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 .
Response to Amendment
This Final Rejection is filed response to Applicant Arguments/Remarks Made in an Amendment filed 1/12/2026.
Claims 6, 13, 19, 21, 24, 30, 33, 36, and 38 are amended.
Claims 21, 31, and 34 are canceled.
New Claims 40-42 are added.
Claims 1, 6, 8, 13, 15, 19, & 22, 24, 27, 30, 33, & 35-42 remain pending.
Response to Arguments
Argument 1, Applicant Argues on in Applicant Arguments/Remarks Made in an Amendment filed on 1/12/2026 pg. 12-15, that prior art fails to teach the primary claim limitations, “determining one or more competitors by eliminating at least one of the plurality of related businesses identified, wherein the at least one of the plurality of related businesses identified are eliminated based on a weighting of mentions of the one or more seed terms, wherein the weighting of the mentions is determined based on at least a recency of the mention, a form of the external documentation in which the mention is derived, and an engagement with the external documentation in which the mention is derived”.
Response to Argument 1, the examiner respectfully disagrees. While applicant argues that the definition of applicant’s specification definition of weighting is more specific than the weighting of McClusky.
Firstly, McClusky teaches eliminating competitive companies not relevant an identified problem specific companies by removing specific companies that may not have mentioned the user queried problem kernel or problem element.
This is supported by Col. 40, lines 7-10, Col. 41, lines 43-47, and Col. 43, Lines 20-37, “a user seeking to identify the unmet technical needs and/or technical problems associated with a specific company or organization or subset of companies or organizations, e.g….a current/potential competitor or set of current/potential competitors, may perform the following… causing the system to list or export the flagged problem kernels or problem elements for that technical area of interest, and/or the specific companies that have most mentioned that problem kernel or problem element… a user interface provides a user with the ability to selectively choose one or more of all these options, e.g., increasing trends being weighted higher from a problem intensity standpoint, trends with the highest recent growth rates, trends which are increasingly associated with corporations, etc. which can increase the likelihood a particular problem kernel will be scored high enough rise to the point that it is displayed”. Thus a user may weight certain results higher than others and thus eliminate certain query results, that may be competitor companies, due to how often those companies are mentioned in certain ways or how close a cosine similarity for a queried problem word is for a portion of text found in an associated document that may belong to a specific and now determined competitor company. The examiner notes that the BRI for, “a form of the external documentation in which the mention is derived, and an engagement with the external documentation in which the mention is derived”, would encompass how a user may actuate user interface objects either flag documents and elements ass not pertinent to the search or cause the system to display more data associated with a result, wherein the user may effectively weight data by selecting external documents from a form of document assigned to individuals and by selecting how keyword mentions may be found in specific document type (e.g., in conference proceedings or news articles). Wherein when a user selects to display more results with documents with keywords from news articles, the user is effectively selecting a type of engagement with from external documents of a news article documentation type.
While applicant argues that McClusky does not teach limitations related to dependent claim 39, “wherein the engagement with the external documentation in which the mention is derived is determined based on viewership, wherein the weighting of the one or more seed terms in the external documentation corresponds to the viewership of the external documentation”, it is noted that Rubchinsky is used to teach this limitation as Rubchinsky teaches recommending media documentation external to a client device, such as on a website database, and containing a query answer that may be given a weighted score based on the number of views by users. This is supported by para. [0063], “The answers and recommendations are captured to media scoring module 245 (910) which also tracks the number of views by users. The media scoring module 245 calculates a voting score for each media file based on the scaled answers, pop-to-drop ratio, percentage of video viewed, comments, social distribution, recommendations and number of views, and updates the website database 150… Media scoring module 245 then assigns a final ranking score to the media file on the results interface 250, based on the relative voting scores among all media files over time (960). Process in FIG. 7 is repeated per media file”.
While applicant argues that McClusky and Schwaber do not teach the primary claim limitation of , “generating a competitive analysis report, wherein the competitive analysis report includes at least a favorability of the target business in comparison to the one or more competitors and an aggregate competitive score over time which is continuously updated in real time based on additional seed terms identified in the external documentation related to the one or more competitors and manual changes to the plurality of seed terms by the user, wherein the competitive score over time is displayed to the user within the user interface utilizing visual insight”. It is noted that a combination of McClusky and Schwaber is used to teach the above limitation. In response to applicant's arguments against the references individually, one cannot show nonobviousness by attacking references individually where the rejections are based on combinations of references. See In re Keller, 642 F.2d 413, 208 USPQ 871 (CCPA 1981); In re Merck & Co., 800 F.2d 1091, 231 USPQ 375 (Fed. Cir. 1986). One would have been motivated to combine favorability metrics of Schwarber with keyword query and display of competitors related to a company problem that is performed, recognized, and thus updated in real-time of McClusky and would have had a reasonable expectation of success in doing so, because the combination saves a user time by quickly providing potentially advantageous and desirable information related to determining relative success of the companies.
Although the claims are interpreted in light of the specification, limitations from the specification are not read into the claims. See In re Van Geuns, 988 F.2d 1181, 26 USPQ2d 1057 (Fed. Cir. 1993).
Argument 2, Applicant Argues on in Applicant Arguments/Remarks Made in an Amendment filed on 1/12/2026 pg. 15-17, that prior art fails to teach the dependent claim 6. 13, and 19 limitations, ” determining whether the one or more seed terms are above or below a similarity threshold by performing a semantic relatedness analysis of the plurality of seed terms identified for the target business by tokenizing the plurality of seed terms using n-Gram tokenization and converting a plurality of tokenized seed terms into a plurality of vectors, wherein each of the plurality of vectors are compared using cosine similarity, and wherein one or more of the plurality of seed terms are determined to be above or below the similarity threshold based on a cosine similarity distance and requiring manual verification by the user within the user interface”.
Response to Argument 2, the examiner respectfully disagrees. McClusky teaches in
Col. 8, lines 43-51, “For example, the cosine similarity between previously trained word, word n-gram, sentence, or paragraph vectors using, for example, Word2Vec, … processes to generate the vectors for “problem words” or “problem sentences” or “problem paragraphs,” and vectors for unclassified portions of text can be calculated to score the probability that a portion of the text references a problem”.
Col. 8, lines 42-67 and Col. 9, lines 1-4, “the cosine similarity between previously trained word, word n-gram, sentence, or paragraph vectors using, for example, Word2Vec, … processes to generate the vectors for “problem words” or “problem sentences” or “problem paragraphs… such predictions are then evaluated and accepted or refined based on various methods, including but not limited to meeting quantitative thresholds based on the estimated probability of the text indicating a technical problem from a single model … using active learning to generate a set of portions of text the classifier is least certain about for further human evaluation”
Thus, finding a semantic relatedness encompasses how a cosine similarity analysis is performed on the plurality of query words that may be identified as related to an industry problem tokenized into n-grams and vectors. Wherein human evaluation is one of the methods to evaluate and accept the cosine similarity predictions. The examiner notes that the BRI for above a similarity threshold encompasses n-grams that are above a quantitative threshold cosine similarity score that is based on an estimated probability that text from a document contains a relevant technical problem kernel. Thus a semantic relatedness analysis is being performed between search seed terms and identified text from documents and articles that may be associated with competing companies.
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 (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
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.
Claim(s) 1, 6, 8, 13, 15, 19, 21, 24, 27, 30-31, & 33-38, is/are rejected under 35 U.S.C. 103 as being unpatentable over U.S. Patent NO. 11392651 “McClusky”, in light of U.S. Patent Application Publication NO. 20100153183 “Ulwick”, and further in light of U.S. Patent Application Publication NO. 20150278731 “Schwaber”.
Claim 1:
McClusky teaches a method for competitive analysis, the method comprising: receiving data related to a target business, wherein the target business is identified based on a user selection within a user interface(i.e. Col. 38, lines 38-55, “a user seeking to identify the unmet technical needs and/or technical problems associated with a specific company or organization … e.g. … a current/potential competitor or set of current/potential competitors, may perform the following … a. select one or more source document databases… c. enter user query words into a user interface to narrow the scope of the analysis, such as one or more companies or organizations, one or more industries, etc… initiating the automatic filtering of the documents using the user query words and automatic analysis of the documents meeting the user query criteria ”, wherein document data is retrieved related to the user’s target input query for a potential competitor); identifying a plurality of seed terms associated with the target business using a machine learning model with Natural Language Processing to analyze the data related to the target business using one or more text analysis techniques (i.e. Col. 20, lines 55-66, “one or more of the computers can have logic to automatically identify materials words within documents based on context utilizing machine learning techniques, as described. Exemplary systems use a named entity recognizer as described above, e.g., a bi-directional LSTM that feeds one or more classifiers, to determine if a word or clause might be a material Technology Element”, wherein the BRI for seed terms encompasses identifying terms, such as material technology elements”, that match user input query words related to a target competitor business. Wherein the seed terms may be identified using cosine similarity to compare process words or n-grams and the term vectors of the process Technology Elements and/or their synonyms and any words or n-grams with scores lower than a threshold value are labeled with the application Technology Element they are most similar to); training the machine learning model with Natural Language Processing based on one or more seed terms of the plurality of seed terms which are manually verified by the user within the user interface (i.e. Col. 9, lines 30-66, “individual sentences are identified as a problem kernel, but displayed as full paragraphs (or summarized paragraphs) to the user… a subset of sentences receiving “borderline scores” from the initial Deficiency Recognizer (e.g., scores between 0.75 and 0.9) is manually evaluated and annotated as problems or not problems”, wherein it is noted that the search display may display seed terms related to industry problems that may be manually verified by the user as labeled correctly and used for training data. Wherein it is noted in Col. 39, lines 51-54, that the searching user may manually flag certain results in order verify the pertinence of displayed query word results generated from seeded terms); identifying a plurality of related businesses to the target business by searching a knowledge corpus using the one or more seed term (i.e. Col. 21, lines 45-54, “one or more application databases 162 include a stored list of words or n-grams indicating a system/component … application databases could include specific product or brand names (e.g., Apple iPhone, Boeing 747, etc.)”, wherein businesses related to a user’s query may be identified by searching a knowledgebase of related vectors), wherein the knowledge corpus is comprised of external documentation and publicly available information related to a plurality of businesses (i.e. Col. 21, lines 54-64, “some exemplary embodiments one or more of the computers can leverage knowledge-based approaches such as including logic to access existing publicly available or paid lexical knowledge database”, wherein a database may be comprised of public documents and externally paid databases); determining one or more competitors (i.e. Col. 40, lines 7-10, Col. 41, lines 43-47, “a user seeking to identify the unmet technical needs and/or technical problems associated with a specific company or organization or subset of companies or organizations, e.g….a current/potential competitor or set of current/potential competitors, may perform the following… causing the system to list or export the flagged problem kernels or problem elements for that technical area of interest, and/or the specific companies that have most mentioned that problem kernel or problem element”, wherein a user may weight and further filter a query by filtering the documents based on being owned by, authored by, or mentioning the specific corporation(s) or organization(s) with specific financial, demographic, geographic metrics, etc. mentioning specific technology or problem elements, mentioning specific industries) by eliminating at least one of the plurality of related businesses identified (i.e. Col. 22, lines 14-24, “cosine similarity is used to compare application words or n-grams and the term vectors of the application Technology Elements and/or their synonyms and any words or n-grams with scores lower than a threshold value are labeled with the application Technology Element they are most similar to (i.e., where the score is closest to 0)”, wherein having a cosine similarity closer to zero indicates that a higher likelihood of a query word being similar to a certain technology element having a similarity score in the case that a user has queried a brand name. Thus query words having score below a threshold value are kept, as a lower score indicates being above a threshold similarity value. As such query words that have a score indicative of having a low similarity value would be below such a threshold similarity value and disregarding as being likely associated with a certain technology element, and thus be eliminated as competitors for the user input query for a problem for a specific company), wherein the at least one of the plurality of related businesses identified are eliminated based on a weighting of mentions of the one or more seed terms, wherein the weighting of the mentions is determined based on at least a recency of the mention, a form of the external documentation in which the mention is derived, and an engagement with the external documentation in which the mention is derived (i.e. Col. 43, Lines 20-37, “a user interface provides a user with the ability to selectively choose one or more of all these options, e.g., increasing trends being weighted higher from a problem intensity standpoint, trends with the highest recent growth rates, trends which are increasingly associated with corporations, etc. which can increase the likelihood a particular problem kernel will be scored high enough rise to the point that it is displayed”, wherein businesses may be eliminated from being displayed based on user selected weights such as a weight concerning recently increasing trending mentions of problems, a weight that is currently rising or falling based on the period and most-recent mentioned data, and as well as weights being based query terms being engaged in specific document types (in conference proceedings or in news articles). The examiner notes that the BRI for a recency of a mention encompasses how the user interface may adjust the display as a user selects an option to narrow and eliminate display results where increasing trends being weighted higher from a problem intensity standpoint. Wherein the BRI for form in which mention is derived encompasses how selection of types of forms may be narrowed based on forms mostly assigned to individuals and mostly assigned to universities. Wherein the BRI for engagement with the form encompasses how mentions may be found in specific document type (e.g., in conference proceedings or news articles); and
generating a competitive analysis report (i.e. Col. 39, lines 10-15, actuate a user interface object, such as an icon, causing the system to list or export the flagged problem kernels or problem elements for that technical area of interest, and/or the specific companies that have most mentioned that problem kernel or problem element), wherein the competitive analysis report includes(i.e. Col. 47, lines 39-44, “recognition is performed in real-time, e.g., user search input is accepted and then problem kernels are recognized in real-time in documents that relate to the query search terms to generate any of the various outputs described herein (e.g., any of the computer displays, printouts, reports, saved data, etc.)”, wherein report results are updated in real time based on classified query terms identified in business related databases) and(i.e. Col. 35, lines 16-45“The user interface can quickly move between ranked, scored documents or problem kernel displayed as described herein, such as with the characteristics described above, moving from document to document (or from problem kernel to problem kernel) …FIG. 22 shows a Search Statistics display, which shows various tabs with more detailed statistics including tables and graphs of assignees, countries, problem kernels by year, products, and industries, etc. This would be used to provide additional information to the user to create more focused search queries.”, wherein it is noted that the BRI for manual changes encompasses how users may manually add additional seed terms representing problem kernels upon review of related search statistics),
While McClusky teaches generating a competitive analyses report or problems related to a user queried business, McClusky may not explicitly teach
wherein the competitive analysis report includes at least a favorability of the target business in comparison to the one or more competitors
wherein the competitive score over time is displayed to the user within the user interface utilizing visual insights.
However Ulwick teaches
wherein the competitive analysis report includes at least a favorability of the target business in comparison to the one or more competitors (i.e. para. [0284], “The Customer Evaluation Criteria report can generate a list of all Desired Outcomes or Predictive Metrics for a project… Several reports are available from within the Competitive Analysis report category, including, but not limited to: (1) competitor comparison, (2) areas of opportunity”, wherein the report may include an analysis of one business’s competitive favorability verses another competitor)
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to add wherein the competitive analysis report includes at least a favorability of the target business in comparison to the one or more competitors, to the neural network searching and retrieval of seed term related information of McClusky, with how a search may further generate a competitive analysis report specifically highlighting areas of strength or weakness of a business compared to its competitors, as taught by Ulwick. One would have been motivated to combine Ulwick with McClusky, and would have had a reasonable expectation of success in doing so, because the combination saves a user time by quickly providing potentially advantageous and desirable information related to a user’s query.
While McClusky and Ulwick teach displaying a competitive score over time, McClusky and Ulwick may not explicitly teach
wherein the competitive score over time is displayed to the user within the user interface utilizing visual insights.
However, Schwarber teaches
wherein the competitive score over time is displayed to the user within the user interface utilizing visual insights (i.e. para. [0069], Fig. 8, “the functional performance index for companies within the same sector or industry can be compared to determine the relative success of the companies”, wherein it is noted that the BRI for a competitive score over time encompasses displayed trend data. The examiner notes the BRI for an aggregate competitive score over time encompasses that a company's performance index changes over time, the associated trend data can be utilized to see how a company's index has changed relative to the industry benchmark or relative to its peers/competitors).
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to add wherein the competitive score over time is displayed to the user within the user interface utilizing visual insights, to the neural network searching and retrieval of seed term related information of McClusky-Ulwick-Schwarber, with displaying trend data indicating competitive performance over time is displayed for each company, as taught by Schwarber. One would have been motivated to combine Schwarber with McClusky-Ulwick-Schwarber, and would have had a reasonable expectation of success in doing so, because the combination saves a user time by quickly providing potentially advantageous and desirable information related to determining relative success of the companies.
Claim 6:
McClusky, Ulwick, and Schwarber teach the method of claim 1.
McClusky teaches further comprising: determining whether the one or more seed terms are above or below a similarity threshold (i.e. Col. 8, lines 44-52, “the cosine similarity between previously trained word, word n-gram, sentence, or paragraph vectors using, for example, Word2Vec, Glove, FastText, Sentence2Vec, or Paragraph2Vec processes to generate the vectors for “problem words” or “problem sentences” or “problem paragraphs,” and vectors for unclassified portions of text can be calculated to score the probability that a portion of the text references a problem”, wherein the query words have a cosine similarity threshold for how close the words are to similar technological concepts. Wherein it is noted that the user may input query words related to a problem and analysis may return potentially relevant competitors) by performing a semantic relatedness analysis of the plurality of seed terms identified for the target business by tokenizing the plurality of seed terms using n-Gram tokenization and converting a plurality of tokenized seed terms into a plurality of vectors (Col. 8, lines 43-51, For example, the cosine similarity between previously trained word, word n-gram, sentence, or paragraph vectors using, for example, Word2Vec, … processes to generate the vectors for “problem words” or “problem sentences” or “problem paragraphs,” and vectors for unclassified portions of text can be calculated to score the probability that a portion of the text references a problem) wherein each of the plurality of vectors are compared using cosine similarity, and wherein one or more of the plurality of seed terms are determined to be above or below the similarity threshold based on a cosine similarity distance and requiring manual verification by the user within the user interface (Col. 8, lines 42-67 and Col. 9, lines 1-4, “the cosine similarity between previously trained word, word n-gram, sentence, or paragraph vectors using, for example, Word2Vec, … processes to generate the vectors for “problem words” or “problem sentences” or “problem paragraphs… such predictions are then evaluated and accepted or refined based on various methods, including but not limited to meeting quantitative thresholds based on the estimated probability of the text indicating a technical problem from a single model … using active learning to generate a set of portions of text the classifier is least certain about for further human evaluation”, wherein finding a semantic relatedness encompasses how a cosine similarity analysis is performed on the plurality of query words that may be identified as related to an industry problem tokenized into n-grams and vectors. Wherein human evaluation is one of the methods to evaluate and accept the cosine similarity predictions. The examiner notes that the BRI for above a similarity threshold encompasses n-grams that are above a quantitative threshold cosine similarity score that is based on an estimated probability that text from a document contains a relevant technical problem).
;
eliminating, by the user, the one or more seed terms below the similarity threshold (i.e. Col. 21, lines 15-20, lines , “cosine similarity is used to compare material words or n-grams and the term vectors of the material Technology Elements and/or their synonyms and any words or n-grams with scores lower than a threshold value are labeled with the material Technology Element they are most similar to (i.e., where the score is closest to 0)”, wherein having a cosine similarity closer to zero indicates that a higher likelihood of a query word being similar to a certain technology element. Thus query words having score below a threshold value are kept, as a lower score indicates being above a threshold similarity value. As such query words that have a score indicative of having a low similarity value would be below such a threshold similarity value and disregarding as being likely associated with a certain technology element); verifying, by the user, the one or more seed terms above the similarity threshold (i.e. Col. 19, lines 33-37, a human can review the associated word for any word embeddings that are added in this method and then accept them for inclusion and add the word to the Technology Element ontology list); storing the one or more verified seed terms in a target business knowledge corpus (i.e. Col. 21, lines 45-54, “one or more application databases 162 include a stored list of words or n-grams indicating a system/component … application databases could include specific product or brand names (e.g., Apple iPhone, Boeing 747, etc.)”, wherein verified query words may be stored in an application database related to specific fields of knowledge); and training the machine learning model with Natural Language Processing based on one or more eliminated seed terms and one or more verified seed terms (i.e. Col. 26, lines 6-13, After a certain number of examples have been gathered, the Problem Element Recognizer Logic can then be retrained on the human-corrected output to improve performance. In some embodiments, as part of the manual, additional problem elements may be discovered, and a new Problem Element Recognizer Logic may be trained that includes these additional problem elements).
Claim 8:
Claim 8 is the system claim reciting similar limitations to Claim 1 and is rejected for similar reasons.
Claim 13:
Claim 13 is the system claim reciting similar limitations to Claim 6 and is rejected for similar reasons.
Claim 15:
Claim 15 is the product claim reciting similar limitations to Claim 1 and is rejected for similar reasons.
Claim 19:
Claim 19 is the product claim reciting similar limitations to Claim 6 and is rejected for similar reasons.
Claim 21:
McClusky, Ulwick, and Schwarber teach the method of claim 1,
McClusky further teaches wherein the text analysis techniques include at least keyword extraction and the data related to the target business includes internal documentation, external documentation, and manual input received from the user within the user interface, wherein the manual input is ontology terms relating to the target business taxonomy (i.e. Col. 41, lines 50-57, “potentially high value opportunities across virtually all technologies are automatically identified. In FIG. 27, the semantic engine corresponds to the search logic 24, described above. The various sources correspond to numerous document source databases 94 and the problem kernels correspond to the various portions of ranked, scored documents”, wherein it is noted that documents may be from internal company document and paid external document databases, and that taxonomy keywords may be manually input by reviewers to provide more context issues related to potential problems related to businesses).
Claim 24:
McClusky, Ulwick, and Schwarber teach the method of claim 1.
McClusky further teaches
wherein the one or more potential related businesses are listed according to an aggregate number of identifications, wherein the aggregate number of identifications may be categorized according to categories of the target business identified by the user in the user interface (i.e. Col. 45, lines 60-69, “a user seeking to identify the unmet technical needs and/or technical problems associated with a specific company or organization or subset of companies … or a current/potential competitor or set of current/potential competitors, may perform the following… c. enter user query words into a user interface to narrow the scope of the analysis, such as one or more companies or organizations ”, wherein potential competitors are listed according to identifications such as how competitors may have been trying to solve the same technical problems of companies being queried by the user) wherein the competitive analysis report includes insights for each of the one or more competitors according to the categories of the target business identified by the user in the user interface (i.e. Col. 40, lines 4-60, “additional text within the document, associated industries, companies, etc., problem trend, etc. and optionally using user-selected display parameters; and… flagged problem kernels or problem elements for that technical area of interest, and/or the specific companies that have most mentioned that problem kernel or problem element”, wherein an exported report includes insights such as most mentioned problem elements for relevant queried companies). .
Claim 27:
McClusky, Ulwick, and Schwarber teach the method of claim 1.
Ulwick further teaches wherein the favorability is determined based on a sentiment analysis of the target business as compared to the one or more competitors (i.e. para. [0284], (i.e. para. [0020], A proprietary index called the opportunity score is used to determine the strength of the underlying market conditions driving these findings, and this score has been shown to be a valid empirical estimator of customer demand/sentiment and hence the consequential business value of fulfilling the market needs appropriately).
Claim 30:
Claim 30 is the system claim reciting similar limitations to Claim 21 and is rejected for similar reasons.
Claim 31:
Claim 31 is the system claim reciting similar limitations to Claim 22 and is rejected for similar reasons.
Claim 33:
Claim 33 is the product claim reciting similar limitations to Claim 21 and is rejected for similar reasons.
Claim 34:
Claim 34 is the product claim reciting similar limitations to Claim 22 and is rejected for similar reasons.
Claim 35:
McClusky, Ulwick, and Schwarber teach the method of claim 1.
Ulwick further teaches
wherein the visual insights include a breakdown of the external documentation (i.e. para. [0161], “Selecting the "input customer sets" option allows the user to input data relating to new internal and external customer sets (e.g. South American End Users, South American Manufacturing, etc.) for prioritizing. After the new customer sets have been defined the user selects the "done" button”, wherein external documentation encompasses an external customer set) related to each of the one or more competitors (para. [0284], “Several reports are available from within the Competitive Analysis report category, including, but not limited to: (1) competitor comparison, (2) areas of opportunity (for competitors) and (3) analyze individual competitor”, wherein a visual report may include a breakdown of competitive analysis based insights drawn from external documentation such as external customer sets).
Claim 36:
McClusky, Ulwick, and Schwarber teach the method of claim 1.
McClusky further teaches wherein the determining of the one or more competitors further comprises:
Characterizing (i.e. Col. 27, lines 9-20, in Problem Expression 1, “bearing failure” is a consequence of “materials degrading” and these relationships can be identified and stored along with the problem element using common sentence dependency mapping techniques available in natural language processing libraries, e.g., Spacy, NLTK, CoreNLP. In some exemplary embodiments, these relationships are used to create a problem element ontology. In some exemplary embodiments, the problem element ontology is shown in response to a user query. In other exemplary embodiments, the user can select problem elements to view surrounding or related elements from the problem element ontology), utilizing one or more Natural Language Processing (NLP) techniques (i.e. Col. 20, lines 62-68 and Col 21, lines 1-3, “Exemplary systems use a named entity recognizer as described above, e.g., a bi-directional LSTM that feeds one or more classifiers, to determine if a word or clause might be a material Technology Element. Exemplary systems use classifier algorithms, e.g., a convolutional neural network, Naïve Bayes, Support Vector Machines, Maximum Entropy, Random Forests, to automatically identify materials words”, wherein the BRI for NLP techniques encompasses the LTSM logic used to automatically identify materials words within documents based on context utilizing machine learning techniques), a manner in which the one or more potential competitors are mentioned within the external documentation (i.e. Col. 27, lines 23-25, “problem kernels are analyzed to determine various metrics associated with their overall seriousness, criticality, or importance to a market or a set of users”, wherein it is noted that problem words may be characterized using NLP mapping techniques and assigned an appropriate score weight based on types and frequencies of mentions in a corpus of external documents. Wherein the BRI for a manner encompasses a context, such as seriousness, in which a competitor mentions a problem kernel); and
assigning a greater weighting to the one or more seed terms based on the manner in which the one or more competitors are mentioned, wherein the manner in which the one or more competitors are mentioned corresponds to one or more goals of the target business identified by the user within the user interface (i.e. Col. 27, lines 45-65, “problem elements are analyzed to determine various metrics associated with their overall seriousness, criticality, or importance to a market or a set of users. Metrics of problem intensity could include, but are not limited to, the number of instances such problem elements appear in a corpus of documents, … the number of companies mentioning the problem element, the size, market power, or typical patenting behavior of companies mentioning such problem elements”, wherein a greater scored weight for a problem term may be determined based on a competing number of higher power companies mentioning the problem in a corpus of external documents. Wherein the BRI for a goal would be wherein the goal is to identify relevant problems to a user query and the most serious and important problems to a user query would have a comparatively higher weight) .
Claim 37:
McClusky, Ulwick, and Schwarber teach the method of claim 1.
McClusky further teaches
wherein the weighting of the mentions are adjusted from default weighting settings based on the target business, wherein the target business is a product or service offered by a company (MC, Col. 27, lines 45-65, “Problem elements are analyzed to determine various metrics associated with their overall seriousness, criticality, or importance to a market or a set of users. Metrics of problem intensity could include, but are not limited to, the number of instances such problem elements appear in a corpus of documents, how recently a problem element has appeared in a document, how long has a problem element been appearing in documents, whether a problem element is appearing more or less frequently in documents across time, the timing and various measures of the aggregate growth or decline of a problem element across such instances, the number of companies mentioning the problem element, the size, market power, or typical patenting behavior of companies mentioning such problem elements, the monetary value of the industries and/or applications involved in the problem element, how frequently the problem element appears in documents mentioning the related application or applications”, wherein the scored weight is for a problem is adjusted based on the criticality of a mention of the problem in documents based on the target business).
Claim 38:
McClusky, Ulwick, and Schwarber teach the method of claim 1.
McClusky further teaches wherein the weighting of the mentions are adjusted from default weighting settings based on input from the user (i.e. Col. 43, Lines 20-37, a user interface provides a user with the ability to selectively choose one or more of all these options, e.g., increasing trends being weighted higher from a problem intensity standpoint, trends with the highest recent growth rates, trends which are increasingly associated with corporations, etc. which can increase the likelihood a particular problem kernel will be scored high enough rise to the point that it is displayed) , wherein the user assigns a greater weighting to a first form of external documentation and a lesser weighting to a second form of external documentation (i.e. Col. 43, lines 10-35, “trends over time are automatically calculated and analyzed over N years, e.g., five or ten or twenty years, for trends, such as, but not limited to… mostly assigned to individuals, mostly assigned to universities… exemplary embodiments, a user interface provides a user with the ability to selectively choose one or more of all these options”, wherein a user may select increase trends being weighted higher when external documents belong to a university than when external documents belong to an individual), and wherein the external documentation is continuously added to the knowledge corpus as it is made publicly available (i.e. Col. 21, lines 54-64, “some exemplary embodiments one or more of the computers can leverage knowledge-based approaches such as including logic to access existing publicly available or paid lexical knowledge database, such as Freebase, DBPedia, Yago, etc. to identify materials words and n-grams”, wherein it is noted that DBPedia is a database that is constantly and continually updated in real time from publicly available Wikipedia articles).
Claim(s) 22 is/are rejected under 35 U.S.C. 103 as being unpatentable over U.S. Patent NO. 11392651 “McClusky”, in light of U.S. Patent Application Publication NO. 20100153183 “Ulwick”, and further in light of U.S. Patent Application Publication NO. 20150278731 “Schwaber”, as applied to Claim 22 above, and further in light of U.S. Patent Application Publication NO. 20210287261 “Medalion”.
Claim 22:
McClusky, Ulwick, and Schwarber teach the method of claim 1.
McClusky teaches further comprising: performing a semantic relatedness analysis on the plurality seed terms identified for the target business by tokenizing the plurality of seed terms using n-Gram tokenization and converting a plurality of tokenized seed terms into a plurality vectors, wherein the plurality of vectors correspond to the plurality of seed terms, and wherein each of the plurality of vectors are compared using cosine similarity (i.e. Col. 8, lines 43-51, “For example, the cosine similarity between previously trained word, word n-gram, sentence, or paragraph vectors using, for example, Word2Vec, … processes to generate the vectors for “problem words” or “problem sentences” or “problem paragraphs,” and vectors for unclassified portions of text can be calculated to score the probability that a portion of the text references a problem”, wherein the BRI for a semantic relatedness analysis encompasses how a cosine similarity analysis is performed on the plurality of query words that may be identified as related to an industry problem tokenized into n-grams and vectors), wherein the one or more seed terms of the plurality of seed terms determined to be above a similarity threshold based on a distance determined using the cosine similarity (i.e. Col. 8, lines 42-67 and Col. 9, lines 1-4, “the cosine similarity between previously trained word, word n-gram, sentence, or paragraph vectors using, for example, Word2Vec, … processes to generate the vectors for “problem words” or “problem sentences” or “problem paragraphs… such predictions are then evaluated and accepted or refined based on various methods, including but not limited to meeting quantitative thresholds based on the estimated probability of the text indicating a technical problem from a single model … using active learning to generate a set of portions of text the classifier is least certain about for further human evaluation”, wherein human evaluation is one of the methods to evaluate and accept the cosine similarity predictions. The examiner notes that the BRI for above a similarity threshold encompasses n-grams that are above a quantitate threshold cosine similarity score).
While McClusky teaches using cosine similarity to compare seed terms identified for the target business field that may be evaluated and verified by manual human evaluation, McClusky may not explicitly teach the seed terms classification
require manual verification by the client within the user interface.
However, Medalion teaches a supervised machine learning method that to
require manual verification by the client within the user interface (i.e. para. [0016], “A method may be used to train a neural network architecture via supervised learning to predict missing categories for businesses based on their given description. … the present disclosure may use the trained neural network to classify new businesses or users (e.g., when signing up for software services), store the classifications within a database, and/or display the classification to the new business or user for confirmation”, wherein a querying user may use a displayed interface to confirm a machine learning generated classification related to a business seed term inquiry).
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to require manual verification by the client within the user interface, to the neural network semantic analysis using cosine similarity and n-grams of seed term related information of McClusky-Ulwick-Schwarber, with how a classification from a neural network is displayed to a querying user for confirmation within a displayed window, as taught by Medalion. One would have been motivated to combine required manual verification Medalion with the cosine similarity threshold calculation and presentation of McClusky-Ulwick-Schwarber, and would have had a reasonable expectation of success in doing so, because the combination creates a more accurate model by using user relevant feedback to prevent mistakes in further iterations.
Claim(s) 39 is/are rejected under 35 U.S.C. 103 as being unpatentable over U.S. Patent NO. 11392651 “McClusky”, in light of U.S. Patent Application Publication NO. 20100153183 “Ulwick”, and further in light of U.S. Patent Application Publication NO. 20150278731 “Schwaber”, as applied to Claim 1 above, and further in light of U.S. Patent Application Publication NO. 20140006415 “Rubchinsky”.
Claim 39:
McClusky, Ulwick, and Schwarber teach the method of claim 1,.
McClusky, Ulwick, and Schwarber may not explicitly teach
wherein the engagement with the external documentation in which the mention is derived is determined based on viewership, wherein the weighting of the one or more seed terms in the external documentation corresponds to the viewership of the external documentation.
However, Rubchinsky teaches
wherein the engagement with the external documentation in which the mention is derived is determined based on viewership, wherein the weighting of the one or more seed terms in the external documentation corresponds to the viewership of the external documentation (i.e. para. [0060], “The answers and recommendations are captured to media scoring module 245 (910) which also tracks the number of views by users. The media scoring module 245 calculates a voting score for each media file based on the scaled answers, pop-to-drop ratio, percentage of video viewed, comments, social distribution, recommendations and number of views, and updates the website database 150… Media scoring module 245 then assigns a final ranking score to the media file on the results interface 250, based on the relative voting scores among all media files over time (960). Process in FIG. 7 is repeated per media file”, wherein media documentation external to a client device, such as on a website database, and containing a query answer may be given a weighted score based on the number of views by users)
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to wherein the engagement with the external documentation in which the mention is derived is determined based on viewership, wherein the weighting of the one or more seed terms in the external documentation corresponds to the viewership of the external documentation and the n-grams of seed term weighting of display of related problem keyword information of McClusky-Ulwick-Schwarber, with how external media documentation containing query keywords may have additional scoring weights corresponding to the viewership of the documentation, which determines the overall relevance of the document as an answer to a user query, as taught by Rubchinsky. One would have been motivated to combine required document weighting Rubchinsky with the document weightings of McClusky-Ulwick-Schwarber, and would have had a reasonable expectation of success in doing so, because the combination creates a more logical method to evaluate the media files and award greater importance to those with a higher perceived high quality.
Allowable Subject Matter
Claim 40-42 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
U.S. Patent Application Publication No. 20190221080 “Reetz”, teaches in para. [0037], databases associated with the wagering companies that hosts data associated with betting on racing events. In an instance, the databases associated with the wagering companies may store information, such as, but not limited to, upcoming racing events, venue information associated with upcoming racing events, live racing events and associated venues, payouts associated with each type of bet (e.g. Win Bets, Exacta Bets, Trifecta Bets) in a competition, and odds of winning associated with each competitor in the racing event etc. Furthermore, the database associated with the wagering companies may also include additional information like statistics related to performances of competitors (e.g. horses in a horseracing event) in previous competitions (e.g. horseracing competitions). Further, the online platform 100 may fetch the information stored by the databases associated with the competitions. Accordingly, based upon receiving the information, the online platform 100 may maintain an internal repository corresponding to the information pertaining to historical performances of competitors, upcoming competitions, live competitions, estimated payouts in a live and/or the upcoming competitions etc.
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to DAVID H TAN whose telephone number is (571)272-7433. The examiner can normally be reached M-F 7:30-4:30.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Cesar Paula can be reached at (571) 272-4128. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/D.T./ Examiner, Art Unit 2145
/CESAR B PAULA/ Supervisory Patent Examiner, Art Unit 2145
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