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 .
The following NON-FINAL Office Action is in response to Applicant’s communication filed 04/30/2025 regarding Application 19/194,191. The following is the first action on the merits.
Priority Acknowledgment
Examiner acknowledges Applicant’s claim as a continuation of Application 17/815,247 and acknowledges claim to priority filing date 07/27/2022.
Status of Claim(s)
Claim(s) 1-20 is/are currently pending and are rejected as follows.
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.
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Claim(s) 1, 16, and 20 is/are rejected on the ground of nonstatutory double patenting as being unpatentable over Claim(s) 1, and 18 of U.S. Patent No. 12,237,261. Although the claims at issue are not identical, they are not patentably distinct from each other because limitations below are anticipated by the claims provided in the previously granted patent.
Support for this determination is given in the citations provided below:
Claim(s) 1, 16, and 20 –
train, using a personal data set of each of a plurality of users, a neural network to predict financial health assessment scores, the personal data set of each of the plurality of the users including a data entry regarding a financial health assessment score previously determined, with respect to each respective user of the plurality of users, upon completion of a financial health assessment survey by each respective user of the plurality of users, wherein the financial health assessment survey includes a plurality of queries regarding financial health of each respective user of the plurality of users, wherein the predicting the financial health assessment scores includes implementing a survey algorithm that utilizes the responses provided by each respective user of the plurality of users to the financial health assessment survey as inputs for outputting the financial health assessment score, wherein the financial health assessment score is a numeric value falling within a range of values with a first end of the range of values indicative of a minimum assessed financial health condition and an opposing second end of the range of values indicative of a maximum assessed financial health condition; (Wilson: Claim(s) 1 and 18: “generating a predictive model during training of a machine learning program including a neural network of the machine learning program…a training data set utilized during the training of the machine learning program comprising a personal data set of each of a plurality of ... users, the personal data set of each of the plurality of the…users including a data entry regarding a financial health assessment score previously determined with respect to each respective ... user of the plurality of ... users upon completion of a financial health assessment survey by each respective…user of the plurality of ... users, wherein the financial health assessment survey includes each respective ... user providing a response to each of a plurality of queries regarding financial health of each respective…user, wherein the financial health assessment score of each respective ... user of the plurality of ... users is determined by a survey algorithm that utilizes the responses provided by each respective ... user as inputs for outputting the financial health assessment score of each respective ... user of the plurality of…users, wherein the financial health assessment score of each respective ... user of the plurality of…users is a numeric value falling within a range of values with a ... end of the range of values indicative of a minimum assessed financial health condition and an opposing second end of the range of values indicative of a maximum assessed financial health condition.”)
deploy the neural network to predict the financial health assessment scores; (Wilson: Claim(s) 1 and 18: “deploying the predictive model to predict financial health assessment scores;”)
apply the deployed neural network to a personal data set of a user to generate a predicted financial health assessment score for the user, the generating of the predicted financial health assessment score including correlating, via the deployed neural network, the personal data set of the user to one or more most similar personal data sets of the plurality of users; (Wilson: Claim(s) 1 and 18: “Applying the deployed predictive model to a personal data set of a second user to generate a predicted financial health assessment score for the second user, the generating of the predicted financial health assessment score including correlating, via the deployed predictive model, the personal data set of the second user to one or more most similar personal data sets of the plurality of first users;”)
apply the deployed neural network to a test personal data set to generate a test financial health assessment score for the user, the test personal data set being different than the personal data set of the user due to a change to at least one data entry that represents a potential relationship change between the user and an entity that is different from a current relationship between the user and the entity due to the user's performance of one or more future interactions, the generating of the test financial health assessment score including correlating, via the deployed neural network, the test personal data set of the user to at least one similar personal data set of the plurality of users; (Wilson: Claim(s) 1 and 18: “applying the deployed predictive model to a test personal data set to generate a test financial health assessment score for the second user, the test personal data set of the second user being different than the personal data set of the second user due to a change to at least one data entry that represents a potential relationship change between the second user and a first entity that is different from a current relationship between the second user and the first entity due to the second user's performance of one or more future interactions, the generating of the test financial health assessment score including correlating, via the deployed predictive model, the test personal data set of the second user to at least one similar personal data set of the plurality of first users;”)
determine that the test financial health assessment score is closer to the maximum assessed financial health condition than the predicted financial health assessment score; and (Wilson: Claim(s) 1 and 18: “determining that the test financial health assessment score is closer to the maximum assessed financial health condition than the predicted financial health assessment score…”)
send, based on the determining that the test financial health assessment score is closer to the maximum assess financial health condition, a communication to a user device of the user, the communication indicating a predicted improvement in the user's financial health if the user were to implement the potential relationship change resulting in the change to the at least one data entry, wherein the communication includes information facilitating a selection by the user to change the current relationship between the user and the entity in accordance with the change to the at least one data entry of the test personal data set of the user (Wilson: Claim(s) 1 and 18: “…based thereon sending a communication to a user device of the second user, the communication indicating a predicted improvement in the second user's financial health if the second user were to implement the potential relationship change resulting in the change to the at least one data entry, wherein the communication includes information facilitating a selection by the second user to change the relationship present between the second user and the first entity in accordance with the change to the at least one data entry of the test personal data set of the second user.”)
Therefore Claim(s) 1, 16, and 20 of Applicant’s claims are rejected under double
patenting in view of the rationale provided above as the content within Applicant’s claim
limitations are directed to patentably indistinct subject matter from the previously granted patent.
Statutory Subject Matter with Regard to 35 U.S.C. 101
Claim(s) 1-20 have been analyzed under the Alice/Mayo framework and determined to be statutory with regards to 35 U.S.C. 101 for the following reasons. First, under Step 1 of the Alice/Mayo framework, it must be considered whether the claims are directed to one or more of the statutory classes. In the instant case, Claim(s) 1-15 are directed towards an apparatus. Claim(s) 16-19 is directed towards a method comprising at least one step. Claim(s) 20 is directed towards an apparatus. Accordingly, these claims fall under the four statutory category of invention and will be further analyzed under Step 2 of the Alice/Mayo framework. Under Step 2A, Prong 1 of the Alice Mayo framework the claims were directed towards the training of a neural network model based on a catered data set and the deployment and application of the model to make determinations for the sending of communications as it relates to the models training and data set. The limitations as they are currently presented were not deemed to recite any abstract idea and therefore was deemed statutory subject matter with regards to 35 U.S.C. 101.
Reasons for Overcoming Pertinent Prior Art
Claim(s) 1-20 are deemed by Examiner to overcome the pertinent prior art, as any
applicable prior art does not disclose, either fully or in combination with other art, such as to
read on applicant’s claimed limitations in their entirety. Listed below is the prior art most
applicable reasons for application, and their deficiencies.
Bloom (US 11,663,668 B1) discloses a predictive model performing unsupervised training via cluster analysis, and a predictive assessment score for a different user correlating the personal data set of the different user to at least one of the first users but does not disclose a test data set that is closer to a maximum score than the predicted assessment score, the test data including a data entry change between the second user and a first entity, or a communication facilitating a change between a second user and a first entity based on the data entry change.
Dibner-Dunlap (US 2019/0378207 A1) discloses a predictive financial health score, correlating a different user data to similar first user data, and a communication facilitating a change between a different user and a first entity based on the data entry change but does not disclose a predictive model performing unsupervised training via cluster analysis, or a test data set of a second user with a data entry change in relationship between a second user and a first entity
Zaluski (US 2022/04058371 A1) discloses a predictive model performing unsupervised training via cluster analysis, and a predictive assessment score for a second user correlating the personal data set of the different user to at least one of the different users, and generating a test data set for a second user, but does not disclose the test data including a data entry change between the different user and a first entity, or a communication facilitating a change between a different user and a first entity based on the data entry change.
Kim (US 2023/0214916 A1) discloses a predictive model for generating a test financial health score for user, correlating a different user data set to a plurality of first user financial scores, the scores having a minimum and maximum but does not disclose the predictive model performing unsupervised training via cluster analysis, a communication facilitating a change between a different user and a first entity based on the data entry change, or determining financial health assessments based on input from a plurality of respective first users.
Bowman (US 2019/0019246 A1) discloses a model for determining a financial health assessment score for a first user of a plurality of first users via inputs received from the respective first user, and the scores having a minimum and maximum assessed values, but does not discloses correlating a different user data set to similar first users, a predictive model performing unsupervised training via cluster analysis, or communication facilitating a change between a different user and a first entity based on the data entry change.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Shriberg (US 11,942,194 B2) discloses a method for mental health evaluation and assessment via responses
Deshmukh (US 11,900,400 B2) discloses a method for enhanced survey information synthesis
Jain (US 2021/0241139 A1) discloses a method for using machine learning to improve processes for achieving readiness
Gebara (US 11,875,408 B2) discloses a method for accurate evaluation of a financial portfolio
Prasad (US 2023/0237165 A1) discloses a metho for evaluating, validating, and implementing change requests to a system environment based on artificial intelligence
Williams (US 2023/0179955 A1) discloses a method for dynamic and adaptive systems for rewarding or disincentivizing behaviors
Sandstrom (US 2020/0285645 A1) discloses a method for online trained object property estimations
Lewis (US 2020/0143946 A1) discloses a method for patient risk scoring and evaluation systems
Srinivasan (US 2020/0134637 A1) discloses a method for pre-filling and/or predicting response data by using artificial intelligence to improve survey response data
Reynolds (US 2020/0090063 A1) discloses a method for generating a decision making algorithm to achieve an objective
Xiong (CN 110796262 A) discloses a method for test data optimization using a machine learning model
Johansen (US 2017/0193604 A1) discloses a method for intelligence assessment and alert system
Bright (US 2011/0099122 A1) discloses a method for providing customers personalized product information
I. Ismath and A. Kirupananda, ("TrustCredit - An Individual Credit Scoring Mechanism Using Alternative Mobile Interaction Touchpoints,") discloses a method for predicting a financial health score for users using a plurality of user data
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Philip N Warner whose telephone number is (571)270-7407. The examiner can normally be reached Monday-Friday 7am-4:00pm.
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/Philip N Warner/Examiner, Art Unit 3624
/Jerry O'Connor/Supervisory Patent Examiner,Group Art Unit 3624
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