Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
DETAILED ACTION
Claim 1 is canceled. Claims 2-21 are new and pending and are considered in this Non-Final Office action.
Continuation
This application is a continuation application of U.S. application no. 16/668,565 filed on 10/30/2019 and 17/181,519 filed on 02/22/2021 (“Parent Application”). See MPEP §201.07. In accordance with MPEP §609.02 A. 2 and MPEP §2001.06(b) (last paragraph), the Examiner has reviewed and considered the prior art cited in the Parent Application. Also, in accordance with MPEP §2001.06(b) (last paragraph), all documents cited or considered ‘of record’ in the Parent Application are now considered cited or ‘of record’ in this application. Additionally, Applicant(s) are reminded that a listing of the information cited or ‘of record’ in the Parent Application need not be resubmitted in this application unless Applicants desire the information to be printed on a patent issuing from this application. See MPEP §609.02 A. 2. Finally, Applicants are reminded that the prosecution history of the Parent Application is relevant in this application. See e.g., Microsoft Corp. v. Multi-Tech Sys., Inc., 357 F.3d 1340, 1350, 69 USPQ2d 1815, 1823 (Fed. Cir. 2004) (holding that statements made in prosecution of one patent are relevant to the scope of all sibling patents).
Information Disclosure Statement
The information disclosure statements (IDS) submitted on 9/26/2025 and 2/3/2026 are acknowledged. The submission is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner. The initialed and dated copy of Applicant’s IDS form 1449 is attached to the instant Office action.
Specification
The amendment made to the Specification 9/26/2025 is entered.
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/apply/applying-online/eterminal-disclaimer.
Claim 2-6, 8-13, 15-19 and 21 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-4, 6-11, 13, 15-18 and 20 of U.S. Patent No. 12,260,419. The subject matter claimed in the instant application is fully disclosed in the referenced Patent since the referenced Patent and the instant application are claiming common subject matter, as follows:
Claims of instant App.
Claims of Patent 12,260,419
2-3 (method), 6 (method), 9-10 (system), 13(system), 15-16 (CRM), 19(CRM)
1 (method), 8 (system), 15 (CRM)
4 (method), 11 (system), 17 (CRM)
6 (method), 13 (system), 20 (CRM)
5 (method), 12 (system), 18 (CRM)
2-4 (method), 9-11 (system), 16-18 (CRM)
8 (method), 21 (CRM)
7 (method), 14 (system)
The chart above maps claims containing similar subject matter as between the instant application and the noted U.S. Patent No. 12,260,419. It is clear that all the elements of claims 2-6, 8-13, 15-19 and 21 of the instant application are to be found in the claims noted above in U.S. Patent No. 12,260,419, and although the claims of the two applications are not identical, they are not patentably distinct from each other because the differences between the above-noted claims of U.S. Patent No. 12,260,419 and the corresponding claims of the instant application merely relate to non-functional details or obvious variants of substantially similar structural/functional limitations, which would have been deemed obvious by one skilled in the art.
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 therefore, subject to the
conditions and requirements of this title.
Claims 2-21 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-patentable subject matter. The claims are directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more. In accordance with Step 1, it is first noted that the claimed computer-implemented method in claims 2-8; the system in claim 9-14; and the claimed non-transitory computer-readable storage device in claim 15-21.
In accordance with Step 2A, Prong One, claims 2-21, the claimed invention recites an abstract idea. Specifically, the independent claim(s) recite(s) (abstract idea recited in italics and additional
elements recited in bold):
Claim 2:
A computer-implemented method, comprising: identifying, using one or more processors based on a sampled portion of a data file, a type of demographic information in the data file comprising one or more fields of mislabeled demographic information; generating a score indicating a probability that the type of the demographic information was identified correctly, wherein the score is adjusted from a baseline score for at least one of a plurality of fields of the demographic information; in response to the type of the demographic information being identified correctly, generating an updated data file labeling the one or more fields of the mislabeled demographic information based on the type of the demographic information; and inserting, based on the type of the demographic information, information into one or more missing fields of the demographic information in the updated data file.
Claim 9:
A system, comprising: a memory configured to store operations; and one or more processors configured to perform the operations, the operations comprising: identifying, based on a sampled portion of a data file, a type of demographic information in the data file comprising one or more fields of mislabeled demographic information; generating a score indicating a probability that the type of the demographic information was identified correctly, wherein the score is adjusted from a baseline score for at least one of a plurality of fields of the demographic information; in response to the type of the demographic information being identified correctly, generating an updated data file labeling the one or more fields of the mislabeled demographic information based on the type of the demographic information; and inserting, based on the type of the demographic information, information into one or more missing fields of the demographic information in the updated data file.
Claim 15:
A non-transitory computer-readable storage device having instructions stored thereon, execution of which, by one or more processors, causes the one or more processors to perform operations comprising: identifying, based on a sampled portion of a data file, a type of demographic information in the data file comprising one or more fields of mislabeled demographic information; generating a score indicating a probability that the type of the demographic information was identified correctly, wherein the score is adjusted from a baseline score for at least one of a plurality of fields of the demographic information; in response to the type of the demographic information being identified correctly, generating an updated data file labeling the one or more fields of the mislabeled demographic information based on the type of the demographic information; and inserting, based on the type of the demographic information, information into one or more missing fields of the demographic information in the updated data file.
The above-recited italicized limitations viewed as an abstract idea are mental processes (i.e., concepts performed in the human mind (including an observation, evaluation, judgment, opinion). Specifically, the recited claimed limitations set forth an arrangement observed information (i.e. demographic information) is evaluated to determine and revise mislabeled nomenclatures and incorrectly identified demographic
information.
According to Step 2A, Prong Two, this judicial exception is not integrated into a practical application
because the use of one or more processors, a system, comprising: a memory configured to store operations; and one or more processors, a non-transitory computer-readable storage device having instructions stored thereon, execution of which, by one or more processors for receiving/transmitting data; processing data (e.g., identifying, based on a sampled portion of a data file, a type of demographic information in the data file comprising one or more fields of mislabeled demographic information; generating a score indicating a probability that the type of the demographic information was identified correctly, wherein the score is adjusted from a baseline score for at least one of a plurality of fields of the demographic information; in response to the type of the demographic information being identified correctly, generating an updated data file labeling the one or more fields of the mislabeled demographic information based on the type of the demographic information; and inserting, based on the type of the demographic information, information into one or more missing fields of the demographic information in the updated data file; etc.); storing data; displaying data and repeating steps is merely implementing the abstract idea steps of valuing an idea in the manner of “apply it”. Using
one or more processors, a system, comprising: a memory configured to store operations; and one or more processors, a non-transitory computer-readable storage device having instructions stored thereon, execution of which, by one or more processors to receive and process
data resulting from this kind of evaluative analysis merely implements the abstract idea in the manner of “apply it” and constrains the abstract idea to a particular technological environment. The recited additional elements generating an updated data file labeling the one or more fields of the mislabeled demographic information based on the type of the demographic information; and inserting, based on the type of the demographic information, information into one or more missing fields of the demographic information in the updated data file are steps of generally linking the abstract idea to a computerized environment, such that the computer automates a manual process of updating a data file with identified missing information. The claim(s) does/do not include additional elements that are sufficient to practically apply the judicial exception because they, whether taken separately or as a whole, merely use conventional computer components or technology to receive, process, store and display data and thus do not provide an inventive concept in the claims.
In accordance with Step 2B, the claims only recite the above bolded additional elements. The additional
elements are recited at a high-level of generality (i.e., as a generic computer for evaluating data files for incorrectly identified demographic information to revise mislabeled nomenclatures) such that it amounts no more than mere instructions to apply the exception using a generic computer component. Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. Further, as evidence of generic computer implementation and an indication that the claimed invention does not amount to significantly more, it is first noted in ¶0050-0064 of Applicant’s Specification, “a computing device can include but are not limited to, a personal computer, a mobile device such as a mobile phone, workstation, embedded system, game console, television, set-top box, or any other computing device. Further, a computing device can include, but is not limited to, a device having a processor and memory, including a non-transitory memory, for executing and storing instructions. The memory may tangibly embody the data and program instructions in a non-transitory manner. Software may include one or more applications and an operating system. Hardware can include, but is not limited to, a processor, a memory, and a graphical user interface display. The computing device may also have multiple processors and multiple shared or separate memory components. For example, the computing device may be a part of or the entirety of a clustered or distributed computing environment or server farm. Various embodiments may be implemented, for example, using one or more well- known computer systems, such as computer system 800 shown in FIG. 8. One or more computer systems 800 may be used, for example, to implement any of the embodiments discussed herein, as well as combinations and sub-combinations thereof. Computer system 800 may include one or more processors (also called central processing units, or CPUs), such as a processor 804… Computer system 800 may also include a main or primary memory 808, such as random access memory (RAM). Main memory 808 may include one or more levels of cache. Main memory 808 may have stored therein control logic (i.e., computer software) and/or data… Computer system 800 may also be any of a personal digital assistant (PDA), desktop workstation, laptop or notebook computer, netbook, tablet, smart phone, smart watch or other wearable, appliance, part of the Internet-of-Things, and/or embedded system, to name a few non-limiting examples, or any combination thereof… In some embodiments, a tangible, non-transitory apparatus or article of manufacture comprising a tangible, non-transitory computer useable or readable medium having control logic (software) stored thereon may also be referred to herein as a computer program product or program storage device. This includes, but is not limited to, computer system 800, main memory 808, secondary memory 810, and removable storage units 818 and 822, as well as tangible articles of manufacture embodying any combination of the foregoing. Such control logic, when executed by one or more data processing devices (such as computer system 800), may cause such data processing devices to operate as described herein. Based on the teachings contained in this disclosure, it will be apparent to persons skilled in the relevant art(s) how to make and use embodiments of this disclosure using data processing devices, computer systems and/or computer architectures other than that shown in FIG. 8. In particular, embodiments can operate with software, hardware, and/or operating system embodiments other than those described herein. As evidence of conventional computer implementation, it is noted in the MPEP, the courts have recognized that “receiving or transmitting data over a network, e.g., using the Internet to gather data” (See buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (e.g. computer receives and sends data files containing demographic information) and “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” (e.g. updating data files according to evaluating demographic information) to be well‐understood, routine, and conventional functions when they are claimed in a merely generic manner (See MPEP 2106.05(d)). From the interpretation of the MPEP and the Specification, one would reasonably deduce that the additional elements are merely embodies generic computers and generic computing functions.
Dependent claims 3, 10, 16 recite the additional element, “training a machine learning model using a labeled training set curated from a data source to identify a heading based at least in part on structure and content of the data file with information describing a medical provider.” The recitation of training a machine learning model describes how the data files introduced in the independent claims are merely used to train a machine learning model. However, the claim fails to introduce how the machine learning model is implemented or applies to the invention in a manner that practically applies the judicial exception or amounts to significantly more. Additionally, Applicant’s Specification, ¶0032, recites “the machine learning model may be based on a plurality of machine learning algorithms to identify different types of demographic information. In some embodiments, the plurality of machine learning algorithms may be supervised machine learning algorithms including, but are not limited to, support vector machines, linear regression, logistic regression, naive Bayes, linear discriminant analysis, decision trees, k-nearest neighbor algorithm, neural networks, and similarity learning.” According to the Applicant’s Specification, any generic machine learning model may be trained in the above additional element. Therefore, it is clear that this limitation generally links the use of generic machine learning technology to the use of the judicial exception. These dependent claims do not remedy any deficiencies.
Dependent claims 4-5, 11-12, 17-18 recite limitations that further describe evaluative steps of the judicial exception. These claims further introduce analysis of data to identify types of demographic information. These claims elements that narrow the metes and bounds of the abstract idea but do not practically apply the abstract idea or provide ‘something more’. The dependent claims do not remedy any deficiencies.
Dependent claims 6, 13 and 19 recite limitations that further describe evaluative steps of the judicial exception. These claims further introduce scoring metrics for evaluating a match of demographic information. These claims elements that narrow the metes and bounds of the abstract idea but do not practically apply the abstract idea or provide ‘something more’. The dependent claims do not remedy any deficiencies.
Dependent claims 7-8, 14 and 20-21 recite limitations that further describe transmission of information, as a result of evaluative steps. Specifically, these claims recite a generation of an alert notification and an updated data file. The mere transmission of information presented in these dependent claims narrow the metes and bounds of the abstract idea but do not practically apply the abstract idea or provide ‘something more’. The dependent claims do not remedy any deficiencies.
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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claim(s) 2-21 are rejected under 35 U.S.C. 103 as being unpatentable over Wu et al. (United States Patent Application Publication, 2019/0287685, hereinafter referred to as Wu) in view of Jagota (United States Patent Application Publication, 2013/0297661) in further view of Akbulut et al. (US Patent Application Publication, 2016/0283350, hereinafter referred to as Akbulut).
As per Claim 2, Wu discloses a computer-implemented method, comprising:
identifying, using one or more processors based on a sampled portion of a data file, a type of demographic information in the data file comprising one or more fields of mislabeled demographic information (Wu: ¶0003, 0024, 0038-0040 and0049: Electronic medical records containing demographic information and a patient’s history of present illness (HPI’s) are collected and analyzed. An HPI classifier receives unprocessed medical records for preprocessing by a natural language processor. A tokenizer converts each word or group of words in the medical record into a token. The tokenizer can analyze the shape of words or phrases (i.e. metadata) using simple rules to develop a token (which represents a type of demographic information, such as name, location, date, etc.). The tokenizer can identify and tokenize names and titles together (e.g., "Dr. Smith") and/or certain medical abbreviations/nomenclatures (e.g., "obstructive sleep apnea," "cardiac arrest," and "Type 2 diabetes.") and even nomenclatures that have are mislabeled or have different meanings depending on context (i.e. “pt” can refer to “patient” or “physical therapy”).).
in response to the type of the demographic information being identified correctly, generating an updated data file labeling the one or more fields of the mislabeled demographic information based on the type of the demographic information (Wu: ¶0003, 0031, 0049, 0062-0068: A machine learning model can be trained to validate its correct operation of processing and generating a classification/label of medical records (i.e. demographic information or medical history). A model evaluator can monitor and evaluate third-party medical records (i.e. demographic information or medical history) for misclassification. Accordingly, a medical system can transmit a corrective action (i.e. generate, update, or delete medical record) as a feedback notification to the data source.); and
inserting, based on the type of the demographic information, information into one or more… fields of the demographic information in the updated data file (WU: Fig. 7 and ¶0066 and 0071-0072: The medical support system can perform the action of generating and updating a medical data file in response to identifying a correct classification of medical demographic information.).
Wu does not explicitly disclose; however, Jagota discloses:
generating a score indicating a probability that the type of the demographic information was identified correctly, wherein the score is adjusted from a baseline score for at least one of a plurality of fields of the demographic information (Jagota: Fig. 7 and ¶0091-0095: Source data in the file identifies if the column of the data fields has a header and the header is compared to the demographic information in the column to determine if there is a match. A match is determined according to a mapping score in comparison to a baseline threshold where the mapping is updated.).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to combine Wu with Jagota’s content match scoring because the references are analogous/compatible, since each is directed toward features of analyzing data content in a data file for nomenclatures, and because incorporating Jagota’s content match scoring in Wu would have served Wu’s pursuit of effectively using a model with a certain threshold to correctly classify data contents (See Wu, ¶0049); and further obvious since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Wu does not explicitly disclose; however, Akbulut discloses:
inserting… information into one or more missing fields (Akbulut: ¶0014: Data analysis program analyzes data to identify and provide data for missing and inaccurate structured content.).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention
to combine Wu with Akbulut’s analysis of unstructured data to identify structured content because the
references are analogous/compatible, since each is directed toward features of predicting erroneous
unstructured data in demographic records, and because incorporating Akbulut’s analysis of unstructured
data to identify structured content in Wu would have served Wu’s pursuit of effectively updating or
generating a medical record with correct classification (See Wu, ¶0071 and 0076); and further obvious
since the claimed invention is merely a combination of old elements, and in the combination each element
merely would have performed the same function as it did separately, and one of ordinary skill in the art
would have recognized that the results of the combination were predictable.
Claims 8 and 15 recite limitations already addressed by the rejection of claim 2; therefore, the same rejection applies. See Wu Fig. 11 for a system with memory and one or more processors. See Wu, ¶0053-0054, for non-transitory computer-readable storage device.
As per Claim 3, Wu in view of Jagota in further view of Akbulut discloses the computer-implemented method of claim 2.
While Wu’s disclosure trains a machine learning model to identify content of the data file with information describing a medical provider (Wu: ¶0049: A model evaluator trains a machine learning model See ¶0026 of example information in data file including information describing a medical provider.), Wu does not explicitly disclose; however, Jagota discloses further comprising: training a machine learning model using a labeled training set curated from a data source to identify a heading based at least in part on structure and content of the data file with information... (Jagota: ¶0033-0034: A machine learning classifier implements a probabilistic scoring method to match strings of data. See ¶0126-0128 whereas for entities scored probabilistically are used in a training set, whereas the structure of the training sets is curated from identified headers (See Tables IX and X).)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to combine Wu with Jagota’s content match scoring because the references are analogous/compatible, since each is directed toward features of analyzing data content in a data file for nomenclatures, and because incorporating Jagota’s content match scoring in Wu would have served Wu’s pursuit of effectively using a model with a certain threshold to correctly classify data contents (See Wu, ¶0049); and further obvious since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Claims 10 and 16 recite limitations already addressed by the rejection of claim 3; therefore, the same rejection applies.
As per Claim 4, Wu in view of Jagota in further view of Akbulut discloses the computer-implemented method of claim 2, further comprising: cross-checking the at least one of the plurality of fields of the demographic information against known demographic information (Wu: ¶0024, 0038-0040, 0044-0045, 0062: Electronic medical records containing demographic information and a patient’s history of present illness (HPI’s) are analyzed. A HPI classifier receives unprocessed medical records for preprocessing by a natural language processor. A tokenizer converts each word or group of words in the medical record into a token. The tokenizer can analyze the shape of words or phrases (i.e. metadata) using simple rules to develop a token (which represents a type of demographic information, such as name, location, date, etc.). A tensor generator vectorizes each token and inputs them into the machine learning model (e.g., the neural network). The neural network is trained using the preprocessed HPI(s) and HPI classification(s) (e.g., collectively referred to as the samples). The samples are divided such that are some of the samples are used for training and some are used for validation (e.g., cross- checking to confirm the model works after training). Known outcomes/results can be used to verify performance of the training model, which can also be validated with a test data set. In some examples, a set of known, "gold standard", or other reference data can be divided into a training data set to train the model and a test data set to test the trained network model to validate its correct operation.).)
Claims 11 and 17 recite limitations already addressed by the rejection of claim 4; therefore, the same rejection applies.
As per Claim 5, Wu in view of Jagota in further view of Akbulut discloses the computer-implemented method of claim 2, wherein identifying the type of the demographic information further comprises: analyzing semantic content of the at least one of the plurality of fields of the demographic information to identify the type of the demographic information; analyzing a shape of the at least one of the plurality of fields of the demographic information to identify the type of the demographic information; or analyzing metadata of the at least one of the plurality of fields of the demographic information to identify the type of the demographic information (Wu: ¶0003, 0024 and 0038-0040: Electronic medical records containing demographic information and a patient’s history of present illness (HPI’s) are analyzed. An HPI classifier receives unprocessed medical records for preprocessing by a natural language processor. A tokenizer converts each word or group of words in the medical record into a token. The tokenizer can analyze the shape of words or phrases using simple rules to develop a token (which represents a type of demographic information, such as name, location, date, etc.). The named entity recognizer scans the tokenized records for numbers, dates, named entities, medical terms, abbreviations, and/or misspelling and replaces these elements with standardized tokens. As an example, “Mar. 12, 2018” is replaced with three tokens representing month, day and year, namely "DATE," "DATE," and "DATE," respectively.).
Claims 12 and 18 recite limitations already addressed by the rejection of claim 5; therefore, the same rejection applies.
As per Claim 6, Wu in view of Jagota in further view of Akbulut discloses the computer-implemented method of claim 2.
Wu does not explicitly disclose; however, Jagota discloses wherein generating the score further comprises: adjusting the baseline score to increase the score based on a matching between a heading and content of a field of the demographic information, or to decrease the score based on a mismatching between the heading and the content of the field of demographic information (Jagota: Fig. 7 and ¶0091-0095: Source data in the file identifies if the column of the data has a header and the header is compared to the demographic information in the column to determine if there is a match. A match is determined according to a mapping score in comparison to a baseline threshold where the mapping is updated, whereas a decrease or no score means that there is no match and a high score confirms a match.).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to combine Wu with Jagota’s content match scoring because the references are analogous/compatible, since each is directed toward features of analyzing data content in a data file for nomenclatures, and because incorporating Jagota’s content match scoring in Wu would have served Wu’s pursuit of effectively using a model with a certain threshold to correctly classify data contents (See Wu, ¶0049); and further obvious since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
As per Claim 7, Wu in view of Jagota in further view of Akbulut discloses he computer-implemented method of claim 2, further comprising: generating an alert notifying an administration device that two or more of the plurality of fields of the demographic information have at least one of same semantic content, a same shape or same metadata (Wu: ¶0067: A notification transmission on whether a HPI (history of present illness) demographic information classification is correct, meaning it has a semantic match is transmitted to a data source.).
Claims 14 and 20 recite limitations already addressed by the rejection of claim 7; therefore, the same rejection applies.
As per Claim 8, discloses the computer-implemented method of claim 2, further comprising: transmitting the updated data file to a third-party (Wu: ¶0003, 0049, and 0065-0066: A model evaluator monitors and evaluates third-party medical records for misclassification. A medical system can transmit a corrective action (i.e. generate, update, or delete medical record) as a feedback notification to the data source (i.e. third party).).
Claim 21 recites limitations already addressed by the rejection of claim 8; therefore, the same rejection applies.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Whitman (US 2014/0195544): Methods, systems and computer program products are provided for predicting data. A name or title is obtained from a taste profile. There is an index into a data set based on the name or title, and a set of terms and corresponding term weights associated with the name or title are retrieved. A sparse vector is constructed based on the set of terms and term weights. The sparse vector is input to a training model including target data. The target data includes a subset of test data which has a correspondence to a predetermined target metric of data. A respective binary value and confidence level is output for each term, corresponding to an association between the term and the target metric.
Lu (US 2021/0173825): The disclosed embodiments provide a system that identifies duplicate entities. During operation, the system selects training data for a first machine learning model based on confidence scores representing likelihoods that pairs of entities in an online system are duplicates. Next, the system updates parameters of the first machine learning model based on features and labels in the training data. The system then identifies a first subset of additional pairs of the entities as duplicate entities based on scores generated by the first machine learning model from values of the features for the additional pairs and a first threshold associated with the scores. The system also determines a canonical entity in each of the duplicate entities based on additional features. Finally, the system updates content outputted in a user interface of the online system based on the identified first subset of the additional pairs.
Zhong et al. (US 2021/0303638): The disclosed embodiments provide a system for processing user-generated input. During operation, the system obtains a first embedding produced by an embedding model from an input string representing an entity and a hierarchy of clusters of embeddings generated by the embedding model from a set of standardized entities. Next, the system searches the hierarchy of clusters for a subset of the embeddings that are within a threshold proximity to the first embedding in a vector space. The system then calculates embedding match scores between the input string and a first subset of the standardized entities represented by the subset of the embeddings based on distances between the subset of the embeddings and the first embedding in the vector space. Finally, the system modifies, based on the embedding match scores, content outputted in response to the input string within a user interface of an online system.
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/ALLISON M NEAL/Primary Examiner, Art Unit 3625
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