Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on May 27, 2026 has been entered. Accordingly, Claims 1, 3-11, and 13-20 are pending in this application. Claims 9, 10, 19, and 20 have been cancelled. Claims 1 and 11 are independent claims and have been amended.
Claim Interpretation
The Examiner has interpreted the following claim limitations in the following manner:
“target convergence attributes” has been interpreted as “attributes about previous customers or clients” in accordance with paragraph [0014] of the Applicant’s Specification.
“high target convergence attribute pattern” has been interpreted as “a multidimensional dataset, wherein each dimension represents an attribute, that meets a similarity distance threshold” in accordance with paragraph [0020] of the Applicant’s Specification.
“first system data” has been interpreted as “attributes about a new customers or clients” in accordance with paragraph [0025] of the Applicant’s Specification.
“first system target convergence” has been interpreted as “attributes about new customers or clients that meet a similarity distance threshold” in accordance with paragraph [0064] of the Applicant’s Specification.
“second system attribute clusters” has been interpreted as “a multidimensional dataset of attributes about previous customers or clients that has been clustered by similarity” in accordance with paragraph [0066] of the Applicant’s Specification.
“an advantage cluster” has been interpreted as “a cluster of attributes most likely to contribute to success” in accordance with paragraph [0078] of the Applicant’s Specification.
“a compatibility datum” has been interpreted as “an indication of whether the customer or client has attributes most likely to contribute to success as a client or customer” in accordance with paragraph [0089] of the Applicant’s Specification.
“desirability reflecting interaction dynamics and transactional efficiency” has been interpreted as “how quickly each client responds to messages, how quickly each client pays its bills, the amount of work generated by each client, the nature of the work generated by each client, the revenue generated based on the work associated with each client, and the like” in accordance with paragraph [0014] of the Applicant’s Specification.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1, 3-5, 11, and 13-15 are rejected under 35 U.S.C. 103 as being unpatentable over Dorle (PG Pub. No. 2021/0350395 A1) and further in view of Baker (PG Pub. No. 2022/0391707 A1), Tsibulevskiy (PG Pub. No. 2022/0319219 A1), Shavlik (PG Pub. No. 2015/0046219 A1), and Venkatesan (PG Pub. No. 2018/0060929 A1).
Regarding Claim 1, Dorle discloses an apparatus for data structure generation to determine a compatibility datum, the apparatus comprising:
at least one processor (see Dorle, paragraph [0048], where the system may include a processor); and
a memory communicatively coupled to the at least one (see Dorle, paragraph [0180], where instructions on the computer-readable storage medium 2710 are read and stored the instructions in storage 2727 or in random access memory (RAM) 2720), the memory containing instructions configuring the at least one processor to:
identify one or more target convergence attributes (see Dorle, paragraph [0048], where the data analyzer may receive a query from a user; the query may obtain a prospect assessment requirement associated with a plurality of prospects … the data analyzer may implement a first artificial intelligence component to identify a plurality of attributes associated with the prospect assessment requirement); wherein identifying the one or more target convergence attributes comprises:
identifying a target convergence attribute data set, wherein the target convergence attribute data set comprises a plurality of data points (see Dorle, paragraph [0048], where the data analyzer may receive a query from a user; the query may obtain a prospect assessment requirement associated with a plurality of prospects … the data analyzer may implement a first artificial intelligence component to identify a plurality of attributes associated with the prospect assessment requirement), the plurality of data points comprising:
ratings on a plurality of test attributes (see Dorle, paragraph [0056], where the plurality of attributes may refer to measurable factors associated with the prospect assessment requirement; for example, the plurality of attributes may include measurable factors such as prospect demographic insights, prospect name, prospect organization name, the designation of prospect liaison … and the like); and
target values (see Dorle, paragraph [0061], where system 110 may identify a threshold cluster significance value … in an example, the first artificial intelligence component may identify a threshold cluster significance value based on the prospect data) related to desirability reflecting interaction dynamics and transactional efficiency (see Dorle, paragraph [0055], where prospect data may include prospect entitlements, demographic details for the prospect, products, and services utilized by the prospect, prospect growth potential, prospect purchase likelihood, past lead conversion rate, average deal size, the financial value of past purchases, number of purchase orders, frequency of prior orders, existing discounts, existing service terms, highest value of past purchases, and the like; in an example, the prospect data may also include revenue, budget, buying power, function, number of employees working in the prospect organization, industry of the prospect organization);
identifying one or more target convergence attributes as a function of the target convergence attribute data set (see Dorle, paragraph [0057], where the plurality of prospect clusters may include the plurality of prospects segmented into clusters based on the plurality of attributes; for example, the plurality of prospects may be clustered according to a time duration of association with an organization, length of association, the average frequency of transactions … and the like);
identify a high target convergence attribute pattern (see Dorle, paragraph [0048], where the data analyzer may receive a query from a user; the query may obtain a prospect assessment requirement associated with a plurality of prospects … the data analyzer may implement a first artificial intelligence component to identify a plurality of attributes associated with the prospect assessment requirement);
obtain first system data, wherein the first system data is obtained from a first system data source (see Dorle, paragraph [0048], where the data analyzer may obtain prospect data from a plurality of data sources);
determine first system target convergence as a function of the high target convergence attribute pattern and the first system data (see Dorle, paragraph [0048], where the data analyzer may implement the first artificial intelligence component to map the plurality of attributes with the plurality of prospects to create a plurality of prospect clusters);
identify a plurality of second system attribute clusters (see Dorle, paragraph [0048], where the data analyzer may implement the first artificial intelligence component to map the plurality of attributes with the plurality of prospects to create a plurality of prospect clusters; each of the plurality of prospect clusters may comprise at least one prospect from the plurality of prospects);
locate in the plurality of second system attribute clusters an advantage cluster (see Dorle, paragraph [0048], where the data analyzer may implement to the first artificial intelligence component to determine a cluster significance value for each of the plurality of prospect clusters; the cluster significance value may be associated with the significance of a prospect cluster amongst the plurality of prospect clusters);
determine advantage cluster applicability as a function of the advantage cluster and the first system data (see Dorle, paragraph [0048], where the data analyzer may implement the first artificial intelligence component to map the plurality of attributes with the plurality of prospects to create a plurality of prospect clusters; each of the plurality of prospect clusters may comprise at least one prospect from the plurality of prospects; the data analyzer may implement to the first artificial intelligence component to determine a cluster significance value for each of the plurality of prospect clusters; the cluster significance value may be associated with the significance of a prospect cluster amongst the plurality of prospect clusters); and
determine a compatibility datum as a function of the first system target convergence and the advantage cluster applicability (see Dorle, paragraph [0061], where a high cluster significance value may indicate the prospect may be of significant importance; such an indication may facilitate the creation of effective prospect retention measures for efficient client relationship management).
Dorle does not disclose:
wherein identifying the high target convergence attribute pattern comprises iteratively training an attribute pattern machine learning model using training data configured to correlate system data inputs to target convergence attribute outputs;
wherein the first system data source comprises data in an image format, wherein the first system data comprises a web crawler configured to automatically search and collect data related to the first system;
process the first system data, using a machine vision system to extract textual data from the image format, wherein processing the first system data comprises pre-processing at least an image in the image format using a de-skew process;
determine a visual element data structure as a function of the compatibility datum, wherein the visual element depicts elements of the first system data that were used to determine the first system target convergence and where they originated from, a comparison between the compatibility datum and a threshold value, and how the advantage cluster applicability changed in comparison to a previous calculation for the same first and second systems, wherein the visual element data structure includes a rule causing a visual element describing the compatibility datum to be displayed when a user selects the compatibility datum using a graphical user interface (GUI), and wherein the visual element data structure applies rules based on a comparison between a numerical value and a threshold; and
transmit the visual element data structure to a user device comprising a second system.
Baker discloses wherein identifying the high target convergence attribute pattern comprises iteratively training an attribute pattern machine learning model using training data configured to correlate system data inputs to target convergence attribute outputs (see Baker, paragraph [0024], where any large complex machine learning system, such as the student learning system 11, has a large number of parameters for which good values need to be found to try to minimize some measure of the cost of errors in the pattern recognition process; see also paragraphs [0027], [0028], where the illustrative training method is an iterative process of stochastic gradient descent on a log-likelihood error cost function … the pseudo-code for this well-known training process is as follows: initialize each weight; do until a stopping criterion is reached).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to combine Dorle with Baker for the benefit of training a machine learning model hyperparameters (see Baker, Abstract).
Dorle in view of Baker does not disclose:
wherein the first system data source comprises data in an image format, wherein the first system data comprises a web crawler configured to automatically search and collect data related to the first system; and
process the first system data, using a machine vision system to extract textual data from the image format, wherein processing the first system data comprises pre-processing at least an image in the image format using a de-skew process;
determine a visual element data structure as a function of the compatibility datum, wherein the visual element depicts elements of the first system data that were used to determine the first system target convergence and where they originated from, a comparison between the compatibility datum and a threshold value, and how the advantage cluster applicability changed in comparison to a previous calculation for the same first and second systems, wherein the visual element data structure includes a rule causing a visual element describing the compatibility datum to be displayed when a user selects the compatibility datum using a graphical user interface (GUI), and wherein the visual element data structure applies rules based on a comparison between a numerical value and a threshold; and
transmit the visual element data structure to a user device comprising a second system
Dorle in view of Baker, and Tsibulevskiy discloses:
wherein the first system data source comprises data in image format (see Tsibulevskiy, paragraph [0034], where block 210 includes searching (e.g., text search, barcode search, graphic search, symbol search, object search) within a figure (e.g., image, TIFF, JPG, PDF, video, CAD file) for a reference (e.g., alphanumeric, part number) referring to an element (e.g., part, object) of the figure; the searching can include computer vision, OpenCV, OCR, barcode reading, edge detection, segmentation, image segmentation, character segmentation, text area detection, object detection, feature detection, sketch detection, or other image processing algorithms); and
process the first system data, using a machine vision system to extract textual data from the image format (see Tsibulevskiy, paragraph [0034], where block 210 includes searching (e.g., text search, barcode search, graphic search, symbol search, object search) within a figure (e.g., image, TIFF, JPG, PDF, video, CAD file) for a reference (e.g., alphanumeric, part number) referring to an element (e.g., part, object) of the figure; the searching can include computer vision, OpenCV, OCR, barcode reading, edge detection, segmentation, image segmentation, character segmentation, text area detection, object detection, feature detection, sketch detection, or other image processing algorithms), wherein processing the first system data comprises pre-processing at least an image in the image format using a de-skew process (see Tsibulevskiy, paragraph [0027], where for example, the image can be pre-processed (e.g., horizontal or vertical de-skew, noise reduction, despeckle, binarization, line removal, layout analysis/zoning, line and word detection, script recognition, character isolation/segmentation, script recognition, character isolation/segmentation, scrip recognition, normalize aspect ratio, or scale, or others).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to combine Dorle and Baker with Tsibulevskiy for the benefit of analyzing content (see Tsibulevskiy, Abstract).
Dorle in view of Baker and Tsibulevskiy does not disclose:
wherein the first system data comprises a web crawler configured to automatically search and collect data related to the first system; and
determine a visual element data structure as a function of the compatibility datum, wherein the visual element depicts elements of the first system data that were used to determine the first system target convergence and where they originated from, a comparison between the compatibility datum and a threshold value, and how the advantage cluster applicability changed in comparison to a previous calculation for the same first and second systems, wherein the visual element data structure includes a rule causing a visual element describing the compatibility datum to be displayed when a user selects the compatibility datum using a graphical user interface (GUI), and wherein the visual element data structure applies rules based on a comparison between a numerical value and a threshold; and
transmit the visual element data structure to a user device comprising a second system.
Shavlik discloses wherein the first system data comprises a web crawler configured to automatically search and collect data related to the first system (see Shavlik, paragraph [0006], where in a first aspect, an automated lead scoring system includes a web crawling process executing on a computing system, the web crawling process configured to collect data from one or more disparate, unaffiliated data sources to obtain information describing attributes of an avatar, the attributes including information descriptive of a prospective customer and actions taken by the prospective customer in response to exposure to a product or service).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to combine Dorle, Baker, and Tsibulevskiy with Shavlik for the benefit of crawling the web to collect data about prospective customers to be compared to known users of a product or service (see Shavlik, Abstract).
Dorle in view of Baker, Tsibulevskiy, and Shavlik does not disclose:
determine a visual element data structure as a function of the compatibility datum, wherein the visual element depicts elements of the first system data that were used to determine the first system target convergence and where they originated from, a comparison between the compatibility datum and a threshold value, and how the advantage cluster applicability changed in comparison to a previous calculation for the same first and second systems, wherein the visual element data structure includes a rule causing a visual element describing the compatibility datum to be displayed when a user selects the compatibility datum using a graphical user interface (GUI), and wherein the visual element data structure applies rules based on a comparison between a numerical value and a threshold; and
transmit the visual element data structure to a user device comprising a second system.
Venkatesan discloses:
wherein the visual element depicts elements of the first system data that were used to determine the first system target convergence (see Venkatesan, paragraph [0013], where Fig. 3 illustrates a graphical user interface (GUI) for lead score customization based on lead property) and where they originated from (see Venkatesan, paragraph [0014], where Fig. 4 illustrates a GUI for lead score customization based on email activity; see also paragraph [0023], where application activity may include actions performed on a user’s web application and website such as configuring a particular feature, downloading a brochure, signing up for a seminar, and the like), a comparison between the compatibility datum and a threshold value (see Venkatesan, paragraph [0027], where upon calculating the lead score, the lead is classified into a category; these categories may be defined as hot leads, warm leads, and cold leads; in some embodiments, a hot lead is a lead score that is greater than or equal to 70), and how the advantage cluster applicability changed in comparison to a previous calculation for the same first and second systems (see Venkatesan, paragraph [0028], where if the activity of the user associated with the lead is less than 7 days, the lead may be classified as active; if the activity is more than 7 days, the lead may be classified as slipping away, and if the activity is greater than 30 days, the lead may be classified as inactive), wherein the visual element data structure includes a rule causing a visual element describing the compatibility datum to be displayed when a user selects the compatibility datum using a graphical user interface (GUI) (see Venkatesan, paragraph [0039], where Fig. 8 illustrates a GUI 800 for customizing a view for lead score, according to an embodiment of the present invention; as discussed above, leads may be filtered based on the lead scores, and those leads that are pertinent may be displayed), and wherein the visual element data structure applies rules based on a comparison between a numerical value and a threshold (see Venkatesan, paragraph [0027], where upon calculating the lead score, the lead is classified into a category; these categories may be defined as hot leads, warm leads, and cold leads; in some embodiments, a hot lead is a lead score that is greater than or equal to 70); and
transmit the visual element data structure to a user device comprising a second system (see Venkatesan, paragraph [0013], where Fig. 3 illustrates a graphical user interface (GUI) for lead score customization based on lead property).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to combine Dorle, Baker, Tsibulevskiy and Shavlik with Venkatesan for the benefit of adjusting a lead score using a combination of implicit and explicit lead scores (see Venkatesan, Abstract).
Regarding Claim 3, Dorle in view of Baker, Tsibulevskiy, Shavlik, and Venkatesan discloses the apparatus of Claim 1, wherein identifying the one or more target convergence attributes as a function of the target convergence attribute data set comprises identifying the test attributes whose degree of correlation with target value is above a threshold (see Dorle, paragraph [0061], where system 110 may identify a threshold cluster significance value … in an example, the first artificial intelligence component may identify a threshold cluster significance value based on the prospect data).
Regarding Claim 4, Dorle in view of Baker, Tsibulevskiy, Shavlik, and Venkatesan discloses the apparatus of Claim 1, wherein identifying the high target convergence attribute pattern comprises training an attribute pattern machine learning model using a supervised learning algorithm (see Dorle, paragraph [0062], where the second artificial intelligence component may implement various supervised learning models).
Regarding Claim 5, Dorle in view of Baker, Tsibulevskiy, Shavlik, and Venkatesan discloses the apparatus of Claim 4, wherein determining the first system target convergence comprises:
inputting the first system data into the attribute pattern machine learning model (see Dorle, paragraph [0048], where the data analyzer may obtain prospect data from a plurality of data sources; and
receiving first system target convergence from the attribute pattern machine learning model (see Dorle, paragraph [0048], where the data analyzer may implement the first artificial intelligence component to map the plurality of attributes with the plurality of prospects to create a plurality of prospect clusters).
Regarding Claim 11, Dorle discloses a method of data structure generation to determine a target compatibility datum, the method comprising:
identifying, by at least one processor, one or more target convergence attributes (see Dorle, paragraph [0048], where the data analyzer may receive a query from a user; the query may obtain a prospect assessment requirement associated with a plurality of prospects … the data analyzer may implement a first artificial intelligence component to identify a plurality of attributes associated with the prospect assessment requirement); wherein identifying the one or more target convergence attributes comprises:
identifying a target convergence attribute data set, wherein the target convergence attribute data set comprises a plurality of data points (see Dorle, paragraph [0048], where the data analyzer may receive a query from a user; the query may obtain a prospect assessment requirement associated with a plurality of prospects … the data analyzer may implement a first artificial intelligence component to identify a plurality of attributes associated with the prospect assessment requirement), the plurality of data points comprising:
ratings on a plurality of test attributes (see Dorle, paragraph [0056], where the plurality of attributes may refer to measurable factors associated with the prospect assessment requirement; for example, the plurality of attributes may include measurable factors such as prospect demographic insights, prospect name, prospect organization name, the designation of prospect liaison … and the like); and
target values (see Dorle, paragraph [0061], where system 110 may identify a threshold cluster significance value … in an example, the first artificial intelligence component may identify a threshold cluster significance value based on the prospect data) related to desirability reflecting interaction dynamics and transactional efficiency (see Dorle, paragraph [0055], where prospect data may include prospect entitlements, demographic details for the prospect, products, and services utilized by the prospect, prospect growth potential, prospect purchase likelihood, past lead conversion rate, average deal size, the financial value of past purchases, number of purchase orders, frequency of prior orders, existing discounts, existing service terms, highest value of past purchases, and the like; in an example, the prospect data may also include revenue, budget, buying power, function, number of employees working in the prospect organization, industry of the prospect organization);
identifying one or more target convergence attributes as a function of the target convergence attribute data set (see Dorle, paragraph [0057], where the plurality of prospect clusters may include the plurality of prospects segmented into clusters based on the plurality of attributes; for example, the plurality of prospects may be clustered according to a time duration of association with an organization, length of association, the average frequency of transactions … and the like);
identify a high target convergence attribute pattern (see Dorle, paragraph [0048], where the data analyzer may receive a query from a user; the query may obtain a prospect assessment requirement associated with a plurality of prospects … the data analyzer may implement a first artificial intelligence component to identify a plurality of attributes associated with the prospect assessment requirement);
obtain first system data, wherein the first system data is obtained from a first system data source (see Dorle, paragraph [0048], where the data analyzer may obtain prospect data from a plurality of data sources);
determine first system target convergence as a function of the high target convergence attribute pattern and the first system data (see Dorle, paragraph [0048], where the data analyzer may implement the first artificial intelligence component to map the plurality of attributes with the plurality of prospects to create a plurality of prospect clusters);
identifying, by at least one processor, a plurality of second system attribute clusters (see Dorle, paragraph [0048], where the data analyzer may implement the first artificial intelligence component to map the plurality of attributes with the plurality of prospects to create a plurality of prospect clusters; each of the plurality of prospect clusters may comprise at least one prospect from the plurality of prospects);
locating, by at least one processor, in the plurality of second system attribute clusters an advantage cluster (see Dorle, paragraph [0048], where the data analyzer may implement to the first artificial intelligence component to determine a cluster significance value for each of the plurality of prospect clusters; the cluster significance value may be associated with the significance of a prospect cluster amongst the plurality of prospect clusters);
determining, by at least one processor, advantage cluster applicability as a function of the advantage cluster and the first system data (see Dorle, paragraph [0048], where the data analyzer may implement the first artificial intelligence component to map the plurality of attributes with the plurality of prospects to create a plurality of prospect clusters; each of the plurality of prospect clusters may comprise at least one prospect from the plurality of prospects; the data analyzer may implement to the first artificial intelligence component to determine a cluster significance value for each of the plurality of prospect clusters; the cluster significance value may be associated with the significance of a prospect cluster amongst the plurality of prospect clusters); and
determining, by at least one processor, a compatibility datum as a function of the first system target convergence and the advantage cluster applicability (see Dorle, paragraph [0061], where a high cluster significance value may indicate the prospect may be of significant importance; such an indication may facilitate the creation of effective prospect retention measures for efficient client relationship management).
Dorle does not disclose:
wherein identifying the high target convergence attribute pattern comprises iteratively training an attribute pattern machine learning model using training data configured to correlate system data inputs to target convergence attribute outputs;
wherein the first system data source comprises data in an image format, wherein the first system data comprises a web crawler configured to automatically search and collect data related to the first system;
process the first system data, using a machine vision system to extract textual data from the image format, wherein processing the first system data comprises pre-processing at least an image in the image format using a de-skew process;
determine a visual element data structure as a function of the compatibility datum, wherein the visual element depicts elements of the first system data that were used to determine the first system target convergence and where they originated from, a comparison between the compatibility datum and a threshold value, and how the advantage cluster applicability changed in comparison to a previous calculation for the same first and second systems, wherein the visual element data structure includes a rule causing a visual element describing the compatibility datum to be displayed when a user selects the compatibility datum using a graphical user interface (GUI), and wherein the visual element data structure applies rules based on a comparison between a numerical value and a threshold; and
transmit the visual element data structure to a user device comprising a second system.
Baker discloses wherein identifying the high target convergence attribute pattern comprises iteratively training an attribute pattern machine learning model using training data configured to correlate system data inputs to target convergence attribute outputs (see Baker, paragraph [0024], where any large complex machine learning system, such as the student learning system 11, has a large number of parameters for which good values need to be found to try to minimize some measure of the cost of errors in the pattern recognition process; see also paragraphs [0027], [0028], where the illustrative training method is an iterative process of stochastic gradient descent on a log-likelihood error cost function … the pseudo-code for this well-known training process is as follows: initialize each weight; do until a stopping criterion is reached).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to combine Dorle with Baker for the benefit of training a machine learning model hyperparameters (see Baker, Abstract).
Dorle in view of Baker does not disclose:
wherein the first system data source comprises data in an image format, wherein the first system data comprises a web crawler configured to automatically search and collect data related to the first system; and
process the first system data, using a machine vision system to extract textual data from the image format, wherein processing the first system data comprises pre-processing at least an image in the image format using a de-skew process;
determine a visual element data structure as a function of the compatibility datum, wherein the visual element depicts elements of the first system data that were used to determine the first system target convergence and where they originated from, a comparison between the compatibility datum and a threshold value, and how the advantage cluster applicability changed in comparison to a previous calculation for the same first and second systems, wherein the visual element data structure includes a rule causing a visual element describing the compatibility datum to be displayed when a user selects the compatibility datum using a graphical user interface (GUI), and wherein the visual element data structure applies rules based on a comparison between a numerical value and a threshold; and
transmit the visual element data structure to a user device comprising a second system
Dorle in view of Baker, and Tsibulevskiy discloses:
wherein the first system data source comprises data in image format (see Tsibulevskiy, paragraph [0034], where block 210 includes searching (e.g., text search, barcode search, graphic search, symbol search, object search) within a figure (e.g., image, TIFF, JPG, PDF, video, CAD file) for a reference (e.g., alphanumeric, part number) referring to an element (e.g., part, object) of the figure; the searching can include computer vision, OpenCV, OCR, barcode reading, edge detection, segmentation, image segmentation, character segmentation, text area detection, object detection, feature detection, sketch detection, or other image processing algorithms); and
process the first system data, using a machine vision system to extract textual data from the image format (see Tsibulevskiy, paragraph [0034], where block 210 includes searching (e.g., text search, barcode search, graphic search, symbol search, object search) within a figure (e.g., image, TIFF, JPG, PDF, video, CAD file) for a reference (e.g., alphanumeric, part number) referring to an element (e.g., part, object) of the figure; the searching can include computer vision, OpenCV, OCR, barcode reading, edge detection, segmentation, image segmentation, character segmentation, text area detection, object detection, feature detection, sketch detection, or other image processing algorithms), wherein processing the first system data comprises pre-processing at least an image in the image format using a de-skew process (see Tsibulevskiy, paragraph [0027], where for example, the image can be pre-processed (e.g., horizontal or vertical de-skew, noise reduction, despeckle, binarization, line removal, layout analysis/zoning, line and word detection, script recognition, character isolation/segmentation, script recognition, character isolation/segmentation, scrip recognition, normalize aspect ratio, or scale, or others).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to combine Dorle and Baker with Tsibulevskiy for the benefit of analyzing content (see Tsibulevskiy, Abstract).
Dorle in view of Baker and Tsibulevskiy does not disclose:
wherein the first system data comprises a web crawler configured to automatically search and collect data related to the first system; and
determine a visual element data structure as a function of the compatibility datum, wherein the visual element depicts elements of the first system data that were used to determine the first system target convergence and where they originated from, a comparison between the compatibility datum and a threshold value, and how the advantage cluster applicability changed in comparison to a previous calculation for the same first and second systems, wherein the visual element data structure includes a rule causing a visual element describing the compatibility datum to be displayed when a user selects the compatibility datum using a graphical user interface (GUI), and wherein the visual element data structure applies rules based on a comparison between a numerical value and a threshold; and
transmit the visual element data structure to a user device comprising a second system.
Shavlik discloses wherein the first system data comprises a web crawler configured to automatically search and collect data related to the first system (see Shavlik, paragraph [0006], where in a first aspect, an automated lead scoring system includes a web crawling process executing on a computing system, the web crawling process configured to collect data from one or more disparate, unaffiliated data sources to obtain information describing attributes of an avatar, the attributes including information descriptive of a prospective customer and actions taken by the prospective customer in response to exposure to a product or service).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to combine Dorle, Baker, and Tsibulevskiy with Shavlik for the benefit of crawling the web to collect data about prospective customers to be compared to known users of a product or service (see Shavlik, Abstract).
Dorle in view of Baker, Tsibulevskiy, and Shavlik does not disclose:
determine a visual element data structure as a function of the compatibility datum, wherein the visual element depicts elements of the first system data that were used to determine the first system target convergence and where they originated from, a comparison between the compatibility datum and a threshold value, and how the advantage cluster applicability changed in comparison to a previous calculation for the same first and second systems, wherein the visual element data structure includes a rule causing a visual element describing the compatibility datum to be displayed when a user selects the compatibility datum using a graphical user interface (GUI), and wherein the visual element data structure applies rules based on a comparison between a numerical value and a threshold; and
transmit the visual element data structure to a user device comprising a second system.
Venkatesan discloses:
wherein the visual element depicts elements of the first system data that were used to determine the first system target convergence (see Venkatesan, paragraph [0013], where Fig. 3 illustrates a graphical user interface (GUI) for lead score customization based on lead property) and where they originated from (see Venkatesan, paragraph [0014], where Fig. 4 illustrates a GUI for lead score customization based on email activity; see also paragraph [0023], where application activity may include actions performed on a user’s web application and website such as configuring a particular feature, downloading a brochure, signing up for a seminar, and the like), a comparison between the compatibility datum and a threshold value (see Venkatesan, paragraph [0027], where upon calculating the lead score, the lead is classified into a category; these categories may be defined as hot leads, warm leads, and cold leads; in some embodiments, a hot lead is a lead score that is greater than or equal to 70), and how the advantage cluster applicability changed in comparison to a previous calculation for the same first and second systems (see Venkatesan, paragraph [0028], where if the activity of the user associated with the lead is less than 7 days, the lead may be classified as active; if the activity is more than 7 days, the lead may be classified as slipping away, and if the activity is greater than 30 days, the lead may be classified as inactive), wherein the visual element data structure includes a rule causing a visual element describing the compatibility datum to be displayed when a user selects the compatibility datum using a graphical user interface (GUI) (see Venkatesan, paragraph [0039], where Fig. 8 illustrates a GUI 800 for customizing a view for lead score, according to an embodiment of the present invention; as discussed above, leads may be filtered based on the lead scores, and those leads that are pertinent may be displayed), and wherein the visual element data structure applies rules based on a comparison between a numerical value and a threshold (see Venkatesan, paragraph [0027], where upon calculating the lead score, the lead is classified into a category; these categories may be defined as hot leads, warm leads, and cold leads; in some embodiments, a hot lead is a lead score that is greater than or equal to 70); and
transmit the visual element data structure to a user device comprising a second system (see Venkatesan, paragraph [0013], where Fig. 3 illustrates a graphical user interface (GUI) for lead score customization based on lead property).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to combine Dorle, Baker, Tsibulevskiy and Shavlik with Venkatesan for the benefit of adjusting a lead score using a combination of implicit and explicit lead scores (see Venkatesan, Abstract).
Regarding Claim 13, Dorle in view of Baker, Tsibulevskiy, Shavlik, and Venkatesan discloses the method of Claim 11, wherein identifying the one or more target convergence attributes as a function of the target convergence attribute data set comprises, identifying, by the at least one processor, the test attributes whose degree of correlation with target value is above a threshold (see Dorle, paragraph [0061], where system 110 may identify a threshold cluster significance value … in an example, the first artificial intelligence component may identify a threshold cluster significance value based on the prospect data).
Regarding Claim 14, Dorle in view of Baker, Tsibulevskiy, Shavlik, and Venkatesan discloses the method of Claim 11, wherein identifying the high target convergence attribute pattern comprises, training, by the at least one processor, an attribute pattern machine learning model using a supervised learning algorithm (see Dorle, paragraph [0062], where the second artificial intelligence component may implement various supervised learning models).
Regarding Claim 15, Dorle in view of Baker, Tsibulevskiy, Shavlik, and Venkatesan discloses the method of Claim 14, wherein determining the first system target convergence comprises:
inputting, by the at least one processor, the first system data into the attribute pattern machine learning model (see Dorle, paragraph [0048], where the data analyzer may obtain prospect data from a plurality of data sources; and
receiving, by the at least one processor, the first system target convergence from the attribute pattern machine learning model (see Dorle, paragraph [0048], where the data analyzer may implement the first artificial intelligence component to map the plurality of attributes with the plurality of prospects to create a plurality of prospect clusters).
Claims 6, 7, 16, and 17 are rejected under 35 U.S.C. 103 as being unpatentable over Dorle, Baker, Tsibulevskiy, Shavlik, and Venkatesan as applied to Claims 1, 3-5, 11, and 13-15 above, and further in view of Srinivasan (PG Pub. No. 2021/0279606 A1).
Regarding Claim 6, Dorle in view of Baker, Tsibulevskiy, Shavlik, and Venkatesan discloses the apparatus of Claim 1, wherein identifying the plurality of second system attribute clusters comprises:
Dorle does not disclose:
identifying second system data;
inputting second system data into an attribute classifier; and
receiving a plurality of second system attributes from the attribute classifier.
Srinivasan discloses:
identifying second system data (see Srinivasan, paragraph [0003], where a processor of a computer having memory may retrieve a new attribute to be added to each of the plurality of entities);
inputting second system data into an attribute classifier (see Srinivasan, paragraph [0017], where to infer the presence of new attributes, the described solutions combine attribute rule mining on structured data with attribute classifiers based on unstructured data to infer new attributes); and
receiving a plurality of second system attributes from the attribute classifier (see Srinivasan, paragraph [0017], where to infer the presence of new attributes, the described solutions combine attribute rule mining on structured data with attribute classifiers based on unstructured data to infer new attributes).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to combine Dorle with Srinivasan for the benefit of automatically detecting new attributes for entities (see Srinivasan, Abstract).
Regarding Claim 7, Dorle in view of Baker, Tsibulevskiy, Shavlik, Venkatesan, and Srinivasan discloses the apparatus of Claim 6, wherein identifying the plurality of second system attribute clusters comprises:
inputting the plurality of second system attributes into a clustering algorithm (see Dorle, paragraph [0048], where the data analyzer may implement the first artificial intelligence component to map the plurality of attributes with the plurality of prospects to create a plurality of prospect clusters; each of the plurality of prospect clusters may comprise at least one prospect from the plurality of prospects); and
receiving a plurality of second system attribute clusters from the clustering algorithm (see Dorle, paragraph [0048], where the data analyzer may implement the first artificial intelligence component to map the plurality of attributes with the plurality of prospects to create a plurality of prospect clusters; each of the plurality of prospect clusters may comprise at least one prospect from the plurality of prospects).
Regarding Claim 16, Dorle in view of Baker, Tsibulevskiy, Shavlik, and Venkatesan discloses the method of Claim 11, wherein identifying the plurality of second system attribute clusters comprises:
Dorle does not disclose:
identifying, by the at least one processor, second system data;
inputting, by the at least one processor, the second system data into an attribute classifier; and
receiving, by the at least one processor, a plurality of second system attributes from the attribute classifier.
Srinivasan discloses:
identifying, by the at least one processor, second system data (see Srinivasan, paragraph [0003], where a processor of a computer having memory may retrieve a new attribute to be added to each of the plurality of entities);
inputting, by the at least one processor, second system data into an attribute classifier (see Srinivasan, paragraph [0017], where to infer the presence of new attributes, the described solutions combine attribute rule mining on structured data with attribute classifiers based on unstructured data to infer new attributes); and
receiving, by the at least one processor, a plurality of second system attributes from the attribute classifier (see Srinivasan, paragraph [0017], where to infer the presence of new attributes, the described solutions combine attribute rule mining on structured data with attribute classifiers based on unstructured data to infer new attributes).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to combine Dorle with Srinivasan for the benefit of automatically detecting new attributes for entities (see Srinivasan, Abstract).
Regarding Claim 17, Dorle in view of Baker, Tsibulevskiy, Shavlik, Venkatesan, and Srinivasan discloses the method of Claim 6, wherein identifying the plurality of second system attribute clusters comprises:
inputting, by the at least one processor, the plurality of second system attributes into a clustering algorithm (see Dorle, paragraph [0048], where the data analyzer may implement the first artificial intelligence component to map the plurality of attributes with the plurality of prospects to create a plurality of prospect clusters; each of the plurality of prospect clusters may comprise at least one prospect from the plurality of prospects); and
receiving, by the at least one processor, a plurality of second system attribute clusters from the clustering algorithm (see Dorle, paragraph [0048], where the data analyzer may implement the first artificial intelligence component to map the plurality of attributes with the plurality of prospects to create a plurality of prospect clusters; each of the plurality of prospect clusters may comprise at least one prospect from the plurality of prospects).
Claims 8 and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Dorle, Baker, Tsibulevskiy, Shavlik, and Venkatesan as applied to Claims 1, 3-5, 11, and 13-15 above, and further in view of Kothandaraman (PG Pub. No. 2020/0019822 A1).
Regarding Claim 8, Dorle in view of Baker, Tsibulevskiy, Shavlik, and Venkatesan discloses the apparatus of Claim 1, wherein locating in the plurality of second system attribute clusters the advantage cluster comprises:
Dorle does not disclose:
identifying a target process;
inputting the target process into the impact metric machine learning model;
inputting a second system attribute cluster into the impact metric machine learning model;
receiving the impact metric from the impact metric machine learning model; and
determining the advantage cluster as a function of an impact metric.
Dorle in view of Kothandaraman discloses:
identifying a target process (see Kothandaraman, paragraph [0019], where various inputs permit identifying KPIs that were successfully used in monitoring automation of one process in one industry to be applied to other processes within the same industry or even other industries);
inputting the target process into the impact metric machine learning model (see Kothandaraman, paragraph [0019], where various inputs permit identifying KPIs that were successfully used in monitoring automation of one process in one industry to be applied to other processes within the same industry or even other industries);
inputting a second system attribute cluster into the impact metric machine learning model (see Kothandaraman, Claim 15, where the process includes classifying, using a machine learning classification model, the user-selected KPIs, into at least two subsets including at least a subset of usable KPIs and a subset of unusable KPIs, the usable KPIs being selected for use in determining the impact of automation on the automated process);
receiving the impact metric from the impact metric machine learning model (see Kothandaraman, Claim 15, where the process includes ranking, using a trained ranking model, the user-selected KPIs from each of the subset of usable KPIs and the subset of unusable KPIs in a descending order of usability); and
determining the advantage cluster as a function of an impact metric (see Dorle, paragraph [0048], where the data analyzer may implement to the first artificial intelligence component to determine a cluster significance value for each of the plurality of prospect clusters; the cluster significance value may be associated with the significance of a prospect cluster amongst the plurality of prospect clusters).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to combine Dorle with Kothandaraman for the benefit of evaluating identifying KPIs (key performance indicators) associated with processes (see Kothandaraman, Abstract).
Regarding Claim 18, Dorle in view of Baker, Tsibulevskiy, Shavlik, and Venkatesan discloses the method of Claim 11, wherein locating in the plurality of second system attribute clusters the advantage cluster comprises:
Dorle does not disclose:
identifying, by the at least one processor, a target process;
inputting, by the at least one processor, the target process into the impact metric machine learning model;
inputting, by the at least one processor, a second system attribute cluster into the impact metric machine learning model;
receiving, by the at least one processor, the impact metric from the impact metric machine learning model; and
determining, by the at least one processor, the advantage cluster as a function of an impact metric.
Dorle in view of Kothandaraman discloses:
identifying, by the at least one processor, a target process (see Kothandaraman, paragraph [0019], where various inputs permit identifying KPIs that were successfully used in monitoring automation of one process in one industry to be applied to other processes within the same industry or even other industries);
inputting, by the at least one processor, the target process into the impact metric machine learning model (see Kothandaraman, paragraph [0019], where various inputs permit identifying KPIs that were successfully used in monitoring automation of one process in one industry to be applied to other processes within the same industry or even other industries);
inputting, by the at least one processor, a second system attribute cluster into the impact metric machine learning model (see Kothandaraman, Claim 15, where the process includes classifying, using a machine learning classification model, the user-selected KPIs, into at least two subsets including at least a subset of usable KPIs and a subset of unusable KPIs, the usable KPIs being selected for use in determining the impact of automation on the automated process);
receiving, by the at least one processor, the impact metric from the impact metric machine learning model (see Kothandaraman, Claim 15, where the process includes ranking, using a trained ranking model, the user-selected KPIs from each of the subset of usable KPIs and the subset of unusable KPIs in a descending order of usability); and
determining, by the at least one processor, the advantage cluster as a function of an impact metric (see Dorle, paragraph [0048], where the data analyzer may implement to the first artificial intelligence component to determine a cluster significance value for each of the plurality of prospect clusters; the cluster significance value may be associated with the significance of a prospect cluster amongst the plurality of prospect clusters).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to combine Dorle with Kothandaraman for the benefit of evaluating identifying KPIs (key performance indicators) associated with processes (see Kothandaraman, Abstract).
Response to Arguments
Applicant’s Arguments, filed May 27, 2026, have been fully considered, but they are not persuasive at least in view of the new grounds of rejection.
Conclusion
The prior art made of record and not relied upon is considered pertinent to the Applicant’s disclosure:
Guha (PG Pub. No. 2010/0114899 A1), which concerns business intelligence analytics on unstructured data.
Kovega (PG Pub. No. 2020/0092317 A1), which concerns detecting abnormal user activity.
Mukhopadhyay (PG Pub. No. 2020/0074169 A1), which concerns extracting structured information from image documents.
Mangal (PG Pub. No. 2020/0184748 A1), which concerns passenger selection and screening for automated vehicles.
Guan (PG Pub. No. 2022/0300804 A1), which concerns few-shot learning for multi-task recommendation systems.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to FARHAD AGHARAHIMI whose telephone number is (571)272-9864. The examiner can normally be reached M-F 9am - 5pm ET.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Apu Mofiz can be reached at 571-272-4080. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/FARHAD AGHARAHIMI/Examiner, Art Unit 2161
/APU M MOFIZ/Supervisory Patent Examiner, Art Unit 2161
Prosecution Insights