Office Action Predictor
Last updated: April 15, 2026
Application No. 18/160,339

Affinity Data System for Data Management

Non-Final OA §103
Filed
Jan 27, 2023
Examiner
SWIFT, CHARLES M
Art Unit
2196
Tech Center
2100 — Computer Architecture & Software
Assignee
International Business Machines Corporation
OA Round
1 (Non-Final)
81%
Grant Probability
Favorable
1-2
OA Rounds
3y 0m
To Grant
99%
With Interview

Examiner Intelligence

Grants 81% — above average
81%
Career Allow Rate
706 granted / 872 resolved
+26.0% vs TC avg
Strong +57% interview lift
Without
With
+56.7%
Interview Lift
resolved cases with interview
Typical timeline
3y 0m
Avg Prosecution
52 currently pending
Career history
924
Total Applications
across all art units

Statute-Specific Performance

§101
10.0%
-30.0% vs TC avg
§103
55.7%
+15.7% vs TC avg
§102
17.0%
-23.0% vs TC avg
§112
6.1%
-33.9% vs TC avg
Black line = Tech Center average estimate • Based on career data from 872 resolved cases

Office Action

§103
DETAILED ACTION This office action is in response to application filed on 1/27/2023. Claims 1 – 20 are pending. 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 . Claim Rejections - 35 USC § 103 The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action: A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made. Claim(s) 1 – 4, 8, 10 – 13, 17, 19 and 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Carnes et al (US 20220200894, hereinafter Carnes), in view of Zhang et al (US 20200351900, hereinafter Zhang). As per claim 1, Carnes discloses: A computer implemented method comprising: determining, by a number of processor units, connections in affinity data for a set of workloads for an application; (Carnes figure 8 and [0056]: “receiving a request for a path in a network including a plurality of network elements interconnected to one another via links, wherein the request includes values for a plurality of criteria, wherein the plurality of criteria include one or more of trust, privacy, and secrecy (step 102); utilizing a multi-criteria path selection process to determine the path through the plurality of network elements over the links based on the plurality of criteria and the associated values (step 104);”.) and assigning, by the number of processor units, weights to the connections based on a number of hops for the connections, wherein the weights indicate an affinity to components related to the set of workloads. (Carnes [0032]: “Privacy relates to the concept of network obfuscation. In this case, the value for privacy is related to the number of additional nodes the flow is routed through. For example, a value of 0 for privacy would be the direct, shortest path. However, a value of 5 would result in the flow being routed through 5 additional nodes… the privacy can be a value of a number of hops, including either a minimum or maximum and/or a range of values.”; [0022]: “Each link in the network will be assigned a value for each of these parameters (either by static assignment or by dynamic measurement), and each one of these criteria will correspond to a weight in an algorithm that seeks to select the best path (or k best paths, k>1) (either by minimization or maximization) through the network. In mathematical terms, this can also be thought of as a multivariable optimization problem, where local minima, local maxima and saddle points are calculated. The present disclosure contemplates any approach to multivariable optimization based on the various criteria described herein and the visualizations. The multivariable optimization includes a multi-criteria path selection algorithm that attempts to find the local and/or global minimum or maximum of a function defined by more than one variable.”) Carnes did not disclose: wherein the set of workloads are for an application; However, Zhang teaches: wherein the set of workloads are for an application; (Zhang [0043]: “each physical node may include network elements (e.g., OTN switch, router) and/or compute servers and storage elements (e.g., datacenters) capable of invoking a subset of service functions selected from a catalog of service functions or executing applications on behalf of users.”) It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teaching of Zhang into that of Carnes in order to have the set of workloads are for an application. Carnes [0056] teaches receiving pathing requests, however, one of ordinary skill in the art can easily see that the pathing request can be generated by an application, such as shown in Zhang [0043]. Applicants have thus merely claimed the combination of known parts in the field to achieve the predictable results of selecting best path for a request and is therefore rejected under 35 USC 103. As per claim 2, the combination of Carnes and Zhang further teach: The computer implemented method of claim 1, wherein the weights indicate a level of affinity for the connections. (Carnes [0032]: “Privacy relates to the concept of network obfuscation. In this case, the value for privacy is related to the number of additional nodes the flow is routed through. For example, a value of 0 for privacy would be the direct, shortest path. However, a value of 5 would result in the flow being routed through 5 additional nodes… the privacy can be a value of a number of hops, including either a minimum or maximum and/or a range of values.”; [0022]: “Each link in the network will be assigned a value for each of these parameters (either by static assignment or by dynamic measurement), and each one of these criteria will correspond to a weight in an algorithm that seeks to select the best path (or k best paths, k>1) (either by minimization or maximization) through the network. In mathematical terms, this can also be thought of as a multivariable optimization problem, where local minima, local maxima and saddle points are calculated. The present disclosure contemplates any approach to multivariable optimization based on the various criteria described herein and the visualizations. The multivariable optimization includes a multi-criteria path selection algorithm that attempts to find the local and/or global minimum or maximum of a function defined by more than one variable.”) As per claim 3, the combination of Carnes and Zhang further teach: The computer implemented method of claim 1 further comprising: collecting, by the number of processor units, the affinity data for incoming connections and outgoing connections using a rolling window. As per claim 4, the combination of Carnes and Zhang further teach: The computer implemented method of claim 1 further comprising: collecting, by the number of processor units, the affinity data from a first number of sources; and collecting, by the number of processor units, the affinity data from a second number of sources iteratively. (Zhang [0069]) As per claim 8, the combination of Carnes and Zhang further teach: The computer implemented method of claim 1, wherein the affinity data comprises connections between servers processing a workload for the application. (Carnes [0032]) As per claim 10, it is the system variant of claim 1 and is therefore rejected under the same rationale. As per claim 11, it is the system variant of claim 2 and is therefore rejected under the same rationale. As per claim 12, it is the system variant of claim 3 and is therefore rejected under the same rationale. As per claim 13, it is the system variant of claim 4 and is therefore rejected under the same rationale. As per claim 17, it is the system variant of claim 8 and is therefore rejected under the same rationale. As per claim 19, it is the computer readable storage medium variant of claim 1 and is therefore rejected under the same rationale. As per claim 20, it is the computer readable storage medium variant of claim 2 and is therefore rejected under the same rationale. Claim(s) 5 – 7 and 14 – 16 is/are rejected under 35 U.S.C. 103 as being unpatentable over Carnes and Zhang, and in view of Fornash et al (US 20200004582, hereinafter Fornash). As per claim 5, the combination of Carnes and Zhang did not teach: The computer implemented method of claim 1 further comprising: migrating, by the number of processor units, the application; and migrating, by the number of processor units, selected data for a current set of workloads for the application based on an affinity of the selected data to the application. However, Fornash teaches: The computer implemented method of claim 1 further comprising: migrating, by the number of processor units, the application; and migrating, by the number of processor units, selected data for a current set of workloads for the application based on an affinity of the selected data to the application. (Fornash [0039] – [0041]) It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teaching of Fornash into that of Carnes and Zhang in order to migrate the application and migrate the selected data for a current set of workloads for the application based on an affinity of the selected data to the application. Zhang [0069] teaches migration of tasks to different datacenters. One of ordinary skill in the art can easily recognize the claimed limitations are merely commonly known steps for migrating applications and its associated data from one location to another, such as shown in Fornash [0039] – [0041], applicants have merely claimed the combination of known parts in the field to achieve predictable results of workload migration and is therefore rejected under 35 USC 103. As per claim 6, the combination of Carnes, Zhang and Fornash further teach: The computer implemented method of claim 5 further comprising: migrating, by the number of processor units, a number of applications with the application as an application group based on an affinity of the number of applications to the application. (Fornash [0039] – [0041]) As per claim 7, the combination of Carnes, Zhang and Fornash further teach: The computer implemented method of claim 6, further comprising: placing, by the number of processor units, the number of applications in the application group that have a weight equal to or less than a threshold distance. (Zhang [0069]) As per claim 14, it is the system variant of claim 5 and is therefore rejected under the same rationale. As per claim 15, it is the system variant of claim 6 and is therefore rejected under the same rationale. As per claim 16, it is the system variant of claim 7 and is therefore rejected under the same rationale. Claim(s) 9 and 18 is/are rejected under 35 U.S.C. 103 as being unpatentable over Carnes and Zhang, and in view of Aiken (US 20020143953). As per claim 9, the combination of Carnes and Zhang did not teach: The computer implemented method of claim 1, wherein a connection in the affinity data comprises a server name, an application name, a source IP address, and a destination IP address. However, Fornash teaches: The computer implemented method of claim 1, wherein a connection in the affinity data comprises a server name, an application name, a source IP address, and a destination IP address. (Aiken [0055]) It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teaching of Aiken into that of Carnes and Zhang in order to have a connection in the affinity data comprises a server name, an application name, a source IP address, and a destination IP address. Aiken [0055] has shown that the claimed limitations are merely commonly known data required for migration purpose, applicants have merely claimed the combination of known parts in the field to achieve predictable results of workload migration and is therefore rejected under 35 USC 103. As per claim 18, it is the system variant of claim 9 and is therefore rejected under the same rationale. Conclusion The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Ji et al (US 20230363057) teaches “determining a suitability to serve as a relay device for a relayed communication between a first device and a second device via one or more relay devices to be selected among one or more relay device candidates; transmitting a message as part of a relay discovery procedure, and with the apparatus acting as one of the one or more relay device candidates; and performing at least one of a transmit power adjustment and/or a radio resource selection for transmitting the message based, at least in part, on the determined suitability.”; Siemens et al (US 20220239595) teaches “the network device may determine that a number of data points in a first node is greater than a maximum node capacity; generate second nodes; update the first node to refer to the second nodes; distribute the data points among the second nodes; and program a hardware table with the updated first node and the second nodes.”; Ramaswamy et al (US 20220231949) teaches “a method for network-aware load balancing for data messages traversing a software-defined wide area network (SD-WAN) (e.g., a virtual network) including multiple connection links between different elements of the SD-WAN. The method includes receiving, at a load balancer in a multi-machine site, link state data relating to a set of SD-WAN datapaths including connection links of the multiple connection links. The load balancer, in some embodiments, provides load balancing for data messages sent from a machine in the multi-machine site to a set of destination machines (e.g., web servers, database servers, etc.) connected to the load balancer over the set of SD-WAN datapaths. The load balancer selects, for the data message, a particular destination machine (e.g., a frontend machine for a set of backend servers) in the set of destination machines by performing a load balancing operation based on the received link state data.”. Any inquiry concerning this communication or earlier communications from the examiner should be directed to CHARLES M SWIFT whose telephone number is (571)270-7756. The examiner can normally be reached Monday - Friday: 9:30 AM - 7PM. Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice. If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, April Blair can be reached at 5712701014. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300. Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000. /CHARLES M SWIFT/Primary Examiner, Art Unit 2196
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Prosecution Timeline

Jan 27, 2023
Application Filed
Oct 18, 2023
Response after Non-Final Action
Feb 28, 2026
Non-Final Rejection — §103
Mar 23, 2026
Examiner Interview Summary
Mar 23, 2026
Applicant Interview (Telephonic)
Mar 24, 2026
Response Filed

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Study what changed to get past this examiner. Based on 5 most recent grants.

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Prosecution Projections

1-2
Expected OA Rounds
81%
Grant Probability
99%
With Interview (+56.7%)
3y 0m
Median Time to Grant
Low
PTA Risk
Based on 872 resolved cases by this examiner. Grant probability derived from career allow rate.

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