DETAILED ACTION
Introduction
1. This office action is in response to Applicant's submission filed on 12/12/2024. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA . Claims 1-20 are currently pending and examined below.
Drawings
2. The drawings filed on 12/12/2024 have been accepted and considered by the Examiner.
Priority
3. The Applicants priority to United States Provisional Application # 63/137828, filed January 15, 2021, has been accepted and considered in this office action.
Double Patenting
4. The non-statutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper time-wise extension of the "right to exclude" granted by a patent and to prevent possible harassment by multiple assignees. A non-statutory obviousness-type double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Omum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); and In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed e-terminal disclaimer (e-TD) in compliance with 37 CFR 1.321 (c) or 1.321(d) may be used to overcome an actual or provisional rejection based on a non-statutory double patenting ground provided the conflicting application or patent either is shown to be commonly owned with this application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. Effective January 1, 1994, a registered attorney or agent of record may sign an e-terminal disclaimer. An e-terminal disclaimer signed by the assignee must fully comply with 37 CFR 3.73(b).
Claim 1-20 of the instant Application are rejected on the ground of non-statutory obviousness-type double patenting as being unpatentable over claims 1-20 of U.S. Patent # 11514464. Although the conflicting claims are not identical, they are not patentably distinct from each other because the claims of the present application are broader in scope than those of U.S. Patent # 11514464 and hence the claims of U.S. Patent # 11514464 can anticipate those of the present invention. That is, the claims of U.S. Patent # 11514464 contain every limitation of the claims of the present application or the claims of the present application are obvious variants thereof. It should be noted that this is in fact a non-provisional non-statutory obviousness-type double patenting rejection because the conflicting claims have in fact been patented.
As an example; claim 15 of the instant application and claim 11 of U.S. Patent # 11514464 both teach a computer-implemented method for conducting a survey to collect and analyze mixed medium responses from a plurality of respondents, the method comprising providing a survey interface to a plurality of user devices associated with the plurality of respondents, wherein the survey interface is configured to receive a response dataset from each respondent that includes data of at least two response mediums; detecting, for each response dataset, one or more topics described in that response dataset; detecting one or more attributes for each of the one or more topics; and displaying, at a user interface, at least one of the one or more topics and the one or more attributes, wherein the user interface comprises one or more controls configured to receive a request from a user for one or more raw response data related to the one or more topics and/or the one or more attributes. One of ordinary skill in the art would recognize that it would have been obvious at the time of the invention to drop narrower limitations in order to have a patent with wider applicability and freedom to operate. In other words, the narrower claim 11 of U.S. Patent # 11514464 anticipate the broader claim 15 of the instant application. Also, removal of the additional steps is obvious: In re Karlson, 136 USPQ 184 (1963): "Omission of an element and its function is an obvious expedient if the remaining elements perform the same functions as before".
Claim 1-20 of the instant Application are also rejected on the ground of non-statutory obviousness-type double patenting as being unpatentable over claims 1-18 of U.S. Patent # 12190340. Although the conflicting claims are not identical, they are not patentably distinct from each other because the claims of the present application are broader in scope than those of U.S. Patent # 12190340 and hence the claims of U.S. Patent # 12190340 can anticipate those of the present invention. That is, the claims of U.S. Patent # 12190340 contain every limitation of the claims of the present application or the claims of the present application are obvious variants thereof. It should be noted that this is in fact a non-provisional non-statutory obviousness-type double patenting rejection because the conflicting claims have in fact been patented.
As an example; claim 15 of the instant application and claim 14 of U.S. Patent # 12190340 both teach a computer-implemented method for conducting a survey to collect and analyze mixed medium responses from a plurality of respondents, the method comprising providing a survey interface to a plurality of user devices associated with the plurality of respondents, wherein the survey interface is configured to receive a response dataset from each respondent that includes data of at least two response mediums; detecting, for each response dataset, one or more topics described in that response dataset; detecting one or more attributes for each of the one or more topics; and displaying, at a user interface, at least one of the one or more topics and the one or more attributes, wherein the user interface comprises one or more controls configured to receive a request from a user for one or more raw response data related to the one or more topics and/or the one or more attributes. One of ordinary skill in the art would recognize that it would have been obvious at the time of the invention to drop narrower limitations in order to have a patent with wider applicability and freedom to operate. In other words, the narrower claim 14 of U.S. Patent # 12190340 anticipate the broader claim 15 of the instant application. Also, removal of the additional steps is obvious: In re Karlson, 136 USPQ 184 (1963): "Omission of an element and its function is an obvious expedient if the remaining elements perform the same functions as before".
Claim Rejections - 35 USC § 102
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 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 the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
5. Claims 1-4, 15-16, 6-8 and 11-12 are rejected under 35 U.S.C. 102 (a) (2) as being anticipated by Blecha-Ward (U.S. Patent Application Publication n# 2021/0065223 A1). Blecha-Ward is already of the record, having been disclosed by the Examiner in the prosecution of the parent Application 18070248.
With regards to claim 15, Blecha-Ward teaches a system for conducting a survey to collect and analyze mixed medium responses from a plurality of respondents, the system comprising: a processor; and a memory comprising instructions whereby the processor is operable to perform the steps of providing a survey interface to a plurality of user devices associated with the plurality of respondents, wherein the survey interface is configured to receive a response dataset from each respondent that includes data of at least two response mediums (Para 6 and figure 1, teaches provides a survey data analysis platform and methods for collecting, analyzing, and delivering processed and actionable survey data to producers and managers of events. Para 27, teaches that surveys can be submitted through an electronic medium e.g., a web portal, mobile application, etc. Para 61, further teaches that surveys may be submitted via electronic media boards, televisions, video monitors, and other electronic media. Paragraphs 6, 27 along with figure 1 of Blech-Ward teach the hardware such as servers, computers, memory and processor etc.);
detecting, for each response dataset, one or more topics described in that response dataset (Para 68, teaches that the text of an open-ended attendee survey response may be analyzed using NLP to ascertain consumer-identified topics or issues that relate to a survey category, in order to properly categorize the attendee data to facilitate analysis and response to such open language data);
detecting one or more attributes for each of the one or more topics (Para 51, teaches that Survey category scores may be individually provided to the event producer, along with relative priority or weighting values of the category with respect to the calculated importance attendees attribute to each category. Para 54 and figure 4, teach a module that highlights attendee survey data responses that indicate qualitative insights to provide specific direction and information on quantitative feedback and categorical responses that have been computed);
and displaying, at a user interface, at least one of the one or more topics and the one or more attributes, wherein the user interface comprises one or more controls configured to receive a request from a user for one or more raw response data related to the one or more topics and/or the one or more attributes (Para 53 and figure 4, teach a module that highlights attendee survey data responses that indicate qualitative insights to provide specific direction and information on quantitative feedback and categorical responses that have been computed. For example, if scores for audio are low, qualitative comments provide detail that it is the volume being too loud that warranted vs. being too soft or unclear. In response, the event producer may then adjust the volume levels of the venue sound system at an appropriate opportunity to provide a better overall attendee experience).
With regards to claim 15, this is a method claim for the corresponding system claim 1. These two claims are related as method and apparatus of using the same, with each claimed system element's function corresponding to the claimed method step. Accordingly, claim 15 is similarly rejected under the same rationale as applied above with respect to system claim 1.
With regards to claim 2, Blecha-Ward teaches the system of claim 1, wherein the one or more attributes includes a sentiment of a respondent corresponding to that response dataset (Para 19, teaches that the data analysis system may include an artificial intelligence language analysis module operable to analyze features the attendee's free form language that indicate sentiment e.g., slang, acronyms, tone, abbreviations, etc. in the narrative responses to survey questions).
With regards to claim 3, Blecha-Ward teaches the system of claim 1, wherein the response dataset comprises time-indexed raw response data received from the plurality of respondents (Paragraphs 9-10, teach that the data received for each of these categories can be received on a continuous basis during the event or experience and incorporated into the raw data set for the data analysis system to analyze, and the data analysis may be updated in real time during the event/experience that may be provided through the dashboard modules in the dynamic dashboard accessible to the event/experience producer. The dynamic dashboard may include several dashboard modules, each present different forms of survey data analysis, including a general satisfaction score calculated from the survey data provided by attendees, raw response volumes to each survey question and survey category; prioritization analysis of each category to inform the event producer as to which survey categories are the most important to the overall experience of the attendees; survey response results for particular categories of attendees based on various criteria such as location of attendee e.g., assigned seat or section, etc., age, gender, the people with whom the person attended the event e.g., alone, with friends, with family, with significant other, with work colleagues, etc, ticket category e.g., season ticket holder vs. single game ticket, price paid or for attendee ticket, seat assignment, and/or other profile data collected through the survey or otherwise and time of survey response relative to event).
With regards to claim 4, Blecha-Ward teaches the system of claim 1, wherein the processor is further operable to display the one or more raw response data to the user (Paragraphs 9-10, teach that the data received for each of these categories can be received on a continuous basis during the event or experience and incorporated into the raw data set for the data analysis system to analyze, and the data analysis may be updated in real time during the event/experience that may be provided through the dashboard modules in the dynamic dashboard accessible to the event/experience producer. The dynamic dashboard may include several dashboard modules, each present different forms of survey data analysis, including a general satisfaction score calculated from the survey data provided by attendees, raw response volumes to each survey question and survey category; prioritization analysis of each category to inform the event producer as to which survey categories are the most important to the overall experience of the attendees; survey response results for particular categories of attendees based on various criteria such as location of attendee e.g., assigned seat or section, etc., age, gender, the people with whom the person attended the event e.g., alone, with friends, with family, with significant other, with work colleagues, etc, ticket category e.g., season ticket holder vs. single game ticket, price paid or for attendee ticket, seat assignment, and/or other profile data collected through the survey or otherwise and time of survey response relative to event).
With regards to claim 16, this is a method claim for the corresponding system claim 4. These two claims are related as method and apparatus of using the same, with each claimed system element's function corresponding to the claimed method step. Accordingly, claim 16 is similarly rejected under the same rationale as applied above with respect to system claim 4.
With regards to claim 6, Blecha-Ward teaches the system of claim 1, wherein the detecting one or more attributes comprises a multi-modal analysis (Para 19, teaches that the data analysis system may include an artificial intelligence language analysis module operable to analyze features the attendee's free form language that indicate sentiment e.g., slang, acronyms, tone, abbreviations, etc. in the narrative responses to survey questions. The artificial intelligence analysis module may conduct analysis of unstructured information in the attendees' free form responses that assign values to positive, negative, or neutral text, thereby converting the attendees' language into a dataset having assigned values in pre-determined categories. The sentiment analysis values for each question and each category of question may be collected and statistically analyzed to provide an overall attendee satisfaction score for each question and category, providing attendee opinion data for the aspects of the event addressed in the free form response section of the survey. The AI language analysis may be based on natural language processing or computational linguistics. The text of an open-ended attendee survey response may be analyzed using NLP to ascertain consumer-identified topics or issues that relate to a survey category and analyze the attendee sentiment relating to that survey category based on the language used by the attendee in their narrative response e.g., analyzing the language in the response for emotive language, such as positive and negative adjectives, positive and negative slang or abbreviations, the nouns to which the positive and negative language elements refer e.g., the food, staff service, etc., and other aspects of the language used in the attendee's responses).
With regards to claim 7, Blecha-Ward teaches the system of claim 6, wherein the multi-modal analysis includes correlating separate sentiment analysis of the at least two response mediums with each other (Para 19, teaches that the data analysis system may include an artificial intelligence language analysis module operable to analyze features the attendee's free form language that indicate sentiment e.g., slang, acronyms, tone, abbreviations, etc. in the narrative responses to survey questions. The artificial intelligence analysis module may conduct analysis of unstructured information in the attendees' free form responses that assign values to positive, negative, or neutral text, thereby converting the attendees' language into a dataset having assigned values in pre-determined categories. The sentiment analysis values for each question and each category of question may be collected and statistically analyzed to provide an overall attendee satisfaction score for each question and category, providing attendee opinion data for the aspects of the event addressed in the free form response section of the survey. The AI language analysis may be based on natural language processing or computational linguistics. The text of an open-ended attendee survey response may be analyzed using NLP to ascertain consumer-identified topics or issues that relate to a survey category and analyze the attendee sentiment relating to that survey category based on the language used by the attendee in their narrative response e.g., analyzing the language in the response for emotive language, such as positive and negative adjectives, positive and negative slang or abbreviations, the nouns to which the positive and negative language elements refer e.g., the food, staff service, etc., and other aspects of the language used in the attendee's responses).
With regards to claim 12, Blecha-Ward teaches the system of claim 1, wherein the at least two response mediums include a first response medium that describes a qualitative response and a second response medium that describes a quantitative response (Para 53 and figure 4, teach a module that highlights attendee survey data responses that indicate qualitative insights to provide specific direction and information on quantitative feedback and categorical responses that have been computed. For example, if scores for audio are low, qualitative comments provide detail that it is the volume being too loud that warranted vs. being too soft or unclear. In response, the event producer may then adjust the volume levels of the venue sound system at an appropriate opportunity to provide a better overall attendee experience).
With regards to claim 8, Blecha-Ward teaches the system of claim 1, wherein a first response medium of the at least two response mediums is a text medium associated with a text sentiment analysis (Para 19, teaches that the text of an open-ended attendee survey response may be analyzed using NLP to ascertain consumer-identified topics or issues that relate to a survey category and analyze the attendee sentiment relating to that survey category based on the language used by the attendee in their narrative response).
With regards to claim 11, Blecha-Ward teaches the system of claim 1, wherein the user interface is configured to display a sequence of pre-configured text prompts, wherein each of the sequence of pre-configured text prompts include a question or instruction for providing the response dataset to the survey interface. (Para 50 and figures 1-2, teach that the attendees may be prompted to respond to a survey during the event by several prompts. A prompting message and a corresponding machine-readable code e.g., an optical label such as a QR code may be presented onsite at the event on electronic media boards e.g., a scoreboard, or other media to allow the attendees capture the machine-readable code with a mobile-computing device and be directed to the survey through a web-portal or mobile application. The machine-readable code may be presented through various means at the event, such as a large digital screen e.g., a scoreboard, or large digital display over a stage, etc., smaller digital screens mounted in a venue, digital or tangible posters or signage at the event, or other means. Attendees may be prompted by notifications sent to their mobile computing devices e.g., text messages, push notifications, etc. providing a link to the survey. The notifications or machine code may also prompt the attendee to download a mobile application e.g., associated with the event, the entertainers present at the event, the event producer, or others, notify the attendee about details or schedule of the event, and/or provide safety and emergency information).
Allowable Subject Matter
6. Claims 19-20 would be allowable if the double patenting rejection is overcome. Claims 5, 9-10 and 13-14 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims and further if the double patenting rejection is overcome. The prior art of record, alone or in combination, does not currently suggest or teach the invention as outlined in these claims. The Examiner shall outline more detailed reasons for allowance as and when the Application goes to allowability.
Conclusion
7. The following prior art, made of record but not relied upon, is considered pertinent to applicant's disclosure: Nicolov (U.S. Patent Application Publication # 2009/0306967 A1), Anisingaraju (U.S. Patent Application Publication # 2016/0203217 A1). These references are also included in the PTO-892 form attached with this office action.
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Any inquiry concerning this communication or earlier communications from the examiner should be directed to NEERAJ SHARMA whose contact information is given below. The examiner can normally be reached on Monday to Friday 8 am to 5 pm. If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Pierre Louis-Desir can be reached on 571-272-7799 (Direct Phone). The fax number for the organization where this application or proceeding is assigned is 571-273-8300.
/NEERAJ SHARMA/
Primary Examiner, Art Unit 2659
571-270-5487 (Direct Phone)
571-270-6487 (Direct Fax)
neeraj.sharma@uspto.gov (Direct Email)
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