DETAILED ACTION
Status of Claims
This is a first office action on the merits in response to the application filed on9/12/2024.
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 .
Priority
This application claims priority of Provisional Application 63/663262 filed on 6/24/2024. Applicant's claim for the benefit of this prior-filed application is acknowledged.
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 non-statutory subject matter;
When considering subject matter eligibility under 35 U.S.C. 101, it must be determined whether the claim is directed to one of the four statutory categories of invention, i.e., process, machine, manufacture, or composition of matter. If the claim does fall within one of the statutory categories, it must then be determined whether the claim is directed to a judicial exception (i.e., law of nature, natural phenomenon, and abstract idea), and if so, it must additionally be determined whether the claim is a patent-eligible application of the exception. If an abstract idea is present in the claim, any element or combination of elements in the claim must be sufficient to ensure that the claim amounts to significantly more than the abstract idea itself.
In the instant case (Step 1), claims 10-18 are directed toward a process, claims 19-20 are directed toward a product, and claims 1-9 are directed toward a system; which are statutory categories of invention.
Additionally (Step 2A Prong One), the independent claims are directed toward a system comprising: a memory storing program code; and one or more processing units to execute the program code to cause the system to: receive a description of a project; instruct a text generation model to generate text describing a plurality of tasks using a prompt including the description; determine a hierarchical structure of the plurality of tasks based on the generated text; and generate a project plan file including the hierarchical structure and the plurality of tasks (claims 1, 10, and 19) (Organizing Human Activity and Mental Processes), which are considered to be abstract ideas (See MPEP 2106). The steps/functions disclosed above and in the independent claims are directed toward the abstract idea of Organizing Human Activity because the claimed limitations are analyzing project descriptions to determine a hierarchy of tasks that need to be performed to generate a project plan as to how to execute the tasks, which is managing how humans interact for commercial purposes. The steps/functions disclosed above and in the independent claims are directed toward the abstract idea of Mental Processes because the claimed limitations are analyzing project descriptions to determine a hierarchy of tasks that need to be performed to generate a project plan as to how to execute the tasks, which can be done in the human mind.
Dependent claims 2-9, 11-18, and 20 further narrow the abstract idea identified in the independent claims, where any additional elements introduced are discussed below.
Step 2A Prong Two: In this application, even if not directed toward the abstract idea, the independent claims additionally recite “a system comprising: a memory storing program code; and one or more processing units to execute the program code to cause the system to; instruct a text generation model to (claim 1)”; “instruct a text generation model to (claim 10)”; “One or more non-transitory computer-readable media storing program code that, when executed by a computing system, causes the computing system to perform operations; instructing a text generation model to (claim 19)”, which are additional elements that do not integrate the judicial exception (e.g. abstract idea) into a practical application because the claimed structure merely adds the words to apply it with the judicial exception and mere instructions to implement an abstract idea on a computer (See MPEP 2106) and are recited at such a high level of generality. These limitations can also be viewed as nothing more than an attempt to generally link the use of the judicial exception to the technological environment of a computer. Even when viewed in combination, the additional elements in the claims do no more than use the computer components as a tool. There is no change to the computer or other technology that is recited in the claim, and thus the claims do not improve computer functionality or other technology.
In addition, dependent claims 2-9, 11-18, and 20 further narrow the abstract idea and dependent claims 3-4, 7-9, 13, 16-18, and 20 additionally recite “a plurality of project plan files (claims 3 and 12); a script (claims 4, 7-8, 13, 16-17, and 20); execute the script (claims 4, 7-8, 13, 16-17, and 20); instruct a text generation model (claims 6 and 15); a plurality of text generation models (claims 9 and 18)”, which are additional elements that do not integrate the judicial exception (e.g. abstract idea) into a practical application because the claimed structure merely adds the words to apply it with the judicial exception and mere instructions to implement an abstract idea on a computer (See MPEP 2106.05(f)).
Step 2B: When analyzing the additional element(s) and/or combination of elements in the claim(s) other than the abstract idea per se the claim limitations amount(s) to no more than: a general link of the use of an abstract idea to a particular technological environment and merely amounts to the application or instructions to apply the abstract idea on a computer (See MPEP 2106). Further, method; System; and Product Independent claims 1, 10, and 19 recite “a system comprising: a memory storing program code; and one or more processing units to execute the program code to cause the system to; instruct a text generation model to (claim 1)”; “instruct a text generation model to (claim 10)”; “One or more non-transitory computer-readable media storing program code that, when executed by a computing system, causes the computing system to perform operations; instructing a text generation model to (claim 19)”; however, these elements merely facilitate the claimed functions at a high level of generality and they perform conventional functions and are considered to be general purpose computer components which is supported by Applicant’s specification in Paragraphs 0018 and 0029-0030 and Figures 1 and 9. The Applicant’s claimed additional elements are mere instructions to implement the abstract idea on a general purpose computer and generally link of the use of an abstract idea to a particular technological environment. When viewed as a whole, these additional claim element(s) do not provide meaningful limitation(s) to transform the abstract idea into a patent eligible application of the abstract idea such that the claim(s) amounts to significantly more than the abstract idea itself.
In addition, claims 2-9, 11-18, and 20 further narrow the abstract idea identified in the independent claims. The Examiner notes that the dependent claims merely further define the data being analyzed and how the data is being analyzed. Similarly, claims 3-4, 7-9, 13, 16-18, and 20 additionally recite “a plurality of project plan files (claims 3 and 12); a script (claims 4, 7-8, 13, 16-17, and 20); execute the script (claims 4, 7-8, 13, 16-17, and 20); instruct a text generation model (claims 6, and 15); a plurality of text generation models (claims 9 and 18)”, which do not account for additional elements that amount to significantly more than the abstract idea because the claimed structure merely amounts to the application or instructions to apply the abstract idea on a computer and does not move beyond a general link of the use of an abstract idea to a particular technological environment (See MPEP 2106.05). The additional limitations of the independent and dependent claim(s) when considered individually and as an ordered combination do not amount to significantly more than the abstract idea. The examiner has considered the dependent claims in a full analysis including the additional limitations individually and in combination as analyzed in the independent claim(s). Therefore, the claim(s) are rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 6 and 15 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Regarding Claims 6 and 15: Claims 6 and 15 recite “instructing a text generation model”. It is unclear to the Examiner if this is a new text generation model or the same text generation model recited in the independent claim from which they depend as it appears to be an antecedent basis issue. The Examiner interprets the claims to be instructing the text generation model of the independent claims. Appropriate correction is required.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claim(s) 1-20 is/are rejected under 35 U.S.C. 102(a)(2) as being taught by Kuusela et al. (US 2025/0149165 A1).
Regarding Claim 1: Kuusela et al. teach a system comprising: a memory storing program code; and one or more processing units to execute the program code to cause the system to (See Figure 1, Paragraph 0038, and claim 1):
receive a description of a project (See Figure 2, Paragraph 0044 – “input elements to receive data associated with a patient to be treated (e.g., plan objectives)”, Paragraph 0068, and claim 1);
instruct a text generation model to generate text describing a plurality of tasks using a prompt including the description (See Figure 2, Paragraph 0044 – “display results of predictions for AI-generated text”, Paragraph 0048, Paragraph 0068 – “text that indicates the one or more decisions and/or treatment attributes”, and claim 1);
determine a hierarchical structure of the plurality of tasks based on the generated text (See Figure 2, Paragraph 0057 – “The analytics server can input the text including the patient attributes into the machine-learning language processing model and execute the machine-learning language processing model. Based on the input, the language machine learning model may output a response in text or additional types of information, such as media image data containing image scans, charts/graphs, and the like”, Paragraph 0068 – “The task can be a task of a hierarchy of tasks associated with generating a radiotherapy treatment plan … A hierarchy of tasks may be or include, in any order or in a specific order and among other tasks, in some cases, objective template selection, optimization, and radiation dose calculation. Each task may correspond to a different treatment attribute or involve a different decision involved in performing the task”, and claim 1 – “a hierarchy of tasks associated with generating treatment plans”);
and generate a project plan file including the hierarchical structure and the plurality of tasks (See Figure 2, Paragraph 0050, Paragraph 0054 – “a machine learning language processing model to generate a radiotherapy treatment plan”, Paragraph 0057, Paragraph 0068 – “The task can be a task of a hierarchy of tasks associated with generating a radiotherapy treatment plan”, and claim 1).
Regarding Claim 2: Kuusela et al. teach the limitations of claim 1. Kuusela et al. further teach wherein the prompt includes a prompt template, the description, a hierarchical task id format and second text describing a second plurality of tasks of a second project plan file (See Figure 2, Paragraph 0044, Paragraph 0057, Paragraph 0068 – “A hierarchy of tasks may be or include, in any order or in a specific order and among other tasks, in some cases, objective template selection, optimization”, and claim 1).
Regarding Claim 3: Kuusela et al. teach the limitations of claim 2. Kuusela et al. further teach the one or more processing units to execute the program code to cause the system to: instruct the text generation model to select one of a plurality of project plan files using a second prompt including the description and descriptions associated with each of the plurality of project plan files; and in response to instructing the text generation model to select one of the plurality of project plan files: receive a selection of the second project plan file; and determine the second text describing the second plurality of tasks of the second project plan file (See Figure 2, Paragraph 0054, Paragraph 0057 – “The user can respond to the response from the machine learning language processing model with text following the response and submit the user input response. This process can repeat any number of times to simulate”, Paragraph 0069 – “The analytics server can generate a prompt from the identified patient attributes and treatment attributes and label the prompt with the decisions for treatment. The analytics server can input the labeled prompt into the machine learning language processing model”, Paragraph 0073 – “The analytics server can generate the prompt according to a defined template that indicates how to format the prompt and/or where to include different types of attributes”, and claim 1).
Regarding Claim 4: Kuusela et al. teach the limitations of claim 2. Kuusela et al. further teach wherein the generated text includes a script, and the one or more processing units to execute the program code to cause the system to: execute the script to generate second text describing a third one or more tasks, wherein determination of the hierarchical structure comprises determination of the hierarchical structure of the plurality of tasks and the third one or more tasks based on the generated text and the generated second text, and wherein the generated project plan file includes the hierarchical structure, the plurality of tasks, and the third one or more tasks (See Figure 2, Paragraph 0046 – “transcripts of instructions given”, Paragraph 0054, Paragraph 0057, Paragraph 0069 – “The machine learning language processing model can be trained on a set of transcriptions”, Paragraph 0073, and claim 1).
Regarding Claim 5: Kuusela et al. teach the limitations of claim 1. Kuusela et al. further teach wherein the description describes an update to the project, and wherein the prompt includes a prompt template, the description, and second text describing a second plurality of tasks of a second project plan file (See Figure 2, Paragraph 0054, Paragraph 0057 – “The user can respond to the response from the machine learning language processing model with text following the response and submit the user input response. This process can repeat any number of times to simulate”, Paragraph 0069 – “The analytics server can generate a prompt from the identified patient attributes and treatment attributes and label the prompt with the decisions for treatment. The analytics server can input the labeled prompt into the machine learning language processing model”, Paragraph 0073 – “The analytics server can generate the prompt according to a defined template that indicates how to format the prompt and/or where to include different types of attributes”, and claim 1).
Regarding Claim 6: Kuusela et al. teach the limitations of claim 5. Kuusela et al. further teach the one or more processing units to execute the program code to cause the system to: instruct a text generation model to generate a summary of differences between the text describing the plurality of tasks and the second text describing the second plurality of tasks of a second project plan file; receive a third description of a third project; instruct the text generation model to generate third text describing a third plurality of tasks using a third prompt including the third description and the summary; determine a second hierarchical structure of the third plurality of tasks based on the generated third text; and generate a third project plan file including the second hierarchical structure and the third plurality of tasks (See Figure 2, Paragraph 0050, Paragraph 0054 – “a machine learning language processing model to generate a radiotherapy treatment plan”, Paragraph 0057, Paragraph 0068 – “The task can be a task of a hierarchy of tasks associated with generating a radiotherapy treatment plan”, Paragraph 0069, Paragraph 0078 – “receive, from the interaction interface, a third input”, Paragraph 0080, and claim 1).
Regarding Claim 7: Kuusela et al. teach the limitations of claim 5. Kuusela et al. further teach wherein the generated text includes a script, the one or more processing units to execute the program code to cause the system to: execute the script to generate second text describing a third one or more tasks, wherein determination of the hierarchical structure comprises determination of the hierarchical structure of the plurality of tasks and the third one or more tasks based on the generated text and the generated second text, and wherein the generated project plan file includes the hierarchical structure, the plurality of tasks, and the third one or more tasks (See Figure 2, Paragraph 0046 – “transcripts of instructions given”, Paragraph 0054, Paragraph 0057, Paragraph 0068 – “The task can be a task of a hierarchy of tasks associated with generating a radiotherapy treatment plan”, Paragraph 0069 – “The machine learning language processing model can be trained on a set of transcriptions”, Paragraph 0073, Paragraph 0078, and claim 1).
Regarding Claim 8: Kuusela et al. teach the limitations of claim 1. Kuusela et al. further teach wherein the generated text includes a script, the one or more processing units to execute the program code to cause the system to: execute the script to generate second text describing a second one or more tasks, wherein determination of the hierarchical structure comprises determination of the hierarchical structure of the plurality of tasks and the second one or more tasks based on the generated text and the generated second text, and wherein the generated project plan file includes the hierarchical structure, the plurality of tasks, and the second one or more tasks (See Figure 2, Paragraph 0046 – “transcripts of instructions given”, Paragraph 0054, Paragraph 0057, Paragraph 0068 – “The task can be a task of a hierarchy of tasks associated with generating a radiotherapy treatment plan”, Paragraph 0069 – “The machine learning language processing model can be trained on a set of transcriptions”, Paragraph 0073, Paragraph 0078, and claim 1).
Regarding Claim 9: Kuusela et al. teach the limitations of claim 1. Kuusela et al. further teach wherein instruction of the text generation model comprises instruction of each of a plurality of text generation models to generate text describing a plurality of tasks using a prompt including the description, the one or more processing units to execute the program code to cause the system to: instruct the text generation model to select one of the generated texts, wherein the hierarchical structure of the plurality of tasks is determined based on the selected generated text, and wherein the generated project plan file includes the hierarchical structure and the plurality of tasks described by the selected generated text (See Figure 2, Paragraph 0046 – “transcripts of instructions given”, Paragraph 0054, Paragraph 0057, Paragraph 0068 – “The task can be a task of a hierarchy of tasks associated with generating a radiotherapy treatment plan”, Paragraph 0069 – “The machine learning language processing model can be trained on a set of transcriptions”, Paragraph 0073, Paragraph 0078, and claim 1).
Regarding Claims 10-20: Claims 10-20 recite limitations already addressed by the rejections of claims 1-9 above; therefore the same rejections apply.
Conclusion
The prior art made of record, but not relied upon is considered pertinent to Applicant's disclosure is listed on the attached PTO-892 and should be taken into account / considered by the Applicant upon reviewing this office action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MATTHEW D HENRY whose telephone number is (571)270-0504. The examiner can normally be reached 9-5 Monday-Friday.
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/MATTHEW D HENRY/Primary Examiner, Art Unit 3625