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 .
Notice to Applicant
The following is a Non-Final Office action. In response to Examiner’s Final Rejection of 11/12/2025, Applicant, on 2/12/2026, amended claims 1, 10 and 19. Claims 1, 4, 6-10, 13 and 15-20 are pending in this application and have been rejected below.
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this
application is eligible for continued examination under 37 CFR 1.114, and the fee set
forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action
has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on
2/12/2026 has been entered.
Response to Arguments
Applicant’s arguments filed July 12, 2026 have been fully considered but they are not persuasive and/or are moot in view of the revised rejections. Applicant’s arguments will be addressed herein below in the order in which they appear in the response filed July 12, 2026.
On Pg. 7-8 of the Remarks, regarding 35 U.S.C. § 101 rejections, Applicant states the present application is a practical application as it specific decision- support tool that automates the narrowing of potential business candidates. By transforming the raw probability data (the vector) into a concrete executable action (a recommendation for selection), the invention provides a specific, tangible utility streamlining entity selection-that amounts to a practical application under MPEP
2106.04(d) and Improvement to User Interaction: By "causing a graphical user interface... to display the recommendation," the system transforms the raw output vector into a specific format that "facilitates selection" by the user. Examiner respectfully disagrees. The present claims amount to no more than utilizing computer elements as tools to evaluate and recommend. Examiner finds the present claims improve an existing business process of classification and there are currently no functional advancements to any technology or technological field, in order for the claim elements to be considered significantly more than the abstract idea itself. Applicant has not identified anything in the claimed invention that shows or even submits the technology is being improved or there was a problem in the technology that the claimed invention solves. Utilizing generic computer structure and technology (see par. 57-60, 65, 69-73) to identify, schedule and execute a job are all, both individually and in combination, generic computer functions such as receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network); electronic recordkeeping, Ultramercial, 772 F.3d at 716, 112 USPQ2d at 1755 (updating an activity log) and storing and retrieving information in memory, Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015); OIP Techs., 788 F.3d at 1363, 115 USPQ2d at 1092-93 (See MPEP 2106.05(d)(II).
On Pg. 8-9 of the Remarks, regarding 35 U.S.C. § 101 rejections, Applicant states (1)Technical Solution to Bias (Ratio) and (2)Specific Application (GUI Control) are significantly more under step 2B. In response, regardless of the complexity and/or granularity of the type of digital content, and user interface with special programming, without meaningful limitations within the claims that amount to significantly more than the abstract idea is a judicial exception (i.e. abstract idea). Applicants mentioned GUI control is M2106.05(f) – field of use.
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, 4, 6-10, 13 and 15-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. Claims 1, 4, 6-10, 13 and 15-20 are directed to extrapolating from crawled data to generate classifications.
Claim 1 recites a method for extrapolating from crawled data to generate classifications, Claim 10 recites a system for extrapolating from crawled data to generate classifications and Claim 19 recites an article of manufacture for extrapolating from crawled data to generate classifications, which include receiving an evaluation request with an input list comprising at least one entity and a respective website identifier of the at least one entity, wherein the evaluation request comprises an evaluation type; identifying, from a plurality of categorization labels, a subset of categorization labels that correspond to a requesting entity that generated the evaluation request based on a compatibility with the evaluation type; crawling information from at least one website corresponding to the website identifier; generating at least one text body by parsing the crawled information; calculate a ratio of an amount of critical terms in the at least one text body and a total amount of terms in the at least one text body; determine, based on the weighted terms and the ratio, whether the at least one text body corresponds to a given categorization label; generate an output vector indicating categorization labels from the subset of categorization labels that the at least one entity corresponds, and generate a recommendation for selecting one or more matching entities based on the output vector; and transmitting the output vector to the requesting entity for causing a graphical interface of the requesting entity to display the recommendation to facilitate selection of a matching entity.
As drafted, this is, under its broadest reasonable interpretation, within the Abstract idea grouping of “Methods of Organizing Human Activity” – managing interactions. The recitation of “system”, “memory”, “hardware processor”, “computing device”, and “computer readable medium”, provide nothing in the claim elements to preclude the step from being “Methods of Organizing Human Activity”- managing interactions. Accordingly, the claim recites an abstract idea.
This judicial exception is not integrated into a practical application. The claims primarily recite the additional element of using computer components to perform each step. The “system”, “memory”, “hardware processor”, “computing device”, and “computer readable medium” is recited at a high-level of generality, such that it amounts no more than mere instructions to apply the exception using a computer component. See MPEP 2106.05(f). Furthermore, the claim 1, claim 10 and claim 19 recite using one or more machine learning analysis techniques. The specification discloses the machine learning analysis at a high-level of generality, providing examples of different techniques that may be applied. The general use of a machine learning analysis does not provide a meaningful limitation to transform the abstract idea into a practical application. Therefore, currently, the machine learning processing is solely used a tool to perform the instructions of the abstract idea. Accordingly, the additional elements do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claims also fail to recite any improvements to another technology or technical field, improvements to the functioning of the computer itself, use of a particular machine, effecting a transformation or reduction of a particular article to a different state or thing, and/or an additional element applies or uses the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception. See 84 Fed. Reg. 55. In particular, there is a lack of improvement to a computer or technical field in data analysis.
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements when considered both individually and as an ordered combination do not amount to significantly more than the abstract idea. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements of “system”, “memory”, “hardware processor”, “computing device”, and “computer readable medium” is insufficient to amount to significantly more. (See MPEP 2106.05(f) – Mere Instructions to Apply an Exception – “Thus, for example, claims that amount to nothing more than an instruction to apply the abstract idea using a generic computer do not render an abstract idea eligible.” Alice Corp., 134 S. Ct. at 235). Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept.
The claim fails to recite any improvements to another technology or technical field, improvements to the functioning of the computer itself, use of a particular machine, effecting a transformation or reduction of a particular article to a different state or thing, adding unconventional steps that confine the claim to a particular useful application, and/or meaningful limitations beyond generally linking the use of an abstract idea to a particular environment. See 84 Fed. Reg. 55. Viewed individually or as a whole, these additional claim element(s) do not provide meaningful limitation(s) to transform the abstract idea into a patent eligible application of the abstract idea such that the claim(s) amounts to significantly more than the abstract idea itself. With regards to receiving data and step 2B, it is M2106.05(d)- Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information) and Storing and retrieving information in memory, Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015). With regards to machine learning and Step 2B- the machine learning is a tool to apply the abstract idea.
Examiner concludes that the additional elements in combination fail to amount to significantly more than the abstract idea based on findings that each element merely performs the same function(s) in combination as each element performs separately. The claim is not patent eligible. Thus, taken alone, the additional elements do not amount to significantly more than the above-identified judicial exception (the abstract idea). Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually.
Dependent Claims 4, 6-9, 13, 15-18 and 20 recite wherein parsing the crawled information further comprises: removing stop words and punctuation from the crawled information; and classifying, using object recognition, images from the at least one website into text describing contents of the images; wherein the evaluation type is one of a partner and a client; wherein a different subset of categorization labels are used for a different requesting entity; wherein crawling the information further comprises utilizing a proxy service that hides an IP address of a web crawler; wherein crawling the information further comprises executing a script that circumvents security measures of the at least one website; wherein a categorization label of the subset of categorization labels indicates a relationship of the requesting entity to the at least one entity; and further narrowing the abstract idea. These recited limitations in the dependent claims do not amount to significantly more than the above-identified judicial exceptions in Claims 1, 10 and 19. Regarding Claims, 8 and 13, 17-18, and the additional elements of “web crawler” and “hardware processor”- it is M2106.05(d)- Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information).
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, 4, 6-7 and 15-16 are rejected under 35 U.S.C. 103 as being unpatentable over Bailey et al., US Publication No. 20220365998A1, [hereinafter Bailey], in view of Nia et al., "A Framework for a Large-Scale B2B Recommender System," 2019 IEEE 14th International Conference on Intelligent Systems and Knowledge Engineering (ISKE), Dalian, China, 2019, pp. 337-343, [hereinafter Nia] and in further view of Miller et al., US Publication No. 20220253871A1, [hereinafter Miller].
Regarding Claim 1,
Bailey teaches
A method for extrapolating from crawled data to generate classifications, the method comprising: receiving an evaluation request with an input list comprising at least one entity and a respective website identifier of the at least one entity, wherein the evaluation request comprises an evaluation type (Bailey Par. 27; Par. 35;Par. 32-“Entity recognition engine 108 uses machine learning and natural language processing to locate and classify named entities in target content found by web crawler 106. In some embodiments, entity recognition engine 108 receives a webpage as input, tokenizes the data, and applies an NLP-based ML model to identify which tokens correspond to named companies. Applying the ML model may include generating one or more ML feature vectors for the web page based on the tokens and feeding the input ML vectors into an ML model, such as an artificial neural network that performs forward propagation using the ML vector. An ML model may be trained to learn contextual patterns in the sequence of tokens. For instance, the ML model may extrapolate meaning based on what tokens precede and/or come after a given token. A token in one context may correspond to an entity name but in another context may have a different meaning. For example, the word “oracle” may be used to refer to a company or have a different meaning depending on the context in which it is used. The trained ML model may learn the different contexts such that when it is applied, it may accurately distinguish between when a given token is used to refer to a company and when it is used for another purpose.”; Par. 40-41; Par. 72-73-“ Interface page 304 presents available filters based on negative news event signal categories. Example tags include bankruptcy, industry or competitive, customer dissatisfaction, financial challenges, executive or key person departure, layoffs, legal issues, regulatory issues, and security challenges. The event signal filters may be evaluated based on current event information as extracted by AI-powered web crawler 106. In the present example, the user has selected “Executive or Key Person Departure” and “Layoffs”. Responsive to the user submitting the filters, data enrichment service 104 may search for entities tagged with one of these two labels based on recent events and add these entities to the entity list if the other filter criteria, if any, are also satisfied; Par. 80);
identifying, from a plurality of categorization labels, a subset of categorization labels that correspond to a requesting entity that generated the evaluation request based on a compatibility with the evaluation type (Bailey Par. 35; Par. 22-23-“In some embodiments, a system includes an AI-powered web crawler to generate and populate a segment for a defined task. The AI-powered web crawler may find and categorize content reflecting recent events that are potentially relevant to a set of entities. The AI-powered web crawler may further leverage machine learning to identify reliable web-based sources of news, perform named entity recognition to determine whether web content is relevant to a set of target entities, and classify web content. For example, if the web crawler finds target content, such as a news article on a trusted website, then one or more machine learning models may be applied to (a) identify one or more named entities that are the subject of the content and (b) assign one or more event category labels for the target content Based on the output of the one or more machine learning models, the system may associate an entity with one or more event categories, also referred to herein as event signals. The system may then generate segments based in part on which entities are associated with a particular event category. In some embodiments, users may define filter criteria or parameters for generating a segment. For example, the filter criteria may specify which event categories the system uses to select and/or exclude entities. Additionally or alternatively, event categories may be weighted, with weights assigned by users or machine-learning processes, and entities may be filtered as a function of the assigned weights. Once the system identifies entities satisfying the filter criteria, the system may then identify a set of one or more individuals associated with each entity and populate the segment with all or a subset of data records for these individuals. Individuals that are employees or members of entities that do not satisfy the filter criteria may be excluded from the segment.”; Par. 72-73);
crawling information from at least one website corresponding to the website identifier (Bailey Par. 22-23-“In some embodiments, a system includes an AI-powered web crawler to generate and populate a segment for a defined task. The AI-powered web crawler may find and categorize content reflecting recent events that are potentially relevant to a set of entities. The AI-powered web crawler may further leverage machine learning to identify reliable web-based sources of news, perform named entity recognition to determine whether web content is relevant to a set of target entities, and classify web content. For example, if the web crawler finds target content, such as a news article on a trusted website, then one or more machine learning models may be applied to (a) identify one or more named entities that are the subject of the content and (b) assign one or more event category labels for the target content Based on the output of the one or more machine learning models, the system may associate an entity with one or more event categories, also referred to herein as event signals. The system may then generate segments based in part on which entities are associated with a particular event category.”; Par. 31);
generating at least one text body by parsing the crawled information (Bailey Par. 33-“In some embodiments, NLP engine 110 parses webpages retrieved by web crawler 106 for relevant information. NLP engine 110 may process unstructured text and other content from a variety of sources, including news articles, press releases, government filings, social media, job listings, and blog posts. If relevant information is found, NLP engine 110 may store and incorporate the data into a database and/or other data repository. Fig. 4; Par. 75);
…for causing a graphical interface of the requesting entity to display the recommendation to facilitate selection of a matching entity. (Bailey Par. 78-79-FIG. 5C illustrates example interface page 512 for defining segment filters in accordance with some embodiments. When a user selects the “Compare Contact Fields” step in display area 506, the GUI presents interface element 514 through which the user may define filter criteria to refine the segment generated by the previous step of the application flow. In the example illustrated, the step compares the titles of each contacts to a specified title. The user may indicate whether an exact title match is required or if a similar match is sufficient. The user may further specify whether individuals with the title should be included in the list or excluded from the list. Additionally or alternatively, the user may compare the contact fields to other contact attributes, such as office location, years with the company, prior interactions, and/or other values.; Par. 81)
Bailey teaches classification analysis and the feature is expounded upon by Nia:
applying a machine learning algorithm on the at least one text body, wherein the machine learning algorithm is configured to weight each term in the at least one text body based on whether the term is present in a list of critical terms for the subset of categorization labels (Nia Pg. 3 – 4 and related text- Based on previous equations and assuming that the overall similarity of the two documents is a weighted average of their similarities computed from different perspectives. The weights are the amounts of information in the descriptions…. Jaccard similarity is computed as the number of shared terms over the number of all unique terms in both strings. Matching Coefficient is a very simple vector-based approach which simply counts the number of similar terms, (dimensions), on which both vectors are non zero. Overlap coefficient is similar to the Dice’s coefficient, but considers two strings a full match if one is a subset of the other. );
calculate a ratio of an amount of critical terms in the at least one text body and a total amount of terms in the at least one text body; determine, based on the weighted terms and the ratio, whether the at least one text body corresponds to a given categorization label;(Nia Pg. 3- 5 and related text- At the end Lin defines the Similarity Theorem as: The similarity between A and B is measured by the ratio between the amount of information needed to state the commonality of A and B and the information needed to fully describe what Aand B are; The title or description for an industrial product is often a very short text, between 10 to 60 characters. As depicted in the proposed model in Fig. 1, to be able to accurately identify an item we propose to predict the category first based on the provided input. Thus this is a short-text classification problem. Out of many different techniques for text classification, CNN, initially designed for computer vision, has become increasingly popular and proven outstanding performance in NLP) ;
generate an output vector indicating categorization labels from the subset of categorization labels that the at least one entity corresponds (Nia Pg. 3-5- We use a CNN to identify the category of the input query. The CNN is trained based on the labeled data set for product categories. According to Fig. 1 The categories of products are {C1, C2, ...Cn}, so for any provided input, the classifier will provide a probability {p1, p2, ...pn}, p1 being the probability of the input belonging to C1 and so on. A ranking would pick the category with the most likelihood, being Ck. This provides a context to the next step which extracts the features and having the context improves the quality of the feature extraction... The features extracted from the input will be represented as a vector and matched against the pre-extracted and stored feature vectors of all the items in the database. Cosinesimilarity );
and generate a recommendation for selecting one or more matching entities based on the output vector(Nia Pg. 3-5- A variety of components are required in the proposed framework. We suggest a framework that will break down the complexity of the problem by classification of the input; mapping it to the right category as shown in Fig. 1 and implement a cascade of category specific recommenders. A category recommender component is built using mainly unsupervised methods such as NER and clustering on the labelled data. Afterwards a human category expert is involved to validate the features for the category.)
and transmitting the output vector to the requesting entity … (Nia Pg. 3-5- We use a CNN to identify the category of the input query. The CNN is trained based on the labrecomeled data set for product categories. According to Fig. 1 The categories of products are {C1, C2, ...Cn}, so for any provided input, the classifier will provide a probability {p1, p2, ...pn}, p1 being the probability of the input belonging to C1 and so on. A ranking would pick the category with the most likelihood, being Ck. This provides a context to the next step which extracts the features and having the context improves the quality of the feature extraction... The features extracted from the input will be represented as a vector and matched against the pre-extracted and stored feature vectors of all the items in the database. Cosinesimilarity );
Bailey and Nia are directed to classification and recommender analysis. It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have improve upon data analysis of Bailey, as taught by Nia, by utilizing additional analysis with a reasonable expectation of success of arriving at the claimed invention. One of ordinary skill in the art would have been motivated to make the modification to the teachings of Bailey with the motivation of improving experience and minimizing information overload (Nia Abstract).
Regarding Claim 2 and Claim 11- Cancelled
Regarding Claim 3 and Claim 12,- Cancelled
Regarding Claim 5 and Claim 14 – Cancelled
Regarding Claim 6 and Claim 15, Bailey in view of Miller teach The method of claim 1, … and The system of claim 10, …
wherein the evaluation type is one of a partner and a client. (Bailey Par. 72-73; Par. 99 -“ In some embodiments, various deployment models may be implemented by a computer network, including but not limited to a private cloud, a public cloud, and a hybrid cloud. In a private cloud, network resources are provisioned for exclusive use by a particular group of one or more entities (the term “entity” as used herein refers to a corporation, organization, person, or other entity). The network resources may be local to and/or remote from the premises of the particular group of entities. In a public cloud, cloud resources are provisioned for multiple entities that are independent from each other (also referred to as “tenants” or “customers”). The computer network and the network resources thereof are accessed by clients corresponding to different tenants. Such a computer network may be referred to as a “multi-tenant computer network.” Several tenants may use a same particular network resource at different times and/or at the same time.”);
Regarding Claim 7 and Claim 16, Bailey in view of Miller teach The method of claim 1, … and The system of claim 10, …
wherein a different subset of categorization labels are used for a different requesting entity. (Bailey Par. 72-75-“ FIG. 3C illustrates example interface page 306 for defining filter criteria based on time-sensitive event signals in accordance with some embodiments. As illustrated, user interface page 306 presents an option to define a threshold timeframe for a layoff event signal. In the present example, the user has selected within the last year. Thus, companies that have not had a layoff within the last year may be excluded from the generated entity list. In some embodiments, users may save a combination of filter criteria using an entity list name. For example, the user may define a list “Fortune 500 companies with layoffs in the past year” and another list “Small cap companies with layoffs in the past 6 months” to reflect different filter criteria associated lists. The named entity lists may be imported and reused by different application flows. For a given list, the segments that are generated by system 100 for each application flow may vary depending on the current event signals extracted by data enrichment service 104. For instance, a company that was on a list when a first application flow is executed may have been removed when a second application flow that uses the same entity list is executed. Additionally or alternatively, a company that was not included may have been added. As may be appreciated from the example interfaces illustrated above, the number of possible filter options and event signals may be extremely large. By leveraging real-time monitoring and machine learning, system 100 may generate segments based on information that a user does not know or has no way of knowing.”);
Regarding Claim 10,
Bailey teaches
A system for extrapolating from crawled data to generate classifications, the system comprising: at least one memory; and at least one hardware processor coupled with the at least one memory and configured, individually or in combination, to: receive an evaluation request with an input list comprising at least one entity and a respective website identifier of the at least one entity, wherein the evaluation request comprises an evaluation type (Bailey Par. 27; Par. 35; Par. 107-109; Par. 32-“Entity recognition engine 108 uses machine learning and natural language processing to locate and classify named entities in target content found by web crawler 106. In some embodiments, entity recognition engine 108 receives a webpage as input, tokenizes the data, and applies an NLP-based ML model to identify which tokens correspond to named companies. Applying the ML model may include generating one or more ML feature vectors for the web page based on the tokens and feeding the input ML vectors into an ML model, such as an artificial neural network that performs forward propagation using the ML vector. An ML model may be trained to learn contextual patterns in the sequence of tokens. For instance, the ML model may extrapolate meaning based on what tokens precede and/or come after a given token. A token in one context may correspond to an entity name but in another context may have a different meaning. For example, the word “oracle” may be used to refer to a company or have a different meaning depending on the context in which it is used. The trained ML model may learn the different contexts such that when it is applied, it may accurately distinguish between when a given token is used to refer to a company and when it is used for another purpose.”; Par. 40-41; Par. 72-73; Par. 80);
identify, from a plurality of categorization labels, a subset of categorization labels that correspond to a requesting entity that generated the evaluation request based on a compatibility with the evaluation type (Bailey Par. 35; Par. 22-23-“In some embodiments, a system includes an AI-powered web crawler to generate and populate a segment for a defined task. The AI-powered web crawler may find and categorize content reflecting recent events that are potentially relevant to a set of entities. The AI-powered web crawler may further leverage machine learning to identify reliable web-based sources of news, perform named entity recognition to determine whether web content is relevant to a set of target entities, and classify web content. For example, if the web crawler finds target content, such as a news article on a trusted website, then one or more machine learning models may be applied to (a) identify one or more named entities that are the subject of the content and (b) assign one or more event category labels for the target content Based on the output of the one or more machine learning models, the system may associate an entity with one or more event categories, also referred to herein as event signals. The system may then generate segments based in part on which entities are associated with a particular event category. In some embodiments, users may define filter criteria or parameters for generating a segment. For example, the filter criteria may specify which event categories the system uses to select and/or exclude entities. Additionally or alternatively, event categories may be weighted, with weights assigned by users or machine-learning processes, and entities may be filtered as a function of the assigned weights. Once the system identifies entities satisfying the filter criteria, the system may then identify a set of one or more individuals associated with each entity and populate the segment with all or a subset of data records for these individuals. Individuals that are employees or members of entities that do not satisfy the filter criteria may be excluded from the segment.”; Par. 72-73);
crawl information from at least one website corresponding to the website identifier (Bailey Par. 22-23-“In some embodiments, a system includes an AI-powered web crawler to generate and populate a segment for a defined task. The AI-powered web crawler may find and categorize content reflecting recent events that are potentially relevant to a set of entities. The AI-powered web crawler may further leverage machine learning to identify reliable web-based sources of news, perform named entity recognition to determine whether web content is relevant to a set of target entities, and classify web content. For example, if the web crawler finds target content, such as a news article on a trusted website, then one or more machine learning models may be applied to (a) identify one or more named entities that are the subject of the content and (b) assign one or more event category labels for the target content Based on the output of the one or more machine learning models, the system may associate an entity with one or more event categories, also referred to herein as event signals. The system may then generate segments based in part on which entities are associated with a particular event category.”; Par. 31);
generate at least one text body by parsing the crawled information (Bailey Par. 33-“In some embodiments, NLP engine 110 parses webpages retrieved by web crawler 106 for relevant information. NLP engine 110 may process unstructured text and other content from a variety of sources, including news articles, press releases, government filings, social media, job listings, and blog posts. If relevant information is found, NLP engine 110 may store and incorporate the data into a database and/or other data repository. Fig. 4; Par. 75);
…for causing a graphical interface of the requesting entity to display the recommendation to facilitate selection of a matching entity. (Bailey Par. 78-79-FIG. 5C illustrates example interface page 512 for defining segment filters in accordance with some embodiments. When a user selects the “Compare Contact Fields” step in display area 506, the GUI presents interface element 514 through which the user may define filter criteria to refine the segment generated by the previous step of the application flow. In the example illustrated, the step compares the titles of each contacts to a specified title. The user may indicate whether an exact title match is required or if a similar match is sufficient. The user may further specify whether individuals with the title should be included in the list or excluded from the list. Additionally or alternatively, the user may compare the contact fields to other contact attributes, such as office location, years with the company, prior interactions, and/or other values.; Par. 81)
Bailey teaches classification analysis and the feature is expounded upon by Nia:
apply a machine learning algorithm on the at least one text body, wherein the machine learning algorithm is configured to weight each term in the at least one text body based on whether the term is present in a list of critical terms for the subset of categorization labels (Nia Pg. 3 – 4 and related text- Based on previous equations and assuming that the overall similarity of the two documents is a weighted average of their similarities computed from different perspectives. The weights are the amounts of information in the descriptions…. Jaccard similarity is computed as the number of shared terms over the number of all unique terms in both strings. Matching Coefficient is a very simple vector-based approach which simply counts the number of similar terms, (dimensions), on which both vectors are non zero. Overlap coefficient is similar to the Dice’s coefficient, but considers two strings a full match if one is a subset of the other. );
calculate a ratio of an amount of critical terms in the at least one text body and a total amount of terms in the at least one text body; determine, based on the weighted terms and the ratio, whether the at least one text body corresponds to a given categorization label;(Nia Pg. 3- 5 and related text- At the end Lin defines the Similarity Theorem as: The similarity between A and B is measured by the ratio between the amount of information needed to state the commonality of A and B and the information needed to fully describe what Aand B are; The title or description for an industrial product is often a very short text, between 10 to 60 characters. As depicted in the proposed model in Fig. 1, to be able to accurately identify an item we propose to predict the category first based on the provided input. Thus this is a short-text classification problem. Out of many different techniques for text classification, CNN, initially designed for computer vision, has become increasingly popular and proven outstanding performance in NLP) ;
generate an output vector indicating categorization labels from the subset of categorization labels that the at least one entity corresponds (Nia Pg. 3-5- We use a CNN to identify the category of the input query. The CNN is trained based on the labeled data set for product categories. According to Fig. 1 The categories of products are {C1, C2, ...Cn}, so for any provided input, the classifier will provide a probability {p1, p2, ...pn}, p1 being the probability of the input belonging to C1 and so on. A ranking would pick the category with the most likelihood, being Ck. This provides a context to the next step which extracts the features and having the context improves the quality of the feature extraction... The features extracted from the input will be represented as a vector and matched against the pre-extracted and stored feature vectors of all the items in the database. Cosinesimilarity );
and generate a recommendation for selecting one or more matching entities based on the output vector(Nia Pg. 3-5- A variety of components are required in the proposed framework. We suggest a framework that will break down the complexity of the problem by classification of the input; mapping it to the right category as shown in Fig. 1 and implement a cascade of category specific recommenders. A category recommender component is built using mainly unsupervised methods such as NER and clustering on the labelled data. Afterwards a human category expert is involved to validate the features for the category.)
and transmit the output vector to the requesting entity … (Nia Pg. 3-5- We use a CNN to identify the category of the input query. The CNN is trained based on the labrecomeled data set for product categories. According to Fig. 1 The categories of products are {C1, C2, ...Cn}, so for any provided input, the classifier will provide a probability {p1, p2, ...pn}, p1 being the probability of the input belonging to C1 and so on. A ranking would pick the category with the most likelihood, being Ck. This provides a context to the next step which extracts the features and having the context improves the quality of the feature extraction... The features extracted from the input will be represented as a vector and matched against the pre-extracted and stored feature vectors of all the items in the database. Cosinesimilarity );
Bailey and Nia are directed to classification and recommender analysis. It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have improve upon data analysis of Bailey, as taught by Nia, by utilizing additional analysis with a reasonable expectation of success of arriving at the claimed invention. One of ordinary skill in the art would have been motivated to make the modification to the teachings of Bailey with the motivation of improving experience and minimizing information overload (Nia Abstract).
Regarding Claim 19,
Bailey teaches
A non-transitory computer readable medium storing thereon computer executable instructions for extrapolating from crawled data to generate classifications, including instructions for: receiving an evaluation request with an input list comprising at least one entity and a respective website identifier of the at least one entity, wherein the evaluation request comprises an evaluation type (Bailey Par. Par. 27; Par. 35; 107-109; Par. 112; Par. 32-“Entity recognition engine 108 uses machine learning and natural language processing to locate and classify named entities in target content found by web crawler 106. In some embodiments, entity recognition engine 108 receives a webpage as input, tokenizes the data, and applies an NLP-based ML model to identify which tokens correspond to named companies. Applying the ML model may include generating one or more ML feature vectors for the web page based on the tokens and feeding the input ML vectors into an ML model, such as an artificial neural network that performs forward propagation using the ML vector. An ML model may be trained to learn contextual patterns in the sequence of tokens. For instance, the ML model may extrapolate meaning based on what tokens precede and/or come after a given token. A token in one context may correspond to an entity name but in another context may have a different meaning. For example, the word “oracle” may be used to refer to a company or have a different meaning depending on the context in which it is used. The trained ML model may learn the different contexts such that when it is applied, it may accurately distinguish between when a given token is used to refer to a company and when it is used for another purpose.”; Par. 40-41; Par. 72-73; Par. 80);
identifying, from a plurality of categorization labels, a subset of categorization labels that correspond to a requesting entity that generated the evaluation request based on a compatibility with the evaluation type (Bailey Par. 35; Par. 22-23-“In some embodiments, a system includes an AI-powered web crawler to generate and populate a segment for a defined task. The AI-powered web crawler may find and categorize content reflecting recent events that are potentially relevant to a set of entities. The AI-powered web crawler may further leverage machine learning to identify reliable web-based sources of news, perform named entity recognition to determine whether web content is relevant to a set of target entities, and classify web content. For example, if the web crawler finds target content, such as a news article on a trusted website, then one or more machine learning models may be applied to (a) identify one or more named entities that are the subject of the content and (b) assign one or more event category labels for the target content Based on the output of the one or more machine learning models, the system may associate an entity with one or more event categories, also referred to herein as event signals. The system may then generate segments based in part on which entities are associated with a particular event category. In some embodiments, users may define filter criteria or parameters for generating a segment. For example, the filter criteria may specify which event categories the system uses to select and/or exclude entities. Additionally or alternatively, event categories may be weighted, with weights assigned by users or machine-learning processes, and entities may be filtered as a function of the assigned weights. Once the system identifies entities satisfying the filter criteria, the system may then identify a set of one or more individuals associated with each entity and populate the segment with all or a subset of data records for these individuals. Individuals that are employees or members of entities that do not satisfy the filter criteria may be excluded from the segment.”; Par. 72-73);
crawling information from at least one website corresponding to the website identifier (Bailey Par. 22-23-“In some embodiments, a system includes an AI-powered web crawler to generate and populate a segment for a defined task. The AI-powered web crawler may find and categorize content reflecting recent events that are potentially relevant to a set of entities. The AI-powered web crawler may further leverage machine learning to identify reliable web-based sources of news, perform named entity recognition to determine whether web content is relevant to a set of target entities, and classify web content. For example, if the web crawler finds target content, such as a news article on a trusted website, then one or more machine learning models may be applied to (a) identify one or more named entities that are the subject of the content and (b) assign one or more event category labels for the target content Based on the output of the one or more machine learning models, the system may associate an entity with one or more event categories, also referred to herein as event signals. The system may then generate segments based in part on which entities are associated with a particular event category.”; Par. 31);
generating at least one text body by parsing the crawled information (Bailey Par. 33-“In some embodiments, NLP engine 110 parses webpages retrieved by web crawler 106 for relevant information. NLP engine 110 may process unstructured text and other content from a variety of sources, including news articles, press releases, government filings, social media, job listings, and blog posts. If relevant information is found, NLP engine 110 may store and incorporate the data into a database and/or other data repository. Fig. 4; Par. 75);
…for causing a graphical interface of the requesting entity to display the recommendation to facilitate selection of a matching entity. (Bailey Par. 78-79-FIG. 5C illustrates example interface page 512 for defining segment filters in accordance with some embodiments. When a user selects the “Compare Contact Fields” step in display area 506, the GUI presents interface element 514 through which the user may define filter criteria to refine the segment generated by the previous step of the application flow. In the example illustrated, the step compares the titles of each contacts to a specified title. The user may indicate whether an exact title match is required or if a similar match is sufficient. The user may further specify whether individuals with the title should be included in the list or excluded from the list. Additionally or alternatively, the user may compare the contact fields to other contact attributes, such as office location, years with the company, prior interactions, and/or other values.; Par. 81)
Bailey teaches classification analysis and the feature is expounded upon by Nia:
applying a machine learning algorithm on the at least one text body, wherein the machine learning algorithm is configured to weight each term in the at least one text body based on whether the term is present in a list of critical terms for the subset of categorization labels (Nia Pg. 3 – 4 and related text- Based on previous equations and assuming that the overall similarity of the two documents is a weighted average of their similarities computed from different perspectives. The weights are the amounts of information in the descriptions…. Jaccard similarity is computed as the number of shared terms over the number of all unique terms in both strings. Matching Coefficient is a very simple vector-based approach which simply counts the number of similar terms, (dimensions), on which both vectors are non zero. Overlap coefficient is similar to the Dice’s coefficient, but considers two strings a full match if one is a subset of the other. );
calculate a ratio of an amount of critical terms in the at least one text body and a total amount of terms in the at least one text body; determine, based on the weighted terms and the ratio, whether the at least one text body corresponds to a given categorization label;(Nia Pg. 3- 5 and related text- At the end Lin defines the Similarity Theorem as: The similarity between A and B is measured by the ratio between the amount of information needed to state the commonality of A and B and the information needed to fully describe what Aand B are; The title or description for an industrial product is often a very short text, between 10 to 60 characters. As depicted in the proposed model in Fig. 1, to be able to accurately identify an item we propose to predict the category first based on the provided input. Thus this is a short-text classification problem. Out of many different techniques for text classification, CNN, initially designed for computer vision, has become increasingly popular and proven outstanding performance in NLP) ;
generate an output vector indicating categorization labels from the subset of categorization labels that the at least one entity corresponds (Nia Pg. 3-5- We use a CNN to identify the category of the input query. The CNN is trained based on the labeled data set for product categories. According to Fig. 1 The categories of products are {C1, C2, ...Cn}, so for any provided input, the classifier will provide a probability {p1, p2, ...pn}, p1 being the probability of the input belonging to C1 and so on. A ranking would pick the category with the most likelihood, being Ck. This provides a context to the next step which extracts the features and having the context improves the quality of the feature extraction... The features extracted from the input will be represented as a vector and matched against the pre-extracted and stored feature vectors of all the items in the database. Cosinesimilarity );
and generate a recommendation for selecting one or more matching entities based on the output vector(Nia Pg. 3-5- A variety of components are required in the proposed framework. We suggest a framework that will break down the complexity of the problem by classification of the input; mapping it to the right category as shown in Fig. 1 and implement a cascade of category specific recommenders. A category recommender component is built using mainly unsupervised methods such as NER and clustering on the labelled data. Afterwards a human category expert is involved to validate the features for the category.)
and transmitting the output vector to the requesting entity … (Nia Pg. 3-5- We use a CNN to identify the category of the input query. The CNN is trained based on the labrecomeled data set for product categories. According to Fig. 1 The categories of products are {C1, C2, ...Cn}, so for any provided input, the classifier will provide a probability {p1, p2, ...pn}, p1 being the probability of the input belonging to C1 and so on. A ranking would pick the category with the most likelihood, being Ck. This provides a context to the next step which extracts the features and having the context improves the quality of the feature extraction... The features extracted from the input will be represented as a vector and matched against the pre-extracted and stored feature vectors of all the items in the database. Cosinesimilarity );
Bailey and Nia are directed to classification and recommender analysis. It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have improve upon data analysis of Bailey, as taught by Nia, by utilizing additional analysis with a reasonable expectation of success of arriving at the claimed invention. One of ordinary skill in the art would have been motivated to make the modification to the teachings of Bailey with the motivation of improving experience and minimizing information overload (Nia Abstract).
Claims 4, 8-9, 13 and 17-18 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Bailey et al., US Publication No. 20220365998A1, [hereinafter Bailey], in view of Nia et al., "A Framework for a Large-Scale B2B Recommender System," 2019 IEEE 14th International Conference on Intelligent Systems and Knowledge Engineering (ISKE), Dalian, China, 2019, pp. 337-343, [hereinafter Nia] and in further view of Miller et al., US Publication No. 20220253871A1, [hereinafter Miller].
Regarding Claim 4 and Claim 13, Bailey in view of Nia teach The method of claim 1, wherein parsing the crawled information further comprises:… and The system of claim 10, wherein the at least one hardware processor is configured to parse the crawled information by:…
Bailey in view of Nia teach classification analysis and the feature is expounded upon by Miller:
removing stop words and punctuation from the crawled information; and classifying, using object recognition, images from the at least one website into text describing contents of the images. (Miller Par. 134-“ In aspects, methods also comprise the processor analyzing a data harmonized evaluation dataset for the presence of undesirable duplicate characters or undesirable duplicate system-identified according to preprogrammed data deduplication standards and removing any identified undesirable duplicate characters or identified undesirable system-identified terms according to a data deduplication protocol to generate a deduplicated dataset and subjecting the deduplicated dataset to further processing to generate semantic vectors and lexical vectors therefrom. In aspects, deduplication is repeated one or more times. In aspects, counts of frequency are made before application of deduplication step(s)..; Par. 172; Par. 271; Par. 184; Par. 204-206”)
Bailey, Nia and Miller are directed to classification analysis. It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have improve upon data analysis of Bailey in view of Nia, as taught by Miller, by utilizing additional notification/alerting with a reasonable expectation of success of arriving at the claimed invention. One of ordinary skill in the art would have been motivated to make the modification to the teachings of Bailey in view of Nia with the motivation of performing automated analysis of product information data records with improved speed and accuracy and the use of such analysis in the performance of other computer-facilitated automated tasks such as generating regulatory submissions, assessing supply chain risk, and the like (Miller Par. 2).
Regarding Claim 8 and Claim 17, Bailey in view of Nia in further view of Miller teach The method of claim 1, wherein crawling the information further comprises:… and The system of claim 10, wherein the at least one hardware processor is configured:…
Bailey in view of Nia teach classification analysis and the feature is expounded upon by Miller:
utilizing a proxy service that hides an IP address of a web crawler. (Miller Par. 303-“ In aspects, S(s)/F(s) comprise SI access, use, or transfer monitoring, such as by employing Database activity monitoring (DAM) software/tools, which are known. Examples of other commercially available data security tools that can be applied to a SOTI (or that corresponding components can be incorporated into an SOTI) include Sophos Intercept X for Server, IBM Security Guardium, Oracle Audit Vault and Database Firewall, Imperva Data Security, Trend Micro ServerProtect, and SQL Secure. In aspects, firewall Functions comprise a list of authorized commands, which can vary, e.g., with level of User/Entity authorization. Firewall and authorization features, such as IP address evaluation, etc., can also overlap, as can reporting of unusual information, attacks on the System, and the like. In aspects, FM(s), DR(s), etc., are segmented from other DS(s)/FM(s), e.g., PRR(s) are segmented from CI. Typically, each type of DS is identified with one or more identifiers, including type identifier(s), which aid in segmentation and protection of SI. Typically, a SOTI will comprise a CIM, which can comprise or consist of a key management system, which stores access keys, policies, protocols, monitoring method(s), etc.”)
Bailey, Nia and Miller are directed to classification analysis. It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have improve upon data analysis of Bailey in view of Nia, as taught by Miller, by utilizing additional notification/alerting with a reasonable expectation of success of arriving at the claimed invention. One of ordinary skill in the art would have been motivated to make the modification to the teachings of Bailey in view of Nia with the motivation of performing automated analysis of product information data records with improved speed and accuracy and the use of such analysis in the performance of other computer-facilitated automated tasks such as generating regulatory submissions, assessing supply chain risk, and the like (Miller Par. 2).
Regarding Claim 9 and Claim 18, Bailey in view of Nia in further view of Miller teach The method of claim 1, wherein crawling the information further comprises:… and The system of claim 10, wherein the at least one hardware processor is configured:…
Bailey in view of Nia teaches classification analysis and the feature is expounded upon by Miller:
executing a script that circumvents security measures of the at least one website. (Miller Par. 332-“ Accordingly, in aspects, certain components (FM(s)) can comprise the ability to interact with internet resources or receive Input, DO, etc., or share results, applications, etc., via the internet, but other component(s) such as MLM code, specialized corpus/SN components, matching ranking data, and data/metadata containing SI or SI-derived information such as PRRs, are secured by firewall(s), encryption, and maintained generally “offline” with respect to internet access.; Par. 391; Par. 582”)
Bailey, Nia and Miller are directed to classification analysis. It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have improve upon data analysis of Bailey in view of Nia, as taught by Miller, by utilizing additional notification/alerting with a reasonable expectation of success of arriving at the claimed invention. One of ordinary skill in the art would have been motivated to make the modification to the teachings of Bailey in view of Nia with the motivation of performing automated analysis of product information data records with improved speed and accuracy and the use of such analysis in the performance of other computer-facilitated automated tasks such as generating regulatory submissions, assessing supply chain risk, and the like (Miller Par. 2).
Regarding Claim 20, Bailey in view of Nia in further view of Miller teach The method of claim 1, …
wherein a categorization label of the subset of categorization labels… (Bailey Par. 35- ML signal detection engine 114 applies one or more ML models to classify events based on the contents of web content retrieved by web crawler 106. In some embodiments, ML signal detection engine 114 categorizes extracted content and/or entities referenced in the content with one or more category tags. Event categories, which may also be referred to as signal classifiers or event buckets, may be pre-defined. Example categories include new product launches, expansion, layoffs, acquisition, initial public offering (IPO), and personnel changes. The event classifiers may vary depending on the particular implementation and be configurable by an end user. A subset of the assigned classifiers may be selected for audit by a human analyst team to ensure accuracy and improve the ML model results. When an auditor verifies or changes an event classifier, the labeled example, including the event classifier and ML feature vector, may be used to retrain and/or tune the ML model.”)
Bailey in view of Nia teach classification analysis and the feature is expounded upon by Miller:
…indicates a relationship of the requesting entity to the at least one entity. (Miller Par. 121 In aspects, by automatic operation of the processor component, the method comprises comparing recognized terms, alphanumeric characters, records, or a combination thereof, contained in a PIDC dataset associated with a combination of elements identified by the query to corresponding system-identified terms, extraneous alphanumeric characters, records, or a combination thereof in the evaluation submission to (a) assess whether there is an error or omission in the one or more evaluation submission terms or records, (b) identify a relationship or potential relationship between evaluation submission and one or more PIDC datasets identified in the query”; Par. 251- Metadata associated with DS(s) used in SOTI/MOTI (e.g., PRRs/CPISs) can provide particularly clear functional data characteristics such as, e.g., protecting SI by imparting access level/SI status information; enabling identification of relationships, opportunities, POC(s), and the like through linking/tagging, e.g., with other DS(s) in the same or other DR(s) (e.g., other PRRs, Records in a RIDR, etc.), synonym content or links to relevant rules/corpus (or corpora) for synonym application, lemmatization, etc., and rules for structure of DSF(s), priority of DSF(s), etc., which can be used in matching, match evaluation (determining hits/misses), ranking hits, assessing confidence of Functions, and the like.)
Bailey, Nia and Miller are directed to classification analysis. It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have improve upon data analysis of Bailey in view of Nia, as taught by Miller, by utilizing additional notification/alerting with a reasonable expectation of success of arriving at the claimed invention. One of ordinary skill in the art would have been motivated to make the modification to the teachings of Bailey in view of Nia with the motivation of performing automated analysis of product information data records with improved speed and accuracy and the use of such analysis in the performance of other computer-facilitated automated tasks such as generating regulatory submissions, assessing supply chain risk, and the like (Miller Par. 2).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure: US Publication No. 20190350187A1 to Kocer et al.- Abstract-“ A configurable nozzle includes a nozzle body having a reception chamber configured to receive an application mixture. The nozzle body includes a nozzle orifice. At least one orifice assembly is coupled with the nozzle body, the at least one orifice assembly includes an orifice plate movably coupled with the nozzle body. The orifice plate extends along at least a portion of the nozzle orifice, and movement of the orifice plate changes one or more of the size or shape of the nozzle orifice. An orifice actuator is coupled with the orifice plate, and the orifice actuator is configured to move the orifice plate.”
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Sincerely,
/CHESIREE A WALTON/ Examiner, Art Unit 3624
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