DETAILED ACTION
1. 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 Amendment
2. Receipt of Applicant’s Amendment filed on 06/22/2026 is acknowledged. The amendment includes the amending of claims 1, 11, 14, and 19-20, the cancellation of claims 3-4 and 13, and the amending of the specification.
Terminal Disclaimer
3. The terminal disclaimer filed on 06/22/2026 disclaiming the terminal portion of any patent granted on this application which would extend beyond the expiration date of U.S. Patent 12,417,232, U.S. Patent Application 19/075883, and U.S. Patent Application 19/076030 has been reviewed and is accepted. The terminal disclaimer has been recorded.
Specification
4. The objection raised in the Office Action mailed on 03/20/2026 has been overcome by applicant’s amendment received on 06/22/2026.
Double Patenting
5. The rejections raised in the Office Action mailed on 03/20/2026 have been overcome by applicant’s submission of a Terminal Disclaimer received on 06/22/2026.
Claim Rejections - 35 USC § 101
6. 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.
7. Claims (1-2 and 5-10), (11-12 and 14-18), and (19-20) are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter 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.
Under the 2019 PEG, 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 (step 1). 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) (step 2A prong 1), and if so, it must additionally be determined whether the claim is integrated into a practical application (step 2A prong 2). If an abstract idea is present in the claim without integration into a practical application, 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 (step 2B).
In the instant case, claims (1-2 and 5-10), (11-12 and 14-18), and (19-20) are directed to a computer-method, computer system, and tangible, non-transitory computer-readable medium respectively. Thus, each of the claims falls within one of the four statutory categories. However, the claims also fall within the judicial exception of an abstract idea.
Under Step 2A Prong 1, the test is to identify whether the claims are “directed to” a judicial exception. The examiner notes that the claimed invention is directed to an abstract idea in that the instant application is directed to mental processes, specifically recommending patient intervention.
The examiner further notes that claims (1-2 and 5-10), (11-12 and 14-18), and (19-20) recite a computer-method, computer system, and tangible, non-transitory computer-readable medium for recommending patient intervention which is similar to themes defined above of method of mental processes such as performing the recommendation of information, and is similar to the abstract idea identified in the 2019 PEG in grouping “c” in that the claims recite certain methods of mental processes such as performing the recommendation of patient intervention. The limitations, substantially comprising the body of the claim, recite a process of recommending patient intervention. The examiner notes that the claimed invention recommends patient intervention. Because the limitations above closely follow the steps in recommending patient intervention, and the steps of the claims involve mental processes, the claim recites an abstract idea consistent with the “mental processes” grouping set forth in the 2019 PEG.
Claim 1:
A computer-implemented method for providing event-specific intervention recommendations, the method comprising: acquiring at least two data streams of a patient by using one or more sensors;
wherein at least one of the data streams includes images;
generating at least one entity-feature-graph based on the acquired at least two data streams of the patient;
selecting at least one intervention based on the generated entity-feature-graph and a trained graph classification model;
wherein the entity-feature-graph is transformed into an embedding space by a transformation process that makes use of distances between each pair of entities in the embedding space to encode corresponding probabilities that respective pairs of the entities are the same; and
outputting an information of the selected intervention to a user.
These limitations, as drafted, is an apparatus that, under its broadest reasonable interpretation, covers the performance of mental processes specifically recommending patient intervention. Recommending patient intervention has long before the modern computer was invented, and continues to be predominantly a product of human endeavor. The instant application is directed to recommending patient intervention. Additionally, the generation of a graph can be performed by a human via their mind and/or pen & paper. Furthermore, the selection of an intervention based on a generated graph and a model can be performed by a human via their mind and/or pen & paper. Moreover, the transformation of an entity-feature-graph into an embedding space and the subsequent vectorization of probabilities can be performed by a human via their mind and/or pen & paper. Because the limitations above closely follow the steps of recommending patient intervention, and the steps involved human judgments, observations and evaluations that can be practically or reasonably performed in the human mind and/or pen & paper, the claim recites an abstract idea consistent with the “mental process” grouping set forth in the 2019 PEG.
The mere nominal recitation of generic computing components such as one or more sensors does not take the claim out of certain methods of mental processes grouping. Therefore, the limitation is directed to an abstract idea.
If the claims are directed toward the judicial exception of an abstract idea, it must then be determined under Step 2A Prong 2 whether the judicial exception is integrated into a practical application. The Examiner notes that considerations under Step 2A Prong 2 comprise most the consideration previously evaluated in the context of Step 2B. The Examiner submits that the considerations discussed previously determined that the claim does not recite “significantly more” at Step 2B would be evaluated the same under Step 2A Prong 1 and result in the determination that the claim does not integrate the abstract idea into a practical application. Specifically, the acquisition of patient data that includes images is simply a data gathering step that is an insignificant extra-solution activity and does not integrate the abstract idea into a practical application. Furthermore, the output of an intervention is simply a data output step that is an insignificant extra-solution activity and does not integrate the abstract idea into a practical application
The instant application fails to integrate the judicial exception into a practical application because the instant application merely recites words “apply it” (or an equivalent) with the judicial exception or merely includes instructions to implement an abstract idea. The instant application is directed to an apparatus instructing the reader to implement the identified apparatus of mental processes of recommending patient intervention. The elements of the claim do not themselves amount to an improvement to the computer, to a technology or another technical field.
Here, the claim elements entirely comprise the abstract idea, leaving little if any aspects of the claim for further consideration under Step 2A Prong 2. In short, the claims have failed to integrate a practical application (see at least 84 Fed. Reg. (4) at 55). Under the 2019 PEG, this supports the conclusion that the claim is directed to an abstract idea, and the analysis proceeds to Step 2B.
While many considerations in Step 2A need not be reevaluated in Step 2B because the outcome will be the same. Here, on the basis of the additional elements other than the abstract idea, considered individually and in combination as discussed above, the Examiner respectfully submits that the claim 1 does not contain any additional elements that individually or as an ordered combination amount to an inventive concept and the claims are ineligible.
With respect to the dependent claims do not recite anything that is found to render the abstract idea as being transformed into a patent eligible invention. The dependent claims are merely reciting further embellishments of the abstract idea and do not claim anything that amounts to significantly more than the abstract idea itself.
With respect to the dependent claims, they have been considered and are not found to be reciting anything that amounts to being significantly more than the abstract idea. Claims 2 and 5-10 are directed to further embellishments of the central theme of the abstract idea in that the claims are directed to further embellishments of the recommending patient intervention of the steps of claim 1 and do not amount to significantly more.
Specifically, claim 2 recites the training of a model which can be performed by a human via their mind and/or pen & paper and does not amount to significantly more.
Furthermore, claim 5 recites the processing of images for location information which can be performed by a human via their mind and/or pen & paper and does not amount to significantly more.
Moreover, claim 6 recites the determination that a patient has suffered an accident via the analysis of an image which can be performed by a human via their mind and/or pen & paper and does not amount to significantly more. Furthermore, the execution of a determined intervention of contacting an emergency contact via a telephonic connection is not significantly more than the abstract idea.
Additionally, claim 7 recites the processing of images for determining that a patient has undergone surgery and is underactive and subsequently adapting a patient therapy which can be performed by a human via their mind and/or pen & paper and does not amount to significantly more.
Furthermore, claim 8 recites the determination that a patient is watching tv which can be performed by a human via their mind and/or pen & paper and does not amount to significantly more. Additionally, the execution of a determined intervention of increasing equipment difficulty is not significantly more than the abstract idea.
Moreover, claim 9 recites the updating of a model which can be performed by a human via their mind and/or pen & paper and does not amount to significantly more.
Additionally, claim 10 recites the acquiring of defined data streams that is simply a data gathering step that is an insignificant extra-solution activity and does not integrate the abstract idea into a practical application. Moreover, the mere nominal recitation of generic computing components such as a camera, sound recorder presence sensor, temperature sensor, sound-level sensor, and door sensor does not take the claim out of certain methods of mental processes grouping. Therefore, the limitation is directed to an abstract idea.
Claim 11:
A computer system for providing event-specific intervention recommendations, the system comprising one or more processors configured to execute the following steps: acquiring at least two data streams of a patient by using one or more sensors;
wherein at least one of the data streams includes images;
generating at least one entity-feature-graph based on the acquired at least two data streams of the patient;
selecting at least one intervention based on the generated entity-feature-graph and a trained graph classification model;
wherein the entity-feature-graph is transformed into an embedding space by a transformation process that makes use of distances between each pair of entities in the embedding space to encode corresponding probabilities that respective pairs of the entities are the same; and
outputting an information of the selected intervention to a user.
These limitations, as drafted, is an apparatus that, under its broadest reasonable interpretation, covers the performance of mental processes specifically recommending patient intervention. Recommending patient intervention has long before the modern computer was invented, and continues to be predominantly a product of human endeavor. The instant application is directed to recommending patient intervention. Additionally, the generation of a graph can be performed by a human via their mind and/or pen & paper. Furthermore, the selection of an intervention based on a generated graph and a model can be performed by a human via their mind and/or pen & paper. Moreover, the transformation of an entity-feature-graph into an embedding space and the subsequent vectorization of probabilities can be performed by a human via their mind and/or pen & paper. Because the limitations above closely follow the steps of recommending patient intervention, and the steps involved human judgments, observations and evaluations that can be practically or reasonably performed in the human mind and/or pen & paper, the claim recites an abstract idea consistent with the “mental process” grouping set forth in the 2019 PEG.
The mere nominal recitation of generic computing components such as one or more processors and one or more sensors do not take the claim out of certain methods of mental processes grouping. Therefore, the limitation is directed to an abstract idea.
If the claims are directed toward the judicial exception of an abstract idea, it must then be determined under Step 2A Prong 2 whether the judicial exception is integrated into a practical application. The Examiner notes that considerations under Step 2A Prong 2 comprise most the consideration previously evaluated in the context of Step 2B. The Examiner submits that the considerations discussed previously determined that the claim does not recite “significantly more” at Step 2B would be evaluated the same under Step 2A Prong 1 and result in the determination that the claim does not integrate the abstract idea into a practical application. Specifically, the acquisition of patient data that includes images is simply a data gathering step that is an insignificant extra-solution activity and does not integrate the abstract idea into a practical application. Furthermore, the output of an intervention is simply a data output step that is an insignificant extra-solution activity and does not integrate the abstract idea into a practical application
The instant application fails to integrate the judicial exception into a practical application because the instant application merely recites words “apply it” (or an equivalent) with the judicial exception or merely includes instructions to implement an abstract idea. The instant application is directed to an apparatus instructing the reader to implement the identified apparatus of mental processes of recommending patient intervention. The elements of the claim do not themselves amount to an improvement to the computer, to a technology or another technical field.
Here, the claim elements entirely comprise the abstract idea, leaving little if any aspects of the claim for further consideration under Step 2A Prong 2. In short, the claims have failed to integrate a practical application (see at least 84 Fed. Reg. (4) at 55). Under the 2019 PEG, this supports the conclusion that the claim is directed to an abstract idea, and the analysis proceeds to Step 2B.
While many considerations in Step 2A need not be reevaluated in Step 2B because the outcome will be the same. Here, on the basis of the additional elements other than the abstract idea, considered individually and in combination as discussed above, the Examiner respectfully submits that the claim 11 does not contain any additional elements that individually or as an ordered combination amount to an inventive concept and the claims are ineligible.
With respect to the dependent claims do not recite anything that is found to render the abstract idea as being transformed into a patent eligible invention. The dependent claims are merely reciting further embellishments of the abstract idea and do not claim anything that amounts to significantly more than the abstract idea itself.
With respect to the dependent claims, they have been considered and are not found to be reciting anything that amounts to being significantly more than the abstract idea. Claims 12 and 14-18 are directed to further embellishments of the central theme of the abstract idea in that the claims are directed to further embellishments of the recommending patient intervention of the steps of claim 11 and do not amount to significantly more.
Specifically, claim 12 recites the training of a model which can be performed by a human via their mind and/or pen & paper and does not amount to significantly more.
Furthermore, claim 14 recites the classification of a graph which can be performed by a human via their mind and/or pen & paper and does not amount to significantly more.
Moreover, claim 15 recites the processing of images for location information which can be performed by a human via their mind and/or pen & paper and does not amount to significantly more.
Additionally, claim 16 recites the determination that a patient has suffered an accident via the analysis of an image which can be performed by a human via their mind and/or pen & paper and does not amount to significantly more. Furthermore, the execution of a determined intervention of contacting an emergency contact via a telephonic connection is not significantly more than the abstract idea.
Furthermore, claim 17 recites the updating of a model which can be performed by a human via their mind and/or pen & paper and does not amount to significantly more.
Moreover, claim 18 recites the acquiring of defined data streams that is simply a data gathering step that is an insignificant extra-solution activity and does not integrate the abstract idea into a practical application. Moreover, the mere nominal recitation of generic computing components such as a camera, sound recorder presence sensor, temperature sensor, sound-level sensor, and door sensor does not take the claim out of certain methods of mental processes grouping. Therefore, the limitation is directed to an abstract idea.
Claim 19:
A tangible, non-transitory computer-readable medium having instructions thereon which, upon being executed by one or more processors, alone or in combination, provide for execution of a method for providing event-specific intervention recommendations, the method comprising: acquiring at least two data streams of a patient by using one or more sensors;
wherein at least one of the data streams includes images;
generating at least one entity-feature-graph based on the acquired at least two data streams of the patient;
selecting at least one intervention based on the generated entity-feature-graph and a trained graph classification model;
wherein the graph classification is performed based on using a graph neural network that is given as input the entity-feature-graph together with probability information that indicates for each pair of entities of a set of entities within the entity-feature-graph a likelihood that both entities of a respective pair are the same; and
outputting an information of the selected intervention to a user.
These limitations, as drafted, is an apparatus that, under its broadest reasonable interpretation, covers the performance of mental processes specifically recommending patient intervention. Recommending patient intervention has long before the modern computer was invented, and continues to be predominantly a product of human endeavor. The instant application is directed to recommending patient intervention. Additionally, the generation of a graph can be performed by a human via their mind and/or pen & paper. Furthermore, the selection of an intervention based on a generated graph and a model can be performed by a human via their mind and/or pen & paper. Moreover, the classification of a graph via the use of a neural network utilizing an entity-feature-graph and probability information can be performed by a human via their mind and/or pen & paper. Because the limitations above closely follow the steps of recommending patient intervention, and the steps involved human judgments, observations and evaluations that can be practically or reasonably performed in the human mind and/or pen & paper, the claim recites an abstract idea consistent with the “mental process” grouping set forth in the 2019 PEG.
The mere nominal recitation of generic computing components such as tangible, non-transitory computer-readable medium, one or more processors, and one or more sensors do not take the claim out of certain methods of mental processes grouping. Therefore, the limitation is directed to an abstract idea.
If the claims are directed toward the judicial exception of an abstract idea, it must then be determined under Step 2A Prong 2 whether the judicial exception is integrated into a practical application. The Examiner notes that considerations under Step 2A Prong 2 comprise most the consideration previously evaluated in the context of Step 2B. The Examiner submits that the considerations discussed previously determined that the claim does not recite “significantly more” at Step 2B would be evaluated the same under Step 2A Prong 1 and result in the determination that the claim does not integrate the abstract idea into a practical application. Specifically, the acquisition of patient data that includes images is simply a data gathering step that is an insignificant extra-solution activity and does not integrate the abstract idea into a practical application. Furthermore, the output of an intervention is simply a data output step that is an insignificant extra-solution activity and does not integrate the abstract idea into a practical application
The instant application fails to integrate the judicial exception into a practical application because the instant application merely recites words “apply it” (or an equivalent) with the judicial exception or merely includes instructions to implement an abstract idea. The instant application is directed to an apparatus instructing the reader to implement the identified apparatus of mental processes of recommending patient intervention. The elements of the claim do not themselves amount to an improvement to the computer, to a technology or another technical field.
Here, the claim elements entirely comprise the abstract idea, leaving little if any aspects of the claim for further consideration under Step 2A Prong 2. In short, the claims have failed to integrate a practical application (see at least 84 Fed. Reg. (4) at 55). Under the 2019 PEG, this supports the conclusion that the claim is directed to an abstract idea, and the analysis proceeds to Step 2B.
While many considerations in Step 2A need not be reevaluated in Step 2B because the outcome will be the same. Here, on the basis of the additional elements other than the abstract idea, considered individually and in combination as discussed above, the Examiner respectfully submits that the claim 19 does not contain any additional elements that individually or as an ordered combination amount to an inventive concept and the claims are ineligible.
With respect to the dependent claims do not recite anything that is found to render the abstract idea as being transformed into a patent eligible invention. The dependent claims are merely reciting further embellishments of the abstract idea and do not claim anything that amounts to significantly more than the abstract idea itself.
With respect to the dependent claims, they have been considered and are not found to be reciting anything that amounts to being significantly more than the abstract idea. Claim 20 is directed to further embellishments of the central theme of the abstract idea in that the claims are directed to further embellishments of the recommending patient intervention of the steps of claim 19 and do not amount to significantly more.
Specifically, claim 20 recites the training of a model which can be performed by a human via their mind and/or pen & paper and does not amount to significantly more.
Response to Arguments
8. Applicant's arguments filed on 06/22/2026 have been fully considered but they are not persuasive.
Applicants argue on Pages 08-09 that “it is respectfully submitted that claims 1-20 are not directed to an abstract idea under Prong One of Step 2A of the Subject Matter Eligibility Test set forth in MPEP § 2106. Specifically, claims 1-20 are not directed to a mathematical concept, certain methods of organizing human activity, or a mental process. Rather, the claims are directed to a machine learning method for using a trained graph classification model that utilizes a generated entity-feature-graph from data streams of a patient obtained by one or more sensors, for selecting an intervention, where the graph classification is performed based on using a graph neural network that is given as input the entity-feature-graph together with probability information that indicates for each pair of entities of a set of entities within the entity-feature-graph a likelihood that both entities of a respective pair are the same, which is not a mental process as alleged in the Office Action”. However, the limitations of “generating at least one entity-feature-graph based on the acquired at least two data streams of the patient”, “selecting at least one intervention based on the generated entity-feature-graph and a trained graph classification model”, and “wherein the entity-feature-graph is transformed into an embedding space by a transformation process that makes use of distances between each pair of entities in the embedding space to encode corresponding probabilities that respective pairs of the entities are the same” can be performed by a human via their mind and/or pen & paper. Furthermore, the additional element limitations of “acquiring at least two data streams of a patient by using one or more sensors, wherein at least one of the data streams includes images” (which is simply acquiring patient sensor data which is a data gathering step that is an insignificant extra-solution activity) and “outputting an information of the selected intervention to a user” (which is a data output step that is an insignificant extra-solution activity) do not integrate the abstract idea into a practical application.
Applicants argue on Pages 09-10 that “Nor is the human mind capable of performing such machine learning processes or implementing a model that is trained via machine learning as is the subject of the claims, and as has been acknowledged by already by the Office. For example, in Example 39 of the USPTO Subject Matter Eligibility Examples ("Example 39"), the Office indicates that training a neural network cannot be practically performed in the human mind. The claim limitations at issue here are far more computationally complex training steps than those of Example 39”. However, unlike in example 39, the independent claims are not reciting a training process to train a machine learning model. Rather, claim 2 of example 47 holds as the independent claims merely recite the use of a trained graph classification model without detailing how such a model operates (See “selecting at least one intervention based on the generated entity-feature-graph and a trained graph classification model, wherein the entity-feature-graph is transformed into an embedding space by a transformation process that makes use of distances between each pair of entities in the embedding space to encode corresponding probabilities that respective pairs of the entities are the same”). The aforementioned limitation does not detail how the claimed trained graph classification model operates (rather, a transformation is used to transform the entity-feature graph into an embedding space via the use of distances for encoding corresponding probabilities). Such a transformation (i.e. vectorization) process can be performed by a human via their mind and/or pen & paper.
Applicants argue on Page 10 that “Moreover, regardless of whether claims 1-20 are viewed as reciting an exception under Prong One of Step 2A of the Subject Matter Eligibility Test, it is respectfully submitted that clams 1-20 provide a practical application under Prong Two of Step 2A of the Subject Matter Eligibility Test. As set forth in MPEP § 2106, an element or combination of elements, including those asserted to be directed to an abstract idea, that reflect a technical improvement (e.g., to a computer or any other technology or technical field) provide that a claim is directed to a practical application. See MPEP § 2106.05(a). In this regard, the claims are directed to improvements to the technological field of machine learning by reducing error variance and generating more reliable recommendations”. However, the purported improvements of reducing error variance and generating reliable recommendations are not an improvement to a computing technology, let alone any other technology. Improving a mental process is still a mental process. Reducing errors that results in more reliable recommendations of patient interventions is improving a mental process. No actual improvement to a computing technology is reflected.
Applicants argue on Page 10 that “Further, MPEP § 2106.05(a) explicitly states that "[a]n indication that the claimed invention provides an improvement can include a discussion in the specification that identifies a technical problem and explains the details of an unconventional technical solution expressed in the claim, or identifies technical improvements realized by the claim over the prior art." Here, the published specification describes a number of technical problems that are overcome by the features of the claims. In particular, paragraphs [0003], [0013] and [0015] of the published specification describe technical problems associated with working with large amounts of data associated with emergency events that must be aggregated and analyzed to determine a recommended action in an expedient manner within the context of time-constrained and high-pressure tasks, such as emergency events. The present applications overcomes this technical problem by using a trained classification model that utilizes a generated entity-feature-graph to generate an intervention. The graph classification model is able to quickly process high volumes of data from a plurality of data streams and swiftly generate a response to situation in public safety or smart cities by transforming the entity-feature-graph into an embedding space by a transformation process that makes use of distances between each pair of entities in the embedding space to encode probabilities that respective pairs of entities are the same. See published specification, paragraphs [0016], [0026], [0054], and [0061]”.
However, the cited paragraphs of the published application of the purported improvement do not reflect any actual improvement to a computing technology or any other technology. Indeed, Paragraph 13 of the published application simply states that the purported invention improves reliability and efficiency of informed decisions which is not a technological improvement to a computing technology and/or any other technology.
Furthermore, Paragraph 15 of the published application simply states that the purported invention aggregates information faster than a human and reduces the cognitive burden of a human which is not a technological improvement to a computing technology and/or any other technology. Indeed, these purported improvements are conclusive in nature and do not improve any technological field. Indeed, the claimed acquiring is simply a data gathering step that is an insignificant extra-solution activity and does not integrate the abstract idea into a practical application. Moreover, the claimed generating, selection, and transformation can all be performed by a human via their mind and/or pen & paper. Furthermore, the claimed outputting is simply a data output step that is an insignificant extra-solution activity and does not integrate the abstract idea into a practical application.
Additionally, there is no improvement to a computing technology and/or any technology in Paragraph 16 of the published application of the specification.
Furthermore, Paragraph 26 of the published application simply states there is more information than a human can efficiently consume. However, such large data is not any actual improvement to a computing technology and/or any other technology.
Moreover, Paragraph 54 of the published application simply states the reduction of errors for more reliable recommended interventions from a graph neural network model which is not an improvement to a computing technology, a machine learning model and/or the training of it, or any other technology. The instant specification is utterly devoid of any explanation of what the claimed graph neural network model constitutes. Rather, the GNN is merely mentioned in a conclusory manner without defining it in any sort of detail, unlike in Desjardins (which dealt with specific improvements to specific ML model operations (the training of that model for reducing catastrophic forgetting).
Furthermore, there is no improvement to a computing technology and/or any technology reflected in Paragraph 61 of the published application.
Applicants argue on Page 11 that “Applicant also refers to the recent Ex Parte Desjardins Memo for further guidance that claims that provide improvements to machine learning like the present claims provide a practical application. The Ex Parte Desjardins Memo provides that the Appeals Review Panel ("ARP") evaluated the claims as a whole and discerned that a certain claim limitation of the application in issue reflected the improvement disclosed in the specification, and as such, the claims as a whole integrated what would otherwise be a judicial exception into a practical application at Step 2A Prong Two. The ARP in determining this holding also determined that the specification identified improvements as to how the machine learning model itself operated, including training machine learning model to learn new tasks while protecting knowledge about previous tasks to overcome the problem of "catastrophic forgetting" encountered in continual learning systems. Similarly, as discussed above and in the present specification, the present invention overcomes problems and provide improvements in machine learning which are reflected in the claims (e.g. "acquiring at least two data streams of a patient by using one or more sensors, wherein at least one of the data streams includes images," "generating at least one entity-feature-graph based on the acquired at least two data streams of the patient," "selecting at least one intervention based on the generated entity- feature-graph and a trained graph classification model, , wherein the graph classification is performed based on using a graph neural network that is given as input the entity-feature- graph together with probability information that indicates for each pair of entities of a set of entities within the entity-feature-graph a likelihood that both entities of a respective pair are the same," or "selecting at least one intervention based on the generated entity-feature-graph and a trained graph classification model, wherein the entity-feature-graph is transformed into an embedding space by a transformation process that makes use of distances between each pair of entities in the embedding space to encode corresponding probabilities that respective pairs of the entities are the same" which overcomes the technical problem of processing large amounts of multi-modal data from sensors to generate an accurate recommendation (intervention) using a trained graph classification model). See paragraphs [0003], [0013] [0015], [0016], [0026], [0054], and [0061]. of the published specification”. However, unlike in Desjardins, there is no actual improvement to the training of a machine learning model and/or for specific ML operations (reducing catastrophic forgetting). Rather, the cited paragraphs of the published application of the purported improvement do not reflect any actual improvement to a machine learning model and/or the training of it. Indeed, Paragraph 13 of the published application simply states that the purported invention improves reliability and efficiency of informed decisions which is not a technological improvement to a computing technology, ML model, and/or any other technology.
Furthermore, Paragraph 15 of the published application simply states that the purported invention aggregates information faster than a human and reduces the cognitive burden of a human which is not a technological improvement to a computing technology, ML model, and/or any other technology. Indeed, these purported improvements are conclusive in nature and do not improve any technological field. Indeed, the claimed acquiring is simply a data gathering step that is an insignificant extra-solution activity and does not integrate the abstract idea into a practical application. Moreover, the claimed generating, selection, and transformation can all be performed by a human via their mind and/or pen & paper. Furthermore, the claimed outputting is simply a data output step that is an insignificant extra-solution activity and does not integrate the abstract idea into a practical application.
Additionally, there is no improvement to a computing technology, ML operations and/or training, or any other technology in Paragraph 16 of the published application of the specification.
Furthermore, Paragraph 26 of the published application simply states there is more information than a human can efficiently consume. However, such large data is not any actual improvement to a computing technology, a machine learning model and/or the training of it, or any other technology.
Moreover, Paragraph 54 of the published application simply states the reduction of errors for more reliable recommended interventions from a graph neural network model which is not an improvement to a computing technology, a machine learning model and/or the training of it, or any other technology. The instant specification is utterly devoid of any explanation of what the claimed graph neural network model constitutes. Rather, the GNN is merely mentioned in a conclusory manner without defining it in any sort of detail, unlike in Desjardins (which dealt with specific improvements to specific ML model operations (the training of that model for reducing catastrophic forgetting).
Furthermore, there is no improvement to a computing technology, ML operations and/or training, or any technology reflected in Paragraph 61 of the published application.
Applicants argue on Page 12 that “Applicant has provided several paragraphs which detail the improvements to the technology or technical field of machine learning. Like in Ex Parte Desjardins, the present invention provides improvements that enhance the functionality of the computers of the machine learning system to provide more accurate and fast recommendations, specifically in each case providing improvements in the technological field of machine learning models and addressing technical problems of existing machine learning approaches. Next, the Memo emphasizes that it is critical that Examiners look at the claim as a whole when considering whether the claims reflect the improvements to technology discussed in the specification and that Examiners should be careful to avoid oversimplifying the claims by looking at them generally and failing to account for the specific requirements of the claims. The Ex Parte Desjardins decision emphasized that "Examiners and panels should not evaluate claims at such a high level of generality" such that potentially meaningful technical limitations are dismissed without adequate explanation”. However, as explained above, Paragraphs 13, 15-16, 26, 54, and 61 of the published application do not provide support for any improvement to a computing technology, ML operations and/or training, or any other technology. Providing faster and more accurate recommendations is a purported improvement to a mental process, which is still a mental process as humans provide recommended medical intervention recommendations. The additional elements of acquiring sensor data (which is a data gathering step that is an insignificant extra-solution activity and does not integrate the abstract idea into a practical application) and outputting an intervention (which is a data output step that is an insignificant extra-solution activity and does not integrate the abstract idea into a practical application) do not provide any improvement to a computing technology, or any other technology. The cited paragraphs (or anywhere else in the specification) do not provide any improvement to a computing technology, or any other technology.
Applicants argue on Page 12 that “the Office Action merely concludes that Applicant has recited data gathering steps or merely recites words similar to "apply it," to implement an identified apparatus of a mental process. See Office Action, pages 22 and 23. Applicant disagrees with this broad oversimplification and instead submits that the claims, as a whole, integrate the improvements described in the specification. For example, as noted above, the technical problems identified in the specification include problems of machine learning-based recommendations, and the accuracy and speed of such generated recommendations. This problem is solved through the use of the trained graph classification model, the generated entity-feature-graph, and the transformation into an embedding space or use of a graph neural network. The claims reflect this improvement by reciting the above amended features”. However, all that is claimed in the independent claims is “acquiring at least two data streams of a patient by using one or more sensors, wherein at least one of the data streams includes images” (which is simply acquiring patient sensor data which is a data gathering step that is an insignificant extra-solution activity and does not integrate the abstract idea into a practical application) and “outputting an information of the selected intervention to a user” (which is a data output step that is an insignificant extra-solution activity and does not integrate the abstract idea into a practical application). Moreover, as explained above, the claimed generating, selection, and transformation can all be performed by a human via their mind and/or pen & paper.
Applicants argue on Pages 12-13 that “Thus, especially when reviewed under the recent guidance provided in the Ex Parte Desjardins Memo, the claims integrate any alleged abstract idea into a practical application by providing improvements to machine learning models, in particular those used for machine learning generated interventions, which is evidenced by the technological problems and solutions described in the specification, and which are reflected in the current claims. It is respectfully submitted that the Step 2A Prong 2 analysis included in the Office Action is not in line with the subsequently issued guidance provided in the Ex Parte Desjardins Memo by relying on an oversimplification of the claims that evaluates the claims a high level of generality without considering the claim as a whole and dismisses the potentially meaningful technical limitations, which have been described in the published specification, without adequate explanation. Accordingly, it is respectfully submitted that the 101 rejections should be withdrawn, and it is further respectfully submitted that the claims do integrate the alleged abstract idea into a practical application based on the updated guidance provided by the Ex Parte Desjardins Memo for at least the reasons provided above”. However, as explained above, unlike in Desjardins, there is no actual improvement to the training of a machine learning model and/or for specific ML operations (reducing catastrophic forgetting). Moreover, the examiner did not overly simply the claims in a high level of generality as the applicants assert. Rather, the examiner pointed to each limitation (“generating at least one entity-feature-graph based on the acquired at least two data streams of the patient”, “selecting at least one intervention based on the generated entity-feature-graph and a trained graph classification model”, and “wherein the entity-feature-graph is transformed into an embedding space by a transformation process that makes use of distances between each pair of entities in the embedding space to encode corresponding probabilities that respective pairs of the entities are the same”) that can be performed by a human via their mind and/or pen & paper. Furthermore, the examiner pointed to the additional elements of “acquiring at least two data streams of a patient by using one or more sensors, wherein at least one of the data streams includes images” (which is simply acquiring patient sensor data which is a data gathering step that is an insignificant extra-solution activity and does not integrate the abstract idea into a practical application) and “outputting an information of the selected intervention to a user” (which is a data output step that is an insignificant extra-solution activity and does not integrate the abstract idea into a practical application).
Applicants argue on Pages 13-14 that “The claims are also eligible under Step 2B for reciting unconventional features. MPEP § 2106.05(d)(I) states that "[i]f the element is not widely prevalent or in common use, or is otherwise beyond those elements recognized in the art or by the courts as being well- understood, routine, or conventional, then the element will in most cases favor eligibility." Here, it is respectfully submitted that the claims include unconventional features of selecting at least one intervention based on the generated entity-feature-graph and a trained graph classification model, wherein the entity-feature-graph is transformed into an embedding space by a transformation process that makes use of distances between each pair of entities in the embedding space to encode corresponding probabilities that respective pairs of the entities are the same, and selecting at least one intervention based on the generated entity-feature-graph and a trained graph classification model, wherein the graph classification is performed based on using a graph neural network that is given as input the entity-feature-graph together with probability information that indicates for each pair of entities of a set of entities within the entity-feature-graph a likelihood that both entities of a respective pair are the same. Therefore, it is respectfully submitted that the claims recite an inventive concept sufficient to transform any alleged abstract idea into a patent-eligible invention, as the amended claims are novel and non-obvious and free of prior art rejections as argued below. While the analysis of eligibility is different from that of novelty and obviousness, the patentability of claims over the prior art is a factor for consideration in the Alice analysis. See e.g., Ultramercial, Inc. V. Hulu, LLC, 772 F.3d 709, 715 (Fed. Cir. 2014) (noting that "any novelty in implementation of the idea is a factor to be considered only in the second step of the Alice analysis."). Accordingly, because Applicant believes the claims are novel and non-obvious and recite a combination of features which are unconventional and go beyond what is well-understood, routine, or conventional, the claims recite an inventive concept, this ensures that the claim, as a whole, amounts to significantly more than the alleged abstract idea”. However, as explained above, the additional elements of “acquiring at least two data streams of a patient by using one or more sensors, wherein at least one of the data streams includes images” (which is simply acquiring patient sensor data which is a data gathering step that is an insignificant extra-solution activity and does not integrate the abstract idea into a practical application) and “outputting an information of the selected intervention to a user” (which is a data output step that is an insignificant extra-solution activity and does not integrate the abstract idea into a practical application) do not reflect any improvement to a computing technology, ML model operations and/or training, or any other technology.
Conclusion
9. The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
U.S. PGPUB 2019/0251480 issued to Duran et al. on 14 August 2019. The subject matter disclosed therein is pertinent to that of claims 1-2, 5-12, and 14-20 (e.g., methods to process patient sensor data).
U.S. PGPUB 2019/0148025 issued to Duran et al. on 16 May 2019. The subject matter disclosed therein is pertinent to that of claims 1-2, 5-12, and 14-20 (e.g., methods to process patient sensor data).
10. 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.
Contact Information
11. Any inquiry concerning this communication or earlier communications from the examiner should be directed to Mahesh Dwivedi whose telephone number is (571) 272-2731. The examiner can normally be reached on Monday to Friday 8:20 am – 4:40 pm.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Charles Rones can be reached (571) 272-4085. The fax number for the organization where this application or proceeding is assigned is (571) 273-8300.
Information regarding the status of an application may be obtained from the Patent Application Information Retrieval (PAIR) system. Status information for published applications may be obtained from either Private PAIR or Public PAIR. Status information for unpublished applications is available through Private PAIR only. For more information about the PAIR system, see http://pair-direct.uspto.gov. Should you have questions on access to the Private PAIR system, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free).
Mahesh Dwivedi
Primary Examiner
Art Unit 2168
July 22, 2026
/MAHESH H DWIVEDI/Primary Examiner, Art Unit 2168
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