DETAILED ACTION
Notice of Pre-AIA or AIA Status
The Action is res e present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
The Action is responsive to the Application filed 01/16/2025.
Please note claims 1-7 and 9-21 are pending and stand rejected in which claims 1, 9 and 10 are independent.
Information Disclosure Statement
The information disclosure statements filed 03/12/2026, and 04/15/2025 are in compliance with 37 CFR 1.97(c) and herein have been considered. Its corresponding PTO-1449 have been electronically signed as attached.
Foreign Priority
Applicant’s claim for the benefit of a prior-filed China Patent Application CHINA 202211531138.5, filed 12/01/2022, under 35 U.S.C. 119(a)-(d) or under 35 U.S.C. 120, 121, or 365(c) is acknowledged.
Claim Interpretation
A “patent is invalid for indefiniteness if its claims, read in light of the specification delineating the patent, and the prosecution history, fail to inform, with reasonable certainty, those skilled in the art about the scope of the invention.” Nautilus, Inc. v. Biosig Instruments, Inc., 134 S.Ct. 2120, 2124, 110 USPQ2d 1688 (2014). The Office does not interpret claims when examining patent applications in the same manner as the courts. The Office construes claims by giving them their broadest reasonable interpretation during prosecution in an effort to establish a clear record of what the applicant intends to claim. See, MPEP 2173.02 (Determining Whether Claim Language is Definite). Such claim construction during prosecution may effectively result in a lower threshold for ambiguity than a court's determination. Id. However, Applicant has the ability to amend the claims during prosecution to ensure that the meaning of the language is clear and definite prior to issuance or provide a persuasive explanation (with evidence as necessary) that a person of ordinary skill in the art would not consider the claim language unclear. Id. (citing In re Buszard, 504 F.3d 1364, 1366 (Fed. Cir. 2007) (claims are given their broadest reasonable interpretation during prosecution “to facilitate sharpening and clarifying the claims at the application.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(d):
(d) REFERENCE IN DEPENDENT FORMS.—Subject to subsection (e), a claim in dependent form shall contain a reference to a claim previously set forth and then specify a further limitation of the subject matter claimed. A claim in dependent form shall be construed to incorporate by reference all the limitations of the claim to which it refers.
Claims 20-21 are rejected under 35 U.S.C. 112(d) as being of improper dependent form for failing to further limit the subject matter of the claim upon which it depends, or for failing to include all the limitations of the claim upon which it depends.
As per claim 20, the claim recites “The computer-readable storage medium of claim 16 …” to which claim 16 recites “The computer device of claim 9, …”.
As per claim 21, the claim depends upon claim 20 and inherits the deficiency of failing to include all the limitations of the claim upon which it depends.
Applicant may cancel the claim(s), amend the claim(s) to place the claim(s) in proper dependent form, rewrite the claim(s) in independent form, or present a sufficient showing that the dependent claim(s) complies with the statutory requirements.
Claim Rejections - 35 USC § 102
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.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 9, 11, 1-2, 10 and 17 are rejected under 35 U.S.C. § 102(a)(2) as being clearly anticipated by
LI, FENG: “METHOD AND DEVICE FOR DISPLAYING A SEARCH RESULT”, (China Patent Application Document CN 105138535 A, DATE PUBLISHED 2015-12-09 and DATE FILED 2015-06-30, hereafter “LI").
As per claim 9, LI teaches a computer device, comprising:
a processor, a storage device, and a bus (See Pages 6-8, in the context "computer device" may include a processor and memory, storage medium and coupled to elements. Here memory teaches storage device and “coupled” reads on connected by computer bus.),
Wherein the storage device stores a machine-readable instruction executable by the processor, the processor communicates with the storage device through the bus when the computer device is running, and the machine readable instruction, when executed by the processor, causes the processor to perform (See Page 6, "computer" is an intelligent electronic device may by running a predetermined program or instruction to execute numerical calculation and/or logic calculation such as a predetermined processing process, which may include a processor and memory, pre-stored in the memory of the subsisting instruction is executed by the processor to execute a predetermined process, or perform a predetermined process) the steps of the search result presentation method comprising:
in response to receiving search information, acquiring a search result matching the search information (See Page 15, searching result according to the searching intention of the user to display, the search result can be various forms of display and improve the display efficiency of the user and search result acquire the speed of search result so as to improve the user experience),
wherein the search result comprises one of key information and structured information of target webpage content (See Pages 12-13, calculating the degree of correlation of the search information of the search result corresponding to the search intention and displaying of search result that is greater than or equal to threshold value. Here the information of search result as being displayed is interpreted key information. as selectable, each search result of the correlation degree is less than the threshold value of the default display form to perform display. wherein a default display form can be by title, description and URL (Uniform Resource Locator, URL) of three elements, after users by preliminarily selecting the title and description, click URL to jump. Here a display form by elements of title, description and URL teaches structured information of target webpage content); and
presenting the search result in a presentation form corresponding to the search result (See Pages 12-13, the displaying of key information, above threshold of correlation value, of the search result or displaying selected elements in structured display form in which both displaying are interpreted presenting the search result in a presentation form corresponding to the search result),
wherein the presentation form corresponding to the key information comprises prominently presenting a keyword in the key information that matches the search information (See Abstract and Page 13, determining the search intent through preset arithmetic according to the search information, the search result of the search information by the search intent corresponding to the display form to display. wherein a default display form can be by title, description and URL (Uniform Resource Locator, URL) of three elements, after users by preliminarily selecting the title and description, click URL to jump. Here the user searches by selecting the title and description and the title and description that reads on keywords are displayed as key information in the search result), and
the presentation form corresponding to the structured information comprises presenting field information of a plurality of key fields of the target webpage content in a structured manner (See Pages 12-13, the displaying of key information, above threshold of correlation value, of the search result or displaying selected elements in structured display form in which both displaying are interpreted presenting the search result in a presentation form corresponding to the search result and wherein a default display form can be by title, description and URL (Uniform Resource Locator, URL) of three elements, after users by preliminarily selecting the title and description, click URL to jump.).
As per claim 11, LI teaches the computer device of claim 9, wherein the acquiring a search result matching the search information comprises:
acquiring individual search results (See LI: Page 13, each search result of the correlation degree is less than the threshold value of the default display form to perform display.),
wherein the individual search results comprise the key information and the structured information (See Pages 12-13, calculating the degree of correlation of the search information of the search result corresponding to the search intention and displaying of search result that is greater than or equal to threshold value. Here the information of search result as being displayed is interpreted key information. as selectable, each search result of the correlation degree is less than the threshold value of the default display form to perform display. wherein a default display form can be by title, description and URL (Uniform Resource Locator, URL) of three elements, after users by preliminarily selecting the title and description, click URL to jump. Here a display form by elements of title, description and URL teaches structured information of target webpage content); and
selecting, from the key information and the structured information, information with a higher corresponding correlation degree as the search result matching the search information based on a first correlation degree between the key information and the search information and a second correlation degree between the structured information and the search information (See Pages 12-13, calculating the degree of correlation of the search information of the search result corresponding to the search intention and displaying of search result that is greater than or equal to threshold value. Here the information of search result as being displayed is interpreted key information. as selectable, each search result of the correlation degree is less than the threshold value of the default display form to perform display. wherein a default display form can be by title, description and URL (Uniform Resource Locator, URL) of three elements, after users by preliminarily selecting the title and description, click URL to jump. Here the display form by elements of title, description and URL teaches structured information of target webpage content).
As per claims 1-2, the claims recite a search result presentation method, comprising operations recited as the steps performed by the processor of the device of claims 9 and 11, respectively and rejected above under 35 USC § 102 as clearly anticipated by LI.
Accordingly, claims 1-2 are rejected along the same rationale that rejected claims 9 and 11, respectively.
As per claims 10 and 17, the claims recite a computer-readable storage medium, wherein a computer program is stored on the computer-readable storage medium, and the computer program, when executed by a processor, causes the processor to perform (See LI: Page 8, the necessary task of program code or code segments can be stored in a machine-or computer-readable medium (such as the storage medium). (one or more) processor is able to execute necessary tasks.) the steps of the search result presentation method comprising operations recited as the steps performed by the processor of the device of claims 9 and 11, respectively and rejected above under 35 USC § 102 as clearly anticipated by LI.
Accordingly, claims 10 and 17 are rejected along the same rationale that rejected claims 9 and 11, respectively.
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 of this title, 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.
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 is incorrect, any correction of the statutory basis 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 text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action.
The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or non-obviousness.
For application naming 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.
Claims 14 and 5, are rejected under 35 U.S.C. 103 as being unpatentable over
LI, as applied to claims 9, 11, 1-2, 10 and 17 above, and further in view of
Fan; Yuan: “SYSTEM FOR REAL-TIME INTRUSION DETECTION OF SQL INJECTION WEB ATTACKS”, (U.S. Patent Application publication US 20090049547 A1, DATE PUBLISHED 2009-02-19 and DATE FILED 2007-08-13, hereafter “Fan").
As per claim 14, LI teaches the computer device of claim 11, wherein selecting, from the key information and the structured information, the information with the higher corresponding correlation degree as the search result matching the search information based on the first correlation degree between the key information and the search information and the second correlation degree between the structured information and the search information (See Pages 12-13, calculating the degree of correlation of the search information of the search result corresponding to the search intention and displaying of search result that is greater than or equal to threshold value. Here the information of search result as being displayed is interpreted key information. as selectable, each search result of the correlation degree is less than the threshold value of the default display form to perform display. wherein a default display form can be by title, description and URL (Uniform Resource Locator, URL) of three elements, after users by preliminarily selecting the title and description, click URL to jump).
However, LI does not explicitly teach inputting the search information, and the key information and the structured information corresponding to the search information into a pre-trained neural network model to obtain a first correlation score for characterizing the first correlation degree and a second correlation score for characterizing the second correlation degree output by the neural network model.
On the other hand, as an analogous art on machine learning for search result, Fan teaches as inputting the search information, and the key information and the structured information corresponding to the search information into a pre-trained neural network model to obtain a first correlation score for characterizing the first correlation degree and a second correlation score for characterizing the second correlation degree output by the neural network model (See [0057] and [0059]-[0060], the Web log are parsed. Step 302 is followed by step 304 where parameters of interest are extracted. The list of extracted parameters from the network traffic 12 was described above. Step 304 is followed by step 305 where the data is normalized and scored in accordance with the operation of the normalizing and scoring engine 70 (See. FIG. 8). Step 305 is followed by step 306 where the results from the normalizing and scoring engine are sent to the SOM (Self Organization Map) engine 62 of the training module 60. The SOM (Self Organization Map) engine 62 is one type of neural network. the Web log is parsed. Step 302 is followed by step 304 where parameters of interest are extracted. Step 304 is followed by step 305 where the data is normalized and scored. The results from the normalizing and scoring engine are sent to the SOM (Self Organization Map) engine 62 of the training module 60. The SOM (Self Organization Map) engine 62 is one type of neural network. Step 306 is followed by step 308 where the SOM output data is normalized by Normalizing and Scoring engine 70 to generate Normalized data. Normalization is needed when different parameters have different data range levels. For example, if the parameter score range is from 0-10 and the (http) status code range is from 0-500, then certain normalization is needed to prevent one attribute from contributing more than other attributes (which is not desired). Step 308 is followed by step 310 where the normalized output data is analyzed. Here the selected parameters of interest read on search, key and structural information and normalized parameter scores read on the correlation scores).
It would have been obvious to one having ordinary skill in the art at the time of the Applicant's application was filed to combine the teaching of Fan with Li reference because LI is dedicated to searching result according to the searching intention of the user display and improve the display efficiency and user search result to obtain the speed of the searching result, and Fan is dedicated to intrusion detection of SQL injection web attacks, and more specifically, to a system for standard query language (SQL) injection attack detection which has low false alarms, as well as, a high detection rate, and the combined teaching would have enabled LI to use neural network to capture, extract and normalize the real-time data, and to feed into the trained neural network for calculating more accurate correlation scores of the input search information.
LI in view of Fan further teaches the following:
presenting information with a higher correlation score as the search result matching the search information based on the first correlation score and the second correlation score (See Fan: [0060], where the normalized output data is analyzed. This data may be analyzed visually in graphs, reports, etc. The data may be analyzed by a computerized process.).
As per claim 5, the claim recites a search result presentation method, comprising operations recited as the steps performed by the processor of the device of claim 14 and rejected above under 35 USC § 103 as being unpatentable over LI in view of Fan.
Accordingly, claim 5 is rejected along the same rationale that rejected claim 14.
Claims 15 and 6, are rejected under 35 U.S.C. 103 as being unpatentable over
LI, as applied to claims 9, 11, 1-2, 10 and 17 above, in view of
Fan, as applied to claim 14 above, and further in view of
Zadeh et al.: “METHODS AND SYSTEMS FOR APPLICATIONS FOR Z-NUMBERS”, (U.S. Patent Application Publication US 20140201126 A1, DATE PUBLISHED 2014-07-17 and DATE FILED 2014-03-18, hereafter “Zadeh").
As per claim 15, the claim recites a first limitation “the computer device of claim 14, wherein the neural network model is pre-trained according to the following steps: acquiring sample search information, sample key information and sample structured information of webpage content corresponding to the sample search information, and sample labels”.
LI in view of Fan teaches the computer device of claim 14, wherein the neural network model is pre-trained according to the following steps:
acquiring search information, key information and structured information of webpage content corresponding to the search information (See LI: Page 15, searching result according to the searching intention of the user to display, the search result can be various forms of display and improve the display efficiency of the user and search result acquire the speed of search result so as to improve the user experience), as previously described in claim 9 rejection above.
LI in view of Fan does not explicitly teach acquiring each of the said information is sample information, nor taches acquiring and sample labels as required by “acquiring sample search information, sample key information and sample structured information of webpage content corresponding to the sample search information, and sample labels”.
However, Zadeh teaches sample input, samples or results, training samples … etc. across the reference (See [1764], for example, some key data samples may be marked as the key representative samples and an elevated weight is assigned to such samples during the training, e.g., to simulate the training with multiple instances of such training sample) and further teaches the labels for supervised training or for making association with object features (See [1808], for examples, the labels for supervised training or for making association with object features).
It would have been obvious to one having ordinary skill in the art at the time of the Applicant's application was filed to combine the teaching of Zadeh with LI in view of Fan reference because LI is dedicated to searching result according to the searching intention of the user display and improve the display efficiency and user search result to obtain the speed of the searching result, Fan is dedicated to intrusion detection of SQL injection web attacks, and more specifically, to a system for standard query language (SQL) injection attack detection which has low false alarms, as well as, a high detection rate, and Zadeh is dedicated to providing very efficient and fast algorithms for image processing, learning machines, NLP, pattern recognition, classification, SVM, deep learning, and the like, for all the applications and usages, and the combined teaching would have enabled LI in view of Fan to use sample information for input to neural network training and retraining for optimizing and improving the learning rate of the neural network.
LI in view of Fan and further in view of Zadeh further teaches the following:
wherein the sample labels are used to mark first sample correlation degrees between the sample key information and the sample search information and second sample correlation degrees between the sample structured information and the sample search information (See LI: Pages 12-13, calculating the degree of correlation of the search information of the search result corresponding to the search intention and displaying of search result that is greater than or equal to threshold value. Here the information of search result as being displayed is interpreted key information. as selectable, each search result of the correlation degree is less than the threshold value of the default display form to perform display. wherein a default display form can be by title, description and URL (Uniform Resource Locator, URL) of three elements, after users by preliminarily selecting the title and description, click URL to jump. Here a display form by elements of title, description and URL teaches structured information of target webpage content; and Zadeh: [1764] and [1808], for examples, some key data samples may be marked as the key representative samples and an elevated weight is assigned to such samples during the training, e.g., to simulate the training with multiple instances of such training sample; and , the labels for supervised training or for making association with object features);
inputting the sample search information, the sample key information, and the sample structured information into the neural network model to be trained to obtain first sample correlation scores for characterizing the first sample correlation degrees and second sample correlation scores for characterizing the second sample correlation degrees output by the neural network model (See Fan: [0057] and [0059]-[0060], the Web log are parsed. Step 302 is followed by step 304 where parameters of interest are extracted. The list of extracted parameters from the network traffic 12 was described above. Step 304 is followed by step 305 where the data is normalized and scored in accordance with the operation of the normalizing and scoring engine 70 (See. FIG. 8). Step 305 is followed by step 306 where the results from the normalizing and scoring engine are sent to the SOM (Self Organization Map) engine 62 of the training module 60. The SOM (Self Organization Map) engine 62 is one type of neural network. the Web log is parsed. Step 302 is followed by step 304 where parameters of interest are extracted. Step 304 is followed by step 305 where the data is normalized and scored. The results from the normalizing and scoring engine are sent to the SOM (Self Organization Map) engine 62 of the training module 60. The SOM (Self Organization Map) engine 62 is one type of neural network. Step 306 is followed by step 308 where the SOM output data is normalized by Normalizing and Scoring engine 70 to generate Normalized data. Normalization is needed when different parameters have different data range levels. For example, if the parameter score range is from 0-10 and the (http) status code range is from 0-500, then certain normalization is needed to prevent one attribute from contributing more than other attributes (which is not desired). Step 308 is followed by step 310 where the normalized output data is analyzed; and Zadeh: [1767], upon training the correlation/association, given an input data (e.g., a document), the reconstruction of labels (as output via, for example, correlation layer), presents the classification of the document based on those labels; one or more labels are identified in the output, indicating that the document is determined to belong to both classes/types; the output (multi-valued or continuous) indicates the degree in which the document is determined to be of the class/type; and the values output at the labels are thresholded (or further discretized) to simplify the presentation and further usage, an output less than 15% is zeroed, or an output more than 85% is turned to 100%); and
determining a loss value of a current training based on the first sample correlation scores, the second sample correlation scores, and the sample labels, and adjusting parameters of the neural network model to be trained based on the loss value (See Zadeh: [1404], a Bayes classifier is used, to minimize the total average loss (due to incorrect decisions), e.g. for the ones used for Guassian pattern classes, a perceptron for 2-pattern classes is used and the least mean square (LMS) delta rule for training perceptions is used, to minimize the error between the actual response and the desired response (for the training purposes).
As per claim 6, the claim recites a search result presentation method, comprising operations recited as the steps performed by the processor of the device of claim 15 and rejected above under 35 USC § 103 as being unpatentable over LI in view of Fan and further in view of Zadeh.
Accordingly, claim 6 is rejected along the same rationale that rejected claim 15.
Claims 12-13, 16, 3-4, 7, 18 and 19 are rejected under 35 U.S.C. 103 as being unpatentable over
LI, as applied to claims 9, 11, 1-2, 10 and 17, above, and further in view of
Zadeh.
As per claim 12, LI does not explicitly teach the computer device of claim 9, wherein the structured information is predetermined by:
extracting the structured information corresponding to the target webpage content based on a key field combination set for a webpage category to which the target webpage content belongs, for the target webpage content.
However, Zadeh teaches the computer device of claim 9, wherein the structured information is predetermined by:
extracting the structured information corresponding to the target webpage content based on a key field combination set for a webpage category to which the target webpage content belongs, for the target webpage content (See Zadeh: [2062] and [1481], the content (such as images) whether in web page or a document link to other knowledgebase entities by fetching the content in an automatic search (e.g., by bots or background processes), analyzing the content within a context and/or by using feature detectors/classifiers, importing the features of the content into Z-web, using the knowledgebase to automatically annotate the content and associate such annotation with the content (for a later search), e.g., via indexing.; and existing attributes and relationships (e.g., age, son, daughter) other attributes and relationships are extracted from an attribute/relationship database based on context and existing attributes).
It would have been obvious to one having ordinary skill in the art at the time of the Applicant's application was filed to combine the teaching of Zadeh with LI reference because LI is dedicated to searching result according to the searching intention of the user display and improve the display efficiency and user search result to obtain the speed of the searching result, Zadeh is dedicated to providing very efficient and fast algorithms for image processing, learning machines, NLP, pattern recognition, classification, SVM, deep learning, and the like, for all the applications and usages, and the combined teaching would have enabled LI to use sample information for input to neural network training and retraining for optimizing and improving the learning rate of the neural network.
LI in view of Zadeh further teaches the following:
wherein the structured information comprises field information of a plurality of key fields indicated by the key field combination (See Zadeh: [1481], existing attributes and relationships (e.g., age, son, daughter) other attributes and relationships are extracted from an attribute/relationship database based on context and existing attributes).
As per claim 13, LI in view of Zadeh teaches the computer device of claim 9, wherein the key information is determined by:
extracting individual text segments in the target webpage content (See Zadeh: [1292] and [2198], dissecting the sentences into its components automatically later on, and extract the relevant and important words and information the selection is made using a fuzzy parameter, we apply the multi-level thresholding. In one embodiment, we apply a model fitting method. In one embodiment, we apply the above to segment the document images, face, text, or the like);
determining a correlation degree between each text segment and the search information (See Zadeh: [1800], the correlation between the blocked object (e.g., eyes) and the blocking object (e.g., dark glasses) is learned by sequential learning or by using two instances of the classifier/feature detector; and Fan: [0083], a joint score is calculated by a Web layer to Database layer correlator 86 to determine a correlation score. This correlation score between the two scores provides a more accurate way and lower the false alarm rate); and
selecting a text segment with a highest corresponding correlation degree as the key information, or selecting at least one text segment with a corresponding correlation degree greater than a first set threshold and integrating the at least one text segment into the key information (See Zadeh: [2959]-[2960], the item(s) matched to the object in the image is displayed to the user in an overlay or in a hovering pane or window, e.g., with a rolling display of selectable options for the user. In one embodiment, the browser goes to the merchant's catalog item webpage (landing page) upon selection the corresponding option to choose; directing or providing the user's browsers with scripts corresponding to See & Shop platform. In one embodiment, the scripts/code analyze the webpage (or rendered document object model) to determine images on the webpage, e.g., belonging to the same nodal hierarchy as elements (e.g., HTML elements) having a particular ID or class.).
As per claim 16, Li in view of Zadeh teaches the computer device of claim 9, wherein prominently presenting the keyword in the key information that matches the search information comprises:
performing word segmentation on the key information to obtain individual segmented words (See Zadeh: [1292], dissecting the sentences into its components automatically later on, and extract the relevant and important words and information.); and
calculating a correlation degree between each obtained segmented word and the search information, and setting a segmented word with a correlation degree between the segmented word and the search information greater than a second set threshold in the key information as in a prominent presentation form (See Zadeh: [1292], the degree of matching to a specific template (e.g. for English grammar), to find (for example) the subject and the verb in the sentence, is done by fuzzy membership function and other fuzzy concepts).
As per claims 3-4 and 7, the claims recite a search result presentation method, comprising operations recited as the steps performed by the processor of the device of claims 12-13 and 16, respectively and rejected above under 35 USC § 103 as being unpatentable over LI in view of Zadeh.
Accordingly, claims 3-4 and 7 are rejected along the same rationale that rejected claims 12-13 and 16, respectively.
As per claims 18 and 19, the claims recite a computer-readable storage medium, wherein a computer program is stored on the computer-readable storage medium, and the computer program, when executed by a processor, causes the processor to perform (See LI: Page 8, the necessary task of program code or code segments can be stored in a machine-or computer-readable medium (such as the storage medium). (one or more) processor is able to execute necessary tasks.) the steps of the search result presentation method comprising operations recited as the steps performed by the processor of the device of claims 12-13, respectively and rejected above under 35 USC § 103 as being unpatentable over LI in view of Zadeh.
Accordingly, claims 18-19 are rejected along the same rationale that rejected claims 12-13, respectively.
Claims 20-21, are rejected under 35 U.S.C. 103 as being unpatentable over
LI, as applied to claims 9, 11, 1-2, 10 and 17 above, in view of
Zadeh, as applied to claim 16 above, and further in view of
Fan.
As per claim 20, LI in view of Zadeh teaches the computer-readable storage medium of claim 16, wherein selecting, from the key information and the structured information, the information with the higher corresponding correlation degree as the search result matching the search information based on the first correlation degree between the key information and the search information and the second correlation degree between the structured information and the search information as described above in claim 9 and 16 rejections.
However, LI in view of Zadeh does not explicitly teach inputting the search information, and the key information and the structured information corresponding to the search information into a pre-trained neural network model to obtain a first correlation score for characterizing the first correlation degree and a second correlation score for characterizing the second correlation degree output by the neural network model.
However, Fan teaches inputting the search information, and the key information and the structured information corresponding to the search information into a pre-trained neural network model to obtain a first correlation score for characterizing the first correlation degree and a second correlation score for characterizing the second correlation degree output by the neural network model (See Fan: [0057] and [0059]-[0060], the Web log are parsed. Step 302 is followed by step 304 where parameters of interest are extracted. The list of extracted parameters from the network traffic 12 was described above. Step 304 is followed by step 305 where the data is normalized and scored in accordance with the operation of the normalizing and scoring engine 70 (See. FIG. 8). Step 305 is followed by step 306 where the results from the normalizing and scoring engine are sent to the SOM (Self Organization Map) engine 62 of the training module 60. The SOM (Self Organization Map) engine 62 is one type of neural network. the Web log is parsed. Step 302 is followed by step 304 where parameters of interest are extracted. Step 304 is followed by step 305 where the data is normalized and scored. The results from the normalizing and scoring engine are sent to the SOM (Self Organization Map) engine 62 of the training module 60. The SOM (Self Organization Map) engine 62 is one type of neural network. Step 306 is followed by step 308 where the SOM output data is normalized by Normalizing and Scoring engine 70 to generate Normalized data. Normalization is needed when different parameters have different data range levels. For example, if the parameter score range is from 0-10 and the (http) status code range is from 0-500, then certain normalization is needed to prevent one attribute from contributing more than other attributes (which is not desired). Step 308 is followed by step 310 where the normalized output data is analyzed. Here the selected parameters of interest read on search, key and structural information and normalized parameter scores read on the correlation scores).
It would have been obvious to one having ordinary skill in the art at the time of the Applicant's application was filed to combine the teaching of Fan with LI in view of Zadeh reference because LI is dedicated to searching result according to the searching intention of the user display and improve the display efficiency and user search result to obtain the speed of the searching result, Fan is dedicated to intrusion detection of SQL injection web attacks, and more specifically, to a system for standard query language (SQL) injection attack detection which has low false alarms, as well as, a high detection rate, and Zadeh is dedicated to providing very efficient and fast algorithms for image processing, learning machines, NLP, pattern recognition, classification, SVM, deep learning, and the like, for all the applications and usages, and the combined teaching would have enabled LI in view of Zadeh to use query result for input to neural network training and retraining for optimizing and improving the learning rate of the neural network.
LI in view of Zadeh and further in view of Fan further teaches the following:
presenting information with a higher correlation score as the search result matching the search information based on the first correlation score and the second correlation score (See Fan: [0060], where the normalized output data is analyzed. This data may be analyzed visually in graphs, reports, etc. The data may be analyzed by a computerized process.).
As per claim 21, LI in view of Zadeh and further in view of Fan teaches the computer-readable storage medium of claim 20, wherein the neural network model is pretrained according to the following steps:
acquiring sample search information, sample key information and sample structured information of webpage content corresponding to the sample search information, and sample labels (See LI: Page 15, searching result according to the searching intention of the user to display, the search result can be various forms of display and improve the display efficiency of the user and search result acquire the speed of search result so as to improve the user experience; and Zadeh: [1764] and [1808], for examples, some key data samples may be marked as the key representative samples and an elevated weight is assigned to such samples during the training, e.g., to simulate the training with multiple instances of such training sample; and the labels for supervised training or for making association with object features),
wherein the sample labels are used to mark first sample correlation degrees between the sample key information and the sample search information and second sample correlation degrees between the sample structured information and the sample search information (See LI: Pages 12-13, calculating the degree of correlation of the search information of the search result corresponding to the search intention and displaying of search result that is greater than or equal to threshold value. Here the information of search result as being displayed is interpreted key information. as selectable, each search result of the correlation degree is less than the threshold value of the default display form to perform display. wherein a default display form can be by);
inputting the sample search information, the sample key information, and the sample structured
information into the neural network model to be trained to obtain first sample correlation scores for characterizing the first sample correlation degrees and second sample correlation scores for characterizing the second sample correlation degrees output by the neural network model (See title, description and URL (Uniform Resource Locator, URL) of three elements, after users by preliminarily selecting the title and description, click URL to jump. Here a display form by elements of title, description and URL teaches structured information of target webpage content; and Zadeh: [1764] and [1808], for examples, some key data samples may be marked as the key representative samples and an elevated weight is assigned to such samples during the training, e.g., to simulate the training with multiple instances of such training sample; and , the labels for supervised training or for making association with object features); and
determining a loss value of a current training based on the first sample correlation scores, the second sample correlation scores, and the sample labels, and adjusting parameters of the neural network model to be trained based on the loss value (See Zadeh: [1404], a Bayes classifier is used, to minimize the total average loss (due to incorrect decisions), e.g. for the ones used for Guassian pattern classes, a perceptron for 2-pattern classes is used and the least mean square (LMS) delta rule for training perceptions is used, to minimize the error between the actual response and the desired response (for the training purposes).
Related Prior Arts
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure can be found in the PTO-892 Notice of Reference Cited.
Conclusion
Examiner has cited particular columns and line numbers in the references applied to the claims above for the convenience of the applicant. Although the specified citations are representative of the teachings of the art and are applied to specific limitations within the individual claim, other passages and figures may apply as well. It is respectfully requested from the applicant in preparing responses, to fully consider the references in entirety as potentially teaching all or part of the claimed invention, as well as the context of the passage as taught by the prior art or disclosed by the Examiner. SEE MPEP 2141.02 [R-5] VI. PRIOR ART MUST BE CONSIDERED IN ITS ENTIRETY, INCLUDING DISCLOSURES THAT TEACH AWAY FROM THE CLAIMS: A prior art reference must be considered in its entirety, i.e., as a whole, including portions that would lead away from the claimed invention. W.L. Gore & Associates, Inc. v. Garlock, Inc., 721 F.2d 1540, 220 USPQ 303 (Fed. Cir. 1983), cert. denied, 469 U.S. 851 (1984) In re Fulton, 391 F.3d 1195, 1201, 73 USPQ2d 1141, 1146 (Fed. Cir. 2004). >See also MPEP §2123.
In the case of amending the Claimed invention, Applicant is respectfully requested to indicate the portion(s) of the specification which dictate(s) the structure relied on for proper interpretation and also to verify and ascertain the metes and bounds of the claimed invention.
Contact Information
Any inquiry concerning this communication or earlier communications from the examiner should be directed to KUEN S LU whose telephone number is (571)272-4114. The examiner can normally be reached on M-F, 8-19, Mid-Flex 2 hours.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Mr. Aleksandr Kerzhner can be reached on 571-270-1760. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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KUEN S LU /Kuen S Lu/
Art Unit 2165
Primary Patent Examiner
August 6, 2026
