DETAILED ACTION
Status of Claims
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
This action is in reply to an application filed 24 October 2025, which claims domestic priority to a provisional application filed 25 October 2024.
Claims 1-20 are currently pending and have been examined.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
Claims 1-7 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), as being indefinite for failing to particularly point out and distinctly claim the subject matter which applicant regards as the invention.
Claim 1 recites the phrase sources based the at least one in the retrieving limitation. It is unclear what this phrase refers to.
To the extent that other claims rely on claims that are rejected under 35 USC 112 and fail to correct the deficiencies of the claims they rely on, those other claims are rejected for the same reasons as the claims they rely on. Appropriate correction is required.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Step 1
Claims 1-20 are within the four statutory categories. Claims 1-7 are drawn to a computer-implemented method, which is within the four statutory categories (i.e. process). Claims 8-14 are drawn to a system for predicting, which is within the four statutory categories (i.e. machine). Claims 15-20 are drawn to a non-transitory computer-readable medium, which is within the four statutory categories (i.e. manufacture).
Prong 1 of Step 2A
Claim 8 recites: a system comprising:
a computer system comprising one or more processors and memory storing computer executable instructions that, when executed by the one or more processors, configure the computer system to at least:
receiving a first request from a client system to provide a summary information for a patient of a healthcare provider for a visit, the summary information comprising patient history information describing at least one of a medical condition and a medical history of the patient;
determining at least one of a category of visit and a plurality of medical complaints for the patient, the category of visit including acute, wellness, follow-up, and generic;
obtain information regarding a chief complaint for the visit;
retrieving data relevant to the first request from a plurality of sources based on the at least one of the category of visit and the plurality of medical complaints;
formatting the data into intermediate representation (IR);
storing the IR at the computer system;
generating a first input for a generative machine learning model, the first input comprising a first prompt based on the first request;
generating, by the generative machine learning model, a first query result for the first input, the first query result comprising a narrative section and a structured section extracted from one or more portions of the data;
generating, by the generative machine learning model, at least one suggested follow-on query selectable as a further request;
formatting the first query result and the suggested follow-on query into a first brief summary; and
providing the first brief summary to a user interface at the client system.
The underlined limitations as shown above, given the broadest reasonable interpretation, cover the abstract idea of a certain method of organizing human activity because they recite a process that could be described as managing personal behavior or relationships or interactions between people (i.e. social activities, teaching, and following rules or instructions – in this case the collaborative steps of a caregiver asking another caregiver for a summary of needed information prior to a patient visit), e.g. see MPEP 2106.04(a)(2). Any limitations not identified above as part of the abstract idea(s) are deemed “additional elements,” and will be discussed in further detail below.
Furthermore, the abstract idea for claims 1 and 15 are identical as the abstract idea for claim 8, because the only difference between claims 1, 8 and 15 is that claim 1 recites a method, whereas claim 8 recites a system and claim 15 recites a non-transitory computer-readable media.
Dependent claims 2-7, 9-14 and 16-20 include other limitations, for example claims
2-4, 9-11 and 16-18 disclose various data sources, and claims 5-7, 12-14, 19 and 20 describes generation of output, but these only serve to further narrow the abstract idea, and a claim may not preempt abstract ideas, even if the judicial exception is narrow, e.g. see MPEP 2106.04. Additionally, any limitations in dependent claims 2-7, 9-14 and 16-20 not addressed above are deemed additional elements to the abstract idea, and will be further addressed below. Hence dependent claims 2-7, 9-14 and 16-20 are nonetheless directed towards fundamentally the same abstract idea as independent claims 1, 8 and 15.
Prong 2 of Step 2A
Claims 1-20 are not integrated into a practical application because the additional elements (i.e. any limitations that are not identified as part of the abstract idea) amount to no more than limitations which:
amount to mere instructions to apply an exception – for example, the recitation of generating by the machine learning model and the structural components of the computer, which amounts to merely invoking a computer as a tool to perform the abstract idea, e.g. see paragraphs 39 and 183 of the present Specification, see MPEP 2106.05(f); and/or
generally link the abstract idea to a particular technological environment or field of use – for example, the claim language limiting the data to patient related data, which amounts to limiting the abstract idea to the field of healthcare, see MPEP 2106.05(h); and/or
adding insignificant extrasolution activity to the abstract idea, for example mere data gathering, selecting a particular data source or type of data to be manipulated, and/or insignificant application (e.g. see MPEP 2106.05(g)).
Additionally, dependent claims 2-7, 9-14 and 16-20 include other limitations, but these limitations also amount to no more than mere instructions to apply the exception (e.g. the hovering window and URL of claims 7 and 14), generally linking the abstract idea to a particular technological environment or field of use (e.g. the types of data disclosed in dependent claims 2-7, 9-14 and 16-20), and/or do not include any additional elements beyond those already recited in independent claims 1, 8 and 15, and hence also do not integrate the aforementioned abstract idea into a practical application.
Step 2B
Claims 1-20 do not include additional elements that are sufficient to amount to “significantly more” than the judicial exception because the additional elements (i.e. the non-underlined limitations above – in this case, the structural components of the computer), as stated above, are directed towards no more than limitations that amount to mere instructions to apply the exception, generally link the abstract idea to a particular technological environment or field of use, and/or add insignificant extra-solution activity to the abstract idea, wherein the insignificant extra-solution activity comprises limitations which:
amount to elements that have been recognized as well-understood, routine, and conventional activity in particular fields, as demonstrated by:
The Specification expressly disclosing that the additional elements are well-understood, routine, and conventional in nature:
paragraphs 39 and 183 of the Specification discloses that the additional elements (i.e. the machine learning model and the structural components of the computer) comprise a plurality of different types of generic computing systems that are configured to perform generic computer functions (i.e. receive and process data ) that are well-understood, routine, and conventional activities previously known to the pertinent industry (i.e. healthcare);
Relevant court decisions: The following are examples of court decisions demonstrating well-understood, routine and conventional activities, e.g. see MPEP 2106.05(d)(II):
i. Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016) (using a telephone for image transmission); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network); but see DDR Holdings, LLC v. Hotels.com, L.P., 773 F.3d 1245, 1258, 113 USPQ2d 1097, 1106 (Fed. Cir. 2014) ("Unlike the claims in Ultramercial, the claims at issue here specify how interactions with the Internet are manipulated to yield a desired result‐‐a result that overrides the routine and conventional sequence of events ordinarily triggered by the click of a hyperlink." (emphasis added));
ii. Performing repetitive calculations, Flook, 437 U.S. at 594, 198 USPQ2d at 199 (recomputing or readjusting alarm limit values); Bancorp Services v. Sun Life, 687 F.3d 1266, 1278, 103 USPQ2d 1425, 1433 (Fed. Cir. 2012) ("The computer required by some of Bancorp’s claims is employed only for its most basic function, the performance of repetitive calculations, and as such does not impose meaningful limits on the scope of those claims.");
iii. Electronic recordkeeping, Alice Corp. Pty. Ltd. v. CLS Bank Int'l, 573 U.S. 208, 225, 110 USPQ2d 1984 (2014) (creating and maintaining "shadow accounts"); Ultramercial, 772 F.3d at 716, 112 USPQ2d at 1755 (updating an activity log); and
iv. Storing and retrieving information in memory, Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015); OIP Techs., 788 F.3d at 1363, 115 USPQ2d at 1092-93.
Dependent claims 2-7, 9-14 and 16-20 include other limitations, but none of these limitations are deemed significantly more than the abstract idea because, as stated above, the aforementioned dependent claims do not recite any additional elements not already recited in independent claims 1, 8 and 15, and/or the additional elements recited in the aforementioned dependent claims similarly amount to mere instructions to apply the exception (e.g. the hovering window and URL of claims 7 and 14), generally linking the abstract idea to a particular technological environment or field of use (e.g. the types of data disclosed in dependent claims 2-7, 9-14 and 16-20), and hence do not amount to “significantly more” than the abstract idea.
Thus, taken alone, the additional elements do not amount to significantly more than the abstract idea identified above. Furthermore, looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually, and there is no indication that the combination of elements improves the functioning of a computer or improves any other technology, and their collective functions merely provide conventional computer implementation.
Therefore, whether taken individually or as an ordered combination, claims 1-20 are nonetheless rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter. Appropriate correction is required.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102 of this title, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1-20 are rejected under 35 U.S.C. 103 as being obvious over Amarasingham et al. (U.S. PG-Pub 2024/0312583 A1), further in view of Sasidharan et al. (U.S. PG-Pub 2025/0118399 A1), hereinafter Sasidharan.
As per claims 1, 8 and 15, Amarasingham discloses a computer-implemented method, a non-transitory computer-readable medium storing computer-executable instructions and a system (Amarasingham, see Figs. 1-3.) comprising:
a computer system comprising one or more processors and memory storing computer executable instructions that, when executed by the one or more processors, configure the computer system (Amarasingham, see Fig. 1A.) to at least:
receiving a first request from a client system to provide a summary information for a patient of a healthcare provider for a visit, the summary information comprising patient history information describing at least one of a medical condition and a medical history of the patient (Amarasingham, see paragraph 402 wherein a user requests summary of a patient condition.);
determining at least one of a category of visit and a plurality of medical complaints for the patient, the category of visit including acute, wellness, follow-up, and generic (Identified summary information includes identification of chief complaint on visit, e.g. acute NSTEMI and cardiogenic shock, see Amarasingham, see Fig. 6A.);
obtain information regarding a chief complaint for the visit (Identified summary information includes identification of chief complaint on visit, e.g. acute NSTEMI and cardiogenic shock, see Amarasingham, see Fig. 6A.);
retrieving data relevant to the first request from a plurality of sources based on the at least one of the category of visit and the plurality of medical complaints (Identified summary located plural information related to visit with complaint, see Fig. 6A. Data is initially received from a plurality of sources, see Fig. 3 #302.);
formatting the data into intermediate representation (IR) (See generated summary of Amarasingham, Fig. 6A and paragraph 29.);
storing the IR at the computer system (See generated summary of Amarasingham, Fig. 6A and paragraph 29.);
generating a first input for a generative machine learning model, the first input comprising a first prompt based on the first request (Additional users request summaries based on previously made summaries, system provides summaries based on type of user, see paragraph 31.);
generating, by the generative machine learning model, a first query result for the first input, the first query result comprising a narrative section and a structured section extracted from one or more portions of the data (See AI generated summary of Amarasingham, see Fig. 6A and paragraphs 29-31. The Office notes the result is both narrative and structured.);
generating, by the generative machine learning model, at least one suggested follow-on query selectable as a further request (System generates selectable hyperlinks to provide information corresponding to a further request, see paragraph 129 and Figs. 6A-6C.);
formatting the first query result and the suggested follow-on query into a first brief summary (Summary is provided with hyperlinks, see Amarasingham, Figs. 6A-6C.); and
providing the first brief summary to a user interface at the client system (Summary is provided with hyperlinks, see Amarasingham, Figs. 6A-6C.).
Although it is the Office’s position that Amarasingham discloses all of the elements above, the Office will provide a secondary reference in the interest of expediting prosecution. Sasidharan teaches that it was old and well known in the art of healthcare communications before the effective filing date of the claimed invention to provide generating, by the generative machine learning model, at least one suggested follow-on query selectable as a further request (Sasidharan discloses generating a suggested selectable follow-on query, see paragraphs 126-130.) in order to provide additional relevant information so as to increase knowledge of the patient’s condition.
Therefore, it would have been obvious to one of ordinary skill in the art of healthcare communications before the effective filing date of the claimed invention to modify the patient summary generation system of Amarasingham to include preparing, by at least one of the one or more computing devices, a service bundle including at least one appropriate medical treatment for the patient, as taught by Sasidharan, in order to create a patient summary generation system that can provide additional relevant information so as to increase knowledge of the patient’s condition. Moreover, merely adding a well-known element into a well-known system, to produce a predictable result to one of ordinary skill in the art, does not render the invention patentably distinct over such combination (see MPEP 2141).
Both Amarasingham and Sasidharan are directed to the electronic processing of patient healthcare data and specifically to the creation of patient clinical summaries.
As per claims 2-5, 9-12 and 16-19, Amarasingham/Sasidharan discloses claims 1, 8 and 15, discussed above. Amarasingham also discloses:
2,9,16. wherein retrieving the data includes accessing an electronic health record (EHR) to retrieve records including at least one of medical history, records from prior medical visits, laboratory results, diagnostic results, past abnormal observation, previously noted chief complaints, known allergies, medication history, immunization records, insurance information, social history, family history, previous recommendations, past and future appointments, and messages sent through the HER (See Amarasingham paragraphs 24-27.);
3,10,17. wherein accessing the EHR includes limiting a range of accessed records to items created within a predetermined time period prior to receiving the first request (Amarasingham considers limiting data input to a predefined time period, see paragraphs 24 and 84);
4,11,18. updating the data relevant to the first request based on updated information received from the patient at a visit to generate updated data, the updated information including at least one of vitals, one or more chief complaints for the visit, and intake notes at the visit (See Amarasingham paragraphs 74, 75, 89, 130 and Fig. 6C.);
5,12,19. receiving a second request from the client system based on the first brief summary (System is operative to generate new summaries, on request or automatically, based on updated information, see paragraphs 22, 74, and 75.);
retrieving data relevant to the second request, the data relevant to the second request including the updated data (System is operative to generate new summaries, on request or automatically, based on updated information, see paragraphs 22, 74, and 75. Identified summary located plural information related to visit with complaint, see Fig. 6A.);
generating a second input for the generative machine learning model, the second input comprising a second prompt and one or more portions of the data relevant to the second request (System is operative to generate new summaries, on request or automatically, based on updated information, see paragraphs 22, 74, and 75. Additional users request summaries based on previously made summaries, system provides summaries based on type of user, see paragraph 31.);
generating, by the generative machine learning model, a second query result from the second input based on the data relevant to the second request (System is operative to generate new summaries, on request or automatically, based on updated information, see paragraphs 22, 74, and 75. See AI generated summary of Amarasingham, see Fig. 6A and paragraphs 29-31.);
formatting the second query result into a second brief summary (System is operative to generate new summaries, on request or automatically, based on updated information, see paragraphs 22, 74, and 75. See generated summary of Amarasingham, Figs. 6A-6C.); and
providing the second brief summary to the user interface at the client system (System is operative to generate new summaries, on request or automatically, based on updated information, see paragraphs 22, 74, and 75. See generated summary of Amarasingham, Figs. 6A-6C.).
As per claims 6, 7, 13, 14 and 20, Amarasingham/Sasidharan discloses claims 1, 8 and 15, discussed above. Amarasingham also discloses:
6,13,20. wherein the narrative section includes a first selection from the first query result arranged into natural language text (See generated summary of Amarasingham, Figs. 6A-6C.), and the structured section includes a second selection from the first query result used to populate data within the first brief summary (Summary is provided with hyperlinks, see Amarasingham, Figs. 6A-6C.);
7,14. wherein at least one of the narrative section and the structured section includes one of a Uniform Resource Locator (URL) and a hovering window to enable display of additional information regarding a specific portion of the first brief summary (Summary is provided with hyperlinks, see Amarasingham, Figs. 6A-6C. It is old and well known to provide hovering information over a hyperlink.).
Amarasingham fails to explicitly disclose a plurality of preset windows. Sasidharan teaches that it was old and well known in the art of healthcare communications before the effective filing date of the claimed invention to provide a plurality of preset windows (Sasidharan discloses generating pop-up windows, see paragraphs 126-130.) in order to provide additional relevant information so as to increase knowledge of the patient’s condition.
Therefore, it would have been obvious to one of ordinary skill in the art of healthcare communications before the effective filing date of the claimed invention to modify the patient summary generation system of Amarasingham to include a plurality of preset windows, as taught by Sasidharan, in order to create a patient summary generation system that can provide additional relevant information so as to increase knowledge of the patient’s condition. Moreover, merely adding a well-known element into a well-known system, to produce a predictable result to one of ordinary skill in the art, does not render the invention patentably distinct over such combination (see MPEP 2141).
Conclusion
Unused but cited relevant prior art includes:
Sasidharan et al. (U.S. PG-Pub 2023/0048252 A1) discloses a system and method for treatment guideline display.
Any inquiry of a general nature or relating to the status of this application or concerning this communication or earlier communications from the Examiner should be directed to Mark Holcomb, whose telephone number is 571.270.1382. The Examiner can normally be reached on Monday-Friday (8-5). If attempts to reach the Examiner by telephone are unsuccessful, the Examiner’s supervisor, Kambiz Abdi, can be reached at 571.272.6702.
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/MARK HOLCOMB/
Primary Examiner, Art Unit 3685
28 August 2026
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