DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application is being examined under the pre-AIA first to invent provisions.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-4,6,8-11,13,15-18 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Referring to claims 1, 8, 15, and consequently their dependent claims, “identify a team via the MLM to process the received dataset, wherein identification of the team comprises determining the team from a set of teams compatible with the received dataset by matching specialties of teams in the set of teams to the received dataset and previously encountered datasets by the identified team, based on rank and the summary of the set of elements of the received dataset” is understood to refer to “identify a team via the MLM to process the received dataset, wherein identification of the team comprises determining the [[team]]team, from a set of teams compatible with the received dataset by matching specialties of individual teams in the set of teams to the received dataset and previously encountered datasets by the individual teams, based on the rank and the summary of the set of elements of the received dataset”. See for example the specification at paragraphs 85-87, cited by Applicant in support of their amendments.
The following is a quotation of 35 U.S.C. 112(d):
(d) REFERENCE IN DEPENDENT FORMS.—Subject to subsection (e), a claim in dependent form shall contain a reference to a claim previously set forth and then specify a further limitation of the subject matter claimed. A claim in dependent form shall be construed to incorporate by reference all the limitations of the claim to which it refers.
The following is a quotation of pre-AIA 35 U.S.C. 112, fourth paragraph:
Subject to the following paragraph [i.e., the fifth paragraph of pre-AIA 35 U.S.C. 112], a claim in dependent form shall contain a reference to a claim previously set forth and then specify a further limitation of the subject matter claimed. A claim in dependent form shall be construed to incorporate by reference all the limitations of the claim to which it refers.
Claims 3, 10, 17 rejected under 35 U.S.C. 112(d) or pre-AIA 35 U.S.C. 112, 4th paragraph, as being of improper dependent form for failing to further limit the subject matter of the claim upon which it depends, or for failing to include all the limitations of the claim upon which it depends.
Referring to claims 3, 10, and 17, particularly in view of the 112b rejection above, it is unclear how, if at all, these claims further distinguish from the independent claims from which they depend. Applicant may cancel the claim(s), amend the claim(s) to place the claim(s) in proper dependent form, rewrite the claim(s) in independent form, or present a sufficient showing that the dependent claim(s) complies with the statutory requirements.
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 1-4, 6, 8-11, 13, 15-18 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
At step 1, if no statutory category rejection was given above, then the claims have been determined to have a statutory category.
At step 2a, prong one, referring to claims 1, 8, and 15, there is disclosed a generic computer that receives data comprising a set of elements, ranks the dataset using a machine learning model, generates a summary of the set of elements via an artificial intelligence engine, identifies a team via the MLM, and transmits the dataset to the team identified. Claim 1 recited herein, “A system for routing data transmissions using machine learning models and enriching data using artificial intelligence, the system comprising: at least one non-transitory storage device; and at least one processing device coupled to the at least one non-transitory storage device, wherein the at least one processing device is configured to: receive a dataset comprising a set of elements, wherein the set of elements at least partially comprises an incident report; rank the received dataset among a plurality of datasets via a machine learning model (MLM), wherein ranking the received dataset determines priority of the received dataset within the plurality of datasets; generate a summary of the set of elements within the received dataset via an artificial intelligence engine, wherein the summary of the set of elements further comprises references to previously encountered datasets associated with the incident report; identify a team via the MLM to process the received dataset, wherein identification of the team comprises determining the team from a set of teams compatible with the received dataset by matching specialties of teams in the set of teams to the received dataset and previously encountered datasets by the identified team, based on rank and the summary of the set of elements of the received dataset; and transmit the received dataset to the team identified by the MLM.” Claims 8 and 15 are similar.
The limitations of ranking, generating, identifying, as crafted, are processes that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of additional elements that do not integrate the judicial exception into a practical application. That is, nothing in these claim elements as emphasized precludes the step from practically being performed in the mind, possibly with the aid pen and paper. For example, these steps perform steps of observation, evaluation, judgment, or opinion.
If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of additional elements that do not integrate the judicial exception into a practical application, then it falls within the "Mental Processes" grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
At step 2a, prong two, this judicial exception is not integrated into a practical application. In particular the claim additionally recites a generic computer, receiving, use of a MLM, use of an AI engine, and transmission.
In each of the limitations, the computer is recited at a high level of generality. This amounts to no more than mere instructions to apply the exception using a generic computer. See MPEP 2106.05(f).
The limitations of receiving and transmitting are mere data gathering and output recited at a high level of generality, and thus are insignificant extra-solution activity. See MPEP 2106.05(g) (“whether the limitation is significant”). In addition, all uses of the recited judicial exceptions require such data gathering and output, and, as such, these limitations do not impose any meaningful limits on the claim. These limitations amount to necessary data gathering and outputting. See MPEP 2106.05.
The limitations of use of the MLM and AI engine provide nothing more than mere instructions to implement an abstract idea on a generic computer. See MPEP 2106.05(f). MPEP 2106.05(f) provides the following considerations for determining whether a claim simply recites a judicial exception with the words “apply it” (or an equivalent), such as mere instructions to implement an abstract idea on a computer: (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; (2) whether the claim invokes computers or other machinery merely as a tool to perform an existing process; and (3) the particularity or generality of the application of the judicial exception.
The judicial exception of ranking and generating is performed using the MLM and AI engine. The MLM and AI engines are used to generally apply the abstract idea without placing any limits on how the MLM or AI engine function. Rather, these limitations only recite the outcome of ranking and generating and do not include any details about how the ranking or generating are accomplished. See MPEP 2106.05(f).
The recitation of ranking and generating also merely indicates a field of use or technological environment in which the judicial exception is performed. Although the additional use of the MLM and AI engine limits the identified judicial exceptions, this type of limitation merely confines the use of the abstract idea to a particular technological environment (AI) and thus fails to add an inventive concept to the claims. See MPEP 2106.05(h).
Even when viewed in combination, the additional elements in this claim do no more than automate the mental processes a person may perform, using the computer components as a tool. Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. Regarding the AI/MLM elements in particular, these are necessarily applied in the order presented by the abstract idea. The invention as a whole is practiced on a generic computer and transmission is the output step of analysis. The claim is directed to an abstract idea.
At step 2b, the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements of a generic computer, receiving and transmitting data, and use of an MLM and AI engine amounts to no more than mere instructions to apply the exception using generic computer components. Mere instructions to apply an exception using generic computer components cannot provide an inventive concept.
The additional element of using the MLM and AI engine are at best mere instructions to “apply” the abstract ideas, which cannot provide an inventive concept. See MPEP 2106.05(f).
Additional elements of receiving and transmitting were both found to be insignificant extra-solution activity in Step 2A, Prong Two, because they were determined to be insignificant limitations as necessary data gathering and outputting. However, a conclusion that an additional element is insignificant extra-solution activity in Step 2A, Prong Two should be re-evaluated in Step 2B. See MPEP 2106.05, subsection I.A. At Step 2B, the evaluation of the insignificant extra-solution activity consideration takes into account whether or not the extra-solution activity is well understood, routine, and conventional in the field. See MPEP 2106.05(g). As discussed in Step 2A, Prong Two above, the recitations of receiving and transmitting are recited at a high level of generality. These elements amount to receiving or transmitting data over a network and are well-understood, routine, conventional activity. See MPEP 2106.05(d), subsection II.
As discussed in Step 2A, Prong Two above, the recitation of a computer to perform limitations amounts to no more than mere instructions to apply the exception using a generic computer component.
Even when considered in combination, these additional elements represent mere instructions to implement an abstract idea or other exception on a computer and insignificant extra-solution activity, which do not provide an inventive concept.
Further referring to claim 2-4, this further performs steps of observation, evaluation, judgment, or opinion.
Further referring to claim 6, this further performs steps of observation, evaluation, judgment, or opinion.
Referring to claims 9-11, 13, and 16-18, see claims 2-4, 6 above.
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, 8-11, and 15-18 is/are rejected under 35 U.S.C. 103 as being unpatentable over US20210014136 to Rath and US5666481 to Lewis.
Referring to claim 1, Rath discloses a system for routing data transmissions using machine learning models and enriching data using artificial intelligence, the system comprising: at least one non-transitory storage device; and at least one processing device coupled to the at least one non-transitory storage device, wherein the at least one processing device is configured to:
receive a dataset comprising a set of elements, wherein the set of elements at least partially comprises an incident report (Figure 4, 414, “Receive support ticket”.);
rank the received dataset among a plurality of datasets via a machine learning model (MLM), wherein ranking the received dataset determines priority of the received dataset within the plurality of datasets (Paragraphs 50-52, “The production server 314 can create a support agent -workload data structure to reflect that support agents with multiple support tickets in their backlog will be impacted in their ability to give all of their attention to another new support ticket, even if their skills make them suitable for the new support ticket. Therefore, the production server 314 can compute the projected workloads of those support agents who have been shortlisted for a given open support ticket, based on the support agents' skills and experiences. Besides the total number of support tickets currently assigned to each support agent—the training server 312 can consider other aspects of the support tickets in a typical backlog, including but not limited to the priority distribution, the escalation status, the probability of escalation predicted by an escalation prediction system, the support ticket complexity, and the support ticket life-stage, which can range from being newly opened to being resolved pending closure. The training server 312 can compute a support agent's projected workload by down-weighting support tickets that are already resolved and pending closure as closed and by up-weighting those support tickets that are predicted to have a high escalation probability by the escalation prediction system or are already escalated. A projected workload can be an estimated amount of assistance to be provided by a person. For example, after identifying Bob and Dana as support agents who had skills for handling the remote mount problem in the closed support ticket 100 and experiences resolving support tickets that had a low complexity, the training server 312 trained the support ticket projected workloads machine-learning model 328 to use the support agent-workload data structure to identify Bob and Dana as support agents who had projected workloads that would have permitted resolving support tickets that had a low complexity. In another example, after identifying Bob and Dana as support agents who have skills for handling the remote mount problem in the open support ticket 200 and only Dana as a support agent who has experiences resolving support tickets that have a high complexity, the production server 312 applies the support ticket projected workloads machine-learning model 342 to the support agent-workload data structure to identify Dana as a support agent who has a projected workload that permits resolving support tickets that have a high complexity. In some embodiments, the production server 312 applies the support ticket projected workloads machine-learning model 342 to the support agent-workload data structure to identify Bob as a support agent who has a projected workload that permits resolving support tickets that have a high complexity, even though Bob has no experience resolving support tickets that have a high complexity, because Bob mat be the only support agent who is available. A total number can be a whole amount of a set of entities. A priority can be a condition of an entity being regarded as more important than another entity. A predicted probability of escalation can be an estimated likelihood of a request to increase a level of support. A support ticket life-stage can be a work phase of a request logged on a work tracking system detailing a problem that needs to be addressed.” Priority, escalation, and weighting being factors in the workload projection MLM.);
generate a summary of the set of elements within the dataset via an artificial intelligence engine (Paragraph 94-96, “After being trained, a support ticket is received, block 414. The system receives a support ticket for determining the support ticket's topic(s) and complexity. For example, and without limitation, this can include the production support tickets assignment system 332 receiving support tickets, which includes the support ticket 200 that contains the subsequent communication 202 and the support ticket's metadata 204, as depicted by FIG. 2. Following the receipt of a support ticket, a topic of the support ticket is determined, block 416. The system identifies a support ticket's topic(s). By way of example and without limitation, this can include the production server 314 applying the support ticket topics machine-learning model 336 to the analysis of the support ticket 200 by the natural language processor machine-learning model 334, thereby identifying a remote mount problem as the topic of the support ticket 200.Having received a support ticket, a complexity of the support ticket is estimated, block 418. The system estimates a support ticket's complexity. In embodiments, this can include the production server 314 applying the support ticket complexities machine-learning model 338 to the natural language analysis of the support ticket 200 by the natural language processor machine-learning model 334, thereby estimating a high complexity from the customer's description of their remote mount problem because the support ticket 200 includes multiple machine language error messages.”);
identify a team via the MLM, based on rank, the summary of the set of elements of the received dataset, and context describing an environment identified within the summary of the set of elements to process the received dataset, wherein the team identified via the MLM has previously encountered sets of elements matching the generated summary; and transmit the dataset to the team identified by the MLM (Paragraph 40-44, “The servers 312 and/or 314 can extract information about support agents that qualify for the given support ticket in one or more aspects. Such information can include the support agent's skills in handling support tickets with topics relevant to the current support ticket and support tickets of similar complexity, the support agent's experience with the specific customer who submitted the current support ticket, the support agent's availability in terms of time zone, work hours and paid time off, and the support agent's current workload. Therefore, the training server 312 may create a support agent-topical skills matrix. In addition to capturing the topics extracted for each closed support ticket, the support ticket -topical skills matrix captures information about the corresponding support agent. The training server 312 can weigh each support ticket's topic(s) for every support agent by the factors specific to each support ticket, as well as the context in which each support ticket was handled, so as to provide an estimate of every support agent's performances handling their support tickets[.] Standalone support ticket-specific factors that capture a support agent's performance include, but are not limited to, the support ticket resolution time, the sentiment score, and the escalation status of the support ticket. The weights may be normalized for each customer (such as the median support ticket resolution time for support tickets by the same customer) because every customer can have their own baseline and distinct preferences for higher or lower sentiment. A resolution time can be a chronological measure of the action of solving a problem. A sentiment score can be a measure of a view of or attitude toward a situation or event. An escalation status can be the condition of an increased level of support. Context specific factors include, but are not limited to, the support agent's ticket backlog and the support agent's workload at the time a support ticket was assigned to the support agent. The training server 312 can enable the weights of these factors to be changed as per end-user preferences, and can enable the weights to be altered such that they are higher weights for more recent support tickets and lower weights for older support tickets. A support agent ticket backlog can be an accumulation of requests logged on a work tracking system detailing a problem that needs to be addressed and which is yet to be completed by a person who is responsible for providing an act of assistance. A support agent workload can be the amount of assistance to be provided by a person who is responsible for providing acts of assistance[.] The training server 312 can weigh the topics, and then aggregate all support tickets for each support agent, thereby creating one summary row for each support agent, which captures each support agent's overall skills, both in terms of volume or quantity and in terms of support quality, in handling support tickets that have certain product-related topics, which are the columns in the matrix. To determine a support agent skill match, the servers 312 and/or 314 can extract the support agent skill summary vector for each support agent from the support agent-skills matrix and rescale the support agent skill summary vector by the relatedness of the topics previously handled to the topic(s) identified in the current support ticket. This rescaling may serve to highlight any experience that a support agent had with the current support ticket's topic(s) and closely related topics, while downplaying the experience the support agent had with unrelated topics. Then the servers 312 and/or 314 can aggregate topics across the support agent skill summary vector to generate a skill score that represents the level of matching between a support agent's skill in handling topics and the current support ticket's topic(s). Although the summary rows in the support agent-topical skills matrix are described as capturing a support agent's skills for handling each topic that is a column in the matrix, the summary rows may also be described as capturing a support agent's experiences for resolving each topic that is a column in the matrix. For example, after identifying the topic of the closed support ticket 100 was a remote mount problem, the training server 312 trained the support agent topical skills machine-learning model 326 to use the summary rows for the support agents in the support agent-topical skills matrix to identify Bob and Dana as support agents who had skills handling a remote mount problem. In another example, after identifying the topic of the open support ticket 200 is a remote mount problem, the production server 314 applies the support agent topical skills machine-learning model 340 to the summary rows for the support agents in the support agent-topical skills matrix to identify Bob and Dana as support agents who have skills handling a remote mount problem.” Paragraph 97-101, “Subsequent to identifying a topic of a support ticket, a first set of support agents are identified who have skills handling the topic of the support ticket, block 420. The system identifies support agent who have skills to handle the support ticket's topic(s). For example, and without limitation, this can include the production server 314 applying the support agent topical skills machine-learning model 340 to the summary rows for the support agents in the support agent-topical skills matrix to identify Bob and Dana as support agents who have skills handling the remote mount problem of the open support ticket 200. After estimating a support ticket's complexity, a machine-learning model identifies a second set of support agents who have experiences resolving support tickets of the estimated complexity, block 422. The system identifies support agents who have experiences resolving support ticket of the estimated complexity. By way of example and without limitation, this can include the production server 314 applying the support agent complexity experiences machine-learning model 340 to the summary rows for the support agents in the support agent-complexity experiences matrix to identify only Dana as a support agent who has experiences resolving support tickets that have the high complexity of the remote mount problem described in the open support ticket 200. Following identification of support agents who can handle a support ticket's topic and resolve support tickets of the estimated complexity, workload availabilities of some of the identified support agents are projected for the support ticket, block 424. The system projects the workload availabilities of identified support agents. In embodiments, this can include the production server 312 applying the support ticket projected workloads machine-learning model 342 to the support agent-workload data structure to project the workloads of Bob and Dana, who were identified as support agents who had skills to handle remote mount problems. The production server 314 also references the support agent-availability data structure to verify that Bob and Dana will have projected availabilities to be assigned the open support ticket 200, such that Bob and Dana both have the projected workload availability for accepting assignment of the open support ticket. Having identified available support agents, support agent scores are generated based on the support agents' skills handling the topic of the support tickets, experiences resolving support tickets of the estimated complexity, and projected workload availabilities for the support ticket, block 426. The system scores support agents for optimal assignment of a support ticket. For example, and without limitation, this can include the production support tickets assignment system 332 generating a score of 45 for assigning the open support ticket 200 to Bob. This score of 45 is based on Bob having skills handling the remote mount problem of the open support ticket 200, no experiences resolving high complexity support tickets such as the open support ticket 200, a projected workload that would have permitted resolving a high complexity support ticket, and the projected availability to be assigned the open support ticket 200. Additionally, the production support tickets assignment system 332 generates a score of 90 for assigning the open support ticket 200 to Dana. This score of 90 is based on Dana having skills handling the remote mount problem of the open support ticket 200, experiences resolving high complexity support tickets such as the open support ticket 200, a projected workload that would have permitted resolving the high complexity support ticket 200, and the projected availability to be assigned the open support ticket 200. Subsequent to scoring support agents, the support agent scores are used to assign a support ticket to a support agent from at least one of the sets of support agents, block 428. The system optimally assigns a support ticket to a support agent. By way of example and without limitation, this can include the production support tickets assignment system 332 assigning the open support ticket 200 to Dana because Dana's score of 90 is higher than Bob's score of 45, which reflects that Dana is the only support agent who is sufficiently qualified to resolve the open support ticket 200 since Dana has the experiences which Bob lacks resolving the high complexity support tickets such as the open support ticket 200”. Further, see above paragraphs 50-52 discussing prioritization as a factor in workload projection.).
Although Rath does not specifically disclose wherein the summary of the set of elements further comprises references to previously encountered datasets associated with the incident report, this is very well known in the art. In a related field of computing, an example of this is shown by Lewis, from the abstract, “An improved method and apparatus of resolving faults in a communications network. The preferred system uses a trouble ticket data structure to describe communications network faults. Completed trouble tickets are stored in a library and when an outstanding trouble ticket is received, the system uses at least one determinator to correlate the outstanding communications network fault to data fields in the set of data fields of the trouble ticket data structure to determine which completed trouble tickets in the library are relevant to the outstanding communications network fault. The system retrieves a set of completed trouble tickets from the library that are similar to the outstanding trouble ticket and uses at least a portion of the resolution from at least one completed trouble ticket to provide a resolution of the outstanding trouble ticket. The determinators may be macros, rules, a decision tree derived from an information theoretic induction algorithm and/or a neural network memory derived from a neural network learning algorithm. The system may adapt the resolution from a retrieved trouble ticket to provide the resolution using null adaptation, parameterized adaptation, abstraction/respecialization adaptation, or critic-based adaptation techniques.” It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to reference previously encountered datasets because, as shown by Lewis, this may allow an outstanding issue to be resolved.
Referring to claim 2, Rath discloses wherein individual elements within the received dataset are ranked on priority according to a predetermined set of indicators (Rath, See paragraphs 50-52 above.).
Referring to claim 3, Rath discloses wherein identification of the team via the MLM further comprises determining the team from a set of teams compatible with the received dataset based on rank and the summary of the set of elements of the received dataset (see above.).
Referring to claim 4, Rath discloses wherein the summary of the set of elements generated by the artificial intelligence engine provides a context for the received dataset (Rath, Paragraph 94-96, “After being trained, a support ticket is received, block 414. The system receives a support ticket for determining the support ticket's topic(s) and complexity. For example, and without limitation, this can include the production support tickets assignment system 332 receiving support tickets, which includes the support ticket 200 that contains the subsequent communication 202 and the support ticket's metadata 204, as depicted by FIG. 2. Following the receipt of a support ticket, a topic of the support ticket is determined, block 416. The system identifies a support ticket's topic(s). By way of example and without limitation, this can include the production server 314 applying the support ticket topics machine-learning model 336 to the analysis of the support ticket 200 by the natural language processor machine-learning model 334, thereby identifying a remote mount problem as the topic of the support ticket 200.Having received a support ticket, a complexity of the support ticket is estimated, block 418. The system estimates a support ticket's complexity. In embodiments, this can include the production server 314 applying the support ticket complexities machine-learning model 338 to the natural language analysis of the support ticket 200 by the natural language processor machine-learning model 334, thereby estimating a high complexity from the customer's description of their remote mount problem because the support ticket 200 includes multiple machine language error messages.”).
Referring to claims 8-11, 15-18, see rejection of claims 1-4 above.
Claim(s) 6,13 is/are rejected under 35 U.S.C. 103 as being unpatentable over Rath and Lewis as applied to claims 1,8 above, and further in view of Official notice (root cause analysis).
Referring to claims 6,13, although Rath does not specifically disclose wherein the summary of the set of elements identifies potential causes of the incident report, identifying a root cause is very well known in the art. In a related field of computing, examiner takes official notice for root cause analysis. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to identify a cause because this aids in diagnosis and resolution.
Response to Arguments
Applicant's arguments filed 27 April 2026 have been fully considered but they are not persuasive.
Regarding Applicant’s argument (page 9) that the human mind cannot use team specialties, current data, and historical data as factors for matching, using trained MLM, as this goes beyond “simple” observation, evaluation, or judgment, examiner notes that no particular number of specialties or datasets are claimed. For a minimal number, a human certainly could perform such a match, perhaps without even the use of pen and paper. For example, imagine that there are only two types of problems, two teams, and two previous incidents. Regarding what may be “simple”, this is relative, and humans are certainly capable of “complex” observation, evaluation, judgment, or opinion. Regarding the use of MLM/AI, no particular argument is given. See rejection above and previous.
Regarding Applicant’s argument (page 10) that the invention is regarding the “technology of routing data transmissions”, again, examiner instead characterizes this as work assignment with an extrasolution step of notification via transmission. The “technical solutions” described by Applicant are regarding the improvements to the process of work assignment.
Regarding Applicant’s argument (page 10) the improvements are reflected in an ordered combination, regarding the AI/MLM elements in particular, these are necessarily applied in the order presented by the abstract idea. The invention as a whole is practiced on a generic computer and transmission is the output step of analysis.
Regarding Applicant’s argument (page 11-12) that Rath does not disclose the claims as amended, and in particular that Rath does not identify based on rank and summary, pointing to paragraph 64 in particular, see newly cited paragraphs 40-44 and 50-52 above.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to GABRIEL L CHU whose telephone number is (571)272-3656. The examiner can normally be reached weekdays 8 am to 5 pm.
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, Ashish Thomas can be reached at (571)272-0631. 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.
/GABRIEL CHU/ Primary Examiner, Art Unit 2114
Prosecution Insights