DETAILED ACTION
Status of Claims
This action is in reply to an application filed 24 October 2025, which claims priority to a provisional application filed 25 October 2024.
The Office notes that the provisional application does not have support for several of the claimed elements, and specifically for the limitations directed to a device containing two cameras or one camera. Accordingly, the elements unsupported by the provisional application have a filing date consisting of 9 December 2000.
Claims 1-20 are currently pending and 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 within the four statutory categories. Claims 1-9 are drawn to a computer-implemented method, which is within the four statutory categories (i.e. process). Claims 10-16 are drawn to a system, which is within the four statutory categories (i.e. machine). Claims 17-20 are drawn to a non-transitory computer readable media storing instructions, which is within the four statutory categories (i.e. manufacture).
Prong 1 of Step 2A
Claim 1 recites: A system comprising:
a computer comprising one or more processors and one or more computer-readable media storing instructions which, when executed by the one or more processors, cause the computer to at least:
receive a query to provide a summary of patient-specific information regarding a condition for a particular patient;
determine a category for the query, wherein determining includes selecting the category from a plurality of categories including at least one of New Admit, New to Me, and Rounded on Before;
retrieve data relevant to the query from an electronic health record (EHR) database, wherein the data as retrieved includes at least structured and unstructured content;
process the data as retrieved based on the category;
filter the data as processed based on the category;
generate, by a generative machine learning model on the computer, a narrative summary including a first portion of the data as filtered including at least a portion of the unstructured content;
generate a structured summary including a second portion of the data as filtered including at least a portion of the structured content; and
format the narrative summary and the structured summary into an output, wherein processing performed for a first selected category differs from processing performed for a second selected category.
The underlined limitations as shown above, given the broadest reasonable interpretation, cover the abstract ideas 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 for a summary of needed information for a patient), 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 17 are identical as the abstract idea for claim 10, because the only difference between claims 1, 10 and 17 is that claim 1 recites a method, whereas claim 10 recites a system and claim 17 recites a non-transitory computer-readable media.
Dependent claims 2-9, 11-16 and 18-20 include other limitations, for example claims 2-9, 11-16 and 18-20 recite filtering or processing data, 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 2-9, 11-16 and 18-20 not addressed above are deemed additional elements to the abstract idea, and will be further addressed below. Hence dependent claims 2-9, 11-16 and 18-20 are nonetheless directed towards fundamentally the same abstract idea as independent claims 1, 10 and 17.
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 generative by a generative 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 276, 335 and 336 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 2-9, 11-16 and 18-20 include other limitations, but these limitations also amount to no more than mere instructions to apply the exception (e.g. the recitation of the machine learning model in claims 6, 14 and 20), generally linking the abstract idea to a particular technological environment or field of use (e.g. the types of processing disclosed in claims 3, 8, 9, 12, 16 and 19, and the types of data disclosed in dependent claims 2-9, 11-16 and 18-20), and/or do not include any additional elements beyond those already recited in independent claims 1, 10 and 17, 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 276, 335 and 336 of the Specification discloses that the additional elements (i.e. the mlm 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);
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; and
v. Electronically scanning or extracting data from a physical document, Content Extraction and Transmission, LLC v. Wells Fargo Bank, 776 F.3d 1343, 1348, 113 USPQ2d 1354, 1358 (Fed. Cir. 2014) (optical character recognition).
Dependent claims 2-9, 11-16 and 18-20 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, 10 and 17, and/or the additional elements recited in the aforementioned dependent claims similarly amount to no more than mere instructions to apply the exception (e.g. the recitation of the machine learning model in claims 6, 14 and 20), generally linking the abstract idea to a particular technological environment or field of use (e.g. the types of processing disclosed in claims 3, 8, 9, 12, 16 and 19, and the types of data disclosed in dependent claims 2-9, 11-16 and 18-20), 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.
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 1-5, 9-13 and 17-19 are rejected under 35 U.S.C. 103 as being obvious over Amarasingham et al. (U.S. PG-Pub 2023/0104655), further in view of Kaliraman et al. (U.S. PG-Pub 2023/0130914 A1), hereinafter Kaliraman.
As per claims 1, 2, 10, 11, 17 and 18, Amarasingham discloses a computer-implemented method, a non-transitory computer-readable media storing instructions and a system (Amarasingham, see Figs. 1-3.) comprising:
a computer comprising one or more processors and one or more computer-readable media storing instructions which, when executed by the one or more processors, cause the computer (Amarasingham, see Fig. 1A.) to at least:
receive a query to provide a summary of patient-specific information regarding a condition for a particular patient (Amarasingham, see Fig. 4 #402 wherein a user requests summary of a patient condition.);
determine a category for the query, wherein determining includes selecting the category from a plurality of categories including at least one of New Admit, New to Me, and Rounded on Before (System provides a request for summary based in response to a patient handover, which would comprise the category of New to Me.);
retrieve data relevant to the query from an electronic health record (EHR) database, wherein the data as retrieved includes at least structured and unstructured content (Identified summary located plural information related to visit with complaint, see Fig. 6A. Data is initially received from a plurality of EHR sources, see Fig. 3 #302, and includes structured and unstructured data, see paragraphs 25, 29 and 37.);
process the data as retrieved based on the category (Data is processed and filtered based on handover request, see Fig. 3 #308-324.);
filter the data as processed based on the category (Data is processed and filtered based on handover request, see Fig. 3 #308-324.);
generate, by a generative machine learning model on the computer, a narrative summary including a first portion of the data as filtered including at least a portion of the unstructured content (See AI generated summary of Amarasingham, see Figs. 6A-6C and paragraphs 29-31. The Office notes the result is both unstructured and structured.);
generate a structured summary including a second portion of the data as filtered including at least a portion of the structured content (See AI generated summary of Amarasingham, see Figs. 6A-6C and paragraphs 29-31. The Office notes the result is both unstructured and structured. Summary is provided with hyperlinks, see Amarasingham, Figs. 6A-6C.); and
format the narrative summary and the structured summary into an output … (See Amarasingham, Figs. 6A-6C.).
Amarasingham fails to explicitly disclose:
1,10,17.wherein processing performed for a first selected category differs from processing performed for a second selected category; and
2,11,18.wherein processing includes, for the first selected category, providing a first set of processing modules, and for the second selected category, providing a second set of processing modules.
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 wherein processing performed for a first selected category differs from processing performed for a second selected category and providing different processing for different categories (Kaliraman discloses determination of a handoff type and different processing of data based thereon, see paragraphs 38, 43 and 57; also see event of Fig. 12.) 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 wherein processing performed for a first selected category differs from processing performed for a second selected category, as taught by Kaliraman, 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 Kaliraman are directed to the electronic processing of patient healthcare data and specifically to the creation of patient clinical summaries.
As per claims 3-5, 12, 13, 9 and 19, Amarasingham/Kaliraman discloses claims 1, 10 and 17, discussed above. Amarasingham also discloses:
3. wherein filtering includes considering the data as processed according to a semantic knowledge graph selected based on the category (Amarasingham utilizes a semantic knowledge graph [KG] based on handover request, see paragraph 54.);
4,12,19. wherein the data is processed according to the semantic knowledge graph by applying enrichment that prioritizes selected data in a predefined hierarchy model based on the category and at least one of a reason for visit and a chief complaint for the particular patient (KG uses a hierarchy model based on handover request and chief compliant, see Amarasingham paragraphs 54, 56, 57, 95 and 96.);
5,13. wherein the predefined hierarchy model includes prioritizing data related to changes in the condition for the particular patient during a predetermined time window (Amarasingham considers limiting data input to a predefined time period, see paragraphs 24 and 84. Engine may prioritize data and omit items of les importance based on conditions, etc., see paragraph 36. Predictive model determines whether patient’s condition will deteriorate during a predefined time period, see paragraph 61. See also prioritization module of paragraphs 65-68 and 123-128.); and
9. determining a role of an originator of the query, the role determining a level of permissions assigned to the originator, wherein processing is modified according to the role as determined (Additional users request summaries based on previously made summaries, system provides summaries based on type of user, see paragraph 31; i.e. the user role determines their permissions for a given summary type.).
Claims 6, 7, 14, 15 and 20 are rejected under 35 U.S.C. 103 as being obvious over Amarasingham/Kaliraman further in view of Siracusano et al. (U.S. PG-Pub 2024/0411994 A1), hereinafter Kaliraman.
As per claims 6, 7, 14, 15 and 20, Amarasingham/Kaliraman discloses claims 1, 10 and 17, discussed above. Amarasingham also discloses:
6,14,20. transforming the data into an intermediate representation normalized to clinical terminologies (See generated summary of Amarasingham, Fig. 6A and paragraph 29.), and
7,15. caching the intermediate representation as keyed to at least a selected one of the particular patient, the category, and a time window, and reusing the cache in responding to updated queries related to the particular patient and the category (Additional users request summaries based on previously made summaries, system provides summaries based on particular patient, see Amarasingham paragraph 31.);
Amarasingham fails to explicitly disclose:
6,14,20. filtering the intermediate representation to meet a token budget for the generative machine learning model on the computer.
Siracusano 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 filtering the intermediate representation to meet a token budget for the generative machine learning model on the computer (Siracusano, see paragraphs 71, 96, 171, 191 and 196.) in order to provide needed data in a cost-efficient manner.
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/Kaliraman to include filtering the intermediate representation to meet a token budget for the generative machine learning model on the computer, as taught by Siracusano, in order to create a patient summary generation system that can provide needed data in a cost-efficient manner. 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 Siracusano are directed to the electronic processing of patient healthcare data and specifically to the creation of patient clinical summaries.
Claims 8 and 16 are rejected under 35 U.S.C. 103 as being obvious over Amarasingham/Kaliraman further in view of Gill et al. (U.S. PG-Pub 2022/0148689 A1), hereinafter Gill.
As per claims 8 and 16, Amarasingham/Kaliraman discloses claims 1, 10 and 17, discussed above. Amarasingham fails to explicitly disclose wherein processing includes, for the unstructured content, processing the unstructured content through at least one of optical character recognition, image recognition, and chunking processes.
Gill teaches that it was old and well known in the art of healthcare communications before the effective filing date of the claimed invention to process unstructured content using optical character recognition (Gill, paragraphs 88 and 136.) in order to utilize all available patient data when providing healthcare services.
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/Kaliraman to process unstructured content using optical character recognition, as taught by Gill, in order to create a patient summary generation system that can utilize all available patient data when providing healthcare services. 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 Gill are directed to the electronic processing of patient healthcare data and specifically to the creation of patient reports.
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
2 September 2026
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