DETAILED ACTION
Continued Examination Under 37 CFR 1.114
A request for continued examination (“RCE”) under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on February 11, 2026 has been entered.
Acknowledgments
The present application is being examined under the pre-AIA first to invent provisions.
This office action is in response to the RCE noted above.
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 based on deleting one or more redundant words from the one or more sentences;
generating one or more importance scores for the one or more sentences in the text data, the one or more importance scores indicatinq importance of the one or more sentences relative to each other,
generating a time series plot based on the text data, the time series plot representinq changes over time for one or more features from 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 based on deletinq one or more redundant words from the one or more sentences:
generating one or more importance scores for the one or more sentences in the text data, the one or more importance scores indicatinq importance of the one or more sentences relative to each other: and
generating a time series plot based on the text data, the time series plot representinq changes over time for one or more features from 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 § 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, 4-7, 9-13 & 15-20 are rejected under 35 U.S.C. 103 as being anticipated by Amarasingham et al. (US 20230104655 A1) (“Amarasingham”) in view of Lucas et al (US 20200176098 A1) (“Lucas”).
As per claims 1 and 13, Amarasingham describes a method comprising:
receiving a patient identifier corresponding to a patient (see Fig. 3 and ¶ [0020]);
accessing a plurality of patient records based on the patient identifier (see Fig. 3 and ¶ [0020]);
generating text data by extracting textual information from the plurality of patient records using one or more text machine learning models (see Fig. 3 and ¶¶ [0038], [0088]);
generating clinical data by processing the text data using one or more extraction machine learning models (see Fig. 3 and ¶¶ [0038], [0088]);
generating summarized clinical data by processing the clinical data using one or more summary machine learning models (see Fig. 3 and ¶¶ [0038], [0089]);
outputting the summarized clinical data (see Fig. 3 and ¶ [0112]);
for claim 13, training one or more risk prediction machine learning models to generate predicted risk measures based on the summarized clinical data (see Fig. 3 and ¶¶ [0038], [0058]).
Amarasingham further teaches:
retrieving records and extracting clinical data (see ¶¶ [0055]–[0079]);
generating summarized clinical data by grouping context, de-duplication, and creating a summary document with references (see ¶¶ [0141]–[0149], Fig. 11);
outputting the summary via GUI and generating risk measures from summarized data (see ¶¶ [0088]–[0111]; Figs. 6–7).
However, Amarasingham does not expressly teach:
generating one or more compressed sentences for one or more sentences in the text data based on deleting one or more redundant words from the one or more sentences;
generating one or more importance scores for the one or more sentences in the text data, the one or more importance scores indicating importance of the one or more sentences relative to each other;
generating a time series plot based on the text data, the time series plot representing changes over time for one or more features from the text data.
Lucas, however, teaches these limitations:
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, effectively deleting redundant words (see FIG. 3, FIG. 5, and ¶¶ [0154]–[0167]);
generating one or more importance scores for the one or more sentences in the text data, where the importance scores indicate importance of the one or more sentences relative to each other, as shown by explicit sentence-level “Score/Weight” examples and discussion of weighting and ranking (see FIG. 4, and ¶¶ [0161]–[0167]);
generating a time series plot based on the text data by extracting and normalizing temporal events (such as timestamps) suitable for timeline or time-series representations, so that the time series plot represents changes over time for one or more features from the text data (see FIG. 2, and ¶¶ [0096]–[0103], [0213]–[0216]).
A person of ordinary skill in the art 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. These are predictable uses yielding expected benefits, with no teaching away.
The combination of Amarasingham and Lucas does not specifically show summarized clinical data that includes, all together, compressed sentences, importance scores, and a time series plot.
However, it would have been obvious for someone skilled in the art to include the outputs described by Lucas—such as compressed sentences, importance scores, and time-related features—as part of the summarized clinical data in Amarasingham’s system. Amarasingham explains how to generate summarized clinical data from patient records (see ¶¶ [0055]–[0079], [0141]–[0149]).
Lucas describes:
making compressed sentences by parsing and labeling to keep only the important parts (FIG. 3; FIG. 5; ¶¶ [0154]–[0167]);
assigning importance scores to sentences and ranking them (FIG. 4; ¶¶ [0161]–[0167]);
extracting time information from text to create timelines or time series (FIG. 2; ¶¶ [0096]–[0103], [0213]–[0216]).
Because both references use step-by-step processing, combining these known features into one structured clinical data summary would have been a routine and predictable design choice. This combination would provide practical benefits, such as clearer, shorter, and more useful summaries. There is nothing in the art that teaches away from this combination. 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]).
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 February 11, 2026 have been fully considered but they are not persuasive.
Response to 101 arguments:
Applicant argument (Page 7):“The amended claims do not recite a judicial exception, at all, and are therefore patent eligible. … The Office alleges that the claims recite ‘certain methods of organizing human activity’ and/or ‘steps [that] can be performed mentally.’ … Applicant respectfully disagrees, and submits that 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 response:Applicant’s argument is not persuasive. The claims are directed to collecting, processing, and summarizing information from patient records, which is a form of organizing human activity. The claimed steps, including generating summaries and recommendations, are activities that can be performed by people mentally or with pen and paper. Using machine learning models in these steps does not change the fact that the claims are drawn to an abstract idea.
Applicant argument (Page 9):“Here, even if the amended claims are found to recite a judicial exception (which they do not), they are eligible because they ‘reflect[] an improvement in the functioning of a computer, or an improvement to other technology or technical field,’ and further ‘integrate’ the alleged ‘judicial exception into a practical application of the exception.’ … Applicant respectfully submits that this is a clear technological improvement to text analysis and summarization.”
Examiner response:This argument is not persuasive. The claims do not describe any improvement to how computers work, nor do they improve another technology. The claims use computers and machine learning models simply as tools for processing information. There is no indication in the claims that the computer is doing anything it could not do before, or that the technology itself is improved. The claims just use existing computer technology to carry out the abstract idea.
Applicant argument (Page 11):“…‘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., more than 50%) that the claim is ineligible under 35 U.S.C. 101.’ … Applicant respectfully submits that the present claims are clearly eligible as discussed above, and that the present claims at least reflect a ‘close call’ such that unpatentability cannot be established by a preponderance of the evidence, as required.”
Examiner response:Applicant’s argument is not persuasive. Based on the record, it is more likely than not that the claims are directed to an abstract idea without any additional features that make them patent eligible. The claims do not add anything meaningful beyond the abstract idea itself, and the analysis supports the conclusion that the claims are ineligible under 35 U.S.C. 101.
Applicant argument (Page 11):“…the Appeals Review Panel explained that the claims at issue were directed to eligible subject matter because they improved the functioning of the computer, such as by improving the training of the model itself. … Applicant submits that the present claims similarly reflect improvements to the machine learning technology itself … The Appeals Review Panel reminded Examiners and panels to ‘not evaluate claims at such a high level of generality.’ … Applicant respectfully submits that the present rejections similarly ‘evaluate [the] claims at [] a high level of generality’ without adequate explanation.”
Examiner response:Applicant’s argument is not persuasive. The claims do not include any details about improving computer function or machine learning technology. Rather, the claims use computer components to perform general data processing steps. There is no specific new algorithm or hardware described. The rejection considers the claims as a whole and is not based on an overly general view of the invention.
Response to 103 arguments:
Applicant’s arguments with respect to claim have been considered but are not persuasive.
Applicant argument (Page 12):“For example, in rejecting pre-amended claim 1, the Office argues that Lucas teaches ‘generating one or more compressed sentences for one or more sentences in the text data’ because Lucas describes ‘sentence parsing’ to ‘isolate[] salient content.’ Final Office Action, p. 11. However, Applicant respectfully submits that there is clear difference between simply identifying ‘salient content,’ as the Office argues, and generating compressed sentences by ‘deleting one or more redundant words,’ as recited in the amended claims.”
Examiner response:Applicant’s argument is not persuasive. Lucas describes parsing and processing sentences to retain only the most relevant or salient portions (see, e.g., Figs. 3–5, [0154]-[0167]). In Lucas, parsing and sequence labeling are used to extract the essential information from a sentence, which necessarily involves removing less relevant or redundant words. The result is a shortened or compressed sentence containing only the key content. The distinction between “isolating salient content” and “deleting redundant words” is not meaningful, as both approaches produce a compressed sentence. Therefore, Lucas teaches or suggests this claim feature.
Applicant argument (Page 13):“Similarly, in rejecting pre-amended claim 1, the Office argues that Lucas teaches ‘generating one or more importance scores for the one or more sentences in the text data,’ because Lucas describes a ‘Score/Weight’ for sentences. Final Office Action, p. 11. However, Applicant notes that the discussion in Lucas regarding sentence-level scores or weights are actually related to the confidence of the extraction (e.g., a ‘high weight’ that the patient actually took medicine at a given time), rather than to the ‘importance’ of a given sentence. [0161]. That is, Lucas clearly fails to teach or suggest that the ‘importance scores indicat[e] importance of the one or more sentences relative to each other,’ as recited in the amended claims.”
Examiner response:Applicant’s argument is not persuasive. Lucas assigns scores or weights to sentences to determine their relevance or importance in the extracted clinical information (see, e.g., Fig. 4, [0161]-[0167]). These scores are used to rank and prioritize sentences for inclusion in a summary, which matches the claimed use of “importance scores” to indicate the significance of each sentence relative to others. Whether the score is described as “confidence” or “importance,” the effect is the same: the system distinguishes which sentences are more relevant. Therefore, this feature is taught or suggested by Lucas.
Applicant argument (Page 13):“Further, in rejecting pre-amended claim 1, the Office argues that Lucas teaches ‘generating a time series plot based on the text data,’ because Lucas describes ‘time-related features’ that are ‘suitable for timeline/time-series representations.’ Final Office action, p. 11. However, Applicant notes that even if Lucas does teach time-related features that are ‘suitable’ for time-series analysis, the Office appears to concede that Lucas does not, in fact, actually teach the generation of ‘a time series plot,’ as recited in the present claims. Further, Lucas clearly fails to teach or suggest generating a ‘time series plot representing changes over time for one or more features from the text data,’ as recited in the amended claims.”
Examiner response:Applicant’s argument is not persuasive. Lucas describes extracting and organizing time-related features for use in timeline or time-series representations (see, e.g., Fig. 2, [0096]-[0103], [0213]-[0216]). Lucas explains how clinical events and features are normalized and structured for visualization over time, which inherently suggests generating a time series plot to show changes in features. Additionally, Amarasingham discusses generating and displaying summarized clinical data, including visualizations such as plots (see, e.g., [0055], [0088], [0111]). The combination would have led a person of ordinary skill to generate and display a time series plot as claimed. Thus, this limitation is taught or would have been obvious over the cited art.
Applicant argument (Page 13):“For at least these reasons, Applicant respectfully submits that claims 1 and 13 are allowable, and requests withdrawal of the rejections and allowance of claims 1 and 13. The rejections to claims 2-12 and 14-20 incorporate the rejections to independent claims 1 and 13, respectively. As stated above, the rejections to independent claims 1 and 13 are believed to be overcome. Accordingly, the rejections to claims 2-12 and 14-20 are also believed to be overcome. Therefore, the withdrawal of the rejections to these claims is respectfully requested.”
Examiner response:Applicant’s arguments have been fully considered but are not persuasive. The combination of Amarasingham and Lucas teaches or suggests all the limitations of the independent claims, either expressly or by obvious modification. The dependent claims do not recite additional features that distinguish over the cited art. Accordingly, the rejections under 35 U.S.C. 103 are maintained.
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
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/MAMON OBEID/Supervisory Patent Examiner, Art Unit 3687
