DETAILED ACTION
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 .
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 9-28 are rejected under 35 U.S.C. 101 because the claims are directed toward an abstract idea without significantly more.
Claim 9, as recited is rejected under 35 U.S.C. 101 because, the claimed invention is directed to a judicial exception (an abstract idea) and does not include additional elements that amount to significantly more than the judicial exception
Step 1:
Claim 9 is directed towards a “system”, which is a machine, and thus falls within a statutory category under the most recent guideline of 35 U.S.C. 101
Step 2A, Prong 1
Claim 9 recites instructions to “receive said one or more documents from said user, said one or more documents comprising one or more college application documents, said one or more college application documents comprising (i) a grade point average of said user, (ii) one or more standardized test scores of said user, (iii) said one or more extracurricular activities of said user, and (iv) one or more college admissions essays”; “associate said one or more college admissions essays with an editor”; “receive one or more essay scores and one or more edited versions of said one or more documents from said editor, wherein said one or more edited versions comprise one or more deletions, additions, comments, and feedbacks, wherein said one or more essay scores are based on (i) semantic content and (ii) emotional tone of said one or more college admissions essays determined by said natural language processing of said one or more college admissions essays”; “generate one or more composite scores based on (i) said grade point average of said user, (ii) said one or more standardized test scores of said user, (iii) said one or more extracurricular activities of said user, and (iv) said one or more essay scores”; and “display said one or more composite scores and said one or more edited versions of said one or more documents to said user.” These limitations collectively recite the collection, evaluation of information, i.e., gathering information and evaluating and editing said information. As characterized by the USPTO guidance and case law, such activities fall within the abstract-idea groupings of mental processes (e.g. observations, evaluations, and judgments that could be performed in the human mind or with pen and paper) and organizing /transmitting information. Reference can be made to latest patent eligibility guidelines. Accordingly, claim 9 recites an abstract idea.
Step 2A, Prong 2
The claim is implemented on a “computing system” that uses “one or more computer processors; and memory comprising machine-executable instructions that, upon execution by said one or more computer processors, implements an online editing marketplace for scoring one or more college application documents of a user based on natural language processing, wherein said online editing marketplace”. These are generic computer components performing their well-understood, routine, and conventional functions of storing and executing instructions, receiving requests, and sending content.
The claim does not recite any specific improvement to computer functionality (e.g., a particular translation algorithm, model architecture, data structure, memory organization, caching mechanism, latency-reduction technique, or network protocol that improves the operation of the computer or network). Nor does it effect a transformation of a physical article or use the abstract idea in any other manner that imposes a meaningful limit on the claim’s scope. Therefore, the claim 9 does not integrate the abstract idea into a practical application under Step 2A, Prong 2.
Step 2B
Beyond the abstract idea, the additional elements are the generic “server,” “one or more processors,” and “memory” performing their conventional functions. Implementing the abstract idea on generic computer components does not amount to significantly more. Alice, 573 U.S. at 223–24).
The ordered combination of limitations mirrors the abstract idea itself performed using routine computer operations. There is no recited unconventional hardware, no technical improvement to the functioning of the computer itself, and no nonconventional arrangement of known components etc.
Accordingly, claim 9 does not include an “inventive concept” sufficient to transform the abstract idea into a patent-eligible application.
Therefore , claim 9 is directed to an abstract idea and does not recite additional elements that integrate the exception into a practical application or amount to significantly more than the exception itself. Claim 9 is therefore rejected under 35 U.S.C. § 101. Dependent claims 10-28 do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements when considered both individually and as an ordered combination do not amount to significantly more than the abstract idea.
The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception. With respect to integration of the abstract idea into a practical application, the additional element of using a generic computing device the determining and data gathering steps amount to no more than mere instructions to apply the exception using a generic computer. The current specification on paragraphs 0068 - 0069, clearly specifies that “… The computer system 1101 can regulate various aspects of the present disclosure, such as, for example, the process 200 of FIG. 2 and/or the process 300 of FIG. 3. The computer system 1101 can be an electronic device of a user or a computer system that is remotely located with respect to the electronic device. The electronic device can be a mobile electronic device. [0069] The computer system 1101 includes a central processing unit (CPU, also "processor" and "computer processor" herein) 1105, which can be a single core or multi core processor, or a plurality of processors for parallel processing. The computer system 1101 also includes memory or memory location 1110 (e.g., random-access memory, read-only memory, flash memory), electronic storage unit 1115 (e.g., hard disk), communication interface 1120 (e.g., network adapter) for communicating with one or more other systems, and peripheral devices 1125, such as cache, other memory, data storage and/or electronic display adapters. The memory 1110,storage unit 1115,interface 1120 and peripheral devices 1125 are in communication with the CPU 1105 through a communication bus (solid lines), such as a motherboard. The storage unit 1115 can be a data storage unit (or data repository) for storing data. The computer system 1101 can be operatively coupled to a computer network ("network") 1130 with the aid of the communication interface 1120. The network 1130 can be the Internet, an internet and/or extranet, or an intranet and/or extranet that is in communication with the Internet. The network 1130 in some cases is a telecommunication and/or data network. The network 1130 can include one or more computer servers, which can enable distributed computing, such as cloud computing. The network 1130, in some cases with the aid of the computer system 1101, can implement a peer-to-peer network, which may enable devices coupled to the computer system 1101 to behave as a client or a server. [0070] The CPU 1105 can execute a sequence of machine-readable instructions, which can be embodied in a program or software. The instructions may be stored in a memory location, such as the memory 1110. The instructions can be directed to the CPU 1105, which can subsequently program or otherwise configure the CPU 1105 to implement methods of the present disclosure. Examples of operations performed by the CPU 1105 can include fetch, decode, execute, and writeback.” The additional elements have been considered both individually and as an ordered combination in the significantly more consideration. The inclusion of the computer or memory and controller to perform the selecting and generating steps amount to no more than mere instructions to apply the exception using generic computer components. Mere instructions to apply an exception using a generic computing device cannot provide an inventive concept. Therefore, claim 9 as drafted is not patent eligible. The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements when considered both individually and as an ordered combination do not amount to significantly more than the abstract idea.
Thus, taken alone, the additional elements do not amount to significantly more than the above-identified judicial exception (the abstract idea). Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely provide conventional computer implementation. Independent claim 9 is therefore not drawn to eligible subject matter as they are directed to an abstract idea without significantly more.
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/apply/applying-online/eterminal-disclaimer.
Claims 9-28 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-17 of U.S. Patent No. 11,714,967. Although the claims at issue are not identical, they are not patentably distinct from each other because claims 9-28 of the instant application are similar in scope and content of the patented claims 1-17 of the patent issued to the same Applicant.
It is clear that all the elements of the application claims 9-28 are to be found in patented claims 1-17 (as the application claims 9-28 fully encompasses patented claims 1-17). The difference between the application claims and the patent claims lies in the fact that the patent claim includes many more elements and is thus much more specific. Thus the invention of claims 1-17 of the patent is in effect a “species” of the “generic” invention of the application claims 9-28. It has been held that the generic invention is “anticipated” by the “species”. See In re Goodman, 29 USPQ2d 2010 (Fed. Cir. 1993). Since application claims 9-28 is anticipated by claims 1-17 of the patent, it is not patentably distinct from of the patented claims.
Application No: 18/789,446
Patent No: 11, 714,967
9. A computer-implemented system comprising: one or more computer processors; and memory comprising machine-executable instructions that, upon execution by said one or more computer processors, implements an online editing marketplace for scoring one or more college application documents of a user based on natural language processing, wherein said online editing marketplace is configured to: receive said one or more documents from said user, said one or more documents comprising one or more college application documents, said one or more college application documents comprising (i) a grade point average of said user, (ii) one or more standardized test scores of said user, (iii) said one or more extracurricular activities of said user, and (iv) one or more college admissions essays; associate said one or more college admissions essays with an editor; receive one or more essay scores and one or more edited versions of said one or more documents from said editor, wherein said one or more edited versions comprise one or more deletions, additions, comments, and feedbacks, wherein said one or more essay scores are based on (i) semantic content and (ii) emotional tone of said one or more college admissions essays determined by said natural language processing of said one or more college admissions essays; generate one or more composite scores based on (i) said grade point average of said user, (ii) said one or more standardized test scores of said user, (iii) said one or more extracurricular activities of said user, and (iv) said one or more essay scores; and display said one or more composite scores and said one or more edited versions of said one or more documents to said user.
1. A method, comprising: (a) obtaining a plurality of essay prompts; (b) processing each essay prompt of said plurality of essay prompts using a natural language processing (NLP) algorithm to extract at least one feature from said each essay prompt; (c) using said at least one feature from said each essay prompt, generating two or more subsets of said plurality of essay prompts, wherein an essay prompt in a subset satisfies a measure of similarity with respect to each other essay prompt in said subset such that a single essay can be responsive to each essay prompt in said subset; and (d) providing an output comprising a report comprising said two or more subsets of said plurality of essay prompts.
2. The method of claim 1, further comprising recommending a theme or topic for said single essay.
11. The method of claim 1, wherein said one or more features comprise one or more of a subject of an essay prompt, a call of an essay prompt, a category of an essay prompt, a topic of an essay prompt, and an allowed length of an essay.
12. The method of claim 1, wherein said one or more features comprise one or more quantitative features.
13. The method of claim 1, wherein said NLP algorithm comprises a rules-based algorithm, a statistical algorithm, a machine learning algorithm, a semantic analysis algorithm, or any combination thereof.
10. The computer-implemented system of claim 9, wherein said one or more essay scores are based on at least 10 features.
5. The method of claim 1, wherein generating said two or more subsets of said plurality of essay prompts comprises processing said at least one feature from said each essay prompt using a clustering algorithm.
6. The method of claim 5, wherein said clustering algorithm comprises a hierarchical clustering algorithm.
7. The method of claim 5, wherein said clustering algorithm comprises a centroid-based clustering algorithm.
11. The computer-implemented system of claim 10, wherein said at least 10 features comprise a subject of an essay prompt, a call of an essay prompt, a category of an essay prompt, a topic of an essay prompt, an allowed length of an essay, or any combination thereof.
5. The method of claim 1, wherein generating said two or more subsets of said plurality of essay prompts comprises processing said at least one feature from said each essay prompt using a clustering algorithm.
6. The method of claim 5, wherein said clustering algorithm comprises a hierarchical clustering algorithm.
7. The method of claim 5, wherein said clustering algorithm comprises a centroid-based clustering algorithm.
12. The computer-implemented system of claim 9, wherein said online editing marketplace comprises a research request subsystem configured to utilize a machine learning natural language processing algorithm to automatically process a user research request.
11. The method of claim 1, wherein said one or more features comprise one or more of a subject of an essay prompt, a call of an essay prompt, a category of an essay prompt, a topic of an essay prompt, and an allowed length of an essay.
12. The method of claim 1, wherein said one or more features comprise one or more quantitative features.
13. The method of claim 1, wherein said NLP algorithm comprises a rules-based algorithm, a statistical algorithm, a machine learning algorithm, a semantic analysis algorithm, or any combination thereof.
13. The computer-implemented system of claim 12, wherein said machine learning natural language processing algorithm is configured to search a database of previous answers to find an answer having sufficient similarity with said user research request.
1. A method, comprising: (a) obtaining a plurality of essay prompts; (b) processing each essay prompt of said plurality of essay prompts using a natural language processing (NLP) algorithm to extract at least one feature from said each essay prompt; (c) using said at least one feature from said each essay prompt, generating two or more subsets of said plurality of essay prompts, wherein an essay prompt in a subset satisfies a measure of similarity with respect to each other essay prompt in said subset such that a single essay can be responsive to each essay prompt in said subset; and (d) providing an output comprising a report comprising said two or more subsets of said plurality of essay prompts. 13. The method of claim 1, wherein said NLP algorithm comprises a rules-based algorithm, a statistical algorithm, a machine learning algorithm, a semantic analysis algorithm, or any combination thereof.
14. The method of claim 13, wherein said NLP algorithm comprises a machine learning algorithm.
14. The computer-implemented system of claim 13, wherein said research request subsystem comprises a database storing information about a plurality of essay editors, wherein said editor is among said plurality of essay editors.
1. A method, comprising: (a) obtaining a plurality of essay prompts; (b) processing each essay prompt of said plurality of essay prompts using a natural language processing (NLP) algorithm to extract at least one feature from said each essay prompt; (c) using said at least one feature from said each essay prompt, generating two or more subsets of said plurality of essay prompts, wherein an essay prompt in a subset satisfies a measure of similarity with respect to each other essay prompt in said subset such that a single essay can be responsive to each essay prompt in said subset; and (d) providing an output comprising a report comprising said two or more subsets of said plurality of essay prompts.
13. The method of claim 1, wherein said NLP algorithm comprises a rules-based algorithm, a statistical algorithm, a machine learning algorithm, a semantic analysis algorithm, or any combination thereof.
14. The method of claim 13, wherein said NLP algorithm comprises a machine learning algorithm.
15. The computer-implemented system of claim 9, wherein said editor is associated in response to said editor claiming said one or more documents.
1. A method, comprising: (a) obtaining a plurality of essay prompts; (b) processing each essay prompt of said plurality of essay prompts using a natural language processing (NLP) algorithm to extract at least one feature from said each essay prompt; (c) using said at least one feature from said each essay prompt, generating two or more subsets of said plurality of essay prompts, wherein an essay prompt in a subset satisfies a measure of similarity with respect to each other essay prompt in said subset such that a single essay can be responsive to each essay prompt in said subset; and (d) providing an output comprising a report comprising said two or more subsets of said plurality of essay prompts.
13. The method of claim 1, wherein said NLP algorithm comprises a rules-based algorithm, a statistical algorithm, a machine learning algorithm, a semantic analysis algorithm, or any combination thereof.
14. The method of claim 13, wherein said NLP algorithm comprises a machine learning algorithm.
16. The computer-implemented system of claim 15, wherein said editor is associated automatically by said online editing marketplace.
1. A method, comprising: (a) obtaining a plurality of essay prompts; (b) processing each essay prompt of said plurality of essay prompts using a natural language processing (NLP) algorithm to extract at least one feature from said each essay prompt; (c) using said at least one feature from said each essay prompt, generating two or more subsets of said plurality of essay prompts, wherein an essay prompt in a subset satisfies a measure of similarity with respect to each other essay prompt in said subset such that a single essay can be responsive to each essay prompt in said subset; and (d) providing an output comprising a report comprising said two or more subsets of said plurality of essay prompts.
13. The method of claim 1, wherein said NLP algorithm comprises a rules-based algorithm, a statistical algorithm, a machine learning algorithm, a semantic analysis algorithm, or any combination thereof.
14. The method of claim 13, wherein said NLP algorithm comprises a machine learning algorithm.
17. The computer-implemented system of claim 16, wherein said editor is configured to provide said one or more essay scores within a predetermined limited time.
13. The method of claim 1, wherein said NLP algorithm comprises a rules-based algorithm, a statistical algorithm, a machine learning algorithm, a semantic analysis algorithm, or any combination thereof.
18. The computer-implemented system of claim 9, wherein said one or more standardized test scores comprises a Scholastic Aptitude Test score, an American College Testing score, an Advanced Placement score, or any combination thereof.
13. The method of claim 1, wherein said NLP algorithm comprises a rules-based algorithm, a statistical algorithm, a machine learning algorithm, a semantic analysis algorithm, or any combination thereof.
19. The computer-implemented system of claim 9, wherein said one or more college application documents comprises a set of user provided information and preferences.
13. The method of claim 1, wherein said NLP algorithm comprises a rules-based algorithm, a statistical algorithm, a machine learning algorithm, a semantic analysis algorithm, or any combination thereof.
14. The method of claim 13, wherein said NLP algorithm comprises a machine learning algorithm.
15. The method of claim 1, further comprising providing a recommendation of an essay that is responsive to each essay prompt of a subset of said two or more subsets of said plurality of essay prompts.
20. The computer-implemented system of claim 19, further comprising generating a personalized school list for said user.
1. A method, comprising: (a) obtaining a plurality of essay prompts; (b) processing each essay prompt of said plurality of essay prompts using a natural language processing (NLP) algorithm to extract at least one feature from said each essay prompt; (c) using said at least one feature from said each essay prompt, generating two or more subsets of said plurality of essay prompts, wherein an essay prompt in a subset satisfies a measure of similarity with respect to each other essay prompt in said subset such that a single essay can be responsive to each essay prompt in said subset; and (d) providing an output comprising a report comprising said two or more subsets of said plurality of essay prompts.
21. The computer-implemented system of claim 20, wherein said personalized school list comprises admission statistics, enrollment statistics, academic statistics, campus statistics, research statistics, faculty statistics, or any combination thereof for a plurality of schools.
13. The method of claim 1, wherein said NLP algorithm comprises a rules-based algorithm, a statistical algorithm, a machine learning algorithm, a semantic analysis algorithm, or any combination thereof.
22. The computer-implemented system of claim 21, wherein said personalized school list comprises admission statistics of said plurality of schools.
13. The method of claim 1, wherein said NLP algorithm comprises a rules-based algorithm, a statistical algorithm, a machine learning algorithm, a semantic analysis algorithm, or any combination thereof.
14. The method of claim 13, wherein said NLP algorithm comprises a machine learning algorithm.
15. The method of claim 1, further comprising providing a recommendation of an essay that is responsive to each essay prompt of a subset of said two or more subsets of said plurality of essay prompts.
23. The computer-implemented system of claim 22, wherein said personalized school list is generated based on said set of user provided information and preferences.
10. The method of claim 1, wherein said report is provided to a user.
24. The computer-implemented system of claim 9, wherein said one or more composite scores are displayed in a graphical representation, a textual representation, or both.
13. The method of claim 1, wherein said NLP algorithm comprises a rules-based algorithm, a statistical algorithm, a machine learning algorithm, a semantic analysis algorithm, or any combination thereof.
14. The method of claim 13, wherein said NLP algorithm comprises a machine learning algorithm.
15. The method of claim 1, further comprising providing a recommendation of an essay that is responsive to each essay prompt of a subset of said two or more subsets of said plurality of essay prompts.
25. The computer-implemented system of claim 24, wherein said one or more composite scores are displayed said graphical representation and said textual representation.
1. A method, comprising: (a) obtaining a plurality of essay prompts; (b) processing each essay prompt of said plurality of essay prompts using a natural language processing (NLP) algorithm to extract at least one feature from said each essay prompt; (c) using said at least one feature from said each essay prompt, generating two or more subsets of said plurality of essay prompts, wherein an essay prompt in a subset satisfies a measure of similarity with respect to each other essay prompt in said subset such that a single essay can be responsive to each essay prompt in said subset; and (d) providing an output comprising a report comprising said two or more subsets of said plurality of essay prompts.
26. The computer-implemented system of claim 9, wherein said one or more college application documents comprise a resume of said user, financial data of said user, demographic data of said user, or any combination thereof.
13. The method of claim 1, wherein said NLP algorithm comprises a rules-based algorithm, a statistical algorithm, a machine learning algorithm, a semantic analysis algorithm, or any combination thereof.
14. The method of claim 13, wherein said NLP algorithm comprises a machine learning algorithm.
15. The method of claim 1, further comprising providing a recommendation of an essay that is responsive to each essay prompt of a subset of said two or more subsets of said plurality of essay prompts.
16. The method of claim 1, wherein said at least one feature comprises at least 5 features.
17. The method of claim 1, wherein said user provides said plurality of essay prompts.
27. The computer-implemented system of claim 9, wherein said one or more composite scores are generated further based on a prospective major.
13. The method of claim 1, wherein said NLP algorithm comprises a rules-based algorithm, a statistical algorithm, a machine learning algorithm, a semantic analysis algorithm, or any combination thereof.
14. The method of claim 13, wherein said NLP algorithm comprises a machine learning algorithm.
15. The method of claim 1, further comprising providing a recommendation of an essay that is responsive to each essay prompt of a subset of said two or more subsets of said plurality of essay prompts.
16. The method of claim 1, wherein said at least one feature comprises at least 5 features.
17. The method of claim 1, wherein said user provides said plurality of essay prompts.
28. The computer-implemented system of claim 9, wherein said one or more composite scores are generated further based on a number of the courses reported by said user.
13. The method of claim 1, wherein said NLP algorithm comprises a rules-based algorithm, a statistical algorithm, a machine learning algorithm, a semantic analysis algorithm, or any combination thereof.
14. The method of claim 13, wherein said NLP algorithm comprises a machine learning algorithm.
15. The method of claim 1, further comprising providing a recommendation of an essay that is responsive to each essay prompt of a subset of said two or more subsets of said plurality of essay prompts.
16. The method of claim 1, wherein said at least one feature comprises at least 5 features.
17. The method of claim 1, wherein said user provides said plurality of essay prompts.
Claims 9-28 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-2, 4-12 and 15-17 of U.S. Patent No. 12,079,582. Although the claims at issue are not identical, they are not patentably distinct from each other because claims 9-28 of the instant application are similar in scope and content of the patented claims 1-2, 4-12 and 15-17 of the patent issued to the same Applicant.
It is clear that all the elements of the application claims 9-28 are to be found in patented claims 1-2, 4-12 and 15-17 (as the application claims 9-28 fully encompasses patented claims 1-2, 4-12 and 15-17). The difference between the application claims and the patent claims lies in the fact that the patent claim includes many more elements and is thus much more specific. Thus the invention of claims 1-2, 4-12 and 15-17 of the patent is in effect a “species” of the “generic” invention of the application claims 9-28. It has been held that the generic invention is “anticipated” by the “species”. See In re Goodman, 29 USPQ2d 2010 (Fed. Cir. 1993). Since application claims 9-28 is anticipated by claims 1-2, 4-12 and 15-17 of the patent, it is not patentably distinct from of the patented claims.
Application No: 18/789,446
Patent No: 12,079,582
9. A computer-implemented system comprising: one or more computer processors; and memory comprising machine-executable instructions that, upon execution by said one or more computer processors, implements an online editing marketplace for scoring one or more college application documents of a user based on natural language processing, wherein said online editing marketplace is configured to: receive said one or more documents from said user, said one or more documents comprising one or more college application documents, said one or more college application documents comprising (i) a grade point average of said user, (ii) one or more standardized test scores of said user, (iii) said one or more extracurricular activities of said user, and (iv) one or more college admissions essays; associate said one or more college admissions essays with an editor; receive one or more essay scores and one or more edited versions of said one or more documents from said editor, wherein said one or more edited versions comprise one or more deletions, additions, comments, and feedbacks, wherein said one or more essay scores are based on (i) semantic content and (ii) emotional tone of said one or more college admissions essays determined by said natural language processing of said one or more college admissions essays; generate one or more composite scores based on (i) said grade point average of said user, (ii) said one or more standardized test scores of said user, (iii) said one or more extracurricular activities of said user, and (iv) said one or more essay scores; and display said one or more composite scores and said one or more edited versions of said one or more documents to said user.
1. A computer-implemented method for generating essay recommendations for a user, comprising: (a) receiving a set of user provided information and preferences; (b) processing the set of user provided information and preferences to generate a personalized school list for the user, the personalized school list comprising a plurality of schools; (c) retrieving a plurality of essay prompts for the plurality of schools in the personalized school list for the user; (d) processing, using a natural language processing algorithm, the plurality of essay prompts to generate a plurality of features for the plurality of essay prompts; (e) identifying, based on the plurality of features, a subset of essay prompts in the plurality of essay prompts such that a single essay can be responsive to each essay prompt in the subset; and (f) providing a report to the user, wherein the report comprises (1) an applicability of at least one of the user's written essays to the subset of essay prompts, or (2) an essay prompt in the subset of essay prompts.
2. The computer-implemented method of claim 1, wherein the set of user provided information and preferences comprises any one of: grade point average (GPA), standardized test scores (e.g., Scholastic Aptitude Test (SAT) scores, and American College Testing (ACT) scores.
5. The computer-implemented method of claim 1, wherein the plurality of features comprises a semantic feature.
6. The computer-implemented method of claim 1, wherein the report is generated by processing the at least one of the user's written essays using a natural language processing algorithm to generate a second plurality of features.
11. A system comprising one or more computer processors; and memory comprising machine-executable instructions that, upon execution by said one or more computer processors, implements an online editing marketplace, wherein said online editing marketplace is configured to: (g) receive one or more documents from a user, said one or more documents comprising college application documents; (h) associate said one or more documents with an editor in response to said editor claiming said one or more documents; (i) receive edited versions of said one or more documents from said editor; (j) generate, using a natural language processing algorithm, emotional tone scores of the edited versions of said one or more documents; and (k) display said emotional tone scores and said edited versions of said one or more documents to said user.
12. The system of claim 11, wherein said one or more documents comprise one or more essays of the user.
10. The computer-implemented system of claim 9, wherein said one or more essay scores are based on at least 10 features.
9. The computer-implemented method of claim 1, wherein the identifying identifies a plurality of subsets of essay prompts.
11. The computer-implemented system of claim 10, wherein said at least 10 features comprise a subject of an essay prompt, a call of an essay prompt, a category of an essay prompt, a topic of an essay prompt, an allowed length of an essay, or any combination thereof.
4. The computer-implemented method of claim 1, wherein the plurality of features comprises an essay prompt, a call of the essay prompt, a category of the essay prompt, a topic of the essay prompt, an allowed length of the essay, or any combination thereof.
12. The computer-implemented system of claim 9, wherein said online editing marketplace comprises a research request subsystem configured to utilize a machine learning natural language processing algorithm to automatically process a user research request.
1. A computer-implemented method for generating essay recommendations for a user, comprising: (a) receiving a set of user provided information and preferences; (b) processing the set of user provided information and preferences to generate a personalized school list for the user, the personalized school list comprising a plurality of schools; (c) retrieving a plurality of essay prompts for the plurality of schools in the personalized school list for the user; (d) processing, using a natural language processing algorithm, the plurality of essay prompts to generate a plurality of features for the plurality of essay prompts; (e) identifying, based on the plurality of features, a subset of essay prompts in the plurality of essay prompts such that a single essay can be responsive to each essay prompt in the subset; and (f) providing a report to the user, wherein the report comprises (1) an applicability of at least one of the user's written essays to the subset of essay prompts, or (2) an essay prompt in the subset of essay prompts.
2. The computer-implemented method of claim 1, wherein the set of user provided information and preferences comprises any one of: grade point average (GPA), standardized test scores (e.g., Scholastic Aptitude Test (SAT) scores, and American College Testing (ACT) scores.
5. The computer-implemented method of claim 1, wherein the plurality of features comprises a semantic feature.
6. The computer-implemented method of claim 1, wherein the report is generated by processing the at least one of the user's written essays using a natural language processing algorithm to generate a second plurality of features.
13. The computer-implemented system of claim 12, wherein said machine learning natural language processing algorithm is configured to search a database of previous answers to find an answer having sufficient similarity with said user research request.
6. The computer-implemented method of claim 1, wherein the report is generated by processing the at least one of the user's written essays using a natural language processing algorithm to generate a second plurality of features.
7. The computer-implemented method of claim 1, wherein the receiving the set of user provided information and preferences is through a computer implemented marketplace.
8. The computer-implemented method of claim 1, wherein the providing the report to the user is through a computer implemented marketplace.
9. The computer-implemented method of claim 1, wherein the identifying identifies a plurality of subsets of essay prompts.
14. The computer-implemented system of claim 13, wherein said research request subsystem comprises a database storing information about a plurality of essay editors, wherein said editor is among said plurality of essay editors.
6. The computer-implemented method of claim 1, wherein the report is generated by processing the at least one of the user's written essays using a natural language processing algorithm to generate a second plurality of features.
7. The computer-implemented method of claim 1, wherein the receiving the set of user provided information and preferences is through a computer implemented marketplace.
8. The computer-implemented method of claim 1, wherein the providing the report to the user is through a computer implemented marketplace.
9. The computer-implemented method of claim 1, wherein the identifying identifies a plurality of subsets of essay prompts.
15. The computer-implemented system of claim 9, wherein said editor is associated in response to said editor claiming said one or more documents.
6. The computer-implemented method of claim 1, wherein the report is generated by processing the at least one of the user's written essays using a natural language processing algorithm to generate a second plurality of features.
7. The computer-implemented method of claim 1, wherein the receiving the set of user provided information and preferences is through a computer implemented marketplace.
8. The computer-implemented method of claim 1, wherein the providing the report to the user is through a computer implemented marketplace.
9. The computer-implemented method of claim 1, wherein the identifying identifies a plurality of subsets of essay prompts.
10. The computer-implemented method of claim 1, wherein the personalized school list comprises admission statistics, enrollment statistics, academic statistics, campus statistics, research statistics, faculty statistics, any combination thereof for the plurality of schools.
16. The computer-implemented system of claim 15, wherein said editor is associated automatically by said online editing marketplace.
6. The computer-implemented method of claim 1, wherein the report is generated by processing the at least one of the user's written essays using a natural language processing algorithm to generate a second plurality of features.
7. The computer-implemented method of claim 1, wherein the receiving the set of user provided information and preferences is through a computer implemented marketplace.
8. The computer-implemented method of claim 1, wherein the providing the report to the user is through a computer implemented marketplace.
9. The computer-implemented method of claim 1, wherein the identifying identifies a plurality of subsets of essay prompts.
10. The computer-implemented method of claim 1, wherein the personalized school list comprises admission statistics, enrollment statistics, academic statistics, campus statistics, research statistics, faculty statistics, any combination thereof for the plurality of schools.
17. The computer-implemented system of claim 16, wherein said editor is configured to provide said one or more essay scores within a predetermined limited time.
6. The computer-implemented method of claim 1, wherein the report is generated by processing the at least one of the user's written essays using a natural language processing algorithm to generate a second plurality of features.
7. The computer-implemented method of claim 1, wherein the receiving the set of user provided information and preferences is through a computer implemented marketplace.
8. The computer-implemented method of claim 1, wherein the providing the report to the user is through a computer implemented marketplace.
9. The computer-implemented method of claim 1, wherein the identifying identifies a plurality of subsets of essay prompts.
10. The computer-implemented method of claim 1, wherein the personalized school list comprises admission statistics, enrollment statistics, academic statistics, campus statistics, research statistics, faculty statistics, any combination thereof for the plurality of schools.
18. The computer-implemented system of claim 9, wherein said one or more standardized test scores comprises a Scholastic Aptitude Test score, an American College Testing score, an Advanced Placement score, or any combination thereof.
6. The computer-implemented method of claim 1, wherein the report is generated by processing the at least one of the user's written essays using a natural language processing algorithm to generate a second plurality of features.
7. The computer-implemented method of claim 1, wherein the receiving the set of user provided information and preferences is through a computer implemented marketplace.
8. The computer-implemented method of claim 1, wherein the providing the report to the user is through a computer implemented marketplace.
9. The computer-implemented method of claim 1, wherein the identifying identifies a plurality of subsets of essay prompts.
10. The computer-implemented method of claim 1, wherein the personalized school list comprises admission statistics, enrollment statistics, academic statistics, campus statistics, research statistics, faculty statistics, any combination thereof for the plurality of schools.
19. The computer-implemented system of claim 9, wherein said one or more college application documents comprises a set of user provided information and preferences.
6. The computer-implemented method of claim 1, wherein the report is generated by processing the at least one of the user's written essays using a natural language processing algorithm to generate a second plurality of features.
7. The computer-implemented method of claim 1, wherein the receiving the set of user provided information and preferences is through a computer implemented marketplace.
8. The computer-implemented method of claim 1, wherein the providing the report to the user is through a computer implemented marketplace.
9. The computer-implemented method of claim 1, wherein the identifying identifies a plurality of subsets of essay prompts.
10. The computer-implemented method of claim 1, wherein the personalized school list comprises admission statistics, enrollment statistics, academic statistics, campus statistics, research statistics, faculty statistics, any combination thereof for the plurality of schools.
20. The computer-implemented system of claim 19, further comprising generating a personalized school list for said user.
6. The computer-implemented method of claim 1, wherein the report is generated by processing the at least one of the user's written essays using a natural language processing algorithm to generate a second plurality of features.
7. The computer-implemented method of claim 1, wherein the receiving the set of user provided information and preferences is through a computer implemented marketplace.
8. The computer-implemented method of claim 1, wherein the providing the report to the user is through a computer implemented marketplace.
9. The computer-implemented method of claim 1, wherein the identifying identifies a plurality of subsets of essay prompts.
10. The computer-implemented method of claim 1, wherein the personalized school list comprises admission statistics, enrollment statistics, academic statistics, campus statistics, research statistics, faculty statistics, any combination thereof for the plurality of schools.
21. The computer-implemented system of claim 20, wherein said personalized school list comprises admission statistics, enrollment statistics, academic statistics, campus statistics, research statistics, faculty statistics, or any combination thereof for a plurality of schools.
6. The computer-implemented method of claim 1, wherein the report is generated by processing the at least one of the user's written essays using a natural language processing algorithm to generate a second plurality of features.
7. The computer-implemented method of claim 1, wherein the receiving the set of user provided information and preferences is through a computer implemented marketplace.
8. The computer-implemented method of claim 1, wherein the providing the report to the user is through a computer implemented marketplace.
9. The computer-implemented method of claim 1, wherein the identifying identifies a plurality of subsets of essay prompts.
10. The computer-implemented method of claim 1, wherein the personalized school list comprises admission statistics, enrollment statistics, academic statistics, campus statistics, research statistics, faculty statistics, any combination thereof for the plurality of schools.
22. The computer-implemented system of claim 21, wherein said personalized school list comprises admission statistics of said plurality of schools.
6. The computer-implemented method of claim 1, wherein the report is generated by processing the at least one of the user's written essays using a natural language processing algorithm to generate a second plurality of features.
7. The computer-implemented method of claim 1, wherein the receiving the set of user provided information and preferences is through a computer implemented marketplace.
8. The computer-implemented method of claim 1, wherein the providing the report to the user is through a computer implemented marketplace.
9. The computer-implemented method of claim 1, wherein the identifying identifies a plurality of subsets of essay prompts.
10. The computer-implemented method of claim 1, wherein the personalized school list comprises admission statistics, enrollment statistics, academic statistics, campus statistics, research statistics, faculty statistics, any combination thereof for the plurality of schools.
23. The computer-implemented system of claim 22, wherein said personalized school list is generated based on said set of user provided information and preferences.
6. The computer-implemented method of claim 1, wherein the report is generated by processing the at least one of the user's written essays using a natural language processing algorithm to generate a second plurality of features.
7. The computer-implemented method of claim 1, wherein the receiving the set of user provided information and preferences is through a computer implemented marketplace.
8. The computer-implemented method of claim 1, wherein the providing the report to the user is through a computer implemented marketplace.
9. The computer-implemented method of claim 1, wherein the identifying identifies a plurality of subsets of essay prompts.
10. The computer-implemented method of claim 1, wherein the personalized school list comprises admission statistics, enrollment statistics, academic statistics, campus statistics, research statistics, faculty statistics, any combination thereof for the plurality of schools.
24. The computer-implemented system of claim 9, wherein said one or more composite scores are displayed in a graphical representation, a textual representation, or both.
15. The system of claim 11, wherein said emotional tone scores are displayed to said user with a graphical representation, a textual representation, a numerical representation, or any combination thereof.
16. The system of claim 15, wherein said emotional tone scores are displayed to said user with the graphical representation and the numerical representation.
25. The computer-implemented system of claim 24, wherein said one or more composite scores are displayed said graphical representation and said textual representation.
15. The system of claim 11, wherein said emotional tone scores are displayed to said user with a graphical representation, a textual representation, a numerical representation, or any combination thereof.
16. The system of claim 15, wherein said emotional tone scores are displayed to said user with the graphical representation and the numerical representation.
17. The system of claim 11, wherein said emotional tone scores are displayed to said user with an individual representation for each emotional tone score.
26. The computer-implemented system of claim 9, wherein said one or more college application documents comprise a resume of said user, financial data of said user, demographic data of said user, or any combination thereof.
6. The computer-implemented method of claim 1, wherein the report is generated by processing the at least one of the user's written essays using a natural language processing algorithm to generate a second plurality of features.
7. The computer-implemented method of claim 1, wherein the receiving the set of user provided information and preferences is through a computer implemented marketplace.
8. The computer-implemented method of claim 1, wherein the providing the report to the user is through a computer implemented marketplace.
9. The computer-implemented method of claim 1, wherein the identifying identifies a plurality of subsets of essay prompts.
10. The computer-implemented method of claim 1, wherein the personalized school list comprises admission statistics, enrollment statistics, academic statistics, campus statistics, research statistics, faculty statistics, any combination thereof for the plurality of schools.
27. The computer-implemented system of claim 9, wherein said one or more composite scores are generated further based on a prospective major.
6. The computer-implemented method of claim 1, wherein the report is generated by processing the at least one of the user's written essays using a natural language processing algorithm to generate a second plurality of features.
7. The computer-implemented method of claim 1, wherein the receiving the set of user provided information and preferences is through a computer implemented marketplace.
8. The computer-implemented method of claim 1, wherein the providing the report to the user is through a computer implemented marketplace.
9. The computer-implemented method of claim 1, wherein the identifying identifies a plurality of subsets of essay prompts.
10. The computer-implemented method of claim 1, wherein the personalized school list comprises admission statistics, enrollment statistics, academic statistics, campus statistics, research statistics, faculty statistics, any combination thereof for the plurality of schools.
28. The computer-implemented system of claim 9, wherein said one or more composite scores are generated further based on a number of the courses reported by said user.
6. The computer-implemented method of claim 1, wherein the report is generated by processing the at least one of the user's written essays using a natural language processing algorithm to generate a second plurality of features.
7. The computer-implemented method of claim 1, wherein the receiving the set of user provided information and preferences is through a computer implemented marketplace.
8. The computer-implemented method of claim 1, wherein the providing the report to the user is through a computer implemented marketplace.
9. The computer-implemented method of claim 1, wherein the identifying identifies a plurality of subsets of essay prompts.
10. The computer-implemented method of claim 1, wherein the personalized school list comprises admission statistics, enrollment statistics, academic statistics, campus statistics, research statistics, faculty statistics, any combination thereof for the plurality of schools.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Please see attached form PTO-892.
The following is closest related prior art.
Lau et al., (US 10,565,213 B2) teach a method, apparatus, and a computer-readable storage device for determining similarities. A plurality of sets having social network service members as entities is constructed by associating each of the social network service members with each of a predetermined selection of professional outcomes by school of graduation of the member. One of the plurality of sets is selected. A similarity algorithm calculates the similarity score of at least some of the plurality of sets in the plurality of sets to the selected one of the plurality of sets, and presents for rendering the k sets in the plurality of sets with the highest similarity scores. The similarity algorithm used may be a cosine similarity algorithm using the sets as vectors.
Apokatanidis (US 2019/0304320 A1) teaches automated essay test generation and assessment processor device aspects identify phrases of discrete words appearing in a text data representation of a reference corpus of a subject matter as key concepts of the subject matter as a function of repetition of the first phrase within the reference corpus and an association with an organizational indicator of the reference corpus. Aspect processor devices further identify a text item that comprises a discrete objective value and that appears in association with the key concept as a question fact that is linked to the key concept, and generate an essay question comprising an instruction to compose an essay answer that associates the key concept to a generic domain attribute of the linked question fact.
Wich-Vila (US 10,353,720 B1) teaches a computerized method, system and media that assists the user in creating optimal answers for an academic or employment application and interview. The computer processor: computes a “brand” for the user to characterize and differentiate himself from other applicants; guides them through brainstorming their unique stories or anecdotes, and then allocating their stories or personal traits amongst the discrete pieces of an application; checks to make sure that an optimal mix of stories is being used; guides a user on a strategy for soliciting strong letters of recommendation; advises on the best type of resume to construct; breaks down a specific set of essay or interview questions into sub-questions; compiles and rearranges the sub-questions into a rough draft; provides guidance on how to create a final draft; automatedly solicits editing feedback from third parties; and, prepares the user for an interview, to include recording and critiquing a mock videotaped interview.
Zhang et al., (US 10,964,224 B1) teach systems and methods are provided for providing feedback on a user's writing behavior in generating a constructed response. An electronic process log for the constructed response is received. The electronic process log is processed to generate a vector having a predetermined number of elements. The vector comprises information related to the user's actions in generating the constructed response and includes (i) data indicating types of actions performed by the user in generating the constructed response, (ii) time points associated with the actions, and (iii) locations associated with the actions, each location indicating whether an associated action occurred within a word, between words, within a phrase, between phrases, within a sentence, between sentences, within a paragraph, or between paragraph. The vector is compared to one or more vectors associated with other constructed responses, and feedback is generated based on the comparison.
Donaldson et al., (US 2020/0273364 A1) teach a computer-based method and system to prevent plagiarized essays from successfully being represented as original authentic work. Rather than relying on access to a database of all known essays, the present invention uses a small pool of no fewer than three essays of verified single authorship to calculate a unique fingerprint for any given author. This fingerprint is a function of the unique presence and use of generic “stopwords” in the pool. The fingerprint can then be applied to an essay of unverified authorship to generate a classification as to the new essay's authenticity.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to VIJAY B CHAWAN whose telephone number is (571)272-7601. The examiner can normally be reached 7-5 Monday thru Thursday.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Richemond Dorvil can be reached at 571-272-7602. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/VIJAY B CHAWAN/Primary Examiner, Art Unit 2658