DETAILED ACTION
This Office action is responsive to the following communication: Application filed on 16 September 2025.
Claim(s) 1-20 is/are pending and present for examination. Claim(s) 1 and 11 is/are in independent form.
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 (IDS) submitted on 16 September 2025 is being considered by the examiner.
Drawings
The drawings were received on 16 September 2025. These drawings are accepted.
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 therefore, 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.
As per claims 1 and 11, the claim(s) recite(s) “obtain a dataset…”, “generate… a set of clusters…”, “identify one or more outliers…”, “classify the one or more outliers across one or more axes…”, “output a report…”, and “update the clustering model…”.
The limitations directed towards “generate… a set of clusters…”, “identify one or more outliers…”, “classify the one or more outliers across one or more axes…”, and “updating the clustering model…” are interpreted to be the observation or judgment and, therefore, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. That is, other than reciting “a processor” and “a memory” in claim 1, nothing in the claim element precludes the step from practically being performed in the mind.
For example, the “generate… a set of clusters” in the context of this claim encompasses the user mentally evaluating a data set to determine data points which may be grouped into a cluster. For example, “identify” in the context of this claim encompasses the user mentally evaluating and making identifying data points which are outliers of said clusters. For example, “classify” in the context of this claim encompasses the user mentally assigning outliers to areas of one or more axes. For example, “update the clustering model” in the context of this claim encompasses the user mentally determining an appropriate response to provide in view of feedback. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
Under step 2A, Prong 2, of the 2019 Revised Guidance, 84 Fed. Reg., we determine whether any of the additional elements beyond the abstract idea integrate the abstract ideas into a practical application. 2019 Guidance, 84 Fed. Reg. 54; MPEP §§ 2106.04(d), 2106.05. The 2019 Guidance provides exemplary considerations that are indicative of an additional element or combination of elements integrating the judicial exception into a practical application, such as an additional element reflecting an improvement in the functioning of a computer or an improvement to other technology or technical field. Id. at 55; see also MPEP § 2106.05(a). This judicial exception is not integrated into a practical application by additional elements. In particular, the claim recites using a processor to perform the steps. The processor in both steps is recited at a high-level of generality such that it amounts no more than mere instructions to apply the exception using a generic computer component.
In addition to the claim limitations, which were determined to recite concepts identified as abstract ideas, certain elements of claims 1 and 11 also constitute insignificant extra-solution activity to the judicial exception. In particular, the claim recites "obtain a dataset.” This limitation reasonably can be characterized as merely constituting the insignificant pre-solution activity of data gathering:
“An example of pre-solution activity is a step of gathering data for use in a claimed process, e.g., a step of obtaining information about credit card transactions, which is recited as part of a claimed process of analyzing and manipulating the gathered information by a series of steps in order to detect whether the transactions were fraudulent.” See MPEP § 2106.05(g).
The Federal Circuit has held that data gathering steps "cannot make an otherwise nonstatutory claim statutory." CyberSource Corp. v. Retail Decisions, Inc., 654 F.3d 1366, 1370 (Fed. Cir. 2011) (quoting In re Grams, 888 F.2d 835, 840 (Fed. Cir. 1989)). In this case, the pre-solution activity of obtaining a dataset of data points for analysis may be considered a step of gathering data for use in a claimed process such as resolving conflicting attributes. This is highly analogous with the example provided above regarding insignificant pre-solution activity of data gathering
Additionally, the claimed feature of “output a report” is merely insignificant extra-solution activity, i.e., necessary data outputting. See MPEP 2106.05(g). At step 2A, prong two, considering these limitations individually and the claim as a whole, the claim fails to integrate the abstract idea into a practical application. The elements directed to “obtain” and “output” do not integrate the abstract idea into a practical application because they do not impose a meaningful limit on the judicial exception and provide only insignificant extra solution activity that is mere data gathering in conjunction with the abstract idea.
At Step 2B, all claim elements, with the exception of the processor and memory, correspond to concepts determined to be abstract ideas for the reasons discussed above in connection with Prong One of the analysis and/or merely constitute extra-solution activity under Prong Two. Applicant's lack of a detailed disclosure of computer hardware or functional requirements and the lack of details describing a computer-specific implementation of the recited functions (such as might have been indicated by inclusion of a detailed flow chart depicting unconventional computer operations and/or routines for performing each of the claimed steps), persuades us that the omitted details are well-understood, routine, and conventional. See, e.g., MPEP § 2106.07(a)(III)(A).
Consistent with the Berkheimer Memorandum, the claims merely recite generic computer components performing generic computing functions that are well-understood, routine, and conventional. 5 See Alice, 573 U.S. at 225 (The "use of a computer to obtain data, adjust account balances, and issue automated instructions; all of these computer functions are 'well-understood, routine, conventional activit[ies]' previously known to the industry.") ( quoting Mayo, 566 U.S. at 71-73); see also Benson, 409
U.S. at 65 (Noting that a "computer operates then upon both new and previously stored data. The general-purpose computer is designed to perform operations under many different programs."); FairWarning IP, LLC v. Iatric Sys., Inc., 839 F.3d 1089, 1096 (Fed. Cir. 2016) (noting that using generic computing components like a microprocessor or user interface does not transform an otherwise abstract idea into eligible subject matter); Mortg. Grader, Inc. v. First Choice Loan Servs. Inc., 811 F.3d 1314, 1324-25 (Fed. Cir. 2016) (indicating components such as an "interface" are generic computer components that do not satisfy the inventive concept requirement); and MPEP § 2106.05(d)(II) (citing Alice and Mayo) accord Berkheimer Memo 3-4.
In this case, the "obtain" and “output” limitations are clearly well-understood, routine, and conventional; see MPEP 2106.05(d)(II), "receiving or transmitting data over a network." The claim(s) do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the computing of measures only add well-understood, routine and conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception (Storing and retrieving information in memory, Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015); OIP Techs., 788 F.3d at 1363, 115 USPQ2d at 1092-93). The claims provide that the measures may be computed by program code that may be stored in memory. Therefore, the computing is nothing more than what can be handled by a conventional search engine and does not provide significantly more than the judicial exception. The claim(s) is/are not patent eligible.
The claim(s) do not include additional elements that are sufficient to amount to significantly more than the judicial exception. The claim(s) is/are not patent eligible.
As per claims 2 and 12, the limitations are directed towards receiving the dataset from one or more external systems. These additional elements represent mere extra-solution activities to the judicial exception. These elements do not integrate the abstract idea into a practical application because they do not impose a meaningful limit on the judicial exception and provide only insignificant extra solution activity that is mere data processing in conjunction with the abstract idea.
The claim(s) do not include additional elements that are sufficient to amount to significantly more than the judicial exception because receiving a dataset only adds well-understood, routine and conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception (See Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015); OIP Techs., 788 F.3d at 1363, 115 USPQ2d at 1092-93). The claim(s) is/are not patent eligible does not integrate the abstract idea into a practical application.
As per claims 3, 4, 13, and 14, the limitations are interpreted to be the observation or judgment made by a user and, therefore, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. That is, nothing in the claim element precludes the step from practically being performed in the mind. Furthermore, it is noted that the limitation of “storing labeled data points…” represent mere extra-solution activities to the judicial exception.
As per claims 6, 7, 9, 10, 16, 17, 19, and 20, the limitations are interpreted to be the observation or judgment made by a user and, therefore, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. That is, nothing in the claim element precludes the step from practically being performed in the mind. Furthermore, it is noted that the limitation of “storing labeled data points…” represent mere extra-solution activities to the judicial exception.
As per claims 8 and 18, the limitations are directed towards displaying a report on a graphical user interface. These additional elements represent mere extra-solution activities to the judicial exception. These elements do not integrate the abstract idea into a practical application because they do not impose a meaningful limit on the judicial exception and provide only insignificant extra solution activity that is mere data processing in conjunction with the abstract idea.
The claim(s) do not include additional elements that are sufficient to amount to significantly more than the judicial exception because outputting data only adds well-understood, routine and conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception (See Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015); OIP Techs., 788 F.3d at 1363, 115 USPQ2d at 1092-93). The claim(s) is/are not patent eligible does not integrate the abstract idea into a practical application.
The claim(s) do not include additional elements that are sufficient to amount to significantly more than the judicial exception because outputting a report to a GUI only adds well-understood, routine and conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception (See Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015); OIP Techs., 788 F.3d at 1363, 115 USPQ2d at 1092-93). The claim(s) is/are not patent eligible does not integrate the abstract idea into a practical application.
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)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claim(s) 1-3, 9-13, 19, and 20 is/are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Lucena et al, U.S. Patent No. 11,107,562, filed on 13 September 2018, and issued on 31 August 2021.
As per independent claim 1, Lucena teaches:
A system for identifying outlier data and generating corrective action, wherein the system comprises:
at least a processor {See Lucena, Figure 1}; and
a memory communicatively connected to the at least a processor, wherein the memory contains instructions configuring the at least a processor to {See Lucena, Figure 1}:
obtain a dataset comprising data points {See Lucena, Figure 5, lines 33-48, wherein this reads over “Data regarding a service provided by one or more of the health care providers 102 may include a variety of information. For example, the data may indicate a name of a patient, social security number or other identifying information for a patient, date of birth of a patient, address of a patient, location where a service was rendered, identifier of a health care provider that provided the service (e.g., a name, number, etc. of a doctor), a type of service provided (e.g., a medical code or value associated with a service), a description of a service provided (e.g., notes, comments, etc.), billing instructions, insurance information for a patient (e.g., identifying an insurance company to bill a visit to), images for a service provided (e.g., still images, videos, medical imaging images, etc.), audio for a service provided, data from a medical device (e.g., data from an electronic medical device), etc.”};
generate, using a clustering model, a set of clusters based on inherent relationships between the data points {See Lucena, column 6, line 54 - column 7, line 1, wherein this reads over “The clustering component 124 may analyze the services data 128 to cluster the health care providers 102. For example, the service provider 106 may analyze the services data 128 to determine types of services that have been provided by a health care provider to patients, types of services that have recently been provided by a health care provider to patients (e.g., over the last week, month, year, etc.), types of services that have been provided most frequently by a health care provider (e.g., a type of service that the health care provider performs the most, second most, etc.), a number of times a type of service has been provided by a health care provider, a number of times a type of service has been provided by a health care provided to a specific patient, a number of patients that have received a type of service from a health care provider, and so on.”};
identify one or more outliers of the data points {See Lucena, column 9, lines 30-36, wherein this reads over “In some examples, the clustering component 124 may cluster health care providers, categorize health care providers, and/or identify outlier health care providers based on a number of times a type of service has been provided by a health care provided to a specific patient and/or a number of patients that have received a type of service from a health care provider.”};
classify the one or more outliers across one or more axes, wherein the one or more axes are associated with one or more dimensions of data of an admission process {See Lucena, column 9, lines 36-48, wherein this reads over “ In one illustration, if a health care provider has provided a particular type of service to a particular patient multiple times, the multiple performances of the particular type of service for the particular patient may be counted once (or weighted less than other times the particular type of service has been performed for other patients) in order to cluster or categorize the health care provider. In another illustration, if a health care provider lies outside a cluster due to the health care provider performing a same type of service to a particular patient multiple times (or more than a threshold), the health care provider may be associated with the cluster (or may not be flagged as being outside the cluster).”};
output a report of the one or more identified outliers, wherein the report comprises a recommended corrective action {See Lucena, column 15, line 62 – column 16, line 7, wherein this reads over “In one illustration, if a health care provider has provided a particular type of service to a particular patient multiple times, the multiple performances of the particular type of service for the particular patient may be counted once (or weighted less than other times the particular type of service has been performed for other patients) in order to cluster or categorize the health care provider. In another illustration, if a health care provider lies outside a cluster due to the health care provider performing a same type of service to a particular patient multiple times (or more than a threshold), the health care provider may be associated with the cluster (or may not be flagged as being outside the cluster).”}; and
update the clustering model based on feedback regarding an effectiveness of previously implemented corrective actions {See Lucena, column 8, lines 47-56, wherein this reads over “In some examples, the clustering component 124 may update a profile or account associated with a health care provider, such as by updating a profile in the health care provider profiles 130 to indicate a category that is being applied to the health care provider. In some examples, each health care provider that has been subjected to clustering processing may be clustered into a cluster (e.g., associated with a nearest cluster) and/or categorized into a category associated with the cluster.”}.
As per dependent claim 2, Lucena teaches:
The system of claim 1, wherein the at least a processor is further configured to receive the dataset from one or more external systems and wherein dataset comprises at least an electronic health record (EHR) {See Lucena, column 5, lines 54-55, wherein this reads over “Moreover, in some examples, the data is part of a medical record.”}.
As per dependent claim 3, Lucena teaches:
The system of claim 1, wherein the at least a processor is further configured to investigate the one or more outliers, wherein investigating the one or more outliers comprises flagging recurring outlier features {See Lucena, column 9, lines 23-29, wherein this reads over “In some examples, the clustering component 124 may categorize (or otherwise flag) a health care provider that lies outside a cluster. For example, if a health care provider claims to be a pediatrician, but lies outside a pediatrician cluster, the health care provider may be categorized as potentially practicing outside an area that is designed by the health care provider.”}.
As per dependent claim 9, Lucena teaches:
The system of claim 1, wherein the at least a processor is further configured to perform a contextual analysis of the one or more outliers, wherein the contextual analysis comprises identifying one or more relevant features including at least one of temporal context and environmental factors {See Lucena, column 9, lines 42-54, wherein this reads over “In another illustration, if a health care provider lies outside a cluster due to the health care provider performing a same type of service to a particular patient multiple times (or more than a threshold), the health care provider may be associated with the cluster (or may not be flagged as being outside the cluster). Such processing may help accurately cluster, categorize, and/or identify health care providers in situations where, for example, a health care provider may provide a non-routine service for the same patient multiple times (e.g., a patient happens to need a certain treatment many times and the certain treatment is outside what a doctor regularly does).”}.
As per dependent claim 10, Lucena teaches:
The system of claim 1, wherein the at least a processor is further configured to verify the one or more outliers using a verification model employing data validation techniques comprising at least one of type checks {See Lucena, column 9, lines 30-42, wherein this reads over “In some examples, the clustering component 124 may cluster health care providers, categorize health care providers, and/or identify outlier health care providers based on a number of times a type of service has been provided by a health care provided to a specific patient and/or a number of patients that have received a type of service from a health care provider. In one illustration, if a health care provider has provided a particular type of service to a particular patient multiple times, the multiple performances of the particular type of service for the particular patient may be counted once (or weighted less than other times the particular type of service has been performed for other patients) in order to cluster or categorize the health care provider.”}.
As per independent claim 11, Lucena teaches:
A method for identifying outlier data and generating corrective action, wherein the method comprises:
obtaining, using at least a processor, a dataset comprising data points {See Lucena, Figure 5, lines 33-48, wherein this reads over “Data regarding a service provided by one or more of the health care providers 102 may include a variety of information. For example, the data may indicate a name of a patient, social security number or other identifying information for a patient, date of birth of a patient, address of a patient, location where a service was rendered, identifier of a health care provider that provided the service (e.g., a name, number, etc. of a doctor), a type of service provided (e.g., a medical code or value associated with a service), a description of a service provided (e.g., notes, comments, etc.), billing instructions, insurance information for a patient (e.g., identifying an insurance company to bill a visit to), images for a service provided (e.g., still images, videos, medical imaging images, etc.), audio for a service provided, data from a medical device (e.g., data from an electronic medical device), etc.”};
generating, using a clustering model, a set of clusters based on inherent relationships between the data points {See Lucena, column 6, line 54 - column 7, line 1, wherein this reads over “The clustering component 124 may analyze the services data 128 to cluster the health care providers 102. For example, the service provider 106 may analyze the services data 128 to determine types of services that have been provided by a health care provider to patients, types of services that have recently been provided by a health care provider to patients (e.g., over the last week, month, year, etc.), types of services that have been provided most frequently by a health care provider (e.g., a type of service that the health care provider performs the most, second most, etc.), a number of times a type of service has been provided by a health care provider, a number of times a type of service has been provided by a health care provided to a specific patient, a number of patients that have received a type of service from a health care provider, and so on.”};
identifying, using the at least a processor, one or more outliers of the data points {See Lucena, column 9, lines 30-36, wherein this reads over “In some examples, the clustering component 124 may cluster health care providers, categorize health care providers, and/or identify outlier health care providers based on a number of times a type of service has been provided by a health care provided to a specific patient and/or a number of patients that have received a type of service from a health care provider.”};
classifying, using the at least a processor, the one or more outliers across one or more axes, wherein the one or more axes are associated with one or more dimensions of data of an admission process {See Lucena, column 9, lines 36-48, wherein this reads over “ In one illustration, if a health care provider has provided a particular type of service to a particular patient multiple times, the multiple performances of the particular type of service for the particular patient may be counted once (or weighted less than other times the particular type of service has been performed for other patients) in order to cluster or categorize the health care provider. In another illustration, if a health care provider lies outside a cluster due to the health care provider performing a same type of service to a particular patient multiple times (or more than a threshold), the health care provider may be associated with the cluster (or may not be flagged as being outside the cluster).”};
outputting a report of the one or more identified outliers, wherein the report comprises a recommended corrective action {See Lucena, column 15, line 62 – column 16, line 7, wherein this reads over “In one illustration, if a health care provider has provided a particular type of service to a particular patient multiple times, the multiple performances of the particular type of service for the particular patient may be counted once (or weighted less than other times the particular type of service has been performed for other patients) in order to cluster or categorize the health care provider. In another illustration, if a health care provider lies outside a cluster due to the health care provider performing a same type of service to a particular patient multiple times (or more than a threshold), the health care provider may be associated with the cluster (or may not be flagged as being outside the cluster).”}; and
updating, using the at least a processor, the clustering model based on feedback regarding an effectiveness of previously implemented corrective actions {See Lucena, column 8, lines 47-56, wherein this reads over “In some examples, the clustering component 124 may update a profile or account associated with a health care provider, such as by updating a profile in the health care provider profiles 130 to indicate a category that is being applied to the health care provider. In some examples, each health care provider that has been subjected to clustering processing may be clustered into a cluster (e.g., associated with a nearest cluster) and/or categorized into a category associated with the cluster.”}.
As per dependent claim 12, Lucena teaches:
The method of claim 11, further comprising receiving, using the at least a processor, the dataset from one or more external systems and wherein the dataset comprises at least an electronic health record (EHR) {See Lucena, column 5, lines 54-55, wherein this reads over “Moreover, in some examples, the data is part of a medical record.”}.
As per dependent claim 13, Lucena teaches:
The method of claim 11, further comprising investigating, using the at least a processor, the one or more outliers, wherein investigating the one or more outliers comprises flagging recurring outlier features {See Lucena, column 9, lines 23-29, wherein this reads over “In some examples, the clustering component 124 may categorize (or otherwise flag) a health care provider that lies outside a cluster. For example, if a health care provider claims to be a pediatrician, but lies outside a pediatrician cluster, the health care provider may be categorized as potentially practicing outside an area that is designed by the health care provider.”}.
As per dependent claim 19, Lucena teaches:
The method of claim 11, further comprising performing, using the at least a processor, a contextual analysis of the one or more outliers, wherein the contextual analysis comprises identifying one or more relevant features including at least one of temporal context and environmental factors {See Lucena, column 9, lines 42-54, wherein this reads over “In another illustration, if a health care provider lies outside a cluster due to the health care provider performing a same type of service to a particular patient multiple times (or more than a threshold), the health care provider may be associated with the cluster (or may not be flagged as being outside the cluster). Such processing may help accurately cluster, categorize, and/or identify health care providers in situations where, for example, a health care provider may provide a non-routine service for the same patient multiple times (e.g., a patient happens to need a certain treatment many times and the certain treatment is outside what a doctor regularly does).”}.
As per dependent claim 20, Lucena teaches:
The method of claim 11, further comprising verifying, using the at least a processor, the one or more outliers using a verification model employing data validation techniques comprising at least one of type checks {See Lucena, column 9, lines 30-42, wherein this reads over “In some examples, the clustering component 124 may cluster health care providers, categorize health care providers, and/or identify outlier health care providers based on a number of times a type of service has been provided by a health care provided to a specific patient and/or a number of patients that have received a type of service from a health care provider. In one illustration, if a health care provider has provided a particular type of service to a particular patient multiple times, the multiple performances of the particular type of service for the particular patient may be counted once (or weighted less than other times the particular type of service has been performed for other patients) in order to cluster or categorize the health care provider.”}.
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.
Claim(s) 5 and 15 is/are rejected under 35 U.S.C. 103 as being unpatentable over Lucena, in view of Mounzer et al, USPGPUB No. 2025/0391548, filed on 25 June 2024, and published on 25 December 2025.
As per dependent claim 5, Lucena, in combination with Mounzer, discloses:
The system of claim 1, wherein the at least a processor is further configured to collect, using a web-crawler, governance data of the dataset, wherein the web-crawler comprises an incremental crawler configured to retrieve the governance data as regulations change {See Mounzer, [0092], wherein this reads over “In one or more embodiments, web crawler may allow for a data store, such as data repository 136, to be populated with laws, regulations, ordinances, and the like provided by the local authorities. In one or more embodiments, some requirements may be categorized based on at least a geographic location, wherein web crawler may be configured to retrieve requirements based on the at least a geographic location, consistent with details described in this disclosure. As a nonlimiting example, training examples 120 may be retrieved from, synthesized, and/or dynamically updated using online resources using a web crawler. As another nonlimiting example, the contact information, address, business hours, insurance policies, and the like pertaining to a hospital or medical practitioner may be retrieved using a web crawler for apparatus 100 to perform subsequent tasks, as described below”}.
Lucena is directed to the invention of clustering data regarding health care providers. Mounzer is directed to the method for automating pre-procedural coordination workflows. Specifically, Mounzer discloses that “web crawler may allow for a data store, such as data repository 136, to be populated with laws, regulations, ordinances, and the like provided by the local authorities” wherein “some requirements may be categorized based on at least a geographic location, wherein web crawler may be configured to retrieve requirements based on the at least a geographic location, consistent with details described in this disclosure.” See Mounzer, [0092]. Additionally, Mounzer discloses that “training examples 120 may be retrieved from, synthesized, and/or dynamically updated using online resources using a web crawler” and “the contact information, address, business hours, insurance policies, and the like pertaining to a hospital or medical practitioner may be retrieved using a web crawler for apparatus 100 to perform subsequent tasks, as described below.” See Mounzer, [0092]. That is, Mounzer discloses that a web crawler (i.e., using a web-crawler) may be used to retrieve laws, regulations, and ordinances (i.e., retrieve the governance data as regulations change). Wherein Mounzer is directed to automating workflows, it would have been obvious to one of ordinary skill in the art at the effective time of the instant filing date to improve the prior art of Lucena with that of Mounzer such that governance data such as rules and regulations may be collected to be used to validate the data of Lucena. One of ordinary skill in the art would have been motivated to make the aforementioned combination such that the validation of data and determination of outliers may be done in view of newly updated governance data which has been retrieved.
As per dependent claim 15, Lucena, in combination with Mounzer, discloses:
The method of claim 11, further comprising collecting, using a web-crawler, governance data of the dataset, wherein the web-crawler comprises an incremental crawler configured to retrieve the governance data as regulations change {See Mounzer, [0092], wherein this reads over “In one or more embodiments, web crawler may allow for a data store, such as data repository 136, to be populated with laws, regulations, ordinances, and the like provided by the local authorities. In one or more embodiments, some requirements may be categorized based on at least a geographic location, wherein web crawler may be configured to retrieve requirements based on the at least a geographic location, consistent with details described in this disclosure. As a nonlimiting example, training examples 120 may be retrieved from, synthesized, and/or dynamically updated using online resources using a web crawler. As another nonlimiting example, the contact information, address, business hours, insurance policies, and the like pertaining to a hospital or medical practitioner may be retrieved using a web crawler for apparatus 100 to perform subsequent tasks, as described below”}.
Allowable Subject Matter
Claims 4, 6-8, 14, and 16-18 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to PAUL KIM whose telephone number is (571)272-2737. The examiner can normally be reached Monday-Friday, 9AM-5PM.
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/Paul Kim/
Primary Examiner
Art Unit 2166
/PK/
Prosecution Insights