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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 27 August 2026 has been entered.
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 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:
filtering the first set of data to exclude, from a candidate set of records for distance determination, one or more records that do not share a common ancestor with the first record;
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 is included in the candidate set and 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 is included in the candidate set and 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 filtering a first set of data based on a data analysis, 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)). The additional element of updating data is merely a data storing step and is insignificant extra-solution activity and is well known (see MPEP 2106.05(d)(II))
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)). The additional element of updating data is merely storing information in memory is recognized as well-understood, routine, and conventional (see MPEP 2106.05(d)(II)).
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
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-3, 5, 8-13, 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 Ezra et al. (US Pre-Grant Publication 2014/0244935), in view of Wang et al. (US Pre-Grant Publication 2022/0391690), and further 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:
filtering the first set of data to exclude, from a candidate set of records eligible for distance determination, one or more records that do not share a common ancestor with the first record;
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 is included in the candidate set and 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 is included in the candidate set and 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;
Ezra teaches:
filtering the first set of data to exclude, from a candidate set of records eligible for distance determination, one or more records that do not share a common ancestor with the first record (see Ezra paragraph [0231]. Ezra shows to filter a search and limit it to only sibling or members that belong to the same branch as a data record).
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 Ezra because Ezra provides a way to improve searching by limiting the search to only a subset of all candidate objects. This will provide an additional way to identify relevant records in Bess while limiting the search for relevant records to a smaller subset of records.
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 is included in the candidate set and 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 is included in the candidate set and 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.
Claims 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 Ezra et al. (US Pre-Grant Publication 2014/0244935), in view of Wang et al. (US Pre-Grant Publication 2022/0391690), and further 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.
Claim 6 is rejected under 35 U.S.C. 103 as being unpatentable over Bess (US Pre-Grant Publication 2014/0244300), in view of Ezra et al. (US Pre-Grant Publication 2014/0244935), in view of Wang et al. (US Pre-Grant Publication 2022/0391690), and further 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.
Claim 7 is rejected under 35 U.S.C. 103 as being unpatentable over Bess (US Pre-Grant Publication 2014/0244300), in view of Ezra et al. (US Pre-Grant Publication 2014/0244935), in view of Wang et al. (US Pre-Grant Publication 2022/0391690), and further 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
Applicant's arguments filed 27 August 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 filtering electronically stored records for eligibility for distance determination, generating vector embeddings of record values, determining distances between embeddings, using key value identifiers to identify records stored in separate database structures, 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 filtering or records, 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 “filtering electronically stored records,” “generating vector,” “using key value identifiers to identify records,” “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.
To rephrase, the Examiner does not dispute that computing technology is used in the performance of the claimed steps. However, the Examiner notes that the MPEP 2106.04(a)(2)(III)(C), as cited above, establishes that the use of generic computing technology to perform what are otherwise data analysis and calculation steps is not enough to establish patent eligibility in view of 35 USC 101.
The claimed and described steps are data analysis steps, and thus fall into the category of “mental process steps.” Simply because these data analyses must be carried out using generic machines and generic computing technology does not, on its own, integrate the mental process steps into a practical application or render a claimed invention significantly more than the included abstract ideas.
Applicant argues that “Moreover, the claims include storing and updating key value identifiers in database structures, providing information to an application from the connected records, and materially modifying electronically stored records in separate database structures, 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. If Applicant wishes to argue that these features integrate the mental process steps into a practical application or provide significantly more than the mental process steps, Applicant is requested to show, relying on citations to the specification as originally filed, how these particular steps as claimed improve the processing of a computer or provide a solution to a problem in the field of endeavor.
Applicant argues that “In addition to the foregoing, amended claim 1 provides further basis for withdrawal of the rejection of Claims 1-20 under 35 U.S.C. § 101. Specifically, amended claim 1 recites, inter alia, "filtering the first set of data to exclude, from a candidate set of records eligible for distance determination, one or more records that do not share a common ancestor with the first record.” This amendment further clarifies that record filtering, which operates before the substantive analysis begins, determines which records the system's subsequent vector-embedding generation and distance-determination operations will be performed on.”
In response to this argument, the concept of “filtering the first set of data to exclude, from a candidate set of records eligible for distance determination, one or more records that do not share a common ancestor with the first record” is merely a data analysis step. A human being is capable of filtering data based on a condition such as the claimed “records that do not share a common ancestor with the first record.” Even if the idea of filtering records before analyzing them was an improvement to a data analysis or mental process, an improved mental process step is still a mental process and is thus patent ineligible.
Applicant continues, arguing that “The effect of this limitation is that the first vector embedding is compared against fewer vector embeddings than would be required if every record of the first set of data were subject to distance determination. The newly included filtering limitation provides a direct reduction in the computational work the underlying processor and memory must perform. This is precisely the type of improvement identified as integrating a claim into a practical application: a specific technical means of reducing the amount of data that must be processed to reach a result. Filtering to reduce the population of records subject to downstream vector-embedding and distance computation is not an improvement to a human's mental judgment; it is a change to what work the machine performs. A person evaluating records mentally does not "generate vector embeddings" or compute distances between them at all, and therefore has no analogous computational burden to reduce.”
Applicant appears to be claiming that the improvement to the mental process of searching is achieved with a “a direct reduction in the computational work the underlying processor and memory must perform.” Examiner requests Applicant provide a citation to the specification as originally filed that discusses the improvement to memory that is achieved using the claimed steps.
Applicant argues that “Even if the Examiner were to maintain that any individual step, viewed in isolation, corresponds to a mental process, the claim as an ordered combination amounts to significantly more. The claim recites a specific sequence in which (1) at least one record that does not share a common ancestor with the first record is excluded from a candidate set of records eligible for distance determination, (2) the recited distances are determined using second and third records that are included in the candidate set and share a common ancestor with the first record, (3) a fourth record is identified from that analysis, (4) bidirectional key-value cross-references are written into both the first and fourth records, and (5) an application request for information from the fourth record is answered by providing information about the first records.”
Applicant appears to be conflating mental process steps with additional elements beyond the mental process steps. Using Applicant’s numbering in the above arguments, steps (1), (2), (3), and (4) are all data analysis steps, or mental process steps. An ordered combination of mental process steps remains a mental process, albeit one in an ordered combination.
Element (5) has additional elements of “request[ing] information” and “answer[ing]” the request. However, it is noted that a step of gathering information, such as a request, and a step of providing information, such as outputting an answer to a request, are, without more, recognized in the MPEP as being insignificant extra solution activity (see MPEP 2106.05(g)(3)).
Applicant has not shown, with citations to the specification as originally filed, how step (5), in part or as a whole, improves the processing of a computer or provides a solution to a problem known in the field of endeavor that relies upon the additional elements beyond the mental process.
Applicant argues that “This ordered combination reflects a specific technical solution, namely reducing the records subject to expensive similarity computation and persisting a structural, key-based reference for future efficient lookup, to the specific technical problem described in the specification of synchronizing data across independently maintained, differently structured data hierarchies without relying on manually specified hard mappings that become stale as the underlying data evolves.”
In response to this argument, Applicant is requested to identify, with citations to the original specification as filed, where the original specification discusses “reducing the records subject to expensive similarity computation” and “persisting a structural, key-based reference for future efficient lookup,” particularly with regard to the alleged improvements to a computing process or a solution to a problem known in the field of endeavor.
As noted above, the steps of “ancestry- based eligibility filtering, vector-distance comparison, cross-database record identification, and reciprocal record updating” are data analysis steps that a human being equipped with generic computing hardware is capable of performing.
Applicant’s reference to the idea that the steps are performed “without relying on manually specified hard mappings that become stale as the underlying data evolves” indicates that the claimed steps are merely directed towards automating an otherwise manual process. As noted in MPEP 2106.05(a)(I), automation of a manual process can be found under the heading “Examples that the courts have indicated may not be sufficient to show an improvement in computer-functionality.”
iii. Mere automation of manual processes, such as using a generic computer to process an application for financing a purchase, Credit Acceptance Corp. v. Westlake Services, 859 F.3d 1044, 1055, 123 USPQ2d 1100, 1108-09 (Fed. Cir. 2017) or speeding up a loan-application process by enabling borrowers to avoid physically going to or calling each lender and filling out a loan application, LendingTree, LLC v. Zillow, Inc., 656 Fed. App'x 991, 996-97 (Fed. Cir. 2016) (non-precedential);
Because Applicant appears to be arguing that the improvement supported by the claims and specification is mere automation of a manual process, the claims remain rejected under 35 USC 101.
Applicant argues that “The rejection also fails to provide factual support that this particular ordered combination of ancestry- based eligibility filtering, vector-distance comparison, cross-database record identification, and reciprocal record updating was well-understood, routine, and conventional.”
As noted above, these steps are data analysis steps and thus mental process steps. A human being equipped with a generic computer is capable of performing these data analysis steps. The filtering, comparison, and identification steps are not additional elements beyond the mental process.
The updating steps appear to be generic data storing step. While a specific type of data is stored, no specifics are claimed regarding the manner of storage itself. As such, the additional element of updating data appears to merely be storing information in memory and is thus recognized as well-understood, routine, and conventional (see MPEP 2106.05(d)(II)).
Applicant is invited to provide a citation to the specification as originally filed supporting the idea that the manner of storing data is a solution to a problem known in the art or provides an improvement to computing technology.
Applicant’s remaining arguments with respect to claims have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Conclusion
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.
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/CHARLES D ADAMS/ Primary Examiner, Art Unit 2165
