DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA . In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
Status of the Claims
The pending claims in the present application are claims 1 and 3-17 of the Reply to Office Action of 08 December 2025 (hereinafter referred to as the “Reply”).
Priority
Receipt is acknowledged of certified copies of papers required by 37 CFR 1.55.
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claim 12 is rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventors, at the time the application was filed, had possession of the claimed invention. The subject matter at issue is the recited “calculating, for each pair of words that co-occur in the extracted sentences, a ratio of a number of sentences in which both words occur to a number of sentences in which either word occurs” of claim 12.
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 and 3-17 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The paragraphs below provide rationales for the rejection. The rationales are based on the multi-step subject matter eligibility test outlined in MPEP 2106.
Step 1 of the eligibility analysis involves determining whether a claim falls within one of the four enumerated categories of patentable subject matter recited in 35 USC 101. (See MPEP 2106.03(I).) That is, Step 1 asks whether a claim is to a process, machine, manufacture, or composition of matter. (See MPEP 2106.03(II).) Referring to the pending claims, the “system” of claims 1, 3-9, and 12-17 constitutes a machine under 35 USC 101, the “method” of claim 10 constitutes a machine under the statute, and the “proposal support program stored on a non-transitory computer-readable storage medium” of claim 11 constitutes a manufacture under the statute. Accordingly, claims 1 and 3-17 meet the criteria of Step 1 of the eligibility analysis. The claims, however, fail to meet the criteria of subsequent steps of the eligibility analysis.
The next step of the eligibility analysis, Step 2A, involves determining whether a claim is directed to a judicial exception. (See MPEP 2106.04(II).) This step asks whether a claim is directed to a law of nature, a natural phenomenon (product of nature) or an abstract idea. (See id.) Step 2A is a two-prong inquiry. (See MPEP 2106.04(II)(A).) Prong One and Prong Two are addressed below.
In the context of Step 2A of the eligibility analysis, Prong One asks whether a claim recites an abstract idea, law of nature, or natural phenomenon. (See MPEP 2106.04(II)(A)(1).) Using claim 1 as an example, the claim recites the following abstract idea limitations:
“A proposal support ... comprising: ...” - See below regarding MPEP 2106.04(a), certain methods of organizing human activity, and mental processes
“... a generation process for generating a co-occurrence network by crawling at least one information source out of a first information source related to a first field and a second information source related to a second field different from the first field to extract sentences containing field names, performing morphological analysis on the extracted sentences to identify words, and calculating co-occurrence probabilities between words that co-occur in the extracted sentences; ...” - See below regarding MPEP 2106.04(a), certain methods of organizing human activity, and mental processes
“... an acquisition process for acquiring a specific first word from the co-occurrence network generated in the generation process by receiving a user selection from ... the co-occurrence network showing nodes sized according to word occurrence counts and links representing co-occurrence relationships, in which each first word of a first word group in a first sentence group including a first field name in the at least one information source is a node, and a co-occurrence relationship between two first words is a link connecting the nodes; ...” - See below regarding MPEP 2106.04(a), certain methods of organizing human activity, and mental processes
“... an extraction process for extracting company names in the second field related to the specific first word, from a second sentence group including a second field name and the specific first word acquired in the acquisition process, from the at least one information source; ...” - See below regarding MPEP 2106.04(a), certain methods of organizing human activity, and mental processes
“... an analysis process for, based on occurrence information regarding a specific second word that co-occurs with the specific first word in a second word group in the second sentence group, associating the specific first word, the specific second word, and the company names in the second field extracted by the extraction process; and ...” - See below regarding MPEP 2106.04(a), certain methods of organizing human activity, and mental processes
“... an output process for outputting an analysis result from the analysis process in a displayable manner.” - See below regarding MPEP 2106.04(a), certain methods of organizing human activity, and mental processes
The above-listed limitations of claim 1, when applying their broadest reasonable interpretations in light of their context in the claim as a whole, fall under enumerated groupings of abstract ideas outlined in MPEP 2106.04(a). For example, limitations of the claim can be characterized as: commercial interactions, including business relations, associated with analyzing and establishing relationships between companies for purposes of generating proposals; and managing personal behavior or relationships or interactions between people, associated with workflow steps for skimming documents for useful information, which fall under the certain methods of organizing human activity grouping of abstract ideas (see MPEP 2106.04(a)). Limitations of the claim also can be characterized as: concepts performed in the human mind, including observation (e.g., the recited “acquiring” step), and evaluation, judgment, and/or opinion (e.g., the recited “generating,” “extracting,” “associating,” and “outputting” steps), which fall under the mental processes grouping of abstract ideas (see MPEP 2106.04(a)). Accordingly, for at least these reasons, claim 1 fails to meet the criteria of Step 2A, Prong One of the eligibility analysis.
In the context of Step 2A of the eligibility analysis, Prong Two asks if the claim recites additional elements that integrate the judicial exception into a practical application. (See MPEP 2106.04(II)(A)(2).) Continuing to use claim 1 as an example, the claim recites the following additional element limitations:
The “system comprising: a processor that executes a program; and a storage device that stores the program, wherein the processor is configured to execute” - See below regarding MPEP 2106.05(a)-(c), (f), and (h)
The “user selection” is from “a graphical display” - See below regarding MPEP 2106.05(a)-(c), (f), and (h)
The above-listed additional element limitations of claim 1, when applying their broadest reasonable interpretations in light of their context in the claim as a whole, are analogous to: accelerating a process of analyzing audit log data when the increased speed comes solely from the capabilities of a general-purpose computer, mere automation of manual processes, instructions to display two sets of information on a computer display in a non-interfering manner, without any limitations specifying how to achieve the desired result, and arranging transactional information on a graphical user interface in a manner that assists traders in processing information more quickly, which courts have indicated may not be sufficient to show an improvement in computer-functionality (see MPEP 2106.05(a)(I)); a commonplace business method being applied on a general purpose computer, gathering and analyzing information using conventional techniques and displaying the result, and selecting a particular generic function for computer hardware to perform from within a range of fundamental or commonplace functions performed by the hardware, which courts have indicated may not be sufficient to show an improvement to technology (see MPEP 2106.05(a)(II)); a general purpose computer that applies a judicial exception, such as an abstract idea, by use of conventional computer functions, and merely adding a generic computer, generic computer components, or a programmed computer to perform generic computer functions, which do not qualify as a particular machine or use thereof (see MPEP 2106.05(b)(I)); a machine that is merely an object on which the method operates, which does not integrate the exception into a practical application (see MPEP 2106.05(b)(II)); use of a machine that contributes only nominally or insignificantly to the execution of the claimed method, which does not integrate a judicial exception (see MPEP 2106.05(b)(III)); transformation of an intangible concept such as a contractual obligation or mental judgment, which is not likely to provide significantly more (see MPEP 2106.05(c)); remotely accessing user-specific information through a mobile interface and pointers to retrieve the information without any description of how the mobile interface and pointers accomplish the result of retrieving previously inaccessible information, which courts have found to be mere instructions to apply an exception, because they recite no more than an idea of a solution or outcome (see MPEP 2106.05(f)); use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea, a commonplace business method or mathematical algorithm being applied on a general purpose computer, and requiring the use of software to tailor information and provide it to the user on a generic computer, which courts have found to be mere instructions to apply an exception, because they do no more than merely invoke computers or machinery as a tool to perform an existing process (see MPEP 2106.05(f)); mere data gathering in the form of obtaining information about transactions using the Internet to verify transactions and consulting and updating an activity log, and selecting a particular data source or type of data to be manipulated in the form of selecting information, based on types of information and availability of information in an environment, for collection, analysis, and display, which courts have found to be insignificant extra-solution activity (see MPEP 2106.05(g)); and specifying that the abstract idea of monitoring audit log data relates to transactions or activities that are executed in a computer environment, because this requirement merely limits the claims to the computer field, i.e., to execution on a generic computer, which courts have described as merely indicating a field of use or technological environment in which to apply a judicial exception (see MPEP 2106.05(h)). For at least these reasons, claim 1 fails to meet the criteria of Step 2A, Prong Two of the eligibility analysis.
The next step of the eligibility analysis, Step 2B, asks whether a claim recites additional elements that amount to significantly more than the judicial exception. (See MPEP 2106.05(II).) The step involves identifying whether there are any additional elements in the claim beyond the judicial exceptions, and evaluating those additional elements individually and in combination to determine whether they contribute an inventive concept. (See id.) The ineligibility rationales applied at Step 2A, Prong Two, also apply to Step 2B. (See id.) For all of the reasons covered in the analysis performed at Step 2A, Prong Two, claim 1 fails to meet the criteria of Step 2B. Further, claim 1 also fails to meet the criteria of Step 2B because at least some of the additional elements are analogous to storing and retrieving information in memory, which courts have recognized as well-understood, routine, conventional activity, and as insignificant extra-solution activity (see MPEP 2106.05(d)(II)). As a result, claim 1 is rejected under 35 USC 101 as ineligible for patenting.
Regarding claims 3-9, the claims depend from claim 1, and expand upon limitations introduced by claim 1. The dependent claims are rejected at least for the same reasons as claim 1. For example, the dependent claims recite abstract idea elements similar to the abstract idea elements of claim 1, that fall under the same abstract idea groupings as the abstract idea elements of claim 1 (e.g., the “wherein in the analysis process, ... calculates, as the occurrence information, a co-occurrence probability between the specific first word and the specific second word in the second sentence group, and associates the specific first word, the specific second word, and the company names in the second field, based on the co-occurrence probability” of claim 3, the “wherein in the analysis process, ... calculates, as the occurrence information, an occurrence count of the specific second word in the second sentence group, and associates the specific first word, the specific second word, and the company names in the second field, based on the occurrence count” of claim 4, the “wherein in the analysis process, ... calculates, as the occurrence information, a number of companies related to the specific first word in the second field in the second sentence group, and associates the specific first word, the specific second word, and the company names in the second field, based on the number of companies in the second field” of claim 5, the “wherein in the output process, ... outputs the first field name, the second field name, and the occurrence information, and outputs connection information in which the specific first word, the specific second word, and the company names in the second field are connected in a displayable manner” of claim 6, the “wherein in the output process, ... outputs the first field name, the second field name, and the co-occurrence probability, which is the occurrence information, and outputs a graph showing combinations of the specific first word and the specific second word connected based on the co-occurrence probability and the company names in the second field in a displayable manner” of claim 7, the “wherein in the output process, ... outputs the first field name, the second field name, and the occurrence count, which is the occurrence information, and outputs a graph showing combinations of the specific first word and the specific second word connected based on the occurrence count and the company names in the second field in a displayable manner” of claim 8, the “wherein in the output process, ... outputs the first field name, the second field name, and the number of companies in the second field, which is the occurrence information, and also outputs a graph showing combinations of the specific first word and the specific second word connected based on the number of companies in the second field, and the company names in the second field in a displayable manner” of claim 9, the “wherein in the generation process, ... calculates the co-occurrence probabilities by calculating, for each pair of words that co-occur in the extracted sentences, a ratio of a number of sentences in which both words occur to a number of sentences in which either word occurs” of claim 12, the “wherein ... stores a co-occurrence network table including fields for a first word, a first occurrence count indicating an occurrence count of the first word, a second word, a second occurrence count indicating an occurrence count of the second word, and a co-occurrence probability between the first word and the second word” of claim 13, the “wherein in the generation process, ... performs the morphological analysis by breaking down the extracted sentences into words and retaining only words corresponding to nouns” of claim 14, the “wherein in the generation process, ... crawls the at least one information source using a crawling condition that includes a crawling word combined with the first field name under an AND condition, and a crawling period specifying a range of publication dates of documents to be crawled” of claim 15, the “wherein ... further executes a second crawling process for crawling the at least one information source using the specific first word as a crawling word to extract the second sentence group from the second information source related to the second field” of claim 16, and the “wherein in the analysis process, ... determines whether to associate the specific first word, the specific second word, and the company names by comparing the occurrence information to a threshold value, and associates the specific first word, the specific second word, and the company names only when the occurrence information exceeds the threshold value” of claim 17). The dependent claims recite further additional elements that are similar to the additional elements of claim 1, that fail to warrant eligibility for the same reasons as the additional elements of claim 1 (e.g., the “system ... the processor” of claims 3-9 and 12-17, and the “storage device” of claim 13). Accordingly, claims 3-9 and 12-17 also are rejected as ineligible under 35 USC 101.
Regarding claim 10, while the claim is of different scope relative to claim 1, the claim recites limitations similar to the limitations of claim 1. As such, the rejection rationales applied to reject claim 1 also apply for purposes of rejecting claim 10. Claim 10 is, therefore, also rejected as ineligible under 35 USC 101.
Regarding claim 11, while the claim is of different scope relative to claims 1 and 10, the claim recites limitations similar to the limitations of claims 1 and 10. As such, the rejection rationales applied to reject claims 1 and 10 also apply for purposes of rejecting claim 11. Claim 11 is, therefore, also rejected as ineligible under 35 USC 101.
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, 4-6, 8-11, and 16 are rejected under 35 U.S.C. 103 as being unpatentable over U.S. Pat. App. Pub. No. 2023/0237512 A1 to Kaur et al. (hereinafter referred to as “Kaur”), in view of U.S. Pat. App. Pub. No. 2023/0244873 A1 to Nakamura (hereinafter referred to as “Nakamura”), and further in view of U.S. Pat. App. Pub. No. 2023/0281391 A1 to Mamy Randriamihaja et al. (hereinafter referred to as “Mamy”).
Regarding claim 1, Kaur discloses the following limitations:
“A proposal support system comprising: a processor that executes a program; and a storage device that stores the program, wherein the processor is configured to execute: ...” - The “system 100” (para. [0039], FIG. 1), “Processor 104” and “Instructions” (FIG. 1), and “Memory 106” and “Instructions” (FIG. 1), and their operations, in Kaur, reads on the recited limitation.
The combination of Kaur and Nakamura (hereinafter referred to as “Kaur/Nakamura”) teaches limitations below of claim 1 that do not appear to be disclosed in their entirety by Kaur:
“... a generation process for generating a co-occurrence network by crawling at least one information source out of a first information source related to a first field and a second information source related to a second field different from the first field to extract sentences containing field names, performing morphological analysis on the extracted sentences to identify words, and calculating co-occurrence probabilities between words that co-occur in the extracted sentences; ...” - Kaur discloses, “Large financial institutions have a need to process large numbers of financial documents, many of which are quite lengthy and contain large volumes of information” (para. [0002]), “there is a need for a mechanism for automatically processing financial documents in order to generate knowledge graphs that convey information relating to entities of interest and relationships between those entities that is contained within the documents” (para. [0003]), “Further, FDKGG device 202 is illustrated as being able to access an entity-specific person, organization, location, and event data repository 206(1) and a financial document types database 206(2). The knowledge graph generation module 302 may be configured to access these databases for implementing a method for automatically processing financial documents to generate knowledge graphs that convey information relating to entities of interest and relationships between those entities” (para. [0072]), “In process 400 of FIG. 4, at step S402, the knowledge graph generation module 302 receives a financial document” (para. [0076]), “At step S404, the knowledge graph generation module 302 extracts raw text information from the financial document” (para. [0077]), “At step S406, the knowledge graph generation module 302 identifies entities that are named in the financial document based on the extracted raw text. The document may contain names of many individual persons or just one person; also, the document may contain a name of a company or commercial organization, such as a corporation, or many such organizations may be named. The document may also include various other types of entities, such as, for example, any one or more of a title (e.g., a job title), a location, an amount, an event, and/or a date. In an exemplary embodiment, the entity identification may be performed by applying a Natural Language Processing (NLP) algorithm that uses a named entity recognition technique to classify the raw text into categories, such as, for example, an individual name category, an organizational name category, a title category, a location category, an amount category, an event category, and a date category” (para. [0078]), “At step S408, the knowledge graph generation module 302 determines relationship information that corresponds to pairs of entities that have been identified in step S406” (para. [0079]), and “At step S410, the knowledge graph generation module 302 uses the entities identified in step S406 and the relationship information determined in step S408 to construct a knowledge graph” (para. [0080]), and “As shown in illustration 700, the relation between two entities may be represented with the concatenation of the final hidden states corresponding to their respective start tokens [E1] and [E2]. Moreover, in order to avoid the BERT model possibly learning the contextual representation specifically dependent on the particular entities, the concept of masking the entities with a certain probability where one or both of the entity instances is replaced with a [BLANK] symbol” (para. [0093]). Operation of the knowledge graph generation module for generating knowledge graphs that convey information relating to entities of interest and relationships between entities, using financial documents of different large financial institutions (those differences amounting to differences in their respective fields), wherein the process for generating the knowledge graphs includes going through the financial documents, extracting raw text information, identifying names and relationships, and considering probabilities in that context, in Kaur, reads on the recited “a generation process for generating a co-occurrence network by crawling at least one information source out of a first information source related to a first field and a second information source related to a second field different from the first field to extract sentences containing field names, performing ... analysis on the extracted sentences to identify words, and calculating co-occurrence probabilities between words that co-occur in the extracted sentences” limitation. Kaur does not use the word “morphological” with respect to the analysis performed. Nakamura discloses, “Further, in the finding statement structuring step, the medical information processing apparatus 16 structures the accepted finding statement using the known natural language processing (an example of “extraction step”, an example of “specifying step”), and acquires the structured result. The natural language processing is a technology of allowing a computer to process natural languages used in daily life, and is processing including morphological analysis of decomposing a sentence into words, syntactic analysis of analyzing relationships between words obtained by the morphological analysis and building a syntax tree illustrating the structure of dependencies between words, and the like” (para. [0064]). The NLP including morphological analysis, in Nakamura, reads on the recited “morphological” limitation. Note the use of NLP also in Kaur.
Nakamura discloses “natural language processing” (para. [0064]), similar to the claimed invention and to Kaur. It would have been obvious to a person having ordinary skill in the art, before the effective filing date of the claimed invention, to have recognized that the NLP, of Kaur, includes morphological analysis, as in Nakamura, because Nakamura says so (see para. [0064]). Additionally or alternatively, it would have been obvious to the person having ordinary skill in the art, before the effective filing date of the claimed invention, to have modified the NLP, of Kaur, to include morphological analysis, of Nakamura, to facilitate word extraction and specifying of relationships between words, per Nakamura (see para. [0064]).
The combination of Kaur, Nakamura, and Mamy (hereinafter referred to as “Kaur/Nakamura/Mamy”) teaches limitations below of claim 1 that do not appear to be taught in their entirety by Kaur/Nakamura:
“... an acquisition process for acquiring a specific first word from a co-occurrence network generated in the generation process by receiving a user selection from a graphical display of the co-occurrence network showing nodes sized according to word occurrence counts and links representing co-occurrence relationships, in which each first word of a first word group in a first sentence group including a first field name in the at least one information source is a node, and a co-occurrence relationship between two first words is a link connecting the nodes; ...” - See the aspects of Kaur that have been referenced above. Kaur also discloses, “At step S410, the knowledge graph generation module 302 uses the entities identified in step S406 and the relationship information determined in step S408 to construct a knowledge graph. In the knowledge graph, the entities identified in step S406 are depicted as nodes, and the relationships between the entities are illustrated by connectors between the nodes and/or edges of the nodes. Then, at step S412, the knowledge graph generation module 302 outputs the knowledge graph. In an exemplary embodiment, the outputting of the knowledge graph may be effected by displaying the knowledge graph via a graphical user interface (GUI) that is displayed at a user terminal” (para. [0080]), and “FIG. 10 is an example of a knowledge graph 1000 that is generated as a result of executing a method for automatically processing financial documents to generate knowledge graphs that convey information relating to entities of interest and relationships between those entities, according to an exemplary embodiment. As shown in knowledge graph 1000, an example of downstream task, open-domain question answering, may include one-hop queries, path queries, and conjunctive queries” (para. [0098]). The question answering that acquires words from the knowledge graph, in which the words are in boxed groups associated with multi-word queries, the queries including organization names that are nodes of the knowledge graph, and relationship terminology for links between nodes, in Kaur, reads on the recited “an acquisition process for acquiring a specific first word from a co-occurrence network generated in the generation process ..., in which each first word of a first word group in a first sentence group including a first field name in the at least one information source is a node, and a co-occurrence relationship between two first words is a link connecting the nodes” limitation. Mamy discloses, “Referring to FIG. 5, the invention of the present disclosure may include a knowledge graph 500. In an embodiment, the knowledge graph 500 may be generated based on the NER, entity normalization, relation classification (e.g., binary, multi-class, or multi-label), and/or triplet extraction. The knowledge graph 500 may include one or more nodes 502 and one or more edges 504. A node 502 may include a normalized biomedical entity, wherein an edge 504 may represent a biomedical relationship. Accordingly, various characteristics of the nodes 502 and/or edges 504 may be modified to communicate a particular aspect to the viewer. As a non-limiting example, the color of the node 502 may be modified to indicate various entity categories. Further, as another non-limiting example, the color of the edge 504 may be modified to indicate various relation types. Yet further, the size of the node 502 may be modified to indicate characteristics of the underlying normalized biomedical entity, for example, the determined relevance, popularity, length, or other metrics. While an edge 504 may represent an adjacent relationship between two nodes 502, a transitive relationship may be formed by the indirect connection of two nodes 502. For example, as shown in FIG. 5, a first node and a second node may be in a transitive relationship if each of the first and second nodes shares an edge with a third node. The relationships among nodes described herein may be estimated (e.g., via creation of knowledge graph embeddings and their use to estimate distances between nodes). The potential modification of node 502 and edge 504 visual characteristics may be referred to herein as nodal indications and edge indications, respectively. For example, a node 502 corresponding to a first entity category may include a first nodal indication (e.g., a red color), and a node 502 corresponding to a second entity category may include a second nodal indication (e.g., a green color). In an embodiment, the size of a node 502 may refer to the frequency of the entity value associated with node 502. For example, nodal size may be a function of the frequency in which an entity value appears in a particular set of texts” (para. [0184]), and “the knowledge graph 500 may be configured to receive a selection input (i.e., a mouse click from a user) corresponding to one of the plurality of nodes or the plurality of edges. Such a selection input may cause the system to generate a summary representation, for example, comprising the subject, the object, the predicate, the relation, the syntactic unit, or the original text or portions thereof, based on the selection input. Accordingly, such a summary representation may be displayed on the client device 102-106” (para. [0185]). The nodes of the knowledge graph including nodes whose sizes indicate relevance, popularity, frequency of appearance, or other metrics, and also including edges for relationships between the nodes, wherein the knowledge graph receives selection inputs of nodes and edges, in Mamy, reads on the recited “by receiving a user selection from a graphical display of the co-occurrence network showing nodes sized according to word occurrence counts and links representing co-occurrence relationships” limitation.
Mamy discloses “information analysis” with “information extraction and relation extraction” (para. [0002]), similar to the claimed invention and to Kaur/Nakamura. It would have been obvious to a person having ordinary skill in the art, before the effective filing date of the claimed invention, to have modified the knowledge graph interface and usage thereof, of Kaur, to include the selection, node size, and edge features, of Mamy, to visually convey metrics, and to facilitate generating summary representations, per Mamy (see paras. [0184] and [0185]).
Kaur/Nakamura/Mamy teaches the limitations below of claim 1:
“... an extraction process for extracting company names in the second field related to the specific first word, from a second sentence group including a second field name and the specific first word acquired in the acquisition process, from the at least one information source; ...” - See the aspects of Kaur that have been referenced above. Additionally, the “Identify Entities Named in Document S406” step (FIG. 4) that “identifies entities that are named in the financial document based on the extracted raw text. The document may contain names of many individual persons or just one person; also, the document may contain a name of a company or commercial organization, such as a corporation, or many such organizations may be named” (para. [0078]), when applied to multiple financial documents, wherein the extraction of information from the financial documents, including information about organizations, events, and the like, in Kaur, reads on the recited limitation.
“... an analysis process for, based on occurrence information regarding a specific second word that co-occurs with the specific first word in a second word group in the second sentence group, associating the specific first word, the specific second word, and the company names in the second field extracted by the extraction process; and ...” - See the aspects of Kaur and that have been referenced above. The “Determine Relationships Between Entities S408” step, that “determines relationship information that corresponds to pairs of entities that have been identified in step S406. In an exemplary embodiment, the determination of the relationship information is made by applying, to the entities identified in step S406, an artificial intelligence (AI) algorithm that is trained by using historical data that relates to one or more of the identified entities” (para. [0079]), when applied to text in multiple financial documents, following the extracting of text therefrom and the identifying of entities therein, in Kaur, reads on the recited limitation.
“... an output process for outputting an analysis result from the analysis process in a displayable manner.” - Performing “outputting of the knowledge graph may be effected by displaying the knowledge graph via a graphical user interface (GUI) that is displayed at a user terminal” (para. [0080]), in Kaur, reads on the recited limitation.
Regarding claim 4, Kaur/Nakamura/Mamy teaches the following limitations:
“The proposal support system according to claim 1, wherein in the analysis process, the processor calculates, as the occurrence information, an occurrence count of the specific second word in the second sentence group, and associates the specific first word, the specific second word, and the company names in the second field, based on the occurrence count.” - See the aspects of Kaur and Mamy that have been referenced above. With the process shown in FIG. 4, being performed on multiple financial documents, to identify entities (named companies) and relationships in text of the financial documents, wherein appearance of the entities and relationships in the text amount to an occurrence count of 1, in Kaur, reads on the recited limitation. Additionally, the frequency values, in Mamy, read on the recited “occurrence count” limitation.
Regarding claim 5, Kaur/Nakamura/Mamy teaches the following limitations:
“The proposal support system according to claim 1, wherein in the analysis process, the processor calculates, as the occurrence information, a number of companies related to the specific first word in the second field in the second sentence group, and associates the specific first word, the specific second word, and the company names in the second field, based on the number of companies in the second field.” - See the aspects of Kaur and Mamy that have been referenced above. With the process shown in FIG. 4, being performed by the processor, wherein the processor associates multiple companies with a term in the text of multiple financial documents, in Kaur, reads on the recited limitation. Additionally, the frequency values, in Mamy, read on the recited “occurrence count” limitation.
Regarding claim 6, Kaur/Nakamura/Mamy teaches the following limitations:
“The proposal support system according to claim 1, wherein in the output process, the processor outputs the first field name, the second field name, and the occurrence information, and outputs connection information in which the specific first word, the specific second word, and the company names in the second field are connected in a displayable manner.” - See the aspects of Kaur that have been referenced above. The displaying of the knowledge graphs, involving the processor causing display of “names of many individual persons or just one person; also, the document may contain a name of a company or commercial organization, such as a corporation, or many such organizations may be named. The document may also include various other types of entities, such as, for example, any one or more of a title (e.g., a job title), a location, an amount, an event, and/or a date. In an exemplary embodiment, the entity identification may be performed by applying a Natural Language Processing (NLP) algorithm that uses a named entity recognition technique to classify the raw text into categories, such as, for example, an individual name category, an organizational name category, a title category, a location category, an amount category, an event category, and a date category” (para. [0078]) and “relationship information that corresponds to pairs of entities” (para. [0079]), such that “the entities identified in step S406 are depicted as nodes, and the relationships between the entities are illustrated by connectors between the nodes and/or edges of the nodes” (para. [0080]), in Kaur, reads on the recited limitation.
Regarding claim 8, Kaur/Nakamura/Mamy teaches the following limitations:
“The proposal support system according to claim 4, wherein in the output process, the processor outputs the first field name, the second field name, and the occurrence count, which is the occurrence information, and outputs a graph showing combinations of the specific first word and the specific second word connected based on the occurrence count and the company names in the second field in a displayable manner.” - See the aspects of Kaur and Mamy that have been cited above. The processor displaying entity data, including entity name data, via nodes for the entities, based on the entity name data appearing in financial documents, such that the knowledge graph shows the entity and other node data connected by edges, in Kaur, reads on the recited limitation. Additionally or alternatively, the frequency values, of Mamy, read on the recited “occurrence count” limitation.
Regarding claim 9, Kaur/Nakamura/Mamy teaches the following limitations:
“The proposal support system according to claim 5, wherein in the output process, the processor outputs the first field name, the second field name, and the number of companies in the second field, which is the occurrence information, and also outputs a graph showing combinations of the specific first word and the specific second word connected based on the number of companies in the second field, and the company names in the second field in a displayable manner.” - See the aspects of Kaur that have been referenced above. The processor displaying entity data, including entity name data for multiple entities (companies), extracted at least once (or possible multiple times) from financial documents, to display the knowledge graphs showing entities as nodes connected by various edges representing relationships, wherein the number of nodes and edges are based on the number of appearances of terms in the financial documents, in Kaur, reads on the recited limitation.
Claims 10 and 11, while of different scope, recite limitations similar to those recited by claim 1. As such, the rationales applied to reject claim 1 also apply to claims 10 and 11. Claims 10 and 11 are, therefore, also rejected under 35 USC 103 as obvious in view of Kaur/Nakamura/Mamy.
Regarding claim 16, Kaur/Nakamura/Mamy teaches the following limitations:
“The proposal support system according to claim 1, wherein the processor further executes a second crawling process for crawling the at least one information source using the specific first word as a crawling word to extract the second sentence group from the second information source related to the second field.” - See the aspects of Kaur that have been referenced above. Continual operation of the system and processes of Kaur on incoming financial documents, resulting in cycles of repeated extraction, identification of entities and relationships, forming knowledge graphs, and then updating with incoming financial documents, in Kaur, reads on the recited limitation.
Claims 3, 7, and 14 are rejected under 35 U.S.C. 103 as being unpatentable over Kaur, in view of Nakamura, further in view of Mamy, and further in view of U.S. Pat. App. Pub. No. 2019/0354544 A1 to Hertz et al. (hereinafter referred to as “Hertz”).
Regarding claim 3, the combination of Kaur, Nakamura, Mamy, and Hertz (hereinafter referred to as “Kaur/Nakamura/Mamy/Hertz”) teaches limitations below that do not appear to be taught in their entirety by Kaur/Nakamura/Mamy:
“The proposal support system according to claim 1, wherein in the analysis process, the processor calculates, as the occurrence information, a co-occurrence probability between the specific first word and the specific second word in the second sentence group, and associates the specific first word, the specific second word, and the company names in the second field, based on the co-occurrence probability.” - See the aspects of Kaur that have been referenced above. The “present invention also applies an aggregation layer to take into account the evidence found and assign a confidence score to the relationship between companies” (para. [0027]), wherein the “confidence score of each detection as well as other signals, which either increase or decrease the probability of the relation” (para. [0137]), in Hertz, when applied in the context of conveying information relating to entities of interest and relationships between the entities, based on the extracting text, identifying entities, and determining relationships, in Kaur, reads on the recited limitation.
Hertz discloses, “determining relationships and association significance between entities” (Abstract), similar to the claimed invention and to Kaur/Nakamura/Mamy. It would have been obvious to a person having ordinary skill in the art, before the effective filing date of the claimed invention, to have modified the determining of relationships between entities (companies), in Kaur, to include the confidence score and probability of relationship aspects, of Hertz, to “avoid introducing too many false positives into the knowledge graph,” as taught by Hertz (para. [0169]).
Regarding claim 7, Kaur/Nakamura/Mamy/Hertz teaches the following limitations:
“The proposal support system according to claim 3, wherein in the output process, the processor outputs the first field name, the second field name, and the co-occurrence probability, which is the occurrence information, and outputs a graph showing combinations of the specific first word and the specific second word connected based on the co-occurrence probability and the company names in the second field in a displayable manner.” - See the aspects of Kaur, Nakamura, Mamy, and Hertz that have been referenced above. The processor causing display of entities and their relationships based on occurrences in text of financial documents, in the form the knowledge graphs of words for the entities and relationships, in Kaur, reads on the recited limitation, except for the recited “co-occurrence probability” limitation. The confidence score and probability aspects, in Hertz, read on the recited “co-occurrence probability” aspect of the limitation. The rationales for combining the teachings of the cited references, from the rejection of claim 3, also apply to this rejection of claim 7.
Regarding claim 14, Kaur/Nakamura/Mamy/Hertz teaches the following limitations:
“The proposal support system according to claim 1, wherein in the generation process, the processor performs the morphological analysis by breaking down the extracted sentences into words and retaining only words corresponding to nouns.” - See the aspects of Kaur, Nakamura, and Hertz that have been referenced above. Hertz also discloses, ““Term” refers to single words or strings of highly-related or linked words or noun phrases. “Term extraction” (also term recognition or term mining) is a type of IE process used to identify or find and extract relevant terms from a given document, and therefore have some relevance, to the content of the document. Such activities are often referred to as “Named Entity Extraction” and “Named Entity Recognition” and “Named Entity Mining” and in connection with additional processes, e.g., Calais “Named Entity Tagging” (or more generally special noun phrase tagger) and the like” (para. [0009]). The extracting and recognizing of terms, with named entity extraction, recognition, mining, and tagging, in Hertz, reads on the recited limitation. The rationales for combining the teachings of the cited references from the rejection of claims 1 and 3, also apply to this rejection of claim 14.
Claim 12 is rejected under 35 U.S.C. 103 as being unpatentable over Kaur, in view of Nakamura, further in view of Mamy, and further in view of U.S. Pat. App. Pub. No. 2017/0228369 A1 to Zelenkov (hereinafter referred to as “Zelenkov”).
Regarding claim 12, the combination of Kaur, Nakamura, Mamy, and Zelenkov (hereinafter referred to as “Kaur/Nakamura/Mamy/Zelenkov”) teaches limitations below of claim 12 that do not appear to be taught in their entirety by Kaur/Nakamura/Mamy:
“The proposal support system according to claim 1, wherein in the generation process, the processor calculates the co-occurrence probabilities by calculating, for each pair of words that co-occur in the extracted sentences, a ratio of a number of sentences in which both words occur to a number of sentences in which either word occurs.” - See the aspects of Kaur and Mamy that have been referenced above. While both Kaur and Mamy disclose calculating probabilities, they do not disclose or suggest sufficient specifics of how the calculations are preformed. Zelenkov discloses, “acquiring by the server, an indication of the digital text to be processed, the digital text comprising a plurality of sentences; parsing by the server, each of plurality of sentences into one or more concept phrases, each of the one or more concept phrases having at least one word; the parsing being executed by applying at least one parsing parameter; executing, by the server, a first analysis to generate a context-independent relation (CIR) value for a given concept phrase of the one or more concept phrases, the CIR value representing a first ratio of a co-inclusion of: (i) at least one word of the given concept phrase and (ii) at least one word of each of the remaining concept phrases of the one or more concept phrases; executing, by the server, a second analysis to generate a context-dependent relation (CDR) value for the given concept phrase, the CDR value representing a second ratio of: (i) a number of sentences where the given concept phrase co-occurs with another concept phrase of the one or more concept phrases to (ii) a total number of the plurality of sentences containing the other concept phrase within the digital text; determining by the server, a total CIR weight and a total CDR weight for each of the concept phrases; determining by the server, for each of the concept phrase, a concept meaning value, based at least in part on its respective total CIR weight and the total CDR weight; determining by the server, for a given sentence of the plurality of sentences, a sentence meaning value, based at least in part of the concept meaning value of each concept phrase contained in the given sentence; ranking by the server, each sentence based at least on the determined sentence meaning value; and generating by the server, the summary of the digital text, the summary of the digital text comprising at least one sentence extracted from the digital text based on its determined ranking” (para. [0014]). Calculating, for words of sentences, the CIR, the CDR, and the concept meaning value, in Zelenkov, when applied in the context of determining entities, relationships, and probabilities, in Kaur, reads on the recited limitation.
Zelenkov discloses, “processing a text” (para. [0002]), similar to the claimed invention and to Kaur/Nakamura/Mamy. It would have been obvious to a person having ordinary skill in the art, before the effective filing date of the claimed invention, to have modified the determining of entities, relationships, and their probabilities, in Kaur, to include use of the values and weights, of Zelenkov, as a mathematical means for determining the intended (or most informative) meaning of words, phrases, and sentences, per Zelenkov (see para. [0011]).
Claims 13 and 17 are rejected under 35 U.S.C. 103 as being unpatentable over Kaur, in view of Nakamura, further in view of Mamy, and further in view of WIPO Int’l Pub. No. 2022/239640 A1 to Agatsuma et al. (hereinafter referred to as “Agatsuma”).
Regarding claim 13, the combination of Kaur, Nakamura, Mamy, and Agatsuma (hereinafter referred to as “Kaur/Nakamura/Mamy/Agatsuma”) teaches limitations below of claim 13 that do not appear to be taught in their entirety by Kaur/Nakamura/Mamy:
“The proposal support system according to claim 1, wherein the storage device stores a co-occurrence network table including fields for a first word, a first occurrence count indicating an occurrence count of the first word, a second word, a second occurrence count indicating an occurrence count of the second word, and a co-occurrence probability between the first word and the second word.” - Agatsuma discloses, “FIG. 7 is an example of the synonym candidate table 115 managed by the storage unit 110 shown in FIG. The synonym candidate table 115 manages information on word pairs that are synonym candidates. A word pair is a combination of two words belonging to the same category extracted from the words in word table 114 . As shown in the figure, the example synonym candidate table 115 includes word A 1151, word B 1152, correct/incorrect information 1153, word category 1154, co-occurrence frequency 1155, edit distance 1156, category association probability 1157, number of appearances 1158, and extraction source It consists of multiple records with each item of text 1159” (English-language translation, p. 7), and “Subsequently, the synonym candidate generation unit 140 generates word A, word B, correctness information (=“unknown”), the category to which word A and word B belong, the co-occurrence frequency obtained in S1514, and the edit distance obtained in S1515. , the category association probabilities of word A and word B, the number of occurrences of word A and word B obtained from the word table 114, and the original sentences (text data) from which word A and word B are extracted, Generate records stored in corresponding items (word A 1151, word B 1152, correct/incorrect information 1153, word category 1154, co-occurrence frequency 1155, edit distance 1156, category related probability 1157, number of appearances 1158, extraction source text 1159) It is registered in the synonym candidate table 115 (S1516)” (English-language translation, p. 11). The table including words, number of appearances of words, co-occurrence frequency, and category related probability, in Agatsuma, reads on the recited limitation.
Agatsuma discloses extracting synonyms from document data (see English-language translation, p. 1), similar to the claimed invention and to Kaur/Nakamura/Mamy. It would have been obvious to a person having ordinary skill in the art, before the effective filing date of the claimed invention, to have modified the extraction and analyses performed on the financial documents, in Kaur, to include use of the tables and metrics, of Agatsuma, for higher accuracy, per Agatsuma (see English-language translation, p. 1).
Regarding claim 17, Kaur/Nakamura/Mamy/Agatsuma teaches the following limitations:
“The proposal support system according to claim 1, wherein in the analysis process, the processor determines whether to associate the specific first word, the specific second word, and the company names by comparing the occurrence information to a threshold value, and associates the specific first word, the specific second word, and the company names only when the occurrence information exceeds the threshold value.” - See the aspects of Agatsuma that have been referenced above. Agatsuma also discloses, “Subsequently, the synonym extraction rule application unit 150 compares the relationship feature amount in the currently selected record with the threshold values in the threshold value table 117, and determines whether or not there is a value less than the threshold value (S1614). Specifically, the synonym extraction rule application unit 150 calculates the relationship feature amount (the co-occurrence frequency 1155, the edit distance 1156, the category relationship probability of each of word A and word B in the category relationship probability 1157, the number of appearances 1158 ), it is determined whether there is any one below the threshold in the corresponding threshold table 117 . In the above determination, the synonym extraction rule application unit 150 stores the threshold of the category relevance probability of word A and word B in the category relevance probability threshold 1172 of the common category threshold table 117 to which word A and word B belong. Use the values provided. If there is even one relational feature amount less than the threshold (S1614: YES), the synonym extraction rule application unit 150 registers the word pair of the selected record in the non-synonym dictionary 122 (S1621)” (English-language translation, pp. 11 and 12). The comparing of relationship feature amounts for words to threshold values, to determine if the words are identified as synonyms or non-synonyms, in Agatsuma, reads on the recited limitation. The rationales for combining the teachings of the cited references, from the rejection of claim 13, also apply to this rejection of claim 17.
Claim 15 is rejected under 35 U.S.C. 103 as being unpatentable over Kaur, in view of Nakamura, further in view of Mamy, and further in view of WIPO Int’l Pub. No. 2020/243116 A1 to Fernandez et al. (hereinafter referred to as “Fernandez”).
Regarding claim 15, the combination of Kaur, Nakamura, Mamy, and Fernandez (hereinafter referred to as “Kaur/Nakamura/Mamy/Fernandez”) teaches limitations below of claim 15 that do not appear to be taught in their entirety by Kaur/Nakamura/Mamy:
“The proposal support system according to claim 1, wherein in the generation process, the processor crawls the at least one information source using a crawling condition that includes a crawling word combined with the first field name under an AND condition, and a crawling period specifying a range of publication dates of documents to be crawled.” - Fernandez discloses, “Resource Crawler 120 is configured to crawl a computer network in search of content and to retrieve found content. Specifically, Resource Crawler 120 is configured to crawl Network 115 and identify Content Sources 195 connected thereto” (para. [037]), “The received content is optionally stored in Video Processing System 100, and may include text, images, audio, video, multimedia, executable computing instructions, and/or any other types of content discussed herein. In some embodiments, the received content is stored along with records of its source, date information, confidence ratings (e.g., content from one source may be considered more reliable than content from a different source” (para. [038]), “News articles are collected by an RSS feeds crawler, which processes 500 news feeds every 30 minutes. The RSS feeds come from a manually generated list of 3,526 feeds from the main media sources of seven countries: United States, Australia, Spain, Canada, United Kingdom, Portugal and Ireland. Feeds are also manually categorized in seven category groups: politics, sports, general news, lifestyle and hobbies, science and technology, business and finance, and entertainment. The feeds crawler visits each feed, crawls it, and stores in the DB each article URL, published date, title and description if provided” (para. [0129]), and “a resource crawler configured to crawl a computer network in search of content and to retrieve found content, the content including both text and image content; content analysis logic configured to identify entities within the retrieved content” (claim 1). The crawling based on named entities and dates, including published dates, in Fernandez, reads on the recited limitation.
Fernandez discloses analysis of content and use of a knowledge graph (see Abstract), similar to the claimed invention and to Kaur/Nakamura/Mamy. It would have been obvious to a person having ordinary skill in the art, before the effective filing date of the claimed invention, to have modified the crawling of Kaur/Nakamura/Mamy, to include the word and published date features, of Fernandez, to “improve the reliability of entity and/or relationship identifications,” per Fernandez (para. [091]).
Response to Arguments
On pp. 8-11 of the Reply, the applicant requests reconsideration and withdrawal of the claim rejection under 35 USC 101. More specifically, the applicant contends, “the amendments ... transform these claims from abstract business methods into concrete technical implementation that provide specific improvements to computer functionality and go well beyond mere automation of manual processes.” (Reply, p. 8.) The applicant emphasizes the following recited limitation as being indicative of technical implementation detail that overcomes the abstract idea characterization: “a generation process for generating a co-occurrence network by crawling at least one information source out of a first information source related to a first field and a second information source related to a second field different from the first field to extract sentences containing field names, performing morphological analysis on the extracted sentences to identify words, and calculating co-occurrence probabilities between words that co-occur in the extracted sentences.” (Reply, p. 8.) The examiner disagrees with the applicant’s contentions. The limitation emphasized by the applicant recites abstract idea elements only. For example, the limitation can be interpreted as a generation process for generating a knowledge graph by reading text of a document from a first set of records related to a first subject and text of another document related to a second subject to extract sentences containing names, performing grammatical analysis on the sentences to identify words and phrases of interest, and thinking about how those words or phrases relate. This is but one example an entirely non-technical, non-technological, non-computerized interpretation of claim limitations that does not warrant eligibility.
The applicant also contends that the recited “acquiring a specific first word from a co-occurrence network generated in the generation process by receiving a user selection from a graphical display of the co-occurrence network showing nodes sized according to word occurrence counts and links representing co-occurrence relationships, in which each first word of a first word group in a first sentence group including a first field name in the at least one information source is a node, and a co-occurrence relationship between two first words is a link connecting the nodes” limitation “integrates the abstract data analysis into a specific graphical user interface that provides a meaningful visual representation of complex linguistic relationships.” (Reply, p. 9.) The applicant also contends that “the claims are directed to a practical application because they provide a specific technological solution to the technical problem of efficient identifying cross-domain business opportunities by using a co-occurrence network to analyze relationships between words across different business fields and automatically extracting and associating company names with specific words based on occurrence information.” (Reply, p. 9.) The applicant contends that this is “a concrete improvement in computer-based data analysis that enables automated cross-filed business intelligence that would be impractical to perform manually across large document databases.” (Reply, p. 9.) The examiner disagrees with the applicant’s contentions. The claim limitations emphasized by the applicant include a mix of abstract idea elements and additional elements. The additional elements amount to use (operation) of any generic, conventional graphical user interface. That is, the recited interface features appear to encompass no more than displaying information and receiving inputs. This is not an improvement to interfaces or any other computers or technology. It is the mere application of generic, conventional interface and computer technology. Thus, there is no eligibility-warranting improvement under MPEP 2106.05(a) or any other rationale. The remaining abstract idea elements of the claim appear to be nothing more than analyzing knowledge graphs made of word nodes and relationship edges, which can easily be performed mentally/manually.
The applicant also contends that the new dependent claims add further technical specificity. (Reply, p. 9.) The examiner concedes that the dependent claims add, to some extent, further technical specificity. But that technical specificity amounts to nothing more than abstract idea elements being performed by generic, conventional computer hardware. Such technical specificity does not warrant eligibility. See, e.g., MPEP 2106.05(a) and (f), and the explanation provided in the 35 USC 101 section above.
The applicant also contends that the “amended claims provide specific improvements to computer functionality in natural language processing an data visualization that go well beyond the “generic computer” implementation identified by the Examiner. The combination of morphological analysis, co-occurrence probability calculations, structured data store, and interactive graphical display creates a specialized system for linguistic analysis that cannot be performed manually or with conventional business methods.” (Reply, p. 10.) The applicant emphasizes the claimed crawling limitation. (Reply, p. 10.) The examiner disagrees with the applicant’s contentions. The lack of an eligibility-warranting improvement under MPEP 2106.05(a) has already been addressed in the preceding paragraphs. Further, there is no specialized system being claimed. Morphological analysis, co-occurrence probability calculations, and linguistic analysis are abstract idea elements. The abstract idea elements being implemented using the claimed system features reads like performing the abstract idea elements using a general purpose computer, which is not grounds for eligibility. See the 35 USC 101 section above, especially the ineligibility rationales provided from MPEP 2106.05(a), (f), and other subsections.
On pp. 11-15 of the Reply, the applicant requests reconsideration and withdrawal of the claim rejections under 35 USC 103. More specifically, the applicant contends that Kaur/Gavlak fails to disclose, teach, or suggest the recited limitations. (Reply, pp. 11-14.) The examiner finds the contentions unpersuasive. Gavlak is no longer included in any of the rejections, and thus, contentions about deficiencies of Gavlak are moot.
The applicant also contends that “While Kaur teaches extracting entities and relationships from financial documents and constructing knowledge graphs with entities as nodes ..., Kaur’s nodes represent entities (persons, organizations, locations, events) rather than words, and Kaur’s edges represent relationships between entities rather than co-occurrence relationships between words.” (Reply, p. 12.) The examiner disagrees with the applicant’s contentions. Kaur’s nodes can represent entities and Kaur’s edges can represent relationships between the entities, but those entities come from financial documents, and thus, are expressed in words. See, e.g., FIG. 10 of Kaur. So Kaur’s nodes represent entities and words, not one or the other.
The applicant also contends that “Kaur does not disclose or teach crawling information sources with specific crawling conditions to extract sentences containing field names. As shown in Kaur, the system merely “receives a financial document” and “extracts raw text information from the financial document” using optical character recognition operations,” and “This passive document reception and text extraction is different from the active crawling process with field-specific targeting as recited in amended claim 1.” (Reply, p. 12.) The examiner disagrees with the applicant’s contentions. Kaur discloses use of OCR to extract raw text. See para. [0077] of Kaur. Kaur also discloses use of NLP on that extracted raw text. See para. [0078] of Kaur. The combination of OCR and NLP in Kaur, which acts on words (that form sentences) reads on the recited “generation process” limitation.
The applicant also contends that “While Kaur describes applying Natural Language Processing algorithms for named entity recognition, it teaches classification,” and “This entity classification approach does not teach morphological analysis to break down sentences into words as a preliminary step for co-occurrence network generation.” (Reply, p. 12.) The examiner disagrees with the applicant’s contentions. NLP includes morphological analysis, as explained by Nakamura in para. [0064].
The applicant also contends that Kaur uses an AI algorithm and that “This AI-based relationship determination using historical training data is fundamentally different from calculating co-occurrence probabilities based on statistical word co-occurrence analysis in sentences. Kaur’s approach focuses on entity relationships rather than statistical word co-occurrence analysis.” (Reply, p. 13.) The examiner disagrees with the applicant’s contentions. Kaur’s approach might focus on entity relationships, but the entities are expressed as words, and thus, the relationships between those entities/words is a statistical word co-occurrence analysis.
The applicant also contends that “Kaur ... does not teach interactive user selection from co-occurrence networks with nodes sized according to word occurrence counts.” (Reply, p. 13.) The applicant’s have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument. See the references to Mamy in the 35 USC 103 section above.
Regarding claims 3 and 7, the applicant contends that Hertz’s ML approach “is distinct from the mathematical co-occurrence probability calculations recited in claim 3. Hertz focuses on entity relationship extraction rather than statistical word co-occurrence analysis.” (Reply, p. 14.) The examiner disagrees with the applicant’s contentions. As stated above, entity relationships extraction is word relationship extraction because entities are expressed as words, particularly in Kaur. Statistics about entities appearing in the same documents in Kaur makes the entity relationship extraction also a statistical word co-occurrence analysis, especially when modified to include the confidence score and probability aspects of Hertz.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Such prior art includes the following:
Zarkar, Raghvendra. “Natural Language Processing.” Medium, 17 January 2022 (last accessed on 25 February 2026 via https://medium.com/@raghvendra.zarkar18/natural-language-processing-65f82c8dd7e0).
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to THOMAS Y. HO, whose telephone number is (571)270-7918. The examiner can normally be reached Monday through Friday, 9:30 AM to 5:30 PM Eastern.
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/THOMAS YIH HO/Primary Examiner, Art Unit 3624