DETAILED ACTION
This action is responsive to the Amendment filed on 01/28/2026. Claims 1-21 had been previously canceled. Claims 22 and 26 have been amended. Claims 22-29 are pending in the case. Claims 22 and 26 are independent claims.
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 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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
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 22-24 and 26-28 are rejected under 35 U.S.C. § 103 as being unpatentable over Brown et al. (US Patent Application Pub. No. 2015/0186156, hereinafter “Brown”), in view of Kamat et al. (US Patent Application Pub. No. 2018/0315130, hereinafter “Kamat”), in further view of Willcock et al. (US Patent Application Pub. No. 2012/0130819, hereinafter “Willcock”).
As to independent claims 22 and 26, Brown shows a computer-implemented method [figs. 16-20] and a concomitant smart phone [¶ 34], comprising:
displaying one or more interactive graphical psychometric tests in a user interface of a smart phone; receiving psychometric data for a user from the user interface of the smart phone based on the one or more interactive graphical psychometric tests [displaying (fig. 10) and receiving responses (¶¶ 55 & 138-140) to one or more interactive graphical psychometric tests (e.g. one or more interactive graphical prompts “to select a personality (e.g., attitude, etc.)” (¶ 138), “to select a preference of the user to utilize to interpret input and perform tasks” (¶ 139), etc.) in a user interface (fig. 10) of a smart phone (¶ 34)], wherein the psychometric data for the user includes a user score [e.g. the psychometric data for the user may include a user score (fig. 1, 6, & 12; ¶¶ 38, 42, 91-92, 127, 154, & 195)];
based on the psychometric data of the user, selecting an interactive virtual assistant from a plurality of interactive virtual assistants to display in the user interface of the smart phone; displaying the selected interactive virtual assistant in the user interface of the smart phone to prompt the user to select one of a plurality of options [based on the varied user input possibilities outlined above, a specific interactive virtual assistant, out of many interactive virtual assistant possibilities, is selected and displayed to prompt the user to select one of a plurality of options (¶¶ 24-28, 134, 138-140, & 199)];{…}
conducting a dynamic interactive electronic communication session with the user via the selected interactive virtual assistant [conducting a electronic communication session by the user with the selected interactive virtual assistant (¶ 02)], wherein the interactive virtual assistant provides personalized financial guidance using machine learning algorithms that {…} adapt responses based on the psychometric data and financial information to customize prompts, phrases, and frequencies of contact for individual users [Brown shows a “Finance Virtual Assistant” (fig. 11; ¶ 38) that provides personalized financial guidance using machine learning algorithms (¶ 55) that adapt responses based on the psychometric data (¶¶ 134, 154, & 199) and financial information (¶¶ 95-98, 131, & 140), which may have the intended result to customize prompts, phrases, and frequencies of contact for individual users (¶¶ 27, 89-108, 136-139, & 190-203).];
Even though Brown is replete with teachings describing both the providing of personalized financial guidance and the training of data from a plurality of users with various psychometric traits (Brown: ¶¶ 42-43 & 88-89), Brown does not appear to explicitly focus on the training of the machine learning algorithms themselves, and thus would not appear to explicitly recite that “the interactive virtual assistant provides personalized financial guidance using machine learning algorithms that are trained on data from a plurality of users with various psychometric traits” as apparently intended. In an analogous art, Kamat shows:
the interactive virtual assistant provides personalized financial guidance using machine learning algorithms that are trained on data from a plurality of users with various psychometric traits [“{…} the system may utilize or be described as a tax assistant (or in other fields, more generically an “assistant”). A tax assistant helps the user with preparing for a tax preparation process. The tax assistant may provide assistance to the user over time, such as throughout the year or beginning at the tax return season when the user begins receiving tax-related documents. {…}” (Kamat: ¶ 19)
“{…} various steps described herein can be accomplished by using unique parameters in a recurrent neural network seeded with the implication database 118. Parameters from the database 118 may include user information 120 (such as an income level, occupation, family characteristics, demographics, and other potentially relevant information) from a user account and/or prior tax returns, tax laws and regulations, among other information. The network will be trained by the user 102, and other similar users, as tax implications are either verified or denied by the user 102.
{…} a machine learning model will consult a database of tax implications and related documents to determine the probability of whether a particular document or trigger has a tax implication. Tax implications can initially be procured by the tax professional or by the system 100. In some embodiments, all or some of the user-verified data and/or tax-professional-verified data are saved to the database. Thus, the database becomes more valuable and accurate as usage increases. Over time, the model may become personalized for each user 102 based on user behavior. This allows for a lower error rate over time.” (Kamat: ¶¶ 53-54)]
One of ordinary skill in the art, having the teachings of Brown and Kamat before them prior to the effective filing date of the claimed invention, would have been motivated to incorporate Kamat’s training teachings into Brown. The rationale for doing so would have been that Brown had already explicitly conceded both a desire of “creating a rich user experience that adapts to different contexts” (Brown: ¶ 28) as well as a willingness to “include a multitude of other tools that similarly provide value to end-users, trainers, and others” (Brown: ¶ 30), and Kamat would have aided in this endeavor by teaching a technique that “helps the user” (Kamat: ¶ 19) and “allows for a lower error rate over time.” (Kamat: ¶ 54). Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of Brown-Kamat (hereinafter, the “Brown-Kamat” combination) in order to obtain at least the above aspects of the invention as recited in claims 22 and 26.
Brown-Kamat does not appear to explicitly recite a “generating an aggregated insight based on emotional annotations associated with members of a combination of cluster groups associated with the user, wherein a cluster group is based on a minimum absolute value distance between the user score and a population median score” as apparently intended. In an analogous art, Willcock shows:
displaying one or more interactive graphical psychometric tests in a user interface of a smart phone; receiving psychometric data for a user from the user interface of the smart phone based on the one or more interactive graphical psychometric tests[“Collection of user emotional preference is achieved through the user answering one or more multimedia survey 60. In an implementation of the multimedia survey 60, the survey comprises a plurality of queries, and with each query of the multimedia survey 60, a set of multimedia objects is presented to the user. The user answers the multimedia survey 60 by selecting one or more multimedia object for each query. In an exemplary embodiment, the survey form is displayed on the web browser of the user device 28.” (Wilcock: ¶ 33)], wherein the psychometric data for the user includes a user score [“The analysis module 41 can perform three kinds of analyses that assign users to different emotional code categories. The first type of analysis is category analysis, Category analysis is to analyze the score for each category associated with the images selected by the respondent, and deduce which category this user should be assigned to. In operation, an expert assigns each image a score for each category to which the user can be assigned. In one embodiment, the score is between −10 to 10; and there are four to eight categories chosen by the expert. The category analysis module reads the images stored in the survey result, extract the category scores for those images that the user selects, and tally them up. The combination of tallied scores of each category is the emotional code. The category with the highest total score is recorded in the emotional code as the user's primary category.” (¶ 42) | See also ¶¶ 45-46.]; {…}
and generating an aggregated insight based on emotional annotations associated with members of a combination of cluster groups associated with the user, wherein a cluster group is based on a minimum absolute value distance between the user score and a population median score [“In operation, an expert chooses two or more axes for each question, in which each axis correspond to a degree of an emotional state that a question is trying to measure. For each question the expert assigns each image a score for each axis. The expert also assigns each category to which a user may be assigned a score for each axis. The statistical analysis module retrieves all the selected images of all survey questions from the survey result database 48. The mathematical distance between the axis scores for each category and the axis scores for the selected images is calculated. The mathematical distance for each category in all the selected images is aggregated. The combination of the aggregated mathematical distance for each category is the emotional code. The category with the shortest aggregated distance will be recorded as the user's primary category. In an exemplary embodiment, there are two axes. The emotional code, and all other relevant information related to this emotional code category are stored in the user profile database 43.” (Willcock: ¶ 45)].
One of ordinary skill in the art, having the teachings of Brown, Kamat, and Willcock before them prior to the effective filing date of the claimed invention, would have been motivated to incorporate Willcock’s aggregated insight generation techniques into Brown-Kamat. The rationale for doing so would have been that like Brown-Kamat, Willcock was also devoted to “providing customized content to a user through collecting the user's emotional preference” (Willcock: ¶ 01). Moreover, Brown-Kamat had already explicitly conceded both a desire of “creating a rich user experience that adapts to different contexts” (Brown: ¶ 28) as well as a willingness to “include a multitude of other tools that similarly provide value to end-users, trainers, and others” (Brown: ¶ 30). Thus, by incorporating Willcock’s techniques, Brown-Kamat would have been enabled to “utilize users' emotional preferences to target potential customers more effectively. The present invention thus overcomes the technical problem in the art that existing computers are not able to covert the emotional characteristics of a human to a machine-readable language. The emotional reflex of a human which is a type of external technical data can now be technically processed and stored in the computers. By utilizing technical solutions such as transmitting the surveys through the communication network e.g. the Internet, and equipping the user with an the existing computer that is able to display surveys and allows the user to use mouse click to select an image, coupled with the techniques to extract user's emotional code from his responses, the performance of the existing computers are greatly enhanced as they can now analyze not only factual but also emotional information of the user before recommending a decision. In essence, human emotional preferences can thus be machine-processed similar to other external technical data” (Willcock: ¶ 12). Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of Brown, Kamat, and Willcock (hereinafter, the “Brown-Kamat-Willcock” combination) in order to obtain the invention as recited in claims 22 and 26.
As to dependent claims 23 and 27, Brown-Kamat-Willcock further shows:
wherein the selecting of the interactive virtual assistant from the plurality of interactive virtual assistants includes: assigning the user to one of a plurality of personality outcomes based upon the psychometric data received from the user [selecting of the interactive virtual assistant comprises assigning the user to one of a plurality of personalities based upon the psychometric data received from the user (Brown: ¶¶ 90 & 101 | see also Brown: ¶¶ 24, 84, & 138)].
As to dependent claims 24 and 28, Brown-Kamat-Willcock further shows:
wherein prompting the user to select one of a plurality of options includes one or more of: prompting the user to rate one of a plurality of financial transactions; prompting the user to select one of a plurality of financial savings options; and prompting the user to select one of a plurality of financial services options [the prompting of the user to select the one of the plurality of options may comprise prompting the user to rate one of a plurality of transactions (Brown: fig. 5; ¶¶ 124 & 160-161) and/or select one of a plurality of financial services options corresponding to the financial assistant (Brown: ¶¶ 29, 38, 102, 110, 131, & 140)].
Claims 25 and 29 are rejected under 35 U.S.C. § 103 as being unpatentable over Brown-Kamat-Willcock in further view of Jones (US Patent Application Pub. No. 2018/0211227, hereinafter “Jones”).
As to dependent claims 25 and 29, Brown-Kamat-Willcock further shows choosing between multiple levels of the psychometric traits (Brown: fig. 10, ¶¶ 55 & 138-140 | Willcock: ¶¶ 42-46). However, Brown-Kamat-Willcock does not appear to explicitly teach doing so via “one or more slidable user interface features configured to move along a continuous scale between two end points” as apparently intended. In an analogous art, Jones shows:
one or more interactive graphical psychometric tests include one or more slidable user interface features configured to move along a continuous scale between two end points representative of a first level of a psychometric trait and a second level of the psychometric trait [Jones shows one or more interactive graphical psychometric tests/test questions (which also share the same intended use of ultimately selecting an appropriate assistant for a particular user (Jones: Abstract)), each of which include one or more slidable user interface features configured to move along a continuous scale between two end points representative of a first level of a psychometric trait and a second level of the psychometric trait (Jones: ¶¶ 90-91). For even further context/examples one or more slidable user interface features configured to move along a continuous scale between two end points representative of a first level of a psychometric trait and a second level of the psychometric trait, see also ¶¶ 84-89 & 92-101.].
One of ordinary skill in the art, having the teachings of Brown, Kamat, Willcock, and Jones before them prior to the effective filing date of the claimed invention, would have been motivated to incorporate Jones’s slidable user interface techniques into the Brown-Kamat-Willcock combination. The rationale for doing so would have been that not only had Jones already found it to be an intuitive and effective means of embodying interactive graphical psychometric tests to choose psychometric traits in an analogous field of endeavor (accurately matching an assistant to a user (Jones: Abstract)), but also Brown had already explicitly conceded both a desire of “creating a rich user experience that adapts to different contexts” (Brown: ¶ 28) as well as a willingness to “include a multitude of other tools that similarly provide value to end-users, trainers, and others” (Brown: ¶ 30), which would have reasonably encompassed alternating Brown’s drop-down-based design choices (Brown: ¶¶ 138-139) for Jones’s slider-based design choices (Jones: ¶¶ 84-101). Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of Brown, Kamat, Willcock, and Jones in order to obtain the invention as recited in claims 25 and 29.
Response to Arguments
Applicant’s prior art arguments have been fully considered but are moot in view of the new grounds of rejection presented above.
Conclusion
Applicant’s amendments necessitated the new grounds of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicants are reminded of the extension of time policy as set forth in 37 C.F.R. § 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 C.F.R. § 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.
It is noted that any citation to specific pages, columns, lines, or figures in the prior art references and any interpretation of the references should not be considered to be limiting in any way. A reference is relevant for all it contains and may be relied upon for all that it would have reasonably suggested to one having ordinary skill in the art. In re Heck, 699 F.2d 1331, 1332-33, 216 U.S.P.Q. 1038, 1039 (Fed. Cir. 1983) (quoting In re Lemelson, 397 F.2d 1006, 1009, 158 U.S.P.Q. 275, 277 (C.C.P.A. 1968)).
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/ALVARO R CALDERON IV/
Examiner, Art Unit 2171
/KIEU D VU/Supervisory Patent Examiner, Art Unit 2171
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