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 .
This Final Office Action is in response to the application 19/293,100 filed on 02/06/2026.
Status of Claims:
Claims 1 and 11 are amended in this Office Action.
Claims 1-20 are pending in this Office Action.
Response to Arguments
CLAIM REJECTIONS UNDER 35 U.S.C. § 101
After reviewing the Applicant’s arguments filed in the remarks filed 02/06/2026 (pg. 1-7) regarding to claims 1 and 11, the Examiner respectfully submits that the arguments are not persuasive.
The applicant submitted that the amended claims do not recite a mental process. The applicant submitted that claims now amended to incorporate additional limitations “generating the output further comprises: producing a standardized data dictionary defining one or more of a schema field, allowed format, and classification metadata; retrieving updated regulatory data from an external source using an application programming interface; updating the output as a function of temporal changes in the retrieved updated regulatory data; transmitting the output to one or more downstream systems wherein the one or more downstream systems are configured to execute a plurality of automated compliance commands” and submitted that the limitations cannot be performed in the human mind mentally or with pen and paper because the claim recites machine functionality rather than human cognition.
The examiner respectfully disagrees with the Applicant and updated the claim rejections under 35 USC § 101 with respect to the amended claims. Please refer to the rejections under 35 USC § 101 below for further details.
CLAIM REJECTIONS UNDER 35 U.S.C. § 103
Applicant’s arguments filed on 02/06/2026 (pages 7-10) regarding claim rejections under 35 U.S.C 103 have been fully considered. However, after further examination, the Examiner respectfully submits that the arguments are not persuasive.
The applicant argues that the office has not asserted that the prior arts of record teach, suggest, or motivate “producing a standardized data dictionary defining one or more of a schema field, allowed format, and classification metadata, retrieving updated regulatory data from an external source using an application programming interface, updating the output as a function of temporal changes in the retrieved updated regulatory data, and transmitting the output to one or more downstream systems wherein the one or more downstream systems are configured to execute a plurality of automated compliance commands.”
The examiner respectfully disagrees with the Applicant; the Examiner respectfully submits that Dean discloses “Col 7 line 35-40: FIG. 6 illustrates an example mapping 600 of target data to a target schema, according to some embodiments. Shown are example schema elements 602 (e.g., person, person ID, humanName, etc.). Also shown are target fields 604, some of which have candidate tokens or target data (e.g., 31240040, John, A, Smith, etc.). Also shown are confidence values 606”. The system of Dean is related to transforming data for a target schema wherein the target schema with transformed data can have further elements such as person, person ID, humanName, etc., to provide definitions of the data. For example, Element labeled as “humanName” can correspond to a name of a person and element dateOfBirth can define a corresponding date to be a birthdate of the person. Therefore, Dean at least teaches “producing a standardized data dictionary defining one or more of a schema field, allowed format, and classification metadata”.
Dean also discloses “Fig. 1 & Col 2 line 38-54: FIG. 1 is an example environment 100 for transforming data for a target schema, according to some embodiments. Shown are a system 102, a database 104, and a database 106. In some implementations, database 104 may be referred to as a source database, which may provide the raw input data. Also, database 106 may be referred to as a target database, which may store the transformed data for a target schema. As described in more detail herein, system 102 receives input data from database 104. In some embodiments, the input data may be raw data organized in a semi-structured schema. For example, the input data may be HL7 data. While some embodiments are described herein in the context of health data such as HL7 data, these embodiments and others may be applied to any type of data. As described in more detail herein, system 102 transforms the input data for a target schema, and stores the transformed data in database 106”. The system of Dean can process or transform input data and the output can be transmitted to a downstream model such as a database. Thus, the database such as database 106, can receive the output of the system and a corresponding command can be executed such as to store the transformed data to the database. Therefore, Dean at least teaches “transmitting the output to one or more downstream systems wherein the one or more downstream systems are configured to execute a plurality of automated compliance commands”.
Rogynskyy discloses “Fig. 7 & [0119]: FIG. 7 illustrates a series of electronic activities between two nodes, N1 702 and N2 704. N1 702 may correspond to a node associated with an entity whose electronic activities are ingested by the node graph generation system 200, while node N2 704 may correspond to a node external to the entity associated with the node N1. A node profile 715 for node N2 is maintained by the node profile manager 220. Before the electronic activity 710 was ingested by the node graph generation system 200, the node profile included the five fields, name, email, phone, company and job title. This information was previously included in the node profile and may have been determined by ingesting information from a system of record…[0121]: Node N2 can then send back a response email to node N1 that includes a signature 726 in the body of the electronic activity. The node profile manager can update, from the successful transmission of the email response and the parsing of the signature, the node profile of N2 by increasing the confidence score of the name of John Smith, the title from the signature, the company name 2 times (one of which was derived by matching the domain name of the email to the domain name of the group node in the node graph) as it is included in the email address and in the signature, and further add a new value for the phone number, which is extracted from the signature. The extracted phone number can represent his direct office number, while the phone number previously maintained in the node profile can be a general company number. In some embodiments, the system can be configured to classify phone numbers as a general company number or a direct office number based on the frequency of the number appearing in different node profiles”. The system of Roggynskyy is related to electronic activities and record objects between different nodes and updating to a node corresponding to a record object. A communication between two nodes can be executed where data can be exchanged such as updates to data fields of the nodes. The system can retrieve data such as relating to fields, name, email, phone, company and job title of an external source such as a node and update the data within a schema of a node based on data such as change or addition of phone number, or a change in confidence of the data. Therefore, Roggynskyy at least teaches “retrieving updated regulatory data from an external source using an application programming interface, updating the output as a function of temporal changes in the retrieved updated regulatory data”.
Please refer to the rejections under 35 USC § 103 below for further details.
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.
Independent Claims 1 and 11:
Step 1:
Claim 1 recites “An apparatus …”, therefore the claim is a machine.
Claim 11 recites “A method…”, the claim recites a series of steps and therefore is process.
Step 2A Prong One:
Claims 1 and 11 recite limitations “map… the PII data to at least a data schema as a function of the one or more model constraints”; “identifying… at least a PII datum of the PII data”; “categorizing…the at least a PII datum to one or more categories of a plurality of categories”; “mapping…the PII data to the at least a data schema”; “modify… the data schema based on a refinement datum, wherein the refinement datum is generated based on a temporal datum of the one or more model constraints”; “generate…an output as a function of the refinement datum and the data schema”; and “producing a standardized data dictionary defining one or more of a schema field, allowed format, and classification metadata”; “updating the output as a function of temporal changes in the retrieved updated regulatory data”. The limitations are processes that, under their 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(n) “apparatus”, “AI-PII model”, “processor”, “external source using an application programming interface”; downstream systems nothing in the claim element precludes the step from practically being performed in a human mind or with the aid of pen and paper.
For example, limitations “map… the PII data to at least a data schema as a function of the one or more model constraints” and “mapping…the PII data to the at least a data schema” in the context of this claim encompass a user mentally, and with the aid of pen and paper performing a mapping operation wherein the received data is mapped into a respective received schema.
Limitation “identifying… at least a PII datum of the PII data” in the context of this claim encompass a user mentally, and with the aid of pen and paper read the data and identify particular datum of the data. One of ordinary skills in the art can, for example, scan a set of data and determine portions of the set of data and consider them as datum.
Limitation “categorizing…the at least a PII datum to one or more categories of a plurality of categories” in the context of this claim encompass a user mentally, and with the aid of pen and paper get the identified datum and put them into categories that they belong to. One of ordinary skills in the art can determine a category that would describe the datum and insert the datum to the particular category.
Limitation “modify… the data schema based on a refinement datum, wherein the refinement datum is generated based on a temporal datum of the one or more model constraints” in the context of this claim encompass a user mentally, and with the aid of pen and paper identify data such as a temporal datum and modify the schema corresponding to the temporal datum.
Limitation “generate…an output as a function of the refinement datum and the data schema” in the context of this claim encompass a user mentally, and with the aid of pen and paper creating an output that is based on the created schema and data.
Limitation “producing a standardized data dictionary defining one or more of a schema field, allowed format, and classification metadata” in the context of this claim encompass a user mentally, and with the aid of pen and paper creating a dictionary for defining other data such as schema field, allowed format, and classification metadata. For example, one of ordinary skills in the art can write particular definitions or descriptions that describe particular fields, format or data within an output.
Limitation “updating the output as a function of temporal changes in the retrieved updated regulatory data” in the context of this claim encompass a user mentally, and with the aid of pen and paper performing a generic update to an entity such as an output based on the retrieved regulatory data.
Step 2A Prong Two:
The judicial exception is not integrated into a practical application. The claim recites the additional elements “using the at least a processor” and “using the AI-PII model”; “an external source using an application programming interface”. The limitations 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).
The claim recites the additional elements “receive, using the at least a processor, personally identifiable information (PII) data”; “receive, using the at least a processor, one or more model constraints”; “retrieving updated regulatory data…”. These limitation amounts to data gathering which is considered to be insignificant extra solution activity (MPEP 2106.05(g)).
The claim recites the additional elements “transmitting the output to one or more downstream systems…”. The limitations amounts to mere generic transmission and presentation of collected and analyzed data which is considered to be insignificant extra solution activity (MPEP 2106.05(g)).
The claim recites the additional elements “…wherein the one or more downstream systems are configured to execute a plurality of automated compliance commands”. The limitation amounts to simply appending well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception (MPEP 2106.05(d)).
The computer system in these steps are recited at a high-level of generality (i.e., as a generic processor performing a generic computer function) such that it amounts no more than mere instructions to apply the exception using a generic computer component. 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 claim is 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. The limitations “using the at least a processor”; “using the AI-PII model”; “receive, using the at least a processor, personally identifiable information (PII) data”; “receive, using the at least a processor, one or more model constraints”; “retrieving updated regulatory data from an external source using an application programming interface”; “retrieving updated regulatory data from an external source using an application programming interface”, “transmitting the output to one or more downstream systems wherein the one or more downstream systems are configured to execute a plurality of automated compliance commands” are recognized by the courts as well-understood, routine , and conventional activities when they are claimed in a merely generic manner (see MPEP 2106.05 (g)) and amount to “apply it” (see MPEP 2106.05(f)).
Dependent claims 2 and 12:
Step 2A Prong Two:
The judicial exception is not integrated into a practical application. The claim recites the additional elements “the one or more model constraints comprises internal data”. This limitation amounts to data gathering which is considered to be insignificant extra solution activity (MPEP 2106.05(g)).
The computer system in these steps are recited at a high-level of generality (i.e., as a generic processor performing a generic computer function) such that it amounts no more than mere instructions to apply the exception using a generic computer component. 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 claim is directed to an abstract idea.
Step 2B:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The limitation “the one or more model constraints comprises internal data” is recognized by the courts as well-understood, routine , and conventional activities when they are claimed in a merely generic manner (see MPEP 2106.05(g)).
Dependent claims 3 and 13:
Step 2A Prong Two:
The judicial exception is not integrated into a practical application. The claim recites the additional elements “the output comprises a data dictionary for the PII data”. The limitations amounts to mere generic transmission and presentation of collected and analyzed data which is considered to be insignificant extra solution activity (MPEP 2106.05(g)).
The computer system in these steps are recited at a high-level of generality (i.e., as a generic processor performing a generic computer function) such that it amounts no more than mere instructions to apply the exception using a generic computer component. 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 claim is directed to an abstract idea.
Step 2B:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The limitation “the output comprises a data dictionary for the PII data” is recognized by the courts as well-understood, routine , and conventional activities when they are claimed in a merely generic manner (see MPEP 2106.05(g)).
Dependent claims 4 and 14:
Step 2A Prong One:
Claims 4 and 14 recite limitations “conditionally update, using the at least a processor, the regulatory data based on the temporal datum”; and “modify, using the at least a processor, the output as a function of the updated regulatory data”. The limitations are processes that, under their 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(n) “apparatus”, “AI-PII model”, “processor” nothing in the claim element precludes the step from practically being performed in a human mind or with the aid of pen and paper.
For example, limitation “conditionally update, using the at least a processor, the regulatory data based on the temporal datum” in the context of this claim encompass a user mentally, and with the aid of pen and paper performing a generic update to an entity such as a regulatory data based on available data such as temporal datum.
Limitation “modify, using the at least a processor, the output as a function of the updated regulatory data” in the context of this claim encompass a user mentally, and with the aid of pen and paper creating an output that is based on the created schema and data.
Step 2A Prong Two:
The judicial exception is not integrated into a practical application. The claim recites the additional elements “using an application programming interface”. The limitations 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).
The claim recites the additional elements “retrieve…regulatory data of the one or more model constraints from an external source”. These limitation amounts to data gathering which is considered to be insignificant extra solution activity (MPEP 2106.05(g)).
The computer system in these steps are recited at a high-level of generality (i.e., as a generic processor performing a generic computer function) such that it amounts no more than mere instructions to apply the exception using a generic computer component. 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 claim is 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. The limitations “retrieve, using an application programming interface, regulatory data of the one or more model constraints from an external source” are recognized by the courts as well-understood, routine , and conventional activities when they are claimed in a merely generic manner (see MPEP 2106.05 (g)) and amount to “apply it” (see MPEP 2106.05(f)).
Dependent claims 5 and 15:
Step 2A Prong Two:
The judicial exception is not integrated into a practical application. The claim recites the additional elements “receive a predefined data schema of the at least a data schema from a user interface”. The limitation amounts to data gathering which is considered to be insignificant extra solution activity (MPEP 2106.05(g)).
The computer system in these steps are recited at a high-level of generality (i.e., as a generic processor performing a generic computer function) such that it amounts no more than mere instructions to apply the exception using a generic computer component. 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 claim is directed to an abstract idea.
Step 2B:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The limitation “receive a predefined data schema of the at least a data schema from a user interface” is recognized by the courts as well-understood, routine , and conventional activities when they are claimed in a merely generic manner (see MPEP 2106.05(g)).
Dependent claims 6 and 16:
Step 2A Prong One:
Claims 6 and 16 recite limitations “identifying one or more gap datums”; and “determining…a remediation datum based on the one or more identified gap datums.”. The limitations are processes that, under their 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(n) “apparatus”, “AI-PII model”, “processor” nothing in the claim element precludes the step from practically being performed in a human mind or with the aid of pen and paper.
For example, limitation “identifying one or more gap datums” in the context of this claim encompass a user mentally, and with the aid of pen and paper read the data and identify particular data that are considered as gap datums. One of ordinary skills in the art can, for example, scan a set of data and determine portions of the set of data and consider them as gap datums.
Limitation “determining…a remediation datum based on the one or more identified gap datums” in the context of this claim encompass a user mentally, and with the aid of pen and paper determining data that is identified as remediation datum based on the identified gap datums. On of ordinary skills in the art can scan the gap datums and make determinations to what remediation datum can be.
Step 2A Prong Two:
The judicial exception is not integrated into a practical application. The claim recites the additional elements “using an automated feedback loop”. The limitations 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).
The computer system in these steps are recited at a high-level of generality (i.e., as a generic processor performing a generic computer function) such that it amounts no more than mere instructions to apply the exception using a generic computer component. 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 claim is 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. The limitation “using an automated feedback loop” is recognized by the courts as well-understood, routine , and conventional activities when they are claimed in a merely generic manner (see MPEP 2106.05 (g)) and amount to “apply it” (see MPEP 2106.05(f)).
Dependent claims 7 and 17:
Step 2A Prong One:
Claims 7 and 17 recite limitations “train the AI-PII model on PII training data, wherein the PII training data comprises historical PII data mapped to historical data schemas”. The limitations are processes that, under their 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(n) “apparatus”, “AI-PII model”, “processor” nothing in the claim element precludes the step from practically being performed in a human mind or with the aid of pen and paper.
For example, limitation “train the AI-PII model on PII training data, wherein the PII training data comprises historical PII data mapped to historical data schemas” in the context of this claim encompass a user mentally, and with the aid of pen and paper inputting data such as historical data into particular function to such as a model to get particular outputs. Selections of desired outputs can be considered over the undesired outputs and data with similar features as the historical data can be considered in subsequent iterations.
Dependent claims 8 and 18:
Step 2A Prong One:
Claims 8 and 18 recite limitations “assign rewards based on a classification” and “update a mapping policy based on a cumulative reward and the classification”. The limitations are processes that, under their 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(n) “apparatus”, “AI-PII model”, “processor” nothing in the claim element precludes the step from practically being performed in a human mind or with the aid of pen and paper.
For example, limitation “assign rewards based on a classification” in the context of this claim encompass a user mentally, and with the aid of pen and paper assigning a value to a classification that is considered as a reward value.
Limitation “update a mapping policy based on a cumulative reward and the classification” in the context of this claim encompass a user mentally, and with the aid of pen and paper performing an update that is based on the reward and the classification.
Step 2A Prong Two:
The judicial exception is not integrated into a practical application. The claim recites the additional elements “train the AI-PII model using a reinforcement learning model”. The limitations 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).
The computer system in these steps are recited at a high-level of generality (i.e., as a generic processor performing a generic computer function) such that it amounts no more than mere instructions to apply the exception using a generic computer component. 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 claim is 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. The limitation “train the AI-PII model using a reinforcement learning model” is recognized by the courts as well-understood, routine , and conventional activities when they are claimed in a merely generic manner (see MPEP 2106.05 (g)) and amount to “apply it” (see MPEP 2106.05(f)).
Dependent claims 9 and 19:
Step 2A Prong One:
Claims 9 and 19 recite limitations “categorize…the at least a PII datum to the one or more categories” and “identifying one or more key words of the PII data”; “classifying…the one or more key words based on a rules-based function.”. The limitations are processes that, under their 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(n) “apparatus”, “AI-PII model”, “processor” nothing in the claim element precludes the step from practically being performed in a human mind or with the aid of pen and paper.
For example, limitation “categorize…the at least a PII datum to the one or more categories” in the context of this claim encompass a user mentally, and with the aid of pen and paper get the identified datum and put them into categories that they belong to. One of ordinary skills in the art can determine a category that would describe the datum and insert the datum to the particular category.
Limitation “identifying one or more key words of the PII data” in the context of this claim encompass a user mentally, and with the aid of pen and paper read the data and identify particular key words of the data. One of ordinary skills in the art can, for example, scan a set of data and determine portions of the set of data and consider them as key words.
Limitation “classifying…the one or more key words based on a rules-based function” in the context of this claim encompass a user mentally, and with the aid of pen and paper performing a classification on the retrieved keywords based on particular function such as a rules-based function.
Step 2A Prong Two:
The judicial exception is not integrated into a practical application. The claim recites the additional elements “using the AI-PII model” and “using a classifier”. The limitations 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).
The computer system in these steps are recited at a high-level of generality (i.e., as a generic processor performing a generic computer function) such that it amounts no more than mere instructions to apply the exception using a generic computer component. 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 claim is 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. The limitations “using the AI-PII model” and “using a classifier” are recognized by the courts as well-understood, routine , and conventional activities when they are claimed in a merely generic manner (see MPEP 2106.05 (g)) and amount to “apply it” (see MPEP 2106.05(f)).
Dependent claims 10 and 20:
Step 2A Prong One:
Claims 10 and 20 recite limitations “execute a downstream command as a function of the output”. The limitations are processes that, under their 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(n) “apparatus”, “AI-PII model”, “processor” nothing in the claim element precludes the step from practically being performed in a human mind or with the aid of pen and paper.
For example, limitation “execute a downstream command as a function of the output” in the context of this claim encompass a user mentally, and with the aid of pen and paper perform a particular function that is based on a given command with the output data.
Step 2A Prong Two:
The judicial exception is not integrated into a practical application. The claim recites the additional elements “transmit the output to one or more downstream models”. The limitations amounts to mere generic transmission and presentation of collected and analyzed data which is considered to be insignificant extra solution activity (MPEP 2106.05(g)).
The claim recites the additional elements “receive the output”. This limitation amounts to data gathering which is considered to be insignificant extra solution activity (MPEP 2106.05(g)).
The computer system in these steps are recited at a high-level of generality (i.e., as a generic processor performing a generic computer function) such that it amounts no more than mere instructions to apply the exception using a generic computer component. 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 claim is 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. The limitations “transmit the output to one or more downstream models” and “receive the output” are recognized by the courts as well-understood, routine , and conventional activities when they are claimed in a merely generic manner (see MPEP 2106.05 (g)).
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-5, 7-15, and 17-20 are rejected under 35 U.S.C. 103 as being unpatentable over Dean et al. (US Patent 11249960) “Dean” in view of Roggynskyy et al. (US PGPUB 20190361905) “Roggynskyy”.
Regarding claim 1, Dean teaches an apparatus for generating an output using an artificial intelligence personally identifiable information (AI-PII) model, wherein the apparatus comprises: at least a computing device, wherein the computing device comprises: a memory; and at least a processor communicatively connected to the memory, wherein the memory contains instructions (Fig. 7 & Col 9 line 22-27: FIG. 7 is a block diagram of an example computer system 700, which may be used for embodiments described herein. The computer system 700 is operationally coupled to one or more processing units such as processor 706, a memory 701, and a bus 709 that couples various system components, including the memory 701 to the processor 706) configuring the at least a processor to: receive, using the at least a processor, personally identifiable information (PII) data (Col 2 line 26-32: In some embodiments, a system receives input data, where the input data includes multiple segments, and where each segment includes one or more source fields containing target data. The system further characterizes the input data based at least in part on predetermined metrics, where the predetermined metrics determine a structure of the input data... Col 5 line 32-59: One metric category may be referred to as a token metrics. The system scans the input data to determine characteristics of individual tokens. For example, the system may determine if a token begins with a capital letter, which may indicate a proper name of person (e.g., a patient name, etc.), etc. The system may determine if a token contains numbers, which may indicate an age, a phone number, an address, etc. The system may determine if a token is alphanumeric, which indicates an address, prescription drug identifier, product identifier, etc… Examiner’s note: The system receives input data wherein the data can associates with personally identifiable information data such as name of person, age, a phone number, an address, etc.); receive, using the at least a processor, one or more model constraints (Col 6 line 65-67 and Col 7 line 14: Referring again to FIG. 2, at block 206, the system maps the target data in the source fields of the segments to target fields of a target schema based at least in part on the characterizing of block 204. In some implementations, the system receives or accesses the target schema. As indicated herein, in various implementations, the target schema is a structured schema. The system parses the target schema into objects in order to build a schema tree structure in memory. The system then maps tokens from the story to the target schema based on the characterizing. The particular structure schema may vary, and will depend on the particular implementations. For example, the target schema may be a unified data module for healthcare (UDMH). The system records which tokens are mapped to which schema fields, and stores the mapping in a suitable storage location.…Examiner’s note: The system receives a target schema that is structured with particular fields for data to be mapped to. Thus, the target schema is equivalent to a schema with corresponding model constraints); map, using an AI-PII model, the PII data to at least a data schema as a function of the one or more model constraints (Col 7 line 6-7: The system then maps tokens from the story to the target schema based on the characterizing) by: identifying, using the AI-PII model, at least a PII datum of the PII data (Col 5 line 32-39: The system scans the input data to determine characteristics of individual tokens. For example, the system may determine if a token begins with a capital letter, which may indicate a proper name of person (e.g., a patient name, etc.), etc. The system may determine if a token contains numbers, which may indicate an age, a phone number, an address, etc… Examiner’s note: The system scans portions of the received data to determine characteristics of individual tokens. Thus, at least a datum of the data is determined); categorizing, using the AI-PII model, the at least a PII datum to one or more categories of a plurality of categories (Col 5 line 32-39: The system scans the input data to determine characteristics of individual tokens. For example, the system may determine if a token begins with a capital letter, which may indicate a proper name of person (e.g., a patient name, etc.), etc. The system may determine if a token contains numbers, which may indicate an age, a phone number, an address, etc. The system may determine if a token is alphanumeric, which indicates an address, prescription drug identifier, product identifier, etc. The system may determine how many characters there are in a token, or if a token fits a certain regular expression (regex), which may indicate a phone number, social security number, medical record number, etc. The system may determine if the token is in a standard field (e.g., first name, last name, etc.), where the system may determine if the data conforms to the field (e.g., letters conform to a last name field, etc.). They system may determine if the value of the token conforms to an expected range of a known field. For example, an numeric value between 18 and 30 that may conform to a body mass index (BMI) field but would not conform to a heart rate field. Conversely, a numeric value between 40 and 100 may conform to a heart rate field, but would not conform to a BMI field. For a given numeric value, the system may identify or eliminate particular candidate target fields for matching. The system may take into account average or standard deviations, as well as other contextual information to characterize a given token… Examiner’s note: The system scans the data and determines their categories such that whether data belongs to a person’s name, an age, a phone number, an address etc.); and mapping, using the AI-PII model, the PII data to the at least a data schema (Col 6 line 65-67 & col 7 line 1-15: Referring again to FIG. 2, at block 206, the system maps the target data in the source fields of the segments to target fields of a target schema based at least in part on the characterizing of block 204. In some implementations, the system receives or accesses the target schema. As indicated herein, in various implementations, the target schema is a structured schema. The system parses the target schema into objects in order to build a schema tree structure in memory. The system then maps tokens from the story to the target schema based on the characterizing. The particular structure schema may vary, and will depend on the particular implementations. For example, the target schema may be a unified data module for healthcare (UDMH). The system records which tokens are mapped to which schema fields, and stores the mapping in a suitable storage location. Example embodiments directed to the mapping of the target data in the source fields to target fields of a target schema are described in more detail below, in connection with FIG. 5.); producing a standardized data dictionary defining one or more of a schema field, allowed format, and classification metadata (Col 7 line 35-40: FIG. 6 illustrates an example mapping 600 of target data to a target schema, according to some embodiments. Shown are example schema elements 602 (e.g., person, person ID, humanName, etc.). Also shown are target fields 604, some of which have candidate tokens or target data (e.g., 31240040, John, A, Smith, etc.). Also shown are confidence values 606… Examiner’s note: The target fields presented in a target schema can correspond to standardized data dictionary where they can define particular data such as data fields/formats); transmitting the output to one or more downstream systems wherein the one or more downstream systems are configured to execute a plurality of automated compliance commands (Fig. 1 & Col 2 line 38-54: FIG. 1 is an example environment 100 for transforming data for a target schema, according to some embodiments. Shown are a system 102, a database 104, and a database 106. In some implementations, database 104 may be referred to as a source database, which may provide the raw input data. Also, database 106 may be referred to as a target database, which may store the transformed data for a target schema. As described in more detail herein, system 102 receives input data from database 104. In some embodiments, the input data may be raw data organized in a semi-structured schema. For example, the input data may be HL7 data. While some embodiments are described herein in the context of health data such as HL7 data, these embodiments and others may be applied to any type of data. As described in more detail herein, system 102 transforms the input data for a target schema, and stores the transformed data in database 106…Examiner’s note: The output of the system can be transmitted to a downstream model such as a database. Thus, the database such as database 106, can receive the output of the system and a corresponding command can be executed such as to store the transformed data to the database).
Dean does not explicitly teach modify, using the at least a processor, the data schema based on a refinement datum, wherein the refinement datum is generated based on a temporal datum of the one or more model constraints and generate, using the AI-PII model, an output as a function of the refinement datum and the data schema; retrieving updated regulatory data from an external source using an application programming interface; updating the output as a function of temporal changes in the retrieved updated regulatory data.
Roggynskyy teaches modify, using the at least a processor, the data schema based on a refinement datum, wherein the refinement datum is generated based on a temporal datum of the one or more model constraints ([0101] Each of the representations 662 of the node profile can include fields and corresponding values. For example, in the first representation 662a, the field “First Name” is associated with 2 different values, John and Johnathan. The first representation 662a also includes the field “Title” which is associated with the value “Director.” In contrast, the second representation 662b and the third representation 662c both include an additional value “CEO” for the field “Title.” Furthermore, in the third representation 662c, the field “Company Name” is associated with 2 different values, Acme and NewCo in contrast with the first two representations 662a and 662b of the node profile. The values of the field Last Name and Cell Phone Number remain the same in all three representations 662 of the node profile… [0382]: Similarly, the system can be configured to detect changes to a node profile and generate a timeline of changes to values of fields of the node profile. For instance, the system can be configured to detect that a node has changed jobs or gets a new title, among others, based on monitoring electronic activities accessible to the system. For instance, the system can determine that a node has changed jobs if the system detects bounce back activity from the email address of the node and also detects that a person with the same name, phone number in the email signature as the node is sending emails from a new email address, perhaps, around the same time that the system detects bounce back email activity from the email address of the node. Similarly, the system can detect a change in the job title based on a change in a signature of the node. The system can then identify a date that the signature was first changed to reflect the new title and mark that date as a date of the title change. In this way, the system can detect when users or nodes get promotions, demotions, join new divisions, leave jobs, start new jobs, among others…Examiner’s note: The system collects data from activities and input the data to a corresponding schema with fields such as present in fig. 6B. The system also modifies data within the schema based on temporal datum such as data that is changed relating to time); and generate, using the AI-PII model, an output as a function of the refinement datum and the data schema ([0383] In some embodiments, the system can be configured to provide companies access to data collected, generated and managed by the system . The data managed by the system can be used to provide insights to the companies, improve the accuracy of data maintained in one or more systems of record of the companies, among others. In some embodiments, the companies that receive access to the data managed by the system can provide access to data maintained by one or more systems of record of the company as well as electronic communications servers of the company, phone servers of the company, as well as other data sources maintained or under the control of the company); retrieving updated regulatory data from an external source using an application programming interface (Fig. 7 & [0119]: FIG. 7 illustrates a series of electronic activities between two nodes, N1 702 and N2 704. N1 702 may correspond to a node associated with an entity whose electronic activities are ingested by the node graph generation system 200, while node N2 704 may correspond to a node external to the entity associated with the node N1. A node profile 715 for node N2 is maintained by the node profile manager 220. Before the electronic activity 710 was ingested by the node graph generation system 200, the node profile included the five fields, name, email, phone, company and job title. This information was previously included in the node profile and may have been determined by ingesting information from a system of record... Examiner’s note: The system can retrieve data such as relating to fields, name, email, phone, company and job title of an external source such as node N2); updating the output as a function of temporal changes in the retrieved updated regulatory data ([0121]: Node N2 can then send back a response email to node N1 that includes a signature 726 in the body of the electronic activity. The node profile manager can update, from the successful transmission of the email response and the parsing of the signature, the node profile of N2 by increasing the confidence score of the name of John Smith, the title from the signature, the company name 2 times (one of which was derived by matching the domain name of the email to the domain name of the group node in the node graph) as it is included in the email address and in the signature, and further add a new value for the phone number, which is extracted from the signature. The extracted phone number can represent his direct office number, while the phone number previously maintained in the node profile can be a general company number. In some embodiments, the system can be configured to classify phone numbers as a general company number or a direct office number based on the frequency of the number appearing in different node profiles… Examiner’s note: The system can update the data within a schema of a node based on data such as change or addition of phone number, or a change in confidence of the data). It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the Roggynskyy teachings in the Dean system. Skilled artisan would have been motivated to incorporate using temporal data to modify a schema of data taught by Roggynskyy in the Dean system so data can be updated with the most recent or accurate data thus enhances operational efficiency and improves user experience with the data. This close relation between both of the references highly suggests an expectation of success.
Regarding claim 2, Dean in view of Roggynskyy teaches all of limitations of claim 1. Dean further teaches wherein the one or more model constraints comprises internal data (Fig. 6 col 4 line 25-31: As described in more detail herein in connection with FIGS. 4, 5, and 6, embodiments avoid this problem by applying metrics to the target data in the source fields of the segments. As described in more detail herein, the system automatically maps raw messages (e.g., HL7 messages, etc.) to a generalized, structured target schema such as a unified data model for healthcare (UDMH)… Col 7 line 35-44: FIG. 6 illustrates an example mapping 600 of target data to a target schema, according to some embodiments. Shown are example schema elements 602 (e.g., person, person ID, humanName, etc.). Also shown are target fields 604, some of which have candidate tokens or target data (e.g., 31240040, John, A, Smith, etc.)... Examiner’s note: The schema can contain constraints that can be considered as internal data such as data model for healthcare or person, person ID, humanName, etc.).
Regarding claim 3, Dean in view of Roggynskyy teaches all of limitations of claim 1. Dean further teaches wherein the output comprises a data dictionary for the PII data (Col 7 line 35-40: FIG. 6 illustrates an example mapping 600 of target data to a target schema, according to some embodiments. Shown are example schema elements 602 (e.g., person, person ID, humanName, etc.). Also shown are target fields 604, some of which have candidate tokens or target data (e.g., 31240040, John, A, Smith, etc.). Also shown are confidence values 606. ).
Regarding claim 4, Dean in view of Roggynskyy teaches all of limitations of claim 1. Dean does not explicitly teach wherein the at least a processor is further configured to: retrieve, using an application programming interface, regulatory data of the one or more model constraints from an external source; conditionally update, using the at least a processor, the regulatory data based on the temporal datum; modify, using the at least a processor, the output as a function of the updated regulatory data.
Roggynskyy teaches retrieve, using an application programming interface, regulatory data of the one or more model constraints from an external source (Fig. 7 & [0119]: FIG. 7 illustrates a series of electronic activities between two nodes, N1 702 and N2 704. N1 702 may correspond to a node associated with an entity whose electronic activities are ingested by the node graph generation system 200, while node N2 704 may correspond to a node external to the entity associated with the node N1. A node profile 715 for node N2 is maintained by the node profile manager 220. Before the electronic activity 710 was ingested by the node graph generation system 200, the node profile included the five fields, name, email, phone, company and job title. This information was previously included in the node profile and may have been determined by ingesting information from a system of record... Examiner’s note: The system can retrieve data such as relating to five fields, name, email, phone, company and job title of an external source such as node N2.); conditionally update, using the at least a processor, the regulatory data based on the temporal datum ([0121]: Node N2 can then send back a response email to node N1 that includes a signature 726 in the body of the electronic activity. The node profile manager can update, from the successful transmission of the email response and the parsing of the signature, the node profile of N2 by increasing the confidence score of the name of John Smith, the title from the signature, the company name 2 times (one of which was derived by matching the domain name of the email to the domain name of the group node in the node graph) as it is included in the email address and in the signature, and further add a new value for the phone number, which is extracted from the signature. The extracted phone number can represent his direct office number, while the phone number previously maintained in the node profile can be a general company number. In some embodiments, the system can be configured to classify phone numbers as a general company number or a direct office number based on the frequency of the number appearing in different node profiles… Examiner’s note: The system can update the data within a schema of a node based on temporal data such as change or addition of phone number, or a change in confidence of the data); and modify, using the at least a processor, the output as a function of the updated regulatory data ([0383] In some embodiments, the system can be configured to provide companies access to data collected, generated and managed by the system . The data managed by the system can be used to provide insights to the companies, improve the accuracy of data maintained in one or more systems of record of the companies, among others. In some embodiments, the companies that receive access to the data managed by the system can provide access to data maintained by one or more systems of record of the company as well as electronic communications servers of the company, phone servers of the company, as well as other data sources maintained or under the control of the company. Please refer to claim 1 for the motivational statement.
Regarding claim 5, Dean in view of Roggynskyy teaches all of limitations of claim 1. Dean does not explicitly teach wherein the at least a processor is further configured to receive a predefined data schema of the at least a data schema from a user interface.
Roggynskyy teaches wherein the at least a processor is further configured to receive a predefined data schema of the at least a data schema from a user interface ([0401] The node graph generation system can be configured to hide or otherwise prevent or block from display one or more fields in the member node profile. The member node, such as the owner of the member node profile, can establish the configuration as to which fields, or values thereof, to hide from display. The node graph generation system can provide access control options via a computing device to a member node or user thereof. The node graph generation system can generate a graphical user interface or other type of user interface to present the access control options, as well as receive selections or modifications to such access control options. Using the access control interface generated and provided by the node graph generation system, the user can control which fields are presented for display via the web page, for example. In some cases, the user can control which accounts can access the member node profile of the user, or, on a more granular level, control which account can access which fields or values in the member node profile.). It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the Roggynskyy teachings in the Dean system. Skilled artisan would have been motivated to incorporate configurating as to which fields, or values thereof, to hide from display by a user taught by Roggynskyy in the Dean system to produce a desired schema that a user would be interested in. Thus, user experience with the system would increase. This close relation between both of the references highly suggests an expectation of success.
Regarding claim 7, Dean in view of Roggynskyy teaches all of limitations of claim 1. Dean does not explicitly teach train the AI-PII model on PII training data, wherein the PII training data comprises historical PII data mapped to historical data schemas.
Roggynskyy teaches train the AI-PII model on PII training data, wherein the PII training data comprises historical PII data mapped to historical data schemas ([0286] The system 200 can be configured to assign respective opportunity contact roles to one or more contacts involved in an opportunity. The system 200 can be configured to determine the opportunity contact role of a contact involved in the opportunity based on the contact's involvement. In some embodiments, system 200 can determine the contact's role based on a function the contact is serving. The function can be determined based on the contact's title, the context of electronic activities the contact is involved in, and other signals that can be derived from the electronic activities and node graph. In addition, the system 200 can assign the contact a specific opportunity contact role based on analyzing past deals or opportunities in which the contact has been involved and determining which opportunity contact role the contact has been assigned in the past. Based on historical role assignments, the system 200 can predict which role the contact should be assigned for the present opportunity. In this way, the system 200 can make recommendations to the owner of the opportunity record object to add contacts to the opportunity or assign the contact an opportunity contact role.). It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the Roggynskyy teachings in the Dean system. Skilled artisan would have been motivated to incorporate formatting data based on historical role assignments taught by Roggynskyy in the Dean system to improve accuracy and prevent any data that was not desired by older iterations. Thus, user experience with the system would increase. This close relation between both of the references highly suggests an expectation of success.
Regarding claim 8, Dean in view of Roggynskyy teaches all of limitations of claim 1. Dean does not explicitly teach to train the AI-PII model using a reinforcement learning model, wherein the reinforcement learning model is configured to: assign rewards based on a classification; and update a mapping policy based on a cumulative reward and the classification.
Roggynskyy teaches to train the AI-PII model using a reinforcement learning model, wherein the reinforcement learning model is configured to: assign rewards based on a classification; and update a mapping policy based on a cumulative reward and the classification ([0085] As more and more data is ingested and processed as described herein, the node graph generated by the node graph generation system 200 as well as node profiles of nodes can get richer and richer with more information. The additional information, as will be described herein, can be used to populate missing fields or add new values to existing fields, reinforce field values that have low confidence scores and further increase the confidence score of field values, adjust confidence scores of certain data points, and identify patterns or make deductions based on the values of various fields of node profiles of nodes included in the graph… [0119]: A node profile 715 for node N2 is maintained by the node profile manager 220. Before the electronic activity 710 was ingested by the node graph generation system 200, the node profile included the five fields, name, email, phone, company and job title. This information was previously included in the node profile and may have been determined by ingesting information from a system of record. At that time, the confidence score of each of the fields is 1. When the first electronic activity is ingested by the system 200, the node profile manager can update the node profile 715 and increase the confidence score of values of fields that can be verified by the electronic activity. By virtue of the electronic activity being successfully transmitted from N1 to N2, the node profile manager 220 can update the confidence score of the email value j@acme.com and the company name Acme by parsing the email address and determining that the domain name of the email matches a domain name of the company node, to which N2 belongs… Examiner’s note: The system implements an adjusting of confidence for each field of data wherein the confidence is increased based on processed activities and the higher confidence gives the data within a field a higher accuracy. This is equivalent to the reinforcement learning model wherein a reward such as an increase in confidence is applied to the data. A mapping such as data to corresponding field is corresponding to the reward). It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the Roggynskyy teachings in the Dean system. Skilled artisan would have been motivated to incorporate adjusting confidence value or reward of data to data field taught by Roggynskyy in the Dean system to determine whether data stored in a particular field is accurate or not, thus improves the accuracy and performance of the system. Thus, user experience with the system would increase. This close relation between both of the references highly suggests an expectation of success.
Regarding claim 9, Dean in view of Roggynskyy teaches all of limitations of claim 1. Dean further teaches wherein the at least a processor is further configured to
categorize, using the AI-PII model, the at least a PII datum to the one or more categories by: identifying one or more key words of the PII data (Fig. 3 & col 3 line 63-67 & col 4 line 1-3: Segment 306 includes a source field 314, which contains name information. As shown, source field 314 contains name information (e.g., John Smith), and source field 314 includes subfields such as source subfield 316. Source subfield 316 contains first name information (e.g., John). In various embodiments source field 314 and source subfield 316 are sources of target data to be transformed for the target schema… Col 4 line 65-67 & col 5 line 1-9: In various embodiments, the system computes metrics on each token such as token 402. These metrics provide the contextual information for characterizing the input data, and, more particularly, each token of the input data. In various embodiments, a token is the target data in a given source field. In this example, token 402 is a person's name, John Smith. As described in more detail herein, the system determines an appropriate target field in the target schema to place the token or target data. This process is referred to as a mapping process, which is described in more detail herein... Examiner’s note: the system processes input data such as to apply tokens to the input data to particular categories. For example, the system can apply a name token to a particular name found in the input); and classifying, using a classifier, the one or more key words based on a rules-based function (Fig. 6 & Col 7 line 35-44: FIG. 6 illustrates an example mapping 600 of target data to a target schema, according to some embodiments. Shown are example schema elements 602 (e.g., person, person ID, humanName, etc.). Also shown are target fields 604, some of which have candidate tokens or target data (e.g., 31240040, John, A, Smith, etc.). Also shown are confidence values 606...).
Regarding claim 10, Dean in view of Roggynskyy teaches all of limitations of claim 1. Dean further teaches wherein the at least a processor is further configured to transmit the output to one or more downstream models, wherein the one or more downstream models is configured to: receive the output; and execute a downstream command as a function of the output (Fig. 1 & Col 2 line 38-54: FIG. 1 is an example environment 100 for transforming data for a target schema, according to some embodiments. Shown are a system 102, a database 104, and a database 106. In some implementations, database 104 may be referred to as a source database, which may provide the raw input data. Also, database 106 may be referred to as a target database, which may store the transformed data for a target schema. As described in more detail herein, system 102 receives input data from database 104. In some embodiments, the input data may be raw data organized in a semi-structured schema. For example, the input data may be HL7 data. While some embodiments are described herein in the context of health data such as HL7 data, these embodiments and others may be applied to any type of data. As described in more detail herein, system 102 transforms the input data for a target schema, and stores the transformed data in database 106…Examiner’s note: The output of the system can be transmitted to a downstream model such as a database. Thus, the database such as database 106, can receive the output of the system and a corresponding command can be executed such as to store the transformed data to the database).
Regarding claim 11, note the rejections of claim 1. The instant claims recite substantially same limitations as the above-rejected claims and are therefore rejected under the same prior-art teachings.
Regarding claim 12, note the rejections of claim 2. The instant claims recite substantially same limitations as the above-rejected claims and are therefore rejected under the same prior-art teachings.
Regarding claim 13, note the rejections of claim 3. The instant claims recite substantially same limitations as the above-rejected claims and are therefore rejected under the same prior-art teachings.
Regarding claim 14, note the rejections of claim 4. The instant claims recite substantially same limitations as the above-rejected claims and are therefore rejected under the same prior-art teachings.
Regarding claim 15, note the rejections of claim 5. The instant claims recite substantially same limitations as the above-rejected claims and are therefore rejected under the same prior-art teachings.
Regarding claim 17, note the rejections of claim 7. The instant claims recite substantially same limitations as the above-rejected claims and are therefore rejected under the same prior-art teachings.
Regarding claim 18, note the rejections of claim 8. The instant claims recite substantially same limitations as the above-rejected claims and are therefore rejected under the same prior-art teachings.
Regarding claim 19, note the rejections of claim 9. The instant claims recite substantially same limitations as the above-rejected claims and are therefore rejected under the same prior-art teachings.
Regarding claim 20, note the rejections of claim 10. The instant claims recite substantially same limitations as the above-rejected claims and are therefore rejected under the same prior-art teachings.
Claims 6 and 16 are rejected under 35 U.S.C. 103 as being unpatentable over Dean et al. (US Patent 11249960) “Dean” in view of Roggynskyy et al. (US PGPUB 20190361905) “Roggynskyy” and Kottler et al. (US PGPUB 20240202170) “Kottler”.
Regarding claim 6, Dean in view of Roggynskyy teaches all of limitations of claim 1. Dean in view of Roggynskyy does not explicitly teach wherein the at least a processor is further configured to modify the data schema by: identifying one or more gap datums and determining, using an automated feedback loop, a remediation datum based on the one or more identified gap datums.
Kottler teaches wherein the at least a processor is further configured to modify the data schema by: identifying one or more gap datums ( [0048]: Control begins at block 300 with a machine learning model of the discrepancy analyzer 140 receiving input of data sets from diverse source data systems, where the data sets include one or more data fields with missing values…[0049]: Control begins at block 400 with a machine learning model of the discrepancy analyzer 140 receiving input of data sets from diverse source data systems, where the data sets include one or more data fields with incorrect values… Examiner’s note: The system receives data and identify the data fields with incorrect values or missing values. The incorrect values or missing values can be equivalent to the gap datums of the application); and determining, using an automated feedback loop, a remediation datum based on the one or more identified gap datums ([0048]: In block 302, the machine learning model of the discrepancy analyzer 140 identifies the missing values and recommendations for fixing the missing values based on historic learning. In block 304, the machine learning model of the discrepancy analyzer 140 generates output with the missing values and the recommendations for fixing the missing values…[0049]: In block 402, the machine learning model of the discrepancy analyzer 140 identifies the incorrect values and recommendations for fixing the incorrect values based on historic learning. In block 404, the machine learning model of the discrepancy analyzer 140 generates output with the incorrect values and the recommendations for fixing the incorrect values. With embodiments, the data standardization service 145 applies the recommendations to the data sets to generate fixed data 192.). It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the Kottler teachings in the Dean and Roggynskyy system. Skilled artisan would have been motivated to incorporate identifying and fixing missing values or correct values taught by Kottler in the Dean and Roggynskyy system to maintain data quality and improve efficiency and accuracy in operations relating to the data. This close relation between both of the references highly suggests an expectation of success.
Regarding claim 16, note the rejections of claim 6. The instant claims recite substantially same limitations as the above-rejected claims and are therefore rejected under the same prior-art teachings.
Prior Art
The prior arts made of record and not relied upon is considered pertinent to applicant's disclosure.
Williams et al. (US PGPUB 20190332697) is directed to an apparatus a network interface to receive a first dataset and a second dataset, wherein the first dataset is associated with the second data set is provided. The apparatus further includes a query engine to generate a first schema from the first dataset and a second schema from the second dataset, wherein the first schema and the second schema are in a common format. The apparatus includes a validation engine to generate a matrix for comparison of data transformations, wherein the matrix includes the first schema and the second schema in the common format. The validation engine is to compare the first schema and the second schema to validate of the second dataset.
Ortel (US PGPUB 20120005241) is directed to a computer system receives data defining a database schema in a common representation, creates a data model based on the input file, identifies one or more database types of a plurality of database types for which a schema is to be generated, and causes a database specific schema file to be generated for each of the one or more database types based on the data model.
Goddijn et al. (US PGPUB 20240152493) is directed to system and method for data cleaning and/or transformation according to certain embodiments. For example, a method includes: receiving a raw source dataset including one or more data types; matching the raw source dataset to a target schema corresponding to a domain, the target schema including one or more standardized variables; and transforming the one or more data types in the raw source dataset to the one or more standardized variables.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in
this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any extension fee pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to CAO DANG VUONG whose telephone number is (571)272-1812. The examiner can normally be reached on M-F 7:30-5 EST.
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/C.D.V./Examiner, Art Unit 2153 03/20/2026
/KAVITA STANLEY/Supervisory Patent Examiner, Art Unit 2153