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 .
Status of Claims
This action is in reply to the application filed on 6/20/24.
Claims 1-20 are currently pending and have been examined.
IDS
The information disclosure statement (IDS) submitted on 06/20/24 have been considered by the examiner. The submission is in compliance with the provisions of 37 CFR 1.97.
Foreign Priority/Priority Date
Acknowledgment is made of Applicant's claim for foreign priority based on application KR10-2021-0184190, filed in Korea on 12/21/21. A certified copy of priority document has been received on 06/20/24. Accordingly, a priority date of 12/21/21 has been given to this application.
Examiner Request
The Applicant is requested to indicate where in the specification there is support for amendments to claims should Applicant amend. The purpose of this is to reduce potential 35 USC 112(a) issues that can arise when claims are amended without adequate support in the specification. The Examiner thanks the Applicant in advance.
Claim Objections
Claims 3, 4, 10, 11, 17, 18 are objected to because of the following informalities: These claims recite the limitation “the standard item data”. Examiner is interpreting these recitations to refer to “preset standard item data” in respective parent claims 2, 9, 16, and has not given a 112(b) antecedent basis rejection at this time; however, it is recommended that Applicant amend the dependent claims to recite “the preset standard item data” for improved clarity and coherence throughout the claims. Appropriate correction is required.
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 1-20 are rejected under 35 U.S.C.101 because the claimed invention is directed to a judicial exception (an abstract idea) without significantly more.
Step 1
Claims 1-7 are drawn to a method, Claims 8-14 are drawn to a device, and Claims 15-20 are drawn to a non-transitory computer readable storage medium, each of which are within the four statutory categories. Claims 1-20 are further directed to an abstract idea on the grounds set out in detail below.
Step 2A Prong 1
Claim 1 recites implementing the steps of:
receiving image data of a medical record of a patient;
converting the image data of the medical record into text data;
extracting item data from the text data;
evaluating reliability of the extracted item data; and
providing patient information regarding the extracted item data if the evaluated reliability is equal to or greater than a pre-determined threshold value.
These steps amount to managing personal behavior or relationships or interactions
between people and therefore recite certain methods of organizing human activity. Determining the reliability of extracted item data from text data that has been converted from image data and providing patient information regarding the extracted item data when the reliability meets or exceeds a predetermined threshold is a personal behavior that may be performed by a healthcare provider.
Independent claims 8, 15 recite similar limitations and also recites an abstract idea under the same analysis.
The above claims are therefore directed to an abstract idea.
Step 2A Prong 2
Claim 1 does not recite any additional elements; therefore, no additional elements are present to integrate the judicial exception into a practical application.
Regarding Claims 8 and 15, this judicial exception is not integrated into a practical application because the additional elements within the claims only amount to:
A. Instructions to Implement the Judicial Exception. MPEP 2106.05(f)
The independent claims additionally recite:
a device for providing patient information, the device comprising: a memory configured to store one or more instructions; and a processor configured to execute the one or more instructions stored in the memory, as implementing the steps of the abstract idea (Claim 8)
a non-transitory computer readable storage medium storing computer executable instructions, wherein the instructions, when executed by a processor as implementing the steps of the abstract idea (Claim 15)
The broad recitation of general purpose computing elements at a high level of generality only amounts to mere instructions to implement the abstract idea using computing components as tools.
Regarding the device for providing patient information, the device comprising: a memory configured to store one or more instructions; and a processor configured to execute the one or more instructions stored in the memory of Claim 8: these elements are all understood to be general purpose computing components functioning in their ordinary capacities ([0047], “The memory 210 may include, but is not limited to, magnetic storage media or flash storage media”; [0053], “The processor 250 may include any type of device capable of processing data. Here, “processor” may mean, for example, a data processing device built into hardware that has a physically structured circuit in order to perform a function represented by code or instructions included in a program. Examples of data processing devices built into hardware include a microprocessor, a central processing unit (CPU), a processor core, a multiprocessor, an application-specific integrated circuit (ASIC), and a field programmable gate array (FPGA), but are not limited thereto”).
Regarding the non-transitory computer readable storage medium storing computer executable instructions, wherein the instructions, when executed by a processor of Claim 15, no specifics are provided other than to reiterate the claim language (see at least paras. [0021], [0022], [0079]) and as such, this element is given its broadest reasonable interpretation as a general purpose computing element functioning in its ordinary capacity to implement the steps of the abstract idea.
These elements are therefore not sufficient to integrate the abstract idea into a practical application. Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually.
The above claims, as a whole, are therefore directed to an abstract idea.
Step 2B
The present claims do not include additional elements that are sufficient to amount to
more than the abstract idea because the additional elements or combination of elements amount to no more than a recitation of:
A. Instructions to Implement the Judicial Exception. MPEP 2106.05(f)
As explained above, claims 8 and 15 only recite the aforementioned computing elements as tools for performing the steps of the abstract idea, and mere instructions to perform the abstract idea using a computer is not sufficient to amount to significantly more than the abstract idea. MPEP 2106.05(f). (Claim 1 does not recite any additional elements, and therefore, does not recite additional elements that are sufficient to amount to significantly more than the abstract idea).
Thus, taken alone, the additional elements do not amount to significantly more than the
above-identified judicial exception. Looking at the limitations as an ordered combination adds
nothing that is not already present when looking at the elements taken individually. Their
collective functions merely provide conventional computer implementation.
Dependent Claims
Dependent claims recite additional subject matter which further narrows or defines the abstract idea embodied in the claims. For example, Claims 4-7, 12-14, 18-20 recite limitations which further narrow the scope of the independent claims. Claims 2, 3, 9-11, 16, 17 further recites limitations that are certain methods of organizing human activity as described below.
Claim 2 recites limitations pertaining to wherein the evaluating reliability of the item data includes: comparing the extracted item data with preset standard item data; and evaluating the reliability of the item data by determining whether essential items of the preset standard item data are included in the extracted item data, which is also certain methods of organizing human activity including managing personal behaviors, as performing a data comparison and evaluating reliability by determining whether essential items are included in extracted data are personal behaviors that may be performed by healthcare personnel.
Claim 3 recites limitations pertaining to re-extracting item data using a pre-trained machine learning model if the essential items of the standard item data are not included in the extracted item data, which amounts to certain methods of organizing human activity but for recitation of “pre-trained machine learning model”, as re-extracting data if essential items of the standard items are not included in the extracted data is a personal behavior that may be performed by a healthcare provider. No particulars of the pre-trained machine learning model are provided (see paras. [0009], [0016], [0079]); therefore, this element is understood to amount to mere instructions to apply the abstract idea on a computer. MPEP 2106.05(f). This is not sufficient to integrate the judicial exception into a practical application or amount to significantly more than the judicial exception.
Claim 9 recites limitations pertaining to compare the extracted item data with preset standard item data, and evaluate the reliability of the item data by determining whether essential items of the preset standard item data are included in the extracted item data, which is also certain methods of organizing human activity including managing personal behaviors, as performing a data comparison and evaluating reliability by determining whether essential items are included in extracted data are personal behaviors that may be performed by healthcare personnel. Claim 9 also recites “the processor” as implementing the steps of the abstract idea. As discussed above with respect to the independent claims, recitation of a processor only amounts to mere instructions to apply the abstract idea. MPEP 2106.05(f). This is not sufficient to integrate the judicial exception into a practical application or amount to significantly more than the judicial exception.
Claim 10 recites limitations pertaining to re-extract item data using a pre-trained machine learning model if the essential items of the standard item data are not included in the extracted item data, which amounts to certain methods of organizing human activity but for recitation of “pre-trained machine learning model”, as re-extracting data if essential items of the standard items are not included in the extracted data is a personal behavior that may be performed by a healthcare provider. No particulars of the pre-trained machine learning model are provided (see paras. [0009], [0016], [0079]); therefore, this element is understood to amount to mere instructions to apply the abstract idea on a computer. MPEP 2106.05(f). This is not sufficient to integrate the judicial exception into a practical application or amount to significantly more than the judicial exception. Claim 10 also recites “the processor” as implementing the steps of the abstract idea. As discussed above with respect to the independent claims, recitation of a processor only amounts to mere instructions to apply the abstract idea. MPEP 2106.05(f). This is not sufficient to integrate the judicial exception into a practical application or amount to significantly more than the judicial exception.
Claim 11 recites limitations pertaining compare the extracted item data with standard item data using at least one of a rule-based model, a Burt model, a decision tree model, and a neural network model, which is also certain methods of organizing human activity including managing personal behaviors, as performing a data comparison using a rule-based model or decision tree model are personal behaviors that may be performed by healthcare personnel. Claim 11 also recites “the processor” as implementing the steps of the abstract idea. As discussed above with respect to the independent claims, recitation of a processor only amounts to mere instructions to apply the abstract idea. MPEP 2106.05(f). This is not sufficient to integrate the judicial exception into a practical application or amount to significantly more than the judicial exception.
Claim 17 recites limitations pertaining to re-extracting item data using a pre-trained machine learning model if the essential items of the standard item data are not included in the extracted item data, which amounts to certain methods of organizing human activity but for recitation of “pre-trained machine learning model”, as re-extracting data if essential items of the standard items are not included in the extracted data is a personal behavior that may be performed by a healthcare provider. No particulars of the pre-trained machine learning model are provided (see paras. [0009], [0016], [0079]); therefore, this element is understood to amount to mere instructions to apply the abstract idea on a computer. MPEP 2106.05(f). This is not sufficient to integrate the judicial exception into a practical application or amount to significantly more than the judicial exception.
The dependent claims have been given the full two-part analysis including analyzing the additional limitations both individually and in combination. The dependent claims, when analyzed individually, and in combination, are also held to be patent ineligible under 35 U.S.C. 101 as they include all of the limitations of their respective parent claim. respectively. The additional recited limitations of the dependent claims fail to establish that the claims do not recite an abstract idea because the additional recited limitations of the dependent claims merely further narrow the abstract idea. Beyond the limitations which recite the abstract idea, the claims recite additional elements consistent with those identified above with respect to the independent claims which encompass adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f). Accordingly, these additional elements do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
Dependent claims 2-7, 9-14, 16-20 recite additional subject matter which amounts to additional elements consistent with those identified in the analysis of the independent claims above. As discussed above with respect to the independent claims and integration of the abstract idea into a practical application, recitation of these additional elements (e.g., a processor) only amounts to invoking computers as a tool to perform the abstract idea. 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.
Dependent claims 2-7, 9-14, 16-20, when analyzed as a whole, are held to be patent ineligible under 35 U.S.C. 101 because the additional recited limitation(s) fail(s) to establish that the claim(s) is/are not directed to an abstract idea without significantly more. These claims fail to remedy the deficiencies of their parent claims above, and are therefore rejected for at least the same rationale as applied to their parent claims above, and incorporated herein.
For the reasons stated, Claims 1-20 fail the Subject Matter Eligibility Test and are consequently rejected under 35 U.S.C. 101.
Claim Rejections - 35 USC § 102
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 the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claim(s) 1-2, 4-9, 11-16, 18-20 is/are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Lucas et. al. (US Publication 20210151192A1).
Regarding Claim 1, Lucas discloses
receiving image data of a medical record of a patient ([0009] teaches on capturing, with a mobile device, a next generation sequencing (NGS) report comprising NGS medical information about a sequenced patient; [0050]-[0051] teaches on the specifics of the image capture process; an algorithm is applied to scan image data of a physical copy of a document using the device’s camera);
converting the image data of the medical record into text data ([0053] teaches on submitting the scanned document for optical character recognition (OCR) to convert the document text into machine-readable format);
extracting item data from the text data ([0056] teaches on using a predefined model for processing extracted data; [0061] teaches on identifying a document region to extract, identifying text from the extracted region and providing it to an NLP algorithm to extract patient information such as name, diagnosis, notable genetic mutations, etc.; [0070] teaches on extracting text immediately following/preceding the identified word “Mutation”; the system can identify mutations represented by alphanumeric phrases such as KRAS, BRAF, PIK3CA; [0070] teaches on an example of extracting the mutation “RRAS”);
evaluating reliability of the extracted item data ([0056] teaches on the extraction guidelines including “reliability checks” to ensure the information is correct, e.g., a diagnosis date may not occur before the patient’s birth date; [0070] teaches on identifying alphanumeric phrases extracted from text such as KRAS, BRAF, PIK3CA which represent mutations; they are compared against a database/dictionary of known mutations to find one or more matches; if the system reads mutation “KRAS” as “RRAS”, it would not find the latter in the mutation database/dictionary; the system checks corresponding variant Exon 2 100C->A to determine that the mutation corresponding to that variant is “KRAS” – interpreted as “evaluating reliability of extracted data item” in which RRAS is extracted data item; Examiner interprets the system determining RRAS is not a known mutation and performing check for corresponding variant to read on “evaluating reliability” of extracted item RRAS; the system performs a comparison, e.g., a comparability score, to determine likelihood of a match – further “evaluating reliability” of the extracted item; [0084] further teaches on OCR post-processing for a section that should include particular data (“Name” or “DOB”); “in an example, a first region of interest may provide a series of gene variants while a second region of interest may provide the expression level/status of those gene variants. In this example, there are a known number of genes, each having a plurality of possible variants, and a query to a molecular pathology service may be initiated to validate whether a recognized gene and variant combination is valid/known or if the combination is actually an unrecognized variant, an OCR introduced error, or if the unknown combination originated from the document” – “validating” step is interpreted as evaluating the reliability of the extracted gene/variant data); and
providing patient information regarding the extracted item data if the evaluated reliability is equal to or greater than a pre-determined threshold value ([0070] teaches on the system determining that extracted mutation RRAS is not found; the system performs a comparison, e.g., a comparability score, to determine likelihood of a match; if the match likelihood is above a certain threshold, the extracted term should be modified to the matching term and will replace the term accordingly – replacing the term RRAS with KRAS is interpreted as “providing patient information” regarding “the extracted item data” (correcting RRAS to KRAS) when the evaluated reliability of KRAS rather than RRAS is above a certain threshold; the corrected “KRAS” mutation is interpreted as the patient data provided).
Regarding Claim 2, Lucas discloses the limitations of claim 1. Lucas further discloses wherein the evaluating reliability of the item data includes: comparing the extracted item data with preset standard item data ([0056] teaches on features derived from a document; each feature has a list of expected key health information types, e.g. a header may expect a patient name, date of birth, report date, institution name, diagnosis, etc., which are interpreted as “preset standard item data”; [0061] teaches on a model indicating that a page should expect to present a patient header, two tables, a chart and a graph (“preset standard item data’); a region mask may be applied to the capture to verify that any regions expected to be present are actually within the capture – comparing the extracted item data with preset standard item data; [0070] teaches on identifying alphanumeric phrases extracted from text such as KRAS, BRAF, PIK3CA which represent mutations; they are compared against a database/dictionary of known mutations (“preset standard item data”) to find one or more matches; if the system reads mutation “KRAS” as “RRAS”, it would not find the latter (RRAS) in the mutation database/dictionary); and evaluating the reliability of the item data by determining whether essential items of the preset standard item data are included in the extracted item data ([0061] teaches on using a “region mask” to verify that expected regions (preset standard data items) are present; upon verifying that a region is present, the region may be extracted – verifying that an expected region is present and subsequently performing extraction is interpreted as determining whether essential items, e.g., the expected regions, are included; [0070] teaches on identifying alphanumeric phrases extracted from text such as KRAS, BRAF, PIK3CA which represent mutations; they are compared against a database/dictionary of known mutations to find one or more matches; if the system reads mutation “KRAS” as “RRAS”, it would not find the latter in the mutation database/dictionary; the system checks corresponding variant Exon 2 100C->A to determine that the mutation corresponding to that variant is “KRAS”; the system may rely on various comparators, such as the number of characters in each term, the number of matching characters in each term (such as the second character in each term is an “R,” the third character in each term is an “A,” and the fourth character in each term is an “S”), a comparability score between the non-identical characters (such as “K” and “R” may be given a higher comparability score than “K” and “Q,” as the former set of characters more closely resemble one another than the latter – interpreted as “evaluating reliability of item data” by determining whether essential items of preset item are present (e.g., K vs. R in RRAS).
Regarding Claim 4, Lucas discloses the limitations of claim 2. Lucas further discloses wherein the comparing the extracted item data with standard item data is performed using at least one of a rule-based model, a Burt model, a decision tree model, and a neural network model ([0069] teaches on using an MLA (machine learning algorithm) or DLNN (deep learning neural network, interpreted as reading on “neural network model”) for processing sections of the NGS report).
Regarding Claim 5, Lucas discloses the limitations of claim 1. Lucas further discloses wherein the medical record includes at least one of a pathology report, a genetic examination report, a regional imaging examination report (CT and MRI), a nuclear medicine imaging examination report (PET/CT), and a prescription ([0009] teaches on capturing an image of a “next generation sequencing” (NGS) report comprising NGS medical information about a sequenced patient; [0059] teaches on NGS being a type of “genetic testing”; per [0066] the NGS report includes information about a patient including genes, mutations, variants or expression data; the foregoing are interpreted as reading on “genetic examination report”, which fulfills claim requirements per claim construction “at least one of”).
Regarding Claim 6, Lucas discloses the limitations of claim 1. Lucas further discloses wherein the item data includes at least one of the name, date of birth, age, sex, treatment history, biopsy results, genetic examination results, imaging examination results, prescription drug, and medicines of the patient ([0061] teaches on identifying a document region to extract, identifying text from the extracted region and providing it to an NLP algorithm to extract patient information such as name, diagnosis, notable genetic mutations, or gene expression count information – the latter are interpreted as “genetic examination results”; [0070] teaches on the system extracting alphanumeric phrases around the identified word “mutation”, e.g., BRAF, KRAS, PIK3CA – interpreted as item data including “genetic examination results”).
Regarding Claim 7, Lucas The method of claim 1. Lucas further discloses wherein the patient information includes at least one of patient’s diagnosis, stage, whether surgery is performed, surgery name, customized treatment information, treatment process, and information on clinical trials in which the patient is able to participate ([0070] teaches on the system detecting the patient has genetic mutation KRAS; a genetic mutation is interpreted as reading on the broadest reasonable interpretation of “patient’s diagnosis”).
Regarding Claim 8, Lucas discloses the limitations of Claim 1. Claim 8 recites the same or substantially similar limitations as Claim 1, and the discussion above with respect to Claim 1 is equally applicable to Claim 8. Claim 8 recites the additional limitations, which are also taught by Lucas: a device for providing patient information, the device comprising: a memory configured to store one or more instructions; and a processor configured to execute the one or more instructions stored in the memory, wherein the instructions, when executed by the processor, cause the processor to ([0043] teaches on a device such as a mobile device, tablet or personal computer for implementing the system of Lucas; the computing devices of Lucas are treated as synonymous to a device comprising a memory and processor for executing instructions, consistent with the instant specification in paras. [0046], [0047], [0052]-[0053]).
Regarding Claim 9, Lucas discloses the limitations of Claim 2. Claim 9 recites the same or substantially similar limitations as Claim 2, and the discussion above with respect to Claim 2 is equally applicable to Claim 9. Claim 9 is rejected for the same reasons as Claim 2.
Regarding Claim 11, Lucas discloses the limitations of Claim 4. Claim 11 recites the same or substantially similar limitations as Claim 4, and the discussion above with respect to Claim 4 is equally applicable to Claim 11. Claim 11 is rejected for the same reasons as Claim 4.
Regarding Claim 12, Lucas discloses the limitations of Claim 5. Claim 12 recites the same or substantially similar limitations as Claim 5, and the discussion above with respect to Claim 5 is equally applicable to Claim 12. Claim 12 is rejected for the same reasons as Claim 5.
Regarding Claim 13, Lucas discloses the limitations of Claim 6. Claim 13 recites the same or substantially similar limitations as Claim 6, and the discussion above with respect to Claim 6 is equally applicable to Claim 13. Claim 13 is rejected for the same reasons as Claim 6.
Regarding Claim 14, Lucas discloses the limitations of Claim 7. Claim 14 recites the same or substantially similar limitations as Claim 7, and the discussion above with respect to Claim 7 is equally applicable to Claim 14. Claim 14 is rejected for the same reasons as Claim 7.
Regarding Claim 15, Lucas discloses the limitations of Claim 1. Claim 15 recites the same or substantially similar limitations as Claim 1, and the discussion above with respect to Claim 1 is equally applicable to Claim 15. Claim 15 recites the additional limitations, which are also taught by Lucas: a non-transitory computer readable storage medium storing computer executable instructions, wherein the instructions, when executed by a processor, cause the processor to ([0043] teaches on a device such as a mobile device, tablet or personal computer for implementing the system of Lucas; the computing devices of Lucas are treated as synonymous to a device comprising a memory and processor for executing instructions, consistent with the instant specification in paras. [0046], [0047], [0052]-[0053]).
Regarding Claim 16, Lucas discloses the limitations of Claim 2. Claim 16 recites the same or substantially similar limitations as Claim 2, and the discussion above with respect to Claim 2 is equally applicable to Claim 16. Claim 16 is rejected for the same reasons as Claim 2.
Regarding Claim 18, Lucas discloses the limitations of Claim 4. Claim 18 recites the same or substantially similar limitations as Claim 4, and the discussion above with respect to Claim 4 is equally applicable to Claim 18. Claim 18 is rejected for the same reasons as Claim 4.
Regarding Claim 19, Lucas discloses the limitations of Claim 5. Claim 19 recites the same or substantially similar limitations as Claim 5, and the discussion above with respect to Claim 5 is equally applicable to Claim 19. Claim 19 is rejected for the same reasons as Claim 5.
Regarding Claim 20, Lucas discloses the limitations of Claim 6. Claim 20 recites the same or substantially similar limitations as Claim 6, and the discussion above with respect to Claim 6 is equally applicable to Claim 20. Claim 20 is rejected for the same reasons as Claim 6.
Claim Rejections - 35 USC § 103
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.
Claim(s) 3, 10, 17 is/are rejected under 35 U.S.C. 103 as being unpatentable over Lucas et. al. (US Publication 20210151192A1) as applied to Claims 1, 8 and 15 above, respectively, in view of Radakovic et. al. (US Publication 20110280481A1).
Regarding Claim 3, Lucas discloses the limitations of Claim 2. Lucas further discloses further comprises using a pre-trained machine learning model [to detect] if the essential items of the standard item data are not included in the extracted item data ([0070] teaches on determining that an extracted mutation is read as “RRAS” rather than “KRAS”; KRAS is interpreted to be standard item data as it is a known mutation included in a database of mutations; the MLA may rely on matching characters (second character is R, third character is A, fourth character is S; “K” is the essential item missing from the standard item data in order to match the mutation to known mutation “KRAS”; [0065] teaches on the MLA being trained with a training dataset; [0070] also teaches on the MLA being trained to identify “mutation” and subsequently text immediately following or preceding that word and compare them against a database)
Radakovic, which is directed to correction of errors in a textual document undergoing OCR, teaches: re-extracting item data [when an error is detected] ([0037] teaches on the system learning from an initial error and automatically re-applying a correction to the OCR process – re-applying is interpreted as “re-extracting”; the OCR engine may make classifications based on one or more features of the document; the classification process may be performed using a machine-learning based algorithm; [0021] teaches on an example of determining that an ‘8’ has been mischaracterized as ‘s’ and the system will attempt to correct similar instances – interpreted as re-extracting item data to correct an image of ‘s’ for ‘8’).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, to modify the teachings of Lucas with these teachings of Radakovic, to perform a re-extraction of item data when an error is detected, e.g., such as when essential items of standard item data are not included in extracted data, with the motivation of automatically correcting an error in an OCR document (Radakovic [0021]).
Regarding Claims 10 and 17, Lucas/Radakovic teach the limitations of Claim 3. Claims 10 and 17 recite the same or substantially similar limitations as Claim 3, and the discussion above with respect to Claim 3 is equally applicable to Claims 10 and 17. Claims 10 and 17 are rejected for the same reasons as Claim 3.
Conclusion
The following relevant prior art not cited is made of record:
US Patent 9576272B1, teaching on determining validity of a document image that has been converted to text using OCR
US Patent 11461816B1, teaching on validation of a healthcare provider bill using image data converted to text
US Patent 20200373003A1, teaching on an automatic medical scan triaging system
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ANNE-MARIE K ALDERSON whose telephone number is (571)272-3370. The examiner can normally be reached on Mon-Fri 9:00am-5:00pm EST and generally schedules interviews in the timeframe of 2:00-5:00pm EST.
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/ANNE-MARIE K ALDERSON/Primary Examiner, Art Unit 3682