DETAILED ACTION
This Final Office Action is in response to Applicant's amendments and arguments filed on February 20, 2026. Applicant has amended claims 1, 8 and 15 and added claims 22-23. Currently, claims 1-23 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 .
Response to Amendments
The 35 U.S.C. 101 rejections of claims 1-20 are maintained in light of applicant’s amendments to claims 1, 8 and 15. Applicant’s amendments necessitated the new grounds for rejection in this office action.
The 35 U.S.C. 103 rejections of claims 1-20 are withdrawn in light of applicant’s amendments to claims 1, 8 and 15. Applicant’s amendments necessitated the new grounds for rejection in this office action.
Response to Arguments
Applicant’s remarks submitted on 2/20/26 have been considered but are not persuasive. Applicant argues on p. 10 of the remarks that the 101 rejections are improper. Examiner disagrees. Applicant argues that the claims are not directed to organized human activity because the claims are directed to understanding which webpages have the most impact on a computer transaction. Examiner disagrees and notes that the claims use a webpage and computer transaction as a tool for implementing the abstract idea itself. Applicant further argues that the allocating resources integrates the abstract idea into practical application. Examiner notes that the amended language is subject to a 112 rejection. Examiner further notes the limitation is not properly tethered to the claim and considered extra solution activity and, in addition, merely generally links to a computing environment for implementing the abstract idea itself. Therefore, the claims remain rejected under 101. Applicant argues on p. 11 of the remarks that the 103 rejections are improper. Examiner disagrees. Applicant argues that Ciabarra does not teach "for each visit, aggregating the value of the commercial transactions at each
node along the path of the visit in a conversion funnel". Examiner disagrees and notes that para [0044] shows "The expected conversion rate can be computed based upon a total number of the plurality of non-suspect sessions and a number of the plurality of non-suspect sessions that included an action defined as a conversion. The expected conversion rate can computed using sessions occurring on the website overall, where the plurality of suspect sessions are associated with the website." This can be considered aggregating conversions given broadest reasonable interpretation. Applicant argues that these transactions determine a drop off value as opposed to potential business value. Examiner notes it is obvious that drop off value can be considered to show business value because it is a different expression of value but still shows a type of business value from the negative perspective. Moreover, it would be obvious that such a calculation can be done to show it in a potential perspective. Moreover, the amended language is shown by the newly cited Dua reference which also explicitly shows a sales funnel and analysis of such conversions to calculate business value for the nodes at para [0025]-[0027]. Therefore, the claims remain rejected under 103.
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, 8 and 15 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. There is no support in the specification for “allocating additional computing resources to a server that supports a particular webpage, where the webpage corresponds to the a least one identified node.” At best, para [0102] shows support of allocating resources but not for a particular website corresponding to identified nodes. For purposes of compact prosecution, this is being interpreted as allocating resources generally. Claims 2-7, 9-14, 16-23 inherit the same deficiencies as claims 1, 8 and 15 and thus rejected for the same reasons.
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 1-23 are clearly drawn to at least one of the four categories of patent eligible subject matter recited in 35 U.S.C. 101 (methods and non-transitory computer readable medium). Claims 1-23 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more. Claims 1, 8 and 15 recite the abstract idea of monitoring a transaction and receiving transaction trace data that identifies a plurality of transactions and receiving data describing a conversion funnel where the data includes a given path is represented by a node along the given path and identifying a subset of transactions from the plurality of transactions as converting transactions where each transaction in the subset of transactions reaches a given conversion page and resulted in a commercial transaction occurring on the given conversion page and identifying visits associated with the converting transactions as converting visits and for each converting visit, determining paths traversed during a given converting visit and accumulating quantity of the commercial transaction at each node along the paths traversed during a given visit, thereby yielding an aggregated business value for each node and for each node in the conversion funnel, calculating a business value weight for a given node by dividing the aggregated business value for the given node by a number of converting visits which passed through the given node and for each node in the conversion funnel, determining potential business value for a given node by multiplying the number of visits that entered the given node with business value weight assigned to the given node and identifying at least one node having largest potential business value from amongst the nodes in the conversion funnel and claim 15 expands on the abstract idea by including receiving a sequence of two or more state changes such that each state change along a given path transitions the web page into another state, and each state is represented by a node on the given path where states include an entry state, a conversion state and one or more connecting (intermediate) states. The claims are directed to a type of monitoring and identifying transaction activities. Under prong 1 of Step 2A, these claims are considered abstract because the claims are certain methods of organizing human activity such as commercial interactions including business relations. Applicant’s claims are organized human activity such as commercial interactions including business relations because the claims show monitoring transactions and visits (human activity) and organization of that data by making calculations and determinations from that data. Under prong 2 of Step 2A, the judicial exception is not integrated into a practical application because the claims (the judicial exception and any additional elements individually or in combination such as computer implemented, computer transaction, distributed computing environment, a monitoring server, where the data includes an entry webpage, a conversion webpage, and one or more paths interconnecting the entry web page to the conversion webpage and allocating additional computing resources to a server that supports a particular webpage, where the webpage corresponds to the at least one identified node and a non-transitory computer-readable medium having computer- executable instructions that, upon execution of the instructions by a processor of a computer, cause the computer to perform steps are not an improvement to a computer or a technology, the claims do not apply the judicial exception with a particular machine, the claims do not effect a transformation or reduction of a particular article to a different state or thing nor do the claims apply 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 claims as a whole is more than a drafting effort designed to monopolize the exception. These limitations at best are merely implementing an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f). 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 individually or in combination such as computer implemented, computer transaction, distributed computing environment, a monitoring server, where the data includes an entry webpage, a conversion webpage, and one or more paths interconnecting the entry web page to the conversion webpage and allocating additional computing resources to a server that supports a particular webpage, where the webpage corresponds to the at least one identified node and a non-transitory computer-readable medium having computer- executable instructions that, upon execution of the instructions by a processor of a computer, cause the computer to perform steps (as evidenced by para [0050], [0067]-[0071], [0074]-[0076], [00225]-[00232] of applicant’s own specification) are well understood, routine and conventional in the field. Dependent claims 2, 4-7, 9, 11-14, 16, 18-21 also do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements either individually or in combination are merely an extension of the abstract idea itself by further showing for each node in the conversion funnel, calculating a drop off value, where the drop off value is a number of visits which entered a given node but did not continue traversing a path in the conversion funnel and for each node in the conversion funnel, determining a business value drop off for a given node by multiplying the drop off value for a given node by the business value weight assigned to the given node and ranking the nodes of the conversion funnel according to the highest business value drop off and identifying a given transaction by identifying a service call executing during one of the transactions in the set of transactions, where an identifier for the service matches a business relevant extractor rule and quantifying business value of given commercial transaction using parameters identified by the business relevant extractor rule and identifying visits associated with the converting transactions by designating transaction within a predefined time period as a visit and generating a visualization of the conversion funnel, where the potential business value is shown for each node in the conversion funnel. Dependent claims 3-4, 6, 10-11, 13, 17-20, 22-23 do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements individually or in combination such as wherein the transaction trace data is captured by agents instrumented into web browsers and computer transactions, originating from same web browser and wherein identifying converting transactions includes extracting identification data for services called by the computer transactions from the transaction trace data and comparing the identification data with a service identification pattern characterizing services causing a commercial transaction; and in response to a match, indicating a particular computer transaction as a converting transaction and in response to a match, extracting data for an actual service call from the transaction trace data, where the extracted data includes service call parameter names and values comparing extracted service call parameter names with a parameter identification pattern, where the parameter identification pattern is associated with the service identification pattern; and in response of a match of a service call parameter name with a parameter identification pattern, applying a service value parameter processing rule on the parameter value for the matching service call parameter name, where the service value parameter processing rule is associated with the service identification pattern and defines a procedure to calculate a business value quantity from the parameter value (as evidenced by para [0050], [0067]-[0071], [0074]-[0076], [00225]-[00232] of applicant’s own specification) are well understood, routine and conventional in the field.
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.
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.
Claims 1-22 are rejected under 35 U.S.C. 103 as being unpatentable over Griefeneder et al. (US 2017/0039554 A1) (hereinafter Griefeneder) in view of Ciabarra (US 20190260818 A1) in view of Dua et al. (US 2021/0150548 A1) (hereinafter Dua).
Claims 1, 8 and 15:
Griefeneder, as shown, discloses the following limitations of claims 1, 8 and 15:
A computer-implemented method (and corresponding method and non-transitory computer readable medium – see para [0146]-[0149], showing equivalent computing functionality) for monitoring a distributed computer transaction executing in a distributed computing environment (see para [0073], "A distributed current top category measure extraction unit 212 is associated to each distributed processing unit 202 which cyclically fetches 211 end-to-end transaction trace data records 107 representing currently executed transactions from its associated distributed event processing unit 202."), comprising: receiving, by a monitoring server, transaction trace data that identifies a plurality of computer transactions which executed in the distributed computing environment (see para [0074], "Data records that may be used to store end-to-end transaction trace data and a top category list are conceptually depicted in FIG. 3. An end-to-end transaction trace data record 107 as shown in FIG. 3a, may contain but is not limited to an identifier 301 which uniquely identifies an individual transaction trace, the identifier 301 may be a combination of an identifier for a process execution on a specific computer system and an identifier for a specific thread execution by the process, or combination of an identifier for a web-browser session, an identifier for an individual content view on the web-browser and an identifier for an action executed on the content, a type 302 which may distinguish transaction traces starting at a web-browser from transaction trace starting at a backend process, classification parameters 303 which specify the coordinates of the transaction trace in a multidimensional classification space, and transaction performance and trace data 310 which may describe the processing steps performed to fulfill the monitored transaction on the granularity level of individual method execution);
identifying, by the monitoring server, a subset of computer transactions from the plurality of computer transaction as converting transactions, where each computer transaction in the subset of computer transactions reaches a given conversion page and resulted in a commercial transaction occurring on the given conversion page (see para [0005]-[0008], where showing conversions and sessions that terminate shows that the conversions can be considered a subset of the computer transactions);
identifying, by the monitoring server, visits associated with the converting transactions as converting visits (see para [0095], "In still other embodiments, a top category detection mechanism as described herein may not only be performed on individual transactions, but also on visits describing a set of transactions describing a specific interaction of an end user with the monitored applications. Calculation and monitoring of such visits may be performed according to the teachings of U.S. patent application Ser. No. 13/722,026 “Method And System For Tracing End-To-End Transaction, Including Browser Side Processing And End User Performance Experience” by Greifeneder et al. which is included herein by reference in its entirety. Following the procedures of the disclosed techniques, top categories may be calculated according to the frequency of visits, the number of converted visits (i.e. visits which resulted in a purchase of the customer), the visit conversion rate (i.e. number of visits vs. number of converted visits), the sum of money spent on visits or the number of unique (visits from different users) or recurring (visits from the same user).");
Griefeneder, however, does not specifically disclose receiving, by the monitoring server, data describing a conversion funnel, where the data includes an entry webpage, a conversion webpage, and one or more paths interconnecting the entry web page to the conversion webpage, such that each webpage along a given path is represented by a node along the given path. In analogous art, Ciabarra discloses the following limitations:
receiving, by the monitoring server, data describing a conversion funnel, where the data includes an entry webpage, a conversion webpage, and one or more paths interconnecting the entry web page to the conversion webpage, such that each webpage along a given path is represented by a node along the given path (see para [0005]-[0007], "a website may present one or more webpages to a user, each webpage (and/or change in a webpage) representing a different stage in a conversion funnel (e.g., multiple stages that end in a particular action that a user performs on a particular webpage (sometimes referred to as a conversion event)). In such an illustrative example, a session may include one or more stages that a user progressed through until the user ended the session or a conversion event occurred. With such a website, information related to the sessions may be stored to be used by the detection system described above. The detection system, for each stage of the conversion funnel, may calculate a typical conversion rate for one or more sessions that are determined to be similar to one or more suspect sessions. In such an example, a suspect session may be a session that is identified as having an attribute of interest (sometimes referred to as a suspect attribute). In some examples, the one or more suspect sessions may be limited to sessions that have been determined to be terminated based upon the suspect attribute (e.g., an event that occurred)." And Fig 6A);
for each converting visit, determining, by the monitoring server, paths traversed during a given converting visit and accumulating quantity of the commercial transaction at each node along the paths traversed during a given visit, thereby yielding an aggregated business value for each node (see para [0044], "After identifying the non-suspect sessions, the detection system computes an expected conversion rate for each stage in the set of stages based on the plurality of non-suspect sessions. The expected conversion rate is the percent of users that progress past the particular stage when the issue or error is not encountered. The expected conversion rate can be computed based upon a total number of the plurality of non-suspect sessions and a number of the plurality of non-suspect sessions that included an action defined as a conversion. The expected conversion rate can computed using sessions occurring on the website overall, where the plurality of suspect sessions are associated with the website." ); and
for each node in the conversion funnel, calculating, by the monitoring server, a business value weight for a given node by dividing the aggregated business value for the given node by a number of converting visits which passed through the given node (see para [0043], " After determining the stages, the detection system computes a suspect conversion rate for each stage in the set of stages based on the plurality of suspect sessions. The suspect conversion rate can be determined by dividing the number of sessions that progress past the stage (e.g., a conversion) by the total number of sessions including that stage. The suspect conversion rate can be computed based upon a total number of the plurality of suspect sessions associated with the set of stages and a number of the suspect sessions associated with the set of stages that included an action defined as a conversion. The detection system can also determine a common session attribute of the plurality of suspect sessions (e.g., all of the sessions used a particular operating system, or a particular web browser, etc.). The common session attribute is identified based on at least a threshold number of the plurality of suspect sessions having the common session attribute. The detection system then identifies a plurality of non-suspect sessions having the common session attribute. The plurality of non-suspect sessions do not include the one or more events associated with the suspect attribute. That is, the non-suspect sessions did not encounter the same error or issue that were encountered in the suspect sessions. Thus, the non-suspect sessions are not included in the plurality of suspect sessions."); and
for each node in the conversion funnel, determining, by the monitoring server, potential business value for a given node by multiplying the number of visits that entered the given node with business value weight assigned to the given node (see para [0134], "The presentation of information may also include sixth column 636, which may include a value representing missed opportunity for each stage. For example, the “view bag” stage is indicated to have $1.3K of missed opportunity. In such an example, the $1.3K may be computed by multiplying an average conversion value (as discussed further below) by the number of sessions that dropped off for the “view bag” stage." and see para [0141]-[0142], "User interface may further include ninth column 730, which may include a missed-sales value for each stage. For example, the “place order” stage is indicated to have 11 missed sales. In such an example, the 11 missed sales may be computed by multiplying a drop-off value for a stage with an under-conversion value for the stage. User interface may further include tenth column 740, which may include a value representing missed opportunity for each stage. The value included in tenth column 740 may different than the value included in sixth column 636 in FIG. 6A. In particular, the value in tenth column 740 may be computed by multiplying a missed-sales value by an average conversion value. While the average conversion value may be computed similarly as descried above in FIG. 6A, the missed-sales value is different. For example, the “place order” stage is indicated to have about $4.2K of missed opportunity.").
Ciabarra further recited the additional limitation of claim 21 of:
receiving, by the monitoring server, data describing a conversion funnel, where the data includes a webpage and a sequence of two or more state changes occurring on the webpage, such that each state change along a given path transitions the web page into another state, and each state is represented by a node on the given path, where states of the webpage include an entry state, a conversion state and one or more connecting (intermediate?) states (see para [0027], "A session may include one or more “stages” that a user progresses through until the user ends the session. The “stages” may be defined based on interactions made by the user (e.g., opening a webpage, selecting an element or item of the page, or inputting information in a particular location). For example, each stage may be associated with a particular interaction that beings the stage (e.g., opening a page, selecting an element, or submitting data) and a particular interaction that ends the stage (e.g., opening another page, selecting another element, or submitting other data). Each stage may be associated with one or more changes or updates to a webpage (e.g., a visual change or a change in the information obtained by the web server). For example, each webpage of the website presented to the user may correspond to a different stage. In some examples, stages may be a combination of multiple different webpages and/or different interactions with particular webpages. Each of the stages may be associated with a unique identifier." and see para [0028], "An example of a set of stages during a session is provided below. In this example, a user opens a homepage of a website, which is associated with a first stage. During the first stage, the user browses the page and selects a link to open a second page on the website, thereby ending the first stage and beginning a second stage. The second stage can be associated with the second page. During the second stage, the user browses the second page and inputs information to the second page by selecting items or elements of the page or by inputting text into a field of the second page. This information is submitted to the website (which can be performed automatically by the website or performed manually by the user selecting a submit button). The submission of such information can end the second stage and beginning a third stage. The third stage can be associated with a third page. The third page may present confirmation of the information selected or input by the user. Not every session with a particular website will include the same stages nor will the stages always occur in the same order. Different sessions may include different stages and the stages may occur in different orders. And, a particular stage or sequence of stages may occur more than once in a given session. In addition, while the stages are described as being associated with a particular “webpage,” the stages can be associated with particular in-line updates to blocks, fields, or elements of the same webpage (e.g., the same URL)." and see para [0071]-[0073], [0090])
It would have been obvious to one or ordinary skill in the art at the time of the invention to combine the teachings of Ciabarra with Griefeneder because including a conversion funnel enables more monitoring of the performance of a website (see Ciabarra, para [0002]).
Moreover, it would have been obvious to one of ordinary skill in the art at the time of the invention to include the method for identifying issues related to digital interactions on websites as taught by Ciabarra in the method for real-time, load-driven multidimensional and hierarchical classification of monitored transaction executions of Griefeneder, since 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.
Griefeneder and Ciabarra do not specifically disclose identifying, by the monitoring server, at least one node having largest potential business value from amongst the nodes in the conversion funnel. In analogous art, Dua gives the following limitations:
identifying, by the monitoring server, at least one node having largest potential business value from amongst the nodes in the conversion funnel (see para [0025]-[0027], "In an embodiment, ASR 102 may apply lead scoring techniques to help sales and marketing departments identify which prospects are potentially most valuable to the company and its current sales funnel. In an example of lead scoring, point values may be assigned to different actions a lead or customer may take in the sales funnel, and/or to different attribute data about customers. For example, clicking a link may be two points, and watching a half a video may be three points, while watching an entire video may be four points. Other actions include reading a blog post, filling out a form, reading an email, calling a helpline, requesting a price quote, etc. The points may also be applied to various demographic or customer information, such as age, location, purchase history, credit score, income, etc. The points may be used to determine which prospects are “hot”, or most likely ready to purchase a product or service. This may help salespeople better use their time and resources to focus on the warmer or hot prospects. This may lead to higher conversions, higher close rates, increased sales, and more satisfied customers. By focusing on customers who have met a threshold point total, have a highest number of points, or who have taken a specific step or action, salespeople may improve their performance and better utilize their resources, including computing resources and bandwidth. However the effectiveness of the lead scoring may be limited by how accurately points are assigned to various activities that indicate a customer's readiness to purchase, or take another action. A company may need to determine what action(s) and/or demographic information shift a customer's focus from interest (e.g., wanting to learn more) to intent (e.g., ready to purchase)."); and
allocating additional computing resources to a server that supports a particular webpage, where the webpage corresponds to the at least one identified node (see para [0027], "By focusing on customers who have met a threshold point total, have a highest number of points, or who have taken a specific step or action, salespeople may improve their performance and better utilize their resources, including computing resources and bandwidth. However the effectiveness of the lead scoring may be limited by how accurately points are assigned to various activities that indicate a customer's readiness to purchase, or take another action. A company may need to determine what action(s) and/or demographic information shift a customer's focus from interest (e.g., wanting to learn more) to intent (e.g., ready to purchase)." where it is obvious to one of ordinary skill in the art that improving utilization of computing resources and bandwidth shows allocating additional computing resources given the interpretation of the claims with the pending 112 rejection and broadest reasonable interpretation where a webpage is a computing resource tool for the salespeople).
It would have been obvious to one or ordinary skill in the art at the time of the invention to combine the teachings of Dua with Griefeneder and Ciabarra because identifying the largest potential business value enables salespersons to more effectively use their resources (see Dua, para [0001-[0002]).
Moreover, it would have been obvious to one of ordinary skill in the art at the time of the invention to include the system for automatic segmentation and ranking of leads and referrals as taught by Dua in the Griefeneder and Ciabarra combination, since 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 2, 9 and 16:
Griefeneder does not specifically disclose for each node in the conversion funnel, calculating a drop off value, where the drop off value is a number of visits which entered a given node but did not continue traversing a path in the conversion funnel. In analogous art, Ciabarra discloses the following limitations:
for each node in the conversion funnel, calculating a drop off value, where the drop off value is a number of visits which entered a given node but did not continue traversing a path in the conversion funnel (see para [0046], "The detection system can also determine a drop-off value for each stage in the set of stages. The drop-off value is the number of the plurality of suspect sessions that terminated in the stage. The detection system can also compute a missed-conversion value for each stage in the set of stages. The missed-conversion value can be computed based upon an average conversion value, the under-conversion rate, and the drop-off value. Then, the detection system can present the missed-conversion value to the user (e.g., via the user interface) so that the user may identify whether a webpage corresponding to the stage includes a technical error. The average conversion value can be calculated based upon an average value for suspect sessions that ended at the stage. The average conversion value can also be calculated based upon an average value for suspect sessions that converted. The detection system can further identify an underperforming stage based upon the missed-conversion value exceeding a threshold. An identification of the underperforming stage and information related the underperforming stage can also be presented with the missed-conversion value.");
for each node in the conversion funnel, determining a business value drop off for a given node by multiplying the drop off value for a given node by the business value weight assigned to the given node (see para [0046], "The detection system can also determine a drop-off value for each stage in the set of stages. The drop-off value is the number of the plurality of suspect sessions that terminated in the stage. The detection system can also compute a missed-conversion value for each stage in the set of stages. The missed-conversion value can be computed based upon an average conversion value, the under-conversion rate, and the drop-off value. Then, the detection system can present the missed-conversion value to the user (e.g., via the user interface) so that the user may identify whether a webpage corresponding to the stage includes a technical error. The average conversion value can be calculated based upon an average value for suspect sessions that ended at the stage. The average conversion value can also be calculated based upon an average value for suspect sessions that converted. The detection system can further identify an underperforming stage based upon the missed-conversion value exceeding a threshold. An identification of the underperforming stage and information related the underperforming stage can also be presented with the missed-conversion value."); and
ranking the nodes of the conversion funnel according to the highest business value drop off (Fig 6, showing the different missed opportunity (636) for the different nodes (626)).
It would have been obvious to one of ordinary skill in the art at the time of the invention to include the method for identifying issues related to digital interactions on websites as taught by Ciabarra in the method for real-time, load-driven multidimensional and hierarchical classification of monitored transaction executions of Griefeneder, since 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 3, 10 and 17:
Further, Griefeneder discloses the following limitations:
wherein the transaction trace data is captured by agents instrumented into web browsers (see para [0004], "Monitoring systems capable to identify, trace and measure individual transaction executions starting from a web-browser side activity, over sending a response to a web-server, processing this request and returning a corresponding response and finally rendering the response on the web-browser, provide large sets of transaction specific measurement data that allow assessing performance and functionality of monitored transaction executions. This transaction trace data typically also contains, beside measurements, data describing the execution context of the monitored transactions." and see para [0029], "FIG. 1 provides an overview of a monitoring system consisting in agents deployed to monitored web-browsers and application processes, and a monitoring server receiving transaction trace data fragments to create end-to-end transaction trace data which is analyzed to identify top-frequency transaction categories as input for statistical analysis." and see para [0121])
Claims 4-6, 11-13, 18-20:
Further, Griefeneder discloses the following limitations:
identifying a given commercial transaction by identifying a service call executing during one of the computer transactions in the set of computer transactions, where an identifier for the service matches a business relevant extractor rule (see para [0065], "The top category list 112 representing a given historic time period is used by the historic top category description extractor 114 to create data that statistically describes the identified top categories. The historic top category description extractor fetches the end-to-end transaction traces corresponding to each transaction category that fall into the considered historic reference time period and creates transaction category specific data in form of time series or statistical parameters like quantiles that describe the transaction categories within the considered time period. The created category description data is stored in a historic top category description repository 119." and see para [0093], "For a top category list as used in the described embodiments that detects top categories according to their transaction execution frequency, the type of the quantity measure may be “transaction frequency” and the measurement value may be the constant value 1 for each analyzed transaction. Some other embodiments may detect top transaction categories based on the execution time of each transaction. In this case, the quantity measure may be the “transaction execution time” and the measurement value of the quantity measure may be the execution time of each transaction. Using such a measure would create a top category list containing the categories of transactions that in sum require the highest amount of execution time.")
quantifying business value of given commercial transaction using parameters identified by the business relevant extractor rule (see para [0093], "Calculating a category quantity measure may include calculating response time or CPU usage of the transaction, determining if the transaction was successful or failed, determining if the transaction execution caused a financial revenue or other technical or financial parameters describing the transaction execution. Those measurement values may be determined by analyzing and processing the transaction performance and trace data 310 which may, next to performance measure data, also contain data describing exceptions or errors occurred during the transaction execution to detect transaction failures, or execution context data like captured method parameter values or return values which could be used to deduct financial or otherwise business relevant events associated with the monitored transaction 131" and see para [0094], "transaction trace data may be analyzed for method calls indicating the economic impact of the transaction execution, like the value of money for which goods were purchased by the transaction.")
identifying visits associated with the converting transactions by designating computer transaction which originate from same web browser within a predefined time period as a visit (see para [0027], "Variants of those embodiments may analyze the proportions of measurement values of different transaction categories by considering multiple dimensions. Continuing the above example, the comparison of measurement values may in addition consider deviations according to the geographic location of the browsers originating the monitored transactions. This may e.g. reveal that the above identified browser version specific response time degradation only occurs for browser situated in a specific geographic location with a specific language. This may indicate that the performance problem is in addition to a specific web-browser version also related to the adaptation of the monitored application to the specific language. Further areas of applicability will become apparent from the description provided herein. The description and specific examples in this summary are intended for purposes of illustration only and are not intended to limit the scope of the present disclosure." And see para [0064], "monitoring server receives transaction data fragments 125 from various browser agents 127 and agents 136 deployed to backend processes and forwards those transaction trace data fragments 125 to an event correlator 102, which identifies and combines transaction trace data fragments 125 describing parts of individual end-to-end transactions into end-to-end transaction trace data records 107. Completed end-to-end transaction trace data records 107 are stored 105 in a transaction repository 106 for further analysis and visualization. A transaction repository may store end-to-end transaction trace data either in main memory, on a hard disc or in a database or in a combination thereof. A historic top category extractor 110 cyclically fetches 108 end-to-end transaction trace data records 107 from the transaction repository 106 corresponding to a specific historic time period. The historic time period may be described as the 24 hours of yesterday, the last week, the same day or yesterday within the last week or similar. The historic top category extractor 110 analyzes the fetched end-to-end transaction traces 107 to identify a list of predictive and limited size, the list contains those transaction categories within a multi-dimensional and hierarchic classification space that contain the most transactions. This identified list of transactions categories is optimized to contain the transaction categories representing the largest sets of transactions while having the most specific transaction classification characteristics. The top category extractor 110 creates an estimated top category list 112 which fulfills those contradicting requirements while maintaining a maximum allowed number of categories in the list. The historic top category extractor evaluates each transaction trace only once and also maintains a limited memory consumption during the calculation of the top category list, depending only of the maximum allowed size of the top category list." and see para [0057], [0136])
Claims 7, 14 and 21:
Griefeneder does not specifically disclose generating a visualization of the conversion funnel, where the potential business value is shown for each node in the conversion funnel. In analogous art, Ciabarra discloses the following limitations:
generating a visualization of the conversion funnel, where the potential business value is shown for each node in the conversion funnel (see para [0031], "Sessions having a certain set of attributes may have a lower conversion rate compared to the average conversion rate. Such sessions may be referred to as “suspect sessions” as further described below. An “under-conversion rate” can be determined by subtracting the conversion rate for one or more sessions to the average conversion rate. The under-conversion rate represents the number of missed conversion events (e.g., potential conversions that were abandoned). The under-conversion rate can be used to determine a “missed opportunity” or “missed opportunity value.” For example, a total missed opportunity value can be determined by multiplying an average conversion value by the number missed conversions (the difference between the average conversion rate and the determined conversion rate for a particular set of sessions)." and see para [0126], "User interface 600 may include summary area 610 to summarize information related to an entire conversion funnel for a particular suspect attribute. The summary information may include an identification of the suspect attribute (see 612), a number of sessions that include the suspect attribute (see 614), a conversion rate for the sessions that include the suspect attribute (see 616), the amount of value received for conversions that include the suspect attribute (see 618), a number of conversions that include the suspect attribute (also see 618), an estimate of an amount of opportunity value (e.g., an amount of value that may be received if the suspect attribute is fixed) for the suspect attribute (see 620), an estimate of a number of potential sales that may be realized if the suspect attribute is fixed (also see 620), or the like.")
It would have been obvious to one of ordinary skill in the art at the time of the invention to include the method for identifying issues related to digital interactions on websites as taught by Ciabarra in the method for real-time, load-driven multidimensional and hierarchical classification of monitored transaction executions of Griefeneder, since 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.
Claim 22;
Further, Griefeneder discloses the following limitations:
wherein identifying converting transactions includes extracting identification data for services called by the computer transactions from the transaction trace data and comparing the identification data with a service identification pattern characterizing services causing a commercial transaction; and in response to a match, indicating a particular computer transaction as a converting transaction (see para [0016], "The system determines the category exactly matching the classification coordinates of the incoming transaction and all categories with more generic classification coordinates also matching the incoming transaction. The incoming transaction accounts for the transaction frequency of the exact matching category and for the frequency of the categories with matching, more generic classification coordinates. As an example, for an incoming transaction with a browser geolocation “Vienna”, a browser type and version “Internet Explorer 9”, an operating system and version “Windows 8.1”, action “buy” on a “product detail” page and a network link type “DSL”, the exactly matching category would have the same classification characteristics. The more generic matching categories contains all categories with classification characteristic matching any combination of classification characteristics of more generic hierarchical classification levels. In this example those would e.g. include for the geolocation classification “Austria” on state/country level, “Europe” on continent level and “All”, for the browser type and version classification “Internet Explorer”, “Desktop Browser” and “All” and so on." and see para [0094], " In other embodiments, transaction trace data may be analyzed for method calls indicating the economic impact of the transaction execution, like the value of money for which goods were purchased by the transaction. This value may be used to calculated and identify top transaction categories to detect those transaction categories with the highest economic impact." and see para [0089]-[0090])
Claim 23 is rejected under 35 U.S.C. 103 as being unpatentable over Griefeneder, Ciabarra and Dua, as applied above, and further in view of Ertl (US 2020/0409933 A1).
Claim 23:
Griefeneder, Ciabarra and Dua do not specifically disclose in response to a match, extracting data for an actual service call from the transaction trace data, where the extracted data includes service call parameter names and values comparing extracted service call parameter names with a parameter identification pattern, where the parameter identification pattern is associated with the service identification pattern. In analogous art, Ertl discloses the following limitations:
in response to a match, extracting data for an actual service call from the transaction trace data, where the extracted data includes service call parameter names and values comparing extracted service call parameter names with a parameter identification pattern, where the parameter identification pattern is associated with the service identification pattern (see para [0058]-[0060], " A service topology and measurement extractor component 224 may also cyclically fetch 223 new transaction traces from the transaction repository 222 for analysis and extraction of service topology and measurement data. The end-to-end transaction trace data stored in the transaction repository 222 may in addition be analyzed by a service topology and measurement extractor 224 to extract service topology and measurement data from those end-to-end transaction traces. The extracted service topology data may consist of services used by transaction executions and service call dependencies. A transaction execution may e.g. call a first specific service and the execution of this specific service may cause the call of a second specific service. During the analysis performed by the service topology and measurement extractor 224 this call sequence may be identified and the topology model may be updated to indicating a call dependency from the first service to the second service. This service topology data may be integrated with already existing topology data describing processes and host computing systems of the monitored environment to enrich the topology model with data describing the services provided by specific processes. Measurement data created by the service topology and measurement extractor 224 may describe performance, functionality and resource consumption of individual transactions. Performance measurements may include the response time of the services executed by the transactions and the overall transaction response time as perceived by the user that initiated the transaction. Functionality related measurements may include capturing error indications for the whole transaction execution or for individual service executions. The captured error indications may be used to derive error rate measurements on transaction and service level or number of exceptions thrown and caught during transaction execution. In addition, functionality related measurements may be derived from service and method execution parameters and return values that indicate erroneous behavior. Resource consumption measurements may include CPU or memory usage of individual transactions. The extracted measurement data may be stored in form of time series the measurement repository 212."); and
in response of a match of a service call parameter name with a parameter identification pattern, applying a service value parameter processing rule on the parameter value for the matching service call parameter name, where the service value parameter processing rule is associated with the service identification pattern and defines a procedure to calculate a business value quantity from the parameter value (see para [0019]-[0020], " When an abnormal operating condition is identified in those other embodiments and transactions affected by those abnormal operating conditions were identified, visits containing affected transactions may be identified in a next step. An impact rate may be calculated for each identified affected visit, e.g. based on a ratio between affected and not affected transactions contained in a visit. Further, visits themselves may be marked as affected, based on either a relative or absolute number of affected transactions they contain. When affected visits are identified, also not affected visits may be determined, in a similar way as not affected transactions are identified, as described above. Affected and not affected visits may then be used to calculate visit-based impact rates on an overall basis and also for grouped visits. Criteria to create visit groups may contain but are not limited to the geolocation of the web-browser that performed the visits, type and version of the web-browser and the operating system executing the web browser." and see para [0072], [0085]-[0087], [0097]).
It would have been obvious to one or ordinary skill in the art at the time of the invention to combine the teachings of Ertl with Griefeneder, Ciabarra and Dua because matching such service calls and parameters with trace data enables more effective monitoring of conditions for a business and improve related efficiencies (see Ertl, para [0003]-[0005]).
Moreover, it would have been obvious to one of ordinary skill in the art at the time of the invention to include the system for business impact analysis as taught by Ertl in the Griefeneder, Ciabarra and Dua combination, since 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.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Taylor et al. (US 2021/0067544 A1), a system for real-time mitigation of fraud and otherwise invalid traffic in a mobile ad environment that comprises four major sub-systems: prevention, detection, control and reporting, which work in cohesion with one another to achieve the common goal of the system
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/SUJAY KONERU/
Primary Examiner, Art Unit 3624