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 .
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 June 3, 2026 has been entered.
Priority
Acknowledgment is made of applicant’s claim for foreign priority under 35 U.S.C. 119 (a)-(d). The certified copy has been filed in parent Application No. KR10-2023-0197437, filed on December 29, 2023.
Response to Amendment
In the amendment filed on May 26, 2026, the following has occurred: claim(s) 1 and 12 have been amended. Now, claim(s) 1-5, 9-16, 20-21 are pending.
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.
Claim(s) 1-5, 9-16, 20-21 is/are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Claim 1: Step 2A Prong One
Claim 1 recite(s)
acquiring clinical trial data included in an area specified by a user, the original data is in a tabular format, the clinical trial data is unstructured data having different structural features from the original data, and the clinical trial data is in a one-dimensional list structure devoid of a row and column structure of the original data;
generating structured data using the clinical trial data by inputting the clinical trial data using a prompt designed to distinguish rows on the basis of patterns in the one-dimensional list structure, so that the clinical trial data is converted into a tabular format by distinguishing rows on the basis of patterns in the clinical trial data, and the structured data is generated by filling predetermined values in where data is absent in the clinical trial data converted into the tabular format; and
recording key-value data included in the structured data as data on an item corresponding to a key
These limitations, as drafted given the broadest reasonable interpretation, but for the recitation of generic computer components, encompass managing interactions between people, including following rules or instructions, which is a subgrouping of Certain Methods of Organizing Human Activity. For example, but for the “by the one or more processors,” language, the claim encompasses a user following instructions to specify an area and acquire clinical trial data from that specified area, a user following instructions to determine structured data using the clinical trial data, and a user following instructions to record key-value data included in the structured data. These steps could be accomplished by a user following rules or instructions.
Claim 1: Step 2A Prong Two
This judicial exception is not integrated into a practical application because the remaining
elements amount to no more than general purpose computer components programmed to perform the abstract idea, insignificant extra-solution activity, and generally linking the abstract idea to a technical environment.
Claim 1, directly or indirectly, recites the following generic computer components configured to implement the abstract idea: “by the one or more processors,”. As set forth in the 2019 Eligibility Guidance, 84 Fed. Reg. at 55 “merely including instructions to implement an abstract idea on a computer” is an example of when an abstract idea has not been integrated into a practical application.
Additionally, the claim recites “wherein the key-value data is recorded in a second system different from the first system” at a high degree of generality, amount no more than storing and retrieving information in memory, Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015); OIP Techs., 788 F.3d at 1363, 115 USPQ2d at 1092-93. As set forth in MPEP 2106.05(d)(II), computer functions as well‐understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity, is an example of when an abstract idea has not been integrated into a practical application.
Additionally, the claims recite “to a generative artificial intelligence (Al) model that is fine-tuned” at a high degree of generality, amount no more than generally linking the abstract idea to a particular technical environment. The recitation is also similar to adding the words “apply it” to the abstract idea. As set forth in MPEP 2106.05(f), merely reciting the words “apply it” or an equivalent, is an example of when an abstract idea has not been integrated into a practical application.
Claim 1: Step 2B
The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements of using a computer configured to perform above identified functions amounts to no more than mere instructions to apply the exception using generic computer components. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. See Alice 573 U.S. at 223 (“mere recitation of a generic computer cannot transform a patent-ineligible abstract idea into a patent-eligible invention.”)
Insignificant, extra solution, data gathering activity has been found to not amount to significantly more than an abstract idea (See MPEP 2106.05(g)). Therefore, whether considered alone or in combination, the additional elements do not amount to significantly more than the abstract idea.
Additionally, generally linking the abstract idea to a particular technological environment does not amount to significantly more than the abstract idea (See MPEP 2016.05(h) and Affinity Labs of Texas v. DirectTV, LLC, 838 F.3d 1253, 120 USP12d 1201 (Fed. Cir. 2016)).
Claims 2-5, 9-10 incorporate the abstract idea identified above and recite additional limitations that expand on the abstract idea. For example, claim 2 describes that the area is specified by a user using a camera, and performing optical character recognition (OCR) on the area. Similarly, claim 3 describes instructions for the user to follow. Similarly, claim 4 further describes that the area is specified by the user. Similarly, claim 5 describes that the clinical trial data is acquired by performing OCR on the area. Finally, claims 9-10 describe instructions for extracting the key-value data, and defines the key-value data. Therefore, these claims recite limitations that fall into the Certain Methods of Organizing Human Activity grouping of abstract ideas.
Dependent claims 2-5, 9-10 recite additional subject matter of “a camera” in claim 2. The additional limitation, given the broadest reasonable interpretation, encompasses a generic computer component. Claim 2 recites the following generic computer component configured to implement the abstract idea: “a camera”. As set forth in the 2019 Eligibility Guidance, 84 Fed. Reg. at 55 “merely including instructions to implement an abstract idea on a computer” is an example of when an abstract idea has not been integrated into a practical application.
The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements of using a computer configured to perform above identified functions amounts to no more than mere instructions to
apply the exception using generic computer components. Mere instructions to apply an exception
using a generic computer component cannot provide an inventive concept. See Alice 573 U.S. at
223 (“mere recitation of a generic computer cannot transform a patent-ineligible abstract idea into a patent-eligible invention.”)
Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely provide conventional computer implementation and do not impose a meaningful limit to integrate the abstract idea into a practical application.
The claims are not patent eligible.
Claim 11 recites the same functions as claim 1, but in a non-transitory computer-readable recording medium.
The additional element of “a computer program” encompasses a generic computer component and does not integrate the abstract idea into a practical application. As set forth in the 2019 Eligibility Guidance, 84 Fed. Reg. at 55 “merely including instructions to implement an abstract idea on a computer” is an example of when an abstract idea has not been integrated into a practical application.
Therefore, whether considered alone or in combination, the additional elements do not amount to significantly more than the abstract idea. The claim is not patent eligible.
Claim 12 recites the same functions as claim 1, but in system form.
Claims 13-16, 20-21 incorporate the abstract idea identified above and recite additional limitations that expand on the abstract idea. For example, claim 13 describes that the area is specified by a user using a camera, and performing optical character recognition (OCR) on the area. Similarly, claim 14 describes instructions for the user to follow. Similarly, claim 15 further describes that the area is specified by the user. Similarly, claim 16 describes that the clinical trial data is acquired by performing OCR on the area. Finally, claims 20-21 describe instructions for extracting the key-value data, and defines the key-value data. Therefore, these claims recite limitations that fall into the Certain Methods of Organizing Human Activity grouping of abstract ideas.
Dependent claims 13-16, 20-21 recite additional subject matter which amount to limitations consisted with the additional elements in independent claim 12 (such as claim 13 recites additional limitations that amount to generic computer components).
Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely provide conventional computer implementation and do not impose a meaningful limit to integrate the abstract idea into a practical application.
The claims are not patent eligible.
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 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.
Claims 1-2, 4-5, 9-13, 15-16, 20-21 are rejected under 35 U.S.C. 103 as being unpatentable over Rosales (U.S. Patent Pre-Grant Publication No. 2009/0299977) in view of Hill et al. (U.S. Patent Pre-Grant Publication No. 2025/0157598).
As per independent claim 1, Rosales discloses a method performed in a system for a recording clinical trial data, the system comprising one or more processors and the method comprising the steps of:
by the one or more processors (See [0022]: A computer system includes a processor and a program storage device readable by the computer system, embodying a program of instructions executable by the processor to perform method steps for automatically labeling unstructured data from electronic medical records), acquiring clinical trial data included in an area specified by a user, wherein the clinical trial data is acquired from original data recorded in a first system (See [0039]: Data pattern may be searched for from within a set of source data that includes patient medical records within which the question of whether the patients have the particular medical finding being searched for is either known or knowable, which the Examiner is interpreting a set of source data to encompass original data recorded in a first system), the original data is in a tabular format (See [0039]-[0040]: The data pattern may be searched for from within a set of source data that includes patient medical records within which the question of whether the patients have the particular medical finding being searched for is either known or knowable, which the Examiner is interpreting the patient medical records to encompass a tabular format), the clinical trial data is unstructured data having different structural features from the original data (See [0042]: The unstructured data of the patient medical records are represented as U1, U2, U3, U4, U5, etc., which the Examiner is interpreting the unstructured data to encompass the clinical trial data is unstructured data), and the clinical trial data is in a one-dimensional list structure devoid of a row and column structure of the original data (See [0034]: The purpose of the data pattern is to quickly identify one or more locations within the unstructured data fragments of the electronic medical records that may be relevant to one or more particular medical findings being searched for and accordingly, the data pattern may be relatively small and may consist of a single keyword or a portion of a word, which the Examiner is interpreting identify one or more locations within the unstructured data fragments of the electronic medical records to encompass a one-dimensional list structure devoid of a row and column structure of the original data);
by the one or more processors (See [0022]: A computer system includes a processor and a program storage device readable by the computer system, embodying a program of instructions executable by the processor to perform method steps for automatically labeling unstructured data from electronic medical records), generating structured data using the clinical trial data (See Fig. 2 and [0034], [0050]: An association between particular matched data patterns in context and whether there is a true association, false association, or no association at all for a particular medical finding, here illustrated for the medical finding V1 (although it is to be understood that where such a table is generated, may be generated for all medical findings), which the Examiner is interpreting the output displayed in Fig. 2 to encompass generating structured data), by inputting the clinical trial data to a generative artificial intelligence (Al) model that is fine-tuned using a prompt designed to distinguish rows on the basis of patterns in the one-dimensional list structure (See [0041], [0052]-[0054]: Automatic labeling of subsequent unstructured data, computer learning techniques may be used to train classifier(s) for the automatic detection of medical findings from subsequent unstructured data, to this end, the association between patterns in context with particular medical findings may be used as labels for building a machine learning classifier, which the Examiner is interpreting the machine learning classifier to encompass a generative artificial intelligence (Al) model that is fine-tuned using a prompt designed to distinguish rows on the basis of patterns in the one-dimensional list structure as the training of the classifier(s) (Step S17) may be performed using the generated training data, using sophisticated computer learning algorithms, or more simply, by ascertaining simple relationships between contexts and medical findings), so that the clinical trial data is converted into a tabular format by distinguishing rows on the basis of patterns in the clinical trial data (See Fig. 2 and [0034], [0050]: An association between particular matched data patterns in context and whether there is a true association, false association, or no association at all for a particular medical finding, here illustrated for the medical finding V1 (although it is to be understood that where such a table is generated, may be generated for all medical findings), which the Examiner is interpreting the output displayed in Fig. 2 to encompass converting the unstructured data into a tabular format), and the structured data is generated by filling predetermined values in where data is absent in the clinical trial data converted into the tabular format (See [0047]-[0048]: By repeating these steps for each match, general associations between various contexts and medical findings may be gathered (Step S16) and by this process, training data may be collected, as these general associations may then be used to train classifiers based on the identified associations, which the Examiner is interpreting the tables to encompass the clinical trial data converted into the tabular format, and interpreting the data being used to associate patterns with particular medical findings is regarded as source data as it also includes data representing the medical findings in the structured data fields to encompass filling predetermined values in where data is absent.)
While Rosales discloses by the one or more processors, wherein the clinical trial data is acquired from original data recorded in a first system, the original data is in a tabular format, the clinical trial data is unstructured data having different structural features from the original data, and the clinical trial data is in a one-dimensional list structure devoid of a row and column structure of the original data, Rosales may not explicitly teach acquiring clinical trial data included in an area specified by a user; and
by the one or more processors, recording key-value data included in the structured data as data on an item corresponding to a key, wherein the key-value data is recorded in a second system different from the first system.
Hill teaches a method by the one or more processors, acquiring clinical trial data included in an area specified by a user (See [0097], [0145]: The inputs their protocol registration number into the system and it automatically acquires the unstructured data from the relevant public registry, which the Examiner is interpreting the unstructured data to encompass clinical data, and interpreting the user selection ([0097]) to encompass an area specified by a user), wherein the clinical trial data is acquired from original data recorded in a first system, the original data is in a tabular format, the clinical trial data is unstructured data having different structural features from the original data, and the clinical trial data is in a one-dimensional list structure devoid of a row and column structure of the original data; and
by the one or more processors (See [0183]: A processor 1214, which can be a micro-controller, digital signal processor (DSP), or other processing component, processes these various signals, such as for display on the computer system 1200 or transmission to other devices via a communication link 1224), recording key-value data included in the structured data as data on an item corresponding to a key (See [0145]: A key-value pair consists of two related data elements: A key, which is a constant that defines the data set and a value, which is a variable that belongs to the set), wherein the key-value data is recorded in a second system different from the first system (See [0145]-[0146]: This may include, for example, using an OCR model (such as Tesseract OCR) to sequence inputs, and an image processing library, for example, the OpenCV library may be used for image reading and processing, and PyTesseract library may be used for OCR, which the Examiner is interpreting the PyTesseract to encompass the second system.)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed to modify the method of Rosales to include acquiring clinical trial data included in an area specified by a user; and recording key-value data included in the structured data as data on an item corresponding to a key, wherein the key-value data is recorded in a second system different from the first system as taught by Hill. One of ordinary skill in the art before the effective filing date of the claimed invention would have been motivated to modify Rosales with Hill with the motivation providing data in a machine-readable, structured format (See Background of Hill in Paragraph [0004]).
Claim(s) 11 mirrors claim 1 only within (a) different statutory category/categories, and is rejected for the same reason as claim 1.
Claim(s) 12 mirrors claim 1 only within (a) different statutory category/categories, and is rejected for the same reason as claim 1.
As per claim 2, Rosales/Hill discloses the method of claim 1 as described above. Rosales may not explicitly teach wherein in the acquiring step, the area is specified by being photographed by the user with a camera, and the clinical trial data is acquired by performing optical character recognition (OCR) on the area.
Hill further teaches wherein in the acquiring step, the area is specified by being photographed by the user with a camera (See [0183]: The system may also include a camera for obtaining image data), and the clinical trial data is acquired by performing optical character recognition (OCR) on the area (See [0145]: Executing a key value pairing may include identifying the keys and pairs and matching them to each other, and using an OCR model (such as Tesseract OCR) to sequence inputs, and an image processing library, which the Examiner is interpreting using an OCR model (such as Tesseract OCR) to sequence inputs to encompass the clinical trial data is acquired by performing optical character recognition (OCR) on the area.)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed to modify the method of Rosales to include in the acquiring step, the area is specified by being photographed by the user with a camera, and the clinical trial data is acquired by performing optical character recognition (OCR) on the area as taught by Hill. One of ordinary skill in the art before the effective filing date of the claimed invention would have been motivated to modify Rosales with Hill with the motivation providing data in a machine-readable, structured format (See Background of Hill in Paragraph [0004]).
Claim(s) 13 mirrors claim 2 only within (a) different statutory category/categories, and is rejected for the same reason as claim 2.
As per claim 4, Rosales/Hill discloses the method of claim 1 as described above. Rosales may not explicitly teach wherein in the acquiring step, the area is specified by being designated by the user.
Hill teaches a method wherein in the acquiring step, the area is specified by being designated by the user (See [0097], [0145]: The inputs their protocol registration number into the system and it automatically acquires the unstructured data from the relevant public registry, which the Examiner is interpreting the user selection ([0097]) to encompass the area is specified by being designated by the user.)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed to modify the method of Rosales to include in the acquiring step, the area is specified by being designated by the user as taught by Hill. One of ordinary skill in the art before the effective filing date of the claimed invention would have been motivated to modify Rosales with Hill with the motivation providing data in a machine-readable, structured format (See Background of Hill in Paragraph [0004]).
Claim(s) 15 mirrors claim 4 only within (a) different statutory category/categories, and is rejected for the same reason as claim 4.
As per claim 5, Rosales/Hill discloses the method of claims 1 and 4 as described above. Rosales may not explicitly teach wherein in the acquiring step, the clinical trial data is acquired by performing OCR on the area.
Hill teaches a method wherein in the acquiring step, the clinical trial data is acquired by performing OCR on the area (See [0097], [0145]: The inputs their protocol registration number into the system and it automatically acquires the unstructured data from the relevant public registry, which the Examiner is interpreting the unstructured data to encompass clinical data, and the OCR model to encompass performing OCR on the area.)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed to modify the method of Rosales to include in the acquiring step, the clinical trial data is acquired by performing OCR on the area as taught by Hill. One of ordinary skill in the art before the effective filing date of the claimed invention would have been motivated to modify Rosales with Hill with the motivation providing data in a machine-readable, structured format (See Background of Hill in Paragraph [0004]).
Claim(s) 16 mirrors claim 5 only within (a) different statutory category/categories, and is rejected for the same reason as claim 5.
As per claim 9, Rosales/Hill discloses the method of claim 1 as described above. Rosales may not explicitly teach wherein the recording step comprises the step of extracting the key-value data from the structured data, and the key-value data includes one key and at least one value.
Hill teaches a method wherein the recording step comprises the step of extracting the key-value data (See [0145]: Executing a key value pairing may include identifying the keys and pairs and matching them to each other, which the Examiner is interpreting identifying to encompass extracting) from the structured data, and the key-value data includes one key and at least one value (See [0145]: A key-value pair consists of two related data elements: A key, which is a constant that defines the data set and a value, which is a variable that belongs to the set, which the Examiner is interpreting a key and a value to encompass the key-value data includes one key and at least one value.)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed to modify the method of Rosales to include the recording step comprises the step of extracting the key-value data from the structured data, and the key-value data includes one key and at least one value as taught by Hill. One of ordinary skill in the art before the effective filing date of the claimed invention would have been motivated to modify Rosales with Hill with the motivation providing data in a machine-readable, structured format (See Background of Hill in Paragraph [0004]).
Claim(s) 20 mirrors claim 9 only within (a) different statutory category/categories, and is rejected for the same reason as claim 9.
As per claim 10, Rosales/Hill discloses the method of claim 1 as described above. Rosales may not explicitly teach wherein in the recording step, the key-value data is recorded as the data on the corresponding item by mapping the key and the corresponding item.
Hill teaches a method wherein in the recording step, the key-value data is recorded as the data on the corresponding item by mapping the key and the corresponding item (See [0145]: A key-value pair consists of two related data elements: A key, which is a constant that defines the data set and a value, which is a variable that belongs to the set, and executing a key value pairing may include identifying the keys and pairs and matching them to each other, which the Examiner is interpreting the matching to encompass mapping.)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed to modify the method of Rosales to include the recording step, the key-value data is recorded as the data on the corresponding item by mapping the key and the corresponding item as taught by Hill. One of ordinary skill in the art before the effective filing date of the claimed invention would have been motivated to modify Rosales with Hill with the motivation providing data in a machine-readable, structured format (See Background of Hill in Paragraph [0004]).
Claim(s) 21 mirrors claim 10 only within (a) different statutory category/categories, and is rejected for the same reason as claim 10.
Claims 3, 14 are rejected under 35 U.S.C. 103 as being unpatentable over Rosales (U.S. Patent Pre-Grant Publication No. 2009/0299977) in view of Hill et al. (U.S. Patent Pre-Grant Publication No. 2025/0157598) in further view of Miyamoto (U.S. Patent Pre-Grant Publication No. 2018/0213147).
As per claim 3, Rosales/Hill discloses the method of claims 1-2 as described above. Rosales/Hill may not explicitly teach wherein in the acquiring step, a camera guide for the OCR is provided to the user.
Miyamoto teaches a method wherein in the acquiring step, a camera guide for the OCR is provided to the user (See [0031]: The display control module displays an operation guide to capture an image suitable to OCR on the live view image being displayed in an overlapping manner, which the Examiner is interpreting an operation guide to encompass a camera guide.)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed to modify the method of Rosales/Hill to include the acquiring step, a camera guide for the OCR is provided to the user as taught by Miyamoto. One of ordinary skill in the art before the effective filing date of the claimed invention would have been motivated to modify Rosales/Hill with Miyamoto with the motivation of easily perform image capturing with an image quality suitable to OCR processing (See Background of the Invention of Miyamoto in Paragraph [0005]).
Claim(s) 14 mirrors claim 3 only within (a) different statutory category/categories, and is rejected for the same reason as claim 3.
Response to Arguments
In the Remarks filed on May 26, 2026, the Applicant argues that the newly amended and/or added claims overcome the 35 U.S.C. 101 rejection(s) and 35 U.S.C. 103 rejection(s). The Examiner does not acknowledge that the newly added and/or amended claims overcome the 35 U.S.C. 101 rejection(s) and 35 U.S.C. 103 rejection(s).
The Applicant argues that:
(1) the user provides some input to the system, the user does not perform the process. Instead, the one or more processors and the generative artificial intelligence model perform the data conversion and storage to the second system. (Step 2A, Prong One: NO). Applicant(s) respectfully submit(s) that the claims recite additional elements that integrate the alleged judicial exception into a practical application because the recited claims solve the problems stated in the specification. More specifically, clinical data is acquired from original data recorded in a first system, structured data is generated using the clinical data by inputting the clinical trial data to a generative artificial intelligence (AI) model that is fine-tuned using a prompt designed to distinguish rows on the basis of patterns in the one-dimensional list structure, and key-value data from the structured data is recorded in a second system different than the first system. Moreover, the various types of data have specific characteristics, namely, (1) the original data is in a tabular format, (2) the clinical trial data is unstructured data having different structural features from the original data, (3) the clinical trial data is in a one-dimensional list structure devoid of a row and column structure of the original data, (4) the clinical trial data is converted into a tabular format by distinguishing rows on the basis of patterns in the clinical trial data, and (5) the structured data is generated by filling predetermined values in where data is absent in the clinical trial data converted into the tabular format. Applicant(s) respectfully submit(s) that the foregoing elements integrate the judicial exception into a practical application to solve the problems stated in the specification. As a result, applicant(s) respectfully submit(s) that claims 1 and 11-12, as a whole, integrate any alleged method of organizing human activity into a practical application, and are, therefore, eligible because they are not directed to the alleged judicial exception (abstract idea). (Step 2A-Prong 2: YES);
(2) Applicant(s) respectfully submit(s) that the claims recite additional elements that are sufficient to amount to significantly more that the alleged judicial exception. More specifically, clinical data is acquired from original data recorded in a first system, structured data is generated using the clinical data by inputting the clinical trial data to a generative artificial intelligence (AI) model that is fine-tuned using a prompt designed to distinguish rows on the basis of patterns in the one-dimensional list structure, and key-value data from the structured data is recorded in a second system different than the first system. Moreover, the various types of data have specific characteristics, namely, (1) the original data is in a tabular format, (2) the clinical trial data is unstructured data having different structural features from the original data, (3) the clinical trial data is in a one-dimensional list structure devoid of a row and column structure of the original data, (4) the clinical trial data is converted into a tabular format by distinguishing rows on the basis of patterns in the clinical trial data, and (5) the structured data is generated by filling predetermined values in where data is absent in the clinical trial data converted into the tabular format. Applicant(s) respectfully submit(s) that the foregoing elements are sufficient to amount to significantly more that the alleged judicial exception. As a result, applicant(s) respectfully submit(s) that claims 1 and 11-12, as a whole, recite additional elements that amount to significantly more than the alleged judicial exception, and are, therefore, eligible. (Step 2B: YES). For at least the foregoing reasons, applicant(s) respectfully submit(s) that claims 1 and 11-12 recite eligible subject matter. Claims 1 and 11-12 are, therefore, allowable under 35 U.S.C. § 101. Reconsideration and withdrawal of the rejections are respectfully requested. In addition, applicant(s) respectfully submit(s) that claims 2-5, 9-10, 13-16 and 20-21 are allowable under 35 U.S.C. § 101 because they depend directly or indirectly from claims 1 and 12, which are allowable for the reasons stated above. Reconsideration and withdrawal of the rejections are respectfully requested;
(3) Office Action stated that Rosales discloses ''generating structured data using the clinical trial data (See Fig. 2 and [0034], [0050]: An association between particular matched data patterns in context and whether there is a true association, false association, or no association at all for a particular medical finding, here illustrated for the medical finding VI (although it is to be understood that where such a table is generated, may be generated for all medical findings), which the Examiner is interpreting the output displayed in Fig. 2 to encompass generating structured data)." (page 10). Rosales states that ''The classifier may be trained using a machine learning technique." (paragraph [0015]). Applicant(s) respectfully submit(s) that Rosales' machine learning taught classifier is not the same as a generative artificial intelligence model. As a result, Rosales does not disclose, teach or suggest using a generative artificial intelligence model as recited in claims 1 and 11-12. Moreover, applicant(s) respectfully submit(s) Rosales does not disclose, teach or suggest that the generative artificial intelligence model is ''fine-tuned using a prompt designed to distinguish rows on the basis of patterns in the one-dimensional list structure'' as recited in claims 1 and 11-12. Applicant(s) respectfully submit(s) that Hill does not cure the stated deficiencies of Rosales. For at least the foregoing reasons, applicant(s) respectfully submit(s) that Rosales and Hill, either alone or in combination, do not disclose, teach or suggest each and every element of claims 1 and 11-12. Claims 1 and 11-12 are, therefore, allowable under 35 U.S.C. § 103. Reconsideration and withdrawal of the rejections are respectfully requested. Applicant(s) respectfully submit(s) that the deficiencies detailed above in reference to cited references are sufficient to traverse the rejections. As a result, any additional distinctions between the claims and the cited references are unnecessary and applicant(s) reserve(s) the right to present additional arguments against the statements and characterizations stated in the Office Action, and the cited reference in the event the rejections are not withdrawn;
(4) Applicant(s) respectfully submit(s) that claims 2-5, 9-10, 13-16 and 20-21 are allowable under 35 U.S.C. § 103 because they depend directly or indirectly from claims I and 12, which are allowable for the reasons stated above. Reconsideration and withdrawal of the rejections are respectfully requested.
In response to argument (1), the Examiner does not find the Applicant’s argument(s) persuasive. The Examiner maintains that the Applicant’s newly amended independent claims are directed to an abstract idea without significantly more. The “one or more processors” are recited at a high-level of generality that amounts to “merely including instructions to implement an abstract idea on a computer” and using a computer as a tool, and the “generative artificial intelligence model perform the data conversion and storage to the second system” is recited at a high-level of generality that amounts to no more than generally linking the abstract idea to a particular technical environment and adding the words “apply it” to the abstract idea. The Examiner maintains that the Applicant’s newly amended claims are similar to “iii. Mere automation of manual processes, such as using a generic computer to process an application for financing a purchase, Credit Acceptance Corp. v. Westlake Services, 859 F.3d 1044, 1055, 123 USPQ2d 1100, 1108-09 (Fed. Cir. 2017)” and that the courts have indicated may not be sufficient to show an improvement in computer-functionality (See MPEP2106.05(a)(I)). The 35 U.S.C. 101 rejection(s) stand.
In response to argument (2), the Examiner does not find the Applicant’s argument(s) persuasive. The Examiner maintains that the claimed limitations, as drafted given the broadest reasonable interpretation, but for the recitation of generic computer components, encompass managing interactions between people, including following rules or instructions, which is a subgrouping of Certain Methods of Organizing Human Activity. For example, but for the “by the one or more processors,” language, the claim encompasses a user following instructions to specify an area and acquire clinical trial data from that specified area, a user following instructions to determine structured data using the clinical trial data, and a user following instructions to record key-value data included in the structured data. These steps could be accomplished by a user following rules or instructions. The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements of using a computer configured to perform above identified functions amounts to no more than mere instructions to apply the exception using generic computer components. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. See Alice 573 U.S. at 223 (“mere recitation of a generic computer cannot transform a patent-ineligible abstract idea into a patent-eligible invention.”) Insignificant, extra solution, data gathering activity has been found to not amount to significantly more than an abstract idea (See MPEP 2106.05(g)). Therefore, whether considered alone or in combination, the additional elements do not amount to significantly more than the abstract idea. Additionally, generally linking the abstract idea to a particular technological environment does not amount to significantly more than the abstract idea (See MPEP 2016.05(h) and Affinity Labs of Texas v. DirectTV, LLC, 838 F.3d 1253, 120 USP12d 1201 (Fed. Cir. 2016)). The 35 U.S.C. 101 rejection(s) stand.
In response to argument (3), the Examiner does not find the Applicant’s argument(s) persuasive. The Examiner maintains that Rosales teaches the newly amended claimed portion of “by the one or more processors, generating structured data using the clinical trial data by inputting the clinical trial data to a generative artificial intelligence (Al) model that is fine-tuned using a prompt designed to distinguish rows on the basis of patterns in the one-dimensional list structure, so that the clinical trial data is converted into a tabular format by distinguishing rows on the basis of patterns in the clinical trial data, and the structured data is generated by filling predetermined values in where data is absent in the clinical trial data converted into the tabular format” as the Applicant’s recitation of a “generative AI model” given the broadest reasonable interpretation recites a computer process that utilizes pattern recognition to identify rows. The Examiner maintains that the disclosure of Rosales in [0041], [0052]-[0054]: Automatic labeling of subsequent unstructured data, computer learning techniques may be used to train classifier(s) for the automatic detection of medical findings from subsequent unstructured data, to this end, the association between patterns in context with particular medical findings may be used as labels for building a machine learning classifier, which the Examiner is interpreting the machine learning classifier to encompass a generative artificial intelligence (Al) model that is fine-tuned using a prompt designed to distinguish rows on the basis of patterns in the one-dimensional list structure as the training of the classifier(s) (Step S17) may be performed using the generated training data, using sophisticated computer learning algorithms, or more simply, by ascertaining simple relationships between contexts and medical findings. The 35 U.S.C. 103 rejection(s) stand.
In response to argument (4), the Examiner does not find the Applicant’s argument(s) persuasive. The dependent claims 2-5, 9-10, 13-16 and 20-21 are rejected individually and due to their dependence on the rejected independent claims as recited above in the 35 U.S.C. 103 rejection(s). The 35 U.S.C. 103 rejection(s) stand.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Ephrat et al. (U.S. Patent Pre-Grant Publication No. 2013/0054512), describes a contextual analysis system that extracts data elements from an unstructured text input; determines whether the extracted data elements are relevant to a predetermined context; and determines, for the extracted elements deemed as relevant, whether the information contained in the relevant data elements complies with a guideline.
Harding et al. (U.S. Patent Publication No. 10,468,126), describes a clinical activity network that uniformly represents actions and data updates occurring in a source healthcare system.
Ahmed et al. (“Artificial intelligence with multi-functional machine learning platform development for better healthcare and precision medicine”), describes implementation of artificial intelligence in healthcare is a compelling vision that has the potential in leading to the significant improvements for achieving the goals of providing real-time, better personalized and population medicine at lower costs.
Nothaft et al. (U.S. Patent Pre-Grant Publication No. 2023/0187074), describes a system and method for analyzing a data store of de-identified patient data to generate one or more dynamic user interfaces usable to predict an expected response of a particular patient population or cohort when provided with a certain treatment.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Bennett S Erickson whose telephone number is (571)270-3690. The examiner can normally be reached Monday - Friday: 9:00am - 5:00pm.
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, Robert Morgan can be reached at (571) 272-6773. 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.
/Bennett Stephen Erickson/Primary Examiner, Art Unit 3683
