the user quantity, which further narrows the abstract idea.
Dependent claim 10 discloses the limitation of the displaying is based on determining that the user is accessing, via the user device, a digital aggregation platform of a financial entity, which further narrows the abstract idea. Note that the technical element “the user device” is recited at a high level of generality. It does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
Dependent claim 11 discloses the limitation of the accessing, processing, applying and displaying is based on receiving a request, via the user device, from the user to determine how the user quantity stored in the one or more user registers compares to average stored quantities of individuals with remuneration levels similar to the user, which further narrows the abstract idea. Note that the technical element “the user device” is recited at a high level of generality. It does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
Dependent claim 13 discloses the limitation of the attribute level comprises a net worth of the user, which further narrows the abstract idea.
Dependent claim 14 discloses the limitation of the attribute level comprises a yearly remuneration level of the user, which further narrows the abstract idea.
Dependent claim 15 discloses the limitation of the determining, applying and displaying is based on receiving a request, via the user device, from the user for the generated output, which further narrows the abstract idea. Note that the technical element “the user device” is recited at a high level of generality. It does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
Dependent claim 16 discloses the limitation of the investment data of the multiple users includes investment percentages of assets, and wherein the results of the comparative analysis compare the one or more investments of the user to the investment percentages of assets, which further narrows the abstract idea.
Dependent claim 18 discloses the limitation of the ascertained numerical levels include remuneration levels of the plurality of users, and wherein the ascertained stored quantities include saved financial assets, which further narrows the abstract idea.
Dependent claim 19 discloses the limitation of the one or more user registers include one or more financial accounts, and wherein the user quantity includes one or more financial assets, which further narrows the abstract idea.
Dependent claim 20 discloses the limitation of the at least one user input that is received includes a remuneration amount of the user, which further narrows the abstract idea.
Thus, the dependent claims do not include any additional elements that integrate the abstract idea into a practical application or are sufficient to amount to significantly more than the judicial exception when considered both individually and as an ordered combination. Therefore, the dependent claims are directed to an abstract idea. Thus, the claims 1-20 are not patent-eligible.
Response to Arguments
Applicant's arguments filed 4/30/26 have been fully considered but they are not persuasive.
The applicant’s 35 USC 103 arguments are moot because the prior art rejections are withdrawn. The examiner is withdrawing the prior art rejections because the amended claims contain new scope narrowing elements which, in combination with the existing elements, sufficiently narrow the claimed scope to overcome the existing prior art and additional art searched. See Examiner Comment Relating to Prior Art above.
In response to applicant's argument that:
“35 U.S.C. §101… each independent claim recites a particular technique for training and deploying a machine learning model that cannot practically be performed in the human mind, including explicit steps of generating groupings, computing relationships via a specified front-end algorithm, identifying outliers relative to a normal pattern, and reducing dimensionality based on those determinations before deployment and user-facing outputs,”
the examiner respectfully disagrees. The examiner is not disputing the premise that computers will generally increase the speed and efficiency in carrying out some repetitive processes. Thus, the applicant’s business process/ideas could very well be carried out faster and more efficiently using generic computers. The “generic computer” concept, as articulated by the Alice court, is not predicated upon the inquiry of whether the claimed computer’s computing capability can be matched by human.
Neither is the examiner arguing that the claim language lacks such generic electronic devices. Rather, the examiner has determined that such devices amount to nothing more than “generic computers”, used and recited at such a high level that they do not integrate the abstract idea into a practical application.
In response to applicant's argument that:
“Example 39,”
the examiner respectfully disagrees. The claimed invention is not the same as Example 39. Processing user inputs to determine user’s assets/savings as compared to others is not the same as training a neural network for facial detection by the use of an expanded training set of facial that is developed by applying mathematical transformation functions on an acquired set of facial images, with the transformations that can include offine transformations; and then training the neural networks further using stochastic learning with backpropagation which is a type of machine learning algorithm that uses the gradient of a mathematical loss function to adjust the weights of the network. Also, Example 39 deals with false positives when classifying non-facial images by performing an iterative training algorithm. The claimed invention does not have the elements and the steps recited in Example 39. One must read Example 39 narrowly in deference to the Alice Court’s emphatic prohibition against patenting abstract ideas that lack genuine innovation beyond the use of generic computers. Implementing a business process/idea by processing data using generic computers is not patentable.
In response to applicant's argument that:
“Ex Parte Desjardins … The present specification identifies the technical problem that deep neural networks require increased computational time and power to train, particularly with unstructured data, and describes an AI program design in which a front-end algorithm generates groupings, determines relationships, identifies outliers, and reduces dimensionality to streamline subsequent model training and deployment,”
the examiner respectfully disagrees. Desjardins is directed towards solving “catastrophic forgetting problem in machine learning”. Thus, it’s improving machine learning technology. The claimed invention, however, are directed more towards general training of machine learning model. It does not improve machine learning technology. E.g., the newly added language can be reasonably interpreted as machine learning training step of (1) establishing data validity, and (2) eliminating data outliers (from training data). It does not improve machine learning technology.
In Desjardins, the claims are solving a technical machine learning problem. I.e., the technological problem being the machine learning models may be subject to “catastrophic forgetting” when trained on multiple tasks, losing knowledge of a previous task when a new task is learned. And the Desjardins technological solution being training machine learning models on multiple tasks, such that once the model has been trained, the model can be used for each of the multiple tasks with an acceptable level of performance. Desjardins provides specific computations (such as, for example, “computing, based on the first values of the plurality of parameters determined by training the machine learning model on the first machine learning task, an approximation of a posterior distribution over possible values of the plurality of parameters”, in Ex parte Desjardins). Desjardins also have specific usage of the computed value within the machine learning mechanism (such as, for example, “training the machine learning model on the second machine learning task by training the machine learning model on the second training data to adjust the first values of the plurality of parameters to optimize performance of the machine learning model on the second machine learning task while protecting performance of the machine learning model on the first machine learning task (emphasis examiner’s)”.
In response to applicant's argument that:
“Example 47,”
the examiner respectfully disagrees. The claimed invention is not the same as Example 47, claim 3. Notifying user and displaying the results of the analysis is not the same as detecting and dropping “malicious network packets”, and blocking future traffic from the source address. Example 47, claim 3 focuses on the technology of blocking anomaly network packets. The claimed invention does not have the elements and the steps recited in example 47, claim 3. One must read example 47, claim 3 narrowly in deference to the Alice Court’s emphatic prohibition against patenting abstract ideas that lack genuine innovation beyond the use of generic computers. Implementing a business process/idea by processing data using generic computers is not patentable.
In response to applicant's argument that:
“significantly more … The Examiner's prior characterization that the "machine learning model" is recited at a high level no longer applies in light of the inserted limitations defining how the model is trained via a front-end algorithm and dimensionality reduction keyed to identified outliers in the training data,”
the examiner respectfully disagrees. As stated above, the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because, when considered separately and as an ordered combination, they do not add significantly more (also known as an “inventive concept”) to the exception. The additional element of using a computer hardware amounts to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. Accordingly, these additional elements, do not change the outcome of the analysis, when considered separately and as an ordered combination. See Claim Rejections - 35 USC § 101 above.
In response to applicant's argument that:
“On this record, the Examiner has not provided the factual showings required to conclude that the specific combination of front-end algorithmic grouping, relationship analysis, outlier detection, and dimensionality reduction steps applied to unstructured resource-utilization training data were well-understood, routine, and conventional at the relevant time,”
the examiner respectfully notes that the office action did not raise the issue of whether anything is “well-understood, routine or conventional”. Rather, the examiner has determined that the claims’ technical elements amount to nothing more than “generic computers”, used in such a way that it does not integrate the abstract idea into a practical application. The examiner respectfully refers the applicant to the discussion above on the abstract idea determination and the technical specific elements. See Claim Rejections - 35 USC § 101 above.
Conclusion
Accordingly, 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 extension fee 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 date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MARK H GAW whose telephone number is (571)270-0268. The examiner can normally be reached Mon-Fri: 9am -5pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Mike 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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/MARK H GAW/Examiner, Art Unit 3693
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