DETAILED ACTION
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
This Office Action is sent in response to Applicant's Communication received on January 19, 2023 for application number 18/156,498. This Office hereby acknowledges receipt of the following and placed of record in file: Specification, Drawings, Abstract, Oath/Declaration, and Claims.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 01/19/2023 is noted. The submission is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
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 1-25 are rejected under 35 U.S.C. 101 because the claimed invention is directed to Judicial Exceptions without significantly more. The claims recite mathematical relationships, mathematical formulas or equations, mathematical calculation and a mental process. This judicial exception is not integrated into a practical application because the recitation of generic computer and generic computer components does not sufficient to integrate the recited judicial exception into a practical application. The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the claims only recites generic computer components, which are well-understood, routine, and conventional.
Revised Patent Subject Matter Eligibility Guidance
The USPTO has published revised guidance on the application of § 101. USPTO’s 2019 Revised Patent Subject Matter Eligibility Guidance, 84 Fed. Reg. 50 (Jan. 7, 2019) (“Guidance”). Under the Guidance, the Examiner first look to whether the claim recites:
(1) any judicial exceptions, including certain groupings of abstract ideas (i.e., mathematical concepts, certain methods of organizing human activity such as a fundamental economic practice, or mental processes) (Guidance, Step 2A, prong 1); and
(2) additional elements that integrate the judicial exception into a practical application (see Manual of Patent Examining Procedure (MPEP) § 2106.05(a)-(c), (e)-(h) (9th Ed., Rev. 08.2017, 2018)) (Guidance, Step 2A, prong 2).
Only if a claim (1) recites a judicial exception and (2) does not integrate that exception into a practical application, do the Examiner then look to whether the claim:
(3) adds a specific limitation beyond the judicial exception that is not “well-understood, routine, conventional” in the field (see MPEP § 2106.05(d)); or
(4) simply appends well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception. (Guidance (Step 2B)).
Evaluate Step 2A Prong One
(a) identify the specific limitation(s) in the claim that recites an abstract idea;
(b) determine whether the identified limitation(s) falls within at least one of the groupings of abstract ideas enumerated in the 2019 Revised Patent Subject Matter Eligibility Guidance.
In TABLE 1 below, the Examiner identifies in italics the specific claim limitations that recite an abstract idea.
TABLE 1
Independent Claim 1, 13, 24
Analysis Under Revised Guidance
(a) A computer-implemented method for generating a schema describing a first node for performing mapping tasks, the method comprising: obtaining an output schema for the first node; ;;.
(b) extracting, from the output schema, configuration data for the first node, wherein the configuration data includes an object and an action associated with the first node
“extracting…. configuration data” is an abstract idea, i.e., a “mathematical concept” to mine data in a schema that includes objects and actions.
(c) analyzing a dataset of historical mappings to identify previous mappings that reference the object or the action associated with the first node
“analyzing a dataset of historical mappings to identify previous mappings …” is an abstract idea, i.e., a “mental process” which identifies data that references previous data.
(d) and generating a modified output schema describing the first node based, at least in part, on the identified previous mappings and the output schema, wherein the modified output schema is devoid of at least one portion of output schema
“generating modified output schema …” is an abstract idea, i.e., a “mathematical concept” which outputs a schema that identifies previous data that is relevant.
In view of the above analysis, Claim 1 recites an abstract idea under the Revised Guidance because the limitations (b) – (d) each recite mathematical relationship, mathematical calculation and/or a mental process. Dependent claims 2-12 also recite abstract idea because they include limitations (b) – (d) by virtue of their dependencies to claim 1.
Dependent claims 2-12 further recites additional limitations. However, these limitations also recite abstract idea, i.e., “mathematical concept – mathematical formulas or equations, mathematical calculations” and i.e., a “mental process” similar to the limitations of claims 1, discussed above.
Evaluate Step 2A Prong Two:
Evaluate whether the claim as a whole integrated the recited Judicial exception into a Practical Application of the exception.
Having determined that the claims recites a judicial exception, the analysis under the Guidance turns now to determining whether there are “additional element that integrate the judicial exception into a practical application”. The examiner determines whether the recited judicial exception is integrated into a practical application that exception by: (1) identifying whether there are any additional elements recited in the claim beyond the judicial exceptions; and (2) evaluating those additional elements individually and in combination to determine whether they integrate the exception into a practical application”.
Independent claim 13 further recite “computer readable storage medium” and Independent claim 24 further recite “one or more computer processors” and “computer readable storage media”, which is a generic/conventional computer storage. Claims 1, 13, and 24 do not recite any additional element that integrate the judicial exception into a practical application. The recitation of generic computer and generic computer components does not sufficient to integrate the recited judicial exception into a practical application. Guidance at MPEP 2106.04 (“Performance of a claim limitation using generic computer components does not necessarily preclude the claim limitation from being in the mathematical concepts grouping.”)
As discussed above, independent claims 1, 13, and 24 recites the mathematic calculation steps and mental process to extract configuration data, analyze historical mappings data to identify previous mappings that are associated with a first node and generate modified output schema based on identified data. These limitations are processes that, under broadest reasonable interpretation, covers performance of the limitation in the mind, but for the recitations of generic computer components. That is, other than reciting a “computer readable storage medium”, “one or more computer processors” and “computer readable storage media”, nothing in the claim element precludes the step from practically being performed in a human mind or with the aid of pen and paper. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind, then it falls within the “Mental Processes” grouping of abstract ideas (concepts performed in the human mind including an observation, evaluation, judgment, and opinion).
Evaluate Step 2B:
Evaluate whether the claim provide an inventive concept, i.e., does the claim recite additional element(s) or a combination of elements that amount to significantly more than the judicial exception in the claim?
At Step 2B, the evaluation of the insignificant extra-solution activity consideration takes into account whether or not the extra-solution activity is well-known. See MPEP 2106.05(g). The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The claim does not add any specific limitations beyond what is well-understood, routine, and conventional. Here, claims 13 and 24 recite ““computer readable storage medium”, “one or more computer processors” and “computer readable storage media”, which are mere generic computer components that are recited at a high level of generality, and, as disclosed in the specification, is also well-understood, routine, conventional activity when expressed at this high level of generality. Mere instructions to apply an exception using a generic computer component cannot integrate a judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B. Therefore, the claims do not provide an inventive concept (significantly more than the abstract idea) and is not eligible.
These additional elements are recited at a high-level of generality such that they amount to no more than mere instructions to apply the exception using a generic computer components. Further, the claim recitations of obtaining data.
Obtaining data is mere data gathering and output recited at a high level of generality, and thus are insignificant extra-solution activity to the judicial exception with no evidence of improvement. Accordingly, the additional elements do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea, thus fail to integrate the abstract idea into a practical application. See MPEP 2106.05(g).
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements of obtaining data (receiving or transmitting over a network), are well-understood, routine and conventional activity according to MPEP 2106.05(d)(II)(i), thus, cannot provide an inventive concept.
As a result, representative claim(s) 1, 13, and 24 do not recite any elements, or ordered combination of elements, which transforms the abstract idea into a patent-eligible subject matter. In addition, the claim(s) does not recite (i) an improvement to the functionality of a computer or other technology or technical field (see MPEP 2106.05(a); (ii) a “particular machine” to apply or use the judicial exception (see MPEP 2106.05(b); (iii) a particular transformation of an article to a different state or thing (see 2106.05(c). Further, the claim does not recite any improvement to computer functionality or specify how the one or more processors are used to improve functionality of a computing device. Considering the claim(s) as a whole, the additional elements fail to apply or use the abstract idea in a meaningful way and the additional limitations recited beyond the judicial exception itself fail to integrate the exception into a practical application. Accordingly, the claims 1-12 of this application are rejected.
Claims 2-3, 8-10, 14-15, 20-22, and 25 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claim(s) recite(s) the abstract idea of a mental process, for example the claims are directed toward the mental process of determining mappings that meet a predetermined usage and frequency of occurrence of mappings, and identifying and editing or removing portions of a schema that do not refer to mappings, under broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. The limitations associated with determining mappings that meet a predetermined usage and frequency of occurrence of mappings, and identifying and editing or removing portions of a schema are considered to be an abstract idea that falls in the “Mental Processes” grouping of abstract ideas.
Claims 4 and 16 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claim(s) recite(s) the abstract idea of a mathematical concept, for example the claims are directed toward the mathematical calculation of comparing mappings with historical mappings and increasing a usage score that is associated with mappings, under broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. The limitations associated with comparing mappings with historical mappings and increasing a usage score are considered to be an abstract idea that falls in the “Mathematical Concept” grouping of abstract ideas.
Claims 5-7, 11-12, 17-19, and 23 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claim(s) recite(s) the abstract idea of a mental process, for example the claims are directed toward the mental process of generating supplementary information associated with an output schema, under broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. The limitations associated with generating supplementary information associated with an output schema are considered to be an abstract idea that falls in the “Mental Processes” grouping of abstract ideas.
The claim(s) do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional element of using a processor to perform the extracting, analyzing, generating, and determining steps amounts to no more than mere instructions to apply the exception using a generic computer component. The limitations related to obtaining data are considered by the examiner to be well-understood, routine and conventional activity according to MPEP 2106.05(d)(II)(i), because the inventive subject matter is directed toward data management in generating a schema. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. Because of these reasons the claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception. The claim(s) 1-25 are rejected.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102 of this title, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1-3, 5-15, and 17-25 are rejected under 35 U.S.C. 103 as being unpatentable over Jochum et al. (US 2022/0164363)(hereinafter Jochum) in view of Tung et al. (US 2020/0050605)(hereinafter Tung).
Regarding claim 1, Jochum teaches a computer-implemented method for generating a schema describing a first node for performing mapping tasks, the method comprising: obtaining an output schema for the first node (see Fig. 3, para [0064], discloses obtaining output schema in selecting transformed table for leaf node (first node) whose similarity to a target table is the higher than remaining transformed tables, according to schema-level matcher); extracting, from the output schema, configuration data for the first node, wherein the configuration data includes an object and an action associated with the first node (see para [0020-0021], para [0023], discloses extracting transformed table (object) and ranking (action) transformed tables in order of similarity to target table for respective leaf node).
Jochum does not explicitly teach analyzing a dataset of historical mappings to identify previous mappings that reference the object or the action associated with the first node; and generating a modified output schema describing the first node based, at least in part, on the identified previous mappings and the output schema, wherein the modified output schema is devoid of at least one portion of output schema.
Tung teaches analyzing a dataset of historical mappings to identify previous mappings that reference the object or the action associated with the first node (see Fig. 1, para [0011], para [0024], discloses analyzing patterns (historical mappings) and tracking usage patterns to determine (identify) necessary schema elements); and generating a modified output schema describing the first node based, at least in part, on the identified previous mappings and the output schema (see Fig. 1, Fig. 3, para [0031-0032], para [0042], discloses generating a modified knowledge graph schema in implementing knowledge graph schema pruning), wherein the modified output schema is devoid of at least one portion of output schema (see para [0025], para [0027], discloses pruning of knowledge graph schema based on query statistics and attribute usage to determine data to retain based on historical usage statistics).
Jochum/Tung are analogous arts as they are each from the same field of endeavor of database systems.
Before the effective filing date of the invention it would have been obvious to a person of ordinary skill in the art to modify the system of Jochum to analyze historical mappings from disclosure of Tung. The motivation to combine these arts is disclosed by Tung as “improves the quality of the knowledge graph dataset by reducing information stored on the knowledge graph that are determined not to be helpful to the query result analysis” (para [0009]) and analyzing historical mappings is well known to persons of ordinary skill in the art, and therefore one of ordinary skill would have good reason to pursue the known options within his or her technical grasp that would lead to anticipated success.
Regarding claim 13, Jochum teaches a computer program product for generating a schema describing a first node for performing mapping tasks, the computer program product comprising a computer readable storage medium having program instructions embodied therewith (see para [0097], discloses medium), the program instructions executable by a processing unit to cause the processing unit to perform a method comprising (see para [0094], discloses processor): obtaining an output schema for the first node (see Fig. 3, para [0064], discloses obtaining output schema in selecting transformed table for leaf node (first node) whose similarity to a target table is the higher than remaining transformed tables, according to schema-level matcher); extracting, from the output schema, configuration data for the first node, wherein the configuration data includes an object and an action associated with the first node (see para [0020-0021], para [0023], discloses extracting transformed table (object) and ranking (action) transformed tables in order of similarity to target table for respective leaf node).
Jochum does not explicitly teach analyzing a dataset of historical mappings to identify previous mappings that reference the object or the action associated with the first node; and generating a modified output schema describing the first node based, at least in part, on the identified previous mappings and the output schema, wherein the modified output schema is devoid of at least one portion of output schema.
Tung teaches analyzing a dataset of historical mappings to identify previous mappings that reference the object or the action associated with the first node (see Fig. 1, para [0011], para [0024], discloses analyzing patterns (historical mappings) and tracking usage patterns to determine (identify) necessary schema elements); and generating a modified output schema describing the first node based, at least in part, on the identified previous mappings and the output schema (see Fig. 1, Fig. 3, para [0031-0032], para [0042], discloses generating a modified knowledge graph schema in implementing knowledge graph schema pruning), wherein the modified output schema is devoid of at least one portion of output schema (see para [0025], para [0027], discloses pruning of knowledge graph schema based on query statistics and attribute usage to determine data to retain based on historical usage statistics).
Jochum/Tung are analogous arts as they are each from the same field of endeavor of database systems.
Before the effective filing date of the invention it would have been obvious to a person of ordinary skill in the art to modify the system of Jochum to analyze historical mappings from disclosure of Tung. The motivation to combine these arts is disclosed by Tung as “improves the quality of the knowledge graph dataset by reducing information stored on the knowledge graph that are determined not to be helpful to the query result analysis” (para [0009]) and analyzing historical mappings is well known to persons of ordinary skill in the art, and therefore one of ordinary skill would have good reason to pursue the known options within his or her technical grasp that would lead to anticipated success.
Regarding claim 24, Jochum teaches computer system for generating a schema describing a first node for performing mapping tasks, comprising: one or more computer processors; one or more computer readable storage media (see para [0094], discloses memory and processor); and computer program instructions, the computer program instructions being stored on the one or more computer readable storage media for execution by the one or more computer processors, the computer program instructions including instructions to: obtain an output schema for the first node (see Fig. 3, para [0064], discloses obtaining output schema in selecting transformed table for leaf node (first node) whose similarity to a target table is the higher than remaining transformed tables, according to schema-level matcher); extract, from the output schema, configuration data for the first node, wherein the configuration data includes an object and an action associated with the first node (see para [0020-0021], para [0023], discloses extracting transformed table (object) and ranking (action) transformed tables in order of similarity to target table for respective leaf node).
Jochum does not explicitly teach analyze a dataset of historical mappings to identify previous mappings that reference the object or the action associated with the first node; and generate a modified output schema describing the first node based, at least in part, on the identified previous mappings and the output schema, wherein the modified output schema is devoid of at least one portion of output schema.
Tung teaches analyzing a dataset of historical mappings to identify previous mappings that reference the object or the action associated with the first node (see Fig. 1, para [0011], para [0024], discloses analyzing patterns (historical mappings) and tracking usage patterns to determine (identify) necessary schema elements); and generating a modified output schema describing the first node based, at least in part, on the identified previous mappings and the output schema (see Fig. 1, Fig. 3, para [0031-0032], para [0042], discloses generating a modified knowledge graph schema in implementing knowledge graph schema pruning), wherein the modified output schema is devoid of at least one portion of output schema (see para [0025], para [0027], discloses pruning of knowledge graph schema based on query statistics and attribute usage to determine data to retain based on historical usage statistics).
Jochum/Tung are analogous arts as they are each from the same field of endeavor of database systems.
Before the effective filing date of the invention it would have been obvious to a person of ordinary skill in the art to modify the system of Jochum to analyze historical mappings from disclosure of Tung. The motivation to combine these arts is disclosed by Tung as “improves the quality of the knowledge graph dataset by reducing information stored on the knowledge graph that are determined not to be helpful to the query result analysis” (para [0009]) and analyzing historical mappings is well known to persons of ordinary skill in the art, and therefore one of ordinary skill would have good reason to pursue the known options within his or her technical grasp that would lead to anticipated success.
Regarding claims 2, 14, and 25, Jochum/Tung teach a method of claim 1, a product of claim 13, and a system of claim 24.
Jochum does not explicitly teach wherein modifying the output schema comprises: determining a set of mappings that meet a predetermined usage requirement based, at least in part, on the identified previous mappings; identifying one or more portions of the output schema that do not refer to at least one mapping of the set of mappings; and editing or removing the identified one or more portions of the output schema.
Tung teaches wherein modifying the output schema comprises: determining a set of mappings that meet a predetermined usage requirement based, at least in part, on the identified previous mappings (see Fig. 1, para [0029], para [0032], discloses determining portions of schema that are most useful based on historical usage patterns and frequency of occurrence); identifying one or more portions of the output schema that do not refer to at least one mapping of the set of mappings; and editing or removing the identified one or more portions of the output schema (see Fig. 1, para [0032-0034], discloses identifying portions that are considered candidate traversal paths of schema that are most useful and removing other candidate traversal paths).
Regarding claims 3 and 15, Jochum/Tung teach a method of claim 1 and a product of claim 13.
Jochum does not explicitly teach wherein determining a set of mappings that meet a predetermined usage requirement comprises: for each of the identified previous mappings: determining a frequency of occurrence of the identified previous mapping in the dataset of historical mappings; and responsive to determining frequency of occurrence of the identified previous mapping exceeds a predetermined usage value, including the identified previous mapping in the set of mappings.
Tung teaches wherein determining a set of mappings that meet a predetermined usage requirement comprises: for each of the identified previous mappings: determining a frequency of occurrence of the identified previous mapping in the dataset of historical mappings (see Fig. 1, para [0032-0033], discloses determining a frequency of occurrence and co-occurrence of entities and relationships based on needs of schema elements); and responsive to determining frequency of occurrence of the identified previous mapping exceeds a predetermined usage value, including the identified previous mapping in the set of mappings (see para [0032, 0034], para [0040], discloses a predetermined threshold amount and calculating a centrality score and identifying path with highest score).
Regarding claims 5 and 17, Jochum/Tung teach a method of claim 1 and a product of claim 13.
Jochum does not explicitly teach generating supplementary information describing the at least one portion of output schema that devoid from modified output schema; and associating the supplementary information with the modified output schema.
Tung teaches generating supplementary information describing the at least one portion of output schema that devoid from modified output schema (see para [0034-0035], discloses calculating an information gain (supplementary information) for keeping or removing candidate traversal paths); and associating the supplementary information with the modified output schema (see para [0038], para [0043], discloses associating information gain calculation with knowledge graph).
Regarding claims 6 and 18, Jochum/Tung teach a method of claim 1 and a product of claim 13.
Jochum does not explicitly teach wherein associating the supplementary information with the modified output schema comprises: including the supplementary information in the modified output schema.
Tung teaches wherein associating the supplementary information with the modified output schema comprises: including the supplementary information in the modified output schema (see para [0038],para [0050], discloses including information gain calculations in query results provided in a knowledge graph format).
Regarding claims 7 and 19, Jochum/Tung teach a method of claim 1 and a product of claim 13.
Jochum does not explicitly teach wherein the first node is an input node of a flow, and wherein the method further comprises: communicating the modified output schema to a client.
Tung teaches wherein the first node is an input node of a flow, and wherein the method further comprises: communicating the modified output schema to a client (see Fig. 2, para [0044], para [0050], discloses displaying knowledge graph format query results).
Regarding claims 8 and 20, Jochum/Tung teach a method of claim 1 and a product of claim 13.
Jochum further teaches wherein the first node is a downstream node of a flow, and wherein the method further comprises: obtaining an input schema for the first node (see Fig. 3, para [0064], discloses obtaining output schema in selecting transformed table for leaf node (first node) whose similarity to a target table is the higher than remaining transformed tables, according to schema-level matcher); extracting, from the input schema, second configuration data for the first node, wherein the second configuration data includes a second object and a second action associated with the first node (see para [0020-0021], para [0023], discloses extracting transformed table (object) and ranking (action) transformed tables in order of similarity to target table for respective leaf node).
Jochum does not explicitly teach analyzing the dataset of historical mappings to identify previous mappings that reference the second object or the second action associated with the first node; and modifying the input schema to generate a modified input schema describing the first node based, at least in part, on the identified previous mappings, wherein the modified input schema is devoid of at least a portion of input schema.
Tung teaches analyzing the dataset of historical mappings to identify previous mappings that reference the second object or the second action associated with the first node (see Fig. 1, para [0011], para [0024], discloses analyzing patterns (historical mappings) and tracking usage patterns to determine (identify) necessary schema elements); and modifying the input schema to generate a modified input schema describing the first node based, at least in part, on the identified previous mappings (see Fig. 1, Fig. 3, para [0031-0032], para [0042], discloses generating a modified knowledge graph schema in implementing knowledge graph schema pruning), wherein the modified input schema is devoid of at least a portion of input schema (see para [0025], para [0027], discloses pruning of knowledge graph schema based on query statistics and attribute usage to determine data to retain based on historical usage statistics).
Regarding claims 9 and 21, Jochum/Tung teach a method of claim 1 and a product of claim 13.
Jochum does not explicitly teach determining a second set of mappings that meet a predetermined usage requirement based, at least in part, on the identified previous mapping that reference the second object or the second action associated with the first node; identifying one or more portions of the input schema that do not refer to at least one mapping of the second set of mappings; and editing or removing the identified one or more portions of the input schema.
Tung teaches determining a second set of mappings that meet a predetermined usage requirement based, at least in part, on the identified previous mapping that reference the second object or the second action associated with the first node (see Fig. 1, para [0029], para [0032], discloses determining portions of schema that are most useful based on historical usage patterns and frequency of occurrence); identifying one or more portions of the input schema that do not refer to at least one mapping of the second set of mappings; and editing or removing the identified one or more portions of the input schema (see Fig. 1, para [0032-0034], discloses identifying portions that are considered candidate traversal paths of schema that are most useful and removing other candidate traversal paths).
Regarding claims 10 and 22, Jochum/Tung teach a method of claim 1 and a product of claim 13.
Jochum does not explicitly teach wherein determining a second set of mappings that meet a predetermined usage requirement comprises: for each of the identified previous mappings that reference the second object or the second action associated with a one or more first nodes: determining a frequency of occurrence of the identified previous mapping in the dataset of historical mappings; and responsive to determining frequency of occurrence of the identified previous mapping exceeds a predetermined usage value, including the identified previous mapping in the second set of mappings.
Tung teaches wherein determining a second set of mappings that meet a predetermined usage requirement comprises: for each of the identified previous mappings that reference the second object or the second action associated with a one or more first nodes: determining a frequency of occurrence of the identified previous mapping in the dataset of historical mappings (see Fig. 1, para [0032-0033], discloses determining a frequency of occurrence and co-occurrence of entities and relationships based on needs of schema elements); and responsive to determining frequency of occurrence of the identified previous mapping exceeds a predetermined usage value, including the identified previous mapping in the second set of mappings (see para [0032, 0034], para [0040], discloses a predetermined threshold amount and calculating a centrality score and identifying path with highest score).
Regarding claims 11 and 23, Jochum/Tung teach a method of claim 1 and a product of claim 13.
Jochum does not explicitly teach generating second supplementary information describing the at least one portion of input schema that devoid from modified input schema; and associating the second supplementary information with the modified input schema.
Tung teaches generating second supplementary information describing the at least one portion of input schema that devoid from modified input schema (see para [0034-0035], discloses calculating an information gain (supplementary information) for keeping or removing candidate traversal paths); and associating the second supplementary information with the modified input schema (see para [0038], para [0043], discloses associating information gain calculation with knowledge graph).
Regarding claim 12, Jochum/Tung teach a method of claim 1.
Jochum does not explicitly teach wherein associating the second supplementary information with the modified input schema comprises: including the second supplementary information in the modified input schema.
Tung teaches wherein associating the second supplementary information with the modified input schema comprises: including the second supplementary information in the modified input schema (see para [0038],para [0050], discloses including information gain calculations in query results provided in a knowledge graph format).
Claims 4 and 16 are rejected under 35 U.S.C. 103 as being unpatentable over Jochum et al. (US 2022/0164363)(hereinafter Jochum) in view of Tung et al. (US 2020/0050605)(hereinafter Tung) as applied to claims 1 and 13, and in further view of Alexander et al. (US 2024/0193167) (hereinafter Alexander).
Regarding claims 4 and 16, Jochum/Tung teach a method of claim 1 and a product of claim 13.
Jochum/Tung does not explicitly teach for each mapping between the object and action associated with the first node: comparing the mapping with the dataset of historical mappings to determine whether or not to identify the mapping as a previous mapping; and responsive to identifying the mapping as a previous mapping, increasing a usage score value associated with the previous mapping, wherein a usage score value represents a frequency of occurrence of the associated mapping in the dataset of historical mappings.
Alexander teaches for each mapping between the object and action associated with the first node: comparing the mapping with the dataset of historical mappings to determine whether or not to identify the mapping as a previous mapping (see Fig. 5, para [0092], para [0111], discloses comparing representative utterances to reference semantic representations and determine an aggregated TF-IDF score or similar relevance score based on relative frequency or usage of words); and responsive to identifying the mapping as a previous mapping, increasing a usage score value associated with the previous mapping, wherein a usage score value represents a frequency of occurrence of the associated mapping in the dataset of historical mappings (see Fig. 5, para [0079], para [0092], discloses similar relevance score representing frequency of occurrence and adjusting threshold criteria).
Jochum/Tung/Alexander are analogous arts as they are each from the same field of endeavor of database systems.
Before the effective filing date of the invention it would have been obvious to a person of ordinary skill in the art to modify the system of Jochum/Tung to compare the mapping with the dataset of historical mappings from disclosure of Alexander. The motivation to combine these arts is disclosed by Alexander as “improving the visibility of semantically similar conversations” (para [0072]) and comparing the mapping with the dataset of historical mappings is well known to persons of ordinary skill in the art, and therefore one of ordinary skill would have good reason to pursue the known options within his or her technical grasp that would lead to anticipated success.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. See Galitsky US Patent No. 12,106,054.
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/Courtney Harmon/Primary Examiner, Art Unit 2159