DETAILED ACTION
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on XXXXXXXXXXXXXX has been entered.
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Status of Claims
Claims X are canceled.
Claims X are new.
Claims 1-20 are pending and have been examined.
This action is in reply to the papers filed on 08/29/2025 (effective filing date 09/14/2022).
Information Disclosure Statement
The information disclosure statement(s) submitted: 08/29/2025, has/have been considered by the Examiner and made of record in the application file.
Amendment
The present Office Action is based upon the original patent application filed on xxx as modified by the amendment filed on xxx.
Terminal Disclaimer
The terminal disclaimer filed on xxx disclaiming the terminal portion of any patent granted on this application which would extend beyond the expiration date of US Pat. No. xxxx has been reviewed and has been placed in the file.
Examiner acknowledges Applicant’s filed Terminal Disclaimer to prior art patent McCauley et al. US Pat. No. 5,930,775. A terminal disclaimer may be filed to overcome or obviate a nonstatutory double patenting rejection (37 CFR 1.321; MPEP 706.02; 1490).
Double Patenting - Withdrawn
The double patenting rejection is withdrawn per the filed terminal disclaimer noted above.
Examiner’s Amendment
Authorization for this examiner’s amendment was given in a communication with Craig C. Largent on 19 August 2013.
An examiner’s amendment to the record appears below. Should the changes and/or additions be unacceptable to applicant, an amendment may be filed as provided by 37 CFR 1.312. To ensure consideration of such an amendment, it MUST be submitted no later than the payment of the issue fee.
--- Claims 1-3 are Amended by Examiner’s Amendment as Follows ---
20. (Original) The method of claim 15, the processor further configured to provide a listing of rental properties if the buyer is not pre-qualified for the mortgage, the selected amount for the homeowner's insurance, or both.
20. (Currently Amended by Examiner’s Amendment) The system of claim 15, the processor further configured to provide a listing of rental properties if the buyer is not pre-qualified for the mortgage, the selected amount for the homeowner's insurance, or both.
Examiner’s Amendment
Authorization for this examiner’s amendment was given in a communication with Frank L. Johnson on 28 August 2017.
An examiner’s amendment to the record appears below. Should the changes and/or additions be unacceptable to applicant, an amendment may be filed as provided by 37 CFR 1.312. To ensure consideration of such an amendment, it MUST be submitted no later than the payment of the issue fee.
--- Claims 1-11 are Amended by Examiner’s Amendment as Follows ---
Claims 1-11. (Withdrawn).
Claims 1-11. (Canceled by Examiner’s Amendment).
Reasons For Allowance
Prior-Art Rejection withdrawn
Claims xxx are allowed. Independent claims X, Y, and Z all contain the same inventive scope. The closest prior art (See PTO-892, Notice of References Cited) does not teach the claimed:
The invention teaches… and the prior-art teaches…, however, the prior-art does not teach…
The closest prior-art (xxx) teach the features as disclosed in Non-final Rejection (xxxx), however, these cited references do not teach and the prior-art does not teach at least the following combination of features and/or elements:
determining, at a second time after associating the information corresponding to the first loyalty card with the logged location, that a second user computing device is located within a specified distance of the logged location using a second positioning system of the second user computing device; in response to determining that the second user computing device is located within the specified distance of the logged location of the first user computing device at the first time of detecting: retrieving information corresponding to a second loyalty card, the second loyalty card being associated with the merchant and the second user computing device; and displaying, by the second user computing device, data describing the second loyalty card.
Claim Rejections - 35 USC §101 - Withdrawn
Per Applicant’s amendments and arguments and considering new guidance in the MPEP, the rejections are withdrawn. Specifically, in Applicant’s Remarks (dated 03/14/2017, pgs. 8-11), Applicant traverses the 35 USC §101 rejections arguing that the amended claims recite new limitations that are not abstract, amount to significantly more, are directed to a practical application, etc… For example, Applicant argues….
In support of their arguments, Applicant cites to the following recent Fed. Cir. court cases (i.e., Alice Corp. v. CLS Bank Int’l, SRI Int’l, Inc. v. Cisco Systems, Inc., Ultramercial, Inc. v. Hulu, LLC, Berkheimer, Core Wireless, McRO, Enfish, Bascom, DDR, etc…).
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/process/file/efs/guidance/eTD-info-I.jsp.
Claims 1-20 are rejected on the ground of anticipatory-nonstatutory double patenting as being unpatentable over claims 1-20 of U.S. Patent No. 12,417,505.
Claim Rejections - 35 USC § 101
35 U.S.C. § 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. § 101 as being directed to non-statutory subject matter because the claimed invention is directed to an abstract idea without significantly more. These claims recite a method, system, and computer readable medium for detecting reliability across the internet after scraping.
Claim 8 recites [a] method, comprising: extracting, by a device, code from data associated with a set of guidelines, wherein the set of guidelines are associated with a first entity; determining, based on extracting the code, other data associated with style; training, based on the set of guidelines, a machine learning model, wherein the machine learning model determines whether a webpage is likely to be authorized by the first entity based on the other data; determining, based on output from the machine learning model, that a plurality of webpages are unlikely to be authorized by the first entity; transmitting, by the device, an indication of the plurality of webpages; and updating, by the device, the machine learning model based on feedback.
The claims are being rejected according to the 2019 Revised Patent Subject Matter Eligibility Guidance (Federal Register, Vol. 84, No. 5, p. 50-57 (Jan. 7, 2019)).
Step 1: Does the Claim Fall within a Statutory Category?
Yes. Claims 8-14 recite a method and, therefore, are directed to the statutory class of a process. Claims 1-7 recite a system/apparatus and, therefore, are directed to the statutory class of machine. Claims 15-20 recite a non-transitory computer readable medium/computer product and, therefore, are directed to the statutory class of a manufacture.
Step 2A, Prong One: Is a Judicial Exception Recited?
Yes. The following tables identify the specific limitations that recite an abstract idea. The column that identifies the additional elements will be relevant to the analysis in step 2A, prong two, and step 2B.
Claim 8: Identification of Abstract Idea and Additional Elements, using Broadest Reasonable Interpretation
Claim Limitation
Abstract Idea
Additional Element
8. A method, comprising:
No additional elements are positively claimed.
The preamble is given little patentable weight.
extracting, by a device, code from data associated with a set of guidelines, wherein the set of guidelines are associated with a first entity;
This limitation includes the step(s) of: extracting, by a device, code from data associated with a set of guidelines, wherein the set of guidelines are associated with a first entity.
But for the device, this limitation is directed to processing and/or communicating known information for detecting reliability across the internet after scraping which may be categorized as any of the following:
mathematical concept (mathematical relationships, mathematical formulas or equations, mathematical calculations)
and/or
mental process – concepts performed in the human mind (including an observation, evaluation, judgment, opinion)
and/or
certain method of organizing human activity –
fundamental economic principles or practices (including hedging, insurance, mitigating risk), and/or
commercial or legal interactions (including agreements in the form of contracts; legal obligations; advertising, marketing or sales activities or behaviors; business relations).
extracting, by a device, code from data associated with a set of guidelines…
The ‘guidelines’ and ‘first entity’ are NOT considered Additional Elements. They are interpreted as purely software or code.
determining, based on extracting the code, other data associated with style;
This limitation includes the step(s) of: determining, based on extracting the code, other data associated with style.
No additional elements are positively claimed.
This limitation is directed to processing and/or communicating known information for detecting reliability across the internet after scraping which may be categorized as any of the following:
mathematical concept (mathematical relationships, mathematical formulas or equations, mathematical calculations)
and/or
mental process – concepts performed in the human mind (including an observation, evaluation, judgment, opinion)
and/or
certain method of organizing human activity –
fundamental economic principles or practices (including hedging, insurance, mitigating risk), and/or
commercial or legal interactions (including agreements in the form of contracts; legal obligations; advertising, marketing or sales activities or behaviors; business relations).
No additional elements are positively claimed.
training, based on the set of guidelines, a machine learning model, wherein the machine learning model determines whether a webpage is likely to be authorized by the first entity based on the other data;
This limitation includes the step(s) of: training, based on the set of guidelines, a machine learning model, wherein the machine learning model determines whether a webpage is likely to be authorized by the first entity based on the other data.
No additional elements are positively claimed.
This limitation is directed to processing and/or communicating known information to facilitate a loyalty program which may be categorized as any of the following:
mathematical concept (mathematical relationships, mathematical formulas or equations, mathematical calculations)
and/or
mental process – concepts performed in the human mind (including an observation, evaluation, judgment, opinion)
and/or
certain method of organizing human activity –
fundamental economic principles or practices (including hedging, insurance, mitigating risk), and/or
commercial or legal interactions (including agreements in the form of contracts; legal obligations; advertising, marketing or sales activities or behaviors; business relations).
No additional elements are positively claimed.
The ‘guidelines,’ ‘machine learning model,’ ‘webpage,’ and ‘first entity’ are NOT considered Additional Elements. They are interpreted as purely software or code.
determining, based on output from the machine learning model, that a plurality of webpages are unlikely to be authorized by the first entity;
This limitation includes the step(s) of: determining, based on output from the machine learning model, that a plurality of webpages are unlikely to be authorized by the first entity.
No additional elements are positively claimed.
This limitation is directed to processing and/or communicating known information to facilitate a loyalty program which may be categorized as any of the following:
mathematical concept (mathematical relationships, mathematical formulas or equations, mathematical calculations)
and/or
mental process – concepts performed in the human mind (including an observation, evaluation, judgment, opinion)
and/or
certain method of organizing human activity –
fundamental economic principles or practices (including hedging, insurance, mitigating risk), and/or
commercial or legal interactions (including agreements in the form of contracts; legal obligations; advertising, marketing or sales activities or behaviors; business relations).
No additional elements are positively claimed.
The ‘machine learning model,’ ‘webpage,’ and ‘first entity’ are NOT considered Additional Elements. They are interpreted as purely software or code.
transmitting, by the device, an indication of the plurality of webpages; and
This limitation includes the step(s) of: transmitting, by the device, an indication of the plurality of webpages.
But for the device, this limitation is directed to processing and/or communicating known information for detecting reliability across the internet after scraping which may be categorized as any of the following:
mathematical concept (mathematical relationships, mathematical formulas or equations, mathematical calculations)
and/or
mental process – concepts performed in the human mind (including an observation, evaluation, judgment, opinion)
and/or
certain method of organizing human activity –
fundamental economic principles or practices (including hedging, insurance, mitigating risk), and/or
commercial or legal interactions (including agreements in the form of contracts; legal obligations; advertising, marketing or sales activities or behaviors; business relations).
transmitting, by the device, an indication…
updating, by the device, the machine learning model based on feedback.
This limitation includes the step(s) of: updating, by the device, the machine learning model based on feedback.
But for the device, this limitation is directed to processing and/or communicating known information for detecting reliability across the internet after scraping which may be categorized as any of the following:
mathematical concept (mathematical relationships, mathematical formulas or equations, mathematical calculations)
and/or
mental process – concepts performed in the human mind (including an observation, evaluation, judgment, opinion)
and/or
certain method of organizing human activity –
fundamental economic principles or practices (including hedging, insurance, mitigating risk), and/or
commercial or legal interactions (including agreements in the form of contracts; legal obligations; advertising, marketing or sales activities or behaviors; business relations).
updating, by the device, the machine learning model…
As shown above, under Step 2A, Prong One, the claims recite a judicial exception (an abstract idea). The claims are directed to the abstract idea of detecting reliability across the internet after scraping, which, pursuant to MPEP 2106.04, is aptly categorized as a is aptly categorized as a mathematical concept, mental process, and/or a method of organizing human activity. Therefore, under Step 2A, Prong One, the claims recite a judicial exception.
Next, the aforementioned claims recite additional functional elements that are associated with the judicial exception, including: a device for implementing the method claims, a processor and computer readable medium for implementing the CRM claims, and processor and memory for implementing the system claims. Examiner understands these limitations to be insignificant extrasolution activity. (See Accenture, 728 F.3d 1336, 108 U.S.P.Q.2d 1173 (Fed. Cir. 2013), citing Cf. Diamond v. Diehr, 450 U.S. 175, 191-192 (1981) ("[I]nsignificant post-solution activity will not transform an unpatentable principle in to a patentable process.”).
The aforementioned claims also recite additional technical elements including: a device for implementing the method claims, a processor and computer readable medium for implementing the CRM claims, and processor and memory for implementing the system claims. These limitations are recited at a high level of generality and appear to be nothing more than generic computer components. Claims that amount to nothing more than an instruction to apply the abstract idea using a generic computer do not render an abstract idea eligible. Alice Corp., 134 S. Ct. at 2358, 110 USPQ2d at 1983. See also 134 S. Ct. at 2389, 110 USPQ2d at 1984.
Step 2A, Prong Two: Is the Abstract Idea Integrated into a Practical Application?
No. The judicial exception is not integrated into a practical application. The additional elements listed above that relate to computing components are recited at a high level of generality (i.e., as generic components performing generic computer functions such as communicating, receiving, processing, analyzing, and outputting/displaying data) such that they amount to no more than mere instructions to apply the exception using generic computing components. Simply implementing the abstract idea on a generic computer is not a practical application of the abstract idea. Additionally, the claims do not purport to improve the functioning of the computer itself. There is no technological problem that the claimed invention solves. Rather, the computer system is invoked merely as a tool. Accordingly, the 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. Therefore, these claims are directed to an abstract idea.
Furthermore, looking at the elements individually and in combination, under Step 2A, Prong Two, the claims as a whole do not integrate the judicial exception into a practical application because they fail to: improve the functioning of a computer or a technical field, apply the judicial exception in the treatment or prophylaxis of a disease, apply the judicial exception with a particular machine, effect a transformation or reduction of a particular article to a different state or thing, or apply the judicial exception beyond generally linking the use of the judicial exception to a particular technological environment. Rather, the claims merely use a computer as a tool to perform the abstract idea(s), and/or add insignificant extra-solution activity to the judicial exception, and/or generally link the use of the judicial exception to a particular technological environment.
Step 2B: Does the Claim Provide an Inventive Concept?
Next, under Step 2B, the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements, when considered both individually and as an ordered combination, do not amount to significantly more than the abstract idea. Furthermore, looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. Simply put, as noted above, there is no indication that the combination of elements improves the functioning of a computer (or any other technology), and their collective functions merely provide conventional computer implementation. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements relating to computing components amount to no more than applying the exception using a generic computing components. Mere instructions to apply an exception using a generic computing component cannot provide an inventive concept. Furthermore, the broadest reasonable interpretation of the claimed computer components (i.e., additional elements) includes any generic computing components that are capable of being programmed to communicate, receive, send, process, analyze, output, or display data.
Additionally, pursuant to the requirement under Berkheimer, the following citations are provided to demonstrate that the additional elements, identified as extra-solution activity, amount to activities that are well-understood, routine, and conventional. See MPEP 2106.05(d).
Capturing an image (code) with an RFID reader. Ritter, US Patent No. 7734507 (Col. 3, Lines 56-67); “RFID: Riding on the Chip” by Pat Russo. Frozen Food Age. New York: Dec. 2003, vol. 52, Issue 5; page S22.
Receiving or transmitting data over a network. Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362; OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014).
Storing and retrieving information in memory. Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015); OIP Techs., 788 F.3d at 1363, 115 USPQ2d at 1092-93.
Outputting/Presenting data to a user. Mayo, 566 U.S. at 79, 101 USPQ2d at 1968; OIP Techs., Inc. v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1092-93 (Fed. Cir. 2015); MPEP 2106.05(g)(3).
Using a machine learning model to determine user segment characteristics for an ad campaign. https://whites.agency/blog/how-to-use-machine-learning-for-customer-segmentation/.
Thus, taken alone and in combination, the additional elements do not amount to significantly more than the above-identified judicial exception (the abstract idea), and are ineligible under 35 USC 101.
Independent system claim 1 and CRM claim 15 also contains the identified abstract ideas, with the additional elements of a processor and storage medium, which are a generic computer components, and thus not significantly more for the same reasons and rationale above.
Dependent claims 2-7, 9-14, and 16-20 further describe the abstract idea. The additional elements of the dependent claims fail to integrate the abstract idea into a practical application and do not amount to significantly more than the abstract idea. Thus, as the dependent claims remain directed to a judicial exception, and as the additional elements of the claims do not amount to significantly more, the dependent claims are not patent eligible.
As such, the claims are not patent eligible.
Invention Could be Performed Manually
It is conceivable that the invention could be performed manually without the aid of machine and/or computer. For example, Applicant claims extracting code from data, determining data associated with a style, training a model, determining that a webpage is unlikely to be authorized, transmitting an indication, and updating the model, etc… Each of these features could be performed manually and/or with the aid of a simple generic computer to facilitate the transmission of data.
See also Leapfrog Enterprises, Inc. v. Fisher-Price, Inc., and In re Venner, which stand for the concept that automating manual activity and/or applying modern electronics to older mechanical devices to accomplish the same result is not sufficient to distinguish over the prior art. Here, applicant is merely claiming computers to facilitate and/or automate functions which used to be commonly performed by a human.
Leapfrog Enterprises, Inc. v. Fisher-Price, Inc., 485 F.3d 1157, 82 USPQ2d 1687 (Fed. Cir. 2007) "[a]pplying modern electronics to older mechanical devices has been commonplace in recent years…"). The combination is thus the adaptation of an old idea or invention using newer technology that is commonly available and understood in the art.
In In re Venner, 262 F.2d 91, 95, 120 USPQ 193, 194 (CCPA 1958), the court held that broadly providing an automatic or mechanical means to replace manual activity which accomplished the same result is not sufficient to distinguish over the prior art. MPEP 2144.04, III Automating a Manual Activity.
MPEP 2144.04 III - Automating a Manual Activity and In re Venner, 262 F.2d 91, 95, 120 USPQ 193, 194 (CCPA 1958) further stand for and provide motivation for using technology, hardware, computer, or server to automate a manual activity.
Therefore, the Office finds no improvements to another technology or field, no improvements to the function of the computer itself, and no meaningful limitations beyond generally linking the use of an abstract idea to a particular technological environment. Therefore, based on the two-part Alice Corp. analysis, there are no limitations in any of the claims that transform the exception (i.e., the abstract idea) into a patent eligible application.
Claim Rejections - Not an Ordered Combination
None of the limitations, considered as an ordered combination provide eligibility, because taken as a whole, the claims simply instruct the practitioner to implement the abstract idea with routine, conventional activity.
Claim Rejections - Preemption
Allowing the claims, as presently claimed, would preempt others from detecting reliability across the internet after scraping. Furthermore, the claim language only recites the abstract idea of performing this method, there are no concrete steps articulating a particular way in which this idea is being implemented or describing how it is being performed.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 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.
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 nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1, 8, 15 are rejected under 35 U.S.C. 103 as being unpatentable over: Damian 2018/0013789; in view of Wang et al. 2020/0336509; in view of Harpur et al. 2018/0054320.
19/315,138 – Claim 1. Damian 2018/0013789 teaches A system, comprising: one or more memories; and one or more processors, coupled to the one or more memories (Damian 2018/0013789 [0006; 0008; 0012; 0020-0025; Figs. 1, 2-A, 2-B]), configured to: extract code from data (Damian 2018/0013789 [0033 - Upon receiving a message, module 38 may parse a header of the respective message to extract a document indicator comprising, for instance, an electronic address of a sender of the respective message and/or a domain name of the email server delivering the respective message. Module 38 may then transmit document indicator 42 to security server 14 and in response, receive assessment indicator 44 from server…][0049 - use content analysis to determine whether a HTML document located at a fraud candidate domain is an illegitimate clone of a legitimate webpage. Such determinations may include analyzing a set of graphic elements of the document under scrutiny (e.g., images, logos, color schemes, fonts, font style, font size, etc.) and comparing such elements to graphic elements harvested from a set of legitimate webpages][0051 - Another example of content analysis based on text comprises identifying and extracting contact information from an electronic document such as a HTML document or email message (e.g., an address, a contact telephone number, a contact email address, etc.). Content analyzer 56 may then try to match the respective contact data to a blacklist of similar data extracted from known fraudulent documents…][0052 - content analysis methods identifies snippets of code placed within an electronic document, such as traffic tracking code. An example of such code is used by web analytics services (e.g., Google® Analytics®) to calculate and report various data related to the use of a webpage: number of visits, referrers, country of origin for the visits, etc. Such code typically comprises a unique client ID (e.g., tracking ID) that allows the respective analytic service to associate the respective electronic document with a particular client…]) associated with a set of guidelines, wherein the guidelines are associated with a first entity (Damian 2018/0013789 [0041 – rules for fraud analysis…][0049 - determinations may include analyzing a set of graphic elements of the document under scrutiny (e.g., images, logos, color schemes, fonts, font style, font size, etc.) and comparing such elements to graphic elements harvested from a set of legitimate webpages] However, a document may be fraudulent also when it is sufficiently similar to a particular legitimate document. In one such example, a webpage may try to deceive users by masquerading as a legitimate webpage of a financial institution (e.g., a bank, an insurance company, etc.). Some embodiments of content analyzer 56 therefore use content analysis to determine whether a HTML document located at a fraud candidate domain is an illegitimate clone of a legitimate webpage. Such determinations may include analyzing a set of graphic elements of the document under scrutiny (e.g., images, logos, color schemes, fonts, font style, font size, etc.) and comparing such elements to graphic elements harvested from a set of legitimate webpages.); determine, based on extracting the code, other data associated with style (Damian 2018/0013789 [0049 - analyzing a set of graphic elements of the document under scrutiny (e.g., images, logos, color schemes, fonts, font style, font size, etc.) and comparing such elements to graphic elements harvested from a set of legitimate webpages]); train, based on the set of guidelines, a machine learning model (Damian 2018/0013789 [0049 - analyzing a set of graphic elements of the document under scrutiny (e.g., images, logos, color schemes, fonts, font style, font size, etc.) and comparing such elements to graphic elements harvested from a set of legitimate webpages] However, a document may be fraudulent also when it is sufficiently similar to a particular legitimate document. In one such example, a webpage may try to deceive users by masquerading as a legitimate webpage of a financial institution (e.g., a bank, an insurance company, etc.). Some embodiments of content analyzer 56 therefore use content analysis to determine whether a HTML document located at a fraud candidate domain is an illegitimate clone of a legitimate webpage. Such determinations may include analyzing a set of graphic elements of the document under scrutiny (e.g., images, logos, color schemes, fonts, font style, font size, etc.) and comparing such elements to graphic elements harvested from a set of legitimate webpages.), wherein the machine learning model determines whether a webpage is likely to be authorized by the first entity based on the other data (Damian 2018/0013789 [0049 - comparing such elements to graphic elements harvested from a set of legitimate webpages] However, a document may be fraudulent also when it is sufficiently similar to a particular legitimate document. In one such example, a webpage may try to deceive users by masquerading as a legitimate webpage of a financial institution (e.g., a bank, an insurance company, etc.). Some embodiments of content analyzer 56 therefore use content analysis to determine whether a HTML document located at a fraud candidate domain is an illegitimate clone of a legitimate webpage. Such determinations may include analyzing a set of graphic elements of the document under scrutiny (e.g., images, logos, color schemes, fonts, font style, font size, etc.) and comparing such elements to graphic elements harvested from a set of legitimate webpages. [0050 - determine an inter-document distance indicative of a degree of similarity between a target document and a reference document (either fraudulent or legitimate), and determine whether the target document is legitimate according to the calculated distance] Content analysis may further comprise analysis of a text part of the respective electronic document. Such text analysis may include searching for certain keywords, computing the frequency of occurrence of certain words and/or word sequences, determining the relative position of certain words with respect to other words, etc. Some embodiments determine an inter-document distance indicative of a degree of similarity between a target document and a reference document (either fraudulent or legitimate), and determine whether the target document is legitimate according to the calculated distance.); determine, based on output from the machine learning model, that a plurality of webpages are unlikely to be authorized by the first entity (Damian 2018/0013789 [0004 - used to identify fraudulent web documents and to issue a warning and/or block access to such documents] Software running on an Internet user's computer system may be used to identify fraudulent web documents and to issue a warning and/or block access to such documents. Several approaches have been proposed for identifying fraudulent webpages. Exemplary strategies include matching a webpage's address to a list of known fraudulent and/or trusted addresses (techniques termed black- and white-listing, respectively). To avoid such detection, fraudsters frequently change the address of their websites. [0031 - further display a notification to the user (e.g., a warning screen, icon, explanation etc.) and/or may notify a system administrator of client system] In some embodiments, in a step 212, anti-fraud module 38 determines according to assessment indicator 44 whether the requested document is likely to be fraudulent. When yes, a step 214 allows client system 10 (e.g., application 36) to access the respective document, for instance by transmitting the original access request to its intended destination. When no, a step 216 may block access to the respective document. Some embodiments may further display a notification to the user (e.g., a warning screen, icon, explanation etc.) and/or may notify a system administrator of client system 10.); transmit an indication of the plurality of webpages (Damian 2018/0013789 [0024 - Output devices 26 may include display devices (e.g., monitor, liquid crystal display) and speakers, as well as hardware interfaces/adapters such as graphic cards, allowing client system 10 to communicate data to a user]); and update the machine learning model based on feedback.
Damian 2018/0013789 may not expressly disclose the “determines whether a webpage is likely to be authorized by the first entity based on the other data …” and “train a machine learning model…”, however, Wang et al. 2020/0336509 teaches (Wang et al. 2020/0336509 [Abstract - A first model predicts whether a page is for a derived entity based on features of the page. Responsive to predicting the page is not for a derived entity, a second model predicts whether the page is for a real-world entity or an imposter based on features of the page…][0005 - online system retrieves multiple machine-learning models, each of which is trained based on labels for a set of the nodes and features of the corresponding pages … the online system uses a second machine-learning model to predict whether the page is for a real-world entity or an imposter of a real-world entity based on the features of the page…][0028 - a page that has been verified to be for a real-world entity based on a set of features of the page…][0031 - The machine-learning model(s) may be trained by the machine-learning module 245 using any suitable techniques or algorithms (e.g., supervised, semi-supervised, or unsupervised learning methods). In some embodiments, the machine-learning model(s) may be trained based on features of pages verified and/or predicted to be for real-world entities, imposters of real-world entities, derived entities that violate a policy of the online system 140, and/or derived entities that comply with the policy…]). Before the effective filing date of the claimed invention, it would have been obvious for one of ordinary skill in the art to have modified Damian 2018/0013789 to include the features as taught by Wang et al. 2020/0336509. One of ordinary skill in the art would have been motivated to do so to utilize well known trained model algorithms useful in detecting certain features in a webpage or dataset which should prove to improve user experience, maximize profits, and optimize revenue.
Damian 2018/0013789 may not expressly disclose the “update the machine learning model”, however, Harpur et al. 2018/0054320 teaches (Harpur et al. 2018/0054320 [0052 - The feedback may then be analyzed using natural language processing rules and dictionaries 108 (act 804) and the model of conversation states may be adjusted or updated based on the feedback using conventional machine learning techniques…]). Before the effective filing date of the claimed invention, it would have been obvious for one of ordinary skill in the art to have modified Damian 2018/0013789 to include the features as taught by Harpur et al. 2018/0054320. One of ordinary skill in the art would have been motivated to do so to utilize well known trained model algorithms useful in detecting certain features in a webpage or dataset which should prove to improve user experience, maximize profits, and optimize revenue.
19/315,138 – Claim 8. Damian 2018/0013789 further teaches A method, comprising: extracting, by a device, code (Damian 2018/0013789 [0006; 0008; 0012; 0020-0025; Figs. 1, 2-A, 2-B]) …
19/315,138 – Claim 15. Damian 2018/0013789 further teaches A non-transitory computer-readable medium storing a set of instructions, the set of instructions comprising: one or more instructions that, when executed by one or more processors of a device (Damian 2018/0013789 [0006; 0008 - a non-transitory computer-readable medium stores instructions; 0012; 0018; 0020-0025; Figs. 1, 2-A, 2-B]), cause the device to: …
Claims 8 and 15, have similar limitations as of Claim 1, therefore they are REJECTED under the same rationale as Claim 1.
Claims 2, 9, 16 are rejected under 35 U.S.C. 103 as being unpatentable over: Damian 2018/0013789; in view of Wang et al. 2020/0336509; in view of Harpur et al. 2018/0054320; in view of Kohavi 2022/0070216.
19/315,138 – Claim 2. Damian 2018/0013789 further teaches The system of claim 1, wherein the machine learning model, when determining whether the webpage is likely to be authorized by the first entity (Damian 2018/0013789 [0049 - comparing such elements to graphic elements harvested from a set of legitimate webpages] However, a document may be fraudulent also when it is sufficiently similar to a particular legitimate document. In one such example, a webpage may try to deceive users by masquerading as a legitimate webpage of a financial institution (e.g., a bank, an insurance company, etc.). Some embodiments of content analyzer 56 therefore use content analysis to determine whether a HTML document located at a fraud candidate domain is an illegitimate clone of a legitimate webpage. Such determinations may include analyzing a set of graphic elements of the document under scrutiny (e.g., images, logos, color schemes, fonts, font style, font size, etc.) and comparing such elements to graphic elements harvested from a set of legitimate webpages. [0050 - determine an inter-document distance indicative of a degree of similarity between a target document and a reference document (either fraudulent or legitimate), and determine whether the target document is legitimate according to the calculated distance] Content analysis may further comprise analysis of a text part of the respective electronic document. Such text analysis may include searching for certain keywords, computing the frequency of occurrence of certain words and/or word sequences, determining the relative position of certain words with respect to other words, etc. Some embodiments determine an inter-document distance indicative of a degree of similarity between a target document and a reference document (either fraudulent or legitimate), and determine whether the target document is legitimate according to the calculated distance.), determines whether programming indicia related to the webpage are similar to that used by the first entity.
Damian 2018/0013789 may not expressly disclose the “determines whether a webpage is likely to be authorized by the first entity based on the other data …” and “train a machine learning model…”, however, Wang et al. 2020/0336509 teaches (Wang et al. 2020/0336509 [Abstract - A first model predicts whether a page is for a derived entity based on features of the page. Responsive to predicting the page is not for a derived entity, a second model predicts whether the page is for a real-world entity or an imposter based on features of the page…][0005 - online system retrieves multiple machine-learning models, each of which is trained based on labels for a set of the nodes and features of the corresponding pages … the online system uses a second machine-learning model to predict whether the page is for a real-world entity or an imposter of a real-world entity based on the features of the page…][0028 - a page that has been verified to be for a real-world entity based on a set of features of the page…][0031 - The machine-learning model(s) may be trained by the machine-learning module 245 using any suitable techniques or algorithms (e.g., supervised, semi-supervised, or unsupervised learning methods). In some embodiments, the machine-learning model(s) may be trained based on features of pages verified and/or predicted to be for real-world entities, imposters of real-world entities, derived entities that violate a policy of the online system 140, and/or derived entities that comply with the policy…]). Before the effective filing date of the claimed invention, it would have been obvious for one of ordinary skill in the art to have modified Damian 2018/0013789 to include the features as taught by Wang et al. 2020/0336509. One of ordinary skill in the art would have been motivated to do so to utilize well known trained model algorithms useful in detecting certain features in a webpage or dataset which should prove to improve user experience, maximize profits, and optimize revenue.
Damian 2018/0013789 may not expressly disclose the “webpage are similar…” features, however, Kohavi 2022/0070216 teaches (Kohavi 2022/0070216 [0016 - analysis and comparison of the webpage or parts of the webpage to known phishing pages or genuine webpages or parts of genuine webpages…] Performing machine learning based image analysis and comparison of the webpage or parts of the webpage to known phishing pages or genuine webpages or parts of genuine webpages of known phishing page targets/brands; [0017 - analysis of the webpage CSS and comparison of the webpage CSS to the CSS of known phishing pages or genuine webpages] Performing analysis of the webpage CSS and comparison of the webpage CSS to the CSS of known phishing pages or genuine webpages of known phishing page brands/targets; [0026] In some embodiments, the analyzing the downloaded webpage includes one or more of computing webpage fingerprints for multiple HTML page elements and comparing the webpage fingerprints to existing webpage fingerprints of known phishing page targets and known phishing sites, performing image analysis and comparison of the page favicon of the webpage to favicons known to be used in phishing pages and also genuine favicons of known phishing page targets, and performing machine learning based image analysis and comparison of the webpage or parts of the webpage to known phishing pages or genuine webpages or parts of genuine webpages of known phishing page targets/brands.). Before the effective filing date of the claimed invention, it would have been obvious for one of ordinary skill in the art to have modified Damian 2018/0013789 to include the features as taught by Kohavi 2022/0070216. One of ordinary skill in the art would have been motivated to do so to utilize well known trained model algorithms useful in detecting certain features in a webpage or dataset which should prove to improve user experience, maximize profits, and optimize revenue.
19/315,138 – Claim 9. The method of claim 8, wherein the machine learning model, when determining whether the webpage is likely to be authorized by the first entity, determines whether programming indicia related to the webpage are similar to that used by the first entity.
19/315,138 – Claim 16. The non-transitory computer-readable medium of claim 15, wherein the machine learning model, when determining whether the webpage is likely to be authorized by the first entity, determines whether programming indicia related to the webpage are similar to that used by the first entity.
Claims 9 and 16, have similar limitations as of Claim 2, therefore they are REJECTED under the same rationale as Claim 2.
Claims 3, 10, 17 are rejected under 35 U.S.C. 103 as being unpatentable over: Damian 2018/0013789; in view of Wang et al. 2020/0336509; in view of Harpur et al. 2018/0054320; in view of Quine et al. 2002/0023138.
19/315,138 – Claim 3. Damian 2018/0013789 further teaches The system of claim 1, wherein the data associated with the set of guidelines is received during a registration procedure with the system (Damian 2018/0013789 [0041 - registration]).
Damian 2018/0013789 may not expressly disclose the “guidelines is received during a registration procedure…” features, however, Quine et al. 2002/0023138 teaches (Quine et al. 2002/0023138 [0087] Accordingly, forwarding system 44 can be programmed with a rule to disallow registration of corporate e-mail addresses for forwarding unless the registration is received from an authorized representative. As an alternative, forwarding system 44 could be programmed with a rule to allow registration and forwarding a corporate e-mail address, but only to another address with the same domain. For traditional consumer ISPs, such as AOL, where the ISP does not assert per se ownership of the content of e-mail messages of its subscribers, no rules may be implemented.). Before the effective filing date of the claimed invention, it would have been obvious for one of ordinary skill in the art to have modified Damian 2018/0013789 to include the features as taught by Quine et al. 2002/0023138. One of ordinary skill in the art would have been motivated to do so to utilize well known trained model algorithms useful in detecting certain features in a webpage or dataset which should prove to improve user experience, maximize profits, and optimize revenue.
19/315,138 – Claim 10. The method of claim 8, wherein the data associated with the set of guidelines is received during a registration procedure with a system associated with the device.
19/315,138 – Claim 17. The non-transitory computer-readable medium of claim 15, wherein the data associated with the set of guidelines is received during a registration procedure with a system associated with the device.
Claims 10 and 17, have similar limitations as of Claim 3, therefore they are REJECTED under the same rationale as Claim 3.
Claims 4, 11, 18 are rejected under 35 U.S.C. 103 as being unpatentable over: Damian 2018/0013789; in view of Wang et al. 2020/0336509; in view of Harpur et al. 2018/0054320; in view of Ramos, SR. et al. 2010/0325528.
19/315,138 – Claim 4. Damian 2018/0013789 further teaches The system of claim 1, wherein the data associated with the set of guidelines includes at least one of a style guide or an example copy (Damian 2018/0013789 [0048 – template; 0049 - style]).
Damian 2018/0013789 may not expressly disclose the “a style guide or an example copy…” features, however, Ramos, SR. et al. 2010/0325528 teaches (Ramos, SR. et al. 2010/0325528 [0055 - rules of a particular style guide][0060 - - reference text inserted into the text of the document for the depicted reference follows the rules of the particular style guide, including those formatting elements]). Before the effective filing date of the claimed invention, it would have been obvious for one of ordinary skill in the art to have modified Damian 2018/0013789 to include the features as taught by Ramos, SR. et al. 2010/0325528. One of ordinary skill in the art would have been motivated to do so to utilize well known trained model algorithms useful in detecting certain features in a webpage or dataset which should prove to improve user experience, maximize profits, and optimize revenue.
19/315,138 – Claim 11. The method of claim 8, wherein the data associated with the set of guidelines includes at least one of a style guide or an example copy.
19/315,138 – Claim 18. The non-transitory computer-readable medium of claim 15, wherein the data associated with the set of guidelines includes at least one of a style guide or an example copy.
Claims 11 and 18, have similar limitations as of Claim 4, therefore they are REJECTED under the same rationale as Claim 4.
Claims 5, 12, 19 are rejected under 35 U.S.C. 103 as being unpatentable over: Damian 2018/0013789; in view of Wang et al. 2020/0336509; in view of Harpur et al. 2018/0054320; in view of Bhattacharya et al. 2020/0322340.
19/315,138 – Claim 5. Damian 2018/0013789 further teaches The system of claim 1, wherein the data associated with the set of guidelines includes a plurality of webpages that are approved by the first entity (Damian 2018/0013789 [Abstract]).
Damian 2018/0013789 may not expressly disclose the “plurality of webpages…” features, however, Bhattacharya et al. 2020/0322340 teaches (Bhattacharya et al. 2020/0322340 [Abstract; 0012; 0013; 0024; 0032-0036; Fig. 1]). Before the effective filing date of the claimed invention, it would have been obvious for one of ordinary skill in the art to have modified Damian 2018/0013789 to include the features as taught by Bhattacharya et al. 2020/0322340. One of ordinary skill in the art would have been motivated to do so to utilize well known trained model algorithms useful in detecting certain features in a webpage or dataset which should prove to improve user experience, maximize profits, and optimize revenue.
19/315,138 – Claim 12. The method of claim 8, wherein the data associated with the set of guidelines includes a plurality of webpages that are approved by the first entity.
19/315,138 – Claim 19. The non-transitory computer-readable medium of claim 15, wherein the data associated with the set of guidelines includes a plurality of webpages that are approved by the first entity.
Claims 12 and 19, have similar limitations as of Claim 5, therefore they are REJECTED under the same rationale as Claim 5.
Claims 6, 7, 13, 14, 20 are rejected under 35 U.S.C. 103 as being unpatentable over: Damian 2018/0013789; in view of Wang et al. 2020/0336509; in view of Harpur et al. 2018/0054320.
19/315,138 – Claim 6. Damian 2018/0013789 further teaches The system of claim 1, wherein the one or more processors are further configured to: receive a plurality of webpages, including the webpage; discard a subset of the plurality of webpages that are determined as not being associated with the first entity; and apply the machine learning model on others of the plurality of webpages, excluding the subset of the plurality of webpages, from discarding the subset (Damian 2018/0013789 [Abstract; 0006-0008]).
19/315,138 – Claim 13. The method of claim 8, further comprising: receiving a plurality of webpages, including the webpage; discarding a subset of the plurality of webpages that are determined as not being associated with the first entity; and applying the machine learning model on other of the plurality of webpages, excluding the subset of the plurality of webpages, from discarding the subset.
19/315,138 – Claim 20. The non-transitory computer-readable medium of claim 15, wherein the one or more instructions further cause the device to: receive a plurality of webpages, including the webpage; discard a subset of the plurality of webpages that are determined as not being associated with the first entity; and apply the machine learning model on other of the plurality of webpages, excluding the subset of the plurality of webpages, from discarding the subset.
Claims 13 and 20, have similar limitations as of Claim 6, therefore they are REJECTED under the same rationale as Claim 6.
19/315,138 – Claim 7. Damian 2018/0013789 further teaches The system of claim 1, wherein the machine learning model outputs a score related to whether the webpage is likely to be authorized by the first entity, and wherein the one or more processors are further configured to: display a visual indicator of whether the score satisfies a threshold (Damian 2018/0013789 [Abstract; 0006-0008; 0018; 0024; 0030]).
19/315,138 – Claim 14. The method of claim 8, wherein the machine learning model outputs a score related to whether the webpage is likely to be authorized by the first entity, and wherein the method further comprises: displaying a visual indicator of whether the score satisfies a threshold.
Claim 14, has similar limitations as of Claim 7, therefore it is REJECTED under the same rationale as Claim 7.
Examiner’s Response to Arguments
Per Applicants’ amendments/arguments, the rejections are withdrawn.
Applicant's arguments have been considered but are moot in view of the new ground(s) of rejection.
Applicants’ amendments have necessitated the new grounds of rejection noted above.
Examiner’s Response: Claim Rejections – 35 USC §112
Per Applicants’ amendments/arguments, the rejections are withdrawn.
Applicant's arguments have been considered but are moot in view of the new ground(s) of rejection.
Applicants’ amendments have necessitated the new grounds of rejection noted above.
Examiner’s Response: Claim Rejections – 35 USC §101
Per Applicants’ amendments/arguments, the rejections are withdrawn. See notes above for additional reasoning and rationale for dropping 35 USC 101 rejection including Applicant’s amendments, arguments, lack of abstract idea, and practical integration.
Applicant's arguments have been considered but are moot in view of the new ground(s) of rejection.
Applicants’ amendments have necessitated the new grounds of rejection noted above.
Regarding Claims 1-15, on page(s) 6-12 of Applicant’s Remarks (dated 12/27/2016), Applicants traverse the 35 USC §101 rejections arguing the following:
Examiner’s Response: Claim Rejections – 35 USC § 102 / § 103
Per Applicants’ amendments/arguments, the rejections are withdrawn. See notes above for additional reasoning and rationale for dropping prior-art rejection including Applicant’s amendments and arguments and unique combination of features and elements not taught by the prior-art without hindsight reasoning.
Applicant's arguments have been considered but are moot in view of the new ground(s) of rejection.
Applicants’ amendments have necessitated the new grounds of rejection noted above.
Regarding Claim X, on page(s) 8-9 of Applicant’s Remarks / After Final Amendments (dated 07/15/2011), Applicant(s) argues that the cited reference(s) (Ellis and Vandermolen) fails to teach, describe, or suggest the amended features. Specifically, Applicant(s) argues that cited reference(s) do not teach, describe, or suggest the following: . With respect, Applicant’s arguments are deemed unpersuasive and the amended feature(s) remain rejected as follows.
With respect, Applicant’s arguments are deemed unpersuasive and the amended feature(s) remain rejected as follows.
Any comments considered necessary by applicant must be submitted no later than the payment of the issue fee and, to avoid processing delays, should preferably accompany the issue fee. Such submissions should be clearly labeled “Comments on Statement of Reasons for Allowance.”
Conclusion
PERTINENT PRIOR ART – Patent Literature
The prior-art made of record and considered pertinent to applicant's disclosure.
Tripathi et al. 2020/0089692 [0100 - filtering the acquired web content to obtain information relating to the entity; identifying probable associations of the entity from the information relating to the entity using an ontology; determining for each of the probable associations, at least one of: a recency attribute, a frequency attribute, a proximity attribute, a semantics attribute; determining a probability score for each of the probable associations of the entity]
Epstein et al. 2018/0239826 [0077 - determining whether the images and/or text included in the digital content is authenticate, authorized]
Farjon et al. 2022/0174092 [0036 - identifying a malicious web page that impersonates a legitimate web page, including a parser extracting HMTL source and a certificate for a web page intended for access by a user via a web browser]
Speegle et al. US8,943,588 [FIG. 1 provides a particular embodiment of a system 110 including a website analyzer]
Chhabra et al. 2021/0174118 [0002 - illegal distributions may be identified by web crawlers]
Kumar et al. 2008/0021981 [0041 - a trust rank that a web crawler, which is associated with the entity that receives the request, associates with the advertisement webpage]
Brunn et al. 2014/0283122 [0035 - a website that causes its authors to register with the website and provide other information may be classified as a trusted source. As a result, the delivery system (100) may be confident enough that the authored generated content actually belongs to the author. The delivery system (100) may use a trust policy to determine whether an online resource is a trusted source. The trust policy may consider one or multiple factors when determining whether the delivery system has enough confidence that the content was authored by the author without authentication. For example, the trust policy may consider a uniform resource locator (URL) of an online resource containing the author specific content. Also, the trust policy may determine that the authorship of content is trusted if the content includes a digital signature. Further, the trust policy may consider whether an identity of the author is identified….][0048 - crawling engine (300) determines the publisher and/or author of the content and determines whether the author and/or publisher already have information stored in the database. If the content is not from a trusted site, the crawling engine (300) can check the authenticity of the author. In some examples, the content is signed by an author who is allegedly the author. In such situations, the crawling engine authenticates the signer…]
Azaz et al. 2024/0037811 teaches “applying a model trained on a set of guidelines…” [0043] The color selection unit 210 of FIG. 4 includes an input component 405 and an output component 425 which may correspond substantially to the input and output components 305, 325 of FIG. 3. In the embodiment of FIG. 4, the color selection component is configured to generate color recommendations based on user specific telemetry data. In embodiments, color recommendations based on user specific preference data are generated using a user-specific color selection model 415 which is a machine learning (ML) model. In embodiments, the user-specific color selection model 415 is trained by a model training unit 420 using a user-specific telemetry training data 430 derived from telemetry data 435 which may be retrieved from telemetry service 435. In embodiments, the model 415 is trained to learn rules for identifying colors to recommend to a user based on past color choices and context information pertaining to the user's color choices from the user's telemetry data and apply these rules to the user's color choices, color patterns, color palette, and the like in the current document. In embodiments, rules may be learned to determine a “tone/sentiment” pertaining to the document based on the colors or color palette currently utilized in the document. Color recommendations may then be made based on the determined tone/sentiment of the document. The training data 430 for the user-specific color selection model may be occasionally updated based on new user telemetry data and the model may be retrained and/or new models may be generated so that color recommendations based on user-specific preferences are based on current user preferences. [0071] wherein the color selection ML model is trained to learn rules for identifying the at least one color for the color recommendation based on the telemetry data for the plurality of users).
PERTINENT PRIOR ART – Non-Patent Literature (NPL)
The NPL prior-art made of record and considered pertinent to applicant's disclosure.
J. Li, N. Zaman, A. Rayes and E. Custodio, "Semantics-Enhanced Online Intellectual Capital Mining Service for Enterprise Customer Centers," in IEEE Transactions on Services Computing, vol. 10, no. 3, pp. 436-447, 1 May-June 2017, doi: 10.1109/TSC.2015.2474861.
THIS ACTION IS MADE FINAL
Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any extension fee pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
THIS ACTION IS MADE FINAL
Applicant’s amendment necessitated new grounds of rejection and FINAL Rejection.
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any extension fee pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the date of this final action.
Contact Information
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/MATTHEW T SITTNER/
Primary Examiner, Art Unit 3629b
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