DETAILED ACTION
Status of Claims
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on September 17, 2025 has been entered.
Claims 1, 3, 8, 13, 15 and 20 have been amended.
Claims 2, 6, 11-12, 14, 18 and 23-24 have been cancelled.
Claims 27-28 have been added.
Claims 1, 3-5, 7-10, 13, 15-17, 19-22 and 27-28 are currently pending and have been examined.
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 .
Response to Amendments
Applicant’s amendment necessitated the new ground(s) of rejection presented in this Office action.
The rejection of claims 1, 3-5, 7-10, 13, 15-17, 19-22 and 27-28 under 35 USC § 101 is maintained.
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 1, 3-5, 7-10, 13, 15-17, 19-22 and 27-28 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention.
As per claims 1 and 13 recites “monitor[ing], in real time, the response activity over an elapsed time period […]. Applicant’s disclosure does not describe that the monitoring of response activity during an elapsed time is happening in real time. Applicant’s disclosures describe at least when location is selected, it includes the current real-time location of members, see Applicant’s disclosure paragraph 0088. Appropriate correction is required.
Claim Rejections- 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1, 3-5, 7-10, 13, 15-17, 19-22 and 27-28 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more. Per MPEP 2106.03 Eligibility Step 1: The Four Categories of Statutory Subject Matter [R-07.2022]. Step 1 is directed to determining whether or not the claims fall within a statutory class. Herein, claims 1, 3-5, 7-10 and 27 falls within statutory class of a process and claims 13, 15-17, 19-22 and 28 falls within statutory class of a machine. Hence, the claims qualify as potentially eligible subject matter under 35 U.S.C §101. With Step 1 being directed to a statutory category, per MPEP 2106.04 Eligibility Step 2A: Whether a Claim is Directed to a Judicial Exception [R-07.2022]. Step 2 is the two-part analysis from Alice Corp. (also called the Mayo test). The 2019 PEG makes two changes in Step 2A: It sets forth new procedure for Step 2A (called “revised Step 2A”) under which a claim is not “directed to” a judicial exception unless the claim satisfies a two-prong inquiry. The two-prong inquiry is as follows: Prong One: evaluate whether the claim recites a judicial exception (an abstract idea enumerated in the 2019 PEG, a law of nature, or a natural phenomenon). If claim recites an exception, then Prong Two: evaluate whether the claim recites additional elements that integrate the exception into a practical application of the exception. The claim(s) recite(s) the following abstract idea indicated by non-boldface font and additional limitations indicated by boldface font:
Claims 1 and 13:
[at least one processor; and at least one memory including computer program code; the at least one memory and the computer program code configured to, with the at least one processor, cause the system at least to:]
receive[ing], from a server, a request from the requestor wherein the request includes a qualifying criterion, a sample size and a predetermined time period for response;
generate[ing], by the processor, an initial member population including one or more candidates, each of whom is relevant to the qualifying criterion, wherein: a relevance score, for each member of the initial member population is generated, reflecting a relevance to the qualifying criterion; and the relevance score is based on context of the qualifying criterion;
generate[ing], by the processor, a final member population including a subset of the initial user population based on a candidate preference and a candidate profile standing;
send[ing], by the server, a first request to a first subset of the final member population, wherein the first subset of the final member population is generated by determining an oversampling number, wherein the oversampling number includes an aggregate of the sample size and a predetermined percentage of the sample size;
monitor[ing], in real-time, the response activity over, an elapsed time period, and determine[ing] based on the monitoring, whether a number of respondents to the first request is more than or equal to the sample size, wherein the elapsed time period is indicative of the time passed since the first request was sent;
dynamically trigger, based on the monitored response activity, a subsequent request to a subsequent subset of the final member population if the number of respondents to the first request is less than the sample size and if the elapsed time period is less than the predetermined time period for response;
determine[ing], in the elapsed time period, whether a total number of respondents to the first request and the subsequent request is more than or equal to the sample size;
send[ing] the subsequent request to the subsequent subset of the final member population if the total number of respondents to the first request and the subsequent request is less than the sample size and if the elapsed time period is less than the predetermined time period for response;
and transmit[ing], by the server, to the requestor a visual representation comprising the response obtained by the total number of respondents when the total number of respondents is equal or more than the sample size and the elapsed time period exceeds the predetermined time period for response.
Per Prong One of Step 2A, the identified recitation of an abstract idea falls within at least one of the Abstract Idea Groupings consisting of: Mathematical Concepts, Mental Processes, or Certain Methods of Organizing Human Activity. Particularly, the identified recitation falls within Mental Processes, concepts performed in the human mind including observations, evaluation, judgement and opinion and Certain Methods of Organizing Human Activity such as commercial or legal interactions including advertising, marketing or sales activities or behaviors, business relations and managing personal behavior or relationships or interactions between people including social activities, teaching, and following rules or instructions. Per Prong Two of Step 2A, this judicial exception is not integrated into a practical application because the claim as a whole does not integrate the identified abstract idea into a practical application. The processor, memory and server is recited at a high level of generality, i.e., as a generic computing and processing system. This processor, memory and server is no more than mere instructions to apply the exception using a generic computing devices each comprising at least a processor and memory. Further, processor configured to cause receiving/determining/transmitting data is mere instruction to apply an exception using a generic computer component which cannot integrate a judicial exception into a practical application. Accordingly, this/these additional element(s) does/do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. Thus, since the claims are directed to the determined judicial exception in view of the two prongs of Step 2A, MPEP 2106.05 Eligibility Step 2B: Whether a Claim Amounts to Significantly More [R-07.2022]is directed to Step 2B. Therein, the additional elements and combinations therewith are examined in the claims to determine whether the claims as a whole amounts to significantly more than the judicial exception. It is noted here that the additional elements are to be considered both individually and as an ordered combination. In this case, the claims each at most comprise additional elements of a processor, memory and server. Taken individually, the additional limitations each are generically recited and thus does not add significantly more to the respective limitations. Further, executing all the steps/functions by a user/service subsystem is mere instruction to apply an exception using a generic computer component which cannot provide an inventive concept in Step 2B (or, looking back to Step 2A, cannot integrate a judicial exception into a practical application). For further support, the Applicant’s specification supports the claims being directed to use of a generic processor, memory and server type structure at paragraphs 0039-0040: “The processor 530 may include a microprocessor, an analogue circuit, a digital circuit, a mixed-signal circuit, a logic circuit, an integrated circuit, a Central Processing Unit (CPU), a Graphics Processing Unit (GPU), a Digital Signal Processor (DSP), a Field Programmable Gate Array (FPGA), an Application Specific Integrated Circuit (ASIC), etc., or any combination thereof. […] the client device 500 may further include a memory 520. The memory may be used by the processor 530 to permanently or temporarily store.” Paragraph 0032, page 13: “Use of the term ‘server’ herein can mean a single computing device or a plurality of interconnected computing devices which operate together to perform a particular function. That is, the server may be contained within a single hardware unit or be distributed among several or many different hardware units.” See also figure 1.
Taken as an ordered combination, the claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the limitations are directed to limitations referenced in Alice Corp. that are not enough to qualify as significantly more when recited in a claim with an abstract idea include, as a non-limiting or non-exclusive examples: i. Adding the words "apply it" (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, e.g., a limitation indicating that a particular function such as creating and maintaining electronic records is performed by a computer, as discussed in Alice Corp., 134 S. Ct. at 2360, 110 USPQ2d at 1984 (see MPEP § 2106.05(f)); ii. Simply appending well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception, e.g., a claim to an abstract idea requiring no more than a generic computer to perform generic computer functions that are well-understood, routine and conventional activities previously known to the industry, as discussed in Alice Corp., 134 S. Ct. at 2359-60, 110 USPQ2d at 1984 (see MPEP § 2106.05(d)); iii. Adding insignificant extra-solution activity to the judicial exception, e.g., mere data gathering in conjunction with a law of nature or abstract idea such as a step of obtaining information about credit card transactions so that the information can be analyzed by an abstract mental process, as discussed in CyberSource v. Retail Decisions, Inc., 654 F.3d 1366, 1375, 99 USPQ2d 1690, 1694 (Fed. Cir. 2011) (see MPEP § 2106.05(g)); or v. Generally linking the use of the judicial exception to a particular technological environment or field of use, e.g., a claim describing how the abstract idea of hedging could be used in the commodities and energy markets, as discussed in Bilski v. Kappos, 561 U.S. 593, 595, 95 USPQ2d 1001, 1010 (2010) or a claim limiting the use of a mathematical formula to the petrochemical and oil-refining fields, as discussed in Parker v. Flook. The courts have recognized the following computer functions inter alia to be well-understood, routine, and conventional functions when they are claimed in a merely generic manner: performing repetitive calculations; receiving, processing, and storing data (e.g., the present claims); electronically scanning or extracting data; electronic recordkeeping; automating mental tasks (e.g., process/machine for performing the present claims); and receiving or transmitting data (e.g., the present claims). The dependent claims 3-5, 7-10, 15-17, 19-22 and 27-28 do not cure the above stated deficiencies, and in particular, the dependent claims further narrow the abstract idea without reciting additional elements that integrate the exception into a practical application of the exception or providing significantly more than the abstract idea. Claims 3 and 15 further limit the abstract idea that each of the one or more candidates is selected based on its association with the at least one population profile identifier (a more detailed abstract idea remains an abstract idea). Claims 4 and 16 further limit the abstract idea that the candidate preference includes a time availability for response to the first request (a more detailed abstract idea remains an abstract idea). Claims 5 and 17 further limit the abstract idea that the candidate profile standing is indicative of seniority, stature or standing of each of the one or more candidates within the user population (a more detailed abstract idea remains an abstract idea). Claims 7 and 19 further limit the abstract idea that the visual representation includes one or more of the following: statistics, graphics, charts, and table (a more detailed abstract idea remains an abstract idea). Claims 8 and 20 further limit the abstract idea that the at least one population profile identifier includes one or more of the following: age group, gender, birth date, current geolocation, religion, nationality and country of residence (a more detailed abstract idea remains an abstract idea). Claims 9 and 21 further limit the abstract idea that the initial member population includes the one or more candidates located within a selected geolocation (a more detailed abstract idea remains an abstract idea). Claims 10 and 22 further limit the abstract idea that the first request sent to the first subset of the final member population is sent at a predetermined time interval (a more detailed abstract idea remains an abstract idea). And claims 27 and 28 further limit the abstract idea that the processor comprises of a request understanding module, wherein: the request understanding module comprises of one or more machine learning models; and the request understanding module is configured for recognizing entity, correcting a query, rewriting a query or recognition of an intent based on the query (a more detailed abstract idea remains an abstract idea. The identified recitation of the dependents claims falls within the Mental Processes, concepts performed in the human mind including observations, evaluation, judgement and opinion and Certain Methods of Organizing Human Activity such as commercial or legal interactions including advertising, marketing or sales activities or behaviors, business relations and managing personal behavior or relationships or interactions between people including social activities, teaching, and following rules or instructions. The recited one or more machine learnings models, merely links the abstract idea to a computer environment. In this way, the one or more machine learnings models involvement is merely a field of use which only contributes nominally and insignificantly to the recited method/system, which indicates absence of integration. Claims 1 and 13 uses the one or more machine learnings models as a tool, in its ordinary capacity, to carry out the abstract idea. As to this level of computer involvement, mere automation of manual processes using generic computers does not necessarily indicate a patent-eligible improvement in computer technology. Considered as a whole, the claimed method/system does not improve the functioning of the computer itself or any other technology or technical field. Since there are no elements or ordered combination of elements that amount to significantly more than the judicial exception, the claims are not eligible subject matter under 35 USC §101. Thus, viewed as a whole, these additional claim element(s) do not provide meaningful limitation(s) to transform the abstract idea into a patent eligible application of the abstract idea such that the claim(s) amounts to significantly more than the abstract idea itself. Therefore, the claim(s) are rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.
Response to Arguments
Applicant's arguments filed on 9/17/2025 have been fully considered but they are not persuasive.
With regard to the 35 U.S.C. 101 rejection. Applicant argues that (1) “the claims are not directed to a judicial exception”. (2) “the claims integrates it into a practical application under Prong 2 of Step 2A” and (3) “the claimed invention amounts to significantly more than the alleged abstract idea” (Remarks, pages 7-13).
With regard to the 35 U.S.C. 103, Applicant’s arguments (Remarks, pages 13-17) with respect to claim(s) 1, 3-4, 7-10, 13, 15-16 and 19-22 have been considered. Please see the updated rejection below of Smargon in view of Batty and Aggarwal. Applicant argues that (4) Aggarwal does not teach or suggest “wherein the first subset of the final member population is generated by determining an oversampling number, wherein the oversampling number includes an aggregate of the sample size and a predetermined percentage of the sample size.”
In response to Applicant’s argument (1), Examiner respectfully disagrees. Please see above the 35 U.S.C. 112 (a) rejection. In addition, Claims 1 and 13 recites a method and system for dynamically matching a request from a requestor with a member population as described in the Applicant's disclosure in paragraph 0046 "enable the user to dynamically connect with other users of the network based on dynamic requests, and dynamically determining connections to other users of the network based on predetermined attributes that the user has selected." Claims 1 and 13 recites a concept related to Mental Processes, concepts performed in the human mind including observations (qualifying criterion which includes: age group, gender, birth date, current geolocation, religion, nationality and country of residence, sample size, predetermined time period for response, oversampling number), evaluation (a relevance score for each member of the initial member population, total number of respondents, elapsed time period, sample size), judgement (final member population including a subset of the initial member population based on the candidate preference and candidate profile standing) and opinion (a visual representation of the response obtained when the total number of respondents is equal or more than the sample size and the elapsed time period exceeds the predetermined time period for response) and Certain Methods of Organizing Human Activity such as commercial or legal interactions including advertising, marketing or sales activities or behaviors, business relations and managing personal behavior or relationships or interactions between people including social activities, teaching, and following rules or instructions i.e., connect/matching with other users. The recited one or more machine learnings models in the newly added claims 27-28, merely links the abstract idea to a computer environment. Therefore, claims 1 and 13 recites an abstract idea falling within the Guidance's subject-matter grouping to the group of Mental Processes, concepts performed in the human mind including observations, evaluation, judgement and opinion and Certain Methods of Organizing Human Activity such as commercial or legal interactions including advertising, marketing or sales activities or behaviors, business relations and managing personal behavior or relationships or interactions between people including social activities, teaching, and following rules or instructions. The rejection is maintained.
In response to Applicant’s argument (2), Examiner respectfully disagrees. Please see above the 35 U.S.C. 112 (a) rejection. In addition, Per Prong Two of Step 2A, this judicial exception is not integrated into a practical application because the claim as a whole does not integrate the identified abstract idea into a practical application. The processor, memory and server is recited at a high level of generality, i.e., as a generic processor performing a generic computer function of receiving/determining/transmitting data. This generic processor limitation is no more than mere instructions to apply the exception using a generic computer component. Considering the claims as a whole, these additional limitations merely add generic computer activities i.e., receiving/determining/transmitting. The recited processor, memory and server, merely links the abstract idea to a computer environment. In this way, the processor, memory and server involvement is merely a field of use which only contributes nominally and insignificantly to the recited method, which indicates absence of integration. Claims 1 and 13 uses the processor, memory and server as a tool, in its ordinary capacity, to carry out the abstract idea. The recited one or more machine learnings models in claims 27 and 28, merely links the abstract idea to a computer environment. In this way, the one or more machine learnings models involvement is merely a field of use which only contributes nominally and insignificantly to the recited method/system, which indicates absence of integration. Claims 27 and 28 uses the one or more machine learnings models as a tool, in its ordinary capacity, to carry out the abstract idea. As to this level of computer involvement, mere automation of manual processes using generic computers does not necessarily indicate a patent-eligible improvement in computer technology. Considered as a whole, the claimed method does not improve the functioning of the computer itself or any other technology or technical field. Further, a processor configured to cause receiving/determining/transmitting data to a device is mere instruction to apply an exception using a generic computer component which cannot integrate a judicial exception into a practical application. Accordingly, this/these additional element(s) does/do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. Merely adding generic computer components to perform the method is not sufficient. Thus, the claim must include more than mere instructions to perform the method on a generic component or machinery to qualify as an improvement to an existing technology. See MPEP § 2106.05(f) for more information about mere instructions to apply an exception. As per MPEP 2106.05 (a) II. Improvements to any other technology or technical field please see the examples that the courts have indicated may not be sufficient to shown an improvement to technology such as gathering and analyzing information using conventional techniques and displaying the result, TLI Communications, 823 F.3d at 612-13, 118 USPQ2d at 1747-48. The rejection is maintained.
In response to Applicant’s argument (3), Examiner respectfully disagrees. Please see above the 35 U.S.C. 112 (a) rejection. In addition, the claims each at most comprise additional elements of a processor, memory and server. Taken individually, the additional limitations each are generically recited and thus does not add significantly more to the respective limitations. Further, executing all the steps/functions by a user/service subsystem is mere instruction to apply an exception using a generic computer component which cannot provide an inventive concept in Step 2B (or, looking back to Step 2A, cannot integrate a judicial exception into a practical application). For further support, the Applicant’s specification supports the claims being directed to use of a generic processor, memory and server type structure at paragraphs 0039-0040: “The processor 530 may include a microprocessor, an analogue circuit, a digital circuit, a mixed-signal circuit, a logic circuit, an integrated circuit, a Central Processing Unit (CPU), a Graphics Processing Unit (GPU), a Digital Signal Processor (DSP), a Field Programmable Gate Array (FPGA), an Application Specific Integrated Circuit (ASIC), etc., or any combination thereof. […] the client device 500 may further include a memory 520. The memory may be used by the processor 530 to permanently or temporarily store.” Paragraph 0032, page 13: “Use of the term ‘server’ herein can mean a single computing device or a plurality of interconnected computing devices which operate together to perform a particular function. That is, the server may be contained within a single hardware unit or be distributed among several or many different hardware units.” See also figure 1. Taken as an ordered combination, the claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the limitations are directed to limitations referenced in Alice Corp. that are not enough to qualify as significantly more when recited in a claim with an abstract idea include, as a non-limiting or non-exclusive examples:
i. Adding the words "apply it" (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, e.g., a limitation indicating that a particular function such as creating and maintaining electronic records is performed by a computer, as discussed in Alice Corp., 134 S. Ct. at 2360, 110 USPQ2d at 1984 (see MPEP § 2106.05(f)); ii. Simply appending well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception, e.g., a claim to an abstract idea requiring no more than a generic computer to perform generic computer functions that are well-understood, routine and conventional activities previously known to the industry, as discussed in Alice Corp., 134 S. Ct. at 2359-60, 110 USPQ2d at 1984 (see MPEP § 2106.05(d)); iii. Adding insignificant extra-solution activity to the judicial exception, e.g., mere data gathering in conjunction with a law of nature or abstract idea such as a step of obtaining information about credit card transactions so that the information can be analyzed by an abstract mental process, as discussed in CyberSource v. Retail Decisions, Inc., 654 F.3d 1366, 1375, 99 USPQ2d 1690, 1694 (Fed. Cir. 2011) (see MPEP § 2106.05(g)); or v. Generally linking the use of the judicial exception to a particular technological environment or field of use, e.g., a claim describing how the abstract idea of hedging could be used in the commodities and energy markets, as discussed in Bilski v. Kappos, 561 U.S. 593, 595, 95 USPQ2d 1001, 1010 (2010) or a claim limiting the use of a mathematical formula to the petrochemical and oil-refining fields, as discussed in Parker v. Flook. The courts have recognized the following computer functions inter alia to be well-understood, routine, and conventional functions when they are claimed in a merely generic manner: performing repetitive calculations; receiving, processing, and storing data (e.g., the present claims); electronically scanning or extracting data; electronic recordkeeping; automating mental tasks (e.g., process/machine for performing the present claims); and receiving or transmitting data (e.g., the present claims). In addition, the recited one or more machine learnings models, merely links the abstract idea to a computer environment. Claims 1 and 13 uses the one or more machine learnings models as a tool, in its ordinary capacity, to carry out the abstract idea. As to this level of computer involvement, mere automation of manual processes using generic computers does not necessarily indicate a patent-eligible improvement in computer technology. The Examiner has adequately supported the finding that the recited processor, memory, server and one more machine learnings for recognizing queries is well-understood, routine and conventional. As explained above, claims 1 and 13 is directed to an abstract idea on a processor, memory, server and one or more machine learnings, used in its ordinary capacity performing well-understood, routine, and conventional activities. The rejection is maintained.
In response to Applicant’s argument (4). Examiner respectfully disagrees. Smargon teaches generating, by the processor (Figure 2) a final member population including a subset of the initial member population based on a candidate preference and a candidate profile standing in ¶ 0047: “The query module 240 allows the user initiating the survey to generate a query to potential respondents to the survey based on selection criteria provided by the user. Multiple queries may be used for one survey. For example, the user may want to survey two groups of respondents (e.g., males and females) using the same survey. For such a survey, a specific count of responses for each group may also be requested.” And sending, by the server (Figures 1 and 2), a first request to a first subset of the final member population in ¶ 0052: “The query module 240 can generate invitations to participate in a survey to a selected set of potential respondents from a pool of potential respondents identified by the query.” Smargon teaches in ¶ 0129: “a starting size of the pool of invitations can be based on a number of survey responses that the user initiating the survey desires to receive.” Smargon teaches the sample size i.e., invitee pool. See also ¶ 0096. And ¶ 0111: “If a selected respondent does not respond (as described for block 410) to the survey invitation before the expiration date or time, the process can automatically select an alternate respondent and send an invitation to participate in the survey.” Smargon describes that additional/alternate respondents are available to participate in the survey. Smargon in view of Batty is silent with regard to the following limitations. However Aggarwal in an analogous art of survey analysis and distribution for the purpose of providing the following limitations as shown does: wherein the first subset of the final member population is generated by determining an oversampling number, wherein the oversampling number includes an aggregate of the sample size and a predetermined percentage of the sample size in ¶ 0470-0471: “The graphical user interface 2600 also provides tools for calculating an appropriate sample size for a survey. For example, after the number of users is reduced to a more manageable subset via application of filters, the user survey service can help obtain a representative survey result by applying a sample size calculation as an aid towards determining how large a set of survey candidates should be.
In this example, the user survey service calculates a sample size in view of a desired confidence level (which may be expressed as a percentage) as to the accuracy of the survey data.” Aggarwal also teaches “The higher the confidence level, the larger the calculated sample size will be. Typically, a high confidence level (e.g., 90% or more) is desirable. In at least one embodiment, the confidence level can be set within a range of 95-99%,” Aggarwal describes that based on the predetermined percentage, larger sample size are calculated. See also ¶ 0473: “A sample size that is initially calculated based on confidence level can be adjusted based on the expected response rate to get to the actual calculated sample size.”
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 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.
Claims 1, 3-4, 7-10, 13, 15-16 and 19-22 are rejected under 35 U.S.C. 103 as being unpatentable over Aaron Smargon (US 2012/0226743 A1) hereinafter “Smargon” in both view of Batty, III et al., (US 2023/0063036 A1) hereinafter “Batty” and Aggarwal et al., (US 2018/0084107 A1) hereinafter “Aggarwal”.
Claim 1:
Smargon as shown discloses a method for dynamically matching a request from a requestor with a member population, the method:
receiving, from a server (Figures 1 and 2), a request from the requestor wherein the request includes a qualifying criterion, a sample size and a predetermined time period for response (¶ 0033: “The survey creation interface allows the user initiating the survey to define various attributes of the survey including the properties and content, including layout of the survey. The survey properties can include […] expiration date and time, respondent selection criteria (for example, who and how many), and respondent data requests.” See also ¶ 0048: “Many different criteria or combinations of criteria may be used for the query. The query criteria can be combined with AND, OR, and other logical operators. Example criteria include selection based on demographic information, such as the age or age range, sex, gender, or location.”);
generating, by the processor (Figure 2), an initial member population including one or more candidates, each of whom is relevant to the qualifying criterion; (¶ 0007: “identifying users eligible for the survey based on the survey attributes; selecting potential respondents from the users identified as eligible;” see also claim 4: “wherein selecting potential respondents comprises querying a database of user information for users that satisfy the respondent selection criteria”);
Smargon teaches in ¶ 0094: “The user data store 280 can store profile information for users of the social network service.” Smargon is silent with regard to the following limitations. However Batty in an analogous art of survey/query management for the purpose of providing the following limitations as shown does:
wherein: a relevance score, for each member of the initial member population is dynamically generated in response to the received request, reflecting a relevance to the qualifying criterion and the relevance score is based on context of the qualifying criterion; (¶ 0023: “the dynamic matching service 112 generates dynamic matches between sets of participants (e.g., by using the matching criteria 132 to dynamically generate a recommendation score for each pairing of a mentor 102 and a mentee 104). […] each time a participant (e.g., one of the mentors 102 or one of the mentees 104) completes matching criteria 132, the dynamic matching service 112 computes a recommendation score for that participant against each participant in the other set of participants.” See also ¶ 0020: “matching criteria 132 can include queries, responses, defined relationships between queries and responses, defined weights for queries and responses, and any other suitable criteria.” And ¶ 0054: “Another query could ask whether a participant wishes to only be matched with participants in another category from the same home state or region as the participant, wishes to be only matched with participants in another category from a different home state or region, or does not care”);
Both Smargon and Batty teach survey/queries management. Smargon teaches in ¶the Abstract “Customized multimedia surveys are provided in a social network environment.” Batty teaches in ¶ 0013: “a dynamic set of queries can be answered by multiple people (e.g., multiple mentors, mentees, or both) .” Thus, they are deemed to be analogous references as they are reasonably pertinent to each other and are directed towards solving similar problems within the same environment. One of ordinary skill in the art would have recognized that applying the known technique of Batty would have yielded predictable results and resulted in an improved system. It would have been recognized that applying the technique of Batty to the teaching of Smargon would have yielded predictable results because the level of ordinary skill in the art demonstrated by the references applied shows the ability to incorporate such as wherein: a relevance score, for each member of the initial member population is dynamically generated in response to the received request, reflecting a relevance to the qualifying criterion; and the relevance score is based on context of the qualifying criterion into similar systems. Further, as noted by Batty “the dynamic matching service can perform one or more of the following actions: limit which participants can be matched across sets/categories of participants (e.g., which mentors are eligible to be matched with a given mentee or group of mentees), establish a lock out period during which participants may not respond to or modify responses to matching criteria, define a number of matches for each participant, or define a time out period (e.g., a time period after which a matching request from one set of participants to another set of participants will be automatically denied). ” (Batty, ¶ 0034).
In addition, Smargon teaches:
generating, by the processor (Figure 2) a final member population including a subset of the initial member population based on a candidate preference and a candidate profile standing (¶ 0047: “The query module 240 allows the user initiating the survey to generate a query to potential respondents to the survey based on selection criteria provided by the user. Multiple queries may be used for one survey. For example, the user may want to survey two groups of respondents (e.g., males and females) using the same survey. For such a survey, a specific count of responses for each group may also be requested.”);
sending, by the server (Figures 1 and 2), a first request to a first subset of the final member population (¶ 0052: “The query module 240 can generate invitations to participate in a survey to a selected set of potential respondents from a pool of potential respondents identified by the query.”);
Smargon teaches in ¶ 0129: “a starting size of the pool of invitations can be based on a number of survey responses that the user initiating the survey desires to receive.” Smargon teaches as explained above the sample size. See also ¶ 0096. Smargon in view of Batty is silent with regard to the following limitations. However Aggarwal in an analogous art of survey analysis and distribution for the purpose of providing the following limitations as shown does:
wherein the first subset of the final member population is generated by determining an oversampling number, wherein the oversampling number includes an aggregate of the sample size and a predetermined percentage of the sample size (¶ 0470-0471: “The graphical user interface 2600 also provides tools for calculating an appropriate sample size for a survey. For example, after the number of users is reduced to a more manageable subset via application of filters, the user survey service can help obtain a representative survey result by applying a sample size calculation as an aid towards determining how large a set of survey candidates should be. In this example, the user survey service calculates a sample size in view of a desired confidence level (which may be expressed as a percentage) as to the accuracy of the survey data.” See also ¶ 0473: “ Survey candidates also can be selected from outside the filtered population, if needed. Alternatively, other techniques can be used to select survey candidates.”);
Both Smargon and Aggarwal teach survey analysis and distribution. Smargon teaches in the Abstract “Customized multimedia surveys are provided in a social network environment.” Aggarwal teaches in the Abstract “a user survey service that can be used for conducting user surveys related to UC services.” Thus, they are deemed to be analogous references as they are reasonably pertinent to each other and are directed towards solving similar problems within the same environment. One of ordinary skill in the art would have recognized that applying the known technique of Aggarwal would have yielded predictable results and resulted in an improved system. It would have been recognized that applying the technique of Aggarwal to the teaching of Smargon in view of Batty would have yielded predictable results because the level of ordinary skill in the art demonstrated by the references applied shows the ability to incorporate such as wherein the first subset of the final member population is generated by determining an oversampling number, wherein the oversampling number includes an aggregate of the sample size and a predetermined percentage of the sample size into similar systems. Further, as noted by Aggarwal “All of these text boxes can be editable to allow a survey administrator to adapt the survey according to, for example, the target audience and/or the information being sought.” (Aggarwal, ¶ 0459).
Further, Smargon teaches:
monitoring, in real-time, the response activity over an elapsed time period, and determining based on the monitoring whether a number of respondents to the first request is more than or equal to the sample size (¶ 0100: “The process can store the responses received in the survey data store 290 and may provide updates to the user who initiated the survey regarding how many of the invited respondents have responded to the survey. If the user who initiated the survey set up an expiration date or time for the survey or set up a threshold number of responses for closing the survey, the process closes the survey to additional responses if the conditions for closing the survey are satisfied.” And ¶ 0046: “surveys can be conducted in real-time. For example, a user could initiate a survey that is posted to a real-time discussion forum on the social network to solicit responses and the responses to the survey can be collected, processed, and displayed to users of the forum as the results are tabulated (i.e., while some users may still be responding to the survey).”);
wherein the elapsed time period is indicative of the time passed since the first request was sent (¶ 0038: “when a certain number or percent of their respondent quota has responded to an active survey, when a certain amount of time has passed since the survey was issued, or when a survey is about to expire),”);
dynamically triggering, based on the monitored response activity (Figures 1 and 2), a subsequent request to a subsequent subset of the final member population if the number of respondents to the first request is less than the sample size and if the elapsed time period is less than the predetermined time period for response (¶ 0111: “If a selected respondent does not respond (as described for block 410) to the survey invitation before the expiration date or time, the process can automatically select an alternate respondent and send an invitation to participate in the survey.”);
determining, in the elapsed time period, whether a total number of respondents to the first request and the subsequent request is more than or equal to the sample size; (¶ 0100: “The process can store the responses received in the survey data store 290 and may provide updates to the user who initiated the survey regarding how many of the invited respondents have responded to the survey. If the user who initiated the survey set up an expiration date or time for the survey or set up a threshold number of responses for closing the survey, the process closes the survey to additional responses if the conditions for closing the survey are satisfied.”);
sending the subsequent request to the subsequent subset of the final member population if the total number of respondents to the first request and the subsequent request is less than the sample size and if the elapsed time period is less than the predetermined time period for response (¶ 0111: “ If a selected respondent does not respond (as described for block 410) to the survey invitation before the expiration date or time, the process can automatically select an alternate respondent and send an invitation to participate in the survey.”);
and transmitting, by the server (Figures 1 and 2), to the requestor a visual representation comprising the response obtained by the total number of respondents when the total number of respondents is equal or more than the sample size and the elapsed time period exceeds the predetermined time period for response (¶ 0100: “If the user who initiated the survey set up an expiration date or time for the survey or set up a threshold number of responses for closing the survey, the process closes the survey to additional responses if the conditions for closing the survey are satisfied.” And ¶ 0101: “ after the results of the survey have been collected, the process compiles the responses received into survey results. The survey results can be published in various formats including a graphical representation of the results (e.g., a bar graph, a pie chart, or a histogram), a textual representation (e.g., a list or a table), or a combination thereof.)”;
Claim 13:
The limitations of claim 13 encompasses substantially the same scope as claim 1. Accordingly, those similar limitations are rejected in substantially the same manner as claim 1, as described above. The following limitations differs from claim 1:
Smargon as shown discloses a system, the system:
at least one processor; and at least one memory including computer program code; the at least one memory and the computer program code configured to, with the at least one processor, cause the system at least to: (Figures 1 and 2);
Claims 3 and 15:
Smargon as shown discloses the following limitations:
wherein each of the one or more candidates is selected based on its association with the qualifying criterion (¶ 0047: “The query module 240 allows the user initiating the survey to generate a query to potential respondents to the survey based on selection criteria provided by the user. Multiple queries may be used for one survey. For example, the user may want to survey two groups of respondents (e.g., males and females) using the same survey. For such a survey, a specific count of responses for each group may also be requested.”);
Claims 4 and 16:
Smargon in view of Batty is silent with regard to the following limitations. However Aggarwal in an analogous art of survey analysis and distribution for the purpose of providing the following limitations as shown does:
wherein the candidate preference includes a time availability for response to the first request (¶ 0353: “A UC system can provide the user survey service the end user's presence information, and the user survey service can target users based on their availability. Therefore, users can be contacted specifically at a time when they are available, and not be disturbed while they are busy. This should increase the possibility of the user actually completing the survey.”);
Both Smargon and Aggarwal teach survey analysis and distribution. Smargon teaches in the Abstract “Customized multimedia surveys are provided in a social network environment.” Aggarwal teaches in the Abstract “a user survey service that can be used for conducting user surveys related to UC services.” Thus, they