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
The present Office Action is in response to the Request for Continued Examination filed 06/04/2026.
Request for Continued Examination
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 4 June 2026 has been entered.
Response to Amendment
In the response filed 06/04/2026, the following occurred: Claim 19 is amended.
Claims 19 and 21-29 are pending and have been examined.
Priority
The effective filing date is 11/09/2023.
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.
Claims 19 and 21-29 are rejected for lacking written description.
Claim 19 is rejected under 35 U.S.C. §112(a) for lacking written description for the recitation of “determining, using the machine learning model and for each document included in the subset of documents, one or more positions within the free-form content, wherein the one or more positions (1) specify positions at which at least one matching unstructured data element is located and (2) are inferred by the machine learning model from the free-form content without reference to pre-embedded structured references”. This is a new matter rejection. New matter added to the claims shall be rejected under 112a. MPEP § 2163.06(I).
The Applicant’s Specification (at para. 0008, 0064, 0081) makes no reference to this subject matter and thus fails to provide support for this limitation. For example, there is no disclosure describing the particular determination as recited. Also, the disclosure appears to be silent as to the recited free-form content, one or more positions thereof, specific locations of matching unstructured data element(s), free-form content input, and pre-embedded structured references.
Further, a portion of this (“without reference to pre-embedded structured references”) appears to be a negative limitation used to overcome the prior art. Regarding negative limitations, MPEP states that: "Any negative limitation or exclusionary proviso must have basis in the original disclosure. If alternative elements are positively recited in the specification, they may be explicitly excluded in the claims. … The mere absence of a positive recitation is not basis for an exclusion. Any claim containing a negative limitation, which does not have basis in the original disclosure, should be rejected under 35 U.S.C. 112(a) or pre-AIA 35 U.S.C. 112, first paragraph, as failing to comply with the written description requirement". MPEP 2173.05(i). Clarification is respectfully requested.
The rejection of claim 19 also applies to dependent claims 21-29.
Note: 35 U.S.C. §101
Subject matter eligibility was determined in Office action mailed 03/06/2026.
The Applicant amended the independent claim to recite “obtaining data indicating a set of clinical trial documents associated with a clinical trial; receiving data indicating a user-specified change to a data field within a first document included in the set of clinical trial documents; determining a key-value pair associated with the data field, wherein the key-value pair comprises (i) a key specifying a classification for the data field, and (ii) a value representing content of the data field as modified by the user-specified change; extracting, using a machine learning model, unstructured data elements contained within other documents included in the set of clinical trial documents; executing, using the machine learning model, a pairing algorithm to match the unstructured data elements from the other documents against the key; identifying, using the machine learning model, a subset of documents from the other documents based on matching against the key, wherein each document included in the subset of documents contains at least one unstructured data element that matches the key; and propagating the value of the key-value pair to the at least one extracted unstructured data element within each document included in the subset of documents.”
The claim as a whole provides a practical application under subject eligibility analysis Step 2A Prong 2, since it provides an improvement to another technological field, i.e., a technical solution to a technical problem of clinical trial documentation. MPEP § 2106.05(a). As indicated in the specification: “[0009] Advantageously, this enables clinical trial documentation to be prepared in an efficient and accurate manner. A user may be able to accurately enter relevant information relevant to their clinical trial, and clinical trial documentation may be prepared and output in a manner (e.g., in a structured data format) that can be easily imported by the clinical trial registration body.” See also the specification para. 0063. The subject matter eligibility of claim 19 also applies to dependent claims 21-29.
Subject Matter Free of Prior Art
Note: Applicant’s Remarks filed 4 Jun 2026 pertain entirely to 35 U.S.C. § 103.
The cited prior art of record fails to expressly teach or suggest, either alone or in combination, the features found within the independent claim 19 as follows:
determining a key-value pair associated with the data field, wherein the key-value pair comprises (i) a key specifying a classification for the data field, and (ii) a value representing content of the data field as modified by the user-specified change;
parsing, using a machine learning model, free-form content of other documents included in the set of clinical trial documents to identify unstructured data elements;
determining, using the machine learning model and for each document included in the subset of documents, one or more positions within the free-form content, wherein the one or more positions (1) specify positions at which at least one matching unstructured data element is located and (2) are inferred by the machine learning model from the free-form content without reference to pre-embedded structured references; and
propagating the value of the key-value pair to the free-form content at each of the one or more positions within each document included in the subset of documents.
The most remarkable prior art of record is as follows:
Broverman et al. (US 2004/0249664) for teaching a system used to perform a computer-assisted methodology for design assistance for clinical trial protocols (see at least the abstract and para. 0095).
Ramanujam (US 2024/0086647) for teaching artificial intelligence-enabled system and method for authoring a scientific document, e.g., documents that describe clinical trial methods and results, using machine learning and natural language processing with minimal user intervention (see at least the abstract, Figs. 3, 9 and para. 1, 42, 46, 61) (see Applicant’s disclosure at para. 0013-0014).
Zovic et al. (WO 2020/243846) for teaching a system and method for automated file reporting including document content analysis, a key value structure, and region data extracted via optical character recognition (see at least para. 0064-0067).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Witchey et al. (US 12,462,246 B1) for teaching token-based digital private data exchange systems, methods, and apparatus including records 114 comprising field-value pairs 116 (see Fig. 1 and col. 6, lines 58-60).
Manchanda et al. for teaching Accelerating Clinical Trial Documentation Through Human Aided Medical Writing Automation System (see abstract).
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/J.M.W./Examiner, Art Unit 3683
/CHRISTOPHER L GILLIGAN/Primary Examiner, Art Unit 3683
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