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 .
Response to Amendment
Applicant's Amendment, filed December 12, 2025, has been fully considered and entered. Accordingly, Claims 1, 3-11, and 13-20 are pending in this application. Claims 1 and 11 are independent claims and have been amended.
In view of Applicant’s Amendment, the rejection of Claims 1, 3-11, and 13-20 under 35 U.S.C. 112(a) has been withdrawn.
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, 9-11, 13-15, 19, and 20 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), Guan (PG Pub. No. 2022/0300804 A1), and Lyons (PG Pub. No. 2021/0319219 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 includes charts comparing first system target convergence to similar measurements of other systems and images of the first system data comprising a logo; 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 includes charts comparing first system target convergence to similar measurements of other systems and images of the first system data comprising a logo; 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 includes charts comparing first system target convergence to similar measurements of other systems and images of the first system data comprising a logo; and
transmit the visual element data structure to a user device comprising a second system.
Guan discloses wherein the first system data comprises a web crawler configured to automatically search and collect data related to the first system (see Guan, paragraph [0029], where a customer can maintain a publicly viewable web site, which can be crawled using a web crawler to glean data regarding the customer).
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 Guan for the benefit of automatic profile creation for enterprises in B2B contexts (see Guan, paragraphs [0002], [0003]).
Dorle in view of Baker, Tsibulevskiy, and Guan does not disclose:
determine a visual element data structure as a function of the compatibility datum, wherein the visual element includes charts comparing first system target convergence to similar measurements of other systems and images of the first system data comprising a logo; and
transmit the visual element data structure to a user device comprising a second system.
Lyons discloses:
determine a visual element data structure as a function of the compatibility datum, wherein the visual element includes charts comparing first system target convergence to similar measurements of other systems and images of the first system data comprising a logo (see Lyons, paragraph [0088], where as shown in a screen shot 400 of Fig. 4A, in a particular example, the prospects may be presented to the user in a table; as illustrated, the table may include demographic information such as the company name, country, state, city revenue, and a link to the company’s website … the prospect information, in some embodiments, may be organized by classification information; see also paragraph [0081], where as illustrated in Fig. 4B, the user is presented with a list of potential matches to review, each match including a prospective client name 402, a potentially matching prospective client (or current client) name 404, and a percentage match 406, representing system confidence in the match); and
transmit the visual element data structure to a user device comprising a second system (see Lyons, paragraph [0088], where as shown in a screen shot 400 of Fig. 4A, in a particular example, the prospects may be presented to the user in a table; as illustrated, the table may include demographic information such as the company name, country, state, city revenue, and a link to the company’s website … the prospect information, in some embodiments, may be organized by classification information; see also paragraph [0081], where as illustrated in Fig. 4B, the user is presented with a list of potential matches to review, each match including a prospective client name 402, a potentially matching prospective client (or current client) name 404, and a percentage match 406, representing system confidence in the match).
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 Guan with Lyons for the benefit of identifying similar prospects based on the characteristics of existing clients (see Lyons, Abstract).
Regarding Claim 3, Dorle in view of Baker, Tsibulevskiy, Guan, and Lyons 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, Guan, and Lyons 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, Guan, and Lyons 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 9, Dorle in view of Baker, Tsibulevskiy, Guan, and Lyons discloses the apparatus of Claim 1, wherein the memory contains instructions configuring the at least processor to:
determine a visual element data structure as a function of the compatibility datum and transmit the visual element data structure to a user device (see Dorle, paragraph [0181], where the computer system 2700 further includes an output device 2725 to provide at least some of the results of the execution as output including, but not limited to, visual information to users).
Regarding Claim 10, Dorle in view of Baker, Tsibulevskiy, Guan, and Lyons discloses the apparatus of Claim 9, wherein the visual element data structure configures the user device to display a visual element to a user (see Dorle, paragraph [0181], where the computer system 2700 further includes an output device 2725 to provide at least some of the results of the execution as output including, but not limited to, visual information to users).
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;
processing, by at least one processor, 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;
determining, by at least one processor, a visual element data structure as a function of the compatibility datum, wherein the visual element includes charts comparing first system target convergence to similar measurements of other systems and images of the first system data comprising a logo; and
transmitting, by at least one processor, 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
processing, by at least one processor, 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;
determining, by at least one processor, a visual element data structure as a function of the compatibility datum, wherein the visual element includes charts comparing first system target convergence to similar measurements of other systems and images of the first system data comprising a logo; and
transmitting, by at least one processor, 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
processing, by at least one processor, 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
determining, by at least one processor, a visual element data structure as a function of the compatibility datum, wherein the visual element includes charts comparing first system target convergence to similar measurements of other systems and images of the first system data comprising a logo; and
transmitting, by at least one processor, the visual element data structure to a user device comprising a second system.
Guan discloses wherein the first system data comprises a web crawler configured to automatically search and collect data related to the first system (see Guan, paragraph [0029], where a customer can maintain a publicly viewable web site, which can be crawled using a web crawler to glean data regarding the customer).
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 Guan for the benefit of automatic profile creation for enterprises in B2B contexts (see Guan, paragraphs [0002], [0003]).
Dorle in view of Baker, Tsibulevskiy, and Guan does not disclose:
determining, by at least one processor, a visual element data structure as a function of the compatibility datum, wherein the visual element includes charts comparing first system target convergence to similar measurements of other systems and images of the first system data comprising a logo; and
transmitting, by at least one processor, the visual element data structure to a user device comprising a second system.
Lyons discloses:
determining, by at least one processor, a visual element data structure as a function of the compatibility datum, wherein the visual element includes charts comparing first system target convergence to similar measurements of other systems and images of the first system data comprising a logo (see Lyons, paragraph [0088], where as shown in a screen shot 400 of Fig. 4A, in a particular example, the prospects may be presented to the user in a table; as illustrated, the table may include demographic information such as the company name, country, state, city revenue, and a link to the company’s website … the prospect information, in some embodiments, may be organized by classification information; see also paragraph [0081], where as illustrated in Fig. 4B, the user is presented with a list of potential matches to review, each match including a prospective client name 402, a potentially matching prospective client (or current client) name 404, and a percentage match 406, representing system confidence in the match); and
transmitting, by at least one processor, the visual element data structure to a user device comprising a second system (see Lyons, paragraph [0088], where as shown in a screen shot 400 of Fig. 4A, in a particular example, the prospects may be presented to the user in a table; as illustrated, the table may include demographic information such as the company name, country, state, city revenue, and a link to the company’s website … the prospect information, in some embodiments, may be organized by classification information; see also paragraph [0081], where as illustrated in Fig. 4B, the user is presented with a list of potential matches to review, each match including a prospective client name 402, a potentially matching prospective client (or current client) name 404, and a percentage match 406, representing system confidence in the match).
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 Guan with Lyons for the benefit of identifying similar prospects based on the characteristics of existing clients (see Lyons, Abstract).
Regarding Claim 13, Dorle in view of Baker, Tsibulevskiy, Guan, and Lyons 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, Guan, and Lyons 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, Guan, and Lyons 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).
Regarding Claim 19, Dorle in view of Baker, Tsibulevskiy, Guan, and Lyons discloses the method of Claim 11, further comprising:
determining, by the at least one processor, a visual element data structure as a function of the compatibility datum and, using at least a processor, transmitting the visual element data structure to a user device (see Dorle, paragraph [0181], where the computer system 2700 further includes an output device 2725 to provide at least some of the results of the execution as output including, but not limited to, visual information to users).
Regarding Claim 20, Dorle in view of Baker, Tsibulevskiy, Guan, and Lyons discloses the method of Claim 19, wherein the visual element data structure configures the user device to display a visual element to a user (see Dorle, paragraph [0181], where the computer system 2700 further includes an output device 2725 to provide at least some of the results of the execution as output including, but not limited to, visual information to users).
Claims 6, 7, 16, and 17 are rejected under 35 U.S.C. 103 as being unpatentable over Dorle, Baker, Tsibulevskiy, Guan, and Lyons as applied to Claims 1, 3-5, 9-11, 13-15, 19, and 20 above, and further in view of Srinivasan (PG Pub. No. 2021/0279606 A1).
Regarding Claim 6, Dorle in view of Baker, Tsibulevskiy, Guan, and Lyons 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, Guan, Lyons, 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, Guan, and Lyons 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, Guan, Lyons, 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, Guan, and Lyons as applied to Claims 1, 3-5, 9-11, 13-15, 19, and 20 above, and further in view of Kothandaraman (PG Pub. No. 2020/0019822 A1).
Regarding Claim 8, Dorle in view of Baker, Tsibulevskiy, Guan, and Lyons 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, Guan, and Lyons 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 December 12, 2025, 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.
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/FARHAD AGHARAHIMI/Examiner, Art Unit 2161
/APU M MOFIZ/Supervisory Patent Examiner, Art Unit 2161