FIRST-PARTY COMPUTING SYSTEM SPECIFIC QUERY RESPONSE GENERATION FOR INTEGRATION OF THIRD-PARTY COMPUTING SYSTEM FUNCTIONALITY

Application No. 18/314,027

• Filed: May 08, 2023
• Priority: Jun 10, 2016 — provisional 62/348,695
  • Jun 23, 2016 — provisional 62/353,802
  • Jul 08, 2016 — provisional 62/360,123
  • Sep 01, 2016 — CIP of 9729583
  • Jun 10, 2017 — CIP of 9851966
  • Jul 27, 2017 — provisional 62/537,839
  • Aug 04, 2017 — provisional 62/541,613
  • Aug 18, 2017 — provisional 62/547,530
  • Oct 13, 2017 — provisional 62/572,096
  • Dec 22, 2017 — CIP of 10/019,597
  • Jun 01, 2018 — CIP of 10/181,051
  • Aug 04, 2018 — CIP of 10/289,870
  • Sep 07, 2018 — provisional 62/728,435
  • Oct 13, 2018 — continuation of 10/282,692
  • Mar 04, 2019 — provisional 62/813,584
  • May 03, 2019 — continuation of 10/510,031
  • Jun 14, 2019 — provisional 62/861,916
  • Dec 13, 2019 — CIP of 10/692,033
  • Jan 17, 2020 — provisional 62/962,329
  • Mar 04, 2020 — CIP of 10/796,260
  • Jun 15, 2020 — CIP of 10/909,488
  • Jan 18, 2021 — CIP of 17/151,334
• Examiner: GAVIN, KRISTIN ELIZABETH
• Art Unit: 3624
• Tech Center: 3600 — Transportation & Electronic Commerce
• Assignee: OneTrust LLC
• OA Round: 3 (Non-Final)

Office Action

DETAILED ACTION
This non-final Office action is responsive to the amendments filed April 8th, 2026. Claims 1, 8, and 15 have been amended. Claims 1-20 are presented for examination.

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 .

Priority
Applicant’s claim for the benefit of a prior-filed application under 35 U.S.C. 119(e) or under 35 U.S.C. 120, 121, 365(c), or 386(c) is acknowledged.

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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 04/08/26 has been entered.

Response to Arguments
Applicant’s arguments, see page 10, filed 04/08/26, with respect to claim 8 have been fully considered and are persuasive. The claim objection of 01/30/26 has been withdrawn.
Applicant's arguments regarding claim rejections under 35 USC 101 filed 04/08/26 have been fully considered but they are not persuasive.
On pages 10-12 of the provided remarks, Applicant argues that the amended claims are not directed to an abstract idea and recite additional elements that transform them into patent eligible subject matter. Beginning on page 11 of the provided remarks, Applicant argues “A. The claims integrate the allegedly-recited judicial exception into a practical application (Prong Two of Step 2A).” Citing paragraph [0025-0026] of the as-filed Specification, Applicant argues “claim 1 provides efficiencies and improvements to generating customized estimate for integrating a third-party computing functionality onto the first party system based on historical integrations of tenant integration durations.” Examiner respectfully disagrees and asserts that the argued operations could be practically carried out by the human mind. For example, Applicant argues that the claims “(1) identifying similarly situated entity computing systems to the first party computing system; (2) determining integration data for each of the similarly situated entity computing systems with respect to the third-party computing functionality; and (3) determining predictive integration data that is specific to the first party computing system based on the integration data for the similarly situated entities” is not a conventional operation. Examiner respectfully disagrees and asserts that the claims high-level recitation of the term “identifying”, “determining”, and “determining” is analogous to the observation, judgement, evaluation and opinion of the human mind. Further, as the amended claims have removed the “computing hardware” as the executing engine of the argued functions, Examiner asserts that the following claim elements are more conventional as they are not limited to a specific technical execution or component. Applicant’s arguments are not persuasive.
Applicant continues on page 12 of the provided remarks to cite McRO, Inc. v. Bandai Namco Games, 837 F. 3d 1299 (Fed. Cir. 2016) and argue that the system behavior is rule-based and dynamic. Examiner asserts that it was not simply the “rule-based and dynamic” nature of the cited McRO that determined patent-eligibility but the following, per MPEP 2106.05(a)(II) “The court relied on the specification's explanation of how the claimed rules enabled the automation of specific animation tasks that previously could not be automated. 837 F.3d at 1313, 120 USPQ2d at 1101. The McRO court indicated that it was the incorporation of the particular claimed rules in computer animation that "improved [the] existing technological process", unlike cases such as Alice where a computer was merely used as a tool to perform an existing process. 837 F.3d at 1314, 120 USPQ2d at 1102. The McRO court also noted that the claims at issue described a specific way (use of particular rules to set morph weights and transitions through phonemes) to solve the problem of producing accurate and realistic lip synchronization and facial expressions in animated characters, rather than merely claiming the idea of a solution or outcome, and thus were not directed to an abstract idea. 837 F.3d at 1313, 120 USPQ2d at 1101.” In contrast, the argued “(1) identifying similarly situated entity computing systems to the first party computing system; (2) determining integration data for each of the similarly situated entity computing systems with respect to the third party computing functionality; and (3) determining predictive integration data that is specific to the first party computing system based on the integration data for the similarly situated entities” does present a specific way to improve query results and therefore does not integrate the judicial exception into a practical application. The 35 USC 101 rejection is maintained. Applicant’s arguments are not persuasive.
Applicant’s arguments, see pages 12-17, filed 04/08/26, with respect to the rejection(s) of claim(s) 1-20 under 35 USC 103 have been fully considered and are persuasive. Therefore, the rejection has been withdrawn. However, upon further consideration, a new ground(s) of rejection is made in view of Jain (U.S 6,795,858 B1) in view of Ignatyev (U.S 2017/0318083 A1) in view of Muthuvaradharajan (U.S 2017/0083377 A1) in view of Tkatch (U.S 2010/0286992 A1).

Claim Objections
Claim 2 is objected to because of the following informalities: the limitation "generating, by the computing hardware" lacks antecedent basis and should recite "generating, by computing hardware". Appropriate correction is required.
Claim 3 is objected to because of the following informalities: the limitation “accessing, by the computing hardware” lacks antecedent basis and should recite “accessing, by computing hardware”. Appropriate correction is required.
Claim 4 is objected to because of the following informalities: the limitation “identifying, by the computing hardware” lacks antecedent basis and should recite “identifying, by computing hardware”. Appropriate correction is required.
Claim 6 is objected to because of the following informalities: the limitation “determining, by the computing hardware” lacks antecedent basis and should recite “determining, by computing hardware”. Appropriate correction is required.

Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.

Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter;
When considering subject matter eligibility under 35 U.S.C. 101, it must be determined whether the claim is directed to one of the four statutory categories of invention, i.e., process, machine, manufacture, or composition of matter. If the claim does fall within one of the statutory categories, it must then be determined whether the claim is directed to a judicial exception (i.e., law of nature, natural phenomenon, and abstract idea), and if so, it must additionally be determined whether the claim is a patent-eligible application of the exception. If an abstract idea is present in the claim, any element or combination of elements in the claim must be sufficient to ensure that the claim amounts to significantly more than the abstract idea itself.

Claims 1-7
Step 1: Independent claim 1 (method), and dependent claims 2-7, respectively, fall within at least one of the four statutory categories of 35 U.S.C. 101: (i) process; (ii) machine; (iii) manufacture; or (iv) composition of matter. Claim 1 is directed to a method (i.e. process).
Step 2A Prong 1: The independent claims recite receiving, a query from a first-party computing system related to integrating third-party computing functionality into the first-party computing system; identifying, a set of third-party vendors that provide the third-party computing functionality; accessing, integration data related to each respective third-party vendor in the set of third-party vendors with relation to integrating the third-party computing functionality; identifying, a set of tenants, the set of tenants including, for each respective third-party vendor, a respective tenant that has previously integrated the third-party computing functionality from the respective third-party vendor into a respective tenant computing system associated with the respective tenant; determining, second integration data associated with the set of tenants integrating the third-party computing functionality, wherein the second integration data comprises historical integration duration data associated with completion of prior integrations of the third-party computing functionality by the respective set of tenants; generating, based on the first integration data and the second integration data, data responsive to the query, the data comprising at least a respective integration delay prediction for the third-party computing functionality for each respective third-party vendor that is specific to the first-party computing system, the respective integration delay prediction being predicted based on at least the historical integration duration data; and taking, an action based on the respective integration delay prediction, wherein the action comprises initiating, network communication or computing operations for integrating the third-party computing functionality from a particular third-party vendor into the first-party computing system. (Certain Method of Organizing Human Activity & Mental Process), which are considered to be abstract ideas (See PEG 2019 and MPEP 2106.05). [Examiner notes the underlined limitations above recite the abstract idea].
The steps/functions disclosed above and in the independent claims recite the abstract idea of Certain Methods of Organizing Human Activity because the claimed limitations are identifying a set of third-party vendors and tenants to respond to the received query; generating an integration delay prediction for the third-party computing functionality; and taking an action with respect to the integration delay prediction, which is managing relationships and interactions. The Applicant’s claimed limitations are determining a third-party vendor to fulfill a query based on a determined integration delay prediction, which recite the abstract idea of Organizing Human Activity.
The steps/functions disclosed above and in the independent claims recite the abstract idea of Mental Process because the claimed limitations are identifying a set of third-party vendors and tenants to respond to the received query; determining second integration data with respect to tenants; generating an integration delay prediction for the third-party computing functionality; and taking an action with respect to the integration delay prediction, which is observation, judgment, evaluation, and opinions of the human mind. The Applicant’s claimed limitations are determining a third-party vendor to fulfill a query based on a determined integration delay prediction, which recite the abstract idea of Mental Process.
In addition, dependent claims 2-7 further narrow the abstract idea and recite further defining the actions taken; identifying the set of tenants; generating data responsive to the query; and modifying the integration delay prediction. These processes are similar to the abstract idea noted in the independent claims because they further the limitations of the independent claims which recite a certain method of organizing human activity which include managing personal interactions as well as mental processes. Accordingly, these claim elements do not serve to confer subject matter eligibility to the claims since they recite abstract ideas.
Step 2A Prong 2: In this application, the above “receiving, a query from a first-party computing system related to integrating third-party computing functionality into the first-party computing system; accessing, integration data related to each respective third-party vendor in the set of third-party vendors; wherein the action comprises initiating, network communication” steps/functions of the independent claims would not account for additional elements that integrate the judicial exception (e.g. abstract idea) into a practical application because receiving/storing data and displaying data merely add insignificant extra-solution activity and merely adds the words to apply it with the judicial exception. Also, the claimed “a first-party computing system; third-party computing” would not account for additional elements that integrate the judicial exception (e.g. abstract idea) into a practical application because the claimed structure merely adds the words to apply it with the judicial exception and mere instructions to implement an abstract idea on a computer (See PEG 2019 and MPEP 2106.05).
In addition, dependent claims 2-7 further narrow the abstract idea and dependent claims 2 & 3 additionally recite “generating, by the computing hardware, a user interface that includes a listing of each respective third-party vendor that increases or decreases a ranking of each respective third-party vendor in the listing based on the respective integration delay prediction” and “accessing, by the computing hardware, a first set of attributes for the first-party computing system” which do not account for additional elements that integrate the judicial exception (e.g. abstract idea) into a practical application because receiving/storing data and displaying data merely add insignificant extra-solution activity and the claimed “computing hardware” which do not account for additional elements that integrate the judicial exception (e.g. abstract idea) into a practical application because the claimed structure merely adds the words to apply it with the judicial exception and mere instructions to implement an abstract idea on a computer (See PEG 2019 and MPEP 2106.05).
The claimed “a first-party computing system; third-party computing” are recited so generically (no details whatsoever are provided other than that they are general purpose computing components and regular office supplies) that they represent no more than mere instructions to apply the judicial exception on a computer. These limitations can also be viewed as nothing more than an attempt to generally link the use of the judicial exception to the technological environment of a computer. Even when viewed in combination, the additional elements in the claims do no more than use the computer components as a tool. There is no change to the computers and other technology that is recited in the claim, and thus the claims do not improve computer functionality or other technology (See PEG 2019).
Step 2B: When analyzing the additional element(s) and/or combination of elements in the claim(s) other than the abstract idea per se the claim limitations amount(s) to no more than: a general link of the use of an abstract idea to a particular technological environment and merely amounts to the application or instructions to apply the abstract idea on a computer (See MPEP 2106.05 and PEG 2019). Further, method claims 1-7 recite “a first-party computing system; third-party computing”; however, these elements merely facilitate the claimed functions at a high level of generality and they perform conventional functions and are considered to be general purpose computer components which is supported by Applicant’s specification in Paragraphs 0091-94 and Figures 1, 6, and 7. The Applicant’s claimed additional elements are mere instructions to implement the abstract idea on a general purpose computer and generally link of the use of an abstract idea to a particular technological environment. Also, the above “receiving, a query from a first-party computing system related to integrating third-party computing functionality into the first-party computing system; accessing, integration data related to each respective third-party vendor in the set of third-party vendors with relation to integration of the third-party computing functionality; wherein the action comprises initiating, network communication” steps/functions of the independent claims would not account for significantly more than the abstract idea because receiving data and displaying/presenting data (See MPEP 2106.05) have been identified as well-known, routine, and conventional steps/functions to one of ordinary skill in the art. When viewed as a whole, these additional claim element(s) do not provide meaningful limitation(s) to transform the abstract idea into a patent eligible application of the abstract idea such that the claim(s) amounts to significantly more than the abstract idea itself.
In addition, claims 2-7 further narrow the abstract idea identified in the independent claims. The Examiner notes that the dependent claims merely further define the data being analyzed and how the data is being analyzed. Similarly, claims 2-3 additionally recite “generating, by the computing hardware, a user interface that includes a listing of each respective third-party vendor that increases or decreases a ranking of each respective third-party vendor in the listing based on the respective integration delay prediction” and “accessing, by the computing hardware, a first set of attributes for the first-party computing system” which do not account for additional elements that amount to significantly more than the abstract idea because receiving data and displaying/presenting data (See MPEP 2106.05) have been identified as well-known, routine, and conventional steps/functions to one of ordinary skill in the art and the claimed “computing hardware” which do not account for additional elements that amount to significantly more than the abstract idea because the claimed structure merely amounts to the application or instructions to apply the abstract idea on a computer and does not move beyond a general link of the use of an abstract idea to a particular technological environment (See MPEP 2106.05). The additional limitations of the independent and dependent claim(s) when considered individually and as an ordered combination do not amount to significantly more than the abstract idea. The examiner has considered the dependent claims in a full analysis including the additional limitations individually and in combination as analyzed in the independent claim(s). Therefore, the claim(s) are rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.

Claims 8-14
Step 1: Independent claim 8 (system) and dependent claims 9-14, respectively, fall within at least one of the four statutory categories of 35 U.S.C. 101: (i) process; (ii) machine; (iii) manufacture; or (iv) composition of matter. Claim 8 is directed to a system (i.e. machine).
Step 2A Prong 1: The independent claims recite receiving, from a fist-party computing system having a first set of attributes, a first request to integrate third-party computing functionality into the first-party computing system; identifying a set of third-party computing systems that provide the third-party computing functionality; accessing tenant computing system integration data for the third-party computing functionality, the tenant computing system integration data comprising integration data for each of a plurality of tenant computing systems operated by respective tenant entities that have previously integrated the third-party computing functionality, wherein the tenant computing system integration data further comprises historical integration duration data associated with completion of prior integrations of the third-party computing functionality for each of a plurality of tenant computing systems; generating a respective integration timing prediction for each third-party computing system in the set of third-party computing systems with respect to integrating the third-party computing functionality into the first-party computing system based on the tenant computing system integration data and the historical integration duration data; customizing each respective integration timing prediction to the first-party computing system by: identifying a subset of the plurality of tenant computing systems that share a subset of the first set of attributes; and generating a modified respective integration timing prediction for each third-party computing system based on a subset of the tenant computing system integration data that omits the tenant computing system integration data for the plurality of tenant computing systems that are not in the subset of the plurality of tenant computing systems; generating a graphical user interface comprising a listing of the set of third-party computing systems and an indication of the modified respective integration timing prediction; providing the graphical user interface to a user computing device in the first-party computing system and; initiating network communication or computing operations for integrating the third-party computing functionality into the first-party computing system. (Certain Method of Organizing Human Activity & Mental Process), which are considered to be abstract ideas (See PEG 2019 and MPEP 2106.05). [Examiner notes the underlined limitations above recite the abstract idea].
The steps/functions disclosed above and in the independent claims recite the abstract idea of Certain Methods of Organizing Human Activity because the claimed limitations are identifying a set of third-party entities to respond to the received query; generating a respective integration timing prediction for the third-party computing functionality; customizing each respective integration timing prediction; and initiating computing operations for integrating the third-party computing functionality into the first-party computing system, which is managing relationships and interactions. The Applicant’s claimed limitations are determining a third-party vendor to fulfill a query based on a determined integration timing prediction, which recite the abstract idea of Organizing Human Activity.
The steps/functions disclosed above and in the independent claims recite the abstract idea of Mental Process because the claimed limitations are identifying a set of third-party entities to respond to the received query; generating an integration timing prediction for the third-party computing functionality; customizing each respective integration timing prediction by identifying a subset that shares a set of attributes and generating a modified respective integration timing prediction; and initiating computing operations for integrating the third-party computing functionality into the first-party computing system, which is observation, judgement, and evaluation of the human mind. The Applicant’s claimed limitations are determining a third-party vendor to fulfill a query based on a determined integration timing prediction, which recite the abstract idea of Mental Process.
In addition, dependent claims 9-14 further narrow the abstract idea and recite further defining increasing or decreasing a ranking of the third-party entities based on the timing predication; customizing each respective integration timing prediction; the set of attributes of third-party entities; anonymizing integration data prior to generating the prediction; and the request to integrate the third-party computing functionality. These processes are similar to the abstract idea noted in the independent claims because they further the limitations of the independent claims which recite a certain method of organizing human activity which include managing personal interactions as well as mental processes. Accordingly, these claim elements do not serve to confer subject matter eligibility to the claims since they recite abstract ideas.
Step 2A Prong 2: In this application, the above “receiving, from a fist-party computing system having a first set of attributes, a first request to integrate third-party computing functionality into the first-party computing system; accessing tenant computing system integration data for the third-party computing functionality, the tenant computing system integration data comprising integration data for each of a plurality of tenant computing systems operated by respective tenant entities that have previously integrated the third-party computing functionality, wherein the tenant computing system integration data further comprises historical integration duration data associated with completion of prior integrations of the third-party computing functionality for each of a plurality of tenant computing systems; generating a graphical user interface comprising a listing of the set of third-party computing systems and an indication of the modified respective integration timing prediction; and providing the graphical user interface to a user computing device in the first-party computing system; initiating network communication for integrating the third-party computing functionality into the first-party computing system” steps/functions of the independent claims would not account for additional elements that integrate the judicial exception (e.g. abstract idea) into a practical application because receiving/storing data and displaying data merely add insignificant extra-solution activity and merely adds the words to apply it with the judicial exception. Also, the claimed “A system comprising: a non-transitory computer-readable medium storing instructions; and a processing device communicatively coupled to the non-transitory computer-readable medium, wherein the processing device is configured to execute the instructions and thereby perform operations; a fist-party computing system; a set of third-party computing systems; tenant computing systems; a graphical user interface; user computing device; a second third-party computing system” would not account for additional elements that integrate the judicial exception (e.g. abstract idea) into a practical application because the claimed structure merely adds the words to apply it with the judicial exception and mere instructions to implement an abstract idea on a computer (See PEG 2019 and MPEP 2106.05). In addition, dependent claims 9-14 further narrow the abstract idea.
The claimed “A system comprising: a non-transitory computer-readable medium storing instructions; and a processing device communicatively coupled to the non-transitory computer-readable medium, wherein the processing device is configured to execute the instructions and thereby perform operations; a fist-party computing system; a set of third-party computing systems; tenant computing systems; a graphical user interface; user computing device; a second third-party computing system” are recited so generically (no details whatsoever are provided other than that they are general purpose computing components and regular office supplies) that they represent no more than mere instructions to apply the judicial exception on a computer. These limitations can also be viewed as nothing more than an attempt to generally link the use of the judicial exception to the technological environment of a computer. Even when viewed in combination, the additional elements in the claims do no more than use the computer components as a tool. There is no change to the computers and other technology that is recited in the claim, and thus the claims do not improve computer functionality or other technology (See PEG 2019).
Step 2B: When analyzing the additional element(s) and/or combination of elements in the claim(s) other than the abstract idea per se the claim limitations amount(s) to no more than: a general link of the use of an abstract idea to a particular technological environment and merely amounts to the application or instructions to apply the abstract idea on a computer (See MPEP 2106.05 and PEG 2019). Further, system claims 8-14 recite “A system comprising: a non-transitory computer-readable medium storing instructions; and a processing device communicatively coupled to the non-transitory computer-readable medium, wherein the processing device is configured to execute the instructions and thereby perform operations; a fist-party computing system; a set of third-party computing systems; tenant computing systems; a graphical user interface; user computing device; a second third-party computing system”; however, these elements merely facilitate the claimed functions at a high level of generality and they perform conventional functions and are considered to be general purpose computer components which is supported by Applicant’s specification in Paragraphs 0091-94 and Figures 1, 6, and 7. The Applicant’s claimed additional elements are mere instructions to implement the abstract idea on a general purpose computer and generally link of the use of an abstract idea to a particular technological environment. Also, the above “receiving, from a fist-party computing system having a first set of attributes, a first request to integrate third-party computing functionality into the first-party computing system; accessing tenant computing system integration data for the third-party computing functionality, the tenant computing system integration data comprising integration data for each of a plurality of tenant computing systems operated by respective tenant entities that have previously integrated the third-party computing functionality, wherein the tenant computing system integration data further comprises historical integration duration data associated with completion of prior integrations of the third-party computing functionality for each of a plurality of tenant computing systems; generating a graphical user interface comprising a listing of the set of third-party computing systems and an indication of the modified respective integration timing prediction; and providing the graphical user interface to a user computing device in the first-party computing system; initiating network communication for integrating the third-party computing functionality into the first-party computing system” steps/functions of the independent claims would not account for significantly more than the abstract idea because receiving data and displaying/presenting data (See MPEP 2106.05) have been identified as well-known, routine, and conventional steps/functions to one of ordinary skill in the art. When viewed as a whole, these additional claim element(s) do not provide meaningful limitation(s) to transform the abstract idea into a patent eligible application of the abstract idea such that the claim(s) amounts to significantly more than the abstract idea itself.
In addition, claims 9-14 further narrow the abstract idea identified in the independent claims. The Examiner notes that the dependent claims merely further define the data being analyzed and how the data is being analyzed. The additional limitations of the independent and dependent claim(s) when considered individually and as an ordered combination do not amount to significantly more than the abstract idea. The examiner has considered the dependent claims in a full analysis including the additional limitations individually and in combination as analyzed in the independent claim(s). Therefore, the claim(s) are rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.

Claims 15-20
Step 1: Independent claims 15 (method) and dependent claims 16-20, respectively, fall within at least one of the four statutory categories of 35 U.S.C. 101: (i) process; (ii) machine; (iii) manufacture; or (iv) composition of matter. Claim 15 is directed to a method (i.e. process).
Step 2A Prong 1: The independent claims recite receiving, by computing hardware, a request to integrate functionality provided by a third-party computing system operated by a third-party vendor having a first set of attributes into a first-party computing system operated by a first-party entity having a second set of attributes; identifying, by the computing hardware, a set of third-party vendors that provide the functionality, the set of third-party vendors having a third set of attributes; accessing, by the computing hardware, tenant computing system integration data for the functionality provided by the third-party computing system, the tenant computing system integration data comprising integration data for each of a plurality of tenant computing systems operated by respective tenant entities that have previously integrated the functionality from any of the set of third-party vendors, the tenant entities having a fourth set of attributes, wherein the tenant computing system integration data further comprises historical integration duration data associated with completion of prior integrations of the third-party computing functionality for each of the respective tenant entities; causing, by the computing hardware, at least one of a rules-based model or a machine-learning model to process the first set of attributes and the third set of attributes to generate a set of similarly situated third-party vendors to the third-party vendor; causing, by the computing hardware, at least one of the rules-based model or the machine-learning model to process the second set of attributes and the fourth set of attributes to generate a set of similarly situated tenant entities to the first-party entity; analyzing, by the computing hardware, the tenant computing system integration data for the set of similarly situated tenant entities and the set of similarly situated third-party vendors to determine integration timing data for each of the set of similarly situated tenant entities and the set of similarly situated third-party vendors; generating, by the computing hardware, a timing prediction for integrating the functionality provided by the third-party computing system into the first-party computing system based on the integration timing data that is specific to the first-party computing system and historical integration duration data; and causing, by the computing hardware, performance of an action with respect to the first-party computing system based on the timing prediction, wherein the action comprises initiating network communication or computing operations for integrating the functionality provided by the third-party computing system into the first-party computing system (Certain Method of Organizing Human Activity & Mental Process), which are considered to be abstract ideas (See PEG 2019 and MPEP 2106.05). [Examiner notes the underlined limitations above recite the abstract idea].
The steps/functions disclosed above and in the independent claims recite the abstract idea of Certain Methods of Organizing Human Activity because the claimed limitations are identifying a set of third-party vendors to respond to the received request; processing attributes to determine a set of similarly situated third-party vendors and tenant entities; analyze the sets to determine integration timing data; generating an timing prediction for the third-party computing functionality; and causing an action with respect to the timing prediction comprising initiating computing operations for integrating the functionality provided by the third-party computing system into the first-party computing system, which is managing relationships and interactions. The Applicant’s claimed limitations are determining a third-party vendor to fulfill a request based on a determined timing prediction, which recite the abstract idea of Organizing Human Activity.
The steps/functions disclosed above and in the independent claims recite the abstract idea of Mental Process because the claimed limitations are identifying a set of third-party vendors to respond to the received request; processing attributes to determine a set of similarly situated third-party vendors and tenant entities; analyze the sets to determine integration timing data; generating an timing prediction for the third-party computing functionality; and causing an action with respect to the timing prediction comprising initiating computing operations for integrating the functionality provided by the third-party computing system into the first-party computing system, which is observation, judgment, and evaluation of the human mind. The Applicant’s claimed limitations are determining a third-party vendor to fulfill a query based on a determined timing prediction, which recite the abstract idea of Mental Process.
In addition, dependent claims 16-20 further narrow the abstract idea and recite further defining the actions taken; the set of attributes; setting a benchmark for completing integration; and tracking actual timing data of integration. These processes are similar to the abstract idea noted in the independent claims because they further the limitations of the independent claims which recite a certain method of organizing human activity which include managing personal interactions as well as mental processes. Accordingly, these claim elements do not serve to confer subject matter eligibility to the claims since they recite abstract ideas.
Step 2A Prong 2: In this application, the above “receiving, by computing hardware, a request to integrate functionality provided by a third-party computing system operated by a third-party vendor having a first set of attributes into a first-party computing system operated by a first-party entity having a second set of attributes; accessing, by the computing hardware, tenant computing system integration data for the functionality provided by the third-party computing system, the tenant computing system integration data comprising integration data for each of a plurality of tenant computing systems operated by respective tenant entities that have previously integrated the functionality from any of the set of third-party vendors, the tenant entities having a fourth set of attributes, wherein the tenant computing system integration data further comprises historical integration duration data associated with completion of prior integrations of the third-party computing functionality for each of the respective tenant entities” steps/functions of the independent claims would not account for additional elements that integrate the judicial exception (e.g. abstract idea) into a practical application because receiving/storing data and displaying data merely add insignificant extra-solution activity and merely adds the words to apply it with the judicial exception. Also, the claimed “computing hardware; third-party computing systems; first-party computing systems; a plurality of tenant system computing systems” would not account for additional elements that integrate the judicial exception (e.g. abstract idea) into a practical application because the claimed structure merely adds the words to apply it with the judicial exception and mere instructions to implement an abstract idea on a computer (See PEG 2019 and MPEP 2106.05).
In addition, dependent claims 16-20 further narrow the abstract idea and dependent claims 17 & 19 additionally recite “generating, by the computing hardware, a user interface that includes a listing of the set of third-party vendors that increases or decreases a ranking of the third-party vendor in the listing based on the timing prediction” and “facilitating at least one of modification of the timing prediction based on the actual timing data or transfer of the actual timing data to a third-party computing entity for use in future timing determinations related to integration of the functionality provided by the third-party computing system” which do not account for additional elements that integrate the judicial exception (e.g. abstract idea) into a practical application because receiving/storing data and displaying data merely add insignificant extra-solution activity and the claimed “computer hardware” which do not account for additional elements that integrate the judicial exception (e.g. abstract idea) into a practical application because the claimed structure merely adds the words to apply it with the judicial exception and mere instructions to implement an abstract idea on a computer (See PEG 2019 and MPEP 2106.05).
Independent claim 15 recites the following limitation, “causing, by the computing hardware, at least one of a rules-based model or a machine-learning model to process”. The “rules-based model or a machine-learning model” are recited so generically (no details whatsoever are provided other than that they are general purpose computing components) that they represent no more than mere instructions to apply the judicial exception on a computer. These limitations can also be viewed as nothing more than an attempt to generally link the use of the judicial exception to the technological environment of a computer. These limitations would not account for additional elements that integrate the judicial exception (e.g. abstract idea) into a practical application because the claimed structure merely adds the words to apply it with the judicial exception and mere instructions to implement an abstract idea on a computer (See PEG 2019 and MPEP 2106.05).
The claimed “computing hardware; third-party computing systems; first-party computing systems; a plurality of tenant computing systems” are recited so generically (no details whatsoever are provided other than that they are general purpose computing components and regular office supplies) that they represent no more than mere instructions to apply the judicial exception on a computer. These limitations can also be viewed as nothing more than an attempt to generally link the use of the judicial exception to the technological environment of a computer. Even when viewed in combination, the additional elements in the claims do no more than use the computer components as a tool. There is no change to the computers and other technology that is recited in the claim, and thus the claims do not improve computer functionality or other technology (See PEG 2019).
Step 2B: When analyzing the additional element(s) and/or combination of elements in the claim(s) other than the abstract idea per se the claim limitations amount(s) to no more than: a general link of the use of an abstract idea to a particular technological environment and merely amounts to the application or instructions to apply the abstract idea on a computer (See MPEP 2106.05 and PEG 2019). Further, method claims 15-20 recite “computing hardware; third-party computing systems; first-party computing systems; a plurality of tenant computing systems”; however, these elements merely facilitate the claimed functions at a high level of generality and they perform conventional functions and are considered to be general purpose computer components which is supported by Applicant’s specification in Paragraphs 0091-94 and Figures 1, 6, and 7 The Applicant’s claimed additional elements are mere instructions to implement the abstract idea on a general purpose computer and generally link of the use of an abstract idea to a particular technological environment. Also, the above “receiving, by computing hardware, a request to integrate functionality provided by a third-party computing system operated by a third-party vendor having a first set of attributes into a first-party computing system operated by a first-party entity having a second set of attributes; accessing, by the computing hardware, tenant computing system integration data for the functionality provided by the third-party computing system, the tenant computing system integration data comprising integration data for each of a plurality of tenant computing systems operated by respective tenant entities that have previously integrated the functionality from any of the set of third-party vendors, the tenant entities having a fourth set of attributes, wherein the tenant computing system integration data further comprises historical integration duration data associated with completion of prior integrations of the third-party computing functionality for each of the respective tenant entities” steps/functions of the independent claims would not account for significantly more than the abstract idea because receiving data and displaying/presenting data (See MPEP 2106.05) have been identified as well-known, routine, and conventional steps/functions to one of ordinary skill in the art. When viewed as a whole, these additional claim element(s) do not provide meaningful limitation(s) to transform the abstract idea into a patent eligible application of the abstract idea such that the claim(s) amounts to significantly more than the abstract idea itself.
Next, when the “machine learning” is evaluated as an additional element, this feature is recited at a high level of generality and encompasses well-understood, routine, and conventional prior art activity. See, e.g., Balsiger et al., US 2012/0054642, noting in paragraph [0077] that “Machine learning is well known to those skilled in the art.” See also, Djordjevic et al. US 2013/0018651, noting in paragraph [0019] that “As known in the art, a generative model can be used in machine learning to model observed data directly.” See also, Bauer et al., US 2017/0147941, noting at paragraph [0002] that “Problems of understanding the behavior or decisions made by machine learning models have been recognized in the conventional art and various techniques have been developed to provide solutions.” Accordingly, the use of machine learning to process attribute data does not add significantly more to the claim.
In addition, claims 16-20 further narrow the abstract idea identified in the independent claims. The Examiner notes that the dependent claims merely further define the data being analyzed and how the data is being analyzed. Similarly, claims 17 & 19 additionally recite “generating, by the computing hardware, a user interface that includes a listing of the set of third-party vendors that increases or decreases a ranking of the third-party vendor in the listing based on the timing prediction” and “facilitating at least one of modification of the timing prediction based on the actual timing data or transfer of the actual timing data to a third-party computing entity for use in future timing determinations related to integration of the functionality provided by the third-party computing system” which do not account for additional elements that amount to significantly more than the abstract idea because receiving data and displaying/presenting data (See MPEP 2106.05) have been identified as well-known, routine, and conventional steps/functions to one of ordinary skill in the art and the claimed “computing hardware” which do not account for additional elements that amount to significantly more than the abstract idea because the claimed structure merely amounts to the application or instructions to apply the abstract idea on a computer and does not move beyond a general link of the use of an abstract idea to a particular technological environment (See MPEP 2106.05). The additional limitations of the independent and dependent claim(s) when considered individually and as an ordered combination do not amount to significantly more than the abstract idea. The examiner has considered the dependent claims in a full analysis including the additional limitations individually and in combination as analyzed in the independent claim(s). Therefore, the claim(s) are rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.

Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, 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.
Claim(s) 1-5, 7-12, and 14-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Jain (U.S 6,795,858 B1) in view of Ignatyev (U.S 2017/0318083 A1) in view of Muthuvaradharajan (U.S 2017/0083377 A1) in view of Tkatch (U.S 2010/0286992 A1).
Claim 1
Regarding Claim 1, Jain discloses the following:
A method comprising [see at least Col 4 lines 66-67 & Col 5 line 1 for reference to a method for selecting a best server from a group of servers that can provide similar content to a client; Figure 2 and related text regarding the method for selecting a best server]
receiving, a query from a first-party computing system related to integrating third-party computing functionality into the first-party computing system [see at least Col 5 lines 6-8 for reference to the client issuing a request for content using a specific domain name]
identifying, a set of third-party vendors that provide the third-party computing functionality [see at least Col 6 lines 33-35 for reference to the server selection system identifying a group of content servers that can provide the material the client has requested; Figure 2 and related text regarding item 201 ‘Identify Group of Content Servers’]
accessing, integration data related to each respective third-party vendor in the set of third-party vendors [see at least Col 6 lines 37-45 for reference to the server selection system employing performance metrics of each of the servers including round trip time, server load, drop rate, available bandwidth, administrative distance, number of hops, and whether or not a server is in a particular subnetwork; Col 7 lines 1-12 for reference to the use of Pathchar to collect information about each server including using of knowledge about earlier hops and the round trip distribution to this hop to assess bandwidth, drop rate, latency, and queue characteristics; Figure 2 and related text regarding item 203 ‘Obtain Performance Metrics’]
identifying, a set, the set including, for each respective third-party vendor, a respective that has previously integrated the third-party computing functionality from the respective third-party vendor into a respective computing system [see at least Col 7 lines 36-40 for reference to the server selection system eliminating servers with metrics that are individually worse that another server’s metrics; Col 7 lines 50-57 for reference to a group of servers being categorized into a group of acceptable servers based on falling within a significance window; Figure 2 and related text regarding item 205 ‘Eliminate Servers With Metrics Worse Than All Corresponding Metrics Of Another Server’ and item 217 ‘Identify Servers Falling Within Significance Window’]
determining, second integration data associated with the set integrating the third-party computing functionality [see at least Col 7 lines 50-55 for reference to the determination of the significance window; Col 8 lines 20-31 for reference to the significance window being catered to the individual characteristics of a particular network; Col 8 lines 32-35 for reference to changes over time in the value of metrics and errors in measuring metric values increasing the significance window; Figure 2 and related text regarding item 215 ‘Determine Significance Window’; Figures 3A & 3B and related text regarding item 303 and 311 ‘significance window’]
generating, based on the first integration data and the second integration data, data responsive to the query, the data comprising at least a respective integration delay prediction for the third-party computing functionality for each respective third-party vendor that is specific to the first-party computing system [see at least Col 8 lines 8-19 for reference to the cycle continuing until there are no remaining metrics and only one server remains after applying significance windows; Col 9 lines 6-10 for reference to servers 307 or 309 being determined as the best servers following the application of metrics A & B; Figure 2 and related text regarding item 217 ‘Identify Servers Falling Within Significance Window’, item 219 ‘Remove Servers Falling Outside Significance Window From Group of Servers’; Figures 3A and 3B and related text regarding the significance of ranking metrics]
taking, an action with respect to the respective integration delay prediction [see at least Col 8 lines 14-15 for reference to the address of the best server being sent to the client’s local DNS server; Col 9 lines 6-10 for reference to servers 307 or 309 being determined as the best servers following the application of metrics A & B; Figures 3A and 3B and related text regarding the significance of ranking metrics]
While Jain discloses the limitations above, it does not disclose identifying, a set of tenants, the set of tenants including, for each respective third-party vendor, a respective tenant that has previously integrated the third-party computing functionality from the respective third-party vendor into a respective tenant computing system; determining, second integration data associated with the set of tenants integrating the third-party computing functionality, wherein the second integration data comprises historical integration duration data associated with completion of prior integrations of the third-party computing functionality by the respective set of tenants; the respective integration delay prediction being predicted based on at least the historical integration duration delay; and wherein the action comprises initiating, network communication or computing operations for integrating the third-party computing functionality from a particular third-party vendor into the first-party computing system.
However, Ignatyev discloses:
identifying, a set of tenants, the set of tenants including, for each respective third-party vendor, a respective tenant that has previously integrated the third-party computing functionality from the respective third-party vendor into a respective tenant computing system [see at least Paragraph 0010 (Provisional Paragraph 0010) for reference to the processing of vectors using machine learning to identify usage metrics or trends in such metrics among users of an account or tenant; Paragraph 0029 (Provisional Paragraph 0006) for reference to the system determining the set of tenants having the one or more tenant characteristics that are the same as those of the new tenant; Paragraph 0063 (Provisional Paragraph 0035) for reference to each tenant datastore containing tenant specific data that is used as part of providing a range of tenant - specific business services or functions, including but not limited to ERP, CRM, eCommerce, Human Resources management, payroll, etc.; Paragraph 0090 for reference to accessing and processing data regarding resource usage and possible demand (such as indicators of possible demand based on machine learning or other data processing techniques) across multiple tenants, embodiments of the inventive system and methods may be able to better allocate resources or “predict” potential resource demand across an industry or set of tenants; Figure 2 (Provisional Figure 2) and related text regarding item 208 ‘Multi-tenant Distributed Computing Platform’, items 217A-Z ‘Tenants’, and items 226 ‘Tenant Datastore’]
determining, by the computing hardware, second integration data associated with the set of tenants integrating the third-party computing functionality, wherein the second integration data comprises historical integration duration data associated with completion of prior integrations of the third-party computing functionality by the respective set of tenants [see at least Paragraph 0090 for reference to accessing and processing data regarding resource usage and possible demand (such as indicators of possible demand based on machine learning or other data processing techniques) across multiple tenants, embodiments of the inventive system and methods may be able to better allocate resources or “predict” potential resource demand across an industry or set of tenants; Figure 4b (Provisional Figure 4b) and related text regarding demand placed upon the infrastructure resources over time for a specific tenant or account; Paragraph 0151 for reference to the inventive methods may be used to analyze historical data concerning the currently existing tenants and their observed “signature” vectors including capturing information regarding one or more “attributes” of the new tenant and existing tenant]
Before the effective filing date, it would have been obvious to one of ordinary skill in the art to modify the method of Jain to include the tenant computing systems of Ignatyev. Doing so would characterize resource usage data by account or tenant, and to process that data to enable platform operators and administrators to make more optimal decisions regarding allocation or allocation changes for platform infrastructure, as stated by Ignatyev (Paragraph 0006).

While the combination of Jain and Ignatyev disclose the limitations above, they do not disclose wherein the second integration data comprises historical integration duration data associated with completion of prior integrations of the third-party computing functionality by the respective set of tenants; the respective integration delay prediction being predicted based on at least the historical integration duration delay; and wherein the action comprises initiating, network communication or computing operations for integrating the third-party computing functionality from a particular third-party vendor into the first-party computing system.
However, Muthuvaradharajan discloses the following:
wherein the second integration data comprises historical integration duration data associated with completion of prior integrations of the third-party computing functionality by the respective set of tenants [see at least Paragraph 0016 for reference to historical data that reflect statuses of execution of the software associated with the set of configurations; Paragraph 0017 for reference to statuses can reflect, for example, whether the execution of the software task has halted due to a failure, whether an execution of the software task has completed or in progress, and/or one or more outputs (e.g., text, graphics, etc.) generated during the execution of the software task, etc.; Paragraph 0018 for reference to data that reflect the historical and current execution statuses of the software task can be stored in database 104, and the data can then be acquired by execution status receiving module; Figures 2A-2C and related text regarding examples of historical and current data that reflects the execution statuses of software]
the respective integration delay prediction being predicted based on at least the historical integration duration delay [see at least Paragraph 0016 for reference to the system integrating with various ERP/APS systems, trigger jobs, detect and identify failure of jobs in ERP/APS system, infer a solution for the failure based on historical outcomes of providing the inferred solution, and trigger the failed jobs with the inferred solution provided; Paragraph 0019 for reference to determination can be made based on, for example, data that reflects the execution status of the task received by execution status receiving module 101, as well as an association between one or more pre stored configurations and one or more historical execution statuses of the task; Paragraph 0021 for reference to each inferred reason can be associated with a configuration targeted at the reason of failure]
Before the effective filing date, it would have been obvious to one of ordinary skill in the art to modify the respective integration delay prediction of Ignatyev to include the historical integration data of Muthuvaradharajan. Doing so implementation of change of a status of execution (e.g., to resolve a failed execution of a task) can be performed more efficiently and accurately, as stated by Muthuvaradharajan (Paragraph 0016).

While the combination of Jain, Ignatyev, and Muthuvaradharajan disclose the limitations above, they do not disclose wherein the action comprises initiating, network communication or computing operations for integrating the third-party computing functionality from a particular third-party vendor into the first-party computing system.
However, Tkatch discloses the following:
wherein the action comprises initiating, network communication or computing operations for integrating the third-party computing functionality from a particular third-party vendor into the first-party computing system [see at least Paragraph 0012 for reference to the business solution enabling the third party business application to be integrated with the business software on the hosted multi-tenant business software system; Paragraph 0029 for reference to the customization module processing one or more business solutions which includes declarative descriptions of customizations needed to integrate a third party software with the business software’; Figure 4 and related text regarding item 408 ‘Activate the Registered Business Solution’]
Before the effective filing date, it would have been obvious to one of ordinary skill in the art to modify the tenant computing system of Ignatyev to include the action of integration of third-party computing functionality of Tkatch. Customizations to generic solutions may be created by partners of the developer of the hosted multi-tenant business software system, as stated by Tkatch (Paragraph 0017).

Claim 2
While the combination of Jain, Ignatyev, Muthuvaradharajan, and Tkatch disclose the limitations above, regarding Claim 2, Jain discloses the following:
generating, by the computing hardware, a user interface that includes a listing of each respective third-party vendor that increases or decreases a ranking of each respective third-party vendor in the listing based on the respective integration delay prediction [see at least Col 8 lines 14-15 for reference to the address of the best server being sent to the client’s local DNS server; Col 9 lines 6-10 for reference to servers 307 or 309 being determined as the best servers following the application of metrics A & B; Figures 3A and 3B and related text regarding the significance of ranking metrics]
Claim 3
While the combination of Jain, Ignatyev, Muthuvaradharajan, and Tkatch disclose the limitations above, regarding Claim 3, Jain discloses the following:
wherein identifying the set of tenants comprises: accessing, by the computing hardware, a first a set of attributes for the first-party computing system [see at least Col 6 lines 37-45 for reference to the server selection system employing performance metrics of each of the servers including round trip time, server load, drop rate, available bandwidth, administrative distance, number of hops, and whether or not a server is in a particular subnetwork; Col 7 lines 1-12 for reference to the use of Pathchar to collect information about each server including using of knowledge about earlier hops and the round trip distribution to this hop to assess bandwidth, drop rate, latency, and queue characteristics; Figure 2 and related text regarding item 203 ‘Obtain Performance Metrics’]
identifying, by the computing hardware, a set of potential tenants, each respective potential tenant having a second set of attributes [see at least Col 7 lines 36-40 for reference to the server selection system eliminating servers with metrics that are individually worse that another server’s metrics; Col 7 lines 50-57 for reference to a group of servers being categorized into a group of acceptable servers based on falling within a significance window; Figure 2 and related text regarding item 205 ‘Eliminate Servers With Metrics Worse Than All Corresponding Metrics Of Another Server’ and item 217 ‘Identify Servers Falling Within Significance Window’]
comparing, by the computing hardware, the first set of attributes to the second set of attributes to identify a respective set of shared attributes for each potential tenant [see at least Col 7 lines 50-55 for reference to the determination of the significance window; Col 8 lines 20-31 for reference to the significance window being catered to the individual characteristics of a particular network; Col 8 lines 32-35 for reference to changes over time in the value of metrics and errors in measuring metric values increasing the significance window; Figure 2 and related text regarding item 215 ‘Determine Significance Window’; Figures 3A & 3B and related text regarding item 303 and 311 ‘significance window’]
determining which respective set of shared attributes includes at least a threshold number of shared attributes [see at least Col 7 lines 50-55 for reference to the determination of the significance window; Col 8 lines 20-31 for reference to the significance window being catered to the individual characteristics of a particular network, for example the server with the best drop rate metric along with any server better than a 10% rate; Col 8 lines 42-52 for reference to significance windows being determined by the use of percentages or percentiles such as falling within the top 25% or server loads of less than 85%; Figure 2 and related text regarding item 215 ‘Determine Significance Window’; Figures 3A & 3B and related text regarding item 303 and 311 ‘significance window’]
Claim 4
While the combination of Jain, Ignatyev, Muthuvaradharajan, and Tkatch disclose the limitations above, regarding Claim 4, Jain discloses the following:
wherein generating the data responsive to the query further comprises generating each respective integration delay prediction by: identifying, by the computing hardware, each respective tenant that has previously integrated the third-party computing functionality from each respective third-party vendor [see at least Col 7 lines 36-40 for reference to the server selection system eliminating servers with metrics that are individually worse that another server’s metrics; Col 7 lines 50-57 for reference to a group of servers being categorized into a group of acceptable servers based on falling within a significance window; Figure 2 and related text regarding item 205 ‘Eliminate Servers With Metrics Worse Than All Corresponding Metrics Of Another Server’ and item 217 ‘Identify Servers Falling Within Significance Window’]
determining, for each respective tenant that has previously integrated the third-party computing functionality from each respective third-party vendor, from the second integration data, a respective actual integration time [see at least Col 6 lines 56-67 for reference to the use of Traceroute to measure round trip time; Col 7 lines 1-12 for reference to the use of Pathchar to collect information about each server including using of knowledge about earlier hops and the round trip distribution to this hop to assess bandwidth, drop rate, latency, and queue characteristics; Figure 2 and related text regarding item 203 ‘Obtain Performance Metrics’]
generating each respective integration delay prediction based on each respective actual integration time [see at least Col 7 lines 21-23 for reference to performance metrics being periodically updated and stored in memory for a later use; Col 7 lines 32-34 for reference to values for performance metrics being updated periodically or determined dynamically; Col 7 lines 36-40 for reference to the server selection system eliminating servers with metrics that are individually worse that another server’s metrics; Col 7 lines 50-57 for reference to a group of servers being categorized into a group of acceptable servers based on falling within a significance window; Figure 2 and related text regarding item 205 ‘Eliminate Servers With Metrics Worse Than All Corresponding Metrics Of Another Server’ and item 217 ‘Identify Servers Falling Within Significance Window’]
Claim 5
While the combination of Jain, Ignatyev, Muthuvaradharajan, and Tkatch disclose the limitations above, regarding Claim 5, Jain discloses the following:
modifying each respective integration delay prediction based on one or more of: a number of the respective set of shared attributes for each respective tenant; or a relation between each respective actual integration time and a respective predicted integration time for each respective tenant that had previously integrated the third-party computing functionality from each respective third-party vendor prior to integration [see at least Col 8 lines 32-35 for reference to changes over time in the value of metrics and errors in measuring metric values increasing the significance window; Figure 2 and related text regarding item 215 ‘Determine Significance Window’; Figures 3A & 3B and related text regarding item 303 and 311 ‘significance window’]
Claim 7
While the combination of Jain, Ignatyev, Muthuvaradharajan, and Tkatch disclose the limitations above, regarding Claim 7, Jain discloses the following:
wherein generating the data responsive to the query comprises using a limited portion of the first integration data and second integration data to calculate each respective integration delay prediction [see at least Col 8 lines 8-19 for reference to the cycle continuing until there are no remaining metrics and only one server remains after applying significance windows; Col 9 lines 6-10 for reference to servers 307 or 309 being determined as the best servers following the application of metrics A & B; Figure 2 and related text regarding item 217 ‘Identify Servers Falling Within Significance Window’, item 219 ‘Remove Servers Falling Outside Significance Window From Group of Servers’; Figures 3A and 3B and related text regarding the significance of ranking metrics]
where the limited portion includes data related to prior integrations of the third-party computing functionality that included at least one of: a similar volume of data encompassed by integrating the third-party computing functionality into the first-party computing system; a similar time period in which the third-party computing functionality is planned to be integrated into the first-party computing system; or a tenant that operates in a related field to the first-party computing system [see at least Col 9 lines 6-10 for reference to servers 307 or 309 being determined as the best servers following the application of metrics A & B; Figure 2 and related text regarding item 217 ‘Identify Servers Falling Within Significance Window’, item 219 ‘Remove Servers Falling Outside Significance Window From Group of Servers’; Figures 3A and 3B and related text regarding the significance of ranking metrics]
Claim 8
Regarding Claim 8, Jain discloses the following:
A system comprising [see at least Col 4 lines 66-67 & Col 5 line 1 for reference to systems for selecting a best server from a group of servers that can provide similar content to a client; Figure 1 and related text regarding the network level view of the present invention]
a non-transitory computer-readable medium storing instructions [see at least Col 4 lines 43-47 for reference to the invention pertaining to computer program products including a machine readable medium on which is stored program instructions, tables or lists, and/or data structures for implementing a method as described above; Col 9 lines 20-30 for reference to the present invention relating to machine readable media including program instructions, state information, etc. for performing various operations]
a processing device communicatively coupled to the non-transitory computer-readable medium, wherein the processing device is configured to execute the instructions and thereby perform operations comprising [see at least Col 5 lines 42-47 for reference to the sever selection system being implemented on any suitable computation device or network node; Col 9 lines 20-30 for reference to the present invention relating to machine readable media including program instructions, state information, etc. for performing various operations; Figure 1 and related text regarding the network level view of the present invention]
receiving, from a fist-party computing system having a first set of attributes, a first request to integrate third-party computing functionality into the first-party computing system [see at least Col 5 lines 6-8 for reference to the client issuing a request for content using a specific domain name]
identifying a set of third-party computing systems that provide the third-party computing functionality [see at least Col 6 lines 33-35 for reference to the server selection system identifying a group of content servers that can provide the material the client has requested; Figure 2 and related text regarding item 201 ‘Identify Group of Content Servers’]
accessing tenant computing system integration data for the third-party computing functionality, the computing system integration data comprising integration data for each of a plurality of computing systems operated by respective entities that have previously integrated the third-party computing functionality [see at least Col 6 lines 37-45 for reference to the server selection system employing performance metrics of each of the servers including round trip time, server load, drop rate, available bandwidth, administrative distance, number of hops, and whether or not a server is in a particular subnetwork; Col 7 lines 1-12 for reference to the use of Pathchar to collect information about each server including using of knowledge about earlier hops and the round trip distribution to this hop to assess bandwidth, drop rate, latency, and queue characteristics; Figure 2 and related text regarding item 203 ‘Obtain Performance Metrics’]
generating a respective integration timing prediction for each third-party computing system in the set of third-party computing systems with respect to integrating the third-party computing functionality into the first-party computing system based on the computing system integration data [see at least Col 8 lines 8-19 for reference to the cycle continuing until there are no remaining metrics and only one server remains after applying significance windows; Col 9 lines 6-10 for reference to servers 307 or 309 being determined as the best servers following the application of metrics A & B; Figure 2 and related text regarding item 217 ‘Identify Servers Falling Within Significance Window’, item 219 ‘Remove Servers Falling Outside Significance Window From Group of Servers’; Figures 3A and 3B and related text regarding the significance of ranking metrics]
customizing each respective integration timing prediction to the first-party computing system by: identifying a subset of the plurality of computing systems that share a subset of the first set of attributes [see at least Col 6 lines 33-35 for reference to the server selection system identifying a group of content servers that can provide the material the client has requested; Figure 2 and related text regarding item 201 ‘Identify Group of Content Servers’]
generating a modified respective integration timing prediction for each third-party computing system based on a subset of the computing system integration data that omits the computing system integration data for the plurality of computing systems that are not in the subset of the plurality of computing systems [see at least Col 7 lines 36-40 for reference to the server selection system eliminating servers with metrics that are individually worse that another server’s metrics; Col 7 lines 50-57 for reference to a group of servers being categorized into a group of acceptable servers based on falling within a significance window; Figure 2 and related text regarding item 205 ‘Eliminate Servers With Metrics Worse Than All Corresponding Metrics Of Another Server’ and item 217 ‘Identify Servers Falling Within Significance Window’]
generating a listing of the set of third-party computing systems and an indication of the modified respective integration timing prediction; and providing to a user computing device in the first-party computing system [see at least Col 8 lines 14-15 for reference to the address of the best server being sent to the client’s local DNS server; Col 9 lines 6-10 for reference to servers 307 or 309 being determined as the best servers following the application of metrics A & B; Figures 3A and 3B and related text regarding the significance of ranking metrics]
While Jain discloses the limitations above, it does not disclose accessing tenant computing system integration data for the third-party computing functionality, the tenant computing system integration data comprising integration data for each of a plurality of tenant computing systems operated by respective tenant entities that have previously integrated the third-party computing functionality, wherein the tenant computing system integration data further comprises historical integration duration data associated with completion of prior integrations of the third-party computing functionality for each of a plurality of tenant computing systems; generating a respective integration timing prediction for each third-party computing system in the set of third-party computing systems with respect to integrating the third-party computing functionality into the first-party computing system based on the tenant computing system integration data and the historical integration duration data; identifying a subset of the plurality of tenant computing systems that share a subset of the first set of attributes; and generating a modified respective integration timing prediction for each third-party computing system based on a subset of the tenant computing system integration data that omits the tenant computing system integration data for the plurality of tenant computing systems that are not in the subset of the plurality of tenant computing systems; generating a graphical user interface comprising a listing of the set of third-party computing systems and an indication of the modified respective integration timing prediction; providing the graphical user interface to a user computing device in the first-party computing system; initiating network communication or computing operations for integrating the third-party computing functionality into the first-party computing system.
However, Ignatyev discloses the following:
accessing tenant computing system integration data for the third-party computing functionality, the tenant computing system integration data comprising integration data for each of a plurality of tenant computing systems operated by respective tenant entities that have previously integrated the third-party computing functionality [see at least Paragraph 0063 (Provisional Paragraph 0035) for reference to each tenant datastore containing tenant specific data that is used as part of providing a range of tenant - specific business services or functions, including but not limited to ERP, CRM, eCommerce, Human Resources management, payroll, etc.; Figure 2 (Provisional Figure 2) and related text regarding item 208 ‘Multi-tenant Distributed Computing Platform’, items 217A-Z ‘Tenants’, and items 226 ‘Tenant Datastore’]
generating a respective integration timing prediction for each third-party computing system in the set of third-party computing systems with respect to integrating the third-party computing functionality into the first-party computing system based on the tenant computing system integration data [see at least Paragraph 0090 for reference to accessing and processing data regarding resource usage and possible demand (such as indicators of possible demand based on machine learning or other data processing techniques) across multiple tenants, embodiments of the inventive system and methods may be able to better allocate resources or “predict” potential resource demand across an industry or set of tenants; Figure 4b (Provisional Figure 4b) and related text regarding demand placed upon the infrastructure resources over time for a specific tenant or account]
identifying a subset of the plurality of tenant computing systems that share a subset of the first set of attributes [see at least Paragraph 0010 (Provisional Paragraph 0010) for reference to the processing of vectors using machine learning to identify usage metrics or trends in such metrics among users of an account or tenant; Paragraph 0029 (Provisional Paragraph 0006) for reference to the system determining the set of tenants having the one or more tenant characteristics that are the same as those of the new tenant; Paragraph 0090 for reference to accessing and processing data regarding resource usage and possible demand (such as indicators of possible demand based on machine learning or other data processing techniques) across multiple tenants, embodiments of the inventive system and methods may be able to better allocate resources or “predict” potential resource demand across an industry or set of tenants]
generating a modified respective integration timing prediction for each third-party computing system based on a subset of the tenant computing system integration data that omits the tenant computing system integration data for the plurality of tenant computing systems that are not in the subset of the plurality of tenant computing systems [see at least Paragraph 0018 for reference to the system determining based on the fitness metric that the set of tenants should be modified; Paragraph 0029 (Provisional Paragraph 0006) for reference to the system determining the set of tenants having the one or more tenant characteristics that are the same as those of the new tenant; Paragraph 0090 for reference to accessing and processing data regarding resource usage and possible demand (such as indicators of possible demand based on machine learning or other data processing techniques) across multiple tenants, embodiments of the inventive system and methods may be able to better allocate resources or “predict” potential resource demand across an industry or set of tenants]
generating a graphical user interface comprising a listing of the set of third-party computing systems and an indication of the modified respective integration timing prediction [see at least Paragraph 0063 (Provisional Paragraph 0034) for reference to the user interface maintaining multiple user interfaces including a graphical user interface; Paragraph 0096 for reference to the system creating a dashboard for the display of certain usage data for multiple tenants of the platform; Figure 3 (Provisional Figure 3) and related text regarding item 303 ‘user interface’]
providing the graphical user interface to a user computing device in the first-party computing system [see at least Paragraph 0063 (Provisional Paragraph 0034) for reference to the user interface maintaining multiple user interfaces including a graphical user interface; Figure 3 (Provisional Figure 3) and related text regarding item 303 ‘user interface’]
Before the effective filing date, it would have been obvious to one of ordinary skill in the art to modify the method of Jain to include the tenant computing systems of Ignatyev. Doing so would characterize resource usage data by account or tenant, and to process that data to enable platform operators and administrators to make more optimal decisions regarding allocation or allocation changes for platform infrastructure, as stated by Ignatyev (Paragraph 0006).

While the combination of Jain and Ignatyev disclose the limitations above, they do not disclose wherein the tenant computing system integration data further comprises historical integration duration data associated with completion of prior integrations of the third-party computing functionality for each of a plurality of tenant computing systems; generating a respective integration timing prediction for each third-party computing system in the set of third-party computing systems with respect to integrating the third-party computing functionality into the first-party computing system based on the tenant computing system integration data and the historical integration duration data; initiating network communication or computing operations for integrating the third-party computing functionality into the first-party computing system.
However, Muthuvaradharajan discloses the following:
wherein the tenant computing system integration data further comprises historical integration duration data associated with completion of prior integrations of the third-party computing functionality for each of a plurality of tenant computing systems [see at least Paragraph 0016 for reference to historical data that reflect statuses of execution of the software associated with the set of configurations; Paragraph 0017 for reference to statuses can reflect, for example, whether the execution of the software task has halted due to a failure, whether an execution of the software task has completed or in progress, and/or one or more outputs (e.g., text, graphics, etc.) generated during the execution of the software task, etc.; Paragraph 0018 for reference to data that reflect the historical and current execution statuses of the software task can be stored in database 104, and the data can then be acquired by execution status receiving module; Figures 2A-2C and related text regarding examples of historical and current data that reflects the execution statuses of software]
generating a respective integration timing prediction for each third-party computing system in the set of third-party computing systems with respect to integrating the third-party computing functionality into the first-party computing system based on the tenant computing system integration data and the historical integration duration data [see at least Paragraph 0016 for reference to the system integrating with various ERP/APS systems, trigger jobs, detect and identify failure of jobs in ERP/APS system, infer a solution for the failure based on historical outcomes of providing the inferred solution, and trigger the failed jobs with the inferred solution provided; Paragraph 0019 for reference to determination can be made based on, for example, data that reflects the execution status of the task received by execution status receiving module 101, as well as an association between one or more pre stored configurations and one or more historical execution statuses of the task; Paragraph 0021 for reference to each inferred reason can be associated with a configuration targeted at the reason of failure]
Before the effective filing date, it would have been obvious to one of ordinary skill in the art to modify the respective integration delay prediction of Ignatyev to include the historical integration data of Muthuvaradharajan. Doing so implementation of change of a status of execution (e.g., to resolve a failed execution of a task) can be performed more efficiently and accurately, as stated by Muthuvaradharajan (Paragraph 0016).

While the combination of Jain, Ignatyev, and Muthuvaradharajan disclose the limitations above, they do not disclose initiating network communication or computing operations for integrating the third-party computing functionality into the first-party computing system.
However, Tkatch discloses the following:
initiating network communication or computing operations for integrating the third-party computing functionality into the first-party computing system [see at least Paragraph 0012 for reference to the business solution enabling the third party business application to be integrated with the business software on the hosted multi-tenant business software system; Paragraph 0029 for reference to the customization module processing one or more business solutions which includes declarative descriptions of customizations needed to integrate a third party software with the business software’; Figure 4 and related text regarding item 408 ‘Activate the Registered Business Solution’]
Before the effective filing date, it would have been obvious to one of ordinary skill in the art to modify the tenant computing system of Ignatyev to include the action of integration of third-party computing functionality of Tkatch. Customizations to generic solutions may be created by partners of the developer of the hosted multi-tenant business software system, as stated by Tkatch (Paragraph 0017).
Claim 9
While the combination of Jain, Ignatyev, Muthuvaradharajan, and Tkatch disclose the limitations above, regarding Claim 9, Jain discloses the following:
wherein the operations further comprise increasing or decreasing a ranking of each third-party computing system in the listing of the set of third-party computing systems based on each modified respective integration timing prediction [see at least Col 8 lines 14-15 for reference to the address of the best server being sent to the client’s local DNS server; Col 9 lines 6-10 for reference to servers 307 or 309 being determined as the best servers following the application of metrics A & B; Figures 3A and 3B and related text regarding the significance of ranking metrics]
Claim 10
While the combination of Jain, Ignatyev, Muthuvaradharajan, and Tkatch disclose the limitations above, regarding Claim 10, Jain discloses the following:
wherein customizing each respective integration timing prediction to the first-party computing system further comprises: determining a number of shared attributes between the subset of the plurality of computing systems and the first-party computing system [see at least Col 6 lines 33-35 for reference to the server selection system identifying a group of content servers that can provide the material the client has requested; Col 7 lines 50-57 for reference to a group of servers being categorized into a group of acceptable servers based on falling within a significance window; Figure 2 and related text regarding item 201 ‘Identify Group of Content Servers’, item 205 ‘Eliminate Servers With Metrics Worse Than All Corresponding Metrics Of Another Server’, and item 217 ‘Identify Servers Falling Within Significance Window’]
generating each modified respective integration timing prediction for each third-party by weighting the subset of the computing system integration data according to the number of shared attributes for each of the subset of the plurality of computing systems [see at least Col 7 lines 42-45 for reference to the server selection should obtain the priorities for each of the metrics; Col 8 lines 35-41 for reference to the significance window being set by multiplying difference between the highest and lowest metric values with a multiplier; Figure 2 and related text regarding item 207 ‘Obtain Metric Priority’]
While Jain discloses the limitations above, it does not disclose determining a number of shared attributes between the subset of the plurality of tenant computing systems and the first-party computing system; and generating each modified respective integration timing prediction for each third-party by weighting the subset of the tenant computing system integration data according to the number of shared attributes for each of the subset of the plurality of tenant computing systems.
However, Ignatyev discloses the following:
determining a number of shared attributes between the subset of the plurality of tenant computing systems and the first-party computing system [see at least Paragraph 0029 (Provisional Paragraph 0006) for reference to the system determining the set of tenants having the one or more tenant characteristics that are the same as those of the new tenant]
generating each modified respective integration timing prediction for each third-party by weighting the subset of the tenant computing system integration data according to the number of shared attributes for each of the subset of the plurality of tenant computing systems [see at least Paragraph 0018 for reference to the system determining based on the fitness metric that the set of tenants should be modified; Paragraph 0029 (Provisional Paragraph 0006) for reference to the system determining the set of tenants having the one or more tenant characteristics that are the same as those of the new tenant; Paragraph 0090 for reference to accessing and processing data regarding resource usage and possible demand (such as indicators of possible demand based on machine learning or other data processing techniques) across multiple tenants, embodiments of the inventive system and methods may be able to better allocate resources or “predict” potential resource demand across an industry or set of tenants]
Before the effective filing date, it would have been obvious to one of ordinary skill in the art to modify the method of Jain to include the tenant computing systems of Ignatyev. Doing so would characterize resource usage data by account or tenant, and to process that data to enable platform operators and administrators to make more optimal decisions regarding allocation or allocation changes for platform infrastructure, as stated by Ignatyev (Paragraph 0006).
Claim 11
While the combination of Jain, Ignatyev, Muthuvaradharajan, and Tkatch disclose the limitations above, regarding Claim 11, Jain discloses the following:
wherein the first set of attributes identify at least one of a geographic location of the first-party computing system, an industry of a first entity that operates the first-party computing system, a volume of data that will be utilized by the third-party computing functionality, a desired time period for integrating the third-party computing functionality, or a number of data infractions experienced by the first entity [see at least Col 6 lines 37-45 for reference to the server selection system employing performance metrics of each of the servers including round trip time, server load, drop rate, available bandwidth, administrative distance, number of hops, and whether or not a server is in a particular subnetwork; Col 7 lines 1-12 for reference to the use of Pathchar to collect information about each server including using of knowledge about earlier hops and the round trip distribution to this hop to assess bandwidth, drop rate, latency, and queue characteristics; Figure 2 and related text regarding item 203 ‘Obtain Performance Metrics’; Examiner notes ‘drop rate’ as analogous to ‘number of data infractions experienced’]
Claim 12
While the combination of Jain, Ignatyev, Muthuvaradharajan, and Tkatch disclose the limitations above, regarding Claim 12, Jain discloses the following:
customizing each respective integration timing prediction to the first-party computing system further comprises modifying each modified respective integration timing prediction based on integration timing data for the first-party computing system that defines an integration performance for the first-party computing system with respect to one or more integration timing predictions for one or more prior third-party computing functionality integrations by the first-party computing system [see at least Col 8 lines 32-35 for reference to changes over time in the value of metrics and errors in measuring metric values increasing the significance window; Figure 2 and related text regarding item 215 ‘Determine Significance Window’; Figures 3A & 3B and related text regarding item 303 and 311 ‘significance window’]
Claim 14
While the combination of Jain, Ignatyev, Muthuvaradharajan, and Tkatch disclose the limitations above, regarding Claim 14, Jain discloses the following:
wherein the first request to integrate the third-party computing functionality into the first-party computing system comprises at least one of: a request to modify a provider of the third-party computing functionality from a first third-party computing system to a second third-party computing system; or a request to integrate the third-party computing function, where the third-party computing functionality is not currently available on the first-party computing system [see at least Col 5 lines 6-8 for reference to the client issuing a request for content using a specific domain name; Col 6 lines 5-16 for reference to the request by the client including the best server providing optimal content including a reliable connection, less expensive, optimize traffic patterns, and work best for all users as a whole]
Claim 15
Regarding Claim 15, Jain discloses the following:
A method comprising [see at least Col 4 lines 66-67 & Col 5 line 1 for reference to a method for selecting a best server from a group of servers that can provide similar content to a client; Figure 2 and related text regarding the method for selecting a best server]
receiving, by computing hardware, a request to integrate functionality provided by a third-party computing system operated by a third-party vendor having a first set of attributes into a first-party computing system operated by a first-party entity having a second set of attributes [see at least Col 5 lines 6-8 for reference to the client issuing a request for content using a specific domain name; Col 6 lines 5-16 for reference to the request by the client including the best server providing optimal content including a reliable connection, less expensive, optimize traffic patterns, and work best for all users as a whole]
identifying, by the computing hardware, a set of third-party vendors that provide the functionality, the set of third-party vendors having a third set of attributes [see at least Col 6 lines 33-35 for reference to the server selection system identifying a group of content servers that can provide the material the client has requested; Figure 2 and related text regarding item 201 ‘Identify Group of Content Servers’]
accessing, by the computing hardware, computing system integration data for the functionality provided by the third-party computing system, the computing system integration data comprising integration data for each of a plurality of computing systems operated by respective entities that have previously integrated the functionality from any of the set of third-party vendors, the entities having a fourth set of attributes [see at least Col 6 lines 37-45 for reference to the server selection system employing performance metrics of each of the servers including round trip time, server load, drop rate, available bandwidth, administrative distance, number of hops, and whether or not a server is in a particular subnetwork; Col 7 lines 1-12 for reference to the use of Pathchar to collect information about each server including using of knowledge about earlier hops and the round trip distribution to this hop to assess bandwidth, drop rate, latency, and queue characteristics; Figure 2 and related text regarding item 203 ‘Obtain Performance Metrics’]
process the first set of attributes and the third set of attributes to generate a set of similarly situated third-party vendors to the third-party vendor [see at least Col 6 lines 33-35 for reference to the server selection system identifying a group of content servers that can provide the material the client has requested; Figure 2 and related text regarding item 201 ‘Identify Group of Content Servers’]
process the second set of attributes and the fourth set of attributes to generate a set of similarly situated entities to the first-party entity [see at least Col 7 lines 50-57 for reference to the server selection system taking the server with the lowest load to determine the significance window and all servers falling within this window are categorized into a group of acceptable servers; Col 8 lines 20-31 for reference to the significance window being catered to the individual characteristics of a particular network; Figure 2 and related text regarding item 213 ‘Identify Server With Best Value for Metric’ and item 217 ‘Identify Servers Falling Within Significance Window’]
analyzing, by the computing hardware, the computing system integration data for the set of similarly situated entities and the set of similarly situated third-party vendors to determine integration timing data for each of the set of similarly situated entities and the set of similarly situated third-party vendors [see at least Col 7 lines 50-55 for reference to the determination of the significance window; Col 8 lines 20-31 for reference to the significance window being catered to the individual characteristics of a particular network; Col 8 lines 32-35 for reference to changes over time in the value of metrics and errors in measuring metric values increasing the significance window; Figure 2 and related text regarding item 215 ‘Determine Significance Window’; Figures 3A & 3B and related text regarding item 303 and 311 ‘significance window’]
generating, by the computing hardware, a timing prediction for integrating the functionality provided by the third-party computing system into the first-party computing system based on the integration timing data that is specific to the first-party computing system [see at least Col 8 lines 8-19 for reference to the cycle continuing until there are no remaining metrics and only one server remains after applying significance windows; Col 9 lines 6-10 for reference to servers 307 or 309 being determined as the best servers following the application of metrics A & B; Figure 2 and related text regarding item 217 ‘Identify Servers Falling Within Significance Window’, item 219 ‘Remove Servers Falling Outside Significance Window From Group of Servers’; Figures 3A and 3B and related text regarding the significance of ranking metrics]
causing, by the computing hardware, performance of an action with respect to the first-party computing system based on the timing prediction [see at least Col 8 lines 14-15 for reference to the address of the best server being sent to the client’s local DNS server; Col 9 lines 6-10 for reference to servers 307 or 309 being determined as the best servers following the application of metrics A & B; Figures 3A and 3B and related text regarding the significance of ranking metrics]
While Jain discloses the limitations above, it does not disclose accessing, by the computing hardware, tenant computing system integration data for the functionality provided by the third-party computing system, the tenant computing system integration data comprising integration data for each of a plurality of tenant computing systems operated by respective tenant entities that have previously integrated the functionality from any of the set of third-party vendors, the tenant entities having a fourth set of attributes, wherein the tenant computing system integration data further comprises historical integration duration data associated with completion of prior integrations of the third-party computing functionality for each of the respective tenant entities; causing, by the computing hardware, at least one of a rules-based model or a machine-learning model to process the first set of attributes and the third set of attributes to generate a set of similarly situated third-party vendors to the third-party vendor; causing, by the computing hardware, at least one of the rules-based model or the machine-learning model to process the second set of attributes and the fourth set of attributes to generate a set of similarly situated tenant entities to the first-party entity; or analyzing, by the computing hardware, the tenant computing system integration data for the set of similarly situated tenant entities and the set of similarly situated third-party entities to determine integration timing data for each of the set of similarly situated tenant entities and the set of third-party vendors; generating, by the computing hardware, a timing prediction for integrating the functionality provided by the third-party computing system into the first-party computing system based on the integration timing data that is specific to the first-party computing system and historical integration duration data; wherein the action comprises initiating network communication or computing operations for integrating the functionality provided by the third-party computing system into the first-party computing system.
However, Ignatyev discloses the following:
accessing, by the computing hardware, tenant computing system integration data for the functionality provided by the third-party computing system, the tenant computing system integration data comprising integration data for each of a plurality of tenant computing systems operated by respective tenant entities that have previously integrated the functionality from any of the set of third-party vendors, the tenant entities having a fourth set of attributes [see at least Paragraph 0063 (Provisional Paragraph 0035) for reference to each tenant datastore containing tenant specific data that is used as part of providing a range of tenant - specific business services or functions, including but not limited to ERP, CRM, eCommerce, Human Resources management, payroll, etc.; Figure 2 (Provisional Figure 2) and related text regarding item 208 ‘Multi-tenant Distributed Computing Platform’, items 217A-Z ‘Tenants’, and items 226 ‘Tenant Datastore’]
causing, by the computing hardware, at least one of a rules-based model or a machine-learning model to process the first set of attributes and the third set of attributes to generate a set of similarly situated third-party vendors to the third-party vendor [see at least Paragraph 0010 (Provisional Paragraph 0010) for reference to the processing of vectors using machine learning to identify usage metrics or trends in such metrics among users of an account or tenant; Paragraph 0090 for reference to accessing and processing data regarding resource usage and possible demand (such as indicators of possible demand based on machine learning or other data processing techniques) across multiple tenants, embodiments of the inventive system and methods may be able to better allocate resources or “predict” potential resource demand across an industry or set of tenants]
causing, by the computing hardware, at least one of the rules-based model or the machine-learning model to process the second set of attributes and the fourth set of attributes to generate a set of similarly situated tenant entities to the first-party entity [see at least Paragraph 0010 (Provisional Paragraph 0010) for reference to the processing of vectors using machine learning to identify usage metrics or trends in such metrics among users of an account or tenant; Paragraph 0090 for reference to accessing and processing data regarding resource usage and possible demand (such as indicators of possible demand based on machine learning or other data processing techniques) across multiple tenants, embodiments of the inventive system and methods may be able to better allocate resources or “predict” potential resource demand across an industry or set of tenants; Paragraph 0106 for reference to the system determining optimal allocation of new tenants by comparing their attributes to existing tenants’ attributes and their usage “signatures”]
analyzing, by the computing hardware, the tenant computing system integration data for the set of similarly situated tenant entities and the set of similarly situated third-party vendors to determine integration timing data for each of the set of similarly situated tenant entities and the set of similarly situated third-party vendors [see at least Paragraph 0090 for reference to accessing and processing data regarding resource usage and possible demand (such as indicators of possible demand based on machine learning or other data processing techniques) across multiple tenants, embodiments of the inventive system and methods may be able to better allocate resources or “predict” potential resource demand across an industry or set of tenants; Figure 4b (Provisional Figure 4b) and related text regarding demand placed upon the infrastructure resources over time for a specific tenant or account]
Before the effective filing date, it would have been obvious to one of ordinary skill in the art to modify the method of Jain to include the machine learning analysis of Ignatyev. Doing so would characterize resource usage data by account or tenant , and to process that data to enable platform operators and administrators to make more optimal decisions regarding allocation or allocation changes for platform infrastructure, as stated by Ignatyev (Paragraph 0006).

While the combination of Jain and Ignatyev disclose the limitations above, they do not disclose wherein the tenant computing system integration data further comprises historical integration duration data associated with completion of prior integrations of the third-party computing functionality for each of the respective tenant entities; generating, by the computing hardware, a timing prediction for integrating the functionality provided by the third-party computing system into the first-party computing system based on the integration timing data that is specific to the first-party computing system and historical integration duration data; wherein the action comprises initiating, by the computing hardware, network communication or computing operations for integrating the third-party computing functionality from a particular third-party vendor of the set of third-party vendors into the first-party computing system.
However, Muthuvaradharajan discloses the following:
wherein the tenant computing system integration data further comprises historical integration duration data associated with completion of prior integrations of the third-party computing functionality for each of the respective tenant entities [see at least Paragraph 0016 for reference to historical data that reflect statuses of execution of the software associated with the set of configurations; Paragraph 0017 for reference to statuses can reflect, for example, whether the execution of the software task has halted due to a failure, whether an execution of the software task has completed or in progress, and/or one or more outputs (e.g., text, graphics, etc.) generated during the execution of the software task, etc.; Paragraph 0018 for reference to data that reflect the historical and current execution statuses of the software task can be stored in database 104, and the data can then be acquired by execution status receiving module; Figures 2A-2C and related text regarding examples of historical and current data that reflects the execution statuses of software]
generating, by the computing hardware, a timing prediction for integrating the functionality provided by the third-party computing system into the first-party computing system based on the integration timing data that is specific to the first-party computing system and historical integration duration data [see at least Paragraph 0016 for reference to the system integrating with various ERP/APS systems, trigger jobs, detect and identify failure of jobs in ERP/APS system, infer a solution for the failure based on historical outcomes of providing the inferred solution, and trigger the failed jobs with the inferred solution provided; Paragraph 0019 for reference to determination can be made based on, for example, data that reflects the execution status of the task received by execution status receiving module 101, as well as an association between one or more pre stored configurations and one or more historical execution statuses of the task; Paragraph 0021 for reference to each inferred reason can be associated with a configuration targeted at the reason of failure]
Before the effective filing date, it would have been obvious to one of ordinary skill in the art to modify the respective integration delay prediction of Ignatyev to include the historical integration data of Muthuvaradharajan. Doing so implementation of change of a status of execution (e.g., to resolve a failed execution of a task) can be performed more efficiently and accurately, as stated by Muthuvaradharajan (Paragraph 0016).

While the combination of Jain, Ignatyev, and Muthuvaradharajan disclose the limitations above, they do not disclose wherein the action comprises initiating, by the computing hardware, network communication or computing operations for integrating the third-party computing functionality from a particular third-party vendor of the set of third-party vendors into the first-party computing system.
However, Tkatch discloses the following:
wherein the action comprises initiating, by the computing hardware, network communication or computing operations for integrating the third-party computing functionality from a particular third-party vendor of the set of third-party vendors into the first-party computing system [see at least Paragraph 0012 for reference to the business solution enabling the third party business application to be integrated with the business software on the hosted multi-tenant business software system; Paragraph 0029 for reference to the customization module processing one or more business solutions which includes declarative descriptions of customizations needed to integrate a third party software with the business software’; Figure 4 and related text regarding item 408 ‘Activate the Registered Business Solution’]
Before the effective filing date, it would have been obvious to one of ordinary skill in the art to modify the tenant computing system of Ignatyev to include the action of integration of third-party computing functionality of Tkatch. Customizations to generic solutions may be created by partners of the developer of the hosted multi-tenant business software system, as stated by Tkatch (Paragraph 0017).
Claim 16
While the combination of Jain, Ignatyev, Muthuvaradharajan, and Tkatch disclose the limitations above, regarding Claim 16, Jain discloses the following:
wherein the second set of attributes identify at least one of a geographic location of the first-party computing system, an industry of the first-party entity that operates the first-party computing system, a volume of data that will be utilized by the functionality provided by the third-party computing system, a desired time period for integrating the functionality provided by the third-party computing system, or a number of prior data infractions experienced by the first-party entity [see at least Col 6 lines 37-45 for reference to the server selection system employing performance metrics of each of the servers including round trip time, server load, drop rate, available bandwidth, administrative distance, number of hops, and whether or not a server is in a particular subnetwork; Col 7 lines 1-12 for reference to the use of Pathchar to collect information about each server including using of knowledge about earlier hops and the round trip distribution to this hop to assess bandwidth, drop rate, latency, and queue characteristics; Figure 2 and related text regarding item 203 ‘Obtain Performance Metrics’; Examiner notes ‘drop rate’ as analogous to ‘number of data infractions experienced’]
Claim 17
While the combination of Jain, Ignatyev, Muthuvaradharajan, and Tkatch disclose the limitations above, regarding Claim 17, Jain discloses the following:
wherein the action comprises one or more of: generating, by the computing hardware, a user interface that includes a listing of the set of third-party vendors that increases or decreases a ranking of the third-party vendor in the listing based on the timing prediction [see at least Col 8 lines 14-15 for reference to the address of the best server being sent to the client’s local DNS server; Col 9 lines 6-10 for reference to servers 307 or 309 being determined as the best servers following the application of metrics A & B; Figures 3A and 3B and related text regarding the significance of ranking metrics]
Claim 18
While the combination of Jain, Ignatyev, Muthuvaradharajan, and Tkatch disclose the limitations above, regarding Claim 18, Jain discloses the following:
generating the timing prediction by weighting the integration timing data according to a number of shared attributes between the first-party entity and each entity in the set of similarly situated tenant entities [see at least Col 7 lines 42-45 for reference to the server selection should obtain the priorities for each of the metrics; Col 8 lines 35-41 for reference to the significance window being set by multiplying difference between the highest and lowest metric values with a multiplier; Figure 2 and related text regarding item 207 ‘Obtain Metric Priority’]
Claim 19
While the combination of Jain, Ignatyev, Muthuvaradharajan, and Tkatch disclose the limitations above, regarding Claim 19, Jain discloses the following:
setting, by the computing hardware, a benchmark for completing integration of the functionality provided by the third-party computing system [see at least Col 7 lines 42-45 for reference to the server selection should obtain the priorities for each of the metrics; Col 7 lines 50-52 for reference to the server selection system taking the server with the lowest load and determining the significance window; Figure 2 and related text regarding item 207 ‘Obtain Metric Priority’ and item 213 ‘Identify Server With Best Value for Metric’]
tracking, by the computing hardware during integration of the functionality provided by the third-party computing system into the first-party computing system, actual timing data [see at least Col 6 lines 56-67 for reference to the use of Traceroute to measure round trip time; Col 7 lines 1-12 for reference to the use of Pathchar to collect information about each server including using of knowledge about earlier hops and the round trip distribution to this hop to assess bandwidth, drop rate, latency, and queue characteristics; Figure 2 and related text regarding item 203 ‘Obtain Performance Metrics’]
facilitating at least one of modification of the timing prediction based on the actual timing data or transfer of the actual timing data to a third-party computing entity for use in future timing determinations related to integration of the functionality provided by the third-party computing system [see at least Col 7 lines 21-23 for reference to performance metrics being periodically updated and stored in memory for a later use; Col 7 lines 32-34 for reference to values for performance metrics being updated periodically or determined dynamically; Col 7 lines 36-40 for reference to the server selection system eliminating servers with metrics that are individually worse that another server’s metrics; Col 7 lines 50-57 for reference to a group of servers being categorized into a group of acceptable servers based on falling within a significance window; Figure 2 and related text regarding item 205 ‘Eliminate Servers With Metrics Worse Than All Corresponding Metrics Of Another Server’ and item 217 ‘Identify Servers Falling Within Significance Window’]
Claim 20
While the combination of Jain, Ignatyev, Muthuvaradharajan, and Tkatch disclose the limitations above, regarding Claim 20, Jain discloses the following:
wherein the first set of attributes define at least a number of infractions incurred by the third-party vendor, a geographic location in which the third-party vendor operates, a relative integration time for the third-party vendor compared to pre-integration predicted integration times, or a number of prior integrations of the functionality provided by the third-party computing system [see at least Col 6 lines 37-45 for reference to the server selection system employing performance metrics of each of the servers including round trip time, server load, drop rate, available bandwidth, administrative distance, number of hops, and whether or not a server is in a particular subnetwork; Col 7 lines 1-12 for reference to the use of Pathchar to collect information about each server including using of knowledge about earlier hops and the round trip distribution to this hop to assess bandwidth, drop rate, latency, and queue characteristics; Figure 2 and related text regarding item 203 ‘Obtain Performance Metrics’; Examiner notes ‘drop rate’ as analogous to ‘number of data infractions experienced’]

Claim(s) 6 is/are rejected under 35 U.S.C. 103 as being unpatentable over Jain (U.S 6,795,858 B1) in view of Ignatyev (U.S 2017/0318083 A1) in view of Muthuvaradharajan (U.S 2017/0083377 A1) in view of Tkatch (U.S 2010/0286992 A1), as applied in claim 1, in view of Perryman (U.S 2016/0112505 A1).
Claim 6
While the combination of Jain, Ignatyev, Muthuvaradharajan, and Tkatch discloses the limitations above, it does not disclose wherein identifying the set of tenants comprises: determining, by the computing hardware, a first geographic location of the first-party computing system; and identifying the set of tenants by determining that each respective tenant computing system is in the first geographic location.
Regarding Claim 6, Perryman discloses the following:
wherein identifying the set of tenants comprises: determining, by the computing hardware, a first geographic location of the first-party computing system [see at least Paragraph 0053-54 for reference to the assignment request being analyzed using an estimated physical location of the computing device; Paragraph 0059 for reference to the assignment request is analyzed to determine the geographic location of the computing device by inspecting the IP address; Figure 4 and related text regarding item 400 ‘Analyze metadata in the assignment request to determine the geographic location of the computing device]
identifying the set of tenants by determining that each respective tenant computing system is in the first geographic location [see at least Paragraph 0032 for reference to the network/location proximity algorithm being used to determine the location of the computing devices to assist; Paragraph 0054 for reference to selection of the entry server being based on the geographic location; Paragraph 0060 for reference to identified entry servers being selected based on proximity to the determined geographic location of the computing device]
Before the effective filing date, it would have been obvious to one of ordinary skill in the art to modify the identification of tenants of Jain to include the consideration of geographic location of Perryman. Doing so would allow entry servers to provide faster, higher quality data transmissions to achieve better load balancing, lower latency, and higher throughput in delivering the data streams than may be otherwise achieved using solely the application server, as stated by Perryman (Paragraph 0039).

Claim(s) 13 is/are rejected under 35 U.S.C. 103 as being unpatentable over Jain (U.S 6,795,858 B1) in view of Ignatyev (U.S 2017/0318083 A1) in view of Muthuvaradharajan (U.S 2017/0083377 A1) in view of Tkatch (U.S 2010/0286992 A1), as applied in claim 8, in view of Wong (U.S 2014/0007038 A1).
Claim 13
While the combination of Jain, Ignatyev, Muthuvaradharajan, and Tkatch disclose the limitations above, they do not disclose wherein accessing the tenant computing system integration data comprises anonymizing the tenant computing system integration data prior to generating each respective integration timing prediction.
Regarding Claim 13, Wong discloses the following:
wherein accessing the tenant computing system integration data comprises anonymizing the tenant computing system integration data prior to generating each respective integration timing prediction [see at least Paragraph 0026 for reference to the project management application generating templates by anonymizing tenant specific details associated with the critical tasks; Figure 1 and related text regarding the exemplary multi-tenant data processing system]
Before the effective filing date, it would have been obvious to one of ordinary skill in the art to modify the accessing of integration data of Jain to include the anonymization of Wong. Doing so would improve collaboration, integration, and community-based cooperation between customer tenants without sacrificing data security, as stated by Wong (Paragraph 0004).

Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
da Costa, Daniel Alencar, et al. "An empirical study of delays in the integration of addressed issues." 2014 IEEE international conference on software maintenance and evolution. IEEE, 2014.
DOCUMENT ID
INVENTOR(S)
TITLE
US 2013/0254895 A1
Neil et al.
NON-HARMFUL INSERTON OF DATA MIMICKING COMPUTER NETWORK ATTACKS
CN104679937A
Li et al.
An Error Estimation And Parameter Self-adapting Adjusting Method For Implicit Projection Algorithm

Any inquiry concerning this communication or earlier communications from the examiner should be directed to KRISTIN ELIZABETH GAVIN whose telephone number is [phone]. The examiner can normally be reached M-F 7:30-4:30 PM EST.
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/KRISTIN E GAVIN/
Primary Examiner, Art Unit 3624

Prosecution Timeline

• May 08, 2023: Application Filed
• Jul 14, 2025: Non-Final Rejection mailed — §101, §103
• Sep 26, 2025: Interview Requested
• Oct 07, 2025: Examiner Interview Summary
• Oct 07, 2025: Applicant Interview (Telephonic)
• Oct 14, 2025: Response Filed
• Jan 30, 2026: Final Rejection mailed — §101, §103
• Mar 30, 2026: Response after Non-Final Action
• Apr 08, 2026: Request for Continued Examination
• Apr 21, 2026: Response after Non-Final Action
• Jun 16, 2026: Non-Final Rejection mailed — §101, §103 (current)