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 .
DETAILED ACTION
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 07/28/2025 has been entered.
Status of Claims
This action is in response to applicant arguments filled on 07/28/2025 for application 19063420.
Claims 1-20 have been amended.
Claims 1-20 are currently pending and have been examined.
Detailed Action
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.
The text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claim(s) 1-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Meehan et al. (US 2023/0153740 A1) in view of Jain et al. (US 20230017196 A1) and in further in view of Adams et al. (US 2018/0005293 A1)
In claim 1, a method for generating offerings of service to one or more users in a computing environment using one or more processors to execute instructions that are configured to cause actions, comprising:
Meehan teaches:
Obtaining one of a plurality of a service categories associated with the one or more subjects based on one or more services provided by a healthcare organization (Para. 70 wherein “the system may apply the at least one pre-determined variable standard and the selected category (from the user or potential recipient), to a source entity suggestion model, which may in-turn output or generate at least one suggested source entity name that matches the specifications outlined by the user (e.g., matches the pre-determined variable standard(s) and the selected category).”
Meehan does not explicitly teach however Adams teaches:
wherein the service category corresponds to a match that is predicted to be a broadest hierarchical selection from the plurality of service categories that is also associated with a confidence score that satisfies a threshold value for a quality of the hierarchical match with the one or more subjects (Para. 89 wherein “ thresholds may be set in the platform 400 (which may be configurable on a merchant-by-merchant or category-by-category basis, or the like), which are used to determine whether there is enough confidence in a match to a particular product to begin to show potentially matching products (which may be presented in order of relevance based on facts known at a particular point in the dialog).”
Meehan further teaches:
displaying an offering panel based on the service category and an availability of the one or more services and an offering model, wherein the offering panel displays information associated with an available service (para. 45 and 52);
Meehan does not explicitly teach however Jain teaches:
collecting one or more physical interactions between one or more of users and the offering panel, wherein an efficacy of the offering panel is based on a frequency and a duration of the one or more physical interactions by the one or more users (Para. 112, 115, 329, and 345 wherein monitoring frequency and duration of an activity of a user is taught);
employing a deficiency model to compare the one or more physical interactions of the one or more users with the offering panel to one or more previous physical interactions of the one or more users with one or more efficacy deficient offering panels (Para. 131, 218, wherein characteristic of user activity data can be compared to threshold values is taught. Para. 257 teaches “content can be displayed can be determined based on comparing user activity data for different users against triggers and/or conditions specified by the configured rules. User devices of users whose activity data satisfies at least one condition or trigger, can be provided the content for display whereas user devices of users whose activity data does not satisfy at least one condition or trigger are prevented from receiving the content”); and
collecting one or more potential sources of other information to retrain the offering model and modify the display of the offering panel to improve its efficacy of for the one or more users and identify one or more causes of efficacy deficiency for the one or more users (Para. 22,2, 494 and 514 wherein a dynamic model is trained based on users responses ).
It would have been obvious to one of ordinary skill to combine the system, methods, and apparatuses for using machine learning to categorize and select suggested source entities as taught in Meehan with the platform for enabling personalized recommendations using intelligent dialog as taught in Adams further with the healthcare system and method for rules engine that dynamically adapts application behavior as taught in Jain. The well-known elements described are merely a combination of old elements, and in combination, each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
As per claim 2, Meehan teaches the method of Claim 1, further comprising:
obtaining content from a content panel based on one or more of a markup language, an encoding, or a format associated with the content panel (Para. 95 wherein natural language processor is used to determine content).
As per claim 3, Meehan teaches the method of Claim 1, further comprising:
obtaining one or more subjects associated with content based on information included in the content and one or more evaluations of the content by a subject model (Para. 95).
As per claim 4, Meehan teaches the method of Claim 1, further comprising:
selecting the offering model for retraining based on one or more performance metrics for its efficacy falling below a threshold value (Para. 103 wherein “Para. 103 “ the step of applying the previously tagged variable training dataset to the source entity suggestion model to train the source entity suggestion model. In some embodiments, the system may train the source entity suggestion model by applying the previously tagged variable training dataset, such that the source entity suggestion model may determine which variables of each source entity it considers in the future may comprise positive or negative feedback.”).
As per claim 5, Meehan teaches the method of Claim 1, further comprising:
retraining the offering model based on one or more other metrics for efficacy associated with one or more other offering models and a training model, wherein the training model includes one or more of a machine learning model or a large language model (Para. 103-104).
As per claim 6, Meehan teaches the method of Claim 1, further comprising:
retraining the offering model to generate one or more other offering panels for display to the one or more users (Para. 103 and 104).
As per claim 7, Meehan teaches the method of Claim 1, further comprising:
collecting one or more non-efficacy deficient offering panels based on one or more physical interaction metrics for input data that exceeds one or more defined threshold values (Para. 101); and
retraining the offering model with training data that includes the input data (Para. 101, 102, and 118).
Claims 8-20 recite substantially similar limitations as seen above and hence are rejected for similar rationale as noted above.
Response to Arguments
Applicant's arguments with respect to the 103 rejection above have been considered but are moot in view of the new ground(s) of rejection. It is respectfully submitted that the Examiner has applied new passages and citations to amended claims at the present time. The Examiner notes that amended limitations were not in the previously pending claims as such, Applicant's remarks with regard to the application of all applied references to the amended limitations are addressed in the above Office Action.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MAROUN P KANAAN whose telephone number is (571)270-1497. The examiner can normally be reached Monday-Friday 8:00-5:00.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Mamon Obeid can be reached at (571) 270-1813. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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MAROUN P. KANAAN
Primary Examiner
Art Unit 3687
/M.P.K/Primary Examiner, Art Unit 3687 /MAROUN P KANAAN/Primary Examiner, Art Unit 3687