DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Status of the Claims
The status of the claims as of the response filed 2/5/2026 is as follows: Claim 13 remains cancelled. Claims 1 and 11-12 are currently amended. Claims 2-10 and 14 are as previously presented. Claims 1-12 and 14 are currently pending in the application and have been considered below.
Response to Amendment
Rejection Under 35 USC 101
The claims have been amended but the 35 USC 101 rejections for claims 1-12 and 14 are upheld.
Response to Arguments
Rejection Under 35 USC 101
On page 12 of the response filed 2/5/2026 Applicant argues that by adding an attention mechanism of a neural network to the claims “it becomes possible to efficiently and accurately determine and output indexes such as ‘the risk,’ ‘the degree of contribution,’ and ‘the degree of mutual relevance’” which “constitutes an improvement in computer functionality and is clearly not an abstract idea. Applicant’s arguments are fully considered, but are not persuasive. Examiner concedes that the neural network and attention mechanism are now positively recited in the claims and are not considered part of the abstract idea, but respectfully disagrees that such elements amount to an improvement in computer functionality. There are currently no details about how these additional elements would perform these clinical determinations in a more accurate or efficient manner that would amount to a technical improvement to the field of neural networks. Rather, the neural network with attention mechanism appears to be recited at a high level of generality in a manner that amounts to instructions to “apply” the otherwise-abstract steps of making clinical risk, degree of contribution, and mutual relevance determinations in an automated/digitized manner. Examiner notes that per MPEP 2106.05(f)(2), “‘claiming the improved speed or efficiency inherent with applying the abstract idea on a computer’ does not integrate a judicial exception into a practical application or provide an inventive concept,” and Applicant has not provided any evidence that the outputting of data with a neural network with an attention mechanism provides any technical improvements beyond the improved speed or efficiency inherent with applying data analysis operations in a computing environment. Alternatively, these components may be considered insignificant extra-solution activity in the form of mere data gathering because they are merely invoked as a means of providing the information necessary for the main parameter extraction and data visualization steps of the invention (see MPEP 2106.05(g)).
Examiner further notes that use of neural networks with attention mechanisms to provide outputs of clinical risk as well as degrees of contribution and mutual relevance of input parameters is well-understood, routine, and conventional in the art, as evidenced by Applicant’s Remarks filed 9/22/2025 at Pg 12 (noting “The attention mechanism is known as a component present in transformers such as generative pre-trained transformers (GPT)”); sections 1 & 2.3 of Jin et al. (Reference U on Pg 2 of the PTO-892 mailed 11/6/2025); sections 1 & 2.2 of Zhang et al. (Reference X on the PTO-892 mailed 11/6/2025); abstract & “attention mechanism” section of Kamal et al. (Reference W on the PTO-892 mailed 11/6/2025); and abstract & [0027] of Zhang et al. (US 20200402665 A1). Accordingly, these elements do not provide an inventive concept.
On page 13 Applicant further submits that the claimed features directed to displaying a directed graph with each of a risk, the first parameter, and the second parameter as nodes and including first and second unidirectional edges displayed in different display modes “make it possible to allow a user to better understand output results of a machine learning model” and thus provide an improvement to a technological environment and integrate any recited abstract idea into a practical application. Applicant’s arguments are fully considered, but are not persuasive. Examiner notes that these data representation features could be achieved by a human actor managing their personal behavior to draw or otherwise manually visually represent clinical risk, degree of contribution, and degree of mutual relevance in graph form (e.g. with various connected nodes and edges presented with different visual attributes like bolded or dashed connection lines, different colored lines, lines of different thicknesses, etc.) and thus are part of the abstract idea itself. Examiner notes that providing improvements to the abstract idea itself (i.e. the analysis, extraction, and display of clinical data relationships) does not provide a technological improvement; see MPEP 2106.05(a)(III): “it is important to keep in mind that an improvement in the abstract idea itself (e.g. a recited fundamental economic concept) is not an improvement in technology. For example, in Trading Technologies Int’l v. IBG, 921 F.3d 1084, 1093-94, 2019 USPQ2d 138290 (Fed. Cir. 2019), the court determined that the claimed user interface simply provided a trader with more information to facilitate market trades, which improved the business process of market trading but did not improve computers or technology.” Similarly, providing complex data correlations and relationships to a user in an easy-to-understand visual manner merely improves the business practice of clinical risk analysis and interpretability and does not improve computers or technology.
On pages 14-15 Applicant analogizes the claims to those found eligible in Desjardins, particularly submitting that “the Office should not evaluate the claims at a very high level of generality as features that can merely be performed by a clinician ‘since most of the claim is directed to clear technical features which result in the technical improvement described in the specification.’” The cited portion of the specification describes how diagnostic support information and medical treatment information whose medical relevance is unknown can be visualized and presented to a user for visual understanding of the relevance between these types of information. Applicant’s arguments are fully considered, but are not persuasive. Examiner respectfully notes that the fact patterns of Desjardins and the instant case are different. In Desjardins, the claims reflected an improvement to how a machine learning model itself is trained and operates to address the technical problem of ‘catastrophic forgetting’ encountered in continual learning systems, which was identified and explained as a technical problem in the specification. In contrast, the instant specification does not outline a specific technical problem in machine learning technology whose solution is reflected in the claims. As noted on Pgs 4 & 20 of the specification, the invention appears to seek to solve a problem rooted in the drawbacks of conventional business practices of clinical data visualization/representation (i.e. a lack of inclusion of medical treatment information that is not related to a disease in the graphical arrangement) rather than a technical problem rooted in computer technology or another technical field. The use of node- and edge-based visual arrangements of various types of data outputs of a clinical risk model (e.g. a neural network with an attention mechanism) to facilitate visual understanding of the data does not amount to a technical improvement to a technical problem as in Desjardins, and instead amounts to an abstract idea without significantly more as explained in more detail below.
For the reasons outlined above, the 35 USC 101 rejections are upheld for claims 1-12 and 14.
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 1-12 and 14 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1
In the instant case, claims 1-10 and 14 are directed to a device (i.e. a machine), claim 11 is directed to a method (i.e. a process), and claim 12 is directed to a computer-readable non-transient storage medium (i.e. a manufacture). Thus, each of the claims falls within one of the four statutory categories. Nevertheless, the claims fall within the judicial exception of an abstract idea.
Step 2A – Prong 1
Independent claims 1, 11, and 12 recite steps that, under their broadest reasonable interpretations, cover certain methods of organizing human activity, e.g. managing personal behavior, relationships, or interactions between people. Specifically, claim 1 (as representative) recites:
A medical information processing device for supporting interpretation of output results of a machine learning model that outputs a risk representing a probability of a subject will suffer from a target disease in response to input of a plurality of parameters obtained at the time of medical treatment of the subject according to a plurality of examination items, the medical information processing device comprising processing circuitry configured to:
extract a first parameter related to the risk from among the plurality of parameters on the basis of a corresponding relationship in which the risk and the plurality of parameters are associated, and a degree of contribution of each of the plurality of parameters input to the machine learning model at the time of causing the machine learning model to output the risk to the risk;
extract a second parameter related to the first parameter from among the plurality of parameters on the basis of degrees of mutual relevance of one of the plurality of parameters input to the machine learning model at the time of causing the machine learning model to output the risk and the degree of contribution of each of the plurality of parameters to the risk; and
cause a display unit to display the risk, the first parameter, and the second parameter
wherein
the machine learning model is a neural network having an attention mechanism,
the attention mechanism is trained to output the risk, the degree of contribution, and the degree of mutual relevance in response to the plurality of parameters being input to the neural network,
the processing circuitry is further configured to:
input the plurality of parameters into the neural network,
extract the first parameter from among the plurality of parameters on the basis of the degree of contribution output by the attention mechanism in response to the plurality of parameters being input to the neural network and the corresponding relationship,
extract the second parameter from among the plurality of parameters on the basis of the degree of contribution and the degree of mutual relevance output by the attention mechanism in response to the plurality of parameters being input to the neural network, and
display a directed graph with each of the risk, the first parameter, and the second parameter as a node on the display unit,
the directed graph includes a first node corresponding to the risk, a second node corresponding to the first parameter, and a third node corresponding to the second parameter,
a first unidirectional edge is provided between the first node and the second node,
a second unidirectional edge is provided between the first node and the third node,
a third unidirectional edge is provided between the second node and the third node,
the first unidirectional edge is displayed with the degree of contribution of the first parameter to the risk,
the second unidirectional edge is displayed with the degree of contribution of the second parameter to the risk, and
the third unidirectional edge is displayed with the degrees of mutual relevance between the first parameter and the second parameter, and
the processing circuitry configured to display at least the first unidirectional edge and the second unidirectional edge in display modes that are different from each other.
But for the recitation of generic computer components like processing circuitry and a display unit, the italicized functions, when considered as a whole, describe a clinical risk analysis and data interpretation operation that could be achieved by a clinician or other medical professional managing their personal behavior. For example, a clinician could observe the outputs of a neural network model with an attention mechanism (e.g. a risk of a disease, a degree of contribution of each input parameter, and a degree of mutual relevance of each input parameter) and identify (i.e. extract) input parameters that they have determined contributed most and least to the risk based on the output degree of contribution and degree of mutual relevance for each parameter. The clinician could then visually communicate such determinations, e.g. by drawing or otherwise creating a graphical representation of a directed graph with various connected nodes and edges presented with different visual attributes (e.g. bolded or dashed connection lines, different colors, different thicknesses, etc.) in accordance with the observed degrees of contribution and mutual relevance of the parameters. Thus, the steps recited in claim 1 (and analogous claims 11 and 12) recite an abstract idea in the form of a certain method of organizing human activity.
Dependent claims 2-10 and 14 inherit the limitations that recite an abstract idea from their dependence on claim 1, and thus these claims also recite an abstract idea under the Step 2A – Prong 1 analysis. In addition, claims 2-10 and 14 recite additional limitations that further describe the abstract idea identified in the independent claims. Specifically, claims 2-8 specify further criteria for extracting and making determinations about the first and second parameters from claim 1, each of which are types of determinations that a clinician would be capable of making when deciding which model inputs are most or least relevant to a particular output based on the observed outputs of a neural network model with an attention mechanism. Claim 9 recites determining a display mode at the time of displaying all of the data types, which a clinician could achieve by choosing how they would prefer to visually convey all of the determined information, e.g. using different colors, fonts, text sizes, connection line representations, etc. Claim 10 recites that the model outputs the degree of contribution and degree of mutual relevance of the pieces of medical treatment information, each of which are types of data that a clinician would be capable of observing when specifically output by a machine learning model such as a neural network with an attention mechanism. Claim 14 recites extracting a third parameter in a similar manner as the first and second parameters of claim 1, as well as displaying the directed graph with additional nodes and unidirectional edges associated with the third parameter. A clinician could similarly accomplish these functions by managing their personal behavior to identify/extract a third parameter based on observed outputs of a neural network model and drawing or otherwise visually representing the additional components of the directed graph as explained for similar steps of claim 1 above. Thus, dependent claims 2-10 and 14 also recite an abstract idea in the form of a certain method of organizing human activity.
However, recitation of an abstract idea is not the end of the analysis. Each of the claims must be analyzed for additional elements that indicate the abstract idea is integrated into a practical application to determine whether the claim is considered to be “directed to” an abstract idea.
Step 2A – Prong 2
The judicial exception is not integrated into a practical application. In particular, independent claims 1, 11, and 12 do not include additional elements that integrate the abstract idea into a practical application. Claim 1 includes the additional elements of a medical information processing device comprising a processing circuitry configured to perform the steps, claim 11 includes the additional element of a medical information processing device, and claim 12 includes the additional elements of a computer-readable non-transient storage medium storing a program executed by a computer. Each of claims 1, 11, and 12 also include the additional element of a display unit to display the information, as well as inputting the plurality of parameters into the neural network and extracting the parameters based on information output by the attention mechanism in response to the plurality of parameters being input to the neural network. These additional elements, when considered in the context of each claim as a whole, merely serve to automate the functions that could be achieved by a clinician or other medical professional managing their personal behavior (e.g. extracting and visually arranging information), and thus amount to instructions to “apply” the abstract idea with generic computer components. For example, a clinician can make determinations about the most and least relevant contributing parameters and their interrelations based on observing model outputs of risk, degree of contribution, and degree of mutual relevance for each parameter input to the model, then visually convey the determined information, e.g. in the form of a directed graph with connected nodes and edges rendered in various styles. The additional elements of a processing device / computer executing a stored program, a display unit, and a neural network with an attention mechanism merely digitize/automate these functions such that they are implemented in a computing/electronic environment, and thus amount to instructions to “apply” the exception with computer components (see MPEP 2106.05(f)).
Examiner notes that the machine learning model comprising a neural network with an attention mechanism appears to merely be invoked and utilized as a source of the risk, degree of contribution, and degree of mutual relevance data outputs for the main parameter extraction and data visualization steps of the invention. Accordingly, these components can also be considered insignificant extra-solution activity in the form of mere data gathering because the steps of inputting parameters to the neural network and receiving outputs from the attention mechanism are merely invoked as a means of providing the information necessary for the main parameter extraction and data visualization steps of the invention (see MPEP 2106.05(g)). Accordingly, claims 1, 11, and 12 as a whole are each directed to an abstract idea without integration into a practical application.
The judicial exception recited in dependent claims 2-10 and 14 is also not integrated into a practical application under a similar analysis as above. Claims 2-10 and 14 are performed with the same additional elements identified in independent claim 1 without introducing any new additional elements, and thus do not provide integration into a practical application.
Accordingly, the additional elements of claims 1-12 and 14 do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. Claims 1-12 and 14 are directed to an abstract idea.
Step 2B
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements of processing circuitry / a computer executing a stored program and a display unit for performing the functional steps of the invention amount to mere instructions to apply the exception using generic computer components. As evidence of the generic nature of the above recited additional elements, Examiner notes at least Pg 5 L10-15, Pg 5 L17-22, and Pg 7 L9 – Pg 8 L6 of Applicant’s specification, where the display unit, memory, and processing circuitry are disclosed in terms of examples of known, generic types of computer hardware such that one of ordinary skill in the art would understand that any generic processing or computing device executing stored instructions in combination with a display could be used to implement the invention.
As explained in para. 18 above, the machine learning model comprising a neural network with an attention mechanism is considered a means of data gathering for the main steps of the invention such that it amounts to insignificant extra-solution activity. Examiner further notes that use of neural networks with attention mechanisms to provide outputs of clinical risk as well as degrees of contribution and mutual relevance of input parameters is well-understood, routine, and conventional in the art, as evidenced by Applicant’s Remarks filed 9/22/2025 at Pg 12 (noting “The attention mechanism is known as a component present in transformers such as generative pre-trained transformers (GPT)”); sections 1 & 2.3 of Jin et al. (Reference U on Pg 2 of the PTO-892 mailed 11/6/2025); sections 1 & 2.2 of Zhang et al. (Reference X on the PTO-892 mailed 11/6/2025); abstract & “attention mechanism” section of Kamal et al. (Reference W on the PTO-892 mailed 11/6/2025); and abstract & [0027] of Zhang et al. (US 20200402665 A1). Accordingly, these elements do not provide an inventive concept.
Analyzing these additional elements as an ordered combination adds nothing that is not already present when considering the elements individually; the overall effect of the computer implementation, neural network with an attention mechanism, and display unit in combination is to digitize and/or automate a clinical risk analysis and data interpretation operation that could otherwise be achieved as a certain method of organizing human activity. Thus, when considered as a whole and in combination, claims 1-12 and 14 are not patent eligible.
Subject Matter Free from Prior Art
The prior art of record fails to expressly teach or suggest, either alone or in combination, each and every feature of the independent claims, as explained in detail in paras. 24-25 of the non-final rejection mailed 11/6/2025. Upon completion of an updated prior art search, no additional relevant prior art was identified.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to KAREN A HRANEK whose telephone number is (571)272-1679. The examiner can normally be reached M-F 8:00-4:00 ET.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Shahid Merchant can be reached on 571-270-1360. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/KAREN A HRANEK/ Primary Examiner, Art Unit 3684