DETAILED ACTION
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
This communication is responsive to the original application filed on 9/18/23. This action is Non-Final. Claims 1 – 9, 19 – 20 and 22 – 30 are pending and have been examined.
Priority
Acknowledgment is made of applicant’s claim for foreign priority under 35 U.S.C. 119 (a)-(d) for Chinese Application No.: CN202210619839.8, filed on 06/02/2022, and 371 of Parent Application No. PCT/CN2022/128370, filed on 10/28/2022.
Receipt is acknowledged of certified copies of papers required by 37 CFR 1.55.
Drawings
The applicant’s drawings submitted are acceptable for examination purposes.
Specification
The applicant’s specification submitted is acceptable for examination purposes.
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 – 9, 19 – 20 and 22 – 30 are rejected under 35 U.S.C. § 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1: Statutory Category - MPEP §§ 2106.03
Claims 1–9 recite a method (process), claims 19 and 22 – 28 recite an electronic device (machine), and claims 20 and 29 – 30 recite a non-transitory computer readable storage medium (manufacture), which fall within the statutory categories under § 101.
Independent Claims 1, 19 and 20 Analysis
Step 2A, Prong 1: Recited Judicial Exception (abstract idea) - MPEP §§ 2106.04(II)(A)(1), 2106.04(a)(2)
The representative claim 1 recites the following abstract idea limitations:
determining a type and a style of each of at least one first search result in response to the at least one first search result being push information (as drafted, this limitation is a process that, under the broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. That is, nothing in the limitation precludes the step from practically being performed in the mind. This limitation, in the context of this claim, encompasses the user thinking about determining search results. Thus, this limitation recites an abstract mental process under 2019 PEG because it can be performed in the human mind either through observation, evaluation and judgment);
based on a preset mapping relationship, determining a first score corresponding to each of the at least one first search result according to the type and the style of each of the at least one first search result (as drafted, this limitation is a process that, under the broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. That is, nothing in the limitation precludes the step from practically being performed in the mind. This limitation, in the context of this claim, encompasses the user thinking about scoring search results. Thus, this limitation recites an abstract mental process under 2019 PEG because it can be performed in the human mind either through observation, evaluation and judgment);
acquiring an updated order of the plurality of first search results by adjusting the initial order in order of the first score from largest to smallest (as drafted, this limitation is a process that, under the broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. That is, nothing in the limitation precludes the step from practically being performed in the mind. This limitation, in the context of this claim, encompasses the user thinking about adjusting the order. Thus, this limitation recites an abstract mental process under 2019 PEG because it can be performed in the human mind either through observation, evaluation and judgment and opinion.).
Step 2A, Prong 2: Integration into a Practical Application (additional elements) - MPEP §§ 2106.04(II)(A)(2), MPEP 2106.04(d), 2106.05(a)-(c),(e)-(h).
The claim recites the following additional elements:
acquiring a plurality of first search results corresponding to a search request and an initial order of the plurality of first search results corresponding to the search request (This limitation amounts to data gathering which is considered to be insignificant extra solution activity (see MPEP 2106.05(g)).);
Step 2B: Significantly more or amounting to an inventive concept (Transformation or Technological Improvement) - MPEP §§ 2106.05
Claim recites additional elements of “acquiring … search results…;” at a very high level of generality and without imposing meaningful limitations on the scope of the claim. In addition, paragraphs 15 – 17 of the instant specification describes generic off-the-shelf computer-based elements for implementing the claimed invention, which does not amount to significantly more than the abstract idea and is not enough to transform an abstract idea into eligible subject matter. Such generic, high-level, and nominal involvement of a computer or computer-based elements for carrying out the invention merely serves to tie the abstract idea to a particular technological environment, which is not enough to render the claims patent-eligible, as noted at pg. 74624 of Federal Register/Vol. 79, No. 241, citing Alice, which in turn cites Mayo. Further, See, e.g., Alice Corp. Pty. Ltd. v. CLS Bank Int'l, 134 S. Ct. 2347, 2359-60, 110 USPQ2d 1976, 1984 (2014). See also OIP Techs. v. Amazon.com, 788 F.3d 1359, 1364, 115 USPQ2d 1090, 1093-94 (Fed. Cir. 2015) ("Just as Diehr could not save the claims in Alice, which were directed to 'implement[ing] the abstract idea of intermediated settlement on a generic computer', it cannot save O/P's claims directed to implementing the abstract idea of price optimization on a generic computer.") (citations omitted). See also, Affinity Labs of Texas LLC v. DirecTV LLC, 838 F.3d 1253, 1257-1258 (Fed. Cir. 2016) (mere recitation of a GUI does not make a claim patent-eligible); Intellectual Ventures I LLC v. Capital One Bank, 792 F.3d 1363, 1370 (Fed. Cir. 2015) ("the interactive interface limitation is a generic computer element".)
The additional elements are broadly applied to the abstract idea at a high level of generality ("similar to how the recitation of the computer in the claims in Alice amounted to mere instructions to apply the abstract idea of intermediated settlement on a generic computer,") as explained in MPEP § 2106.05(f)) and they operate in a well-understood, routine, and conventional manner.
MPEP § 2106.05 (d)(II) sets forth the following:
The courts have recognized the following computer functions as well-understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g. at a high level of generality) as insignificant extra-solution activity.
Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec...; TLI Communications LLC v. AV Auto. LLC...; OIP Techs., Inc., v. Amazon.com, Inc... ; buySAFE, Inc. v. Google, Inc...;
Performing repetitive calculations, Flook ... ; Bancorp Services v. Sun Life...;
Electronic recordkeeping, Alice Corp...; Ultramercial... ;
Storing and retrieving information in memory, Versata Dev. Group, Inc. v. SAP Am., Inc...;
Electronically scanning or extracting data from a physical document, Content Extraction and Transmission, LLC v. Wells Fargo Bank...; and
A web browser's back and forward button functionality, Internet Patent Corp. v. Active Network, Inc...
Courts have held computer-implemented processes not to be significantly more than an abstract idea (and thus ineligible) where the claim as a whole amounts to nothing more than generic computer functions merely used to implement an abstract idea, such as an idea that could be done by a human analog (i.e., by hand or by merely thinking).
In addition, when taken as an ordered combination, the ordered combination adds nothing that is not already present as when the elements are taken individually. There is no indication that the combination of elements integrates the abstract idea into a practical application. Their collective functions merely provide conventional computer implementation. Therefore, when viewed as a whole, these additional claim elements do not provide meaningful limitations to transform the abstract idea into a practical application of the abstract idea or that the ordered combination amounts to significantly more than the abstract idea itself.
Dependent Claims Analysis
The dependent claims have been fully considered as well, however, similar to the findings for independent claims above, these claims are similarly directed to the above-mentioned groupings of abstract ideas set forth in the 2019 PEG, without integrating it into a practical application and with, at most, a general purpose computer that serves to tie the idea to a particular technological environment, which does not add significantly more to the claims. The ordered combination of elements in the dependent claims (including the limitations inherited from the parent claim(s)) add nothing that is not already present as when the elements are 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. Accordingly, the subject matter encompassed by the dependent claims fails to amount to significantly more than the abstract idea. Specifically,
Claims 2, 22 and 29 add the limitation of, “determining the initial order of the plurality of first search results based on a relevance of each of the plurality of first search results to the search request, and a number of clicks of each of the plurality of first search results within a preset duration.” This limitation is a process that, under the broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. That is, nothing in the limitation precludes the step from practically being performed in the mind. This limitation, in the context of this claim, encompasses the user thinking about determining search results. Thus, this limitation recites an abstract mental process under 2019 PEG because it can be performed in the human mind either through observation, evaluation and judgment and opinion.
Claims 3, 23 and 30 add the limitations of:
determining at least one vertical class label to which the search request is currently mapped; This limitation is a process that, under the broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. That is, nothing in the limitation precludes the step from practically being performed in the mind. This limitation, in the context of this claim, encompasses the user thinking about determining labels. Thus, this limitation recites an abstract mental process under 2019 PEG because it can be performed in the human mind either through observation, evaluation and judgment and opinion.
calculating a second score of each of the at least one vertical class label based on a preset value index; This limitation is a function that, under the broadest reasonable interpretation, covers a mathematical concept and does not transform the abstract idea.
determining a relevance of each of the plurality of first search results to each of the at least one vertical class label; This limitation is a process that, under the broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. That is, nothing in the limitation precludes the step from practically being performed in the mind. This limitation, in the context of this claim, encompasses the user thinking about determining search results. Thus, this limitation recites an abstract mental process under 2019 PEG because it can be performed in the human mind either through observation, evaluation and judgment and opinion.
determining a third score of each of the plurality of first search results based on the relevance of each of the plurality of first search results to each of the at least one vertical class label, and the second score of each of the at least one vertical class label; and This limitation is a process that, under the broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. That is, nothing in the limitation precludes the step from practically being performed in the mind. This limitation, in the context of this claim, encompasses the user thinking about determining search results. Thus, this limitation recites an abstract mental process under 2019 PEG because it can be performed in the human mind either through observation, evaluation and judgment and opinion.
determining the initial order of the plurality of first search results based on the third score corresponding to each of the plurality of first search results. This limitation is a process that, under the broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. That is, nothing in the limitation precludes the step from practically being performed in the mind. This limitation, in the context of this claim, encompasses the user thinking about determining search results. Thus, this limitation recites an abstract mental process under 2019 PEG because it can be performed in the human mind either through observation, evaluation and judgment and opinion.
Claims 4 and 24 add the limitations of:
determining a historical search frequency and a historical click value rate corresponding to each of the plurality of first search results; and This limitation is a process that, under the broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. That is, nothing in the limitation precludes the step from practically being performed in the mind. This limitation, in the context of this claim, encompasses the user thinking about determining search results. Thus, this limitation recites an abstract mental process under 2019 PEG because it can be performed in the human mind either through observation, evaluation and judgment and opinion.
determining a first search result with the historical search frequency greater than a first threshold and/or the historical click value rate greater than a second threshold as the push information. This limitation is a process that, under the broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. That is, nothing in the limitation precludes the step from practically being performed in the mind. This limitation, in the context of this claim, encompasses the user thinking about determining search results. Thus, this limitation recites an abstract mental process under 2019 PEG because it can be performed in the human mind either through observation, evaluation and judgment and opinion.
Claims 5 and 25 add the limitations of:
acquiring historical search results for a specified historical duration; This limitation amounts to data gathering which is considered to be insignificant extra solution activity (see MPEP 2106.05(g)).
determining a fourth score corresponding to each of the historical search results based on a preset value index; and This limitation is a process that, under the broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. That is, nothing in the limitation precludes the step from practically being performed in the mind. This limitation, in the context of this claim, encompasses the user thinking about determining search results. Thus, this limitation recites an abstract mental process under 2019 PEG because it can be performed in the human mind either through observation, evaluation and judgment and opinion.
determining the mapping relationship between each type and each style and the first score based on an average value of the fourth scores of the historical search results with a same type and a same style. This limitation is a process that, under the broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. That is, nothing in the limitation precludes the step from practically being performed in the mind. This limitation, in the context of this claim, encompasses the user thinking about determining search results. Thus, this limitation recites an abstract mental process under 2019 PEG because it can be performed in the human mind either through observation, evaluation and judgment and opinion.
Claims 6 and 26 add the limitations:
determining a current arrangement position of each of the plurality of first search results; and This limitation is a process that, under the broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. That is, nothing in the limitation precludes the step from practically being performed in the mind. This limitation, in the context of this claim, encompasses the user thinking about determining search results. Thus, this limitation recites an abstract mental process under 2019 PEG because it can be performed in the human mind either through observation, evaluation and judgment and opinion.
inserting a second search result into a specified display position in the arrangement position, wherein the second search result is a predetermined search result having a specified page feature or a specified page content. This limitation is a mere generic transmission and presentation of collected and analyzed data which is considered to be insignificant extra solution activity (MPEP 2106.05(g)).
Claims 7 and 27 add the limitations of:
inserting the second search result into the specified display position in the arrangement position in response to there being one second search result; or, This limitation is a mere generic transmission and presentation of collected and analyzed data which is considered to be insignificant extra solution activity (MPEP 2106.05(g)).
inserting a plurality of second search results into each of the specified display positions in the arrangement position in a preset order in response to there being the plurality of second search results. This limitation is a mere generic transmission and presentation of collected and analyzed data which is considered to be insignificant extra solution activity (MPEP 2106.05(g)).
Claims 8 and 28 add the limitations of:
acquiring the plurality of first search results corresponding to the search request and device attribute information of initiating the search request; This limitation amounts to data gathering which is considered to be insignificant extra solution activity (see MPEP 2106.05(g)).
determining a reference order associated with the device attribute information based on a preset mapping relationship; and This limitation is a process that, under the broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. That is, nothing in the limitation precludes the step from practically being performed in the mind. This limitation, in the context of this claim, encompasses the user thinking about determining search results. Thus, this limitation recites an abstract mental process under 2019 PEG because it can be performed in the human mind either through observation, evaluation and judgment and opinion.
determining the initial order of the plurality of first search results based on the reference order. This limitation is a process that, under the broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. That is, nothing in the limitation precludes the step from practically being performed in the mind. This limitation, in the context of this claim, encompasses the user thinking about determining search results. Thus, this limitation recites an abstract mental process under 2019 PEG because it can be performed in the human mind either through observation, evaluation and judgment and opinion.
Claim 9 adds the limitation of, “a device type, a network type used by a device and attribute information of a user to which the device belongs.” These steps are recited at a high-level of generality (i.e., as a generic processor performing a generic computer function) such that it amounts no more than mere instructions to apply the exception using a generic computer component. Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea (see MPEP 2106.05(f)).
Therefore, claims 1 – 9, 19 – 20 and 22 – 30 are directed to an abstract idea and do not recite additional elements sufficient to amount to significantly more. The dependent claims provide specific implementations but do not transform the abstract idea into a patent-eligible application. Therefore, the claims are not patent-eligible under 35 U.S.C. § 101.
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.
Claims 1 – 9, 19 – 20 and 22 – 30 are rejected under 35 U.S.C. 103 as being unpatentable over Wu et al., U.S. Patent Application Publication No.: 2021/0224286 (Hereinafter “Wu”), and further in view of Qiao et al., U.S. Patent Application Publication No.: 2013/0018894 (Hereinafter “Qiao”).
Regarding claim 1, Wu teaches, a method for sorting search results, comprising:
acquiring a plurality of first search results corresponding to a search request and an initial order of the plurality of first search results corresponding to the search request (Wu [0004]: “In the conventional technology, after a user inputs a search keyword, a corresponding search result list may be acquired according to the search keyword, text similarities are then calculated by using an algorithm model, sorting is performed according to similarity scores, and a search result list obtained after sorting is returned.”);
determining a type and a style of each of at least one first search result in response to the at least one first search result being push information (Wu [0031]: “A vertical search is a professional search engine for an industry, is a subdivision and extension of a search engine, performs one-time integration on a type of special information in a library, directionally extracts required data according to fields, processes the data, and returns the processed data to a user in a form. For example, in “Search”, the vertical search is specifically a search for a particular type of results, for example, an official account search or a mini program search.”);
based on a preset mapping relationship, determining a first score corresponding to each of the at least one first search result according to the type and the style of each of the at least one first search result (Wu [0033]: “During the semantic matching, semantic matching weight vectors of the search results are determined, to fully mine the search needs of the user, and semantic matching weight vectors are determined according to different search needs of the user to adjust the impact of the semantic matching and the accurate matching on final similarities, so that semantic matching scores are determined according to the semantic matching weight vectors, and finally, similarities of the search results are determined according to the accurate matching scores and the semantic matching scores. This is more suitable for a scenario of a variety of search needs of the user, and search results with higher similarities better meet the needs of the user, thereby improving accuracy and making search results with higher similarities better meet the needs of the user.”); and
Wu doesn’t clearly teach, acquiring an updated order of the plurality of first search results by adjusting the initial order in order of the first score from largest to smallest. However, Qiao [0133] teaches, “Turning back to FIG. 12, step 1250 involves performing, using the sentiment data (e.g., accessed in step 1240), at least one operation associated with the search results (e.g., accessed in step 1230). In one embodiment, step 1250 may involve processing (e.g., using search result processing component 1380) the search results (e.g., 1365) based on the sentiment data (e.g., 1340) to generate processed search results (e.g., 1375). The processing may involve filtering the search results (e.g., removing at least one search result or data associated therewith from the search results) based on the sentiment data (e.g., accessed in step 1240), ranking the search results (e.g., reordering the search results or data associated therewith) based on the sentiment data (e.g., accessed in step 1240), some combination thereof, etc.”).
It would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to incorporate the teaching of Wu et al. to the Qiao’s system by adding the feature of ranking search results. The references (Wu and Qiao) teach features that are analogous art and they are directed to the same field of endeavor, such as databases. Ordinary skilled artisan would have been motivated to do so to provide Wu’s system with enhanced data. (See Qiao [Abstract], [0133], [0276]). One of the biggest advantages of network machine learning database algorithms is their ability to improve over time. Machine learning technology typically improves efficiency and accuracy thanks to the ever-increasing amounts of data that are processed.
Regarding claim 2, the method according to claim 1, wherein acquiring the initial order of the plurality of first search results corresponding to the search request comprises:
determining the initial order of the plurality of first search results based on a relevance of each of the plurality of first search results to the search request, and a number of clicks of each of the plurality of first search results within a preset duration (Qiao [0161]: “Accordingly, in one embodiment, a GUI (e.g., 1600C) may be generated and/or displayed that advantageously includes sentiment data associated with other data (e.g., the words “steering,” “fuel economy,” “engine,” “acceleration,” “handling,” “braking,” etc.) that is not part of the query (e.g., which includes the word “performance” but does not include the words “steering,” “fuel economy,” “engine,” “acceleration,” “handling” or “braking”). As such, a user entering the query need not know the other data associated with the word performance or spend the time and effort to enter those words in as part of the query. Thus, the GUI may provide valuable and relevant information by displaying the sentiment (e.g., associated with sentiment data) of one or more features (e.g., “steering,” “fuel economy,” “engine,” “acceleration,” “handling,” “braking,” etc.) of the Toyota Land Cruiser as determined from at least one search result or document. Further, the sentiment data (e.g., of GUI 1600C) may be displayed contemporaneously with the corresponding search results (e.g., of GUI 1600B) as an image or GUI (e.g., 1600A) in one embodiment, thereby providing even more valuable and relevant information related to the initial query (e.g., which may be displayed in region 1610 of GUI 1600A).”)
Regarding claim 3, the method according to claim 1, wherein acquiring the initial order of the plurality of first search results corresponding to the search request comprises:
determining at least one vertical class label to which the search request is currently mapped (Wu [0252]: “Based on the foregoing embodiments, the following describes a specific application scenario of the embodiments. In this embodiment of this disclosure, the search result processing method may be applied to a vertical search in a “Search” function in WeChat®, for example, a scenario of an official account search in “Search”. Herein, the Search function may allow a user to search for information such as Moments, articles, official accounts, novels, music, and stickers according to a search keyword.”)
calculating a second score of each of the at least one vertical class label based on a preset value index (Wu [0259]: “Compared with the conventional technology, a performance analysis is performed according to the method in the embodiments of this disclosure. For an interpretation recognition task, an objective is to determine whether two texts have the same meaning. Based on two different datasets, that is, a dataset 1 and a dataset 2, the accuracy of the matching between the search results and the search keyword and the index F1-score that measures the accuracy of the binary classification model are counted.”)
determining a relevance of each of the plurality of first search results to each of the at least one vertical class label (Wu [0166]: “In this embodiment of this disclosure, the corresponding training sample set is constructed based on the initial training sample set according to different training targets, thereby improving the accuracy of the similarity model. The two-tuple data is pairwise data, and one piece of the pairwise data includes two texts and a label. For example, the label is 0 or 1, and 0 or 1 is used for representing whether the two texts are similar. If pairwise data is (A, B, 1), it indicates that a text A and a text B are similar.”)
determining a third score of each of the plurality of first search results based on the relevance of each of the plurality of first search results to each of the at least one vertical class label, and the second score of each of the at least one vertical class label (Qiao 0084]: “As shown in FIG. 1B, step 140 involves determining a third score associated with the theme and/or the semantic key. In one embodiment, the third score may be determined in step 140 based on the sentiment data of the corresponding theme elements and/or semantic sub-keys (e.g., in column 930 of data structure 900). In one embodiment, the third score determined in step 140 may include at least a portion of the combined score data (e.g., a combined positive sentiment score, a combined negative sentiment score, a net sentiment score, some combination thereof, etc.) in column 950 of data structure 900 of FIG. 9.”); and
determining the initial order of the plurality of first search results based on the third score corresponding to each of the plurality of first search results (Qiao [0133]: “one or more search results (or data associated therewith) that are not associated with the sentiment data (e.g., accessed in step 1240) may be removed from the search results in step 1250. As another example, the search results (or data associated therewith) may be ordered based on a respective score, a respective category of sentiment, a respective degree of sentiment, a respective classification of sentiment, etc.”)
Regarding claim 4, the method according to claim 1, further comprising:
determining a historical search frequency and a historical click value rate corresponding to each of the plurality of first search results (Wu [0044]: “Specifically, word segmentation is performed on the search keyword and the search result according to a preset high-frequency word set. Specifically, a high-frequency word is obtained through division when the search keyword and the search result include the high-frequency word in the preset high-frequency word set, and the remaining parts of a text are divided by word, to respectively obtain the word segmentation results corresponding to the search keyword and the search result.); and
determining a first search result with the historical search frequency greater than a first threshold and/or the historical click value rate greater than a second threshold as the push information (Qiao [Abstract]: “A method, computer-readable medium, and a computer system for automatically generating sentiment data are disclosed. One or more portions of at least one document may be determined to be associated with at least one sentiment of one or more other portions of the at least one document. One or more scores associated with the at least one sentiment may be automatically determined based on at least one respective attribute of the one or more portions.”).
Regarding claim 5, the method according to claim 1, further comprising:
acquiring historical search results for a specified historical duration (Wu [0148-0149]: “The performing step 700 specifically includes: first, acquiring an original search click record set according to a search click behavior of a user, each original search click record in the original search click record set including at least a search keyword, an exposure list, and a click result, the click result being a clicked search result.);
determining a fourth score corresponding to each of the historical search results based on a preset value index (Wu [0152]: “The original search click record set is then filtered based on the preset rule, the original search click records that meet the preset rule are filtered out, and an initial training sample set is obtained according to the filtered original search click record set.); and
determining the mapping relationship between each type and each style and the first score based on an average value of the fourth scores of the historical search results with a same type and a same style (Qiao [0079]: “Turning back to FIG. 1B, step 135 involves determining that the second portion and the fourth portion are theme elements associated with a theme and/or are semantic sub-keys associated with a semantic key. In one embodiment, a theme associated with the second and fourth portions (e.g., as theme elements) may be determined in step 135.”).
Regarding claim 6, the method according to claim 1, wherein acquiring the updated order of the plurality of first search results by adjusting the initial order in order of the first score from largest to smallest, the method further comprises:
determining a current arrangement position of each of the plurality of first search results (Wu [0184-0186]: “First, a three-tuple data training set is constructed according to the initial training sample set. The constructing specifically includes: performing the following processing for each initial training sample in the initial training sample set: 1) Confidences of search results in an exposure list corresponding to the initial training sample are determined The determining specifically includes: a. A position of a click result of the initial training sample in the corresponding exposure list is respectively counted.,); and
inserting a second search result into a specified display position in the arrangement position, wherein the second search result is a predetermined search result having a specified page feature or a specified page content (Qiao [0209]: “Each piece of three-tuple data in the three-tuple data training set includes at least a search keyword, a first search result, and a second search result, and a similarity between the search keyword and the first search result is greater than a similarity between the search keyword and the second search result.”)
Regarding claim 7, the method according to claim 6, wherein inserting the second search result into the specified display position in the arrangement position comprises:
inserting the second search result into the specified display position in the arrangement position in response to there being one second search result (Wu [0179-0180]: “Step 720. Construct a three-tuple data training set according to the initial training sample set, and train the first similarity model according to the three-tuple data training set, to obtain a trained second similarity model. Each piece of three-tuple data in the three-tuple data training set includes at least a search keyword, a first search result, and a second search result, and a similarity between the search keyword and the first search result is greater than a similarity between the search keyword and the second search result.”); or,
inserting a plurality of second search results into each of the specified display positions in the arrangement position in a preset order in response to there being the plurality of second search results (Wu [0182]: “Therefore, in this embodiment of this disclosure, a sorted optimization target may further be used to retrain the first similarity model, to construct the three-tuple data training set. The first similarity model is retrained based on the three-tuple data training set, and a principle of the target is to optimize the sorted results by using differences in similarities of different texts to the same text. For example, the three-tuple data is (the search keyword, doc1, doc2), and it is known that a matching degree of (the search keyword, doc1) is greater than a matching degree of (the search keyword, doc2). Through such three-tuple data training, the similarity model may finally enable that a similarity score calculated for (the search keyword, doc1) is greater than that of (the search keyword, doc2). That is, doc1 in the search results corresponding to the search keyword may rank higher than doc2.”).
Regarding claim 8, the method according to claim 1, wherein acquiring the plurality of first search results corresponding to the search request and the initial order of the plurality of first search results corresponding to the search request comprises:
acquiring the plurality of first search results corresponding to the search request and device attribute information of initiating the search request (Wu [0149-0150]: “First, acquiring an original search click record set according to a search click behavior of a user, each original search click record in the original search click record set including at least a search keyword, an exposure list, and a click result, the click result being a clicked search result. In this embodiment of this disclosure, original record data may be acquired from an original search click behavior log of the user. Generally, each record includes a search keyword, an exposure list, and a click result of the user. The exposure list includes a plurality of search results.);
determining a reference order associated with the device attribute information based on a preset mapping relationship (Wu [0159]: “The user searches a search keyword, and clicks a plurality of search results that are in the returned exposure list and correspond to a plurality of click results. That is, a one-time search of the user corresponds to a plurality of clicks. In this case, it indicates that the reference value of the click results of the user is relatively low, and an association between the click results and the search keyword may not be high. Therefore, the reliability of this record is relatively low, which adversely affects the training of the similarity model.); and
determining the initial order of the plurality of first search results based on the reference order (Wu [0247-0250]: “Input a search keyword of a user and all returned search results. Sequentially input the search keyword and the search results into the similarity model and respectively calculate similarities between the search keyword and the search results. Use a similarity result as a feature, provide the similarity result to a sorting model. Finally, the sorting model obtains a sorted search result at least according to the similarity result. This is not limited to the sorting model. The sorting model may sort the search results sequentially in descending order of similarity, or may combine other features or factors simultaneously to finally obtain the sorted search results.”).
Regarding claim 9, the method according to claim 8, wherein the device attribute information comprises at least one of: a device type, a network type used by a device and attribute information of a user to which the device belongs (Wu [0028-0029]: “In FIG. 1 is a schematic diagram of an application architecture of embodiments according to an embodiment of this disclosure, including at least a terminal 130 and a server 110. The terminal 130 may be any smart device such as a smartphone, a tablet computer, a portable personal computer, or a smart television, various applications (APP) such as a browser and a social application capable of performing an information search may be installed and run on the terminal 130, and a user may search for required information by using the APP of the terminal 130. … The server 110 may provide the terminal 130 with various network services. For different terminals or applications on the terminal, the server 110 may be considered as a backend server providing corresponding network services. The server 110 may be one server, a server cluster formed by a plurality of servers, or a cloud computing center, which is not limited. The terminal 130 and the server 110 may be connected through the Internet 120, to communicate with each other.”).
Regarding claim 19, Wu teaches, an electronic device, comprising:
a processor (Wu [0007]: processor); and a memory for storing instructions executable by the processor (Wu [0007]: memory), wherein, the processor is configured to perform actions of:
acquiring a plurality of first search results corresponding to a search request and an initial order of the plurality of first search results corresponding to the search request (Wu [0004]: “In the conventional technology, after a user inputs a search keyword, a corresponding search result list may be acquired according to the search keyword, text similarities are then calculated by using an algorithm model, sorting is performed according to similarity scores, and a search result list obtained after sorting is returned.”);
determining a type and a style of each of at least one first search result in response to the at least one first search result being push information (Wu [0031]: “A vertical search is a professional search engine for an industry, is a subdivision and extension of a search engine, performs one-time integration on a type of special information in a library, directionally extracts required data according to fields, processes the data, and returns the processed data to a user in a form. For example, in “Search”, the vertical search is specifically a search for a particular type of results, for example, an official account search or a mini program search.”);
based on a preset mapping relationship, determining a first score corresponding to each of the at least one first search result according to the type and the style of each of the at least one first search result (Wu [0033]: “During the semantic matching, semantic matching weight vectors of the search results are determined, to fully mine the search needs of the user, and semantic matching weight vectors are determined according to different search needs of the user to adjust the impact of the semantic matching and the accurate matching on final similarities, so that semantic matching scores are determined according to the semantic matching weight vectors, and finally, similarities of the search results are determined according to the accurate matching scores and the semantic matching scores. This is more suitable for a scenario of a variety of search needs of the user, and search results with higher similarities better meet the needs of the user, thereby improving accuracy and making search results with higher similarities better meet the needs of the user.”); and
Wu doesn’t clearly teach, acquiring an updated order of the plurality of first search results by adjusting the initial order in order of the first score from largest to smallest. However, Qiao [0133] teaches, “Turning back to FIG. 12, step 1250 involves performing, using the sentiment data (e.g., accessed in step 1240), at least one operation associated with the search results (e.g., accessed in step 1230). In one embodiment, step 1250 may involve processing (e.g., using search result processing component 1380) the search results (e.g., 1365) based on the sentiment data (e.g., 1340) to generate processed search results (e.g., 1375). The processing may involve filtering the search results (e.g., removing at least one search result or data associated therewith from the search results) based on the sentiment data (e.g., accessed in step 1240), ranking the search results (e.g., reordering the search results or data associated therewith) based on the sentiment data (e.g., accessed in step 1240), some combination thereof, etc.”).
It would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to incorporate the teaching of Wu et al. to the Qiao’s system by adding the feature of ranking search results. The references (Wu and Qiao) teach features that are analogous art and they are directed to the same field of endeavor, such as databases. Ordinary skilled artisan would have been motivated to do so to provide Wu’s system with enhanced data. (See Qiao [Abstract], [0133], [0276]). One of the biggest advantages of network machine learning database algorithms is their ability to improve over time. Machine learning technology typically improves efficiency and accuracy thanks to the ever-increasing amounts of data that are processed.
Regarding claim 20, Wu teaches, a non-transitory computer-readable storage medium having computer instructions stored thereon, wherein the computer instructions are configured to cause a computer to perform actions of:
acquiring a plurality of first search results corresponding to a search request and an initial order of the plurality of first search results corresponding to the search request (Wu [0004]: “In the conventional technology, after a user inputs a search keyword, a corresponding search result list may be acquired according to the search keyword, text similarities are then calculated by using an algorithm model, sorting is performed according to similarity scores, and a search result list obtained after sorting is returned.”);
determining a type and a style of each of at least one first search result in response to the at least one first search result being push information (Wu [0031]: “A vertical search is a professional search engine for an industry, is a subdivision and extension of a search engine, performs one-time integration on a type of special information in a library, directionally extracts required data according to fields, processes the data, and returns the processed data to a user in a form. For example, in “Search”, the vertical search is specifically a search for a particular type of results, for example, an official account search or a mini program search.”);
based on a preset mapping relationship, determining a first score corresponding to each of the at least one first search result according to the type and the style of each of the at least one first search result (Wu [0033]: “During the semantic matching, semantic matching weight vectors of the search results are determined, to fully mine the search needs of the user, and semantic matching weight vectors are determined according to different search needs of the user to adjust the impact of the semantic matching and the accurate matching on final similarities, so that semantic matching scores are determined according to the semantic matching weight vectors, and finally, similarities of the search results are determined according to the accurate matching scores and the semantic matching scores. This is more suitable for a scenario of a variety of search needs of the user, and search results with higher similarities better meet the needs of the user, thereby improving accuracy and making search results with higher similarities better meet the needs of the user.”); and
Wu doesn’t clearly teach, acquiring an updated order of the plurality of first search results by adjusting the initial order in order of the first score from largest to smallest. However, Qiao [0133] teaches, “Turning back to FIG. 12, step 1250 involves performing, using the sentiment data (e.g., accessed in step 1240), at least one operation associated with the search results (e.g., accessed in step 1230). In one embodiment, step 1250 may involve processing (e.g., using search result processing component 1380) the search results (e.g., 1365) based on the sentiment data (e.g., 1340) to generate processed search results (e.g., 1375). The processing may involve filtering the search results (e.g., removing at least one search result or data associated therewith from the search results) based on the sentiment data (e.g., accessed in step 1240), ranking the search results (e.g., reordering the search results or data associated therewith) based on the sentiment data (e.g., accessed in step 1240), some combination thereof, etc.”).
It would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to incorporate the teaching of Wu et al. to the Qiao’s system by adding the feature of ranking search results. The references (Wu and Qiao) teach features that are analogous art and they are directed to the same field of endeavor, such as databases. Ordinary skilled artisan would have been motivated to do so to provide Wu’s system with enhanced data. (See Qiao [Abstract], [0133], [0276]). One of the biggest advantages of network machine learning database algorithms is their ability to improve over time. Machine learning technology typically improves efficiency and accuracy thanks to the ever-increasing amounts of data that are processed.
Regarding claim 22, the device according to claim 19, wherein acquiring the initial order of the plurality of first search results corresponding to the search request comprises:
determining the initial order of the plurality of first search results based on a relevance of each of the plurality of first search results to the search request, and a number of clicks of each of the plurality of first search results within a preset duration (Qiao [0161]: “Accordingly, in one embodiment, a GUI (e.g., 1600C) may be generated and/or displayed that advantageously includes sentiment data associated with other data (e.g., the words “steering,” “fuel economy,” “engine,” “acceleration,” “handling,” “braking,” etc.) that is not part of the query (e.g., which includes the word “performance” but does not include the words “steering,” “fuel economy,” “engine,” “acceleration,” “handling” or “braking”). As such, a user entering the query need not know the other data associated with the word performance or spend the time and effort to enter those words in as part of the query. Thus, the GUI may provide valuable and relevant information by displaying the sentiment (e.g., associated with sentiment data) of one or more features (e.g., “steering,” “fuel economy,” “engine,” “acceleration,” “handling,” “braking,” etc.) of the Toyota Land Cruiser as determined from at least one search result or document. Further, the sentiment data (e.g., of GUI 1600C) may be displayed contemporaneously with the corresponding search results (e.g., of GUI 1600B) as an image or GUI (e.g., 1600A) in one embodiment, thereby providing even more valuable and relevant information related to the initial query (e.g., which may be displayed in region 1610 of GUI 1600A).”).
Regarding claim 23, the device according to claim 19, wherein acquiring the initial order of the plurality of first search results corresponding to the search request comprises:
determining at least one vertical class label to which the search request is currently mapped (Wu [0252]: “Based on the foregoing embodiments, the following describes a specific application scenario of the embodiments. In this embodiment of this disclosure, the search result processing method may be applied to a vertical search in a “Search” function in WeChat®, for example, a scenario of an official account search in “Search”. Herein, the Search function may allow a user to search for information such as Moments, articles, official accounts, novels, music, and stickers according to a search keyword.”);
calculating a second score of each of the at least one vertical class label based on a preset value index (Wu [0259]: “Compared with the conventional technology, a performance analysis is performed according to the method in the embodiments of this disclosure. For an interpretation recognition task, an objective is to determine whether two texts have the same meaning. Based on two different datasets, that is, a dataset 1 and a dataset 2, the accuracy of the matching between the search results and the search keyword and the index F1-score that measures the accuracy of the binary classification model are counted.”);
determining a relevance of each of the plurality of first search results to each of the at least one vertical class label (Wu [0166]: “In this embodiment of this disclosure, the corresponding training sample set is constructed based on the initial training sample set according to different training targets, thereby improving the accuracy of the similarity model. The two-tuple data is pairwise data, and one piece of the pairwise data includes two texts and a label. For example, the label is 0 or 1, and 0 or 1 is used for representing whether the two texts are similar. If pairwise data is (A, B, 1), it indicates that a text A and a text B are similar.”);
determining a third score of each of the plurality of first search results based on the relevance of each of the plurality of first search results to each of the at least one vertical class label, and the second score of each of the at least one vertical class label (Qiao 0084]: “As shown in FIG. 1B, step 140 involves determining a third score associated with the theme and/or the semantic key. In one embodiment, the third score may be determined in step 140 based on the sentiment data of the corresponding theme elements and/or semantic sub-keys (e.g., in column 930 of data structure 900). In one embodiment, the third score determined in step 140 may include at least a portion of the combined score data (e.g., a combined positive sentiment score, a combined negative sentiment score, a net sentiment score, some combination thereof, etc.) in column 950 of data structure 900 of FIG. 9.”); and
determining the initial order of the plurality of first search results based on the third score corresponding to each of the plurality of first search results (Qiao [0133]: “one or more search results (or data associated therewith) that are not associated with the sentiment data (e.g., accessed in step 1240) may be removed from the search results in step 1250. As another example, the search results (or data associated therewith) may be ordered based on a respective score, a respective category of sentiment, a respective degree of sentiment, a respective classification of sentiment, etc.”).
Regarding claim 24, the device according to claim 19, wherein the processor is further configured to perform actions of:
determining a historical search frequency and a historical click value rate corresponding to each of the plurality of first search results (Wu [0044]: “Specifically, word segmentation is performed on the search keyword and the search result according to a preset high-frequency word set. Specifically, a high-frequency word is obtained through division when the search keyword and the search result include the high-frequency word in the preset high-frequency word set, and the remaining parts of a text are divided by word, to respectively obtain the word segmentation results corresponding to the search keyword and the search result.); and
determining a first search result with the historical search frequency greater than a first threshold and/or the historical click value rate greater than a second threshold as the push information (Qiao [Abstract]: “A method, computer-readable medium, and a computer system for automatically generating sentiment data are disclosed. One or more portions of at least one document may be determined to be associated with at least one sentiment of one or more other portions of the at least one document. One or more scores associated with the at least one sentiment may be automatically determined based on at least one respective attribute of the one or more portions.”).
Regarding claim 25, the device according to claim 19, wherein the processor is further configured to perform actions of:
acquiring historical search results for a specified historical duration (Wu [0148-0149]: “The performing step 700 specifically includes: first, acquiring an original search click record set according to a search click behavior of a user, each original search click record in the original search click record set including at least a search keyword, an exposure list, and a click result, the click result being a clicked search result.);
determining a fourth score corresponding to each of the historical search results based on a preset value index (Wu [0152]: “The original search click record set is then filtered based on the preset rule, the original search click records that meet the preset rule are filtered out, and an initial training sample set is obtained according to the filtered original search click record set.); and
determining the mapping relationship between each type and each style and the first score based on an average value of the fourth scores of the historical search results with a same type and a same style (Qiao [0079]: “Turning back to FIG. 1B, step 135 involves determining that the second portion and the fourth portion are theme elements associated with a theme and/or are semantic sub-keys associated with a semantic key. In one embodiment, a theme associated with the second and fourth portions (e.g., as theme elements) may be determined in step 135.”).
Regarding claim 26, the device according to claim 19, wherein acquiring the updated order of the plurality of first search results by adjusting the initial order in order of the first score from largest to smallest, the method further comprises:
determining a current arrangement position of each of the plurality of first search results (Wu [0184-0186]: “First, a three-tuple data training set is constructed according to the initial training sample set. The constructing specifically includes: performing the following processing for each initial training sample in the initial training sample set: 1) Confidences of search results in an exposure list corresponding to the initial training sample are determined The determining specifically includes: a. A position of a click result of the initial training sample in the corresponding exposure list is respectively counted.,); and
inserting a second search result into a specified display position in the arrangement position, wherein the second search result is a predetermined search result having a specified page feature or a specified page content (Qiao [0209]: “Each piece of three-tuple data in the three-tuple data training set includes at least a search keyword, a first search result, and a second search result, and a similarity between the search keyword and the first search result is greater than a similarity between the search keyword and the second search result.”).
Regarding claim 27, the device according to claim 26, wherein inserting the second search result into the specified display position in the arrangement position comprises:
inserting the second search result into the specified display position in the arrangement position in response to there being one second search result (Wu [0179-0180]: “Step 720. Construct a three-tuple data training set according to the initial training sample set, and train the first similarity model according to the three-tuple data training set, to obtain a trained second similarity model. Each piece of three-tuple data in the three-tuple data training set includes at least a search keyword, a first search result, and a second search result, and a similarity between the search keyword and the first search result is greater than a similarity between the search keyword and the second search result.”); or,
inserting a plurality of second search results into each of the specified display positions in the arrangement position in a preset order in response to there being the plurality of second search results (Wu [0182]: “Therefore, in this embodiment of this disclosure, a sorted optimization target may further be used to retrain the first similarity model, to construct the three-tuple data training set. The first similarity model is retrained based on the three-tuple data training set, and a principle of the target is to optimize the sorted results by using differences in similarities of different texts to the same text. For example, the three-tuple data is (the search keyword, doc1, doc2), and it is known that a matching degree of (the search keyword, doc1) is greater than a matching degree of (the search keyword, doc2). Through such three-tuple data training, the similarity model may finally enable that a similarity score calculated for (the search keyword, doc1) is greater than that of (the search keyword, doc2). That is, doc1 in the search results corresponding to the search keyword may rank higher than doc2.”).
Regarding claim 28, the device according to claim 19, wherein acquiring the plurality of first search results corresponding to the search request and the initial order of the plurality of first search results corresponding to the search request comprises:
acquiring the plurality of first search results corresponding to the search request and device attribute information of initiating the search request (Wu [0149-0150]: “First, acquiring an original search click record set according to a search click behavior of a user, each original search click record in the original search click record set including at least a search keyword, an exposure list, and a click result, the click result being a clicked search result. In this embodiment of this disclosure, original record data may be acquired from an original search click behavior log of the user. Generally, each record includes a search keyword, an exposure list, and a click result of the user. The exposure list includes a plurality of search results.);
determining a reference order associated with the device attribute information based on a preset mapping relationship (Wu [0159]: “The user searches a search keyword, and clicks a plurality of search results that are in the returned exposure list and correspond to a plurality of click results. That is, a one-time search of the user corresponds to a plurality of clicks. In this case, it indicates that the reference value of the click results of the user is relatively low, and an association between the click results and the search keyword may not be high. Therefore, the reliability of this record is relatively low, which adversely affects the training of the similarity model.); and
determining the initial order of the plurality of first search results based on the reference order (Wu [0247-0250]: “Input a search keyword of a user and all returned search results. Sequentially input the search keyword and the search results into the similarity model and respectively calculate similarities between the search keyword and the search results. Use a similarity result as a feature, provide the similarity result to a sorting model. Finally, the sorting model obtains a sorted search result at least according to the similarity result. This is not limited to the sorting model. The sorting model may sort the search results sequentially in descending order of similarity, or may combine other features or factors simultaneously to finally obtain the sorted search results.”).
Regarding claim 29, the non-transitory computer-readable storage medium according to claim 20, wherein acquiring the initial order of the plurality of first search results corresponding to the search request comprises:
determining the initial order of the plurality of first search results based on a relevance of each of the plurality of first search results to the search request, and a number of clicks of each of the plurality of first search results within a preset duration (Qiao [0161]: “Accordingly, in one embodiment, a GUI (e.g., 1600C) may be generated and/or displayed that advantageously includes sentiment data associated with other data (e.g., the words “steering,” “fuel economy,” “engine,” “acceleration,” “handling,” “braking,” etc.) that is not part of the query (e.g., which includes the word “performance” but does not include the words “steering,” “fuel economy,” “engine,” “acceleration,” “handling” or “braking”). As such, a user entering the query need not know the other data associated with the word performance or spend the time and effort to enter those words in as part of the query. Thus, the GUI may provide valuable and relevant information by displaying the sentiment (e.g., associated with sentiment data) of one or more features (e.g., “steering,” “fuel economy,” “engine,” “acceleration,” “handling,” “braking,” etc.) of the Toyota Land Cruiser as determined from at least one search result or document. Further, the sentiment data (e.g., of GUI 1600C) may be displayed contemporaneously with the corresponding search results (e.g., of GUI 1600B) as an image or GUI (e.g., 1600A) in one embodiment, thereby providing even more valuable and relevant information related to the initial query (e.g., which may be displayed in region 1610 of GUI 1600A).”).
Regarding claim 30, the non-transitory computer-readable storage medium according to claim 20, wherein acquiring the initial order of the plurality of first search results corresponding to the search request comprises:
determining at least one vertical class label to which the search request is currently mapped (Wu [0252]: “Based on the foregoing embodiments, the following describes a specific application scenario of the embodiments. In this embodiment of this disclosure, the search result processing method may be applied to a vertical search in a “Search” function in WeChat®, for example, a scenario of an official account search in “Search”. Herein, the Search function may allow a user to search for information such as Moments, articles, official accounts, novels, music, and stickers according to a search keyword.”);
calculating a second score of each of the at least one vertical class label based on a preset value index (Wu [0259]: “Compared with the conventional technology, a performance analysis is performed according to the method in the embodiments of this disclosure. For an interpretation recognition task, an objective is to determine whether two texts have the same meaning. Based on two different datasets, that is, a dataset 1 and a dataset 2, the accuracy of the matching between the search results and the search keyword and the index F1-score that measures the accuracy of the binary classification model are counted.”);
determining a relevance of each of the plurality of first search results to each of the at least one vertical class label (Wu [0166]: “In this embodiment of this disclosure, the corresponding training sample set is constructed based on the initial training sample set according to different training targets, thereby improving the accuracy of the similarity model. The two-tuple data is pairwise data, and one piece of the pairwise data includes two texts and a label. For example, the label is 0 or 1, and 0 or 1 is used for representing whether the two texts are similar. If pairwise data is (A, B, 1), it indicates that a text A and a text B are similar.”);
determining a third score of each of the plurality of first search results based on the relevance of each of the plurality of first search results to each of the at least one vertical class label, and the second score of each of the at least one vertical class label (Qiao 0084]: “As shown in FIG. 1B, step 140 involves determining a third score associated with the theme and/or the semantic key. In one embodiment, the third score may be determined in step 140 based on the sentiment data of the corresponding theme elements and/or semantic sub-keys (e.g., in column 930 of data structure 900). In one embodiment, the third score determined in step 140 may include at least a portion of the combined score data (e.g., a combined positive sentiment score, a combined negative sentiment score, a net sentiment score, some combination thereof, etc.) in column 950 of data structure 900 of FIG. 9.”); and
determining the initial order of the plurality of first search results based on the third score corresponding to each of the plurality of first search results (Qiao [0133]: “one or more search results (or data associated therewith) that are not associated with the sentiment data (e.g., accessed in step 1240) may be removed from the search results in step 1250. As another example, the search results (or data associated therewith) may be ordered based on a respective score, a respective category of sentiment, a respective degree of sentiment, a respective classification of sentiment, etc.”).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant’s disclosure.
Stegman, US 2023/0106319, Adaptive Search Result Re-ranking
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Ramanath, US 2020/0004835, Generating candidates for search using scoring/retrieval architecture
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Epstein, US 2012/0191684, Methods and Apparatuses for searching content
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Any inquiry concerning this communication or earlier communications from the examiner should be directed to SABA AHMED whose telephone number is (571) 270-0236. The examiner can normally be reached on MON – FRI: 8AM – 5PM EST.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Boris Gorney can be reached on 571-270-5626. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/SABA AHMED/
Examiner, Art Unit 2154
/BORIS GORNEY/Supervisory Patent Examiner, Art Unit 2154
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