DETAILED ACTION
This is in reference to communication received 12 September 2025. Claims 1 – 20 are pending for examination. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
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 (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Independent claim 1, representative of claims 17, in part is directed toward a statutory category of invention, the claim appears to be directed toward a judicial exception namely an abstract idea. Claim 1 recites invention directed transcribing of medical records of patients. Subsequent to receiving data of a patient, it is processed and data elements in the received data is extracted that reflects the language semantics of a particular user (e.g., medical professional) and mapped to pre-determined data representation based on data categories, data field of the patient data record is populated (e.g. filled), stored and made available to medical professional when they query data record of the patient for selecting a particular treatment for administering to the patient, which, pursuant to MPEP 2106.04, is aptly categorized as a method of organizing human activity (i.e. transcribing). Therefore, under Step 2A, Prong One, the claims recite a judicial exception.
The independent claims further recite the additional functional element of using a trained language extraction machine learning model which trained using patient data entries associated with particular user (e.g., a medical professional) for extracting data elements from the patient data and data categories represented by the data elements, such that the language extraction machine learning model accounts for the particular user’s habit of entering words in relation to one another, transcribe the received data into a medical record of the patient which is made available to the user when they query for the patient’s medical record for treating the patient. Not only do these features fail to integrate the abstract idea into a practical application (see below), but it can also reasonably be seen as the conventional application of well-known machine learning concepts to build and train a model to implement the abstract idea on a computer, and merely uses a computer as a tool to perform the abstract idea. See MPEP 2106.05(f).
Represented claim 17, which do recite statutory categories (machine, product of manufacture, for example), the same analysis as above applies to these claims since the method steps are the same. However, the judicial exception is not integrated into a practical application. This claim add the generic computer components (additional elements) of a system comprising one or more hardware processors and a memory to perform the method addressed above (claim 1).
The processor and database are recited at a high-level of generality such that they amount to 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. The claims are directed to an abstract idea.
The claims 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 element of the processor, memory, and non-transitory machine-readable medium amounts to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. The claims are not patent eligible.
When taken as an ordered combination, nothing is added that is not already present when the elements are taken individually. When viewed as a whole, the marketing activities amount to instructions applied using generic computer components.
As for dependent claims 2 – 16 and 18 – 20, these claims recite limitations that further define the same abstract idea with details regarding results that the trained language model can provide, details regarding the data, data elements, identifying that data record are converted into a standardized representation, how the data fields of the patient data record will be populated, allowing user (e.g., a medical professional, an abstractor) to manually populate the data record or optionally, populate the data fields with pre-determined data representations (e.g., default values), defining that the quality of the user’s work is determined, and report is provided, defining what medical application comprises. Defining where the patient data can be received from, defining that the language extraction machine learning model comprises a neural network, and defining that the populated data records enables reporting to a regional and/or national data records of patients. Thus, the dependent claims merely provide additional non-structural (and predominantly non-functional) details that fail to meaningfully limit the claims or the abstract idea(s).
Therefore, claims 1 – 20 are not drawn to eligible subject matter, as they are directed to an abstract idea without significantly more.
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.
Claims 1 – 4, 6 – 7 and 14 – 20 are rejected under 35 U.S.C. 103 as being unpatentable over Biesterfeld et al. US Publication 2019/0361980 in view of Nuance’s published user-guide “Dragon Naturally Speaking Installation and User Guide” hereinafter referred to as Nuance and Reiner US Publication 2010/0145720.
Regarding claim 1 and representative claim 17, Biesterfeld teaches computer-implemented system and method extracting patient information for storage and retrieval in a medical records database (Biesterfeld, The NLP Model(s) 150 are generally used to process input data to perform natural language processing, which may include normalization of the data (e.g., normalizing capitalization, spelling, and the like), as well as more rigorous NLP such as entity identification and extraction, sentiment analysis, determination of the meanings or concepts present in the data, and the like [Biesterfeld, 0016] comprising:
a medical records database [Biesterfeld, Fig. 2 and associated disclosure];
one or more processors programmed and configured (Biesterfeld, a computer-readable storage medium having computer readable program code embodied therewith, the computer-readable program code executable by one or more computer processors to perform an operation) [Biesterfeld, claim 11] for:
receiving patient data of a patient (Biesterfeld, The method includes receiving a data set comprising a plurality of records, wherein each of the plurality of records contains a respective one or more fields. The method also includes identifying a first group of fields in the plurality of records, wherein each of the first group of fields has a common metadata attribute) [Biesterfeld, 0003];
processing the patient data using a learning system with Artificial Intelligence (AI)-assisted clinical extraction tool (Biesterfeld, The NLP Model(s) 150 are generally used to process input data to perform natural language processing, which may include normalization of the data (e.g., normalizing capitalization, spelling, and the like), as well as more rigorous NLP such as entity identification and extraction, sentiment analysis, determination of the meanings or concepts present in the data, and the like [Biesterfeld, 0016, 0034], the processing comprising:
the language extraction machine learning model is trained using a set of training data (Biesterfeld, if the normalization used by the Ingestion System 105 involves transforming each value into a lowercase equivalent, "Peanut," "PEANUT," and "peanut" may still be considered to match, in an embodiment. In some embodiments, the normalization process involves reducing all words to their stem. For example, "running," "ran," and "runs," may be converted to "run." In various embodiment, the Metric Generator 140 may determine that "run" does or does not match with "running," "ran," or "runs." In various embodiments, the normalization process may include other similar operations, such as converting input values based on a predefined dictionary of terms) [Biesterfeld, 0021];
Biesterfeld does not explicitly teach Language extraction machine learning model has been trained with data that reflects medical language semantics and a particular user of a medical application. However, Nuance teaches to efficiently create initial drafts of medical documents. Nuance teaches Before you can begin using Dragon, you must let Dragon create a user profile for each person or healthcare provider who is dictating. Your user profile stores acoustic information about your voice that Dragon uses to recognize what you say. This profile also stores any changes you make to the standard vocabulary – any special words, names, acronyms, and abbreviations you add. Nuance further recites Immediately after you create your profile, before you begin dictating, you train Dragon to understand your voice. … After it adapts to your voice, Dragon’s Accuracy wizard appears and prompts you to first adapt Dragon to your writing style, then schedule regular Acoustic and Language Model tuning, and optionally schedule Data Collection. [Nuance, page 36, 39, 41].
Therefore, at the time of filing, it would have been obvious to one of ordinary skill in the art to modify Biesterfeld by adopting teachings of Nuance.
Biesterfeld in view of Nuance teaches system and method further comprising:
executing a trained language extraction machine learning model that has been trained with data that reflects medical language semantics and a particular user of a medical application's prior habit of entering other patient data (as responded to above) [Nuance, page 36, 39, 41], wherein the trained language extraction machine learning model indicates probabilities of a data element representing multiple data categories, the probabilities being generated or updated by the training (Nuance, Perform Language Model Optimization to update your language model. The language model contains statistical information that predicts which words are most likely to occur in the context of the user’s speech. Language Model Optimization uses text extracted from a user's .DRA files to add commonly used word sequences to the language model. Based on the speech data collected, Language Model Optimization may change the language model you selected when you created your User Profile. For example, Dragon may change Best-Match III to Inland Northern US (Great Lakes area) - BestMatch III.) [Nuance, page 222], the executing including:
Biesterfeld in view of Nuance does not explicitly teach mapping of data. However, Reiner teaches system and method for conversion of unstructured, free text data (contained within medical reports) into standardized, structured data [Reiner, 0020]. Reiner teaches mapping said synonymous nomenclature to a standardized lexicon such that a single set of structured data elements is recorded as report data in a report database [Reiner, 0021].
Therefore, at the time of filing, it would have been obvious to one or ordinary skill in the art to modify Biesterfeld in view of Nuance by adopting teachings of Reiner to generate a single set of structure data elements for recording as a report in a report database.
Biesterfeld in view of Nuance and Reiner teaches system and method further comprising:
extracting data elements from the patient data and data categories represented by the data elements (Reiner, Based upon any of these prospective analyses, the end-user can elect to incorporate the updates analyses into his/her "user and/or context specific default", and the program 110 will save same to the database 113, 114 in step 405. In step 406, in the future, whenever similar structured data is reported, these updated default parameters will be incorporated by the program 110 into the "automated analyses" function) [Reiner, 0177-0178];
mapping at least some of the extracted data elements to pre-determined data representations based on the data categories and identified patient data entry habits associated with the particular user to form structured data (Nuance, Dragon’s Accuracy wizard appears and prompts you to first adapt Dragon to your writing style, then schedule regular Acoustic and Language Model tuning, and optionally schedule Data Collection.) [Nuance, 41], including calculating a probability of one set of words following another set of words based on the data entry habits associated with the particular user, and selecting a data category associated with the highest probability for the data element from the multiple data categories (Reiner, Once the lexicon, ontology, and synonymous terms have been established by the program 110, the program 110 can extract and characterize free-text report data in an automated fashion. These extracted data elements are then mapped by the program 110 to the structured data elements contained within the ontology and presented to the authoring physician for verification, on the display 102.) [Reiner, 0102, 0094];
populating fields of a data record of the patient based on the pre-determined data representations (Reiner, Once the lexicon, ontology, and synonymous terms have been established by the program 110, the program 110 can extract and characterize free-text report data in an automated fashion. These extracted data elements are then mapped by the program 110 to the structured data elements contained within the ontology and presented to the authoring physician for verification, on the display 102) [Reiner, 0102];
storing the populated data record in a database accessible by the medical application (Reiner, medical data which could be accessed by the program 110 in data mining analysis, are stored within the EMR (i.e., a) clinical, b) molecular, c) laboratory, d) pathology, e) imaging, f) clinical testing, g) demographic, h) occupational/environmental, i) quality, and j) socio-cultural) [Reiner, 0188];
receiving a medical record query through the medical application; and in response to and based on a medical records query, retrieving record(s) from the database and transmitting patient data from the retrieved record(s) for access by the medical application (Reiner, Once the input data has been completed, the databases 113, 114 are queried by the program 110, and a number of cases meeting the search criteria are presented by the program 110 to the end-user on the display 102) [Reiner, 0113], the patient data useable for selecting a particular treatment for administering to the patient (Reiner, The program 110 will then create a list of differential diagnoses (using artificial intelligence techniques such as neural networks), based upon these inputted data and provide a statistical probability for each of the listed diagnoses in step 504.) [Reiner, 0208].
Regarding claim 2 and representative claim 19, as combined and under the same rationale as above, Biesterfield in view of Nuance and Reiner teaches system and method, wherein the AI-assisted clinical extraction tool comprises a natural language processor (Biesterfeld, The NLP Model(s) 150 are generally used to process input data to perform natural language processing, which may include normalization of the data (e.g., normalizing capitalization, spelling, and the like), as well as more rigorous NLP such as entity identification and extraction, sentiment analysis, determination of the meanings or concepts present in the data, and the like [Biesterfeld, 0016];
wherein the language extraction machine learning model is trained using a set of training data comprising at least one of: a common text data model, dictionaries, hierarchical text data, or tagged text data (Biesterfeld, if the normalization used by the Ingestion System 105 involves transforming each value into a lowercase equivalent, "Peanut," "PEANUT," and "peanut" may still be considered to match, in an embodiment. In some embodiments, the normalization process involves reducing all words to their stem. For example, "running," "ran," and "runs," may be converted to "run." In various embodiment, the Metric Generator 140 may determine that "run" does or does not match with "running," "ran," or "runs." In various embodiments, the normalization process may include other similar operations, such as converting input values based on a predefined dictionary of terms) [Biesterfeld, 0021].
Regarding claim 3 and representative claim 20, as combined and under the same rationale as above, Biesterfield in view of Nuance and Reiner teaches system and method, wherein the language extraction machine learning model is trained using the tagged text data, and wherein the tagged text data is derived from the other patient data and indicate at least one of a data category for the text data, or a data representation mapped to the text data (Biesterfeld, if the normalization used by the Ingestion System 105 involves transforming each value into a lowercase equivalent, "Peanut," "PEANUT," and "peanut" may still be considered to match, in an embodiment. In some embodiments, the normalization process involves reducing all words to their stem. For example, "running," "ran," and "runs," may be converted to "run." In various embodiment, the Metric Generator 140 may determine that "run" does or does not match with "running," "ran," or "runs." In various embodiments, the normalization process may include other similar operations, such as converting input values based on a predefined dictionary of terms) [Biesterfeld, 0021].
Regarding claim 4, as combined and under the same rationale as above, Biesterfield in view of Nuance and Reiner teaches system and method, wherein the processing comprises converting the extracted data elements to a standardized data format based on a data table that maps multiple alternative expressions representing the same information to a single standardized expression (Biesterfeld, the Ingestion System 105 extracts the value from the selected field. At block 640, the Ingestion System 105 normalizes the value. In various embodiments, this may include standardizing capitalization, converting words to a standard dictionary, and the like.) [Biesterfeld, 0044].
Regarding claim 6, as combined and under the same rationale as above, Biesterfield in view of Nuance and Reiner teaches system and method, further comprising:
displaying a first field representing the extracted data elements from the patient data in a user interface (Reiner, extracted data elements are then mapped by the program 110 to the structured data elements contained within the ontology and presented to the authoring physician for verification, on the display 102) [Reiner, 0102];
displaying, in the user interface, a first option to manually populate the first field of the data record and a second option to automatically populate the first field based on the extracted data elements and pre-determined data representations (Reiner, If the authoring physician determines that the data extraction, characterization, and/or mapping are erroneous, he/she is presented with a number of alternative options: modify the free text, select from a list of related structured data elements, request automated query of the report database to identify similar terms used in free text reports (context-specific) and associated structured data elements) [Reiner, 0102-0105].
receiving, from the interface, a selection of the first option or the second option (Reiner, If the authoring physician determines that the data extraction, characterization, and/or mapping are erroneous, he/she is presented with a number of alternative options) [Reiner, 0102]
based on a selection of the first option, populating the first field with manually edited data received via a second field of the interface (Reiner, modify the free text (unstructured) data used within the report; select from a list of related structured data elements (which are contained within the lexicon/ontology) [Reiner, 0103-0104);
based on a selection of the second option, populating the first field with the predetermined data representations (request automated query of the report database to identify similar terms used in free text reports (context-specific) and associated structured data elements) [Reiner, 0105]; and
storing the data record with the populated first field (Reiner, medical data which could be accessed by the program 110 in data mining analysis, are stored within the EMR (i.e., a) clinical, b) molecular, c) laboratory, d) pathology, e) imaging, f) clinical testing, g) demographic, h) occupational/environmental, i) quality, and j) socio-cultural) [Reiner, 0188].
Regarding claim 7, as combined and under the same rationale as above, Biesterfield in view of Nuance and Reiner teaches system and method, wherein the method further comprises:
determining, based on probabilities indicated in the trained language extraction machine learning model, a confidence level of populating the first field based on the data representations; and displaying the confidence level adjacent to the second option (Reiner, The program 110 will then create a list of differential diagnoses (using artificial intelligence techniques such as neural networks), based upon these inputted data and provide a statistical probability for each of the listed diagnoses in step 504) [Reiner, 0208].
Regarding claim 14, as combined and under the same rationale as above, Biesterfield in view of Nuance and Reiner teaches system and method, wherein the patients data are received from one or more sources comprising at least one of:
an EMR (electronic medical record) system,
a PACS (picture archiving and communication system),
a Digital Pathology (DP) system,
an LIS (laboratory information system),
a RIS (radiology information system), patient reported outcomes,
a wearable device, or
a social media website
(Reiner, medical data which could be accessed by the program 110 in data mining analysis, are stored within the EMR (i.e., a) clinical, b) molecular, c) laboratory, d) pathology, e) imaging, f) clinical testing, g) demographic, h) occupational/environmental, i) quality, and j) socio-cultural) [Reiner, 0188].
Regarding claim 15, as combined and under the same rationale as above, Biesterfield in view of Nuance and Reiner teaches system and method, wherein the language extraction machine learning model indicates probabilities of a data element based on the data entry habits associated with the particular user (Nuance, Language model contains statistical information that predicts which words are most likely to occur in the context of the user’s speech) [Nuance, page 267].
Regarding claim 16 and representative claim 18, as combined and under the same rationale as above, Biesterfield in view of Nuance and Reiner teaches system and method, wherein the language extraction machine learning model comprises a neural network (Many computing systems and models require large amounts of curated data in order to operate. For example, deep question and answer (QA) systems, many machine learning models, neural networks, and similar cognitive systems depend on carefully curated corpuses containing a large number of documents in order to return satisfactory results) [Reiner, 0208].
Regarding claim 19, as combined and under the same rationale as above, Biesterfield in view of Nuance and Reiner teaches system and method, wherein the language extraction machine learning model comprises a natural language processor;
wherein the language extraction machine learning model comprises training using a set of training data comprising at least one of: a common text data model, dictionaries, hierarchical text data, or tagged text data (Biesterfeld, if the normalization used by the Ingestion System 105 involves transforming each value into a lowercase equivalent, "Peanut," "PEANUT," and "peanut" may still be considered to match, in an embodiment. In some embodiments, the normalization process involves reducing all words to their stem. For example, "running," "ran," and "runs," may be converted to "run." In various embodiment, the Metric Generator 140 may determine that "run" does or does not match with "running," "ran," or "runs." In various embodiments, the normalization process may include other similar operations, such as converting input values based on a predefined dictionary of terms) [Biesterfeld, 0021].
Claims 8 – 10 are rejected under 35 U.S.C. 103 as being unpatentable over Biesterfeld et al. US Publication 2019/0361980 in view of Nuance’s published user-guide “Dragon Naturally Speaking Installation and User Guide” hereinafter referred to as Nuance, Reiner US Publication 2010/0145720 and Kelata et al. US Publication 2013/0246962.
Regarding claim 8, as combined and under the same rationale as above, Biesterfield in view of Nuance and Reiner teaches system and method, further comprising:
identifying a human abstractor responsible for abstracting patients data of a set of patients into data records of the set of patients (Reiner, Based upon any of these prospective analyses, the end-user can elect to incorporate the updates analyses into his/her "user and/or context specific default", and the program 110 will save same to the database 113, 114 in step 405. In step 406, in the future, whenever similar structured data is reported, these updated default parameters will be incorporated by the program 110 into the "automated analyses" function) [Reiner, 0177-0178];
determining a subset of the set of patients for whom the abstraction is incomplete (Biesterfeld, the Metric Generator 140 also generates a metric corresponding to the number or percentage of fields in the group that cannot be normalized) [Biesterfeld, 0022];
determining a first percentage representing a ratio between the subset of the set of patients and the set of patients (Biesterfeld, the Metric Generator 140 may generate a metric corresponding to the number or percentage of such fields in the identified group also generates a metric corresponding to the number or percentage of fields in the group that cannot be normalized) [Biesterfeld, 0022]; and
Biesterfeld in view of Nuance and Reiner does not explicitly teach using a progress as a percentage in the report. However Kaleta teaches that a progress bar (also sometimes referred to as a status bar, or a completion status bar, etc.) is commonly used to convey a completion status of a task or a process [Kaleta, 0003].
Therefore, at the time of filing, it would have been obvious to one or ordinary skill in the art to modify Biesterfeld in view of Nuance and Reiner by adopting teachings of Kelata to provide a visual cue to a user to indicate when a subtask of a task of interest to the user is complete.
Biesterfeld in view of Nuance Reiner and Kelata teaches system and method further comprising:
displaying the first percentage and identification information of the abstractor in a second interface as part of a progress report of the abstractor (Kelata, A progress bar can display a completion status indicator that allows a user to visualize a completion status of an ongoing task or operation) [Kelata, 0029].
Regarding claim 9, as combined and under the same rationale as above, B Biesterfield in view of Nuance, Reiner teaches system and method, further comprising and Kelata teaches system and method, further comprising:
determining a second percentage of completion of abstraction for the data record of each of the subset of the set of patients (Biesterfeld, the Metric Generator 140 may generate a metric corresponding to the number or percentage of such fields in the identified group also generates a metric corresponding to the number or percentage of fields in the group that cannot be normalized) [Biesterfeld, 0022]; and
displaying information related to the second percentages in the second interface as part of the progress report (Kelata, A progress bar can display a completion status indicator that allows a user to visualize a completion status of an ongoing task or operation) [Kelata, 0029].
Regarding claim 10, as combined and under the same rationale as above, Biesterfield in view of Nuance, Reiner teaches system and method, further comprising and Kelata teaches system and method, further comprising:
determining a predicted time of completion of manual population of remaining unpopulated fields of the data record of each of the subset of the set of patients (Biesterfeld, the Metric Generator 140 also generates a metric corresponding to the number or percentage of fields in the group that cannot be normalized) [Biesterfeld, 0022]; and
displaying the predicted time of completion as part of the progress report (Kelata, upon receiving a progress value, under-reporter 215 can determine an estimated completion time for the task at hand (e.g., using a lookup table) [Kelata, 0056].
Claims 11 – 13 are rejected under 35 U.S.C. 103 as being unpatentable over Biesterfeld et al. US Publication 2019/0361980 in view of Nuance’s published user-guide “Dragon Naturally Speaking Installation and User Guide” hereinafter referred to as Nuance, Reiner US Publication 2010/0145720 and Barnes et al. US Publication 2017/0076046.
Regarding claim 11, Biesterfeld in view or Nuance and Reiner does not explicitly recite data to include patient tumor information (e.g. cancer) and history of care (treatement) as a part of patient data records. However, Barnes teaches informatics platform for integrated clinical care. Barnes teaches the system may provide a method for a physician to initiate or request a formal curbside consultation (second opinion). For example, collaborators through the system may be able to review, discuss, and make recommendations for a patient without all being in the same hospital system [Barnes, 0251].
Therefore, at the time of filing, it would have been obvious to one or ordinary skill in the art to modify Biesterfeld in view or Nuance and Reiner by adopting teachings of Barnes, and implement a collaboration interface that facilitates communication of patient specific information.
as combined and under the same rationale as above, Biesterfield in view of Nuance and Reiner and Barnes teaches system and method, wherein the fields of the data record of the patient include tumor information (Barnes, relevant patient information ( e.g., age, gender, clinical problems, allergies and current medications), tumor information ( e.g., type of cancer, size, location, staging and TNM (T=tumor invasive score, N=nodal involvement score, M=metastasis score)) and other related demographic information with the ability to edit clinical values) [Barnes, 0079] and history of care (Barnes, the collaboration tool 60 can document the chats and consultations regarding a patient and can include the documented chats and consultations as part of the patient's medical record) [Barnes, 0144];
wherein the medical application comprises a quality of care evaluation tool (Barnes, The system can evaluate patient data and search for pre-determined "flags.") [Barnes, 0247]; and
wherein the populated data record enables the quality of care evaluation tool to determine a quality of care administered to the patient based on
(1) the history of care and the tumor information included in the populated data record(Barnes, the collaboration tool 60 can document the chats and consultations regarding a patient and can include the documented chats and consultations as part of the patient's medical record) [Barnes, 0144]; and
(2) a quality of care metrics definition (Barnes, variants are then filtered based on several metrics including quality, frequency in the population, and germline/somatic classification.) [Barnes, 0289].
Regarding claim 12, as combined and under the same rationale as above, Biesterfield in view of Nuance and Reiner teaches system and method, further comprising and Barnes teaches system and method, wherein the data elements of the data record of the patient include descriptive information of patients and tumor (Barnes, relevant patient information ( e.g., age, gender, clinical problems, allergies and current medications), tumor information ( e.g., type of cancer, size, location, staging and TNM (T=tumor invasive score, N=nodal involvement score, M=metastasis score)) and other related demographic information with the ability to edit clinical values) [Barnes, 0079];
wherein the medical application comprises a medical research tool (Barnes, Multi-Variable Neuro-Oncological Interactive Chronological Visualization Tool) [Barnes, 0269]; and
wherein the populated data record enables the medical research tool to determine a correlation between descriptive information of the patients and descriptive information of the tumor included in the populated data record (Barnes, Item lb marks an area wherein relevant Neuro-Oncological data is charted automatically in the time domain and displayed simultaneously to visualize new data correlations and relationships.) [Barnes, 0270].
Regarding claim 13, as combined and under the same rationale as above, Biesterfield in view of Nuance and Reiner teaches system and method, further comprising Barnes teaches system and method, wherein the populated data record enables reporting to a regional and/or national data record of patients (Barnes, structured reporting functionality that provides system aggregated patient information, clinical trial query results specific to the patient and a mechanism to capture multi-disciplinary tumor board recommendations;) [Barnes, 0076].
Claim 5 is rejected under 35 U.S.C. 103 as being unpatentable over Biesterfeld et al. US Publication 2019/0361980 in view of Nuance’s published user-guide “Dragon Naturally Speaking Installation and User Guide” hereinafter referred to as Nuance, Reiner US Publication 2010/0145720 and Anand et al. US Publication 2015/0019248.
Regarding claim 5, Biesterfeld in view of Nuance and Riener does not teach detecting an error in data and updating the data to remove error. However, Anand teaches system and method for automated determination of a gap in care. By extracting clinical data of any format from respective different sources, a data repository normalized to a generic format is created. A human readable data representation of medical quality data is translated into a machine generic data representation used for storing the extracted clinical data. Anand teaches when an error is identified, the error is corrected and/or avoided. In the example of the error in the data source, the mining may be performed without the error (e.g., performing the mining without including a value having the error or the data source having the error). The error is avoided by not including the error or the unreliable data source. In other examples, the error is fixed manually or automatically by inference, and the operation continues using the fixed data [Anand, 0103].
Therefore, at the time of filing, it would have been obvious to one or ordinary skill in the art to modify Biesterfeld in view or Nuance and Reiner by adopting teachings of Anand to identify a gap in care for a patient in an automated process to back-stop a medical professional.
as combined and under the same rationale as above, Biesterfield in view of Nuance, Reiner and Anand teaches system and method, wherein the processing comprises detecting an error in the extracted data elements based on comparing the extracted data elements against a threshold (Anand, Data repair and/or error forgiveness is important in the medical environment i.e. threshold) [Anand, 0104] and updating the extracted data elements to remove the error (Anand, when an error is identified, the error is corrected and/or avoided. In the example of the error in the data source, the mining may be performed without the error (e.g., performing the mining without including a value having the error or the data source having the error). The error is avoided by not including the error or the unreliable data source. In other examples, the error is fixed manually or automatically by inference, and the operation continues using the fixed data;
Response to Arguments
Applicant's argument that pending amended claimed amended invention is eligible for patent under 35 USC 101 because the claimed invention recite elements which integrate a practical application and recites “significantly more” than the alleged abstract idea is acknowledged and considered.
However, upon further review, it is deemed that the pending claimed invention is not eligible for patent under 35 USC 101 and have been responded to in the Rejection under 35 USC 101 section.
Applicant's argument that pending claimed amended invention is eligible for patent because cited prior art does not teach the added limitations in the claimed invention is acknowledged and considered.
However, while performing a updated search, a new prior art was found that teach the added limitations. Therefore, applicant’s arguments are moot under new grounds of rejection.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
The Nine “Cs” of Clinical Documentation Improvement
Medical Dictation & Transcription
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Naresh Vig whose telephone number is (571)272-6810. The examiner can normally be reached Mon-Fri 06:30a - 04:00p.
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/NARESH VIG/Primary Examiner, Art Unit 3622
December 9, 2025