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 .
DETAILED ACTION
Response to Amendment
In the amendment dated 11/14/2025, the following occurred: Claims 1, 2, 4-9 and 11-20 were amended.
Claims 1-20 are currently pending.
Claim Objections
Claims 6 and 13 are objected to for the following informality: “…adjust[ing]… the one or more visualization attributes of the treatment plan in accordance with an input received after presenting the medical visualization.” Should read “…adjust[ing]… the one or more visualization attributes of the radiation therapy treatment plan in accordance with an input received after presenting the medical visualization.”
Claims 7, 14 and 20 are objected to for the following informality: “…wherein the medical visualization is a comparison between the one or more visualization attribute of the treatment plan at two different times.” Should read “…wherein the medical visualization is a comparison between the one or more visualization attributes of the radiation therapy treatment plan at two different times.”
Appropriate corrections/clarification is required.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Claims 1, 8 and 15 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1
The claim recites a method, a non-transitory computer readable medium (CRM) and a system for generating and presenting visualizations, which are within a statutory category.
Step 2A1
Regarding claims 1, 8 and 15, the limitation of (claim 1 being representative) receiving one or more visualization attributes associated with a radiation therapy treatment of a patient; executing a […] model using the one or more visualization attributes and one or more configurations of a […] module to generate machine-readable code instructing the […] module to generate a medical visualization of the radiation therapy treatment corresponding to the one or more visualization attributes, […] rendering the medical visualization of the one or more visualization attributes; transmitting the machine-readable code, to the […] module, causing the […] module to retrieve at least one library and execute the machine-readable code to generate the medical visualization of the radiation therapy treatment; and in response to receiving the medical visualization from the […] module, presenting the medical visualization as drafted, is a process that, under the broadest reasonable interpretation, covers a method organizing human activity but for the recitation of generic computer components. That is other than reciting (in claim 1) a processor on a user device, (in claim 8) a non-transitory computer readable medium, a processor and a user device and (in claim 15) a processor and a user device, the claimed invention amounts to managing personal behavior or interaction between people (i.e., rules or instructions). For example, but for the non-transitory computer readable medium, processor and user device, the claims encompass receiving visualization attributes, generating a medical visualization corresponding to the visualization attributes and present the medical visualization in the manner described in the identified abstract idea, supra. The Examiner notes that certain “method[s] of organizing human activity” includes a person’s interaction with a computer (see MPEP 2106.04(a)(2)(II)). If a claim limitation, under its broadest reasonable interpretation, covers managing personal behavior or interactions between people, but for the recitation of generic computer components, then it falls within the “Certain Methods of Organizing Human Activity – Managing Personal Behavior Relationships, Interactions Between People (e.g. social activities, teaching, following rules or instructions)” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
Step 2A2
This judicial exception is not integrated into a practical application. In particular, claims 1 and 15 recite the additional elements of a processor and a user device. Claim 8 recites the additional elements of a non-transitory computer readable medium, a processor and a user device. These additional elements are not exclusively defined by the applicant and are recited at a high-level of generality (i.e., a generic computer components for enabling access to medical information or for performing generic computer functions. See Specification at para. [0039] and [0040]) such that they amounts to no more than mere instructions to apply the exception using a generic computer component. As set forth in MPEP 2106.04(d) “merely including 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. Accordingly, even in combination, these additional elements do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
Claims 1, 8 and 15 further recite the additional elements of a user interface comprising an interaction interface. This additional element merely generally links the abstract idea to a particular technological environment or field of use. MPEP 2106.04(d)(I) indicates that generally linking an abstract idea to a particular technological environment or field of use cannot provide a practical application.
The claims further recite the additional elements of a machine-learning language processing model to generate machine-readable code and a visualization software module to generate a medical visualization which is interpreted as “apply it” to the abstract idea. MPEP 2106.04(d)(I) indicates that merely saying “apply it” or equivalent to the abstract idea cannot provide a practical application.
Accordingly, even in combination, these additional elements do not integrate the abstract idea into a practical application.
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 the non-transitory computer readable medium, processor and user device to perform the noted steps amounts to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept (“significantly more”). Moreover, using generic computer components to perform abstract ideas does not provide a necessary 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.
Also, as discussed above with respect to integration of the abstract idea into a practical application, the additional element of user interface comprising an interactive user interface was determined to generally link the abstract idea to a particular technological environment or field of use. MPEP 2106.05(A) indicates that generally linking an abstract idea to a particular technological environment or field of use cannot provide significantly more.
As discussed above with respect to integration of the abstract idea into a practical application, the additional element of using a machine-learning language processing model to generate machine-readable code and a visualization software module to generate a visualization were found to be “apply it” to the abstract idea. This has been re-evaluated under the “significantly more” analysis and determined to be insufficient to provide significantly more. MPEP2106.05(1)(A) indicates that merely saying “apply it’ or equivalent to the abstract idea cannot provide an inventive concept (“significantly more’). As such the claim is not patent eligible.
Accordingly, even in combination, these additional elements do not provide significantly more. As such the claim is not patent eligible.
The examiner notes that: A well-known, general-purpose computer has been determined by the courts to be a well-understood, routine and conventional element (see, e.g., Alice Corp. v. CLS Bank; see also MPEP 2106.05(d)); Receiving and/or transmitting data over a network (“a communications network”) has also been recognized by the courts as a well - understood, routine and conventional function (see, e.g., buySAFE v. Google; MPEP 2016(d)(II)); and Performing repetitive calculations is/are also well-understood, routine and conventional computer functions when they are claimed in a merely generic manner (see, e.g., Parker v. Flook; MPEP 2016.05(d)).
Claims 2-7, 9-14 and 16-20 are similarly rejected because they either further define/narrow the abstract idea and/or do not further limit the claim to a practical application or provide as inventive concept such that the claims are subject matter eligible even when considered individually or as an ordered combination. Claim(s) 2, 9 and 16 further merely describe(s) the machine-readable code. Claim(s) 3, 4, 10, 11, 17 and 18 further merely describe(s) the machine-learning language processing model. Claim(s) 5, 12 and 19 further merely describe(s) recalibrating the machine- learning language processing model. Claim(s) 6 and 13 further merely describe(s) adjusting the one or more visualization attributes. Claim(s) 7, 14 and 20 further merely describe(s) the medical visualization.
Claim Rejections - 35 USC § 102
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 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.
Claims 1-20 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Peltola (US 2022/0296923).
REGARDING CLAIM 1
Peltola discloses A method for generating machine-readable code for visualization of radiation therapy treatment plan data comprising: presenting, by a processor on a user device, a user interface comprising an interaction interface for a medical professional operating the user device ([0028] teaches an electronic platform that includes graphical user interfaces (GUIs) (interpreted by examiner as presenting the user interface comprising an interactive interface) displayed on the end-user devices 140, and/or the administrator computing device 150 and that a physician operating the physician device 120b (interpreted by examiner as the medical professional operating the user device) may access the platform, input patient attributes or characteristics and other data); receiving, by the processor from the interaction interface, one or more visualization attributes associated with a radiation therapy treatment of a patient ([0028] teaches the platform identifies and displays treatment attributes (e.g., RTTP that includes different radiation parameters) (interpreted by examiner as the received one or more visualization attributes). [0035] teaches treatment data received from a physician operating the physician device 120b and [0036] teaches retrieve/receive data associated with a particular patient's treatment plan. [0041] teaches the analytics server receives patient data (e.g., physical attributes and diagnosis) and treatment data and [0080] teaches the data received (e.g., patient/treatment data) may be visual (also interpreted by examiner as the received one or more visualization attributes associated with a radiation therapy treatment of a patient)); executing, by the processor, a machine-learning language processing model using the one or more visualization attributes and one or more configurations of a visualization software module to generate machine-readable code instructing the visualization software module to generate a medical visualization of the radiation therapy treatment corresponding to the one or more visualization attributes, wherein the machine-learning language processing model is configured to generate the machine-readable code corresponding to the visualization software module for rendering the medical visualization of the one or more visualization attributes ([0029] teaches radiation parameters may include, but are not limited to, different treatment modalities, field geometry settings for external beam radiotherapy, side effect predictions, organ and/or tumor segmentation, machine therapy attributes, dosage administration attributes (e.g., dosage amount), treatment frequency, treatment timing, etc. (interpreted by examiner as the one or more visualization attributes and one or more configurations of a visualization software module) [0033] and [0035] teach the analytics server uses inputs to train the machine-learning or other computer models and executes various models to analyze the retrieved data (interpreted by examiner as executing a machine-learning language processing model using the one or more visualization attributes and one or more configurations of a visualization software module) [0095] teaches a plan optimizer (interpreted by examiner as the visualization software module) generating a treatment plan and [0096] teaches the output generated by the AI model may be ingested by the plan optimizer and [0102] teaches the plan optimizer 330 may display the suggested treatment plan 340 (interpreted by examiner as the medical visualization) on a computer of a clinic where a radiotherapy technician or a treating oncologist can review the treatment plan (interpreted by examiner as the visualization software module for rendering the medical visualization of the one or more visualization attributes) [0020] teaches the plan optimizer may be a set of computer-readable instructions stored on a non-transitory computer medium and configured to be executed by a processor to carry out this functionality (interpreted by examiner as the machine-readable code instructing the visualization software module to generate a medical visualization of the radiation therapy treatment). [0028] teaches a physician operating the physician device 120b may access the platform, input patient attributes or characteristics and other data, and further instruct the analytics server 110a to optimize the patient's treatment plan and [0095] teaches instructing the plan optimizer to generate a new plan (interpreted by examiner as executing, by the processor, a machine-learning language processing model to generate machine-readable code)) transmitting, by the processor, the machine-readable code, to the visualization software module for execution by the visualization software module ([0020] teaches the plan optimizer may be a set of computer-readable instructions configured to be executed by a processor to carry out this functionality (interpreted by examiner as means for transmitting the machine-readable code to the software module for execution)); and in response to receiving the medical visualization from the visualization software module, presenting, by the processor, the medical visualization via the user interface based on causing the visualization software to execute the machine-readable code ([0028] teaches the analytics server optimize dosage distribution and display the results (interpreted by examiner as the medical visualization) on the end-user devices and may display the predicted dose distribution and/or radiation parameters (also interpreted by examiner as the medical visualization) on the physician device [0035] teaches executing various models 111 to analyze the retrieved data and displays the results via the electronic platform on the administrator computing device 150, the electronic physician device 120b, and/or the end-user devices 140 (interpreted by examiner as the user interface) and [0006] teaches displaying, by the processor, a heat map having a set of segments where each segment corresponds to a first coordinate and a second coordinate of the anatomical region of the patient, wherein a visual attribute of each segment corresponds to a calculated dose distribution value and [0088] teaches visualizing the AI model's predictions. (All of the above are interpreted by the examiner as presenting the medical visualization in response to receiving the medical visualization from the visualization software module, via the user interface based on causing the visualization software to execute the machine-readable code)).
REGARDING CLAIMS 8 and 15
Claims 8 and 15 are analogous to Claim 1 thus Claims 8 and 15 are similarly analyzed and rejected in a manner consistent with the rejection of Claim 1.
REGARDING CLAIM 2
Peltola discloses the limitation of claim 1.
Peltola further discloses:
The method of claim 1, wherein the machine-readable code comprises a digital library to generate the medical visualization (Peltola [0035] teaches the models 111 stored within the analytics server 110a or the system database 110b (interpreted by examiner as the digital library)).
REGARDING CLAIM 3
Peltola discloses the limitation of claim 1.
Peltola further discloses:
The method of claim 1, wherein the machine-learning language processing model is specifically trained for a plan optimizer that generates the radiation therapy treatment of the patient (Peltola at [0006] teaches that the artificial intelligence model trained using training dataset and [0029] teaches the predicted dose distribution values can be ingested by the plan optimizer to iteratively improve the patient's treatment plan (interpreted by examiner as wherein the machine-learning language processing model is specifically trained for a plan optimizer that generates the radiation therapy treatment of the patient)).
REGARDING CLAIM 4
Peltola discloses the limitation of claim 1.
Peltola further discloses:
The method of claim 1, wherein the machine-learning language processing model is specifically trained for the medical visualization using at least one configuration of the visualization software module (Peltola [0102] the plan optimizer (interpreted by examiner as the visualization software module) may iteratively revise the patient's treatment plan and with each iteration, the plan optimizer may transmit new treatment plan data back to the machine-learning model (interpreted by examiner as the machine-learning language processing model) whereby the machine-learning model can recalculate/re-predict new dose distribution data based on the revised treatment data generated by the plan optimizer (iteration 322). The plan optimizer 330 and the machine-learning model 320 may repeat the iteration 322 until the patient's treatment plan is optimized (interpreted by examiner as wherein the machine-learning language processing model is specifically trained for the medical visualization using at least one configuration of the visualization software module)).
REGARDING CLAIM 5
Peltola discloses the limitation of claim 1.
Peltola further discloses:
The method of claim 1, further comprising: recalibrating, by the processor, the machine-learning language processing model using an input corresponding to a quality of the medical visualization (Peltola at [0110] teaches the analytics server may use user interactions to further train and re-calibrate the AI model. When an end user performs an activity on the electronic platform that displays the results predicted via the AI model, the analytics server may track and record details of the user's activity. For instance, when a predicted result is displayed on a user's electronic device, the analytics server may monitor the user's electronic device to identify whether the user has interacted with the predicted results by editing, deleting, accepting, or revising the results. The analytics server may also identify a timestamp of each interaction, such that the analytics server records the frequency of modification, duration of revision/correction (interpreted by examiner as recalibrating the machine-learning language processing model using an input corresponding to a quality of the medical visualization)).
REGARDING CLAIM 6
Peltola discloses the limitation of claim 1.
Peltola further discloses:
The method of claim 1, further comprising: adjusting, by the processor, the one or more visualization attributes of the treatment plan in accordance with an input received after presenting the medical visualization (Peltola at [0028] teaches the analytics server 110a may utilize the methods and systems described herein to optimize dosage distribution and display the results on the end-user devices or adjust the configuration of one of end-user devices 140 (e.g., the radiotherapy machine 140d) [0040] teaches adjusting the medical device 140d based on the radiation parameters generated by the analytics server 110a. For instance, the radiotherapy machine may adjust the gantry and couch based on angles and other attributes/parameters determined by the analytics server 110a. The analytics server 110a may transmit instructions to the radiotherapy machines indicating any number or type of radiation parameters (e.g., field geometry settings) to facilitate such adjustments. [0058] teaches medical professional adjusting one or more attributes of the patient's treatment plan (interpreted by examiner as means for adjusting the one or more visualization attributes of the treatment plan in accordance with an input received after presenting the medical visualization)).
REGARDING CLAIM 7
Peltola discloses the limitation of claim 1.
Peltola further discloses:
The method of claim 1, wherein the medical visualization is a comparison between the one or more visualization attributes of the treatment plan at two different times (Peltola at [0029] teaches evaluating the radiation parameters. [0061] teaches the analytics server may label the training dataset, such that the AI model can differentiate between desirable and undesirable outcomes. Labeling the training dataset may be performed automatically and/or using human intervention. For instance, the analytics server may analyze a treatment plan, DVH, and/or achieved values for clinical goals for a previously treated patient and may identify various hot spots or cold spots (e.g., by comparing the dosage received with the plan objective thresholds). [0065] teaches The AI model may output dose distribution values for individual patients based on their respective characteristics, and the outputs can be compared against the labels. Also, [0071] teaches the AI model may ingest all the data within the training dataset to identify hidden patterns and connections between data points and [0072] teaches the analytics server may compare the predicted values with true and actual values within the training dataset (interpreted by examiner as wherein the medical visualization is a comparison between the one or more visualization attributes of the treatment plan at two different times)).
REGARDING CLAIMS 9-14 and 16-20
Claims 9-14 and 16-20 are analogous to Claims 2-7 thus Claims 9-14 and 16-20 are similarly analyzed and rejected in a manner consistent with the rejection of Claim 2-7.
Response to Arguments
Drawing Objections
Regarding the drawing objection(s), the Applicant has amended Figure 3 to overcome the basis/bases of objection.
Claim Objections
Regarding the claim objection(s), the Applicant has amended claims 1, 8 and 15 to overcome the basis/bases of objection. However, the Applicant has not overcome the basis/bases of the objection of claims 6, 7, 13, 14 and 20.
Rejection under 35 U.S.C. § 101
Regarding the rejection of claims 1-20, the Examiner has considered the Applicant’s arguments, but does not find them persuasive. Applicant argues:
Furthermore, amended claim 1 recites that the processor may execute "a machine-learning model using one or more visualization attributes." As a non-limiting example, the Specification describes that "visualization attributes indicate that the user is interested in viewing a DVH of the patient's organs (both OAR and PTV). The machine-learning language processing model will then determine a set of libraries to generate the DVH table and underlying code for a visualization engine to generate the DVH." See para. [0075]. The machine-learning language processing model is therefore further configured to generate the machine-readable code based on input indicating what visualization should be presented. As a result, the claims are not directed to a method of organizing human activity, as alleged by the Office Action, but rather a specific technological process for generating machine-readable code using a machine-learning language processing model that interprets visualization attributes to produce executable code for rendering radiation therapy treatment plans… Even if the claims were considered to recite an abstract idea (which is not conceded here), the claims, at least as amended, integrate any would-be abstract idea into a practical application. In particular, the recited method of generating machine-readable code for visualization of radiation therapy treatment plan data addresses technical problems associated with constraints of conventional visualization software. The Specification describes that "[c]urrent software solutions, however, are often cumbersome, featuring outdated interfaces and limited customization options." See para. [0005]. The Specification further describes that "integrating new visual features into treatment planning software involves prolonged discussions between customers and software providers, which can take a long time to result in an actual product update." See para. [0006]. Additionally, the Specification describes that "a particular visualization engine may use certain specific libraries to generate the visualization." See para. [0078].
Regarding 1, The Examiner respectfully disagrees. The claims simply execute a machine learning language model using some specific information (one or more visualization attributes), and is interpreted as an additional element that is applied to the abstract idea, i.e. a tool to perform their abstract idea. The Examiner notes that certain “method[s] of organizing human activity” includes a person’s interaction with a computer (see MPEP 2106.04(a)(2)(II)). Hence, a person can interact with a computer, apply machine learning technology and perform the claims abstract idea. As a result, the use of machine learning language model cannot not provide a practical application, as MPEP 2106.04(d)(I) indicates that merely saying “apply it” or equivalent to the abstract idea cannot provide a practical application. Moreover, the specification does not claim a technical solution to a technical problem. Instead the specification focuses on improving the accuracy and effectiveness of radiotherapy treatments [0003] and improving treatment plan visualizations to improve the efficiency and efficacy of treatment planning [0007]. The claim is in ineligible.
Rejection under 35 U.S.C. § 102
Regarding the rejection of claims 1-20, the Examiner has considered the Applicant’s arguments, but does not find them persuasive. Applicant argues:
Claims 1-20 were rejected under 35 U.S.C. § 102 as allegedly being unpatentable over U.S. 2022/0296923 (Peltola). Applicant respectfully traverses these rejections.
Per the Parties' agreement during the Examiner Interview, the claims (as amended and presented herein) overcome this rejection. Therefore, Applicant requests that this rejection be withdrawn.
Regarding 1, The Examiner respectfully disagrees. After further consideration of the cited reference Peltola, the Examiner confirms Applicants claimed invention is anticipated by Peltola. Please refer to the updated rejection above. Given the broadest reasonable interpretation the cited reference in combination teaches the claimed features.
Conclusion
Applicant’s amendment necessitated the new grounds of rejection presented in this Office action. 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.
The prior art made of record though not relied upon in the present basis of rejection are noted in the attached PTO 892 and include:
Princ (US 20220414525) teaches machine learning approach for solving beam angle optimization. Nord (US 2014/0275700) teaches method and apparatus for using patient-experience outcomes when developing radiation-therapy treatment plans.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to LIZA TONY KANAAN whose telephone number is (571)272-4664. The examiner can normally be reached on Mon-Thu 9:00am-6:00pm ET.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Robert Morgan can be reached on 571-272-6773. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/LIZA TONY KANAAN/Examiner, Art Unit 3683
/ROBERT W MORGAN/Supervisory Patent Examiner, Art Unit 3683