DETAILED ACTION
The following is a Final Office action. In response to Examiner’s communication of 4/16/26, Applicant, on 7/9/2026, amended claims 1, 2, 6, 8, 9, 13-15, and 19. Claims 1-20 are pending in the present application and have been rejected as described below.
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 .
Information Disclosure Statement
Applicant filed an Information Disclosure Statement (IDS) on 7/9/2026. This filing is in compliance with 37 C.F.R. 1.97.
As required by M.P.E.P. 609(C), the applicant's submission of the Information Disclosure Statement is acknowledged by the examiner and the cited references have been considered in the examination of the claims now pending. As required by M.P.E.P. 609(C), a copy of the PTOL -1449 form, initialed and dated by the examiner, is attached to the instant office action.
Response to Amendment
Applicant’s presentation of arguments is acknowledged.
Revised 35 USC § 101 rejections of claims 1-20 regarding abstract ideas are applied in light of Applicant’s amendments and explanations.
Revised 35 USC § 102 and 103 rejections of claims 1-20 have been applied in light of Applicant’s arguments.
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 a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more. Here, under considerations of the broadest reasonable interpretation of the claimed invention, Examiner finds that the Applicant invented a method and system for determining capabilities of agents for processing requests and routing a request to one or more agents to process the request. Examiner formulates an abstract idea analysis, following the framework described in the MPEP, as follows:
Step 1: The claims are directed to a statutory category, namely a "method" (claims 1-7) and "system" (claims 8-20).
Step 2A - Prong 1: The claims are found to recite limitations that set forth the abstract idea(s), namely, regarding claim 1:
A method comprising: defining… regions for corresponding agents of a network within a multidimensional space, wherein the corresponding agents include artificial intelligence agents and each region for a corresponding agent contains embeddings representing capabilities of the corresponding agent for processing requests;
wherein boundaries for a region for the corresponding agent are defined by ranges for dimensions of the embeddings contained in the region
identifying… one or more regions in the multidimensional space encompassing an embedding of a request based on dimensions of the embedding of the request being within the ranges for the one or more regions;
routing… the request to one or more agents associated with the one or more regions to process the request;
Independent claims 8 and 14 recites substantially similar claim language.
Dependent claims 2-7, 9-13, and 15-20 recite the same or similar abstract idea(s) as independent claims 1, 8, and 14 with merely a further narrowing of the abstract idea(s) to particular data characterization and/or additional data analyses performed as part of the abstract idea.
The limitations in claims 1-20 above falling well-within the groupings of subject matter identified by the courts as being abstract concepts, specifically the claims are found to correspond to the category of:
"Certain methods of organizing human activity- fundamental economic principles or practices (including hedging, insurance, mitigating risk); commercial or legal interactions (including agreements in the form of contracts; legal obligations; advertising, marketing or sales activities or behaviors; business relations); managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions)" as the limitations identified above are directed to determining capabilities of agents for processing requests and routing a request to one or more agents to process the request and thus is a method of organizing human activity including at least commercial or business interactions or relations and/or a management of user personal behavior; and/or
"Mental processes - concepts performed in the human mind (including an observation, evaluation, judgement, opinion)" as the limitations identified above include mere data observations, evaluations, judgements, and/or opinions, e.g. including user observation and evaluation by determining capabilities of agents for processing requests and routing a request to one or more agents to process the request, which is capable of being performed mentally and/or using pen and paper.
Step 2A - Prong 2: Claims 1-20 are found to clearly be directed to the abstract idea identified above because the claims, as a whole, fail to integrate the claimed judicial exception into a practical application, specifically the claims recite the additional elements of:
" via at least one processor… / An apparatus comprising: a router to enable communications; memory; and at least one processor configured to perform operations including: / One or more non-transitory computer readable storage media encoded with processing instructions that, when executed by one or more processors, cause the one or more processors to perform operations including:" (claims 1, 8, and 14) however the aforementioned elements merely amount to generic components of a general purpose computer used to "apply" the abstract idea (MPEP 2106.0S(f)) and thus fails to integrate the recited abstract idea into a practical application, furthermore the high-level recitation of processing data from a generic "processor" is at most an attempt to limit the abstract to a particular field of use (MPEP 2106.0S(h), e.g.: "For instance, a data gathering step that is limited to a particular data source (such as the Internet) or a particular type of data (such as power grid data or XML tags) could be considered to be both insignificant extra-solution activity and a field of use limitation. See, e.g., Ultramercial, 772 F.3d at 716, 112 USPQ2d at 1755 (limiting use of abstract idea to the Internet); Electric Power, 830 F.3d at 1354, 119 USPQ2d at 1742 (limiting application of abstract idea to power grid data); Intellectual Ventures I LLC v. Erie lndem. Co., 850 F.3d 1315, 1328-29, 121 USPQ2d 1928, 1939 (Fed. Cir. 2017) (limiting use of abstract idea to use with XML tags).") and/or merely insignificant extra-solution activity (MPE 2106.05(g)) and thus further fails to integrate the abstract idea into a practical application;
Step 2B: Claims 1-20 do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements as described above with respect to Step 2A Prong 2 merely amount to a general purpose computer that attempts to apply the abstract idea in a technological environment (MPEP 2106.0S(f)), including merely limiting the abstract idea to a particular field of use of analysis via a "processor", as explained above, and/or performs insignificant extra-solution activity, e.g. data gathering or output, (MPEP 2106.0S(g)), as identified above, which is further found under step 2B to be merely well-understood, routine, and conventional activities as evidenced by MPEP 2106.0S(d)(II) (describing conventional activities that include transmitting and receiving data over a network, electronic recordkeeping, storing and retrieving information from memory, electronically scanning or extracting data from a physical document, and a web browser's back and forward button functionality). Therefore, similarly the combination and arrangement of the above identified additional elements when analyzed under Step 2B also fails to necessitate a conclusion that the claims amount to significantly more than the abstract idea directed to determining capabilities of agents for processing requests and routing a request to one or more agents to process the request.
Claims 1-20 are accordingly rejected under 35 USC§ 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea(s)) without significantly more.
Note: The analysis above applies to all statutory categories of invention. As such, the presentment of any claim otherwise styled as a machine or manufacture, for example, would be subject to the same analysis.
For further authority and guidance, see:
MPEP § 2106
https://www.uspto.gov/patents/laws/examination-policy/subject-matter-eligibility
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102(A)(1) that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(A)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claims 1-6 and 8-19 are rejected under 35 U.S.C. 102(A)(1) as being anticipated by U.S. Patent Number 11558509 to Jayapalan et al. (hereafter referred to as Jayapalan).
As per claim 1, Jayapalan teaches:
A method comprising: defining, via at least one processor, regions for corresponding agents of a network within a multidimensional space (Col. 7 lines 22-34 teach the outputs of mapping module 430 are fed as inputs to a knowledge graph module 440. Knowledge graph module 440 may comprise a graph database 444. Knowledge graph module 440 may also comprise a graph neural network 442 that can be used to learn a suitable embedding of the graph data in graph database 444. Specifically, while the graph data may initially be represented within a high dimensional vector space, a graph neural network may help learn representations of the data in a (possibly continuous) low-dimensional vector space. Such low dimensional embeddings may allow a system to more readily detect relevant patterns that are obscured or simply undetectable when the data is represented within the higher dimensional vector space).
wherein the corresponding agents include artificial intelligence agents (Col. 4 lines 56-61 teach likewise, bots, or other digital assistants, may comprise systems that are focused on helping a user complete a single task. For example, a bot may be built into a webpage to help explain key information to a user or provide assistance in filling out a form. These bots may comprise artificial intelligences that are trained on a highly specific domain).
and each region for a corresponding agent contains embeddings representing capabilities of the corresponding agent for processing requests (Col. 7 lines 35-49 teach outputs from knowledge graph module 440 may be passed to an application programming interface (API) graph module 450 (“API module 450”). API module 450 may further include several sub-modules, including retrieval module 452, inference module 454, and query module 456. In particular, once the graph data contained within knowledge graph module 440 has been transformed into a suitable embedding, the embedded representation of the data can be used to retrieve data, infer or predict new data, and/or query the knowledge graph for answers that can be found by looking at connections in the data. Of course, it may be appreciated that the knowledge graph can be continually updated with new data, and that new, and possibly more suitable, embeddings can be learned as the knowledge grows or is otherwise modified).
wherein boundaries for a region for the corresponding agent are defined by ranges for dimensions of the embeddings contained in the region (Col. 8 lines 32-44 and FIGS. 5A-C show a sequence for classifying the graph and/or predicting value(s) of a target node (node 530). Referring first to FIG. 5A, a sample neighborhood is selected for graph 500. This is a particular subset of the graph, where nodes are sufficiently close together according to a suitable metric. For example, a target node 530 has three adjacent nodes (node 531, node 532, and node 533). Each of these nodes has one or more adjacent nodes. In this example, the shaded nodes represent nodes that are sufficiently close together within the selected embedding space according to selected criteria, while the unshaded nodes represent nodes that may be related by other relationships that are not relevant within the selected embedding. (Examiner asserts that the distances between notes constitutes a dimensional range)).
identifying, via the at least one processor, one or more regions in the multidimensional space encompassing an embedding of a request (Col. 8 lines 32-44 and FIGS. 5A-C show a sequence for classifying the graph and/or predicting value(s) of a target node (node 530). Referring first to FIG. 5A, a sample neighborhood is selected for graph 500. This is a particular subset of the graph, where nodes are sufficiently close together according to a suitable metric. For example, a target node 530 has three adjacent nodes (node 531, node 532, and node 533). Each of these nodes has one or more adjacent nodes. In this example, the shaded nodes represent nodes that are sufficiently close together within the selected embedding space according to selected criteria, while the unshaded nodes represent nodes that may be related by other relationships that are not relevant within the selected embedding).
based on dimensions of the embedding of the request being within the ranges for the one or more regions (Col. 8 lines 32-44 and FIGS. 5A-C show a sequence for classifying the graph and/or predicting value(s) of a target node (node 530). Referring first to FIG. 5A, a sample neighborhood is selected for graph 500. This is a particular subset of the graph, where nodes are sufficiently close together according to a suitable metric. For example, a target node 530 has three adjacent nodes (node 531, node 532, and node 533). Each of these nodes has one or more adjacent nodes. In this example, the shaded nodes represent nodes that are sufficiently close together within the selected embedding space according to selected criteria, while the unshaded nodes represent nodes that may be related by other relationships that are not relevant within the selected embedding. (Examiner asserts that the distances between notes constitutes a dimensional range)).
routing, via the at least one processor, the request to one or more agents associated with the one or more regions to process the request (Col 9 lines 43-64 teach once an intent for the call has been determined, the system could proceed to step 804, which comprises route pairing using a knowledge graph. The output of step 804 is a route recommendation. A route recommendation may include a subset or ranking of all available agents that may be paired with a selected customer associated with an incoming call. For example, a route recommendation for a selected customer could be a list of five agents from the set of all agents that provide the best “match” to the customer according to selected criteria. In some cases, the list of agents could be ranked. In step 806 the system optimizes routes based on the routing recommendations determined in step 804. In particular, a route selection system is used to select final customer-agent pairings (routes) for all incoming calls based on an optimized schedule that may consider route recommendations across many customers in a manner that avoids collisions between pairs. For example, if a selected agent is ranked as the best match for two different customers, the route selection system may match one of the two customers with a different agent that was also well matched to that customer).
As per claim 8, Jayapalan teaches:
An apparatus comprising: a router to enable communications; memory; and at least one processor configured to perform operations including (Col. 2 lines 32-52 teach a system for intelligently routing calls between users and agents includes a device processor and a non-transitory computer readable medium storing instructions. The instructions are executable by the device processor to receive a call from a user, determine an intent for the call from the user, retrieve a set of available agents that can receive the call from the user, and determine, based on the determined intent for the call, an objective function for matching the user to an available agent in the set of available agents. The instructions are further executable to retrieve a route knowledge graph, provide, as input to the route knowledge graph, information about the intent, information about the set of available agents, and information about the objective function, and receive, as output from the route knowledge graph, a route recommendation, where the route recommendation includes information about a recommended agent from the set of available agents. The instructions are also executable to pass the route recommendation to a route selection system, and route, based on a route selection provided by the route selection system, the call from the user to the recommended agent).
The remainder of the claim language is substantially similar to that found in regard to claim 1 and is rejected for the same reasons put forth in regard to claim 1.
As per claim 14, Jayapalan teaches:
One or more non-transitory computer readable storage media encoded with processing instructions that, when executed by one or more processors, cause the one or more processors to perform operations including (Col. 2 lines 32-52 teach a system for intelligently routing calls between users and agents includes a device processor and a non-transitory computer readable medium storing instructions. The instructions are executable by the device processor to receive a call from a user, determine an intent for the call from the user, retrieve a set of available agents that can receive the call from the user, and determine, based on the determined intent for the call, an objective function for matching the user to an available agent in the set of available agents. The instructions are further executable to retrieve a route knowledge graph, provide, as input to the route knowledge graph, information about the intent, information about the set of available agents, and information about the objective function, and receive, as output from the route knowledge graph, a route recommendation, where the route recommendation includes information about a recommended agent from the set of available agents. The instructions are also executable to pass the route recommendation to a route selection system, and route, based on a route selection provided by the route selection system, the call from the user to the recommended agent).
The remainder of the claim language is substantially similar to that found in regard to claim 1 and is rejected for the same reasons put forth in regard to claim 1.
As per claims 2, 9, and 15, Jayapalan teaches each of the limitations of claims 1, 8, and 14.
In addition, Jayapalan teaches:
wherein the embeddings represent capabilities of the corresponding agent include embeddings of intents and contexts for the corresponding agent (Col. 7 lines 22-34 teach the outputs of mapping module 430 are fed as inputs to a knowledge graph module 440. Knowledge graph module 440 may comprise a graph database 444. Knowledge graph module 440 may also comprise a graph neural network 442 that can be used to learn a suitable embedding of the graph data in graph database 444. Col. 8 lines 32-44 and FIGS. 5A-C show a sequence for classifying the graph and/or predicting value(s) of a target node (node 530). Referring first to FIG. 5A, a sample neighborhood is selected for graph 500. This is a particular subset of the graph, where nodes are sufficiently close together according to a suitable metric. For example, a target node 530 has three adjacent nodes (node 531, node 532, and node 533). Each of these nodes has one or more adjacent nodes. In this example, the shaded nodes represent nodes that are sufficiently close together within the selected embedding space according to selected criteria, while the unshaded nodes represent nodes that may be related by other relationships that are not relevant within the selected embedding. Col. 10 lines 21-29 and FIG. 10 teaches a schematic view of an architecture for intelligent call routing, according to an embodiment. As seen in FIG. 10, several inputs are fed into a route knowledge graph 1002. As described herein, a “route knowledge graph” is a knowledge graph that can be utilized to predict or otherwise generate route recommendations. In some cases, a route knowledge graph can be represented within an embedding space using a graph neural network, as already described above).
As per claims 3, 10, and 16, Jayapalan teaches each of the limitations of claims 1, 8, and 14.
In addition, Jayapalan teaches:
wherein identifying the one or more regions comprises identifying a plurality of regions in the multidimensional space encompassing the embedding of the request (Col. 8 lines 32-44 and FIGS. 5A-C show a sequence for classifying the graph and/or predicting value(s) of a target node (node 530). Referring first to FIG. 5A, a sample neighborhood is selected for graph 500. This is a particular subset of the graph, where nodes are sufficiently close together according to a suitable metric. For example, a target node 530 has three adjacent nodes (node 531, node 532, and node 533). Each of these nodes has one or more adjacent nodes. In this example, the shaded nodes represent nodes that are sufficiently close together within the selected embedding space according to selected criteria, while the unshaded nodes represent nodes that may be related by other relationships that are not relevant within the selected embedding).
wherein routing the request to one or more agents comprises routing the request to each of a plurality of agents associated with the plurality of regions (Col. 10 lines 21-29 and FIG. 10 teach a schematic view of an architecture for intelligent call routing, according to an embodiment. As seen in FIG. 10, several inputs are fed into a route knowledge graph 1002. As described herein, a “route knowledge graph” is a knowledge graph that can be utilized to predict or otherwise generate route recommendations. In some cases, a route knowledge graph can be represented within an embedding space using a graph neural network, as already described above).
As per claims 4, 11, and 17, Jayapalan teaches each of the limitations of claims 1, 8, and 14.
In addition, Jayapalan teaches:
identifying, via the at least one processor, a region associated with a corresponding agent nearest to the embedding of the request in the multidimensional space (Col. 8 lines 32-44 and FIGS. 5A-C show a sequence for classifying the graph and/or predicting value(s) of a target node (node 530). Referring first to FIG. 5A, a sample neighborhood is selected for graph 500. This is a particular subset of the graph, where nodes are sufficiently close together according to a suitable metric. For example, a target node 530 has three adjacent nodes (node 531, node 532, and node 533). Each of these nodes has one or more adjacent nodes. In this example, the shaded nodes represent nodes that are sufficiently close together within the selected embedding space according to selected criteria, while the unshaded nodes represent nodes that may be related by other relationships that are not relevant within the selected embedding).
when the embedding of the request corresponds to a location in the multidimensional space outside the regions of the corresponding agents (Col 9 lines 43-64 teach once an intent for the call has been determined, the system could proceed to step 804, which comprises route pairing using a knowledge graph. The output of step 804 is a route recommendation. A route recommendation may include a subset or ranking of all available agents that may be paired with a selected customer associated with an incoming call. For example, a route recommendation for a selected customer could be a list of five agents from the set of all agents that provide the best “match” to the customer according to selected criteria. In some cases, the list of agents could be ranked. In step 806 the system optimizes routes based on the routing recommendations determined in step 804. In particular, a route selection system is used to select final customer-agent pairings (routes) for all incoming calls based on an optimized schedule that may consider route recommendations across many customers in a manner that avoids collisions between pairs. For example, if a selected agent is ranked as the best match for two different customers, the route selection system may match one of the two customers with a different agent that was also well matched to that customer. (See also Col. 7 lines 35-49 and Col. 10 lines 21-29)).
As per claims 5, 12, and 18, Jayapalan teaches each of the limitations of claims 1, 8, and 14.
In addition, Jayapalan teaches:
wherein defining the regions for corresponding agents comprises defining the regions for the corresponding agents handling high volume requests (Col. 10 lines 43-50 teach route knowledge graph 1002 can also receive call center conditions 1012 as inputs. Such inputs may be relevant when a large number of agents are localized at one or more call centers. Call center conditions could include, but are not limited to, call volume at the call center, information about phone and/or internet outages, business hours at the call center, as well as other suitable information).
wherein routing the request to one or more agents comprises directly routing the request to the one or more agents. (Col 9 lines 43-64 teach once an intent for the call has been determined, the system could proceed to step 804, which comprises route pairing using a knowledge graph. The output of step 804 is a route recommendation. A route recommendation may include a subset or ranking of all available agents that may be paired with a selected customer associated with an incoming call. For example, a route recommendation for a selected customer could be a list of five agents from the set of all agents that provide the best “match” to the customer according to selected criteria. In some cases, the list of agents could be ranked. In step 806 the system optimizes routes based on the routing recommendations determined in step 804. In particular, a route selection system is used to select final customer-agent pairings (routes) for all incoming calls based on an optimized schedule that may consider route recommendations across many customers in a manner that avoids collisions between pairs. For example, if a selected agent is ranked as the best match for two different customers, the route selection system may match one of the two customers with a different agent that was also well matched to that customer).
As per claims 6, 13, and 19, Jayapalan teaches each of the limitations of claims 1, 8, and 14.
In addition, Jayapalan teaches:
receiving the request, via the at least one processor, from a source agent of the network when the source agent is unable to identify at least one agent to process the request. (Col. 12 lines 24-40 teaches in step 1306, the system can determine an updated objective function based on the updated intent. In step 1308, the system can use a route knowledge graph to determine an updated route recommendation. In step 1310, the system can determine if the updated route recommendation includes a second agent that is a better match than the current first agent. For example, in some cases, the system may include the current agent (first agent) in the set of available agents that is checked based on the updated intent. In such a case, the first agent may be returned as part of the route recommendation. If the first agent is ranked ahead of all other recommended agents, the system may determine no better match exists. If there is another agent ranked ahead of the first agent (for example, the second agent), or if the first agent is not recommended at all according to the updated route recommendation, the system may determine that there is a better match).
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 of this title, 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 set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied 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.
Claims 7 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over U.S. Patent Number 11558509 to Jayapalan et al. (hereafter referred to as Jayapalan) in view of U.S. Patent Application Publication Number 2021/0374563 to Jezewski (hereafter referred to as Jezewski).
As per claims 7 and 20, Jayapalan teaches each of the limitations of claims 1 and 14 respectively.
Jayapalan teaches determining capabilities of agents for processing requests and routing a request to one or more agents to process the request but does not explicitly teach removing overlapping segments from the regions for the corresponding agents as described by the following citations from Jezewski:
wherein defining the regions for the corresponding agents comprises: removing overlapping segments from the regions for the corresponding agents (Paragraph Number [0306] teaches the program applies interface object components (functions, patterns, attributes) to problem objects. For example, if the program finds a possible system conflict object in the problem system in step 6, apply the system conflict objects, functions, & attributes to the problem system intersection object, so if the system conflict object has an associated function in its definition like ‘diverging intents’ or ‘trade-off’ or ‘resource competition’ or ‘antagonistic agent’, apply those to the problem system intersection and see if they fit in the problem system intersection. An intersection is defined as “an overlap at the intersection point involving different directions”, so it's likely to match the ‘resource competition’ and ‘diverging intents’ components of the system conflict object definition, but may not depending on the problem definition (the intersection may just be an incidental routing object, rather than a competition for that position, and may allow multiple objects occupying the same position, and the directions may not indicate different intents if similar objects are in both directions). Now it's clearer that the intersection has an ambiguity in the position sequence attribute (a variant of the position overlap where only one agent can possess the resource at a given time), creating a possible conflict (determined by which agent arrives in the position first and which agent gets the position resource first). The diverging direction attribute inherent to the intersection has not been converted to a diverging intent, but it could be if the different directions indicate different intents, and if that difference is relevant to the resolution of the conflict about which agent is allowed in the position first. The mapping function has also identified a possible trade-off in the ambiguity, indicating that only one agent can occupy the position at a given time, so only one agent can go first (a scarce resource of occupation sequence or saved time that may be causative in the system, especially if the agent changes the intersection or removes some of its value by occupying it first)).
Both Jayapalan and Jezewski are directed to routing request to agents. Jayapalan discloses determining capabilities of agents for processing requests and routing a request to one or more agents to process the request. Jezewski improves upon Jayapalan by disclosing removing overlapping segments from the regions for the corresponding agents. One of ordinary skill in the art would be motivated to further include removing overlapping segments from the regions for the corresponding agents, to efficiently provide for distinct clustering and removal of information that is unnecessarily duplicated. Accordingly, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system and method of determining capabilities of agents for processing requests and routing a request to one or more agents to process the request in Jayapalan to further utilize removing overlapping segments from the regions for the corresponding agents in Jezewski, since the claimed invention is merely a combination of old elements, and in combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Response to Argument
Applicant’s arguments filed 7/9/2026 have been fully considered but they are not fully persuasive.
Applicant argues that the amended claims recite eligible subject matter. (See Applicant’s Remarks, 7/9/2026, pgs. 9-12). Examiner disagrees with Applicant’s assertions. As described above in the 35 USC 101 rejection, the claim recites an abstract idea that is analogous to organizing human activities and utilizes technology to facilitate implementation of the abstract ideas. Each of the steps set forth by the Examiner as abstract constitute actions that could be performed by a person and are written as directions to be performed (e.g. defining, identifying, routing, etc.). The application of a computer components to these abstract concepts does not rise to the level of eligibility in and of itself. Additionally, the problems solved by applying the computer components are solely directed towards improving the abstract idea rather than the underlying technology. Examiner can not find any improvement to technology recited in the claims. The computer components as claimed are recited at a high level of generality and merely use computer technology as a tool to apply the abstract idea.. (See e.g. MPEP 2106.05(f): (1) Whether the claim recites only the idea of a solution or outcome i.e., the claim fails to recite details of how a solution to a problem is accomplished. The recitation of claim limitations that attempt to cover any solution to an identified problem with no restriction on how the result is accomplished and no description of the mechanism for accomplishing the result, does not integrate a judicial exception into a practical application or provide significantly more because this type of recitation is equivalent to the words "apply it"). Additionally, other than an output of data there is no particular application of the technology recited by the claims. There is nothing in the claims that improves the underlying technology but instead the technology is present to facilitate implementation of the abstract ideas in a particular technological environment. Accordingly, the 35 USC 101 rejection has been maintained.
Applicant argues that the Jayapalan reference does not teach the amended claim limitations recited in the independent claims. (See Applicant’s Remarks, 7/9/2026, pgs. 12). Examiner respectfully disagrees and notes that Applicant’s arguments are moot in that new citations / explanations from the Jayapalan reference that have been applied to the newly amended claim language. In response to Applicant’s assertions, Examiner directs Applicant to review the new 35 USC 102 and 103 rejections presented above.
Conclusion
Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a). A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any extension fee pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MATTHEW H. DIVELBISS whose telephone number is (571) 270-0166. The fax phone number is 571-483-7110. The examiner can normally be reached on M-Th, 7:00 - 5:00. 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, Jerry O'Connor can be reached on (571) 272-6787.
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/MATTHEW H DIVELBISS/Examiner, Art Unit 3624
/Jerry O'Connor/Supervisory Patent Examiner,Group Art Unit 3624
Prosecution Insights