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 .
Notice to Applicant
This communication is in response to the amendment filed 2/23/2026. Claims 1-6, 9-13, 15, 16, 19, and 20 have been amended. Claims 7, 8, 14, 17, and 18 have been canceled. Claims 21-25 have been added. Claims 1-6, 9-13, 15, 16, and 19-25 remain pending and have been examined.
Specification
Applicant’s proposed amendments to the specification filed 2/23/2026 are acknowledged and entered.
Response to Arguments
A. Applicant's arguments with respect to the rejection under 35 USC 101 have been fully considered but they are not persuasive.
Applicant argues starting on page 16 of the response that claim 1 recites statutory subject matter under Step 2A Prong 2 and Step 2B, citing to Examples 25 and 45 of the Eligibility Examples. Examiner respectfully disagrees.
Applicant asserts that claim 1 is analogous to the process of opening a rubber press in Example 25, citing to the limitations reciting “mapping the prompt information to output information using a machine-trained pattern completion model,” “wherein the output information is a sequence of output text tokens that contain control instructions for controlling the output system to deliver the guidance based on the guidance objective, the control instructions specifying content to be presented and commands that govern operation of the output system,” and “controlling the operation of the output system based on the output information to deliver the content and to modify physical conditions in an environment in response to the commands.” However, Examiner respectfully disagrees with Applicant’s assertion that receiving repeated measurements from a state sensing system and “generating and executing commands based on a result of data analysis” renders claim 1 analogous to the claim in Example 25.
Example 25 sets out that the recited judicial exception (a mathematical formula/calculations) constituted eligible subject matter because it improved the process of controlling the rubber mold and the process of rubber molding. Examiner maintains that the particularity of the apparatus being controlled and the specific manner in which the recited mathematical equation was recited as used to control that apparatus is critical to the rationale provided. Examples 25 and 45 are not directed to general mathematical equations recited broadly as outputted to a non-specific device in order to accomplish a broad array of possible results. The present claims do not recite a particular apparatus comparable to the rubber mold of Example 25, and do not recite control of a particular apparatus to yield a particular improvement. Rather, claim 1 recites generating text instructions based on input information which “expresses a guidance objective, a physiological state of a user measured by a state-sensing system, and a self-reported emotional state of the user,” determining “control instructions” using the text instructions, and using these “control instructions” to “deliver the content and to modify physical conditions in an environment.” The elements such as receiving the input information, generating the prompt information, mapping the prompt information to text “that contain control instructions for controlling the output system,” and providing the content or environment modifications all fall within the scope of the described abstract idea. The additional elements recited as used in each step are only involved as tools to implement these steps, such as a “machine-trained pattern completion model,” recited at a high level of generality as “used” to map the prompt information to output information, and a “state sensing system” likewise recited at a high level of generality as measuring the physiological state of the user.
Examiner respectfully disagrees with Applicant’s assertions regarding the decision in Ex parte Desjardins on similar grounds. That decision was based on a conclusion that the claim at issue was directed to improving the use or trainability of a particular machine learning model, rather than merely applying a model to perform data manipulation as part of an abstract idea.
With respect to Applicant’s arguments on page 21 that claim 1 “recites a specific way of interacting with a pattern completion model that goes well beyond the traditional manner of using such a model,” Examiner notes that the elements of generating the prompt information and performing that function a plurality of times fall within the scope of the abstract idea. A human being is capable of generating text instructions from patient information such as “combining the measurement "68 bpm" with the pre-generated text fragment "My current heart beats per minute are,".” Examiner respectfully disagrees that this constitutes a form of “prompt generation” which improves the use of a machine learning model.
Examiner further respectfully disagrees with Applicant’s assertion on page 24 that claim 20 recites “a specific interaction between two machine trained models” in a manner which integrates the claim into a practical application. Simply reciting that the input information or output information “is further processed by another machine-trained model” does not amount to a “specific interaction” between two models. The use of two models at the level recited only amounts to mere instructions to use each as a tool and does not incorporate specific interactions beyond the use of each.
The rejection under 35 USC 101 is maintained.
B. Applicant’s arguments with respect to the rejection of claims 15-19 under 35 USC 103 have been fully considered and are persuasive. The corresponding rejection has been withdrawn.
Claim Objections
The previous objection to claim 8 is moot due to cancelation of the claim in the amendments filed 2/23/2026.
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 1-6, 9-13, 15, 16, and 19-25 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 1-6, 9-13, and 21-24 are drawn to a method, claims 15, 16, 19, and 25 are drawn to a computing system, and claim 20 is drawn to a computer readable storage medium, each of which is within the four statutory categories. Examiner notes that the computer readable storage medium of claim 20 is construed as expressly extruding transitory signals per-se based on paragraph 112 of the specification as originally filed.
Step 2A(1)
Claim 1 recites, in part, performing the steps of:
receiving a first part of input information that describes a guidance objective;
receiving a second part of input information that expresses a physiological state of a user;
generating prompt information that describes the input information by assembling plural parts of the input information, including the first and second parts, into a sequence of input text tokens, wherein the generating includes combining the second part of the input information with a pre-generated text fragment;
mapping the prompt information to output information,
wherein the output information is a sequence of output text tokens that contain control instructions for controlling the output system to deliver guidance based on the guidance objective, the control instructions specifying content to be presented and commands that govern operation of the output system;
delivering the content and modifying physical conditions in an environment in response to the commands,
wherein the receiving a second part, generating, mapping, and controlling are performed for a plurality of passes of operation, and
wherein, for each pass of the plurality of passes after a first pass, the generating appends added information to prompt information generated in a prior pass, the added information including an instance of control instructions generated in the prior pass and input information that expresses an updated measurement.
These steps amount to a form of managing personal behavior or relationships or interactions between people and therefore fall within the scope of an abstract idea in the form of a method of organizing human activity. Fundamentally the process is that of generating text instructions based on a guidance objective and physiological state of a user, and delivering content and modifying the environment of a user according to those instructions. A person could perform this as part of providing an individual with content and an environment according to a particular objective and the state of the user.
Examiner notes that the “output system” in the limitations above is only recited as a description of the intended purpose and content of the control instructions, i.e. that the control instructions are for controlling an output system, and does not perform any function itself in the above limitations.
Claim 15 recites, in part, performing the steps of:
receiving input information that expresses a guidance objective, a physiological state of a user and a self-reported emotional state of the user;
generating prompt information that describes the input information, wherein the prompt information includes a sequence of input text tokens, wherein the generating includes combining the physiological state with a pre-generated text fragment;
mapping the prompt information to output information, the output information including a sequence of output text tokens that contain control instructions for controlling the output system to deliver guidance based on the guidance objective the control instructions specifying content to be presented and commands that govern operation of the output system; and
delivering the content and modifying physical conditions in an environment in response to the commands.
These steps amount to a form of managing personal behavior or relationships or interactions between people and therefore fall within the scope of an abstract idea in the form of a method of organizing human activity. Fundamentally the process is that of generating text instructions based on a guidance objective, an emotional state, and a physiological state of a user, and delivering content and modifying the environment of a user according to those instructions. A person could perform this as part of providing an individual with content and an environment according to a particular objective and the state of the user.
Examiner notes that the “output system” in the limitations above is only recited as a description of the intended purpose and content of the control instructions, i.e. that the control instructions are for controlling an output system, and does not perform any function itself in the above limitations.
Claim 20 recites, in part, performing the steps of:
receiving input information that expresses a guidance objective, a physiological state of a user, a self-reported emotional state of the user, and a selected environment;
generating prompt information that describes the input information; and
mapping the prompt information to output information, the output information containing control instructions for controlling an output system to provide guidance in achieving the guidance objective via generated multi-sensory content, the control instructions specifying content to be presented and commands that govern operation of the output system,
These steps amount to a form of managing personal behavior or relationships or interactions between people and therefore fall within the scope of an abstract idea in the form of a method of organizing human activity. Fundamentally the process is that of using a guidance objective, a physiological state of a user, an emotional state, and a selected environment to determine content and commands for operating an output system. A person could perform this as part of using an individual’s state and guidance preferences to determine guidance for that individual for delivery via multi-sensory content.
Examiner notes that the “output system” in the limitations above is only recited as a description of the intended purpose and content of the control instructions, i.e. that the control instructions are for controlling an output system, and does not perform any function itself in the above limitations.
Step 2A(2)
This judicial exception is not integrated into a practical application because the additional elements within the claims only amount to:
A. Instructions to Implement the Judicial Exception. MPEP 2106.05(f)
Claim 1 recites additional elements of a) a state-sensing system used to measure the physiological state, b) a machine-trained pattern completion model used to map the prompt information to the output information, and c) the output system performing the function of delivering the content and modifying the physical conditions in the environment.
Claim 15 recites additional elements of a) a store used to store computer-readable instructions, b) a processing system for executing the computer-readable instructions to perform the subsequently recited operations, c) a state-sensing system used to measure the physiological state of the user, b) machine-trained weights used to map the prompt information to the output information, and c) the output system performing the function of delivering the content and modifying the physical conditions in the environment.
Claim 20 recites additional elements of a) a computer-readable storage medium used to store computer-readable instructions, b) a processing system used to execute the computer-readable instructions to perform the subsequently recited operations, c) a state-sensing system used to measure the physiological state of the user, d) a machine-trained pattern completion model used to map the prompt information to the output information, and e) another machine-trained model used to further process the input or output information and trained by reinforcement learning to promote the guidance objective.
Paragraph 25 states that a state sensing system includes one or more sensing devices while describing the sensed data as captured by “sensing devices that measure heart beat rate, blood pressure, respiratory rate, and/or temperature,” “an electroencephalogram sensing device,” “a sensing device that measures pupil dilation or a sensing device that determines direction of gaze,” or “any type of electrodermal sensing device.” The state-sensing system is therefore construed as encompassing generic physiological monitoring devices.
Paragraphs 32, 35, and 36 describe a pattern completion component, stating that the pattern completion component may be implemented using a machine-trained model such as a publicly available transformer model. Paragraph 24 states that “a "machine-trained model" refers to computer-implemented logic for executing a task using machine-trained weights that are produced in a training operation,” while paragraphs 75 and 79 further describe the model using reinforcement learning. Based on the scope of the disclosure the machine-trained weights, machine-trained pattern completion model, and the “another machine-trained model” used further process the input or output information are construed as encompassing general-purpose machine learning models.
Paragraphs 39 and 132 state that an output system may include any of an audio output system, visual output system, lighting system, odor output system, haptic output system, an HVAC system, or a workflow-modifying system. Based on the scope of the disclosure the output system is construed as encompassing generic forms of media output devices.
Paragraphs 110-114 describe a computing system including processors, such as CPUs, GPUs, ASICs, and others, and computer readable storage devices including RAM and other hardware memory unites storing instructions for execution by the processors. The store used to store computer-readable instructions and the computer-readable storage medium are each construed as encompassing generic forms of computer memory, while the processing system is likewise construed as encompassing generic computer processing devices.
Each of the above elements amounts to instructions to implement functions of the abstract idea using computing elements as tools. For example, the state-sensing system is recited at a high level of generality as used to obtain the physiological information and disclosed broadly as encompassing devices for collecting respective types of information, and the storage mediums and processing systems are recited at a high level of generality as used to store and execute instructions while likewise being disclosed at a high level of generality. The machine-trained weights and machine-trained pattern completion model are similarly each recited at a high level of generality as used to map prompt information to output information or to “further process” the input or output information, and are disclosed as encompassing generic forms of models such as those trained using reinforcement learning. The output system is likewise recited only at a high level of generality as controlled to deliver the content and modify the physical environment, and is disclosed as encompassing a plurality of generic forms of output systems.
B. Insignificant Extra-Solution Activity. MPEP 2106.05(g)
Claim 20 further recites the additional element of further processing the input information and/or output information. However, this element amounts to insignificant extra-solution activity based on only being nominally or tangentially related to the invention. Specifically, the element only recites the further processing of the input information and/or output information at a high level of generality and no further operation is recited as impacted by the further processing.
The above elements are therefore not sufficient to integrate the abstract idea into a practical application.
The above claims, as a whole, are therefore directed to an abstract idea.
Step 2B
The present claims do not include additional elements that are sufficient to amount to more than the abstract idea because the additional elements or combination of elements amount to:
A. Instructions to Implement the Judicial Exception. MPEP 2106.05(f)
As explained above, claims 1, 15, and 20 only recite the state-sensing system, output system, store used to store computer-readable instructions, processing system, machine-trained weights, computer-readable storage medium, machine-trained pattern completion model, and “another” machine-trained model as tools for performing the steps of the abstract idea, and mere instructions to perform the abstract idea using a computer is not sufficient to amount to significantly more than the abstract idea. MPEP 2106.05(f)
B. Insignificant Extra-Solution Activity. MPEP 2106.05(g)
Claim 20 recites the additional element of further processing the input information and/or output information. However, this element amounts to insignificant extra-solution activity based on only being nominally or tangentially related to the invention. Specifically, the element only recites the further processing of the input information and/or output information at a high level of generality and no further operation is recited as impacted by the further processing.
C. Well-Understood, Routine and Conventional Activities. MPEP 2106.05(d)
Furthermore, the element of processing the input information and/or output information recited in claim 20 likewise amounts to well-understood routine and conventional activity. As explained above, this element is claimed as insignificant extra-solution activity, and further constitutes a form of performing repetitive calculations.
Thus, taken alone, the additional elements do not amount to significantly more than the above-identified judicial exception. Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually.
Depending Claims
Claim 2 recites wherein the physiological state of the user expresses: a vital sign; or electrodermal activity; or body movement; or an eye-related characteristic; or a voice-related characteristic; or any combination. These limitations fall within the scope of the abstract idea as set out above.
Claim 3 recites wherein the prompt information also includes a self-reported emotional state. These limitations fall within the scope of the abstract idea as set out above.
Claim 3 recites the additional element of an input system used to receive the emotional state.
Paragraphs 27 and 47 describe an input system used to receive an emotional state of a user as including a user interface page having sections for free-form text entries or drop-down menus. The input system is construed as encompassing generic forms of graphical user interfaces.
The input system amounts to instructions to implement functions of the abstract idea using computing elements as tools. Specifically, the input system is only recited at a high level of generality as used to receive the emotional state of the user, and is disclosed broadly as encompassing generic forms of user interface elements. These elements are not sufficient to integrate the abstract idea into a practical application or to amount to significantly more than the abstract idea.
Claim 4 recites wherein the guidance objective expressed in the prompt information is a therapeutic goal of the guidance. These limitations fall within the scope of the abstract idea as set out above.
Claim 5 recites wherein the therapeutic goal is: reduction of stress; or meditation; or inducement of sleep; or inducement of attentiveness; or control of a specified emotion or compulsion; or management of memory; or ability to complete a task within a specified environment; enhancement of productivity; or any combination. These limitations fall within the scope of the abstract idea as set out above.
Claim 6 recites wherein, for at least one pass of the plurality of passes, the prompt information also describes a selected environment. These limitations fall within the scope of the abstract idea as set out above.
Claim 9 recites wherein at least some of the input text tokens describe a type of output modality to use, and at least some of the input text tokens describe a format of control information to be provided in the output text tokens. These limitations fall within the scope of the abstract idea as set out above.
Claim 10 recites the additional elements of the input information and/or the output information being further processed by a reward-driven machine-trained model, the reward-driven machine-trained model having been trained by reinforcement learning to promote the guidance objective.
As set out above, paragraph 24 states that “a "machine-trained model" refers to computer-implemented logic for executing a task using machine-trained weights that are produced in a training operation,” while paragraphs 75, 79, 80, and 106 further describe the model using reward-driven reinforcement learning to promote an identified therapeutic goal. Based on the scope of the disclosure the “reward-driven machine-trained model” is construed as encompassing general-purpose machine learning models having been trained using reinforcement learning.
The recited reward-driven machine-trained model amounts to instructions to implement functions of the abstract idea using computing elements as tools. Specifically, the reward-driven machine-trained model is only recited at a high level of generality as used to “further process” the input or output information, and is disclosed broadly as encompassing generic forms of models trained using reinforcement learning.
The element reciting processing the input information and/or output information amounts to insignificant extra-solution activity based on only being nominally or tangentially related to the invention. Specifically, this element only recites the further processing of the input information and/or output information at a high level of generality and no further operation is recited as impacted by the further processing.
This element likewise amounts to well-understood routine and conventional activity. As explained above, this element is claimed as insignificant extra-solution activity, and further constitutes a form of performing repetitive calculations.
The above elements are therefore not sufficient to integrate the abstract idea into a practical application or to amount to significantly more than the abstract idea.
Claims 11 and 16 recite synthesizing visual content based on the output information. These limitations fall within the scope of the abstract idea as set out above.
Claims 11 and 16 further recite the additional element of another machine-trained model as performing the function of synthesizing visual content based on the output information.
Paragraphs 40 and 41 describe a machine-trained model as used to synthesize visual content, providing examples such as publicly available systems including DALL-E as well as that the model may include transformer-based neural networks, recurrent neural networks, and other forms of models. The machine trained model is construed as encompassing generic forms of machine trained models.
The recited machine-trained model amounts to instructions to implement functions of the abstract idea using computing elements as tools. Specifically, the machine-trained model is only recited at a high level of generality as used to synthesize the visual content, and is disclosed broadly as encompassing generic forms of trained models. The above elements are therefore not sufficient to integrate the abstract idea into a practical application or to amount to significantly more than the abstract idea.
Claim 12 recites providing multi-sensory content. These limitations fall within the scope of the abstract idea as set out above.
Claim 12 further recite the additional element of the output system as used to provide the multi-sensory content.
Paragraphs 39 and 132 state that an output system may include any of an audio output system, visual output system, lighting system, odor output system, haptic output system, an HVAC system, or a workflow-modifying system. Based on the scope of the disclosure the output system is construed as encompassing generic forms of media output devices.
The recited output system amounts to instructions to implement functions of the abstract idea using computing elements and machines as tools. Specifically, the recited output system is only recited at a high level of generality as used for providing multi-sensory content, and is disclosed broadly as encompassing generic forms or previously available systems. The output system is therefore not sufficient to integrate the abstract idea into a practical application or to amount to significantly more than the abstract idea.
Claim 13 recites the additional elements of the output system including: an audio output system for delivering audio content; or a visual output system for delivering visual content; or a lighting system for controlling lighting; or an odor output system for delivering scents; or a haptic output system for delivering a tactile experience; or an HVAC system for controlling heating, cooling, and/or ventilation, or a workflow-modifying system for controlling workflow of the user; or any combination.
Paragraphs 39, 51, and 54-56 describe various embodiments of audio output systems, visual output systems, lighting systems, odor output systems, haptic systems, HVAC systems, and workflow-modifying systems. The audio output system is described as including speakers and commercially available audio systems. The visual output system is described as including commercially available output devices including display monitors. The odor output system is described in terms of its function of delivering scents, and as including commercially available systems. The lighting system is disclosed as encompassing the overhead lighting in a physical space. The haptic output system is disclosed as providing tactile output and encompassing commercially available devices. The HVAC system is only disclosed in terms of its function of controlling heating, cooling, and ventilation such as via fans. The workflow-modifying system is similarly only disclosed in terms of its function of controlling applications of the user such as by temporarily silencing alerts or alarms. Each of these elements is therefore construed as encompassing generic forms of each respective type of output system.
The above elements amount to instructions to implement functions of the abstract idea using computing elements and machines as tools. Specifically, each of the recited output systems is only recited at a high level of generality as used for delivering or controlling a corresponding output type, i.e. the lighting system controlling lighting, the audio system for delivering audio content, and the visual system for delivering visual content, and is disclosed broadly as encompassing generic forms or previously available systems. The above elements are therefore not sufficient to integrate the abstract idea into a practical application or to amount to significantly more than the abstract idea.
Claim 19 recites the mapping as generating instances of output information that promote the guidance objective. These limitations fall within the scope of the abstract idea as set out above.
Claim 19 recites the additional elements of a) “a combination of a machine-trained pattern completion model and a reward-driven machine-trained model” used to perform the mapping and further processing, wherein the machine-trained pattern completion model includes weights that express patterns observed in a corpus of text fragments, and wherein the reward-driven machine-trained model includes weights that have been trained to generate the instances of output information.
Paragraphs 32, 35, and 36 describe a pattern completion component, stating that the pattern completion component may be implemented using a machine-trained model such as a publicly available transformer model. Paragraph 24 states that “a "machine-trained model" refers to computer-implemented logic for executing a task using machine-trained weights that are produced in a training operation,” while paragraphs 75 and 79 further describe the model using reinforcement learning. Based on the scope of the disclosure the machine-trained weights, machine-trained pattern completion model, and the “another machine-trained model” used further process the input or output information are construed as encompassing general-purpose machine learning models.
As set out above, paragraph 24 states that “a "machine-trained model" refers to computer-implemented logic for executing a task using machine-trained weights that are produced in a training operation,” while paragraphs 75, 79, 80, and 106 further describe the model using reward-driven reinforcement learning to promote an identified therapeutic goal. Based on the scope of the disclosure the “reward-driven machine-trained model” is construed as encompassing general-purpose machine learning models having been trained using reinforcement learning.
Furthermore, the recitation of these models being used in combination is construed as encompassing each model being simply being used during the mapping given that the claim does not further specify what constitutes “a combination of” with respect to the models.
The above elements amount to instructions to implement functions of the abstract idea using computing elements and machines as tools. The “combination of a machine-trained pattern completion model and a reward-driven machine-trained model” is only broadly recited as used to perform the mapping, and the reward-driven machine-trained model is only then broadly recited as performing the further processing.
The element of further processing the input information and/or output information amounts to insignificant extra-solution activity based on only being nominally or tangentially related to the invention. Specifically, the element only recites the further processing of the input information and/or output information at a high level of generality and no further operation is recited as impacted by the further processing. This element likewise amounts to well-understood routine and conventional activity. As explained above, this element is claimed as insignificant extra-solution activity, and further constitutes a form of performing repetitive calculations.
The above elements are therefore not sufficient to integrate the abstract idea into a practical application or to amount to significantly more than the abstract idea.
Claim 21 recites wherein, for at least one pass of the plurality of passes, the prompt information also describes a selected environment, wherein the physiological state of the user includes a vital sign of the user, wherein the prompt information also includes a self-reported emotional state, wherein the content to be presented includes a text narrative that describes the selected environment, wherein the method further includes synthesizing visual content based on the text narrative, and wherein the output information also provides information that identifies progress towards the guidance objective.
Claim 21 further recites additional elements of a) an input system used to receive the emotional state, and b) another machine-trained model used to synthesize the visual content based on the text narrative.
Paragraphs 27 and 47 describe an input system used to receive an emotional state of a user as including a user interface page having sections for free-form text entries or drop-down menus. The input system is construed as encompassing generic forms of graphical user interfaces.
Paragraphs 40 and 41 describe a machine-trained model as used to synthesize visual content, providing examples such as publicly available systems including DALL-E as well as that the model may include transformer-based neural networks, recurrent neural networks, and other forms of models. The machine trained model is construed as encompassing generic forms of machine trained models.
These elements amount to instructions to implement functions of the abstract idea using computing elements as tools. The input system is only recited at a high level of generality as used to receive the emotional state of the user, and is disclosed broadly as encompassing generic forms of user interface elements. The machine-trained model is similarly only recited at a high level of generality as used to synthesize the visual content, and is disclosed broadly as encompassing generic forms of trained models. These elements are not sufficient to integrate the abstract idea into a practical application or to amount to significantly more than the abstract idea.
Claim 22 recites wherein the guidance objective expressed in the prompt information is reduction in stress wherein the self-reported emotional state is anxiousness, and wherein the vital sign is heart rate. These limitations fall within the scope of the abstract idea as set out above.
Claim 23 recites assessing that the output information does not promote the guidance objective, and based on a result of the assessing, modifying the output information or requesting generation of new output information.
Claim 23 recites additional elements of a) the machine-trained pattern completion model as including weights that express patterns observed in a corpus of text fragments and as used to generate the new output information, and b) a reward-driven machine-trained model used to assess that the output information does not promote the objective and as including weights that have been trained by reinforcement learning.
Paragraphs 32, 35, and 36 describe a pattern completion component, stating that the pattern completion component may be implemented using a machine-trained model such as a publicly available transformer model. Paragraph 24 states that “a "machine-trained model" refers to computer-implemented logic for executing a task using machine-trained weights that are produced in a training operation,” while paragraphs 75 and 79 further describe the model using reinforcement learning. Based on the scope of the disclosure the machine-trained weights, machine-trained pattern completion model, and the “another machine-trained model” used further process the input or output information are construed as encompassing general-purpose machine learning models.
As set out above, paragraph 24 states that “a "machine-trained model" refers to computer-implemented logic for executing a task using machine-trained weights that are produced in a training operation,” while paragraphs 75, 79, 80, and 106 further describe the model using reward-driven reinforcement learning to promote an identified therapeutic goal. Based on the scope of the disclosure the “reward-driven machine-trained model” is construed as encompassing general-purpose machine learning models having been trained using reinforcement learning.
These elements amount to instructions to implement functions of the abstract idea using computing elements as tools. The machine-trained pattern completion model is only recited at a high level of generality as used to generate the new output information, and is disclosed broadly as encompassing generic forms of trained models. The reward-driven machine-trained model is likewise recited at a high level of generality as used to assess that the output information does not promote the objective, and is disclosed broadly as encompassing general-purpose machine learning models having been trained using reinforcement learning. These elements are not sufficient to integrate the abstract idea into a practical application or to amount to significantly more than the abstract idea.
Claim 24 recites wherein the method further includes assessing whether the output information promotes the guidance objective to produce a recommendation, wherein the prompt information includes the recommendation.
Claim 24 recites additional elements of a) the machine-trained pattern completion model as including weights that express patterns observed in a corpus of text fragments and as receiving the recommendation, and b) a reward-driven machine-trained model used to assess whether the output information promotes the objective to produce the recommendation and as including weights that have been trained by reinforcement learning.
Paragraphs 32, 35, and 36 describe a pattern completion component, stating that the pattern completion component may be implemented using a machine-trained model such as a publicly available transformer model. Paragraph 24 states that “a "machine-trained model" refers to computer-implemented logic for executing a task using machine-trained weights that are produced in a training operation,” while paragraphs 75 and 79 further describe the model using reinforcement learning. Based on the scope of the disclosure the machine-trained weights, machine-trained pattern completion model, and the “another machine-trained model” used further process the input or output information are construed as encompassing general-purpose machine learning models.
As set out above, paragraph 24 states that “a "machine-trained model" refers to computer-implemented logic for executing a task using machine-trained weights that are produced in a training operation,” while paragraphs 75, 79, 80, and 106 further describe the model using reward-driven reinforcement learning to promote an identified therapeutic goal. Based on the scope of the disclosure the “reward-driven machine-trained model” is construed as encompassing general-purpose machine learning models having been trained using reinforcement learning.
These elements amount to instructions to implement functions of the abstract idea using computing elements as tools. The machine-trained pattern completion model is only recited at a high level of generality as used to receive the recommendation, and is disclosed broadly as encompassing generic forms of trained models. The reward-driven machine-trained model is likewise recited at a high level of generality as used to assess whether the output information promotes the objective to produce the recommendation, and is disclosed broadly as encompassing general-purpose machine learning models having been trained using reinforcement learning. These elements are not sufficient to integrate the abstract idea into a practical application or to amount to significantly more than the abstract idea.
Claim 25 recites wherein, for at least one pass of the plurality of passes, the prompt information also describes a selected environment, wherein the physiological state of the user includes a vital sign of the user, wherein the content to be presented includes a text narrative that describes the selected environment, wherein the operations further include synthesizing visual content based on the text narrative, and wherein the output information also provides information that identifies progress towards the guidance objective.
Claim 25 further recites additional element of another machine-trained model used to synthesize the visual content based on the text narrative.
Paragraphs 40 and 41 describe a machine-trained model as used to synthesize visual content, providing examples such as publicly available systems including DALL-E as well as that the model may include transformer-based neural networks, recurrent neural networks, and other forms of models. The machine trained model is construed as encompassing generic forms of machine trained models.
The recited machine-trained model amounts to instructions to implement functions of the abstract idea using computing elements as tools. Specifically, the machine-trained model is only recited at a high level of generality as used to synthesize the visual content, and is disclosed broadly as encompassing generic forms of trained models. The above elements are therefore not sufficient to integrate the abstract idea into a practical application or to amount to significantly more than the abstract idea.
Claims 1-6, 9-13, 15, 16, and 19-25 are therefore rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.
35 USC § 112
The previous rejection of claims 18 and 19 under 35 USC 112(b) is moot due to cancelation of the claims in the amendment filed 2/23/2026.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Nell et al (US Patent Application Publication 2022/0043986);
Harper et al (US Patent Application Publication 2023/0032131);
Shazeer et al (US Patent Application Publication 2022/0374608);
Gruenberg et al (WO 2020/232296);
Heimerl et al (US Patent Application Publication 2021/0217532);
Tan et al (US Patent Application Publication 2023/0263440);
Taylor et al (US Patent Application Publication 2024/0233231);
Parasnis et al (US 11,928,319);
Flood et al (US Patent Application Publication 2022/0377543);
Shen et al (US Patent Application Publication 2021/0035556).
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.
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/Gregory Lultschik/Examiner, Art Unit 3682
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