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 .
Information Disclosure Statement
The Information Disclosure Statement(s) filed on 27 June 2024, has been considered by the Examiner.
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 a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Claims 1, 13 and 20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claims recite computer-implemented method, system, and one or more non-transitory computer-readable storage media (i.e., system) for performing the limitations of:
Claim 1, which is representative of claims 13 and 20
[… obtaining …], entity data associated with an entity, the entity data including one or more locations of the entity at respective times; determining, by […] executing a dynamic period algorithm, one or more periods based on the entity data, wherein at least one of the one or more periods includes at least one of the respective times; applying, […], a […] model to the entity data and the one or more periods, wherein applying the […] model includes determining, for at least one period of the one or more periods, one or more confidence values associated with each location at the respective times included in the at least one period based on (i) a frequency associated with each location and (ii) a period distance value relating a current time to the at least one period, and outputting a ranking for each location included in the at least one period based on the one or more confidence values; and generating, […], a data object indicating one or more of the ranked locations.
as drafted, is a system, which under its broadest reasonable interpretation, covers a method of organizing human activity (i.e., managing personal behavior including following rules or instructions) via human interaction with generic computer components. That is, by a human user interacting with a computer with one or more processors (claim 1), a memory and one or more processors (claims 13 and 20), the claimed invention amounts to managing personal behavior or interaction between people, the Examiner notes as stated in 2106.04(a)(2), “certain activity between a person and a computer… may fall within the “certain methods of organizing human activity” grouping”. For example, but for a computer with one or more processors (claim 1), a memory and one or more processors (claims 13 and 20), the claim encompasses collection of data about an entity, organization of the collected data using various algorithms and models, and providing an output for a human user to use based on the organization of the data. If a claim limitation, under its broadest reasonable interpretation, covers managing personal behavior or interactions between people but for the recitation of generic computer components, then it falls within the “method of organizing human activity” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
This judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements of a computer with one or more processors (claim 1), a memory and one or more processors (claims 13 and 20), which implements the abstract idea. The computer with one or more processors (claim 1), a memory and one or more processors (claims 13 and 20) are recited at a high-level of generality (i.e., a general-purpose computers/ computer components implementing generic computer functions; see Applicant’s Specification Fig. 1, paragraphs [0034]-[0036]) such that it amounts no more than mere instructions to apply the exception using generic computer components. Accordingly, these additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
The claim recites the additional elements of “receiving…” and “applying… a machine learning model”. The “receiving…” steps are recited at a high-level of generality (i.e., as a general means of receiving/transmitting data) and amounts to the mere transmission and/or receipt of data, which is a form of extra-solution activity. The “applying… a machine learning model” is recited at a high-level of generality (i.e., using a generic off the shelf machine learning algorithm to make predictions) and amounts to merely linking of the abstract idea to particular technological environment. Accordingly, even in combination, these additional elements do not integrate the abstract idea into a practical application. The claim is directed to an abstract idea.
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements of a computer with one or more processors (claim 1), a memory and one or more processors (claims 13 and 20) to perform the noted steps amounts to no more than mere instructions to apply the exception using generic hardware components. Mere instructions to apply an exception using generic hardware components cannot provide an inventive concept ("significantly more").
Also, as discussed above with respect to integration of the abstract idea into a practical application, the additional elements of “receiving…” and “applying… a machine learning model” were considered extra-solution activity and/or generally linking the abstract idea to particular technological environment. The “receiving…” steps have been re-evaluated under the "significantly more" analysis and determined to amount to be well-understood, routine, and conventional elements/functions. As described in MPEP 2106.0S(d)(II)(i) "Receiving or transmitting data over a network" is well-understood, routine, and conventional. The “applying… a machine learning model” been re-evaluated under the “significantly more” analysis and determined to amount to be well-understood, routine, and conventional elements/functions. As described in Malaviya (20170024531): see below but at least paragraph [0053]; Andrew (20150118667): paragraph [0102], [0116]; Harder (20250022588): paragraph [0022]; use of a machine learning model to make predictions is well-understood, routine and conventional. Well-understood, routine, and conventional elements/functions cannot provide "significantly more." As such the claim is not patent eligible.
Claims 2-12 and 14-19 are similarly rejected because either further define the abstract idea and/or do not further limit the claim to a practical application or provide as inventive concept such that the claims are subject matter eligible.
Claims 2 and 14 further describe the distance value, however does not recite any additional elements, therefore the claim cannot provide significantly more and/or a practical application.
Claims 3 and 15 recite the additional element of “converting… to the standardized format”, however this is recited at a high-level of generality (i.e., generic off-the-shelf converting) and amounts to generally linking the abstract idea to particular technological environment. Accordingly, even in combination, these additional elements do not integrate the abstract idea into a practical application. The claim is directed to an abstract idea.
Also, as discussed above with respect to integration of the abstract idea into a practical application, the additional elements were considered to be generally linking the abstract idea to particular technological environment. This has been re-evaluated under the "significantly more" analysis and determined to amount to be well- understood, routine, and conventional elements/functions. As described in Harder (20250022588): paragraph [0027]; Tal (20230376858): paragraph [0066]; converting data to a standard format is well-understood routine and conventional. Well-understood, routine, and conventional elements/functions cannot provide “significantly more.” As such the claim is not patent eligible.
Claim 4 describes the entity data, however does not recite any additional elements, therefore the claim cannot provide significantly more and/or a practical application.
Claims 5 and 16 further recite the additional element of “wherein the machine learning model is trained using…”, however this is recited at a high-level of generality (i.e., generic off-the-shelf training) and amounts to generally linking the abstract idea to particular technological environment. Accordingly, even in combination, these additional elements do not integrate the abstract idea into a practical application. The claim is directed to an abstract idea.
Also, as discussed above with respect to integration of the abstract idea into a practical application, the additional elements were considered to be generally linking the abstract idea to particular technological environment. This has been re-evaluated under the "significantly more" analysis and determined to amount to be well- understood, routine, and conventional elements/functions. As described in Malaviya (20170024531): see below but at least paragraph [0172]; Andrew (20150118667): paragraph [0116]; Harder (20250022588): paragraph [0103]; training of machine learning models is well-understood routine and conventional. Well-understood, routine, and conventional elements/functions cannot provide “significantly more.” As such the claim is not patent eligible.
Claim 6 further describes the periods, however does not recite any additional elements, therefore the claim cannot provide significantly more and/or a practical application.
Claims 7 and 17 further describes the dynamic period algorithm, however does not recite any additional elements, therefore the claim cannot provide significantly more and/or a practical application.
Claims 9-10 and 18-19 further describe application of the machine learning model, however use of a generic off-the-shelf machine learning model was already considered above and is incorporated herein.
Claim 11 describes updating data, however does not recite any additional elements, therefore the claim cannot provide significantly more and/or a practical application.
Claim 12 recites the additional element of “a random forest model” however this is recited at a high-level of generality (i.e., generic type of machine learning model) and amounts to generally linking the abstract idea to particular technological environment. Accordingly, even in combination, these additional elements do not integrate the abstract idea into a practical application. The claim is directed to an abstract idea.
Also, as discussed above with respect to integration of the abstract idea into a practical application, the additional elements were considered to be generally linking the abstract idea to particular technological environment. This has been re-evaluated under the "significantly more" analysis and determined to amount to be well- understood, routine, and conventional elements/functions. As described in Harder (20250022588): paragraph [0103]; Firooz (20170017901): paragraph [0065]; random forest models are well-understood routine and conventional. Well-understood, routine, and conventional elements/functions cannot provide “significantly more.” As such the claim is not patent eligible.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claim(s) 1, 4-5, 9-11, 13, 16 and 18-20 are rejected under 35 U.S.C. 103 as being unpatentable over U.S. Patent Pub. No. 20170024531 (hereafter “Malaviya”), in view of U.S. Patent Pub. No. 20150118667 (hereafter “Andrew”).
Regarding claim 1, Malaviya teaches computer-implemented method (Malaviya: Figs. 1-5, paragraph [0004], “a computer-implemented method”) comprising:
receiving, by one or more processors, entity data associated with an entity, the entity data including one or more locations of the entity at respective times (Malaviya: Figs. 1-5, paragraph [0004], “receiving or continuously monitoring electronic position information associated with one or more entities (e.g. individuals, objects) within a healthcare facility during a duration of time”, paragraph [0032], “track or map… entities for efficient risk prediction, processing and management, including infection control, for example, by receiving, processing and aggregating position and time data points related to healthcare organizations to dynamically generate mapping data structures with multiple dimensions representative of the aggregated position and time data points”, paragraph [0049], “Devices 102 may also have various electronic components, such as processors, input interfaces (e.g., keyboards, touch screens), output interfaces (e.g., display screens)”, paragraph [0095], “system 200 may be provided in various forms using particularly configured hardware, and includes electronic implementation through the use of various computing equipment, such as servers, data storage devices, processors”);
determining, by the one or more processors executing a dynamic period algorithm, one or more periods based on the entity data, wherein at least one of the one or more periods includes at least one of the respective times (Malaviya: Figs. 1-5, paragraph [0032], “dynamically generate mapping data structures with multiple dimensions representative of the aggregated position and time data points”, paragraphs [0040]-[0041], “user interface components 104 may permit for access and/or modification of a contextual profile… dynamically generate and update or modify mapping data structures representative of multiple dimensions of data points (e.g. position, time, identifiers, descriptors, fields)”, paragraph [0046], “Movement may be provided (e.g., received, determined) through movement data that corresponds to various time points and/or time segments… These combinations of movements and time may be grouped into various movement events”, paragraph [0065], “The map structure can include location points for different time periods”, paragraph [0118], “the system may be configured to apply algorithms”, paragraph [0166], “Various algorithms may be used… Some of these algorithms and/or techniques can be utilized in various combinations with one another”. The Examiner notes that a claim may be rendered obvious where the limiting function is that of making a set of prior-known elements contiguous, i.e., bringing them together. As such, this claim would be obvious to one of ordinary skill in the art at the time of the invention to make the algorithms of Malaviya continuous without undue experimentation or risk of unexpected results, see In re Dulberg, 289 F.2d 522, 523, 129 USPQ 348, 349 (CCPA 1961). MPEP 2144.04);
applying, by the one or more processors, a machine learning model to the entity data and the one or more periods (Malaviya: Figs. 1-5, paragraph [0053], “risk level may be determined, for example, based on machine-learned and/or tracked event data associated with a propensity to create risk, and may be weighted based on risk severity”, paragraph [0082], “run analyses to determine and/or continually refine relationships identified between various factors. In some embodiments, machine-learning and/or neural network techniques are utilized to determine and/or probabilistically estimate the strength of relationships (e.g., through correlations, cross-correlations) as there are a large number of variables whose relationships with one another is not entirely know”, paragraph [0172], “the system 200 may be configured to perform machine-based learning techniques and analyses to heuristically assess probabilities”), wherein applying the machine learning model includes
determining, for at least one period of the one or more periods, one or more confidence values associated with each location at the respective times included in the at least one period (Malaviya: Figs. 1-5, paragraph [0063], “The location or position data points can be linked to a time component to provide an additional dimension to the map data structure. These data points are contextualized based on data stored on contextual profiles such that additional values and scores may be associated with the points (e.g., infection risk probability, healthcare incident risk probability, name of individual, accessibility constraints, medications prescribed, occupation of individual, task being assigned to individual)”, paragraph [0109], “data elements may be associated with various levels of confidence (e.g., some information is known with a high level of certainty”, paragraph [0129], “Various different levels of risk can be identified (e.g., based on seriousness of an adverse outcome), with differing levels of confidence (e.g., providing a confidence score). In some embodiments, the level of risk and the level of confidence associated with a risk may be used to determine a holistic risk rating (e.g., based on an expected value). Electronic indicia relating to one or more risks and/or other associated information or metadata may be stored in data storage 250. In some embodiments, the risk identification unit 218 may be configured to utilize probabilistic models and/or predictive models that may be refined over time/repeated events to determine that a risk is present”) based on […] and
(ii) a period distance value relating a current time to the at least one period (Malaviya: Figs. 1-5, paragraph [0006], “wherein the approximated probabilistic risk level for each individual is re-weighted based at least on distance from the location of the healthcare practitioner, the superimposing generating an initial practitioner location-contextualized electronic map structure”, paragraph [0100], “The positional information may be defined with absolute metrics (e.g., GPS coordinates) or in relative metrics (e.g., distance”), and
outputting a ranking for each location included in the at least one period based on the one or more confidence values (Malaviya: Figs. 1-5, paragraphs [0006]-[0008], “the at least one generated electronic map structure storing the current locations of the one or more individuals as nodes… generating an electronic prioritized list, based on the initial practitioner location-contextualized electronic map structure, providing an ordered list of nodes ranked in accordance to the re-weighted approximated probabilistic risk level of the node”, claim 5, “providing an ordered list of nodes ranked in accordance to the re-weighted approximated probabilistic risk level of the node”); and
generating, by the one or more processors, a data object indicating one or more of the ranked locations (Malaviya: Figs. 1-5, paragraph [0004], “generate at least one electronic map structure storing, as location points, current locations of the one or more individuals and associating, with each of the location points, the approximated probabilistic risk level for the corresponding individual”, paragraph [0032], “dynamically generate mapping data structures with multiple dimensions representative of the aggregated position and time data points”, claim 5, “providing an ordered list of nodes ranked in accordance to the re-weighted approximated probabilistic risk level of the node”. The data structure provides the ranking and teaches what is require under the broadest reasonable interpretation).
Malaviya may not explicitly teach (underlined below for clarity):
determining, for at least one period of the one or more periods, one or more confidence values associated with each location at the respective times included in the at least one period based on (i) a frequency associated with each location and (ii) a period distance value relating a current time to the at least one period,
Andrew teaches determining, for at least one period of the one or more periods, one or more confidence values associated with each location at the respective times included in the at least one period based on (i) a frequency associated with each location and (ii) a period distance value relating a current time to the at least one period (Andrew: paragraph [0055], “uncertainty is calculated by physical factors related to the timing of signals received from the device's acquired satellites, so it can report a reliable estimate of sensor inaccuracy”, paragraph [0081], “likely uncertainty is based on precision of the sensor determining the location”, paragraph [0111], “The target frequency… provides a measurable score. Goals can also be one-time tasks. For example, tasks that need to be accomplished, where the exact location and timing… translated into a set of venues, venue types, and activities”, paragraph [0118], “assessing the user's behavior against the target frequency”),
One of ordinary skill in the art before the effective filing date would have found it obvious to include using frequency as taught by Andrew within the ranking of locations as taught by Malaviya with the motivation of “improving health, reducing carbon footprint, and saving money” (Andrew: Abstract).
Regarding claim 4, Malaviya and Andrew teach the limitations of claim 1, and further teach wherein the entity data includes a plurality of locations corresponding to the entity at the respective times (Malaviya: paragraph [0065], “The map structure can include location points for different time periods. The map structure can include location points for past locations of one or more individuals”, paragraph [0070]-[0071], “indicate the location of various objects, individuals and/or equipment… include time-variant information indicating that various locations and/or objects may have moved and/or otherwise been physically altered over a period of time. The time data may be linked to position data points for an entity”).
The motivation to combine is the same as in claim 1, incorporated herein.
Regarding claim 5, Malaviya and Andrew teach the limitations of claim 1, and further teach wherein the machine learning model is trained using (i) a plurality of training entity data corresponding to a plurality of entities and (ii) a plurality of training periods as inputs to output (a) rankings of locations included in the plurality of training entity data and (b) one or more optimal periods from the plurality of training periods (Malaviya: paragraphs [0006]-[0008], “providing an ordered list of nodes ranked in accordance to the re-weighted approximated probabilistic risk level of the node”, paragraph [0019], “one or more infected entities during a period of potential infection and setting a plurality of position nodes indicating that a plurality of positions are associated”, paragraphs [0065]-[0066], “include location points for different time periods… classification determinations based on tracked information from contextual profiles, such as movement data as aggregated from multiple individuals and other entities”, paragraph [0080], “a probabilistic pathway may be determined that may be optimized in view of particular contextual characteristics related to a particular healthcare issue”, paragraph [0146], “a more efficient usage of a known duration of a healthcare practitioner's time can be provided wherein contextualized profile and location information is harnessed to provide a more effective and efficient approach based on empirical methodology and healthcare predictions”, paragraph [0172], “the system 200 may be configured to perform machine-based learning techniques”).
The motivation to combine is the same as in claim 1, incorporated herein.
Regarding claim 9, Malaviya and Andrew teach the limitations of claim 1, and further teach wherein the dynamic period algorithm determines a plurality of periods based on the entity data (Malaviya: paragraph [0046], “various time points and/or time segments”; Andrew: paragraph [0026], “A storyline is composed of a time-ordered sequence of contexts that partition a given span of time that are arranged into groups at a plurality of hierarchical levels”, paragraph [0029], “a chapter 26 is shown as being divided into five slices… the first slice… the second slice… he third slice… the fourth slice… the fifth slice”), and applying the machine learning model further comprises:
determining, for each period of the plurality of periods, a confidence value associated with each location included in each period of the plurality of periods based on (i) a frequency associated with each location and (ii) a period distance value associated with each period of the plurality of periods (Malaviya: Figs. 1-5, paragraph [0006], “wherein the approximated probabilistic risk level for each individual is re-weighted based at least on distance from the location of the healthcare practitioner, the superimposing generating an initial practitioner location-contextualized electronic map structure”, paragraph [0063], “The location or position data points can be linked to a time component to provide an additional dimension to the map data structure. These data points are contextualized based on data stored on contextual profiles such that additional values and scores may be associated with the points (e.g., infection risk probability, healthcare incident risk probability, name of individual, accessibility constraints, medications prescribed, occupation of individual, task being assigned to individual)”, paragraph [0100], “The positional information may be defined with absolute metrics (e.g., GPS coordinates) or in relative metrics (e.g., distance”), paragraph [0109], “data elements may be associated with various levels of confidence (e.g., some information is known with a high level of certainty”, paragraph [0129], “Various different levels of risk can be identified (e.g., based on seriousness of an adverse outcome), with differing levels of confidence (e.g., providing a confidence score). In some embodiments, the level of risk and the level of confidence associated with a risk may be used to determine a holistic risk rating (e.g., based on an expected value). Electronic indicia relating to one or more risks and/or other associated information or metadata may be stored in data storage 250. In some embodiments, the risk identification unit 218 may be configured to utilize probabilistic models and/or predictive models that may be refined over time/repeated events to determine that a risk is present”; Andrew: paragraph [0055], “uncertainty is calculated by physical factors related to the timing of signals received from the device's acquired satellites, so it can report a reliable estimate of sensor inaccuracy”, paragraph [0081], “likely uncertainty is based on precision of the sensor determining the location”, paragraph [0111], “The target frequency… provides a measurable score. Goals can also be one-time tasks. For example, tasks that need to be accomplished, where the exact location and timing… translated into a set of venues, venue types, and activities”, paragraph [0118], “assessing the user's behavior against the target frequency”), and
outputting a ranking for each location included in each period of the plurality of periods based on respective confidence values (Malaviya: Figs. 1-5, paragraphs [0006]-[0008], “the at least one generated electronic map structure storing the current locations of the one or more individuals as nodes… generating an electronic prioritized list, based on the initial practitioner location-contextualized electronic map structure, providing an ordered list of nodes ranked in accordance to the re-weighted approximated probabilistic risk level of the node”, claim 5, “providing an ordered list of nodes ranked in accordance to the re-weighted approximated probabilistic risk level of the node”).
The motivation to combine is the same as in claim 1, incorporated herein.
Regarding claim 10, Malaviya and Andrew teach the limitations of claim 1, and further teach (a) determining, by the one or more processors, that each confidence value fails to satisfy a confidence threshold value (Malaviya: paragraph [0092], “Various scores may be generated… to compare to defined thresholds”, paragraph [0109], “data elements may be associated with various levels of confidence (e.g., some information is known with a high level of certainty”, paragraph [0129], “Various different levels of risk can be identified (e.g., based on seriousness of an adverse outcome), with differing levels of confidence (e.g., providing a confidence score). In some embodiments, the level of risk and the level of confidence associated with a risk may be used to determine a holistic risk rating (e.g., based on an expected value). Electronic indicia relating to one or more risks and/or other associated information or metadata may be stored in data storage 250. In some embodiments, the risk identification unit 218 may be configured to utilize probabilistic models and/or predictive models that may be refined over time/repeated events to determine that a risk is present”; paragraph [0182], “determines that a risk score has increased beyond a particular threshold, e.g., through the application of a rule by the rules engine 214”);
(b) determining, by the one or more processors executing the dynamic period algorithm, one or more additional periods based on the entity data (Malaviya: paragraph [0046], “various time points and/or time segments”; Andrew: paragraph [0026], “A storyline is composed of a time-ordered sequence of contexts that partition a given span of time that are arranged into groups at a plurality of hierarchical levels”, paragraph [0029], “a chapter 26 is shown as being divided into five slices… the first slice… the second slice… he third slice… the fourth slice… the fifth slice”);
(c) applying, by the one or more processors, the machine learning model to determine, for each period of the one or more additional periods, one or more respective confidence values associated with each location included in each period of the one or more additional periods based on (i) a respective frequency associated with each location and (ii) a respective period distance value associated with each period of the one or more additional periods (Malaviya: Figs. 1-5, paragraphs [0006]-[0008], “the at least one generated electronic map structure storing the current locations of the one or more individuals as nodes… generating an electronic prioritized list, based on the initial practitioner location-contextualized electronic map structure, providing an ordered list of nodes ranked in accordance to the re-weighted approximated probabilistic risk level of the node”, claim 5, “providing an ordered list of nodes ranked in accordance to the re-weighted approximated probabilistic risk level of the node”), and
output a respective ranking for each location included in each period of the one or more additional periods based on the one or more respective confidence values (Malaviya: Figs. 1-5, paragraphs [0006]-[0008], “the at least one generated electronic map structure storing the current locations of the one or more individuals as nodes… generating an electronic prioritized list, based on the initial practitioner location-contextualized electronic map structure, providing an ordered list of nodes ranked in accordance to the re-weighted approximated probabilistic risk level of the node”, claim 5, “providing an ordered list of nodes ranked in accordance to the re-weighted approximated probabilistic risk level of the node”);
(d) iteratively performing steps (a)-(c) until at least one respective confidence value satisfies the confidence threshold value (Malaviya: paragraph [0092], “Various scores may be generated… to compare to defined thresholds”, paragraph [0176], “The process may be iterated to continually identify”, paragraph [0182], “determines that a risk score has increased beyond a particular threshold, e.g., through the application of a rule by the rules engine 214”); and
generating, by the one or more processors, the data object indicating a ranked location corresponding with the at least one respective confidence value (Malaviya: Figs. 1-5, paragraph [0004], “generate at least one electronic map structure storing, as location points, current locations of the one or more individuals and associating, with each of the location points, the approximated probabilistic risk level for the corresponding individual”, paragraph [0032], “dynamically generate mapping data structures with multiple dimensions representative of the aggregated position and time data points”, claim 5, “providing an ordered list of nodes ranked in accordance to the re-weighted approximated probabilistic risk level of the node”. The data structure provides the ranking and teaches what is require under the broadest reasonable interpretation).
The motivation to combine is the same as in claim 1, incorporated herein.
Regarding claim 11, Malaviya and Andrew teach the limitations of claim 1, and further teach determining, by the one or more processors, a location value associated with the entity in a location database is different from a highest ranked location from the at least one period that has a highest confidence value of the one or more confidence values; and updating, by the one or more processors, the location value in the location database to include the highest ranked location (Malaviya: paragraph [0004], “updating one or more contextual digital profiles, each of the one or more contextual digital profiles corresponding to one of the one or more individuals with the contextualized electronic position information”, paragraphs [0006]-[0008], “the at least one generated electronic map structure storing the current locations of the one or more individuals as nodes… generating an electronic prioritized list, based on the initial practitioner location-contextualized electronic map structure, providing an ordered list of nodes ranked in accordance to the re-weighted approximated probabilistic risk level of the node”, paragraph [0070], “compare and/or correlate”, claim 5, “providing an ordered list of nodes ranked in accordance to the re-weighted approximated probabilistic risk level of the node”).
The motivation to combine is the same as in claim 1, incorporated herein.
REGARDING CLAIM(S) 13 and 20
Claim(s) 13 and 20 are analogous to Claim(s) 1, thus Claim(s) 13 and 20 are similarly analyzed and rejected in a manner consistent with the rejection of Claim(s) 1.
REGARDING CLAIM(S) 16
Claim(s) 16 is/are analogous to Claim(s) 5, thus Claim(s) 16 is/are similarly analyzed and rejected in a manner consistent with the rejection of Claim(s) 5.
REGARDING CLAIM(S) 18-19
Claim(s) 18-19 is/are analogous to Claim(s) 9-10, thus Claim(s) 18-19 is/are similarly analyzed and rejected in a manner consistent with the rejection of Claim(s) 9-10.
Claim(s) 2 and 14 are rejected under 35 U.S.C. 103 as being unpatentable over U.S. Patent Pub. No. 20170024531 (hereafter “Malaviya”) and U.S. Patent Pub. No. 20150118667 (hereafter “Andrew”) as applied to claims 1 and 13 above, and further in view of U.S. Patent Pub. No. 20190117138 (hereafter “Budiman”).
Regarding claim 2, Malaviya and Andrew teach the limitations of claim 1, and further teach wherein the period distance value is a difference […] included in the at least one period (Malaviya: Figs. 1-5, paragraph [0006], “wherein the approximated probabilistic risk level for each individual is re-weighted based at least on distance from the location of the healthcare practitioner, the superimposing generating an initial practitioner location-contextualized electronic map structure”, paragraph [0100], “The positional information may be defined with absolute metrics (e.g., GPS coordinates) or in relative metrics (e.g., distance”; Andrew: Figure 1, paragraph [0027], “the user's time can be divided into a sequence of slices. In this example, each slice has a type and a start and end time”, paragraph [0062], “k-means clustering can be applied to find clusters of raw contexts (by location, or a distance function combining location and time)”, paragraph [0068], “specify a time window within which preexisting data may be changed or replaced. Any data outside the window (i.e., older than a certain age)”, paragraph [0085], “different lengths of time”, paragraph [0122], “based on the user's future schedule or current context, it is determined”. The Examiner notes a current time in relation to a start time is used for creating a time window (i.e., period distance value), and teaches what is required under the broadest reasonable interpretation).
Malaviya and Andrew may not explicitly teach (underlined below for clarity):
the period distance value is a difference between a current time and an earliest time included in the at least one period.
Budiman teaches the period distance value is a difference between a current time and an earliest time included in the at least one period (Budiman: paragraphs [0086]-[0090], “there are various ways wear duration (T_wear) can be determined, depending on what actions constitute the wear start time and current time. System 100 can, in some embodiments, determine T_wear by subtracting a T_wear start time from a current time (or wear current time, which is the time used to approximate the current time)”).
One of ordinary skill in the art before the effective filing date would have found it obvious to include using a difference in time as taught by Budiman with the distance measurements for determination of confidence as taught by Malaviya and Andrew with the motivation of “improving the performance of analyte sensors” (Budiman: paragraph [0007]).
REGARDING CLAIM(S) 14
Claim(s) 14 is/are analogous to Claim(s) 2, thus Claim(s) 14 is/are similarly analyzed and rejected in a manner consistent with the rejection of Claim(s) 2.
Claim(s) 3, 12 and 15 are rejected under 35 U.S.C. 103 as being unpatentable over U.S. Patent Pub. No. 20170024531 (hereafter “Malaviya”) and U.S. Patent Pub. No. 20150118667 (hereafter “Andrew”) as applied to claims 1 and 13 above, and further in view of U.S. Patent Pub. No. 20250022588 (hereafter “Harder”).
Regarding claim 3, Malaviya and Andrew teach the limitations of claim 1, and further teach extracting, by the one or more processors, the entity data from a data file (Malaviya: paragraph [0058], “utilizing a “recorded by field” in medical records”, paragraph [0062], “This information may be determined, for example, by directly or indirectly measuring and/or monitoring movements, object states, post discharge monitoring, post procedure and patient records, etc”, paragraphs [0070]-[0073], “static maps (e.g., maps having static information, such as physical maps, blueprints, some electronic files) may be used… This information may be provided from one or more external systems, such as inventory systems, electronic health record systems”; Andrew: paragraph [0062], “algorithms can be used additionally or alternatively to extract”, paragraph [0071], “These labels may be either predefined or automatically extracted”);
[…] converting, by the one or more processors, the one or more non-standardized values from the non-standardized format to the standardized format (Andrew: paragraph [0040], “translating the data into a collection of raw contexts for additional analysis”).
Malaviya and Andrew may not explicitly teach (underlined below for clarity):
determining, by the one or more processors, (i) one or more non-standardized values within the entity data and (ii) a mapping to convert the one or more non-standardized values from a non-standardized format to a standardized format; and converting, by the one or more processors, the one or more non-standardized values from the non-standardized format to the standardized format.
Harder teaches determining, by the one or more processors, (i) one or more non-standardized values within the entity data and (ii) a mapping to convert the one or more non-standardized values from a non-standardized format to a standardized format; and converting, by the one or more processors, the one or more non-standardized values from the non-standardized format to the standardized format (Harder: paragraph [0027], “raw data may be in diverse formats and structures, necessitating extract, transform, load (ETL) processes to ensure consistency and reliability. The data processing module 103 may perform data cleaning on the raw data, by identifying and rectifying anomalies, such as missing values, duplicates, and outliers using sophisticated algorithms. The data processing module 103 may implement transformation processes to convert the data into a standardized format, employing techniques like data parsing and encoding to ensure interoperability between different data sources. The data processing module 103 may normalize the data to ensure that disparate data metrics are scaled to a common range, thereby allowing fair comparisons”).
One of ordinary skill in the art before the effective filing date would have found it obvious to include conversion to a standardized format as taught by Harder with the extraction of data as taught by Malaviya and Andrew with the motivation of “ensure consistency and reliability” (Harder: paragraph [0027]).
Regarding claim 12, Malaviya and Andrew teach the limitations of claim 1, but may not explicitly teach wherein the machine learning model is a trained random forest model.
Harder teaches wherein the machine learning model is a trained random forest model (Harder: paragraph [0103], “the machine-learning module 107 may leverage advanced algorithms and models to analyze complex healthcare data and generate accurate performance rankings. The machine-learning module 107 may employ a variety of machine-learning techniques including supervised learning algorithms like regression models, decision, trees, and ensemble methods (e.g., Random Forests,”)
The motivation to combine is the same as in claim 3, incorporated herein.
REGARDING CLAIM(S) 15
Claim(s) 15 is/are analogous to Claim(s) 3, thus Claim(s) 15 is/are similarly analyzed and rejected in a manner consistent with the rejection of Claim(s) 3.
Claim(s) 6-8 and 17 are rejected under 35 U.S.C. 103 as being unpatentable over U.S. Patent Pub. No. 20170024531 (hereafter “Malaviya”) and U.S. Patent Pub. No. 20150118667 (hereafter “Andrew”) as applied to claims 1 and 13 above, and further in view of U.S. Patent Pub. No. 20170017901 (hereafter “Firooz”).
Regarding claim 6, Malaviya and Andrew teach the limitations of claim 5, but may not explicitly teach wherein each optimal period of the one or more optimal periods corresponds to a respective entity type included in the plurality of entities.
Firooz teaches wherein each optimal period of the one or more optimal periods corresponds to a respective entity type included in the plurality of entities (Firooz: paragraph [0031], “Vector classification instructions, within the server computer, receive one or more sample episodes from a user or other external source. The one or more sample episodes include sample feature vectors that have been assigned a specific classification label”, paragraph [0053], “feature identification instructions 121 may provide instruction to determine the optimal time window size… determining a size of a time duration window for analyzing signal data and step size for advancing the time duration window in order to discover patterns of statistical interest based upon the time duration window. In an embodiment, the feature identification instructions 121 may provide instruction to evaluate the signal data sets by using auto-correlation to find a time duration window and step size that provides signal data of statistical interest. Auto-correlation in this context refers to analyzing the signal data set in order to discover repeating patterns that may be used to define the size of the time duration window and step size”. Determination of time size based on classification teaches what is required under the broadest reasonable interpretation).
One of ordinary skill in the art before the effective filing date would have found it obvious to include using optimal durations with entity type as taught by Firooz with the use of optimal time periods as aught by Malaviya and Andrew with the motivation of “improving the safety, reliability, and quality” (Firooz: paragraph [0033]).
Regarding claim 7, Malaviya, Andrew and Firooz teach the limitations of claim 6, and further teach wherein the dynamic period algorithm is configured to determine the one or more periods based on (i) the entity data and (ii) an optimal period of the one or more optimal periods corresponding to the respective entity type associated with the entity (Malaviya: Figs. 1-5, paragraph [0032], “dynamically generate mapping data structures with multiple dimensions representative of the aggregated position and time data points”; paragraph [0046], “Movement may be provided (e.g., received, determined) through movement data that corresponds to various time points and/or time segments… These combinations of movements and time may be grouped into various movement events”; paragraph [0146], “a more efficient usage of a known duration of a healthcare practitioner's time can be provided wherein contextualized profile and location information is harnessed to provide a more effective and efficient approach based on empirical methodology and healthcare predictions”; Firooz: paragraph [0053], “feature identification instructions 121 may provide instruction to determine the optimal time window size… determining a size of a time duration window for analyzing signal data and step size for advancing the time duration window in order to discover patterns of statistical interest based upon the time duration window. In an embodiment, the feature identification instructions 121 may provide instruction to evaluate the signal data sets by using auto-correlation to find a time duration window and step size that provides signal data of statistical interest. Auto-correlation in this context refers to analyzing the signal data set in order to discover repeating patterns that may be used to define the size of the time duration window and step size”).
The motivation to combine is the same as in claim 6, incorporated herein.
Regarding claim 8, Malaviya, Andrew and Firooz teach the limitations of claim 6, and further teach applying, by the one or more processors, the machine learning model to (i) new entity data and (ii) one or more new periods to determine one or more new optimal periods for one or more respective entity types included in the plurality of entities (Firooz: Fig.4, paragraph [0011], “classify a new set of feature vectors”, paragraph [0032], “assess new signal data sets received by the server computer system”).
The motivation to combine is the same as in claim 6, incorporated herein.
REGARDING CLAIM(S) 17
Claim(s) 17 is/are analogous to Claim(s) 7, thus Claim(s) 17 is/are similarly analyzed and rejected in a manner consistent with the rejection of Claim(s) 7.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
U.S. Patent Pub. No. 20230376858 (hereafter “Tal”) teaches classification of data using training data sets.
U.S. Patent Pub. No. 20230281483 (hereafter “Mallena”) teaches evaluation scores for periods of times using machine learning.
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/A.E.L./Examiner, Art Unit 3684
/Shahid Merchant/Supervisory Patent Examiner, Art Unit 3684