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
This action is in reply to the amendments filed on 20 March 2026.
Claims 1, 2, 3, 8, 11, 12, 13, and 18 are indicated as amended.
Claims 1-20 are currently pending and have been examined.
Response to Amendment
Applicant’s amendments are insufficient to overcome the double patenting rejection previously raised. This rejection is respectfully maintained.
Applicant’s amendments are insufficient to overcome the 101 rejections previously raised. These rejections are respectfully maintained and updated below as necessitated by the amendments to the claims.
Applicant’s amendments are insufficient to overcome the 103 rejections previously raised. These rejections are respectfully maintained and updated below as necessitated by the amendments to the claims.
Response to Arguments
Applicant’s arguments filed on 20 March 2026 have been fully considered but are not persuasive.
Regarding the 101, Applicant argues that the claims do not recite a fundamental economic practice and therefore are not a certain method of organizing human activity. Examiner respectfully disagrees. The rejection categorizes the claim language as illustrating organizing activity relating to a commercial interaction by indicating subject matter relating to marketing activities or behaviors, and/or business relations. The amened claim language stating that the model is trained based on outputs and inputs and is configured with feedback loops that refine the model at different levels and further refine the model based on labeled data collected from opportunities based on behavior does not transform the claim into a patent eligible invention. The description of the trained model does not realize a technical improvement through a detailed training or refinement process. The training is treated as an additional element that and interpreted as merely an “apply it" type limitations because there is no details in the claim specifying how it is trained, just what they model is trained with. The claims do not realize a technical improvement through a specified training process but instead merely use a trained model to output a response/recommendation or result.
The extracting limitation is merely performed “using a machine learning algorithm” and is treated as data observation that is part of the overall method of organizing human activity and as a gathering function performed by a generically recited computer element.
Applicant argues that the claims solve a technical problem unique to AI systems how to coordinate multiple trained models with dynamic, real world feedback to produce adaptive geographic growth predictions in a scalable computing environment. Examiner respectfully disagrees. Feedback loops that periodically update and retrain models, without detailing exactly how the model is modified or even modifiable does not realize a technical improvement. Model retraining enables a model to make the most accurate predictions with the most up to date data. Model retraining does not necessarily change the parameters and variables used in the model itself it simply adapts the model to the current data set so that the existing parameters give healthier and up to date outputs. This is how all AI models operate and unless the details of exactly how this model is revised or modified are claimed this model, where the training/retraining/configuration is merely discussed at a high level, does not illustrate a technical improvement. Unlike Desjardin, the specification does not outline how specific machine learning techniques solve a technical problem such as catastrophic forgetting. Referenced paragraphs [0097, 0160] merely discuss feedback loops to refine the machine learning algorithm and using feedback to fine tune the model at different levels without detailing exactly what that refinement entails, no details regarding how the data results in changes or updates to the model or exactly what parameters or variables of the model are altered. The claims describe that the trained model is based on data and is configured with feedback loops that refine and use language like “to refine” and “to fine tune without actively claiming the refining and fine tuning in specific detail.
The 101 rejections are respectfully maintained and updated below as necessitated by the amendments to the claims because the claim language does not integrate the recited abstract idea into a practical application nor does it amount to significantly more.
Regarding the 103, applicant argues that the references fail to teach the claimed invention and that the examiner has not articulated a sufficient rationale for combining the references. Examiner respectfully disagrees.
Applicant argues that Turner’s cash forecasting is not growth opportunity identification or market competitiveness analysis. Turner teaches forecasting models related to financial outlooks for a market and using market data to derive future outcomes of financial performance. The broadest reasonable interpretation of the claims requires only that the model outputs one or more more outputs related to competitiveness of a market or a future outlooks of a region. This is met by the interpretation of the claims. Shah is combined to teach the growth opportunity model. In response to applicant's arguments against the references individually, one cannot show nonobviousness by attacking references individually where the rejections are based on combinations of references. See In re Keller, 642 F.2d 413, 208 USPQ 871 (CCPA 1981); In re Merck & Co., 800 F.2d 1091, 231 USPQ 375 (Fed. Cir. 1986).
Applicant argues that Shah does not teach updating the gathered data at predetermined times and other elements of Claims 2, 3, and 8. Examiner respectfully disagrees. Shah is not relied upon to teach the updating limitation and is combined to teach the growth opportunity model. The test for obviousness is not whether the features of a secondary reference may be bodily incorporated into the structure of the primary reference; nor is it that the claimed invention must be expressly suggested in any one or all of the references. Rather, the test is what the combined teachings of the references would have suggested to those of ordinary skill in the art. See In re Keller, 642 F.2d 413, 208 USPQ 871 (CCPA 1981). The 103 rejections are respectfully maintained and updated below as necessitated by the amendments to the claims.
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/apply/applying-online/eterminal-disclaimer.
Claims 1-20 are provisionally rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-18 of copending Application No. 18588856. Although the claims at issue are not identical, they are not patentably distinct from each other because the broader claims of the instant application that teaches growth opportunity identification are anticipated by the narrower claims of the co-pending application that teaches specific growth opportunities through optimization of advertisements.
This is a provisional nonstatutory double patenting rejection because the patentably indistinct claims have not in fact been patented.
Claims 1-20 are provisionally rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-20 of copending Application No. 18588432. Although the claims at issue are not identical, they are not patentably distinct from each other because the broader claims of the instant application that teaches growth opportunity identification are anticipated by the narrower claims of the co-pending application that teaches specific growth opportunities through product models and product optimization.
This is a provisional nonstatutory double patenting rejection because the patentably indistinct claims have not in fact been patented.
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 and 11 recite as a whole a method of organizing human activity because the claims recite a method for identifying areas for growth by gathering data, extracting behavior features, processing customer behavior or financial institution behavior features related to market competitiveness or regional outlook, input goals into a model to determine a response based on goal inputs and output data. This methodology illustrates organizing activity relating to a commercial interaction by indicating subject matter relating to marketing activities or behaviors, and/or business relations. The mere nominal recitation of a generic computer system does not take the claims out of the abstract idea grouping. Thus, the claims recite an abstract idea.
This judicial exception is not integrated into a practical application. The claims as a whole merely describe how to generally “apply” the concept of identifying areas for growth using AI and models in a computerized environment. The claimed computer components are recited at a high level of generality and are merely invoked as tools to perform the process. Simply implementing the abstract idea on a generic computer is not a practical application of the abstract idea. Using a machine learning algorithm and using trained models to extract, process, input and output results is considered “apply it” on a computer, see MPEP2106.05f and 2106.05h field of use for the using machine learning and using models. Merely describing that the model is trained based on outputs and inputs and is configured with feedback loops that define the model at different levels and further refine the model based on data collected from opportunities and based on behavior does not integrate the recited abstract idea into a practical application nor does it amount to significantly more. The training itself occurs outside of the scope of the claim and the fact that it is merely “based on” outputs and inputs and is configured with loops that refine the model at different levels and based on different data does not realize a technical improvement. There are no details specifying exactly how the model itself is changed or improved using the data or as a result of that data being analyzed or fed back. The additional elements recited do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing or implementing the abstract idea. The claims fail to recite any improvements to another technology, technical field or to the functioning of the computer itself. There is no use of a particular machine or effecting a transformation or reduction of a particular article to a different state or thing and/or no additional element applies or uses the judicial exception in some other meaningful way beyond generally linking the use of the exception to a particular technological environment.
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed with respect to step 2A, the claims as a whole merely describe how to generally apply the concept of identifying areas of growth in a computerized environment. Thus, even when viewed as a whole, nothing in the claims amounts to significantly more and the claims are ineligible.
Dependent claims 2-10 and 12-20 include all of the limitations of the independent claims and therefore recite the same abstract idea. These claims merely narrow the abstract idea by describing model variables, updating and provided updated gathered data, modifying features to refine outputs. The feedback loop, adjustments, training, filtering, monitoring, refining and updating claimed are merely retraining the model with updated data. This does not change the function, parameters or variables of the model or algorithm itself in an automated learning manner. It merely adapts the model to the updated data so the existing parameters give healthier or more up to date outputs. This does not constitute a technological improvement to the function or capability of the model/algorithm itself and is not sufficient to transform the claims into a patent eligible invention. The same additional elements are considered to “apply it” and “field of use” similar to the independent claims above as there are no details given that transform the claims into a patent eligible invention. Outputting or displaying the results is considered insignificant extra solution activity since it is merely transmission of data. When reconsidered under step 2B this has been re-evaluated and determined to be well understood, routine and conventional activity in the field. The specification does not provide any indication that the display is performed by anything other than a generic off the shelf computer component and the Symantec, TLI and OIP Techs court decisions indicate that the mere collection, receipt or transmission of data over a network is well-understood, routine and conventional when claimed in a merely generic manner, as it is here. Therefore, Claims 1-20 are considered ineligible under 101.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over Turner et al. (20200279198) in view of Shah (US 2023/0316313).
As per Claim 1 Turner teaches:
A system for identifying, using artificial intelligence, a combination of geographic areas for growth comprising:
at least one non-transitory memory; and at least one processing device, the memory containing software code configured to cause the processing device to gather data from a plurality of data sources; extract, using a machine learning algorithm, at least one of a plurality of customer behavior features or a plurality of financial institution behavior features based on the gathered data (Turner in at least [0036, 0076] describes instructions and configurations for making use of targeted balances, thresholds, limits, amounts, indicators, defined by any of the parties as described and using machine learning techniques);
process the customer behavior features and financial institution behavior features using one or more trained foundation models, wherein the trained foundation models are selected based on a plurality of foundation model selection variables, and wherein the trained foundation models output one or more foundation model outputs related to one or more of a competitiveness of a market or a future outlook of a region for a product (Turner in at least [0017] the controller may determine the cash position for the company, the output module may be utilized to provide the case position to a user);
input the foundation model outputs and a plurality of goal inputs into a trained model, wherein: the goal inputs comprise a plurality of financial institution products and one or more of a plurality of financial institution product variants, a plurality of financial institution parameters, a plurality of financial institution regions, a plurality of financial institution growth strategies, a request, a financial institution marketing budget, or a customer focus (Turner in at least [0173] describes “user generated content” when referring to financial data supplied by a user for use in managing cash flows and cash flow analysis, user generate content may include one or more cash forecasts built based on input, direction, advice, projections, plans and goals provided by one or more users);
the trained model is trained based on the foundation model outputs and the goal inputs (Turner in at least [0193] and Fig. 7 illustrate and describe the predictor which may project a cash flow stream value at a time in the future, provided that each associated factor occurs between a set time and a future time, the predictor employs and/or comprises a neural network, the neural network used may be pre-trained, the neural network used may train based on financial transaction available to the predictor and feedback from a user) and is configured with feedback loops that:
refine the trained model at different levels; and further refine the trained model based on labeled market data collected from executed growth opportunities based on market and client behavior (Turner in at least [0100, 0143, 0193, 0201, 0205, 0208, 0211, 0213-0215, 0243] describe the ability to utilize feedback to revise and edit forecast models as well as training based on financial transactions available to the predictor and user feedback, multilevel hierarchical tags can be configured through iterative refinements, updated frequency updates the forecast model with the updated financial information, different forecast levels, such as a macro level of a forecast can be adjusted to help business determine layoffs or other needs, levers may come in different varieties and can be used to quickly model out different scenarios at a high level to see the cash effect using different macro driven assumptions);
output, from the trained model, a natural-language response, wherein the response is based on the goal inputs (Turner in at least [0161] and Fig. 4 illustrate and describe the tag parameters that may be generated in many ways, including explicit selections, search queries, and natural language inputs).
Turner does not explicitly recite that the model is a growth opportunity model that the output response based on goals is specifically a market response. However Shah teaches a system and method for developing a growth strategy. Shah describes using statistical models and machine learning algorithms to analyze goals, train the models and determine growth strategies for a target market or market segment, as is described in at least [0002, 0004-0006, 0009, 0012, 0014-0015, 0073, 0092-0093, 0125, 0163-0178, 0206, 0214, 0224, 0228-0230, 0232-0236, 0250, 0256, 0269, 0287].
Therefore, it would be obvious to one of ordinary skill in the art to modify the trained models with the ability to output market responses based on goal inputs to include strategic growth considerations because each of the elements were known, but not necessarily combined as claimed. The technical ability exists to combine the elements as claimed and the result of the combination is predictable because each of the elements performs the same function as it did independently. By analyzing data for growth opportunities the combination enables the identification of opportunities that are in alignment with the strengths of an entity which will help reach individual target goals and improve overall performance.
As per Claim 2 Turner further teaches:
wherein: the foundation model selection variables comprise one or more of the goal inputs; and the processing device is further configured to: update the gathered data at predetermined times; provide the updated data to the machine learning algorithm through a first feedback loop; and modify the customer behavior features, the financial institution financial features, foundation model outputs based upon the updated data received from the first feedback loop to refine the machine learning algorithm, wherein the first feedback loop provides updated data that contributes to refinement of the trained growth opportunity model at one or more of the different levels (Turner in at least [0017] describes the controller determining the cash position for a company, the output module may be utilized to provide the cash position to a user, the ERP systems provide access to data supporting the business processes in real-time, the data they manage may support a variety of business functions or departments, including manufacturing, purchasing, sales, accounting, human resources, supply chain management, project management, customer relationship management, etc., Turner in at least [0100, 0143, 0193, 0201, 0205, 0208, 0211, 0213-0215, 0243] describe the ability to utilize feedback to revise and edit forecast models as well as training based on financial transactions available to the predictor and user feedback, multilevel hierarchical tags can be configured through iterative refinements, updated frequency updates the forecast model with the updated financial information, different forecast levels, such as a macro level of a forecast can be adjusted to help business determine layoffs or other needs, levers may come in different varieties and can be used to quickly model out different scenarios at a high level to see the cash effect using different macro driven assumptions).
Turner does not explicitly recite that the model is a growth opportunity model that the output response based on goals is specifically a market response. However Shah teaches a system and method for developing a growth strategy. Shah describes using statistical models and machine learning algorithms to analyze goals, train the models and determine growth strategies for a target market or market segment, as is described in at least [0002, 0004-0006, 0009, 0012, 0014-0015, 0073, 0092-0093, 0125, 0163-0178, 0206, 0214, 0224, 0228-0230, 0232-0236, 0250, 0256, 0269, 0287].
Shah is combined based on the reasons and rationale set forth in the rejection of Claim 1 above.
As per Claim 3 Turner further teaches:
wherein the trained model comprises one or more of a logistic regression, a random forest, a gradient boosting, a clustering algorithm, or a deep learning model; and the processing device is further configured to: update the goal inputs at predetermined times; provide the updated goal input data to the trained model in a second feedback loop; and train the trained model based upon the updated goal input data received from the second feedback loop to refine the trained model at one or more of the different levels (Turner in at least [0205] and Fig. 11 illustrate and describe the update frequency as an interval by which the forecast information updates the collected financial data, the information may be updated based on the data collected from the cash management module or updated based on changes from the user interface, (Turner in at least [0100, 0143, 0193, 0201, 0205, 0208, 0211, 0213-0215, 0243] describe the ability to utilize feedback to revise and edit forecast models as well as training based on financial transactions available to the predictor and user feedback, multilevel hierarchical tags can be configured through iterative refinements, updated frequency updates the forecast model with the updated financial information, different forecast levels, such as a macro level of a forecast can be adjusted to help business determine layoffs or other needs, levers may come in different varieties and can be used to quickly model out different scenarios at a high level to see the cash effect using different macro driven assumptions).
Turner does not explicitly recite that the model is a growth opportunity model that the output response based on goals is specifically a market response. However Shah teaches a system and method for developing a growth strategy. Shah describes using statistical models and machine learning algorithms to analyze goals, train the models and determine growth strategies for a target market or market segment, as is described in at least [0002, 0004-0006, 0009, 0012, 0014-0015, 0073, 0092-0093, 0125, 0163-0178, 0206, 0214, 0224, 0228-0230, 0232-0236, 0250, 0256, 0269, 0287].
Shah is combined based on the reasons and rationale set forth in the rejection of Claim 1 above.
As per Claim 4 Turner further teaches:
wherein the processing device is further configured to: receive customer feedback through one or more customer feedback channels, wherein the gathered data further comprises the customer feedback; and adjust the goal inputs based on the customer feedback (Turner in at least [0196] describes the neural network used as employing one or more of a variety of learning models including, supervised learning, unsupervised learning and reinforcement learning, this may employ various back propagation techniques).
As per Claim 5 Turner further teaches:
wherein the trained model is further configured for one or more collaborative filtering, content-based filtering, or hybrid recommendation filtering (Turner in at least [0078] describes the prescriptive application of advanced statistical modeling tools that maybe also be computed over the entire corpus of the financial data set for a customer or many customers, both on demand and in real time).
Turner does not explicitly recite that the model is a growth opportunity model that the output response based on goals is specifically a market response. However Shah teaches a system and method for developing a growth strategy. Shah describes using statistical models and machine learning algorithms to analyze goals, train the models and determine growth strategies for a target market or market segment, as is described in at least [0002, 0004-0006, 0009, 0012, 0014-0015, 0073, 0092-0093, 0125, 0163-0178, 0206, 0214, 0224, 0228-0230, 0232-0236, 0250, 0256, 0269, 0287].
Shah is combined based on the reasons and rationale set forth in the rejection of Claim 1 above.
As per Claim 6 Turner further teaches:
wherein the processing device is further configured to: monitor the trained model performance according to one or more trained model metrics at predetermined times; and refine the trained model according to the trained model performance (Turner in at least [0075] describes the platform that may enrich the raw data it aggregates through a process of filtering, querying correlating, linking, calculating and analyzing, using algorithms it either generates or was generated directly or indirectly through machine learning methods).
Turner does not explicitly recite that the model is a growth opportunity model that the output response based on goals is specifically a market response. However Shah teaches a system and method for developing a growth strategy. Shah describes using statistical models and machine learning algorithms to analyze goals, train the models and determine growth strategies for a target market or market segment, as is described in at least [0002, 0004-0006, 0009, 0012, 0014-0015, 0073, 0092-0093, 0125, 0163-0178, 0206, 0214, 0224, 0228-0230, 0232-0236, 0250, 0256, 0269, 0287].
Shah is combined based on the reasons and rationale set forth in the rejection of Claim 1 above.
As per Claim 7 Turner further teaches:
wherein the market response is based on a market segment including one or more of a demographic segment, a geographic segment, a psychographic segment, a behavioral segment or a regional segment including one or more of a geographic scope, state, province, district, or parish; and comprises a ranking and a likelihood scoring (Turner in at least [0073] the platform aggregates data from these sources into its cloud native platform, i.e. one or more server computers that are accessible by one or more client computers via a computer network, and securely stores, monitors, manages, and protects this data in the cloud on behalf of its customers).
As per Claim 8 Turner further teaches:
wherein the market response comprises a top market; and the processing device is further configured to: update the market segment and the regional segment at predetermined times; provide the updated market segment and regional segment data to the trained model in a third feedback loop; and train the trained model based upon updated market segment data and updated regional segment data received from the third feedback loop to refine the trained model at one of the different levels (Turner in at least [0095] the platform may leverage its big data platform to calculate and derive key performance indicators and other statistics to report and score a company’s operating performance, (Turner in at least [0100, 0143, 0193, 0201, 0205, 0208, 0211, 0213-0215, 0243] describe the ability to utilize feedback to revise and edit forecast models as well as training based on financial transactions available to the predictor and user feedback, multilevel hierarchical tags can be configured through iterative refinements, updated frequency updates the forecast model with the updated financial information, different forecast levels, such as a macro level of a forecast can be adjusted to help business determine layoffs or other needs, levers may come in different varieties and can be used to quickly model out different scenarios at a high level to see the cash effect using different macro driven assumptions).
Turner does not explicitly recite that the model is a growth opportunity model that the output response based on goals is specifically a market response. However Shah teaches a system and method for developing a growth strategy. Shah describes using statistical models and machine learning algorithms to analyze goals, train the models and determine growth strategies for a target market or market segment, as is described in at least [0002, 0004-0006, 0009, 0012, 0014-0015, 0073, 0092-0093, 0125, 0163-0178, 0206, 0214, 0224, 0228-0230, 0232-0236, 0250, 0256, 0269, 0287].
Shah is combined based on the reasons and rationale set forth in the rejection of Claim 1 above.
As per Claim 9 Turner further teaches:
wherein the system further comprises a user interface configured to: provide the user interface to a user device; receive an input from the user device on one or more elements of the user interface; and update the one or more of the data sources, the gathered data, the machine learning algorithm, the customer behavior features, the financial institution behavior features, the trained models, the foundation model selection variables, the goal inputs, or the growth opportunity model in response to the input; and display an updated market response based on the input (Turner in at least [0101] users, via the platform’s user interface may input comments as tags that temporarily or permanently stay connected to data within the platform’s integrated systems, such as comments for variances analysis).
As per Claim 10 Turner further teaches:
wherein the gathered data comprises customer data and financial institution data (Turner in at least [0025] describes the process of projecting the company cash forecast involving gathering historical financial transaction data from financial institutions, ERP system financial data from ERP systems).
As per Claims 11-20 the limitations are substantially similar to those set forth in Claims 1-10 and are therefore rejected based on the same reasons and rationale set forth in the rejection of Claims 1-10 above.
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 STEPHANIE Z DELICH whose telephone number is (571)270-1288. The examiner can normally be reached on Monday - Friday 7-3:30.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Rutao Wu can be reached on 571-272-6045. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/STEPHANIE Z DELICH/Primary Examiner, Art Unit 3623
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