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 .
Claims 1 – 20 are pending.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1 – 20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The independent claim(s) recite(s) receiving a query from a user, retrieve a number of items in response to the query input, determining whether the query is a null and low query based on the number of retrieved items; leveraging a diversity inducing optimization to generate a plurality of diverse reformulated queries, providing a plurality of query results corresponding to the plurality of diverse reformulated queries, which amount to processes that may be performed in the human mind, or a mental process.
This judicial exception is not integrated into a practical application because claim elements such as receiving a query input from a user, retrieve a number of items from a database in response to the query input, injecting a plurality of decoders and leveraging a diversity inducing optimization function to generate a plurality of diverse reformulated queries; providing, to the user, a plurality of query results are mere general linking the use of the judicial exception to a particular technological environment or field of use, see MPEP 2106.05(h), such as that as a machine learning environment using sequence-to-sequence models with decoders injected therein that receives user query input and returns results to the querying user.
The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements such as database, computer storage media storing instructions executed by a processor, a processor, etc. are mere use of a computer as a tool to perform the abstract idea, see MPEP 2106.05(f).
The dependent claims further recite claim elements such as providing the query results to the user in a user interface without providing the plurality of diverse reformulated queries to the user, providing the results and the reformulated queries to the user in a user interface, separating the reformulated queries and the query results within the user interface, which are directed to merely implementing the abstract idea on a computer or using a computer as a tool to perform the abstract idea, see MPEP 2106.05(f), by the use of a user interface for providing results to the user. The dependent claims further recites claim elements such as training a sequence-to-sequence model utilizing historical data, injecting the decoders into the sequence-to-sequence model, which is generally linking the use of the judicial exception to a particular technological environment or field of use, see MPEP 2106.05(h), such as that of a machine learning environment comprising sequence-to-sequence models to be trained. The dependent claims further recites the historical user data comprises a search query and two query reformulations, wherein each of the two query reformulations comprise one or more of dropped tokens, replaced tokes, or added tokens corresponding to the search query, which is adding insignificant extra-solution activity to the judicial exception, see MPEP 2106.05(g).
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claim(s) 1 – 20 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by U.S. Patent Application Publication No. 2022/0172040 issued to Kazi et al (hereinafter referred to as Kazi).
As to claim 1, Kazi discloses a method comprising:
receiving a query input from a user (input search query, see Kazi: Para. 0002, 0014 – 0015, 0103 - 0110);
retrieving a number of items from a database in response to the query input (receiving initial search results, see Kazi: Para. 0002, 0014 – 0015, 0103 - 0110);
determining whether the query input is a null and low query based on the number of retrieved items (no results or user is not satisfied with the results, see Kazi: Para. 0002, 0014 – 0015, 0029, 0103 - 0110);
in response to the query input being the null and low query:
injecting a plurality of decoders and leveraging a diversity inducing optimization function to generate a plurality of diverse reformulated queries (based on the feedback that the user is not satisfied, generating reformulated query suggestions by way of machine learning, see Kazi: Para. 0015 – 0019 and 0059 - 0060, and generating the suggested queries using the query suggestion generator based on output from a neural network and sequence-to-sequence modeling, utilizing single and/or two layer decoders each comprising multiple LSTM units, each LSTM unit predicting an output, see Kazi: Para. 0064 – 0075, 0103 - 0110, and the query suggestions generated by use of the trained model, using optimization algorithms, to take into account different interpretations of different types of data, see Kazi: Para. 0019, 0049, 0083 – 0085, 0101 – 0110, suggested queries based on different interpretations is a diversity);
providing, to the user, a plurality of query results corresponding to the plurality of diverse reformulated queries (providing results for one or more suggested queries, including anticipatory search results before selection of a suggested query, see Kazi: Para. 0037, 0101 – 0110).
As to claim 2, Kazi discloses the method of claim 1, wherein the plurality of query results is provided to the user in a user interface without providing the plurality of diverse reformulated queries to the user (displaying results is a designated query result portion of the interface, separate from the query suggestion portion of the user interface, see Kazi: Para. 0028 – 0029, and providing anticipatory search results, in a live manner, for the query suggestions as a user enters an incomplete original query, the query suggestions only being displayed after user selection of an anticipatory result or indicated interest in an anticipatory result, see Kazi: Para. 0037, the query suggestions results are displayed live before the query suggestions are displayed).
As to claim 3, Kazi discloses the method of claim 1, wherein the plurality of query results and the plurality of diverse reformulated queries is provided to the user in a user interface (providing suggested queries and results (including the anticipatory results) to the user device through the user interface, see Kazi: Para. 0029, 0037, 0101).
As to claim 4, Kazi discloses the method of claim 3, further comprising separating each of the plurality of diverse reformulated queries and the corresponding plurality of query results within the user interface (suggested queries are displayed in a query suggestion portion of the user interface, and the received anticipatory results are displayed when a user selects the suggest query, see Kazi: Para. 0029 – 0030, 0037, 0101 and Fig. 2).
As to claim 5, Kazi discloses the method of claim 1, further comprising: training a sequence-to-sequence model utilizing historical user data (using the original query, user feedback queries and suggested queries, stored in a query history log, to further train a ML model, including a sequence-to-sequence model, see Kazi: Para. 0014 – 0020, 0034 – 0035, 0042 – 0044, 0049, 0101 – 0110); and
injecting the plurality of decoders in the sequence-to-sequence model (generating the suggested queries using the query suggestion generator based on output from a neural network and sequence-to-sequence modeling, utilizing single and/or two layer decoders each comprising multiple LSTM units, each LSTM unit predicting an output, see Kazi: Para. 0064 – 0075, 0101 - 0110).
As to claim 6, Kazi discloses the method of claim 5, wherein the historical user data comprises a search query and two query reformulations (using the original query, user feedback queries and suggested queries, stored in a query history log, to further train a ML model, including a sequence-to-sequence model, see Kazi: Para. 0014 – 0020, 0034 – 0035, 0042 – 0044, 0049, 0101 – 0110).
As to claim 7, Kazi discloses the method of claim 6, wherein each of the two query reformulations comprise one or more of dropped tokens, replaced tokens, or added tokens corresponding to the search query (query suggestion generator adds query tokens, different tokens, etc. to the embeddings for generation of suggested queries, see Kazi: Para. 0064 – 0065, 0106 – 0108).
Claims 8 and 15 are rejected using similar rationale to the rejection of claim 1 above.
Claims 9 and 16 are rejected using similar rationale to the rejection of claim 2 above.
Claims 10 and 17 are rejected using similar rationale to the rejection of claim 3 above.
Claims 11 and 18 are rejected using similar rationale to the rejection of claim 4 above.
Claims 12 and 19 are rejected using similar rationale to the rejection of claim 5 above.
Claims 13 and 14 are rejected using similar rationale to the rejection of claims 6 and 7 above, respectively.
Claim 20 is rejected using similar rationale to the rejection of claims 6 and 7 above.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MARK E HERSHLEY whose telephone number is (571)270-7774. The examiner can normally be reached M-F: 9am-6pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Amy Ng can be reached at (571) 270-1698. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/MARK E HERSHLEY/Primary Examiner, Art Unit 2164