DETAILED ACTION The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA. This action is responsive to the Application filed on 07/20 /2023. Claims 1- 20 are pending in the case. Claims 1, 8 , and 15 are independent claims. Claim Rejections - 35 U.S.C. § 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 - 7 are directed towards the statutory category of a process. Claims 8 - 14 are directed towards the statutory category of a machine. Claims 15 - 20 are directed towards the statutory category of an article of manufacture. With respect to claim 1 : 2A Prong 1 : This claim is directed to a judicial exception. A… method comprising (mental process) : merging… population data with ancillary data from a first ancillary dataset and a second ancillary dataset, wherein (i) the population data has been extracted from a population dataset, and (ii) the populating dataset comprises location and feature information associated with a plurality of entities (mental process) ; generating… location data associated with the plurality of entities based on the merged population data (mental process) ; associating… the location data with external domain data (mental process) ; determining… a plurality of distances between the plurality of entities based on the location data (mental process) ; generating… one or more sets of a plurality of clusters comprising respective ones of the plurality of entities, wherein (i) one of the one or more sets of the plurality of clusters is generated based on respective ones of the plurality of distances associated with the respective ones of the plurality of entities are within a relative distance boundary, (ii) the respective ones of the plurality of entities comprises a first set of shared features or a second set of shared features, the first set of shared features and the second set of shared features determined based on (a) the merged population data, and (b) the association of the location data with the external domain data, and (iii) the one or more sets of the plurality of clusters is usable to train a prediction … model to rank one or more candidate entities (mental process) ; …. 2A Prong 2 : This judicial exception is not integrated into a practical application. Additional elements: computer-implemented (merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea, as discussed in MPEP § 2106.05(f)); by one or more processors (merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea, as discussed in MPEP § 2106.05(f)); prediction machine learning model (merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea, as discussed in MPEP § 2106.05(f) – high level machine learning ) ; and initiating… the performance of one or more prediction-based actions based on the ranking of the one or more candidate entities (adding insignificant extra-solution activity to the judicial exception, as discussed in MPEP § 2106.05(g)) . 2B : The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. Additional elements: computer-implemented (merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea, as discussed in MPEP § 2106.05(f)); by one or more processors (merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea, as discussed in MPEP § 2106.05(f)); prediction machine learning model (merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea, as discussed in MPEP § 2106.05(f) – high level machine learning ) ; and initiating… the performance of one or more prediction-based actions based on the ranking of the one or more candidate entities (MPEP 2106.05(d) indicates that merely “storing and retrieving information in memory” and/or "receiving or transmitting data over a network" are well‐understood, routine, conventional functions when they are claimed in a merely generic manner (as it is in the present claim). Thereby, a conclusion that the claimed step is well-understood, routine, conventional activity is supported under Berkheimer) . With respect to claim 2 : 2A Prong 1 : This claim is directed to a judicial exception. 2A Prong 2 : This judicial exception is not integrated into a practical application. Additional elements: the prediction machine learning model comprises a gradient boosting machine learning model (merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea, as discussed in MPEP § 2106.05(f)) . 2B : The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. Additional elements: the prediction machine learning model comprises a gradient boosting machine learning model (merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea, as discussed in MPEP § 2106.05(f)) . With respect to claim 3 2A Prong 1 : This claim is directed to a judicial exception. determining… a similarity score between a pair of the plurality of entities based on a comparison of one or more features associated with the pair of entities selected for comparison (mental process) . 2A Prong 2 : This judicial exception is not integrated into a practical application. Additional elements: by the one or more processors (merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea, as discussed in MPEP § 2106.05(f)) . 2B : The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. Additional elements: by the one or more processors (merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea, as discussed in MPEP § 2106.05(f)) . With respect to claim 4 : 2A Prong 1 : This claim is directed to a judicial exception. determining… a similarity score for the pair of entities based on a distance function and the embeddings (mental process) . 2A Prong 2 : This judicial exception is not integrated into a practical application. Additional elements: generating, by the one or more processors and using an embedding machine learning model, embeddings for the one or more features (merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea, as discussed in MPEP § 2106.05(f)); and by the one or more processors (merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea, as discussed in MPEP § 2106.05(f)) . 2B : The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. Additional elements: generating, by the one or more processors and using an embedding machine learning model, embeddings for the one or more features (merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea, as discussed in MPEP § 2106.05(f)); and by the one or more processors (merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea, as discussed in MPEP § 2106.05(f)) . With respect to claim 5 : 2A Prong 1 : This claim is directed to a judicial exception. the distance function comprises one of a Euclidean distance, a Manhattan distance, a Minkowski distance, a Jaccard distance, or a Cosine similarity (mental process); 2A Prong 2 : This judicial exception is not integrated into a practical application. 2B : The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. With respect to claim 6 : 2A Prong 1 : This claim is directed to a judicial exception. the first set of shared features is associated with barrier data based on the first ancillary dataset and the second set of shared features is associated with profile data based on the second ancillary dataset (mental process) . 2A Prong 2 : This judicial exception is not integrated into a practical application. 2B : The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. With respect to claim 7 : 2A Prong 1 : This claim is directed to a judicial exception. the respective ones of the plurality of entities associated with the one set of the plurality of clusters comprise per-cluster set similarity scores of at least a predetermined threshold (mental process) . 2A Prong 2 : This judicial exception is not integrated into a practical application. 2B : The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The remaining claims 8 - 20 are rejected under 35 U.S.C. § 101 because the claimed invention is directed to an abstract idea without significantly more for at least the same reasons as those given above with respect to claims 1 - 7 with only the addition of generic computer components under step 2A prong 1. Under the broadest reasonable interpretation, these limitations are process steps that cover mental processes including an observation, evaluation, judgment or opinion that could be performed in the human mind or with the aid of pencil and paper but for the recitation of a generic computer component. If a claim, under its broadest reasonable interpretation, covers a mental process but for the recitation of generic computer components, then it falls within the "Mental Process" grouping of abstract ideas. A person would readily be able to perform this process either mentally or with the assistance of pen and paper. See MPEP § 2106.04(a)(2). Limitations that merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea, as discussed in MPEP § 2106.05(f). These additional elements do not integrate the judicial exception into a practical application under step 2A prong 2. Refer to MPEP §2106.04(d). Moreover, the limitations are merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea, as discussed in MPEP § 2106.05(f). These additional elements do not recite any additional elements/limitations that amount to significantly more. Accordingly, the claimed invention recites an abstract idea without significantly more. Claim Rejections - 35 U.S.C. § 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 of this title, 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. 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 are advised of the obligation under 37 C.F.R. § 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. Claims 1, 2, 6, 8, 9, 13, 15, and 16 are rejected under 35 U.S.C. § 103 as being unpatentable over Tamer et al. (U.S. Pat. App. Pub. No. 2023/0245739 , hereinafter Tamer ) in view of Smart et al. (U.S. Pat. App. Pub. No. 2021/0334275 , hereinafter Smart ) , Sander et al. (Sander, Jörg, Martin Ester, Hans-Peter Kriegel, and Xiaowei Xu. "Density-based clustering in spatial databases: The algorithm gdbscan and its applications." Data mining and knowledge discovery 2, no. 2 (1998): 169-194, hereinafter Sander), and Burges (Burges, Christopher JC. "From ranknet to lambdarank to lambdamart: An overview." Learning 11, no. 23-581 (2010): 81, hereinafter Burges) . As to independent claim s 1, 8, and 15 , Tamer teaches A computer-implemented method comprising ( Title and abstract. Figure 11 and paragraph 36 ): … population data with ancillary data from a first ancillary dataset and a second ancillary dataset, wherein (i) the population data has been extracted from a population dataset, and (ii) the populating dataset comprises location and feature information associated with a plurality of entities ( Paragraph 18, "create a holistic patient record" via "Data identification, ingestion, integration, and staging." Paragraph 18, "The record includes metrics beyond patient-specific clinical information, such as SDoH." Paragraph 20, "address, blockgroup, census tract, latitude, longitude, county, ethnicity, race, language, smoking data, marital status" ); generating, by the one or more processors, location data associated with the plurality of entities based on the merged population data ( Paragraph 18, "The collected data include, e.g., 129 features specific to each patient, including unique identifiers, birthdate, death date (if applicable), domicile address, other location information (e.g., block group, census tract, latitude, longitude, and genocode" ); associating, by the one or more processors, the location data with external domain data ( Paragraph 18, " enriching the data from geo level to patient level". Paragraph 18, "data for, e.g., 171 features, are collected from the patients' neighborhoods (e.g., census block, block groups, census tracts), such as prevalence of chronic diseases, employment, income level, food insecurity, education level, population density, distance or accessibility to facility locations, etc. The metrics also includes data elements that provide an understanding of transportation-related access barriers" );… generating, by the one or more processors, one or more sets of a plurality of clusters comprising respective ones of the plurality of entities,… (ii) the respective ones of the plurality of entities comprises a first set of shared features or a second set of shared features, the first set of shared features and the second set of shared features determined based on (a) the merged population data, and (b) the association of the location data with the external domain data, and (iii) the one or more sets of the plurality of clusters is usable to train a prediction machine learning model… ( Paragraph 14, "The patient cluster identification system and method described herein are operable to identify patient clusters based on a given set of cluster-defining data features (also referred to as metrics or factors) and a given set of cluster-descriptive data features, assign a particular patient to an identified patient cluster." Paragraph 22, "The cluster-defining metrics include variables that provide insights to the patients' demographics, their utilization of healthcare facilities, and SDoH data of their neighborhoods." Shared feature sets derived from merged patient data plus geolinked external domain SDoH. Paragraph 14, "generate one or more recommendations, such as a care plan for the particular patient, targeted programs for one or more patient clusters, and improved workflows" ); and initiating, by the one or more processors, the performance of one or more prediction- based actions… ( Abstract, "generate a recommendation for holistic and targeted care plans and programs." ). Tamer does not appear to expressly teach. Smart teaches merging, by one or more processors ( Paragraph 3, "merges location-based data sets" to "produce a merged data set." ); and determining, by the one or more processors, a plurality of distances between the plurality of entities based on the location data ( Paragraph 3, " based on geographical distances between location-based data." Paragraph 4, "determining locations from a resulting data set within a predefined geographical distance of a location" ) . Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, having the patient clustering of Tamer to include the dataset merging of Smart to support use cases that can include, for example, identifying relevant users, selecting relevant content for users, identifying relevant locations for users, appropriately timing actions, and the like (see Smart at paragraph 2). Tamer does not appear to expressly teach wherein (i) one of the one or more sets of the plurality of clusters is generated based on respective ones of the plurality of distances associated with the respective ones of the plurality of entities are within a relative distance boundary. Sander teaches wherein (i) one of the one or more sets of the plurality of clusters is generated based on respective ones of the plurality of distances associated with the respective ones of the plurality of entities are within a relative distance boundary ( Page 181, "DBSCAN uses a distance-based neighborhood “distance less or equal than Eps”" ). Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, having the patient clustering of Tamer to include the spatial clustering techniques of Sander to cluster point objects as well as spatially extended objects according to both, their spatial and their nonspatial attributes (see Sander at page 170). Tamer does not appear to expressly teach to rank one or more candidate entities; and based on the ranking of the one or more candidate entities. Burges teaches to rank one or more candidate entities; and based on the ranking of the one or more candidate entities ( Page 1, "ranking" ). Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, having the patient clustering of Tamer to include the ranking techniques of Burges to solve real world ranking problems (see Burges at page 1). As to dependent claims 2, 9, and 16 , Burges further teaches the prediction machine learning model comprises a gradient boosting machine learning model ( Page 1, "LambdaMART is the boosted tree version," and "ranking" ). Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, having the patient clustering of Tamer to include the ranking techniques of Burges to solve real world ranking problems (see Burges at page 1). As to dependent claims 6 and 13 , Tamer further teaches the first set of shared features is associated with barrier data based on the first ancillary dataset and the second set of shared features is associated with profile data based on the second ancillary dataset ( Paragraph 16, "The first factor may depend on the amount of time to the care facility, which may include transit time, availability of public transit, the number and distribution of care facilities in a geographical area served by those facilities." Paragraph 18, "The collected data include, e.g., 129 features specific to each patient, including unique identifiers, birthdate, death date (if applicable), domicile address, other location information (e.g., block group, census tract, latitude, longitude, and genocode), demographics (ethnicity, race, age), preferred language, marital status, tobacco use, number and type of encounters, insurance coverage, utilization, disease registry status, Charlson Comorbidity index, etc." ). Claims 3, 7, 10, 14, 17, and 20 are rejected under 35 U.S.C. § 103 as being unpatentable over Tamer in view of Smart, Sander, Burges, and Parandehgheibi et al. ( U.S. Pat. App. Pub. No. 2016/0359680 , hereinafter Parandehgheibi ). As to dependent claims 3, 10, and 17 , the respective rejections of claims 1, 8, and 15 are incorporated. Tamer does not appear to expressly teach determining, by the one or more processors, a similarity score between a pair of the plurality of entities based on a comparison of one or more features associated with the pair of entities selected for comparison. Parandehgheibi teaches determining, by the one or more processors, a similarity score between a pair of the plurality of entities based on a comparison of one or more features associated with the pair of entities selected for comparison ( Paragraph 104, "pair-wise similarity scores can be determined from a pair of feature vectors for a pair of nodes." Paragraph 103, "output of an individual domain-specific learner can be a similarity score or vector between a pair of nodes" ). Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, having the patient clustering of Tamer to include the cluster discovery via multi-domain fusion for application dependency mapping techniques of Parandehgheibi to gather all relevant information for identifying workloads and their interdependencies (see Parandehgheibi at paragraph 5). As to dependent claims 7, 14, and 20 , the respective rejections of claims 1, 8, and 15 are incorporated. Tamer does not appear to expressly teach the respective ones of the plurality of entities associated with the one set of the plurality of clusters comprise per-cluster set similarity scores. Parandehgheibi teaches the respective ones of the plurality of entities associated with the one set of the plurality of clusters comprise per-cluster set similarity scores… ( Paragraph 89, "validating the clusters, such as by calculating silhouette scores. Silhouette scoring is a method of interpretation and validation of consistency within clusters of data. A silhouette score is a measure of how similar an object is to its own cluster compared to other clusters, which can range from −1 to 1, where a high value indicates that the node is well matched ). Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, having the patient clustering of Tamer to include the cluster discovery via multi-domain fusion for application dependency mapping techniques of Parandehgheibi to gather all relevant information for identifying workloads and their interdependencies (see Parandehgheibi at paragraph 5). Tamer does not appear to expressly teach of at least a predetermined threshold. Smart teaches of at least a predetermined threshold ( Paragraph 15, "failing to satisfy a threshold" ). Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, having the patient clustering of Tamer to include the dataset merging of Smart to support use cases that can include, for example, identifying relevant users, selecting relevant content for users, identifying relevant locations for users, appropriately timing actions, and the like (see Smart at paragraph 2). Claims 4, 5, 11, 12, 18, and 19 are rejected under 35 U.S.C. § 103 as being unpatentable over Tamer in view of Smart, Sander, Burges, Parandehgheibi, and Mikolov et al. (Mikolov, Tomas, Kai Chen, Greg Corrado, and Jeffrey Dean. "Efficient estimation of word representations in vector space." arXiv preprint arXiv:1301.3781 (2013), hereinafter Mikolov). As to dependent claims 4, 11, and 18 , the respective rejections of claims 3, 10, and 17 are incorporated. Tamer does not appear to expressly teach generating, by the one or more processors and using an embedding machine learning model, embeddings for the one or more features. Mikolov teaches generating, by the one or more processors and using an embedding machine learning model, embeddings for the one or more features ( Abstract, "model architectures for computing continuous vector representations" The vector representation is an embedding ). Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, having the patient clustering of Tamer to include the embedding techniques of Mikolov to improve accuracy at much lower computational cost (see Mikolov at abstract). Tamer does not appear to expressly teach determining, by the one or more processors, a similarity score for the pair of entities based on a distance function and the embeddings . Parandehgheibi teaches determining, by the one or more processors, a similarity score for the pair of entities based on a distance function and the embeddings ( Paragraph 103, "a similarity score or vector between a pair of nodes in one domain, and the aggregate similarity scores or vectors across all domains can represent a similarity vector or matrix for the pair of nodes" ). Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, having the patient clustering of Tamer to include the cluster discovery via multi-domain fusion for application dependency mapping techniques of Parandehgheibi to gather all relevant information for identifying workloads and their interdependencies (see Parandehgheibi at paragraph 5). As to dependent claims 5, 12, and 19 , Parandehgheibi teaches the distance function comprises one of a Euclidean distance, a Manhattan distance, a Minkowski distance, a Jaccard distance, or a Cosine similarity ( Paragraph 103, "Euclidean distance, Manhattan distance, Minkowski distance, cosine similarity, or Jaccard similarity" ). Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, having the patient clustering of Tamer to include the cluster discovery via multi-domain fusion for application dependency mapping techniques of Parandehgheibi to gather all relevant information for identifying workloads and their interdependencies (see Parandehgheibi at paragraph 5). Conclusion It is noted that any citation to specific pages, columns, lines, or figures in the prior art references and any interpretation of the references should not be considered to be limiting in any way. A reference is relevant for all it contains and may be relied upon for all that it would have reasonably suggested to one having ordinary skill in the art. In re Heck , 699 F.2d 1331, 1332-33, 216 U.S.P.Q. 1038, 1039 (Fed. Cir. 1983) (quoting In re Lemelson , 397 F.2d 1006, 1009, 158 U.S.P.Q. 275, 277 (C.C.P.A. 1968)). Any inquiry concerning this communication or earlier communications from the examiner should be directed to Casey R. Garner whose telephone number is 571-272-2467. The examiner can normally be reached Monday to Friday, 8am to 5pm, Eastern Time. If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Alexey Shmatov can be reached on 571-270-3428. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300. Information regarding the status of an application may be obtained from Patent Center and the Private Patent Application Information Retrieval (PAIR) system. Status information for published applications may be obtained from Patent Center or Private PAIR. Status information for unpublished applications is available through Patent Center and Private PAIR to authorized users only. Should you have questions about access to the Private PAIR system, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) Form at https://www.uspto.gov/patents/uspto-automated - interview-request-air-form. /Casey R. Garner/ Primary Examiner, Art Unit 2123