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 .
Claims 1-18 remain pending in the application under prosecution and have been reexamined.
In the response to this Office action, the Examiner respectfully requests that support be shown for language added to any original claims on amendment and any new claims. That is, indicate support for newly added claim language by specifically pointing to page(s) and line numbers in the specification and/or drawing figure(s). This will assist the Examiner in prosecuting this application.
Examiner cites particular columns and line numbers in the references as applied to the claims below for the convenience of the applicant. Although the specified citations are representative of the teachings in the art and are applied to the specific limitations within the individual claim, other passages and figures may apply as well. It is respectfully requested that, in preparing responses, the applicant fully consider the references in entirety as potentially teaching all or part of the claimed invention, as well as the context of the passage as taught by the prior art or disclosed by the examiner.
Response to Amendment/Argument
Applicant's arguments filed 01/28/2026 have been fully considered but they are not persuasive.
Independent claims have been amended to recite “reweighing logged feedback information of a feedback database; and updating the individual personalized machine learning model based on the reweighed feedback information.” The applied reference US 20180101887 (AKKIRAJU et al) teaches mechanism to improve matching through customer feedback recommending products to choose by observing different tradeoffs such that: the learning module continuously improves the matching models through customer collected feedback; the learning module then modifies customer trait-to-consumption preference model to reflect the changes made by the customer to compute a matching score, wherein the learning module, modifying the rules based on collected consumer feedback, more accurately matches customer traits to the desired consumption preferences; and wherein Once match scores between products and customer are computed, personalized interactive decision support system can individual personalize machine learning model recommending matched products to customers. (see Par. 0076; Par. 0084; Par. 0122-0123].
The amended limitation, “reweighing logged feedback information of a feedback database; and updating the individual personalized machine learning model based on the reweighed feedback information,” is matched receiving, by the visual and interactive decision support module, customer feedback; and updating, by a learning module executing within the personalized interactive decision support system, the machine learning module based on the customer feedback (Par. 0076; Par. 0084; Par. 0122-0123].
The amended claims are found to be anticipated by US 20180101887 (AKKIRAJU et al). Therefore, the rejection is maintained an updated below.
Claim Rejections - 35 USC § 102
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 the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1-18 are rejected under 35 U.S.C. 102(a1)(a2) as being anticipated by US 20180101887 (AKKIRAJU et al).
With respect to claims 1 and 15, AKKIRAJU teaches system and method of providing personalized guideline information for a user in a predetermined domain, in particular in the context of everyday activities, wherein a set of personality types is defined for users of the predetermined domain (improved data processing apparatus and method and more specifically to mechanisms for offering personalized and interactive decision support based on a set of personalized preferences using a machine learning model to predict preferences from traits) [Abstract; Fig. 7, 8, and 11; Par. 0005]
the method comprising:
determining, by a personality type recognizer, a personality type for a user in order to assign the personality type to the user
(correlating, by a personalized product recommendation module executing within a personalized interactive decision support system, at least one customer to a set of consumption preferences using a machine learning model based on a set of traits of the at least one customer to form at least one customer-to-preference correlation) [Par. 0034-0036; Par. 0047-0049];
selecting a personality-typed machine learning model from a model pool of personality-typed machine learning models based on the personality type of the user, wherein the selected personality-typed machine learning model is used to initialize an individual personalized machine learning model of the user
(matching, by the personalized product recommendation module, at least one customer to at least one product within a set of products based on the at least one customer-to-preference correlation and the set of product-to-preference correlations to form at least one product recommendation) [Par. 0034-0036];
generating, by the individual personalized machine learning model of the user, a recommendation prediction, wherein the recommendation prediction is presented as a guideline information to the user
(presenting, by a visual and interactive decision support module executing within the personalized interactive decision support system, the at least one product recommendation) [Abstract; Fig. 7, 8, and 11; Par. 0005; Par. 0034-0036; Par. 0047-0049; Par. 0074-0076; Par. 0113-0115];
reweighing logged feedback information of a feedback database; and updating the individual personalized machine learning model based on the reweighed feedback information
(learning models modified based on received customer feedback (block) for the customer trait-to-consumption preference model to reflect the changes made by the customer, i.e., update the prediction model based on the customer feedback) (Par. 0076; Par. 0084; Par. 0122-0123].
With respect to claim 2, AKKIRAJU teaches method, wherein the personality-typed machine learning models of the model pool were trained based on previously collected data (training the machine learning model based on statistical analysis of a set of observed data for customers with customer traits and expressed or observed preferences) [Par. 0034-0036; Par. 0047-0049].
With respect to claim 3, AKKIRAJU teaches method, wherein a controller module is provided, wherein the controller module is configured to inform the individual personalized machine learning model (at least one product recommendation comprises presenting the at least one product to the at least one customer) [Par. 0047-0049].
With respect to claim 4, AKKIRAJU teaches method, wherein every time the individual personalized machine learning model presents a recommendation prediction to the user, the individual personalized machine learning model is informed by the controller module, wherein the controller module in turn considers the recognized personality type of the user (machine learning model comprising a set of correlation values, wherein each correlation value in the set of correlation values represents an identification of the at least one customer to whom the at least one product is to be targeted, wherein the at least one product recommendation comprises presenting the at least one product to the at least one customer) [Par. 0047-0049].
With respect to claim 5, AKKIRAJU teaches method, wherein said controller module is configured to interact with the user (machine learning model forming a set of product-to-preference correlations presented to the at least one customer) [Par. 0047-0049].
With respect to claim 6, AKKIRAJU teaches method, wherein the control module is configured to collect feedback information, in particular implicit and/or explicit feedback information, from the user when the recommendation prediction is presented to the user (presenting the product recommendation to the customer and receiving customer feedback; and updating, by a learning module executing within the personalized interactive decision support system, the machine learning module based on the customer feedback) [Par. 0047-0049].
With respect to claim 7, AKKIRAJU teaches method, wherein the controller module includes an emotional state recognizer, wherein the emotional state recognizer is employed to handle implicit feedback information of the user such that an emotional state of the user is determined (presenting product recommendation comprising presenting a visualization that shows the at least one product and presenting traits within the set of traits and attributes based on personality traits, values, needs, intent, emotional status) [Fig. 8; Par. 0113-0115].
within the set of attributes having a correlation contributing to the at least one product recommendation).
With respect to claim 8, AKKIRAJU teaches method, wherein the emotional state of the user is considered by the individual personalized learning model of the use (applying customer profile and set of customer traits to generate preferences of the customer profile based on the analysis on customer-traits and demographics) [Par. 0115-0117].
With respect to claim 9, AKKIRAJU teaches method, wherein the feedback information is logged together with the recommendation prediction in a feedback database, wherein the feedback database is shared across all users of the predetermined domain (the learning module being continuously improved through customer collected feedback and the interactive decision support module sending optimal set of products and their attributes and customer feedback to the learning module) [Par. 0074-0076].
With respect to claim 10, AKKIRAJU teaches method, wherein, in particular periodically, feedback information of the users collected by the feedback database is used to update personality-typed machine learning models of the model pool and/or to update users' individual personalized machine learning models (the learning module being continuously improved through customer collected feedback and the interactive decision support module sending optimal set of products and their attributes and customer feedback to the learning module) [Par. 0074-0076].
With respect to claim 11, AKKIRAJU teaches method, wherein, if a final outcome of a user's logged feedback information is known, all feedback information data points of the user are readjusted by the final outcome in a post episode feedback adjustment (the learning module being continuously improved through customer collected feedback and the interactive decision support is made to reflect the changes, attributes and customer feedback to the learning module) [Par. 0074-0076; Par. 0122].
With respect to claim 12, AKKIRAJU teaches method, wherein a user similarity matrix is computed, wherein the user similarity matrix indicates how similar each user is to all other users, such that, based on a similarity score, the logged feedback information of the feedback database is reweighed to create a personalized log for a specific user, wherein the personalized log is used to update the individual personalized machine learning model of the specific user (learning models updated by generating a product recommendation identifying customers having the subset of customer traits that might be interested in the product and applying preferences to a set of correlation values that map consumption preferences based on customer profiles) [Fig. 11; Par. 0120-0122; Par. 0074-0076].
With respect to claim 13, AKKIRAJU teaches method, wherein, depending on a personality type mismatch between the personality type assigned to the user and a specific personality type of a specific personality-typed machine learning model that is to be updated, the logged feedback information of the feedback database is reweighed to create a personality-typed log, wherein the personality-typed log is used to update the specific personality-typed machine learning model having the specific personality type (learning models updated by generating a product recommendation identifying customers having the subset of customer traits that might be interested in the product and applying preferences to a set of correlation values that map consumption preferences based on customer profiles) [Fig. 11; Par. 0120-0122; Par. 0074-0076].
With respect to claim 14, AKKIRAJU teaches method, wherein the predetermined domain includes a gym environment, a smart home environment, a virtual reality environment, a shopping environment and/or a health environment (decision support based on derived insights from social media, enterprise data, or other digital communications based on determined personality traits of the customer to generates a customer profile based on the personality and other customer traits) [Par. 0122-0125].
With respect to claim 16, AKKIRAJU teaches method, wherein the predetermined domain is in the context of everyday activities (the learning module being continuously improved through customer collected traits and the customer profiles) [Par. 0074-0076].
With respect to claim 17, AKKIRAJU teaches method, wherein the feedback information comprises implicit and/or explicit feedback information (learning models modified based on received customer feedback (block) for the customer trait-to-consumption preference model to reflect the changes made by the customer, i.e., update the prediction model based on the customer feedback) [Par. 0076;Par. 0122].
With respect to claim 18, AKKIRAJU teaches method, wherein the feedback information of the users is used periodically (learning models updated or modified based on customer trait-to-consumption preference model collected over different timing periods [Par. 0049-0050; Par. 0076; Par. 0122].
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
WO 2021244734 A1 (LAWRENCE et al) teaching personality type for a user in order to assign the personality type to the user, the personality-typed machine learning model being selected from a model pool of personality-type machine learning models based on the type of the user, where the selected model is used to initialize an individual personalized machine learning (PML) model; recommendation prediction is generated by the individual PML model, and is presented as a guideline information to user wherein the PML models of the model pool are trained based on previously collected data, and a controller module is provided to inform the PLL model.
WO 2022034879 A1 (DAIKI et al) teaching evaluation unit to evaluate multiple types of person models to be generated using the evaluation criteria and the attribute information., and an output unit to output a single type of output three-dimensional person model that is evaluated by the evaluation unit.
EP 4009190 (GREGORI LARS et al) teaching customization based on user profiles and personalization featuring an AI component configured to create new machine learning models (e.g., AI models for search and/or AI models for recommendation) using user feedback stored in the feedback database; at least one machine learning model to be used for the information retrieval is selected based at least in part on an input from the user and the user feedback on the retrieved information is stored in the feedback database in association with the user and with user feedback to "select at least one machine learning model, based at least in part on the input from the user, from among a plurality of machine learning models.
WO 2021050391 A1 (POLLERI et al) teaching machine learning platform to analyze identified data and user provided desired prediction and performance characteristics to select one or more library components and associated API to generate a machine learning application, the machine learning to monitor and evaluate the outputs of the machine learning model to allow for feedbacks and adjustments to the model.
S. Koh, H. J. Wi, B. Hyung Kim and S. Jo, "Personalizing the Prediction: Interactive and Interpretable machine learning," 2019 16th International Conference on Ubiquitous Robots (UR), Jeju, Korea (South), 2019, pp. 354-359.
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.
Contact Information
Any inquiry concerning this communication or earlier communications from the examiner should be directed to PIERRE MICHEL BATAILLE whose telephone number is (571)272-4178. The examiner can normally be reached Monday - Thursday 7-6 ET.
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/PIERRE MICHEL BATAILLE/ Primary Examiner, Art Unit 2136