DETAILED ACTION
Notice of 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 .
Drawings
The drawings are objected to as failing to comply with 37 CFR 1.84(p)(5) because the reference signs used in the specification and as depicted in Fig. 8 are not consistent. Para. 0088 describes reference sign 802 that is not depicted in Fig. 8, but appears to correspond to what is labeled as 804 in Fig. 8. Para. 0089 describes reference sign 804, but appears to correspond to what is labeled as 806 in Fig. 8. Reference sign 806 in Fig. 8 is not mentioned in the specification at all.
Corrected drawing sheets in compliance with 37 CFR 1.121(d) are required in reply to the Office action to avoid abandonment of the application. Any amended replacement drawing sheet should include all of the figures appearing on the immediate prior version of the sheet, even if only one figure is being amended. Each drawing sheet submitted after the filing date of an application must be labeled in the top margin as either “Replacement Sheet” or “New Sheet” pursuant to 37 CFR 1.121(d). If the changes are not accepted by the examiner, the applicant will be notified and informed of any required corrective action in the next Office action. The objection to the drawings will not be held in abeyance.
Claim Objections
Claims 7 and 15 are objected to because of the following informalities:
In claim 7, line 3, “of a webpage associated the” should read “of a webpage associated with the”
In claim 15, line 3, “of a webpage associated the” should read “of a webpage associated with the”
Appropriate correction is required.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Regarding Step 1 of the Alice/Mayo framework, Claims 1-8 are directed to a method (a process), Claims 9-16 are directed to a system (a machine), and Claims 17-20 are directed to a non-transitory computer-readable medium (an article of manufacture), which each fall within one of the four statutory categories of inventions.
Regarding Claim 1
Step 2A, prong 1 (Is the claim directed to a law of nature, a natural phenomenon or an abstract idea).
Claim 1 recites the following mental processes, that in each case under the broadest reasonable interpretation, covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components (e.g., “computer-implemented”, “computer processor”).
A … method for efficient entity classification, comprising: (under the broadest reasonable interpretation, a human can mentally review data and perform entity classification)
partitioning the copy of the knowledge base into a plurality of partitions, each partition of the plurality of partitions specifying a respective subset of the plurality of entities; and (under the broadest reasonable interpretation, a human can view a copy of a knowledge base, such as a simple knowledge graph printed onto paper that has 4 nodes, and mentally (or using pencil and paper) partition the knowledge graph into 2 knowledge graphs each having 2 nodes specifying a particular subset of the entities)
for each partition of the plurality of partitions: classifying, … at least one entity in the respective subset of the plurality of entities with the particular entity class based on the one or more subclasses of the set of subclasses. (under the broadest reasonable interpretation, a human can view a partition of a knowledge base, such as a simple knowledge graph printed onto paper that only has 2 nodes, and classify at least one entity based on the one or more subclasses)
Step 2A, prong 2 (Does the claim recite additional elements that integrate the judicial exception into a practical application?).
The judicial exception is not integrated into a practical application.
Regarding the “computer-implemented” limitation, such limitation is recited at a high-level of generality and amounts to no more than adding the words “apply it” (or an equivalent) with the judicial exception. In particular, the claim only recites the additional element of a computer. This additional element is recited at a high-level of generality and amounts to no more than mere instructions to apply the exception using a generic computer component (a computer). Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea (See MPEP 2106.05(f)).
Regarding the “obtaining, via a query service of a knowledge base, a set of subclasses associated with a particular entity class” limitation, such additional element of a data gathering step is recited at a high level of generality and amounts to extra-solution activity of receiving data, i.e. pre-solution activity of gathering data for use in the claimed process (see MPEP 2106.05(g)).
Regarding the “obtaining a copy of the knowledge base, the copy of the knowledge base specifying a plurality of entities” limitation, such additional element of a data gathering step is recited at a high level of generality and amounts to extra-solution activity of receiving data, i.e. pre-solution activity of gathering data for use in the claimed process (see MPEP 2106.05(g)).
Regarding the “by at least one computer processor” limitation, such limitation is recited at a high-level of generality and amounts to no more than adding the words “apply it” (or an equivalent) with the judicial exception. In particular, the claim only recites the additional element of a processor. This additional element is recited at a high-level of generality and amounts to no more than mere instructions to apply the exception using a generic computer component (a processor). Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea (See MPEP 2106.05(f)).
Accordingly, at Step 2A, prong two, after considering all claim elements individually and as an ordered combination, it is determined that the claims do not integrate the judicial exception into a practical application.
Step 2B (Does the claim recite additional elements that amount to significantly more than the judicial exception?)
In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception.
Regarding the “computer-implemented” limitation, such limitation is recited at a high-level of generality and amounts to no more than adding the words “apply it” (or an equivalent) with the judicial exception, because the limitation merely provides instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. Accordingly, this additional element does not add significantly more than the judicial exception. (See MPEP 2106.05(f)).
Regarding the “obtaining, via a query service of a knowledge base, a set of subclasses associated with a particular entity class” limitation, as discussed above, the additional element of a data gathering step is recited at a high level of generality and amounts to extra-solution activity of receiving data, i.e. pre-solution activity of gathering data for use in the claimed process. The courts have found limitations directed to obtaining information electronically, recited at a high level of generality, to be well-understood, routine, and conventional (see MPEP 2106.05(d)(II), “receiving or transmitting data over a network”, "electronic record keeping," and "storing and retrieving information in memory").
Regarding the “obtaining a copy of the knowledge base, the copy of the knowledge base specifying a plurality of entities” limitation, as discussed above, the additional element of a data gathering step is recited at a high level of generality and amounts to extra-solution activity of receiving data, i.e. pre-solution activity of gathering data for use in the claimed process. The courts have found limitations directed to obtaining information electronically, recited at a high level of generality, to be well-understood, routine, and conventional (see MPEP 2106.05(d)(II), “receiving or transmitting data over a network”, "electronic record keeping," and "storing and retrieving information in memory").
Regarding the “by at least one computer processor” limitation, such limitation is recited at a high-level of generality and amounts to no more than adding the words “apply it” (or an equivalent) with the judicial exception, because the limitation merely provides instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. Accordingly, this additional element does not add significantly more than the judicial exception. (See MPEP 2106.05(f)).
Accordingly, at Step 2B after considering all claim elements individually and as an ordered combination, it is determined that the claims do not integrate the judicial exception into a practical application.
Regarding Claim 2
Step 2A, Prong 2
Regarding the “wherein obtaining the copy of the knowledge base comprises: downloading the copy of the knowledge base” limitation, such additional element of a data gathering step is recited at a high level of generality and amounts to extra-solution activity of receiving data, i.e. pre-solution activity of gathering data for use in the claimed process (see MPEP 2106.05(g)).
Regarding the “wherein the copy of the knowledge base is included in a first file having a first text-based file format; and converting the copy of the knowledge base to a second text-based file format, wherein the second text-based file format is a newline-delimited format such that each entity of the plurality of entities is specified on a separate, independently-processable line” limitation, the limitation merely describes particular data formats, and therefore such limitation amounts to no more than generally linking the use of a judicial exception to a particular technological environment or field of use. As explained by the Supreme Court, a claim directed to a judicial exception cannot be made eligible "simply by having the applicant acquiesce to limiting the reach of the patent for the formula to a particular technological use." Diamond v. Diehr, 450 U.S. 175, 192 n.14, 209 USPQ 1, 10 n. 14 (1981). Thus, limitations that amount to merely indicating a field of use or technological environment in which to apply a judicial exception do not integrate a judicial exception into a practical application.
Step 2B
Regarding the “wherein obtaining the copy of the knowledge base comprises: downloading the copy of the knowledge base” limitation, as discussed above, the additional element of a data gathering step is recited at a high level of generality and amounts to extra-solution activity of receiving data, i.e. pre-solution activity of gathering data for use in the claimed process. The courts have found limitations directed to obtaining information electronically, recited at a high level of generality, to be well-understood, routine, and conventional (see MPEP 2106.05(d)(II), “receiving or transmitting data over a network”, "electronic record keeping," and "storing and retrieving information in memory").
Regarding the “wherein the copy of the knowledge base is included in a first file having a first text-based file format; and converting the copy of the knowledge base to a second text-based file format, wherein the second text-based file format is a newline-delimited format such that each entity of the plurality of entities is specified on a separate, independently-processable line” limitation, such limitation amounts to no more than generally linking the use of a judicial exception to a particular technological environment or field of use as explained above, which does not amount to significantly more than the judicial exception. MPEP 2106.05(h).
Regarding Claim 3
Step 2A, Prong 2
Regarding the “wherein the first text-based file format is a JavaScript Object Notation (JSON) file format and the second-text based file format is a JSON Lines format” limitation, the limitation merely describes particular data formats, and therefore such limitation amounts to no more than generally linking the use of a judicial exception to a particular technological environment or field of use. As explained by the Supreme Court, a claim directed to a judicial exception cannot be made eligible "simply by having the applicant acquiesce to limiting the reach of the patent for the formula to a particular technological use." Diamond v. Diehr, 450 U.S. 175, 192 n.14, 209 USPQ 1, 10 n. 14 (1981). Thus, limitations that amount to merely indicating a field of use or technological environment in which to apply a judicial exception do not integrate a judicial exception into a practical application.
Step 2B
Regarding the “wherein the first text-based file format is a JavaScript Object Notation (JSON) file format and the second-text based file format is a JSON Lines format” limitation, such limitation amounts to no more than generally linking the use of a judicial exception to a particular technological environment or field of use as explained above, which does not amount to significantly more than the judicial exception. MPEP 2106.05(h).
Regarding Claim 4
Step 2A, Prong 1
determining that at least one of a particular entity of the plurality of entities of the knowledge base has been modified or a new entity has been added to the knowledge base after obtaining the copy of the knowledge base; (under the broadest reasonable interpretation, a human can mentally determine if an entity has been added to the knowledge base after the copy was obtained, such as by mentally comparing the copy of the knowledge graph (e.g., on paper), and the most up-to-date knowledge graph, and determining if there are updates to a particular entity)
classifying at least one of the particular entity or the new entity with the particular entity class based on the one or more of the set of subclasses. (under the broadest reasonable interpretation, a human can mentally classify a new or modified entity based on the set of subclasses by assigning a subclass to the entity)
Regarding Step 2A, Prong 2, the claim does not include any additional elements that integrate the judicial exception into a practical application and regarding Step 2B, there are no additional elements recited that amount to significantly more than the judicial exception.
Regarding Claim 5
Step 2A, Prong 2
Regarding the “obtaining a notification from a streaming application programming interface (API) associated with the knowledge base that indicates that at least one of the particular entity of knowledge base has been modified or the new entity has been added to the knowledge base” limitation, such additional element of a data gathering step is recited at a high level of generality and amounts to extra-solution activity of receiving data, i.e. pre-solution activity of gathering data for use in the claimed process (see MPEP 2106.05(g)).
Step 2B
Regarding the “obtaining a notification from a streaming application programming interface (API) associated with the knowledge base that indicates that at least one of the particular entity of knowledge base has been modified or the new entity has been added to the knowledge base” limitation, as discussed above, the additional element of a data gathering step is recited at a high level of generality and amounts to extra-solution activity of receiving data, i.e. pre-solution activity of gathering data for use in the claimed process. The courts have found limitations directed to obtaining information electronically, recited at a high level of generality, to be well-understood, routine, and conventional (see MPEP 2106.05(d)(II), “receiving or transmitting data over a network”, "electronic record keeping," and "storing and retrieving information in memory").
Regarding Claim 6
Step 2A, Prong 1
determining that at least one second entity in the respective subset of the plurality of entities does not correspond to the particular entity class; (under the broadest reasonable interpretation, a human can mentally determine that a second entity does not correspond to a particular entity class)
in response to determining that at least one second entity in the respective subset of the plurality of entities does not correspond to the particular entity class, classifying the at least one second entity as being unassigned. (under the broadest reasonable interpretation, a human can mentally determine that a second entity does not correspond to a particular entity class and therefore assign it to an “Unassigned” class)
Regarding Step 2A, Prong 2, the claim does not include any additional elements that integrate the judicial exception into a practical application and regarding Step 2B, there are no additional elements recited that amount to significantly more than the judicial exception.
Regarding Claim 7
Step 2A, Prong 1
determining a level of popularity of the classified at least one entity based on the indication (under the broadest reasonable interpretation, a human can mentally determine a level of popularity (e.g., low or high) based on the indication of the number of page views, e.g., comparing the page views to a threshold)
Step 2A, Prong 2
Regarding the “for each partition of the plurality of partitions: obtaining an indication of a number of pageviews of a web page associated the classified at least one entity” limitation, such additional element of a data gathering step is recited at a high level of generality and amounts to extra-solution activity of receiving data, i.e. pre-solution activity of gathering data for use in the claimed process (see MPEP 2106.05(g)).
Step 2B
Regarding the “for each partition of the plurality of partitions: obtaining an indication of a number of pageviews of a web page associated the classified at least one entity” limitation, as discussed above, the additional element of a data gathering step is recited at a high level of generality and amounts to extra-solution activity of receiving data, i.e. pre-solution activity of gathering data for use in the claimed process. The courts have found limitations directed to obtaining information electronically, recited at a high level of generality, to be well-understood, routine, and conventional (see MPEP 2106.05(d)(II), “receiving or transmitting data over a network”, "electronic record keeping," and "storing and retrieving information in memory").
Regarding Claim 8
Step 2A, Prong 2
Regarding the “wherein the particular entity class comprises one of a film or a television series, wherein the at least one entity comprises a title of the one of the film or the television series, and wherein the set of subclasses comprises a plurality of further sub-classifications of the one of the film or the television series” limitation, this limitation merely describes the types of data in the knowledge graph, and therefore such limitation amounts to no more than generally linking the use of a judicial exception to a particular technological environment or field of use. As explained by the Supreme Court, a claim directed to a judicial exception cannot be made eligible "simply by having the applicant acquiesce to limiting the reach of the patent for the formula to a particular technological use." Diamond v. Diehr, 450 U.S. 175, 192 n.14, 209 USPQ 1, 10 n. 14 (1981). Thus, limitations that amount to merely indicating a field of use or technological environment in which to apply a judicial exception do not integrate a judicial exception into a practical application.
Step 2B
Regarding the “wherein the particular entity class comprises one of a film or a television series, wherein the at least one entity comprises a title of the one of the film or the television series, and wherein the set of subclasses comprises a plurality of further sub-classifications of the one of the film or the television series” limitation, such limitation amounts to no more than generally linking the use of a judicial exception to a particular technological environment or field of use as explained above, which does not amount to significantly more than the judicial exception. MPEP 2106.05(h).
Regarding Claim 9
Step 2A, Prong 1
Claim 9 recites a system that corresponds to the method of claim 1, and therefore the analysis under Step 2A, Prong 1 with respect to claim 1 also applies to this claim 9.
Step 2A, Prong 2
Claim 9 recites a system that corresponds to the method of claim 1, and therefore the analysis under Step 2A, Prong 2 with respect to claim 1 also applies to this claim 9. While claim 9 recites additional generic computing components (“memories”, “processor”), such additional generic computing components do not change the analysis under Step 2A, Prong 2. These additional elements are recited at a high-level of generality and amount to no more than mere instructions to apply the exception using generic computer components. Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea (See MPEP 2106.05(f)).
Step 2B
Claim 9 recites a system that corresponds to the method of claim 1, and therefore the analysis under Step 2B with respect to claim 1 also applies to this claim 9. While claim 9 recites additional generic computing components (“memories”, “processor”), such additional generic computing components do not change the analysis under Step 2B because the limitations merely provide instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. Accordingly, this additional element does not add significantly more than the judicial exception. (See MPEP 2106.05(f)).
Claims 10-16 depend from claim 9 and recite systems that correspond to the methods of claims 2-8, respectively, and are therefore each rejected for the same reasons explained above with respect to claim 9 and claims 2-8, respectively.
Regarding Claim 17
Step 2A, Prong 1
Claim 17 recites a non-transitory computer-readable medium that corresponds to the method of claim 1, and therefore the analysis under Step 2A, Prong 1 with respect to claim 1 also applies to this claim 17.
Step 2A, Prong 2
Claim 17 recites a non-transitory computer-readable medium that corresponds to the method of claim 1, and therefore the analysis under Step 2A, Prong 2 with respect to claim 1 also applies to this claim 17. While claim 17 recites additional generic computing components (“non-transitory computer-readable medium”, “computing device”), such additional generic computing components do not change the analysis under Step 2A, Prong 2. These additional elements are recited at a high-level of generality and amount to no more than mere instructions to apply the exception using generic computer components. Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea (See MPEP 2106.05(f)).
Step 2B
Claim 17 recites a non-transitory computer-readable medium that corresponds to the method of claim 1, and therefore the analysis under Step 2B with respect to claim 1 also applies to this claim 17. While claim 17 recites additional generic computing components (“non-transitory computer-readable medium”, “computing device”), such additional generic computing components do not change the analysis under Step 2B because the limitations merely provide instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. Accordingly, this additional element does not add significantly more than the judicial exception. (See MPEP 2106.05(f)).
Claims 18-20 depend from claim 17 and recite non-transitory computer-readable mediums that correspond to the methods of claims 2-4, respectively, and are therefore each rejected for the same reasons explained above with respect to claim 17and claims 2-4, respectively.
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, 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 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, 9, and 17 are rejected under 35 U.S.C. 103 as being unpatentable over US 20240289647 A1, hereinafter referenced as ZHAO, in view of US 20230325599 A1, hereinafter referenced as NEZAMI.
Regarding Claim 1
ZHAO teaches:
A computer-implemented method for efficient entity classification, comprising: (ZHAO, para. 0035: “In some embodiments, the target data processing task is entity classification, inter-entity relationship prediction, or entity set mining.”;
ZHAO, para. 0037: “It should be understood that the system shown and the modules thereof can be implemented in various forms. For example, in some embodiments, the system and the modules of the system can be implemented by hardware, software, or a combination of software and hardware.”)
obtaining … the knowledge base, … the knowledge base specifying a plurality of entities; (ZHAO, para. 0026: “For example, the processing device 120 can obtain two or more knowledge graphs from two or more of the servers 110-1, 110-2, 110-3, and . . . by using the network 130, to obtain a shared knowledge graph that integrates knowledge graphs of a plurality of service domains.”;
Examiner’s Note: para. 0021 of the instant specification says that a knowledge base can also be referred to as a knowledge graph)
partitioning … the knowledge base into a plurality of partitions, each partition of the plurality of partitions specifying a respective subset of the plurality of entities; (ZHAO, para. 0098: “In some embodiments, the target subgraph is a heterogeneous graph, and the target subgraph can be split into a plurality of homogeneous graphs, where the homogeneous graph refers to a knowledge graph that includes only one entity type and one relationship type. For example, the target subgraph is a heterogeneous graph a that includes several entity types such as a person, a payment account, and a Wi-Fi account, and a plurality of types of relationships between these entities. The target subgraph a can be split into the following several homogeneous graphs: a homogeneous graph (which can be referred to as a social graph) b that includes only a human and an interpersonal relationship, a homogeneous graph (which can be referred to as a payment graph) c that includes only a payment account and a payment relationship between payment accounts, and a homogeneous graph (which can be referred to as a medium graph) d that includes only a Wi-Fi account and a binding relationship between Wi-Fi accounts.”
Examiner’s Note: As shown in Fig. 4, a knowledge graph can be split into homogenous graphs that each include only one entity type, so this would split the knowledge graph into N homogenous knowledge graphs for N different types)
for each partition of the plurality of partitions: (ZHAO, para. 0102: “In some embodiments, graph data processing can be performed on the homogeneous graphs obtained by means of splitting, so as to extract a plurality of graph features. For more content of the method for performing graph data processing on the graph to obtain a plurality of graph features, refer to step 320 and related description. Details are omitted here for simplicity. For a plurality of different homogeneous graphs, graph data processing can be separately performed on the plurality of homogeneous graphs to obtain a plurality of corresponding set of graph features (one set of graph features can include one or more graph features of the homogeneous graph).”
classifying, by at least one computer processor, at least one entity in the respective subset of the plurality of entities (ZHAO, para. 0035: “In some embodiments, the target data processing task is entity classification, inter-entity relationship prediction, or entity set mining.”)
However, ZHAO fails to explicitly teach:
obtaining, via a query service of a knowledge base, a set of subclasses associated with a particular entity class;
…a copy of … the copy of…
…the copy of …
… with the particular entity class based on the one or more subclasses of the set of subclasses.
However, in a related field of endeavor (searching a knowledge base for named entities, see para. 0035), NEZAMI teaches and makes obvious:
obtaining, via a query service of a knowledge base, a set of subclasses associated with a particular entity class; (NEZAMI, para. 0157: “A knowledge base can be queried by querying the one or more publicly available dump files for the knowledge base using a query-based search. In order to further expand a respective gazetteer, a query-based search can be performed on the one or more dump files for a knowledge base or knowledge bases to be queried to retrieve a set of retrieved named entities that are categorized into one or more sub-classes and/or classes that are similar to the named entity category of the respective gazetteer. The query-based search for a respective gazetteer can be performed on the one or more dump files by extracting the sub-classes and/or classes of the named entities included in the content stored in the one or more dump files, determining which sub-classes and/or classes the extracted sub-classes and/or classes correspond to, …. The sub-classes and/or classes can be extracted from the one or more dump files based on the column or columns of the table or tables that includes those sub-classes and/or classes.”;
Examiner’s Note: the ZHAO-NEZAMI combination now obtains class and sub-class data from a knowledge base using a query-based search of the knowledge base
obtaining a copy of the knowledge base, the copy of the knowledge base specifying a plurality of entities (NEZAMI, para. 0132: “A knowledge base can be searched by searching one or more publicly and/or privately available dump files (i.e., record files, archival files, etc.) for the knowledge base using a pre-trained model.”;
Examiner’s Note: the ZHAO-NEZAMI combination now obtains a dump file (corresponding to recited “copy” of a knowledge graph as in NEZAMI)
partitioning the copy of the knowledge base into a plurality of partitions, each partition of the plurality of partitions specifying a respective subset of the plurality of entities (NEZAMI, para. 0132: “A knowledge base can be searched by searching one or more publicly and/or privately available dump files (i.e., record files, archival files, etc.) for the knowledge base using a pre-trained model.”;
Examiner’s Note: the ZHAO-NEZAMI combination now obtains a dump file (corresponding to recited “copy” of a knowledge graph as in NEZAMI and performs partitioning on the graph as explained above with respect to ZHAO)
classifying, by at least one computer processor, at least one entity in the respective subset of the plurality of entities with the particular entity class based on the one or more subclasses of the set of subclasses. (NEZAMI, para. 0157: “A knowledge base can be queried by querying the one or more publicly available dump files for the knowledge base using a query-based search. In order to further expand a respective gazetteer, a query-based search can be performed on the one or more dump files for a knowledge base or knowledge bases to be queried to retrieve a set of retrieved named entities that are categorized into one or more sub-classes and/or classes that are similar to the named entity category of the respective gazetteer. The query-based search for a respective gazetteer can be performed on the one or more dump files by extracting the sub-classes and/or classes of the named entities included in the content stored in the one or more dump files, determining which sub-classes and/or classes the extracted sub-classes and/or classes correspond to, …. The sub-classes and/or classes can be extracted from the one or more dump files based on the column or columns of the table or tables that includes those sub-classes and/or classes.”;
Examiner’s Note: the ZHAO-NEZAMI combination now obtains class and sub-class data from a knowledge base using a query-based search of the knowledge base as in NEZAMI, and uses those sub-classes to perform the entity classification of ZHAO).
Before the effective filing date, it would have been obvious to one of ordinary skill in the art to combine the teachings of ZHAO and NEZAMI as explained above. As disclosed by NEZAMI, one of ordinary skill would have been motivated to do so in order to utilize publicly-available knowledge bases in the form of available dump files, including the well-known Wikipedia content. (paras. 0132, 0134).
Regarding Claim 9
ZHAO teaches:
A system for efficient entity classification, comprising: one or more memories; and at least one processor each coupled to at least one of the one or more memories and configured to perform operations comprising: (ZHAO, para. 0035: “In some embodiments, the target data processing task is entity classification, inter-entity relationship prediction, or entity set mining.”;
ZHAO, para. 0037: “ It should be understood that the system shown and the modules thereof can be implemented in various forms. For example, in some embodiments, the system and the modules of the system can be implemented by hardware, software, or a combination of software and hardware. The hardware part can be implemented by using dedicated logic. The software part can be stored in a memory and executed by an appropriate instruction execution system, for example, a microprocessor or specially designed hardware. A person skilled in the art can understand that the above methods and systems can be implemented by using computer-executable instructions and/or control code included in the processor.”)
The remaining limitations correspond to the method of claim 1, and therefore this claim is rejected for the same reasons explained above with respect to claim 1.
Regarding Claim 17
ZHAO teaches:
A non-transitory computer-readable medium having instructions stored thereon that, when executed by at least one computing device, cause the at least one computing device to perform operations comprising: (ZHAO, para. 0037: “ It should be understood that the system shown and the modules thereof can be implemented in various forms. For example, in some embodiments, the system and the modules of the system can be implemented by hardware, software, or a combination of software and hardware. The hardware part can be implemented by using dedicated logic. The software part can be stored in a memory and executed by an appropriate instruction execution system, for example, a microprocessor or specially designed hardware. A person skilled in the art can understand that the above methods and systems can be implemented by using computer-executable instructions and/or control code included in the processor.”)
The remaining limitations correspond to the method of claim 1, and therefore this claim is rejected for the same reasons explained above with respect to claim 1.
Claims 2-3, 10-11, and 18-19 are rejected under 35 U.S.C. 103 as being unpatentable over ZHAO in view of NEZAMI and further in view of Yadav, Shweta. "Assessing the efficacy of synthetic data for enhancing machine translation models in low resource domains." International Conference on Big Data Analytics. Cham: Springer Nature Switzerland, 2023, hereinafter referenced as YADAV.
Regarding Claim 2
ZHAO and NEZAMI teach the method of claim 1 as explained above. However, ZHAO fails to explicitly teach:
wherein obtaining the copy of the knowledge base comprises: downloading the copy of the knowledge base, wherein the copy of the knowledge base is included in a first file having a first text-based file format; and
converting the copy of the knowledge base to a second text-based file format, wherein the second text-based file format is a newline-delimited format such that each entity of the plurality of entities is specified on a separate, independently-processable line.
However, in a related field of endeavor (searching a knowledge base for named entities, see para. 0035), NEZAMI teaches and makes obvious:
wherein obtaining the copy of the knowledge base comprises: downloading the copy of the knowledge base, wherein the copy of the knowledge base is included in a first file having a first text-based file format; (NEZAMI, para. 0133: “For example, the pre-trained model can receive, as inputs, a person gazetteer (e.g., GZ.sub.[PER]: {[Jane Doe]; [John Doe]; ...}) and one or more Wikipedia dump files, retrieve a set of retrieved named entities (e.g., [Jayne Doe], [Jon Doe], [John Doe], [Jane Doe], ...) by searching the one or more Wikipedia dump files for named entities that are similar to the named entities in the person gazetteer, processing the set of retrieved named entities to produce a set of processed named entities (e.g., [Jayne Doe], [Jon Doe], ...), and combining the set of processed name entities with the named entities in the person gazetteer (e.g., GZ.sub.[PERj: {[Jane Doe]; [John Doe]; [Jayne Doe]; [Jon Doe]; ...}).”;
Examiner’s Note: NEZAMI teaches acquiring a dump file for Wikipedia (e.g., downloading the file), and notes that the Wikipedia dump file is text searchable; the ZHAO-NEZAMI combination now downloads a dump file of the Wikipedia knowledge base as in NEZAMI)
Before the effective filing date, it would have been obvious to one of ordinary skill in the art to combine the teachings of ZHAO and NEZAMI as explained above. As disclosed by NEZAMI, one of ordinary skill would have been motivated to do so in order to utilize publicly-available knowledge bases in the form of available dump files, including the well-known Wikipedia content. (paras. 0132, 0134).
However, ZHAO and NEZAMI fail to explicitly teach:
converting the copy of the knowledge base to a second text-based file format, wherein the second text-based file format is a newline-delimited format such that each entity of the plurality of entities is specified on a separate, independently-processable line.
However, in a related field of endeavor (obtaining a corpus of data, see p. 123, section 1), YADAV teaches and makes obvious:
converting the copy of the knowledge base to a second text-based file format, wherein the second text-based file format is a newline-delimited format such that each entity of the plurality of entities is specified on a separate, independently-processable line. (YADAV, p. 126, section 3: “The corpus is converted to JSON format …. Once the JSON file is accurately generated next step is to follow the step outlined by openAI to generate a jsonL file.”;
Examiner’s Note: The ZHAO-NEZAMI-YADAV combination now converts the dump file of NEZAMI to JSON Lines format (corresponding to recited “second text-based file format is a newline-delimited format such that each entity of the plurality of entities is specified on a separate, independently-processable line”) as taught by YADAV)
Before the effective filing date, it would have been obvious to one of ordinary skill in the art to combine the teachings of ZHAO, NEZAMI, and YADAV as explained above. As disclosed by YADAV, one of ordinary skill would have been motivated to do so in order to put the knowledge graph into a format that can be used by OpenAI’s models, because OpenAI is one of the leading LLMs available. (YADAV, p. 126, section 3 and footnote 4).
Regarding Claim 3
ZHAO, NEZAMI, and YADAV teach the method of claim 2 as explained above. However, ZHAO and NEZAMI fail to explicitly teach:
wherein the first text-based file format is a JavaScript Object Notation (JSON) file format and the second-text based file format is a JSON Lines format.
However, in a related field of endeavor (obtaining a corpus of data, see p. 123, section 1), YADAV teaches and makes obvious:
wherein the first text-based file format is a JavaScript Object Notation (JSON) file format and the second-text based file format is a JSON Lines format. (YADAV, p. 126, section 3: “The corpus is converted to JSON format …. Once the JSON file is accurately generated next step is to follow the step outlined by openAI to generate a jsonL file.”;
Examiner’s Note: The ZHAO-NEZAMI-YADAV combination now converts the dump file of NEZAMI to JSON format (as in YADAV) and then to JSON Lines format (corresponding to recited “second text-based file format is a newline-delimited format such that each entity of the plurality of entities is specified on a separate, independently-processable line”) as taught by YADAV)
Before the effective filing date, it would have been obvious to one of ordinary skill in the art to combine the teachings of ZHAO, NEZAMI, and YADAV as explained above. As disclosed by YADAV, one of ordinary skill would have been motivated to do so in order to put the knowledge graph into a format that can be used by OpenAI’s models, because OpenAI is one of the leading LLMs available. (YADAV, p. 126, section 3 and footnote 4).
Claim 10 depends from claim 9 and recites a system that corresponds to the method of claim 2, and is therefore rejected for the same reasons explained above with respect to claims 2 and 9.
Claim 11 depends from claim 10 and recites a system that corresponds to the method of claim 3, and is therefore rejected for the same reasons explained above with respect to claims 3 and 10.
Claim 18 depends from claim 17 and recites a non-transitory computer-readable medium that corresponds to the method of claim 2, and is therefore rejected for the same reasons explained above with respect to claims 2 and 17.
Claim 19 depends from claim 18 and recites a non-transitory computer-readable medium that corresponds to the method of claim 3, and is therefore rejected for the same reasons explained above with respect to claims 3 and 18.
Claim 4, 12, and 18 are rejected under 35 U.S.C. 103 as being unpatentable over ZHAO in view of NEZAMI and further in view of US 20210406263 A1, hereinafter referenced as RISCUTIA.
Regarding Claim 4
ZHAO and NEZAMI teach the method of claim 1 as explained above. However, ZHAO and NEZAMI fail to explicitly teach:
determining that at least one of a particular entity of the plurality of entities of the knowledge base has been modified or a new entity has been added to the knowledge base after obtaining the copy of the knowledge base; and
classifying at least one of the particular entity or the new entity with the particular entity class based on the one or more of the set of subclasses.
However, in a related field of endeavor (knowledge graphs, see para. 0004), RISCUTIA teaches and makes obvious:
determining that at least one of a particular entity of the plurality of entities of the knowledge base has been modified or a new entity has been added to the knowledge base after obtaining the copy of the knowledge base; and (RISCUTIA, para. 0076: “The graph builder 108 may include a notification system 112. The notification system 112 may provide an alert regarding any changes detected between the data and the queries 118a, 118b stored in the data storage systems 114a, 114b and the information stored in the entities 104 or the connections 106 of the knowledge graph 102. The notification system 112 may provide the alert through the user access point 126 or directly to users. For example, the graph builder 108 may crawl the data storage system 114b at a time after having generated the knowledge graph 102. The graph builder 108 may detect, based on the knowledge graph 102, that a column has been removed from the first table. The notification system 112 may provide a graph query to the knowledge graph 102 requesting identification of any queries or reports that use the first table.”;
Examiner’s Note: the ZHAO-NEZAMI-RISCUTIA combination now determines if an entity of the knowledge graph has new information after the last time the knowledge graph was updated as in RISCUTIA)
classifying at least one of the particular entity or the new entity with the particular entity class based on the one or more of the set of subclasses. (RISCUTIA, para. 0076: “The graph builder 108 may include a notification system 112. The notification system 112 may provide an alert regarding any changes detected between the data and the queries 118a, 118b stored in the data storage systems 114a, 114b and the information stored in the entities 104 or the connections 106 of the knowledge graph 102. The notification system 112 may provide the alert through the user access point 126 or directly to users. For example, the graph builder 108 may crawl the data storage system 114b at a time after having generated the knowledge graph 102. The graph builder 108 may detect, based on the knowledge graph 102, that a column has been removed from the first table. The notification system 112 may provide a graph query to the knowledge graph 102 requesting identification of any queries or reports that use the first table.”;
Examiner’s Note: the ZHAO-NEZAMI-RISCUTIA combination now determines if an entity of the knowledge graph has new information after the last time the knowledge graph was updated as in RISCUTIA and then updates the classification using the entity classification of ZHAO and the sub-classes of NEZAMI)
Before the effective filing date, it would have been obvious to one of ordinary skill in the art to combine the teachings of ZHAO, NEZAMI, and RISCUTIA as explained above. One of ordinary skill would have been motivated to do so in order to ensure that operations on the knowledge graph are based on the most up-to-date information.
Claim 12 depends from claim 9 and recites a system that corresponds to the method of claim 4, and is therefore rejected for the same reasons explained above with respect to claims 4 and 9.
Claim 20 depends from claim 17 and recites a non-transitory computer-readable medium that corresponds to the method of claim 4, and is therefore rejected for the same reasons explained above with respect to claims 4 and 17.
Claims 5 and 13 are rejected under 35 U.S.C. 103 as being unpatentable over ZHAO in view of NEZAMI and RISCUTIA and further in view of US 20230030187 A1, hereinafter referenced as SHANKAR.
Regarding Claim 5
ZHAO, NEZAMI, and RISCUTIA teach the method of claim 4 as explained above. However, ZHAO, NEZAMI, and RISCUTIA fail to explicitly teach:
obtaining a notification from a streaming application programming interface (API) associated with the knowledge base that indicates that at least one of the particular entity of knowledge base has been modified or the new entity has been added to the knowledge base.
However, in a related field of endeavor (database systems, see para. 0005), SHANKAR teaches and makes obvious:
obtaining a notification from a streaming application programming interface (API) associated with the knowledge base that indicates that at least one of the particular entity of knowledge base has been modified or the new entity has been added to the knowledge base. (SHANKAR, para. 0066: “Streaming API is a specialized API for setting up notifications that trigger when changes are made to data. It uses a publish-subscribe (pub/sub) model in which users can subscribe to channels that broadcast certain types of data changes. The pub/sub model reduces the number of API requests by eliminating the need for polling. Streaming API is useful, for example, in writing apps that would otherwise need to frequently poll for changes.”;
Examiner’s Note: the ZHAO-NEZAMI-RISCUTIA-SHANKAR combination now determines if an entity of the knowledge graph has new information after the last time the knowledge graph was updated as in RISCUTIA and then sends a Streaming API notification as in SHANKAR)
Before the effective filing date, it would have been obvious to one of ordinary skill in the art to combine the teachings of ZHAO, NEZAMI, RISCUTIA, and SHANKAR as explained above. As disclosed by SHANKAR, one of ordinary skill would have been motivated to do so for applications “that would otherwise need to frequently poll for changes.” (para. 0066).
Claim 13 depends from claim 12 and recites a system that corresponds to the method of claim 5, and is therefore rejected for the same reasons explained above with respect to claims 5 and 12.
Claims 6 and 14 are rejected under 35 U.S.C. 103 as being unpatentable over ZHAO and NEZAMI and further in view of US 20220230089 A1, hereinafter referenced as PERAUD.
Regarding Claim 6
ZHAO and NEZAMI teach the method of claim 1 as explained above. However, ZHAO and NEZAMI fail to explicitly teach:
determining that at least one second entity in the respective subset of the plurality of entities does not correspond to the particular entity class; and in response to determining that at least one second entity in the respective subset of the plurality of entities does not correspond to the particular entity class, classifying the at least one second entity as being unassigned.
However, in a related field of endeavor (machine learning classifiers, see para. 0001), PERAUD teaches and makes obvious:
determining that at least one second entity in the respective subset of the plurality of entities does not correspond to the particular entity class; and in response to determining that at least one second entity in the respective subset of the plurality of entities does not correspond to the particular entity class, classifying the at least one second entity as being unassigned. (PERAUD, para. 0104: “To be able to implement this model, the expected taxonomy of topics as well as the proportion of the verbatims that does not belong to the target taxonomy may need to be known. This way, there can be a separate class (i.e., an unknown class) where all the verbatims with unassignable topics can be classified into.”;
Examiner’s Note: the ZHAO-NEZAMI-PERAUD combination now determines if an entity cannot be assigned to a class as in PERAUD, and in that case, assigns it to an “Unknown” class)
Before the effective filing date, it would have been obvious to one of ordinary skill in the art to combine the teachings of ZHAO, NEZAMI, and PERAUD as explained above. As disclosed by PERAUD, one of ordinary skill would have been motivated to do so in order to do a second pass with a clustering algorithm to determine the closest class. (para. 0132).
Claim 14 depends from claim 9 and recites a system that corresponds to the method of claim 6, and is therefore rejected for the same reasons explained above with respect to claims 6 and 9.
Claims 7 and 15 are rejected under 35 U.S.C. 103 as being unpatentable over ZHAO, in view of NEZAMI and further in view of US 20190266126 A1, hereinafter referenced as YAMAMOTO.
Regarding Claim 7
ZHAO and NEZAMI teach the method of claim 1 as explained above. However, ZHAO and NEZAMI fail to explicitly teach:
for each partition of the plurality of partitions: obtaining an indication of a number of pageviews of a web page associated the classified at least one entity; and
determining a level of popularity of the classified at least one entity based on the indication.
However, in a related field of endeavor (databases, see para. 0057), YAMAMOTO teaches and makes obvious:
for each partition of the plurality of partitions: obtaining an indication of a number of pageviews of a web page associated the classified at least one entity; and (YAMAMOTO, para. 0113: “Accordingly, the information (the first index value) that indicates the popularity of the blog, such as the total number of page views, the number of accessed unique users, the total linked number, the total number of comments, the page ranking of the blog, the value that indicates the increasing tendency of the page view for the entire blog”;
Examiner’s Note: the ZHAO-NEZAMI-YAMAMOTO combination now has the entities in the ZHAO knowledge graph relate to blogs and determines the number of page views for the blog as in YAMAMOTO)
determining a level of popularity of the classified at least one entity based on the indication. (YAMAMOTO, para. 0113: “Accordingly, the information (the first index value) that indicates the popularity of the blog, such as the total number of page views, the number of accessed unique users, the total linked number, the total number of comments, the page ranking of the blog, the value that indicates the increasing tendency of the page view for the entire blog”;
Examiner’s Note: the ZHAO-NEZAMI-YAMAMOTO combination now has the entities in the ZHAO knowledge graph relate to blogs and determines the number of page views for the blog in order to determine the popularity of the blog as in YAMAMOTO)
Before the effective filing date, it would have been obvious to one of ordinary skill in the art to combine the teachings of ZHAO, NEZAMI, and YAMAMOTO as explained above. As disclosed by YAMAMOTO, one of ordinary skill would have been motivated to do so in order to determine a popularity, or relative importance of the blog entity to determine if the blog article should be saved in a compressed or non-compressed format. (para. 0092).
Claim 15 depends from claim 9 and recites a system that corresponds to the method of claim 7, and is therefore rejected for the same reasons explained above with respect to claims 7 and 9.
Claims 8 and 16 are rejected under 35 U.S.C. 103 as being unpatentable over ZHAO, in view of NEZAMI and further in view of US 20230133146 A1, hereinafter referenced as ZHU.
Regarding Claim 8
ZHAO and NEZAMI teach the method of claim 1 as explained above. However, ZHAO and NEZAMI fail to explicitly teach:
wherein the particular entity class comprises one of a film or a television series, wherein the at least one entity comprises a title of the one of the film or the television series, and wherein the set of subclasses comprises a plurality of further sub-classifications of the one of the film or the television series.
However, in a related field of endeavor (knowledge bases, see paras. 0008-0010), ZHU teaches and makes obvious:
wherein the particular entity class comprises one of a film or a television series, wherein the at least one entity comprises a title of the one of the film or the television series, and wherein the set of subclasses comprises a plurality of further sub-classifications of the one of the film or the television series. (ZHU, para. 0036: “For step S12, in the case of a smart TV, the film and television skill is a first skill, and the music skill is a second skill. When the skill preferentially hit by “Play Teresa Teng's Tianmimi” is the film and television field skill, whether “Tianmimi” and “Teresa Teng” match each other is determined according to the knowledge base of the film and television field. In the disclosure above, the film title “Tianmimi” in the knowledge base of the film and television field corresponds to filmmakers: Peter Chan, Maggie Cheung, Leon Lai Ming, and Eric Tsang. Therefore, the name semantic slot does not match the character semantic slot in the film and television field.”;
Examiner’s Note: ZHU teaches that a knowledge base can include film and television titles, with sub-categories related to music and filmmakers; the ZHAO-NEZAMI-ZHU combination now modifies the knowledge graph of ZHAO to include information and film and television series, with sub-categories, as in ZHU)
Before the effective filing date, it would have been obvious to one of ordinary skill in the art to combine the teachings of ZHAO, NEZAMI, and ZHU as explained above. One of ordinary skill would have been motivated to do so in order to provide information to a portable entertainment device (see ZHU, para. 0087) that can be provided to the user about a film/television series that is being consumed.
Claim 16 depends from claim 9 and recites a system that corresponds to the method of claim 8, and is therefore rejected for the same reasons explained above with respect to claims 8 and 9.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
US 20210390256 A1 (Liu). “In some embodiments, the dividing of the data and the entity groups may be considered to be performed utilizing a graph (e.g., a knowledge graph) and a graph partitioning method. For example, in the example shown in FIG. 6, the output of the entity correlation analyzer 606 is provided to a graph analyzer 614. The graph analyzer 614 generates a graph 616 based on the correlation data received from the entity correlation analyzer 606. The graph 616 may include nodes that represent entity types and edges that interconnect the nodes that represent correlations between the entity types. The graph analyzer 614 utilizes (and/or includes) a graph partition (or partitioning) module 618, that divides the graph into groups.” (para. 0083).
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/MICHAEL C. LEE/Examiner, Art Unit 2128
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