DETAILED ACTION
Notice to Applicant
The following is a NON-FINAL Office action upon examination of application number 19/329,402, filed on 09/15/2025. Claims 1-20 are pending in the application and have been examined on the merits discussed below.
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Priority
Application 19/329,402 filed 09/15/2025 claims Priority from Provisional Application 63/694,547, filed 09/13/2024.
Information Disclosure Statement
4. The information disclosure statement (IDS) filed on 04/07/2026 has been acknowledged. The submission is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Claim Rejections - 35 USC § 112
5. The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
6. Claims 4 and 11-20 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor, or for pre-AIA the applicant regards as the invention.
7. Claim 4 recites “The method of claim 3, wherein identifying the data indicator is further based on sentiment analysis.” The term “the data indicator” lacks antecedent basis. While claim 3 recites “further comprising identifying data indicators to be monitored based on the goal,” claim 3 does not introduce a singular data indicator (instead in introduces a plurality), therefore rendering the claim indefinite. Appropriate correction is required.
8. Claim 11 recites “A system for artificial intelligence assisted personal data management, the system comprising: a communication interface that communicates over a communication network to receive tracked data regarding a user account from a plurality of external sources over a communication network; a processor that executes instructions stored in memory…” The second “a communication network” renders it unclear whether the same communication network previously introduced ais intended or whether a different communication network is being introduced, therefore rendering the claim indefinite. Appropriate correction is required.
9. Claim 20 recites “wherein the notification is generated based on iterative prompts…” The term “the notification” lacks antecedent basis. While claim 1 recites “a personalized notification,” it does not recite “a notification. It is unclear whether “the notification” refers to the previously introduced “personalized notification” or to a different notification, therefore rendering the claim indefinite. Appropriate correction is required.
10. All claims dependent from above rejected claims are also rejected due to dependency.
Claim Rejections - 35 USC § 101
11. 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.
12. Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-patentable subject matter. The claims are directed to an abstract idea without significantly more.
13. Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The eligibility analysis in support of these findings is provided below, in accordance with MPEP 2106.
With respect to Step 1 of the eligibility inquiry (as explained in MPEP 2106), it is first noted that the claimed method (claims 1-10), system (claims 11-19), and non-transitory, computer-readable storage medium (claim 20) are directed to potentially eligible categories of subject matter (i.e., process, machine, and article of manufacture, respectively), and therefore claims 1-20 satisfy Step 1 of the eligibility inquiry.
With respect to Step 2A Prong One, it is next noted that the claims recite an abstract idea that falls into the “Certain Methods of Organizing Human Activity” abstract idea set forth in MPEP 2106 because the claims recite steps for managing personal data and user goals, which encompasses activity for managing personal behavior or relationships or interactions, and “Mental Processes” or concepts performed in the human mind such as via observation, evaluation, and judgment. With respect to independent claim 1, the limitations reciting the abstract idea are indicated in bold below: receiving tracked data regarding a user account from a plurality of external sources over a communication network; identifying a trend regarding the user account based on one or more sets of the tracked data, each set including data that has been aggregated in accordance with a predefined category; identifying a goal based on one or more iterative conversations with a large language model; generating a visual representation that includes the identified trend and a personalized notification based on the goal, wherein the personalized notification is generated based on iterative prompts generated by the large language model in accordance with the identified and the goal; and dynamically updating the visual representation based on new data received from one or more of the external sources in real-time. These steps cover organizing human activity because the claim recites limitations related to identifying suer goals, analyzing account related data, identifying trends, and generate personalized notifications based on the identified goals and trends, and can also be performed mentally via human evaluation/judgment/opinion perhaps with the aid of pen and paper. Independent claims 10 and 20 recite similar limitations as set forth in claim 1 and are therefore found to recite the same abstract idea as claim 1.
Therefore, because the limitations above set forth activities falling within the “Certain methods of organizing human activity” and “Mental Processes” abstract idea groupings described in MPEP 2106, the additional elements recited in the claims are further evaluated, individually and in combination, under Step 2A Prong Two and Step 2B below.
With respect to Step 2A Prong Two, the judicial exception is not integrated into a practical application. The additional elements are: a communication network and a large language model (claim 1), a communication interface that communicates over a communication network to a communication network, a processor that executes instructions stored in memory, and a large language model (claim 11), a non-transitory, computer-readable storage medium, having embodied thereon a program executable by a processor, artificial intelligence, a communication network, and a large language model (claim 20). These additional elements have been evaluated, but fail to integrate the abstract idea into a practical application because they amount to using generic computing elements or computer-executable instructions (software) to perform the abstract idea, similar to adding the words “apply it” (or an equivalent), which merely serves to link the use of the judicial exception to a particular technological environment. See MPEP 2106.05(f) and 2106.05(h). Furthermore, these additional elements fail to integrate the abstract idea into a practical application because they fail to provide an improvement to the functioning of a computer or to any other technology or technical field, fail to apply the exception with a particular machine, fail to apply the judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition, fail to effect a transformation of a particular article to a different state or thing, and fail to apply/use the abstract idea in a meaningful way beyond generally linking the use of the judicial exception to a particular technological environment.”). Even if the step for receiving is evaluated as an additional element, this activity encompasses, at most, insignificant extra-solution activity, which is not indicative of a practical application, as noted in MPEP 2106.05(g), and is not enough to add significantly more since it is well-understood and conventional activity, as noted in MPEP 2106.05(d)
Accordingly, because the Step 2A Prong One and Prong Two analysis resulted in the conclusion that the claims are directed to an abstract idea, additional analysis under Step 2B of the eligibility inquiry must be conducted in order to determine whether any claim element or combination of elements amount to significantly more than the judicial exception.
With respect to Step 2B of the eligibility inquiry, it has been determined that the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. The additional elements are: a communication network and a large language model (claim 1), a communication interface that communicates over a communication network to a communication network, a processor that executes instructions stored in memory, and a large language model (claim 11), a non-transitory, computer-readable storage medium, having embodied thereon a program executable by a processor, artificial intelligence, a communication network, and a large language model (claim 20). The additional elements have been fully considered, but fail to add significantly more because they merely serve to tie the invention to a particular operating environment (i.e., computer-based implementation) by describing the use of generic computing elements to implement the claimed invention, though at a very high level of generality and without imposing meaningful limitation on the scope of the claim, similar to simply saying "apply it” or “apply it using a general purpose computer,” which is not enough to transform an abstract idea into eligible subject matter. Notably, Applicant’s Specification describes generic off-the-shelf computing elements for implementing the claimed invention and suggests that virtually any generic computing devices could be used to implement the invention (See, e.g., Specification paragraph [0025]). Therefore, these additional elements describe generic computing elements that merely serve to tie the abstract idea to a particular operating environment, which does not add significantly more to the abstract idea. See, e.g., Alice Corp., 134 S. Ct. 2347, 110 USPQ2d 1976.
Even if the receiving step is not deemed part of the abstract idea, this step is at most directed to insignificant extra-solution activity, which has been recognized as well-understood, routine, and conventional, and thus insufficient to add significantly more to the abstract idea. See MPEP 2106.05(d) - Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016) (using a telephone for image transmission); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network).
Even if the artificial intelligence (AI) was evaluated as an element beyond software/code for a generic computer to execute, it is noted that that the claimed use of Artificial Intelligence (AI) is recited at a high level of generality these elements amount to well-understood, routine, and conventional activity in the art, which fails to add significantly more to the claims. See, e.g., Magdon-Ismail et al., US 2009/0055270 (paragraph 39: “Both local and central engines may incorporate analysis techniques, such as artificial intelligence, machine learning and other techniques, which are well known in the art”). See also, Muchkaev, US 2010/0287011 (paragraph 47: “artificial intelligence algorithm such as a search algorithm, a learning algorithm, or any other artificial intelligence algorithm commonly known in the art”).
Even if the large language model (LLM) was evaluated as an element beyond software/code for a generic computer to execute, it is noted that that the claimed use of a large language model (LLM) is recited at a high level of generality these elements amount to well-understood, routine, and conventional activity in the art, which fails to add significantly more to the claims. See, e.g., Radmilac et al., US 2024/0411751 A1 (paragraph 0004: “Conventional LLMs, as well as model networks that further process and analyze outputs from LLMs including interconnected sets of machine learning models, often serve a large and diverse group of users across different enterprises”).
In addition, when taken as an ordered combination, the ordered combination adds nothing that is not already present as when the elements are taken individually. There is no indication that the combination of elements integrate the abstract idea into a practical application. Their collective functions merely provide generic computer implementation. Therefore, when viewed as a whole, these additional claim elements do not provide meaningful limitations to transform the abstract idea into a practical application of the abstract idea or that the ordered combination amounts to significantly more than the abstract idea itself.
Dependent claims 2-10 and 12-19 recite the same abstract idea as recited in the independent claims, and when evaluated under Step 2A Prong One of the eligibility inquiry, merely recite further details of the same abstract idea recited in the independent claims accompanied by, at most, the involvement of the same generic computing elements as the independent claims which, as noted above, are not sufficient to amount to a practical application or significantly more than the abstract idea itself. In particular, dependent claims 2-10 recite “further comprising updating the model based on the new data,” “further comprising identifying data indicators to be monitored based on the goal,” “wherein identifying the data indicator is further based on sentiment analysis,” “wherein identifying the trend is further based on sentiment analysis of user data as expressed in text in relation to the goal,” “wherein identifying the trend is further based on time-synchronized measurements of pairs of data,” “wherein the iterative prompts include an information component and a request component,” “further comprising identifying one or more anomalous trend parameters that are negatively correlated with the goal,” “further comprising determining a cause of the identified anomalous trend parameters, and updating the personalized notification based on the determined cause,” “further comprising updating the personalized notification based on a change in variance in the trend, and triggering a different recommendation to include in the personalized notification when the change is a decrease in the variance,” however, these claims also set forth steps falling within the same Mental Processes, and/or Certain methods of organizing human activity abstract idea groupings recited in the independent claim. The other dependent claims have been fully considered as well; however these claims are also directed to the abstract idea itself without integrating it into a practical application and implemented by, at most, a general purpose computer that serves to tie the idea to a particular technological environment, which does not add significantly more to the claims. The additional elements recited in the dependent claims including “wherein the text is tokenized” (claims 5 and 15) are recited at a high level of generality and fails to yield any discernible improvement to the computer or to any technology, nor set forth any additional function or result that provided meaningful limitation beyond linking the abstract idea to a particular technological environment (i.e., automated/computing environment), and thus fail to integrate the abstract idea into a practical application. When evaluated under Step 2A Prong Two and Step 2B, the additional elements do not amount to a practical application or significantly more since they merely require generic computing devices (or computer-implemented instructions/code) which as noted in the discussion of the independent claims above is not enough to render the claims as eligible.
The ordered combination of elements in the dependent claims (including the limitations inherited from the parent claim(s)) add nothing that is not already present as when the elements are taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely provide generic computer implementation. Accordingly, the subject matter encompassed by the dependent claims fails to amount to a practical application or significantly more than the abstract idea itself.
For more information, see MPEP 2106.
Claim Rejections - 35 USC § 103
14. In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
15. The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102 of this title, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
16. The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
17. Claims 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over Gallix et al., Pub. No.: US 2025/0292910 A1, [hereinafter Gallix], in view of Smith Lewis et al., Pub. No.: US 2024/0289863 A1, [hereinafter Smith].
As per claim 1, Gallix teaches a method for artificial intelligence (AI)-based personal data management, the method (paragraphs 0071, 0076) comprising:
receiving tracked data regarding a user account from a plurality of external sources over a communication network (paragraph 0129, discussing that the user data refer to a collection of data personal to the user, gathered from various sources. This includes data collected by the healthcare system, data provided directly by the user, and data derived from other external sources; paragraph 0191, discussing that current user data may be at least one of a data collected by the healthcare system, a data provided directly by the user, and data derived from other external sources…;
identifying a trend regarding the user account based on one or more sets of the tracked data, each set including data that has been aggregated in accordance with a predefined category (paragraph 0048, discussing utilizing advanced algorithms to extract significant features from the user data, employing the reference data as a benchmark to identify patterns; paragraph 0220, discussing the patient's historical and current health data are examined over time to identify patterns and trends; paragraph 0281, discussing identifying patterns and correlations between various factors and the outcome of interest; paragraph 0388, discussing that the longitudinal data are then analyzed to detect patterns and trends over time; paragraph 0039, discussing that each input layer is configured to handle a type of input data; paragraph 0040, discussing that the model comprises at least two distinct input layers, each specialized for processing a specific type of input data. This modular architecture allows for tailored preprocessing of different data types; paragraph 0300, discussing stratifying the patient into different risk categories for various health outcomes; paragraphs 0042, 0096);
identifying a goal (paragraph 0431, discussing that continuous monitoring of key metrics and targets will allow deviations from planned goals to be identified);
generating a visual representation that includes the identified trend and a personalized notification based on the goal (paragraph 0241, discussing generating personalized health recommendations; paragraph 0369, discussing an innovative digital health platform that integrates individual virtual twin modeling, artificial intelligence (AI), and predictive analytics to enhance individual health prevention and management. The platform generates lifestyle recommendations…; paragraph 0375, discussing personalized recommendations; paragraph 0384, discussing personalized recommendations based on real-time data; paragraph 0593, discussing that the app displays a simple graph or trend line that shows how their results have changed over time; paragraph 0629, discussing a visual report card…the Virtual Twin will display the recommendation…The comprehensive nature of the graph supports the creation of a personalized health plan; paragraph 0400); and
dynamically updating the visual representation based on new data received from one or more of the external sources in real-time (paragraph 0222, discussing that the virtual model of the user is not static; it is updated continually with new patient data; paragraph 0231, discussing that the virtual model of the user is not static; it is updated continually with new user data; paragraph 0242, discussing calibrating the virtual model of the user based on initial data and ongoing updates; paragraph 0243, discussing continuous monitoring and updating; paragraph 0253, discussing that as new data are collected, the first, second and/or virtual model needs to be continually updated. Machine learning algorithms can be used to refine model parameters based on new data; paragraph 0256, discussing real-time updates).
While Gallix describes identifying goals, it does not explicitly teach that the goal is identified based on one or more iterative conversations with a large language model; and generating a visual representation that includes the identified trend and a personalized notification based on the goal, wherein the personalized notification is generated based on iterative prompts generated by the large language model in accordance with the identified and the goal. However, Smith in the analogous art of artificial intelligence systems and teaches these concepts. Smith teaches:
identifying a goal based on one or more iterative conversations with a large language model (paragraph 0083, discussing that the neural network may be trained on a set of user-item interaction data comprising user profiles with demographic data, personality traits, and/or historical item engagement data matched to item metadata attributes including textual descriptions, audio & visual features, popularity indices, and/or embedded category vectors. Notably, user profile data may not only include conversational history but also current conversational attributes, such as the user's current goal inferred by the semantic router described above, their mood, and other factors such as an emotionally intelligent and socially fluent human would pick up during the course of a sales conversation; paragraph 0089, discussing an intelligence and insights module that analyzes inputs from various sources to extract valuable insights and provide data-driven recommendations. This analysis may be performed using advanced machine learning algorithms such as deep learning and predictive analytics. These algorithms may process large amounts of data to identify patterns and trends in user behavior and preferences, allowing the system to better understand the motivations and needs of individual users. The intelligence and insights module may provide one or more data-driven recommendations regarding improvements to responses of respective conversational agents, improvements to one or more services provided, and/or system performance, etc.; paragraph 0099, discussing that conversation logs may be sampled to extract a representative dataset given storage constraints…Conversations may be further grouped by time, subject, user characteristic, or other attributes and summarized at the group level, adding a hierarchical level above the conversational level in which groups of conversations are summarized. In some embodiments, this hierarchical structure may enable administrators to ask very broad questions about a wide range of conversations and receive analyses quickly based on LLM analysis, but also to then further dig into individual conversations with more specific queries; paragraph 0119, discussing analyzing inputs from a plurality of users responsive to interactions with respective conversational agents, extract insights associated with interactions with the plurality of users; and provide data-driven recommendations. In some embodiments, the processor may implement an intelligence and insights module. This module provides reporting and insights on user behavior and trends. It uses generative AI to analyze conversations across all users and provide insights and recommendations); and
generating a visual representation that includes the identified trend and a personalized notification based on the goal, wherein the personalized notification is generated based on iterative prompts generated by the large language model in accordance with the identified and the goal (paragraph 0088, discussing that predictive conversion may not be restricted to direct commerce but may be more generally applied to prompt users to take actions or take steps towards a goal at the right moment; paragraph 0089, discussing an intelligence and insights module that analyzes inputs from various sources to extract valuable insights and provide data-driven recommendations. This analysis may be performed using advanced machine learning algorithms such as deep learning and predictive analytics. These algorithms may process large amounts of data to identify patterns and trends in user behavior and preferences, allowing the system to better understand the motivations and needs of individual users. The intelligence and insights module may provide one or more data-driven recommendations regarding improvements to responses of respective conversational agents, improvements to one or more services provided, and/or system performance, etc.; paragraph 0090, discussing that examples of patterns and trends in user behavior and preferences could include a user's preferred communication style, their purchasing habits, the types of products or services they are interested in, and their engagement levels with different content or media, among others. This information can then be used to deliver more personalized and relevant content and recommendations, improving the overall user experience; paragraph 0094, discussing that predictions made by the module may be based on analysis of data collected from the various inputs. Machine learning algorithms may be used to identify patterns and trends in user behavior and preferences...In some embodiments, predictions are continually refined over time based on the user's interactions with the system, enabling the module to provide increasingly accurate and relevant recommendations to each individual user; paragraph 0097, discussing that an AI system may use a combination of machine learning algorithms, such as Natural Language Processing (NLP), to analyze user behavior and preferences and generate personalized recommendations. The outputs are not just one optimal formula applied across all users, but may be trained with respect to smaller subsets of similar users, or even each individual user, based on their specific needs and preferences. The system may continually update and evolve the formula based on one or more user's interactions, ensuring that the recommendations remain relevant and personalized over time; paragraph 0101, discussing that an admin module may be configured to display, via an interactive admin UI, for example: summarized topics, in granular form and also in a simple, understandable summary generated by an LLM and tuned to highlight important or notable trends; sample conversations for qualitative assessment; charts of topic trends, engagement and user needs over time; recommendations of high priority knowledge gaps limiting agent effectiveness…; paragraph 0116, discussing that the processor may be configured to implement a personalized rapport module. This module uses the user data collected from the interface to create a personalized experience for the user. The AI leverages advances in cognitive and neuroscience to proactively engage the user and create a lasting connection. The output of this module is a set of personalized engagement strategies for the user, among other outputs, which may impact future responses. In some embodiments, the first user profile may be updated, e.g., continually in real-time, or periodically, based on streams of user interaction data, to ensure accuracy and personalization of system outputs; paragraph 0118, discussing that the processor is configured to generate customized content recommendations for the first user based at least in part on the first user profile provide to the first user by the conversational agent; paragraph 0094).
Gallix is directed towards training machine learning models. Smith is directed to artificial intelligence systems. Therefore, they are deemed to be analogous as they both are directed towards solutions for modeling systems. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Gallix with Smith because the references are analogous art because they are both directed to solutions for data management, which falls within applicant’s field of endeavor (artificial intelligence based analytics and management of personal data), and because modifying Gallix to include Smith’s features for including identifying a goal based on one or more iterative conversations with a large language model and generating a visual representation that includes the identified trend and a personalized notification based on the goal, wherein the personalized notification is generated based on iterative prompts generated by the large language model in accordance with the identified and the goal, in the manner claimed, would serve the motivation of improving engagement with users in artificial intelligence driven communications (Smith at paragraph 0002); and further obvious because the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
As per claim 2, the Gallix-Smith combination teaches the method of claim 1. Gallix further teaches further comprising updating the large language model based on the new data (paragraph 0034, discussing that the machine learning model is designed to adapt and improve over time. It achieves this by continuously learning from an ongoing stream of new user data, thereby refining its predictive accuracy. This dynamic adaptation ensures that the model stays relevant and accurate in the long-term, maintaining reliable risk assessments as new health data becomes available. This feature is critical for applications in dynamic health monitoring systems where user conditions may evolve, requiring the model to adjust its predictions based on the latest data; paragraph 0224, discussing model calibration…calibrate the virtual model of the user based on initial data and ongoing updates. This may involve machine learning techniques to refine the model's predictions over time; paragraph 0231, discussing that the virtual model of the user is not static; it is updated continually with new user data; paragraph 0242, discussing calibrating the virtual model of the user based on initial data and ongoing updates; paragraph 0243, discussing continuous monitoring and updating; paragraph 0253, discussing that as new data are collected, the first, second and/or virtual model needs to be continually updated. Machine learning algorithms can be used to refine model parameters based on new data; paragraph 0164, discussing that Natural Language Processing (NLP) and Large Language Models (LLMs) like BERT, GPT, and their derivatives, which are pre-trained on vast corpora, can be fine-tuned for specific healthcare tasks to provide contextually rich representations of the text data. These models can perform tasks such as information extraction, which identifies clinical entities like diseases, medications, or symptoms within the text.).
Examiner notes that Smith, in addition to Gallix as cited above, also teaches: updating the large language model based on the new data (paragraph 0072, discussing that user needs, motivations, and/or other factors may be inferred, e.g., by a profile updating module. In some embodiments, the profile updating module may use one or more calls to an LLM or other updatable reinforcement learning model to input the raw data described above, extract the information relevant to the user profile, and may either pass this information to the vector encoder and then store these as vector encodings or store them in another appropriate format…; paragraph 0121, discussing that a machine learning model, or model, may take one or more inputs and generate one or more outputs. Examples of a machine learning model may include a neural network or other machine learning model described herein, and may take inputs and provide outputs based on the inputs and parameter values of the model. For example, a model may be fed an input or set of inputs for processing based on user feedback data or outputs determined by other models and provide an output or set of outputs. In some cases, outputs may be fed back to the machine learning model as input to train the machine learning model. In some embodiments, a machine learning model may update its configurations based on its assessment of a prediction or instructions against feedback information or outputs of other models. In some embodiments, such as where a machine learning model is a neural network, connection weights may be adjusted to reconcile differences between the neural network's prediction or instructions and feedback data. In some embodiments, one or more neurons (or nodes) of a neural network may require that their respective errors are sent backward through the neural network to them to facilitate the update process. Updates to the connection weights may, for example, be reflective of the magnitude of error propagated backward after a forward pass has been completed. In this way, for example, a machine learning model may be trained to generate better predictions or instructions.
As per claim 3, the Gallix-Smith combination teaches the method of claim 1. Gallix further teaches further comprising identifying data indicators to be monitored based on the goal (paragraph 0034, discussing that the machine learning model is designed to adapt and improve over time. It achieves this by continuously learning from an ongoing stream of new user data, thereby refining its predictive accuracy. This dynamic adaptation ensures that the model stays relevant and accurate in the long-term, maintaining reliable risk assessments as new health data becomes available. This feature is critical for applications in dynamic health monitoring systems where user conditions may evolve, requiring the model to adjust its predictions based on the latest data; paragraph 0042, discussing that each input layer in this model is adept at managing a variety of data types, including but not limited to: numerical data from clinical measurements, time-series data from continuous monitoring devices, categorical data from patient surveys, textual data from clinical notes, image data from diagnostic imaging tools, and audio data from patient interactions, biological and genetics data. This versatility allows the model to integrate and synthesize data across modalities, enhancing its ability to derive comprehensive insights into the user's health status; paragraph 0082, discussing that the invention not only monitors the patient's (i.e. the user's) current state of health, but also uses advanced predictive modeling to anticipate future risks. It integrates physiological, behavioral and environmental data into a digital platform that uses AI to assess and recommend personalized prevention strategies; paragraph 0195, discussing that by monitoring and correlating data from multiple sources, deviations from typical physiological behavior can be detected; paragraph 0257, discussing that the virtual model of the user may be updated through a process that involves continuous data assimilation and model recalibration. The frequency of avatar updates can vary depending on several factors, such as the nature of the data, the specific health conditions being monitored and the objectives of the virtual model of the user; paragraph 0258, discussing that the process is data-driven and depends on the design of the virtual model of the user, available IT resources and the specific requirements of the individual's health monitoring plan; paragraphs 0205, 0218, 0261).
As per claim 4, the Gallix-Smith combination teaches the method of claim 3. Gallix further teaches wherein identifying the data indicator is further based on sentiment analysis (paragraph 0164, discussing that additionally, NLP techniques can be applied for sentiment analysis to gauge patient feedback or emotional states, and for topic modeling to uncover prevalent themes or trends in patient histories).
As per claim 5, the Gallix-Smith combination teaches the method of claim 1. Gallix further teaches wherein identifying the trend is further based on sentiment analysis of user data as expressed in text in relation to the goal (paragraph 0164, discussing that text data is often unstructured and can be challenging to process directly. Initially, medical data is typically structured during the collection process, but unstructured text data may require advanced techniques to extract features. Natural Language Processing (NLP) and Large Language Models (LLMs) like BERT, GPT, and their derivatives, which are pre-trained on vast corpora, can be fine-tuned for specific healthcare tasks to provide contextually rich representations of the text data. These models can perform tasks such as information extraction, which identifies clinical entities like diseases, medications, or symptoms within the text. Additionally, NLP techniques can be applied for sentiment analysis to gauge patient feedback or emotional states, and for topic modeling to uncover prevalent themes or trends in patient histories. Through these advanced methods, text data can be converted into structured information, which can then be integrated with other types of features to improve the overall risk prediction).
Gallix does not explicitly teach wherein the text is tokenized. However, Smith in the analogous art of artificial intelligence systems and teaches this concept. Smith teaches:
wherein the text is tokenized (paragraph 0048, discussing that text may be preprocessed, for example, by some subset of tokenization, consistent casing, spell correction, removal of stop words, stemming, lemmatization, text normalization or other techniques that standardize the text and enhance information density…; paragraph 0135, discussing that an AI system employing one or more of the present techniques may be integrated with blockchain technology, leveraging decentralized data storage and secure cryptographic protocols. This integration would provide a secure and tamper-proof solution for storing user data and interactions, ensuring data privacy and protection. In this embodiment, the AI system would be designed to interact with smart contracts, enabling automated decision-making and improving the speed and efficiency of data processing. However, safeguards would be in place to ensure that the AI system cannot make irreversible transactions to the blockchain without proper authorization. In addition to providing secure data storage, the use of blockchain technology could also enable the creation of unique digital tokens that could be earned and traded by users based on their interactions with the AI system. This would provide a new way of incentivizing user engagement and creating a more immersive experience. The decentralized nature of the blockchain would ensure transparency and accountability in the tracking and distribution of these tokens, further enhancing the user experience).
Gallix is directed towards training machine learning models. Smith is directed to artificial intelligence systems. Therefore, they are deemed to be analogous as they both are directed towards solutions for modeling systems. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Gallix with Smith because the references are analogous art because they are both directed to solutions for data management, which falls within applicant’s field of endeavor (artificial intelligence based analytics and management of personal data), and because modifying Gallix to include Smith’s feature for including wherein the text is tokenized, in the manner claimed, would serve the motivation of ensuring data privacy and protection (Smith at paragraph 0135); and further obvious because the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
As per claim 6, the Gallix-Smith combination teaches the method of claim 1. Gallix further teaches wherein identifying the trend is further based on time-synchronized measurements of pairs of data (paragraph 0041, discussing that said type of input data corresponds to at least one of a numerical data, time-series data…; paragraph 0048, discussing utilizing advanced algorithms to extract significant features from the user data, employing the reference data as a benchmark to identify patterns; paragraph 0220, discussing the patient's historical and current health data are examined over time to identify patterns and trends; paragraph 0388, discussing that the longitudinal data are then analyzed to detect patterns and trends over time; paragraph 0628).
As per claim 7, the Gallix-Smith combination teaches the method of claim 1. Although not explicitly taught by Gallix, Smith in the analogous art of artificial intelligence systems teaches wherein the iterative prompts include an information component and a request component (paragraph 0052, discussing that in some embodiments, this embedding-retrieval pipeline may be applied to Retrieval Augmented Generation (RAG) whereby a targeted search across the embedded vector database is performed in response to a user query, e.g., in order to produce context for a conversational agent to then generate a response, for example by inserting the retrieved text chunks into a system prompt or message used to generate the agent's response to the user; paragraph 0053, discussing that other embodiments may include, for example, running a “codex tool” query inside or in addition to the system prompt which determines whether the agent should search for additional information from the vector database or generate a response using its own internal memory and current context (e.g., system prompt and message history, for example). In some embodiments, this query may be editable by the agent creator, for example it may instruct an agent to check whether a user is asking for information about an insurance plan and, if so, parse the question being asked and pass this to the vector database to retrieve the relevant information from the knowledge base. This retrieved information may then be passed into the system prompt or message history before generating a response to the user's query; paragraph 0058, discussing that a conversational agent's responses may be guided by the context retrieved using this RAG approach in ways beyond simply defining source data, for example updating its system prompt instructions based on the type of query received; paragraph 0071, discussing that these interactions may be direct (e.g., provided by the user explicitly to improve their experience), indirect-active (e.g., the conversational agent may prompt a user with questions or interactions designed to elicit useful information for the user profile), or indirect-passive (e.g., conversational agent infers attributes from interaction history or other provided data about the user, or from other visual or mechanical user interactions such as texts, clicks, dwell times etc.); paragraph 0107, discussing an Interaction Interface, which may provide means through which users interact with the AI system. This may take the form of conversational agents, chatbots, virtual assistants, or other conversational interfaces, or a flexible search box permitting a query or other prompt, or the uploading of an image or video or audio or data file or other formats, or a combination of the above, and provides a simple and intuitive way for users to engage with the AI system; paragraph 0108, discussing that Various Machine Learning Algorithms, which may be used to analyze data described herein, such as to train various machine learning models, which may be employed by one or more components or modules described herein, such as for making predictions, and providing insights and recommendations. These algorithms may utilize techniques such as deep learning, reinforcement learning, and predictive analytics, and are constantly learning and evolving to provide more accurate and meaningful insights and recommendations over time; paragraphs 0074-0076, 0109).
Gallix is directed towards training machine learning models. Smith is directed to artificial intelligence systems. Therefore, they are deemed to be analogous as they both are directed towards solutions for modeling systems. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Gallix with Smith because the references are analogous art because they are both directed to solutions for data management, which falls within applicant’s field of endeavor (artificial intelligence based analytics and management of personal data), and because modifying Gallix to include Smith’s feature for including wherein the iterative prompts include an information component and a request component, in the manner claimed, would serve the motivation of improving engagement with users in artificial intelligence driven communications (Smith at paragraph 0002); and further obvious because the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
As per claim 8, the Gallix-Smith combination teaches the method of claim 1. Gallix further teaches further comprising identifying one or more anomalous trend parameters that are negatively correlated with the goal (paragraph 0048, discussing that Feature Extraction: Utilizes advanced algorithms to extract significant features from the user data, employing the reference data as a benchmark to identify patterns or anomalies indicative of health risks; paragraph 0134, discussing that the user data comprises at least one information that can be linked to an abnormality. For instance, a single information may deviate from typical results, or multiple information may be correlated to identify such deviations; paragraph 0195, discussing that the current user data comprises at least one information that can be linked to an abnormality. For instance, a single information may deviate from typical results, or multiple information may be correlated to identify such deviations).
As per claim 9, the Gallix-Smith combination teaches the method of claim 8. Gallix further teaches further comprising determining a cause of the identified anomalous trend parameters, and updating the personalized notification based on the determined cause (paragraph 0134, discussing that the user data comprises at least one information that can be linked to an abnormality. For instance, a single information may deviate from typical results, or multiple information may be correlated to identify such deviations. By monitoring and correlating data from multiple sources, deviations from typical physiological behavior can be detected, potentially indicating physical, physiological, symptomatic, diagnostic, behavioral, or pathological abnormalities that may require further investigation or intervention; paragraph 0195, discussing that the current user data comprises at least one information that can be linked to an abnormality. For instance, a single information may deviate from typical results, or multiple information may be correlated to identify such deviations. By monitoring and correlating data from multiple sources, deviations from typical physiological behavior can be detected, potentially indicating physical, physiological, symptomatic, diagnostic, behavioral, or pathological abnormalities that may require further investigation or intervention).
As per claim 10, the Gallix-Smith combination teaches the method of claim 1. Gallix further comprising updating the personalized notification based on a change in variance in the trend, and triggering a different recommendation to include in the personalized notification when the change is a decrease in the variance (paragraph 0241, discussing generating personalized health recommendations; paragraph 0369, discussing an innovative digital health platform that integrates individual virtual twin modeling, artificial intelligence (AI), and predictive analytics to enhance individual health prevention and management. The platform generates lifestyle recommendations…; paragraph 0265, discussing triggered updates. In addition to scheduled or automatic updates, certain events can trigger an immediate update of the avatar. For example, the detection of an anomaly in the data (such as a sudden increase in heart rate) may trigger an urgent reassessment of the person's state of health; paragraph 0375, discussing personalized recommendations; paragraph 0384, discussing personalized recommendations based on real-time data; paragraph 0593, discussing that the app displays a simple graph or trend line that shows how their results have changed over time; paragraph 0623, discussing that opening a timeline graph that illustrates the past change in risk. As an example, the patient starts to be treated with antihypertensive 5 years before and the risk of cardiac accident decrease significantly almost immediately. For the future the timeline curve may demonstrate the potential impact of intervention (lifestyle changes or medication) on the physiological age trend of a patient. Each line represents a different scenario and how it might affect the physiological age over time, given a series of years; paragraph 0629, discussing a visual report card…the Virtual Twin will display the recommendation…The comprehensive nature of the graph supports the creation of a personalized health plan).
Examiner notes that Smith, in addition to Gallix as cited above, also teaches updating the personalized notification based on a change in variance in the trend, and triggering a different recommendation to include in the personalized notification when the change is a decrease in the variance (paragraph 0088, discussing that predictive conversion may not be restricted to direct commerce but may be more generally applied to prompt users to take actions or take steps towards a goal at the right moment; paragraph 0089, discussing an intelligence and insights module that analyzes inputs from various sources to extract valuable insights and provide data-driven recommendations. This analysis may be performed using advanced machine learning algorithms such as deep learning and predictive analytics. These algorithms may process large amounts of data to identify patterns and trends in user behavior and preferences, allowing the system to better understand the motivations and needs of individual users. The intelligence and insights module may provide one or more data-driven recommendations regarding improvements to responses of respective conversational agents, improvements to one or more services provided, and/or system performance, etc.; paragraph 0090, discussing that examples of patterns and trends in user behavior and preferences could include a user's preferred communication style, their purchasing habits, the types of products or services they are interested in, and their engagement levels with different content or media, among others. This information can then be used to deliver more personalized and relevant content and recommendations, improving the overall user experience; paragraph 0094, discussing that predictions made by the module may be based on analysis of data collected from the various inputs. Machine learning algorithms may be used to identify patterns and trends in user behavior and preferences...In some embodiments, predictions are continually refined over time based on the user's interactions with the system, enabling the module to provide increasingly accurate and relevant recommendations to each individual user; paragraph 0097, discussing that an AI system may use a combination of machine learning algorithms, such as Natural Language Processing (NLP), to analyze user behavior and preferences and generate personalized recommendations. The outputs are not just one optimal formula applied across all users, but may be trained with respect to smaller subsets of similar users, or even each individual user, based on their specific needs and preferences. The system may continually update and evolve the formula based on one or more user's interactions, ensuring that the recommendations remain relevant and personalized over time; paragraph 0101, discussing that an admin module may be configured to display, via an interactive admin UI, for example: summarized topics, in granular form and also in a simple, understandable summary generated by an LLM and tuned to highlight important or notable trends; sample conversations for qualitative assessment; charts of topic trends, engagement and user needs over time; recommendations of high priority knowledge gaps limiting agent effectiveness…; paragraph 0116, discussing that the processor may be configured to implement a personalized rapport module. This module uses the user data collected from the interface to create a personalized experience for the user. The AI leverages advances in cognitive and neuroscience to proactively engage the user and create a lasting connection. The output of this module is a set of personalized engagement strategies for the user, among other outputs, which may impact future responses. In some embodiments, the first user profile may be updated, e.g., continually in real-time, or periodically, based on streams of user interaction data, to ensure accuracy and personalization of system outputs; paragraph 0118, discussing that the processor is configured to generate customized content recommendations for the first user based at least in part on the first user profile provide to the first user by the conversational agent; paragraph 0133, discussing that if a user is showing increased engagement with a certain type of content, the system may recommend similar content to further enhance the user's virtual experience. Additionally, the system could use biometric data, such as heart rate and brain activity, to make even more informed decisions. For example, if the user's heart rate increases while viewing a certain type of content, the system may recommend different or similar content that could help to keep the user relaxed and engaged in the virtual environment. This embodiment has the potential to revolutionize the way people interact with technology, media, and information in a highly engaging and personalized manner).
Claims 11 and 20 recite substantially similar limitations that stand rejected via the art citations and rationale applied to claim 1, as discussed above. Further, as per claim 11 the Gallix-Smith combination teaches a system for artificial intelligence assisted personal data management, the system comprising: a communication interface and a processor that executes instructions stored in memory (Gallix, paragraph 0076: “a computer program comprising software code adapted to perform a method for obtaining a trained model for prediction and/or a method for assisting the user compliant with any of the above execution modes when the program is executed by a processor”; paragraph 0093: “The term “processor” should not be construed to be restricted to hardware capable of executing software, and refers in a general way to a processing device, which can for example include a computer, a microprocessor, an integrated circuit, or a programmable logic device (PLD). The processor may also encompass one or more Graphics Processing Units (GPU), whether exploited for computer graphics and image processing or other functions. Additionally, the instructions and/or data enabling to perform associated and/or resulting functionalities may be stored on any processor-readable medium such as, e.g., an integrated circuit, a hard disk, a CD (Compact Disc), an optical disc such as a DVD (Digital Versatile Disc), a RAM (Random-Access Memory) or a ROM (Read-Only Memory). Instructions may be notably stored in hardware, software, firmware or in any combination thereof.”; paragraph 0117, discussing that it should be understood that the elements shown in the figures may be implemented in various forms of hardware, software or combinations thereof. Preferably, these elements are implemented in a combination of hardware and software on one or more appropriately programmed general-purpose devices, which may include a processor, memory and input/output interfaces; paragraph 0115). As per claim 20, the Gallix-Smith combination teaches a non-transitory, computer-readable storage medium, having embodied thereon a program executable by a processor to perform a method for artificial intelligence assisted personal data management (Gallix, paragraph 0076: “a computer program comprising software code adapted to perform a method for obtaining a trained model for prediction and/or a method for assisting the user compliant with any of the above execution modes when the program is executed by a processor”; paragraph 0093: “The term “processor” should not be construed to be restricted to hardware capable of executing software, and refers in a general way to a processing device, which can for example include a computer, a microprocessor, an integrated circuit, or a programmable logic device (PLD). The processor may also encompass one or more Graphics Processing Units (GPU), whether exploited for computer graphics and image processing or other functions. Additionally, the instructions and/or data enabling to perform associated and/or resulting functionalities may be stored on any processor-readable medium such as, e.g., an integrated circuit, a hard disk, a CD (Compact Disc), an optical disc such as a DVD (Digital Versatile Disc), a RAM (Random-Access Memory) or a ROM (Read-Only Memory). Instructions may be notably stored in hardware, software, firmware or in any combination thereof; paragraph 0117).
Claim 12 recites substantially similar limitations that stand rejected via the art citations and rationale applied to claim 2, as discussed above.
Claim 13 recites substantially similar limitations that stand rejected via the art citations and rationale applied to claim 3, as discussed above.
Claim 14 recites substantially similar limitations that stand rejected via the art citations and rationale applied to claim 4, as discussed above.
Claim 15 recites substantially similar limitations that stand rejected via the art citations and rationale applied to claim 5, as discussed above.
Claim 16 recites substantially similar limitations that stand rejected via the art citations and rationale applied to claim 6, as discussed above.
Claim 17 recites substantially similar limitations that stand rejected via the art citations and rationale applied to claim 7, as discussed above.
Claim 18 recites substantially similar limitations that stand rejected via the art citations and rationale applied to claim 8, as discussed above.
Claim 19 recites substantially similar limitations that stand rejected via the art citations and rationale applied to claim 9, as discussed above.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Lessans et al., Pub. No.: US 2025/0137675 A1 – describes that LLMs can have token limits for sizes of inputted text during training and/or runtime/inference operations.
Sivabalaselvamani, D., et al. "Artificial Intelligence in data-driven analytics for the personalized healthcare." 2021 international conference on computer communication and informatics (ICCCI). IEEE, 2021 – describes data-driven analytics.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to DARLENE GARCIA-GUERRA whose telephone number is (571) 270-3339. The examiner can normally be reached M-F 7:30a.m.-5:00p.m. EST.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Brian M. Epstein can be reached on (571) 270-5389. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/Darlene Garcia-Guerra/
Primary Examiner, Art Unit 3625
Prosecution Insights