CTFR 18/732,414 CTFR 81599 Notice of Pre-AIA or AIA Status 07-03-aia AIA 15-10-aia The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA. Claim Rejections - 35 USC § 101 07-04-01 AIA 07-04 35 U.S.C. 101 reads as follows: Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title. Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a mental process without significantly more. Representative independent claim 1 recites: “A computer-implemented method comprising: receiving one or more updates to one or more records of a first set of data stored in one or more first database structures, wherein one or more other records of the first set of data include one or more key value identifiers corresponding to of a second set of data stored in one or more second database structures; for at least a first record, of the one or more records, identifiable in the first set of data using a first key value identifier, connecting the first record to the second set of data at least in part by: accessing a first user-specified setting that activates automated identification of a candidate connection for connecting the first set of data to the second set of data; based at least in part on the first user-specified setting: generating a first vector embedding of one or more values of the first record; determining a first distance between the first vector embedding and a second vector embedding of one or more values of a second record of the first set of data, wherein the second record shares a common ancestor with the first record, and wherein the second record includes a second key value identifier corresponding to the second set of data; determining a second distance between the first vector embedding and a third vector embedding of one or more values of a third record of the first set of data, wherein the third record shares a common ancestor with the first record, and wherein the third record includes a third key value identifier corresponding to the second set of data; based at least in part on the first distance and the second distance and based at least in part on the common ancestor with the first record, identifying, for use as a particular candidate connection from the first record to the second set of data, a fourth record in the second set of data using the second key value identifier; updating the fourth record to reference the first record by including the first key value identifier in the fourth record; updating the first record in the first set of data to reference the fourth record by including the second key value identifier in the first record; receiving a request from an application for information from the fourth record, and, in response to the request, providing information about the first record.” Claims 11 and 16 recite similar subject matter. This is directed to a mental process because the subject matter of the claims is directed towards connecting a first record to a second record based on a user-specified setting that results in a series of steps comprising generating vector embeddings, multiple determinations, an identification, two update steps, and a providing information step. All of these steps are mere data analysis calculation steps and may be performed by a human being equipped with a generic computer. The independent claims include additional elements to the mental process in the form of a “receiving one or more updates…” step and a “receiving a request from an application for information” step. Claim 11 includes “one or more non-transitory machine-readable storage media.” Claim 16 includes “one or more processors,” and “one or more non-transitory computer-readable media.” This judicial exception is not integrated into a practical application because the claimed additional elements do not appear to improve the processing of a computer, require the use of a specific machine, effect a transformation or reduction of a particular article to a different state or thing, or provide a technological solution to a technological problem. The “one or more processors” and “one or more non-transitory computer-readable media” are recited at a high level of generality. They appear to be generic computing hardware elements. The recitation of generic hardware is little more than using a computer to perform an abstract idea, see MPEP 2106.05(f)(2). The “receiving” steps are merely data gathering steps and are thus mere pre-solution insignificant activity (see MPEP 2106.05(g). It is noted that none of the additional elements appear to improve the processing of a computer, require the use of a specific machine, effect a transformation or reduction of a particular article to a different state or thing, or provide a technological solution to a technological problem. As such, none of the additional elements appear to integrate the judicial exception into a practical application. None of the additional elements are sufficient to amount to significantly more than the judicial exception, in part or in whole. The recitation of generic hardware of the “one or more processors” and “one or more non-transitory computer-readable media” are little more than using a computer to perform an abstract idea, see MPEP 2106.05(f)(2). The additional elements of “receiving…” data are merely extra-solution activity data gathering and is well understood, routine, and conventional (see MPEP 2106.05(g)). None of the additional elements, in part or in whole, appear to improve the processing of a computer, require the use of a particular machine, effect a transformation or reduction of a particular article to a different state or thing, or add a specific limitation other than what is well understood, routine, or conventional. As such, none of the additional elements appears to be, in part or in whole, significantly more than the judicial exception. Dependent claims 2-10, 12-15, and 17-20 are merely directed towards additional limitations that further define data types or further describe analyses that will occur. It is noted that the claimed data definitions and data analysis and extraction steps do not appear to include additional elements that incorporate the claimed subject matter into a practical application. The dependent claims also do not include additional elements that, in part or in whole, appear to be significantly more than the abstract idea. Dependent claims 8, 15, and 20 each include a “displaying” element as an additional element. “Displaying” data does not incorporate the claim into a practical application. Displaying an output of a data analysis by displaying the integrated extracted source data is insignificant post-solution activity (see MPEP 2106.05(g)(3)). Displaying data is also not, in part or as a whole, significantly more than the abstract idea. Displaying an output of a data analysis is insignificant extra-solution activity and is well known (see MPEP 2106.05(g)((3). Claim Rejections - 35 USC § 103 07-06 AIA 15-10-15 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. 07-20-aia AIA 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. 07-21-aia AIA Claim s 1-3, 5, 8-10, 11-13, 15-18 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Bess (US Pre-Grant Publication 2014/0244300), in view of Wang et al . (US Pre-Grant Publication 2022/0391690), further in view of Balakrishnan et al . (US Pre-Grant Publication 2020/0151663) . As to claim 1, Bess teaches a computer-implemented method comprising: receiving one or more updates to one or more records of a first set of data stored in one or more first database structures, wherein one or more other records of the first set of data include one or more key value identifiers corresponding to a second set of data stored in one or more second database structures (see Bess paragraph [0064]-[0065]. Bess shows that updates may be received to health records (Master patient index databases, or MPI), [0008]. Each of the health records, or first set of data, contain “key value identifiers.” These values are reflected in a set of inverted indices, or second set of data. Paragraph [0065] discusses how these key value identifiers, such as names, exist in an inverted index. Thus, the values in the underlying master patient index correspond to a second set of data stored in the inverted index); for at least a first record, of the one or more records, identifiable in the first set of data using a first key value identifier, connecting the first record to the second set of data (see Bess paragraphs [0064]-[0065]. Whenever a record of the first set of data is updated or added, it is connected to an appropriate inverted index) at least in part by: accessing a first user-specified setting that activates automated identification of a candidate connection for connecting the first set of data to the second set of data (see paragraphs [0031]-[0032]. The system of Bess relies on duplication detection system to connect a record to the appropriate inverted index. As noted in paragraph [0060], these may be set by a user); … based at least in part on the [similarity], identifying, for use as a particular candidate connection from the first record to the second set of data, a fourth record in the second set of data using the second key value identifier (see paragraph [0102] and subsequent paragraphs. Bess teaches to identify duplicate records. As noted in paragraph [0032], a first record may copy information identified as referring to the same entity from a second record based on similarity); updating the fourth record to reference the first record by including the first key value identifier in the fourth record (see Bess paragraph [0064]. An inverted index may be updated to refer to an updated record. Updating the inverted index “includes” identifiers in a record); updating the first record in the first set of data to reference the fourth record by including the second key value in the first record (see Bess paragraph [0032]. The first record can be updated with information from another record. Updating a record includes values in the record); receiving a request from an application for information from the fourth record, and, in response to the request, providing information about the first record (see Bess paragraph [0042]. An inverted index may be used to point to records containing a queried string. As noted in paragraph [0064], the inverted index may be updated in response to changes). Bess does not clearly teach: based at least in part on the first user-specified setting: generating a first vector embedding of one or more values of the first record; determining a first distance between the first vector embedding and a second vector embedding of one or more values of a second record of the first set of data, wherein the second record shares a common ancestor with the first record, and wherein the second record includes a second key value identifier corresponding to the second set of data; determining a second distance between the first vector embedding and a third vector embedding of one or more values of a third record of the first set of data, wherein the third record shares a common ancestor with the first record, and wherein the third record includes a third key value identifier corresponding to the second set of data; based at least in part on the first distance and the second distance and based at least in part on the common ancestor with the first record, identifying, for use as a particular candidate connection from the first record to the second set of data, a fourth record in the second set of data using the second key value identifier; Wang teaches: based at least in part on the first user-specified setting: generating a first vector embedding of one or more values of the first record (see Wang paragraphs [0030]-[0031]. Wang teaches that entity embeddings may be generated for a set of entities. Each of the entities is represented by a vector); determining a first distance between the first vector embedding and a second vector embedding of one or more values of a second record of the first set of data, wherein the second record shares a common ancestor with the first record, wherein the second record includes a second key value identifier corresponding to the second set of data (see Wang paragraphs [0030]-[0031]. A distance may be measured between entities. As noted in Wang , the distance between two entities sharing data elements, including a common ancestor, will be closer than a distance between two entities sharing no common elements. Also see paragraph [0043] for a comparison and result); determining a second distance between the first vector embedding and a third vector embedding of one or more values of a third record of the first set of data, wherein the third record shares a common ancestor with the first record, and wherein the third record includes a third key value identifier corresponding to the second set of data (see Wang paragraphs [0030]-[0031]. A distance may be measured between entities. It is noted that Wang teaches to run this comparison for a third entity and the first entity, in which the third entity may have different elements. As noted in Wang , the distance between two entities sharing data elements, including a common ancestor, will be closer than a distance between two entities sharing no common elements. It is noted that Wang does not explicitly require that the third entity share a common ancestor, however such an element is merely a data value that Wang shows that some entities may possess and all entities are checked for. Also see paragraph [0043] for a comparison and result); It would have been obvious to one of ordinary skill in the art before the earliest filing date of the invention to have modified the teachings of Bess by the teachings of Wang because Wang provides an additional way to measure similarity between two entities. This will provide an additional way for Bess to measure similarity between records, which will help Bess to more accurately identify when records should share data values. Balakrishnan teaches: based at least in part on the first distance and the second distance and based at least in part on the common ancestor with the first record, identifying, for use as a particular candidate connection from the first record to the second set of data, a fourth record in the second set of data using the second key value identifier (see Balakrishnan paragraphs [0041] and [0043] for using vector similarity to determine whether a record should copy data values from another records. It is noted that Wang teaches wherein a record weighted by the similarity checker attribute may be a common ancestor). It would have been obvious to one of ordinary skill in the art before the earliest filing date of the invention to have modified the teachings of Bess by the teachings of Balakrishnan because Balakrishnan provides an additional way to measure similarity between two entities and to append values to a record when records are sufficiently similar. This will provide an additional way for Bess to complete data records and reduce inefficiency, which will help Bess to more accurately identify when records should share data values and what those data values should be. As to claim 2, Bess as modified teaches the computer-implemented method of Claim 1, wherein the first user- specified setting comprises one or more preferred matching fields, and wherein identifying the fourth record for use as the particular candidate connection is further based at least in part on an increased weight of the one or more preferred matching fields (see Bess paragraphs [0071] and [0090]). As to claim 3, Bess as modified teaches the computer-implemented method of Claim 1, wherein the first user-specified setting indicates a preferred common ancestry, and wherein identifying the fourth record for use as the particular candidate connection is further based at least in part on an increased weight of a subset of records sharing the preferred common ancestry (see Wang paragraph [0030]); wherein the subset of records comprises the second record and the third record (see Wang paragraph [0030]). As to claim 5, Bess as modified teaches the computer-implemented method of Claim 1, wherein the first user- specified setting comprises one or more required matching fields, further comprising filtering, from the first set of data, records that do match on the one or more required matching fields (see Bess paragraph [0069]); wherein the first distance and the second distance are determined based at least in part on the first record and the second record remaining after the filtering (see Bess paragraph [0069]). As to claim 8, Bess teaches the computer-implemented method of Claim 1, further comprising accessing a first user-specified rule for connecting the first set of data to the second set of data, wherein the first user-specified rule specifies one or more matching fields of the first set of data (see Bess paragraph [0060]. Users may set scoring rules); determining a first accuracy score for the first user-specified rule and a second accuracy score for the first user-specified setting (see paragraph [0010] and [0088]. Multiple probability scores may be formed. These are accuracy scores because they reflect a probability that records are related); identifying a fifth record in the first set of data that: satisfies an ancestor condition at least in part by sharing a common ancestor with the first record (see Wang paragraph [0030]), and matches the first record on the one or more matching fields (see Wang paragraph [0030]. Also see Bess paragraph [0088]); wherein the fifth record includes a third key value identifier corresponding to a sixth record of the second set of data (see Bess paragraphs [0032], [0064], and [0102]. It is noticed that this appears to be simply a different record. Bess shows analyzing and determining duplicates from records. Thus, it would be obvious that, amongst the multiple records of Bess , another record would exist to be analyzed); based on the first accuracy score and the second accuracy score, selecting the fourth record instead of the sixth record for use as the particular candidate connection from the first record to the second set of data (see Bess paragraphs [0032], [0064], and [0102]. Duplicate records may be determined based on probability, or accuracy, scores. See Balakrishnan paragraphs [0041] and [0043] for using vector similarity to determine whether a record should copy data values from another records); causing display of information about the sixth record of the second set of data in association with a recommendation to connect the first record to the fourth record instead of the fifth record (see Bess paragraph [0172]. Based on a score, a manual review of merging two records may be required). As to claim 9, Bess as modified teaches the computer-implemented method of Claim 8, wherein the second record includes a fourth key value identifier corresponding to a roll-up structure of the second set of data (see Bess paragraphs [0031]-[0032] and [0064]); the method further comprising: based at least in part on the first distance and the second distance and based at least in part on the common ancestor with the first record, identifying, for use as another particular candidate connection from the first record to the second set of data, a fifth record in the second set of data using the fourth key value identifier (see Bess paragraphs [0031]-[0032] and [0064]); updating the fifth record to reference the first record by including the first key value in the fifth record (see Bess paragraphs [0031]-[0032] and [0064]); wherein updating the first record comprises updating the first record to include the fourth key value identifier (see Bess paragraphs [0031]-[0032] and [0064]. All records in the inverted indices are updated to point to any updated data in the first record). As to claim 10, Bess as modified teaches the computer-implemented method of Claim 1, wherein the first user- specified setting indicates that updates are to be automatically applied to connect the first set of data to the second set of data, and wherein another user-specified setting indicates that updates are to be reviewed before being applied to connect the first set of data to another set of data, wherein updating the fourth record and updating the first record are performed automatically in response to identifying the fourth record for use as the particular candidate connection from the first record to the second set of data, without prompting a user for confirmation before updating the fourth record and updating the first record (see Bess paragraphs [0172]-[0174]. Bess includes settings for automatic connection and for manual review of any connections). As to claims 11 and 16, see the rejection of claim 1. As to claims 12 and 17, see the rejection of claim 2. As to claims 13 and 18, see the rejection of claim 3. As to claims 15 and 20, see the rejection of claim 8 . 07-21-aia AIA Claim s 4, 14, and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Bess (US Pre-Grant Publication 2014/0244300), in view of Wang et al . (US Pre-Grant Publication 2022/0391690), further in view of Balakrishnan et al . (US Pre-Grant Publication 2020/0151663), and further in view of Holub et al . (US Patent 11,080,307) . As to claim 4, Bess as modified teaches the computer-implemented method of Claim 1. Bess as modified does not clearly teach wherein the second vector embedding of one or more values of a second record of the first set of data comprises an aggregate vector embedding of a particular cluster of vector embeddings corresponding to a subset of records of the first set of data, the computer-implemented method further comprising determining the aggregate vector embedding at least in part by: clustering vector embeddings of records in the first set of data into a plurality of clusters including the particular cluster, wherein the clustering is based at least in part on connections between records represented by the vector embeddings and records of the second set of data, and aggregating vector embeddings of the particular cluster. Holub teaches: wherein the second vector embedding of one or more values of a second record of the first set of data comprises an aggregate vector embedding of a particular cluster of vector embeddings corresponding to a subset of records of the first set of data (see Holub 3:58-4:8. Clusters may be developed that are based on an aggregate vector of data elements in that cluster), the computer-implemented method further comprising determining the aggregate vector embedding at least in part by: clustering vector embeddings of records in the first set of data into a plurality of clusters including the particular cluster, wherein the clustering is based at least in part on connections between records represented by the vector embeddings and records of the second set of data (see Holub 3:58-4:8), and aggregating vector embeddings of the particular cluster (see Holub 3:58-4:8). It would have been obvious to one of ordinary skill in the art before the earliest filing date of the invention to have modified the teachings of Bess by the teachings of Holub because Holub provides an additional way to identify similarity with other entities. This will provide an additional way for Bess to complete data records and reduce inefficiency by allowing a comparison with entire groups of entities in addition to individually. As to claims 14 and 19, see the rejection of claim 4 . 07-21-aia AIA Claim 6 is rejected under 35 U.S.C. 103 as being unpatentable over Bess (US Pre-Grant Publication 2014/0244300), in view of Wang et al . (US Pre-Grant Publication 2022/0391690), further in view of Balakrishnan et al . (US Pre-Grant Publication 2020/0151663), and further in view of Carus et al . (US Pre-Grant Publication 2016/0350283) . As to claim 6, Bess as modified teaches the computer-implemented method of Claim 1. Bess does not teach wherein the first user-specified setting is subject to a blacklist of fields, further comprising filtering, from the first set of data, one or more particular fields on the blacklist of fields; wherein the first vector embedding is generated based on fields other than the one or more particular fields after the filtering, further comprising generating the second vector embedding and the third vector embedding based on fields other than the one or more particular fields after the filtering. Carus teaches wherein the first user-specified setting is subject to a blacklist of fields, further comprising filtering, from the first set of data, one or more particular fields on the blacklist of fields (see Carus paragraphs [0178]-[0179]. Carus teaches to create a vector space model. This creation includes a filtering step that relies on excluded user-defined attributes in the form of a list of “stopwords”); wherein the first vector embedding is generated based on fields other than the one or more particular fields after the filtering, further comprising generating the second vector embedding and the third vector embedding based on fields other than the one or more particular fields after the filtering (see Carus paragraphs [0178]-[0179]). It would have been obvious to one of ordinary skill in the art before the earliest filing date of the invention to have modified the teachings of Bess by the teachings of Carus because Carus provides additional techniques for creating a vector space. Notably, the techniques of Carus normalize and limit the vector space to only relevant attributes, which will make storing the vector space and searching for similar objects in Bess more efficient . 07-21-aia AIA Claim 7 is rejected under 35 U.S.C. 103 as being unpatentable over Bess (US Pre-Grant Publication 2014/0244300), in view of Wang et al . (US Pre-Grant Publication 2022/0391690), further in view of Balakrishnan et al . (US Pre-Grant Publication 2020/0151663), and further in view of Mones et al . (US Pre-Grant Publication 2017/0154314) . As to claim 7, Bess as modified teaches the computer-implemented method of Claim 1. Bess does not teach wherein the first user- specified setting is subject to an option to exclude fields that have a protected class of information, further comprising filtering, from the first set of data, one or more particular fields predicted to have a protected class of information; wherein the first vector embedding is generated based on fields other than the one or more particular fields after the filtering, further comprising generating the second vector embedding and the third vector embedding based on fields other than the one or more particular fields after the filtering. Mones teaches: wherein the first user- specified setting is subject to an option to exclude fields that have a protected class of information, further comprising filtering, from the first set of data, one or more particular fields predicted to have a protected class of information (see Mones paragraph [0087]. Rules may be designed to filter data based on a protected class of information); wherein the first vector embedding is generated based on fields other than the one or more particular fields after the filtering, further comprising generating the second vector embedding and the third vector embedding based on fields other than the one or more particular fields after the filtering (see Mones paragraph [0087] for the exclusion of protected classes of information. See Wang paragraphs [0030]-[0031] for generating a vector and Balakrishnan paragraphs [0041] and [0043] for using vector similarity to identify related objects). It would have been obvious to one of ordinary skill in the art before the earliest filing date of the invention to have modified the teachings of Bess by the teachings of Mones because Mones provides a user greater control over what data may be analyzed and what data should be excluded from consideration when identifying related records. This will improve a user’s ability to refine and control searches in Bess . Response to Arguments 07-37 AIA Applicant's arguments filed 20 February 2026 have been fully considered but they are not persuasive. 35 USC 101 Response Applicant argues that “The limitations are directed to a concrete, computer- implemented process for managing and synchronizing data between multiple database structures, and not merely to an abstract mental process. The claimed steps, including receiving updates to records in a first database, generating vector embeddings of record values, calculating distances between embeddings, and updating records to reference one another, require computer implementation and manipulation of digital data structures that are not performable in the human mind. The specification supports this by describing, for example, in paragraphs [0100]-[0109] and [0110]-[0135], the generation of vector embeddings, clustering of records, calculation of distances between embeddings using metrics such as cosine distance or Pearson correlation, and the automated establishment of connections between source and target dimensions. These operations are computational in nature, rely on electronic memory and processing resources, and involve transformations of the underlying digital data, which cannot be carried out mentally by a human.” As noted in MPEP 2106.04(a)(2) III C, “claims can recite a mental process even if they are claimed as being performed on a computer. The Supreme Court recognized this in Benson, determining that a mathematical algorithm for converting binary coded decimal to pure binary within a computer’s shift register was an abstract idea. The Court concluded that the algorithm could be performed purely mentally even though the claimed procedures "can be carried out in existing computers long in use, no new machinery being necessary." 409 U.S at 67, 175 USPQ at 675. See also Mortgage Grader, 811 F.3d at 1324, 117 USPQ2d at 1699 (concluding that concept of "anonymous loan shopping" recited in a computer system claim is an abstract idea because it could be "performed by humans without a computer").” MPEP 2106.04(a)(2) III C 1-3 further elaborate on the idea that a claim may still be directed towards an abstract idea despite the use of a generic machine. Thus, though the claims may not be performed wholly in a human mind, the “generating vector,” “calculating distances,” and “updating records “ may be performed by a human with a generic computer. Computational operations, such as those listed by Applicant, also may be performed by a human being with a generic computer. As such, the data analysis and computational steps of the claims are mental process steps directed towards an abstract idea. Applicant argues that “Moreover, the claims include storing and updating key value identifiers in database structures, providing machine-readable output to applications, and supporting optional user- specified settings for weighting, whitelisting, or blacklisting fields which are technical solutions to a technical problem in data management, further distinguishing the claims from mere mental processes. Accordingly, the claims are directed to a practical application of data processing techniques in a computer environment, rather than an abstract idea or mental method.” In response to this argument, it is noted that the storing and output operations appear to be generic storage and output operations. Applicant includes no citations to the specification describing how these particular storage or output operations integrate the mental process steps into a practical application or provide significantly more than the abstract idea. It is noted that the independent claims do not require “supporting optional user- specified settings for weighting, whitelisting, or blacklisting fields.” Additionally, these steps appear as if they are mere data analysis steps. An improved mental process remains a mental process and is patent ineligible. Applicant is reminded that unclaimed features from the specification receive no patentable weight until claimed. Applicant argues that “In particular, claim 1 recites limitations directed towards automated identification of candidate connections between records. Identification is performed using vector embeddings and similarity measures. As described in the specification, the use of vector embeddings in combination with structural constraints enables identification of candidate records without requiring naive pairwise comparison against all records, thereby improving computational efficiency (Specification [0110]-[0118]). Further, the use of common ancestry in claim 1 constrains similarity evaluation to structurally related records, enabling faster and more accurate identification of relationships between records.” In response to this argument, the concept of identifying using vector embeddings with structural constraints is merely a data analysis step. Similarity evaluation is also a mental process step of data analysis. An improved mental process step is still a mental process and thus patent ineligible. Applicant is requested to identify the additional elements in the claims beyond the data analysis, data observations, or data judgment steps and explain how those additional elements integrate the data mental process into a practical application or provide, in part or as a whole, significantly more than the mental process. Applicant argues that “Claim 1 also recites limitations directed to the dynamic handling of updates across different data dimensions. As disclosed, changes made to a source record may automatically propagate to target records using stored key value identifiers and updated connections (Specification [0113]-[0118], [0121]). These persistent connections modify the relational state of the database itself and provide a technical solution to the problem of maintaining synchronized and linked data across heterogeneous data sets that are configured differently.” In response to this argument, it appears that these connections are merely data references, such that any updates resulting from data analysis steps could be performed by a human being equipped with a computer. An improved method of data analysis is still a mental process and is patent ineligible. Applicant argues that “Additionally, claim 1 recites limitations directed to improved interoperability with applications. The stored connections enable real-time or automated analysis, forecasting, and process management. For example, when the system receives a request from an application for information from a fourth record, the system responds by providing information about a first record that is linked via a stored key value identifier, thereby enabling cross-dimension data access.” In response to this argument, it is noted that no applications regarding “real-time or automated analysis, forecasting, and process management” are claimed. Applicant is reminded that unclaimed features and uses from the specification receive no patentable weight until claimed. Applicant argues that “Specifically, amended claim 1 recites, inter alia, "updating the first record in the first set of data to reference the fourth record by including the second key value identifier in the first record". This amendment further clarifies that a machine- readable identifier is written into a stored record, that the internal state of a database structure is modified, and a persistent, addressable relationship is created for subsequent computer operations. The stored key value identifiers are relied upon to service application requests and enable cross-dimension traversal, thereby integrating any abstract data analysis into a practical application that changes how the database operates.” In response to this argument, it is noted that even if “updating” a record was an additional element beyond the mental process, such a step, without more, would appear to be at best a routine method of storing data. Storing data is regarded as insignificant extra-solution activity and does not, on its own, integrate a mental process into a practical application or provide significantly more than a mental process. It is also noted that the subsequent steps of “service application requests” and “cross-dimensional traversal” (neither of which appear to be claimed) appear to be a similarly be directed to mere data retrieval steps. Data retrieval steps are merely extra-solution activity data gathering and are well understood, routine, and conventional (see MPEP 2106.05(g)). Data retrieval steps, on their own, do not integrate a mental process into a practical application or provide significantly more than a mental process. Applicant argues that “In addition, the specification clearly points to features that provide practical applications for claim 1. For example, paragraph [0090] states "[o]ther applications may also request data from other records and receive the updated information for incorporation into domain-specific application functionality such as predictions, forecasting, data analysis, process management, etc." This passage expressly shows that the claimed updating of records and key-value-based connections may be relied upon to service application requests and to supply data used in concrete, real-world application functionality. As described, the features of claim 1 do not merely analyze or organize data in the abstract, but instead updates database records in a manner that directly affects how applications retrieve information and perform operational tasks, thereby integrating the claimed techniques into a practical application that changes how the database operates in response to application requests.” In response to this argument, it is noted that the claims do not require “domain-specific application functionality such as predictions, forecasting, data analysis, process management.” Applicant is reminded that unclaimed features from the specification receive no patentable weight until claimed. 35 USC 103 Response Applicant argues that “In the present response, independent claim 1 is amended to include the following limitations: "updating the fourth record to reference the first record using by including the first key value identifier in the fourth record" and "updating the first record in the first set of data to reference the fourth record using by including the second key value identifier in the first record.” Applicant continues, arguing that “Bess does not disclose or suggest these limitations. Bess is directed to managing a master patient index using inverted index structures for identifying candidate duplicate records and supporting duplicate resolution. Bess does not disclose updating a record in one dataset "by including" a "key value identifier" in that record so that the record "reference[s]" another record, nor does Bess disclose the converse update in the other direction as set forth in amended claim 1. Instead, Bess teaches use of index structures separate from the records to locate candidates and support matching, and Bess further indicates that possible duplicate relationships are not required to be maintained. That disclosure is inconsistent with the amended claim language that requires the "including" updates to be written into the records themselves.” In response to this argument, Bess does disclose updating a record in one dataset “by including” a “key value identifier” in that record, notably see Bess paragraphs [0064] and [0008]. Bess creates a “master patient index (MPI),” or a “dataset.” When a record is updated in the MPI, an inverted index, or “another record,” that is associated with the MPI is updated as well (see Bess paragraph [0064]). It is also noted that updating the inverted index so that the inverted index refers to the newly updated record does disclose the converse update in the other direction. It is also noted that the inverted index stores “values” that correspond to the values in the MPI (see Bell paragraph [0065]-[0066]). It is noted that Applicant provides no specific definitions regarding “references” or “key values.” As such, any update entry to a database, such as adding and updating new “records” in an MPI and a record in an inverted index, teaches the claim limitations to the extent claimed. Applicant is reminded that unclaimed features from the specification receive no patentable weight until claimed. Applicant continues, arguing that “Amended claim 1 also includes the sequence "receiving a request from an application for information from the fourth record, and, in response to the request, providing information about the first record." Bess does not disclose or suggest servicing an application request for information from one record by providing information about a different record in response, based on record-to-record referencing established by including key value identifiers in the records as claimed. Bess is focused on duplicate identification and optional merge or update of duplicate entries, not on establishing record-to-record referencing through inclusion of key value identifiers in the records as set forth in amended claim 1.” In response to this argument, it is noted that Bess includes a database and an inverted index. The inverted index may be searched to identify relevant fields through record-to-record referencing (see Bess paragraphs [0070] and [0108]). While Bess may have additional features or focuses not recited in the claim language, because Bess shows the mapped claim language to the extent claimed, the claimed invention is obvious over Bess in view of Wang et al . in view of Balakrishnan . Applicant continues, arguing that “Wang and Balakrishnan, whether considered alone or in combination with Bess, do not cure these deficiencies. The rejection does not identify, and the references do not disclose, the specific amended limitations requiring "including the first key value identifier in the fourth record" and "including the second key value identifier in the first record," nor do they disclose the cited application-request limitation in the context of those record updates. Accordingly, the applied prior art, taken individually or in any alleged combination, fails to teach or suggest at least the amended limitations quoted above.” In response to this argument, it is noted that neither Wang nor Balakrishnan is relied upon to teach these features. Bess teaches these features for the reasons provided above. The combination of references teaches the claimed subject matter to the extent claimed. Conclusion 07-39 AIA THIS ACTION IS MADE FINAL. 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 nonprovisional extension fee (37 CFR 1.17(a)) 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 mailing date of this final action. Any inquiry concerning this communication or earlier communications from the examiner should be directed to CHARLES D ADAMS whose telephone number is (571)272-3938. The examiner can normally be reached M-F, 9-5:30 EST. Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice. If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Aleksandr Kerzhner can be reached at 5712701760. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300. Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000. /CHARLES D ADAMS/ Primary Examiner, Art Unit 2165 Application/Control Number: 18/732,414 Page 2 Art Unit: 2165 Application/Control Number: 18/732,414 Page 3 Art Unit: 2165 Application/Control Number: 18/732,414 Page 4 Art Unit: 2165 Application/Control Number: 18/732,414 Page 5 Art Unit: 2165 Application/Control Number: 18/732,414 Page 6 Art Unit: 2165 Application/Control Number: 18/732,414 Page 7 Art Unit: 2165 Application/Control Number: 18/732,414 Page 8 Art Unit: 2165 Application/Control Number: 18/732,414 Page 9 Art Unit: 2165 Application/Control Number: 18/732,414 Page 10 Art Unit: 2165 Application/Control Number: 18/732,414 Page 11 Art Unit: 2165 Application/Control Number: 18/732,414 Page 12 Art Unit: 2165 Application/Control Number: 18/732,414 Page 13 Art Unit: 2165 Application/Control Number: 18/732,414 Page 14 Art Unit: 2165 Application/Control Number: 18/732,414 Page 15 Art Unit: 2165 Application/Control Number: 18/732,414 Page 17 Art Unit: 2165 Application/Control Number: 18/732,414 Page 18 Art Unit: 2165 Application/Control Number: 18/732,414 Page 19 Art Unit: 2165 Application/Control Number: 18/732,414 Page 20 Art Unit: 2165 Application/Control Number: 18/732,414 Page 21 Art Unit: 2165 Application/Control Number: 18/732,414 Page 22 Art Unit: 2165 Application/Control Number: 18/732,414 Page 23 Art Unit: 2165 Application/Control Number: 18/732,414 Page 24 Art Unit: 2165 Application/Control Number: 18/732,414 Page 25 Art Unit: 2165 Application/Control Number: 18/732,414 Page 26 Art Unit: 2165 Application/Control Number: 18/732,414 Page 27 Art Unit: 2165
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