DETAILED ACTION
This office action is based on the claims filed on 04/16/2026.
Claims 1, 7, and 18 have been amended.
Claims 1-20 are currently pending and have been examined.
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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination 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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 04/16/2026 has been entered.
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.
Claim 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. Claims 1-6 and 18-20 are drawn to a system and Claims 7-17 are drawn to a method, which are within the four statutory categories (i.e., a machine and a process). Claims 1-20 are further directed to an abstract idea on the grounds set out in detail below.
Under Step 2A, Prong 1, the steps of the claim for the invention represents an abstract idea of a series of steps that recite a process for displaying treatment guidelines and recommendation for supporting decision. This abstract idea could have been performed by a human actor to implement the abstract idea for steps citing a process directed to a user input or interaction with a device and following rules to determine treatment guidelines and associated clinical markers, which both the instant claims and the abstract idea are defined as certain methods of organizing human activity.
Independent Claim 1 recites the steps for:
“a display screen configured to display a patient medical path listing one or more of treatment guidelines and patient medical history, and additionally being configured to display abbreviated representations of at least one of the treatment guidelines and the patient medical history that can be reached directly from the displayed patient medical path, the patient medical history comprising clinical markers automatically identified from health records of the patient,
wherein automatically identifying the clinical markers comprises using a natural language processing pipeline to identify entities from the health records, identifying positive and negative assertions of the identified entities, after identifying the positive and negative assertions of the identified entities identifying relationships between the identified entities, and recognizing and generating binary relationships between the identified entities;
wherein the abbreviated representations each display a limited list of data offered within the treatment guidelines and/or the patient medical history, each of the data in the list being selectable to launch the treatment guidelines and/or the patient medical history and enable the selected data to be seen, and
wherein the abbreviated representations are displayed while in an unlaunched state”
Independent Claim 7 recites the steps for:
“providing a user-interactive electronic form for a user to input rules for displaying a treatment path for a patient wherein the rules for displaying the treatment path include node names, node types, node display confirmation, node display visual effects, and node ordering constraints, and wherein the node ordering constraints are automatically filled by a processor matching the node names and/or the node types to a sequence shown in reference treatment guidelines based on the node names and/or the node types;
automatically acquiring, via the processor, health records of the patient and treatment guidelines corresponding to a condition of the patient, based on the input rules, and identifying, via the processor, clinical markers from the health records via a natural language processing pipeline, wherein identifying clinical markers from the health records via the natural language processing pipeline comprises:
identifying entities from the health records, identifying positive and negative assertions of the identified entities, after identifying the positive and negative assertions of the identified entities identifying relationships between the identified entities, and recognizing and generating binary relationships between the identified entities;
wherein the identification of clinical markers includes entity recognition by a machine learning model trained to perform various steps upon parsing or scanning the health records of the patient, including identifying entities from text of the health records of the patient, including a type of a tumor, a position of the tumor, and a body part at which the tumor is located, wherein identified text is indicated in boxes while assignment/categorization of the identified text by the natural language processing pipeline is depicted;
matching the clinical markers to relevant sections of the treatment guidelines according to implementation of the input rules by the processor including identifying nodes of the treatment guidelines based on a set of guideline selection rules of the input rules and comparing entry markers and exit markers for each of the nodes with information from the clinical markers, wherein the entry markers and the exit markers are defined by a set of node traversal rules of the input rules, and wherein matching the clinical markers to the relevant sections of the treatment guidelines further comprises: determining, by the processor, satisfaction of the node exit markers by comparing information from the clinical markers against the node exit markers to enable progression to subsequent nodes; and clipping the relevant sections into segments based on the node exit markers determined to be satisfied; and
displaying, at a graphical user interface of a display device in real-time, the relevant sections of the treatment guidelines overlaid with summaries of the health records, the summaries correlated to the relevant sections based on the clinical markers, displayed as a graphical representation along a lane and spaced along the lane to align with a corresponding section of the treatment guidelines to provide treatment recommendations based on a path of the treatment guidelines.”
Independent Claim 18, similar to Claim 7, recites the steps for:
“a display screen displaying the patient journey;
a guideline information collection form providing parameters for displaying a treatment path for a patient stored at a guideline database wherein the parameters include, for each node of a plurality of nodes, a node name, a node type, node display constraints, node entry markers, node exit markers, and node ordering constraints;
treatment guidelines corresponding to the treatment path for the patient stored at a memory of a processor; and
a set of modules also stored at the memory of the processor and implemented at the processor, the set of modules including with executable instructions that, when executed, cause the processor to:
process the parameters from the guideline information collection form to generate node traversal rules;
identify clinical markers from health records and the guideline information collection form via a natural language processing pipeline, wherein identifying the clinical markers from the guideline information collection form via the natural language processing pipeline comprises:
identifying entities from the health records, identifying positive and negative assertions of the identified entities,
after identifying the positive and negative assertions of the identified entities identifying relationships between the identified entities, and recognizing and generating binary relationships between the identified entities;
identify the treatment path from the treatment guidelines based on the parameters;
wherein identifying the treatment path from the treatment guidelines based on the parameters comprises locating relevant sections of the treatment guidelines that match the identified clinical markers,
identifying nodes within the relevant sections of the treatment guidelines using the node traversal rules,
comparing the node entry markers and the node exit markers for each identified node with information of the identified clinical markers,
determining if the clinical markers satisfy the node exit markers, clipping the relevant sections into segments based on the node exit markers
determined to be satisfied, and ordering the segments according to a sequence of the treatment guidelines; and
display the treatment path at a display device along with corresponding summaries from medical records of the patient, the summaries correlated to the treatment path based on common clinical markers, the treatment path overlaid with a patient event timeline, the patient event timeline including segments of treatment guidelines ordered sequentially and connected by arrows”.
These limitations, as drafted, given the broadest reasonable interpretation, cover performance of the limitations by a human user/actor that constitute certain methods of organizing human activity. For example, the limitations encompass a data gathering and processing/transformation by a user interacting with a displaying device to manually the ability inquire treatment path following input rules, identifying markers and treatment path to receive treatment guidelines to a patient condition and associated guidelines clinical markers to display on a device proving a recommendation and decision support of a human activity, which are steps that that could have been performed by a human actor interaction and a device to implement the abstract idea. These limitations encompass activity of a single person or multiple people and a computer, following rules or instructions to perform the steps of the claimed invention, e.g., interaction with devices and following rules and/or steps to determine treatment guideline(s) to be displayed for the subject following instruction(s), which constitutes Certain Methods of Organizing Human Activity. Accordingly, the claim limitations recite an abstract idea. Any limitations not identified above as part of the process are deemed "additional elements," and will be discussed in further detail below.
Under Step 2A, Prong 2, this judicial exception is not integrated into a practical application because the remaining elements amount to no more than a component programmed to perform the abstract ideas and linking the abstract idea to a particular technological environment. In particular, the claims recite the additional elements such as “processor, graphical user interface (GUI)/display device, database, memory, modules, natural language processing (NLP), machine learning (ML) model” that is/are disclosed at a high - level of generality and includes known hardware components to perform steps, i.e., input[ting], display[ing], that iteratively takes input data and determine an output performing generic computer functions for displaying treatment guidelines and recommendations such that it amounts no more than adding the words "apply it" (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea because the steps reciting additional elements that are mere data implemented using a general purpose computing components being used in ordinary capacity to perform the steps such that causing the computer system to perform the instructions, see MPEP 2106.05(f), and mere data gathering and outputting process that does not add a meaningful limitation to the above abstract idea, see MPEP 2106.04(d).
For example, the machine learning (ML) model and NLP, is/are recited in the claims in a high level of generality and is in described in the specification in an arbitrary form without disclosing a specific algorithm and implementing the claimed invention for allowing the model to learn patterns and relationships within the data and implement these additional elements to perform the claimed function rather the trained ML model is recited at a high level of generality and describing a general concept of using a ML model to perform task(s) which is a mere in instruction(s) that may be performed by human that it amounts no more than adding the words "apply it" (or an equivalent). Implementing the abstract idea for displaying a treatment guideline on generic computer components is not a practical application of the abstract idea. As set forth in the 2019 Eligibility Guidance, 84 Fed. Reg. at 55 "merely include[ing] instructions to implement an abstract idea on a computer" is an example of when an abstract idea has not been integrated into a practical application, see (Applicant, PGPub 0065, 0067). Accordingly, looking at the claim as a whole, individually and in combination, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
Under step 2B, the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because they do not present improvements to another technology or technical field and the additional elements amount to no more than a generic computer components, recited at a high level of generality, that amounts to no more than adding the words "apply it" (or an equivalent) to apply the exception, e.g. “input, display”, using generic computer component, see MPEP 2106.05(f), mere data gathering that does not add a meaningful limitation to the above abstract idea, see MPEP 2106.04(d). Their collective functions merely provide conventional computer implementation and mere instructions to apply an exception using a generic computer component to the abstract idea cannot provide an inventive concept, see Alice, 573 U.S. at 223 ("mere recitation of a generic computer cannot transform a patent-ineligible abstract idea into a patent-eligible invention"). Therefore, whether considered alone or in combination, the additional elements do not amount to significantly more than the abstract idea
Dependent Claims 2-6, 8-17 and 19-20 include all of the limitations of claim(s) 1, 7, and 18, and therefore likewise incorporate the above-described abstract idea. While the depending claims add additional limitations, such as
As for claims 2-3, 5, 10-12 and 15-17, the claim(s) recite limitations that are under the broadest reasonable interpretation, further define the abstract idea noted in the independent claim(s) that covers performance by a human interaction but for, the recitation of the generic computer components which are similarly rejected because, neither of the claims, further, defined the abstract idea and do not further limit the claim to a practical application or provide an inventive concept. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept ("significantly more").
As for claims 4, 6, 8-9, 13-14, and 19-20, the claim(s) recite limitations that are under the broadest reasonable interpretation, further define the abstract idea noted in the independent claim(s) that covers performance by a human interaction but for, the recitation of the generic computer components which are similarly rejected because, neither of the claims, further, defined the abstract idea and do not further limit the claim to a practical application or provide an inventive concept such that the claims are subject matter eligible. The claims recite additional elements “graphical user interface, clinical marker detection module, natural language processing, marker filter module, summary generation module, database” that implement the identified abstract idea and is/are recited in the claim(s) at a high level as a tool to apply the exceptions and perform the disclosed feature, e.g., “displaying, applying, store, interact with a cursor”. These components are recited at a high level of generality such that it amounts no more than mere instructions to apply the exception using generic computer components. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept ("significantly more").
Response to Arguments
Applicant's arguments filed 11/26/2025 and the declaration under 37 CFR 1.132 filed 04/16/2026 by Kanamarlapudi Anuradha have been fully considered by the Examiner and addressed as the following:
In the remarks, Applicant argues the substance:
Applicant's arguments with respect to the 35 U.S.C. § 101 rejection on page 11-18.
On page 11-12 of the remarks, the Applicant argues “the claims herein set forth a particular approach that is selected to address specific technical problems, not merely to organize human activity or display information on a generic computer. See SMED, paragraphs 5-9 ... uses a specifically configured, multi-stage NLP pipeline performing entity recognition... and ontology linking in a defined sequential order ... which the inventors determined was associated with reduced processing overhead, improved clinical marker extraction accuracy, and faster real-time output generation compared to prior automated approaches relying on keyword search or generic NLP. See SMED, paragraph 9... The claimed features are not merely generic computing elements, but rather specific technical features that...” Examiner respectfully disagree. Examiner respectfully asserts that the claims are given their broadest reasonable interpretation for the purpose of determining whether they encompass a judicial exception. The claims limitations are directed towards a process for determining treatment guidelines and decision making based on data gathering and transformation and inputted rules by a user interacting with a user interface providing input for displaying a treatment path while acquiring and analyzing the patient medical history and treatment guidelines where medical records is/are associated with clinical markers for identifying patient records and relationship of the records, and presenting treatment guidelines following the rules inputted by the user. Such steps have been interpreted, under BRI, as a process for organizing treatment steps and guideline to support decision making as such have been identified as steps that can be performed by a human user organizing data that falls in the course of human behavior which defines the identified abstract idea. The claim(s) recite additional elements, e.g., processor, graphical user interface (GUI)/display device, NLP, ML model”, recited at a high level of generality and as tool(s) to perform the steps of the identified abstract steps that amount no more than adding the words "apply it" (or an equivalent). For example, the NPL and ML model in the claims and specification do not preclude the system from being implemented on a generic computer configured with a specific purpose with specific programming as their collective functions merely provide conventional computer implementation and mere instructions to apply an exception using a generic computer component to the abstract idea.
Moreover, Applicant points to paragraph 5-9 Declaration of the Affidavit filed on 04/16/2026 where the claim(s) as amended recite a specific configured process, e.g., “multi-stage NLP pipeline performing entity recognition”, “automated clipping of relevant guideline sections into segments”, etc., that would improve accuracy, faster output generation as described in the Affiant argument in paragraph 9. Examiner respectfully disagrees to such argument. The Affiant argues on paragraph 9 that the invention uses a specific, multistage technical architecture providing a user-interactive electronic form providing inputs to define rules for displaying a treatment path and using the treatment guidelines and health records of a patient, defines the path through multiple markers and displaying it. Examiner asserts that the steps described by the Affiant are a mere steps for defining the treatment path based on data gathered and processed such as transformed while using NLP and applying rules selected by a user and provide a path through nodes such as tree graph points, based on defined rules which are steps that can be performed by a human managing treatment path which define the abstract idea “Certain Methods of Organizing Human Activity managing interactions between people, (including following rules or instructions)”, for example, the NLP pipeline for performing ontology linking (or mapping) and performing node traversal mechanisms through nodes in a graph or tree-structured data which are features that can be performed by a huma using a machine to structure and organize treatment path. The Affiant approach described in SMED paragraph 9 does not describe how the steps would improve accuracy and faster output generation.
On page 12-13 of the remarks, the Applicant further argues “whether or not the claims recite any abstract ideas, the claims are directed to eligible subject matter under Step 2A at least because any alleged abstract idea is integrated into a practical application under Step 2A, Prong Two... The claimed invention improves the field of automated clinical decision support and treatment guideline display by addressing specific technical problems in doing so ... they are specific technical deficiencies that prevent prior automated systems from accurately and efficiently correlating patient health records to the relevant sections of complex treatment guidelines, resulting in slow processing, high rates of false positives and false negatives in clinical marker extraction, and an inability to automatically determine which path through a branching treatment guideline is most relevant to a specific patient. See SMED, paragraph 5; see also Applicant's specification, paragraph [00104]... The claimed approach provides a non-conventional technical solution that addresses these problems. See SMED, paragraph 9”, Examiner respectfully disagree. The Applicant points to paragraph 5 Declaration of the Affidavit arguing prior automated systems and alleging their technical deficiencies of slow processing and false positive and negative rates and automatically determine path, however, the Affiant did not provide how such deficiencies are addressed by the current invention/system. The Affiant in paragraph 6-7 Declaration of the affidavit describes the alleged Keyword-based search and Generic NPL approaches deficiencies but does not describe how such deficiencies are addressed with the current system. The Affiant, however, describing in SMED paragraphs 9-13 a process for displaying a treatment path where in SMED paragraph 10 describes utilizing a trained NPL pipeline to distinguish between findings claiming that it avoids additional processor processing of data that are not relevant according to the specification, in SMED paragraph 11 describes identifying the relationship between entities, in SMED paragraph 12 describes performing ontology linking, and in SMED 13 describes node traversal mechanism to define entry and exit markers, which are steps for processing health data extracted from medical records via plurality of modules that are executed by a processor of a computing device. As described above, these features describe data processing steps that can be performed by a huma using a machine to structure and organize treatment path, however, none of them require specific hardware beyond what is in a generic computer.
Moreover, Applicant points to the specification [00104] describing automatic extraction of information presented to a user more quickly and in real time however it is described in an arbitrary form with no description of how it is objectively faster or efficient as such do not describe a technical improvement for processing efficiency. For example, neither the claims nor the specification describes how the current invention improves speed of processing and reducing high rates of false positives and false negatives clinical marker extraction. There is nothing in the claims is reciting a solution that is rooted in computer technology any improvement to the technology to overcome a technical problem. The thrust of Applicant's invention is to improve the abstract idea for generating and displaying a treatment path to support clinical decision through leveraging computing technology, e.g., processor, machine learning model, NLP, in an understood manner.
On page 14 of the remarks, the Applicant further argues “This node traversal mechanism was selected over alternative approaches ... thereby reducing the volume of guideline text that must be processed and displayed. See SMED, paragraphs 13 and 14,” Examiner respectfully disagree. As described by the Affiant in SMED, paragraphs 13 and 14 that node traversal is performed by processor implementing a set of node traversal rules input by a user while nowhere describes how the node travels is implemented and its mechanism, e.g., “in-order, or pre-order, or post-order traversal”, rather the claims and specification describes generating a set of node traversal rules in an arbitrary form. The Affiant, for example, in SMED, paragraphs 13 and 14 discussed the technical improvement avoiding additional data processing and improving processing speed however nowhere in the claims or specification describes how implementing the traversal nodes rules is performed to reduce the volume of guideline text rather described at a high level of generality as such no evidence providing reduction of output information or improving processing speed, see (Applicant [00074], [00118]).
Similarly, Applicant argues that the node traversal mechanism of the claimed invention directly reduces the text for query and processing by excluding irrelevant nodes and sections from further processing at the traversal stage pointing to SMED, paragraphs 15 and 19 describing the claimed invention is improving the functioning of technology itself, while, as described above, no where in the claims or specification describes how the generating of the node traversal rules is used to achieve such goal nor describing any mechanism of doing so. The Affiant describes in SMED, paragraphs 15 and 19 comparing information from the marker to the node exit marker to procedure to the subsequent nodes and clipping the relevant section into segments but the Affiant does not nor the specification describe excluding irrelevant nodes, see (Applicant [00109]).
On page 14-15 of the remarks, the Applicant further argues “Amended claims 7 and 18 also recite that the node ordering constraints ... This automatic filling of node ordering constraints provides a further specific technical improvement in processing efficiency... displayed treatment path is consistent with the sequence of the treatment guidelines without requiring additional processing to verify or correct the ordering. See SMED, paragraph 17,” Examiner respectfully disagree. The step for filling of node ordering is considered part of the abstract steps while the automation step does not impart the feature to an improvement of a processing data as described by the Affiant in SMED, paragraph 17 rather mere automation of manual processes using a generic computer to process data as described in the claim and the specification, see (Applicant [00061]).
On page 15 of the remarks, the Applicant further argues “Amended claim 1 further recites displaying abbreviated representations of at least one of the treatment guidelines and the patient medical history while in an unlaunched state ... This abbreviated representation display architecture addresses the technical problem of rendering overhead ... See SMED, paragraph 16; see also Applicant's specification, paragraph [00105] ...,” Examiner respectfully disagree. The function of displaying abbreviated representations while in an unlaunched state is performing the steps of collecting, displaying, and summarizing information while the element is well-known, routine and conventional1 in performing known process in a displaying data through leveraging computing technology in a well understood manner. The Affiant in SMED, paragraph 16, and pointing the specification [000105] “[w]hen the abbreviated representations are in an unlaunched state, selected data listed by each of the abbreviated representations may be at least partially hidden from view, thereby condensing a visual footprint of the abbreviated representations” discussed such abbreviation representation reduces the rendering overhead of the display system and allow rapid access while nothing in the claims or specification indicates. However as mentioned above, there no specific description of how the device captures the data to be abbreviated while in an unblanched state in order to improve data access and reduce overhead of displaying rather the specification and claim(s) disclosing what to display.
On page 15 of the remarks, the Applicant further argues “The Office suggests that the additional elements of the claims amount to no more than generic computer components... However, the claimed approach improves the technology itself because it represents improvements in processing speed, extraction accuracy, and display efficiency beyond what other automated approaches could achieve. See SMED, paragraph 19”, Examiner respectfully disagree. As discussed above with the regards to the Affiant remarks in SMED, paragraph 19 describing the claimed approach is improving the functioning of technology itself, Examiner asserts that the claims at issue do not require any nonconventional computer, network, or other components, or even a non-conventional and non-generic arrangement of known, conventional pieces but merely call for performance of the claimed functions on a set of generic computer components. The elements of the instant process, when taken alone, each execute in a manner conventionally expected of these elements. The elements of the instant underlying process, when taken in combination, together do not offer substantially more than the sum of the functions of the elements when each is taken alone.
On page 17 of the remarks, the Applicant further argues “The claims herein are directly analogous to Core Wireless in that amended claim 1 recites an improved display architecture in which abbreviated representations of treatment guidelines and patient medical history are displayed in an unlaunched state...”, Examiner respectfully disagree. While Core Wireless require a specific, technological solution to a technological problem, where the claim(s) contain precise language delimiting the type of data to be displayed and how to display it specifying a particular manner for accessing summary window and a data list being selectable to launch a respective application and enable the selected data to be seen within the respective application summary window displayed while the one or more applications are in an un-launched state, thus improving upon conventional user interfaces to increase the efficiency of using mobile devices. In contrast, and as mentioned above, the instant invention describes displaying an abbreviated representations however there no specific description of how the device captures the data to be abbreviated while in an unblanched state. Therefore, the claimed invention is not analogues to Core Wireless.
On page 17 of the remarks, the Applicant further argues “The Office suggests that the NLP pipeline and machine learning model are recited at a high level of generality without disclosing a specific algorithm ... The However, as explained above and in the SMED, the NLP pipeline of the claimed invention is not a generic NLP system recited at a high level of generality... This level of specificity is not comparable to a generic recitation of a machine learning model performing a task, and it is directly analogous to the level of specificity that the Appeals Review Panel found sufficient in Ex parte Desjardins, Appeal 2024-000567 (ARP Sept. 26, 2025), where the claims were found to improve the functioning of the computer itself by reducing storage requirements and preserving task performance across sequential training.”, Examiner respectfully disagree. As mentioned above, the NLP pipeline is a process to analyze/transform a gathered data through processing the data on steps that are reciting a process that may be performed by a human flowing instruction and rules applied by the user. Moreover, as mentioned in the final OA mailed 12/16/2025, the limitations recite the abstract idea of determining patient treatment guidelines based on selection rules of user’s input rules supporting decision making form providing input for displaying a treatment path and presenting treatment guidelines following rules as input by the user while reciting ML and NLP at a high level of generality and as a tool to implement the identified abstract steps while the Desjardins claims were found to be directed to methods for training artificial intelligence/machine learning (AI) models and the claims improved the functioning of the computer itself by reducing storage requirements and preserving task performance across sequential training as such improves the operation of a machine learning system by enhancing its training efficiency or preserving prior learning, as such it is not “directed to” an abstract idea under Alice Step 1.
In contrast and as mentioned above, the machine learning (ML) model and NLP, is/are recited in the claims in a high level of generality and is in described in the specification, (see Applicant 00065, 00067), in an arbitrary form without disclosing a specific algorithm and implementing the claimed invention for allowing the model to learn patterns and relationships within the data and implement these additional elements to perform the claimed function rather the trained ML model is recited at a high level of generality and describing a general concept of using a ML model to perform task(s) which is a mere in instruction(s) that may be performed by human that it amounts no more than adding the words "apply it" (or an equivalent).
On page 17-18 of the remarks, the Applicant argues “However, the claimed approach does not merely automate a pre-existing manual process ... These are technical solutions to technical problems that did not exist before computerized clinical decision support systems, and they are not the mere automation of any pre-existing manual process...”, Examiner respectfully disagree. As discussed in the rejection above, the components of the instant system, when taken alone, each execute in a manner conventionally expected of these components. At best, Applicant has claimed features that may improve an abstract idea. However, an improved abstract idea is still abstract, (SAP America v. Investpic *2-3 ("'We may assume that the techniques claimed are "groundbreaking, innovative, or even brilliant," but that is not enough for eligibility. Association for Molecular Pathology v. Myriad Genetics, Inc., 569 U.S. 576, 591 (2013); accord buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1352 (Fed. Cir. 2014). Nor is it enough for subject-matter eligibility that claimed techniques be novel and nonobvious in light of prior art, passing muster under 35 U.S.C. §§ 102 and 103. See Mayo Collaborative Servs. v. Prometheus Labs., Inc., 566 U.S. 66, 89-90 (2012); Synopsys, Inc. v. Mentor Graphics Corp., 839 F.3d 1138, 1151 (Fed. Cir. 2016) ("[A] claim for a new abstract idea is still an abstract idea."'.
There is a fundamental difference between computer functionality improvements, on the
one hand, and uses of existing computers as tools to perform a particular task, on the other. The alleged advantages that Applicants tout do not concern an improvement in computer capabilities but instead relate to an alleged improvement in displaying a treatment path, for which a computer is used as a tool in its ordinary capacity.
Hence, Examiner remains the 101 rejections of claims which have been updated to address Applicant's amendments and remarks and to comply with the 2019 Revised Patent Subject Matter Eligibility Guidance in the above Office Action.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ALAAELDIN ELSHAER whose telephone number is (571)272-8284. The examiner can normally be reached M-Th 8:30-5:30.
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/ALAAELDIN M. ELSHAER/Primary Examiner, Art Unit 3687
1 Page (US 2021/0065888), [0072], [0166]; Barkol (US 2020/0327996), [0170]
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