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
This non-final Office action is in response to applicant’s communication received on August 29, 2024, wherein claims 1-17 are currently pending.
Claim Objections
Claims 1-2 and 14-17 objected to because of the following informalities: Claims 1-2 and 14-17 seems to start every limitation within the individual claim with a capital letter which makes each claim seem to contain multiple separate sentences despite the use of semicolons (i.e. each claim does not seem to follow MPEP § 608.01(m) – “Each claim begins with a capital letter (Applicant’s individual claims have multiple capital letters) and ends with a period (single sentence requirement)…[p]eriods may not be used elsewhere in the claims except for abbreviations…[w]here a claim sets forth a plurality of elements or steps, each element or step of the claim should be separated by a line indentation (also not followed by the Applicant)”). Appropriate correction is required (and Applicant should parse through all the claim to follow MPEP § 608.01(m)).
Applicant may find the following link (pdf) useful in claim writing: https://www.uspto.gov/sites/default/files/documents/Claim drafting.pdf
Claim 3 is objected to because of the following informalities: dependent claim 3 (system claim) is shown (probably mistakenly) to be dependent of claim 1 (method claim) but the way claim 4 is written (recites substantially similar limitations to claim 2 (which is also dependent of claim 1)) and the position of claim 4 make it seems that claim 4 should be dependent of independent claim 3. For examination purposes, claim 4 will be considered as dependent of independent claim 3. Appropriate correction/clarification is required.
Claim 13 is objected to because of the following informalities: Claim 13 is depend on claim 8 (which depends on claim 7) but claim 13 is separated by claims that do not depend on claim 8 (instead being separate by claims depending on claim 5). A series of singular dependent claims is permissible in which a dependent claim refers to a preceding claim which, in turn, refers to another preceding claim. A claim which depends from a dependent claim should not be separated by any claim which does not also depend from said dependent claim. It should be kept in mind that a dependent claim may refer to any preceding independent claim. In general, applicant's sequence will not be changed. See MPEP § 608.01(n).
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitations are: “risk management module” in claims 2 and 4; “language judgment module,” in claim 7; “language judgment module,” and “translation module” in claim 8; “keyword extraction module” in claim 12; “cross-country conversion module,” “language judgment module,” “translation module,” in claim 13; “semantic analysis module,” “classification module,” “recommendation module,” “risk management module,” “content learning module” in claims 14 (independent) and 17; “language judgment module,” “translation module” in claim 15.
Because these claims’ limitations are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 2, 4, 5, and 14-17 is rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claims 2 and 4, recite the limitation “the user’s brainstorm.” There is insufficient antecedent basis for this limitation in the claim.
Claims 5 and 17 recite the limitation “the brainstorming concept.” There is insufficient antecedent basis for this limitation in the claim.
Claim 14-17 (independent claim) recites the limitations “the input module, the category recommendation module, the database module, the risk management module, the content learning module.” There is insufficient antecedent basis for this limitation in the claim.
Claim 15 recites the limitation “the language judgment module.” There is insufficient antecedent basis for this limitation in the claim.
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 1-17 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter.
Regarding Step 1 (MPEP 2106.03) of the subject matter eligibility test per MPEP 2106.03, Claims 1-2 and 14-17 are directed to a method (i.e., process), and claims 3-13 are directed to a system (i.e. machine). Accordingly, all claims are directed to one of the four statutory categories of invention.
(Under Step 2) The claimed invention is directed to an abstract idea without significantly more.
(Under Step 2A, Prong 1 (MPEP 2106.04)) The independent claims (1, 3, 5, 14) are directed to managing intellectual property and abstract intellectual property (e.g. trademark) industry processes (searches, comparing abstract information, risk determinations (in IP field), grouping and categorizing information, etc.,) and the independent claims recite collecting/obtaining information/data (where the information itself is abstract in nature (intellectual property type information from descriptions and figures and also stored intellectual property information and parameters)), data analysis/manipulation (comparing information, evaluations, matching, moving information around (grouping/categorizing), threshold determinations and comparing, risk possibilities determination, etc.,) to determine more data/information, possibly obtaining more abstract information/data, and providing this determined data/information (e.g. reports) for further analysis (using the reports and determined information) and decision-making.
The limitations of the independent claims (1, 3, 5, 14), under the broadest reasonable interpretation, covers methods of organizing human activity (fundamental economic principles or practices (mitigating risk in intellectual property field, making recommendations and making industry decisions based on intellectual property/trademark determinations and issues)) and mental process (concepts performed in the human mind that include observation and evaluation (though search and comparisons/matchings), and judgments/decisions)). If a claims limitation, under its broadest reasonable interpretation, covers the performance of the limitation as fundamental economic principles or practices (including hedging, insurance, mitigating risk); commercial or legal interactions (including agreements in the form of contracts; legal obligations; advertising, marketing or sales activities or behaviors; business relations); managing personal behavior or relationships or interactions between people (including scheduling, social activities, teaching, and following rules or instructions), then it falls within the “organizing human activities” grouping of abstract ideas. (MPEP 2106.04). If claim limitations, under its broadest reasonable interpretation, cover the performance of the limitation as concepts performed in the human mind (including an observation, evaluation, judgment, opinion), the claim limitations fall within the Mental process grouping of abstract ideas. (MPEP 2106.04).
Accordingly, since Applicant's claims fall under organizing human activities grouping and mental processes grouping, the claims recite an abstract idea.
(Under Step 2A, prong 2 (MPEP 2106.04(d))) This judicial exception is not integrated into a practical application because but for the recitation of well-known generic/general-purpose computing/technology components/elements/terms (“electronic device, processor, server, network interface controller, application program (software), user interface, database, login (into the system/network/etc.,), etc.,” (in independent claim 1); “system, electronic device, processor, server, network interface controller, application program (software), user interface, database, login (into the system/network/etc.,), memory, etc.,” (in independent claim 3); “system, electronic device, processor, server, network interface controller, application program (software), user interface, database, login (into the system/network/etc.,), train (stated but no technical details provided), compiler, memory, etc.,” (in independent claim 5); “electronic device, memory, database, login (logs into the system/network/etc.,), etc.,” (in independent claim 14)), in the context of the independent claims (1, 3, 5, 14), the claims encompass the above stated abstract idea (organizing human activity (fundamental economic principles or practices (mitigating risk in intellectual property field, making recommendations and making industry decisions based on intellectual property/trademark determinations and issues)) and mental process (concepts performed in the human mind that include observation and evaluation (though search and comparisons/matchings), and judgments/decisions))).
As shown above, the independent claims (1, 3, 5, 14) recite generic/general-purpose computing/technology components/elements/terms/limitations (“electronic device, processor, server, network interface controller, application program (software), user interface, database, login (into the system/network/etc.,), etc.,” (in independent claim 1); “system, electronic device, processor, server, network interface controller, application program (software), user interface, database, login (into the system/network/etc.,), memory, etc.,” (in independent claim 3); “system, electronic device, processor, server, network interface controller, application program (software), user interface, database, login (into the system/network/etc.,), train (stated but no technical details provided), compiler, memory, etc.,” (in independent claim 5); “electronic device, memory, database, login (logs into the system/network/etc.,), etc.,” (in independent claim 14)) which are recited at a high level of generality performing generic/general purpose computer/computing functions. (MPEP 2106.04). The generic/general-purpose computing/technology components/elements/terms/limitations are no more than mere instructions to apply the judicial exception (the above abstract idea) in an apply-it fashion using generic/general-purpose computing/technology components/elements/terms/limitations (“electronic device, processor, server, network interface controller, application program (software), user interface, database, login (into the system/network/etc.,), etc.,” (in independent claim 1); “system, electronic device, processor, server, network interface controller, application program (software), user interface, database, login (into the system/network/etc.,), memory, etc.,” (in independent claim 3); “system, electronic device, processor, server, network interface controller, application program (software), user interface, database, login (into the system/network/etc.,), train (stated but no technical details provided), compiler, memory, etc.,” (in independent claim 5); “electronic device, memory, database, login (logs into the system/network/etc.,), etc.,” (in independent claim 14)). The CAFC has stated that it is not enough, however, to merely improve abstract processes by invoking a computer merely as a tool. Customedia Techs., LLC v. Dish Network Corp., 951 F.3d 1359, 1364 (Fed. Cir. 2020). The focus of the claims is simply to use computers and a familiar network as a tool to perform abstract processes (discussed above) involving simple information exchange. Carrying out abstract processes involving information exchange is an abstract idea. See, e.g., BSG, 899 F.3d at 1286; SAP America, 898 F.3d at 1167-68; Affinity Labs of Tex., LLC v. DIRECTV, LLC, 838 F.3d 1253, 1261-62 (Fed. Cir. 2016). And use of standard computers and networks to carry out those functions—more speedily, more efficiently, more reliably—does not make the claims any less directed to that abstract idea. See Alice Corp., 573 U.S. at 222-25; Customedia, 951 F.3d at 1364; Trading Techs. Int'l, Inc. v. IBG LLC, 921 F.3d 1084, 1092-93 (Fed. Cir. 2019); SAP America, 898 F.3d at 1167; Intellectual Ventures I LLC v. Symantec Corp., 838 F.3d 1307, 1314 (Fed. Cir. 2016); Electric Power Grp., LLC v. Alstom S.A., 830 F.3d 1350, 1353, 1355 (Fed. Cir. 2016); Intellectual Ventures I LLC v. Capital One Bank (USA), 792 F.3d 1363, 1367, 1370 (Fed. Cir. 2015); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355 (Fed. Cir. 2014). Accordingly, the additional elements (“electronic device, processor, server, network interface controller, application program (software), user interface, database, login (into the system/network/etc.,), etc.,” (in independent claim 1); “system, electronic device, processor, server, network interface controller, application program (software), user interface, database, login (into the system/network/etc.,), memory, etc.,” (in independent claim 3); “system, electronic device, processor, server, network interface controller, application program (software), user interface, database, login (into the system/network/etc.,), train (stated but no technical details provided), compiler, memory, etc.,” (in independent claim 5); “electronic device, memory, database, login (logs into the system/network/etc.,), etc.,” (in independent claim 14)) do not integrate the abstract idea in to a practical application because it does not impose any meaningful limits on practicing the abstract idea – i.e. they are just post-solution/extra-solution activities.
(Under Step 2B (MPEP 2106.05)) The independent claims (1, 3, 5, 14) do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the claims do not recite an improvement to another technology or technical field, an improvement to the functioning of the computer itself, or meaningful limitations beyond generally linking the use of an abstract idea to a particular technological environment. The independent claims recite using known generic/general-purpose computing/technology components/elements/terms/limitations (“electronic device, processor, server, network interface controller, application program (software), user interface, database, login (into the system/network/etc.,), etc.,” (in independent claim 1); “system, electronic device, processor, server, network interface controller, application program (software), user interface, database, login (into the system/network/etc.,), memory, etc.,” (in independent claim 3); “system, electronic device, processor, server, network interface controller, application program (software), user interface, database, login (into the system/network/etc.,), train (stated but no technical details provided), compiler, memory, etc.,” (in independent claim 5); “electronic device, memory, database, login (logs into the system/network/etc.,), etc.,” (in independent claim 14)). For the role of a computer in a computer implemented invention to be deemed meaningful in the context of this analysis, it must involve more than performance of "well-understood, routine, [and] conventional activities previously known to the industry." Alice Corp. v. CLS Bank Int'l, 110 USPQ2d 1976 (U.S. 2014), at 2359 (quoting Mayo, 132 S. Ct. at 1294 (internal quotation marks and brackets omitted)). These activities as claimed by the Applicant are all well-known and routine tasks in the field of art – as can been seen in the specification of Applicant’s application (for example, see Applicant’s specification at, for example, figure 4 and Pages 24-25 [where Applicant recites general-purpose/generic computers/processors/etc., and generic/general-purpose computing components/devices/etc., in Applicant’s specification]) and/or the specification of the below cited art (used in the rejection below and on the PTO-892) and/or also as noted in the court cases in §2106.05 in the MPEP. Further, "the mere recitation of a generic computer cannot transform a patent ineligible abstract idea into a patent-eligible invention." Alice at 2358. None of the hardware offers a meaningful limitation beyond generally linking the system to a particular technological environment, that is, implementation via computers. Adding generic computer components to perform generic functions that are well‐understood, routine and conventional, such as gathering data, performing calculations, and outputting a result would not transform the claims into eligible subject matter. Abstract ideas are excluded from patent eligibility based on a concern that monopolization of the basic tools of scientific and technological work might impede innovation more than it would promote it. The independent claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the claims require no more than a generic computer to perform generic computer functions. The additional elements (“electronic device, processor, server, network interface controller, application program (software), user interface, database, login (into the system/network/etc.,), etc.,” (in independent claim 1); “system, electronic device, processor, server, network interface controller, application program (software), user interface, database, login (into the system/network/etc.,), memory, etc.,” (in independent claim 3); “system, electronic device, processor, server, network interface controller, application program (software), user interface, database, login (into the system/network/etc.,), train (stated but no technical details provided), compiler, memory, etc.,” (in independent claim 5); “electronic device, memory, database, login (logs into the system/network/etc.,), etc.,” (in independent claim 14)) or combination of elements in the claims other than the abstract idea per se amounts to no more than: (i) mere instructions to implement the idea on a computer, and/or (ii) recitation of generic computer structure that serves to perform generic computer functions that are well-understood, routine, and conventional activities previously known to the pertinent industry. Applicant is directed to the following citations and references: Digitech Image., LLC v. Electronics for Imaging, Inc. (758 F.3d 1344 (2014) discussing U.S. Patent No. 6,128,415); and (2) Federal register/Vol. 79, No 241 issued on December 16, 2014, page 74629, column 2, Gottschalk v. Benson. Viewed as a whole, the independent claims do not purport to improve the functioning of the computer itself, or to improve any other technology or technical field. Use of an unspecified, generic computer does not transform an abstract idea into a patent-eligible invention. Thus, the independent claims (1, 3, 5, 14) do not amount to significantly more than the abstract idea itself. See Alice Corp. v. CLS Bank Int'l, 110 USPQ2d 1976 (U.S. 2014).
The dependent claims (2, 4, 6-13, 15-17) further define the independent claims and merely narrow the described abstract idea, but not adding significantly more than the abstract idea. The dependent claims either individually or in combination are merely an extension of the abstract idea itself. The above rejection discussed for the independent claims fully applies to the dependent claims.
The dependent claims (2, 4, 6-13, 15-17) further state using obtained data/information (where the information itself is abstract in nature (intellectual property type information from descriptions and figures and also stored intellectual property information and parameters)), data analysis/manipulation (comparing information, evaluations, matching, moving information around (grouping/categorizing), threshold determinations and comparing, risk possibilities determination, etc.,) to determine more data/information, possibly obtaining more abstract information/data, and providing this determined data/information (e.g. reports) for further analysis (using the reports and determined information) and decision-making. These dependent claims also cover methods of organizing human activity (fundamental economic principles or practices (mitigating risk in intellectual property field, making recommendations and making industry decisions based on intellectual property/trademark determinations and issues)) and mental process (concepts performed in the human mind that include observation and evaluation (though search and comparisons/matchings), and judgments/decisions)).
This judicial exception is not integrated into a practical application because the claims and specification recite additional elements as generic/general-purpose computing/technology components/elements/terms/limitations (no technical/additional elements recited (dependent claim 2); “system” (dependent claim 4); “system, electronic device, database, login (into the system/network/etc.,), machine learning/train (only stated with no technical details provided)” (dependent claims 6-13); “database, login (into the system/network/etc.,), compiler,” (dependent claims 15-17)) performing generic computer/computing/technology functions. (MPEP 2106.04). The dependent claims (2, 4, 6-13, 15-17) merely use the same general technological environment and instructions as the independent claims above to implement the abstract idea. The generic/general-purpose computing/technology components/elements/terms/limitations are no more than mere instructions to apply the judicial exception (the above abstract idea) in an apply-it fashion using generic/general-purpose computing/technology components/elements/terms/limitations (see above). Hence, the additional elements (no technical/additional elements recited (dependent claim 2); “system” (dependent claim 4); “system, electronic device, database, login (into the system/network/etc.,), machine learning/train (only stated with no technical details provided)” (dependent claims 6-13); “database, login (into the system/network/etc.,), compiler,” (dependent claims 15-17)) do not integrate the abstract idea in to a practical application because they does not impose any meaningful limits on practicing the abstract idea – i.e. they are just post-solution/extra-solution activities.
Also, the dependent claims (2, 4, 6-13, 15-17) either individually or in combination are merely an extension of the abstract idea itself and the dependent claims (similar to the independent claims) do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the claims require no more than a generic computer to perform generic computer functions. The additional elements (no technical/additional elements recited (dependent claim 2); “system” (dependent claim 4); “system, electronic device, database, login (into the system/network/etc.,), machine learning/train (only stated with no technical details provided)” (dependent claims 6-13); “database, login (into the system/network/etc.,), compiler,” (dependent claims 15-17)) or combination of elements in the dependent claims other than the abstract idea per se amounts to no more than: (i) mere instructions to implement the idea on a computer, and/or (ii) recitation of generic computer structure that serves to perform generic computer functions that are well-understood, routine, and conventional activities previously known to the pertinent industry. Applicant is directed to the following citations and references: Digitech Image., LLC v. Electronics for Imaging, Inc. (758 F.3d 1344 (2014) discussing U.S. Patent No. 6,128,415); and (2) Federal register/Vol. 79, No 241 issued on December 16, 2014, page 74629, column 2, Gottschalk v. Benson. Viewed as a whole, dependent claims do not purport to improve the functioning of the computer itself, or to improve any other technology or technical field. Use of an unspecified, generic computer does not transform an abstract idea into a patent-eligible invention. Thus, the dependent claims (2, 4, 6-13, 15-17) do not amount to significantly more than the abstract idea itself. See Alice Corp. v. CLS Bank Int'l, 110 USPQ2d 1976 (U.S. 2014).
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-6, 9, 11-12, 14, and 17 are rejected under 35 U.S.C. 103 as being unpatentable over Solmer (US 12,141,732) in view of Jessen et al., (US 2016/0350886), further in view of Plotkin (US 2024/0211685).
As per independent claim 5, Solmer discloses a trademark risk management system for receiving a user end that receives a user input through operating an electronic device, the processor of the electronic device connects to a server via a network interface controller and executes an application program for category recommendation and risk management (Fig. 3B [IP management system], 3E-5A [shows IP management system; with fig. 12 [calculate classification]]; col. 6, lines 53-65 [intellectual property with categories], col. 20, lines 1-15 [modeling…trademarks…classified into…groups (categories); with col. 14, line 1 – col. 15, line 15, line 2 [cluster suggested…classification (filter and weighting)…automated suggestions]], col. 7, lines 51-54 [clearing risk…IP…determine risks (to clear or mitigate); with col. 47, lines 10-11 [maximizing benefits and minimizing…risks]]), the system at least comprises:
an order processing module that generates a new case order and regularly updates the information in the case order to a temporary memory of the system after the user selects the first target country (col. 12, lines 4-58 [request…service…request…analyzed (order processing)…parameters…obtained from…user…user’s request…carried out (order processing)… receive a request from a user via the user interface and analyze the user request to determine, e.g., which service is to be activated and what data are needed for the selected service; see with col. 45, lines 53-57 [criteria received (by user)…criteria include…country of interest], col. 16, lines 51-55 [criteria…different countries; also col. 20, lines 3-9 [regarding various target countries]]]);
an input module for receiving the description text or graphics input by the user, converting the description text to a string for labeling processing, and sending a string information and recording the input language of the string information in the temporary memory (col. 4, lines 33-37 [textual content…captured via semantic analysis of the textual content…semantic modeling may be applied based on any type of textual input – including one or more documents, a segment of a document, a written description, etc.,; with col. 39, lines 39-50 [user input…received…include…parse-able information…parsed…one or more words, phrases, or related concepts; with col. 38, lines 45-46 [label during the interaction with, e.g., semantic map (t-SNE)…tree map (k-means clustering)]], with col. 55, lines 45-46 [discusses input language of intellectual property (document, etc.,), with col. 29, line 51-52 [recorded to memory and/or storage media]]]; col. 40, lines 32-37 [text input], col. 48, lines 19-34 [algorithm…semantic map…caching techniques (memory)…t-SNE positions…recorded and stored in memory or disk such that, upon adding additional data points (whether new documents upon an updated search at a later date, or a supplemental data set from the same initial data), only the new positions are calculated and the new items are merged into the original map (possibly with a visually distinct overlay, e.g., z-axis, alpha-blend, color)]);
a semantic analysis module that receives the string information, analyzes and segments it through a natural language database, and generates and sends semantic analysis results (col. 9, lines 23-42 [semantic analysis of the long document. For example, a standard may be segmented based on semantics of the text and each of such segments (concerning semantically distinction technology description) may then be classified…semantic information server…semantic analysis of the long document. For example, a standard may be segmented based on semantics of the text and each of such segments (concerning semantically distinction technology description) may then be classified; see with col. 30, line 22 – col. 31, line 38 [discusses database(s) linked to language processing]]; col. 27, lines 29-58 [and example – semantic search leveraging result ranking via artificial intelligence may typically be based on a textual description of a query (reflecting an intent of what to look for) or a different means of specifying what to look for, such as a document (e.g., a patent represented by a patent number)….allow a user to express a variety of means to indicate weighting criteria and options…textual description…natural language, document identifiers, as well as traditional filtering options (e.g., classification code, Boolean text or date filters, regular expression matching, or other syntactical filtering operations) may all be utilized to specify the parameters to be applied to the search…reviewing a portfolio of a company, a product of a company, a standard of a standard body, a person or an inventor, a university, or another entity, the relevant information is much more diverse and may include a variety types of information such as technical literature, patents, patent applications, webpages, or any other class of information…specified manually by a unique numerical identifier, e.g., Digital Object Identifier (DOI) number, Uniform Resource Identifier (URL), patent or patent application number, etc.]; see also col. 54, line 9 – col. 55, line 40);
a classification module that analyzes the trademark/intellectual-property category classification code for the semantic analysis results, connects to a database module to judge and generate at least one set of intellectual-property classification codes (see citations above and see col. 9, lines 21-28 [complied with in different sub-areas, the semantic information server…used to provide various services based on semantic analysis of the long document…segmented based on semantics of the text and each of such segments…then be classified; with col. 11, lines 16-17 [semantic modeling…classification code]; with col. 13, line 51 – col. 14, line 29 [classification filters…(with analysis/evaluation/judgement/etc.,)…classification code filter…classification code weighting]; with col. 20, lines 1-15 [modeling…trademarks…classified into…groups (categories)]]);
a category recommendation module that is a computational model trained with a natural language model and intellectual property classification tables and details, combined with the semantic analysis module and the classification module to parse the string information with ambiguous semantics or imprecise descriptions into intellectual-property category recommendation information, and finally generates the recommended application intellectual property category (see citations above and see col. 13, line 65 – col. 14, line 65 [ambiguous…word sense induction (WSI) and/or word sense disambiguation (WSD) (particularly when time-influenced, as in a preferred embodiment) influences retrieval of a document set, by, for example, using identified word senses in the documents included by the date filter compared to senses identified in documents excluded by the date filter, to disambiguate the polysemous words (discuses ambiguity and disambiguation); with col. 16, lines 25-30 [classification…categories of classification], with col. 9, lines 21-28 [complied with in different sub-areas, the semantic information server…used to provide various services based on semantic analysis of the long document…segmented based on semantics of the text and each of such segments…then be classified; with col. 11, lines 16-17 [semantic modeling…classification code], with col. 20, lines 1-15 [modeling…trademarks…classified into…groups (categories)]]);
a search document generation module that comprises a text search unit, a figure search unit, a conversion unit, and a figure comparison unit, the figure search unit receives the input figure and searches for the previous case in the database module, the text search unit receives the input text and searches for the previous case in the database module (col. 6, line 55 – col. 7, line 13 [intellectual property (IP) related…involving searching…areas…characterizing the semantics of the documents…criteria…processing such documents to understand the semantics of the documents and extracting/determining information based on such semantics…semantic information server…launch a search on…documents…analyze… materials may be analyzed for semantics (to make determinations); see with col. 4, lines 33-37 [textual content…captured via semantic analysis of the textual content…semantic modeling may be applied based on any type of textual input – including one or more documents, a segment of a document, a written description, etc.,; with col. 39, lines 39-50 [user input…received…include…parse-able information…parsed…one or more words, phrases, or related concepts], col. 56, lines 22-24 [identify figures], col. 66, lines 6-10 [analyze…semantic analyzer…review figures]]),
further ranks the similarity to generate a risk assessment report (for example, see col. 6, line 53 – col. 8, line 12 [intellectual property (IP)…study (report – includes assessment and analysis, etc.,)…clearance evaluation…clearing…risk (with checking enforceable IP rights)…textual information…analyzed to extract the semantics…similar semantics…determine…risks (service achieved…analyzing textual information for semantics and matching the semantics…assessment); see with col. 8, lines 40-46 [assessment…infringement (type of risk)…invalidity (type of risk)…freedom to operate (type of risk assessment)], with col. 14, lines 48-54 [applied filters…reranking element separately parsed into a secondary interface…ranking element influences either the original search criteria and/or the ranking of retrieval], with col. 12, line 66 – col. 13, line 6 [services (study/report)…include, e.g., a trending topics identifier 551, a classification skew detector 552, a semi-supervised learner 553, and a document summarizer…specialized services…include, e.g., a trending topics predictor 546, an entity analyzer 547, an infringement analyzer (risk), and a 112 analyzer (legal risk)]]; col. 28, line 66 – col. 29, line 38 [ranking…similarities…matched… retrieval ranking or scoring of each document or a set of documents, e.g., user ranking, user tagging, date ranking, citation ranking, term frequency inverse document frequency cosine similarity]);
a content learning module, further including a pattern learning unit, which learns the corresponding trademark/intellectual-property content from the database module for different trademark/intellectual-property categories (col. 32, lines [trending topic…documents…popularity…measured…compared …over set period of time (pattern learning described); with col. 30, line 22 – col. 31, line 28 [using language models], with col. 51, lines 36-64 [different data manipulation, visualization, or interaction tools may be activated based on any of the entities displayed in the semantic map or documents associated with any selected entities…user may select entity…request to perform trending topics analysis (pattern learning done for trend analysis – see col. 53, lines 7-11 [perform trending topic analysis])]]; col. 4, lines 27-61 [AI based semantic systems…modeling and analysis of individual documents/texts/etc.,…integrated AI based system and method for semantic based aggregated modeling, search, visualization, summarization, and various applications…AI based technologies, textual content associated with an entity may be used to characterize or model the entity with respect to different aspects thereof based on semantics embedded in such textual content and captured via semantic analysis of the textual content (language models/modelling (with AI this is part of Large Language Model as Large Language Models are advanced AI systems designed to understand, generate, and process human language (which is what Solmer does)))], with col. 20, lines 1-15 [modeling…trademarks…classified into…groups (categories)])
a risk management module that further comprises a text generation unit and a pattern generation unit, regenerates text and/or figures based on the learning of the content learning module and based on the previous cases matched by the figure search unit and the text search unit in the database module (see citations above and also see col. 28, line 40 – col. 30, line 57 [a flowchart of an exemplary process for using the probabilistic parser and unified search box…parse-able information, e.g., a standard of interest….traverse market intelligence data, such as corporate tree, market newsfeed, to include in modeling/retrieval…aggregated models are generated…dynamically generate semantic models for entities and save in the storage….semantic models for the entities are previously generated, such aggregated entity models are retrieved and used to build, at 807, a unified query based on the escaped text, the aggregated models, and/or any weighting and filtering requirements in the query…aggregated semantic feature vectors and semantic signatures may be weighted or selected based on the topics in the escaped text…search is performed based on the query and relevant entities with matched technologies or products associated with the entity are identified…semantic versus filtering or syntactical…semantic vector may be clustered into related concepts and presented in a searchable fashion to the user via conceptual proximity in order to create a ranked list with an indication of word clustering and/or visualization techniques (such as k-means clustering, or t-SNE visualization) employed; with col. 32, lines [trending topic…documents…popularity…measured…compared …over set period of time (pattern learning described); with col. 30, line 22 – col. 31, line 28 [using language models], with col. 51, lines 36-64 [[different data manipulation, visualization, or interaction tools may be activated based on any of the entities displayed in the semantic map or documents associated with any selected entities…user may select entity…request to perform trending topics analysis (pattern learning done for trend analysis – see col. 53, lines 7-11 [perform trending topic analysis])]]], col. 4, lines 33-37 [textual content…captured via semantic analysis of the textual content…semantic modeling may be applied based on any type of textual input – including one or more documents, a segment of a document, a written description, etc.,; with col. 39, lines 39-50 [user input…received…include…parse-able information…parsed…one or more words, phrases, or related concepts], col. 56, lines 22-24 [identify figures], col. 66, lines 6-10 [analyze…semantic analyzer…review figures]);
wherein, after the user inputs the brainstorming concept for the trademark/intellectual property name/concept/information or figure through the input module, the pattern generation unit combines the semantic analysis module to analyze the brainstorming concept and translate it into pattern generation language ((note that the Applicant does not describe the term “brainstorming” in the specification and the term is used very broadly/open-ended (i.e. any concept can involve “brainstorming” and it is the user is inputting concepts)); see citations above and see (see citations above and also see col. 4, lines 33-37 [textual content…captured via semantic analysis of the textual content…semantic modeling may be applied based on any type of textual input – including one or more documents, a segment of a document, a written description, etc.,; with col. 39, lines 39-50 [user input…received…include…parse-able information…parsed…one or more words, phrases, or related concepts], col. 56, lines 22-24 [identify figures], col. 66, lines 6-10 [analyze…semantic analyzer…review figures], col. 12, line 66 – col. 14, line 35 [trending topics identifier 551, a classification skew detector…summarizer…trending topics predictor…concept cluster…code filter; with col. 16, lines 34-27 [application…detection of trending topics]]),
generate the pattern code (what is trending) corresponding to the brainstorming concept through the pattern generation language (working of semantic information server of Solmer) (see citations above and see col. 8, line 58 – col. 9, line 57 [semantic information server…analyzing (see citations above for discussion on semantic information server)…providing assessment…estimated market trend (pattern code)… semantic information server…semantic information services that the semantic information server…identify trends in technologies and/or industries, semantically model the certain targets, profile certain targets]), and then
generate the regenerated figure corresponding to the brainstorming concept through a compiler unit ((note that compiler unit is just a generic computer element to compile software (as no other definition is provided in the specification)) col. 28, line 40 – col. 30, line 57 [a flowchart of an exemplary process for using the probabilistic parser and unified search box…parse-able information, e.g., a standard of interest….traverse market intelligence data, such as corporate tree, market newsfeed, to include in modeling/retrieval…aggregated models are generated…dynamically generate semantic models for entities and save in the storage….semantic models for the entities are previously generated, such aggregated entity models are retrieved and used to build, at 807, a unified query based on the escaped text, the aggregated models, and/or any weighting and filtering requirements in the query…aggregated semantic feature vectors and semantic signatures may be weighted or selected based on the topics in the escaped text…search is performed based on the query and relevant entities with matched technologies or products associated with the entity are identified…semantic versus filtering or syntactical…semantic vector may be clustered into related concepts and presented in a searchable fashion to the user via conceptual proximity in order to create a ranked list with an indication of word clustering and/or visualization techniques (such as k-means clustering, or t-SNE visualization) employed; with col. 32, lines [trending topic…documents…popularity…measured…compared…over set period of time (pattern learning described); with col. 30, line 22 – col. 31, line 28 [using language models], with col. 51, lines 36-64 [[different data manipulation, visualization, or interaction tools may be activated based on any of the entities displayed in the semantic map or documents associated with any selected entities…user may select entity…request to perform trending topics analysis (pattern learning done for trend analysis – see col. 53, lines 7-11 [perform trending topic analysis])]]], col. 4, lines 33-37 [textual content…captured via semantic analysis of the textual content…semantic modeling may be applied based on any type of textual input – including one or more documents, a segment of a document, a written description, etc.,; with col. 39, lines 39-50 [user input…received…include…parse-able information…parsed…one or more words, phrases, or related concepts], col. 56, lines 22-24 [identify figures], col. 66, lines 6-10 [analyze…semantic analyzer…review figures]), and
during the regeneration of the figure, the figure search unit will compare the similarity with the previous cases that have been searched, so that the similarity of the regenerated figure to the previous case is below a set value ((note that “regeneration” is producing similar text/figure based on the input parameter put into the system (search and reproduces relevant to what it requested)) col. 56, lines 22-24 [identify figures; with col. 66, lines 6-10 [analyze…semantic analyzer…review figures], col. 21, line 60 – col. 22, line 11 [similarity threshold…cosine similarity… value may vary between −1 to 1, or 0-1 …value…define…the relevancy similarity threshold; with col. 23, lines 25-26 [scores of…similar documents (with figures analysis)…lower relevance scores…reduced further…based on a function of…similarity], with col. 13, lines 7-15 [generates/regenerates results (text/figures/etc.,) on the user interface based on input parameters/requests]], see also col. 28, line 40 – col. 29, line 13);
wherein, after the user inputs the brainstorming concept for the trademark name or figure through the input module, the text generation unit combines the semantic analysis module to analyze the brainstorming concept (see citations above and see fig. 5A [see aggregate modeling layer with figure 5D]; and see col. 9, lines 23-42 [semantic analysis of the long document…standard may be segmented based on semantics of the text and each of such segments (concerning semantically distinction technology description) may then be classified…semantic information server…semantic analysis…segmented based on semantics of the text and each of such segments (concerning semantically distinction technology description) may then be classified; see with col. 30, line 22 – col. 31, line 38 [discusses database(s) linked to language processing]]; col. 27, lines 29-58 [and example – semantic search leveraging result ranking via artificial intelligence may typically be based on a textual description of a query (reflecting an intent of what to look for) or a different means of specifying what to look for, such as a document (e.g., a patent represented by a patent number)….allow a user to express a variety of means to indicate weighting criteria and options…textual description…natural language, document identifiers, as well as traditional filtering options (e.g., classification code, Boolean text or date filters, regular expression matching, or other syntactical filtering operations) may all be utilized to specify the parameters to be applied to the search…reviewing a portfolio of a company, a product of a company, a standard of a standard body, a person or an inventor, a university, or another entity, the relevant information is much more diverse and may include a variety types of information such as technical literature, patents, patent applications, webpages, or any other class of information…specified manually by a unique numerical identifier, e.g., Digital Object Identifier (DOI) number, Uniform Resource Identifier (URL), patent or patent application number, etc.]; see also col. 54, line 9 – col. 55, line 40), and then
regenerates the text by combining the content learning module, and at the same time, the text search unit compares the similarity with the previous cases that have been searched, so that the similarity of the regenerated text to the previous case is within the set value (see citations above and see col. 6, line 53 – col. 8, line 12 textual information…analyzed to extract the semantics…similar semantics…determine…risks (service achieved…analyzing textual information for semantics and matching the semantics…assessment); see with col. 8, lines 40-46 [assessment…infringement (type of risk)…invalidity (type of risk)…freedom to operate (type of risk assessment)], col. 28, line 66 – col. 29, line 38 [ranking…similarities…matched… retrieval ranking or scoring of each document or a set of documents, e.g., user ranking, user tagging, date ranking, citation ranking, term frequency inverse document frequency cosine similarity], col. 21, line 60 – col. 22, line 11 [similarity threshold…cosine similarity…value may vary between −1 to 1, or 0-1 …value…define…the relevancy similarity threshold], with col. 13, lines 7-15 [describes generating/regenerating results (text/figures/etc.,) on the user interface based on input parameters/requests]]; also see col. 28, line 40 – col. 29, line 13 [an example shown with details]).
Although Solmer discloses Applicant’s above limitations, Solmer discloses trademarks in various separate embodiments and most limitations are for all types of intellectual property (IP) (e.g. patents, trademarks, etc.,; where Solmer does disclose application of the steps to trademarks – see citations above – e.g. col. 20, lines 1-15 [modeling…trademarks…classified into…groups (categories)). However, it would be obvious to one of ordinary skill in the art to include and combine the various disclosed (albeit separately stated) embodiment and elements of trademarks to show Applicant’s claimed concept as trademarks are taught by Solmer itself (within the same reference) and since one of ordinary skill in the art at the time of the invention would have recognized that applying the known technique and concepts (intellectual property) of Solmer itself (same reference) would have yielded predictable results because the level of ordinary skill in the art demonstrated by the single reference applied shows the ability to incorporate such concepts and features into similar systems; and also since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Solmer does not explclity state a login module through which the user operates the electronic device to authenticate the identity.
Analogous are Jessen discloses a login module through which the user operates the electronic device to authenticate the identity (¶ 0124 [module first authenticates or validates the user…by examining a password and/or username submitted (login/sign-in) from the user]).
Therefore, it would be obvious to one of ordinary skill in the art to include in the system/method of Solmer login module through which the user operates the electronic device to authenticate the identity as taught by analogous art Jessen for security, privacy, and individualized use (so things are viewable and/or usable by the user only) since doing so could be performed readily by any person of ordinary skill in the art, with neither undue experimentation, nor risk of unexpected results (KSR-G/TSM); and also since one of ordinary skill in the art at the time of the invention would have recognized that applying the known technique and concepts of Jessen (it is old and well-known (and broadly used) type of secure use of application to have login and authentication technology of computing/computer based applications with login to individual accounts) would have yielded predictable results because the level of ordinary skill in the art demonstrated by the references applied shows the ability to incorporate such concepts and features into similar systems (KSR-D). (MPEP 2141).
Although Solmer states on col. 4, lines 27-61 “AI based semantic systems…modeling and analysis of individual documents/texts/etc.,…integrated AI based system and method for semantic based aggregated modeling, search, visualization, summarization, and various applications…AI based technologies, textual content associated with an entity may be used to characterize or model the entity with respect to different aspects thereof based on semantics embedded in such textual content and captured via semantic analysis of the textual content (language models/modelling (with AI this is part of Large Language Model as Large Language Models are advanced AI systems designed to understand, generate, and process human language,” neither Solmer nor Jessen explicitly state the term large language model.
Analogous art Plotkin discloses large language model (¶¶ 0587-0589 [using large language model; see with 0007 [intellectual property (patent) analysis etc.,]]).
Therefore, it would be obvious to one of ordinary skill in the art to include in Solmer in view of Jessen large language model as taught by analogous art Plotkin to optimally automate language processing and provide optimized results/insights on the data that is analyzed since doing so could be performed readily by any person of ordinary skill in the art, with neither undue experimentation, nor risk of unexpected results (KSR-G/TSM); and also since one of ordinary skill in the art at the time of the invention would have recognized that applying the known technique and concepts of Plotkin (it is common and well-known to use large language models for understanding, generating, and summarizing human-like text across diverse applications) would have yielded predictable results because the level of ordinary skill in the art demonstrated by the references applied shows the ability to incorporate such concepts and features into similar systems (KSR-D). (MPEP 2141).
As per claim 6, Solmer discloses the system according to claim 5, wherein the figure search unit, upon receiving the input figure, first converts the figure into vector representation through a conversion unit, and then conducts a matching search in the database module through the figure comparison unit to find the previous case (see citations above for claim 5 and also see col. 56, lines 22-24 [identify figures; with col. 66, lines 6-10 [analyze…semantic analyzer…review figures; with col. 28, line 66 – col. 29, line 38 [matching… utilize a dimensionality reduction via, e.g., an auto-encoder and neural network during modeling and training… link analysis algorithms with numerical weightings, distance between code vectors generated…scores which are converted to another…scoring metric…semantic “relevancy”], col. 30, line 38 – col. 31, line 20 [semantic vector representation… semantic vector may be derived based on a representation of data in a lower dimensional space associated with a document or an entity…conversion…pattern matches]], with col. 13, lines 7-15 [generates/regenerates results (text/figures/etc.,) on the user interface based on input parameters/requests; with col. 49, line 54 – col. 50, line 11 [modeled as multiple semantic vectors…matching techniques…matching…performed… pre-modeled entities or aggregated entities of multiple companies can be combined into one directed graph by linking/overlaying the nodes that meet certain criteria, e.g., relevance or time criteria…relevance]]]).
As per claim 9, Solmer discloses the system according to claim 5, wherein further comprises a case processing module that further comprises: an application form generation unit that extracts the identity information of the user authenticated by the login authentication and brings it into the application data, or the user directly inputs the application data in the fields through the electronic device and the input module receives the application data, and the application data is generated by applying the formatting template; wherein, after the case processing module generates the application form, the user's case order is completed (for example, among many, see col. 4, lines 30-36 [textual content…used to characterize or model the entity with respect to different aspects thereof based on semantics embedded in such textual content and captured via semantic analysis of the textual content…applied based on any type of textual input, including one or more documents (forms); with col. 13, lines 16-60 [user interface…search box presented (user requests and inputs (see above)); see with col. 16, lines 47-51 [textual information may be generated in different forms…template…criteria provided by…user]]; with col. 11, line 43 – col. 12, line 48 [ semantic information server 110 may also include a user interface…interact with users to receive instructions and parameters for the requested services and serve as a media on which service results from the semantic information server 110 may be presented (after completion of request)… invoke the search engine 510 in the search layer to gather textual information from different sources relevant to one or more entities based on a search request formulated based on the entity (or entities) at issue. Such searched information is used to perform modeling of the entities based on semantics of the search result. Similarly, when an application in the service layer 540 needs to invoke the search engine 510 in the search layer to gather relevant textual information, the application sends a request formulated based on the needs relevant to the nature of the service so that the request may be analyzed by the search request processor 560 to generate specific instructions for the search engine 510 to carry out the required search…interfaces with the user interface…user interface, information about which entities to be modeled with what parameters (e.g., search scope specified by a range of dates) may be obtained from a user…user's request…carried out…application… obtain specific requests of the user (user fills/inputs and send requests)], col. 13, lines 16-60 [user interface…search box presented (user requests and inputs (see above)); see with col. 16, lines 47-51 [textual information may be generated in different forms…template…criteria provided by…user]]]).
As per claim 11, Solmer discloses the system according to claim 10, wherein the figure comparison unit uses edit distance, cosine similarity, or Jaccard similarity to calculate the similarity during the comparison process, and filters out the figures with a similarity exceeding a set value (col. 56, lines 22-24 [identify figures; with col. 66, lines 6-10 [analyze…semantic analyzer…review figures], col. 21, line 60 – col. 22, line 11 [ a similarity threshold greater than a threshold value is utilized to create a document-pair connection (in some models, such as those using in part cosine similarity, such a value may vary between −1 to 1, or 0-1 when cutting off negatively correlated documents from retrieval between −1 and 0)….value used to…define the relevancy similarity threshold…relevancy similarity values may be constant for modeling across all documents or vary based on criteria, such as distribution of concept relevancy, number of concepts], with col. 13, lines 7-15 [generates/regenerates results (text/figures/etc.,) on the user interface based on input parameters/requests]], col. 28, line 67 – col. 29, line 38 [similarity…matched…above certain threshold…result set chosen… retrieval of a result which may incorporate multiple or arbitrary combinations of methodologies into result retrieval ranking or scoring…cosine similarity]).
As per claim 12, Solmer discloses the system according to claim 5, wherein further comprises a keyword extraction module that extracts keywords from the input content, generates multiple keywords, and uses the keywords to train the model using machine learning algorithms (see citations above for claims 5 and 6 and also see col. 30, line 58 – col. 31, line 36 [AI-based and traditional criteria used by a search engine to perform a retrieval may be optionally included in a search history…information…include weighted keywords, triggered keywords, and concepts (including n-grams), and reversed semantic vectors as disclosed above…conversion into a form that is more human-readable form (e.g., without an unstructured output) may be achieved by capturing residual terms and clustering thereof for display or recording purposes…count of documents discovered Weighted keywords and/or entities determined based on the query Keywords and/or concepts added by the language models]; with col. 29, lines 15-25 [keyword models…modeling and training…cosine similarity; with col. 38, line 34 – col. 39, line 27 [creating training data…used for machine learning… labels may be used as ground truth for the training data for future supervised learning or training…improve training models]], col. 20, lines 22-31 [semantic model for a document or any textual content, in accordance with an embodiment of the present teaching, comprises a semantic feature vector, which is a higher dimensional vector representation based on unigrams, n-grams, topics, concepts and combination thereof, and a semantic signature, which is a compact vector representation of high level semantics contained in the semantic feature vector. Concepts, which are part of the keyword models derived from textual information, represent meaning that may be defined by multiple related terms]], see with col. 34, line 55 – col. 35, line 13 [Rapid Automatic Keyword Extraction (RAKE)…terms may include words, phrases, n-grams, hyphenated expressions, user inputs, keywords identified from models and/or other retrieval functions, or any combination thereof…additional terms in the document that are semantically related to the input terms may then be identified...algorithmic ranking (e.g., Luhn algorithm, RAKE algorithm) of areas of interest is calculated…based on…semantic analysis and intra or within document term occurrences or co-occurrences. Such calculated ranking is for purposes of contextual summarization]).
As per independent claim 1, Solmer discloses trademark risk management method, wherein a user operates an electronic device, the processor of the electronic device connects to a server via a network interface controller and executes an application program to perform category recommendation and risk management (Fig. 3B [IP management system], 3E-5A [shows IP management system; with fig. 12 [calculate classification]]; col. 6, lines 53-65 [intellectual property with categories], col. 20, lines 1-15 [modeling…trademarks…classified into…groups (categories); with col. 14, line 1 – col. 15, line 15, line 2 [cluster suggested…classification (filter and weighting)…automated suggestions]], col. 7, lines 51-54 [clearing risk…IP…determine risks (to clear or mitigate); with col. 47, lines 10-11 [maximizing benefits and minimizing…risks]]), comprising the following steps:
(S100) The user inputs text content through the user interface of the electronic device, and the processor executes an input module to input the text content; (S200) The processor executes a semantic analysis module in the application program to analyze the text content (col. 13, lines 28-29 [inputs from the user], col. 39, lines 39-50 [user input…received…include…parse-able information…parsed…one or more words, phrases, or related concepts; with col. 4, lines 33-61 [textual content…captured via semantic analysis of the textual content…semantic modeling may be applied based on any type of textual input – including one or more documents, a segment of a document, a written description, etc.,], col. 66, lines 6-10 [analyze…semantic analyzer]);
(S300) The semantic analysis module further connects a classification module, a search module, and a database module to classify the text content by industry technology and conduct a matching search in the database module, the matching data is then sent to an intellectual property information disclosure module (col. 4, lines 28-9 [semantic based aggregated modeling, search, visualization, summarization], col. 5, lines 64-65 [understand the trend of the industry sector…technologies], col. 7, lines 56 – col. 8, line 46 [information…analyzed to extract the semantics based on which documents (intellectual property documents – with can also be trademarks, see col. 20, lines 4-9 [different types of textual information or documents…IP…trademarks]) with similar semantics may be searched and analyzed to determine which of the documents may pose risks…achieved by analyzing textual information for semantics and matching the semantics of a…description and that of the relevant documents]);
(S400) The intellectual property information disclosure module analyzes and summarizes the data and presents the information to the user through the user interface of the electronic device (see citations above and see col. 4, lines 39-61 [semantics captured from textual content…utilized for deriving…summarization, assessment, conclusions, or predictions…information…analyzed…generate summary], col. 12, lines 30 – col. 13, line 6 [ modeling layer interfaces with the user interface as well as the search layer in order to carry out the modeling tasks… receive a request from a user via the user interface and analyze the user request…summarizer…analyzers]);
(S500) The analyzed and summarized data is also sent to a category recommendation module, the category recommendation module classifies and summarizes the types of intellectual property in the data, and displays the recommended types of intellectual property for application in a ranked order on the user interface (see citations above and see col. 13, line 65 – col. 14, line 65 [ambiguous…word sense induction (WSI) and/or word sense disambiguation (WSD) (particularly when time-influenced, as in a preferred embodiment) influences retrieval of a document set, by, for example, using identified word senses in the documents included by the date filter compared to senses identified in documents excluded by the date filter, to disambiguate the polysemous words (discuses ambiguity and disambiguation); with col. 16, lines 25-30 [classification…categories of classification], with col. 9, lines 21-28 [complied with in different sub-areas, the semantic information server…used to provide various services based on semantic analysis of the long document…segmented based on semantics of the text and each of such segments…then be classified; with col. 11, lines 16-17 [semantic modeling…classification code], with col. 20, lines 1-15 [modeling…trademarks…classified into…groups (categories)]]);
(S600) The user selects through the user interface (see citations above and also see citations for claim 5 above; and see col. 12, lines 30 – col. 13, line 6 [modeling layer interfaces with the user interface as well as the search layer in order to carry out the modeling tasks…receive a request from a user via the user interface and analyze the user request…summarizer… analyzers]; see with col. 13, lines 63-64 [Users…prompted…list of selectable options], col. 18, lines 5-7 [user may select…visualizing different aspects of semantics associated with data], see also col. 25, lines 7-30);
(S700) The user inputs description through the user interface, and the processor executes the input module to input the description (see citations above and see col. 12, lines 30 – col. 13, line 6 [modeling layer interfaces with the user interface as well as the search layer in order to carry out the modeling tasks…receive a request from a user via the user interface and analyze the user request…summarizer… analyzers]; see with col. 13, lines 63-64 [Users…prompted…list of selectable options], col. 18, lines 5-7 [user may select…visualizing different aspects of semantics associated with data], col. 4, lines 39-61 [semantics captured from textual content…utilized for deriving…summarization, assessment, conclusions, or predictions…information…analyzed…generate summary], col. 12, lines 30 – col. 13, line 6 [ modeling layer interfaces with the user interface as well as the search layer in order to carry out the modeling tasks… receive a request from a user via the user interface and analyze the user request…summarizer…analyzers]; see also (col. 4, lines 33-37 [textual content…captured via semantic analysis of the textual content…semantic modeling may be applied based on any type of textual input – including one or more documents, a segment of a document, a written description, etc.,; with col. 39, lines 39-50 [user input…received…include…parse-able information…parsed…one or more words, phrases, or related concepts; with col. 38, lines 45-46 [label during the interaction with, e.g., semantic map (t-SNE)…tree map (k-means clustering)]]);
(S800) the processor executes the semantic analysis module, the search module conducts a matching search on the analysis results in the database module, and the search results are sent to the recommendation module to generate the recommended application trademark/intellectual property category (see citations above and also see citations for claim 5 above; and see col. 56, lines 22-24 [identify figures; with col. 66, lines 6-10 [analyze…semantic analyzer…review figures; with col. 28, line 66 – col. 29, line 38 [matching… utilize a dimensionality reduction via, e.g., an auto-encoder and neural network during modeling and training… link analysis algorithms with numerical weightings, distance between code vectors generated…scores which are converted to another…scoring metric…semantic “relevancy”], col. 30, line 38 – col. 31, line 20 [semantic vector representation… semantic vector may be derived based on a representation of data in a lower dimensional space associated with a document or an entity…conversion…pattern matches]], with col. 13, lines 7-15 [generates/regenerates results (text/figures/etc.,) on the user interface based on input parameters/requests; with col. 49, line 54 – col. 50, line 11 [modeled as multiple semantic vectors…matching techniques…matching…performed… pre-modeled entities or aggregated entities of multiple companies can be combined into one directed graph by linking/overlaying the nodes that meet certain criteria, e.g., relevance or time criteria…relevance]]]);
(S900) The processor executes the search module to conduct a second matching search in the database module based on the recommended application trademark/intellectual-property category, the second search results are sent to a search document generation module to generate a risk assessment report (col. 4, lines 28-9 [semantic based aggregated modeling, search, visualization, summarization], with col. 20, lines 4-9 [different types of textual information or documents…IP…trademarks]) with similar semantics may be searched and analyzed to determine which of the documents may pose risks…achieved by analyzing textual information for semantics and matching the semantics of a…description and that of the relevant documents], col. 16, lines 25-30 [classification…categories of classification], with col. 9, lines 21-28 [complied with in different sub-areas, the semantic information server…used to provide various services based on semantic analysis of the long document…segmented based on semantics of the text and each of such segments…then be classified; with col. 11, lines 16-17 [semantic modeling…classification code], with col. 20, lines 1-15 [modeling…trademarks…classified into…groups (categories)], col. 6, line 53 – col. 8, line 12 [intellectual property (IP)…study (report – includes assessment and analysis, etc.,)…clearance evaluation…clearing…risk (with checking enforceable IP rights)…textual information…analyzed to extract the semantics…similar semantics…determine…risks (service achieved…analyzing textual information for semantics and matching the semantics…assessment); see with col. 8, lines 40-46 [assessment…infringement (type of risk)…invalidity (type of risk)…freedom to operate (type of risk assessment)], with col. 14, lines 48-54 [applied filters…reranking element separately parsed into a secondary interface…ranking element influences either the original search criteria and/or the ranking of retrieval], with col. 12, line 66 – col. 13, line 6 [services (study/report)…include, e.g., a trending topics identifier 551, a classification skew detector 552, a semi-supervised learner 553, and a document summarizer…specialized services…include, e.g., a trending topics predictor 546, an entity analyzer 547, an infringement analyzer (risk), and a 112 analyzer (legal risk)]]; col. 28, line 66 – col. 29, line 38 [ranking…similarities…matched… retrieval ranking or scoring of each document or a set of documents, e.g., user ranking, user tagging, date ranking, citation ranking, term frequency inverse document frequency cosine similarity]).
Although Solmer discloses Applicant’s above limitations, Solmer discloses trademarks in various separate embodiments and most limitations are for all types of intellectual property (IP) (e.g. patents, trademarks, etc.,; where Solmer does disclose application of the steps to trademarks – see citations above – e.g. col. 20, lines 1-15 [modeling…trademarks…classified into…groups (categories)). However, it would be obvious to one of ordinary skill in the art to include and combine the various disclosed (albeit separately stated) embodiment and elements of trademarks to show Applicant’s claimed concept as trademarks are taught by Solmer itself (within the same reference) and since one of ordinary skill in the art at the time of the invention would have recognized that applying the known technique and concepts (intellectual property) of Solmer itself (same reference) would have yielded predictable results because the level of ordinary skill in the art demonstrated by the single reference applied shows the ability to incorporate such concepts and features into similar systems; and also since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Solmer does not explclity state user completes the login process through a login module.
Analogous are Jessen discloses user completes the login process through a login module (¶ 0124 [module first authenticates or validates the user…by examining a password and/or username submitted (login/sign-in) from the user]).
Therefore, it would be obvious to one of ordinary skill in the art to include in Solmer user completes the login process through a login module as taught by analogous art Jessen for security, privacy, and individualized use (so things are viewable and/or usable by the user only) since doing so could be performed readily by any person of ordinary skill in the art, with neither undue experimentation, nor risk of unexpected results (KSR-G/TSM); and also since one of ordinary skill in the art at the time of the invention would have recognized that applying the known technique and concepts of Jessen (it is old and well-known (and broadly used) type of secure use of application to have login and authentication technology of computing/computer based applications with login to individual accounts) would have yielded predictable results because the level of ordinary skill in the art demonstrated by the references applied shows the ability to incorporate such concepts and features into similar systems (KSR-D). (MPEP 2141).
Solmer does not explclity state trademark type and brand.
Analogous art Jessen discloses trademark type and brand (¶¶ 0068 [trademarks…logos], 0075 [descriptions, designs and logotype trademarks], 0076 [a design, shape or pattern…trademark; with 0053 [figures associated with a trademark, a wordmark, a distinctive logo, or a figurative mark]]).
Therefore, it would be obvious to one of ordinary skill in the art to include in Solmer trademark type and brand as taught by analogous art Jessen to holistically and efficiently consider relevant literature worldwide so as to optimally manage and mitigate risks since doing so could be performed readily by any person of ordinary skill in the art, with neither undue experimentation, nor risk of unexpected results (KSR-G/TSM); and also since each individual element and its function are shown in the prior art, albeit shown in separate references, the difference between the claimed subject matter and the prior art rests not on any individual element or function but in the very combination itself- that is in the substitution of the information regarding trademark type and brand/logo/etc., of the Jessen for the intellectual property in Solmer. Thus, the simple substitution of one known element for another producing a predictable result renders the claim obvious (KSR-B). (MPEP 2141).
As per claim 2, Solmer discloses the method according to claim 1, wherein further comprises the following step after step (S900): (S901) A risk management module regenerates text or graphics based on the similar text or graphics in the risk assessment report and the concept information of the user's brainstorm received by the input module (see citations above in claim 5 that already address this limitation; and also see (note that “regeneration” is producing similar text/figure based on the input parameter put into the system (search and reproduces relevant to what it requested); also note that the Applicant does not describe the term “brainstorming” in the specification and the term is used very broadly/open-ended (i.e. any concept can involve “brainstorming” and it is the user is inputting concepts) fig. 5A [see aggregate modeling layer with figure 5D]; and see col. 9, lines 23-42 [semantic analysis of the long document…standard may be segmented based on semantics of the text and each of such segments (concerning semantically distinction technology description) may then be classified…semantic information server…semantic analysis…segmented based on semantics of the text and each of such segments (concerning semantically distinction technology description) may then be classified], col. 56, lines 22-24 [identify figures; with col. 66, lines 6-10 [analyze…semantic analyzer…review figures], col. 21, line 60 – col. 22, line 11 [similarity threshold…cosine similarity… value may vary between −1 to 1, or 0-1 …value…define…the relevancy similarity threshold; with col. 23, lines 25-26 [scores of…similar documents (with figures analysis)…lower relevance scores…reduced further…based on a function of…similarity], with col. 13, lines 7-15 [generates/regenerates results (text/figures/etc.,) on the user interface based on input parameters/requests]], col. 27, lines 29-58 [and example – semantic search leveraging result ranking via artificial intelligence may typically be based on a textual description of a query (reflecting an intent of what to look for) or a different means of specifying what to look for, such as a document (e.g., a patent represented by a patent number)….allow a user to express a variety of means to indicate weighting criteria and options…textual description…natural language, document identifiers, as well as traditional filtering options (e.g., classification code, Boolean text or date filters, regular expression matching, or other syntactical filtering operations) may all be utilized to specify the parameters to be applied to the search…reviewing a portfolio of a company, a product of a company, a standard of a standard body, a person or an inventor, a university, or another entity, the relevant information is much more diverse and may include a variety types of information such as technical literature, patents, patent applications, webpages, or any other class of information…specified manually by a unique numerical identifier, e.g., Digital Object Identifier (DOI) number, Uniform Resource Identifier (URL), patent or patent application number, etc.]; see also col. 54, line 9 – col. 55, line 40; see also col. 28, line 40 – col. 29, line 13).
As per independent claim 3, Solmer discloses a trademark risk management system for receiving a user end that receives a user through operating an electronic device, the processor of the electronic device connects to a server via a network interface controller and executes an application program for category recommendation and risk management (Fig. 3B [IP management system], 3E-5A [shows IP management system; with fig. 12 [calculate classification]]; col. 6, lines 53-65 [intellectual property with categories], col. 20, lines 1-15 [modeling…trademarks…classified into…groups (categories); with col. 14, line 1 – col. 15, line 15, line 2 [cluster suggested…classification (filter and weighting)…automated suggestions]], col. 7, lines 51-54 [clearing risk…IP…determine risks (to clear or mitigate); with col. 47, lines 10-11 [maximizing benefits and minimizing…risks]]), the system at least comprises:
an input module for receiving the text content input by the user, converting the text content to a string for labeling processing, and sending a string information and recording the input language of the string information in a temporary memory (col. 4, lines 33-37 [textual content…captured via semantic analysis of the textual content…semantic modeling may be applied based on any type of textual input – including one or more documents, a segment of a document, a written description, etc.,; with col. 39, lines 39-50 [user input…received…include…parse-able information…parsed…one or more words, phrases, or related concepts; with col. 38, lines 45-46 [label during the interaction with, e.g., semantic map (t-SNE)…tree map (k-means clustering)]], with col. 55, lines 45-46 [discusses input language of intellectual property (document, etc.,), with col. 29, line 51-52 [recorded to memory and/or storage media]]]; col. 40, lines 32-37 [text input], col. 48, lines 19-34 [algorithm…semantic map…caching techniques (memory)…t-SNE positions…recorded and stored in memory or disk such that, upon adding additional data points (whether new documents upon an updated search at a later date, or a supplemental data set from the same initial data), only the new positions are calculated and the new items are merged into the original map (possibly with a visually distinct overlay, e.g., z-axis, alpha-blend, color)]);
a semantic analysis module that receives the string information and analyzes and segments it through a natural language database to generate and send semantic analysis results (col. 9, lines 23-42 [semantic analysis of the long document. For example, a standard may be segmented based on semantics of the text and each of such segments (concerning semantically distinction technology description) may then be classified…semantic information server…semantic analysis of the long document. For example, a standard may be segmented based on semantics of the text and each of such segments (concerning semantically distinction technology description) may then be classified; see with col. 30, line 22 – col. 31, line 38 [discusses database(s) linked to language processing]]; col. 27, lines 29-58 [and example – semantic search leveraging result ranking via artificial intelligence may typically be based on a textual description of a query (reflecting an intent of what to look for) or a different means of specifying what to look for, such as a document (e.g., a patent represented by a patent number)….allow a user to express a variety of means to indicate weighting criteria and options…textual description…natural language, document identifiers, as well as traditional filtering options (e.g., classification code, Boolean text or date filters, regular expression matching, or other syntactical filtering operations) may all be utilized to specify the parameters to be applied to the search…reviewing a portfolio of a company, a product of a company, a standard of a standard body, a person or an inventor, a university, or another entity, the relevant information is much more diverse and may include a variety types of information such as technical literature, patents, patent applications, webpages, or any other class of information…specified manually by a unique numerical identifier, e.g., Digital Object Identifier (DOI) number, Uniform Resource Identifier (URL), patent or patent application number, etc.]; see also col. 54, line 9 – col. 55, line 40);
a classification module that analyzes the industry category classification code for the semantic analysis results, connects to a database module to judge and generate at least one set of industry classification codes (see citations above and see col. 9, lines 21-28 [complied with in different sub-areas, the semantic information server…used to provide various services based on semantic analysis of the long document…segmented based on semantics of the text and each of such segments…then be classified; with col. 11, lines 16-17 [semantic modeling…classification code]; with col. 13, line 51 – col. 14, line 29 [classification filters…(with analysis/evaluation/judgement/etc.,)…classification code filter…classification code weighting]; with col. 20, lines 1-15 [modeling…trademarks…classified into…groups (categories)]]);
a search module that conducts a matching search in the database module based on the at least one set of industry classification codes and generates data of the matching search results (col. 4, lines 28-9 [semantic based aggregated modeling, search, visualization, summarization], col. 5, lines 64-65 [understand the trend of the industry sector…technologies], col. 7, lines 56 – col. 8, line 46 [information…analyzed to extract the semantics based on which documents (intellectual property documents – with can also be trademarks, see col. 20, lines 4-9 [different types of textual information or documents…IP…trademarks]) with similar semantics may be searched and analyzed to determine which of the documents may pose risks…achieved by analyzing textual information for semantics and matching the semantics of a…description and that of the relevant documents]);
an intellectual property information disclosure module that receives the data and further analyzes it statistically to generate basic intellectual property information (see citations above and see citations for claim 5 above; and see col. 8, lines 14-21 [assessed…evaluation…statistics…analyzed], col. 14, lines 66-67 [advanced…probabilistic (statistical)…methodology; with col. 28, lines 47-48 [engage in probabilistic matching]]);
a recommendation module that also receives the data and classifies and summarizes the types of intellectual property in the data to generate the types of intellectual property recommended for application (see citations above and see col. 4, lines 39-61 [semantics captured from textual content…utilized for deriving…summarization, assessment, conclusions, or predictions…information…analyzed…generate summary], col. 12, lines 30 – col. 13, line 6 [ modeling layer interfaces with the user interface as well as the search layer in order to carry out the modeling tasks…receive a request from a user via the user interface and analyze the user request…summarizer…analyzer; see with col. 13, line 65 – col. 14, line 65 [ambiguous…word sense induction (WSI) and/or word sense disambiguation (WSD) (particularly when time-influenced, as in a preferred embodiment) influences retrieval of a document set, by, for example, using identified word senses in the documents included by the date filter compared to senses identified in documents excluded by the date filter, to disambiguate the polysemous words (discuses ambiguity and disambiguation); with col. 16, lines 25-30 [classification…categories of classification], with col. 9, lines 21-28 [complied with in different sub-areas, the semantic information server…used to provide various services based on semantic analysis of the long document…segmented based on semantics of the text and each of such segments…then be classified; with col. 11, lines 16-17 [semantic modeling…classification code], with col. 20, lines 1-15 [modeling…trademarks…classified into…groups (categories)]]]); and
wherein, when the user selects a trademark/intellectual-property from the types of intellectual property recommended for application, the input module receives the description input by the user again (Fig. 3B [IP management system], 3E-5A [shows IP management system]; col. 6, lines 53-65 [intellectual property with categories], col. 20, lines 1-15 [IP…applications of patents/trademarks/copyrights,]), and
the semantic analysis module receives the string information about the description, analyzes and segments it to generate and send the analysis results of the technical description, the search module conducts a matching search on the analysis results in the database module, and transmits the search results to the recommendation module to generate the recommended application trademark/intellectual-property category (see citations above and also see col. 28, line 40 – col. 30, line 57 [a flowchart of an exemplary process for using the probabilistic parser and unified search box…parse-able information, e.g., a standard of interest….traverse market intelligence data, such as corporate tree, market newsfeed, to include in modeling/retrieval…aggregated models are generated…dynamically generate semantic models for entities and save in the storage….semantic models for the entities are previously generated, such aggregated entity models are retrieved and used to build, at 807, a unified query based on the escaped text, the aggregated models, and/or any weighting and filtering requirements in the query…aggregated semantic feature vectors and semantic signatures may be weighted or selected based on the topics in the escaped text…search is performed based on the query and relevant entities with matched technologies or products associated with the entity are identified…semantic versus filtering or syntactical…semantic vector may be clustered into related concepts and presented in a searchable fashion to the user via conceptual proximity in order to create a ranked list with an indication of word clustering and/or visualization techniques (such as k-means clustering, or t-SNE visualization) employed; with col. 32, lines [trending topic…documents…popularity…measured…compared …over set period of time (pattern learning described); with col. 30, line 22 – col. 31, line 28 [using language models], with col. 51, lines 36-64 [[different data manipulation, visualization, or interaction tools may be activated based on any of the entities displayed in the semantic map or documents associated with any selected entities…user may select entity…request to perform trending topics analysis (pattern learning done for trend analysis – see col. 53, lines 7-11 [perform trending topic analysis])]]], col. 4, lines 33-37 [textual content…captured via semantic analysis of the textual content…semantic modeling may be applied based on any type of textual input – including one or more documents, a segment of a document, a written description, etc.,; with col. 39, lines 39-50 [user input…received…include…parse-able information…parsed…one or more words, phrases, or related concepts], col. 56, lines 22-24 [identify figures], col. 66, lines 6-10 [analyze…semantic analyzer…review figures]),
the search module conducts a second matching search in the database module based on the recommended application trademark category, (col. 4, lines 28-9 [semantic based aggregated modeling, search, visualization, summarization], with col. 20, lines 4-9 [different types of textual information or documents…IP…trademarks]) with similar semantics may be searched and analyzed to determine which of the documents may pose risks…achieved by analyzing textual information for semantics and matching the semantics of a…description and that of the relevant documents], col. 16, lines 25-30 [classification…categories of classification], with col. 9, lines 21-28 [complied with in different sub-areas, the semantic information server…used to provide various services based on semantic analysis of the long document…segmented based on semantics of the text and each of such segments…then be classified; with col. 11, lines 16-17 [semantic modeling…classification code], with col. 20, lines 1-15 [modeling…trademarks…classified into…groups (categories)], col. 6, line 53 – col. 8, line 12 [intellectual property (IP)…study (report – includes assessment and analysis, etc.,)…clearance evaluation…clearing…risk (with checking enforceable IP rights)…textual information…analyzed to extract the semantics…similar semantics…determine…risks (service achieved…analyzing textual information for semantics and matching the semantics…assessment); see with col. 8, lines 40-46 [assessment…infringement (type of risk)…invalidity (type of risk)…freedom to operate (type of risk assessment)], with col. 14, lines 48-54 [applied filters…reranking element separately parsed into a secondary interface…ranking element influences either the original search criteria and/or the ranking of retrieval], with col. 12, line 66 – col. 13, line 6 [services (study/report)…include, e.g., a trending topics identifier 551, a classification skew detector 552, a semi-supervised learner 553, and a document summarizer…specialized services…include, e.g., a trending topics predictor 546, an entity analyzer 547, an infringement analyzer (risk), and a 112 analyzer (legal risk)]]; col. 28, line 66 – col. 29, line 38 [ranking…similarities…matched… retrieval ranking or scoring of each document or a set of documents, e.g., user ranking, user tagging, date ranking, citation ranking, term frequency inverse document frequency cosine similarity]) and
transmits the second search results to a search document generation module to generate a risk assessment report (Fig. 1 [data transmission shown]; col. 4, lines 28-9 [semantic based aggregated modeling, search, visualization, summarization], with col. 20, lines 4-9 [different types of textual information or documents…IP…trademarks]) with similar semantics may be searched and analyzed to determine which of the documents may pose risks…achieved by analyzing textual information for semantics and matching the semantics of a…description and that of the relevant documents], col. 16, lines 25-30 [classification…categories of classification], with col. 9, lines 21-28 [complied with in different sub-areas, the semantic information server…used to provide various services based on semantic analysis of the long document…segmented based on semantics of the text and each of such segments…then be classified; with col. 11, lines 16-17 [semantic modeling…classification code], with col. 20, lines 1-15 [modeling…trademarks…classified into…groups (categories)], col. 6, line 53 – col. 8, line 12 [intellectual property (IP)…study (report – includes assessment and analysis, etc.,)…clearance evaluation…clearing…risk (with checking enforceable IP rights)…textual information…analyzed to extract the semantics…similar semantics…determine…risks (service achieved…analyzing textual information for semantics and matching the semantics…assessment); see with col. 8, lines 40-46 [assessment…infringement (type of risk)…invalidity (type of risk)…freedom to operate (type of risk assessment)], with col. 14, lines 48-54 [applied filters…reranking element separately parsed into a secondary interface…ranking element influences either the original search criteria and/or the ranking of retrieval], with col. 12, line 66 – col. 13, line 6 [services (study/report)…include, e.g., a trending topics identifier 551, a classification skew detector 552, a semi-supervised learner 553, and a document summarizer…specialized services…include, e.g., a trending topics predictor 546, an entity analyzer 547, an infringement analyzer (risk), and a 112 analyzer (legal risk)]]; col. 28, line 66 – col. 29, line 38 [ranking…similarities…matched… retrieval ranking or scoring of each document or a set of documents, e.g., user ranking, user tagging, date ranking, citation ranking, term frequency inverse document frequency cosine similarity]).
Although Solmer discloses Applicant’s above limitations, Solmer discloses trademarks in various separate embodiments and most limitations are for all types of intellectual property (IP) (e.g. patents, trademarks, etc.,; where Solmer does disclose application of the steps to trademarks – see citations above – e.g. col. 20, lines 1-15 [modeling…trademarks…classified into…groups (categories)). However, it would be obvious to one of ordinary skill in the art to include and combine the various disclosed (albeit separately stated) embodiment and elements of trademarks to show Applicant’s claimed concept as trademarks are taught by Solmer itself (within the same reference) and since one of ordinary skill in the art at the time of the invention would have recognized that applying the known technique and concepts (intellectual property) of Solmer itself (same reference) would have yielded predictable results because the level of ordinary skill in the art demonstrated by the single reference applied shows the ability to incorporate such concepts and features into similar systems; and also since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Solmer does not explclity state a login module through which the user operates the electronic device to authenticate the identity and user completes the login process through a login module.
Analogous are Jessen discloses a login module through which the user operates the electronic device to authenticate the identity and user completes the login process through a login module (¶ 0124 [module first authenticates or validates the user…by examining a password and/or username submitted (login/sign-in) from the user]).
Therefore, it would be obvious to one of ordinary skill in the art to include in Solmer a login module through which the user operates the electronic device to authenticate the identity and user completes the login process through a login module as taught by analogous art Jessen for security, privacy, and individualized use (so things are viewable and/or usable by the user only) since doing so could be performed readily by any person of ordinary skill in the art, with neither undue experimentation, nor risk of unexpected results (KSR-G/TSM); and also since one of ordinary skill in the art at the time of the invention would have recognized that applying the known technique and concepts of Jessen (it is old and well-known (and broadly used) type of secure use of application to have login and authentication technology of computing/computer based applications with login to individual accounts) would have yielded predictable results because the level of ordinary skill in the art demonstrated by the references applied shows the ability to incorporate such concepts and features into similar systems (KSR-D). (MPEP 2141).
Solmer does not explclity state trademark type and brand.
Analogous art Jessen discloses trademark type and brand (¶¶ 0068 [trademarks…logos], 0075 [descriptions, designs and logotype trademarks], 0076 [a design, shape or pattern…trademark; with 0053 [figures associated with a trademark, a wordmark, a distinctive logo, or a figurative mark]]).
Therefore, it would be obvious to one of ordinary skill in the art to include in Solmer trademark type and brand as taught by analogous art Jessen to holistically and efficiently consider relevant literature worldwide so as to optimally manage and mitigate risks since doing so could be performed readily by any person of ordinary skill in the art, with neither undue experimentation, nor risk of unexpected results (KSR-G/TSM); and also since each individual element and its function are shown in the prior art, albeit shown in separate references, the difference between the claimed subject matter and the prior art rests not on any individual element or function but in the very combination itself- that is in the substitution of the information regarding trademark type and brand/logo/etc., of the Jessen for the intellectual property in Solmer. Thus, the simple substitution of one known element for another producing a predictable result renders the claim obvious (KSR-B). (MPEP 2141).
As per claim 4, claim 4 discloses substantially similar limitations as claim 2 above; and therefore claim 4 is rejected under the same rationale and reasoning as presented above for claim 2.
As per independent claim 14, Solmer discloses a trademark risk management method, comprising the following steps:
(2)The user creates a case order through the electronic device and selects the first target country, the information in the case order is updated to a temporary memory on a regular basis (col. 12, lines 4-58 [request…service…request…analyzed (order processing)…parameters…obtained from…user…user’s request…carried out (order processing)… receive a request from a user via the user interface and analyze the user request to determine, e.g., which service is to be activated and what data are needed for the selected service; see with col. 45, lines 53-57 [criteria received (by user)…criteria include…country of interest], col. 16, lines 51-55 [criteria…different countries; also col. 20, lines 3-9 [regarding various target countries]]]);
(3)The user inputs the description text or image through the input module of the electronic device, converts the description text into a string, and performs labeling processing to form string information, the input language of the string information is recorded in the temporary memory (col. 4, lines 33-37 [textual content…captured via semantic analysis of the textual content…semantic modeling may be applied based on any type of textual input – including one or more documents, a segment of a document, a written description, etc.,; with col. 39, lines 39-50 [user input…received…include…parse-able information…parsed…one or more words, phrases, or related concepts; with col. 38, lines 45-46 [label during the interaction with, e.g., semantic map (t-SNE)…tree map (k-means clustering)]], with col. 55, lines 45-46 [discusses input language of intellectual property (document, etc.,), with col. 29, line 51-52 [recorded to memory and/or storage media]]]; col. 40, lines 32-37 [text input], col. 48, lines 19-34 [algorithm…semantic map…caching techniques (memory)…t-SNE positions…recorded and stored in memory or disk such that, upon adding additional data points (whether new documents upon an updated search at a later date, or a supplemental data set from the same initial data), only the new positions are calculated and the new items are merged into the original map (possibly with a visually distinct overlay, e.g., z-axis, alpha-blend, color)]);
(4)A semantic analysis module performs semantic analysis on the string information, and a classification module performs trademark classification on the string information; in step 4, the following steps are further comprised: (411)The semantic analysis module analyzes and segments the string information to generate semantic analysis results (col. 9, lines 23-42 [semantic analysis of the long document. For example, a standard may be segmented based on semantics of the text and each of such segments (concerning semantically distinction technology description) may then be classified…semantic information server…semantic analysis of the long document. For example, a standard may be segmented based on semantics of the text and each of such segments (concerning semantically distinction technology description) may then be classified; see with col. 30, line 22 – col. 31, line 38 [discusses database(s) linked to language processing]]; col. 27, lines 29-58 [and example – semantic search leveraging result ranking via artificial intelligence may typically be based on a textual description of a query (reflecting an intent of what to look for) or a different means of specifying what to look for, such as a document (e.g., a patent represented by a patent number)….allow a user to express a variety of means to indicate weighting criteria and options…textual description…natural language, document identifiers, as well as traditional filtering options (e.g., classification code, Boolean text or date filters, regular expression matching, or other syntactical filtering operations) may all be utilized to specify the parameters to be applied to the search…reviewing a portfolio of a company, a product of a company, a standard of a standard body, a person or an inventor, a university, or another entity, the relevant information is much more diverse and may include a variety types of information such as technical literature, patents, patent applications, webpages, or any other class of information…specified manually by a unique numerical identifier, e.g., Digital Object Identifier (DOI) number, Uniform Resource Identifier (URL), patent or patent application number, etc.]; see also col. 54, line 9 – col. 55, line 40);
(412)The classification module classifies the string information based on the semantic analysis results to generate at least one set of intellectual property classification codes (see citations above and see col. 9, lines 21-28 [complied with in different sub-areas, the semantic information server…used to provide various services based on semantic analysis of the long document…segmented based on semantics of the text and each of such segments…then be classified; with col. 11, lines 16-17 [semantic modeling…classification code]; with col. 13, line 51 – col. 14, line 29 [classification filters…(with analysis/evaluation/judgement/etc.,)…classification code filter…classification code weighting]; with col. 20, lines 1-15 [modeling…trademarks…classified into…groups (categories)]]);
(413)The category recommendation module combines the semantic analysis module and the classification module to parse the string information into intellectual property category recommendation information, and finally generates the recommended application intellectual property category (see citations above and see col. 13, line 65 – col. 14, line 65 [ambiguous…word sense induction (WSI) and/or word sense disambiguation (WSD) (particularly when time-influenced, as in a preferred embodiment) influences retrieval of a document set, by, for example, using identified word senses in the documents included by the date filter compared to senses identified in documents excluded by the date filter, to disambiguate the polysemous words (discuses ambiguity and disambiguation); with col. 16, lines 25-30 [classification…categories of classification], with col. 9, lines 21-28 [complied with in different sub-areas, the semantic information server…used to provide various services based on semantic analysis of the long document…segmented based on semantics of the text and each of such segments…then be classified; with col. 11, lines 16-17 [semantic modeling…classification code], with col. 20, lines 1-15 [modeling…trademarks…classified into…groups (categories)]]);
(421)The figure search unit and the text search unit receive the user's input text and/or image and search in the database module to compare and search for intellectual property precedents, generate a search file, and further filter out precedents with a similarity higher than a risk value (see citation above and see citations in claim 5 above; also see col. 6, line 55 – col. 7, line 13 [intellectual property (IP) related…involving searching…areas…characterizing the semantics of the documents…criteria…processing such documents to understand the semantics of the documents and extracting/determining information based on such semantics…semantic information server…launch a search on…documents…analyze…materials may be analyzed for semantics (to make determinations); see with col. 56, lines 22-24 [identify figures], col. 66, lines 6-10 [analyze…semantic analyzer…review figures], col. 6, line 53 – col. 8, line 12 [intellectual property (IP)…study (report – includes assessment and analysis, etc.,)…clearance evaluation…clearing…risk (with checking enforceable IP rights)…textual information…analyzed to extract the semantics…similar semantics…determine…risks (service achieved…analyzing textual information for semantics and matching the semantics…assessment), col. 4, lines 33-37 [textual content…captured via semantic analysis of the textual content…semantic modeling may be applied based on any type of textual input – including one or more documents, a segment of a document, a written description, etc.,], col. 56, lines 22-24 [identify figures; with col. 66, lines 6-10 [analyze…semantic analyzer…review figures], col. 21, line 60 – col. 22, line 11 [a similarity threshold greater than a threshold value is utilized to create a document-pair connection (in some models, such as those using in part cosine similarity, such a value may vary between −1 to 1, or 0-1 when cutting off negatively correlated documents from retrieval between −1 and 0)….value used to…define the relevancy similarity threshold…relevancy similarity values may be constant for modeling across all documents or vary based on criteria, such as distribution of concept relevancy, number of concepts], with col. 13, lines 7-15 [generates/regenerates results (text/figures/etc.,) on the user interface based on input parameters/requests]], col. 28, line 67 – col. 29, line 38 [similarity…matched…above certain threshold…result set chosen…retrieval of a result which may incorporate multiple or arbitrary combinations of methodologies into result retrieval ranking or scoring…cosine similarity]); In step 421, the following step is further comprised:
(5)The risk management module regenerates the text and/or image based on the learning of the content learning module and based on the precedents matched by the figure search unit and the text search unit in the database module (see citations above and also see col. 28, line 40 – col. 30, line 57 [a flowchart of an exemplary process for using the probabilistic parser and unified search box…parse-able information, e.g., a standard of interest….traverse market intelligence data, such as corporate tree, market newsfeed, to include in modeling/retrieval…aggregated models are generated…dynamically generate semantic models for entities and save in the storage….semantic models for the entities are previously generated, such aggregated entity models are retrieved and used to build, at 807, a unified query based on the escaped text, the aggregated models, and/or any weighting and filtering requirements in the query…aggregated semantic feature vectors and semantic signatures may be weighted or selected based on the topics in the escaped text…search is performed based on the query and relevant entities with matched technologies or products associated with the entity are identified…semantic versus filtering or syntactical…semantic vector may be clustered into related concepts and presented in a searchable fashion to the user via conceptual proximity in order to create a ranked list with an indication of word clustering and/or visualization techniques (such as k-means clustering, or t-SNE visualization) employed; with col. 32, lines [trending topic…documents…popularity…measured…compared …over set period of time (pattern learning described); with col. 30, line 22 – col. 31, line 28 [using language models], with col. 51, lines 36-64 [[different data manipulation, visualization, or interaction tools may be activated based on any of the entities displayed in the semantic map or documents associated with any selected entities…user may select entity…request to perform trending topics analysis (pattern learning done for trend analysis – see col. 53, lines 7-11 [perform trending topic analysis])]]], col. 4, lines 33-37 [textual content…captured via semantic analysis of the textual content…semantic modeling may be applied based on any type of textual input – including one or more documents, a segment of a document, a written description, etc.,; with col. 39, lines 39-50 [user input…received…include…parse-able information…parsed…one or more words, phrases, or related concepts], col. 56, lines 22-24 [identify figures], col. 66, lines 6-10 [analyze…semantic analyzer…review figures]).
Although Solmer discloses Applicant’s above limitations, Solmer discloses trademarks in various separate embodiments and most limitations are for all types of intellectual property (IP) (e.g. patents, trademarks, etc.,; where Solmer does disclose application of the steps to trademarks – see citations above – e.g. col. 20, lines 1-15 [modeling…trademarks…classified into…groups (categories)). However, it would be obvious to one of ordinary skill in the art to include and combine the various disclosed (albeit separately stated) embodiment and elements of trademarks to show Applicant’s claimed concept as trademarks are taught by Solmer itself (within the same reference) and since one of ordinary skill in the art at the time of the invention would have recognized that applying the known technique and concepts (intellectual property) of Solmer itself (same reference) would have yielded predictable results because the level of ordinary skill in the art demonstrated by the single reference applied shows the ability to incorporate such concepts and features into similar systems; and also since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Solmer does not explclity state user logs in to the electronic device and authenticates the user's identity and identity information.
Analogous are Jessen discloses user logs in to the electronic device and authenticates the user's identity and identity information (¶ 0124 [module first authenticates or validates the user…by examining a password and/or username submitted (login/sign-in) from the user]).
Therefore, it would be obvious to one of ordinary skill in the art to include in Solmer user logs in to the electronic device and authenticates the user's identity and identity information as taught by analogous art Jessen for security, privacy, and individualized use (so things are viewable and/or usable by the user only) since doing so could be performed readily by any person of ordinary skill in the art, with neither undue experimentation, nor risk of unexpected results (KSR-G/TSM); and also since one of ordinary skill in the art at the time of the invention would have recognized that applying the known technique and concepts of Jessen (it is old and well-known (and broadly used) type of secure use of application to have login and authentication technology of computing/computer based applications with login to individual accounts) would have yielded predictable results because the level of ordinary skill in the art demonstrated by the references applied shows the ability to incorporate such concepts and features into similar systems (KSR-D). (MPEP 2141).
As per claim 17, Solmer discloses the method according to claim 14, wherein further comprises the following steps in step:
after the user inputs the brainstorming concept for the trademark name or image through the input module, the pattern generation unit combines the semantic analysis module to analyze the brainstorming concept and translate it into pattern generation language ((note that the Applicant does not describe the term “brainstorming” in the specification and the term is used very broadly/open-ended (i.e. any concept can involve “brainstorming” and it is the user is inputting concepts)); see citations above and see (see citations above and also see col. 4, lines 33-37 [textual content…captured via semantic analysis of the textual content…semantic modeling may be applied based on any type of textual input – including one or more documents, a segment of a document, a written description, etc.,; with col. 39, lines 39-50 [user input…received…include…parse-able information…parsed…one or more words, phrases, or related concepts], col. 56, lines 22-24 [identify figures], col. 66, lines 6-10 [analyze…semantic analyzer…review figures], col. 12, line 66 – col. 14, line 35 [trending topics identifier 551, a classification skew detector…summarizer…trending topics predictor…concept cluster…code filter; with col. 16, lines 34-27 [application…detection of trending topics]]),
the pattern generation language is used to generate the pattern code corresponding to the brainstorming concept (working of semantic information server of Solmer) (see citations above and see col. 8, line 58 – col. 9, line 57 [semantic information server…analyzing (see citations above for discussion on semantic information server)…providing assessment…estimated market trend (pattern code)… semantic information server…semantic information services that the semantic information server…identify trends in technologies and/or industries, semantically model the certain targets, profile certain targets]),
the pattern code is then compiled by the compiler unit to generate the regenerated pattern corresponding to the brainstorming concept ((note that compiler unit is just a generic computer element to compile software (as no other definition is provided in the specification)) col. 28, line 40 – col. 30, line 57 [a flowchart of an exemplary process for using the probabilistic parser and unified search box…parse-able information, e.g., a standard of interest….traverse market intelligence data, such as corporate tree, market newsfeed, to include in modeling/retrieval…aggregated models are generated…dynamically generate semantic models for entities and save in the storage….semantic models for the entities are previously generated, such aggregated entity models are retrieved and used to build, at 807, a unified query based on the escaped text, the aggregated models, and/or any weighting and filtering requirements in the query…aggregated semantic feature vectors and semantic signatures may be weighted or selected based on the topics in the escaped text…search is performed based on the query and relevant entities with matched technologies or products associated with the entity are identified…semantic versus filtering or syntactical…semantic vector may be clustered into related concepts and presented in a searchable fashion to the user via conceptual proximity in order to create a ranked list with an indication of word clustering and/or visualization techniques (such as k-means clustering, or t-SNE visualization) employed; with col. 32, lines [trending topic…documents…popularity…measured…compared…over set period of time (pattern learning described); with col. 30, line 22 – col. 31, line 28 [using language models], with col. 51, lines 36-64 [[different data manipulation, visualization, or interaction tools may be activated based on any of the entities displayed in the semantic map or documents associated with any selected entities…user may select entity…request to perform trending topics analysis (pattern learning done for trend analysis – see col. 53, lines 7-11 [perform trending topic analysis])]]], col. 4, lines 33-37 [textual content…captured via semantic analysis of the textual content…semantic modeling may be applied based on any type of textual input – including one or more documents, a segment of a document, a written description, etc.,; with col. 39, lines 39-50 [user input…received…include…parse-able information…parsed…one or more words, phrases, or related concepts], col. 56, lines 22-24 [identify figures], col. 66, lines 6-10 [analyze…semantic analyzer…review figures]),
during the regeneration process of the pattern, the figure search unit compares the regenerated pattern with the precedents that have been searched out, this ensures that the regenerated pattern has a similarity with the precedents that is below a set value ((note that “regeneration” is producing similar text/figure based on the input parameter put into the system (search and reproduces relevant to what it requested)) col. 56, lines 22-24 [identify figures; with col. 66, lines 6-10 [analyze…semantic analyzer…review figures], col. 21, line 60 – col. 22, line 11 [similarity threshold…cosine similarity… value may vary between −1 to 1, or 0-1 …value…define…the relevancy similarity threshold; with col. 23, lines 25-26 [scores of…similar documents (with figures analysis)…lower relevance scores…reduced further…based on a function of…similarity], with col. 13, lines 7-15 [generates/regenerates results (text/figures/etc.,) on the user interface based on input parameters/requests]], see also col. 28, line 40 – col. 29, line 13);
the text generation unit combines the semantic analysis module to analyze the brainstorming concept (see citations above for claim 14 and claim 5 and see fig. 5A [see aggregate modeling layer with figure 5D]; and see col. 9, lines 23-42 [semantic analysis of the long document…standard may be segmented based on semantics of the text and each of such segments (concerning semantically distinction technology description) may then be classified…semantic information server…semantic analysis…segmented based on semantics of the text and each of such segments (concerning semantically distinction technology description) may then be classified; see with col. 30, line 22 – col. 31, line 38 [discusses database(s) linked to language processing]]; col. 27, lines 29-58 [and example – semantic search leveraging result ranking via artificial intelligence may typically be based on a textual description of a query (reflecting an intent of what to look for) or a different means of specifying what to look for, such as a document (e.g., a patent represented by a patent number)….allow a user to express a variety of means to indicate weighting criteria and options…textual description…natural language, document identifiers, as well as traditional filtering options (e.g., classification code, Boolean text or date filters, regular expression matching, or other syntactical filtering operations) may all be utilized to specify the parameters to be applied to the search…reviewing a portfolio of a company, a product of a company, a standard of a standard body, a person or an inventor, a university, or another entity, the relevant information is much more diverse and may include a variety types of information such as technical literature, patents, patent applications, webpages, or any other class of information…specified manually by a unique numerical identifier, e.g., Digital Object Identifier (DOI) number, Uniform Resource Identifier (URL), patent or patent application number, etc.]; see also col. 54, line 9 – col. 55, line 40), and then
regenerates the text based on the content learning module, the text generation unit also compares the regenerated text with the precedents that have been searched out, this ensures that the regenerated text has a similarity with the precedents that is below a set value (see citations above and see (see citations above and see col. 6, line 53 – col. 8, line 12 textual information…analyzed to extract the semantics…similar semantics…determine…risks (service achieved…analyzing textual information for semantics and matching the semantics…assessment); see with col. 8, lines 40-46 [assessment…infringement (type of risk)…invalidity (type of risk)…freedom to operate (type of risk assessment)], col. 28, line 66 – col. 29, line 38 [ranking…similarities…matched… retrieval ranking or scoring of each document or a set of documents, e.g., user ranking, user tagging, date ranking, citation ranking, term frequency inverse document frequency cosine similarity], col. 21, line 60 – col. 22, line 11 [similarity threshold…cosine similarity…value may vary between −1 to 1, or 0-1 …value…define…the relevancy similarity threshold], with col. 13, lines 7-15 [describes generating/regenerating results (text/figures/etc.,) on the user interface based on input parameters/requests]]; also see col. 28, line 40 – col. 29, line 13 [an example shown with details])).
Claims 7-8, 13, 15 are rejected under 35 U.S.C. 103 as being unpatentable over Solmer (US 12,141,732) in view of Jessen et al., (US 2016/0350886), further in view of Plotkin (US 2024/0211685), further in view of Saito (US 2020/0401769).
As per claim 7, Solmer in view of Jessen further in view of Plotkin discloses the system according to claim 5 and disclose judgment module input language of the string information of first target country (see citations above for claim 5). But neither Solmer nor Jessen nor Plotkin state determines whether the same as the official language of the target country.
Analogous art Saito discloses same as the official language of the target country (¶¶ 0025 [Japanese, English, Chinese, German, and other “official languages” used in various countries]).
Therefore, it would be obvious to one of ordinary skill in the art to include in Solmer in view of Jessen further in view of Plotkin official language of the target country as taught by analogous art Saito to holistically and efficiently consider relevant literature worldwide so as to optimally manage and mitigate risks since doing so could be performed readily by any person of ordinary skill in the art, with neither undue experimentation, nor risk of unexpected results (KSR-G/TSM); and also since one of ordinary skill in the art at the time of the invention would have recognized that applying the known technique and concepts of Saito would have yielded predictable results because the level of ordinary skill in the art demonstrated by the references applied shows the ability to incorporate such concepts and features into similar systems (KSR-D). (MPEP 2141).
As per claim 8, Solmer in view of Jessen further in view of Plotkin discloses the system according to claim 5 and disclose judgment module input language of the string information of first target country and trademark and figure/image and text analysis and searching (see citations above for claim 5). However, neither Solmer nor Jessen disclose wherein if the language module determines that the input language of the string information is not the same as the official language of the first target country, the string information is translated using a translation module, in addition, the language of the trademark image in the final search document is translated back to the input language of the string information using the translation module.
Plotkin discloses determines that the input language of the string information and the information is translated using a translation module and translating information back to the original (input) language using the translation module (¶ 0413 [Back-translation…involves translate the input text string into another language using machine translation systems…then translating…back to the original language]).
Therefore, it would be obvious to one of ordinary skill in the art to include in Solmer in view of Jessen determines that the input language of the string information and the information is translated using a translation module and translating information back to the original (input) language using the translation module as taught by analogous art Plotkin to accurately process relevant document that may be in different languages and provide optimized results/insights on the data that is analyzed since doing so could be performed readily by any person of ordinary skill in the art, with neither undue experimentation, nor risk of unexpected results (KSR-G/TSM); and also since one of ordinary skill in the art at the time of the invention would have recognized that applying the known technique and concepts of Plotkin (it is common and well-known to use translations for understanding, generating, and summarizing texts/documents/literature across diverse applications) would have yielded predictable results because the level of ordinary skill in the art demonstrated by the references applied shows the ability to incorporate such concepts and features into similar systems (KSR-D). (MPEP 2141).
But neither Solmer nor Jessen nor Plotkin state wherein if the language is not the same as the official language of the first target country the language in the final document.
Analogous art Saito discloses wherein if the language is not the same as the official language of the first target country the language in the final document (¶¶ 0025 [Japanese, English, Chinese, German, and other “official languages” used in various countries; with 0002 [translation…language… character string into a sentence in a second language as a target language], 0039, 0050 [language conversion database 20 stores language conversion data required to make the basic conversion from the first data D1 into the second data D2. As used herein, the “language conversion data” is data defining a basic conversion rule in terms of words and grammar, for example, which is required to change the classification of the language (i.e., to make translation)], 0057-0058 [in relaying the language data, the data conversion system performs at least “translation” as the processing of converting the first data D1 as Japanese language data into the second data D2 as English language data]]).
Therefore, it would be obvious to one of ordinary skill in the art to include in Solmer in view of Jessen further in view of Plotkin official language of the target country as taught by analogous art Saito to holistically and efficiently consider relevant literature worldwide so as to optimally manage and mitigate risks since doing so could be performed readily by any person of ordinary skill in the art, with neither undue experimentation, nor risk of unexpected results (KSR-G/TSM); and also since one of ordinary skill in the art at the time of the invention would have recognized that applying the known technique and concepts of Saito would have yielded predictable results because the level of ordinary skill in the art demonstrated by the references applied shows the ability to incorporate such concepts and features into similar systems (KSR-D). (MPEP 2141).
As per claim 13, Solmer discloses the system according to claim 8, wherein the language judgement module further comprises a cross-country conversion module, after the user selects a second target country; and analyzing the trademark/intellectual property name/data/information to the official language of the second target country before formatting (col. 12, lines 4-58 [request…service…request…analyzed (order processing)…parameters…obtained from…user…user’s request…carried out (order processing)… receive a request from a user via the user interface 505 and analyze the user request to determine, e.g., which service is to be activated and what data are needed for the selected service; see with col. 45, lines 53-57 [criteria received (by user)…criteria include…country of interest; with col. 46, lines 31-38 [assessment with additional countries of interest]], col. 16, lines 51-55 [criteria…different countries; also col. 20, lines 3-9 [regarding various target countries]]]). Additionally, although Solmer discloses Applicant’s above limitations, Solmer discloses trademarks in various separate embodiments and most limitations are for all types of intellectual property (IP) (e.g. patents, trademarks, etc.,; where Solmer does disclose application of the steps to trademarks – see citations above – e.g. col. 20, lines 1-15 [modeling…trademarks…classified into…groups (categories)). However, it would be obvious to one of ordinary skill in the art to include and combine the various disclosed (albeit separately stated) embodiment and elements of trademarks to show Applicant’s claimed concept as trademarks are taught by Solmer itself (within the same reference) and since one of ordinary skill in the art at the time of the invention would have recognized that applying the known technique and concepts (intellectual property) of Solmer itself (same reference) would have yielded predictable results because the level of ordinary skill in the art demonstrated by the single reference applied shows the ability to incorporate such concepts and features into similar systems; and also since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
However, neither Solmer nor Jessen nor Plotkin state the language module first determines whether the input language of the string information is the same as the official language of the second target country, if not, the translation module is used to translate data/information to the official language of the second target country.
Analogous art Saito discloses the language module first determines whether the input language of the string information is the same as the official language of the second target country, if not, the translation module is used to translate data/information to the official language of the second target country (¶¶ 0025 [Japanese, English, Chinese, German, and other “official languages” used in various countries; with 0002 [translation…language… character string into a sentence in a second language as a target language], 0039, 0050 [language conversion database 20 stores language conversion data required to make the basic conversion from the first data D1 into the second data D2. As used herein, the “language conversion data” is data defining a basic conversion rule in terms of words and grammar, for example, which is required to change the classification of the language (i.e., to make translation)], 0057-0058 [in relaying the language data, the data conversion system 10 performs at least “translation” as the processing of converting the first data D1 as Japanese language data into the second data D2 as English language data]]).
Therefore, it would be obvious to one of ordinary skill in the art to include in Solmer in view of Jessen further in view of Plotkin the language module first determines whether the input language of the string information is the same as the official language of the second target country, if not, the translation module is used to translate data/information to the official language of the second target country as taught by analogous art Saito to holistically and efficiently consider relevant literature worldwide so as to optimally manage and mitigate risks since doing so could be performed readily by any person of ordinary skill in the art, with neither undue experimentation, nor risk of unexpected results (KSR-G/TSM); and also since one of ordinary skill in the art at the time of the invention would have recognized that applying the known technique and concepts of Saito would have yielded predictable results because the level of ordinary skill in the art demonstrated by the references applied shows the ability to incorporate such concepts and features into similar systems (KSR-D). (MPEP 2141).
As per claim 15, claim 15 discloses substantially similar limitations as claims 7 and 8 (combined) above; and therefore claim 15 is rejected under the same rationale and reasoning as presented above for claims 7 and 8.
Claims 10 and 16 are rejected under 35 U.S.C. 103 as being unpatentable over Solmer (US 12,141,732) in view of Jessen et al., (US 2016/0350886), further in view of Plotkin (US 2024/0211685), further in view of Fuxman et al., (US 10,146,768).
As per claim 10, Solmer in view of Jessen further in view of Plotkin discloses the system according to claim 5, but neither Solmer nor Jessen nor Plotkin wherein the conversion unit converts the figure from pixels to vectors.
Analogous art Fuxman discloses wherein the conversion unit converts the figure from pixels to vectors (col. 4, line 65 – col. 5, line 5 [language models… responses for the image based on a detected language… information related to the obtained image; see with col. 6, lines 2-10 [techniques…image feature vector determined directly from pixels of an obtained image], col. 23, line 62 – col. 24, line 2 [the image (e.g., image pixels) can be sent from messaging module to the feature vector generator which is described above with reference to FIG. 2…the feature vector generator determines a feature vector based on the image pixels]], col. 16, lines 36-44 [feature vector is a condensed numerical representation of the visual pixel content of the image…feature vector can be generated by a neural network based on the image pixel values]).
Therefore, it would be obvious to one of ordinary skill in the art to include in Solmer in view of Jessen further in view of Plotkin wherein the conversion unit converts the figure from pixels to vectors as taught by analogous art Fuxman to holistically, accurately (greater quality), and efficiently analyze relevant text and images (without the need to specifically recognize objects in an image) so as to optimally manage and mitigate risks since doing so could be performed readily by any person of ordinary skill in the art, with neither undue experimentation, nor risk of unexpected results (KSR-G/TSM); and also since one of ordinary skill in the art at the time of the invention would have recognized that applying the known technique and concepts (applying techniques with LLM and using neural network techniques for image analysis and vectorization in old and well-known) of Fuxman would have yielded predictable results because the level of ordinary skill in the art demonstrated by the references applied shows the ability to incorporate such concepts and features into similar systems (KSR-D). (MPEP 2141).
As per claim 16, Solmer discloses the method according to claim 14, wherein further comprises the following steps after step (421):
figure search unit and converting input into a vector representation after receiving the input information/data (col. 56, lines 22-24 [identify figures; with col. 16, lines 47-49 [information…generated in different forms…as semantic feature vectors], col. 66, lines 6-10 [analyze…semantic analyzer…review figures], col. 20, lines 24-54]); and
the figure comparison unit then compares the figure in the database module and filters out the figures with a similarity exceeding a set value, the filtered figures are considered to be intellectual property precedents (col. 56, lines 22-24 [identify figures; with col. 66, lines 6-10 [analyze…semantic analyzer…review figures], col. 21, line 60 – col. 22, line 11 [ a similarity threshold greater than a threshold value is utilized to create a document-pair connection (in some models, such as those using in part cosine similarity, such a value may vary between −1 to 1, or 0-1 when cutting off negatively correlated documents from retrieval between −1 and 0)….value used to…define the relevancy similarity threshold…relevancy similarity values may be constant for modeling across all documents or vary based on criteria, such as distribution of concept relevancy, number of concepts], with col. 13, lines 7-15 [generates/regenerates results (text/figures/etc.,) on the user interface based on input parameters/requests]], col. 28, line 67 – col. 29, line 38 [similarity…matched…above certain threshold…result set chosen… retrieval of a result which may incorporate multiple or arbitrary combinations of methodologies into result retrieval ranking or scoring…cosine similarity]).
Analogous art Saito discloses the language module first determines whether the input language of the string information is the same as the official language of the second target country, if not, the translation module is used to translate data/information to the official language of the second target country (¶¶ 0025 [Japanese, English, Chinese, German, and other “official languages” used in various countries; with 0002 [translation…language… character string into a sentence in a second language as a target language], 0039, 0050 [language conversion database 20 stores language conversion data required to make the basic conversion from the first data D1 into the second data D2. As used herein, the “language conversion data” is data defining a basic conversion rule in terms of words and grammar, for example, which is required to change the classification of the language (i.e., to make translation)], 0057-0058 [in relaying the language data, the data conversion system 10 performs at least “translation” as the processing of converting the first data D1 as Japanese language data into the second data D2 as English language data]]).
However, neither Solmer nor Jessen nor Plotkin does not explicitly state converting figure into a vector representation.
Analogous art Fuxman discloses converting figure into a vector representation (col. 4, line 65 – col. 5, line 5 [language models… responses for the image based on a detected language… information related to the obtained image; see with col. 6, lines 2-10 [techniques…image feature vector determined directly from pixels of an obtained image], col. 23, line 62 – col. 24, line 2 [the image (e.g., image pixels) can be sent from messaging module to the feature vector generator which is described above with reference to FIG. 2…the feature vector generator determines a feature vector based on the image pixels]], col. 16, lines 36-44 [feature vector is a condensed numerical representation of the visual pixel content of the image…feature vector can be generated by a neural network based on the image pixel values]).
Therefore, it would be obvious to one of ordinary skill in the art to include in Solmer in view of Jessen further in view of Plotkin converting figure into a vector representation as taught by analogous art Fuxman to holistically, accurately (greater quality), and efficiently analyze relevant text and images (without the need to specifically recognize objects in an image) so as to optimally manage and mitigate risks since doing so could be performed readily by any person of ordinary skill in the art, with neither undue experimentation, nor risk of unexpected results (KSR-G/TSM); and also since one of ordinary skill in the art at the time of the invention would have recognized that applying the known technique and concepts (applying techniques with LLM and using neural network techniques for image analysis and vectorization in old and well-known) of Fuxman would have yielded predictable results because the level of ordinary skill in the art demonstrated by the references applied shows the ability to incorporate such concepts and features into similar systems (KSR-D). (MPEP 2141).
Conclusion
The prior art made of record on the PTO-892 and not relied upon is considered pertinent to applicant's disclosure. For example, some of the pertinent art is as follows:
Budzyn (US 2004/0230568): Discusses investigating intellectual property relating to a product, an entity and/or trademark, and, optionally, other information relating to same. In particular, the method relies on certain bits of information which are used to establish search criteria. In one exemplary use, using known computing technology, a searcher will simply input a product's trademark, and one or more lists of relevant patents can be generated, preferably arranged in a hierarchy of importance, along with other relevant information.
Eder (US 2004/0215551): Provides for defining, measuring and continuously monitoring the matrix of value and the matrix of risk for a multi-enterprise commercial organization. A complete matrix of value is developed for each enterprise in the organization using predictive models and vector creation algorithms. The matrices of enterprise value are then used to support the creation of scenarios that contain all enterprise risk factors. A series of scenarios under both normal and extreme conditions are then developed in order to develop a complete matrix of risk for each enterprise in the organization and the organization as a whole. The information from these matrices is then used to calculate and display the matrix of value for the organization, the matrix of risk for the organization and the efficient frontier for organization financial performance. Forecast changes to the organization and its environment are then mapped to the matrices of value and risk for the organization and analyzed using probabilistic simulation models.
Carter (US 2010/0250479): Provides an information gathering module, a semantic abstract generation module, and an intellectual property space identification module. The information gathering module can retrieve information pertaining to intellectual property activities within a particular technical field. The semantic abstract generation module can generate semantic abstracts based on the information retrieved by the information gathering module. The intellectual property space identification module can perform an evaluation of the particular technical field based on the generated semantic abstracts.
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/Gurkanwaljit Singh/
Primary Examiner, Art Unit 3625
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