DETAILED ACTION
Claims 21 – 40, which are currently pending, are fully considered below.
Claims 1 – 20 are cancelled.
Claims 21 – 40 are new.
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 .
Double Patenting
A rejection based on double patenting of the “same invention” type finds its support in the language of 35 U.S.C. 101 which states that “whoever invents or discovers any new and useful process... may obtain a patent therefor...” (Emphasis added). Thus, the term “same invention,” in this context, means an invention drawn to identical subject matter. See Miller v. Eagle Mfg. Co., 151 U.S. 186 (1894); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Ockert, 245 F.2d 467, 114 USPQ 330 (CCPA 1957).
A statutory type (35 U.S.C. 101) double patenting rejection can be overcome by canceling or amending the claims that are directed to the same invention so they are no longer coextensive in scope. The filing of a terminal disclaimer cannot overcome a double patenting rejection based upon 35 U.S.C. 101.
Claims 21, 23 – 26, 28 - 31, 33 – 26 and 38 - 40 are rejected under 35 U.S.C. 101 as claiming the same invention as that of claims 1 – 4, 6 – 14, and 16 - 20 of prior U.S. Patent No. 12/019,640. This is a statutory double patenting rejection.
Both sets of claims are drawn to generating a ranking of a plurality of experts using evaluation data relating to a plurality of experts, to generate corresponding modified evaluation data; and analyzing the identified modified evaluation data to generate expert-specific scores, for each of the plurality of experts; and outputting the ranking of the plurality of experts based on the expert-specific scores.
U.S. Patent No. 12/019,640
Instant Claims
1 and 11. A method for generating a ranking of a plurality of experts, the method comprising: generating the ranking based on search filter criteria, wherein the ranking is generated by: accessing evaluation data related to the plurality of experts, wherein, one or more of a disambiguation model and a normalization model are applied to at least some of the evaluation data, the disambiguation and normalization models being trained machine learning models, and the evaluation data is associated with one or more association factors; and identifying evaluation data related to the search filter criteria, wherein the identification is based on the associations corresponding to the evaluation data; and analyzing the identified evaluation data to generate expert-specific scores, for each of the plurality of experts; and outputting the ranking of the plurality of experts based on the expert-specific scores.
21 and 31. A method for generating a ranking of a plurality of experts, the method comprising: applying one or more trained machine learning models to disambiguate and/or normalize at least some evaluation data relating to a plurality of experts to generate corresponding modified evaluation data; associating the modified evaluation data with one or more association factors; generating the ranking based on search filter criteria, wherein the ranking is generated by: identifying modified evaluation data related to the search filter criteria, wherein the identification is based on the associations corresponding to the modified evaluation data; and analyzing the identified modified evaluation data to generate expert-specific scores, for each of the plurality of experts; and outputting the ranking of the plurality of experts based on the expert-specific scores.
2 and 12. The method of claim 1, wherein the association factors comprise associations with: (i) at least one evaluation data category, (ii) at least one taxonomy category, and (iii) at least one expert of the plurality of experts.
23 and 33. The method of claim 21, wherein the association factors comprise associations with one or more of: (i) at least one evaluation data category, (ii) at least one taxonomy category, and (iii) at least one expert of the plurality of experts
3 and 13. The method of claim 1, wherein generating the rankings based on the search filter criteria further comprises: identifying taxonomy categories associated with the search filter criteria; for each identified taxonomy category, determining (i) a subset of associated taxonomy-specific evaluation data, and (ii) one or more evaluation data categories associated with that evaluation data; for each expert, of the plurality of experts, in respect of each taxonomy category: identifying, in the taxonomy-specific evaluation data, a subset of expert-specific evaluation data, for that expert; in respect of each evaluation data category for that taxonomy category, analyzing the corresponding expert-specific evaluation data to determine a corresponding category score for that category; and based on the category-specific scores, determining a taxonomy score for that taxonomy category; and determining the expert-specific score, wherein the expert-specific score is based on the taxonomy scores for each of the taxonomy categories, for that expert.
4 and 14. The method of claim 3, wherein the taxonomy score is generated by combining the category-specific scores for that taxonomy category.
24 and 34. The method of claim 23, wherein generating the rankings based on the search filter criteria further comprises: determining one or more taxonomy categories associated with the search filter criteria; for each taxonomy category, identifying modified evaluation data associated with that taxonomy category and which comprises taxonomy-specific evaluation data; for each taxonomy-specific evaluation data, associated with each taxonomy category, identifying a segment of that data associated with each expert which comprises expert-specific evaluation data associated with each taxonomy category; for each expert, and in respect of each taxonomy category, determining one or more evaluation data categories associated with the expert-specific evaluation data, for a given taxonomy category; for each evaluation data category, analyzing the corresponding expert-specific evaluation data in that category to determine a category-specific score; combining the category-specific scores to generate an expert-specific taxonomy score, for the given taxonomy category; and generating an expert-specific score by combining the expert-specific taxonomy scores, for each taxonomy category.
6 and 16. The method of claim 3, wherein expert-specific score is determined by combining the taxonomy scores for each taxonomy category, for that expert.
25 and 35. The method of claim 24, wherein analyzing the corresponding expert-specific evaluation data to determine the category-specific score comprises: determining one or more data-specific scores, each data-specific score being determined for a separate expert-specific evaluation data associated with the evaluation data category; and determining the category-specific score by combining the data-specific scores.
7 and 17. The method of claim 6, wherein each expert-specific score is generated using a weighted-combination of each of the corresponding taxonomy scores.
26 and 36. The method of claim 25, wherein determining the data-specific scores comprises: identifying one or more evaluation data dimensions associated with the evaluation data category; for each evaluation data dimension: identifying one or more assessment factors; for each assessment factor, determining a respective factor score; and determining a dimension score, for that evaluation data dimension, using a weighted or un-weighted combination of the factor scores; and determining the data-specific score by combining the dimension scores, for each evaluation data dimension.
8 and 18. The method of claim 1, wherein: the disambiguation model is configured to generate classification labels for different text portions in the evaluation data; and the normalization model is configured to normalize text portions, in the evaluation data, into a standardized format.
28 and 38. The method of claim 21, wherein, the disambiguation involves generating classification labels for different text portions in the evaluation data; and the normalization involves normalizing text portions, in the evaluation data, into a standardized format.
9 and 19. The method of claim 8, wherein the output of the disambiguation model is an input into the normalization model, and wherein, the normalization model normalizes text portions based on the classification labels, generated for those text portions, by the disambiguation model.
29 and 39. The method of claim 28, wherein the normalization involves normalizing text portions based on the classification labels, generated for those text portions, by the disambiguation.
10 and 20. The method of claim 1, wherein the model trained for the disambiguation and normalization models is a consolidated Bi-directional Encoder Representations from Transformers (BERT) model.
30 and 40. The method of claim 21, wherein the one or more trained machine learning models comprise a consolidated Bi-directional Encoder Representations from Transformers (BERT) model.
Conclusion/Contact Information
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ALEXANDRIA Y BROMELL whose telephone number is (571)270-3034. The examiner can normally be reached M-F 8-4.
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, Robert Beausoliel can be reached at 571-272-3645. 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.
/ALEXANDRIA Y BROMELL/Primary Examiner, Art Unit 2167 September 23, 2025