Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
DETAILED ACTION
Status of the Application
Claims 1-20 are currently pending in this case and have been examined and addressed below. This communication is a Non-Final Rejection in response to the Claims filed on 12/23/2024.
Claim Objections
Claims 1, 16, and 17 are objected to because of the following informalities: The claims recite using a respective data standardization machine-learn models machine-learned model in the one or more machine-learned models. This appears to be repetitive and not clear if this is one model chosen from a group of models or several models. Appropriate correction is required.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected because the claimed invention is directed to an abstract idea without significantly more.
Step 1
Claims 1-15 fall within the statutory category of a process. Claim 16 falls within the statutory category of an apparatus or system. Claims 17-20 fall within the statutory category of an article of manufacture.
Step 2A, Prong One
As per Claim 1, 16, and 17, the limitations of generating standardized health data, wherein the standardized health data is generated by converting the sensor data into a standardized format usable by a query response machine-learned model; and generating a model input for the query response machine-learned model comprising the user health query and the standardized health data, under its broadest reasonable interpretation, covers performance of the limitation in the mind. The steps of generating standardized health data by converting the sensor data into a standardized format and generating a model input comprising the user health query and the standardized health data are concepts performed including observation, evaluation, judgement and opinion in the human mind. If a claim limitation, under its broadest reasonable interpretation, covers the performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claims recite an abstract idea.
Step 2A, Prong Two
The judicial exception is not integrated into a practical application because the additional elements and combination of additional elements do not impose meaningful limits on the judicial exception. In particular, Claim 1 recites the additional element of a computing system comprising a processor to execute the method. Claim 16 recites processors and a non-transitory computer-readable media that store instructions to cause the processor to execute the method. Claim 17 recites non-transitory computer-readable media that collectively store instructions to cause the computing device to perform the method. These elements are recited at a high-level of generality, such that it amounts to no more than mere instructions to apply the exception using a generic computer component. Accordingly, these additional elements do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claims also recite the use of data standardization machine-learned models for generating standardized health data. As generating standardized health data is directed to the abstract idea, the use of a machine learning model to execute the abstract idea amounts to mere instructions to apply the exception. As per MPEP 2106.05(f)(2), the use of a mathematical algorithm applied on a general purpose computer has been found by the courts to do no more than invoke computers as a tool and amount to mere instructions to apply the exception. Similarly, the claims recite the use of a respective data standardization machine-learn models machine-learned model in the one or more machine-learned models that is trained to convert the sensor data into the standardized format. As converting the sensor data into standardized format is directed to the abstract idea, the use of a machine learning model to execute the abstract idea amounts to mere instructions to apply the exception. As per MPEP 2106.05(f)(2), the use of a mathematical algorithm applied on a general purpose computer has been found by the courts to do no more than invoke computers as a tool and amount to mere instructions to apply the exception. The claims also recite the additional elements of obtaining health data for a user from a plurality of health data sources, wherein the health data comprises sensor data captured by a sensor; receiving a user health query form the user; providing the model input to the query response machine-learning model; receiving model output form the query response machine learned model; and transmitting the model output for display to the user which amounts to insignificant extra-solution activity, as in MPEP 2106.05(g), because the steps of obtaining health data, receiving user health query, and receiving model output are mere data gathering in conjunction with the abstract idea, and the steps of providing the model input to the machine-learned model and transmitting the model output for display to the user are mere data outputting in conjunction with the abstract idea, where the limitation amounts to necessary data gathering and outputting, (i.e., all uses of the recited judicial exception require such data gathering or data output). See Mayo, 566 U.S. at 79, 101 USPQ2d at 1968; OIP Techs., Inc. v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1092-93 (Fed. Cir. 2015) (presenting offers and gathering statistics amounted to mere data gathering). Because the additional elements do not impose meaningful limitations on the judicial exception, the claim is directed to an abstract idea.
Step 2B
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements when considered both individually and as an ordered combination do not amount to significantly more than the abstract idea. As discussed above with the respect to integration of the abstract idea into a practical application, the additional element of a computing system with processors and non-transitory computer-readable media that store instructions to perform the method of the invention amounts to no more than mere instructions to apply the exception using a generic computing component. The computing system is recited at a high level of generality and is recited as generic computer components by reciting one or more processors including any processing device such as a processor cord, a microprocessor, etc. (Specification, [0067, 0077]) and a non-transitory computer-readable media that store instructions such as RAM, ROM, flash memory devices, etc. (Specification [0077], [0082]), which do not add meaningful limitations to the abstract idea beyond mere instructions to apply an exception. The use of machine-learned models to carry out the abstract idea is also found to be mere instructions to apply the exception, as described above. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. The claims also include the additional elements of obtaining health data for a user from a plurality of health data sources; receiving a user health query form the user; providing the model input to the query response machine-learning model; receiving model output form the query response machine learned model; and transmitting the model output for display to the user which are elements that are well-understood, routine and conventional computer functions in the field of data management because they are claimed at a high level of generality and include receiving or transmitting data as well as storing and retrieving information from memory, which have been found to be well-understood, routine and conventional computer functions by the Court (MPEP 2106.05(d)(II)(i) 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); but see DDR Holdings, LLC v. Hotels.com, L.P., 773 F.3d 1245, 1258, 113 USPQ2d 1097, 1106 (Fed. Cir. 2014) ("Unlike the claims in Ultramercial, the claims at issue here specify how interactions with the Internet are manipulated to yield a desired result‐‐a result that overrides the routine and conventional sequence of events ordinarily triggered by the click of a hyperlink." (emphasis added), (iv) Presenting offers and gathering statistics, OIP Techs., 788 F.3d at 1362-63, 115 USPQ2d at 1092-93, and (iv) Storing and retrieving information in memory, Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015); OIP Techs., 788 F.3d at 1363, 115 USPQ2d at 1092-93). Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of the computer or improves another technology. The claims do not amount to significantly more than the underlying abstract idea.
Dependent Claims 2-15 and 18-20 add further limitations which are also directed to an abstract idea. For example, Claims 2 and 18 include the use of a computing device to produce health data which amounts to mere instructions to apply the exception because the use of a general purpose computing device to execute the abstract idea has been found in MPEP 2106.05(f)(2) to be mee instructions to apply the exception. Claims 3-6, 8-13, and 20 further specify or limit the elements of the independent claims and therefore are directed to the same abstract idea. Claim 7 includes combining the first model output and the second model output into the standardized data which can be performed using human mental evaluation, observation, judgment, and/or opinion. Therefore, this falls into the abstract grouping of a mental process. Claim 14 includes filtering the standardized health data based on the user health query which can be performed using human mental evaluation, observation, judgment, and opinion and is therefore directed to a mental process. Claim 15 describes the query response machine-learned model as generative large language machine learned model which amounts to mere instructions to apply the exception because the use of a known mathematical algorithm such as a large language model applied to the abstract idea amounts to mere instructions to apply the exception, as per MPEP 2106.059f)(2). Claim 19 includes selecting a subset of standardized health data from the standardized health data based on the user health query and generating model input comprising the subset of standardized health data and input query which amounts to a mental process for the same reasons as the independent claims. Because the additional elements do not impose meaningful limitations on the judicial exception and the additional elements are well-understood, routine and conventional functionalities in the art, the claims are directed to an abstract idea and are not patent eligible.
Claim Rejections - 35 USC § 103
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 (i.e., changing from AIA to pre-AIA ) 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.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
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.
Claims 1-3, 8-14, and 16-20 are rejected under 35 U.S.C. 103 as being unpatentable over Massei (US 2025/0125056 A1), hereinafter Massei, in view of Ambati et al. (US 2018/0293462 A1), hereinafter Ambati.
As per Claims 1, 16, and 17, Massei discloses a computing system, comprising:
one or more processors ([0327, 0329] system is executed by data processing apparatus which is a programmable processor, computer, etc.); and one or more non-transitory computer-readable media that store instructions wherein, when executed by the one or more processors, the instructions cause the one or more processors to perform operations ([0327-0328] system is implemented as computer programs encoded on computer storage medium to be executed by data processing apparatus, the computer storage medium includes computer-readable storage device), the operations comprising:
obtaining health data for a user from a plurality of health data sources ([0086-0087] health-related data for a person from a plurality of different data sources such as EMR, collected from app or device, computer files, input; platform pulls the member’s data from multiple sources, see Claim 6);
generating standardized health data ([0087] the health related data for a member is pulled from multiple sources and then converted into standardized formats, see Claim 6);
receiving a health query from the user ([0259-0260] query system for users to seek answers to health questions, user inputs a query into the system);
generating a model input for the query response machine-learned model comprising the health query and the standardized health data (See Fig. 29 where the machine learning model at 2912 receives input from the question/query input by the user related to their health and also receives input from health member account);
providing the model input to the query response machine-learned model ([0083] machine learning/AI processes that take one or more inputs for processing);
receiving model output from the query response machine-learned model ([0083] machine learning/AI processes that process input and predict, identify, or estimate one or more outputs based on the inputs, [0236] output of the model includes recommendations which are personalized insights); and
transmitting the model output for display to the user ([0288] the output is transmitted to the HCP, healthcare provider; [0088] the insights and actionable information content is presented to the member in formats such as charts, ratings, etc. [0197] reports and recommendations are provided to the user).
However, Massei may not explicitly disclose the following which is taught by Ambati: generating standardized data using one or more data standardization machine-learning models ([0028] machine learning model and associated transformers allow data to be converted to format useable by machine learning model);
wherein the health data comprises sensor data captured by a sensor ([0016] data is obtained from a plurality of sources including sensors/medical devices collecting health data such as heart rate associated with an individual), wherein the standardized health data is generated by converting sensor data into a standardized format using a respective data standardization machine-learn models machine -learned model in the one or more machine-learned models that is trained to convert the sensor data into the standardized format, wherein the standardized format is a format usable by a query response machine-learned model ([0020-0021] transformers are applied to feature values to transform the feature values into a common format which can be used for a machine learning model, [0028] machine learning model and associated transformers allow data to be converted to format useable by machine learning model, [0034] data may be from a device such as a sensor to generate sensor values , [0037-0038] data is received from databases and devices including health sensor devices including ECG device, [0071-0072] data received from devices including sensors and medical devices and the received data is transformed into a common ontology which can be used to train and input values into a machine learning model).
Therefore, it would have been obvious to a person of ordinary skill in the art before the filing of the present invention to combine the known concept of converting the sensor data into a format usable by a machine-learning model from Ambati with the known system of using standardized health data as input to a model which generates an output from Massei in order to improve accuracy of a trained machine learning model by using data which is in the same format to eliminate the issues with data from different sources in different ontologies (Ambati [0004]).
As per Claim 2, Massei and Ambati discloses the limitations of Claim 1. Massei also teaches the health data is produced by one or more user computing devices ([0005-0006] receiving health data specific to the user from one or more data sources, each source different from each other data source, Fig. 2 shows data sources, [0085] devices including computer devices).
As per Claim 3, Massei and Ambati discloses the limitations of Claim 2. Massei also teaches the one or more user computing devices comprise one or more of a smartphone, a smartwatch, a fitness band, a wearable computing device, a laptop, a tablet, or an embedded computing system ([0100] data sources include wearable devices such as smart phones, smart watches, glucose monitors, or other wearable health monitoring devices, Fig. 2 shows data sources including smart watch, smart ring, smartphones).
As per Claim 8, Massei and Ambati discloses the limitations of Claim 1. Massei also teaches the standardized format usable by the query response machine-learned model comprises natural language descriptions of one or more anomalous events within the standardized health data ([0237] data inputs from user include data within the health data that is determined to be anomalous).
As per Claim 9, Massei and Ambati discloses the limitations of Claim 8. Massei also teaches the natural language descriptions of the one or more anomalous events within the standardized health data comprises anomaly data representing an anomalous event within the one or more anomalous events that occurred and time data describing when the anomalous event occurred ([0241] text description of the insight is provided which indicates an anomalous event such as cholesterol is above normal range and provides the time data associated with when the anomalous event occurred, i.e. yesterday).
As per Claim 10, Massei and Ambati discloses the limitations of Claim 9. Massei also teaches the standardized format usable by the query response machine-learned model comprises natural language descriptions of an average range for a particular health characteristic within the health data ([0190] determining a patient health data value is deviated from the normal range is based on determining a baseline value for the patient using the average value over time for that health characteristic).
As per Claim 11, Massei and Ambati discloses the limitations of Claim 10. Massei also teaches the anomalous event for a particular health characteristic is described with regards to the average value for the particular health characteristic ([0241] an anomaly such as cholesterol is out of range is compared to the “normal” range, which Examiner interprets as with regards to the average value for the health characteristic, [0190] determining a patient health data value is deviated from the normal range is based on determining a baseline value for the patient using the average value over time for that health characteristic).
As per Claim 12, Massei and Ambati discloses the limitations of Claim 1. Massei also teaches the user health query comprises a natural language prompt including a question about one or more health characteristics of the user ([0227] initiation of query by a user includes a query input, [0259] user enters query into system including a natural language prompt such as a question about health content).
As per Claim 13, Massei and Ambati discloses the limitations of Claim 12. Massei also teaches the natural language prompt specifies a time period for the user health query ([0259] user enters query into system including a natural language prompt such as a question about health content where the question specifies a time period of the request such as since May 24).
As per Claim 14, Massei and Ambati discloses the limitations of Claim 13. Massei also teaches generating the model input for the query response machine-learned model comprising the user health query and the standardized health data comprises: filtering the standardized health data based on the user health query (Fig. 26 the health data is filtered to determine input to model to generate insights, [0240] insights are drawn from member health data and take into account user personalization which filter accordingly, see Fig. 8 where the insight tools are filtered/personalized based on “whole types” such as social data, personal data, categories, etc., Fig. 16 the whole types determines the data which is input into the MI/AI analysis and determines relevant data).
As per Claim 18, Massei and Ambati discloses the limitations of Claim 17. Massei also teaches the health data is produced by a plurality of distinct user computing devices ( [0005-0006] receiving health data specific to the user from one or more data sources, each source different from each other data source, Fig. 2 shows data sources, [0085] devices including computer devices).
As per Claim 19, Massei and Ambati discloses the limitations of Claim 17. Massei also teaches generating the model input for the query response machine-learned model comprising the user health query and the standardized health data comprises: selecting a subset of standardized health data from the standardized health data based on the user health query; and generating the model input for the query response machine-learned model comprising the subset of standardized health data and the input query (Fig. 26 the health data is filtered to determine input to model to generate insights, [0240] insights are drawn from member health data and take into account user personalization which filter accordingly, see Fig. 8 where the insight tools are filtered/personalized based on “whole types” such as social data, personal data, categories, etc., Fig. 16 the whole types determines the data which is input into the MI/AI analysis and determines relevant data).
As per Claim 20, Massei and Ambati discloses the limitations of Claim 17. Massei also teaches the standardized format usable by the query response machine-learned model comprises natural language descriptions of anomalous events within the health data ([0237] data inputs from user include data within the health data that is determined to be anomalous).
Claims 4-7 are rejected under 35 U.S.C. 103 as being unpatentable over Massei (US 2025/0125056 A1), in view of Ambati et al. (US 2018/0293462 A1), in view of Burugapalli et al. (US 2024/0370404 A1).
As per Claim 4, Massei and Ambati discloses the limitations of Claim 1. Ambati also teaches providing sensor data during a first time period from a first health data source of the one or more health data sources ([0016] data is accumulated from a plurality of sources including medical devices such as sensors of ECG device).
Therefore, it would have been obvious to a person of ordinary skill in the art before the filing of the present invention to combine the known concept of converting the sensor data into a format usable by a machine-learning model from Ambati with the known system of using standardized health data as input to a model which generates an output from Massei in order to improve accuracy of a trained machine learning model by using data which is in the same format to eliminate the issues with data from different sources in different ontologies (Ambati [0004]).
Massei and Ambati may not explicitly disclose the following which is taught by Burugapalli: generating standardized health data comprises:
providing, by the computing system, health data during a first time period from a first health data source of the one or more health data sources to a first data standardization machine-learned model trained to generate standardized health data based on data produced by the first health data source ([0022] acquire, aggregate and normalize patient information into standardized patient information, see Fig. 3/[0011]/[0050] where an AI model is used to produce the normalized data); and
receiving, from the first data standardization machine-learned model, first model output, wherein the first model output comprises standardized health data representing the health data during the first time period from the first health data source from the one or more health data sources ([0042-0043] model output of the data from a data source is normalized and output to a zone for that particular data source).
Therefore, it would have been obvious to a person of ordinary skill in the art before the filing of the present invention to combine the known concept of using machine learning model to generate standardized health data from a plurality of sources and times from Burugapalli with the known system of using standardized health data as input to a model which generates an output from Massei and Ambati in order to identify meaningful trends and insights across a plurality of health data records and systems (Burugapalli [0005]).
As per Claim 5, Massei, Ambati, and Burugapalli discloses the limitations of Claim 4. Burugapalli also teaches generating standardized health data comprises:
providing, by the computing system, health data during a second time period from a second health data source from the one or more health data sources to a second data standardization machine-learned model trained to generate standardized health data based on data produced by the second health data source ([0006] handling patient data received at different times from different health systems, [0022] acquire, aggregate and normalize patient information into standardized patient information for various data sources, see Fig. 3/[0011]/[0050] where an AI model is used to produce the normalized data, [0039] data from a specific health system is processed separately from other health systems, [0075] data platform receives updated records, i.e. received at a different time, which are normalized); and
receiving, from the second data standardization machine-learned model, second model output, wherein the second model output comprises standardized health data representing the health data during the first time period from the second health data source from the one or more health data sources ([0042-0043] model output of the data from a data source is normalized and output to a zone for that particular data source).
Therefore, it would have been obvious to a person of ordinary skill in the art before the filing of the present invention to combine the known concept of using machine learning model to generate standardized health data from a plurality of sources and times from Burugapalli with the known system of using standardized health data as input to a model which generates an output from Massei and Ambati in order to identify meaningful trends and insights across a plurality of health data records and systems (Burugapalli [0005]).
As per Claim 6, Massei, Ambati, and Burugapalli discloses the limitations of Claim 5. Burugapalli also teaches the first data standardization machine-learned model is the same model as the second data standardization machine-learned model ([0068] normalizing the data using a machine learning model, where using one machine learning model for all normalization would be the same model for each health system).
Therefore, it would have been obvious to a person of ordinary skill in the art before the filing of the present invention to combine the known concept of using machine learning model to generate standardized health data from a plurality of sources and times from Burugapalli with the known system of using standardized health data as input to a model which generates an output from Massei and Ambati in order to identify meaningful trends and insights across a plurality of health data records and systems (Burugapalli [0005]).
As per Claim 7, Massei, Ambati, and Burugapalli discloses the limitations of Claim 6. Burugapalli also teaches combining the first model output and the second model output into the standardized health data ([0044] data from each zone which has been normalized into a uniform schema from a data source is transferred to a common zone in the platform which includes aggregated health data from all of the health systems).
Therefore, it would have been obvious to a person of ordinary skill in the art before the filing of the present invention to combine the known concept of using machine learning model to generate standardized health data from a plurality of sources and times and generating aggregated data from Burugapalli with the known system of using standardized health data as input to a model which generates an output from Massei and Ambati in order to identify meaningful trends and insights across a plurality of health data records and systems (Burugapalli [0005]).
Claim 15 is rejected under 35 U.S.C. 103 as being unpatentable over Massei (US 2025/0125056 A1), in view of Ambati et al. (US 2018/0293462 A1), in view of Oermann (US 2025/0357007 A1).
As per Claim 15, Massei and Ambati discloses the limitations of Claim 1. Massei may not explicitly disclose the following which is taught by Oermann: the query response machine-learned model is a generative large language machine-learned model ([0008] use of large language models as universal predication engines for medical predictive tasks).
Therefore, it would have been obvious to a person of ordinary skill in the art before the filing of the present invention to combine the known concept of using a generative large language model to generate health predictions and insight from Oermann with the known system of using standardized health data as input to a model which generates an output from Massei and Ambati in order to provide decision support to a physician at the point of care in real-time (Oermann [0005]).
Response to Arguments
Applicant’s arguments, see Pages 7-12, “Rejections under 35 U.S.C. §101”, filed 04/09/2026 with respect to claims 1-20 have been fully considered but they are not persuasive.
Applicant argues that the present claims are not directed to an abstract idea because generating standardized data from sensor data from the plurality of sensors using machine-learned models cannot reasonably be performed in the human mind. Examiner respectfully disagrees. The claims recite generating standardized health data by converting sensor data into standardized forms. The sensor data is recited at a high level of generality such that it could be any data which is collected from a sensor. The specification describes the sensors as fitness bands, smart watches, wearable technology, smartphones, etc. (Para. 22). The format of the data is not specified in the claims and therefore, under broadest reasonable interpretation, would include any data that is collected from sensors. This could include heart rate, blood pressure, steps taken, temperature, etc. (see specification para. 27). A person can understand and analyze this type of data. Additionally, the converting and the format of the standardized health data which results are not specified and thus could be any format of data in which all of the data could be converted into. Therefore, it would be reasonable for analyzing the sensor data to generated standardized data to be performed using human mental processes. The machine-learned model is recited at a high level of generality and claimed merely as using the model to carry out the step of converting the health data, such that this amounts to mere instructions to apply the exception because the use of a mathematical algorithm to carry out the step of the abstract idea amounts to mere instructions to apply the exception, as per MPEP 2106.05(f)(2).
Applicant argues that the present claims integrate the abstract idea into a practical application because the claims include specific features that were specifically designed to achieve an improved technological result. Specifically, Applicant argues that the steps of the claim are directed to improving computer functionality. Examiner respectfully disagrees. Generating standardized data by converting the obtained sensor data into a standardized format and generating a model input comprising the user health query and the standardized health data are steps which are directed to the abstract idea and do not integrate the abstract idea into a practical application. The use of machine learning models to generate the standardized data merely uses known machine learning technology to apply the abstract idea and does not provide an improvement to technology or to a computer itself. The claims also recite providing input to the model, receiving output from the model, and transmitting the output of the model for display. These steps are merely transmitting and receiving data. There is not improvement to machine learning technology or to any other computer functionality recited in these very high-level and broad limitations. They merely are exchanging data within the system and between users. Therefore, there is no improvement recited to computer functionality or to any technology.
Applicant argues that the claims recite an inventive concept that is not well-understood, routine, and conventional in the field because the claim does not simply use a computer to standardize data and input into a model. The claim instead gathers a large amount of data generated by a sensor, converts the data into a natural language that can be input to a large language model, provide the converted data and user query to an LLM so the LLM can provide answers to the user. Examiner notes that the claims do not recite all of these details. Claim 1 (representative) only recites obtaining sensor data from a plurality of sources. The type or format of data is not recited. The claims recite generating standardized health data by converting sensor data to a standardized format, however, this does not specify a natural language or any other particular format of the data beyond “standardized”. Under BRI of the claims, this could be text data. The conversion of sensor data to text could be done by human mental processing and text data can be input into an LLM. The additional elements are recited at a high-level of generality such that they are merely providing data to a model, receiving output from the model, and transmitting the output for display. These steps are merely transmitting and receiving data, which are well-understood, routine, and conventional as determined by the courts (see MPEP 2106.05(d)(II)). Therefore, the claims do not provide an inventive concept or significantly more than the abstract idea.
Applicant’s arguments, see Pages 13-17, “Rejections under 35 U.S.C. §103”, filed 04/09/2026 with respect to claims 1-20 have been fully considered and they are persuasive. However, upon further consideration, a new grounds of rejection have been made in view of Ambati.
Conclusion
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Evangeline Barr whose telephone number is (571)272-0369. The examiner can normally be reached Monday to Friday 8:00 am to 4:00 pm.
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/EVANGELINE BARR/Primary Examiner, Art Unit 3682
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