DETAILED ACTION
This action is in reply to the application filed on 01/27/2025.
Claims 1-20 are pending.
Claims 1-20 have been examined.
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(d):
(d) REFERENCE IN DEPENDENT FORMS.—Subject to subsection (e), a claim in dependent form shall contain a reference to a claim previously set forth and then specify a further limitation of the subject matter claimed. A claim in dependent form shall be construed to incorporate by reference all the limitations of the claim to which it refers.
The following is a quotation of pre-AIA 35 U.S.C. 112, fourth paragraph:
Subject to the following paragraph [i.e., the fifth paragraph of pre-AIA 35 U.S.C. 112], a claim in dependent form shall contain a reference to a claim previously set forth and then specify a further limitation of the subject matter claimed. A claim in dependent form shall be construed to incorporate by reference all the limitations of the claim to which it refers.
Claim 16 rejected under 35 U.S.C. 112(d) or pre-AIA 35 U.S.C. 112, 4th paragraph, as being of improper dependent form for failing to further limit the subject matter of the claim upon which it depends, or for failing to include all the limitations of the claim upon which it depends. The claim recites any of the steps in Claim 1 and does not further narrow nor limit the scope of the claim invention. Applicant may cancel the claim(s), amend the claim(s) to place the claim(s) in proper dependent form, rewrite the claim(s) in independent form, or present a sufficient showing that the dependent claim(s) complies with the statutory requirements.
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 directed to a method, a system, or product which are one of the statutory categories of invention. (Step 1: Yes).
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more. Claims 1-20 are directed to an abstract idea, Method of Organizing Human Activity. The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional computer elements, which are recited at a high level of generality, provide generic computer functions that do not add meaningful limits to practicing the abstract idea.
Claims 1 and 17 are grouped together. Claim 1, for instance, recites, A computer implemented method for facilitating knowledge sharing between one or more startups, one or more experts, and one or more investors, and optimizing investment decisions, the method comprising: receiving an application from one or more companies in a startup pool; using artificial intelligence or machine learning to narrow the one or more companies down to one or more startups; matching the one or more startups with one or more experts in a relevant technical field; receiving one or more data from the one or more startups about the one or more startups; transmitting the one or more data to the one or more experts; receiving one or more analyses from the one or more experts of the one or more startups; and aggregating the one or more analyses to create a consensus rating for each of the one or more startups. These limitations are directed to human resources matching– business relations (commercial interactions). Hence, it falls within the “Certain Methods of Organizing Human Activity” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
Claim 10 is grouped together. Claim 1, for instance, recites, A computer implemented method for training a machine learning model for optimizing investment outcomes, the method comprising: obtaining a dataset of identified investment outcomes; training the machine learning model using the dataset of identified investment outcomes thereby obtaining a trained machine learning model, and storing the trained machine learning model. These limitations are directed to human resources matching– business relations (commercial interactions). Hence, it falls within the “Certain Methods of Organizing Human Activity” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
This judicial exception is not integrated into a practical application. In particular, the claim only recites additional elements such as processing circuitry, a memory, one or more startups, one or more experts, and artificial intelligence recited at a high-level of generality (generating, linking, determining) such that it amounts no more than mere instructions to apply the exception using a generic computer component. Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea
Next the claim as a whole is analyzed to determine whether any element, or combination of elements, is sufficient to ensure the claim amounts to significantly more than an abstract idea. Claims 1-20 do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements are merely performing the abstract idea on a generic device i.e., abstract idea and apply it. There is no improvement to computer technology or computer functionality MPEP 2106.05(a) nor a particular machine MPEP 2106.05(b) nor a particular transformation MPEP 2106.05(c). Given the above reasons, a generic processing device helps to compose a risk profile and purchase insurance based on such risk for a property is not an Inventive Concept. Thus, the claim is not patent eligible.
The dependent claims have been given the full two part analysis (Step 2A – 2-prong tests and step 2B) including analyzing the additional limitations both individually and in combination. The dependent claim(s) when analyzed both individually and in combination are also held to be patent ineligible under 35 U.S.C. 101 because for the same reasoning as above and the additional recited limitation(s) fail(s) to establish that the claim(s) is/are not directed to an abstract idea. The additional limitations of the dependent claim(s) when considered individually and as ordered combination do not amount to significantly more than the abstract idea.
The dependent claims 2 and 18 has been given the full two part analysis (Step 2A – 2-prong tests and step 2B) including analyzing the additional limitations both individually and in combination. The dependent claim(s) when analyzed both individually and in combination are also held to be patent ineligible under 35 U.S.C. 101 because the claims recite transmitting the one or more analyses and the consensus ratings to one or more investors and the additional recited limitation(s) fail(s) to establish that the claim(s) is/are not directed to an abstract idea. The additional elements (such as a machine learning model) of the dependent claim(s) when considered individually and as ordered combination do not amount to significantly more than the abstract idea. Therefore, the claims are not patent eligible.
The dependent claim 3 and 19 has been given the full two part analysis (Step 2A – 2-prong tests and step 2B) including analyzing the additional limitations both individually and in combination. The dependent claim(s) when analyzed both individually and in combination are also held to be patent ineligible under 35 U.S.C. 101 because the claims recite receiving one or more evaluations from the one or more investors and the additional recited limitation(s) fail(s) to establish that the claim(s) is/are not directed to an abstract idea. The additional elements (such as a machine learning model) of the dependent claim(s) when considered individually and as ordered combination do not amount to significantly more than the abstract idea. Therefore, the claims are not patent eligible.
The dependent claim 4 and 20 has been given the full two part analysis (Step 2A – 2-prong tests and step 2B) including analyzing the additional limitations both individually and in combination. The dependent claim(s) when analyzed both individually and in combination are also held to be patent ineligible under 35 U.S.C. 101 because the claims recite making one or more investment decisions and the additional recited limitation(s) fail(s) to establish that the claim(s) is/are not directed to an abstract idea. The additional elements (such as a machine learning model) of the dependent claim(s) when considered individually and as ordered combination do not amount to significantly more than the abstract idea. Therefore, the claims are not patent eligible.
The dependent claim 5 has been given the full two part analysis (Step 2A – 2-prong tests and step 2B) including analyzing the additional limitations both individually and in combination. The dependent claim(s) when analyzed both individually and in combination are also held to be patent ineligible under 35 U.S.C. 101 because the claims recite paying each of the one or more experts and the additional recited limitation(s) fail(s) to establish that the claim(s) is/are not directed to an abstract idea. The additional elements (such as a machine learning model) of the dependent claim(s) when considered individually and as ordered combination do not amount to significantly more than the abstract idea. Therefore, the claims are not patent eligible.
The dependent claim 6 has been given the full two part analysis (Step 2A – 2-prong tests and step 2B) including analyzing the additional limitations both individually and in combination. The dependent claim(s) when analyzed both individually and in combination are also held to be patent ineligible under 35 U.S.C. 101 because the claims recite one or more documents and the additional recited limitation(s) fail(s) to establish that the claim(s) is/are not directed to an abstract idea. The additional elements (such as a machine learning model) of the dependent claim(s) when considered individually and as ordered combination do not amount to significantly more than the abstract idea. Therefore, the claims are not patent eligible.
The dependent claim 7 has been given the full two part analysis (Step 2A – 2-prong tests and step 2B) including analyzing the additional limitations both individually and in combination. The dependent claim(s) when analyzed both individually and in combination are also held to be patent ineligible under 35 U.S.C. 101 because the claims recite referring to information contained int the output of the models and the additional recited limitation(s) fail(s) to establish that the claim(s) is/are not directed to an abstract idea. The additional elements (such as a machine learning model) of the dependent claim(s) when considered individually and as ordered combination do not amount to significantly more than the abstract idea. Therefore, the claims are not patent eligible.
The dependent claim 8 has been given the full two part analysis (Step 2A – 2-prong tests and step 2B) including analyzing the additional limitations both individually and in combination. The dependent claim(s) when analyzed both individually and in combination are also held to be patent ineligible under 35 U.S.C. 101 because the claims recite a predicted company valuation and the additional recited limitation(s) fail(s) to establish that the claim(s) is/are not directed to an abstract idea. The additional elements (such as a machine learning model) of the dependent claim(s) when considered individually and as ordered combination do not amount to significantly more than the abstract idea. Therefore, the claims are not patent eligible.
The dependent claim 9 has been given the full two part analysis (Step 2A – 2-prong tests and step 2B) including analyzing the additional limitations both individually and in combination. The dependent claim(s) when analyzed both individually and in combination are also held to be patent ineligible under 35 U.S.C. 101 because the claims recite r improving the one or more investment decisions over time and the additional recited limitation(s) fail(s) to establish that the claim(s) is/are not directed to an abstract idea. The additional elements (such as a machine learning model) of the dependent claim(s) when considered individually and as ordered combination do not amount to significantly more than the abstract idea. Therefore, the claims are not patent eligible.
The dependent claim 11 has been given the full two part analysis (Step 2A – 2-prong tests and step 2B) including analyzing the additional limitations both individually and in combination. The dependent claim(s) when analyzed both individually and in combination are also held to be patent ineligible under 35 U.S.C. 101 because the claims recite optimizing identified investment outcomes and the additional recited limitation(s) fail(s) to establish that the claim(s) is/are not directed to an abstract idea. The additional elements (such as a machine learning model) of the dependent claim(s) when considered individually and as ordered combination do not amount to significantly more than the abstract idea. Therefore, the claims are not patent eligible.
The dependent claim 12 has been given the full two part analysis (Step 2A – 2-prong tests and step 2B) including analyzing the additional limitations both individually and in combination. The dependent claim(s) when analyzed both individually and in combination are also held to be patent ineligible under 35 U.S.C. 101 because the claims recite return on investment and the additional recited limitation(s) fail(s) to establish that the claim(s) is/are not directed to an abstract idea. The additional elements (such as a machine learning model) of the dependent claim(s) when considered individually and as ordered combination do not amount to significantly more than the abstract idea. Therefore, the claims are not patent eligible.
The dependent claim 13 has been given the full two part analysis (Step 2A – 2-prong tests and step 2B) including analyzing the additional limitations both individually and in combination. The dependent claim(s) when analyzed both individually and in combination are also held to be patent ineligible under 35 U.S.C. 101 because the claims recite one or more inputs and the additional recited limitation(s) fail(s) to establish that the claim(s) is/are not directed to an abstract idea. The additional elements (such as a machine learning model) of the dependent claim(s) when considered individually and as ordered combination do not amount to significantly more than the abstract idea. Therefore, the claims are not patent eligible.
The dependent claim 14 has been given the full two part analysis (Step 2A – 2-prong tests and step 2B) including analyzing the additional limitations both individually and in combination. The dependent claim(s) when analyzed both individually and in combination are also held to be patent ineligible under 35 U.S.C. 101 because the claims recite one or more inputs and the additional recited limitation(s) fail(s) to establish that the claim(s) is/are not directed to an abstract idea. The additional elements (such as a machine learning model) of the dependent claim(s) when considered individually and as ordered combination do not amount to significantly more than the abstract idea. Therefore, the claims are not patent eligible.
The dependent claim 15 has been given the full two part analysis (Step 2A – 2-prong tests and step 2B) including analyzing the additional limitations both individually and in combination. The dependent claim(s) when analyzed both individually and in combination are also held to be patent ineligible under 35 U.S.C. 101 because the claims recite making one or more investment decisions and the additional recited limitation(s) fail(s) to establish that the claim(s) is/are not directed to an abstract idea. The additional elements (such as a machine learning model) of the dependent claim(s) when considered individually and as ordered combination do not amount to significantly more than the abstract idea. Therefore, the claims are not patent eligible.
The dependent claim 16 has been given the full two part analysis (Step 2A – 2-prong tests and step 2B) including analyzing the additional limitations both individually and in combination. The dependent claim(s) when analyzed both individually and in combination are also held to be patent ineligible under 35 U.S.C. 101 because the claims recite matching the one or more startups with one or more experts and the additional recited limitation(s) fail(s) to establish that the claim(s) is/are not directed to an abstract idea. The additional elements (such as a machine learning model) of the dependent claim(s) when considered individually and as ordered combination do not amount to significantly more than the abstract idea. Therefore, the claims are not patent eligible.
Therefore, Claims 1-20 are not drawn to eligible subject matter as they are directed to an abstract idea without significantly more.
Claim Rejections - 35 USC § 103
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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1-4, 10-11, 16-20 are rejected under 35 U.S.C. 103 as being unpatentable over Stockdale, (WO 2021/214571 A1) in view of (Institute for Supply Managmenet, Inc. (WO 2021/207432 A1); hereinafter, IFSM.
Claims 1 and 17 are grouped together. Stockdale teaches: A computer implemented method for facilitating knowledge sharing between one or more startups, one or more experts, and one or more investors, and optimizing investment decisions, the method comprising: receiving an application from one or more companies in a startup pool (Stockdale, see at least “. . . Where appropriate users may select from a dropdown list or other data input tool for such things as industry that may include for example medical, legal, construction, HR, retail, or hospitality. The dropdown list or other tool may have one or more cascading submenus for example if medical is selected the submenu may include doctor, nurse, surgeon, research scientist etc. Providing drop lists for user selection facilitates searching the platform for relevant profiles. Critically in this embodiment excluded characteristics including one or more of name, gender, race and religion are not disclosed on the platform to other users, whether potential employers or employees. Therefore a potential employee remains anonymous until they input a permission confirmation to the system in response to a system request to waive anonymity in respect of a particular expression of interest by a potential employer . . .”);
using artificial intelligence or machine learning to narrow the one or more companies down to one or more startups (Stockdale, see at least “. . . The artificial intelligence engine 300 comprises executable instructions comprising instructions to output to a first user a questionnaire for population with objectives in said categories by the first user. An example for remuneration D is shown in Figure 11. A user specifies £30,000 as their current salary. Their objective is to earn a salary of $50,000 within 5 years, £80,000 within 10 years, £110,000 within 15 years and £150,000 within 20 years. Objectives may be specified in other categories other may be for example for role. A current role may be sales assistant. An objective within 5 years may be a role of sales manager, within 10 years may be a role of regional sales manager, and within 15 years to be sales director. Objectives may be specified for any suitable category. The system receives the objectives from a user ‘α’ in the completed questionnaire. The AI engine searches other user ‘β’ profiles or external sources of equivalent data for matches between the objectives of user ‘α’ and current or requested data in a corresponding category of users ‘β’. Current data is useful because it describes another user β who has already achieved the objective. Requested data is the profile data that a user β has specified is required for an employed position if they are to take up the position. Preferably the AI engine admits the requested data for analysis only if a second user has accepted that user for connection, because in this way it is known that the requested data is viable. When the AI engine has identified a match in one category it searches the data for another user β for differences between the current status of user α and the status of users β in other categories. The engine identifies the trend of by searching for differences for many users β. The system outputs to user α a report based on the differences and changes required to user α data in other categories in order to attain the aspired objective . . .”) The AI tool helps to narrow the scope of the search ;
and aggregating the one or more analyses to create a consensus rating for each of the one or more startups (Stockdale, “. . . Typically it is the employer user who receives the greatest advantage from a weighting system since that user is more likely to receive many first user profiles in response to a job vacancy, whereas an employee user may receive profiles from only a handful of prospective employers. In addition to a comparison of profile data the weighting ranks a user based on a comparison of data and the importance of that data. In one implementation of weighting a user may apply a weighting factor of between 1 and 0. Other possible implementations are not restricted to this range (e.g.1 to 100). A factor of 1 indicates high importance and a factor of 0 indicates low importance. In practice a factor of 0 is not used because it indicates that a user’s profile data in this respect has no importance. In the simplest example shown in Figure 7, a question has a true/false answer giving a non-weighted value of 1 or 0. If a second user allocates a weight of 1 for essential, then the first user score if the answer is true is 1 (i.e.1x1). If the weighting factor is 0.5 for medium importance the score is 0.5 (i.e.1x0.5). Each question is weighted in this way and a total or aggregate weighted score calculated. The highest weighted score is ranked highest for disclosure to the second user down to the lowest weighted score which is ranked last . . .”).
Stockdale does not disclose the following; however, IFSM teaches:
matching the one or more startups with one or more experts in a relevant technical field (Institute for Supply Managmenet, Inc. (WO 2021/207432 A1); hereinafter, IFSM, see at least par. [0162] “The database of professionals and the ability to match them with a job, recommendation for the employer or for contingent workers. The community of practitioners by profession and industry, with the unique data of their actual skills based on the assessment, as opposed to the person self-selecting, is a differentiator . . .” & see at least par. [00164] “] The assessment system may offer talent assessment for a given industry or area of practice for existing employees as well as new hire matches. The new hire match functionality may be applied to any profession. The following are other elements and features. [00165] - The database of professionals and the ability to match them with a job, recommendation for the employer or for contingent workers.”) the concept is directed to matching talents to companies;
receiving one or more data from the one or more startups about the one or more startups (IFSM, see at least par. [0150] “Over time, the assessment system may create job descriptions and facilitate succession planning based on company workforces and skills. The assessment system may facilitate building out a succession plan and provide a risk assessment based on team competencies to future needs. The assessment system may include database functionality to store employees with the associated company, so that when the company creates a new job with noted requirements, the platform may assess the current team’s competencies and skillset. This may be useful for ongoing engagement and for users to keep profiles current, particularly with reassessments.”) the assessment program receives data regarding the companies/start-ups;
transmitting the one or more data to the one or more experts (IFSM, par. [0140] & see at least par. [0141] “Data for various professions, industries, competencies, roles, job descriptions and assessments may be included in one database or a limited set of databases, and tools may be provided to leverage that data and report on it. This data and tools may function by profession.”) The professional could leverage the system to receive information regarding the jobs or employers;
receiving one or more analyses from the one or more experts of the one or more startups (IFSM, see par. [0080] “In various embodiments, the server 110, such as in conjunction with machine learning, may automate and create a bank of smart future job descriptions. The server 110 may also provide tools for succession planning, such as based on company workforces and skills, such as by developing a succession plan and providing a risk assessment based on team competencies to future needs. The database 112 may store data associated with employees of the client company, so that when the company creates a new job with noted requirements, the server 110 may assess the current team’s competencies and skillset.”) the analyses could include succession planning or risk assessment or team competencies;
It would be obvious to one of ordinary skill in the art before the effective filing date to combine the features of matching companies with experts as taught by IFSM with the invention disclosed by Stockdale to help providing multi-degree perspective on matching between employers and professionals (abstract). Therefore, the combination is obvious.
Claim 10 is disclosed: Stockdale teaches: training the machine learning model using the dataset of identified investment outcomes thereby obtaining a trained machine learning model (Stockdale, see at least “. . . he AI engine searches other user ‘β’ profiles or external sources of equivalent data for matches between the objectives of user ‘α’ and current or requested data in a corresponding category of users ‘β’. Current data is useful because it describes another user β who has already achieved the objective . . . . The system determines from the differences between the profiles the changes required and thereby determines at least one action system. An action system comprises one or more actions that if taken by user α would lead to a probable attainment of the desired objective. Figure 14 illustrates that the system identifies the key differences 314 between profiles and from this analysis identifies at least one and preferably a plurality of action systems, or action plans, 316 that can be followed by user α in order to attain their objective.” , and storing the trained machine learning model (see at least “. . . Since it may be the case that the first user would entertain offers of £X the system aims to capture this eventuality by arranging for the second user to submit a variation request that varies the terms specified by the first user. The system is arranged at step 138 to receive from an employer user a variation request relating to an employee user’s profile data, storing the variation associated with the employer user and the employee user and outputting the variation to the employee user . . .”) The system stores the user profile and outputs as part of the trained model.
Stockdale does not disclose the following; however, IFSM teaches:
A computer implemented method for training a machine learning model for optimizing investment outcomes, the method comprising: obtaining a dataset of identified investment outcomes (IFSM, see at least par. [0156]-[0158]) The cited portion discusses investment recommendations by machine learning (in terms of organizational training);
It would be obvious to one of ordinary skill in the art before the effective filing date to combine the features of matching companies with experts as taught by IFSM with the invention disclosed by Stockdale to help providing multi-degree perspective on matching between employers and professionals (abstract). Therefore, the combination is obvious.
Claims 2 and 18 are grouped together. Stockdale in view of IFSM teaches: The method of claim 1. Stockdale further teaches: further comprising transmitting the one or more analyses and the consensus ratings to one or more investors(Stockdale, “. . . Typically it is the employer user who receives the greatest advantage from a weighting system since that user is more likely to receive many first user profiles in response to a job vacancy, whereas an employee user may receive profiles from only a handful of prospective employers. In addition to a comparison of profile data the weighting ranks a user based on a comparison of data and the importance of that data. In one implementation of weighting a user may apply a weighting factor of between 1 and 0. Other possible implementations are not restricted to this range (e.g.1 to 100). A factor of 1 indicates high importance and a factor of 0 indicates low importance. In practice a factor of 0 is not used because it indicates that a user’s profile data in this respect has no importance. In the simplest example shown in Figure 7, a question has a true/false answer giving a non-weighted value of 1 or 0 . . .”).
Claims 3 and 19 are grouped together. Stockdale in view of IFSM teaches: The method of claim 1. Stockdale further teaches: further comprising receiving one or more evaluations from the one or more investors of the one or more startups (Stockdale, see at least “. . . The second user has applied weighting to the numbered questions: Weighting Factors (F) 1: 1, 2: 1, 3: 1, 4: 0.5, 5: 0.5, 6: 1, 7: 0.5, 8: 1, 9: 0.5, 10: 1 The first user has provided responses: Responses (R) 1: 1, 2: 1, 3: 0, 4: 1, 5, 1, 6: 0, 7: 1, 8: 0, 9: 1, 10: 1 (Total = 7) The weighted score for each question is calculated by the product RxF: Weighted Responses (RF) 1: 1 (1x1), 2: 1(1x1), 3: 0(0x1), 4: 0.5(1x0.5), 5: 0.5(1x0.5), 6: 0(0x1), 7: 0.5(1x0.5), 8: 0(0x1), 9: 0.5(1x0.5), 10: 1(1x1) (Weighted total = 5) Figure 9 shows an implementation in which there are 50 answers to be completed The weighting system is not restricted to the use in employment and can be applied usefully in other fields. Another embodiment is shown in Figures 10 to 15. In the broadest sense, this embodiment identifies trends among a multiplicity of users β for output to a user α . . .”) The evaluation comes in form of rating.
Claims 4 and 20 are grouped together. Stockdale in view of IFSM teaches: The method of claim 3. Stockdale teaches: further comprising making one or more investment decisions based at least in part on the one or more analyses, the consensus ratings, and/or the one or more evaluations (Stockdale, see at least “. . . In a modification, the system may be used by those who may be some time away from employment. Children at school often find it difficult to relate the abstract world of academics to real world commerce and industry. The system provides a link between the two worlds. For example, a child may have aspirations to become a doctor, lawyer, builder, games programmer etc. The system determines which academic qualifications are required for any chosen career. In particular the system may identify not only the formal qualifications required but other types of experience or skills that are advantageous to employment or university entrance . . .”) The investment could be in the form of selecting appropriate career choice.
Claim 11. Stockdale in view of IFSM teaches: The method of claim 10. Furthermore, Stockdale teaches: further comprising training a machine learning model for optimizing identified investment outcomes, wherein the training comprises (Stockdale, see at least “. . . he AI engine searches other user ‘β’ profiles or external sources of equivalent data for matches between the objectives of user ‘α’ and current or requested data in a corresponding category of users ‘β’. Current data is useful because it describes another user β who has already achieved the objective . . . . The system determines from the differences between the profiles the changes required and thereby determines at least one action system. An action system comprises one or more actions that if taken by user α would lead to a probable attainment of the desired objective. Figure 14 illustrates that the system identifies the key differences 314 between profiles and from this analysis identifies at least one and preferably a plurality of action systems, or action plans, 316 that can be followed by user α in order to attain their objective.”; training the machine learning model using a dataset of one or more identified investment outcomes, thereby obtaining a further trained machine learning model; and storing the further trained machine learning model (see at least “. . . Since it may be the case that the first user would entertain offers of £X the system aims to capture this eventuality by arranging for the second user to submit a variation request that varies the terms specified by the first user. The system is arranged at step 138 to receive from an employer user a variation request relating to an employee user’s profile data, storing the variation associated with the employer user and the employee user and outputting the variation to the employee user . . .”) The system stores the user profile and outputs as part of the trained model.
Claim 16. Stockdale in view of IFSM teaches claim 1: Furthermore, Stockdale teaches: A non-transitory computer-readable storage medium having stored thereon instructions executable by processing circuitry to perform any of the steps of claim 1 (Stockdale, see at least “. . . The artificial intelligence engine 300 comprises executable instructions comprising instructions to output to a first user a questionnaire for population with objectives in said categories by the first user. An example for remuneration D is shown in Figure 11. A user specifies £30,000 as their current salary. Their objective is to earn a salary of $50,000 within 5 years, £80,000 within 10 years, £110,000 within 15 years and £150,000 within 20 years. Objectives may be specified in other categories other may be for example for role. A current role may be sales assistant. An objective within 5 years may be a role of sales manager, within 10 years may be a role of regional sales manager, and within 15 years to be sales director. Objectives may be specified for any suitable category. The system receives the objectives from a user ‘α’ in the completed questionnaire. The AI engine searches other user ‘β’ profiles or external sources of equivalent data for matches between the objectives of user ‘α’ and current or requested data in a corresponding category of users ‘β’. Current data is useful because it describes another user β who has already achieved the objective. Requested data is the profile data that a user β has specified is required for an employed position if they are to take up the position. Preferably the AI engine admits the requested data for analysis only if a second user has accepted that user for connection, because in this way it is known that the requested data is viable. When the AI engine has identified a match in one category it searches the data for another user β for differences between the current status of user α and the status of users β in other categories. The engine identifies the trend of by searching for differences for many users β. The system outputs to user α a report based on the differences and changes required to user α data in other categories in order to attain the aspired objective . . .”) The AI tool helps to narrow the scope of the search ;
Claims 5-9, 12-15 are rejected under 35 U.S.C. 103 as being unpatentable over Stockdale, (WO 2021/214571 A1) in view of (Institute for Supply Managmenet, Inc. (WO 2021/207432 A1); hereinafter, IFSM in further view of Balan, (US 2020/0034772 A1).
Claim 5 is disclosed. Stockdale in view IFSM teaches: The method of claim 1. However, Balan teaches: further comprising paying each of the one or more experts for the one or more analyses in the form of investment credit in any of the one or more startups (Balan, see at least par. [0093] “. . . It then will get a credit rating for the company and add the AA bond yield to the product of the incremental beta (based on the credit rating) and the market risk premium. It then computes a WACC (weighted average cost of capital) based on the forecasted capital structure of the firm by utilizing 2 metrics described above as well as the capital structure of the firm being valued . . .”).
It would be obvious to one of ordinary skill in the art before the effective filing date to combine the features of paying each one of the experts investment as taught by Balan with the invention disclosed by Stockdale in view of IFSM to help evaluating the company. Therefore, the combination is obvious.
Claim 6 is disclosed. Stockdale in view IFSM teaches: The method of claim 1. However, Balan discloses: wherein the one or more data comprise one or more of: documents; videos; financial data; financial projections; market data; economic data; industry type; founder identification; founder background; expert identification; expert ratings; investor identification (Balan, see at least par. [0083] “. . . Predictions may be made using AI based approach by considering various factors like past performances, industry trends, market conditions, economy, GDP, and the like. The training set may include a vast number of historical financial data of various companies across multiple geographies and multiple industries . . .”).
It would be obvious to one of ordinary skill in the art before the effective filing date to combine the features of paying each one of the experts investment as taught by Balan with the invention disclosed by Stockdale in view of IFSM to help evaluating the company. Therefore, the combination is obvious.
Claim 7 is disclosed: Stockdale in view IFSM teaches: The method of claim 1. However, Balan teaches: wherein the consensus ratings comprise one or more of: a predicted company valuation; a rating scale of 10 (Balan, par. [0090] “In addition to analyzing and visualizing historical data and making comparisons with projections created via machine learning generated financial models, the system also has the functionality of generating valuations for a company utilizing varying valuation methods and each of the 3 financial statements to do so (see, e.g., FIGS. 17, 18, and 19).”).
It would be obvious to one of ordinary skill in the art before the effective filing date to combine the features of paying each one of the experts investment as taught by Balan with the invention disclosed by Stockdale in view of IFSM to help evaluating the company. Therefore, the combination is obvious.
Claim 8 is disclosed: Stockdale in view IFSM teaches: The method of claim 1. However, Balan teaches: The method of claim 1. However, Balan teaches: wherein the transmitting the one or more data to the one or more experts comprises transmitting via a smart device (see at least par. [0099] “. . . An exemplary embodiment generates interactive valuations displayed on a user's computing device. The data content is used to produce proprietary projections . . .”).
It would be obvious to one of ordinary skill in the art before the effective filing date to combine the features of paying each one of the experts investment as taught by Balan with the invention disclosed by Stockdale in view of IFSM to help evaluating the company. Therefore, the combination is obvious.
Claim 9 is disclosed: Stockdale in view IFSM teaches: The method of claim 1. However, Balan teaches: The method of claim 1. However, Balan teaches: The method of claim 4, further comprising improving the one or more investment decisions over time with a machine learning model (Balan, see at least par. [0090] “. . . After this data is stored, the software can use machine learning to analyze historical data in order to generate more improved and more accurate visualizations and projections. The software also uses machine learning to be able to recognize more specific line items during the smart import process in order to accept more specific client financials . . .”).
It would be obvious to one of ordinary skill in the art before the effective filing date to combine the features of paying each one of the experts investment as taught by Balan with the invention disclosed by Stockdale in view of IFSM to help evaluating the company. Therefore, the combination is obvious.
Claim 12 is disclosed: Stockdale in view IFSM teaches: The method of claim 10. However, Balan teaches: wherein the one or more identified investment outcomes comprise one or more of: expert accuracy, return on investment (Balan, see at least par. [0093] “. . . It then will get a credit rating for the company and add the AA bond yield to the product of the incremental beta (based on the credit rating) and the market risk premium. It then computes a WACC (weighted average cost of capital) based on the forecasted capital structure of the firm by utilizing 2 metrics described above as well as the capital structure of the firm being valued . . .”).
It would be obvious to one of ordinary skill in the art before the effective filing date to combine the features of evaluating each one of the experts investment as taught by Balan with the invention disclosed by Stockdale in view of IFSM to help evaluating the company. Therefore, the combination is obvious.
Claim 13 is disclosed: Stockdale in view IFSM teaches: The method of claim 10. However, Balan teaches: wherein the machine learning model uses one or more inputs comprising one or more of: industry type, founder identification, founder background, expert identification and that expert's ratings, investor identification and success, or other variables (Balan, see at least par. [0113] “. . . Various metrics like size, profitability, industry, maturity state, etc., will all be factored into what comparable companies are selected for analysis. After the multiple has been decided through some kind of weighted average calculation based on the relevance level of each comparable company, the correct P&L line item needs to be selected and multiplied by the multiple (i.e. revenue or EBITDA or EBIT) . . .”).
It would be obvious to one of ordinary skill in the art before the effective filing date to combine the features of evaluating each one of the experts investment as taught by Balan with the invention disclosed by Stockdale in view of IFSM to help evaluating the company. Therefore, the combination is obvious.
Claim 14 is disclosed: Stockdale in view IFSM teaches: The method of claim 10. However, Balan teaches: further comprising making one or more investment decisions based at least in part on the stored trained machine learning model (Balan, see at least par. [0090] “. . . After this data is stored, the software can use machine learning to analyze historical data in order to generate more improved and more accurate visualizations and projections. The software also uses machine learning to be able to recognize more specific line items during the smart import process in order to accept more specific client financials . . .”).
It would be obvious to one of ordinary skill in the art before the effective filing date to combine the features of evaluating each one of the experts investment as taught by Balan with the invention disclosed by Stockdale in view of IFSM to help evaluating the company. Therefore, the combination is obvious.
Claim 15 is disclosed: Stockdale in view IFSM teaches: The method of claim 11. However, Balan teaches: further comprising making one or more investment decisions based at least in part on the stored further trained machine learning model (Balan, see at least par. [0090] “. . . After this data is stored, the software can use machine learning to analyze historical data in order to generate more improved and more accurate visualizations and projections. The software also uses machine learning to be able to recognize more specific line items during the smart import process in order to accept more specific client financials . . .”).
It would be obvious to one of ordinary skill in the art before the effective filing date to combine the features of evaluating each one of the experts investment as taught by Balan with the invention disclosed by Stockdale in view of IFSM to help evaluating the company. Therefore, the combination is obvious.
Conclusion
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/TOAN DUC BUI/Examiner, Art Unit 3693
/ELIZABETH H ROSEN/Primary Examiner, Art Unit 3693