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 .
Response to Arguments
Applicant's arguments filed on 6/22/2026 have been fully considered but they are not persuasive.
Applicant argues the 101 on pages 6-12 of the arguments. However, these are not found as persuasive. Having separate models or one model does not create any different on whether the idea is abstract without specifics of the model rather than what the model is used for. There is also no training claimed for the algorithms to show that this is specifics rather than an off the shelf algorithm. Applicant argues that the cost values being used in the different algorithms are seen as an improvement to the art; however, this is not persuasive since you can use as many algorithms or as you want and it does not improve planning but rather the processing times of a computer which is not the art area. The examples, 47 and 49 have different fact patterns to the present invention, those are for specific algorithms and have training in their AI models versus just using the AI as a tool; further, there is no practical application like actual treatment happening. The models being used are recited in the specification and the claims are high level as just AI models/algorithms without actually stating what those algorithms are or specifics of the models. Therefore the 101 strands.
Applicant argues that the prior art does not teach “separate” for the different models. However, this is not seen as persuasive as separate can be seen as just separate lines in code or different parts of an algorithm. Further, both models are executed by the same processor which if they are separate or integral to one another is seen as just obvious as there are only two options on how to write the codes.
Applicant argues that the prior art does not teach the modes, treatment plans or different cost values and transferring the cost values. However, examiner of record respectfully disagrees. Hakala teaches using multiple agents which each have a reward and thus a cost value that are both fed into a global model for the agent 203 and model 400. Also, these are all run by the same processor. See Fig. 1-4. Hakala is used to teach different algorithms and adjust the algorithm as they do the same methods using an ai algorithm. Also, it is the combination of Hakala and McNutt that teaches the claimed invention; Hakala teaches the specifics of the algorithms and McNutt teaches what can be implemented in the algorithms for updating with different cost values. Also, the claims do not specify what the cost values are specifically or the specifics of the algorithms just that they are used to update the algorithms or impute into an algorithm. More details are needed in the claims. Also, algorithms can be written in code that is a single code that have different inputs into different parts of the code or you can have separate algorithms in different code that are still both executed by the same processor. Hakala does teach and shows different models and information and rewards going in to a global model as seen in Fig. 4.
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 and 3-15, and 17-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claim(s) recite(s) an abstract idea directed to organizing human activity and math.
Claims 1, 3-15 and 17-20 are drawn to a method, CRM, and system for outputting a treatment plan. Claims 1, 3-14 do fall into statutory categories of a product and a process. Claims 15, 17-20 do not fall into a statutory category (see below) but would fall into a statutory category if it included “memory”. Claims 1, 3-15, and 17-20 recite the abstract idea of receiving information and executing a models to generate and calculate attributes and values for the treatment plan. The further elements in the dependents do not further limit the abstract idea and fall into the execution steps. The mentions of the processors and CRM elements are seen as being considered in (apply it, MPEP 2106.05(f)). The abstract idea is seen as organizing human activity because it recites managing personal behavior or relationships or interaction between people or is also seen as performing math. This judicial exception is not integrated into a practical application because the additional elements (i.e. the limitations not identified as part of the abstract idea) amount to no more than limitations which amount to mere instruction to apply an exception – for example, the recitation of the processor computer in the computer readable medium, which amounts to merely invoking a computer as a tool to perform the abstract idea see MPEP 2106.05(f) and specification [0090]-[0091] and generally linking the abstract idea to a particular technological environment or field of use – for example, the recitation of a computer and a processor, which amounts to limiting the abstract idea to the field of a computer, see MPEP 2106.05(h). Furthermore, the claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception. The additional elements of outputting information as deemed “additional elements” which do not result in significantly more than the judicial exception. They are seen as post-solution activity where displaying information is not seen as significantly more similar to that of Electric Power Group which deemed gathering data, analyzing it and displaying it did not result in overcoming the judicial exception.
Thus, taken alone, the additional elements do not amount to “significantly more” than the above-identified abstract idea. Furthermore, looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually, and there is no indication that the combination of elements improves the function of a computer or improves any other technology, and their collective functions merely provide conventional computer implementation.
Therefore, whether taken individually or as an ordered combination, claims 1-20 are nonetheless rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.
Claims 15 and 17-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. The claim(s) does/do not fall within at least one of the four categories of patent eligible subject matter because the claimed invention falls under “signals per se” as there is no mention of a memory and the processor can fall into the “signals per se” category which is not a statutory category.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Furthermore, since the reference qualifies under 102(a)(1) it is suggested to look at the exceptions 102(b)(1)(a)/(b) as the inventorship is different between the reference and the current application.
Claim(s) 1, 3-15, and 17-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Hakala et al., US 20220415472, herein referred to as “Hakala” in view of McNutt et al., US 20170083682, herein referred to as “McNutt”.
Regarding claim 1, Hakala discloses: A method comprising: receiving, by a processor (Fig. 1 and 4), a radiation therapy plan objective for a patient (Fig. 1 and 4, [0014], [0024]; specifically processor for determining radiation [0056]; specifically selecting dosage); executing, by the processor, a plan optimizer computer model to generate one or more treatment attributes for a treatment plan complying with the radiation therapy plan objectives ([0014], [0024]; specifically the processor used with machine learning to predict radiotherapy treatment using iterative processes and claim 1), the plan optimizer computer model iteratively calculating the one or more attributes, where with each iteration, the plan optimizer computer model revises the one or more attributes of the treatment plan in accordance with a cost value (claim 1 and [0014], [0024]; specifically iterative process with machine learning and reward value); executing, by the processor, an artificial intelligence model separate from the plan optimizer computer model to calculate a second cost value for the treatment plan (Fig. 4; [0024]; specifically adjusting the reward value which would result in a second cost), and outputting, by the processor, the treatment plan for the patient ([0014], [0024] and [0143]; specifically outputting a radiotherapy treatment) transmitting, by the processor, the second cost value to the plan optimizer computer model, wherein the plan optimizer computer model revised the one or more attributes of the treatment plan in accordance with the second cost value (Fig. 4 and [0020]-[0021], [0121] and claim 1; specifically the different rewards/cumulative rewards resulting in creating the treatment plan).
While Hakala does disclose, Hakala does not explicitly disclose: wherein the artificial intelligence model is trained to calculate the second cost value in accordance with a likelihood of occurrence of a health-problem for the patient after being treated via the treatment plan having the one or more attributes.
However, McNutt does disclose: wherein the artificial intelligence model is trained to calculate the second cost value in accordance with a likelihood of occurrence of a health-problem for the patient after being treated via the treatment plan having the one or more attributes (abstract and [241]; specifically the toxicity that could occur for patient).
It would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to modeling and treatment planning of Hakala to include the ability to train for toxicity as seen in McNutt. The motivation being able to connect treatment to toxicity for better monitoring of patient as seen in McNutt [0276]-[0277].
Regarding claim 3, Hakala does disclose it can look at different issues associated with disease as seen in ([0025]). However, Hakala does not explicitly disclose what those could be. McNutt does disclose: wherein the health-problem corresponds to at least one of xerostomia, reduction of saliva production, headache, hair loss, nausea, vomiting, fatigue, hair loss, skin irritation, memory loss, or speech loss ([0276]; xerostomia, [0337]; salivary gland function).
It would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to modeling and treatment planning of Hakala to include the ability to train for toxicity as seen in McNutt. The motivation being able to connect treatment to toxicity for better monitoring of patient and make connections between different diagnostic factors/side effects as seen in McNutt [0086].
Regarding claim 4, Hakala discloses: wherein the radiation therapy plan objective corresponds to a dose- volume objective ([0051]; specifically dose distribution which is seen as dose-volume objective).
Regarding claim 5, Hakala discloses looking at cancer [0071]. Hakala does not explicitly disclose that a separate health-problem can be another form of cancer. However, McNutt discloses: wherein the health-problem corresponds to developing a secondar cancer (abstract, [0328] and claims 1 and 14).
It would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to modeling and treatment planning of Hakala to include the ability to train for toxicity as seen in McNutt. The motivation being able to connect treatment to toxicity for better monitoring of patient and make connections between different diagnostic factors/side effects as seen in McNutt [0086].
Regarding claim 6, Hakala discloses: wherein the plan optimizer computer model aggregates the second cost value generated by the artificial intelligence model with the cost value generated by the plan optimizer computer model ([0070]-[0075] and Fig. 4 which shows the different agents with different rewards as well as the global model and Fig. 5).
Regarding claim 7, Hakala discloses: wherein the artificial intelligence model is customized for a clinic implementing the treatment plan for the patient ([0013]-[0015] and claim 1).
Regarding claims 8-14, the claims are similarly rejected to claims 1-7 as seen above. The CRM aspect is further seen in Hakala, Fig. 1 and [0062].
Regarding claims 15, and 17-20, the claims are similarly rejected as claims 1-6 above. The processor and system are taught in the claims of Hakala and Fig. 1.
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 JOANNE M RODDEN whose telephone number is (303)297-4276. The examiner can normally be reached Monday - Friday 9:00 AM-5:00 PM MST.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Jonathan Moffat can be reached at 571-272-4390. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/JOANNE M RODDEN/Supervisory Patent Examiner, Art Unit 3794
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