DETAILED ACTION
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
This action is responsive to claims filed 12/01/2025 and Applicant’s communication regarding application 18/753202 filed 12/01/2025.
Claims 12-17 have been examined with this office 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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 has been entered.
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claim 12 and any claims which depend therefrom are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. Applicant introduces new matter to claims 12. Applicant amended claim 12 recite the limit (or an equivalent) "forming a query to a rules engine based on the extracted terms indicating user preference in addition to preset criteria, the query designed to form a list of recommendations that do not meet with user preferences and are not otherwise deemed suitable according to the preset criteria that are to be suppressed and removed as possible candidates for the updated list of recommendations;
executing the query to generate a suppressed recommendation list;
outputting a representation of the updated recommendations list determined by the machine learning model from which entries in the suppressed recommendation list have been removed at the end user device for viewing by a particular end user;”.
The specification reads:
[0017] At the UI 110, the user is prompted to enter any criteria/preferences that they may have for a preferred advisor. The orchestration layer receives the preference information and then accesses a rules engine 145, which can be implemented using a query management tool, to apply a set of rules to the criteria and preferences. The rules can include conditions for removing certain advisors as candidates for matches based on preset criteria. As one example, a lead may prefer a lead to be located within a certain geographical proximity. Based on application of these criteria, the rules engine 145 returns a second list of lead/advisory pairs that match the criteria and preferences of the user. In parallel, the orchestration layer 140 takes the raw scored lead-advisor paring list 125 and applies further existing business protocols 150 that also can suppress out advisors from the list that do not meet a set of propriety business rules, resulting in a refined pairing list. For example, a business protocol might exclude a particular advisor on the basis that the selected advisor is already responsible for a threshold number of clients. The refined list is combined by the orchestration layer 140 with the second list generated by the via the rules engine 145. This combination removes any advisors that do not meet the criteria of the preferences, rules and business protocols. After the recommendations have been completed, a finalized list 160 is passed with corresponding management system information describing the advisor to the UI 110. At the UI 110, a predefined number of top advisor recommendations are presented to the lead in a visualization (an example is shown in FIG. 4). The lead can utilize the UI to select a preferred advisor from the presented advisors shown in the UI.
The specification describes a query designed to form a list of recommendations that match user preferences and are otherwise deemed suitable according to the preset criteria. The applications describe method/system steps that suppress out advisors from the list that do not meet a set of propriety business rules. A suppressed recommendation list of advisors that do not meet user preferences and preset criteria is not disclosed as being generated. Further still, the machine learning model is described being used in parallel with the query to the rules engine to form the refined pairing list. The output of the query to the rules engine (claimed suppressed recommendation list) is not used as in input to the machine learning model. As such, the amended claims as cited adds new matter as claimed and is rejected along with any claims which depend therefrom fail to comply with the written description requirement.
The following is a quotation of 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ) second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claim 12 and any claims which depend therefrom are rejected under 35 U.S.C. 112, second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which applicant regards as the invention. Claim 12 recites the claim limit “forming a query to a rules engine based on the extracted terms indicating user preference in addition to preset criteria, the query designed to form a list of recommendations that do not meet with user preferences and are not otherwise deemed suitable according to the preset criteria that are to be suppressed and removed as possible candidates for the updated list of recommendations;
executing the query to generate a suppressed recommendation list;
outputting a representation of the updated recommendations list determined by the machine learning model from which entries in the suppressed recommendation list have been removed at the end user device for viewing by a particular end user;”. It is not entirely clear from the claims if the “suppressed recommendation list“ contains a list of recommendations that do not meet with user preferences and are not otherwise deemed suitable according to the preset criteria that are to be suppressed and removed as possible candidates for the updated list of recommendations, or if the “suppressed recommendation list “ contains entries where recommendations that do not meet with user preferences and are not otherwise deemed suitable according to the preset criteria that are to be suppressed and removed as possible candidates for the updated list of recommendations have been removed. Therefore, one of ordinary skill in the art would not be reasonably apprised of the scope of the invention.
Prior Art
The claims overcome the prior art of record such that none of the cited prior art reference’s disclosures can be applied to form the basis of a 35 USC § 102 rejection nor can they be combined to fairly suggest in combination, the basis of a 35 USC § 103 rejection when the limitations are read in the particular environment of the claims. The prior art of record teaches "forming a query to a rules engine based on the extracted terms indicating user preference in addition to preset criteria, executing the query to generate a suppressed recommendation list; and outputting a representation of the updated recommendations list determined by the machine learning model from which entries in the suppressed recommendation list have been removed at the end user device for viewing by a particular end user;”. However, none of the prior art of record teaches that “the query designed to form a list of recommendations that do not meet with user preferences and are not otherwise deemed suitable according to the preset criteria that are to be suppressed and removed as possible candidates for the updated list of recommendations”. Therefore, the claims may be allowable if amended to overcome the rejection(s) under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), 2nd paragraph, set forth in this Office action.
Response to Arguments
Applicant's arguments with respect to claims have been considered but are moot in view of the new ground(s) of rejection necessitated by applicant’s amendment to claims. The rejection above serves as the examiner’s response to the applicant’s arguments.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Callery (PGPub No. 20180260903) teaches a machine learning based recommendation for a desired strategy is generated for a user. User data pertaining to previous user decisions pertaining to capitalization is received. Data that is similar to the received user data is automatically queried. The similar data may be useful for generating the recommendation for the desired strategy. An objective that constrains the desired strategy for capitalization is received. The similar user data and the objective are automatically analyzed to generate the recommendation for the desired strategy based on machine learning from the similar user data.
Wolfram (PGPub No. 20140067534) teaches a method for providing to a user commercial information regarding at least one of a product or a service, at least one of 1) a query of the user, or 2) an answer to the query is analyzed, and an activity of the user is inferred based on the analysis of the query and/or the answer. A general product or service to support the activity is determined, and a vendor that provides the general product or service is determined in response to determining the general product or service. Information regarding a specific product or service provided by the vendor is obtained, wherein the specific product or service corresponds to the general product or service. Commercial information corresponding to the specific product or service provided by the vendor is generated, wherein the commercial information is for electronic transmission to the user.
Anthony-Hoppe (US Patent No. 7962347) teaches an apparatus and method for an advisor referral tool for objectively matching professional services between users and advisors in an on-line or computer based environment. The advisor referral tool matches users with professional advisors by executing an advisor matching algorithm to select a subset of advisors from an advisor database based on user selected search criteria. A server computer calculates a percent match value for each advisor in the subset of advisors that corresponds to consumer responses to predetermined psychographic/profile questions. The server computer then creates a best fit advisor list of advisors from the subset of advisors that is based on the calculated percent match value. The best fit advisor list is then transmitted to and displayed on the user's computer.
Gupta (US Patent No. 10332172) teaches a method and system for providing lead recommendations are disclosed. A server system stores profile information for a plurality of members of a server system. The server system then analyzes the stored profile information to identify one or more potential sales lead recommendations for a first member of the server system. The server system then ranks the one or more identified potential sales lead recommendations. The server system selects one or more of the identified sales lead recommendations and transmits the selected one or more identified sales lead recommendations to a client device associated with the first member of the server system.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Gregory A Pollock whose telephone number is (571) 270-1465. The examiner can normally be reached M-F 8 AM - 4 PM.
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/Gregory A Pollock/Primary Examiner, Art Unit 3691
01/09/2026