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
This action is issued in response to Application filed March 11, 2025.
Claims 1-8 are pending.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on June 18, 2025 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
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.
Claim(s) 1-3 and 6-8 is/are rejected under 35 U.S.C. 103 as being unpatentable over Subramanian (U.S. Patent Application No. 2023/0185796) in view of Tamayo (U.S. Patent Application No. 2020/0364243).
Regarding Claim 1, Subramanian discloses a method for processing search requests at a search platform, the search platform being arranged to access a cache with a number of incomplete search results, wherein each incomplete search result comprises at least one first data field, the method comprising
in response to receiving a search request with at least one search parameter from a client, determining, using the cache, one or more of the incomplete search results with first data fields that correspond to the at least one search parameter (par [0005], [0179-0180], Subramanian – receiving a request, wherein the request includes multiple field values and determining a search type according to the request. The request/search is performed in order to identify records associated with the request with errors due to missing data within certain fields);
for each determined incomplete search result, generating at least one second data field using a machine learning model (par [0103], [0119-0121], Subramanian – module identifies fields that are missing values/data from the database and using a machine learning module to predict values of the missing fields based on other known and/or associated field values… par [0182]).
While Subramanian teaches all of the claimed subject matter as stated above. However, Subramanian is not as detailed with respect to the at least one second data field corresponds to the at least one search parameter.
On the other hand, Tamaya discloses the at least one second data field corresponds to the at least one search parameter (par [0021], Tamayo - search each record of the record set for similar records based on the similar records having similar field values at step. The search may be performed by any suitable full text search engine. The search engine may utilize a scoring system in combination with phonetic matching over a plurality of record fields, such as first name, last name, etc.). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate Tamayo’s record matching using deep learning teachings into the Subramanian system. A skilled artisan would have been motivated to combine in order to allow for better tracking and output of search results using artificial intelligence.
As a result, the combination of Subramanian in view of Tamayo, disclose assembling a number of completed search results on the basis of the determined incomplete search results and the generated at least one second data field (par [0105], [0108], Subramanian – one or more modules are used for automated entity field correction and the module(s) may validate whether a predicted missing field value for the result is correct and to confirm… par [0199]);
returning at least one of the completed search results to the client (par [0120], [0124], Subramanian – additional data that was requested to complete the missing field value(s) are returned and validated).
Regarding Claim 2, the combination of Subramanian in view of Tamayo, disclose the method of claim 1, wherein the search request indicates at least two search parameters, wherein at least one first search parameter relates to at least one first data field and at least one second search parameter relates to the at least second data field, wherein the method comprises:
determining the one or more of the incomplete search results with first data fields that correspond to the at least one first data parameter; for each determined incomplete search result, generating the at least one second data field using the machine learning model, wherein the at least one second data field corresponds to the at least one second search parameter and the at least one first data field of each determined incomplete search result (par [0119-0121], [0182], Subramanian… par [0021], Tamayo).
Regarding Claim 3, the combination of Subramanian in view of Tamayo, disclose the method of claim 1, wherein the machine learning model comprises at least one of deep neural network (par [0018], Tamayo – applying a model for record linkage using artificial intelligence, which may take the form of a deep neural network), a gradient boosting tree model, and a generative artificial intelligence model.
Regarding Claim 6, the combination of Subramanian in view of Tamayo, disclose the method of claim 1, further comprising receiving, from the client, a previous search request preceding the search request; generating, using at least an original data source, one or more complete previous search results including at least one first data field and at least one second data field corresponding to one or more search parameters in the previous search request; returning at least one of the generated one or more complete previous search results to the client; wherein the search request from the client requests an update of at least one of the returned complete previous one or more search results, the update requiring an update of at least one second data field and wherein generating at least one second data field using the machine learning model constitutes the update of the at least one second data field (par [0113], [0127], Subramanian – historical data is retrieved based on previously requests… the system may use a machine learning model that is trained based on previous prescription request documents, to become more effective at identifying data elements… also see par [0222], [0225], Subramanian).
Claims 7 and 8 contain similar subject matter as claim 1 above; and are rejected under the same rationale.
Allowable Subject Matter
Claims 4 and 5 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims. The following is a statement of reasons for the indication of allowable subject matter: traversing a hierarchical tree with multiple branches; utilizing the machine learning model to predict a subset of the branches of the hierarchical tree for traversing; skipping traversing the hierarchical tree when the predicted subset of the branches has been traversed.
Points of Contact
Any inquiry concerning this communication or earlier communications from the examiner should be directed to CHELCIE L DAYE whose telephone number is (571) 272-3891. The examiner can normally be reached on Monday-Friday 7:30-4:00pm. 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, Apu Mofiz can be reached on 571-272-4080. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of an application may be obtained from the Patent Application Information Retrieval (PAIR) system. Status information for published applications may be obtained from either Private PAIR or Public PAIR. Status information for unpublished applications is available through Private PAIR only. For more information about the PAIR system, see http://pair-direct.uspto.gov. Should you have questions on access to the Private PAIR system, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free).
Chelcie Daye
Patent Examiner
Technology Center 2100
January 8, 2026
/CHELCIE L DAYE/Primary Examiner, Art Unit 2161