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 the application filed 7 February 2025, which claims foreign priority to a Japanese application filed 16 February 2024.
Claims 1-8 are currently pending and have been examined.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 7 February 2025 has been considered by the Office to the extent indicated.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), first paragraph:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 1-8 are rejected under 35 U.S.C. 112, first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor(s), at the time the application was filed, had possession of the claimed invention.
Claims 1, 7 and 8 recite the step of generating summary text of the explanatory text with use of a large language model with the explanatory text used as an input. The patent application does not provide an adequate formula or algorithm explaining how the summary text is generated. For example, the specification, in paragraph [0031] states the system uses a large language module to generate summary text. The specification does not, however, disclose an adequate formula or algorithm for generating summary text. Therefore, one skilled in the art of healthcare intervention, upon reading the specification, would not conclude that the inventor had possession of the claimed inventions on the day the application was filed.
Claims 1, 7 and 8 recite the step of inferring, based on the summary text and the medical information, a symptom candidate for the symptom in the patient. The patent application does not provide an adequate formula or algorithm explaining how the symptom candidate is inferred. For example, the specification, in paragraph [0045] states the system uses a large language module to infer a symptom candidate. The specification does not, however, disclose an adequate formula or algorithm for inferring a symptom candidate. Therefore, one skilled in the art of healthcare intervention, upon reading the specification, would not conclude that the inventor had possession of the claimed inventions on the day the application was filed.
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 1-8 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 1-8 are within the four statutory categories. Claims 1-6 are drawn to a symptom understanding assist apparatus, which is within the four statutory categories (i.e. machine). Claim 7 is drawn to a symptom understanding assist method, which is within the four statutory categories (i.e. process). Claim 8 is drawn to a computer-readable non-transitory recording medium having recorded thereon a symptom understanding assist program for causing a computer to function as a symptom understanding assist apparatus, which is within the four statutory categories (i.e. manufacture).
Prong 1 of Step 2A
Claim 1 recites: A symptom understanding assist apparatus, comprising
at least one processor, the at least one processor carrying out:
an explanation acquiring process of acquiring explanatory text of an explanation made by a patient about a symptom in the patient;
a summary generating process of generating summary text of the explanatory text with use of a large language model with the explanatory text used as an input;
a medical information acquiring process of acquiring medical information regarding the patient;
a symptom inferring process of inferring, based on the summary text and the medical information, a symptom candidate for the symptom in the patient; and
an outputting process of outputting the summary text and the symptom candidate inferred.
The underlined limitations as shown above, given the broadest reasonable interpretation, cover the abstract idea a certain method of organizing human activity because they recite a process that comprise managing personal behavior or relationships or interactions between people (i.e. social activities, teaching, and following rules or instructions – in this case the use of patient data to infer a diagnosis based thereon – a process performed in a patient encounter.), 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 7 and 8 are identical as the abstract idea for claim 1, because the only difference between claims 1, 7 and 8 is that claim 1 recites an apparatus, whereas claim 7 recites a method and claim 8 recites a non-transitory computer-readable media.
Dependent claims 2-6 include other limitations, for example claims 2, 4, 5 and 6 recite further details regarding the medical information or symptom candidate information and claim 3 recites data conversion, 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 2-6 not addressed above are deemed additional elements to the abstract idea, and will be further addressed below. Hence dependent claims 2-6 are nonetheless directed towards fundamentally the same abstract idea as independent claim 1.
Prong 2 of Step 2A
Claims 1-8 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 LLM 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 paragraph 55 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 medical 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 2-6 include other limitations, but these limitations also amount to no more than mere instructions to apply the exception (e.g. claim 4 presents a trained model generated by machine learning), and/or do not include any additional elements beyond those already recited in independent claims 1, 7 and 8, and hence also do not integrate the aforementioned abstract idea into a practical application.
Step 2B
Claims 1-8 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:
Paragraph 55 of the Specification discloses that the additional elements (i.e. 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 2-6 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, 7 and 8, and/or the additional elements recited in the aforementioned dependent claims similarly amount to mere instructions to apply the exception (e.g. claim 4 presents a trained model generated by machine learning), 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 1-8 are nonetheless rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(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.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claim(s) 1-8 is/are rejected under 35 U.S.C. 102(a)(1 and/or 2) as being anticipated or clearly anticipated by Kannan et al. (U.S. PG-Pub 2019/0311814 A1), hereinafter Kannan.
As per claims 1, 7 and 8, Kannan discloses a computer-readable non-transitory recording medium having recorded thereon a symptom understanding assist program for causing a computer to function as a symptom understanding assist apparatus, a symptom understanding assist method, and a symptom understanding assist apparatus (Kannan, see Figs. 1, 4, 10 and 16.), comprising
at least one processor, the at least one processor carrying out (Kannan, see Figs. 1, 4, 10 and 16.):
an explanation acquiring process of acquiring explanatory text of an explanation made by a patient about a symptom in the patient (Kannan, system acquires patient dialog information including information about symptoms, see paragraphs 72-76.);
a summary generating process of generating summary text of the explanatory text with use of a large language model with the explanatory text used as an input (System processes patient dialog information to summarize and output determined symptoms, see Kannan Fig. 10. Kannan utilizes an LLM, see paragraph 137.);
a medical information acquiring process of acquiring medical information regarding the patient (Kannan, paragraphs 17, 43, 66, 77, 102 and 111.);
a symptom inferring process of inferring, based on the summary text and the medical information, a symptom candidate for the symptom in the patient (Kannan determines potential diagnosis based on symptom and patient medical records, see paragraphs 95-99, 103 and 104.); and
an outputting process of outputting the summary text and the symptom candidate inferred (See Kannan, paragraphs 95-99, 103 and 104; and Figs. 10, 13 and 14 #1470.).
As per claims 2-6, Kannan discloses claim 1, discussed above. Kannan also discloses:
2. wherein the medical information includes at least one selected from the group consisting of personal information regarding the patient, information on findings shown by a medical examination performed on the patient, and medical history information regarding the patient (Kannan, paragraph 17, 43, 66, 77, 102 and 111.);
3. wherein in the explanation acquiring process, the at least one processor converts, into text data, a speech picked up in the medical examination performed on the patient (Kannan, paragraph 81.);
4. wherein in the symptom inferring process, the at least one processor infers the symptom candidate, based on an output obtained by inputting the summary text and the medical information to a trained model generated by machine learning (Kannan discloses of trained machine learning models, see paragraphs 7, 71, 120, 137 and 139.);
5. wherein the medical information includes a diagnosis target image of the patient (Kannan, paragraphs 134 and 139-141.); and
6. wherein the symptom candidate is information to be used in decision making on diagnosis of the patient (Kannan determines potential diagnosis based on symptom and patient medical records, see paragraphs 95-99, 103 and 104.).
Conclusion
The following uncited prior art is noted as being relevant to the current invention:
Yokomichi et al. (U.S. PG-Pub 2020/0066380 A1).
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
30 January 2026