POTENTIAU INVESTORS FOR A NEW BUSINESS ENTITY

PRIORITY CUAIM

[0001] This application claims priority to U.S. Patent Application No. 16/002,929 filed on June 7, 2018 entitled“Computerized Relevance Scoring Engine For Identifying Potential Investors For A New Business Entity.”

TECHNICAU FIEUD

[0002] An apparatus and method is described for identifying venture capital investors for a new business entity based on a description of the new business entity and past investment activity by venture capital investors.

BACKGROUND OF THE INVENTION

[0003] Startup technology companies typically obtain investments from venture capital firms. These venture capital firms invest money in the startup technology companies in return for equity in the companies. There are thousands of venture capital firms across the world, and it is a daunting task to find the best set of potential investors among the many existing venture capital firms. Startup technology companies often rely upon“word of mouth” recommendations or basic website searches to identify the best candidates. This is an inefficient process for the startup technology companies as well as for venture capital firms, as the latter often are approached by startup technology companies that are in a technology space in which the particular venture capital firm is not interested. As used herein, an“investor” is a person or entity that provides money or other material asset to another person or entity in exchange for a potential profitable return. An example of an investor is a venture capital firm.

[0004] What is needed is a mechanism to assist startup technology companies in identifying venture capital firms that are most likely to invest in that company.

SUMMARY OF THE INVENTION

[0005] The embodiments described herein provide a mechanism for inputting a set of criteria regarding a startup technology company, analyzing all existing data regarding all known venture capital firms, generating a relevancy score for each of the venture capital firms as to that particular startup technology company, and providing a report identifying the venture capital firms who are most likely to invest in the startup technology company and identifying the relevant companies in which that venture capital firm has invested in the past.

BRIEF DESCRIPTION OF THE DRAWINGS

[0006] Figure 1 depicts prior art hardware components of a computing device and data store.

[0007] Figure 2 depicts software components of the computing device.

[0008] Figure 3 depicts a relevance scoring engine

[0009] Figure 4 depicts a keyword module used within the relevance scoring engine.

[0010] Figure 5 depicts a deepnet used within the keyword module.

[0011] Figure 6 depicts exemplary key words datasets for a startup technology company and a venture capital firm.

[0012] Figure 7 depicts a report generated by a display engine.

[0013] Figure 8 depicts another report generated by the display engine. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0014] With reference to Figure 1, computing device 110 is depicted. Computing device 110 can be a server, desktop, notebook, mobile device, tablet, or any other computer with network connectivity. Computing device 110 comprises processing unit 130, memory 140, non-volatile storage 150, network interface 160, input device 170, and display device 180. Non-volatile storage 150 can comprise a hard disk drive or solid state drive. Network interface 160 can comprise an interface for wired communication (e.g., Ethernet) or wireless communication (e.g., 3G, 4G, GSM, 802.11). Input device 170 can comprise a keyboard, mouse, touchscreen, microphone, motion sensor, and/or other input device. Display device 180 can comprise an LCD screen, touchscreen, or other display.

[0015] Computing device 110 is coupled (by network interface 160 or another communication port) to data store 120 over network/link 190. Network/link 190 can comprise wired portions (e.g., Ethernet) and/or wireless portions (e.g., 3G, 4G, GSM, 802.11), or a link such as USB, Firewire, PCI, etc. Network/link 190 can comprise the Internet, a local area network (LAN), a wide area network (WAN), or other network.

[0016] With reference to Figure 2, software components of computing device 110 are depicted.

Computing device 110 comprises operating system 210 (such as the operating systems known by the trademarks“Windows,”“Linux,”“MacOS,”“Android,� �� or“iOS”), web server 220 (such as

Apache), and software applications 230. Software applications 230 comprise relevance scoring engine 240 and display engine 250. Operating system 210, web server 220, and software applications 230 each comprise lines of software code that can be stored in memory 140 and executed by processing unit 130 (or plurality of processing units). [0017] Figure 3 depicts additional aspects of relevance scoring engine 240. In the examples that follow, it is assumed that the technology startup company of interest is called“Aingel,” and that a user wishes to obtain a list of venture capital firms who are best suited for investing in Aingel. The user first enters input dataset 310. In one example, input dataset 310 can comprise a written description of the overall business of Aingel. An example of input dataset 310 might be,“Aingel is an AI-driven analytics platform that matches startups with their ideal VCs and helps them fundraise faster.”

[0018] Keyword module 320 parses input dataset 310 to generate a company keyword dataset 311 of keywords and concepts that concisely describe the nature of Company X’s business, as well as company vector representation 312 of those keywords and concepts. Company vector representation 312 is a vectorized transformation of each word in company keyword dataset 311. If input dataset 310 already is a concise list of words, then it is to be understood that company keyword dataset 311 may be the same or very similar to input dataset 310.

[0019] An embodiment of keyword module 320 will now be described with reference to Figure 4. Keyword module 320 receives input dataset 310 and performs sentences and word

tokenization and encoding using deepnet 401. Deepnet 401 converts the words into company keyword dataset 311 and company vector representation 312.

[0020] With reference to Figure 5, deepnet 401 comprises multiple convolution layers, that address the l-ngram, 2-ngram, 3-ngram words convolutions with 100 filters each. It is to be understood that additional convolution layers can be used. For each convolutions layer output 501, max pooling 502 is performed.

[0021] Three dense layers are then applied and the final result will be company vector representation 3122 for the category of business (the actual Y). An example of keywords from company keyword dataset 311 for the example of input dataset 310, described above, might be “(AI, analytics, invest).” Keyword module 320 then can use RMSE to measure the error between the actual Y and the predicted Y.

[0022] By providing input dataset 310 to deepnet 401 and training on the category of business (Y), the first layer will have a latent representation for the description. The latent vector together with company keyword dataset 311 will be used for matching vectors.

[0023] With reference again to Figure 3, data acquisition module 330 then generates the universe of data that will be analyzed to identify the list of venture capital firms who are best suited for investing in Company X. In this example, data store 120 already contains dataset 370 regarding potential investors.

[0024] Dataset 370 optionally comprises data structure 37 F (such as a table in a relational database) for each known venture capital firm, here referred to generically as VCi, where i ranges from 1 to n, where n is the number of known venture capital firms. Each data structure 37 F can contain one or more of the types of data shown in Table 1:

[0025] Data acquisition module 330 accesses web servers 350 over the Internet 340 and obtains additional data 360 about every known venture capital company VCi and updates dataset 370 (or populates dataset 370, if dataset 370 is initially empty) with additional data 360. To obtain the additional data 360, data acquisition module 330 can perform“screen scraping” of websites or servers and/or can use APIs to obtain data from websites or servers, including obtaining data from services known by the trademarks“Twitter,”“facebook,” and“Linkedln.”

[0026] With reference to Figure 6, keyword module 320 analyzes dataset 370 as it did for input dataset 310, and for each company VCi, generates VC keyword dataset 372i and VC vector representation 373i for that company based on data structure 371

[0027] Data analysis module 380 receives input dataset 310, company keyword dataset 311, company vector representation 312, data structure 371 , and VC keyword dataset 372i and VC vector representation 373i for each of the venture capital firms VCi. For each venture capital firm VCi, data analysis module 330 determines how many keywords in VC keyword dataset 372i matched a keyword in company keyword dataset 311 by comparing company vector

representation 312 and VC vector representation 373i.

[0028] An example of a company keyword dataset 311 and VC keyword dataset 372i (for venture capital firm VCi) are shown in Figure 6. In this example, it can be seen that two keyword terms (“AI” and“artificial intelligence) have a strong match between the two datasets. Data analysis module 330 can further determine a relevance score (shown in parentheses in Figure 6) based on comparison of company vector representation 312 and VC vector representation 373 for each term in VC keyword dataset 372i to indicate how relevant each particular term is to company keyword dataset 311.

[0029] Data analysis module 330 performs the following calculations:

Relevance Score for VCi = max(relevance) of all keywords matched * count of keywords matched

“Relevance Score for VCi” is a relevance score for VCi that indicates how well VCi matches with Company X. It takes the largest relevance score of each keyword matched and multiplies it by the number of keywords matched. In the example of Figure 6, the Relevance Score for VCi would be .95 * 2 = 1.90.

[0030] The Relevance Score for VCi can be normalized as follows:

Normalized Score for VCi = Relevance Score for VCi * 100 /Maximum

Relevance Score for Any VC

[0031] Optionally, a Relevance Score VCi _j can be calculated using the above algorithms for each VC _j with whom VCi has co-invested in the past, and then the Relevance Score VCi can take into account the Relevance Scores VCi _k for each of the k companies with which VCi has co-invested. This, a VC will have a higher Relevance Score if its co-investors VC _k for past investments have relatively high Relevance Scores themselves.

[0032] In addition, for each VCi, a portfolio score can be generated for the portfolio of companies in which that VC has invested. These companies can be referred to as Pi _j , where j ranges from 1 to m, where m is the number of companies in which VCi has invested. [0033] A relevance score can be calculated for the portfolio company, Pi _j , in which VCi has invested, using the same approach as for Relevance Score for VCi:

Relevance Score for Pi _j = max(relevance) of all keywords matched * count of keywords matched

[0034] The Relevance Score for Pi _j can be normalized as follows:

Normalized Score for Pi _j = Relevance Score for Pi _j * 100 /Maximum Relevance Score for Any Pij

[0035] Data analysis module 380 also can determine a score that reflects the recency of investments by each VCi in relevant portfolio companies. A normalized score for recency for each VCi can be calculated as follows:

Normalized Recency Score for VCi = 1.0— (number of days since last investment by VCi in relevant space / maximum number of days since last investment by VC in relevant space )

[0036] Data analysis module 380 also can determine a score that reflects the frequency of investments by each VCi in relevant portfolio companies. A normalized score for frequency for each VCi can be calculated as follows:

Normalized Frequency Score for VCi = number of investments in space by VCi / maximum number of investments in space by any VC

[0037] A Comprehensive Rating for VCi can then be calculated by applying a weighting formula against the Normalized Score for VCi , the Normalized Recency Score for VCi, and the

Normalized Frequency Score for VCi. An example for a weighting formula is: Comprehensive Rating for VCi = 100 * (0.5 * Normalized Score for VCi + 0.25 * Normalized Recency Score for VCi + 0.25 * Normalized Frequency Score for

VQ )

[0038] Figure 7 shows exemplary report 701 generated by display engine 250. Report 701 shows Comprehensive Relevancy Scores VCi of all n companies (or a subset thereof). Figure 8 shows exemplary report 601 generated by display engine 250. Report 801 shows a list of the most relevant companies (Pij) in the portfolio of a particular VC (here, VC ₃₁ ) based on the Relevance Score for Pij .

[0039] Thus, by simply inputting key words or a summary regarding a new venture

(Company X), the user will be provided with a report 701 of the venture capital companies that are the most likely to invest in Company X based on their past activity. For each particular venture capital company, a user can be provided with a report 801 showing which companies invested in by a particular venture capital firm are most relevant to the business of Company X.

[0040] In another embodiment, the apparatus and methods described above can be used to generate comprehensive ratings for individuals within each venture capital company (e.g., specific members or investors of the venture capital company) and to identify the relevance of companies in which that individual has invested in the past. Reports similar to report 701 and 801 can then be generates for specific individuals as opposed to venture capital companies as a whole.

[0041] One of ordinary skill in the art will appreciate that relevance scoring engine 240 can be used for other purposes as well. For example, another type of entity - such as an accelerator (e.g., a fixed-term, cohort-based program that provides seed investment,

connections, mentorship, pitch and demonstration opportunities, and educational components to a startup company to accelerate its growth) or incubator (e.g., entity that provides services such as management training or office space to a startup company) - can use relevance scoring engine 240 to find the ideal venture capital firm to visit the entity or make a presentation to the entity based on the collection of startup companies associated with that entity. Here, input dataset 310 would comprise information for all of the startups associated with that entity, and report 701 in this instance would provide a ranking of the VCs that are best suited for the overall collection of startups associated with that entity. When input dataset 310 reflects data for more than one startup, the relevance score for each VCi could be the sum or average score of VCi as to each individual startup in the overall collection of startups reflected in input dataset 310.

[0042] References to the present invention herein are not intended to limit the scope of any claim or claim term, but instead merely make reference to one or more features that may be covered by one or more of the claims. Materials, processes and numerical examples described above are exemplary only, and should not be deemed to limit the claims. It should be noted that, as used herein, the terms“over” and“on” both inclusively include“directly on” (no intermediate materials, elements or space disposed there between) and“indirectly on” (intermediate materials, elements or space disposed there between). Likewise, the term“adjacent” includes“directly adjacent” (no intermediate materials, elements or space disposed there between) and“indirectly adjacent” (intermediate materials, elements or space disposed there between).