DETAILED ACTION
Introduction
This Final Office Action is in response to amendments and remarks filed on January 26, 2026, for the application with serial number 17/698,202.
Claims 1 and 14 are amended.
Claims 1, 3, 5-7, 9-14, 17-19, and 21-25 are pending.
Interview
The Examiner acknowledges the interview conducted on October 30, 2025, in which proposed amendments were discussed with respect to the outstanding rejections.
Response to Remarks/Amendments
35 USC §101 Rejections
The Applicant traverses the rejection of the claims as being directed to an ineligible abstract idea, contending that the present claims recite a method that is rooted in computer technology. See Remarks p. 10. The Examiner respectfully disagrees. The claims recite steps that could be performed mentally or on paper by a human being, but a general purpose computer network with a cloud-based video streaming service is recited for implementation. The claims recite highly generalized steps for manipulating data obtained from video-based questionnaires to predict and determine results. No apparent improvement to a computer-based technology is recited in the claims. Contrary to the Applicant’s assertions, video analysis is not rooted in computer technology. Video and motion pictures employ the use of images that are played back sequentially in rapid succession. Video and motion pictures existed long before computers, and video does not necessarily require a computer. For example, tapes and other media can be used to store still images for video. The present claims merely represent a drafting effort to tie the recited method to video streaming services, which are presently a preferred method of providing video content. The Applicant additionally appears to concede that the present claims use algorithms, which are also ineligible abstract ideas. See Remarks p. 11. Contrary to the Applicant’s assertions, a human being could observe attributes in still images of video and map those attributes to the images to analyze the survey.
The Applicant further contends that the client-server architecture recited in the claims renders the claims eligible. See Remarks p. 12. In response, the Examiner submits that the use of client computers and servers merely amounts to the use of generic computer hardware that does not provide a practical application or significantly more than the recited abstract idea. The machine learning in the claims amounts to, at best, a technological environment for implementing the abstract idea. The abstract idea of facilitating a survey and determining the results is generally linked to a machine learning environment for implementation. No apparent improvement to machine learning is recited in the claims. Similarly, the CDN and DRM provide a technological environment for implementing the abstract idea. The concepts of CDN and DRM are tangential to the inventive concept of determining results of a survey.
The Applicant additionally submits that the claims are subject matter eligible because the claims contain unconventional elements. See Remarks p. 14. In response, the Examiner submits that the Applicant fails to point to any additional elements in the claims that are not conventional. The analysis below, considers additional elements of the claims that are outside the scope of the abstract idea and determines that the elements do not provide a practical application or significantly more than the abstract idea. Again, the Examiner reiterates that the claims do not provide a technical solution to a technical problem. The client-server architecture amounts to generic computer hardware.
The rejection for lack of subject matter eligibility is updated and maintained.
35 USC §103 Rejections
Amendments to independent claims 1 and 14 changed the scope of the claims, necessitating further search and consideration of the prior art. A new search returned the Todd reference, which is cited in the rejection of the independent claims, below. The Applicant’s arguments with respect to independent claims 1 and 14 are moot in light of the newly cited reference.
The rejection of the dependent claims stands or falls with the rejection of the independent claims.
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.
The Manual of Patent Examining Procedure (MPEP) provides detailed rules for determining subject matter eligibility for claims in §2106. Those rules provide a basis for the analysis and finding of ineligibility that follows.
Claims 1, 3, 5-7, 9-14, 17-19, and 21-25 are rejected under 35 U.S.C. 101. The claimed invention is directed to non-statutory subject matter because the claimed invention recites a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more. Although claims(s) 1, 3, 5-7, 9-14, 17-19, and 21-25 are all directed to one of the four statutory categories of invention, the claims are directed to facilitating a survey and determining survey results (as evidenced by exemplary independent claim 1; “a system for facilitating survey;” “determine . . . an end survey result”), an abstract idea. Certain methods of organizing human activity are ineligible abstract ideas, including managing personal behavior or relationships or interactions between people. See MPEP §2106.04(a). The limitations of exemplary claim 1 include: “receive . . . a knowledgebase comprising a set of survey queries;” “receive one or more video frame responses;” “extract, a set of attributes;” “map . . . the one or more video frame responses to each said query;” “process . . . training data comprising said set of attributes and the mapped video frame responses;” and “determine . . . an end survey result;” “create said set of survey queries;” and “test [an] executable survey bot.” The steps are all steps for managing personal behavior related to the abstract ideas of facilitating a survey and determining survey results that, when considered alone and in combination, are part of the abstract idea of facilitating a survey and determining survey results. The dependent claims further recite steps for managing personal behavior that are part of the abstract idea of facilitating a survey and determining survey results. These claim elements, when considered alone and in combination, are considered to be abstract ideas because they are directed to a method of organizing human activity which includes conducting a recorded video survey and evaluating the recorded responses of respondents.
Under step 2A of the subject matter eligibility analysis, a claim that recites a judicial exception must be evaluated to determine whether the claim provides a practical application of the judicial exception. Additional elements of the independent claims amount to generic computer hardware that does not provide a practical application (a system with a processor and memory; and a recording device in independent claim 1; and a recording device of a user in independent claim 14). See MPEP §2106.04(d)[I]. The independent claims additionally recite the transmission of survey questions and responses via video by computing devices, but this language amounts to the use of generic computer network hardware to transmit data. The claims do recite the use of machine learning, but the abstract ideas of facilitating a survey and determining survey results are generally linked to a machine learning algorithm for implementation. Therefore, the machine learning merely amounts to a technological environment that does not provide a practical application or significantly more than the abstract ideas. See MPEP §2106.05(h). Likewise, the abstract idea of facilitating a video survey and determining survey results is generally linked to the newly amended language reciting the use of digital rights management and a content delivery network is generally linked. Therefore, the DRM and CDN merely amount to a technological environment for implementing the abstract idea. The claims do not recite an improvement to another technology or technical field, nor do they recite an improvement to the functioning of the computer itself. See MPEP §2106.05(a). The claims require no more than a generic computer (a system with a processor and memory in independent claim 1; no hardware is recited in independent claim 14) to implement the abstract idea, which does not amount to significantly more than an abstract idea. See MPEP §2106.05(f). Because the claims only recite use of a generic computer, they do not apply the judicial exception with a particular machine. See MPEP §2106.05(b). For these reasons, the claims do not provide a practical application of the abstract idea, nor do they amount to significantly more than an abstract idea under step 2B of the subject matter eligibility analysis. Using a generic computer to implement an abstract idea does not provide an inventive concept. Therefore, the claims recite ineligible subject matter under 35 USC §101.
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, 3, 11, 13, 14, 23, and 25 is/are rejected under 35 U.S.C. 103 as being unpatentable over US 20190341152 A1 to Mellem et al. (hereinafter ‘MELLEM’) in view of US 20200143946 A1 to Lewis (hereinafter ‘LEWIS’), US 20150317676 A1 to Reid et al. (hereinafter ‘REID’), US 20160048603 A1 to Heidel et al. (hereinafter ‘HEIDEL’), and US 20180316941 A1 to Todd (hereinafter ‘TODD’).
Claim 1 (Currently Amended)
MELLEM discloses a system for facilitating survey of an entity through an executable survey bot (see abstract; diagnose mental health disorders using a diagnostic questionnaire)
MELLEM does not explicitly disclose, but LEWIS discloses, wherein the executable survey bot is generated by the system and is specific to the entity (see abstract; The service includes a dynamically-generated questionnaire having an initial question associated with a prioritized predicted risk and a plurality of subsequent questions that are generated based on previous answers and an optimization function that minimizes a total number of questions needed for the service to validate the predicted risk factors. See also ¶[0023]; an electronic device including a processor).
MELLEM further discloses said system comprising a processor (see ¶[0004]; a processor) that executes a set of executable instructions stored in a memory (see again ¶[0004]; an exemplary system includes a memory comprising code and has stored instructions), upon execution of which, the processor causes the system to:
receive, from a database, a knowledgebase comprising a set of survey queries associated with said entity (see claim 1; display a series of test questions from mental health questionnaires), and receive one or more video frame responses corresponding to each survey query of said set of survey queries (see ¶[0008], [0011] and [0018]; the received set of test video data is processed to identify a plurality of video segments corresponding to one question in the series of questions. Training data includes audio and video data recorded for each of the plurality of individuals recording during a training test. Receive a set of answer data representing answers to a series of questions from mental health questionnaires) through a recording device (see ¶[0005]; recording, by the camera, a set of test video data).
MELLEM does not explicitly disclose, but REID discloses, wherein said set of survey queries is in a form of video frame responses and initiated once an authorized user generates a user query (see ¶[0034] and [0044]; an authorized user drafts, publishes, and closes surveys),
MELLEM further discloses wherein said user query is in a form of any or a combination of textual, audio, and video form (see ¶[0005]; a series of questions from mental health questionnaires comprising text and answers for each question. See also ¶[0004], [0071] and [0106]; commands and information from a user can be entered through input devices such as a microphone. The user interface receives input from a user via a keyboard. An exemplary system includes a microphone and camera), wherein said user query is received at a client side of the executable survey bot in the form of a first set of data packets from a user computing device (see ¶[0058]-[0060]; enable one or more remote users of client computing systems 106 to interact over one or more intervening computer network. One or more users (e.g., researchers, physicians, patients) may interact over the computer network 108 with the TDC system 100 to generate a TDC and to use a generated TDC to screen for a plurality of mental health disorders. A subset of the questions, e.g., 15-20 questions out of more than 20 questions (e.g., 200 questions, 600 questions), from the multiple self-administered mental health questionnaires may be selected and used to autonomously screen subjects), and wherein the video frame responses corresponding to said set of survey queries are transmitted in real-time in the form of a second set of data packets to said user computing device from a server side of the executable bot (see ¶[0004]-[0005] and [0008]; the camera outputs video. The control system is configured to perform additional steps, including receiving the set of test video data and the set of test audio data).
MELLEM discloses a machine learning-based diagnostic classifier that includes questionnaires to classify subjects (see abstract). REID discloses monitoring and responding to customer feedback by drafting and publishing surveys with authorized users. It would have been obvious to include the authorized users as taught by REID in the system executing the method of MELLEM with the motivation to classify subjects.
MELLEM further discloses extract, by an extraction engine (see ¶[0004]; one or more processors), a set of attributes pertaining to said set of survey queries. said set of attributes being indicative of nature of the set of survey queries (see abstract and ¶[0012]; build statistical models that include questions as features to classify groups of subjects. Identify a likelihood that a user has a mentally health disorder, including a neuropsychiatric disorder. See also ¶[0027]; labeled training data includes whether the individual has a health issue):
map, by the system, the one or more video frame responses to each said survey query of said set of survey queries based on the extracted set of attributes, and process. through a machine learning (ML) engine. training data comprising said set of attributes and the mapped video frame responses to generate a trained model (see abstract and ¶[0005] and [0008]; a machine learning diagnostic classifier. Process, using a machine learning model, the selection of answers and test video data to output a mental health indication of the user. Test video data is preprocessed to identify video segments that correspond to one question in the series of questions. See also ¶[0011]-[0014]; machine learning techniques process labeled training data to build statistical models. Labeled training data includes a selection of answers to questionnaires for determining features. Subsets of models are generated based on the importance measure of features): and
determine, using the ML engine, an end survey result based on the mapped said set of survey queries with said one or more video frame responses (see ¶[0050]; determine whether a user has a mental health issue).
wherein the system is configured to generate and deploy the executable survey bot that is specific to the entity and is downloadable or accessible as a standalone application for conducting the survey (see ¶[0067]-[0070] and [0105]; application programs and modules that are stored in memory. The Application or process may be performed by a server).
MELLEM does not specifically disclose, but HEIDEL discloses, wherein an authoring portal module is configured to create said set of survey queries in intuitive flow (see ¶[0377]; a survey decision engine webpage that creates six parts of a survey), wherein the authoring portal module is configured to create, update, or delete any or a combination of said set of survey queries (see again ¶[0377]; a survey decision engine webpage that creates six parts of a survey).
MELLEM further discloses and said one or more video frame responses pertaining to said set of survey queries (see abstract and ¶[0005] and [0008]; a machine learning diagnostic classifier. Process, using a machine learning model, the selection of answers and test video data to output a mental health indication of the user. Test video data is preprocessed to identify video segments that correspond to one question in the series of questions).
MELLEM does not specifically disclose, but HEIDEL discloses and wherein the authoring portal module is configured to test the executable survey bot before generating said end survey result (see again ¶[0377]; a focus group template for survey pretests).
MELLEM does not specifically disclose, but TODD discloses, wherein the video frame responses are encoded and segmented to make them ready for adaptive delivery by a video encoding module, wherein the video frame responses are protected from unauthorized access by a multi-protocol dynamic packaging multi-digital rights management (DRM) module (see ¶[0321]; DRM-protected content may be a non-streamable layer. Layers that are non-streamable are protected from theft), and wherein the video frame responses are hosted on a cloud content delivery network (CDN) to provide high availability and reduce latency with adaptive media delivery capability (see again ¶[0321]; an interactive multi-layer session may stream content from a content delivery network).
MELLEM discloses a machine learning-based diagnostic classifier that includes questionnaires to classify and diagnose subjects (see abstract). LEWIS discloses patient risk scoring and evaluation to identify diagnoses for patients (see ¶[0109]) that includes dynamically generated questionnaires to minimize the number of questions needed to validate risk factors. It would have been obvious to include the dynamically generated questionnaires as taught by LEWIS in the system executing the method of MELLEM with the motivation to minimize the number of questions needed to assess risk factors for patients.
MELLEM discloses a machine learning-based diagnostic classifier that includes questionnaires to classify and diagnose subjects (see abstract). HEIDEL discloses a decision engine for research and statistics that includes a survey template for creating and pretesting surveys. It would have been obvious to include the survey template as taught by HEIDEL in the system executing the method of MELLEM with the motivation to diagnose subjects based on surveys/questionnaires.
MELLEM discloses a machine learning-based diagnostic classifier that includes questionnaires provided on and recorded on video (see ¶[0005]). TODD discloses video processing and display of video content that includes DRM content that is hosted on a content delivery network. It would have been obvious for one of ordinary skill in the art at the time of invention to include the DRM content hosted on a content delivery network as taught by TODD in the system executing the method of MELLEM with the motivation to provide security and video content streaming services.
Claim 3 (Previously Presented)
The combination of MELLEM, LEWIS, REID, HEIDEL, and TODD discloses the system as claimed in claim 1.
MELLEM further discloses wherein the nature of the set of survey queries corresponds to any or a combination of open ended, ratings related, satisfaction related, and choice related queries (see ¶[0119]; a multiple choice question , textual response, or any other user input.).
Claim 11 (Previously Presented)
The combination of MELLEM, LEWIS, REID, HEIDEL, and TODD discloses the system as claimed in claim 1.
MELLEM further discloses wherein user responses to said one or more video frame responses of said set of survey queries are stored in any or a combination of textual, audio and video form (see ¶[0005], [0011], and [0018]; record answers, test video data, and test audio data), wherein said user responses are stored in the database coupled to said server (see ¶[0004]; a memory that contains a machine readable medium. An exemplary system includes a display, microphone, camera, memory, and control system. See also ¶[0045], [0063], and [0069]-[0070] & Fig. 1; the processor-based device may take the form of a server computer. Communications networks and computer networks).
Claim 13 (Previously Presented)
The combination of MELLEM, LEWIS, REID, HEIDEL, and TODD discloses the system as claimed in claim 1.
MELLEM further discloses wherein said one or more video frame responses to said set of survey queries are stored in a cloud (see ¶[0052] and [0063]; collected data is processed in a cloud-computing environment).
Claim 14 (Currently Amended)
MELLEM discloses a method for facilitating survey of an entity through an executable survey bot (see abstract; diagnose mental health disorders using a diagnostic questionnaire).
MELLEM does not explicitly disclose, but LEWIS discloses, wherein the executable survey bot is generated by the system and is specific to the entity (see abstract; The service includes a dynamically-generated questionnaire having an initial question associated with a prioritized predicted risk and a plurality of subsequent questions that are generated based on previous answers and an optimization function that minimizes a total number of questions needed for the service to validate the predicted risk factors. See also ¶[0023]; an electronic device including a processor).
MELLEM further discloses said method comprising: receiving, by a processor, from a database, a knowledgebase comprising a set of survey queries associated with said entity (see claim 1; display a series of tests questions from mental health questionnaires), and receiving one or more video frame responses corresponding to each survey query of said set of survey queries (see ¶[0008], [0011] and[0018]; the received set of test video data is processed to identify a plurality of video segments corresponding to one question in the series of questions. Training data includes audio and video data recorded for each of the plurality of individuals recording during a training test. Receive a set of answer data representing answers to a series of questions from mental health questionnaires) through a recording device of a user (see ¶[0005]; recording, by the camera, a set of test video data).
MELLEM does not explicitly disclose, but REID discloses, wherein said set of survey queries is in a form of video frame responses and initiated once an authorized user generates a user query (see ¶[0034] and [0044]; an authorized user drafts, publishes, and closes surveys),
MELLEM further discloses wherein said user query is in a form of any or a combination of textual, audio, and video form (see ¶[0005]; a series of questions from mental health questionnaires comprising text and answers for each question. See also ¶[0004], [0071] and [0106]; commands and information from a user can be entered through input devices such as a microphone. The user interface receives input from a user via a keyboard. An exemplary system includes a microphone and camera), wherein said user query is received at a client side of the executable survey bot in the form of a first set of data packets from a user computing device (see ¶[0058]-[0060]; enable one or more remote users of client computing systems 106 to interact over one or more intervening computer network. One or more users (e.g., researchers, physicians, patients) may interact over the computer network 108 with the TDC system 100 to generate a TDC and to use a generated TDC to screen for a plurality of mental health disorders. A subset of the questions, e.g., 15-20 questions out of more than 20 questions (e.g., 200 questions, 600 questions), from the multiple self-administered mental health questionnaires may be selected and used to autonomously screen subjects), and wherein the one or more video frame responses corresponding to said set of survey queries are transmitted in real-time in the form of a second set of data packets to said user computing device from a server side of the executable bot (see ¶[0004]-[0005] and [0008]; the camera outputs video. The control system is configured to perform additional steps, including receiving the set of test video data and the set of test audio data).
MELLEM discloses a machine learning-based diagnostic classifier that includes questionnaires to classify subjects (see abstract). REID discloses monitoring and responding to customer feedback by drafting and publishing surveys with authorized users. It would have been obvious to include the authorized users as taught by REID in the system executing the method of MELLEM with the motivation to classify subjects.
MELLEM further discloses extracting, via an extraction engine, a set of attributes pertaining to said set of survey queries, said set of attributes are indicative of nature of the set of survey queries (see abstract and ¶[0012]; build statistical models that include questions as features to classify groups of subjects. Identify a likelihood that a user has a mentally health disorder, including a neuropsychiatric disorder. See also ¶[0027]; labeled training data includes whether the individual has a health issue):
mapping the one or more video frame responses to each said survey query of said set of survey queries based on the extracted set of attributes, and processing, through a machine learning (ML) engine, training data comprising said set of attributes and the mapped video frame responses to generate a trained model (see abstract and ¶[0005] and [0008]; a machine learning diagnostic classifier. Process, using a machine learning model, the selection of answers and test video data to output a mental health indication of the user. Test video data is preprocessed to identify video segments that correspond to one question in the series of questions. See also ¶[0011]-[0014]; machine learning techniques process labeled training data to build statistical models. Labeled training data includes a selection of answers to questionnaires for determining features. Subsets of models are generated based on the importance measure of features); and
determining, using the ML engine, an end survey result based on the mapped said set of survey queries with said one or more video frame responses (see ¶[0050]; determine whether a user has a mental health issue).
wherein the system is configured to generate and deploy the executable survey bot that is specific to the entity and is downloadable or accessible as a standalone application for conducting the survey (see ¶[0067]-[0070] and [0105]; application programs and modules that are stored in memory. The Application or process may be performed by a server).
MELLEM does not specifically disclose, but HEIDEL discloses, wherein an authoring portal module is configured to create said set of survey queries in intuitive flow (see ¶[0377]; a survey decision engine webpage that creates six parts of a survey), wherein the authoring portal module is configured to create, update, or delete any or a combination of said set of survey queries see again ¶[0377]; a survey decision engine webpage that creates six parts of a survey).
MELLEM further discloses and said one or more video frame responses pertaining to said set of survey queries (see abstract and ¶[0005] and [0008]; a machine learning diagnostic classifier. Process, using a machine learning model, the selection of answers and test video data to output a mental health indication of the user. Test video data is preprocessed to identify video segments that correspond to one question in the series of questions).
MELLEM does not specifically disclose, but HEIDEL discloses, and wherein the authoring portal module is configured to test the executable survey bot before generating said end survey result (see again ¶[0377]; a focus group template for survey pretests).
MELLEM does not specifically disclose, but TODD discloses, wherein the video frame responses are encoded and segmented to make them ready for adaptive delivery by a video encoding module, wherein the video frame responses are protected from unauthorized access by a multi-protocol dynamic packaging multi-digital rights management (DRM) module (see ¶[0321]; DRM-protected content may be a non-streamable layer. Layers that are non-streamable are protected from theft), and wherein the video frame responses are hosted on a cloud content delivery network (CDN) to provide high availability and reduce latency with adaptive media delivery capability (see again ¶[0321]; an interactive multi-layer session may stream content from a content delivery network).
MELLEM discloses a machine learning-based diagnostic classifier that includes questionnaires to classify and diagnose subjects (see abstract). LEWIS discloses patient risk scoring and evaluation to identify diagnoses for patients (see ¶[0109]) that includes dynamically generated questionnaires to minimize the number of questions needed to validate risk factors. It would have been obvious to include the dynamically generated questionnaires as taught by LEWIS in the system executing the method of MELLEM with the motivation to minimize the number of questions needed to assess risk factors for patients.
MELLEM discloses a machine learning-based diagnostic classifier that includes questionnaires to classify and diagnose subjects (see abstract). HEIDEL discloses a decision engine for research and statistics that includes a survey template for creating and pretesting surveys. It would have been obvious to include the survey template as taught by HEIDEL in the system executing the method of MELLEM with the motivation to diagnose subjects based on surveys/questionnaires.
MELLEM discloses a machine learning-based diagnostic classifier that includes questionnaires provided on and recorded on video (see ¶[0005]). TODD discloses video processing and display of video content that includes DRM content that is hosted on a content delivery network. It would have been obvious for one of ordinary skill in the art at the time of invention to include the DRM content hosted on a content delivery network as taught by TODD in the system executing the method of MELLEM with the motivation to provide security and video content streaming services.
Claim 23 (Previously Presented)
The combination of MELLEM, LEWIS, REID, HEIDEL, and TODD discloses the method as claimed in claim 14.
MELLEM further discloses wherein user responses to said one or more video frame responses of said set of survey queries are stored in any or a combination of textual, audio and video form (see ¶[0005], [0011], and [0018]; record answers, test video data, and test audio data), and wherein said user responses are stored in a database (see ¶[0004]; a memory that contains a machine readable medium. An exemplary system includes a display, microphone, camera, memory, and control system. See also ¶[0045], [0063], and [0069]-[0070] & Fig. 1; the processor-based device may take the form of a server computer. Communications networks and computer networks).
Claim 25 (Previously Presented)
The combination of MELLEM, LEWIS, REID, HEIDEL, and TODD discloses the method as claimed in claim 14.
MELLEM further discloses wherein said one or more video frame responses to said set of survey queries are stored in a cloud (see ¶[0052] and [0063]; collected data is processed in a cloud-computing environment).
Claim(s) 5 and 17 is/are rejected under 35 U.S.C. 103 as being unpatentable over US 20190341152 A1 to MELLEM et al. in view of US 20200143946 A1 to LEWIS, US 20150317676 A1 to REID et al., US 20160048603 A1 to HEIDEL et al., and US 20180316941 A1 to TODD as applied to claim 1, above, and further in view of US 20120232931 A1 to Buisman et al. (hereinafter ‘BUISMAN’).
Claim 5 (Previously Presented)
The combination of MELLEM, LEWIS, REID, HEIDEL, and TODD discloses the system as claimed in claim 1.
The combination of MELLEM, LEWIS, REID, HEIDEL, and TODD does not explicitly disclose, but BUISMAN discloses, wherein said client side of the executable survey bot is represented in the form of any or a combination of an animated character, a personality character, or an actual representation of the entity character (see ¶[0091]; the questionnaire templates and questionnaires can be textual, audio, video, image, avatar, animation, or a combination of these).
MELLEM discloses a machine learning-based diagnostic classifier that includes questionnaires to classify subjects (see abstract). BUISMAN discloses a patient monitoring system that evaluates patient responses to questions to provide the patient with knowledge (see abstract) that includes animated, avatar, and video questionnaires. It would have been obvious to include the questionnaire formats as taught by BUISMAN in the system executing the method of MELLEM with the motivation to provide questionnaires and classify subjects.
Claim 17 (Previously Presented)
The combination of MELLEM, LEWIS, REID, HEIDEL, and TODD discloses the method as claimed in claim 14.
The combination of MELLEM, LEWIS, REID, HEIDEL, and TODD does not explicitly disclose, but BUISMAN discloses, wherein said client side of the executable survey bot is represented in the form of any or a combination of an animated character, a personality character, or an actual representation of the entity character (see ¶[0091]; the questionnaire templates and questionnaires can be textual, audio, video, image, avatar, animation, or a combination of these).
MELLEM discloses a machine learning-based diagnostic classifier that includes questionnaires to classify subjects (see abstract). BUISMAN discloses a patient monitoring system that evaluates patient responses to questions to provide the patient with knowledge (see abstract) that includes animated, avatar, and video questionnaires. It would have been obvious to include the questionnaire formats as taught by BUISMAN in the system executing the method of MELLEM with the motivation to provide questionnaires and classify subjects.
Claim(s) 6 and 18 is/are rejected under 35 U.S.C. 103 as being unpatentable over US 20190341152 A1 to MELLEM et al. in view of US 20200143946 A1 to LEWIS, US 20150317676 A1 to REID et al., US 20160048603 A1 to HEIDEL et al., and US 20180316941 A1 to TODD as applied to claim 1, above, and further in view of US 20190294868 A1 to Martinez (hereinafter ‘MARTINEZ’).
Claim 6 (Previously Presented)
The combination of MELLEM, LEWIS, REID, HEIDEL, and TODD discloses the system as claimed in claim 1.
The combination of MELLEM, LEWIS, REID, HEIDEL, and TODD does not specifically disclose, but MARTINEZ discloses, wherein said one or more video frame responses are arranged in sequential order based on the trained model (see abstract and¶[0023], [0038]. and [0102]; action units in sequences of images used to determine emotions).
MELLEM discloses a machine learning-based diagnostic classifier that includes questionnaires to classify subjects (see abstract). MARTINEZ discloses recognition and annotation of facial expressions that includes using video sequences of participants in surveys to monitor and determine emotional responses. It would have been obvious to include the surveys as taught by MARTINEZ in the system executing the method of MELLEM with the motivation to classify subjects of questionnaires.
Claim 18 (Previously Presented)
The combination of MELLEM, LEWIS, REID, HEIDEL, and TODD discloses the method as claimed in claim 14.
The combination of MELLEM, LEWIS, REID, HEIDEL, and TODD does not specifically disclose, but MARTINEZ discloses, wherein said one or more video frame responses are arranged in sequential order based on the trained model (see abstract and¶[0023], [0038]. and [0102]; action units in sequences of images used to determine emotions).
MELLEM discloses a machine learning-based diagnostic classifier that includes questionnaires to classify subjects (see abstract). MARTINEZ discloses recognition and annotation of facial expressions that includes using video sequences of participants in surveys to monitor and determine emotional responses. It would have been obvious to include the surveys as taught by MARTINEZ in the system executing the method of MELLEM with the motivation to classify subjects of questionnaires.
Claim(s) 7 and 19 is/are rejected under 35 U.S.C. 103 as being unpatentable over US 20190341152 A1 to MELLEM et al. in view of US 20200143946 A1 to LEWIS, US 20150317676 A1 to REID et al., US 20160048603 A1 to HEIDEL et al., and US 20180316941 A1 to TODD as applied to claim 1, above, and further in view of US 20040083394 A1 to Brebner et al. (hereinafter ‘BREBNER’).
Claim 7 (Previously Presented)
The combination of MELLEM, LEWIS, REID, HEIDEL, and TODD discloses the system as claimed in claim 1.
The combination of MELLEM, LEWIS, REID, HEIDEL, and TODD does not specifically disclose, but BREBNER discloses, wherein the user is identified, verified, and then authorized to access the system (see ¶[0003]; a login process with a login identifier and verifies that the user is the authorized user of that identifier).
MELLEM discloses a computer-implemented method that identifies the faces of users answering questionnaires (see ¶[0146]-[0147]). BREBNER discloses user authentication that discloses verifying the identity of a user (see abstract and ¶[0001]). It would have been obvious to verify the identity of a user as taught by BREBNER in the system executing the method of MELLEM with the motivation to identify individuals answering questionnaires and provide security.
Claim 19 (Previously Presented)
The combination of MELLEM, LEWIS, REID, HEIDEL, and TODD discloses the method as claimed in claim 14.
The combination of MELLEM, LEWIS, REID, HEIDEL, and TODD does not specifically disclose, but BREBNER discloses, wherein a user is identified, verified, and then authorized to access the method (see ¶[0003]; a login process with a login identifier and verifies that the user is the authorized user of that identifier).
MELLEM discloses a computer-implemented method that identifies the faces of users answering questionnaires (see ¶[0146]-[0147]). BREBNER discloses user authentication that discloses verifying the identity of a user (see abstract and ¶[0001]). It would have been obvious to verify the identity of a user as taught by BREBNER in the system executing the method of MELLEM with the motivation to identify individuals answering questionnaires and provide security.
Claim(s) 9 and 21 is/are rejected under 35 U.S.C. 103 as being unpatentable over US 20190341152 A1 to MELLEM et al. in view of US 20200143946 A1 to LEWIS, US 20150317676 A1 to REID et al., US 20160048603 A1 to HEIDEL et al., and US 20180316941 A1 to TODD as applied to claim 1, above, and further in view of US 20160370954 A1 to Burningham et al. (hereinafter ‘BURNINGHAM’).
Claim 9 (Original)
The combination of MELLEM, LEWIS, REID, HEIDEL, and TODD discloses the system as claimed in claim 1.
The combination of MELLEM, LEWIS, REID, HEIDEL, and TODD does not specifically disclose, but BURNIGHAM discloses, wherein the system is configured to resume an unfinished survey (see ¶[0076]; provide an option for a respondent to pause a survey and resume the survey using a different distribution channel).
MELLEM discloses a machine learning-based diagnostic classifier that includes questionnaires to classify subjects (see abstract). BURNINGHAM discloses recomposing survey questions for distribution via multiple distribution channels, where a respondent is allowed to resume a paused survey based on a desire to use a different channel. It would have been obvious to include the pausing and resuming of surveys as taught by BURNINGAM in the system executing the method of MELLEM with the motivation to adhere to the desires and preferences of respondents.
Claim 21 (Original)
The combination of MELLEM, LEWIS, REID, HEIDEL, and TODD discloses the method as claimed in claim 14.
The combination of MELLEM, LEWIS, REID, HEIDEL, and TODD does not specifically disclose, but BURNIGHAM discloses, wherein the method is configured to resume an unfinished set of survey queries (see ¶[0076]; provide an option for a respondent to pause a survey and resume the survey using a different distribution channel).
MELLEM discloses a machine learning-based diagnostic classifier that includes questionnaires to classify subjects (see abstract). BURNINGHAM discloses recomposing survey questions for distribution via multiple distribution channels, where a respondent is allowed to resume a paused survey based on a desire to use a different channel. It would have been obvious to include the pausing and resuming of surveys as taught by BURNINGAM in the system executing the method of MELLEM with the motivation to adhere to the desires and preferences of respondents.
Claim(s) 10 and 22 is/are rejected under 35 U.S.C. 103 as being unpatentable over US 20190341152 A1 to MELLEM et al. in view of US 20200143946 A1 to LEWIS, US 20150317676 A1 to REID et al., US 20160048603 A1 to HEIDEL et al., and US 20180316941 A1 to TODD as applied to claim 1, above, and further in view of US 6311190 B1 to Bayer et al. (hereinafter ‘BAYER’).
Claim 10 (Previously Presented)
The combination of MELLEM, LEWIS, REID, HEIDEL, and TODD discloses the system as claimed in claim 1.
The combination of MELLEM, LEWIS, REID, HEIDEL, and TODD does not specifically disclose, but BAYER discloses, wherein the ML engine is configured with language processing engines to receive said user query in any language and provide said one or more video frame responses corresponding to said user query in any language (see abstract and col 6, ln 66-col 7, ln 36; conduct surveys in multiple languages. Translate questions in different languages. Define translations of responses for different languages.).
MELLEM discloses a machine learning-based diagnostic classifier that includes questionnaires to classify subjects (see abstract). BAYER discloses conducting surveys in different languages to sample a population with different countries or regions (see col 1, ln 26-44). It would have been obvious to include the different languages for surveys as taught by BAYER in the system executing the method of MELLEM with the motivation to sample responses from a population with different languages and increase the scope of individuals who can take the survey.
Claim 22 (Previously Presented)
The combination of MELLEM, LEWIS, REID, HEIDEL, and TODD discloses the method as claimed in claim 14.
The combination of MELLEM, LEWIS, REID, HEIDEL, and TODD does not specifically disclose, but BAYER discloses, wherein the ML engine is configured with language processing engines to receive said user query in any language and provide a user response corresponding to said user query in any language (see abstract and col 6, ln 66-col 7, ln 36; conduct surveys in multiple languages. Translate questions in different languages. Define translations of responses for different languages.).
MELLEM discloses a machine learning-based diagnostic classifier that includes questionnaires to classify subjects (see abstract). BAYER discloses conducting surveys in different languages to sample a population with different countries or regions (see col 1, ln 26-44). It would have been obvious to include the different languages for surveys as taught by BAYER in the system executing the method of MELLEM with the motivation to sample responses from a population with different languages and increase the scope of individuals who can take the survey.
Claim(s) 12 and 24 is/are rejected under 35 U.S.C. 103 as being unpatentable over US 20190341152 A1 to MELLEM et al. in view of US 20200143946 A1 to LEWIS, US 20150317676 A1 to REID et al., US 20160048603 A1 to HEIDEL et al., and US 20180316941 A1 to TODD as applied to claim 1, above, and further in view of US 20120226626 A1 to Venkateswaran et al. (hereinafter ‘VENKATESWARAN’).
Claim 12 (Currently Amended)
The combination of MELLEM, LEWIS, REID, HEIDEL, and TODD discloses the system as claimed in claim 1.
The combination of MELLEM, LEWIS, REID, HEIDEL, and TODD does not specifically disclose, but VENKATESWARAN discloses, wherein said end survey result is displayed in a dashboard coupled to the authoring portal module associated with the ML engine (see ¶[0009] and [0027]; receive input data from a user for a questionnaire and create a business intelligence dashboard markup. The dashboard generating module includes a data source for storing the input data.).
MELLEM discloses a machine learning-based diagnostic classifier that includes questionnaires to classify subjects (see abstract). VENKATESWARAN discloses a dashboard for displaying reports and questionnaire results. It would have been obvious to include the dashboard as taught by VENKATESWARAN in the system executing the method of MELLEM with the motivation to display questionnaire results.
Claim 24 (Currently Amended)
The combination of MELLEM, LEWIS, REID, HEIDEL, and TODD discloses the method as claimed in claim 14.
The combination of MELLEM, LEWIS, REID, HEIDEL, and TODD does not specifically disclose, but VENKATESWARAN discloses, wherein said end survey result is displayed in a dashboard coupled to the authoring portal module associated with the ML engine (see ¶[0009] and [0027]; receive input data from a user for a questionnaire and create a business intelligence dashboard markup. The dashboard generating module includes a data source for storing the input data.).
MELLEM discloses a machine learning-based diagnostic classifier that includes questionnaires to classify subjects (see abstract). VENKATESWARAN discloses a dashboard for displaying reports and questionnaire results. It would have been obvious to include the dashboard as taught by VENKATESWARAN in the system executing the method of MELLEM with the motivation to display questionnaire results.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. 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 nonprovisional extension fee (37 CFR 1.17(a)) 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 mailing date of this final action.
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/RICHARD N SCHEUNEMANN/ Primary Examiner, Art Unit 3624
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