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 .
DETAILED ACTION
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 1-6 and 10-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception such as a natural phenomenon, abstract idea, or law of nature, without significantly more and/or a practical application per se, specifically with one or more of:
1) Not integrating a judicial exception into a practical application (see explanation below), and
2) Not reciting elements that would amount to significantly more than the judicial exception (see explanation below).
Accordingly, claims 1-6 and 10-20 are directed towards patent ineligible subject matter under 35 U.S.C. 101.
The independent claims:
When taking the current claim limitations of the present invention, we see that they are directed to sorting overlapping data of interest in document sets and producing analysis thereof for the most related terms in documents such as searching for documents of interest in a library.
Regarding the claim limitations of claim(s) 1, 19, and 20 as recited:
identifying a text set including at least one text element; using a named entity recognition model based on deep learning, identifying keywords in the at least one text element; based on the text set, determining degrees of association of keyword pairs included in a keyword set including the keywords; obtaining information on a query word that is input by a user; and based on at least one among the degrees of association, generating information on response words corresponding to the query word
Step 1: IS THE CLAIM DIRECTED TO A PROCESS, MACHINE, MANUFACTURE OR COMPOSITION OF MATTER?
Yes
Step 2A.1: IS THE CLAIM DIRECTED TO A LAW OF NATURE, A NATURAL PHENOMENON (PRODUCT OF NATURE) OR AN ABSTRACT IDEA?
Yes
Step 2A.2: DOES THE CLAIM RECITE ADDITIONAL ELEMENTS THAT INTEGRATE THE JUDICIAL EXCEPTION INTO A PRACTICAL APPLICATION?
Regarding the independent claims. No, analogous to Solutran, Inc. v. Elavon, Inc., 931 F.3d 1161, 2019 USPQ2d 281076 (Fed. Cir. 2019), the claims are directed to sorting overlapping data of interest in document sets and producing analysis thereof for the most related terms in documents such as searching for documents of interest in a library, such as lacking a clear improvement of function/technology of deep learning which is generically recited as extra solution activity.
Further as demonstrated in Solutran, Inc. v. Elavon, Inc., 931 F.3d 1161, 2019 USPQ2d 281076 (Fed. Cir. 2019), the claims were to methods for electronically processing paper checks, all of which contained limitations setting forth receiving merchant transaction data from a merchant, crediting a merchant’s account, and receiving and scanning paper checks after the merchant’s account is credited. In part one of the Alice/Mayo test, the Federal Circuit determined that the claims were directed to the abstract idea of crediting the merchant’s account before the paper check is scanned. The court first determined that the recited limitations of “crediting a merchant’s account as early as possible while electronically processing a check” is a “long-standing commercial practice” like in Alice and Bilski. 931 F.3d at 1167, 2019 USPQ2d 281076, at *5 (Fed. Cir. 2019). The Federal Circuit then continued with its analysis under part one of the Alice/Mayo test finding that the claims are not directed to an improvement in the functioning of a computer or an improvement to another technology. In particular, the court determined that the claims “did not improve the technical capture of information from a check to create a digital file or the technical step of electronically crediting a bank account” nor did the claims “improve how a check is scanned.” Id.
Regarding the December 5th 2025 Memo in light of September 26, 2025 Appeals Review Panel Decision in Ex parte Desjardins, Appeal 2024-000567 for Application 16/319,040, in deciding if a recited abstract idea does or does not direct the entire claim to an abstract idea, when a claim is considered as a whole.
The claim which demonstrated improvements to technology and/or function recites: "adjust the first values of the plurality of parameters to optimize performance of the machine learning model on the second machine learning task while protecting performance of the machine learning model on the first machine learning task.".
The decision recites that “We are persuaded that constitutes an improvement to how the machine learning model itself operates, and not, for example, the identified mathematical calculation.”
When considering the limitation decided upon, there are clear improvements to machine learning that are not rudimentary or a long-standing practice, for instance adjusting for optimization and protection of performance, as claimed, are improvements to a machine learning models operations, not simply a general mathematical or generic recitation, but rather an improvement to function.
Specifically, Ex Parte Desjardins explained the following:
Enfish ranks among the Federal Circuit's leading cases on the eligibility of technological improvements. In particular, Enfish recognized that “[m]uch of the advancement made in computer technology consists of improvements to software that, by their very nature, may not be defined by particular physical features but rather by logical structures and processes.” 822 F.3d at 1339. Moreover, because “[s]oftware can make non-abstract improvements to computer technology, just as hardware improvements can,” the Federal Circuit held that the eligibility determinations should turn on whether “the claims are directed to an improvement to computer functionality versus being directed to an abstract idea.” Id. at 1336. (Desjardins, page 8).
Further, specifically:
“Paragraph 21 of the Specification, which the Appellant cites, identifies improvements in training the machine learning model itself. Of course, such an assertion in the Specification alone is insufficient to support a patent eligibility determination, absent a subsequent determination that the claim itself reflects the disclosed improvement. See MPEP § 2106.05(a) (citing Intellectual Ventures I LLC v. Symantec Corp., 838 F.3d 1307, 1316 (Fed. Cir. 2016)). Here, however, we are persuaded that the claims reflect such an improvement. For example, one improvement identified in the 8 Appeal2024-000567 Application 16/319,040 Specification is to "effectively learn new tasks in succession whilst protecting knowledge about previous tasks." Spec. ,r 21. The Specification also recites that the claimed improvement allows artificial intelligence (AI) systems to "us[e] less of their storage capacity" and enables "reduced system complexity." Id. When evaluating the claim as a whole, we discern at least the following limitation of independent claim 1 that reflects the improvement: "adjust the first values of the plurality of parameters to optimize performance of the machine learning model on the second machine learning task while protecting performance of the machine learning model on the first machine learning task." We are persuaded that constitutes an improvement to how the machine learning model itself operates, and not, for example, the identified mathematical calculation. Under a charitable view, the overbroad reasoning of the original panel below is perhaps understandable given the confusing nature of existing § 101 jurisprudence, but troubling, because this case highlights what is at stake. Categorically excluding AI innovations from patent protection in the United States jeopardizes America's leadership in this critical emerging technology. Yet, under the panel's reasoning, many AI innovations are potentially unpatentable-even if they are adequately described and nonobvious-because the panel essentially equated any machine learning with an unpatentable "algorithm" and the remaining additional elements as "generic computer components," without adequate explanation. Dec. 24. Examiners and panels should not evaluate claims at such a high level of generality.”
Further in Ex Parte Desjardins, Appeal No. 2024-000567 (PTAB September 26, 2025, Appeals Review Panel Decision) (precedential), the claimed invention was a method of training a machine learning model on a series of tasks. The Appeals Review Panel (ARP) overall credited benefits including reduced storage, reduced system complexity and streamlining, and preservation of performance attributes associated with earlier tasks during subsequent computational tasks as technological improvements that were disclosed in the patent application specification. Specifically, the ARP upheld the Step 2A Prong One finding that the claims recited an abstract idea (i.e., mathematical concept). In Step 2A Prong Two, the ARP then determined that the specification identified improvements as to how the machine learning model itself operates, including training a machine learning model to learn new tasks while protecting knowledge about previous tasks to overcome the problem of “catastrophic forgetting” encountered in continual learning systems. Importantly, the ARP evaluated the claims as a whole in discerning at least the limitation “adjust the first values of the plurality of parameters to optimize performance of the machine learning model on the second machine learning task while protecting performance of the machine learning model on the first machine learning task” reflected the improvement disclosed in the specification. Accordingly, the claims as a whole integrated what would otherwise be a judicial exception instead into a practical application at Step 2A Prong Two, and therefore the claims were
The claim itself does not need to explicitly recite the improvement described in the specification (e.g., “thereby increasing the bandwidth of the channel”). See, e.g., Ex Parte Desjardins, Appeal No. 2024-000567 (PTAB September 26, 2025, Appeals Review Panel Decision) (precedential), in which the specification identified the improvement to machine learning technology by explaining how the machine learning model is trained to learn new tasks while protecting knowledge about previous tasks to overcome the problem of “catastrophic forgetting,” and that the claims reflected the improvement identified in the specification. Indeed, enumerated improvements identified in the Desjardins specification included disclosures of the effective learning of new tasks in succession in connection with specifically protecting knowledge concerning previously accomplished tasks; allowing the system to reduce use of storage capacity; and the enablement of reduced complexity in the system. Such improvements were tantamount to how the machine learning model itself would function in operation and therefore not subsumed in the identified mathematical calculation.
The second paragraph of MPEP § 2106.05(a), subsection I, is revised to add new examples xiii and xiv to the list of examples that may show an improvement in computer functionality:
xiii. An improved way of training a machine learning model that protected the model’s knowledge about previous tasks while allowing it to effectively learn new tasks; Ex Parte Desjardins, Appeal No. 2024-000567 (PTAB September 26, 2025, Appeals Review Panel Decision) (precedential); and
xiv. Improvements to computer component or system performance based upon adjustments to parameters of a machine learning model associated with tasks or workstreams; Ex Parte Desjardins, Appeal No. 2024-000567 (PTAB September 26, 2025, Appeals Review Panel Decision) (precedential).
Step 2B: DOES THE CLAIM RECITE ADDITIONAL ELEMENTS THAT AMOUNT TO SIGNIFICANTLY MORE THAN THE JUDICIAL EXCEPTION?
No. The claims amount to sorting overlapping data of interest in document sets and producing analysis thereof for the most related terms in documents such as searching for documents of interest in a library. This can be performed independent of software/hardware as a mental step or by-hand per se by physically looking or browsing documents including search engine results per se.
• Collecting and comparing known information (Classen)
• Collecting, displaying, and manipulating data (Int. Ventures v. Cap One Financial)
• Collecting information, analyzing it, and displaying certain results of the collection and analysis (Electric Power Group; West View†)
• Comparing information regarding a sample or test subject to a control or target data (Ambry/Myriad CAFC)
• Comparing new and stored information and using rules to identify options (Smartgene)†
Assistance for Applicant in amending to overcome 101:
Limitations that the courts have found to qualify as “significantly more” when recited in a claim with a judicial exception include:
i. Improvements to the functioning of a computer, e.g., a modification of conventional Internet hyperlink protocol to dynamically produce a dual-source hybrid webpage, as discussed in DDR Holdings, LLC v. Hotels.com, L.P., 773 F.3d 1245, 1258-59, 113 USPQ2d 1097, 1106-07 (Fed. Cir. 2014) (see MPEP § 2106.05(a));
ii. Improvements to any other technology or technical field, e.g., a modification of conventional rubber-molding processes to utilize a thermocouple inside the mold to constantly monitor the temperature and thus reduce under- and over-curing problems common in the art, as discussed in Diamond v. Diehr, 450 U.S. 175, 191-92, 209 USPQ 1, 10 (1981) (see MPEP § 2106.05(a));
iii. Applying the judicial exception with, or by use of, a particular machine, e.g., a Fourdrinier machine (which is understood in the art to have a specific structure comprising a headbox, a paper-making wire, and a series of rolls) that is arranged in a particular way to optimize the speed of the machine while maintaining quality of the formed paper web, as discussed in Eibel Process Co. v. Minn. & Ont. Paper Co., 261 U.S. 45, 64-65 (1923) (see MPEP § 2106.05(b));
iv. Effecting a transformation or reduction of a particular article to a different state or thing, e.g., a process that transforms raw, uncured synthetic rubber into precision-molded synthetic rubber products, as discussed in Diehr, 450 U.S. at 184, 209 USPQ at 21 (see MPEP § 2106.05(c));
v. Adding a specific limitation other than what is well-understood, routine, conventional activity in the field, or adding unconventional steps that confine the claim to a particular useful application, e.g., a non-conventional and non-generic arrangement of various computer components for filtering Internet content, as discussed in BASCOM Global Internet v. AT&T Mobility LLC, 827 F.3d 1341, 1350-51, 119 USPQ2d 1236, 1243 (Fed. Cir. 2016) (see MPEP § 2106.05(d)); or
vi. Other meaningful limitations beyond generally linking the use of the judicial exception to a particular technological environment, e.g., an immunization step that integrates an abstract idea of data comparison into a specific process of immunizing that lowers the risk that immunized patients will later develop chronic immune-mediated diseases, as discussed in Classen Immunotherapies Inc. v. Biogen IDEC, 659 F.3d 1057, 1066-68, 100 USPQ2d 1492, 1499-1502 (Fed. Cir. 2011) (see MPEP § 2106.05(e)).
To help in amending the claims and for analysis purposes, example claims 3 and 4 are listed below from the courts, however such example amendment potentials are not limited to the provided examples and alternative amendments are possible using i-vi from the courts. The example below show differences between eligible claims (court claim 4) and ineligible claims (court claim 3), which thus illustrates significantly more which is tied to hardware that is not generally recited in the art. In this case general changing of font size in claim 3 versus a significant step of conditionally changing font size tied to hardware in claim 4.
See below examples based on MPEP and not on the current claim set, to help amend to overcome 101 rejections:
Regarding independent claim examples:
For instance in the example claims, for example claims 3 and 4 below:
Ineligible
3. A computer‐implemented method of resizing textual information within a window displayed in a graphical user interface, the method comprising:
(not significant) generating first data for describing the area of a first graphical element;
(not significant) generating second data for describing the area of a second graphical element containing textual information;
(not significant) calculating, by the computer, a scaling factor for the textual information which is proportional to the difference between the first data and second data.
The claim recites that the step of calculating a scaling factor is performed by “the computer” (referencing the computer recited in the preamble). Such a limitation gives “life, meaning and vitality” to the preamble and, therefore, the preamble is construed to further limit the claim. (See MPEP 2111.02.)
However, the mere recitation of “computer‐implemented” is akin to adding the words “apply it” in conjunction with the abstract idea. Such a limitation is not enough to qualify as significantly more. With regards to the graphical user interface limitation, the courts have found that simply limiting the use of the abstract idea to a particular technological environment is not significantly more. (See, e.g., Flook.)
Whereas in similar claim 4:
Eligible
4. A computer‐implemented method for dynamically relocating textual information within an underlying window displayed in a graphical user interface, the method comprising:
displaying a first window containing textual information in a first format within a graphical user interface on a computer screen;
displaying a second window within the graphical user interface;
constantly monitoring the boundaries of the first window and the second window to detect an overlap condition where the second window overlaps the first window such that the textual information in the first window is obscured from a user’s view;
determining the textual information would not be completely viewable if relocated to an unobstructed portion of the first window;
calculating a first measure of the area of the first window and a second measure of the area of the unobstructed portion of the first window;
calculating a scaling factor which is proportional to the difference between the first measure and the second measure;
scaling the textual information based upon the scaling factor;
(significant step) automatically relocating the scaled textual information, by a processor, to the unobscured portion of the first window in a second format during an overlap condition so that the entire scaled textual information is viewable on the computer screen by the user;
(significant step) automatically returning the relocated scaled textual information, by the processor, to the first format within the first window when the overlap condition no longer exists.
These limitations are not merely attempting to limit the mathematical algorithm to a particular technological environment. Instead, these claim limitations recite a specific application of the mathematical algorithm that improves the functioning of the basic display function of the computer itself. As discussed above, the scaling and relocating the textual information in overlapping windows improves the ability of the computer to display information and interact with the user.
The dependent claims are rejected as follows, for the same reasoning as being directed towards patent ineligible subject matter under 35 U.S.C. 101, and not adding eligible subject matter to the respective parent claim.
Claims 2-6, 10-12, 14, 16, and 18 describe, without further limiting, frequency based classification operations of like kind documents or data sets or words a user can either do in person or by manipulating a search engine.
Claims 13 and 15 discuss sorting of results, which falls into a user manually organizing paper or manipulating data onto a device as extra solution activity i.e. a organization by hand or a search engine.
Claim 17 discusses displaying data, which falls into manipulating data onto a device as extra solution activity i.e. a search engine.
Claims 7-9 have not been rejected under 35 U.S.C. 101 because the claimed invention appears to contain an abstract idea that demonstrates significantly more than the judicial exception, such as significant steps of co-occurrence outside the known metes and bounds of document searching that a human would consciously perform. As such, claims 7-9 appear to be eligible under 35 U.S.C. 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.
Claims 1-6 and 11-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over US 11675790 B1 Jami; Aditya et al. (hereinafter Jami) in view of US 11392651 B1 McClusky; Mark Daniel et al. (hereinafter McClusky).
Re claim 1, Jami teaches
1. A method of generating keyword information performed by an electronic device, the method comprising: (fig. 3c user device)
identifying a text set including at least one text element; (creating a set using co-occurrence and frequency to search a DB with search queries which produces company i.e. entity pairs in clusters in the context of finance specified with a degree of relatedness or association to weight/score entities for classification col 6 line 32 to col 7 line 32 with fig. 3c search tool and fig. 4-5 filtered scoring/weighting amongst edges)
…identifying keywords in the at least one text element; (keyword identification col 26 line 55 – col 27 line 3… and creating a set using co-occurrence and frequency to search a DB with search queries which produces company i.e. entity pairs in clusters in the context of finance specified with a degree of relatedness or association to weight/score entities for classification col 6 line 32 to col 7 line 32 with fig. 3c search tool and fig. 4-5 filtered scoring/weighting amongst edges)
based on the text set, determining degrees of association of keyword pairs included in a keyword set including the keywords; (keyword identification col 26 line 55 – col 27 line 3… and creating a set using co-occurrence and frequency to search a DB with search queries which produces company i.e. entity pairs in clusters in the context of finance specified with a degree of relatedness or association to weight/score entities for classification col 6 line 32 to col 7 line 32 with fig. 3c search tool and fig. 4-5 filtered scoring/weighting amongst edges)
obtaining information on a query word that is input by a user; and (information is produced from the search tool and query such as a co-occurrence knowledge graph col 16 lines 28-41 with fig. 6a-6b…with keyword identification col 26 line 55 – col 27 line 3… and creating a set using co-occurrence and frequency to search a DB with search queries which produces company i.e. entity pairs in clusters in the context of finance specified with a degree of relatedness or association to weight/score entities for classification col 6 line 32 to col 7 line 32 with fig. 3c search tool and fig. 4-5 filtered scoring/weighting amongst edges)
based on at least one among the degrees of association, generating information on response words corresponding to the query word. (isolation into pairs in different classifications by type e.g. clothing versus shoes with edge pairs under nodal joining with sub-clusters, both separated information is produced from the search tool and query such as a co-occurrence knowledge graph col 16 lines 28-41 with fig. 6a-6b…with keyword identification col 26 line 55 – col 27 line 3… and creating a set using co-occurrence and frequency to search a DB with search queries which produces company i.e. entity pairs in clusters in the context of finance specified with a degree of relatedness or association to weight/score entities for classification col 6 line 32 to col 7 line 32 with fig. 3c search tool and fig. 4-5 filtered scoring/weighting amongst edges)
However, while Jami teaches a search tool that a user can interact with using neural networks col 25 lines 34-43 for entity recognition requiring a deep analysis for entities such as companies col 5 line 58 to col 6 line 19 in the context of a learning model col 40 lines 41-55, it fails to teach deep learning with user interaction per se and thus fails to teach:
using a named entity recognition model based on deep learning… (McClusky a recursive deep neural network or model col 26 line 48-56 in the context of entities such as companies col 31 lines 9-20… using user interactions to alter display of data e.g. sorting or grouping on a GUI search tool col 35 lines 45-67 with col 38 line 38 to col 42 line 29 on a GUI e.g. fig. 17)
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Jami to incorporate the above claim limitations as taught by McClusky to allow for a simple substitution of one known element such as user interactions/edits on an interface for searching utilizing deep neural networks for another such as a standard user search tool with general neural network use, to obtain predictable results, thereby improving the neural network to be a deep type otherwise functioning the same way, and allowing a user more flexibility to classify and alter displayed data as demonstrated in McClusky to improve results and/or reduce the amount of time it takes to query the database.
Re claim 19, this claim has been rejected for teaching a broader, or narrower claim based on general inclusion of hardware alone (e.g. processor, memory, instructions), representation of claim 1 omitting/including hardware for instance, otherwise amounting to a virtually identical scope
Re claim 20, this claim has been rejected for teaching a broader, or narrower claim based on general inclusion of hardware alone (e.g. processor, memory, instructions), representation of claim 1 omitting/including hardware for instance, otherwise amounting to a virtually identical scope
Re claim 2, Jami teaches
2. The method of claim 1, wherein the text set includes unstructured data related to finance, and wherein the at least one text element is at least one sentence in the unstructured data related to finance. (col 4 lines 13-23 where a company in business or finance scope… producing isolation into pairs in different classifications by type e.g. clothing versus shoes with edge pairs under nodal joining with sub-clusters, both separated information is produced from the search tool and query such as a co-occurrence knowledge graph col 16 lines 28-41 with fig. 6a-6b…with keyword identification col 26 line 55 – col 27 line 3… and creating a set using co-occurrence and frequency to search a DB with search queries which produces company i.e. entity pairs in clusters in the context of finance specified with a degree of relatedness or association to weight/score entities for classification col 6 line 32 to col 7 line 32 with fig. 3c search tool and fig. 4-5 filtered scoring/weighting amongst edges)
Re claim 3, Jami teaches
3. The method of claim 1, wherein the determining of the degrees of association comprises, based on the text set, identifying total frequency in which the keyword pairs are included in each of the at least one text element. (creating a set using co-occurrence and frequency to search a DB with search queries which produces company i.e. entity pairs in clusters in the context of finance specified with a degree of relatedness or association to weight/score entities for classification col 6 line 32 to col 7 line 32 with fig. 3c search tool and fig. 4-5 filtered scoring/weighting amongst edges)
Re claim 4, Jami teaches
4. The method of claim 3, further comprising, based on the keyword set and the total frequency, determining a co-occurrence graph. (a co-occurrence knowledge graph rendering isolation into pairs in different classifications by type e.g. clothing versus shoes with edge pairs under nodal joining with sub-clusters, both separated information is produced from the search tool and query col 16 lines 28-41 with fig. 6a-6b…with keyword identification col 26 line 55 – col 27 line 3… and creating a set using co-occurrence and frequency to search a DB with search queries which produces company i.e. entity pairs in clusters in the context of finance specified with a degree of relatedness or association to weight/score entities for classification col 6 line 32 to col 7 line 32 with fig. 3c search tool and fig. 4-5 filtered scoring/weighting amongst edges)
Re claim 5, Jami teaches
5. The method of claim 4, wherein the co-occurrence graph includes nodes and edges connecting the nodes, wherein each of the nodes corresponds to one of the keywords included in the keyword set, and (eges and nodes with isolation into pairs in different classifications by type e.g. clothing versus shoes with edge pairs under nodal joining with sub-clusters, both separated information is produced from the search tool and query such as a co-occurrence knowledge graph col 16 lines 28-41 with fig. 6a-6b…with keyword identification col 26 line 55 – col 27 line 3… and creating a set using co-occurrence and frequency to search a DB with search queries which produces company i.e. entity pairs in clusters in the context of finance specified with a degree of relatedness or association to weight/score entities for classification col 6 line 32 to col 7 line 32 with fig. 3c search tool and fig. 4-5 filtered scoring/weighting amongst edges)
wherein a weight of each of the edges is identified based on total frequency in which two keywords corresponding to each of a first node and a second node that are connected to the each of the edges are included together in each of the at least one text element. (pairs in different classifications by type e.g. clothing versus shoes with edge pairs under nodal joining with sub-clusters, both separated information is produced from the search tool and query such as a co-occurrence knowledge graph col 16 lines 28-41 with fig. 6a-6b…with keyword identification col 26 line 55 – col 27 line 3… and creating a set using co-occurrence and frequency to search a DB with search queries which produces company i.e. entity pairs in clusters in the context of finance specified with a degree of relatedness or association to weight/score entities for classification col 6 line 32 to col 7 line 32 with fig. 3c search tool and fig. 4-5 filtered scoring/weighting amongst edges)
Re claim 6, Jami teaches
6. The method of claim 4, wherein the co-occurrence graph is a directed weighted co-occurrence graph. (a co-occurrence knowledge graph rendering isolation into pairs in different classifications by type e.g. clothing versus shoes with edge pairs under nodal joining with sub-clusters, both separated information is produced from the search tool and query col 16 lines 28-41 with fig. 6a-6b…with keyword identification col 26 line 55 – col 27 line 3… and creating a set using co-occurrence and frequency to search a DB with search queries which produces company i.e. entity pairs in clusters in the context of finance specified with a degree of relatedness or association to weight/score entities for classification col 6 line 32 to col 7 line 32 with fig. 3c search tool and fig. 4-5 filtered scoring/weighting amongst edges)
Re claim 11, Jami teaches
11. The method of claim 1, wherein identifying of the keywords comprises identifying a category of each of the keywords, and wherein generating of the information on the response words comprises: identifying at least one keyword related to the query word among the keywords included in the keyword set; and (placing pairs in different classifications by type e.g. clothing versus shoes with edge pairs under nodal joining with sub-clusters, both separated information is produced from the search tool and query such as a co-occurrence knowledge graph col 16 lines 28-41 with fig. 6a-6b…with keyword identification col 26 line 55 – col 27 line 3… and creating a set using co-occurrence and frequency to search a DB with search queries which produces company i.e. entity pairs in clusters in the context of finance specified with a degree of relatedness or association to weight/score entities for classification col 6 line 32 to col 7 line 32 with fig. 3c search tool and fig. 4-5 filtered scoring/weighting amongst edges)
filtering a keyword of a set category from the at least one keyword. (filtering down into pairs in different classifications by type e.g. clothing versus shoes with edge pairs under nodal joining with sub-clusters, both separated information is produced from the search tool and query such as a co-occurrence knowledge graph col 16 lines 28-41 with fig. 6a-6b…with keyword identification col 26 line 55 – col 27 line 3… and creating a set using co-occurrence and frequency to search a DB with search queries which produces company i.e. entity pairs in clusters in the context of finance specified with a degree of relatedness or association to weight/score entities for classification col 6 line 32 to col 7 line 32 with fig. 3c search tool and fig. 4-5 filtered scoring/weighting amongst edges)
Re claim 12, Jami teaches
12. The method of claim 1, wherein the information on the response words includes at least one of information on a first text element in which the query word and the one of the response words are included together in the at least one text element and information on first text data including the first text element among text data included in the text set. (grouped into pairs in different classifications by type e.g. clothing versus shoes with edge pairs under nodal joining with sub-clusters, both separated information is produced from the search tool and query such as a co-occurrence knowledge graph col 16 lines 28-41 with fig. 6a-6b…with keyword identification col 26 line 55 – col 27 line 3… and creating a set using co-occurrence and frequency to search a DB with search queries which produces company i.e. entity pairs in clusters in the context of finance specified with a degree of relatedness or association to weight/score entities for classification col 6 line 32 to col 7 line 32 with fig. 3c search tool and fig. 4-5 filtered scoring/weighting amongst edges)
Re claim 13, while Jami teaches a search tool that a user can interact with using neural networks col 25 lines 34-43 for entity recognition requiring a deep analysis for entities such as companies col 5 line 58 to col 6 line 19 in the context of a learning model col 40 lines 41-55, it fails to teach deep learning with user interaction per se and thus fails to teach:
13. The method of claim 1, wherein generating of the information on the response words comprises: based on information on a sort order of the response words, sorting and providing the information on the response words, (McClusky user interactions to alter display of data e.g. sorting based on co-occurrence or score and a grouping on a GUI search tool col 35 lines 45-67 with col 38 line 38 to col 42 line 29 on a GUI e.g. fig. 17)… using a recursive deep neural network or model col 26 line 48-56 in the context of entities such as companies col 31 lines 9-20)
wherein the information on the sort order of the response words is a size order of a degree of association between the query word and the response words. (McClusky user interactions to alter display of data e.g. sorting based on co-occurrence or score and a grouping on a GUI search tool col 35 lines 45-67 with col 38 line 38 to col 42 line 29 on a GUI e.g. fig. 17)… using a recursive deep neural network or model col 26 line 48-56 in the context of entities such as companies col 31 lines 9-20)
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Jami to incorporate the above claim limitations as taught by McClusky to allow for a simple substitution of one known element such as user sorting on an interface for searching utilizing deep neural networks for another such as a standard user search tool with general neural network use, to obtain predictable results, thereby improving the neural network to be a deep type otherwise functioning the same way, and allowing a user more flexibility to sort data, classify, and alter displayed data as demonstrated in McClusky to improve results and/or reduce the amount of time it takes to query the database.
Re claim 14, Jami teaches
14. The method of claim 1, wherein generating of the information on the response words comprises: in case that the query word includes a plurality of query words, identifying a plurality of response word sets corresponding to each of the plurality of query words; and (grouped into pairs in different classifications by type e.g. clothing versus shoes with edge pairs under nodal joining with sub-clusters, both separated information is produced from the search tool and query such as a co-occurrence knowledge graph col 16 lines 28-41 with fig. 6a-6b…with keyword identification col 26 line 55 – col 27 line 3… and creating a set using co-occurrence and frequency to search a DB with search queries which produces company i.e. entity pairs in clusters in the context of finance specified with a degree of relatedness or association to weight/score entities for classification col 6 line 32 to col 7 line 32 with fig. 3c search tool and fig. 4-5 filtered scoring/weighting amongst edges)
determining at least one keyword that is simultaneously included in the plurality of response word sets as the response words corresponding to the plurality of query words. (simultaneous nodal edge information obtained, and grouped into pairs in different classifications by type e.g. clothing versus shoes with edge pairs under nodal joining with sub-clusters, both separated information is produced from the search tool and query such as a co-occurrence knowledge graph col 16 lines 28-41 with fig. 6a-6b…with keyword identification col 26 line 55 – col 27 line 3… and creating a set using co-occurrence and frequency to search a DB with search queries which produces company i.e. entity pairs in clusters in the context of finance specified with a degree of relatedness or association to weight/score entities for classification col 6 line 32 to col 7 line 32 with fig. 3c search tool and fig. 4-5 filtered scoring/weighting amongst edges)
Re claim 15, while Jami teaches a search tool that a user can interact with using neural networks col 25 lines 34-43 for entity recognition requiring a deep analysis for entities such as companies col 5 line 58 to col 6 line 19 in the context of a learning model col 40 lines 41-55, it fails to teach deep learning with user interaction per se and thus fails to teach:
15. The method of claim 14, wherein the sort order of the response words is determined based on sort rankings of the response words in each of the plurality of response word sets. (McClusky user interactions to alter display of data e.g. sorting based on co-occurrence or score and a grouping on a GUI search tool col 35 lines 45-67 with col 38 line 38 to col 42 line 29 on a GUI e.g. fig. 17)… using a recursive deep neural network or model col 26 line 48-56 in the context of entities such as companies col 31 lines 9-20)
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Jami to incorporate the above claim limitations as taught by McClusky to allow for a simple substitution of one known element such as user sorting on an interface for searching utilizing deep neural networks for another such as a standard user search tool with general neural network use, to obtain predictable results, thereby improving the neural network to be a deep type otherwise functioning the same way, and allowing a user more flexibility to sort data, classify, and alter displayed data as demonstrated in McClusky to improve results and/or reduce the amount of time it takes to query the database.
Re claim 16, Jami teaches
16. The method of claim 1, wherein generating of the information on the response words comprises: classifying the response words into a first response word which is a target keyword and a second response word which is a general keyword; and (creating a set using co-occurrence and frequency to search a DB with search queries which produces company i.e. entity pairs in clusters in the context of finance specified with a degree of relatedness or association to weight/score entities for classification col 6 line 32 to col 7 line 32 with fig. 3c search tool and fig. 4-5 filtered scoring/weighting amongst edges)
classifying and generating information on the first response word and information on the second response word separately, (separate classes and types with pairs using co-occurrence and frequency to search a DB with search queries which produces company i.e. entity pairs in clusters in the context of finance specified with a degree of relatedness or association to weight/score entities for classification col 6 line 32 to col 7 line 32 with fig. 3c search tool and fig. 4-5 filtered scoring/weighting amongst edges)
wherein the first response word includes a keyword corresponding to at least one stock that is listed on an exchange. (variant such as stock symbol col 27 lines 60-67… separate classes and types with pairs using co-occurrence and frequency to search a DB with search queries which produces company i.e. entity pairs in clusters in the context of finance specified with a degree of relatedness or association to weight/score entities for classification col 6 line 32 to col 7 line 32 with fig. 3c search tool and fig. 4-5 filtered scoring/weighting amongst edges)
Re claim 17, Jami teaches
17. The method of claim 3, wherein generating of the information on the response words comprise: identifying first total frequency in which one of the response words is included together with the query word in each of the at least one text element and a category of the response word; and (creating a set using co-occurrence and frequency to search a DB with search queries which produces company i.e. entity pairs in clusters in the context of finance specified with a degree of relatedness or association to weight/score entities for classification col 6 line 32 to col 7 line 32 with fig. 3c search tool and fig. 4-5 filtered scoring/weighting amongst edges)
based on the first total frequency and the category, generating a page where information on the response words is displayed. (a page per se using user software to interface with col 13 lines 11-39 with fig. 3c with col 12 lines 25-67 using for instance Owler CIS as a user interface with user access fig. 2 analogous to a search engine col 2 lines 34-48 with Owler driven user functions… from creating a set using co-occurrence and frequency to search a DB with search queries which produces company i.e. entity pairs in clusters in the context of finance specified with a degree of relatedness or association to weight/score entities for classification col 6 line 32 to col 7 line 32 with fig. 3c search tool and fig. 4-5 filtered scoring/weighting amongst edges)
Re claim 18, Jami teaches
18. The method of claim 1, wherein generating of the information on the response words comprises: among the response words, identifying a first type response word included in a first group and a second type response word included in a second group; and (creating a set using co-occurrence and frequency to search a DB with search queries which produces company i.e. entity pairs in clusters in the context of finance specified with a degree of relatedness or association to weight/score entities for classification col 6 line 32 to col 7 line 32 with fig. 3c search tool and fig. 4-5 filtered scoring/weighting amongst edges)
classifying and generating information on the first type response word and information on the second type response word separately. (isolation of a set using co-occurrence and frequency to search a DB with search queries which produces company i.e. entity pairs in clusters in the context of finance specified with a degree of relatedness or association to weight/score entities for classification col 6 line 32 to col 7 line 32 with fig. 3c search tool and fig. 4-5 filtered scoring/weighting amongst edges)
Claim 10 is/are rejected under 35 U.S.C. 103 as being unpatentable over US 11675790 B1 Jami; Aditya et al. (hereinafter Jami) in view of US 11392651 B1 McClusky; Mark Daniel et al. (hereinafter McClusky) and further in view of US 20110119052 A1 Onodera; Sachiko (hereinafter Onodera).
Re claim 10, while Jami teaches semantic similarities and analysis of entities to derive meaning and relationships, it fails to teach morphemic concepts:
10. The method of claim 1, further comprising: identifying a keyword of a word class that is set in the at least one text element using morpheme analyzing, wherein the keyword set further includes the keyword of the word class that is set. (Onodera 0114-0115 extract a phrase classified into a word class such as a noun or a verb which is used as a keyword, from the morpheme analysis)
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Jami in view of McClusky to incorporate the above claim limitations as taught by Onodera to allow for a simple substitution of one known element such as well-known morphemic analysis of keywords for another such as entity meaning, semantic similarities, and relationship maps, to obtain predictable results, thereby improving the combination to improve evaluation accuracy of entities, and improve CIS or search engine results by better understanding the context and meaning of search queries for reducing erroneous classification of companies for instance.
Allowable Subject Matter
Claims 7-9
Are objected to as being dependent upon a rejected base claim but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims. After searching through patent and non-patent literature, there was no evidence that there exists a limitation in direct relation or an obvious variant to such limitations as a whole as precisely limited. When searching for a secondary prior art for the limitation as recited in the above claims, the most relevant topics pertained to material from the same Inventor and Assignee but did not teach or suggest the aforementioned complex limitations as a whole as precisely limited.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
US 20240419703 A1 KIM; Jaehyuk et al.
NER for keywords
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MICHAEL C COLUCCI whose telephone number is (571)270-1847. The examiner can normally be reached on M-F 9 AM - 5 PM.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Andrew Flanders can be reached at (571)272-7516. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of an application may be obtained from the Patent Application Information Retrieval (PAIR) system. Status information for published applications may be obtained from either Private PAIR or Public PAIR. Status information for unpublished applications is available through Private PAIR only. For more information about the PAIR system, see http://pair-direct.uspto.gov. Should you have questions on access to the Private PAIR system, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative or access to the automated information system, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/MICHAEL COLUCCI/Primary Examiner, Art Unit 2655 (571)-270-1847
Examiner FAX: (571)-270-2847
Michael.Colucci@uspto.gov