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 .
DETAILED ACTION
Status of Claims
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and the fee set forth in 37 CFR 1.17 (e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 07/01/2025 has been entered.
Claims 1,6 and 11 have been amended.
Claims 1-15 are currently pending and have been examined.
Response to Applicant’s Arguments
Applicant’s amendments and arguments filed on 07/01/2025 have been fully considered and discussed in the next section. Applicant is reminded that the claims must be given its broadest, reasonable interpretation.
With regard to claims 1, 6 and 11 rejection under 35 USC § 101:
Applicant argues that “ the generation of a function description model for a target website by processing a plurality of existing function description models, ontology information, and a target website, is not a certain method of organizing human activity using a trained machine learning model as the claims do not describe commercial interactions or business relations. Rather, the claimed process of independent claims 1, 6, and 11 describe how a machine learning model generates a function description model (i.e., one or more actions that are performable on the website) for a target website by training the machine learning model with previous function description models, previous websites, and ontology information and automatically performs those functions using the generated function description model (page 1/10)”.
Applicant’s arguments are considered, but they are moot based on the new ground of rejection of claims 1-15 under 35 USC § 101, where the claims rejection is maintained as the claims are directed to an abstract idea.
Applicant argues that “the specification provides sufficient details such that one of ordinary skill in the art would recognize the claimed invention as providing an improvement. For example, at least paragraphs [0003] and [0090]-[0094] of the subject application as filed describe the technical problem solved by and the technical solution realized by the claimed invention (page 2/10)”. As shown above in at least these paragraphs, the specification describes the invention such that the improvement (i.e., the automated navigation of a target website using existing function description models and ontology information to generate a target function description model for the target website without human intervention) is apparent to one of ordinary skill in the art (paragraph 4/10)”..
Examiner disagrees. An improvement of “ Automated navigation of a target website using existing function description models and ontology information to generate a target function description model for the target website without human intervention” fails to (a) improve another technology or technical field and (b) improve the functioning of the computer itself and (c) applies the abstract idea with or by use of, a particular machine, which is a generic computer performing generic computer functions and are not seen to recite an improvement to another technology or technical field, an improvement to the functioning of the computer itself.
Indeed, the identified improvements recited by Applicant are really, at best improvements to the performance of the abstract idea (e.g., improvements made in the underlying business method (the automated navigation of a target website using existing function description models and ontology information to generate a target function description model for the target website without human intervention) and not in the operations of any additional elements or technology. As such, the examiner finds that any improvement obtained by practicing the claimed invention is an improvement to a business process. Therefore, the claim rejection of claims 1-15 under 35 USC § 101 is maintained.
Examiner notes “ The claims are not a technical improvement to the operation of a computer but the removal of human labor that is just automating a task and not patent eligible, see In re Venner, 262 F.2d 91, 95, 120 USPQ 193, 194 (CCPA 1958).
Examiner also notes: It has been held that “ Although claims are interpreted in light of the specification, limitations from the specification are not read into the claims ( In re Van Geuns, 26 USPQ2d 1057 (CA FC 1993))”.
Applicant argues that “ the amended independent claims 1, 6, and 11 include the components or steps of the invention that provide the improvement described in the specification. For example, independent claims 1, 6, and 11 have been amended to recite, in part, "generating a function description model for the target website without human intervention by processing the plurality of function description models, ontology data, and target website data using the machine learning process" and "automatically performing the one or more actions on the target website using the function description model generated for the target website". That is, by processing multiple existing function description models corresponding to actions that are performable on the website and actions of the function description model are automatically performed on the target website without human intervention. In this manner, amended independent claims 1, 6, and 11 include the components or steps of ontology data that normalizes descriptors across various websites, a function description model, that models the actions that are performable on a target website, is generated for the target website the invention that provide the improvement described in the specification. Accordingly, Applicant respectfully submits that amended independent claims 1, 6, 11 recite a practical application of the alleged abstract idea. As such, Applicant respectfully asserts that claims 1-15 are directed to patentable subject matter under 35 U.S.C. § 101. Applicant respectfully requests that the rejection of claims 1-15 under 35 U.S.C. § 101 be withdrawn (page 4/10)”.
Examiner disagrees. Generating a function description model for the target website without human intervention by processing the plurality of function description models, ontology data, and target website data using the machine learning process" and "automatically performing the one or more actions on the target website using the function description model generated for the target website and / or processing multiple existing function description models corresponding to actions that are performable on the website and actions of the function description model are automatically performed on the target website without human intervention fails to (a) improve another technology or technical field and (b) improve the functioning of the computer itself and (c) applies the abstract idea with or by use of, a particular machine, which is a generic computer performing generic computer functions and are not seen to recite an improvement to another technology or technical field, an improvement to the functioning of the computer itself.
Indeed, the identified improvements recited by Applicant are really, at best improvements to the performance of the abstract idea (e.g., improvements made in the underlying business method (Generating a function description model for the target website without human intervention by processing the plurality of function description models, ontology data, and target website data using the machine learning process" and "automatically performing the one or more actions on the target website using the function description model generated for the target website and / or processing multiple existing function description models corresponding to actions that are performable on the website and actions of the function description model are automatically performed on the target website without human intervention) and not in the operations of any additional elements or technology. As such Applicant's claimed solution is NOT technological and does not addresses a technological problem.
As such, the examiner finds that any improvement obtained by practicing the claimed invention is an improvement to a business process. Second, under Step 2a, Prong 2, the improvement to a technology or technological field must be rooted in the additional element. Additional elements are those elements outside of the identified abstract idea itself. In the instant case the only additional elements are “Machine learning process”.
Furthermore, the use of the additional element of “Machine learning process”, does no more than apply or link the use of the recited judicial exception to a particular technological environment/field of use. As thus amended claims 1,6 and 11 do not recite limitations that transfer the abstract idea into practical application. Accordingly, the claim rejection of claims 1-15 under 35 USC § 101 is maintained.
Examiner notes “ The claims are not a technical improvement to the operation of a computer but the removal of human labor that is just automating a task and not patent eligible, see In re Venner, 262 F.2d 91, 95, 120 USPQ 193, 194 (CCPA 1958).
Examiner also notes: It has been held that “ Although claims are interpreted in light of the specification, limitations from the specification are not read into the claims ( In re Van Geuns, 26 USPQ2d 1057 (CA FC 1993))”.
With regard to claims 1-15 rejection under 35 USC § 103:
Applicant argues that “the combination of Nishant and Phillipps does not appear to teach: 1)generating a function description model for the target website without human intervention by processing the plurality of function description models, ontology data, and target website data using the machine learning process, and 2) automatically performing the one or more actions on the target website using the function description model generated for the target website. As such, Applicants respectfully submit that the combination of Nishant and Phillipps is not understood to teach, disclose, or suggest Applicant's claimed "generating a function description model for the target website without human intervention by processing the plurality of function description models, ontology data, and target website data using the machine learning process" and "automatically performing the one or more actions on the target website using the function description model generated for the target website". Accordingly, Applicant respectfully requests that the rejection of claims 1-5, 6-10, 11-15 under § 103 as being unpatentable over the combination of Nishant and Phillipps be withdrawn (pages 8-9/10)”.
Examiner disagrees. In response to applicant's arguments against the references individually, one cannot show nonobviousness by attacking references individually where the rejections are based on combinations of references. See In re Keller , 642 F.2d 413,208 USPQ871 (CCPA 1981); In re Merck & Co., 800 F.2d 1091, 231 USPQ375 (Fed. Cir. 1986).
Nishant in at least paragraph 137 discloses the display of chat bot interface 150 and intent options on various websites is described with respect to FIGS. 7A-7C and 13A-13C. In particular, FIGS. 7A-7C are example portions of chat bot interface 150, indicating example intention options that may be displayed (at step 610), based on a specific current website a user is navigating; and FIGS. 13A-13C are example screen shots of chat bot interface 150, generated on a web browser of client device 108, as chat bot interface window 1302 is positioned on three different and unconnected websites 1304, 1306 and 1308. More specifically, chat bot 132 may be configured to overlay and maintain chat bot interface window 1302 on each of independent websites 1304-1308 as user 106 navigates across websites 1304-1308 . Nishant also in at least paragraph 140 discloses In addition, chat bot 132, via intent options 700, such as intention options 700-A (FIG. 7A), may provide links to one or more other independent websites for further information (while maintaining (single) chat bot interface window1302 across different websites).
Philips in at least paragprh 49 discloses “ The website interaction module 102, in certain embodiments, generates machine learning ensembles or other machine learning program code for a web server 108, with little or no input from a Data Scientist or other expert, by generating a large number of learned functions from multiple different classes, evaluating, combining, and/or extending the learned functions, synthesizing selected learned functions, and organizing the synthesized learned functions into a machine learning ensemble. The website interaction module 102, in one embodiment, services analysis requests for the web server 108 using the generated machine learning ensembles or other machine learning program code”. Philip also in at least paragprh 58 discloses “the machine learning result may include a prediction that the user will navigate to a certain page if the website is adapted to present navigation links in a certain way. In a certain embodiment, the machine learning result may include a prediction of what suggested search terms and/or search results may be most useful to a user”.
Nishant’s reference was used to address the concept of providing relevant content queries across unconnected websites using a machine learning. Phillips’s reference was used to address a concept of using machine learning real -time adaptive website interaction and using the machine learning to predict that the user will navigate to a certain page if the website is adapted to present navigation links in a certain way. Philip’s reference also used to address the concept of generates machine learning ensembles or other machine learning program code for a web server 108, with little or no input from a Data Scientist or other expert ( i.e. without a human interaction) by generating a large number of learned functions from multiple different classes, evaluating, combining, and/or extending the learned functions, synthesizing selected learned functions, and organizing the synthesized learned functions into a machine learning ensemble.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the application to have combined the teaching of Nishant including chat Bot interface window comprising a machine learning engine, and user intent analysis with teaching of Phillip’s including machine learning for real-time adaptative website interaction with in order to provide in a real -time adaptative website interaction and thus saving time as taught by Phillips over that of Nishant. Therefore, the claim rejection under 35 USC § 103 is maintained.
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 1-15 are rejected under 35 U.S.C.101 because the claimed invention is directed to a judicial exception subject matter, specifically an abstract idea. The analysis for this determination is explained below:
Step 1, determine whether the claim is directed to one of the four statutory categories of invention, i.e., process, machine, manufacture, or composition of matter.
In this case, claim(s) 1-5 are directed to a process (i.e. a method); claims 6-10 are directed to manufacture (i.e. computer program product); claim (s) 11-15 are directed to a machine (i.e. an apparatus).
The claimed invention is directed to at least one judicial exception (i.e a law of nature, a natural phenomenon, or an abstract idea) without significantly more. The claim(s) recite(s) the following abstract idea: Claim 1, as exemplary, recites the limitations of: “ providing a plurality of function description corresponding to a plurality of websites; providing ontology data concerning the plurality of websites; providing target website data concerning a target website ; and processing the plurality of function description , ontology data and target website data to generate a function description for the target website, generating a function description for the target website by processing the plurality of function description, ontology data, and target website data ; automatically performing the one or more actions on the target website”.
The limitations as detailed above, as drafted, falls within the “ mental process,” grouping of abstract ideas as it relates to concepts performed in the human mind (including an observation, evaluation , judgment , opinion). Accordingly, the claim recites an abstract idea (i.e. “PEG” Revised Step 2A Prong One=Yes).
This judicial exception is not integrated into a practical application because the claim only recites the additional elements of “ computer, computing device comprising software such as machine learning process, models and websites” , the additional technical elements above are recited at a high-level of generality (i.e. as a generic processor performing a generic computer function of processing, communicating and displaying) such that it amounts to no more than mere instructions to apply the exception using a generic computer component. The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional technical elements above do not integrate the abstract idea/judicial exception into a practical application because it does not impose any meaningful limits on practicing the abstract idea. More specifically, the additional elements fail to include (1) improvements to the functioning of a computer or to any other technology or technical field (see MPEP 2106.05(a)), (2) applying or using a judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition (see Vanda memo), (3) applying the judicial exception with, or by use of, a particular machine (see MPEP 2106.05(b)), (4) effecting a transformation or reduction of a particular article to a different state or thing (see MPEP 2106.05(c)), or (5) applying or using the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception (see MPEP 2106.05(e) and Vanda memo).
Rather, the limitations merely add the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea (see MPEP 2106.05(f)), or generally link the use of the judicial exception to a particular technological environment or field of use (see MPEP 2106.05(h)). Thus, the claim is “directed to” an abstract idea (i.e. “PEG” Revised Step 2A Prong Two=Yes).
When considering Step 2B of the Alice/Mayo test, the claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the claims do not amount to significantly more than the abstract idea.
More specifically, as discussed above with respect to integration of the abstract idea into a practical application, the additional elements of computer, computing device comprising software such as machine learning process, models, model and websites””, to perform the claimed functions amounts to no more than mere instructions to apply the exception using a generic computer component. “Generic computer implementation” is insufficient to transform a patent-ineligible abstract idea into a patent-eligible invention (See Affinity Labs, _F.3d_, 120 U.S.P.Q.2d 1201 (Fed. Cir. 2016), citing Alice, 134 S. Ct. at 2352, 2357) and more generally, “simply appending conventional steps specified at a high level of generality” to an abstract idea does not make that idea patentable (See Affinity Labs, _F.3d_, 120 U.S.P.Q.2d 1201 (Fed. Cir. 2016), citing Mayo, 132 S. Ct. at 1300). Moreover, “the use of generic computer elements like a microprocessor or user interface do not alone transform an otherwise abstract idea into patent-eligible subject matter (See FairWarning, 120 U.S.P.Q.2d. 1293, citing DDR Holdings, LLC v. Hotels.com, L.P., 773 F.3d 1245, 1256 (Fed. Cir. 2014)). As such, the additional elements of the claim do not add a meaningful limitation to the abstract idea because they would be generic computer functions in any computer implementation. Thus, taken alone, the additional elements do not amount to significantly more than the above-identified judicial exception (the abstract idea). Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of the computer or improves any other technology. Their collective functions merely provide generic computer implementation.
The Examiner notes simply implementing an abstract concept on a computer, without meaningful limitations to that concept, does not transform a patent-ineligible claim into a patent-eligible one (See Accenture, 728 F.3d 1336, 108 U.S.P.Q.2d 1173 (Fed. Cir. 2013), citing Bancorp, 687 F.3d at 1280), limiting the application of an abstract idea to one field of use does not necessarily guard against preempting all uses of the abstract idea (See Accenture, 728 F.3d 1336, 108 U.S.P.Q.2d 1173 (Fed. Cir. 2013), citing Bilski, 130 S. Ct. at 3231), and further the prohibition against patenting an abstract principle “cannot be circumvented by attempting to limit the use of the [principle] to a particular technological environment” (See Accenture, 728 F.3d 1336, 108 U.S.P.Q.2d 1173 (Fed. Cir. 2013), citing Flook, 437 U.S. at 584), and finally merely limiting the field of use of the abstract idea to a particular existing technological environment does not render the claims any less abstract (See Affinity Labs, _F.3d_, 120 U.S.P.Q.2d 1201 (Fed. Cir. 2016), citing Alice, 134 S. Ct. at 2358; Mayo, 132 S. Ct. at 1294; Bilski v. Kappos, 561 U.S. 593, 612 (2010); Content Extraction & Transmission LLC v. Wells Fargo Bank, Nat’l Ass’n, 776 F.3d 1343, 1348 (Fed. Cir. 2014); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355 (Fed. Cir. 2014).
Applicant herein only requires a general purpose computers communicating over a general purpose network (as evidenced from paragraphs 191-195); therefore, there does not appear to be any alteration or modification to the generic activities indicated, and they are also therefore recognized as insignificant activity with respect to eligibility. Finally, the following limitations are considered insignificant extra solution activity as they are directed to merely receiving, storing and/or transmitting data:
providing a plurality of function description models corresponding to a plurality of websites to a machine learning process, wherein each function description model includes one or more actions that are performable on the website;
providing ontology data concerning the plurality of websites to the machine learning process, wherein the ontology data is a predefined dataset with normalized descriptors for a plurality of associated descriptors from the plurality of websites;
providing target website data concerning a target website to the machine learning process;
Thus, taken individually and in combination, the additional elements do not amount to significantly more than the above-identified judicial exception (the abstract idea) (i.e. “PEG” Step 2B=No).
For these reasons, there is no inventive concept in the claim, and thus the claim is not patent eligible. Same analysis of the judicial exception is applied to claims 6 and 11.
The dependent claims 2-4,7-10, 12-14 are rejected under 35 U.S.C.101 appears to merely further limit the abstract idea of “mental process” as it relates to concepts performed in the human mind (including an observation, evaluation , judgment , opinion). The claims merely add further details that narrow that abstract idea of, without significantly more. the claim further narrows the abstract idea and/or recite additional elements previously rejected in the independent claims 1, 6 and 11. Claims 5, and 15 recite additional elements of : “ a HTML website structure; a javascript website structure; and a CSS website structure”. These elements amounts to no more than generally linking the use of the judicial exception to a particular technological environment or field of use – see MPEP 2106.05(h) in Steps 2A- Prong 2 and Step 2B; and therefore only further limit the abstract idea (i.e. “PEG” Revised Step 2A Prong One=Yes), does/do not include any new additional elements that are sufficient to amount to significantly more than the judicial exception, and as such are “directed to” said abstract idea (i.e. “PEG” Step 2A Prong Two=Yes); and do not add significantly more than the idea (i.e. “PEG” Step 2B=No).
Thus, the dependent claims further narrows the abstract idea and/or recite additional elements previously rejected in the independent 1,6,11.
Accordingly, the claim fails to recite any improvements to another technology or technical field, improvements to the functioning of the computer itself, use of a particular machine, effecting a transformation or reduction of a particular article to a different state or thing, adding unconventional steps that confine the claim to a particular useful application, and/or meaningful limitations beyond generally linking the use of an abstract idea to a particular environment. See 84 Fed. Reg. 55. Viewed individually or as a whole, these additional claim element(s) do not provide meaningful limitation(s) to transform the abstract idea into a patent eligible application of the abstract idea such that the claim(s) amounts to significantly more than the abstract idea itself.
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 1,6 and 11 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention.
Claims 1, 6 and 11 recites the limitation (s) of: generating function description model for the target website without human intervention by processing the plurality of function description models, ontology data, and target website data using the machine learning process; and automatically performing the one or more actions on the target website using the function description model generated for the target website.
The specification teaches “ example and as is known in the art, some websites may include portions of website structure or code that are generated dynamically as a user interacts with the website. Accordingly, automation process 10 may enable 202 a user (e.g., user 36) to interact with a website (e.g., website 100) to visually identify spatial regions of website 100 and associate 204 the spatial regions with the portions of webpage structure generated or exposed in response to user 36's interaction with website 100. Accordingly, automation process 10 may associate 206 the generated or exposed structure portions of the website structure with one or more descriptors of website 100 to define a specific data description model. As will be discussed in greater detail below, automation process 10 may define a function description model based, at least in part, upon the user's interactions with the website that result in the dynamic generation of website structure (paragraph 48)”.
The specification does not teach “ generating function description model for the target website without human intervention by processing the plurality of function description models, ontology data, and target website data using the machine learning process; and automatically performing the one or more actions on the target website using the function description model generated for the target website”.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries 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.
Claims 1-15 are rejected under 35 U.S.C. 103 as being unpatentable over Nishant et al, US Pub No: 2019/0012390 A1 in view of Phillipps et al, US Pub 2014/0236875 A1.
Claims 1, 6 and 11:
Nishant discloses:
providing a plurality of function description models corresponding to a plurality of websites to a machine learning process, wherein each function description model includes one or more actions that are performable on the website;
providing ontology data concerning the plurality of websites to the machine learning process, wherein the ontology data is a predefined dataset with normalized descriptors for a plurality of associated descriptors from the plurality of websites;
See at least Paragraphs 137, 140, 167, 167, ( paragraph 137, the display of chat bot interface 150 and intent options on various websites is described with respect to FIGS. 7A-7C and 13A-13C. In particular, FIGS. 7A-7C are example portions of chat bot interface 150, indicating example intention options that may be displayed (at step 610), based on a specific current website a user is navigating; and FIGS. 13A-13C are example screen shots of chat bot interface 150, generated on a web browser of client device 108, as chat bot interface window 1302 is positioned on three different and unconnected websites 1304, 1306 and 1308. More specifically, chat bot 132 may be configured to overlay and maintain chat bot interface window1302on each of independent websites 1304-1308 as user 106 navigates across websites 1304-1308; paragprah 140 discloses In addition, chat bot 132, via intent options 700, such as intention options 700-A (FIG. 7A), may provide links to one or more other independent websites for further information (while maintaining (single) chat bot interface window1302 across different websites). Yet further, chat bot 132, through chat bot interface 150 (such as chat bot interface window 1302), may provide an automated conversational dialog with user 106 to better anticipate and identify user intent, for example, to provide more accurate query results. In this manner, CQ platform 102 may provide relevant and accurate information, as well as additional information that may be useful to user 106; while reducing the amount of time to identify relevant information and while reducing the need to hand-off communication to live agent 118;
processing a plurality of function description models, ontology data and target website data using the machine learning process to generate a function description model for a target website (see at least paragraph 37 (the CQ platform may provide advantages such as 24/7 (i.e., automated) answering of any product query through the chat bot, and product insights (e.g., real-time in-stock information, lead time, alternative parts) beyond basic product information.); paragraph 73 (Chat bot 132 may include an artificial intelligence (AI) (e.g., machine learning) engine configured to conduct an automated conversation with user 106 via text-based communication such as text messages in a chat interface. CQ platform 102, via chat bot 132, may be able to handle at least an initial portion of a query session with user 106, for example, to determine an intent for the query, so that user 106 may be properly routed to live agent 118 to handle the intent. In other cases, CQ platform 102, via chat bot 132, may provide sufficient information or resolution to user 106 without involving live agent 118. In some cases, AI-generated content segments may be distributed throughout a chat session); paragraph 74 (In an example embodiment, chat bot 132 may be configured as a plug-n-play model, such that chat bot 132 may be plugged into any web page, by including an <iframe> within a <div> of an HTML document);
Nishant does not specifically disclose, but Phillips however discloses:
providing a plurality of function description models corresponding to a plurality of websites to a machine learning process (see at least paragraph 62 (the website adaptation module 206 may adapt the website for the user in real-time by adapting a page of the website in response to the user's request for the page. For example, in a further embodiment, the user may request a page of the website, and may receive a customized version of the page with navigation links, marketing offers, or the like configured by the website adaptation module 206. In another embodiment, the website adaptation module 206 may adapt a page of the website for the user while the user is viewing the page. For example, in a further embodiment, the website adaptation module 206 may provide customized navigation links, marketing offers, or the like, while the user is viewing a page of an AJAX-based web application, or the like, which allows the page to be changed without reloading) ; paragraph 63 (the website interaction module 102, in certain embodiments, may be substantially similar to the website interaction module 102 described above with regard to FIG. 1 and FIG. 2A. In the depicted embodiment, the web site interaction module 102 includes an input module 202, a machine learning module 204, and a website adaptation module 206, which may be configured substantially as described above with regard to FIG. 2A. The input module 202, in the depicted embodiment, includes an analytics intercept module 212, a historical information module 214, and an enrichment information module 216. The machine learning module 204, in the depicted embodiment, includes one or more machine learning ensembles 222a-n. The website adaptation module 206, in the depicted embodiment, includes an offer module 232, a navigation module 234, a search module 236, and a test module 238);
providing ontology data concerning the plurality of websites to the machine learning process (see at least paragraph 67 ( the historical information module 214 may receive historical information directly from the web server 108 for the website, by querying databases, monitoring log files, or the like. In another embodiment, the historical information module 214 may provide an interface for the website owner to upload historical information. In view of this disclosure, many types of historical information and ways of receiving historical information are clear. In various embodiments, using the historical information module 214 to receive historical information from an owner of the website allows the input module 202 to provide information to the machine learning module 204 about the user's past behavior, which may suggest certain adaptations to the website)”;
generating a function description model for the target website without human intervention by processing the plurality of function description models, ontology data, and target website data using the machine learning process; and automatically performing the one or more actions on the target website using the function description model generated for the target website (see at least paragraph 49 discloses “ The website interaction module 102, in certain embodiments, generates machine learning ensembles or other machine learning program code for a web server 108, with little or no input from a Data Scientist or other expert, by generating a large number of learned functions from multiple different classes, evaluating, combining, and/or extending the learned functions, synthesizing selected learned functions, and organizing the synthesized learned functions into a machine learning ensemble. The website interaction module 102, in one embodiment, services analysis requests for the web server 108 using the generated machine learning ensembles or other machine learning program code”; paragprh 58 discloses “the machine learning result may include a prediction that the user will navigate to a certain page if the website is adapted to present navigation links in a certain way. In a certain embodiment, the machine learning result may include a prediction of what suggested search terms and/or search results may be most useful to a user”; paragraph 79 (the machine learning result may predict which web pages the user may want to visit or revisit, such as recently visited pages, related pages, pages for items frequently bought together, or the like); paragraph 81 (presenting suggested search terms and/or search results based on a machine learning result may allow the website adaptation module 206 to make relevant web pages for each user easier to find. For example, the machine learning result may predict which web pages a user is most likely to be interested in, and the search module 236 may configure the presentation of search results to display those web pages first. Similarly, the machine learning result may predict that are relevant to a user's interests, which the search module 236 may present as suggested search terms) ;
It would have been obvious to one of ordinary skill in the art before the effective filing date of the application to have combined the teaching of Nishant including chat Bot interface window comprising a machine learning engine, and user intent analysis with teaching of Phillip’s including machine learning for real-time adaptative website interaction with in order to provide in a real -time adaptative website interaction and thus saving time as taught by Phillips over that of Nishant.
Examiner notes “ The claims are not a technical improvement to the operation of a computer but the removal of human labor that is just automating a task and not patent eligible, see In re Venner, 262 F.2d 91, 95, 120 USPQ 193, 194 (CCPA 1958).
Examiner also notes : The independent claims recite an intended use. The functions recited in the claim are not positively claimed limitations but only requires the elements to be able to perform the functions. A recitation of the intended use of the claimed invention must result in a structural difference between the claimed invention and the prior art in order to patentably distinguish the claimed invention from the prior art. If the prior art structure is capable of performing the intended use, then it meets the claim. See MPEP 2114 and Ex parte Masham, 2 USPQ2d 1647 (Bd. Pat. App. & Inter. 1987).
Claims 2, 7 and 12:
The combination of Nishant/ Phillips discloses the limitations as shown above.
Nishant further discloses:
generating a specific function description model of the plurality of function description models ( see at least paragraph 74 (In an example embodiment, chat bot 132 may be configured as a plug-n-play model, such that chat bot 132 may be plugged into any web page, by including an <iframe> within a <div> of an HTML document);
Claims 3, 8 and 13:
The combination of Nishant/ Phillips discloses the limitations as shown above.
Nishant further discloses:
wherein generating a specific function description model of the plurality of function description models includes: identifying one or more interactions with one or more portions of a website structure of a specific website; and associating the one or more interactions with the one or more portions of the website structure with one or more functions of the specific website to define the specific function description model (see at least paragraph 72 ( client component of web channel 130 may include a hypertext markup language (HTML) file with JS and CSS files included in a website associated with CQ platform 102 (referred to as the platform website). The JS and CSS files may be included in the platform web site (avnet.com/huckster.io/element14.com) in an HTML iframe to enable chat bot 132 in the website. Based on the target website, the styling may be changed but all of the JS/CSS files may connect to the same instance of chat bot 134 deployed by CQ platform 102);
Claims 4, 9 and 14:
The combination of Nishant/ Phillips discloses the limitations as shown above.
Nishant further discloses:
wherein identifying one or more interactions with one or more portions of a website structure of a specific website includes: enabling a user to review a specific website to visually interact with one or more spatial regions of the specific website (see at least paragraph 46 (The CQ platform also customizes a standard bot framework to include more features and user interface (UI) elements in order to provide a rich user experience. A natural language processor connector component is customized to support the chat bot on different sites of the CQ platform (e.g., avnet.com, hackster.io and element14.com). A custom extension to the natural language processor root dialog component is created, to include logic based on the site where the chat bot is hosted. A custom regular expression pattern is introduced in the code to identify part numbers. A WebChat client html component is customized to store and retrieve a chat bot conversation identifier from a browser cookie, and allows the user to connect to the existing conversations. In some examples, a WebChat ReactJS component is customized to support overlays, emojis, interstitial windows etc) ;
associating the one or more interactions with the one or more spatial regions of the specific website with the one or more portions of the website structure (see at least paragraph 47 (the CQ platform's customization, the CQ platform may shorten the amount of time it takes for users to access information, thereby increasing the number of projects delivered on time. The CQ platform may streamline the user experience through a combination of AI and direct interface with experts. Users may initially interact with an automated assistant (i.e., the chat bot) for fast answers to everyday questions related to a components search or product design. In cases where a specialist is best suited to support the user, the chat bot can immediately connect the user to the relevant expert); paragraph 72 ( client component of web channel 130 may include a hypertext markup language (HTML) file with JS and CSS files included in a website associated with CQ platform 102 (referred to as the platform website). The JS and CSS files may be included in the platform web site (avnet.com/huckster.io/element14.com) in an HTML iframe to enable chat bot 132 in the website. Based on the target website, the styling may be changed but all of the JS/CSS files may connect to the same instance of chat bot 134 deployed by CQ platform 102);
Claims 5, 10 and 15:
The combination of Nishant/ Phillips discloses the limitations as shown above.
Nishant further discloses:
wherein the website structure includes one or more of: a HTML website structure; a javascript website structure; and a CSS website structure ( see at least paragraph 72 ( client component of web channel 130 may include a hypertext markup language (HTML) file with JS and CSS files included in a website associated with CQ platform 102 (referred to as the platform website). The JS and CSS files may be included in the platform web site (avnet.com/huckster.io/element14.com) in an HTML iframe to enable chat bot 132 in the website. Based on the target website, the styling may be changed but all of the JS/CSS files may connect to the same instance of chat bot 134 deployed by CQ platform 102);
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant’s disclosure.
Rapaport et al, US Pub No: 2014/0344718 A1 teaches contextually based automatic service offering to users of machine system.
James Gall. US Pub No: 20190340230 a1, teaches system and method for generating websites from predefined templates.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Affaf Ahmed whose telephone number is 571-270-1835. The examiner can normally be reached on [M- R 8-6 pm ].
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Ilana Spar can be reached at 571-270-7537. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/AFAF OSMAN BILAL AHMED/Primary Examiner, Art Unit 3622