Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
DETAILED ACTION
This communication is a First Office Action Non-Final on Merits. Claims 1-20, as originally filed, are currently pending and have been considered below.
Priority
Applicant’s claim for the benefit of a prior-filed application under 35 U.S.C. 119(e) or under 35 U.S.C. 120, 121, 365(c), or 386(c) is acknowledged.
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 1-20 are rejected under 35 U.S.C. § 101 because the claimed invention is directed to a judicial exception (an abstract idea) without significantly more.
Step 1: Identifying Statutory Categories
In the instant case, claims 1-8 are directed to a system, claims 9-13 are directed to a method and claims 14-20 are directed to a computer readable medium. Thus, the claims fall within one of the four statutory categories. Nevertheless, the claims fall within the judicial exception of an abstract idea. (Examiner note Spec [0053] the term “computer readable medium” is expressly defined to include any type of computer readable storage device and/or storage disk and to exclude propagating signals.)
Step 2A: Prong 1 Identifying a Judicial Exception
Under Step 2A, prong 1, Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention recites an abstract idea without significantly more. Independent claims 1, 9 and 14 recite methods for selecting corporate targets based on social media content that includes store social media data of users, wherein the users include individuals and companies, and wherein each user has a user profile; for each individual, generate individual data terms from the user profile of the individual and identify a current employer based on the user profile; for each company: generate company data terms from the user profile of the company; and add the individual data terms of the individuals for which the company is identified as the current employer to the company data terms of the company; for each combination of company and company data term, generate a frequency score for the company that is indicative of how often the company data term is used with respect to the company; identify one or more seed companies and a pool of candidate companies from the companies on the social media; identify one or more data-types-of-interest for determining one or more target companies from the pool of candidate companies; for each data-type-of-interest, calculate a respective similarity score for each combination of seed company and candidate company; determine the one or more target companies based on similarity scores of each candidate company; and generate and transmit a report for the one or more target companies.
These limitations as drafted, are a process that, under its broadest reasonable interpretation, covers methods of organizing human activity (including commercial interactions such as business relations, managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions) including interaction between person and computer) and mathematical calculations, but for the recitation of generic computer components. That is, other than reciting the structural elements (such as one or more databases, one or more processors, social media platform, a computer readable medium), the claims are directed to selecting corporate targets and generating a report for one or more target companies. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation of organizing human activity but for the recitation of generic computer components, the claim recites an abstract idea.
Step 2A Prong 2 - This judicial exception is not integrated into a practical application because the claim merely describes how to generally “apply” the concept of receiving data, analyzing it, and providing report for one or more target companies. In particular, the claims only recites the additional element – one or more databases, one or more processors, social media platform, a computer readable medium. The additional elements are recited at a high-level of generality such that it amounts to no more than mere instructions to apply the exception using a generic computer component or merely uses a computer as a tool to perform an abstract idea, as discussed in MPEP 2106.05(f). The claims are directed to an abstract idea. Simply implementing the abstract idea on generic components is not a practical application of the abstract idea. Accordingly, these additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
The claims are directed to an abstract idea. When considered in combination, the claims do not amount to improvements to the functioning of a computer, or to any other technology or technical field, as discussed in MPEP 2106.05(a), applying the judicial exception with, or by use of, a particular machine, as discussed in MPEP 2106.05(b), effecting a transformation or reduction of a particular article to a different state or thing, as discussed in MPEP 2106.05(c), or applying or using 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, as discussed in MPEP 2106.05(e). Accordingly, the additional elements do not integrate the abstract idea into a practical application because they does not impose any meaningful limits on practicing the abstract idea. Therefore, the claims are directed to an abstract idea.
Step 2B: Considering Additional Elements
The claimed invention is directed to an abstract idea without significantly more. The claim does not include additional elements that are sufficient to amount significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the claims describe how to generally “apply” to; provide report for one or more target company based on social media content. The claim(s) do not include additional elements that are sufficient to amount to significantly more than the judicial exception because mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. The independent claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. Even when viewed as a whole, nothing in the claim adds significantly more (i.e., an inventive concept) to the abstract idea. The claims are not patent eligible. The dependent claim(s) when analyzed as a whole are held to be patent ineligible under 35 U.S.C. 101 because the additional recited limitation(s) fail to establish that the claim(s) is/are not directed to an abstract idea. The dependent claims are not significantly more because they are part of the identified judicial exception. See MPEP 2106.05(g). The claims are not patent eligible. With respect to the one or more databases, one or more processors, social media platform, a computer readable medium these limitations are described in Applicant’s own specification as generic and conventional elements. See Applicants specification, Paragraph [0051] details “ The processor(s) 110 may be any suitable processing device or set of processing devices such as, but not limited to, a microprocessor, a microcontroller-based platform, an integrated circuit, etc. [0057] database to store data. [0053] the term “computer readable medium” is expressly defined to include any type of computer readable storage device and/or storage disk and to exclude propagating signals.” These are basic computer elements applied merely to carry out data processing such as, discussed above, receiving, analyzing, transmitting and displaying data, which fall under well-understood, routine and conventional functions of generic computers. Furthermore, the use of such generic computers to receive or transmit data over a network has been identified as a well understood, routine and conventional activity by the courts. See Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); TLI Communications LLC v. AVAuto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016) (using a telephone for image transmission); Presenting offers and gathering statistics, OIP Techs., 788 F.3d at 1362-63, 115 USPQ2d at 1092-93, 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); but see DDR Holdings, LLC v. Hotels.com, L.P., 773 F.3d 1245, 1258, 113 USPQ2d 1097, 1106 (Fed. Cir. 2014) ("Unlike the claims in Ultramercial, the claims at issue here specify how interactions with the Internet are manipulated to yield a desired result-a result that overrides the routine and conventional sequence of events ordinarily triggered by the click of a hyperlink." (emphasis added)); Also see MPEP 2106.05(d) discussing elements that the courts have recognized as well-understood, routine and conventional activities in particular fields. Lastly, the additional elements provides only a result-oriented solution which lacks details as to how the computer performs the claimed abstract idea. Therefore, the additional elements amount to mere instructions to apply the exception. See MPEP 2106.05(f).
Furthermore, these steps/components are not explicitly recited and therefore must be construed at the highest level of generality and amount to mere instructions to implement the abstract idea on a computer. Therefore, the claimed invention does not demonstrate a technologically rooted solution to a computer-centric problem or recite an improvement to another technology or technical field, an improvement to the function of any computer itself, applying the exception with, or by use of, a particular machine, effect a transformation or reduction of a particular article to a different state or thing, add a specific limitation other than what is well-understood, routine and conventional in the field, add unconventional steps that confine the claim to a particular useful application, or provide meaningful limitations beyond generally linking an abstract idea to a particular technological environment such as computing. Viewing the limitations as an ordered combination does not add anything further than looking at the limitations individually. Taking the additional claimed elements individually and in combination, the computer components at each step of the process perform purely generic computer functions. Viewed as a whole, the claims do not purport to improve the functioning of the computer itself, or to improve any other technology or technical field. Use of an unspecified, generic computer does not transform an abstract idea into a patent-eligible invention. Thus, the claims do not amount to significantly more than the abstract idea itself.
Dependent claims 2-8, 10-13, and 15-20 add additional limitations, but these only serve to further limit the abstract idea, and hence are nonetheless directed towards fundamentally the same abstract idea as Independent claims.
Claim 2 recites wherein the one or more databases to store each frequency score and each similarity score. These limitations further narrow the abstract idea of independent claims. The claim does not provide any new additional elements beyond abstract idea. Therefore, whether analyzed individually or as an ordered combination, they fail to integrate the abstract idea into a practical application or provide significantly more than the abstract idea.
Claims 3, 7, 10,12, 15, 19 recites each frequency score includes at least one of an n-gram count or a term frequency-inverse document frequency (TF-IDF) score; each similarity score includes at least one of an n-gram overlap score or a cosine similarity score. These limitations further narrow the abstract idea of mathematical calculations by defining frequency score/similarity score. The claims do not provide any new additional elements beyond abstract idea. Therefore, whether analyzed individually or as an ordered combination, they fail to integrate the abstract idea into a practical application or provide significantly more than the abstract idea.
Claims 4, 16 recite identify the pool of candidate companies by determining which of the companies of the social media platform satisfies one or more prerequisites for comparison to the one or more seed companies. These limitations further narrow the abstract idea of independent claims. The claims do not provide any new additional elements beyond abstract idea. Therefore, whether analyzed individually or as an ordered combination, they fail to integrate the abstract idea into a practical application or provide significantly more than the abstract idea.
Claims 5-6, 8, 11, 13, 17-18, 20 recites generating a ranking of the company data terms based on the respective frequency scores for each combination of company and data type, and wherein calculating the similarity score for each combination of seed company and candidate company includes only comparing a predetermined number of the company data terms that are highest ranked in the ranking of the respective seed company; determining the target companies includes: selecting a predetermined number of the candidate companies with respective highest similarity scores; or selecting each of the candidate companies that have a similarity score greater than a threshold score. These limitations further narrow the abstract idea of independent claims. The claims do not provide any new additional elements beyond abstract idea. Therefore, whether analyzed individually or as an ordered combination, they fail to integrate the abstract idea into a practical application or provide significantly more than the abstract idea
The dependent claims do not integrate into a practical application. As such, the additional elements individually or in combination do not integrate the exception into a practical application, but rather, the recitation of any additional element amounts to merely reciting the words “apply it” (or equivalent) with the judicial exception, or merely includes instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea (See MPEP 2106.05(f)). The dependent claims also do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements are merely used to apply the abstract idea to a technological environment. These limitations do not include an improvement to another technology or technical field, an improvement to the functioning of the computer itself, or meaningful limitations beyond generally linking the use of the abstract idea to a particular technological environment. See MPEP 2106.05d. Thus, the claims do not add significantly more to an abstract idea. The claims are ineligible. Therefore, since there are no limitations in the claim that transform the exception into a patent eligible application such that the claim amounts to significantly more than the exception itself, the claims are rejected under 35 USC 101 as being directed to non-statutory subject matter. See (Alice Corporation Pty. Ltd. v. CLS Bank International, et al.).
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.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over Pogrebezky et al. (US 2020/0242635 A1) in view of Vergo et al. (US2020/0233872) A1
Regarding Claims 1, 9 and 14, Pogrebezky discloses the system/method/medium for selecting corporate targets based on social media content (Abstract lines 1-5 generate a rating score for each company profile stored in the repository by processing a plurality of input metrics. The rating scores can then be used by a CRM application to identify highest ranked company profiles in response to a user input.), the system comprising:
Pogrebezky discloses one or more databases configured to store social media data of users of a social media platform, wherein the users include individuals and companies (Fig 12 #1230 multi-tenant database, [0042] determining the company name can be extracted from one or more URLs, from one or more social handles, or from different HTML attributes.[0063] an indication of whether the company profile includes a first type of social media account for that particular company; an indication of whether the company profile includes a second type of social media account for that particular company, [0243] a “tenant” or an “organization” should be understood as referring to a group of one or more users (typically employees) that share access to common subset of the data within the multi-tenant database 1230. In this regard, each tenant includes one or more users and/or groups associated with); and wherein each user has a user profile on the social media platform ([0238] company profile, [0244] Each enterprise tenant may represent a company, corporate department, business or legal organization, and/or any other entities that maintain data for particular sets of users (such as their respective employees or customers) within the multi-tenant system 1200. ); and
Pogrebezky discloses one or more processors configured to: for each individual, generate individual data terms from the user profile of the individual and identify a current employer based on the user profile ([0016] company executive determination module for automatically generating executive profile information (individual data terms) for a company from multiple sources, Fig 7 # 740-760 extract peoples name and job titles [0195] the company executive determination module 146 performs steps 750 through 780 to analyze each of the verified candidate webpages and extract executive details from those verified candidate webpages [0197]At 780, the company executive determination module 146 extracts people's names and job titles from the HTML tree (of each verified candidate webpage) and then adds the extracted names and job titles to the company profile for that particular company (current employer) as executive information. [0244] Each enterprise tenant may represent a company, corporate department, business or legal organization, and/or any other entities that maintain data for particular sets of users (such as their respective employees or customers) within the multi-tenant system 1200.);
Pogrebezky discloses for each company: generate company data terms from the user profile of the company; and add the individual data terms of the individuals for which the company is identified as the current employer to the company data terms of the company ([0197]At 780, the company executive determination module 146 extracts people's names and job titles from the HTML tree (of each verified candidate webpage) (company data terms)and then adds the extracted names and job titles to the company profile for that particular company as executive information., Fig 7 #770-780);
Pogrebezky discloses for each combination of company and company data term, generate a frequency score for the company that is indicative of how often the company data term is used with respect to the company ([0062] the plurality of input metrics can include any combination of company size in terms of number of employees; a cluster size that indicates a number of company seeds that a particular company profile has in a cluster for that particular company; a reliability score, obtained by crowd source testing, that indicates reliability of each of the seed sources that reflects data correctness of that seed source; a number of company news items that indicates how many news items have been collected on a particular company; and a popularity metric (frequency score) that indicates how many times the particular company profile was selected in the past by other CRM users [0207] FIG. 9, the rating module 142 includes a company scoring function 940 that processes various input metrics 902, 904, 906, 908, 910, 912 to generate a rating score 950 for company profile that is stored in the repository 124. The rating score 950 represent popularity, interest, and size of the company.) ;
Pogrebezky discloses identify one or more seed companies and a pool of candidate companies from the companies on the social media platform ([0072] The seed collection, enrichment and clustering system 100 is a robust highly scalable system for generating a repository of company profiles that can be used other applications (e.g., CRM applications). The seed collection, enrichment and clustering system 100 is designed to collect seeds 108 from any source (e.g. Thomson Reuters, New York company registry, etc.) via dedicated components., [0074] Each collected seed comprises original seed data that includes one or more attributes each having a type and an associated value. As used herein, an “attribute” can refer to a specific piece of information about a company that describes or can be used to identify that company. In most cases, an attribute is a tuple of a type and a value <type, value>, where the type can be different properties of some entity (e.g., if the entity is company or an organization some examples of types can include company name, website address, phone, physical address, stock ticker, industry, Facebook® Handle, etc.). Each value is a specific piece of structured or unstructured information associated with a particular company (e.g., information about a company that describes a company, identifies a company, or that can be processed to identify a company) that has been extracted from a webpage or another source. [0202] At step 830, based on the prefix entered, the application can then search the repository 124 for the top candidate company profiles having the highest rating scores. The number of top candidate company profiles can vary depending on the implementation, and can be configured or specified by the CRM user of the application. For example, in one non-limiting implementation, the number of top candidate company profiles could be the top five candidate company profiles sorted based on scores from highest to lowest.);
Pogrebezky discloses identify one or more data-types for determining from the pool of candidate companies ([0202]The top candidate company profiles can be suggested to the user starting with the most interesting/popular company first based on the ratings/scores for each of the top candidate company profiles.;
Pogrebezky discloses for each data-type, calculate a respective similarity score for each combination of seed company and candidate company ([0114] During the clustering process, each candidate company name is compared to each support indicator using similarity functions. Each cluster can include two types of objects—regular candidate company name and support indicators. [0143] a feature can be calculated by comparing two corresponding attribute values form the two seeds. For instance, the attribute is company name, and if there are two seeds with names abc and abc inc, then they can have the following features could result: name_idf-similarity: 0.8 and name_char-similarity: 0.5. [0149] the similarity module 553 can compare a value of the company name attribute 552 that was extracted from the enriched company seed to a value of the company name attribute 551 of the original company seed to determine a first attribute similarity score 554. The first attribute similarity score 554 can then be scaled based on a first weight (e.g., 0.5) for that particular company name attribute to generate a first weighted similarity product 572. [0203] At step 840, the top candidate company profiles are returned to the CRM system and displayed to the CRM user via user interface of the application sorted based on their scores from highest to lowest);
Pogrebezky discloses determine the one or more companies based on similarity scores of each candidate company ([0200] After the rating scores for each company have been generated and added to their respective company profiles [0202]the number of top candidate company profiles could be the top five candidate company profiles sorted based on scores from highest to lowest. The top candidate company profiles can be suggested to the user starting with the most interesting/popular company first based on the ratings/scores for each of the top candidate company profiles, [0014] generating score/ranks for various attributes that are part of a cluster).; and
Pogrebezky discloses generate and transmit a report for the one or more companies. (Fig 8 # 840 return top n candidate company profile to user, [0203] At step 840, the top candidate company profiles are returned to the CRM system and displayed to the CRM user via user interface of the application sorted based on their scores from highest to lowest so that the CRM user can select one of the top candidate company profiles and use it to create the new account record.)
Pogrebezky does not specifically teach identify one or more data-types-of-interest for determining one or more target companies from the pool of candidate companies; for each data-type-of-interest, calculate a respective similarity score for each combination of seed company and candidate company; determine the one or more target companies based on similarity scores of each candidate company; generate and transmit a report for the one or more target companies
Vergo teaches for each combination of company and company data term, generate a frequency value for the company ([0022] calculate the text similarity between the query text and all of the companies in the search repository using term frequency-inverse document frequency plus cosine similarity (alternatively, Word2Vecec, GLoVe, Doc2Vec, etc. may be used for the word embedding) and measuring scores based on structured attributes in the query and derived attributes for processing the text similarity, yielding a re-ranked list of recommended companies.); identify one or more data-types-of-interest for determining one or more target companies from the pool of candidate companies ([0022] find a set of companies (i.e., entities) that are most similar to target description (i.e., query) of a company by evaluating text similarity between the query text and all of the companies in the search repository and obtaining a ranked list of companies.[0039] In step 102, a list of companies (or description of companies) is used as a list of query entities such that, in step 103, a set of similar companies for each company on the list of query entities is calculated. For example, a list of companies that produce wearable computing technology might consist of Fitbit, Salutron, Jawbone and Runtastic.); for each data-type-of-interest, calculate a respective similarity score for each combination of seed company and candidate company ([0026] The similarity between two companies can be calculated by preprocessing each description to filter out unimportant text, stop words, and boilerplate phrases. The names of specific companies and locations are removed, which generally do not contribute to functional similarity. These words and phrases are identified with a publicly available Natural Language Understanding service. The remaining text is then lemmatized and this process is repeated for the description of all companies in the database. [0027] The similarity between two companies is defined as the cosine of their tf-idf vectors. Fig 4 calculate similarity matrix [0045] The similarity search between each is compiled and expanded to calculate a similarity matrix between each list and the corpus.); determine the one or more target companies based on similarity scores of each candidate company ([0028] To find the n most similar companies to company A, a similarity between company A and all companies in the tf-idf matrix is found. [0030] To form the ground truth, 3000 pairs of company descriptions are annotated by assigning each pair a rank of “1” (strong), “2” or “3” (weak). To evaluate the performance of the model, the similarity scores are calculated from the model (as described above) for all 3000 pairs and then calculated the Spearman's rank correlation coefficient. This is iterated over several values of mindf and maxdf, picking the values that resulted in the best Spearman score.[0031] Finding the top companies that match a list L of companies is done in three steps. For each company in L, similar companies are found, each with its similarity score. A similarity algorithm is employed based on company description text. The similarity techniques are deployed that also take into account structured data, such as number of employees, revenue and location (i.e., company information includes a combination of descriptive (textual) information about the company and structured information about the companies). [0033] the matching companies are sorted by their aggregate scores, aggi, and the top ones are returned. The initial list input by the user can be used to tune the weights of the similarity function to realize a weighted cosine similarity function.); generate and transmit a report for the one or more target companies ([0033] the matching companies are sorted by their aggregate scores, aggi, and the top ones are returned. [0035] The results of the lists are visualized by maintaining a relationship of similarity in a high-dimensional space mapped to a low-dimensional visualization (e.g., shown in 2-D or 3-D for human consumption). More specifically, to help the user find matching companies of interest based on similarity to one or more lists, a t-SNE based visualization, U-map, dimensionality reduction technique, etc. may be used., [0041] In step 105, a final set of the results is ordered based on the voting scheme and presenting them back to the user as a first ranked list.)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have included identify one or more data-types-of-interest for determining one or more target companies from the pool of candidate companies; for each data-type-of-interest, calculate a respective similarity score for each combination of seed company and candidate company; determine the one or more target companies based on similarity scores of each candidate company; generate and transmit a report for the one or more target companies, as disclosed by Vergo in the system disclosed by Pogrebezky, for the motivation of providing a method to help the user find matching companies of interest based on similarity to one or more lists/query ([0035], [0041] Vergo)
Claim 14. Pogrebezky discloses the computer readable medium ([0160] some or all steps of this process, and/or substantially equivalent steps, are performed by execution of processor-readable instructions stored or included on a non-transitory processor-readable medium. comprising instructions, which, when executed, cause a machine to:
Regarding Claims 2. Pogrebezky as modified by Vergo teaches the system of claim 1,
Pogrebezky teaches wherein the one or more databases are further configured to store each frequency score and each similarity score ([0077] the repository 124 where seeds and company profiles are stored can be implemented using a data store or distributed database such as the Apache Cassandra™ database management system, [0102] At 290, the rating module 142 can automatically score or rate company profiles that are stored in the repository 124 to generate a score or rating for each company profile. The scores or ratings that are generated at 290 can then be used by applications when selecting company profiles (e.g., in the process of generating CRM records). A CRM user can input information into a CRM application and the CRM application can then automatically retrieve one or more of the company profiles that have the highest score(s) or rating(s).).
Regarding Claims 3, 10 and15. Pogrebezky as modified by Vergo teaches the system of claim 1,
Pogrebezky does not teach wherein each frequency score includes at least one of an n-gram count or a term frequency-inverse document frequency (TF-IDF) score.
Vergo teaches wherein each frequency score includes at least one of an n-gram count or a term frequency-inverse document frequency (TF-IDF) score. ([0022] calculate the text similarity between the query text and all of the companies in the search repository using term frequency-inverse document frequency plus cosine similarity (alternatively, Word2Vecec, GLoVe, Doc2Vec, etc. may be used for the word embedding) and measuring scores based on structured attributes in the query and derived attributes for processing the text similarity, yielding a re-ranked list of recommended companies.)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have included at least one of an n-gram count or a term frequency-inverse document frequency (TF-IDF) score, as disclosed by Vergo in the system disclosed by Pogrebezky, for the motivation of providing a method to help the user find matching companies of interest based on similarity to one or more lists/query by searching repository using term frequency inverse document frequency ([0022], [0035], [0041] Vergo)
Regarding Claims 4 and 16, Pogrebezky as modified by Vergo teaches the system of claim 1,
Pogrebezky teaches wherein the one or more processors are configured to identify the pool of candidate companies by determining which of the companies of the social media platform satisfies one or more prerequisites for comparison to the one or more seed companies.([0044] a company seed enrichment method and pipeline system are provided for finding and validating enhancement information to be added to company seed data to enrich company seed data. In one implementation, a seed enricher module automatically enriches collected seeds. Each of the collected seeds comprises: original seed data that includes a plurality of attributes each having a type and an associated value. Each value is a specific piece of structured or unstructured information associated with a particular company, [0051] each of values for each attribute within that cluster can be scored to generate a score for each attribute by comparing values for each attribute to a value of a corresponding attribute from the original seed data that was extracted from the home webpage for that company. In one embodiment, each of values for each attribute within each cluster can be scored by (a) selecting a particular enriched company seed from the cluster; (b) extracting values for each attribute of that particular enriched company seed; (c) determining a similarity of each extracted value for each attribute of that particular enriched company seed in comparison to an original value of a corresponding attribute from the original company seed to determine a similarity score for that attribute of that particular enriched company seed, [0063] the plurality of input metrics can include any combination of an indication of whether the company profile includes a ticker symbol that indicates that the particular company is publicly traded; an indication of whether the company profile includes a phone number for that particular company; an indication of whether the company profile includes a physical address for that particular company; an indication of whether the company profile includes a first type of social media account for that particular company (prerequisites) [0203At step 840, the top candidate company profiles are returned to the CRM system and displayed to the CRM user via user interface of the application sorted based on their scores from highest to lowest)
Regarding claims 5, 11 and 17. Pogrebezky as modified by Vergo teaches the system of claim 1,
Pogrebezky teaches wherein, for each combination of company and data type, the one or more processors are configured to generate a ranking of the company data terms based on the respective frequency scores.( [0205]a company scoring generator 900 that can be executed at the rating module 142 to rate company profiles, [0206] FIG. 9, the rating module 142 includes a company scoring function 940 that processes various input metrics 902, 904, 906, 908, 910, 912 to generate a rating score 950 for company profile that is stored in the repository 124. The rating score 950 represent popularity, interest, and size of the company. [0061] each rating score represent popularity, interest, and size of a particular company.[0135] automatically determines/selects which attributes to choose for converting the cluster into a company profile by selecting the best values for a given attribute (or field) from multiple sources. In this way, the profile generator module 164 can effectively filter out any bad values. For each attribute, the company generation algorithm executed by the profile generator module 164 can generate a score/value, and then select the attributes having the highest score/value for inclusion in the company profile. )
Regarding claims 6, 11, 18. Pogrebezky as modified by Vergo teaches the system of claim 5,
Pogrebezky does not specifically teach wherein, to calculate the similarity score for each combination of seed company and candidate company, the one or more processors are configured to only compare a predetermined number of the company data terms that are highest ranked in the ranking of the respective seed company.
Vergo teaches wherein, to calculate the similarity score for each combination of seed company and candidate company, the one or more processors are configured to only compare a predetermined number of the company data terms that are highest ranked in the ranking of the respective seed company. ([0023] the similarity calculations of the invention are based on an assumption that similar companies have overlapping key words in their descriptions or that the words used in the descriptions have similar meaning (i.e. semantics). [0026] The similarity between two companies can be calculated by preprocessing each description to filter out unimportant text, stop words, and boilerplate phrases. The names of specific companies and locations are removed, which generally do not contribute to functional similarity. These words and phrases are identified with a publicly available Natural Language Understanding service. The remaining text is then lemmatized and this process is repeated for the description of all companies in the database. A term frequency-inverse document frequency (tf-idf) model is built using the preprocessed descriptions. [0027]The similarity between two companies is defined as the cosine of their tf-idf vectors [0030] An evaluation metric is defined to measure the performance of the tf-idf model, or generally, a word embedding technique used by the invention. In one embodiment, ground truth data is needed. To form the ground truth, 3000 pairs of company descriptions are annotated by assigning each pair a rank of “1” (strong), “2” or “3” (weak). Finding the top companies that match a list L of companies is done in three steps. For each company in L, similar companies are found, each with its similarity score. A similarity algorithm is employed based on company description text. The similarity techniques are deployed that also take into account structured data, such as number of employees, revenue and location (i.e., company information includes a combination of descriptive (textual) information about the company and structured information about the companies).)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have included to calculate the similarity score for each combination of seed company and candidate company, the one or more processors are configured to only compare a predetermined number of the company data terms that are highest ranked in the ranking of the respective seed company, as disclosed by Vergo in the system disclosed by Pogrebezky, for the motivation of providing a method to help the user find matching companies of interest based on similarity to one or more lists/query by searching repository using term frequency inverse document frequency ([0022], [0035], [0041] Vergo)
Regarding Claims 7, 12, 19. Pogrebezky as modified by Vergo teaches the system of claim 1,
Pogrebezky does not teach wherein each similarity score includes at least one of an n-gram overlap score or a cosine similarity score.
Vergo teaches wherein each similarity score includes at least one of an n-gram overlap score or a cosine similarity score. ([0027]The similarity between two companies is defined as the cosine of their tf-idf vectors [0030] An evaluation metric is defined to measure the performance of the tf-idf model, or generally, a word embedding technique used by the invention. In one embodiment, ground truth data is needed. To form the ground truth, 3000 pairs of company descriptions are annotated by assigning each pair a rank of “1” (strong), “2” or “3” (weak). Finding the top companies that match a list L of companies is done in three steps. For each company in L, similar companies are found, each with its similarity score. A similarity algorithm is employed based on company description text. The similarity techniques are deployed that also take into account structured data, such as number of employees, revenue and location (i.e., company information includes a combination of descriptive (textual) information about the company and structured information about the companies).)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have included wherein each similarity score includes at least one of an n-gram overlap score or a cosine similarity score, as disclosed by Vergo in the system disclosed by Pogrebezky, for the motivation of providing a method to help the user find matching companies of interest based on similarity to one or more lists/query by searching repository using term frequency inverse document frequency ([0022], [0035], [0041] Vergo)
Regarding Claims 8, 13 and 20, Pogrebezky as modified by Vergo teaches the system of claim 1,
Pogrebezky teaches wherein, to determine the target companies, the one or more processors are configured to: select a predetermined number of the candidate companies with respective highest similarity scores ([0202] At step 830, based on the prefix entered, the application can then search the repository 124 for the top candidate company profiles having the highest rating scores. The number of top candidate company profiles can vary depending on the implementation, and can be configured or specified by the CRM user of the application. The number of top candidate company profiles could be the top five candidate company profiles sorted based on scores from highest to lowest. The top candidate company profiles can be suggested to the user starting with the most interesting/popular company first based on the ratings/scores for each of the top candidate company profiles.); or select each of the candidate companies that have a similarity score greater than a threshold score.
Vergo also teaches select a predetermined number of the candidate companies with respective highest similarity scores ([0041] In step 105, a final set of the results is ordered based on the voting scheme and presenting them back to the user as a first ranked list) or select each of the candidate companies that have a similarity score greater than a threshold score.([0036] A second variant uses a force-based layout for a graph, in which each company is a node, and two nodes are connected if their similarity is above a given threshold.)
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Guo (US 10,242,258) discloses evaluating a first plurality of features for both records in the pair of records by calculating term frequency-inverse term frequency (TF-IDF) for each token of each field relevant to each feature and based on the calculated TF-IDF for each token of each field relevant to each feature, computing a similarity score based on the similarity function by adding a weight assigned to the TF-IDF for any token that appears in both records. Fig 21 #2106 consider record for similarity analysis if above threshold.
Gao (US2022/0147523) discloses wherein each frequency score includes at least one of an n-gram count or a term frequency-inverse document frequency (TF-IDF) score. ([0066] compute similarity between two strings is to represent each string as a vector, and then use a vector similarity or distance measure, such as cosine similarity or Euclidean distance. The vector representation can follow a bag-of-words format, where each dimension of the vector corresponds to the strength of a specific word (or character or n-gram sequence of words), typically in the form of the word count or TF-IDF value. TF-IDF stands for Term Frequency-Inverse Document Frequency, which is a weighting technique that is proportional to the count of a word (term frequency) in a string, and inversely proportional to the occurrences of the word in the corpus of all documents or strings (document frequency)., [0081] where the document frequency of word t is previously computed by counting the number of company names that contain t)
Tam (US2021/0097492) discloses determine company 202.1 and company 202.2 are a possible pair of companies 414 based on a similarity of the logos 304 of company 202.1 and company 202.2. Generate possible pairs module 402 may determine company 202.1 and company 202.2 are a possible pair of companies 414 based on a similarity of the names 302 of company 202.1 and company 202.2, e.g., name 302 “LinkedIn” and name 302 “LinkedIn China”. [0093] FIG. 14 illustrates the operation 1400 of extract acquisition phrases module 1400, in accordance with some embodiments. Illustrated in FIG. 14 is company description 1404, extract acquisition phrases module 1402, acquisition phrases 1406, template acquisition phrases 1408, company 1 1410.1, and company 2 1410.2.
Yoshida (WO2022064690A1) discloses the business matching system 10 of the present embodiment is a system that presents candidates for a company suitable as a trading partner when a company makes a transaction with another company. Specifically, the business matching system 10 has the needs of the first company when the target company for matching is the first company and one or more candidate companies that are candidates for the matching destination are the second companies.
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/SANGEETA BAHL/Primary Examiner, Art Unit 3626
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