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 .
Introductory Remarks
In response to communications filed on 2 September 2025, claim(s) 1, 3, 7, 16, and 18 is/are amended per Applicant’s request. Claim(s) 6 is/are cancelled. Claim(s) 22-28 is/are new. Therefore, claims 1, 3-7, 16, 18-20, and 22-28 are presently pending in the application, of which, claim(s) 1, 16, and 25 is/are presented in independent form.
An IDS was received on 8 April 2025 and 2 September 2025; all references have been considered for their English language portions only.
Examiner’s Note
The rejections below group claims that may not be identical, but whose language and scope are so substantively similar as to lend themselves to grouping, in the interests of clarity and conciseness. Any citation to the instant specification herein is made to the PGPub version (if applicable). The examiner notes that no statement has been entered regarding the inventorship of individual claims as required under 37 CFR 1.56, and therefore assumes that all claims have the same inventorship or are directed to inventions that were commonly owned as of the effective filing date of the invention.
Double Patenting
Applicant is advised that should claim 6 be found allowable, claim 22 will be objected to under 37 CFR 1.75 as being a substantial duplicate thereof. When two claims in an application are duplicates or else are so close in content that they both cover the same thing, despite a slight difference in wording, it is proper after allowing one claim to object to the other as being a substantial duplicate of the allowed claim. See MPEP § 608.01(m).
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claim(s) 1, 3-5, 7, 16, 18-20, and 23-27 is/are rejected under 35 U.S.C. 103 as being unpatentable over Boone et al. (U.S. PGPub No. 2007/0198459 A1) (hereinafter Boone) in view of Dixon et al. (U.S. PGPub No. 2017/0083508 A1) (hereinafter Dixon) in view of Kohlmeier et al. (U.S. PGPub No. 2020/0183884 A1) (hereinafter Kohlmeier).
As per claim 1, Boone teaches a computing system (0004) that outputs a ranked list of search results in response to receipt of a query, the computing system comprising:
a processor (0101); and
memory storing instructions that, when executed by the processor, cause the processor to perform acts (0101) comprising:
receiving a query that comprises a first term and a second term (0064-0066);
identifying a document based upon the query (0068);
executing a first search of an index based upon the document and the first term (0064-66) and executing a second search of the index based upon the document and the second term (id. – “read through each term in the query, find the term in the reverse index…”);
determining, based on the second search, that the index includes an entry for the document and the second term, wherein the entry comprises a second ranking score (0064-66)
computing an overall ranking score for the document with respect to the query, wherein the overall ranking score is based upon an aggregate of the first ranking score and the second ranking score (0064-66); and
returning the ranked list of documents based upon the query, where the document is positioned in the ranked list of documents based upon the overall ranking score (0068, 0070, and 0071).
But Boone does not appear to explicitly disclose:
subsequent to identifying the document, executing a first search of an index based upon the document and the first term and executing a second search of the index based upon the document and the second term;
determining, based on the first search, that the index fails to include an entry for the document and the first term;
in response to determining that the index fails to include the entry for the document and the first term, providing the document and the first term to a computer-implemented machine learning model, where the computer-implemented machine learning model outputs a first ranking score based upon the document and the first term;
determining, based on the second search, that the index includes an entry for the document and the second term, wherein the entry comprises a second ranking score obtained as output of the computer-implemented machine learning model prior to receiving the query. (Emphasis added).
Dixon does teach providing the document and the first term to a computer-implemented machine learning model, where the computer-implemented machine learning model outputs a first ranking score based upon the document and the first term. Dixon at 0054. It would have been obvious to one of ordinary skill in the art to incorporate the teachings of Dixon into the invention of Boone in order to providing the document and the first term to a computer-implemented machine learning model, where the computer-implemented machine learning model outputs a first ranking score based upon the document and the first term. This would have been clearly advantageous as it would allow for the algorithm described in Boone to be adjusted and improved over time by the machine learning of Dixon. Dixon does not explicitly state that rankings are obtained prior to receiving a query, but given Boone’s description of generating weightings (see 0057-60) this is obvious. The combination hereinafter BD.
But BD does not appear to explicitly disclose:
subsequent to identifying the document, executing a first search of an index based upon the document and the first term and executing a second search of the index based upon the document and the second term;
determining, based on the first search, that the index fails to include an entry for the document and the first term;
in response to determining that the index fails to include the entry for the document and the first term, providing the document and the first term to a computer-implemented machine learning model, where the computer-implemented machine learning model outputs a first ranking score based upon the document and the first term. (Emphasis added).
Kohlmeier teaches identifying a document, and, subsequent to that, extracting entities and storing those entities in an index. Kohlmeier at Figure 1. The document being a file that is currently being consumed, authored, or edited. See Kohlmeier at 0021. While Kohlmeier does not explicitly disclose “determining that the index fails to include an entry for the document and the first term”, this is implicit since it is adding the terms to the index. Kohlmeier further establishes criteria by which entities from the file are ranked, construed as a computer-implemented model. Kohlmeier at 0057. It would have been obvious to one of ordinary skill in the art to incorporate the teachings of Kohlmeier into the combination of BD in order to subsequent to identifying the document, executing a first search of an index based upon the document and the first term and executing a second search of the index based upon the document and the second term; determining, based on the first search, that the index fails to include an entry for the document and the first term; and in response to determining that the index fails to include the entry for the document and the first term, providing the document and the first term to a computer-implemented machine learning model, where the computer-implemented machine learning model outputs a first ranking score based upon the document and the first term.. This would have been clearly advantageous as it would enable BD to produce search results and rankings for documents that have been changed since Boone’s index for the document was created. The combination hereinafter BDK.
As per claim 16, BDK teaches machine storage media comprising instructions that, when executed by a processor, cause the processor to perform acts (Boone at 0098) comprising:
For the remaining limitations see the examiner’s remarks regarding claim 1.
As per claim 25, BDK teaches a method (Boone at 0006) for outputting a ranked list of documents based upon a query, the method performed by a computing system, the method comprising:
For the remaining limitations see the examiner’s remarks regarding claim 1.
As per claims 3, 18, and 26, see remarks regarding claim 1.
As per claims 4 and 19, see remarks regarding claim 1.
As per claims 5 and 20, see remarks regarding claim 1.
As per claims 7 and 27, see remarks regarding claim 1.
As per claim 23, BDK teaches the computing system of claim 1, wherein the first ranking score is mapped to a combination of the document and the first term in the index (Boone at 0064-66 and 0068).
As per claim 24, BDK teaches the computing system of claim 1, wherein the second ranking score is mapped to a combination of the document and the second term in the index (Boone at 0064-66 and 0068).
Claim(s) 6, 22, and 28 is/are rejected under 35 U.S.C. 103 as being unpatentable over Boone in view of Dixon in view of Kohlmeier as applied to claim 1, and further in view of Xu et al. (U.S. PGPub No. 2012/0233161 A1) (hereinafter Xu).
As per claims 6, 22, and 28, neither Boone, Dixon, nor Kohlmeier appear to explicitly disclose the computing system of claim 1, wherein the computer-implemented model is a neural network. Boone does describe classifying documents into categories using a neural network (Boone at 0092) and Dixon does state that the processor “may be configured to perform neural network learning techniques to set up the models” (Dixon at 0045), but does neither explicitly state the use of a neural network in the indexing/ranking procedures. Xu does explicitly demonstrate this. Xu at 0059. It would have been obvious to one of ordinary skill in the art to incorporate the teachings of Xu into the invention/combination of BDK in order to have the computer-implemented model be a neural network. This would have been clearly advantageous as it would enable the model to adjust and improve over time.
Response to Arguments
Applicant's arguments filed 2 September 2025 have been fully considered but they are not persuasive. Applicant raises several arguments, which are addressed here in the order they were presented by the applicant.
First, the applicant argues that the combined references do not teach any of the limitations of claim 1. See applicant’s remarks at page 10. The examiner disagrees and directs the applicant to the current rejection.
Second, the applicant argues that the combination of references do not suggest ranking search results using an aggregated score comprising a combination of pre-computed ranking scores with newly computed scores based upon a term-document pair that is not found in the index. See applicant’s remarks at page 11. Boone teaches that the ranking scores are precomputed (0057-60), and it is clear that ranking a search containing terms that are not present in the document would still produce an aggregated score comprising a combination of the pre-computed ranking scores (e.g. Boone at 0064-66) and the newly computed scores for terms not present in the document (i.e. scores of zero; see Boone at 0059).
Applicant offers no other arguments beyond arguing allowability for the reasons cited for the independent claim(s) or dependence upon said claims. These arguments are considered met.
Conclusion
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to TYLER J TORGRIMSON whose telephone number is (571)270-5550. The examiner can normally be reached Monday - Friday 9 am - 5:30 pm.
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/TYLER J TORGRIMSON/Primary Examiner, Art Unit 2165