DETAILED ACTION
Status of Claims
This action is in reply to the amendment filed on 05/01/2026.
Claims 1-3, 6, 15, 17 and 20 have been amended.
Claims 1-20 are currently pending and have been examined.
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 .
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 under 35 U.S.C. §101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1:
Claims 1-16 are directed to a method (i.e., a process), claims 1-19 are directed to a system (i.e., a machine) and claim 20 is directed to non-transitory computer readable medium (i.e., a manufacture). Accordingly, claims 1-20 are all within at least one of the four statutory categories.
Step 2A - Prong One:
An “abstract idea” judicial exception is subject matter that falls within at least one of the following groupings: a) mathematical concepts, b) certain methods of organizing human activity, and/or c) mental processes.
Representative independent claim 17 includes limitations that recite an abstract idea. Note that independent claim 17 is the system claim, while claim 1 covers a method claim and claim 20 covers the matching computer readable medium.
Specifically, independent claim 17 recites:
A system comprising:
one or more processors; and
one or more memories storing processor-executable instructions that, when executed by the one or more processors, cause the one or more processors to perform operations comprising:
obtaining first data indicative of at least a first symptom associated with an encounter between an individual and a provider;
obtaining second data comprising sensor data generated by one or more devices associated with the individual;
generating, based at least in part on the first data and the second data, a data set comprising data indicative of one or more temporal patterns in the second data;
determining, based at least in part on the data set, a first temporal pattern, of the one or more temporal patterns in the second data, that is relevant to the encounter; and
determining, based at least in part on the data set, a second temporal pattern, of the one or more temporal patterns in the second data, that is relevant to the encounter;
determining a first metric indicative of how relevant the first temporal pattern is to the encounter and a second metric indicative of how relevant the second temporal pattern is to the encounter; and
generating output text comprising first text indicative of the first temporal pattern and second text indicative of the second temporal pattern, wherein an arrangement of the first text and the second text within the output text is based on the first metric and the second metric.
The Examiner submits that the foregoing underlined limitations constitute: (a) “certain methods of organizing human activity” because 1) determining temporal patterns from detected sensor data and symptoms associated with an encounter between an individual and a provider and 2) generating output text indicative of a temporal pattern are clinical activities performed by clinicians caring for patients and are a part of a medical workflow, which are managing human behavior/interactions between people. Furthermore, these limitations constitute (b) “a mental process” because determining temporal patterns from detected sensor data and symptoms associated with an encounter between an individual and a provider is an observation/evaluation/analysis that can be performed in the human mind or with a pen and paper. The underlined limitations further constitute: (c) mathematical concepts because determining a first metric indicative of how relevant the first temporal pattern is to the encounter and a second metric indicative of how relevant the second temporal pattern is to the encounter and outputting text based on the first metric and the second metric are ways of numerically evaluating relevant medical findings, which are mathematical concepts. The foregoing underlined limitations also relate to claim 17 (similarly to claims 1 and 20).
Accordingly, the claim describes at least one abstract idea.
In relation to claims 4-7 and 11, these claims merely recite specific kinds of data, such as: claim 4 – when the machine learned model is a regression model or a tree-based algorithm, claim 5 - when the machine learned model is a neural network having one or more respective feature weights for the one or more factors, claim 6 – the machine learned model comprises a predictive model trained on a graph having (i) a first node representing the first symptom, (ii) one or more respective nodes representing the one or more factors, and (iii) edges that connect node pairs within the graph and are associated with weights indicative of relevancy between nodes of the node pairs, and wherein to scores comprise weights associated with the edges, claim 7 – the first data is a predefined code associated with the first symptom and claim 11 – first sensor data is indicative of one or more physiological parameters of the individual and second sensor data is indicative of one or more physical activities of the individual.
In relation to claims 2-3, 8-10, 13-16 and 18-19, these claims merely recite determining steps such as: claim 2 – determining the first temporal pattern that is relevant to the encounter comprises: determining one or more candidate causes of the first symptom by accessing a knowledge base and determining the first temporal pattern that is relevant to the encounter based at least in part on the data set and the one or more candidate causes, wherein the first metric is based at least in part on a relevance of the first temporal pattern in view of the one or more candidate causes, claim 3 - determining the first temporal pattern that is relevant to the encounter comprises: determining the first temporal pattern that is relevant to the encounter based at least in part on one or more factors linked to the first symptom by a machine learned model, wherein the first metric is based at least in part on scores generated based on the machine learned model, claim 8 – generating the first data by applying one or more automated processing techniques to the unstructured data, the one or more automated processing techniques including one or more of (i) a natural language processing technique, (ii) a computer vision technique, or (iii) an automated speech recognition technique, claim 9 – determining an amount of variation in the values of the first parameter during the first time period relative to a baseline for the individual; and adding information based on the values of first parameter to the data set when the amount of variation exceeds a threshold amount, claim 10 – determining the amount of variation in the values of the first parameter during the first time period relative to the baseline for the individual comprises: determining a first mean or median value of the values of the first parameter during the first time period and determining a difference between the first mean or median value and a baseline mean or median value for the individual, claim 13 - generating the data set comprises determining an amount of variation in the first activity of the individual during the first time period relative to a baseline for the individual and adding information based on the first activity of the individual during the first time period to the data set when the amount of variation exceeds a threshold amount, claim 14 - determining whether a frequency of the first activity of the individual during the first time period or an amount of the first activity of the individual during the first time period differs from an earlier frequency or amount, respectively, of the first activity of the individual by more than a threshold amount, claim 15- the arrangement of the first text and the second text within the output text is indicative of whether the first temporal pattern or the second temporal pattern is more relevant to the encounter, claim 16 - generating the output text includes using a transformer-based machine learned model to generate the output text, claim 18 - determining the first temporal pattern that is relevant to the encounter at least in part by: determining one or more candidate causes of the first symptom by accessing a knowledge base and determining the first temporal pattern that is relevant to the encounter based at least in part on the data set and the one or more candidate causes and claim 19 - to perform the determining the first temporal pattern that is relevant to the encounter at least in part by: determining the first temporal pattern that is relevant to the encounter based at least in part on one or more factors linked to the first symptom by a machine learned model.
Step 2A - Prong Two:
Regarding Prong Two of Step 2A, it must be determined whether the claim as a whole integrates the abstract idea into a practical application. As noted, it must be determined whether any additional elements in the claim beyond the abstract idea integrate the exception into a practical application in a manner that imposes a meaningful limit on the judicial exception. The courts have indicated that additional elements merely using a computer to implement an abstract idea, adding insignificant extra solution activity, or generally linking use of a judicial exception to a particular technological environment or field of use do not integrate a judicial exception into a “practical application.”
The limitations of claims 1, 17 and 20, as drafted is a process that, under its broadest reasonable interpretation, covers performance of the limitations in the human mind but for the recitation of generic computer components. That is, other than reciting a system, one or more processors, one or more memories storing processor-executable instructions and one or more non-transitory computer-readable media storing processor-executable instructions to perform the limitations, nothing in the claim elements precludes the steps from practically being performed in the human mind. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation within a health care environment in the human mind but for the recitation of generic computer components, then it falls within the “certain methods of organizing human activity” and “Mental Process” grouping of abstract ideas. Accordingly, the claims recite an abstract idea.
The judicial exception is not integrated into a practical application. In particular, the system, one or more processors, one or more memories storing processor-executable instructions and one or more non-transitory computer-readable media storing processor-executable instructions are recited at high levels of generality (i.e., as generic computer components performing generic computer functions of receiving data/inputs, determining and providing data) such that it amounts no more than mere instructions to apply the exception using the generic computer components.
Regarding the additional limitations “a machine learning model”, “accessing a knowledge base”, and “a regression model or a tree-based algorithm”, the Examiner submits that this additional limitation amount to merely using a computer to perform the at least one abstract idea (see MPEP § 2106.05(f)). Regarding the additional limitation “obtaining first data ……” and “obtaining second data ……” the Examiner submits that this additional limitation merely adds insignificant pre-solution activity (data gathering; selecting data to be manipulated) to the at least one abstract idea (see MPEP § 2106.05(g)).
Thus, taken alone, the additional elements do not amount to significantly more than the above identified judicial exception (the abstract idea). Looking at the limitations as an ordered combination add nothing that is not already present when looking at the elements taken individually. For instance, there is no indication that the additional elements, when considered as a whole, reflect an improvements in the functioning of a computer or an improvement to another technology or technical field, apply or us the above-noted implement/use to above-noted judicial exception with a particular machine or manufacture that is integral to the claim, effect a transformation or reduction of a particular article to a different state or thing, or apply or use the judicial exception in some meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is not more than a drafting effort designed to monopolize the exception (see MPEP §2106.05). Their collective functions merely provide conventional computer implementation.
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to the integration of the abstract idea into practical application, the additional elements amount to no more than mere instructions to apply the exception using generic computer components. Mere instructions to apply an exception using generic computer component provide an inventive concept. The claims are not patent eligible.
Step 2B:
Regarding Step 2B, in representative independent claim 17, regarding the additional limitations of the system, one or more processors, one or more memories storing processor-executable instructions and one or more non-transitory computer-readable media storing processor-executable instructions, the Examiner submits that these limitations amount to merely using a computer to perform the at least one abstract idea (see MPEP § 2106.05(f)).
Thus, representative independent claim 17 and analogous independent claims 1 and 20 do not include additional elements (considered both individually and as an ordered combination) that are sufficient to amount to significantly more than the judicial exception for the same reasons to those discussed above with respect to determining that the claim does not integrate the abstract idea into a practical application.
The dependent claims no not include additional elements (considered both individually and as an ordered combination) that are sufficient to amount to significantly more than the judicial exception for the same reason discussed above with respect to determining that the dependent claims do not integrate the at least abstract idea into a practical application.
Therefore, claims 1-20 are ineligible under 35 USC §101.
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 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 set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied 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 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over Ozen Irmak (U.S. 2025/0149174 A1) In view of Kannan (U.S. 2023/0105969 A1).
Claim 1:
Ozen Irmak discloses A computer-implemented method comprising:
obtaining, by one or more processors, first data indicative of at least a first symptom associated with an encounter between an individual and a provider (See Fig. 1 user devices in P0050, Fig. 6 processor 602 in P0230, exemplary health categories in P0061 and entered symptoms in P0090.);
obtaining, by the one or more processors, second data comprising sensor data generated by one or more devices associated with the individual (See Fig. 1 different types of sensors in P0002, P0031 and received inputs from one or more sensors (106, 108) in P0054. Also, see Fig. 4A-4B exemplary sensor data sleep features (Fig. 1-5) mentioned in P0062-P0063.);
generating, by the one or more processors and based at least in part on the first data and the second data, a data set comprising data indicative of one or more temporal patterns in the second data (See sensors monitoring (P0054, P0077-P0078) during an individual’s daily life in P00093 exemplary activity tracker, a sleeping tracker, a blood pressure monitor, or an electroencephalogram weekly in P0101-P0102. Also, see Fig. 4A-4B exemplary sensor data sleep features (sf_1-2 & 4) used as a different set of sleep (health) features, 404 and 454 mentioned in P0063-P0064, exemplary temporal health pattern mentioned in P0105, P0120.); and
determining, by the one or more processors and based at least in part on the data set, a first temporal pattern, of the one or more temporal patterns in the second data, that is relevant to the first symptom (See sensors monitoring (P0053-P0054, P0077-P0078) during an individual’s daily life in P00093 exemplary activity tracker, a sleeping tracker, a blood pressure monitor, or an electroencephalogram as temporal patterns. Also, see relevant symptoms from intakes and diaries is shown Fig. 4A-4B, P0023 P0109.);
determining, by the one or more processors and based at least in part on the data set, a second temporal pattern, of the one or more temporal patterns in the second data, that is relevant to the encounter (With temporal patterns relevant to encounters such as symptoms and conditions associated with the encounter, see P0086-P0090 detecting the worsening or continued state of desirable/undesirable symptoms or unsustainable side-effects of interventions. Also, see exemplary levels of depression-related symptoms in P0122-P0123, data from sensors, diaries and questionnaires (P0078, P0094-P0095, P0101, P0226) can be used to determine temporal patterns relevant to an encounter.); and
determining, by the one or more processors, a first metric indicative of how relevant the first temporal pattern is to the encounter and a second metric indicative of how relevant the second temporal pattern is to the encounter (Besides the diagnostic threshold as a metric indicating levels of depression-related symptoms in P0122-P0123, see P0070-P0072 as score thresholds among assigned first, second and third labels for each or some of the input modules, and the modules can be used to predict that module's health-marker's normalcy);
Although Ozen Irmak discloses a computer-implemented method, system and software to determine temporal patterns in the second data, that is relevant to the first symptom mentioned above and generated output text as a text explaining an alert in P0053, see structured text, narrative text data in P0078-P0079 and [P0227] pre-processing of event diaries include extracting the following information from unstructured text: word-choice, number of words used, repeated words, completion duration, keyword analysis, time of entry, etc.), Ozen Irmak does not explicitly teach when the generated output text is indicative of a temporal pattern and an arrangement of the first text and the second text within the output text being based on the first metric and the second metric. Kannan teaches:
generating, by the one or more processors, output text comprising first text indicative of the first temporal pattern (See Fig. 1 medical text understanding 126 mentioned in P0055-P0056.) and second text indicative of the second temporal pattern, wherein an arrangement of the first text and the second text within the output text is based on the first metric and the second metric (See generated questions via conversation engine in Fig. 10, P0092-P0095 where a user’s summary of symptoms are extracted and arranged, and P0100-0101 where the learned models incorporate correlated symptoms using rule-based engines. Also, see P0122-P0133 indexing content serves as metrics when responding to any question about symptoms, laboratory test, medications and procedures.).
Therefore, it would have been obvious to one of ordinary skill in the art of medical machine learning models before the effective filing date of the claimed invention to modify the method, system and software of Ozen Irmak to include when the generated output text is indicative of a temporal pattern and an arrangement of the first text and the second text within the output text being based on the first metric and the second metric as taught by Kannan in order to address shortcomings associated with current medical decision support systems mentioned in Kannan’s P0008, P0136.
Regarding claim 2, although Ozen Irmak discloses the computer-implemented method of claim 1 mentioned above, Ozen Irmak does not explicitly teach determining candidate causes of the first symptom by accessing a knowledge base and pattern that is relevant to the encounter based the data set and the one or more candidate causes. Kannan teaches:
wherein determining the first temporal pattern that is relevant to the encounter comprises: determining one or more candidate causes of the first symptom by accessing a knowledge base (See [P0099-P0100] These engines may include rule-based engines, wherein rules in a knowledge base specify the strength of relationship between a symptom/finding, and diseases that may cause such symptoms.); and determining the first temporal pattern that is relevant to the encounter based at least in part on the data set and the one or more candidate causes (See Fig. 1, P0014, P0046 knowledge based 118 symptoms, conditions, temporal modifiers and interface of user devices in P0042-P0043.), wherein the first metric is based at least in part on a relevance of the first temporal pattern in view of the one or more candidate causes (See [P0099-P0100] These engines may include rule-based engines, wherein rules in a knowledge base specify the strength of relationship between a symptom/finding, and diseases that may cause such symptoms. Also, see Fig. 1, P0014, P0042-P0043 and P0046.).
Therefore, it would have been obvious to one of ordinary skill in the art of medical machine learning models before the effective filing date of the claimed invention to modify the method, system and software of Ozen Irmak to include determining candidate causes of the first symptom by accessing a knowledge base and pattern that is relevant to the encounter based the data set and the one or more candidate causes as taught by Kannan in order to address shortcomings associated with current medical decision support systems mentioned in Kannan’s P0008, P0136.
Regarding claim 3, although Ozen Irmak and Kannan teach the computer-implemented method of claim 1 mentioned above, and Ozen Irmak teaches wherein the first metric is based at least in part on scores generated based on the machine learned model (Taught in P0070-P0072 as score thresholds among assigned first, second and third labels for each or some of the input modules, and the modules can be used to predict that module's health-marker's normalcy.). However, Ozen Irmak does not explicitly teach determining a temporal pattern that is relevant to the encounter based on factors linked to the first symptom by a machine learned model.
Kannan teaches:
wherein determining the first temporal pattern that is relevant to the encounter comprises: determining the first temporal pattern that is relevant to the encounter based at least in part on one or more factors linked to the first symptom by a machine learned model (See [P0016] a first diagnosis engine based on rules in a knowledge based codifying probabilistic relationships between symptoms/findings and diseases, P0045 and Fig. 1.).
Therefore, it would have been obvious to one of ordinary skill in the art of medical machine learning models before the effective filing date of the claimed invention to modify the method, system and software of Ozen Irmak to include determining a temporal pattern that is relevant to the encounter based on factors linked to the first symptom by a machine learned model as taught by Kannan in order to address shortcomings associated with current medical decision support systems mentioned in Kannan’s P0008, P0136.
Regarding claim 4, although Ozen Irmak and Kannan teach the computer-implemented method of claim 3 mentioned above, Ozen Irmak disclose wherein the machine learned model comprises a regression model or a tree-based algorithm (See P0106-P0107 recurrent regression structures.).
Regarding claim 5, although Ozen Irmak and Kannan teach the computer-implemented method of claim 3 mentioned above, Ozen Irmak discloses wherein the machine learned model comprises a neural network having one or more respective feature weights for the one or more factors (See P0003-P0004, P0107 neural networks determining respective probabilities of the particular events happening based on the respective markers, and to output respective final probabilities of the particular events.).
Regarding claim 6, although Ozen Irmak and Kannan teach the computer-implemented method of claim 3 mentioned above, Ozen Irmak discloses wherein the machine learned model comprises a predictive model trained on a graph having (i) a first node representing the first symptom, (ii) one or more respective nodes representing the one or more factors, and (iii) edges that connect node pairs within the graph and are associated with weights indicative of relevancy between nodes of the node pairs, and wherein the scores comprise the weights associated with the edges (See exemplary machine learning model include relevant symptoms from intakes and diaries is shown in Fig. 2, Fig. 4A-4B, P0023 P0109 where the machine learning model show graphed edges of at least two nodes. Also, see P0069, P0073-P0074 hidden layer, module calculations.).
Regarding claim 7, although Ozen Irmak and Kannan teach the computer-implemented method of claim 1 mentioned above, Ozen Irmak discloses wherein the first data comprises a predefined code associated with the first symptom (See P0043 symptoms that meet certain diagnostic criteria.).
Regarding claim 8, although Ozen Irmak and Kannan teach the computer-implemented method of claim 1 mentioned above, Ozen Irmak discloses wherein obtaining the first data comprises: receiving unstructured data specifying a reason for the encounter (See unstructured narrative text data in P0078.); and generating the first data by applying one or more automated processing techniques to the unstructured data, the one or more automated processing techniques including one or more of (i) a natural language processing technique, (ii) a computer vision technique, or (iii) an automated speech recognition technique (See [P0227] pre-processing of event diaries include extracting the following information from unstructured text: word-choice, number of words used, repeated words, completion duration, keyword analysis, time of entry, etc. Also, see auditory feedback and speech input in P0245.).
Regarding claim 9, although Ozen Irmak and Kannan teach the computer-implemented method of claim 1 mentioned above, Ozen Irmak discloses wherein the sensor data comprises first sensor data that was generated by a first device of the one or more devices and is indicative of values of a first parameter during a first time period (Taught in P00031, P0054, P0078.), and wherein generating the data set comprises: determining an amount of variation in the values of the first parameter during the first time period relative to a baseline for the individual; and adding information based on the values of first parameter to the data set when the amount of variation exceeds a threshold amount (Taught in P0101-P0102 as triggered pre-set intervals of sensor data.).
Regarding claim 10, Ozen Irmak discloses wherein determining the amount of variation in the values of the first parameter during the first time period relative to the baseline for the individual comprises: determining a first mean or median value of the values of the first parameter during the first time period; and determining a difference between the first mean or median value and a baseline mean or median value for the individual (Taught in P0046 pre-specified value greater than 50% as exemplary disorder means.).
Regarding claim 11, Ozen Irmak discloses wherein obtaining the second data comprises obtaining one or both of: first sensor data indicative of one or more physiological parameters of the individual; and second sensor data indicative of one or more physical activities of the individual (See [P0102] Examples of the sensors include, but are not limited to, an activity tracker, a sleeping tracker, a blood pressure monitor, or an electroencephalogram.).
Regarding claim 12, Ozen Irmak discloses wherein obtaining the second data comprises obtaining environmental sensor data indicative of one or more environmental factors associated with the individual (See Fig. 1, [P0049] a user of a platform that uses environment 100 to monitor, identify, and/or predict the behavioral disorders of individual 102.).
Regarding claim 13, Ozen Irmak discloses wherein: the second data further comprises application data (i) generated by an application installed on a mobile device of the individual and configured to receive manual entries of activities (See mobile application in P0002, P0096.), and (ii) indicative of a first activity of the individual during a first time period; generating the data set comprises determining an amount of variation in the first activity of the individual during the first time period relative to a baseline for the individual; and adding information based on the first activity of the individual during the first time period to the data set when the amount of variation exceeds a threshold amount (Taught in P0070-P0072 as score thresholds among assigned first, second and third labels for each or some of the input modules, and the modules can be used to predict that module's health-marker's normalcy.).
Regarding claim 14, Ozen Irmak discloses wherein determining the amount of variation in the first activity of the individual during the first time period relative to the baseline for the individual (See P0101-P0102 as triggered pre-set intervals of sensor data.) comprises: determining whether a frequency of the first activity of the individual during the first time period or an amount of the first activity of the individual during the first time period differs from an earlier frequency or amount, respectively, of the first activity of the individual by more than a threshold amount (Taught in P0070-P0072 as score thresholds among assigned first, second and third labels for each or some of the input modules, and the modules can be used to predict that module's health-marker's normalcy.).
Regarding claim 15, Ozen Irmak and Kannan teach the computer-implemented method of claim 1 mentioned above, Ozen Irmak teaches further comprising:
the arrangement of the first text and the second text within the output text is indicative of whether the first temporal pattern or the second temporal pattern is more relevant to the encounter as indicated by the first metric and the second metric (See exemplary arrangement of text data in P0226-P0227 and [P0255] at least two pieces of input data have different formats, and wherein the formats include one or more of voice, text, selection of an item on a respective device. See generated output text as a text explaining an alert in P0053, see structured text, narrative text data in P0078-P0079 and [P0227] pre-processing of event diaries include extracting the following information from unstructured text: word-choice, number of words used, repeated words, completion duration, keyword analysis, time of entry, etc.).
Regarding claim 16, although Ozen Irmak and Kannan teach the computer-implemented method of claim 1 mentioned above, Ozen Irmak does not explicitly teach when the generated output text is indicative of a temporal pattern. Kannan teaches:
wherein generating the output text includes using a transformer-based machine learned model to generate the output text (See Fig. 1 medical text understanding 126 mentioned in P0055-P0056.).
Therefore, it would have been obvious to one of ordinary skill in the art of medical machine learning models before the effective filing date of the claimed invention to modify the method, system and software of Ozen Irmak to include when the generated output text is indicative of a temporal pattern as taught by Kannan in order to address shortcomings associated with current medical decision support systems mentioned in Kannan’s P0008, P0136.
Claim 17:
Ozen Irmak discloses A system comprising:
one or more processors (See Fig. 1 user devices in P0050, Fig. 6 processor 602 in P0230.); and
one or more memories storing processor-executable instructions that, when executed by the one or more processors, cause the one or more processors to perform operations (See P0230 including instructions stored in the memory 604.) comprising:
obtaining first data indicative of at least a first symptom associated with an encounter between an individual and a provider (See Fig. 1 user devices in P0050, Fig. 6 processor 602 in P0230, exemplary health categories in P0061 and entered symptoms in P0090.);
obtaining second data comprising sensor data generated by one or more devices associated with the individual (See Fig. 1 different types of sensors in P0002, P0031 and received inputs from one or more sensors (106, 108) in P0054. Also, see Fig. 4A-4B exemplary sensor data sleep features (sf_1-5) mentioned in P0062-P0063.);
generating, based at least in part on the first data and the second data, a data set comprising data indicative of one or more temporal patterns in the second data (See sensors monitoring (P0054, P0077-P0078) during an individual’s daily life in P00093 exemplary activity tracker, a sleeping tracker, a blood pressure monitor, or an electroencephalogram weekly in P0101-P0102. Also, see Fig. 4A-4B exemplary sensor data sleep features (sf_1-2 & 4) used as a different set of sleep (health) features, 404 and 454 mentioned in P0063-P0064, exemplary temporal health pattern mentioned in P0105, P0120.); and
determining, based at least in part on the data set, a first temporal pattern, of the one or more temporal patterns in the second data, that is relevant to the encounter (See sensors monitoring (P0053-P0054, P0077-P0078) during an individual’s daily life in P00093 exemplary activity tracker, a sleeping tracker, a blood pressure monitor, or an electroencephalogram as temporal patterns. Also, see relevant symptoms from intakes and diaries is shown Fig. 4A-4B, P0023 P0109.);
determining, based at least in part on the data set, a second temporal pattern, of the one or more temporal patterns in the second data, that is relevant to the encounter (With temporal patterns relevant to encounters such as symptoms and conditions associated with the encounter, see P0086-P0090 detecting the worsening or continued state of desirable/undesirable symptoms or unsustainable side-effects of interventions. Also, see exemplary levels of depression-related symptoms in P0122-P0123, data from sensors, diaries and questionnaires (P0078, P0094-P0095, P0101, P0226) can be used to determine temporal patterns relevant to an encounter.); and
determining a first metric indicative of how relevant the first temporal pattern is to the encounter and a second metric indicative of how relevant the second temporal pattern is to the encounter (Besides the diagnostic threshold as a metric indicating levels of depression-related symptoms in P0122-P0123, see P0070-P0072 as score thresholds among assigned first, second and third labels for each or some of the input modules, and the modules can be used to predict that module's health-marker's normalcy);
Although Ozen Irmak discloses a computer-implemented method, system and software to determine temporal patterns in the second data, that is relevant to the first symptom mentioned above and generated output text as a text explaining an alert in P0053, see structured text, narrative text data in P0078-P0079 and [P0227] pre-processing of event diaries include extracting the following information from unstructured text: word-choice, number of words used, repeated words, completion duration, keyword analysis, time of entry, etc.), Ozen Irmak does not explicitly teach when the generated output text is indicative of a temporal pattern and an arrangement of the first text and the second text within the output text being based on the first metric and the second metric. Kannan teaches:
generating output text comprising first text indicative of the first temporal pattern (See Fig. 1 medical text understanding 126 mentioned in P0055-P0056.) and second text indicative of the second temporal pattern, wherein an arrangement of the first text and the second text within the output text is based on the first metric and the second metric (See generated questions via conversation engine in Fig. 10, P0092-P0095 where a user’s summary of symptoms are extracted and arranged, and P0100-0101 where the learned models incorporate correlated symptoms using rule-based engines. Also, see P0122-P0133 indexing content serves as metrics when responding to any question about symptoms, laboratory test, medications and procedures.).
Therefore, it would have been obvious to one of ordinary skill in the art of medical machine learning models before the effective filing date of the claimed invention to modify the method, system and software of Ozen Irmak to include when the generated output text is indicative of a temporal pattern and an arrangement of the first text and the second text within the output text being based on the first metric and the second metric as taught by Kannan in order to address shortcomings associated with current medical decision support systems mentioned in Kannan’s P0008, P0136.
Regarding claim 18, although Ozen Irmak discloses the system of claim 17 and wherein the processor-executable instructions, when executed by the one or more processors, cause the one or more processors mentioned above, Ozen Irmak does not explicitly teach determining candidate causes of the first symptom by accessing a knowledge base and pattern that is relevant to the encounter based the data set and the one or more candidate causes. Kannan teaches:
to perform the determining the first temporal pattern that is relevant to the encounter at least in part by: determining one or more candidate causes of the first symptom by accessing a knowledge base (See [P0099-P0100] These engines may include rule-based engines, wherein rules in a knowledge base specify the strength of relationship between a symptom/finding, and diseases that may cause such symptoms.); and determining the first temporal pattern that is relevant to the encounter based at least in part on the data set and the one or more candidate causes (See Fig. 1, P0014, P0046 knowledge based 118 symptoms, conditions, temporal modifiers and interface of user devices in P0042-P0043.).
Therefore, it would have been obvious to one of ordinary skill in the art of medical machine learning models before the effective filing date of the claimed invention to modify the method, system and software of Ozen Irmak to include determining candidate causes of the first symptom by accessing a knowledge base and pattern that is relevant to the encounter based the data set and the one or more candidate causes as taught by Kannan in order to address shortcomings associated with current medical decision support systems mentioned in Kannan’s P0008, P0136.
Regarding claim 19, although Ozen Irmak and Kannan teach the computer-implemented system of claim 17, wherein the processor-executable instructions, when executed by the one or more processors, cause the one or more processors mentioned above, Ozen Irmak does not explicitly teach determining a temporal pattern that is relevant to the encounter based on factors linked to the first symptom by a machine learned model. Kannan teaches:
to perform the determining the first temporal pattern that is relevant to the encounter at least in part by: determining the first temporal pattern that is relevant to the encounter based at least in part on one or more factors linked to the first symptom by a machine learned model (See [P0016] a first diagnosis engine based on rules in a knowledge based codifying probabilistic relationships between symptoms/findings and diseases, P0045 and Fig. 1.).
Therefore, it would have been obvious to one of ordinary skill in the art of medical machine learning models before the effective filing date of the claimed invention to modify the method, system and software of Ozen Irmak to include determining a temporal pattern that is relevant to the encounter based on factors linked to the first symptom by a machine learned model as taught by Kannan in order to address shortcomings associated with current medical decision support systems mentioned in Kannan’s P0008, P0136.
Claim 20:
Ozen Irmak discloses One or more non-transitory computer-readable media storing processor-executable instructions that, when executed by one or more processors, cause the one or more processors to perform operations (Fig. 1 user devices in P0050, Fig. 6 processor 602 in P0230 including instructions stored in the memory 604.) comprising:
obtaining first data indicative of at least a first symptom associated with an encounter between an individual and a provider (See Fig. 1 user devices in P0050, Fig. 6 processor 602 in P0230, exemplary health categories in P0061 and entered symptoms in P0090.);
obtaining second data comprising sensor data generated by one or more devices associated with the individual (See Fig. 1 different types of sensors in P0002, P0031 and received inputs from one or more sensors (106, 108) in P0054. Also, see Fig. 4A-4B exemplary sensor data sleep features (sf_1-5) mentioned in P0062-P0063.);
generating, based at least in part on the first data and the second data, a data set comprising data indicative of one or more temporal patterns in the second data (See sensors monitoring (P0054, P0077-P0078) during an individual’s daily life in P00093 exemplary activity tracker, a sleeping tracker, a blood pressure monitor, or an electroencephalogram weekly in P0101-P0102. Also, see Fig. 4A-4B exemplary sensor data sleep features (sf_1-2 & 4) used as a different set of sleep (health) features, 404 and 454 mentioned in P0063-P0064, exemplary temporal health pattern mentioned in P0105, P0120.); and
determining, based at least in part on the data set, a first temporal pattern, of the one or more temporal patterns in the second data, that is relevant to the encounter (See sensors monitoring (P0053-P0054, P0077-P0078) during an individual’s daily life in P00093 exemplary activity tracker, a sleeping tracker, a blood pressure monitor, or an electroencephalogram as temporal patterns. Also, see relevant symptoms from intakes and diaries is shown Fig. 4A-4B, P0023 P0109.);
determining, based at least in part on the data set, a second temporal pattern, of the one or more temporal patterns in the second data, that is relevant to the encounter (With temporal patterns relevant to encounters such as symptoms and conditions associated with the encounter, see P0086-P0090 detecting the worsening or continued state of desirable/undesirable symptoms or unsustainable side-effects of interventions. Also, see exemplary levels of depression-related symptoms in P0122-P0123, data from sensors, diaries and questionnaires (P0078, P0094-P0095, P0101, P0226) can be used to determine temporal patterns relevant to an encounter.); and
determining a first metric indicative of how relevant the first temporal pattern is to the encounter and a second metric indicative of how relevant the second temporal pattern is to the encounter (Besides the diagnostic threshold as a metric indicating levels of depression-related symptoms in P0122-P0123, see P0070-P0072 as score thresholds among assigned first, second and third labels for each or some of the input modules, and the modules can be used to predict that module's health-marker's normalcy);
Although Ozen Irmak discloses a computer-implemented method, system and software to determine temporal patterns in the second data, that is relevant to the first symptom mentioned above and generated output text as a text explaining an alert in P0053, see structured text, narrative text data in P0078-P0079 and [P0227] pre-processing of event diaries include extracting the following information from unstructured text: word-choice, number of words used, repeated words, completion duration, keyword analysis, time of entry, etc.), Ozen Irmak does not explicitly teach when the generated output text is indicative of a temporal pattern and an arrangement of the first text and the second text within the output text being based on the first metric and the second metric. Kannan teaches:
generating output text comprising first text indicative of the first temporal pattern (See Fig. 1 medical text understanding 126 mentioned in P0055-P0056.) and second text indicative of the second temporal pattern, wherein an arrangement of the first text and the second text within the output text is based on the first metric and the second metric (See generated questions via conversation engine in Fig. 10, P0092-P0095 where a user’s summary of symptoms are extracted and arranged, and P0100-0101 where the learned models incorporate correlated symptoms using rule-based engines. Also, see P0122-P0133 indexing content serves as metrics when responding to any question about symptoms, laboratory test, medications and procedures.).
Therefore, it would have been obvious to one of ordinary skill in the art of medical machine learning models before the effective filing date of the claimed invention to modify the method, system and software of Ozen Irmak to include when the generated output text is indicative of a temporal pattern and an arrangement of the first text and the second text within the output text being based on the first metric and the second metric as taught by Kannan in order to address shortcomings associated with current medical decision support systems mentioned in Kannan’s P0008, P0136.
Response to Arguments
Applicant argues that the amended claims are patent eligible because they do not recite a mental process, see pgs. 9-10 of Remarks – Examiner disagrees.
In the instant case, health related symptoms are communicated during an encounter between an individual and a provider, which is equivalent to the individual describing symptoms in a scheduled medical appointment. Furthermore, sensor data already detected from a personal device, a wearable or implanted medical device or an environmental monitoring sensor is presented to a user. The preceding steps of claim 1, can be done in the human mind, where a user would apply one’s knowledge of recording symptoms associated with a medical patient, reviewing the medical patient’s medical history, determining if any of the patient’s medical history contains reoccurring symptoms, emergency room visits, elevated vital signs or which medications were taken. Besides “certain methods of organizing human activity” and “mental process”, independent claims 1, 17 and 20 have determining metric steps, which are mathematical concepts.
Applicant argues that the claims as a whole would integrate any such judicial exception into one or more practical application, with the metric-driven arrangement of output text, liken to Example 37, see pgs. 10-11 of Remarks – Examiner disagrees.
Performing curating a data set with temporal data, and identifying temporal patterns based on such data, enables the efficient generation of an accurate, relevant, and concise summary are operations any generic computer is merely using the computer as a tool to implement the abstract idea (saying “apply it”) and is merely using the computer in the manner in which it was designed to be used, i.e., performing generic computer functions. Unlike Example 37, in the instant case, the use of display icons is not being tracked over a period of time or ranked according to most often used. The recited improvements are nonetheless directed towards improving the abstract idea and not the computer itself – that is, the recited invention may improve determining temporal patterns from detected sensor data and symptoms associated with an encounter between an individual and a provider (i.e. the abstract idea), but there is no evidence to show that it improves the structural or functional properties of the computer itself.
Applicant argues on the basis that the Ozen and Kannan references fail to teach “generating, by the one or more processors and based at least in part on the first data and the second data, a data set comprising data indicative of one or more temporal patterns in the second data”. Rather, Ozen’s conversation engine generates questions (Fig. 10, P0092-P0095) where a user’s summary of symptoms are extracted and arranged. Also, see Ozen’s P0100-0101 where the learned models incorporate correlated symptoms using rule-based engines, P0122-P0133 indexing content serves as metrics when responding to any question about symptoms, laboratory test, medications and procedures and Fig 1 medical text understanding 126 mentioned in P0055-P0056.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. See Croan (US 2008/0215365 A1).
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.
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/T.S.W./Examiner, Art Unit 3687 07/14/2026
/Anita Y Coupe/Supervisory Patent Examiner, Art Unit 3619
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