DETAILED ACTION
Acknowledgments
The present application is being examined under the pre-AIA first to invent provisions.
This office action is in response to the claims file September 11, 2025.
Claims 1-20 have been examined.
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.
Step 1: claims 1-20 are directed to methods. Therefore, these claims fall within the four statutory categories of invention.
Step 2A, prong 1: Claims 1 & 13, for example, recites the abstract idea of generating and outputting summarized clinical data including a recommendation or risk measures. This idea is described by the following claim elements:
1. A method, comprising:
receiving a patient identifier corresponding to a patient;
accessing a plurality of patient records based on the patient identifier; generating text data by extracting textual information from the plurality of patient records using one or more text machine learning models;
generating clinical data by processing the text data;
generating one or more compressed sentences for one or more sentences in the text data:
generating one or more importance scores for the one or more sentences in the text data: and
generating a time series plot based on the text data;
generating summarized clinical data by processing the clinical data. including the one or more compressed sentences, the one or more importance scores, and the time series plot
generating summarized clinical data by processing the clinical data. including the one or more compressed sentences, the one or more importance scores, and the time series plot […]; and
outputting the summarized clinical data.
13. A method, comprising:
accessing a plurality of patient records of a patient;
generating text data by extracting textual information from the plurality of patient records […];
generating clinical data by processing the text data […];
generating one or more compressed sentences for one or more sentences in the text data:
generating one or more importance scores for the one or more sentences in the text data: and
generating a time series plot based on the text data;
generating summarized clinical data by processing the clinical data including the one or more compressed sentences, the one or more importance scores and the time series plot […]; and
training one or more risk prediction machine learning models to generate predicted risk measures based on the summarized clinical data.
The above steps fall into the Certain Methods of Organizing Human Activity grouping of abstract ideas as they involve or managing personal behavior or relationships or interactions between people. These steps involve receiving health records of a patient, generate summarized reports based on the obtained health records and output the generated summarized reports to a user, the reports including a risk prediction or recommendation. Additionally, these steps can be performed mentally or manually (using a pend and a paper) and do not require a machine.
Step 2A, prong 2: claims 1 & 13 recite additional elements that fail to integrate the abstract idea into practical application.
Claims 1 & 13 recite the following additional limitations:
using one or more text machine learning models
using one or more extraction machine learning models
using one or more summary machine learning models
The machine learning models, however, are merely used as a tool to implement the abstract idea. Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a machine, or merely use the machine learning models as a tool to perform an abstract idea, cannot provide an inventive concept. See MPEP 2106.05(f).
The additional elements noted above are recited at a high level of generality, and comprises only a microprocessor and memory to simply perform the generic computer functions of receiving, identifying, calculating, determining & comparing assets data and assigning assets to a room. Additionally, at least ¶ [0044] of the application as filed seems to indicate that the healthcare system 205 used to implement the claimed inventions (e.g. using the claimed machine learning models) is a conventional microprocessor (e.g. general-purpose computer). Generic computers performing generic computer functions, alone, do not integrate the claimed abstract idea into a practical application.
Furthermore, the additional claimed elements, noted above, when viewed individually and as an ordered combination does not integrate the abstract idea into a practical application. The additional elements fail to provide a practical application because there are (1) no actual improvements to the functioning of a computer, (2) nor to any other technology or technical field, (3) nor do the claims apply the judicial exception with, or by use of, a particular machine, ( 4) nor do the claims provide a transformation or reduction of a particular article to a different state or thing, (5) nor provide other meaningful limitations beyond generally linking the use of the judicial exception to a particular technological environment, in view of MPEP §2106.04, (6) nor do the claims apply the judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition, in view of MPEP §2106.04(d)(2).
Step 2B: claim fails to recite additional elements that amount to an inventive concept.
For the reasons identified with respect to Step 2A, prong 2, claims 1 & 13 fail to recite additional elements that amount to an inventive concept. For example, use of machine learning models in its ordinary capacity for economic or other tasks (e.g., to obtain patient data, generate/output summarized clinical data, from the obtained patient records, including recommendations) or simply adding a general-purpose computer system or computer component after the fact to an abstract idea (e.g., a fundamental economic practice) does not integrate a judicial exception into a practical application or provide significantly more (see MPEP 2106.05(g)).
Furthermore, the additional claimed elements, noted above, when viewed individually and as an ordered combination do not provide significantly more than the abstract idea. The additional elements fail to (1) recite any improvements to another technology or technical field; (2) recite any improvements to the functioning of the computer itself; (3) apply the judicial exception with, or by use of, a particular machine; ( 4) effect a transformation or reduction of a particular article to a different state or thing; (5) add a specific limitation other than what is well-understood, routine and conventional in the fie Id; (6) add unconventional steps that confine the claim to a particular useful application; nor (7) provide other meaningful limitations beyond generally linking the use of the judicial exception to a particular technological environment, in view of the MPEP 2106.0S(a-h).
Dependent claim 8 further describe the abstract idea of outputting summary document via a graphical user interface (GUI). This claim includes an additional element of a graphical user interface (GUI). This additional element in combination with the other additional elements do not integrate the abstract idea into a practical application or that provide significantly more than the abstract idea. Therefore, these dependent claims are also not patent eligible.
The remaining dependent claims further describe the abstract idea of generating and outputting summarized clinical data including a recommendation or risk measure. These claims do not include new additional elements that integrate the abstract idea into a practical application or that provide significantly more than the abstract idea. Therefore, these dependent claims are also not patent eligible.
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.
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claims 1, 4-7, 9-13 & 15-20 are rejected under 35 U.S.C. 103(a) as being anticipated by Amarasingham et al. (US 20230104655 A1) (“Amarasingham”) in view of Lucas et al (US 2020/0176098 A1) (“Lucas”).
As per claims 1 & 13: Amarasingham discloses:
receiving a patient identifier (e.g. patient profile) corresponding to a patient (fig. 3 & related text; ¶ [0020]);
accessing a plurality of patient records based on the patient identifier (fig. 3 & related text; ¶ [0020]- For example, hospital, ambulatory, or patient-based medical records of patients and patient profile information may be identified and automatically processed to identify content of various inputs, such as a person, age, sex, reason for visiting the medical facility, current conditions, previous conditions, medications administered, etc. Also, the current conditions a patient may be experiencing can be identified and processed based on a knowledge base of information to achieve additional recommendations, diagnoses, procedures, tests, medications, treatments, etc., to assist the patients in their current health status.);
generating text data by extracting textual information from the plurality of patient records using one or more text machine learning models (fig. 3 & related text; ¶¶ [0038], [0088]- NLP models 202 alone, or in conjunction with third-party tools 208, may extract some or all concepts, including clinical issues, diagnoses, symptoms, treatments, etc., along with their attributes (e.g., section, negation, past, etc.) from the unstructured data. As also discussed above, NLP models 202C and 202D may extract concepts, such as potential barriers to discharge, social determinants of health, and disease identification. Further, NLP model 202B may detect attributes of the concepts, such as negated phrasing, temporality (e.g., past vs current), potentiality (e.g., potential or actual), and possessives (e.g., text in the unstructured data referencing the patient or a patient's family member).);
generating clinical data by processing the text data using one or more extraction machine learning models (fig. 3 & related text; ¶¶ [0038], [0088]- NLP models 202 alone, or in conjunction with third-party tools 208, may extract some or all concepts, including clinical issues, diagnoses, symptoms, treatments, etc., along with their attributes (e.g., section, negation, past, etc.) from the unstructured data. As also discussed above, NLP models 202C and 202D may extract concepts, such as potential barriers to discharge, social determinants of health, and disease identification. Further, NLP model 202B may detect attributes of the concepts, such as negated phrasing, temporality (e.g., past vs current), potentiality (e.g., potential or actual), and possessives (e.g., text in the unstructured data referencing the patient or a patient's family member).);
generating summarized clinical data by processing the clinical data […], using one or more summary machine learning models (fig. 3 & related text; ¶¶ [0038], [0089]-NLP model 202F may receive the output of NLP models 202 from operation 308 and current patient data and generate an output that includes active clinical issues. The current patient data may include note documents, audio data, etc., that was stored in memory storage 104 since the last patient update or the last time summary 114 was generated. NLP model 202F may identify the section of the note, audio, or other current patient data that is most relevant to the information task, and identify the active clinical issues for the patient based on the identified section of the data.); and
outputting the summarized clinical data (fig. 3 & related text, ¶ [0112].
Claim 13: training one or more risk prediction machine learning models to generate predicted risk measures based on the summarized clinical data (fig. 3 & related text; ¶ [0038], [0058]- generate risk scores that predict a patient's outcomes.)
Amarasingham further teaches each function
Retrieving records and extracting clinical data (receiving patient identifier, accessing records and extracting emographics/diagnoses/medications/therapies; ¶¶ [0055]–[0079]).
Generating summarized clinical data by grouping context, de-duplication, and creating a summary document with references (¶¶ [0141]–[0149], Fig 11).
Outputting the summary via GUI and generating risk measures from summarized data (¶¶ [0088]–[0111]; Figs 6–7).
Amarasingham does not expressly teach:
generating one or more compressed sentences for one or more sentences in the text data:
generating one or more importance scores for the one or more sentences in the text data: and
generating a time series plot based on the text data;
Lucas however teaches
Lucas however teaches:
generating one or more compressed sentences for one or more sentences in the text data — via sentence parsing and sequence labeling that isolates salient content (see FIG. 3; FIG. 5; ¶¶ [0154]–[0167]).
generating one or more importance scores for the one or more sentences in the text data — Lucas expressly shows sentence-level “Score/Weight” examples (e.g., 95%, 97%, 85%) and discusses weighting/ranking (see FIG. 4; ¶¶ [0161]–[0167]).
generating time-related features from text by extracting/normalizing temporal events (e.g., timestamps) suitable for timeline/time‑series representations (see FIG. 2; ¶¶ [0096]–[0103], [0213]–[0216]).
A person of ordinary skill would have been motivated to incorporate Lucas’s sentence compression and importance ranking into Amarasingham’s summarization stage to reduce verbosity and highlight key clinical events, and to add time-series plotting for visualizing patient event chronology—predictable uses yielding predictable benefits, with no teaching away.
The combination Amarasingham/Lucas does not expressly disclose that the summarized clinical data includes compressed sentences, importance scores, and a time‑series plot as a composite structure.
However, it would have been obvious to a person of ordinary skill in the art to implement Amarasingham’s summary machine‑learning pipeline using Lucas’s modular NLP outputs and to provide those outputs together as the “clinical data” input. Amarasingham teaches generating summarized clinical data with summary ML from extracted patient records (e.g., ¶¶ 0055–0079, 0141–0149).
Lucas teaches:
sentence compression via parsing and sequence labeling that isolates salient content (FIG. 3; FIG. 5; ¶¶ 0154–0167),
sentence-importance scoring/ranking with explicit “Score/Weight” examples (FIG. 4; ¶¶ 0161–0167),
extracting/normalizing temporal events (e.g., timestamps) suitable for timeline/time-series representations (FIG. 2; ¶¶ 0096–0103, 0213–0216).
Given both references use modular pipelines, aggregating these known feature channels (compressed sentences, importance scores, temporal features) into a single structured “clinical data” object for input to the summary model would have been a routine, predictable design choice that yields expected benefits (salience, brevity, interpretability), with no teaching away and a reasonable expectation of success. See KSR v. Teleflex, 550 U.S. 398, 417–421 (2007); MPEP §§ 2143, 2144.
As per claims 4 & 15: Amarasingham/ Lucas discloses as shown above.
Amarasingham further discloses wherein the plurality of patient records comprise at least one of: (i) clinician notes, (ii) faxed documents, (iii) a continuity of care document (CCD), or (iv) one or more lab reports (¶¶ [0020], [0033]).
As per claims 5 & 16: Amarasingham/ Lucas discloses as shown above.
Amarasingham further discloses wherein generating the clinical data comprises extracting, from the text data, at least one of: (i) demographic information of the patient, (ii) diagnoses of the patient, (iii) medications used by the patient, or (iv) therapies that the patient engages in (¶¶ [0088], [0033]).
As per claims 6 & 17: Amarasingham/ Lucas discloses as shown above.
Amarasingham further discloses wherein generating the clinical data comprises extracting information corresponding to a face-to-face meeting between the patient and a clinician, the information comprising (i) a date when the face-to-face meeting was conducted, (ii) a certification, by the clinician, indicating that the patient would benefit from receiving home health services, (iii) an explanation of why the patient would benefit from home health services, and (iv) an indication of one or more recommended home health services (figs. 6A-6D & related text).
The examiner further notes that the limitations (i), (ii), (iii) and (iv) have been considered but are not given patentable weight because the limitations have been interpreted as non-functional limitations that are not positively claimed:
The noted above limitations are only found in the nonfunctional descriptive material and are not functionally involved in the steps recited. The associating steps would be performed the same regardless of the non-functional limitations.
When descriptive material is not functionally related to the substrate, the descriptive material will not distinguish the invention from prior art in terms of patentability. It has been held that where the printed matter is not functionally related to the substrate, the printed matter will not distinguish the invention from the prior art in terms of patentability.
As per claims 7 & 18: Amarasingham/ Lucas discloses as shown above.
Amarasingham further discloses wherein generating the summarized clinical data comprises at least one of: grouping information, from the clinical data, based on contextual information and removing duplicative information from the grouped information (¶¶ [0107], [0111]).
As per claim 9: Amarasingham/ Lucas discloses as shown above.
Amarasingham further discloses wherein outputting the summarized clinical data comprises generating one or more predicted risk measures based on processing the summarized clinical data using one or more risk prediction machine learning models (¶¶ [0055], [0058]; fig. 3 & related text).
As per claims 10 & 19: Amarasingham/ Lucas discloses as shown above.
Amarasingham further discloses wherein the one or more predicted risk measures comprise at least one of: (i) a medication risk, (ii) a re-hospitalization risk, or (iii) a fall risk (¶¶ [0055], [0058]; fig. 3 & related text).
As per claim 11: Amarasingham/ Lucas discloses as shown above.
Amarasingham further discloses wherein outputting the summarized clinical data comprises generating a recommended care plan based on processing the summarized clinical data using one or more care plan generation machine learning models (¶¶ [0020], [0030], [0067], [0096]).
As per claims 12 & 20: Amarasingham/ Lucas discloses as shown above.
Amarasingham further discloses wherein: the recommended care plan is generated based further on one or more predicted risk measures generated using one or more risk prediction machine learning models, and the predicted risk measures comprise at least one of: (i) a medication risk, (ii) a re-hospitalization risk, or (iii) a fall risk (¶¶ [0020], [0030], [0067], [0096]).
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 2, 3 & 14 rejected under 35 U.S.C. 103 as being unpatentable over Amarasingham in view of Lucas and further in view of Heck (US 20130268284 A1) (“Heck”).
As per claims 2 & 14: Amarasingham/ Lucas discloses as shown above.
Amarasingham further discloses in ¶¶ [0125], [0119]- In the request message, the computing device 122 may include an identifier that identifies that device or the user of the device as being associated with a user type, such as a user being a patient (or family member) or a medical professional.
Amarasingham further discloses a patient referral service to a third party, wherein patients’ records are sent to the third party (¶ [0132])
Amarasingham doesn’t expressly disclose wherein receiving the patient identifier comprises receiving a patient referral, for the patient, for home health services.
Heck, however, discloses wherein receiving the patient identifier comprises receiving a patient referral, for the patient, for home health services (Fig. 4 & related text; ¶¶ [0032], [0036]).
It would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention, to modify Amarasingham’s method of generating /sending summarized reports based on requests, to also include the functionality of transferring medical records based on medical referrals, as disclosed by Heck, in order to create or update medical records that focus on patient issues that are imminent to the patient's well-being and to leave other issues for a later time (Amarasingham, ¶[0004]).
As per claim 3: Amarasingham/ Lucas discloses shown above.
Amarasingham further discloses in ¶¶ [0125], [0119]- In the request message, the computing device 122 may include an identifier that identifies that device or the user of the device as being associated with a user type, such as a user being a patient (or family member) or a medical professional.
Amarasingham further discloses a patient referral service to a third party, wherein patients’ records are sent to the third party (¶ [0132])
Amarasingham doesn’t disclose transmitting, to a referring entity that provided the patient referral, one or more requests for the plurality of patient records in response to receiving the patient referral.
Heck, however, discloses transmitting, to a referring entity that provided the patient referral, one or more requests for the plurality of patient records in response to receiving the patient referral (Fig. 4 & related text; ¶¶ [0032], [0038]).
It would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention, to modify Amarasingham’s method of generating /sending summarized reports based on requests, to also include the functionality of transferring medical records based on medical referrals, as disclosed by Heck, in order to create or update medical records that focus on patient issues that are imminent to the patient's well-being and to leave other issues for a later time (Amarasingham, ¶[0004]).
Claim 8 is rejected under 35 U.S.C. 103 as being unpatentable over Amarasingham in view of Lucas and further in view of Fotsch et al (US 20140058750 A1) (“Fotsch”).
As per claim 8: Amarasingham/ Lucas discloses as shown above.
Amarasingham further discloses wherein outputting the summarized clinical data comprises:
generating a summary document comprising the summarized clinical data (¶¶ [0107], [0111]);
embedding one or more reference indications (doctor’s notes, hyperlinks…etc) in the summary document, wherein each respective reference indication corresponds to a respective element of the summarized clinical data and […] (¶¶ [0129], [0044], [0032], [0075]); and
outputting summary document via a graphical user interface (GUI) (¶¶ [0029], [0031]).
Amarasingham does not expressly disclose that the reference indication includes a link to one or more source records, from the plurality of patient records, from which the clinical data was extracted.
Fotsch, however, clearly discloses a reference indication that includes a link to one or more source records, from the plurality of patient records, from which the clinical data was extracted (fig. 2B & related text; ¶¶ [0065], [0016]).
It would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention, to modify Amarasingham’s method of generating /sending summarized reports based on requests, to also include patient data sources in the patient summary records, as disclosed by Fotsch, in order to create a complete and accurate medical record of the patient thereby enhancing healthcare outcomes for the patient.
Response to Arguments
Applicant's arguments filed September 11, 2025 have been fully considered but they are not persuasive.
Response to 101 arguments:
Applicant argument (Page 8):“The amended claims do not recite a judicial exception, at all, and are therefore patent eligible… the present claims, which relate to techniques for training and using machine learning models to summarize clinical data, do not recite or relate to any method of organizing human activity and cannot be performed mentally.”
Examiner respectfully disagrees.The claims recite high-level data processing and organization of information—extracting text, generating importance scores, plotting time series, and summarizing clinical data—which falls within the “certain methods of organizing human activity” and “mental processes” groupings. Invoking machine-learning models does not remove the claims from these abstract-idea categories.
Applicant argument (Page 9):
“[P]rong two ‘considers the claim as a whole’… the claims ‘reflect[] an improvement to the functioning of a computer or to another technology or technical field’… the described and claimed models can be used to ‘readily and efficiently generate accurate and reliable summarized clinical data… with minimal (or no) manual effort,’ reducing time, effort, and ‘potential for error.’”
Examiner respectfully disagrees. Although the specification describes benefits, the claims recite only generic ML and computing steps without specifying a particular, non-conventional implementation (e.g., specialized data structures, novel algorithms, or hardware improvements). Merely achieving efficiency or accuracy with conventional ML on generic hardware does not integrate the abstract idea into a patent-eligible practical application under step 2A, prong 2.
Applicant argument (Page 10):
“If it is a ‘close call’ as to whether a claim is eligible, an Examiner should only make a rejection when it is more likely than not (i.e., >50%) that the claim is ineligible under 35 U.S.C. 101.”
Examiner respectfully disagrees. The record and application of the Office’s eligibility framework demonstrate by a preponderance of the evidence that the claims are directed to ineligible subject matter. The “close call” standard does not preclude a rejection where the Examiner finds—based on the Guidance—that the high-level data-processing steps are abstract and lack an inventive concept.
Response to prior art arguments:
Applicant’s arguments with respect to claim have been considered but are moot in view of the new ground of rejection.
Prior Art Made of Record
The prior art made of record and not relied upon is considered pertinent to Applicant's disclosure, and is listed in the attached form PTO-892 (Notice of References Cited). Unless expressly noted otherwise by the Examiner, all documents listed on form PTO-892 are cited in their entirety.
US 20190156947 A1 discloses techniques and system configurations to enable the automated collection and evaluation of clinical data within an automated insights information system are disclosed herein. In an example, the information system is adapted to continuously monitor clinical systems for new patient data, process patient data into a standardized and structured format, selectively run algorithms to classify and characterize data, and stores the results of algorithms (such as findings, predictions, and recommendations) that can be used as input to other algorithms, or sent to clinical systems and presented to end users. In a specific example, a method performed in a computing system may include: requesting and obtaining a first and second set of clinical data, analyzing the first and second set of clinical data with respective algorithms, identifying a clinical finding, and generating output from the computing system based on the identified clinical finding.
US 20230274806 A1 discloses [0064] In detail, the transfer-requesting hospital terminal 101 may generate transfer information to be provided to a referral hospital when a patient is transferred based on patient medical treatment information, which is information about medical treating a patient who wants to request medical treatment, and may send the generated transfer information to any referral hospital terminal 102 to remotely perform patient medical treatment request.
Conclusion
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any extension fee pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MAMON OBEID whose telephone number is (571)270-1813. The examiner can normally be reached 8 AM- 5 PM.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Deborah Reynolds can be reached at (571) 272-0734. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/MAMON OBEID/Supervisory Patent Examiner, Art Unit 3687
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