DETAILED ACTION
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 .
Claims 1-20 have been examined.
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 1-8 are drawn to a system which is within the four statutory categories (i.e. machine). Claims 9-16 are drawn to a method which is within the four statutory categories (i.e. process). Claims 17-20 are drawn to a non-transitory medium which is within the four statutory categories (i.e. manufacture).
Step 2A, Prong 1:
Claims 1 recites “obtaining, by one or more processors, data associated with a set of individual profiles corresponding to a set of individuals, where each individual profile of the set of individual profiles comprises a plurality of entries representing a state of an individual; determining, by the one or more processors, a set of attributes associated with each individual corresponding to each individual profile, where each attribute is associated with at least one indicator representing the state of the individual; in response to determining that a first indicator is present for the set of individuals based on the first indicator satisfying an expression level for each individual, determining, by the one or more processors, correlations between a subset of the attributes for each individual of the set of individuals that are being treated for a condition, the correlations indicating a subset of indicators different from the first indicator that are indicative of the condition; and providing, by the one or more processors, output data associated with the correlations indicating the subset of indicators, the output data configured to cause a graphical user interface (GUI) to be displayed that indicates subset of indicators that are indicative of the condition” and the limitations of “determining, by the one or more processors, a set of attributes associated with each individual corresponding to each individual profile,…”, “in response to determining that a first indicator is present for the set of individuals based on the first indicator satisfying an expression level for each individual, determining, by the one or more processors, correlations between a subset of the attributes for each individual of the set of individuals that are being treated for a condition…” are directed to an abstract idea of “certain methods of organizing human activity” with a recitation of generic computing device (one or more processors).
This is a method of managing interactions between people, such as user following rules and instructions. The mere nominal recitation of a generic processor device does not take the claim out of the methods of organizing human interactions grouping. Thus, the claim recites an abstract idea.
Claims 1 and 17 repeat the same limitations and are also rejected for the same reason.
Claims 2, 10, 18, 3, 11, 19 recite “clustering, by the one or more processors, the individual profiles to form a plurality of clusters,…”, “performing, by the one or more processors, a cluster analysis based on the plurality of clusters to identify attributes that are indicative of presence of the condition”, and these limitations are directed to mathematical calculations, therefore the limitations fall within the “mathematical concept” grouping of abstract ideas.
The dependent claims also recite limitations that are directed to an abstract idea of “certain methods of organizing human activity”, such as claims 2, 10, 18 recite “determining, by the one or more processors, the correlations between the subset of the attributes based on the plurality of clusters”, claims 4, 12, 20 recite “determining, by the one or more processors, a second set of correlations indicating a second subset of indicators”, claims 5, 13 recite “determining the correlations between the subset of the attributes comprises: determining, by the one or more processors, correlations between the subset of the attributes for individuals over the period of time, the correlations indicating changes in the subset of indicators that are indicative of the condition”, claims 6, 14 recite “determining, by the one or more processors, a first attribute that is associated with a change pattern over the period of time, the change pattern representing changes in quantity of an indicator represented by samples gathered from individuals over the period of time”, and claim 15 recites “determining, by the one or more processors, a first attribute that is associated with a first change pattern over the period of time, the first change pattern representing first changes in quantity of a first indicator represented by samples gathered from individuals over the period of time; determining, by the one or more processors, a second attribute that is associated with a second change pattern over the period of time, the second change pattern representing first changes in quantity of a second indicator represented by samples gathered from individuals over the period of time; and determining, by the one or more processors, a correlation between the first attribute and the second attribute based on a comparison of the first changes in quantity and the second changes in quantity over time”.
These limitations correspond to a method of managing interactions between people, such as user following rules and instructions and the mere nominal recitation of a generic processor device does not take the claims out of the methods of organizing human interactions grouping.
After considering all claim elements, both individually and in combination and in ordered combination, it has been determined that the claims do not amount to significantly more than the abstract idea itself.
Claims 2-8, 10-16 and 18-20 are ultimately dependent from claims 1, 9, 17 and include all the limitations of claims 1, 9, 17. Therefore, claims 2-8, 10-16 and 18-20 recite the same abstract idea. Claims 2-8, 10-16 and 18-20 describe a further limitation regarding the basis for determining correlations indicating the subset of indicators. These are all just further describing the abstract idea recited in claims 1, 9, 17, without adding significantly more.
Step 2A, Prong 2:
This judicial exception is not integrated into a practical application. In particular, the current claims recite the additional elements of “one or more processors”, using one or more processors to perform the steps of determining a set of attributes associated with the individual and determine the correlations between a subset of the attributes…”, “a graphical user interface (GUI)”, “a non-transitory computer-readable medium storing instructions …”, which are hardware and software elements, these limitations are not enough to qualify as “practical application” being recited in the claims along with the abstract idea since these elements are merely invoked as a tool to apply instructions of the abstract idea in a particular technological environment, and mere instructions to apply/implement/automate an abstract idea in a particular technological environment and merely limiting the use of an abstract idea to a particular field or technological environment do not provide practical application for an abstract idea (MPEP 2106.05(f) & (h)).
Claims also recite other additional limitations beyond abstract idea, including functions such as obtain data from a database and output data are insignificant extra-solution activities (see MPEP 2106.05 (g)), which do not provide a practical application for the abstract idea.
Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claims are directed to an abstract idea.
Step 2B:
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional element of using a processor to perform determining steps amounts to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept.
The claims are not patent eligible.
Claim Rejections - 35 USC § 102
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 the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claims 1-20 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Kenedy et al. (hereinafter Kenedy) (US 2021/0233665 A1).
As per claims 1-8, they are system claims which repeat the same limitations of claims 9-16, the corresponding method claims, as a collection of elements as opposed to a series of process steps. Since the teachings of Kenedy disclose the underlying process steps that constitute the methods of claims 9-16, it is respectfully submitted that they provide the underlying structural elements that perform the steps as well. As such, the limitations of claims 1-8 are rejected for the same reasons given below for claims 9-16.
As per Claim 9, Kenedy discloses a method for processing profiles included in a protected dataset maintained in a secured network location and configured to prevent re-identification of individuals represented by the profiles to determine correlations between indicators representing latent patterns that are indicative of a condition being present in individuals, the method comprising:
obtaining, by one or more processors, data associated with a set of individual profiles corresponding to a set of individuals, where each individual profile of the set of individual profiles comprises a plurality of entries representing a state of an individual (Kenedy; “…The attribute profile of an individual is preferably provided to embodiments of the present invention as a dataset record whose association with the individual can be indicated by a unique identifier contained in the dataset record. An actual attribute of an individual can be represented by an attribute descriptor in attribute profiles, records, datasets, and databases…” in [0044], “…data is obtained…” in [0051]);
determining, by the one or more processors, a set of attributes associated with each individual corresponding to each individual profile, where each attribute is associated with at least one indicator representing the state of the individual (Kenedy; “…Attribute determination system 200 then, based on attributes of large query-attribute-positive and query-attribute-negative populations, determines which attributes and combinations of attributes, extending across the pangenetic (genetic/epigenetic), physical, behavioral, situational, and historical attribute categories, are statistically related to the query attribute…” in [0051]);
in response to determining that a first indicator is present for the set of individuals based on the first indicator satisfying an expression level for each individual, determining, by the one or more processors, correlations between a subset of the attributes for each individual of the set of individuals that are being treated for a condition, the correlations indicating a subset of indicators different from the first indicator that are indicative of the condition (Kenedy; “…In addition to genetic and epigenetic attributes, which can be referred to collectively as pangenetic attributes, numerous other attributes likely influence the development of traits and disorders. These other attributes, which can be referred to collectively as non-pangenetic attributes, can be categorized individually as physical, behavioral, or situational attributes.…” in [0042], “…every attribute remains a potentially important characteristic of the individual and may contribute to predisposition toward some other attribute or subset of attributes queried during subsequent or future implementation of the methods disclosed herein…” in [0043]); and
providing, by the one or more processors, output data associated with the correlations indicating the subset of indicators, the output data configured to cause a graphical user interface (GUI) to be displayed that indicates subset of indicators that are indicative of the condition (Kenedy discloses “…Based on the comparison that is performed, one or more graphic displays can be presented to one or both users indicating regions of similarity and regions of dissimilarity. Using the methods and system described herein, sets of common attributes can be identified, or sets of attributes common to a larger group can be identified and highlighted to the user through the graphical display…” in [0075]).
As per claim 10, Kenedy discloses the method of claim 9, wherein determining the correlations between the subset of the attributes comprises: clustering, by the one or more processors, the individual profiles to form a plurality of clusters, where each individual profile is assigned to a cluster of the plurality of clusters based on similarities between attributes of the subset of attributes, and determining, by the one or more processors, the correlations between the subset of the attributes based on the plurality of clusters (Kenedy: “… a query submitted by a user with respect to their predisposition for a disease can cause the system to immediately cluster individuals like the user ( e.g. similar genetic structure , similar diagnosis , similar attributes ) to provide statistical risk of developing the disease… the system might cluster people having genetic attributes and other key attributes ( e.g. BMI , age , physical activity level ) identical or very similar to the user in order to determine which medications will be most efficacious for that user.” in [0083]).
As per claim 11, Kenedy discloses the method of claim 10, wherein determining the correlations between the subset of the attributes based on the plurality of clusters comprises: performing, by the one or more processors, a cluster analysis based on the plurality of clusters to identify attributes that are indicative of presence of the condition, and determine, by the one or more processors, the subset of indicators that correspond to the attributes that are indicative of the presence of the condition (Kenedy; [0083]).
As per claim 12, Kenedy discloses the method of claim 9, wherein determining the correlations between the subset of the attributes includes: determining, by the one or more processors, a first set of correlations indicating a first subset of indicators, where each indicator of the first subset of indicators satisfies a primary correlation threshold indicative of the condition, and the method further comprising: determining, by the one or more processors, a second set of correlations indicating a second subset of indicators, where each indicator of the second subset of indicators satisfies a secondary correlation threshold indicative of the condition, and where each indicator of the second subset of indicators is not indicated by the first subset of indicators (Kenedy: “…every attribute remains a potentially important characteristic of the individual and may contribute to predisposition toward some other attribute or subset of attributes queried during subsequent or future implementation of the methods disclosed herein…” in [0043], “… A social network can also be auto - formed from a population of individuals to include individuals similar to the user based on a minimum shared set of attributes or based on thresholds for overall degrees of similarity at the pangenetic and / or non - pangenetic levels” in [0080]).
As per claim 13, Kenedy discloses the method of claim 9, wherein obtaining the data associated with a set of individual profiles corresponding to a set of individuals comprises: obtaining, by the one or more processors, the data associated with a set of individual profiles, where each individual profile comprises a plurality of entries having one or more sub-entries that represent the state of the individual over a period of time, and wherein determining the correlations between the subset of the attributes comprises: determining, by the one or more processors, correlations between the subset of the attributes for individuals over the period of time, the correlations indicating changes in the subset of indicators that are indicative of the condition (Kenedy: “…behavioral , situational , and historical attributes are collected from consumer 210 or clinician 220 via describe behavioral attributes use case 244 , describe situational attributes use case 246 , and describe historical attributes use case 248 , respectively…” in [0051]).
As per claim 14, Kenedy discloses the method of claim 13, wherein determining the correlations between the subset of the attributes for individuals over the period of time comprises: determining, by the one or more processors, a first attribute that is associated with a change pattern over the period of time, the change pattern representing changes in quantity of an indicator represented by samples gathered from individuals over the period of time (Kenedy: “…, the nodes and / or edges can be colored or patterned to indicate the degree of similarity or type of associations between individuals . Color coding and / or geometric patterns can similarly be used to visualize the associations between individuals and attributes comprising the network. Color coding and / or geometric patterns can be used to indicate individuals that form a cluster based on shared attributes and / or associations. Color coding and / or patterns can be used to indicate the attributes of interest possessed by an individual in the network. …” in [0096]).
As per claim 15, Kenedy discloses the method of claim 13, wherein determining the correlations between the subset of the attributes for individuals over the period of time comprises: determining, by the one or more processors, a first attribute that is associated with a first change pattern over the period of time, the first change pattern representing first changes in quantity of a first indicator represented by samples gathered from individuals over the period of time; determining, by the one or more processors, a second attribute that is associated with a second change pattern over the period of time, the second change pattern representing first changes in quantity of a second indicator represented by samples gathered from individuals over the period of time; and determining, by the one or more processors, a correlation between the first attribute and the second attribute based on a comparison of the first changes in quantity and the second changes in quantity over time (Kenedy: “…, the nodes and / or edges can be colored or patterned to indicate the degree of similarity or type of associations between individuals . Color coding and / or geometric patterns can similarly be used to visualize the associations between individuals and attributes comprising the network. Color coding and / or geometric patterns can be used to indicate individuals that form a cluster based on shared attributes and / or associations. Color coding and / or patterns can be used to indicate the attributes of interest possessed by an individual in the network. …” in [0096]).
As per claim 16, Kenedy discloses the method of claim 15, wherein the first attribute is further associated with a correlation that satisfies a primary correlation threshold indicative of the condition, and wherein the second attribute is further associated with correlation that satisfies the primary correlation threshold or a secondary correlation threshold indicative of the condition (Kenedy: “… The statistical results obtained from computing the statistical measures , as well as the attribute combinations to which they correspond , can be subjected to inclusion , elimination , filtering , and evaluation based on meeting one or more statistical requirements which may be predetermined , predesignated , preselected or alternatively , computed de novo based on the statistical results . Statistical requirements can include, but are not limited to, numerical thresholds , statistical minimum or maximum values , and statistical significance ( confidence ) values which may collectively be referred to as predetermined statistical thresholds . …” in [0137]).
As per claims 17-20, they are article of manufacture claims which repeat the same limitations of claims 9-12, the corresponding method claims, as a collection of executable instructions stored on machine readable media as opposed to a series of process steps. Since the teachings of Kenedy disclose the underlying process steps that constitute the method of claims 9-12, it is respectfully submitted that they likewise disclose the executable instructions that perform the steps as well. As such, the limitations of claims 17-20, are rejected for the same reasons given above for claims 9-12.
Conclusion
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/DILEK B COBANOGLU/Primary Examiner, Art Unit 3687
