DETAILED ACTION
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, 2, 21 and 22 have been amended. Claim 4 has been cancelled. Claim 23 is new. Claims 1-3 and 5-22 are pending.
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 08/10/2026 has been entered.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-3 and 5-22 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Claims 1-3, 5-20 and 23 (Group I) are drawn to a method for source data review which is within the four statutory categories (i.e. process). Claim 21 (Group II) is drawn to a system for source data review which is within the four statutory categories (i.e. machine). Claim 22 (Group III) is drawn to a non-transitory medium for source data review which is within the four statutory categories (i.e. manufacture).
Claims 21 (Group II) recites a source document review system comprising:
a computing device comprising at least one processor (apply it, MPEP § 2106.05(f)); and
a memory communicatively coupled to the at least one processor, the memory storing instructions which, when executed by the at least one processor, cause the at least one processor to perform operations comprising (apply it, MPEP § 2106.05(f)):
obtaining, from one or more data sources for one or more clinical studies, a plurality of source documents, wherein each source document from the plurality of source documents comprises clinical trial information of the one or more clinical studies in an unstructured format;
converting, by an optical character recognition module, the clinical trial information in the unstructured format into structured digital text data;
extracting, from the structured digital text data and for each source document in the plurality of source documents, data indicative of a plurality of clinical study events, each clinical study event being associated with at least one clinical study entity that is associated with at least one clinical study and each clinical study event having one or more event features;
calculating, for each clinical study event in the plurality of clinical study events based on a predetermined set of rules and further based on the one or more event features of each clinical study event, a degree of regulatory compliance for each clinical study event;
calculating, based on the degree of regulatory compliance for each clinical study event associated with a particular clinical study, a compliance score for the particular clinical study; and
transmitting, based on the compliance score and the degrees of regulatory compliance for each clinical study event, a notification to at least one client device (MPEP § 2106.05(f), apply it) to enable corrective action to be taken.
The bolded limitations, given the broadest reasonable interpretation, cover a mental process and/or a certain method of organizing human activity because it recites a process that is performed in the human mind, but for the recitation of generic computer components and/or fundamental economic practices, commercial or legal interactions, and/or managing personal behavior or relationships or interactions between people. Any limitations not identified above as part of the abstract idea are underlined and deemed “additional elements,” and will be discussed in further detail below.
Furthermore, the abstract idea for Claims 1-3, 5-20 and 23 (Group I) and Claim 22 (Group III) is identical as the abstract idea for Claim 21 (Group II), because the only difference between is they are directed towards different statutory categories. Claim 22 further recites a non-transitory computer-readable storage device (apply it, MPEP § 2106.05(f)).
Dependent Claims 2-3, 5-20 and 23 include other limitations, for example Claim 2 recites wherein extracting the data indicative of the plurality of clinical study events comprises extracting the data by a Natural Language Processing (NLP) model, the NLP model being trained using analyzed feature data from at least a subset of the plurality of source documents and using a subset of one or more corpora of documents pertaining to the clinical trial as contextual data; further comprising generating an updated NLP model by modifying one or more parameters of the NLP model in response to obtaining error data indicating a difference between the data extracted by the NLP model and data in the plurality of source documents, Claim 3 recites wherein at least one event from the one or more events detected by the updated NLP model indicates a correlation between two or more entities from the plurality of entities, Claim 5 recites wherein generating the updated NLP model comprises generating, for each source document in the plurality of source documents, a classification of each respective entity from the plurality of entities, wherein the classification indicates a class of medical ontology for the respective entity based on the analyzed feature data, Claim 6 recites wherein the updated NLP model is configured to generate a plurality of events likely to have occurred among the plurality of entities, wherein the updated NLP model is configured to generate, for each event in the plurality of events, a value indicating a likelihood of association of entities from at least a subset of the plurality of entities, Claim 7 recites wherein training the updated NLP model comprises: providing a training example query for input to the updated NLP model; generating, using the training example query and by the updated NLP model, a training model output representing one or more detected events associated with plurality of entities; obtaining ground truth data, wherein the ground truth data indicates one or more events associated with the plurality of entities; determining a score based on a comparison of the ground truth data and the training model output; and based on the score exceeding a threshold, updating one or more parameters of at least one layer from the plurality of layers, Claim 8 recites detecting, using the updated NLP model, an adverse event from the one or more events, wherein the adverse event indicates that information related to participants in the one or more clinical studies does not follow a protocol from one or more protocols for conducting the one or more clinical studies; and in response to detecting the adverse event, generating data indicative of one or more updates to the information related to the participants found in the source document from the plurality of source documents that includes an entity associated with the adverse event, Claim 9 recites generating, by the updated NLP model, generative prompt data that configures a user interface of a client device, wherein the generative prompt data causes display of a visual representation of annotations corresponding to each respective entity from the plurality of entities, wherein each annotation indicates a class of medical ontology for the respective entity, and wherein the NLP model is trained to generate the generative prompt data using one or more generative visualization techniques, Claim 10 recites providing the generative prompt data to the client device, wherein providing the generative prompt data causes the client device to update the user interface to include one or more graphical elements, each graphical element corresponding to each annotation from the annotations, Claim 11 recites providing, for output by the one or more computers, the user interface including a respective selectable control for providing feedback to an identification of an event from the one or more events for the one or more clinical studies, the identified event corresponding to a graphical element from the one or more graphical elements; receiving, by the user interface, a user selection of one or more of the selectable controls included in the user interface; and updating one or more parameters of the updated NLP model based on the user selection, Claim 12 recites determining, from the one or more events and using the NLP model, one or more instances of non-compliant data in at least one source document from the plurality of source documents, wherein the non-compliant data is associated with an entity from the plurality of entities, Claim 12 recites wherein an instance from the one or more instances of non-compliance comprises a deviation from at least one protocol from one or more protocols for conducting the one or more clinical studies, Claim 14 recites wherein an instance from the one or more instances of non-compliant data indicates a treatment plan that does not follow protocol for the one or more clinical studies, Claim 15 recites identifying, based one or more instances of non-compliant data, an output trend indicating a pattern of non-compliance for the one or more clinical studies, the pattern being associated with at least one (i) a subset of entities from the plurality of entities, or (ii) one or more sites for conducting the one or more clinical studies, Claim 16 recites obtaining one or more documents corresponding to one or more sites for conducting the one or more clinical studies, the one or more documents comprising clinical data for the one or more clinical studies; determining, based on the one or more documents, a plurality of data fields and a plurality of data formats for the clinical data from the one or more documents; identifying, based on the one or more documents, at least one corpus of documents from a subset of the one or more corpora of documents related to the one or more documents; applying, a set of compliance rules to the clinical data for the one or more documents, wherein applying the set of compliance rules comprises: identifying one or more instances of non-compliant data in the clinical data from the one or more documents; generating, based on the one or more instances of non-compliant data in the clinical data and a set of quality indicators for the clinical data, a score representing a compliance rating for the one or more documents; and generating, based on the one or more instances of non-compliant data, a signal indicating one or more fields in at least one document from the one or more documents that include at least one instance from the one or more instances of non-compliant data; and providing at least one of (i) the score for the compliance rating for the at least one document, or (ii) the signal indicating the one or more fields in the at least one document, to a computing device, Claim 17 recites wherein the one or more documents comprises at least one of (i) certification records, (ii) delegation tasks, (iii) training logs, (iv) financial disclosures, or (v) a set of protocols, Claim 18 recites identifying a trend from the one or more instances of non-compliant data in the clinical data, the trend indicating the one or more documents that do not meet at least one protocol from the one or more protocols or at least one rule in the set of compliance rules, Claim 19 recites determining, based on the set of compliance rules, a non-compliance rate of a set of documents, the set of documents associated with a site from the one or more sites and a threshold value for non-compliant data for the site; comparing the non-compliance rate to a threshold value for non-compliant data for the site; and based on the non-compliance rate to the threshold value, providing the signal indicating the one or more instances of non-compliant data in the set of documents to a computing device, Claim 20 recites analyzing the one or more documents, wherein analyzing the one or more documents includes comparing one or more site fields in the one or more documents to one or more fields in the one or more corpora of documents; and based on the analyzing of the one or more documents, generating a set of indicators for a set of fields, each indicator in the set of indicators corresponding to a field in the set of fields, wherein the indicator from the set of indicators for the field in the set of fields represents compliance status of data represented by the field, and Claim 23 recites further comprising determining, based on determining that a compliance score for a selected clinical study event in the plurality of clinical study events indicates that the data indicative of the selected clinical study event comprises non-compliant data; and wherein transmitting the notification to the at least one client device causes the client device to refrain from at least one of:
transmitting the non-compliant data; or storing the non-compliant data, but these only serve to further limit the abstract idea, and hence are nonetheless directed towards fundamentally the same abstract idea as independent Claims 1, 21 and 22.
Furthermore, Claims 1-3 and 5-23 are not integrated into a practical application because the additional elements (i.e. the limitations not identified as part of the abstract idea) amount to no more than limitations which:
amount to mere instructions to apply an exception – for example, the recitation of a computing device comprising at least one processor; and
a memory communicatively coupled to the at least one processor, the memory storing instructions…, a non-transitory computer-readable storage device, a user interface, a client device, which amounts to merely invoking a computer as a tool to perform the abstract idea, e.g. see paragraphs [0035] and [0138-0140] of the present Specification, see MPEP 2106.05(f).
Furthermore, the Claims do not include additional elements that are sufficient to amount to “significantly more” than the judicial exception because, the additional elements (i.e. the elements other than the abstract idea) amount to no more than limitations which:
amount to elements that have been recognized as well-understood, routine, and conventional activity in particular fields, as demonstrated by:
The Specification expressly disclosing that the additional elements are well-understood, routine, and conventional in nature:
Paragraphs [0035], [0071]and [0138-0140] of the Specification discloses that the additional elements (i.e. computing system processor, non-transitory computer readable medium, an optical character recognition module) comprise a plurality of different types of generic computing systems that are configured to perform generic computer functions that are well-understood, routine, and conventional activities previously known to the pertinent industry (i.e. healthcare).
Dependent Claims 2-3, 5-20, and 23 include other limitations, but none of these functions are deemed significantly more than the abstract idea because the additional elements recited in the aforementioned dependent claims similarly represent no more than generic computer components recited at an “apply it” level (user interface, client device).
Thus, taken alone, the additional elements do not amount to “significantly more” than the above-identified abstract idea. Furthermore, looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually, and there is no indication that the combination of elements improves the functioning of a computer or improves any other technology, and their collective functions merely provide conventional computer implementation.
Therefore, whether taken individually or as an ordered combination, Claims 1-3 and 5-23 are nonetheless rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.
2025Attorney Docket No. 317EP.001US01
Response to Arguments
Applicant's arguments filed 08/10/2026 have been fully considered.
Claims Rejections – 35 U.S.C. § 101
Applicant asserts that the claims are not directed to an abstract idea (Remarks, page 15-16). Specifically, Applicant asserts that “arrangement improves the operation of a source document review system in a practical, technological way (Remarks, page 15).” The claims as amended do not result in a practical application. The additional elements are recited an “apply it” level and are not improved as a result of the claimed invention. The claims require the use of computers and OCR, but do not improve either as a result of the abstract idea. The inventive concept is not optical character recognition, as the claimed invention is using an already existing technology for a particular purpose.
Applicant asserts that the instant claims are similar to Example 42 as they “recite a specific improvement to a computer-based clinical trial source document review system (Remarks, page 16).” Unlike Example 42, the claims do not include a technical problem with a technical solution. The clinical documents are OCRed to enable the extracting specific data from clinical study events. This is not technical problem as alleged by Applicant.
Applicant asserts that “subsequent compliance calculations and notification transmission are not incidental post-solution activity; they are part of the claimed practical application because the notification is transmitted based on the event-specific compliance determinations and enables corrective action to be taken at a client device (Remarks, page 16).” Examiner maintains that this is part of the abstract idea, and therefore cannot be used to integrate into a practical application. The additional elements, both alone and in combination, do not result in an improvement to the computer itself or a technological improvement, and therefore do not rise to the level of a practical application.
Regarding new claim 23, Examiner asserts that this is part of the abstract idea as it simply includes rules or instructions for what to do with the data, either refraining from transmitting non-compliant data or storing the non-compliant data. As this is considered part of the abstract idea, it does not result in a practical application of the abstract idea or significantly more. The improvement resulting from the claim, if any, is to the abstract idea itself, not the computing/client device.
Therefore, the claims remain rejected as being directed towards ineligible subject matter.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Rachelle Reichert whose telephone number is (303)297-4782. The examiner can normally be reached M-F 9-5 MT.
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, Jason Dunham can be reached at (571)272-8109. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/RACHELLE L REICHERT/Primary Examiner, Art Unit 3686
Prosecution Insights