DETAILED ACTION
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(s) 1-20 are pending for examination. This action is Non-Final.
Claim Rejections - 35 USC § 112
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.
Claim(s) 20 is/are 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. Claim 20 appears to claim duplicative limitations of claim 17. Claim 20 depends off of claim 17, thus, fails to further limit the subject matter of the claim upon which it depends. 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.
Claim(s) 1-20 is/are rejected under 35 U.S.C. 101 because the claimed invention is directed to abstract idea without significantly more.
Of note - Regarding Claim 17-20; Claim(s) 17-20 is/are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. The claim(s) does/do not fall within at least one of the four categories of patent eligible subject matter for being directed to signals per se. Claim 17 recites “A computer program product, stored on a computer readable medium, comprising instructions that when executed by one or more computers cause the one or more computers to:...” without restricting the media to its non-transitory forms, neither in the claim language or in the specification in at least paragraph [0025]. According to MPEP 2106.03, “Even when a product has a physical or tangible form, it may not fall within a statutory category. For instance, a transitory signal, while physical and real, does not possess concrete structure that would qualify as a device or part under the definition of a machine, is not a tangible article or commodity under the definition of a manufacture (even though it is man-made and physical in that it exists in the real world and has tangible causes and effects), and is not composed of matter such that it would qualify as a composition of matter.” Therefore, claims 17 and its dependent claims 18-20 are rejected under 101 for being directed to signals per se. The applicant is advised to rewrite the claims so that signals per se are not included in the scope, without introducing new subject matter.
For purposes of compact prosecution, and without admission, claim(s) 17-20 is/are reanalyzed under 35 U.S.C. 101 for being directed to a judicial exception without significantly more as if the claim passed step 1.
Step 1: claim(s) 1-20 are directed to a machine, process, and/or manufacture. Therefore, the claims are directed to statutory subject matter under Step 1 (Step 1: YES). See MPEP 2106.03.
Prong 1, Step 2A: claim 1, and similar claim(s) 9 and 17, taken as representative, recites at least the following limitations that recite an abstract idea:
receive a service event data structure related to a service message provided to an application framework;
input the service event data structure to
compare the IT support intent classification to execution criteria for a second
in response to a determination that the IT support intent classification satisfies the execution criteria,
input the service event data structure to the second
cause transmission of a notification for a user device based on the escalation classification.
The above limitations, under their broadest reasonable interpretation, fall within the “Certain Methods of Organizing Human Activity” grouping of abstract ideas, enumerated in MPEP 2106.04(a)(2)(II), in that they recite "commercial interactions" or "legal interactions" include agreements in the form of contracts, legal obligations, advertising, marketing or sales activities or behaviors, and business relations. The broadest reasonable interpretation of these limitations for claim 1, and similar claim(s) 9 and 17 includes receive a service event data structure related to a service message provided to an application framework; input the service event data structure to model to generate an information technology (IT) support intent classification associated with the service event data structure; compare the IT support intent classification to execution criteria for a second model; in response to a determination that the IT support intent classification satisfies the execution criteria, input the service event data structure to the second model to generate an escalation classification associated with the service event data structure; and cause transmission of a notification for a user device based on the escalation classification., thus, claim 1, and similar claim(s) 9 and 17 falls within the “Certain Methods of Organizing Human Activity” grouping of abstract ideas as they recite “commercial interactions" or "legal interactions" in the form of business relations.
The above limitations, under their broadest reasonable interpretation, fall within the “Mental Processes” grouping of abstract ideas, enumerated in MPEP 2106.04(a)(2)(III), in that they recite as concepts performed in the human mind, including observations, evaluations, judgments, and opinions. That is, other than reciting for claim 1, and similar claim(s) 9 and 17, i.e., apparatus w/ processor, instructions (computer program product/medium) and use of artificial intelligence; nothing in these claim element(s) precludes the step(s) from practically being performed in the mind. For example, the broadest reasonable interpretation of these limitations for claim 1, and similar claim(s) 9 and 17, includes receive a service event data structure related to a service message provided to an application framework; input the service event data structure to model to generate an information technology (IT) support intent classification associated with the service event data structure; compare the IT support intent classification to execution criteria for a second model; in response to a determination that the IT support intent classification satisfies the execution criteria, input the service event data structure to the second model to generate an escalation classification associated with the service event data structure; and cause transmission of a notification for a user device based on the escalation classification, which, encompass steps that a user can manually perform in the human mind or by a human using a pen and paper. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “mental processes” grouping of abstract ideas.
Accordingly, these claims recite an abstract idea. (Prong 1, Step 2A: YES). The types of identified abstract ideas are considered together as a single abstract idea for analysis purposes.
Prong 2, Step 2A: Limitations that are not indicative of integration into a practical application include: (1) Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea (MPEP 2106.05(f)), (2) Adding insignificant extra-solution activity to the judicial exception (MPEP 2106.05(g)), (3) Generally linking the use of the judicial exception to a particular technological environment or field of use (MPEP 2106.05(h)). Claim 1, and for similar claim(s) 9 and 17, recite i.e., apparatus w/ processor, instructions (computer program product/medium). These additional elements are described at a high level in Applicant’s specification without any meaningful detail about their structure or configuration. These elements in the steps are recited at a high-level of generality such that it amounts no more than mere instructions to apply the exception using a generic computer component and merely invoke such additional elements as a tool to perform the abstract idea. See MPEP 2106.05(f). Accordingly, these additional elements, even in combination, do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
As such, under Prong 2 of Step 2A, when considered both individually and as a whole, the limitations of claim 1, and similar claim(s) 9 and 17 are not indicative of integration into a practical application (Prong 2, Step 2A: NO). See MPEP 2106.04(d).
Since claim 1, and similar claim(s) 9 and 17 recites an abstract idea and fails to integrate the abstract idea into a practical application, claim 1, and similar claim(s) 9 and 17 is “directed to” an abstract idea under Step 2A (Step 2A: YES). See MPEP 2106.04(d).
Step 2B: The recitation of the additional elements is acknowledged, as identified above with respect to Prong 2 of Step 2A. These additional elements do not add significantly more to the abstract idea for the same reasons as addressed above with respect to Prong 2 of Step 2A.
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception because, when considered separately and as an ordered combination, they do not add significantly more to the exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements of for claim 1, and for similar claim(s) 9 and 17, i.e., apparatus w/ processor, instructions (computer program product/medium); thus, amounts to no more than mere instructions to apply the exception using a generic computer component and do not add anything that is not already present when they are considered individually or in combination. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. Therefore, under Step 2B, there are no meaningful limitations in claim 1, and similar claim(s) 9 and 17 that transform the judicial exception into a patent eligible application such that the claims amount to significantly more than the judicial exception itself (Step 2B: NO). See MPEP 2106.05.
Accordingly, under the Subject Matter Eligibility test, claim 1, and similar claim(s) 9 and 17 is ineligible.
Regarding Claims 2-8, 10-16, and 18-20, claims 2-9, 11-18, and 20 further defines the abstract idea that is present in their respective independent claims and hence are abstract for at least the reasons presented above w/ respect to “Certain Methods of Organizing Human Activity” as the claims recite further concepts of “commercial interactions" or "legal interactions" in the form of business relations and/or further recite “Mental Processes” as the claims recite further concepts that can be performed in the human mind, including observations, evaluations, judgments, and opinions. These dependent claim does not include any additional elements that integrate the abstract idea into a practical application; as such elements are recited at a high level of generality such that it amounts not more than mere instructions to apply the exception using a generic computer component (i.e., claim(s) 3, 10, and 28 – third AI model and claims 6-8 and 14-16 – training AI models). Even in combination, these additional elements do not integrate the abstract idea into a practical application and do no not amount to significantly more than the abstract idea itself. Thus, the aforementioned claims are not patent-eligible.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 1, 4-7, 9, 12-15, 17, and 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Rahman et al. (US 2021/0097229 A1) in view of Kansal et al. (US 2020/0401849 A1).
Regarding Claim 1;
Rahman discloses an apparatus comprising
one or more processors and one or more storage devices storing instructions that are operable (FIG. 1 and FIG. 2), when executed by the one or more processors (FIG. 1 and FIG. 2), to cause the one or more processors to:
receive a service event data structure related to a service message provided to an application framework (FIG. 3 and [0065] - As shown in FIG. 3, at step 302, process 300 includes receiving a service request message. For example, service management system 102 may receive a service request message (e.g., an information technology (IT) service request message) from a computing device (e.g., user device 104)).
input the service event data structure to a first artificial intelligence (AI) model to generate an information technology (IT) support intent classification associated with the service event data structure (FIG. 3 and [0082] - As further shown in FIG. 3, at step 312, process 300 includes determining a classification of the service request message... In some non-limiting embodiments, the service request message classification model may include a convolution neural network model and [0086]).
compare the IT support intent classification to execution criteria... in response to a determination that the IT support intent classification satisfies the execution criteria... cause transmission of a notification for a user device based on the ... classification (FIG. 3 and [0102] - In some non-limiting embodiments, service management system 102 may communicate the classification data based on determining a classification of the service request message. For example, service management system 102 may communicate (e.g., automatically) the classification data based on determining a classification of the service request message using a convolution neural network).
Rahman fails to explicitly disclose
...execution criteria for a second AI model;
...input the service event data structure to the second AI model to generate an escalation classification associated with the service event data structure; and
cause transmission of a notification for a user device based on the escalation classification.
However, in an analogous art, Kansal teaches
...execution criteria for [a second] AI model ([0039] –FIG. 8 is a flowchart depicting exemplary operations that may be employed to current service request records once the random forest model has been generated. In certain embodiments, the current service request records that are to be analyzed are retrieved at operation 802. At operation 804, the data for the independent variables of each service request record in the current set may be extracted and applied individually to the random forest model); ...input the service event data structure to [the second] AI model to generate an escalation classification associated with the service event data structure ([0040] - In certain embodiments employing a classification model for the random forest tree RFT.sub.1, decision node 920 may classify the service request record as likely to escalate or not likely to escalate); and cause transmission of a notification for a user device based on the escalation classification (FIG. 8 – Present customer services requests having high elation probability on user interface for action).
Therefore, it would have been obvious to one of ordinarily skill in the art before the effective filing date of the claimed invention to combine the teachings of Kansal to the classification of the service event data structure of Rahman to include ...execution criteria for [a second] AI model; ...input the service event data structure to [the second AI] model to generate an escalation classification associated with the service event data structure; and cause transmission of a notification for a user device based on the escalation classification.
One would have been motivated to combine the teachings of Kansal to Rahman to do so as it provides / allows machine learning [to] be applied to service request records to identify service requests that are likely to escalate if not properly addressed (Kansal, [0019]).
Regarding Claim 4;
Rahman in view of Kansal disclose the apparatus of claim 1.
Kansal further teaches wherein [the second] AI model is a binary classification model that classifies the service event data structure with an escalation label or a non-escalation label ([0038] - For example, certain embodiments may generate a decision tree to prioritize the service request records in a probability bin into high, medium, and low priority records... Based on the teachings of the present disclosure, it will be appreciated that various levels other than “high”, “medium”, and “low” priority classifications may be employed and [0047] - In certain embodiments, the rule instance may be used to identify a single class of service request records (e.g., high-priority service request records that are to be addressed immediately), as opposed to dividing the service request records within a bin between multiple classes (e.g., multiple priorities levels to the records in the bin)).
Similar rationale and motivation is noted for the combination of Kansal to Rahman in view of Kansal, as per claim 1, above.
Regarding Claim 5;
Rahman in view of Kansal disclose the apparatus of claim 1.
Rahman further discloses wherein the first AI model is a non-binary classification model ([0082] - As further shown in FIG. 3, at step 312, process 300 includes determining a classification of the service request message... In some non-limiting embodiments, the service request message classification model may include a convolution neural network model and [0101] - In some non-limiting embodiments, a last fully connected layer of a plurality of fully connected layers may be an output layer. For example, a third fully connected layer of the three fully connected layers may be an output layer. In some non-limiting embodiments, the output layer may be used by service management system 102 to map an input to the output layer to determine a label (e.g., a label associated with a work group of a plurality of work groups, a label associated with a category of a work group of a plurality of categories of a plurality of work groups, and/or the like) for the input, and a SoftMax function may be applied by service management system 102 to the input to compute a probability associated with the label. In some non-limiting embodiments, a label may include classification data associated with the classification of the service request message).
Kansal further teaches [the second] AI model is a binary classification model. ([0038] - For example, certain embodiments may generate a decision tree to prioritize the service request records in a probability bin into high, medium, and low priority records... Based on the teachings of the present disclosure, it will be appreciated that various levels other than “high”, “medium”, and “low” priority classifications may be employed and [0047] - In certain embodiments, the rule instance may be used to identify a single class of service request records (e.g., high-priority service request records that are to be addressed immediately), as opposed to dividing the service request records within a bin between multiple classes (e.g., multiple priorities levels to the records in the bin)).
Similar rationale and motivation is noted for the combination of Kansal to Rahman in view of Kansal, as per claim 1, above.
Regarding Claim 6;
Rahman in view of Kansal disclose the apparatus of claim 1.
Rahman further discloses wherein the first AI model is a deep learning model trained for intent recognition related to IT service messages ([0090] - For example, service management system 102 may use machine learning techniques to analyze the training data to generate the service request message classification model. In some non-limiting embodiments, generating the service request message classification model (e.g., based on training data obtained from historical data) may be referred to as training the service request message classification model. In some non-limiting embodiments, the machine learning techniques may include supervised techniques, such as artificial neural networks (e.g., convolution neural networks) and/or the like).
Regarding Claim 7;
Rahman in view of Kansal disclose the apparatus of claim 1.
Rahman further discloses wherein the first AI model is trained based on a training dataset that comprises one or more historical services messages for a user ... associated with the service message ([0069] - In some non-limiting embodiments, service management system 102 may receive a service request message based on a computing device (e.g., user device 104) generating the service request message based on an input received from a person and [0090] - In some non-limiting embodiments, generating the service request message classification model (e.g., based on training data obtained from historical data) may be referred to as training the service request message classification model).
Kansal further teaches concepts of identification of a user in a service message ([0019]).
Similar rationale and motivation is noted for the combination of Kansal to Rahman in view of Kansal, as per claim 1, above
Regarding Claim(s) 9 and 12-15; claim(s) 9 and 12-15 is/are directed to a/an method associated with the system claimed in claim(s) 1 and 4-7. Claim(s) 9 and 12-15 is/are similar in scope to claim(s) 1 and 4-7, and is/are therefore rejected under similar rationale.
Regarding Claim(s) 17 and 20; claim(s) 17 and 20 is/are directed to a/an computer program product associated with the system claimed in claim(s) 1. Claim(s) 17 and 20 is/are similar in scope to claim(s) 17, and is/are therefore rejected under similar rationale.
Claim(s) 2, 3, 10, 11, 18, and 19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Rahman et al. (US 2021/0097229 A1) in view of Kansal et al. (US 2020/0401849 A1) and further in view of Krier et al. (US 2020.0302349 A1).
Regarding Claim 2;
Rahman in view of Kansal disclose the apparatus of claim 1.
Rahman further discloses wherein the execution criteria comprises first execution criteria (FIG. 3 and [0102] - In some non-limiting embodiments, service management system 102 may communicate the classification data based on determining a classification of the service request message. For example, service management system 102 may communicate (e.g., automatically) the classification data based on determining a classification of the service request message using a convolution neural network)
Rahman in view of Kansal fail to explicitly disclose wherein the one or more storage devices store instructions that are operable, when executed by the one or more processors, to further cause the one or more processors to wherein the one or more storage devices store instructions that are operable, when executed by the one or more processors, to further cause the one or more processors to: compare the IT support intent classification to second execution criteria for [a third] AI model; and in response to a determination that the IT support intent classification satisfies the second execution criteria, input the service event data structure to [the third] AI model to generate a reply message for the service message.
However, in an analogous art, Krier teaches wherein the one or more storage devices store instructions that are operable, when executed by the one or more processors, to further cause the one or more processors to wherein the one or more storage devices store instructions that are operable, when executed by the one or more processors, to further cause the one or more processors to: compare the IT support intent classification to second execution criteria for [a third] AI model ([0007] - The present disclosure relates to determining and providing recommended actions to address service events. A user, such as an agent, may open a service case via a case management application to address a particular service event and [0064] - The recommendations interface 450 may include recommendation cards of different recommendation types, including a recommendation communication card 452 having information associated with the service event, a recommendation response card 454 having a possible reply message, a recommended resolution card 456 having a possible solution to resolve the service event, a recommended resource card 458 having a possible resource to facilitate resolving the service event, another suitable recommendation card, or any combination thereof. The designer may utilize the recommendations interface 450 to modify any of the possible recommendation cards, such as to change the text associated with each of the recommendation cards, to add additional recommendation cards, to remove one of the recommendation cards, and so forth. Additionally or alternatively, the designer may use the recommendations interface 450 to search for particular recommended actions (e.g., to associate with a particular trend model 346 and [0046] – machine learning routines; and in response to a determination that the IT support intent classification satisfies the second execution criteria, input the service event data structure to [the third] AI model to generate a reply message for the service message ([0064] - Thus, the recommendations interface 450 may enable the designer to modify how each recommended action is provided or presented to the user, in which the user may apply the provided recommended action to address the service events (e.g., by presenting information associated with the recommended action to a customer associated with the service event) and [0073] - If the user decides to apply the recommended action, the user may select a second “apply” icon 530 that is displayed on the recommendation interface 520. Upon selection of the second “apply” icon 530, the case management application may send information associated with the recommended action, such as the identification field 522 and/or the recommendation notes field 524, to a customer or client of the service case that the user is assisting).
Therefore, it would have been obvious to one of ordinarily skill in the art before the effective filing date of the claimed invention to combine the teachings of Krier to the service event data structure of Rahman in view of Kansal to include wherein the one or more storage devices store instructions that are operable, when executed by the one or more processors, to further cause the one or more processors to wherein the one or more storage devices store instructions that are operable, when executed by the one or more processors, to further cause the one or more processors to: compare the IT support intent classification to second execution criteria for [a third] AI model; and in response to a determination that the IT support intent classification satisfies the second execution criteria, input the service event data structure to [the third] AI model to generate a reply message for the service message.
One would have been motivated to combine the teachings of Krier to Rahman in view of Kansal to do so as it provides / allows providing recommended actions to address service events (Krier, [0001]).
Regarding Claim 3;
Rahman in view of Kansal in view Krier disclose the apparatus of claim 2.
Krier further teaches wherein the user device is a first user device ([0007] - A user, such as an agent, may open a service case via a case management application to address a particular service event. and [0064] - Thus, the recommendations interface 450 may enable the designer to modify how each recommended action is provided or presented to the user, in which the user may apply the provided recommended action to address the service events (e.g., by presenting information associated with the recommended action to a customer associated with the service event) and [0073] - If the user decides to apply the recommended action, the user may select a second “apply” icon 530 that is displayed on the recommendation interface 520. Upon selection of the second “apply” icon 530, the case management application may send information associated with the recommended action, such as the identification field 522 and/or the recommendation notes field 524, to a customer or client of the service case that the user is assisting), and wherein the one or more storage devices store instructions that are operable, when executed by the one or more processors, to further cause the one or more processors to: cause transmission of the reply message to a second user device associated with service message ([0064] - Thus, the recommendations interface 450 may enable the designer to modify how each recommended action is provided or presented to the user, in which the user may apply the provided recommended action to address the service events (e.g., by presenting information associated with the recommended action to a customer associated with the service event) and [0073] - If the user decides to apply the recommended action, the user may select a second “apply” icon 530 that is displayed on the recommendation interface 520. Upon selection of the second “apply” icon 530, the case management application may send information associated with the recommended action, such as the identification field 522 and/or the recommendation notes field 524, to a customer or client of the service case that the user is assisting).
Similar rationale and motivation is noted for the combination of Krier to Rahman in view of Kansal in view Krier, as per claim 2, above.
Regarding Claim(s) 10-11; claim(s) 10-11 is/are directed to a/an method associated with the system claimed in claim(s) 2-3. Claim(s) 10-11 is/are similar in scope to claim(s) 2-3, and is/are therefore rejected under similar rationale.
Regarding Claim(s) 18-19; claim(s) 18-19 is/are directed to a/an computer program product associated with the system claimed in claim(s) 2-3. Claim(s) 18-19 is/are similar in scope to claim(s) 2-3, and is/are therefore rejected under similar rationale.
Claim(s) 8 and 16 is/are rejected under 35 U.S.C. 103 as being unpatentable over Rahman et al. (US 2021/0097229 A1) in view of Kansal et al. (US 2020/0401849 A1) and further in view of Gurgu et al. (US 2023/0297887 A1).
Regarding Claim 8;
Rahman in view of Kansal disclose the apparatus of claim 1.
Rahman further discloses the first AI model is trained ([0069] and [0090]).
Rahman in view of Kansal fails to explicitly disclose wherein the first AI model is trained based on a training dataset associated with a set of candidate questions provided by a classification model.
However, in an analogous art, Gurgu teaches wherein the ... AI model is trained based on a training dataset associated with a set of candidate questions provided by a classification model. [0069] - In some embodiments, the training system 110 is configured to automatically recommend training questions to the chatbot builder 12 for training the one or more machine learning models of the intent classification system 100. One or more training questions may be generated based on an input prompt. The prompt may be manually and/or automatically formulated using the source data in the knowledge base 14. In this manner, the generated training questions may be catered to the enterprise's business and [0071]).
Therefore, it would have been obvious to one of ordinarily skill in the art before the effective filing date of the claimed invention to combine the teachings of Gurgu to the training of Rahman in view of Kansal to include wherein the first AI model is trained based on a training dataset associated with a set of candidate questions provided by a classification model.
One would have been motivated to combine the teachings of Gurgu to Rahman in view of Kansal to do so as it provides / allows quality/responsiveness of the answers to depend on the training received (Gurgu, [0028]).
Regarding Claim(s) 16; claim(s) 16 is/are directed to a/an method associated with the apparatus claimed in claim(s) 1. Claim(s) 16 is/are similar in scope to claim(s) 1, and is/are therefore rejected under similar rationale.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ASFAND M SHEIKH whose telephone number is (571)272-1466. The examiner can normally be reached Mon-Fri: 7a-3p (MDT).
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, JESSICA LEMIEUX can be reached at (571)270-3445. 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.
/ASFAND M SHEIKH/Primary Examiner, Art Unit 3626
Prosecution Insights