DETAILED ACTION
This office action is in response to the above identified application filed on February 04, 2025. The application contains claims 1-23.
Claims 1-23 are pending
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 .
Priority
The present application is a Continuation of 18000152, filed 11/29/2022, now U.S. Patent # 12248529 and having 1 RCE-type filing therein. 18000152 is a National Stage entry of PCT/US2022/071054, International Filing Date: 03/09/2022.
Information Disclosure Statement
The information disclosure statements (IDS) were submitted on August 29, 2025 and December 17, 2025. The submissions are in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statements are being considered by the examiner.
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 USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The 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/process/file/efs/guidance/eTD-info-I.jsp.
Claims 1-23 are rejected on the ground of nonstatutory double patenting as being unpatentable over corresponding claims of U.S. Patent No. 12248529 as shown below. Although the claims at issue are not identical, they are not patentably distinct from each other because the claims in the present application are obvious over the corresponding claims in the U.S. Patent.
A comparison of the claims is shown in the comparison tables below.
Present Application
U.S. Patent # 12248529
A computer-implemented method comprising:
receiving a search query posing a question;
generating a plurality of search results based on the search query, each of the plurality of search results having a respective passage relating to the search query;
selecting a set of the respective passages, one of the respective passages in the set being a candidate passage belonging to a primary search result of the plurality of search results, and remaining respective passages in the set being context passages belonging to secondary search results of the plurality of search results;
scoring the candidate passage using the context passages to produce an accuracy score for the candidate passage, the accuracy score indicating a level of consensus between the candidate passage and the context passages; and
providing a portion of the candidate passage for display in a search result page rendered by a browser window on a display based on the accuracy score, the portion of the candidate passage representing an answer to the question posed in the search query.
The computer-implemented method as in claim 1, further comprising:
comparing the accuracy score to an accuracy score threshold;
in response to the accuracy score being greater than the accuracy score threshold, displaying the portion of the candidate passage in the search result page; and
in response to the accuracy score being less than the accuracy score threshold, not displaying the portion of the candidate passage in the search result page.
The computer-implemented method as in claim 1, further comprising:
providing the candidate passage for display in the search result page with the portion of the candidate passage based on the accuracy score.
The computer-implemented method as in claim 1, wherein scoring the candidate passage using the context passages includes:
inputting the candidate passage, the search query, and the context passages into a score prediction engine configured to predict the accuracy score based on the candidate passage, the search query, and the context passages.
The computer-implemented method as in claim 4, wherein scoring the candidate passage using the context passages further includes:
in addition to inputting the candidate passage, the search query, and the context passages into the score prediction engine, inputting respective titles of the candidate passage and the context passages into the score prediction engine.
6. The computer-implemented method as in claim 4, wherein the score prediction engine was trained on a corpus of training records, the corpus of training records including training queries, primary passages selected for the training queries, at least one context passage, and respective accuracy scores for the primary passages.
7. The computer-implemented method as in claim 6, wherein the corpus of training records further includes respective titles of the primary passages for the at least one context passage.
8. The computer-implemented method as in claim 6, wherein the score prediction engine was further trained by applying a loss function that is based on a set of accuracy score thresholds applied to a primary passage, the loss function including, for each of the set of accuracy score thresholds for the primary passage, a sigmoidal cross-entropy loss for that threshold score.
9. The computer-implemented method as in claim 8, wherein the loss function produces as output an average of the sigmoidal cross-entropy loss over the set of accuracy score thresholds.
10. The computer-implemented method as in claim 8, wherein the score prediction engine was trained using a plurality of training stages, and wherein the set of accuracy score thresholds varies between the plurality of training stages.
11. The computer-implemented method as in claim 6, wherein the corpus of training records also includes a set of previously-scored passages, respective accuracy scores of the set of previously-scored passages not being based on a consensus with the primary passages and at least one context passages.
12. A computer program product comprising a non-transitory storage medium, the computer program product including code that, when executed by processing circuitry on which a search engine is configured to execute, causes the processing circuitry to perform a method, the method comprising:
receiving query data representing a search query posing a question;
generating a plurality of search results based on the search query, each of the plurality of search results having a respective passage relating to the search query;
selecting a set of the respective passages, one of the respective passages in the set being a candidate passage belonging to a primary search result of the plurality of search results, and remaining respective passages in the set being context passages belonging to secondary search results of the plurality of search results;
scoring the candidate passage using the context passages to produce an accuracy score for the candidate passage, the accuracy score indicating a level of consensus between the candidate passage and the context passages; and
providing a portion of the candidate passage for display as a short answer in a search result page rendered by a browser window on a display based on the accuracy score, the portion of the candidate passage representing an answer to the question posed in the search query.
13. The computer program product as in claim 12, wherein the method further comprises:
comparing the accuracy score to an accuracy score threshold;
in response to the accuracy score being greater than the accuracy score threshold, displaying the portion of the candidate passage in the search result page; and
in response to the accuracy score being less than the accuracy score threshold, not displaying the portion of the candidate passage in the search result page.
14. The computer program product as in claim 12, wherein the method further comprises:
providing the candidate passage for display in the search result page with the portion of the candidate passage based on the accuracy score.
15. The computer program product as in claim 12, wherein scoring the candidate passage using the context passages includes:
inputting the candidate passage and the context passages into a score prediction engine configured to predict the accuracy score based on the candidate passage and the context passages.
16. The computer program product as in claim 15, wherein scoring the candidate passage using the context passages further includes:
in addition to inputting the candidate passage and the context passages into the score prediction engine, inputting respective titles of the candidate passage and the context passages into the score prediction engine.
17. The computer program product as in claim 15, wherein the method further comprising:
performing a training operation on a corpus of training data to train the score prediction engine, the corpus of training data including candidate passages providing short answers for display and remaining respective passages, from which a top scoring short answer is generated.
18. A computer-implemented method comprising:
receiving a search query;
identifying a plurality of search results based on the search query, each of the plurality of search results having a respective passage relating to the search query; and providing an answer to the search query based on a set of respective passages from top-ranked search results in the plurality of search results by: determining an accuracy score indicating a level of consensus between the set of respective passages, and generating the answer using at least a portion of a respective passage from the set of respective passages based on the accuracy score.
20. The computer-implemented method as in claim 18, wherein the answer is provided as part of a search result page that includes the top-ranked search results.
19. The computer-implemented method as in claim 18, further comprising:
comparing the accuracy score to an accuracy score threshold;
in response to the accuracy score being greater than the accuracy score threshold, displaying the at least a portion in a search result page; and
in response to the accuracy score being less than the accuracy score threshold, not displaying the at least a portion in the search result page.
21. An apparatus, the apparatus comprising:
memory; and controlling circuitry coupled to the memory, the controlling circuitry being configured to:
receive query data representing a search query posing a question;
generate a plurality of search results based on the search query, each of the plurality of search results having a respective passage relating to the search query;
select a set of the respective passages, one of the respective passages in the set being a candidate passage belonging to a primary search result of the plurality of search results, and remaining respective passages in the set being context passages belonging to secondary search results of the plurality of search results;
score the candidate passage using the context passages to produce an accuracy score for the candidate passage, the accuracy score indicating a level of consensus between the candidate passage and the context passages; and
provide a portion of the candidate passage for display as a short answer in a search result page rendered by a browser window on a display based on the accuracy score, the portion of the candidate passage representing an answer to the question posed in the search query.
22. The apparatus as in claim 21, wherein the controlling circuitry is further configured to:
comparing the accuracy score to an accuracy score threshold;
in response to the accuracy score being greater than the accuracy score threshold, displaying the portion of the candidate passage in the search result page; and
in response to the accuracy score being less than the accuracy score threshold, not displaying the portion of the candidate passage in the search result page.
23. The apparatus as in claim 21, wherein the apparatus is further configured to:
provide the candidate passage for display in the search result page with the portion of the candidate passage based on the accuracy score.
A computer-implemented method comprising:
receiving a search query;
generating a plurality of search results based on the search query, each of the plurality of search results having a respective passage relating to the search query;
selecting a set of the respective passages, one of the respective passages in the set being a candidate passage belonging to a top-ranked search result of the plurality of search results, and remaining respective passages in the set being context passages belonging to lower-ranked search results of the plurality of search results;
scoring the candidate passage using the context passages to produce an accuracy score for the candidate passage, the accuracy score indicating a level of consensus between the candidate passage and the context passages; and
in response to the accuracy score being greater than a threshold, providing the candidate passage for display as a short answer in a search result page rendered by a browser window on a display.
The computer-implemented method as in claim 1, further comprising:
in response to the accuracy score being less than the accuracy score threshold, not providing the candidate passage for display as the short answer in the search result page.
…
in response to the accuracy score being greater than a threshold, providing the candidate passage for display as a short answer in a search result page rendered by a browser window on a display.
The computer-implemented method as in claim 1, wherein scoring the candidate passage using the context passages includes:
inputting the candidate passage, the search query, and the context passages into a score prediction engine configured to predict the accuracy score based on the candidate passage, the search query, and the context passages.
The computer-implemented method as in claim 3, wherein scoring the candidate passage using the context passages further includes:
in addition to inputting the candidate passage, the search query, and the context passages into the score prediction engine, inputting respective titles of the candidate passage and the context passages into the score prediction engine.
5. The computer-implemented method as in claim 3, wherein the score prediction engine was trained on a corpus of training records, the corpus of training records including training queries, primary passages selected for the training queries, at least one context passage, and respective accuracy scores for the primary passages.
6. The computer-implemented method as in claim 5, wherein the corpus of training records further includes respective titles of the primary passages for the at least one context passage.
7. The computer-implemented method as in claim 5, wherein the score prediction engine was further trained by applying a loss function that is based on a set of accuracy score thresholds applied to a primary passage, the loss function including, for each of the set of accuracy score thresholds for the primary passage, a sigmoidal cross-entropy loss for that threshold score.
8. The computer-implemented method as in claim 7, wherein the loss function produces as output an average of the sigmoidal cross-entropy loss over the set of accuracy score thresholds.
9. The computer-implemented method as in claim 7, wherein the score prediction engine was trained using a plurality of training stages, and wherein the set of accuracy score thresholds varies between the plurality of training stages.
10. The computer-implemented method as in claim 5, wherein the corpus of training records also includes a set of previously-scored passages, respective accuracy scores of the previously-scored passages not being based on a consensus with the primary passages and at least one context passages.
13. A computer program product comprising a non-transitory storage medium, the computer program product including code that, when executed by processing circuitry on which a search engine is configured to execute, causes the processing circuitry to perform a method, the method comprising:
receiving query data representing a search query;
generating a plurality of search results based on the search query, each of the plurality of search results having a respective passage relating to the search query;
selecting a set of the respective passages, one of the respective passages in the set being a candidate passage belonging to a top-ranked search result of the plurality of search results, and remaining respective passages in the set being context passages belonging to lower-ranked search results of the plurality of search results;
scoring the candidate passage using the context passages to produce an accuracy score for the candidate passage, the accuracy score indicating a level of consensus between the candidate passage and the context passages; and
in response to the accuracy score being greater than a threshold, providing the candidate passage for display as a short answer in a search result page rendered by a browser window on a display.
14. The computer program product as in claim 13, wherein the method further comprises
in response to the accuracy score being less than the accuracy score threshold, not providing the candidate passage for display as the short answer in the search result page.
13. …
in response to the accuracy score being greater than a threshold, providing the candidate passage for display as a short answer in a search result page rendered by a browser window on a display.
15. The computer program product as in claim 13, wherein scoring the candidate passage using the context passages includes:
inputting the candidate passage and the context passages into a score prediction engine configured to predict the accuracy score based on the candidate passage and the context passages.
16. The computer program product as in claim 15, wherein scoring the candidate passage using the context passages further includes:
in addition to inputting the candidate passage and the context passages into the score prediction engine, inputting respective titles of the candidate passage and the context passages into the score prediction engine.
17. The computer program product as in claim 15, wherein the method further comprising:
performing a training operation on a corpus of training data to train the score prediction engine, the corpus of training data including candidate passages providing short answers for display and remaining respective passages, from which a top scoring short answer is generated.
1. A computer-implemented method comprising:
receiving a search query;
generating a plurality of search results based on the search query, each of the plurality of search results having a respective passage relating to the search query; selecting a set of the respective passages, one of the respective passages in the set being a candidate passage belonging to a top-ranked search result of the plurality of search results, and remaining respective passages in the set being context passages belonging to lower-ranked search results of the plurality of search results; scoring the candidate passage using the context passages to produce an accuracy score for the candidate passage, the accuracy score indicating a level of consensus between the candidate passage and the context passages; and
in response to the accuracy score being greater than a threshold, providing the candidate passage for display as a short answer in a search result page rendered by a browser window on a display.
2. The computer-implemented method as in claim 1, further comprising:
in response to the accuracy score being less than the accuracy score threshold, not providing the candidate passage for display as the short answer in the search result page.
18. An apparatus, the apparatus comprising:
memory; and controlling circuitry coupled to the memory, the controlling circuitry being configured to:
receive query data representing a search query;
generate a plurality of search results based on the search query, each of the plurality of search results having a respective passage relating to the search query;
select a set of the respective passages, one of the respective passages in the set being a candidate passage belonging to a top-ranked search result of the plurality of search results, and remaining respective passages in the set being context passages belonging to lower-ranked search results of the plurality of search results;
score the candidate passage using the context passages to produce an accuracy score for the candidate passage, the accuracy score indicating a level of consensus between the candidate passage and the context passages; and
in response to the accuracy score being greater than a threshold, provide the candidate passage for display as a short answer in a search result page rendered by a browser window on a display.
19. The apparatus as in claim 18, wherein the controlling circuitry is further configured to:
in response to the accuracy score being less than the accuracy score threshold, not provide the candidate passage for display as the short answer in the search result page.
18. …
in response to the accuracy score being greater than a threshold, provide the candidate passage for display as a short answer in a search result page rendered by a browser window on a display.
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 1-23 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
The 2019 PEG guidance for subject matter eligibility is applied in the following analyses:
At Step 1
The inventions of claims 1-23 are directed to the statutory categories of a process (claims 1-11 and 18-20), a manufacture (claims 12-17), and a machine (claims 21-23). Thus, the claimed invention is directed to statutory subject matter.
The following analysis refers to representative claim 1, but the same analysis applies to independent claims 12, 18, and 21, which recite similar limitations.
At Step 2A, Prong One
Claims 1, 12, 18, and 21 each recite abstract ideas in the following limitations:
“selecting a set of the respective passages, one of the respective passages in the set being a candidate passage belonging to a primary search result of the plurality of search results, and remaining respective passages in the set being context passages belonging to secondary search results of the plurality of search results”. Selecting passages that contain either an answer to a query or relevant information that provides context to the query involves observation, evaluation, and judgment that can be practically performed in the human mind. Therefore, this limitation may be characterized as a mental process.
“scoring the candidate passage using the context passages to produce an accuracy score for the candidate passage, the accuracy score indicating a level of consensus between the candidate passage and the context passages”. Recited at a high level of generality, comparing passages and determining the general agreement between passages by way of scoring involves observation, evaluation, judgment, and maybe simple mathematics that can all be practically performed in the human mind. Therefore, this limitation may be characterized as a mental process.
At Step 2A, Prong Two
This judicial exception is not integrated into a practical application because the claims recite the additional elements of:
“receiving a search query posing a question; generating a plurality of search results based on the search query, each of the plurality of search results having a respective passage relating to the search query” may be characterized as insignificant extra-solution activity, particularly pre-solution data gathering, see MPEP 2106.05(g).
“providing a portion of the candidate passage for display in a search result page rendered by a browser window on a display based on the accuracy score, the portion of the candidate passage representing an answer to the question posed in the search query” may be characterized as insignificant extra-solution activity, particularly post-solution activity, see MPEP 2106.05(g), wherein “… based on the accuracy score, the portion of the candidate passage representing an answer to the question posed in the search query” is still part of the mental process of scoring and selecting the answer passage as discussed above.
“processing circuitry” and “a search engine” (claim 12) and “memory” and “controlling circuitry” (claim 18) may be characterized as mere instructions to implement an abstract idea on a computer or use a computer as a tool to perform an abstract idea, see MPEP 2106.05(f).
Even when viewed in combination, these additional elements do not integrate the recited judicial exception into a practical application and the claim is directed to the judicial exception.
At Step 2B
Claims 1, 12, 18, and 21 do not include additional elements that are sufficient to amount to significantly more than the judicial exception because as discussed above the additional elements constitute a high-level recitation of a generic computer components which represent mere instructions to apply on a computer and insignificant extra-solution activities including preliminary data gathering and post-solution activities.
As per MPEP 2106.05(II), at Step 2B the conclusions for these additional elements under MPEP §§ 2106.05(a) - (c), (e) (f) and (h) from Step 2A Prong Two are carried over and they do not provide significantly more. The additional elements from Step 2A Prong Two considered to be insignificant extra-solution activity per MPEP § 2106.05(g) are re-evaluated as follows:
“receiving a search query posing a question; generating a plurality of search results based on the search query, each of the plurality of search results having a respective passage relating to the search query”. This limitation is pre-solution data gathering using conventional search engine operation that is well understood, routine, and conventional. The courts have found the “receiving …” functions as well understood and routine activities as well, see MPEP 2106.05(d) [Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information)].
“providing a portion of the candidate passage for display in a search result page rendered by a browser window on a display based on the accuracy score, the portion of the candidate passage representing an answer to the question posed in the search query”. The providing is merely output of the result which is insignificant displaying. As evidenced by the links below, use of display devices to display information such as outputs is well understood, routine and conventional.
The Graphical User Interface: An Introduction
Past, Present and Future of User Interface Software Tools
User Interface Software and Technology
Notifications and the notification list - Ubuntu Documentation
Notifications Pane and Action Center in Windows 10
Designing Notifications for Apps - | UX Magazine
Even when considered in combination, these additional elements do not provide an inventive concept or significantly more.
Therefore, claims 1, 12, 18, and 21 are rejected under 35 USC 101 as being directed to an abstract idea without significantly more.
Dependent claims 2, 13, 19, and 22 each recite additional elements of “comparing the accuracy score to an accuracy score threshold” and displaying or not displaying the result based on the comparison. Both the comparing and determining whether or not to display the result are still mentally performable, and the displaying is insignificant extra-solution activity that is well understood, routine, and conventional as discussed above.
Dependent claims 3, 14, 20, and 23 each recite additional insignificant extra-solution activity elements of displaying search results that is well understood, routine, and conventional as discussed above.
Dependent claims 4, 5, 15, and 16 each recite additional insignificant pre-solution data gathering elements of inputting data that is well understood, routine, and conventional as discussed above.
Dependent claims 6, 7, 11, and 17 each recite additional elements of training “the score prediction engine”. Because the claim does not recite a machine learning, and the additional elements do not answer the question of how the additional elements used to train the score prediction engine are related to the accuracy score prediction, these limitations merely generally link the abstract idea to a technological area – machine learning, see MPEP 2106.05(h).
Dependent claims 8, 9, and 10 each recite additional elements of applying “a loss function” in training “the score prediction engine”. Recited at a high level of generality, “a loss function” application is mathematical operations that can be performed in the human mind with or without the aid of a pen and paper. Because the claim does not recite a machine learning, and the additional elements do not answer the question of how these additional elements are clearly related to the accuracy score prediction, these limitations do not integrate the abstract idea into a practical application; they merely generally link the abstract idea to a technological area – machine learning, see MPEP 2106.05(h).
Therefore, dependent claims 2-11, 13-17, 19-20, and 22-23 are also rejected under 35 USC 101 as being directed to an abstract idea without significantly more.
Conclusion
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/XIAOQIN HU/Examiner, Art Unit 2168
/CHARLES RONES/Supervisory Patent Examiner, Art Unit 2168
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