DETAILED ACTION
In response to communication filed on 29 April 2024, this is first Office Action of the merits.
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Objections
Claims 1, 15 and 18 are objected to because of the following informalities:
Claims 1, 15 and 18 recite “one or more queries associated with health data” should read as -- one or more queries associated with the health data -- as it appears to be a typographical error and may cause antecedent basis issue.
Appropriate corrections are required.
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/apply/applying-online/eterminal-disclaimer.
Claims 1, 15 and 18 provisionally rejected on the ground of nonstatutory double patenting as being unpatentable over claim 1 of copending Application No.19/233,986 (reference application). Although the claims at issue are not identical, they are not patentably distinct from each other. The claims of co-pending application teach the limitations of the claim as shown by comparison below in bold.
This is a provisional nonstatutory double patenting rejection because the patentably indistinct claims have not in fact been patented.
Current Application
(Application# 18649282)
Co-Pending Application# 19/233,986
Difference
Claim 1: A computer-implemented method of processing health data, the computer-implemented method comprising:
Claim 1: A computer-implemented method of processing health data, the computer-implemented method comprising:
receiving, by a computing system comprising one or more processors, one or more queries associated with health data comprising a plurality of health metrics;
receiving, by a computing system comprising one or more processors, one or more queries associated with health
data comprising health information;
Claim limitations health metrics and health information are both referring to the information specific to health
determining, by the computing system, based on inputting the one or more queries into one or more machine-learned models, one or more objectives associated with the one or more queries;
determining, by the computing system, based on inputting the one or more queries into one or more machine-learned models, one or more topics of the one or more queries,
Claim limitations objectives associated and topics are basically different words applied to refer to the same concept of determining a specific information regarding the queries
determining, by the computing system, one or more statistical insights based on the one or more objectives and the health data; and
determining,… one or more analytical techniques based on the one or more topics and the one or more key metrics
Claim limitations key metrics and health data are basically different words applied to refer to
the information specific to health
generating, by the computing system, one or more key indications based on the one or more statistical insights and the health data,
determining, by the computing system, based on performing
the one or more analytical techniques on at least the
health data comprising the one or more key metrics, one or more analytical results
Claim limitations key indications and analytical results are basically different words applied to refer to the information specific to health.
Claim limitations statistical insights and analytical techniques are basically different words applied to refer to performing analysis.
wherein the one or more key indications comprise one or more visualizations associated with at least one health metric of the plurality of health metrics.
the one or more analytical techniques on at least the
health data comprising the one or more key metrics… generating, by the computing system, one or more visualizations based on the analysis.
Claim 1 is rejected on the ground of nonstatutory double patenting as being unpatentable over claim 1 of copending Application No. 19/233,986. The claims of copending Application No. 19/233,986 mentions processing health data. Although the claims at issue are not identical, they are not patentably distinct from each other because instant application recites health metrics, objectives, health data, key indications, statistical insights and copending Application No. 19/233,986 recites health information, topic, key metrics, analytical results, analytical results. Based on the analysis above, these terms are referring to the same information respectively. Therefore, it would have been obvious that the claim limitations in both the current application and copending Application No. 19/233,986 are referring to the same information. As a result, claim 1 of the current application falls entirely within the scope of claim 1 of copending Application No. 19/233,986.
Claims 15 and 18 incorporate substantively all the limitations of claim 1 in a computer-readable medium and system form and are rejected under the same rationale
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-6, 9-13 and 15-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1:
Claims 1-14 are recited as being directed to a “method”. Claims 15-17 are recited as being directed to a “computer-readable medium” and claims 18-20 are being directed to a “system”.
Regarding claim 1,
Step 2A: Prong One:
Claim 1 recites limitations:
determining,… one or more objectives associated with the one or more queries;
determining,… one or more statistical insights based on the one or more objectives and the health data; and
generating,… one or more key indications based on the one or more statistical insights and the health data, wherein the one or more key indications comprise one or more visualizations associated with at least one health metric of the plurality of health metrics.
These claim limitations appear to be reciting a “Mental Process” including evaluation.
A human mind can mentally evaluate to determine one or more objectives associated with the one or more queries. A human being can apply evaluation to determine statistical insights based on the one or more objectives and the health data. A human mind can evaluate to generate one or more key indications based on the one or more statistical insights and health data using a pen and paper.
Step 2A - Prong Two:
The abstract idea does not appear to be integrated into a practical application with the recitation of the following claim language.
Claim 1 further recites limitations:
A computer-implemented method of processing health data, the computer-implemented method comprising:
… by a computing system comprising one or more processors,…
… by the computing system,…
These claim limitations appear to be to merely add the use of generic computer components which are merely executing the abstract idea within a computer device (see MPEP 2106.05(b)) and do not appear to integrate the abstract idea into a particular practical application.
Claim 1 further recites limitations:
receiving,.. one or more queries associated with health data comprising a plurality of health metrics;
These claim limitations as a whole have been identified as insignificant extra-solution activity. Per MPEP 2106.05(g) “An example of pre-solution activity is a step of gathering data for use in a claimed process, e.g., a step of obtaining information about credit card transactions, which is recited as part of a claimed process of analyzing and manipulating the gathered information by a series of steps in order to detect whether the transactions were fraudulent”. Similarly the claim limitations as a whole above appear to be gathering data being received and do not appear to integrate the abstract idea into a practical application.
Claim 1 further recites limitations:
… based on inputting the one or more queries into one or more machine-learned models,…
These claim limitations appear to be reciting machine learning models. Machine learning models do not integrate into a practical application since they are conventional computer functions. Per MPEP (2106.05 (b) (I)), computer that applies a judicial exception, such as an abstract idea, by use of conventional computer functions does not qualify as a particular machine. As a result these claim limitations do not apply to a practical application.
Step 2B:
The abstract idea does not appear to be significantly more with the recitation of the following claim language.
Claim 1 further recites limitations:
A computer-implemented method of processing health data, the computer-implemented method comprising:
… by a computing system comprising one or more processors,…
… by the computing system,…
These claim limitations appear to be to merely add the use of generic computer components which are merely executing the abstract idea within a computer device (see MPEP 2106.05(b)) and do not appear to amount to significantly more.
Claim 1 further recites limitations:
receiving,.. one or more queries associated with health data comprising a plurality of health metrics;
These claim limitations as a whole have been identified as insignificant extra-solution activity. Per MPEP 2106.05(g) “An example of pre-solution activity is a step of gathering data for use in a claimed process, e.g., a step of obtaining information about credit card transactions, which is recited as part of a claimed process of analyzing and manipulating the gathered information by a series of steps in order to detect whether the transactions were fraudulent”. Similarly the claim limitations as a whole above appear to be gathering data in terms of requests, data and content being received and appear to be conventional computer functionality. Also, MPEP 2106.05(d)(II) has identified “Receiving or transmitting data over a network, e.g., using the Internet to gather data” as conventional computer technology. Similarly, the claim limitations identified above appear to be receiving data. As a result, these claim limitations as a whole do not appear to amount to significantly more than the abstract idea itself.
Claim 1 further recites limitations:
… based on inputting the one or more queries into one or more machine-learned models,…
These claim limitations appear to be reciting queries inputted in machine learning models. Machine learning models do not amount to significantly more since they are conventional computer functions. Per MPEP (2106.05 (b) (I)), computer that applies a judicial exception, such as an abstract idea, by use of conventional computer functions does not qualify as a particular machine. As a result, these limitations are merely executing the abstract idea without being significantly more than the abstract. These references provide evidence that inputting queries into machine learning models are conventional computer technology: Buda et al. (US 2018/0157978 A1 – [0040]), Guo et al. (US 2019/0114343 A1 – [0238]), Schueau et al. (US 2019/0188285 – [0072]), Kathirvel et al. (US 2020/0019633 A1 – [0054]) and Wahl et al. (US 2020/0311680 A1 – [0383]).
Claims 15 and 18 incorporate substantively all the limitations of claim 1 in a computer-readable form (wherein claim limitations - One or more tangible non-transitory computer-readable media storing computer-readable instructions that when executed by one or more processors cause the one or more processors to perform operations, the operations comprising: in Step 2A: Prong Two as these claim limitations appear to be to merely add the use of generic computer components which are merely executing the abstract idea within a computer device (see MPEP 2106.05(b)) and do not appear to integrate the abstract idea into a particular practical application. These claim limitations in Step 2B appear to be to merely add the use of generic computer components which are merely executing the abstract idea within a computer device (see MPEP 2106.05(b)) and system form (wherein claim limitations - A computing system comprising: one or more processors; one or more non-transitory computer-readable media storing instructions that when executed by the one or more processors cause the one or more processors to perform operations comprising: in Step 2A: Prong Two as these claim limitations appear to be to merely add the use of generic computer components which are merely executing the abstract idea within a computer device (see MPEP 2106.05(b)) and do not appear to integrate the abstract idea into a particular practical application. These claim limitations in Step 2B appear to be to merely add the use of generic computer components which are merely executing the abstract idea within a computer device (see MPEP 2106.05(b)) and are rejected under the same rationale.
Regarding claim 2,
Step 2A: Prong One:
Claim 2 recites limitations:
…. to determine the one or more objectives based on identifying health-related information in the one or more queries.
These claim limitations appear to be reciting a “Mental Process” including evaluation.
A human mind can mentally evaluate to determine one or more objectives based on identifying health-related information in the queries.
Step 2A - Prong Two:
The abstract idea does not appear to be integrated into a practical application with the recitation of the following claim language.
Claim 2 further recites limitations:
wherein the one or more machine-learned models comprise one or more large language models (LLMs) that are configured,…
These claim limitations appear to be reciting machine learning models. Large language models do not integrate into a practical application since they are conventional computer functions. Per MPEP (2106.05 (b) (I)), computer that applies a judicial exception, such as an abstract idea, by use of conventional computer functions does not qualify as a particular machine. As a result these claim limitations do not apply to a practical application.
Step 2B:
The abstract idea does not appear to be significantly more with the recitation of the following claim language.
Claim 2 further recites limitations:
wherein the one or more machine-learned models comprise one or more large language models (LLMs) that are configured…
These claim limitations appear to be reciting machine learning models. Large language models do not amount to significantly more since they are conventional computer functions. Per MPEP (2106.05 (b) (I)), computer that applies a judicial exception, such as an abstract idea, by use of conventional computer functions does not qualify as a particular machine. As a result, these limitations are merely executing the abstract idea without being significantly more than the abstract. These references provide evidence that large language models are conventional computer technology: Kumar et al. (US 11,855,860 B1 – [Abstract]), Dyngosz et al. (US 12,044,116 B1 – [Abstract]), Lo et al. (US 12,061,970 B1 – [Abstract]), Nouri et al. (US 2024/0038226 A1 – [Abstract]) and Mace et al. (US 2024/0070270 A1 – [Abstract]).
Claims 16 and 19 incorporate substantively all the limitations of claim 2 in a computer-readable medium and system form and are rejected under the same rationale.
Regarding claims 3-6, 9-11, 13, 17 and 20,
Claim 3 further recites limitations:
wherein the one or more objectives comprise one or more statistical analysis techniques to perform on one or more health metrics selected from the plurality of health metrics.
Claim 4 further recites limitations:
wherein the one or more statistical insights comprise one or more relationships between at least two health metrics of the plurality of health metrics.
Claim 5 further recites limitations:
wherein the one or more key indications comprise a description of the one or more relationships between at least two health metrics of the plurality of health metrics.
Claim 6 further recites limitations:
wherein the one or more key indications comprise a scatter plot that indicates one or more relationships between at least two health metrics of the plurality of health metrics.
Claim 9 further recites limitations:
generating… based on inputting the one or more statistical insights into the one or more machine-learned models, the one or more key indications
Claim 10 further recites limitations:
determining,…. a headline that summarizes the one or more statistical insights; and
generating,… the headline in a prominent location relative to the one or more visualizations.
Claim 11 further recites limitations:
determining,… a recommendation that corresponds to at least one statistical insight of the one or more statistical insights, wherein the one or more key indications comprise the recommendation.
Claim 13 further recites limitations:
wherein the plurality of health metrics comprise a plurality of heart rates at a plurality of time intervals, a plurality of body mass values at a plurality of time intervals, a plurality of sleeping hours associated with a plurality of time intervals, or a number of steps associated with a plurality of time intervals.
These claim limitations appear to be reciting a “Mental Process” including evaluation and observation.
A human mind can mentally evaluate to perform statistical analysis to perform health metrics. A human being can apply evaluation to determine relationships between at least two health metrics. A human mind can evaluate to determine description of the one or more relationships between at least two health metrics. A human mind can mentally evaluate to perform scatter plot on a paper to reflect relationships between health metrics. A person can mentally generate indications based on the input in machine learning models. A human being can apply evaluation to determine a headline that summarizes the one or more statistical insights; and generating the headline in a prominent location relative to the one or more visualizations. A human mind can evaluate to determine recommendations. A human being can apply observation to determine that the plurality of health metrics comprise a plurality of heart rates at a plurality of time intervals, a plurality of body mass values at a plurality of time intervals, a plurality of sleeping hours associated with a plurality of time intervals, or a number of steps associated with a plurality of time intervals.
Claims 17 and 20 incorporate substantively all the limitations of claim 3 in a computer-readable medium and system form and are rejected under the same rationale.
Regarding claim 12,
Step 2A: Prong One:
Claim 12 recites limitations:
generating,… one or more secondary indications that are based on the one or more key indications and have a higher level of granularity than the one or more key indications.
These claim limitations appear to be reciting a “Mental Process” including evaluation.
A human mind can mentally evaluate to determine one or more secondary indications that are based on the one or more key indications and have a higher level of granularity than the one or more key indications.
Step 2A - Prong Two:
The abstract idea does not appear to be integrated into a practical application with the recitation of the following claim language.
Claim 12 further recites limitations:
… by the computing system,…
These claim limitations appear to be to merely add the use of generic computer components which are merely executing the abstract idea within a computer device (see MPEP 2106.05(b)) and do not appear to integrate the abstract idea into a particular practical application.
Claim 12 further recites limitations:
further comprising: in response to receiving a request for additional information;…
These claim limitations as a whole have been identified as insignificant extra-solution activity. Per MPEP 2106.05(g) “An example of pre-solution activity is a step of gathering data for use in a claimed process, e.g., a step of obtaining information about credit card transactions, which is recited as part of a claimed process of analyzing and manipulating the gathered information by a series of steps in order to detect whether the transactions were fraudulent”. Similarly the claim limitations as a whole above appear to be gathering data being received and do not appear to integrate the abstract idea into a practical application.
Step 2B:
The abstract idea does not appear to be significantly more with the recitation of the following claim language.
Claim 12 further recites limitations:
… by the computing system,…
These claim limitations appear to be to merely add the use of generic computer components which are merely executing the abstract idea within a computer device (see MPEP 2106.05(b)) and do not appear to amount to significantly more.
Claim 12 further recites limitations:
further comprising: in response to receiving a request for additional information;…
These claim limitations as a whole have been identified as insignificant extra-solution activity. Per MPEP 2106.05(g) “An example of pre-solution activity is a step of gathering data for use in a claimed process, e.g., a step of obtaining information about credit card transactions, which is recited as part of a claimed process of analyzing and manipulating the gathered information by a series of steps in order to detect whether the transactions were fraudulent”. Similarly the claim limitations as a whole above appear to be gathering data in terms of requests, data and content being received and appear to be conventional computer functionality. Also, MPEP 2106.05(d)(II) has identified “Receiving or transmitting data over a network, e.g., using the Internet to gather data” as conventional computer technology. Similarly, the claim limitations identified above appear to be receiving data. As a result, these claim limitations as a whole do not appear to amount to significantly more than the abstract idea itself.
Claim Rejections - 35 USC § 103
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.
Claims 1, 3, 4, 6, 10, 11, 15, 17, 18 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Sundararaman et al. (US 2020/0050949 A1, hereinafter “Sundar”) in view of Baskaran et al. (US 11,715,051 B1, hereinafter “Baskaran”).
Regarding claim 1, Sundar teaches
A computer-implemented method of processing health data, the computer-implemented method comprising: (see Sundar, [0014] “The healthcare industry is one of many industries that produces an enormous amount of data, including healthcare related data in forms of medical records, hospital records, primary care physician records, billing records… and/or the like”; [0087] “systems and/or methods described herein”).
receiving, by a computing system comprising one or more processors, one or more queries associated with health data (see Sundar, [0023] “the user interface may enable a user to submit a query (e.g., in a natural language format) relating to a data file of interest (e.g., a target data file) that is managed by the healthcare data platform”; [0102] “components of a device 400… device 400 may include a bus 410, a processor 420, a memory 430”) comprising a plurality of health metrics; (see Sundar, [0053] “the healthcare data platform may derive, determine, generate, calculate, or compute metrics (e.g., healthcare metrics) based on the grouped data elements… the metrics may be determined by a metric derivation engine of the healthcare data platform, which accesses stored metric definitions and computes the metrics based on the stored definitions”).
determining, by the computing system, based on inputting the one or more queries into one or more machine-learned models, one or more objectives associated with the one or more queries; (see Sundar, [0023] “The digital assistant platform may receive the query, extract one or more keywords from the query, and use the keywords to identify an intent classification and/or an entity associated with the query. The digital assistant platform may extract the keywords from the query using a natural language processing model, and identify the intent classification and/or the entity using a machine learning model”; [0102] “components of a device 400… device 400 may include a bus 410, a processor 420, a memory 430”).
determining, by the computing system, one or more statistical insights based on the one or more objectives and the health data; and (see Sundar, [0069] “the natural language processing model may obtain data identifying, in natural language, a query by the user requesting analytical information in connection with a standardized data set, and may parse the data to identify the keywords, the intent classifications, the entities, and/or the like… based on a query being “What are the metrics used to measure patient wait times?,” the natural language processing model may determine that “metrics” and “patient wait times” are keywords of the query that are related to the analytical information requested by the user… the natural language processing model may determine that “metrics” relate to an intent classification associated with the query, and/or determine that “patient wait times” relate to an entity associated with the query. In such cases, the natural language processing model may determine that a natural language text corresponds to a characteristic based on data relating to other analytical information, data identifying characteristics of analytical information”; [0044]-[0047] “the data sets undergo intelligent processing based on the types of data elements and automate KPI calculation depending on the subject area of the client providing extended insights to the clients… standardized data sets for various subject areas may be created for use in calculating various metrics specific to a given subject area”).
… based on the one or more statistical insights and the health data,… at least one health metric of the plurality of health metrics (see Sundar, [0069] “the natural language processing model may obtain data identifying, in natural language, a query by the user requesting analytical information in connection with a standardized data set, and may parse the data to identify the keywords, the intent classifications, the entities, and/or the like… based on a query being “What are the metrics used to measure patient wait times?,” the natural language processing model may determine that “metrics” and “patient wait times” are keywords of the query that are related to the analytical information requested by the user… the natural language processing model may determine that “metrics” relate to an intent classification associated with the query, and/or determine that “patient wait times” relate to an entity associated with the query. In such cases, the natural language processing model may determine that a natural language text corresponds to a characteristic based on data relating to other analytical information, data identifying characteristics of analytical information”; [0044]-[0047] “the data sets undergo intelligent processing based on the types of data elements and automate KPI calculation depending on the subject area of the client providing extended insights to the clients… standardized data sets for various subject areas may be created for use in calculating various metrics specific to a given subject area”).
Sundar does not explicitly teach generating, by the computing system, one or more key indications; wherein the one or more key indications comprise one or more visualizations associated with at least one health metric.
However, Baskaran discloses set of metrics and teaches
generating, by the computing system, one or more key indications… wherein the one or more key indications comprise one or more visualizations associated with metrics (see Baskaran, [col 171 lines 4-8] “The insights application 4212 requests the events and metrics from the DIQS 108 and causes visual representations, also referred to as visualizations, of one or more of the metrics, key performance indicators”; [col 175 lines 6-17] “The insights application 4212 can process the returned information and can cause a display of visual representations of, for example, the metrics, log files, key performance indicators… the insights application processes the events and metrics to provide the visual representations… processes the events and metrics and sends the processed events to the application 4212 to provide the visual representations”; [see col 10 lines 31-32] “comprises one or more computing devices”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include the functionality of key indications comprising visualization, statistical analysis, relationships, scatter plot, trend along with their types, summaries and recommendation, as being disclosed and taught by Baskaran, in the system taught by Sundar to yield the predictable results of effectively storing and analyzing large volumes of data (see Baskaran, [col 160 lines 23-37] “the data intake and query platform provides various schemas, dashboards and visualizations that make it easy for developers to create applications to provide additional capabilities… This differs significantly from conventional IT monitoring systems that lack the infrastructure to effectively store and analyze large volumes of service-related events”).
Claims 15 and 18 incorporate substantively all the limitations of claim 1 in a computer-readable medium form (see Sundar, [0107] “Device 400 may perform these processes based on to processor 420 executing software instructions stored by a non-transitory computer-readable medium, such as memory 430 and/or storage component 440”) and system form (see Sundar, [0107] “Device 400 may perform these processes based on to processor 420 executing software instructions stored by a non-transitory computer-readable medium, such as memory 430 and/or storage component 440”) and are rejected under the same rationale.
Regarding claim 3, the proposed combination of Sundar and Baskaran teaches
wherein the one or more objectives comprise (see Sundar, [0023] “The digital assistant platform may receive the query, extract one or more keywords from the query, and use the keywords to identify an intent classification and/or an entity associated with the query. The digital assistant platform may extract the keywords from the query using a natural language processing model, and identify the intent classification and/or the entity using a machine learning model”; [0102] “components of a device 400… device 400 may include a bus 410, a processor 420, a memory 430”) one or more statistical analysis techniques to (see Baskaran, [col 147 line 52] “to perform statistical analysis on values extracted”) perform on one or more health metrics selected from the plurality of health metrics (see Sundar, [0014] “The various healthcare clients may employ one or more data analytics tools to access and manipulate the enormous amount of data available from the EDI, for example, to assess trends, measure key performance indicators (KPIs), calculate metrics for driving decisions to deliver better medical care”; [0053] “may generate a plurality of values based on mapping the data elements to the plurality of functions and may determine one or more metrics based on combining the plurality of values according to a metric definition”). The motivation for the proposed combination is maintained.
Claims 17 and 20 incorporate substantively all the limitations of claim 3 in a computer-readable medium and system form and are rejected under the same rationale.
Regarding claim 4, the proposed combination of Sundar and Baskaran teaches
wherein the one or more statistical insights comprise (see Sundar, [0069] “the natural language processing model may obtain data identifying, in natural language, a query by the user requesting analytical information in connection with a standardized data set, and may parse the data to identify the keywords, the intent classifications, the entities, and/or the like… based on a query being “What are the metrics used to measure patient wait times?,” the natural language processing model may determine that “metrics” and “patient wait times” are keywords of the query that are related to the analytical information requested by the user… the natural language processing model may determine that “metrics” relate to an intent classification associated with the query, and/or determine that “patient wait times” relate to an entity associated with the query. In such cases, the natural language processing model may determine that a natural language text corresponds to a characteristic based on data relating to other analytical information, data identifying characteristics of analytical information”; [0044]-[0047] “the data sets undergo intelligent processing based on the types of data elements and automate KPI calculation depending on the subject area of the client providing extended insights to the clients… standardized data sets for various subject areas may be created for use in calculating various metrics specific to a given subject area”) one or more relationships between (see Baskaran, [col 61 lines 11-13] “identify associations or relationships between a particular dataset and… other datasets”) at least two health metrics of the plurality of health metrics (see Sundar, [0014] “calculate metrics for driving decisions to deliver better medical care”; [0053] “may generate a plurality of values based on mapping the data elements to the plurality of functions and may determine one or more metrics based on combining the plurality of values according to a metric definition” – there are plurality of metrics). The motivation for the proposed combination is maintained.
Regarding claim 6, the proposed combination of Sundar and Baskaran teaches
wherein the one or more key indications (see Baskaran, [col 171 lines 4-8] “The insights application 4212 requests the events and metrics from the DIQS 108 and causes visual representations, also referred to as visualizations, of one or more of the metrics, key performance indicators”; [col 175 lines 6-17] “The insights application 4212 can process the returned information and can cause a display of visual representations of, for example, the metrics, log files, key performance indicators… the insights application processes the events and metrics to provide the visual representations… processes the events and metrics and sends the processed events to the application 4212 to provide the visual representations”) comprise a scatter plot that indicates (see Baskaran, [col 148 lines 8-10] “The reporting application allows the user to select a visualization of the statistics in a graph (e.g., bar chart, scatter plot”) one or more relationships between datasets (see Baskaran, [col 61 lines 11-13] “identify associations or relationships between a particular dataset and… other datasets”) at least two health metrics of the plurality of health metrics (see Sundar, [0014] “calculate metrics for driving decisions to deliver better medical care”; [0053] “may generate a plurality of values based on mapping the data elements to the plurality of functions and may determine one or more metrics based on combining the plurality of values according to a metric definition” – there are plurality of metrics). The motivation for the proposed combination is maintained.
Regarding claim 10, the proposed combination of Sundar and Baskaran teaches
wherein the generating, by the computing system, one or more key indications (see Baskaran, [col 171 lines 4-8] “The insights application 4212 requests the events and metrics from the DIQS 108 and causes visual representations, also referred to as visualizations, of one or more of the metrics, key performance indicators”; [col 175 lines 6-17] “The insights application 4212 can process the returned information and can cause a display of visual representations of, for example, the metrics, log files, key performance indicators… the insights application processes the events and metrics to provide the visual representations… processes the events and metrics and sends the processed events to the application 4212 to provide the visual representations”; [see col 10 lines 31-32] “comprises one or more computing devices”) based on the one or more statistical insights and the health data comprises: (see Sundar, [0069] “the natural language processing model may obtain data identifying, in natural language, a query by the user requesting analytical information in connection with a standardized data set, and may parse the data to identify the keywords, the intent classifications, the entities, and/or the like… based on a query being “What are the metrics used to measure patient wait times?,” the natural language processing model may determine that “metrics” and “patient wait times” are keywords of the query that are related to the analytical information requested by the user… the natural language processing model may determine that “metrics” relate to an intent classification associated with the query, and/or determine that “patient wait times” relate to an entity associated with the query. In such cases, the natural language processing model may determine that a natural language text corresponds to a characteristic based on data relating to other analytical information, data identifying characteristics of analytical information”; [0044]-[0047] “the data sets undergo intelligent processing based on the types of data elements and automate KPI calculation depending on the subject area of the client providing extended insights to the clients… standardized data sets for various subject areas may be created for use in calculating various metrics specific to a given subject area”).
determining, by the computing system, a headline that summarizes (see Baskaran, [col 137 lines 12-16] “calculate statistics, reorder the results, create an alert, create summary of the results, or perform some type of aggregation function”; [col 155 lines 57-67] “the summarization engine periodically generates a summary covering data obtained during a latest non-overlapping time period… where the query seeks events meeting a specified criteria, a summary for the time period may only include events within the time period that meet the specified criteria. Similarly, if the query seeks statistics calculated from the events, such as the number of events that match the specified criteria, then the summary for the time period includes the number of events in the period that match the specified criteria”; [see col 10 lines 31-32] “comprises one or more computing devices”) the one or more statistical insights; and (see Sundar, [0069] “the natural language processing model may obtain data identifying, in natural language, a query by the user requesting analytical information in connection with a standardized data set, and may parse the data to identify the keywords, the intent classifications, the entities, and/or the like… based on a query being “What are the metrics used to measure patient wait times?,” the natural language processing model may determine that “metrics” and “patient wait times” are keywords of the query that are related to the analytical information requested by the user… the natural language processing model may determine that “metrics” relate to an intent classification associated with the query, and/or determine that “patient wait times” relate to an entity associated with the query. In such cases, the natural language processing model may determine that a natural language text corresponds to a characteristic based on data relating to other analytical information, data identifying characteristics of analytical information”; [0044]-[0047] “the data sets undergo intelligent processing based on the types of data elements and automate KPI calculation depending on the subject area of the client providing extended insights to the clients… standardized data sets for various subject areas may be created for use in calculating various metrics specific to a given subject area”).
generating, by the computing system, the headline (see Baskaran, [col 137 lines 12-16] “calculate statistics, reorder the results, create an alert, create summary of the results, or perform some type of aggregation function”; [col 155 lines 57-67] “the summarization engine periodically generates a summary covering data obtained during a latest non-overlapping time period… where the query seeks events meeting a specified criteria, a summary for the time period may only include events within the time period that meet the specified criteria. Similarly, if the query seeks statistics calculated from the events, such as the number of events that match the specified criteria, then the summary for the time period includes the number of events in the period that match the specified criteria”; [see col 10 lines 31-32] “comprises one or more computing devices”) in a prominent location relative to the one or more visualizations (see Baskaran, [col 162 line 59 – col 163 line 3] “The IT monitoring application provides a visualization for incident review showing detailed information for notable events. The incident review visualization may also show summary information for the notable events over a time frame, such as an indication of the number of notable events at each of a number of severity levels. The severity level display may be presented as a rainbow chart with the warmest color associated with the highest severity classification. The incident review visualization may also show summary information for the notable events over a time frame, such as the number of notable events occurring within segments of the time frame”). The motivation for the proposed combination is maintained.
Regarding claim 11, the proposed combination of Sundar and Baskaran teaches
wherein the generating, by the computing system, one or more key indications based on the metric (see Baskaran, [col 171 lines 4-8] “The insights application 4212 requests the events and metrics from the DIQS 108 and causes visual representations, also referred to as visualizations, of one or more of the metrics, key performance indicators”; [col 175 lines 6-17] “The insights application 4212 can process the returned information and can cause a display of visual representations of, for example, the metrics, log files, key performance indicators… the insights application processes the events and metrics to provide the visual representations… processes the events and metrics and sends the processed events to the application 4212 to provide the visual representations”; [see col 10 lines 31-32] “comprises one or more computing devices”) the one or more statistical insights and the health data comprises: (see Sundar, [0069] “the natural language processing model may obtain data identifying, in natural language, a query by the user requesting analytical information in connection with a standardized data set, and may parse the data to identify the keywords, the intent classifications, the entities, and/or the like… based on a query being “What are the metrics used to measure patient wait times?,” the natural language processing model may determine that “metrics” and “patient wait times” are keywords of the query that are related to the analytical information requested by the user… the natural language processing model may determine that “metrics” relate to an intent classification associated with the query, and/or determine that “patient wait times” relate to an entity associated with the query. In such cases, the natural language processing model may determine that a natural language text corresponds to a characteristic based on data relating to other analytical information, data identifying characteristics of analytical information”; [0044]-[0047] “the data sets undergo intelligent processing based on the types of data elements and automate KPI calculation depending on the subject area of the client providing extended insights to the clients… standardized data sets for various subject areas may be created for use in calculating various metrics specific to a given subject area”).
determining, by the computing system, a recommendation that corresponds to metrics (see Baskaran, [col 189 lines 16-23] “Recommendation engine 5113 can receive, as input, cloud instance metrics of a cloud instance from metrics data store 5122 and generate a score (classification)… the recommendations engine 5113 may generate scores and/or recommendations using ML model 5240 based on metrics generated during a determined time interval”) at least one statistical insight of the one or more statistical insights, (see Sundar, [0069] “the natural language processing model may obtain data identifying, in natural language, a query by the user requesting analytical information in connection with a standardized data set, and may parse the data to identify the keywords, the intent classifications, the entities, and/or the like… based on a query being “What are the metrics used to measure patient wait times?,” the natural language processing model may determine that “metrics” and “patient wait times” are keywords of the query that are related to the analytical information requested by the user… the natural language processing model may determine that “metrics” relate to an intent classification associated with the query, and/or determine that “patient wait times” relate to an entity associated with the query. In such cases, the natural language processing model may determine that a natural language text corresponds to a characteristic based on data relating to other analytical information, data identifying characteristics of analytical information”; [0044]-[0047] “the data sets undergo intelligent processing based on the types of data elements and automate KPI calculation depending on the subject area of the client providing extended insights to the clients… standardized data sets for various subject areas may be created for use in calculating various metrics specific to a given subject area”) wherein the one or more key indications comprise (see Baskaran, [col 171 lines 4-8] “The insights application 4212 requests the events and metrics from the DIQS 108 and causes visual representations, also referred to as visualizations, of one or more of the metrics, key performance indicators”; [col 175 lines 6-17] “The insights application 4212 can process the returned information and can cause a display of visual representations of, for example, the metrics, log files, key performance indicators… the insights application processes the events and metrics to provide the visual representations… processes the events and metrics and sends the processed events to the application 4212 to provide the visual representations”) the recommendation (see Baskaran, [col 180 line 66 – col 181 line 5] “cause display of visual representation of insights… such insights may include… recommendations”). The motivation for the proposed combination is maintained.
Claims 2, 5, 7-9, 14, 16 and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Sundar in view of Baskaran further in view of Lotti et al. (US 12,118,821 B1, hereinafter “Lotti”).
Regarding claim 2, the proposed combination of Sundar and Baskaran teaches
wherein the one or more machine-learned models comprise… (see Sundar, [0046] “the healthcare data platform may intelligently group the data elements based on machine learning models”) that are configured to determine the one or more objectives (see Sundar, [0023] “The digital assistant platform may receive the query, extract one or more keywords from the query, and use the keywords to identify an intent classification and/or an entity associated with the query. The digital assistant platform may extract the keywords from the query using a natural language processing model, and identify the intent classification and/or the entity using a machine learning model”; [0102] “components of a device 400… device 400 may include a bus 410, a processor 420, a memory 430”) based on identifying health-related information in the one or more queries (see Sundar, [0069] “the natural language processing model may obtain data identifying, in natural language, a query by the user requesting analytical information in connection with a standardized data set, and may parse the data to identify the keywords… based on a query being "What are the metrics used to measure patient wait times?," the natural language processing model may determine that "metrics" and "patient wait times" are keywords of the query that are related to the analytical information requested by the user”).
The proposed combination of Sundar and Baskaran does not explicitly teach machine-learned models comprise one or more large language models (LLMs).
However, Lotti discloses machine learning models and teaches
machine learning model can be one or more large language models (LLMs) (see Lotti, [col 16 lines 16-17] “the generative machine learning model 170 can be a generative large language model (LLM)”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include the functionality of large language model, description, audio indication, inputting into machine learning model and text-based description, as being disclosed and taught by Lotti, in the system taught by the proposed combination of Sundar and Baskaran to yield the predictable results of improved image processing and prompt generation for effective searching and filtering of information (see Lotti, [col 7 lines 60-63] “the improved image processing and prompt generation can be used, for example, for searching and/or filtering with respect to a database”).
Claims 16 and 19 incorporate substantively all the limitations of claim 2 in a computer-readable medium and system form and are rejected under the same rationale.
Regarding claim 5, the proposed combination of Sundar and Baskaran teaches
wherein the one or more key indications comprise processed metrics and events… (see Baskaran, [col 171 lines 4-8] “The insights application 4212 requests the events and metrics from the DIQS 108 and causes visual representations, also referred to as visualizations, of one or more of the metrics, key performance indicators”; [col 175 lines 6-17] “The insights application 4212 can process the returned information and can cause a display of visual representations of, for example, the metrics, log files, key performance indicators… the insights application processes the events and metrics to provide the visual representations… processes the events and metrics and sends the processed events to the application 4212 to provide the visual representations”) the one or more relationships between datasets (see Baskaran, [col 61 lines 11-13] “identify associations or relationships between a particular dataset and… other datasets”) at least two health metrics of the plurality of health metrics (see Sundar, [0014] “calculate metrics for driving decisions to deliver better medical care”; [0053] “may generate a plurality of values based on mapping the data elements to the plurality of functions and may determine one or more metrics based on combining the plurality of values according to a metric definition” – there are plurality of metrics)
The proposed combination of Sundar and Baskaran does not explicitly teach a description of the one or more relationships.
However, Lotti discloses machine learning models and teaches
a description of relationship data (see Lotti, [col 26 lines 43-44] “a description of the 2D or 3D relationship data”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include the functionality of large language model, description, audio indication, inputting into machine learning model and text-based description, as being disclosed and taught by Lotti, in the system taught by the proposed combination of Sundar and Baskaran to yield the predictable results of improved image processing and prompt generation for effective searching and filtering of information (see Lotti, [col 7 lines 60-63] “the improved image processing and prompt generation can be used, for example, for searching and/or filtering with respect to a database”).
Regarding claim 7, the proposed combination of Sundar and Baskaran teaches
wherein the one or more statistical insights (see Sundar, [0069] “the natural language processing model may obtain data identifying, in natural language, a query by the user requesting analytical information in connection with a standardized data set, and may parse the data to identify the keywords, the intent classifications, the entities, and/or the like… based on a query being “What are the metrics used to measure patient wait times?,” the natural language processing model may determine that “metrics” and “patient wait times” are keywords of the query that are related to the analytical information requested by the user… the natural language processing model may determine that “metrics” relate to an intent classification associated with the query, and/or determine that “patient wait times” relate to an entity associated with the query. In such cases, the natural language processing model may determine that a natural language text corresponds to a characteristic based on data relating to other analytical information, data identifying characteristics of analytical information”; [0044]-[0047] “the data sets undergo intelligent processing based on the types of data elements and automate KPI calculation depending on the subject area of the client providing extended insights to the clients… standardized data sets for various subject areas may be created for use in calculating various metrics specific to a given subject area”) comprise a trend associated with (see Baskaran, [col 169 lines 6-10] “The trends can inform the users of highlights, and lowlights… a user interface displaying visual representations of the trends can include an interactive visual representation”) the at least one health metric of the plurality of health metrics, and (see Sundar, [0053] “the healthcare data platform may derive, determine, generate, calculate, or compute metrics (e.g., healthcare metrics) based on the grouped data elements… the metrics may be determined by a metric derivation engine of the healthcare data platform, which accesses stored metric definitions and computes the metrics based on the stored definitions”) wherein the one or more key indications comprise… (see Baskaran, [col 171 lines 4-8] “The insights application 4212 requests the events and metrics from the DIQS 108 and causes visual representations, also referred to as visualizations, of one or more of the metrics, key performance indicators”; [col 175 lines 6-17] “The insights application 4212 can process the returned information and can cause a display of visual representations of, for example, the metrics, log files, key performance indicators… the insights application processes the events and metrics to provide the visual representations… processes the events and metrics and sends the processed events to the application 4212 to provide the visual representations”) based on a type of the trend (see Baskaran, [col 169 lines 6-7] “The trends can inform the users of highlights, and lowlights” – highlights and lowlights are interpreted as types of trends).
The proposed combination of Sundar and Baskaran does not explicitly teach one or more audio indications.
However, Lotti discloses machine learning models and teaches
one or more audio indications (see Lotti, [col 32 lines 19-20] “converts the textual information to audio data the can be provided for presentation via a GUI”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include the functionality of large language model, description, audio indication, inputting into machine learning model and text-based description, as being disclosed and taught by Lotti, in the system taught by the proposed combination of Sundar and Baskaran to yield the predictable results of improved image processing and prompt generation for effective searching and filtering of information (see Lotti, [col 7 lines 60-63] “the improved image processing and prompt generation can be used, for example, for searching and/or filtering with respect to a database”).
Regarding claim 8, the proposed combination of Sundar, Baskaran and Lotti teaches
wherein the one or more audio indications comprise a first audio indication (see Lotti, [col 32 lines 19-20] “converts the textual information to audio data the can be provided for presentation via a GUI”) based on the type of the trend being an upward trend, (see Baskaran, [col 169 lines 6-7] “The trends can inform the users of highlights”). The motivation for the proposed combination is maintained.
Regarding claim 9, the proposed combination of Sundar and Baskaran teaches
wherein the generating, by the computing system, one or more key indications based on the metric (see Baskaran, [col 171 lines 4-8] “The insights application 4212 requests the events and metrics from the DIQS 108 and causes visual representations, also referred to as visualizations, of one or more of the metrics, key performance indicators”; [col 175 lines 6-17] “The insights application 4212 can process the returned information and can cause a display of visual representations of, for example, the metrics, log files, key performance indicators… the insights application processes the events and metrics to provide the visual representations… processes the events and metrics and sends the processed events to the application 4212 to provide the visual representations”; [see col 10 lines 31-32] “comprises one or more computing devices”) the one or more statistical insights and the health data comprises: (see Sundar, [0069] “the natural language processing model may obtain data identifying, in natural language, a query by the user requesting analytical information in connection with a standardized data set, and may parse the data to identify the keywords, the intent classifications, the entities, and/or the like… based on a query being “What are the metrics used to measure patient wait times?,” the natural language processing model may determine that “metrics” and “patient wait times” are keywords of the query that are related to the analytical information requested by the user… the natural language processing model may determine that “metrics” relate to an intent classification associated with the query, and/or determine that “patient wait times” relate to an entity associated with the query. In such cases, the natural language processing model may determine that a natural language text corresponds to a characteristic based on data relating to other analytical information, data identifying characteristics of analytical information”; [0044]-[0047] “the data sets undergo intelligent processing based on the types of data elements and automate KPI calculation depending on the subject area of the client providing extended insights to the clients… standardized data sets for various subject areas may be created for use in calculating various metrics specific to a given subject area”).
generating, by the computing system,… (see Baskaran, [col 171 lines 4-8] “The insights application 4212 requests the events and metrics from the DIQS 108 and causes visual representations, also referred to as visualizations, of one or more of the metrics, key performance indicators”; [col 175 lines 6-17] “The insights application 4212 can process the returned information and can cause a display of visual representations of, for example, the metrics, log files, key performance indicators… the insights application processes the events and metrics to provide the visual representations… processes the events and metrics and sends the processed events to the application 4212 to provide the visual representations”; [see col 10 lines 31-32] “comprises one or more computing devices”) the one or more statistical insights… (see Sundar, [0069] “the natural language processing model may obtain data identifying, in natural language, a query by the user requesting analytical information in connection with a standardized data set, and may parse the data to identify the keywords, the intent classifications, the entities, and/or the like… based on a query being “What are the metrics used to measure patient wait times?,” the natural language processing model may determine that “metrics” and “patient wait times” are keywords of the query that are related to the analytical information requested by the user… the natural language processing model may determine that “metrics” relate to an intent classification associated with the query, and/or determine that “patient wait times” relate to an entity associated with the query. In such cases, the natural language processing model may determine that a natural language text corresponds to a characteristic based on data relating to other analytical information, data identifying characteristics of analytical information”; [0044]-[0047] “the data sets undergo intelligent processing based on the types of data elements and automate KPI calculation depending on the subject area of the client providing extended insights to the clients… standardized data sets for various subject areas may be created for use in calculating various metrics specific to a given subject area”) the one or more key indications (see Baskaran, [col 171 lines 4-8] “The insights application 4212 requests the events and metrics from the DIQS 108 and causes visual representations, also referred to as visualizations, of one or more of the metrics, key performance indicators”; [col 175 lines 6-17] “The insights application 4212 can process the returned information and can cause a display of visual representations of, for example, the metrics, log files, key performance indicators… the insights application processes the events and metrics to provide the visual representations… processes the events and metrics and sends the processed events to the application 4212 to provide the visual representations”; [see col 10 lines 31-32] “comprises one or more computing devices”).
The proposed combination of Sundar and Baskaran does not explicitly teach based on inputting the one or more insights into the one or more machine-learned models.
However, Lotti discloses machine learning models and teaches
based on inputting inputs into the one or more machine-learned models, (see Lotti, [col 14 lines 48-49] “one or more inputs of the machine learning model (e.g., trained machine learning model)”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include the functionality of large language model, description, audio indication, inputting into machine learning model and text-based description, as being disclosed and taught by Lotti, in the system taught by the proposed combination of Sundar and Baskaran to yield the predictable results of improved image processing and prompt generation for effective searching and filtering of information (see Lotti, [col 7 lines 60-63] “the improved image processing and prompt generation can be used, for example, for searching and/or filtering with respect to a database”).
Regarding claim 14, the proposed combination of Sundar and Baskaran teaches
wherein the one or more key indications comprise… (see Baskaran, [col 171 lines 4-8] “The insights application 4212 requests the events and metrics from the DIQS 108 and causes visual representations, also referred to as visualizations, of one or more of the metrics, key performance indicators”; [col 175 lines 6-17] “The insights application 4212 can process the returned information and can cause a display of visual representations of, for example, the metrics, log files, key performance indicators… the insights application processes the events and metrics to provide the visual representations… processes the events and metrics and sends the processed events to the application 4212 to provide the visual representations”) of the one or more statistical insights (see Sundar, [0069] “the natural language processing model may obtain data identifying, in natural language, a query by the user requesting analytical information in connection with a standardized data set, and may parse the data to identify the keywords, the intent classifications, the entities, and/or the like… based on a query being “What are the metrics used to measure patient wait times?,” the natural language processing model may determine that “metrics” and “patient wait times” are keywords of the query that are related to the analytical information requested by the user… the natural language processing model may determine that “metrics” relate to an intent classification associated with the query, and/or determine that “patient wait times” relate to an entity associated with the query. In such cases, the natural language processing model may determine that a natural language text corresponds to a characteristic based on data relating to other analytical information, data identifying characteristics of analytical information”; [0044]-[0047] “the data sets undergo intelligent processing based on the types of data elements and automate KPI calculation depending on the subject area of the client providing extended insights to the clients… standardized data sets for various subject areas may be created for use in calculating various metrics specific to a given subject area”)
The proposed combination of Sundar and Baskaran does not explicitly teach a text-based description.
However, Lotti discloses machine learning models and teaches
a text-based description (see Lotti, [col 5 lines 7-9] “A textual identifier can refer to a textual description related to or describing”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include the functionality of large language model, description, audio indication, inputting into machine learning model and text-based description, as being disclosed and taught by Lotti, in the system taught by the proposed combination of Sundar and Baskaran to yield the predictable results of improved image processing and prompt generation for effective searching and filtering of information (see Lotti, [col 7 lines 60-63] “the improved image processing and prompt generation can be used, for example, for searching and/or filtering with respect to a database”).
Claims 12 and 13 are rejected under 35 U.S.C. 103 as being unpatentable over Sundar in view of Baskaran further in view of Miller et al. (US 10,154,460 B1, hereinafter “Miller”).
Regarding claim 12, the proposed combination of Sundar and Baskaran teaches
further comprising: in response to receiving a request for additional information, (see Sundar, [0067] “may generate a subsequent request for additional information”) generating, by the computing system, one or more secondary indications (see Sundar, [0067] “communicate the request for additional information to the user”) that are based on the one or more key indications and… (see Baskaran, [col 171 lines 4-8] “The insights application 4212 requests the events and metrics from the DIQS 108 and causes visual representations, also referred to as visualizations, of one or more of the metrics, key performance indicators”; [col 175 lines 6-17] “The insights application 4212 can process the returned information and can cause a display of visual representations of, for example, the metrics, log files, key performance indicators… the insights application processes the events and metrics to provide the visual representations… processes the events and metrics and sends the processed events to the application 4212 to provide the visual representations”) the one or more key indications (see Baskaran, [col 171 lines 4-8] “The insights application 4212 requests the events and metrics from the DIQS 108 and causes visual representations, also referred to as visualizations, of one or more of the metrics, key performance indicators”; [col 175 lines 6-17] “The insights application 4212 can process the returned information and can cause a display of visual representations of, for example, the metrics, log files, key performance indicators… the insights application processes the events and metrics to provide the visual representations… processes the events and metrics and sends the processed events to the application 4212 to provide the visual representations”).
The proposed combination of Sundar and Baskaran does not explicitly teach have a higher granularity than the one or more key indications.
However, Miller discloses measuring physical activity of the user and teaches
have a higher level of granularity during working out than when the user is eating or sleeping (see Miller, [col 36 lines 41-42] “a higher measurement or data granularity threshold during a period when the user is exercising or working out and a lower measurement or data granularity threshold (relative to the higher measurement or data granularity threshold) when the user is not working out (such as when the user is eating, working, or sleeping)”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include the functionality of higher level of granularity and plurality of heart rates at plurality of time intervals, as being disclosed and taught by Miller, in the system taught by the proposed combination of Sundar and Baskaran to yield the predictable results of effectively monitoring, collecting and analyzing various aspects of the fitness routine (see Miller, [col 3 lines 1-5] “a sport or fitness enthusiast may desire to monitor, collect, and/or analyze various aspects of the fitness routine (such as their heart rate, workout intensity, workout duration, and so forth) to determine how to improve and adjust their fitness routine to increase its efficacy”).
Regarding claim 13, the proposed combination of Sundar and Baskaran teaches
wherein the plurality of health metrics comprise (see Sundar, [0053] “the healthcare data platform may derive, determine, generate, calculate, or compute metrics (e.g., healthcare metrics) based on the grouped data elements… the metrics may be determined by a metric derivation engine of the healthcare data platform, which accesses stored metric definitions and computes the metrics based on the stored definitions”).
The proposed combination of Sundar and Baskaran does not explicitly teach a plurality of heart rates at a plurality of time intervals.
However, Miller discloses measuring physical activity of the user and teaches
a plurality of heart rates at a plurality of time intervals, (see Miller, [col 11 line 19-22] “the analysis tool can determine a baseline measurement for an individual by iteratively determining medium of a measurement, such as a heart rate measurement, over a period of time”; [col 12 line 62 – col 13 line 3] “The target or baseline heart rate can be a stable or steady heart rate of the individual after a period of time while the event or activity may be occurring. The analysis tool can monitor the heart rate of the user during the event and determine when the event or activity has finished. Upon completion of the activity, the analysis tool can take a first heart rate measurement. After a threshold period of time has passed, the analysis tool can take a second heart rate measurement”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include the functionality of higher level of granularity and plurality of heart rates at plurality of time intervals, as being disclosed and taught by Miller, in the system taught by the proposed combination of Sundar and Baskaran to yield the predictable results of effectively monitoring, collecting and analyzing various aspects of the fitness routine (see Miller, [col 3 lines 1-5] “a sport or fitness enthusiast may desire to monitor, collect, and/or analyze various aspects of the fitness routine (such as their heart rate, workout intensity, workout duration, and so forth) to determine how to improve and adjust their fitness routine to increase its efficacy”).
Citation Of Relevant Prior Art
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
US Patent No. US 11,074,533 B1 (Upadhyay) teaches metrics and key performance indicators, however it does not clarify query being inputted in machine learning model.
US Publication No. 2019/0332892 A1 (Wickesberg) teaches metrics and key performance indicators, however it does not clarify query being inputted in machine learning model.
Conclusion
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/VAISHALI SHAH/Primary Examiner, Art Unit 2156
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