DETAILED ACTION
This is a non-final, first office action on the merits. Claims 1-21 are pending. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claim 21 is rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor, or for pre-AIA the applicant regards as the invention.
Claim 21, line 6, recites the limitation “the conversion rate”. There is insufficient antecedent basis for this limitation in the claim.
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 1-21 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. Specifically, claims 1-21 are directed to an abstract idea without additional elements amounting to significantly more than the abstract idea.
With respect to Step 2A Prong One of the framework, claims 1, 11, and 21 recite an abstract idea. Claims 1, 11, and 21 include “receiving question and response pairs for each participant; identifying the question type; identifying a topic of the question using at least one topic; identifying an entity of the question using at least one name entity recognition (NER); processing the responses based upon the question type; decoding at least one attribute from the processed responses; storing the at least one attribute; performing topic and entity extractions on a question/response pair; decoding the extracted topic and entity to generate an attribute for a plurality of study participants; estimating the conversion rate of a subset of the study participants based upon the rarity of an attribute and the number of the plurality of study participants that are known to have said attribute; estimate a time to field based upon the estimated conversion rate and a number of extended study offers; querying a historical study database to compare the usability study to previous usability studies to estimate duration of the study; and estimate a time to completion for the study based upon the estimated time to field and the estimated duration”.
The limitations above recite an abstract idea under Step 2A Prong One. More particularly, the elements above recite mental processes-concepts performed in the human mind (including an observation, evaluation, judgment, opinion) and mathematical calculations because the elements describe a process for identifying attributes in a plurality of usability study. As a result, claims 1, 11, and 21 recite an abstract idea under Step 2A Prong One.
Claims 2-10 and 12-20 further describe the process for identifying attributes in a plurality of usability study. As a result, claims 2-10 and 12-20 recite an abstract idea under Step 2A Prong One for the same reasons as stated above with respect to claims 1, 11, and 21.
With respect to Step 2A Prong Two of the framework, claims 1, 11, and 21 do not include additional elements that integrate the abstract idea into a practical application. Claims 1, 11, and 21 include additional elements that do not recite an abstract idea under Step 2A Prong One. The additional elements of claims 1, 11, and 21 include a machine learning (ML), a system server, and a database. When considered in view of the claim as a whole, the additional elements do not integrate the abstract idea into a practical application because the additional computing elements are generic computing elements that are merely used as a tool to perform the recited abstract idea. As a result, claims 1, 11, and 21 do not include additional elements that integrate the abstract idea into a practical application under Step 2A Prong Two.
Claims 2-6, 8-10, 12-16, and 18-20 do not include any additional elements beyond those recited with respect to claims 1, 11, and 21. As a result, claims 2-6, 8-10, 12-16, and 18-20 do not include additional elements that integrate the abstract idea into a practical application under Step 2A Prong Two for the same reasons as stated above with respect to claims 1, 11, and 21.
Claims 7 and 17 include additional elements that do not recite an abstract idea under Step 2A Prong One. The additional elements of claims 7 and 17 include a machine learning (ML). When considered in view of the claims as a whole, the additional elements do not integrate the abstract idea into a practical application because the additional computing elements do no more than generally link the use of the recited abstract idea to a particular technological environment. As a result, claims 7 and 17 do not include additional elements that integrate the abstract idea into a practical application under Step 2A Prong Two.
With respect to Step 2B of the framework, claims 1, 11, and 21 do not include additional elements amounting to significantly more than the abstract idea. As noted above, claims 1, 11, and 21 include additional elements that do not recite an abstract idea under Step 2A Prong One. The additional elements of claims 1, 11, and 21 include a machine learning (ML), a system server, and a database. The additional elements do not amount to significantly more than the abstract idea because the additional computing elements are generic computing elements that are merely used as a tool to perform the recited abstract idea. Further, looking at the additional elements as an ordered combination adds nothing that is not already present when considering the additional elements individually. As a result, independent claims 1, 11, and 21 do not include additional elements that amount to significantly more than the abstract idea under Step 2B.
Claims 2-6, 8-10, 12-16, and 18-20 do not include any additional elements beyond those recited with respect to claims 1, 11, and 21. As a result, claims 2-6, 8-10, 12-16, and 18-20 do not include additional elements that amount to significantly more than the abstract idea under Step 2B for the same reasons as stated above with respect to claims 1, 11, and 21.
Claims 7 and 17 include additional elements that do not recite an abstract idea under Step 2A Prong One. The additional elements of claims 7 and 17 include a machine learning (ML). The additional elements do not amount to significantly more than the abstract idea because the additional computing elements do no more than generally link the use of the recited abstract idea to a particular technological environment. Further, looking at the additional elements as an ordered combination adds nothing that is not already present when considering the additional elements individually. As a result, claims 7 and 17 do not include additional elements that amount to significantly more than the abstract idea under Step 2B.
Therefore, the claims are directed to an abstract idea without additional elements amounting to significantly more than the abstract idea. Accordingly, claims 1-21 are rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.
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.
Claims 1-8, 11-18, and 21 are rejected under 35 U.S.C. 103 as being unpatentable over F Rimbach et al. (Internet Marketing for Profit Organizations: A framework for the implementation of strategic internet marketing) - 2010 - pearl.plymouth.ac.uk (hereinafter Rimbach et al.) in view of Terry et al. (US Pub No. 2019/0179903) (hereinafter Terry et al.).
Regarding claims 1 and 11, Rimbach and Terry disclose a method for identifying attributes in a plurality of usability study participants comprising:
receiving question and response pairs for each participant (see Rimbach, page 7, wherein the respondents answered questionnaires distributed directly to them via different media. The surveys included open as well as closed questions to enable a comprehensive evaluation of the strategic frameworks. The answers to those questions were segmented into different groups and analyzed accordingly);
identifying the question type (see Rimbach, page 7, wherein the surveys included open as well as closed questions to enable a comprehensive evaluation of the strategic frameworks. The answers to those questions were segmented into different groups and analyzed accordingly);
identifying a topic of the question (see Rimbach, Table 11-page 88, wherein Initiate topics in online discussion forums Comment on topics in online discussion forums Chat with others);
processing the responses based upon the question type (see Rimbach, page 342, wherein within a questionnaire distributed via email to 153 participants a part A was composed of three closed and three open questions. The three closed questions allowed the individual rating of importance for the three objectives. The 3 open questions allowed the participants to elaborate additional objectives of Internet marketing or alternative terminology. An explorative survey format was chosen as this part of the research primarily focused on the free conceptualization of a new comprehensive structure and not a statistical confirmation of an established approach. Based on 78 responses the following result was assessed in regards to prioritizing the objectives of Internet marketing);
(translating) at least one attribute from the processed responses (see Rimbach, pages 57, 102, & 138-139, wherein……within this analytical process the PO has to differentiate the expectations of inhomogeneous target groups. The expectations for each target group can be translated into a corresponding value curve. Sigel (2006) provides some examples into different shopping behavior which allow customizing the conversion strategy; and page 158, wherein the setup and process of the survey the group individuals selected has already common characteristic (e.g. similar social status, geography, etc.); and page 342, wherein based on 78 responses the following result was assessed in regards to prioritizing the objectives of Internet marketing); and
storing the at least one attribute (see Rimbach, page 58, wherein the features and configuration of the technical infrastructure also determine the marketing possibilities. Based on the Web server's operating system and main applications special modules have to be available to run consumer interactive scripts. Databases, daemons or security settings have an impact on the user interaction).
Rimbach et al. fails to explicitly disclose using at least one topic machine learning (ML) model; identifying an entity of the question using at least one name entity recognition (NER) ML model; decoding at least one attribute from the processed responses; and store the at least one attribute as a vector dictionary.
Analogous art Terry discloses identifying a topic of the question using at least one topic machine learning (ML) model (see Terry, para [0021], wherein deep learning models may be employed to extract entity information from the response……Such a system receives a response message from a human contact, identifies questions within the received response message using machine learning classifiers, cross references the identified questions with approved answer database, and outputs an approved answer from the approved answer database when there is a match.……..The topic of the question is then used for the cross reference against answers by topic);
Analogous art Terry discloses identifying an entity of the question using at least one name entity recognition (NER) ML model (see Terry, para [0021], wherein deep learning models may be employed to extract entity information from the response……Such a system receives a response message from a human contact, identifies questions within the received response message using machine learning classifiers, cross references the identified questions with approved answer database, and outputs an approved answer from the approved answer database when there is a match.……..The topic of the question is then used for the cross reference against answers by topic; and para [0136], wherein after the normalization, documents are further processed through lemmatization (at 1320), name entity replacement (at 1330), the creation of n-grams (at 1340) sentence extraction (at 1350), noun-phrase identification (at 1360) and extraction of out-of-office features and/or other named entity recognition (at 1370). Each of these steps may be considered a feature extraction of the document);
Analogous art Terry discloses decoding at least one attribute from the processed responses (see Terry, para [0170], wherein the response is then translated into all available languages (at 3040) to allow for human operator audit and review. Classification may be performed on all response translations (at 3050); paras [0107]-[0109], wherein a response browser and action accuracy browser may likewise be generated for display to the user…..The browser response display 2200, at FIG. 22, provides the user the ability to filter the responses by a number of features, including message client, conversation type, action taken, date range message series and industry, as seen at 2210. After selecting filters, the report of actions may be run as illustrated at 2220. The applicable responses are then displayed to the user); and
Analogous art Terry discloses a database configured to store the at least one attribute as a vector dictionary (see Terry, para [0182], wherein distributed database, and/or associated caches and servers) that store the one or more sets of instructions; para [0101], wherein attributes of concepts and associations; para [0102], wherein feature vectors; and para [0170], wherein dictionaries are collected for the supported languages (at 3010)).
Rimbach directed to conversion metrics along with customer insight. Terry directed to a system for answering system utilizing approved answers. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of Rimbach, regarding A framework for the implementation of strategic internet marketing, to have included identifying a topic of the question using at least one topic machine learning (ML) model; identifying an entity of the question using at least one name entity recognition (NER) ML model; decoding at least one attribute from the processed responses; and store the at least one attribute as a vector dictionary because both inventions teach improving conversion rate performance. Further, the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Regarding claims 2 and 12, Rimbach and Terry disclose the method of claim 1, wherein the processing the responses includes a Boolean response processing, a quantitative response processing, a single response processing, and a multi-response processing (see Rimbach, Appendix I indicates that output that can only have one of two possible values: true or false; Table 11-page 88, wherein Feedback/rating Submit product specific or general qualitative feedback Quantitative rating of products and services; page 7, wherein the surveys included open as well as closed questions to enable a comprehensive evaluation of the strategic frameworks; and page 190, wherein the questions could be answered with "yes" and "no").
Regarding claims 3 and 13, Rimbach and Terry disclose the method of claim 2, wherein the Boolean response processing includes collecting a binary state for the question topic (see Rimbach, page 190, wherein the questions could be answered with "yes" and "no").
Regarding claims 4 and 14, Rimbach and Terry disclose the method of claim 2, wherein the quantitative response processing, as set forth above with claim 2.
Rimbach et al. fails to explicitly disclose performing an entity extraction on the responses.
Analogous art Terry discloses performing an entity extraction on the responses (see Terry, para [0022], wherein deep learning models may be employed to extract entity information from the response).
One of ordinary skill in the art would have recognized that applying the known technique of Terry would have yielded predictable results and resulted in an improved system for the same reasons as stated above with respect to claim 1.
Regarding claims 5 and 15, Rimbach and Terry disclose the method of claim 2, wherein the single response processing includes performing a topic extraction (see Rimbach, page 231, wherein queries on topics like marketing, accounting or e-learning).
Rimbach et al. fails to explicitly disclose an entity extraction on the responses.
Analogous art Terry discloses performing a topic extraction and an entity extraction on the responses (see Terry, para [0021], wherein the topic of the question is then used for the cross reference against answers by topic; and para [0022], wherein deep learning models may be employed to extract entity information from the response).
One of ordinary skill in the art would have recognized that applying the known technique of Terry would have yielded predictable results and resulted in an improved system for the same reasons as stated above with respect to claim 1.
Regarding claims 6 and 16, Rimbach and Terry disclose the method of claim 2, wherein the multi-response processing includes performing at least two topic extractions (see Rimbach, page 231, wherein queries on topics like marketing, accounting or e-learning).
Rimbach et al. fails to explicitly an entity extraction for each topic on the responses.
Analogous art Terry discloses performing at least two topic extractions and an entity extraction for each topic on the responses (see Terry, para [0159], wherein a response is received (at 2710) and through the classification it is determined that a question is present in the response (at 2720). Topics for the questions are determined by the classifier. The question topic is cross referenced against generic question topics for which approved answers have already been generated (at 2730); and para [0022], wherein deep learning models may be employed to extract entity information from the response).
One of ordinary skill in the art would have recognized that applying the known technique of Terry would have yielded predictable results and resulted in an improved system for the same reasons as stated above with respect to claim 1.
Regarding claims 7 and 17, Rimbach and Terry disclose the method of claim 4, wherein the entity extraction on the response, as set forth above with claim 4.
Rimbach et al. fails to explicitly performed using a response NER ML model selected from a plurality of NER models based upon accuracy of the response NER ML model based on the topic of the question.
Analogous art Terry discloses performed using a response NER ML model selected from a plurality of NER models based upon accuracy of the response NER ML model based on the topic of the question (see Terry, paras [0099]-[0100], wherein perform name entity recognition (NER) to extract concepts related to the business being discussed. Examples of this could include a person, for example. Concepts are extracted which are relevant to the actions associated with the response. In some embodiments, concepts in NER are identified using graph based and deep learning statistical sequential labeling algorithms. Examples of which include Conditional Random Fields (CRF) and Bidirectional Long Short Term Memory (LSTM)…….leverage database-based similarity and unsupervised machine learning measures. Associations may also be extracted between the concepts in the conversation using instance-based classification algorithms; and para [0022], wherein deep learning models may be employed to extract entity information from the response).
One of ordinary skill in the art would have recognized that applying the known technique of Terry would have yielded predictable results and resulted in an improved system for the same reasons as stated above with respect to claim 1.
Regarding claims 8 and 18, Rimbach and Terry disclose the method of claim 1, further comprising fielding the participants in a usability study (see Rimbach, page 62, wherein certain characteristics of the relationship between Web design, usability and Internet marketing success has been roughly described by several researchers).
Regarding claims 11-20 rejected based upon the same rationale as the rejection of claims 1-10, respectively, since it is the system claim corresponding to the method claim. Claim 11 discloses additional feature a system server (see Rimbach, page 26, wherein databases and server).
Regarding claim 21, Rimbach and Terry disclose a method of predicting fulfillment criteria for a usability study comprising:
(translating) the extracted topic and entity to generate an attribute for a plurality of study participants (see Rimbach, pages 57, 102, & 138-139, wherein……within this analytical process the PO has to differentiate the expectations of inhomogeneous target groups. The expectations for each target group can be translated into a corresponding value curve. Sigel (2006) provides some examples into different shopping behavior which allow customizing the conversion strategy accordingly: "Online shopping habits generally mirror offline habits for men and women. Both value the Internet for its 24/7/365 time convenience, product availability, product/pricing comparison facility, and shop-at-home ease. Frustrations include shipping charges, spam, and concerns about credit card security. Men are more likely than women to be bargain hunters online, and men are more likely to fault online shopping because they can't touch or feel what they're buying. They are also more likely to purchase expensive products online, and they appear to have greater trust in Internet shopping generally (Sigel, 2006)."….. a conversion rate of 10% from online shop visits to purchases and 2% to recommendations. That indicates that 10 % of all visitors create a desire to purchase, much more than the conversion to perform recommendations. The 45% conversion rate of forum views to new posts can be interpreted that almost half of all visitors to the forum create the desire to leave an entry. Those figures have to be put into correlation of industry benchmarks or tracked over a period of time to evaluate improvements; and page 158, wherein the setup and process of the survey the group individuals selected has already common characteristic (e.g. similar social status, geography, etc.));
estimating the conversion rate of a subset of the study participants based upon the rarity of an attribute and the number of the plurality of study participants that are known to have said attribute (see Rimbach, page 101, wherein the conversion rate can be calculated for any kind of transaction that has been identified by the PO. The following figure is an example of potential measurements: Fig. 26; page 122, wherein understanding can be developed by first of all segmenting potential customers into logical groups (McKenna, 1988); page 158, wherein based on the setup and process of the survey the group individuals selected has already common characteristic (e.g. similar social status, geography, etc.). The result is illustrated in Figure 44; pages 31-32, wherein number of market participants clearly indicates the magnitude of the Internet as a significant and potentially the most significant sales and information channel for a large spectrum of POs. The number of potential consumers - as an indicator of the platforms significance - is further supported by statistics of the supply side of this marketplace);
estimate a time to field based upon the estimated conversion rate and a number of extended study offers (see Rimbach, page 237, wherein measure the desire for the illustrated content, the conversion TAT has not been defined as the time a user requires to click on external advertising references (practically leaving the node due to more interesting content elsewhere), but as the time in which a consumer develops an interest for the content and explores more individual pages of the node without exiting the node as such. Based on this understanding, two Google Analytics metrics provided interesting data points to evaluate the impact and benefit of information and illustration. An increased 'average time on page' as well as reduction of '% Exit' from the start page suggests that a consumer is (more) attracted by the content or not; and pages 153-154, wherein in studying how people use tools and complete processes, one of the things Norman looked at was actions. He highlighted seven stages of completing a task which can be translated into a set of questions which support to develop an appropriate design for the specific task);
querying a historical study database to compare the usability study to previous usability studies to estimate duration of the study (see Rimbach, page 228, wherein database as well as the application for the front-end and administration area had to be adjusted. The enhanced header of the start page provides a lot of information which the search engines can use to identify the Web site as a potential match to a query; page 31, wherein the statistics reveal three main regions Asia, Europe and North America totaling around 1.3 billion users (and potential customers) which can be targeted via Internet marketing. Considering the number of spoken languages in those three main regions, North America appears as the biggest uniform and therefore potentially most attractive market……Besides the global perspective, individual domestic markets differ significantly from the e-business maturity (e.g. in Finland over 70% of enterprises placed orders over the Internet, in Romania the value is under 5%)2. Within some new statistics (e.g. BDA China3) China has already overtaken the United States in terms of total number Internet users, just 22.1 % of them use the platform actively for online shopping (compared to 71 % in the United States, The Straits Times 2008). This number of market participants clearly indicates the magnitude of the Internet as a significant and potentially the most significant sales and information channel for a large spectrum of POs. The number of potential consumers as an indicator of the platforms significance is further supported by statistics of the supply side of this marketplace; and pages 115 & 157, wherein the usability and design of the Web site, the payment and delivery processes and even the feedback or return policies
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estimate a time to completion for the study based upon the estimated time to field and the estimated duration (see Rimbach, pages 115 & 157, wherein the usability and design of the Web site, the payment and delivery processes and even the feedback or return policies
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Rimbach et al. fails to explicitly disclose performing topic and entity extractions on a question/response pair using a machine learning (ML) model; and decoding the extracted topic.
Analogous art Terry discloses performing topic and entity extractions on a question/response pair using a machine learning (ML) model (see Terry, para [0021], wherein deep learning models may be employed to extract entity information from the response……Such a system receives a response message from a human contact, identifies questions within the received response message using machine learning classifiers, cross references the identified questions with approved answer database, and outputs an approved answer from the approved answer database when there is a match.……..The topic of the question is then used for the cross reference against answers by topic); and
Analogous art Terry discloses decoding the extracted topic and entity to generate an attribute for a plurality of study participants (see Terry, para [0170], wherein the response is then translated into all available languages (at 3040) to allow for human operator audit and review. Classification may be performed on all response translations (at 3050); paras [0107]-[0109], wherein a response browser and action accuracy browser may likewise be generated for display to the user…..The browser response display 2200, at FIG. 22, provides the user the ability to filter the responses by a number of features, including message client, conversation type, action taken, date range message series and industry, as seen at 2210. After selecting filters, the report of actions may be run as illustrated at 2220. The applicable responses are then displayed to the user).
Rimbach directed to conversion metrics along with customer insight. Terry directed to a system for answering system utilizing approved answers. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of Rimbach, regarding A framework for the implementation of strategic internet marketing, to have included performing topic and entity extractions on a question/response pair using a machine learning (ML) model; and decoding the extracted topic and entity to generate an attribute for a plurality of study participants because both inventions teach improving conversion rate performance. Further, the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Claims 9-10 and 19-20 are rejected under 35 U.S.C. 103 as being unpatentable over F Rimbach et al. (Internet Marketing for Profit Organizations: A framework for the implementation of strategic internet marketing) - 2010 - pearl.plymouth.ac.uk (hereinafter Rimbach et al.), in view of Terry et al. (US Pub No. 2019/0179903) (hereinafter Terry et al.), and further in view of Desikan et al. (US Pub No. 2007/0005417) (hereinafter Desikan et al.).
Regarding claims 9 and 19, Rimbach and Terry disclose the method of claim 8, wherein the fielding the participants includes:
detecting fraudulent participants based upon the vector for each participant (see Rimbach, page 157, wherein entries in an online forum may contribute to daily movements on the site. Consumers, which are afraid of fraud, find in that structure security that their purchase behavior follows an established pattern (i.e., a vector that describes their behavior) and they are not exposed to any unforeseeable risk………… These considerations focus on the complete value chain of the products and services as well as the usability and design of the Web site, the payment and delivery processes and even the feedback or return policies);
predicting a conversion rate for the participants based upon the vector for each participant (see Rimbach, page 101, wherein the conversion rate can be calculated for any kind of transaction that has been identified by the PO. The following figure is an example of potential measurements: Fig. 26; page 158, wherein based on the setup and process of the survey the group individuals selected has already common characteristic (e.g. similar social status, geography, etc.); and page 157, wherein entries in an online forum may contribute to daily movements on the site. Consumers, which are afraid of fraud, find in that structure security that their purchase behavior follows an established pattern (i.e., a vector that describes their behavior) and they are not exposed to any unforeseeable risk…………);
selecting a provider based upon the conversion rate of the participants in the provider (see Rimbach, page 173, wherein the process of positioning hyperlinks and banners can be easily outsourced to professional service provider; page 101, wherein the conversion rate can be calculated for any kind of transaction that has been identified by the PO. The following figure is an example of potential measurements: Fig. 26; page 158, wherein based on the setup and process of the survey the group individuals selected has already common characteristic (e.g. similar social status, geography, etc.)); and
onboarding participants from the provider to the usability study (see Rimbach, page 133, wherein based on the scope of the identified Web sites, the potential partners can be contacted and possible marketing campaigns discussed).
Rimbach et al. Terry et al. combined fail to explicitly disclose detecting; screening the participants based upon the vector for each participant.
Analogous art Desikan discloses detecting fraudulent participants based upon the vector for each participant (see Desikan, para [0058], wherein detect index spammers (i.e., Websites that use illegitimate means to rank higher on search results); para [0055], wherein may be used to train an expert system (e.g., neural networks, Bayesian networks, support vector machines, etc.) to classify other Websites as having the policy violation or not; and para [0014], wherein possible fraud or deception on the advertising network or participants of the advertising network by the collection);
Analogous art Desikan discloses screening the participants based upon the vector for each participant (see Desikan, para [0086], wherein an advertising network may check participants in its advertising network for policy compliance to help find unauthorized Websites; and para [0055], wherein may be used to train an expert system (e.g., neural networks, Bayesian networks, support vector machines, etc.) to classify other Websites as having the policy violation or not);
Analogous art Desikan disclose predicting a conversion rate for the participants based upon the vector for each participant (see Desikan, paras [0029]-[0030], wherein a "conversion" is said to occur when a user consummates a transaction related to a previously served ad. What constitutes a conversion may vary from case to case and can be determined in a variety of ways. For example, it may be the case that a conversion occurs when a user clicks on an ad, is referred to the advertiser's Web page, and consummates a purchase there before leaving that Web page; and para [0055], wherein may be used to train an expert system (e.g., neural networks, Bayesian networks, support vector machines, etc.) to classify other Websites as having the policy violation or not).
Rimbach directed to conversion metrics along with customer insight. Desikan directed to a system for reviewing the suitability of websites for participation in an advertising network. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of Rimbach, regarding A framework for the implementation of strategic internet marketing, to have included detecting fraudulent participants based upon the vector; screening the participants based upon the vector for each participant; and predicting a conversion rate for the participants based upon the vector for each participant because both inventions teach improving conversion rate performance. Further, the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Regarding claims 10 and 20, Rimbach and Terry disclose the method of claim 9, further comprising generating a question recommendation for the usability study based upon the vector for each participant (see Rimbach, pages 153-154, wherein in studying how people use tools and complete processes, one of the things Norman looked at was actions. He highlighted seven stages of completing a task which can be translated into a set of questions which support to develop an appropriate design for the specific task:
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Conclusion
The prior arts made of record and not relied upon is considered pertinent to applicant's disclosure. (US Pub No. 2013/0159317; US Pat No. 9,535,902; US Pub No. 2010/0228580; US Pub No. 2015/0081279; US Pub No. 2014/0068407; US Pub No. 2021/0304232; US Pat No. 2006/0161553; US Pub No. 2021/0233031; US Pub No. 2009/0112685; US Pat No. 7,917,491; US Pub No. 2019/0258671; R Oentaryo, EP Lim, M Finegold, D Lo, F Zhu (Detecting click fraud in online advertising: a data mining approach)- Learning Research, 2014 - dl.acm.org; and Carroll, Marty, "Usability and Web analytics: ROI justification for an Internet strategy," Interactive Marketing, vol. 4, No. 3, pp. 223-234, The Usability Company, Jan./Mar. 2003.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to HAFIZ KASSIM whose telephone number is (571)272-8534. The examiner can normally be reached on Mon - Fri (8am - 5pm) EST.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Rutao Wu can be reached on (571) 272-6045. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/HAFIZ A KASSIM/Primary Examiner, Art Unit 3623 08/22/2025