DETAILED ACTION
Claims 1-20 are pending.
Claims 1, 4; 11, 15; and 20 are amended.
Claims 1-20 are rejected.
Notice of AIA Status
The present application is being examined under the pre-AIA first to invent provisions.
Statutory Review under 35 USC § 101
Claims 1-10 are directed towards a method and have been reviewed. Claims 1-10 appear to remain patent-ineligible as being an abstract idea without significantly more.
Claims 11-19 are directed toward a system and have been reviewed. Claims 11-19 are a system that contains hardware (a nontransitory processor-readable medium). Claims 11-19 appear to remain patent-ineligible as being an abstract idea without significantly more.
Claim 20 is directed towards a method and has been reviewed. Claim 20 appears to remain patent-ineligible as being an abstract idea without significantly more.
Examiner Notes/Objections
Claims 1-20 are objected to.
The Examiner cautions against the recent amendments that result in the incorporation of substantial limitations within the confines of the preamble.
At least a portion of the claim elements are now language solely recited in preamble recitations in claims 1, 11, and 20. When reading the preamble in the context of the entire claim, the recitations (see at least “one or more defined success-evaluation criteria” and “a defined number or rate of positive interactions with other users from the plurality of users” and “a defined number of photos in a user’s profile” and “a set of defined evaluation criteria”) is not limiting because the body of the claim describes a complete invention and the language recited solely in the preamble does not provide any distinct definition of any of the claimed invention’s limitations. Thus, the preamble of the claim(s) would not be considered a limitation and would be of no significance to claim construction. See Pitney Bowes, Inc. v. Hewlett-Packard Co., 182 F.3d 1298, 1305, 51 USPQ2d 1161, 1165 (Fed. Cir. 1999). See MPEP § 2111.02.
Response to Arguments
35 U.S.C. 101
Applicant's arguments filed 01/23/2026 have been fully considered but they are not persuasive.
Regarding claim 1, Applicant submits in pp10-11 that the operations of the claims are practical applications and operations that cannot be considered mental steps or otherwise abstract ideas.
In response to Applicant’s arguments, the Examiner relies on MPEP 2106.05(f), which refers to Intellectual Ventures I v. Capital One Fin. Corp., 850 F.3d 1332, 121 USPQ2d 1940 (Fed. Cir. 2017) and “the abstract idea of ‘collecting, displaying, and manipulating data.’”
The (1) “providing” step is displaying data.
The (2) “receiving” step is collecting data.
The (3) “receiving” step is also collecting data.
The (4) “implementing” step is manipulating data.
As a result, claims 1, 11, and 20 remain directed to an abstract idea.
Regarding claim 1, Applicant submits in p11 that the claim elements that were alleged to be directed to mental steps and/or abstract ideas have been amended or canceled.
In response to Applicant’s arguments, the claim elements remain directed to abstract ideas.
The change of the “identifying” from active voice to passive voice does not substantially make it immune to analysis under the patent subject matter eligibility determination.
The detecting of the discrepancy being changed to “a discrepancy [that] is determined” does not substantially make it immune to analysis under the patent subject matter eligibility determination.
The “determining … whether the discrepancy from desired behavior is sufficiently large” being changed to “if the discrepancy from desired behavior is sufficiently large” does remove a mental “determination” step; however, the Examiner notes the use of “if” in the claim limitation, which results in an interpretation of at least the method claim not requiring the remaining portion of the “providing” step if the condition does not apply.
The amendment of the “determining” steps to make them “receiving” steps has changed the analysis of the limitations; however, the “receiving” is still part of an abstract idea of “collecting, displaying, and manipulating data.”
Regarding claim 1, Applicant submits in pp11-12 that amended claim 1 is analogous to the claims found patent-eligible in Enfish, LLC v. Microsoft Corp., 822 F.3d 1327 (Fed. Cir. 2016), where improvements to database functionality were deemed technological in nature.
In response to Applicant’s arguments, amended claim 1 appears to be more analogous to the claims found patent-ineligible in Intellectual Ventures I v. Capital One Fin. Corp., 850 F.3d 1332, 121 USPQ2d 1940 (Fed. Cir. 2017), where the “collecting, displaying, and manipulating [of] data” were deemed an abstract idea.
35 U.S.C. 103
A portion of Applicant’s arguments, see pp12-14, filed 01/23/2026, with respect to the rejection(s) of claim(s) 1-4, 7-10, and 20 under pre-AIA U.S.C. 103(a) have been fully considered and are not persuasive.
While the Examiner did refer to ¶ 0083 of the specification, the changes made to the independent claims incorporating the subject matter of dependent claim 4 did not incorporate anything from the portions of ¶ 0083 referred to by the Examiner (see Remarks p14, “The Examiner noted in the Office Action that she appreciated the support provided from paragraph [0083] of the specification and sees potential in this language” in light of CTFR 10/23/2025 p5, “The Examiner … sees potential in the language involving common knowledge and/or data gathered from other non-end user sources and/or from unsuccessful end user components as the claims currently consider data gathered from successful end user components..”
The changes also did not substantially alter the subject matter incorporated into the independent claims originally from dependent claim 4 with the exception of reiterating that the discrepancy is “from desired behavior to be corrected.”
Claim 1 now recites “wherein the discrepancy from desired behavior to be corrected is between the user’s messaging activities and the information on the user’s profile.”
Dependent claim 4 recited “wherein implementing the suggestion to change the piece of content further comprises correcting a discrepancy between the user’s messaging activities compared to the information on the user’s profile.”
As a result, the reference De remains relevant to the changes made to the independent claims; as a result, claims 1 and 20 remain rejected under pre-AIA 35 U.S.C. 103(a) as being unpatentable over Krishnamoorthy in view of De and Fredinburg and Gobeyn.
Applicant’s arguments, see p14, filed 01/23/2026, with respect to the rejection(s) of claim(s) 4 under pre-AIA U.S.C. 103(a) have been fully considered and are persuasive.
Therefore, the rejection has been withdrawn. However, upon further consideration, a new ground(s) of rejection is made.
Applicant’s arguments, see p15, filed 01/23/2026, with respect to the rejection(s) of claims 5-6 have been fully considered and are not persuasive, as its parent independent claim 1 remains rejected under pre-AIA 35 U.S.C. 103(a).
A portion of Applicant’s arguments, see pp15-17, filed 01/23/2026, with respect to the rejection(s) of claims 11-18 have been fully considered and are not persuasive.
While the Examiner did refer to ¶ 0083 of the specification, the changes made to the independent claims incorporating the subject matter of dependent claim 4 did not incorporate anything from the portions of ¶ 0083 referred to by the Examiner (see Remarks p14, “The Examiner noted in the Office Action that she appreciated the support provided from paragraph [0083] of the specification and sees potential in this language” in light of CTFR 10/23/2025 p5, “The Examiner … sees potential in the language involving common knowledge and/or data gathered from other non-end user sources and/or from unsuccessful end user components as the claims currently consider data gathered from successful end user components..”
The changes also did not substantially alter the subject matter incorporated into the independent claims originally from dependent claim 15 with the exception of reiterating that the discrepancy is “from desired behavior.”
Claim 11 now recites “wherein the discrepancy from desired behavior to be corrected is between the user’s messaging activities and the information on the user’s profile.”
Dependent claim 15 recited “wherein the processor being caused to implement the suggestion to change the piece of content further comprises correcting a discrepancy between the user’s messaging activities compared to the information on the user’s profile.”
As a result, the reference De remains relevant to the changes made to the independent claims; as a result, claim 11 remains rejected under pre-AIA 35 U.S.C. 103(a) as being unpatentable over Krishnamoorthy in view of De and Fredinburg and Gobeyn.
Applicant’s arguments, see pp17-18, filed 01/23/2026, with respect to the rejection(s) of claim(s) 15 under pre-AIA U.S.C. 103(a) have been fully considered and are persuasive.
Therefore, the rejection has been withdrawn. However, upon further consideration, a new ground(s) of rejection is made.
Applicant’s arguments, see p18, filed 01/23/2026, with respect to the rejection(s) of claim 19 have been fully considered and are not persuasive, as its parent independent claim 15 remains rejected under pre-AIA 35 U.S.C. 103(A).
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 1-10 remain rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Claim 1 recites identifying a subset, determining a discrepancy, providing a suggestion to correct a discrepancy, receiving a first value, receiving a second value, and implementing the suggestion to change a piece of content, which are being considered an abstract idea at this time.
(The amendments to change the elements from active voice to passive voice do not substantially alter the analysis of the claims under 35 U.S.C. 101.)
See relevantly MPEP 2106.04(a)(2), Section III, Subsection D referring to an “application program interface for extracting and processing information from a diversity of types of hard copy documents – Content Extraction, 776 F.3d at 1345, 113 USPQ2d at 1356” and MPEP 2016.05(f) referring to “the abstract idea of ‘collecting, displaying, and manipulating data’” Intellectual Ventures I v. Capital One Fin. Corp., 850 F.3d 1332, 121 USPQ2d 1940 (Fed. Cir. 2017).
Identifying and determining serve as mental processes.
Providing a suggestion falls under “displaying.”
Receiving a first value and receiving a second value fall under “collecting.”
Implementing the suggestion to change a piece of content falls under “manipulating.”
Step 2A, Prong Two
This judicial exception of identifying a subset, determining a discrepancy, providing a suggestion to correct a discrepancy, receiving a first value, receiving a second value, and implementing the suggestion to change a piece of content is not integrated into a practical application despite the generically recited computer elements shown below:
a processor-based hosted-services system,
instructions are received from the user to correct the discrepancy
The generically recited computer elements amount to implementing the abstract idea on a computer, merely using a computer as a tool to perform an abstract idea, or generally linking the use of a judicial exception to a particular technological environment or field of use as seen below.
A method of operating a processor-based hosted-services system,
This additional element generally links the use of the judicial exception to a particular technological environment or field of use, as discussed in MPEP 2106.05(h).
A method of operating a processor-based hosted-services system,
The processor in these steps are recited at a high-level of generality (i.e., as a generic processor performing a generic computer function) such that it amounts no more than mere instructions to apply the exception using a generic computer component. Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. (see MPEP 2106.05(f)).
instructions are received from the user to correct the discrepancy
This limitation can be considered post-solution activity (MPEP 2106.05(d)) and amounts to no more than mere instructions to apply the exception.
Step 2B
The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception.
instructions are received from the user to correct the discrepancy
This limitation adds 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 (MPEP 2106.05(I)(A)).
Claim 2 introduces a determining of a value, comparing the value, and determining whether a discrepancy exists, which are further steps in a mental process.
Claim 2 does introduce prompting a user of the existence of a discrepancy.
This additional element is mere generic transmission and presentation of collected and analyzed data which is considered to be insignificant extra solution activity (MPEP 2106.05(g)).
Claim 3 specifies that the comparison is performed in response to a request received from the target user.
This additional element is mere generic transmission and presentation of collected and analyzed data which is considered to be insignificant extra solution activity (MPEP 2106.05(g)).
Claim 4 introduces the user’s stated relationship intent in the user’s profile not matching their own actions or activities, which is considered to be insignificant extra solution activity (MPEP 2106.05(g)).
Claim 5 specifies that the comparison is performed automatically on a periodic basis, independent of any received user requests, merely nominal or token extra-solution components of the claim.
Claim 6 introduces a determining of recency, comparing the recentness, and identifying of a discrepancy, which are further steps in a mental process.
Claim 7 introduces causing comparative information to be presented to a target user.
This additional element is mere generic transmission and presentation of collected and analyzed data which is considered to be insignificant extra solution activity (MPEP 2106.05(g)).
Claim 8 introduces causing presentation of comparative information of other users to a target user.
This additional element is mere generic transmission and presentation of collected and analyzed data which is considered to be insignificant extra solution activity (MPEP 2106.05(g)).
Claim 9 specifies limiting the other users whose comparative information is being presented to users in a certain geographic area, merely nominal or token extra-solution components of the claim.
Claim 10 describes performing a comparison, which is an additional step in a mental process.
Claims 11-19 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Claim 11 recites identifying a subset, determining a discrepancy, providing a suggestion to correct a discrepancy, receiving a first value, receiving a second value, and implementing the suggestion to change a piece of content, which are being considered an abstract idea at this time.
(The amendments to change the elements from active voice to passive voice do not substantially alter the analysis of the claims under 35 U.S.C. 101.)
See relevantly MPEP 2106.04(a)(2), Section III, Subsection D referring to an “application program interface for extracting and processing information from a diversity of types of hard copy documents – Content Extraction, 776 F.3d at 1345, 113 USPQ2d at 1356” and MPEP 2016.05(f) referring to “the abstract idea of ‘collecting, displaying, and manipulating data’” Intellectual Ventures I v. Capital One Fin. Corp., 850 F.3d 1332, 121 USPQ2d 1940 (Fed. Cir. 2017).
Identifying and determining serve as mental processes.
Providing a suggestion falls under “displaying.”
Receiving a first value and receiving a second value fall under “collecting.”
Implementing the suggestion to change a piece of content falls under “manipulating.”
Step 2A, Prong Two
This judicial exception of identifying a subset, determining a discrepancy, providing a suggestion to correct a discrepancy, receiving a first value, receiving a second value, and implementing the suggestion to change a piece of content is not integrated into a practical application despite the generically recited computer elements shown below:
A processor-based hosted-services system
communications ports
a plurality of end user devices,
a processor
prompt the target user…
implementing the suggestion to change the piece of content
The generically recited computer elements amount to implementing the abstract idea on a computer, merely using a computer as a tool to perform an abstract idea, or generally linking the use of a judicial exception to a particular technological environment or field of use as seen below.
A processor-based hosted-services system,
This additional element generally links the use of the judicial exception to a particular technological environment or field of use, as discussed in MPEP 2106.05(h).
A processor-based hosted-services system,
The processor in these steps are recited at a high-level of generality (i.e., as a generic processor performing a generic computer function) such that it amounts no more than mere instructions to apply the exception using a generic computer component. Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. (see MPEP 2106.05(f)).
wherein the processor executes the processor-executable instructions
The generically recited computer element amounts to merely using a computer as a tool to perform an abstract idea (see MPEP 2160.05(f)).
communications ports that provide communications with a plurality of end user devices,
These additional elements are mere generic transmission and presentation of collected and analyzed data which is considered to be insignificant extra solution activity (MPEP 2106.05(g)).
instructions are received from the user to correct the discrepancy
This limitation can be considered post-solution activity (MPEP 2106.05(d)) and amounts to no more than mere instructions to apply the exception.
Step 2B
The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception despite the additional limitations shown below:
instructions are received from the user to correct the discrepancy
This limitation adds 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 (MPEP 2106.05(I)(A)).
a nontransitory processor-readable medium that stores processor executable instructions;
These elements store and retrieve information in memory, which are well-understood, routine, conventional computer functions as recognized by the court decisions listed in MPEP § 2106.05(d).
communications ports that provide communications with a plurality of end user devices,
This performs receiving or transmitting data over a network, which are well-understood, routine, conventional computer functions as recognized by the court decisions listed in MPEP § 2106.05(d), specifically MPEP § 2106.05(d)(II)(i).
Claim 12 introduces a determining of a value, comparing the value, and determining whether a discrepancy exists, which are further steps in a mental process.
Claim 12 does introduce prompting a user of the existence of a discrepancy.
This additional element is mere generic transmission and presentation of collected and analyzed data which is considered to be insignificant extra solution activity (MPEP 2106.05(g)).
Claim 13 specifies that the comparison is performed in response to a request received from the target user.
This additional element is mere generic transmission and presentation of collected and analyzed data which is considered to be insignificant extra solution activity (MPEP 2106.05(g)).
Claim 14 specifies that the comparison is performed in response to an updating of a user profile by the target user.
This additional element stores and retrieves information in memory, which are well-understood, routine, conventional computer functions as recognized by the court decisions listed in MPEP § 2106.05(d).
Claim 15 introduces the user’s stated relationship intent in the user’s profile not matching their own actions or activities, which is considered to be insignificant extra solution activity (MPEP 2106.05(g)).
Claim 16 introduces a determining of recency, comparing the recentness, and identifying of a discrepancy, which are further steps in a mental process.
Claim 17 introduces a determining of frequency, comparing the frequency, and identifying of a discrepancy, which are further steps in a mental process.
Claim 18 introduces a determining of recency, comparing the recentness, and identifying of a discrepancy, which are further steps in a mental process.
Claim 19 introduces a determining of length and identifying of a discrepancy, which are further steps in a mental process.
Claim 20 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Claim 20 recites identifying a subset, determining a discrepancy, providing a suggestion to correct a discrepancy, receiving a first value, receiving a second value, and implementing the suggestion to change a piece of content, which are being considered an abstract idea at this time.
(The amendments to change the elements from active voice to passive voice do not substantially alter the analysis of the claims under 35 U.S.C. 101.)
See relevantly MPEP 2106.04(a)(2), Section III, Subsection D referring to an “application program interface for extracting and processing information from a diversity of types of hard copy documents – Content Extraction, 776 F.3d at 1345, 113 USPQ2d at 1356” and MPEP 2016.05(f) referring to “the abstract idea of ‘collecting, displaying, and manipulating data’” Intellectual Ventures I v. Capital One Fin. Corp., 850 F.3d 1332, 121 USPQ2d 1940 (Fed. Cir. 2017).
Identifying and determining serve as mental processes.
Providing a suggestion falls under “displaying.”
Receiving a first value and receiving a second value fall under “collecting.”
Implementing the suggestion to change a piece of content falls under “manipulating.”
Step 2A, Prong Two
This judicial exception of identifying a subset, determining a discrepancy, providing a suggestion to correct a discrepancy, receiving a first value, receiving a second value, and implementing the suggestion to change a piece of content is not integrated into a practical application despite the generically recited computer elements shown below:
a processor-based hosted-services system,
The generically recited computer elements amount to implementing the abstract idea on a computer, merely using a computer as a tool to perform an abstract idea, or generally linking the use of a judicial exception to a particular technological environment or field of use as seen below.
A method of operating a processor-based hosted-services system,
This additional element generally links the use of the judicial exception to a particular technological environment or field of use, as discussed in MPEP 2106.05(h).
A method of operating a processor-based hosted-services system,
The processor in these steps are recited at a high-level of generality (i.e., as a generic processor performing a generic computer function) such that it amounts no more than mere instructions to apply the exception using a generic computer component. Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. (see MPEP 2106.05(f)).
prompting the target user to take corrective actions to address the discrepancy in order to improve success in finding potential candidates,
This limitation is mere generic transmission and presentation of collected and analyzed data which is considered to be insignificant extra solution activity (MPEP 2106.05(g)).
instructions are received from the user to correct the discrepancy
This limitation can be considered post-solution activity (MPEP 2106.05(d)) and amounts to no more than mere instructions to apply the exception.
Step 2B
The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception despite the additional limitations shown below:
instructions are received from the user to correct the discrepancy
This limitation adds 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 (MPEP 2106.05(I)(A)).
Claim Rejections - 35 USC § 112
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 1-20 are rejected under 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.
Claims 1, 11, and 20 refer to modifying “if the discrepancy from desired behavior is sufficiently large.”
Relatedly, p21, lines 26-28 (or ¶ 0048 of the published application publication) recites “Other discrepancies may be considered non-critical discrepancies, triggering a prompt only if the discrepancy is sufficiently large and/or only if there are a sufficient number of non-critical discrepancies.”
Similar language is present throughout the specification, including at least ¶ 0040, “Criteria may, for example, include maintaining an end user profile with sufficient images or photographs and/or with images or photographs that are sufficiently recent” and ¶ 0091, “the hosted services server computer systems 202 may determine whether one or more identified discrepancies are sufficiently severe, for example critical discrepancies.”
It is not explicitly laid out how large the discrepancy from desired behavior has to be to trigger providing the suggestion to change the piece of content and to modify the user’s messaging behavior.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-20 are rejected under 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
The term “sufficiently” in claims 1, 11, and 20 is a relative term which renders the claim indefinite. The term “sufficiently” is not defined by the claim, the specification does not provide a standard for ascertaining the requisite degree, and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention.
As a result, the phrase “sufficiently large” and the limitation “providing a suggestion to correct the discrepancy from desired behavior by changing the piece of content and modifying the user’s messaging behavior if the discrepancy from desired behavior is sufficiently large” has been rendered indefinite by the use of the term “sufficiently.”
Claim Rejections - 35 USC § 103
The following is a quotation of pre-AIA 35 U.S.C. 103(a) which forms the basis for all obviousness rejections set forth in this Office action:
(a) A patent may not be obtained though the invention is not identically disclosed or described as set forth in section 102 of this title, if the differences between the subject matter sought to be patented and the prior art are such that the subject matter as a whole would have been obvious at the time the invention was made to a person having ordinary skill in the art to which said subject matter pertains. Patentability shall not be negatived by the manner in which the invention was made.
Claims 1-3, 7-10; and 20 are rejected under pre-AIA 35 U.S.C. 103(a) as being unpatentable over Krishnamoorthy et al., U.S. Patent Application Publication No. 2002/0156632 (published October 24, 2002, at least one year prior to the instant application date of March 13, 2013; Patent Application Publication for IDS reference Krishnamoorthy et al., U.S. Patent No. 9,047,611, provided in the IDS as a result of inclusion in a parent application; hereinafter "Krishnamoorthy") in view of Fredinburg et al., U.S. Patent No. 9,275,420 (filed October 5, 2012, prior to the instant application date of March 13, 2013; hereinafter Fredinburg) in further view of De et al., U.S. Patent No. 8,225,413 (published July 17, 2012, prior to the instant application date of March 13, 2013; hereinafter De) in further view of Gobeyn et al., U.S. Patent Application Publication No. 2009/0150330 (hereinafter Gobeyn).
Regarding claim 1, Krishnamoorthy teaches:
A method of operating a processor-based hosted-services system, (Krishnamoorthy ¶ 0032: the user may provide the information in a web form hosted on application server 110)
wherein a subset of users from a plurality of users as successful users are identified, based at least in part on one or more defined success-evaluation criteria that include: a defined number… (Krishnamoorthy ¶ 0021: enhancing module 112 may compare the information and/or requirements included in one or more data objects of the user with that of competing users. The competing users are selected based at least in part on the similarity of requirements included in the data objects of the user and the data objects of the one or more competing users; see this in light of ¶ 0047: Thus, relative score associated with the user's data object is indicative of the likelihood of achieving the requirement included in the data object of the user. For example, if the user's data object has a score of 75, while that of his competitor is 80, then the user has lesser chances of fulfilling his requirement as compared to his competitor)
and wherein a discrepancy from desired behavior is determined between the components of a user's profile and a set of defined evaluation criteria, wherein the discrepancy is a piece of content to be changed, (Krishnamoorthy ¶ 0045-0048: comparison module 208 may compare the one or more data objects [shows claimed 'piece of content'] of the user with that of the competing users for the same requirements … the relative score associated with the data objects is calculated based on the internal parameters, external parameters, and the comparison data received from comparison module 208. Thus, the relative score of data objects may provide the standing of the data objects submitted by the user to that of other data objects submitted by competing users that belong to the same class as that of the user's data object. The relative score may thus be used to compare the user's data object with that of competing user's data objects. Thus, relative score associated with the user's data object is indicative of the likelihood of achieving the requirement included in the data object of the user. For example, if the user's data object has a score of 75, while that of his competitor is 80, then the user has lesser chances of fulfilling his requirement as compared to his competitor; see also FIG. 4, steps 406-408, ¶ 0079-0082: At step 406, comparison module 208 may compare the one or more data objects of the user with that of the competing users for the same requirements ... the absolute score of the data object may provide the measure of how closely the user's requirements may be matched to the corresponding complementary requirements of another user (who provide the service and/or product to match the user requirement). In other words, the absolute score provides an indication of a likelihood of how the user fares against the complimentary requirements)
the method comprising: providing, by at least one processor of the processor-based hosted-services system, a suggestion to correct the discrepancy from desired behavior by changing the piece of content and modifying the user's … behavior if the discrepancy from desired behavior is sufficiently large; (Krishnamoorthy ¶ 0045-0048: Thus, relative score associated with the user's data object is indicative of the likelihood of achieving the requirement included in the data object of the user. For example, if the user's data object has a score of 75, while that of his competitor is 80, then the user has lesser chances of fulfilling his requirement as compared to his competitor; ¶ 0055-0058 the user may be informed that on completing a particular course of study, his relative score may increase from 75 to 82, thereby increasing his chances of acquiring a suitable job, as compared to that of the competing user's data object which may have a relative score of 80 ... improving the absolute score of the user desiring a particular job position from 60 to 80 will increase his chances of meeting his needs based on the available jobs pertaining to his requirements ... feedback generator 212 may also suggest the best match to the user's requirement, by judging the minimum modifications that need to be made in order to achieve the best match judged by a high absolute score, made to compare one or more distinctly comparable data objects that lie in the same domain; see also FIG. 4, steps 408-410, ¶ 0082-0084: the absolute score provides an indication of a likelihood of how the user fares against the complimentary requirements ... At step 410, feedback generator 212 may analyze the one or more scores computed by computing module 210 and provides a listing of the internal parameters, external parameters, optionally including historical parameters, recommendations, or all, that need to be modified, improved, added or removed, in order to enhance the one or more scores associated with the data object)
receiving, by at least one processor of the processor-based hosted-services system, a first value indicative of … a user's profile; and (Krishnamoorthy FIG. 3C-3D, ¶ 0066-0068: comparison module 208 may compare data object 300 of the user with that of the competing users' based on the one or more extracted parameters … The relative score may be based on one or more extracted parameters associated with data object 300 and the comparison data received from comparison module 208; FIG. 4, steps 406-408, ¶ 0079-0085: At step 406, comparison module 208 may compare the one or more data objects of the user with that of the competing users for the same requirements ... At step 408, computing module 210 may compute the one or more scores based at least on one of the comparison data received from comparison module 208 and/or from the information extracted from the one or more internal parameters, external parameters, optionally including historical parameters and/or recommendations)
receiving, by at least one processor of the processor-based hosted-services system, a second value indicative of … a user's profile following a change in the piece of content in the user's profile, (Krishnamoorthy ¶ 0062: feedback generator 212 may receive the modified data objects from the user and re-compute the score associated with the data objects based on one or more changes in the internal parameters, or change of value assigned to the parameters. The change of the parameter may be due to user amendment, change in the external parameters; see also FIG. 3D, ¶ 0068: the feedback may also include suggestions such as, but not limited to, that if the user changes his residence to Los Angeles, his chances of acquiring his desired job will increase by 5% ... if the user performs one or more changes in data object 300 as per the suggestions provided by feedback generator 212, then computing module 210 may re-compute the new relative and absolute score for the user; ¶ 0085: if the user modifies the one or more data objects, based on the suggestions from feedback generator 212, then the computing module 210 may re-compute the one or more scores associated with the one or more data objects)
wherein a difference between the first value and the second value indicates an effectiveness of the change in the piece of content in attracting attention of the another user; (Krishnamoorthy ¶ 0053: the feedback generator may inform the user that on acquiring a particular skill set, the user may improve the chance of acquiring his desired post; ¶ 0055: the user may be informed that on completing a particular course of study, his relative score may increase from 75 to 82, thereby increasing his chances of acquiring a suitable job, as compared to that of the competing user's data object which may have a relative score of 80 ... improving the absolute score of the user desiring a particular job position from 60 to 80 will increase his chances of meeting his needs based on the available jobs pertaining to his requirements FIG. 3D, ¶ 0068: the feedback may also include suggestions such as, but not limited to, that if the user changes his residence to Los Angeles, his chances of acquiring his desired job will increase by 5% ... if the user performs one or more changes in data object 300 as per the suggestions provided by feedback generator 212, then computing module 210 may re-compute the new relative and absolute score for the user)
Krishnamoorthy does not expressly disclose the following:
Krishnamoorthy does not expressly disclose criteria that include: a defined number or rate of positive interactions with other users from the plurality of users, and a defined number of photos in a user’s profile.
Krishnamoorthy does not expressly disclose the user’s messaging behavior.
Krishnamoorthy does not expressly disclose a first value indicative of the defined number of the photos in a user’s profile.
Krishnamoorthy does not expressly disclose a second value indicative of the defined number of the photos in a user’s profile.
Krishnamoorthy further does not expressly disclose:
implementing the suggestion to change the piece of content when the discrepancy exists regarding the piece of content to be changed and instructions are received from the user to correct the discrepancy from desired behavior in order to improve success in finding potential candidates,
and wherein the discrepancy from desired behavior to be corrected is between the user's messaging activities compared to the information on the user's profile.
However, De addresses at least a portion of these limitations by teaching the following:
De teaches criteria that include: a defined number or rate of positive interactions with other users, (De FIG. 3B, col. 15, lines 41-60: Activity on a social network associated with a user's profile can be gauged from a variety of sources, such as a number of messages sent and/or received on the social network, a number of fans on the social network, a number of flags from other users, a number of friends on the social network, frequency of profile updates and page views, etc. If the alleged victim is determined to have a greater level of activity than the alleged impersonator, then an indicator of impersonation can be identified. The strength of such an indicator can depend on the disparity in activity between the alleged victim and the alleged impersonator--a greater disparity can provide a stronger indication of impersonation while a small disparity can provide little, if any, indication of impersonation)
De also teaches the target user’s messaging behavior. (De FIG. 3B, col. 15, lines 41-60: Activity on a social network associated with a user's profile can be gauged from a variety of sources, such as a number of messages sent and/or received on the social network)
De also teaches “a first value of … the photos in a user’s profile” and “a second value indicative of … the photos in a user’s profile.” (De col. 14, lines 15-35: At step 356, photos and/or images contained on the profile page of the alleged victim and the profile page of the alleged impersonator are compared to identify indicators of impersonation and/or indicators contrary to impersonation ... Although subject to the same strength of impersonation factors (e.g., degree of similarity, timing, commonality on the social network, field type, etc.) discussed above, a match of a photo and/or an image can indicate impersonation more strongly than some field matches, such as non-sensitive fields)
and wherein the discrepancy from desired behavior to be corrected is between the user's messaging activities compared to the information on the user's profile. (De FIG. 3B, col. 15, lines 41-60: At step 364, activity on the social network for the alleged impersonator's profile can be compared against activity related to the alleged victim's profile to identify indicators of impersonation and/or indicators contrary to impersonation. Activity on a social network associated with a user's profile can be gauged from a variety of sources, such as a number of messages sent and/or received on the social network ... The strength of such an indicator can depend on the disparity in activity between the alleged victim and the alleged impersonator--a greater disparity can provide a stronger indication of impersonation while a small disparity can provide little, if any, indication of impersonation)
It would have been obvious to one of ordinary skill in the art at the time the invention was made to combine the user profile comparison and suggestions of Krishnamoorthy with the user profile comparison and notifications of De.
In addition, both of the references (Krishnamoorthy and De) disclose features that are directed to analogous art, and they are directed to the same field of endeavor, such as user data comparison and suggestions.
Motivation to do so would be to improve the functioning of Krishnamoorthy as modified performing a comparison and scoring of user-provided data objects with the ability in similar reference De to perform comparison and scoring of user data but with the added functionality of various comparison techniques present in De (see De col. 14, lines 15-35).
Krishnamoorthy in view of De does not expressly disclose criteria that include … a defined number of photos in a user’s profile;
Krishnamoorthy in view of De does not expressly disclose a first value indicative of the defined number of the photos in a user’s profile.
Krishnamoorthy in view of De does not expressly disclose a second value indicative of the defined number of the photos in a user’s profile.
Krishnamoorthy in view of De further does not expressly disclose:
implementing the suggestion to change the piece of content when the discrepancy exists regarding the piece of content to be changed and instructions are received from the user to correct the discrepancy from desired behavior in order to improve success in finding potential candidates,
However, Fredinburg addresses this by teaching:
implementing the suggestion to change the piece of content when the discrepancy exists regarding the piece of content to be changed… (Fredinburg col. 14, line 41-col. 15, line 21: the evaluation module 210 compares the profile impression data for a professional profile impression to the user's source data and determines the difference. The evaluation module 210 determines that work related content (e.g., photos or videos including users wearing suites, posts including work discussion, etc.) ranks higher in the profile impression data than in the user's source data ... the evaluation module 210 generates a summary including difference between the user's source data and the profile impression data and instructs the GUI module 212 to generate a user interface displaying the summary in the user device 115; Fredinburg FIG. 3, col. 16, lines 1-11: The method 300 can include determining 308 user profile impression data based at least in part on the source data and the user review data. For example, the determination module 206 modifies the user's 125 source data based at least in part on the user review data and determines profile impression data including modified source data (e.g., modified posts, photos, videos, etc.))
and instructions are received from the user to correct the discrepancy from desired behavior in order to improve success in finding potential candidates, (Fredinburg FIGs. 4, col. 16, lines 19-46: generating 406 profile preview data. In some implementations, the preview module 204 generates profile preview data by modifying the user's 125 source data based at least in part on the selection of the profile impression; Fredinburg FIGs. 6-7, col. 17-col. 18, line 8, see primarily Fredinburg FIG. 6A, col. 17, lines 3-30: The popup dialog box 606 includes a recommended profile photo 608 for a professional profile impression. The popup dialog box 606 also includes a “Yes” button 610 and a “No” button 612 which are clickable by the user to indicate if the user agrees to change to the recommended profile photo 608; see then Fredinburg FIG. 6B, col. 17, lines 31-41: FIG. 6B depicts a graphic representation of an example user interface 650 for displaying modified user content for a professional profile impression ... The user icon 608 displays the profile photo for the user that is recommended in the profile impression review interface 600; Fredinburg addresses the claimed improving success in finding potential candidates in col. 5, line 57-col. 6, line 63: profile impression is a configuration of a user profile with the social network application 109 that is designed to make a first user 125 appear a certain way to other second users 125 who view the user profile ... the makeover module 103 then modifies the user profile for the first user 125 so that it leaves other second users 125 with the impression that the first user 125 is professional. Accordingly, the profile impression is a configuration of a user profile for a first user 125 that is designed to cause other second users 125 to perceive the first user 125 in a predetermined way)
It would have been obvious to one of ordinary skill in the art at the time the invention was made to combine the user profile comparison and suggestions of Krishnamoorthy as modified with the user profile comparison and suggestions of Fredinburg.
In addition, both of the references (Krishnamoorthy as modified and Fredinburg) disclose features that are directed to analogous art, and they are directed to the same field of endeavor, such as user data comparison and suggestions.
Motivation to do so would be to improve the functioning of Krishnamoorthy as modified as modified performing a comparison of user-provided data objects with the ability in similar reference Fredinburg to perform comparison of user data but with the added functionality of different profile impressions to apply to an existing user profile.
Krishnamoorthy in view of De and Fredinburg does not expressly disclose criteria that include … a defined number of photos in a user’s profile;
Krishnamoorthy in view of De and Fredinburg does not expressly disclose a first value indicative of the defined number of the photos in a user’s profile.
Krishnamoorthy in view of De and Fredinburg does not expressly disclose a second value indicative of the defined number of the photos in a user’s profile.
However, Gobeyn addresses this by teaching the following:
Gobeyn teaches criteria that include … a defined number of photos in a user’s profile; (Gobeyn FIGs. 7-8, ¶ 0060-0063: The chart 800 represents how the digital image records in this subset are separated by the individual rock stars (i.e., instances) they have been deemed to have imaged [shows association with a user's profile] ... The X-axis 804 of the chart 800 represents the individual rock starts (i.e., instances) that have been imaged. The threshold 806 indicates the minimum number of digital image records (zero in this example) needed for a rock star (i.e., an instance) to be considered in the level-of-interest calculation [shows defined number of photos])
Gobeyn teaches a first value indicative of the defined number of the photos in a user’s profile [and] a second value indicative of the defined number of the photos in a user’s profile. (Gobeyn FIG. 7, ¶ 0060-0061: The threshold 706 indicates the minimum number of digital image records (five in this example) needed for a location (i.e., an instance) to be considered in the level-of-interest calculation. In this case, Locations A and D have a number of digital image records associated therewith that meet the threshold 706. In this regard, it is deemed that two locations (i.e., instances) out of the five different locations have at least a meaningful number of images (five in this example) of a car show event; FIG. 8, ¶ 0062-0063: The X-axis 804 of the chart 800 represents the individual rock starts (i.e., instances) that have been imaged ... because the threshold 806 is zero, all imaged rock stars are considered in the level-of-interest calculation)
It would have been obvious to one of ordinary skill in the art at the time the invention was made to combine the user metadata and scoring as in Krishnamoorthy as modified with the user metadata and level of interest calculations of Gobeyn.
In addition, both of the references (Krishnamoorthy as modified and Gobeyn) disclose features that are directed to analogous art, and they are directed to the same field of endeavor, such as user-related data and scoring management.
Motivation to do so would be to improve the functioning of Krishnamoorthy as modified performing a comparison and scoring of user-provided data objects with the ability in similar reference Gobeyn to perform score comparisons of user data but to do so against thresholds.
Regarding claim 2, Krishnamoorthy in view of De and Fredinburg and Gobeyn teaches all the features with respect to claim 1 above including:
determining a value that represents an action taken by the target user; comparing the determined value to an evaluation-criteria value for the successful users; (Krishnamoorthy ¶ 0045-0048: comparison module 208 may compare the one or more data objects of the user with that of the competing users for the same requirements … the relative score associated with the data objects is calculated based on the internal parameters, external parameters, and the comparison data received from comparison module 208. Thus, the relative score of data objects may provide the standing of the data objects submitted by the user to that of other data objects submitted by competing users that belong to the same class as that of the user's data object. The relative score may thus be used to compare the user's data object with that of competing user's data objects. Thus, relative score associated with the user's data object is indicative of the likelihood of achieving the requirement included in the data object of the user. For example, if the user's data object has a score of 75, while that of his competitor is 80, then the user has lesser chances of fulfilling his requirement as compared to his competitor; see also FIG. 4, steps 406-408, ¶ 0079-0082: At step 406, comparison module 208 may compare the one or more data objects of the user with that of the competing users for the same requirements ... the absolute score of the data object may provide the measure of how closely the user's requirements may be matched to the corresponding complementary requirements of another user (who provide the service and/or product to match the user requirement). In other words, the absolute score provides an indication of a likelihood of how the user fares against the complimentary requirements)
determining whether a discrepancy exists between the determined value and the evaluation-criteria value for the successful users; and in response to determining that the discrepancy exists, prompting the user of the existence of the discrepancy. (Krishnamoorthy ¶ 0055-0058 shows prompting: the user may be informed that on completing a particular course of study, his relative score may increase from 75 to 82, thereby increasing his chances of acquiring a suitable job, as compared to that of the competing user's data object which may have a relative score of 80 ... feedback generator 212 may also suggest the best match to the user's requirement, by judging the minimum modifications that need to be made in order to achieve the best match judged by a high absolute score, made to compare one or more distinctly comparable data objects that lie in the same domain; see also FIG. 4, steps 408-410, ¶ 0082-0084 also shows prompting: the absolute score of the data object may provide the measure of how closely the user's requirements may be matched to the corresponding complementary requirements of another user (who provide the service and/or product to match the user requirement). In other words, the absolute score provides an indication of a likelihood of how the user fares against the complimentary requirements ... At step 410, feedback generator 212 may analyze the one or more scores computed by computing module 210 and provides a listing of the internal parameters, external parameters, optionally including historical parameters, recommendations, or all, that need to be modified, improved, added or removed, in order to enhance the one or more scores associated with the data object)
Regarding claim 3, Krishnamoorthy in view of De and Fredinburg and Gobeyn teaches all the features with respect to claim 1 above including:
wherein the user profile is compared to the set of defined evaluation criteria in response to a request received from the target user. (Krishnamoorthy FIG. 4, ¶ 0071: At step 402, receiving module 202 may receive the one or more data objects from application server 110; see this occurring before step 406 of ¶ 0079: At step 406, comparison module 208 may compare the one or more data objects of the user with that of the competing users for the same requirements)
Regarding claim 7, Krishnamoorthy in view of De and Fredinburg and Gobeyn teaches all the features with respect to claim 1 above including:
further comprising causing comparative information to be presented to the target user. (Krishnamoorthy ¶ 0055-0058: the user may be informed [shows 'presentation'] that on completing a particular course of study, his relative score may increase from 75 to 82, thereby increasing his chances of acquiring a suitable job, as compared to that of the competing user's data object which may have a relative score of 80 [shows 'comparative information']; see also FIG. 4, steps 408-410, ¶ 0082-0084: the absolute score of the data object may provide the measure of how closely the user's requirements may be matched to the corresponding complementary requirements of another user (who provide the service and/or product to match the user requirement). In other words, the absolute score provides an indication of a likelihood of how the user fares against the complimentary requirements)
Regarding claim 8, Krishnamoorthy in view of De and Fredinburg and Gobeyn teaches all the features with respect to claim 7 above including:
further comprising causing presentation of comparative information of the other users from the plurality of users to the target user. (Krishnamoorthy ¶ 0055-0058: the user may be informed [shows 'presentation'] that on completing a particular course of study, his relative score may increase from 75 to 82, thereby increasing his chances of acquiring a suitable job, as compared to that of the competing user's data object which may have a relative score of 80 [shows 'comparative information of other users']; see also FIG. 4, steps 408-410, ¶ 0082-0084: the absolute score of the data object may provide the measure of how closely the user's requirements may be matched to the corresponding complementary requirements of another user (who provide the service and/or product to match the user requirement). In other words, the absolute score provides an indication of a likelihood of how the user fares against the complimentary requirements)
Regarding claim 9, Krishnamoorthy in view of De and Fredinburg and Gobeyn teaches all the features with respect to claim 8 above including:
further comprising limiting the other users from the plurality of users to ones in a certain geographic area. (De col. 13, lines 54-64: strength of impersonation may also be based upon how common the similar/matching value is for a field on the social network, within a sub-graph (a collection of users that is connected by acquaintance relationships and reasonably separate from other, unconnected users) on the social network, and/or within a geographic region (e.g., North America, Europe, etc.))
Regarding claim 10, Krishnamoorthy in view of De and Fredinburg and Gobeyn teaches all the features with respect to claim 7 above.
Krishnamoorthy teaches:
further comprising comparing a total number of images of the target user to a total number of images for a set of other users from the plurality of users… (Krishnamoorthy ¶ 0019: The data objects may be one of portable document file, word file, text file, excel file, an online form, an XML file (or other structured document), or a scanned image; ¶ 0045-0048: comparison module 208 may compare the one or more data objects of the user with that of the competing users for the same requirements … the relative score associated with the data objects is calculated based on the internal parameters, external parameters, and the comparison data received from comparison module 208. Thus, the relative score of data objects may provide the standing of the data objects submitted by the user to that of other data objects submitted by competing users that belong to the same class as that of the user's data object. The relative score may thus be used to compare the user's data object with that of competing user's data objects. Thus, relative score associated with the user's data object is indicative of the likelihood of achieving the requirement included in the data object of the user. For example, if the user's data object has a score of 75, while that of his competitor is 80, then the user has lesser chances of fulfilling his requirement as compared to his competitor; see all this in light of Krishnamoorthy ¶ 0066: the relative score assigned to data object 300 [shown above to correspond to an image] of the user may have a score of 70, which is computed by the summation of the values assigned to these parameters [Krishnamoorthy contemplates the claimed 'total number of images' as the value to be compared])
De teaches:
further comprising comparing … for a set of other users who share common demographic information with the target user. (De col. 13, lines 54-64: strength of impersonation may also be based upon how common the similar/matching value is for a field on the social network, within a sub-graph (a collection of users that is connected by acquaintance relationships and reasonably separate from other, unconnected users) on the social network, and/or within a geographic region (e.g., North America, Europe, etc.))
It would have been obvious to one of ordinary skill in the art at the time the invention was made to combine the user profile comparison and suggestions of Krishnamoorthy with the user profile comparison and notifications of De.
Motivation to do so would be to improve the functioning of Krishnamoorthy as modified performing a comparison and scoring of user-provided data objects with the ability in similar reference De to perform comparison and scoring of user data but with the added functionality of various comparison techniques present in De (see De col. 14, lines 15-35).
Regarding claim 20, Krishnamoorthy teaches:
A method of operating a processor-based hosted-services system, (Krishnamoorthy ¶ 0032: the user may provide the information in a web form hosted on application server 110; ¶ 0087: FIG. 5 illustrates an example hardware system 500 to implement score enhancing system 112 according to one embodiment. Hardware system 500 includes at least one processor 502)
wherein a subset of users from a plurality of users as successful users are identified, based at least in part on one or more defined success-evaluation criteria that include: a defined number… (Krishnamoorthy ¶ 0021: enhancing module 112 may compare the information and/or requirements included in one or more data objects of the user with that of competing users. The competing users are selected based at least in part on the similarity of requirements included in the data objects of the user and the data objects of the one or more competing users; see this in light of ¶ 0047: Thus, relative score associated with the user's data object is indicative of the likelihood of achieving the requirement included in the data object of the user. For example, if the user's data object has a score of 75, while that of his competitor is 80, then the user has lesser chances of fulfilling his requirement as compared to his competitor)
and wherein a discrepancy from desired behavior is determined between the components of a user's profile and a set of defined evaluation criteria, wherein the discrepancy is a piece of content to be changed, (Krishnamoorthy ¶ 0045-0048: comparison module 208 may compare the one or more data objects [shows claimed 'piece of content'] of the user with that of the competing users for the same requirements … the relative score associated with the data objects is calculated based on the internal parameters, external parameters, and the comparison data received from comparison module 208. Thus, the relative score of data objects may provide the standing of the data objects submitted by the user to that of other data objects submitted by competing users that belong to the same class as that of the user's data object. The relative score may thus be used to compare the user's data object with that of competing user's data objects. Thus, relative score associated with the user's data object is indicative of the likelihood of achieving the requirement included in the data object of the user. For example, if the user's data object has a score of 75, while that of his competitor is 80, then the user has lesser chances of fulfilling his requirement as compared to his competitor; see also FIG. 4, steps 406-408, ¶ 0079-0082: At step 406, comparison module 208 may compare the one or more data objects of the user with that of the competing users for the same requirements ... the absolute score of the data object may provide the measure of how closely the user's requirements may be matched to the corresponding complementary requirements of another user (who provide the service and/or product to match the user requirement). In other words, the absolute score provides an indication of a likelihood of how the user fares against the complimentary requirements)
the method comprising: provide, by least one processor of the processor-based hosted-services system, a suggestion to correct the discrepancy from desired behavior by changing the piece of content and modifying the user's … behavior if the discrepancy from desired behavior is sufficiently large; (Krishnamoorthy ¶ 0045-0048: Thus, relative score associated with the user's data object is indicative of the likelihood of achieving the requirement included in the data object of the user. For example, if the user's data object has a score of 75, while that of his competitor is 80, then the user has lesser chances of fulfilling his requirement as compared to his competitor; ¶ 0055-0058 describe the claimed 'sufficiently large': the user may be informed that on completing a particular course of study, his relative score may increase from 75 to 82, thereby increasing his chances of acquiring a suitable job, as compared to that of the competing user's data object which may have a relative score of 80 ... improving the absolute score of the user desiring a particular job position from 60 to 80 will increase his chances of meeting his needs based on the available jobs pertaining to his requirements ... feedback generator 212 may also suggest the best match to the user's requirement, by judging the minimum modifications that need to be made in order to achieve the best match judged by a high absolute score, made to compare one or more distinctly comparable data objects that lie in the same domain; see also FIG. 4, steps 408-410, ¶ 0082-0084: the absolute score provides an indication of a likelihood of how the user fares against the complimentary requirements ... At step 410, feedback generator 212 may analyze the one or more scores computed by computing module 210 and provides a listing of the internal parameters, external parameters, optionally including historical parameters, recommendations, or all, that need to be modified, improved, added or removed, in order to enhance the one or more scores associated with the data object)
receive, by at least one processor of the processor-based hosted-services system, a difference between a first value … in a user’s profile and a second value … in a user’s profile following a change in the piece of content in the user’s profile that indicates an effectiveness of the change in the piece of content in attracting attention of the another user; and (Krishnamoorthy ¶ 0053: the feedback generator may inform the user that on acquiring a particular skill set, the user may improve the chance of acquiring his desired post; ¶ 0055: the user may be informed that on completing a particular course of study, his relative score may increase from 75 to 82, thereby increasing his chances of acquiring a suitable job, as compared to that of the competing user's data object which may have a relative score of 80 ... improving the absolute score of the user desiring a particular job position from 60 to 80 will increase his chances of meeting his needs based on the available jobs pertaining to his requirements FIG. 3D, ¶ 0068: the feedback may also include suggestions such as, but not limited to, that if the user changes his residence to Los Angeles, his chances of acquiring his desired job will increase by 5% ... if the user performs one or more changes in data object 300 as per the suggestions provided by feedback generator 212, then computing module 210 may re-compute the new relative and absolute score for the user)
Krishnamoorthy does not expressly disclose the following:
Krishnamoorthy does not expressly disclose criteria that include: a defined number or rate of positive interactions with other users from the plurality of users, and a defined number of photos in a user’s profile;
Krishnamoorthy does not expressly disclose the user’s messaging behavior.
Krishnamoorthy does not expressly disclose a first value indicative of the number of listed interests in a user’s profile.
Krishnamoorthy does not expressly disclose a second value indicative of the number of listed interests in a user’s profile.
Krishnamoorthy further does not expressly disclose:
implementing the suggestion to change the piece of content, in response to determining that the discrepancy exists regarding the piece of content to be changed, and receiving instructions from the target user to correct the discrepancy,
and wherein the discrepancy from desired behavior to be corrected is between the user's messaging activities compared to the information on the user's profile
However, De addresses at least a portion of these limitations by teaching the following:
De teaches criteria that include a defined number or rate of positive interactions with other users from the plurality of users, (De FIG. 3B, col. 15, lines 41-60: Activity on a social network associated with a user's profile can be gauged from a variety of sources, such as a number of messages sent and/or received on the social network, a number of fans on the social network, a number of flags from other users, a number of friends on the social network, frequency of profile updates and page views, etc. If the alleged victim is determined to have a greater level of activity than the alleged impersonator, then an indicator of impersonation can be identified. The strength of such an indicator can depend on the disparity in activity between the alleged victim and the alleged impersonator--a greater disparity can provide a stronger indication of impersonation while a small disparity can provide little, if any, indication of impersonation)
De also teaches the user’s messaging behavior. (De FIG. 3B, col. 15, lines 41-60: Activity on a social network associated with a user's profile can be gauged from a variety of sources, such as a number of messages sent and/or received on the social network)
De also teaches:
and wherein the discrepancy from desired behavior to be corrected is between the user's messaging activities compared to the information on the user's profile. (De FIG. 3B, col. 15, lines 41-60: At step 364, activity on the social network for the alleged impersonator's profile can be compared against activity related to the alleged victim's profile to identify indicators of impersonation and/or indicators contrary to impersonation. Activity on a social network associated with a user's profile can be gauged from a variety of sources, such as a number of messages sent and/or received on the social network ... The strength of such an indicator can depend on the disparity in activity between the alleged victim and the alleged impersonator--a greater disparity can provide a stronger indication of impersonation while a small disparity can provide little, if any, indication of impersonation)
It would have been obvious to one of ordinary skill in the art at the time the invention was made to combine the user profile comparison and suggestions of Krishnamoorthy with the user profile comparison and notifications of De.
In addition, both of the references (Krishnamoorthy and De) disclose features that are directed to analogous art, and they are directed to the same field of endeavor, such as user data comparison and suggestions.
Motivation to do so would be to improve the functioning of Krishnamoorthy as modified performing a comparison and scoring of user-provided data objects with the ability in similar reference De to perform comparison and scoring of user data but with the added functionality of various comparison techniques present in De (see De col. 14, lines 15-35).
Krishnamoorthy in view of De does not expressly disclose criteria that include … a defined number of photos in a user’s profile;
Krishnamoorthy in view of De does not expressly disclose a first value indicative of the number of listed interests in a user’s profile.
Krishnamoorthy in view of De does not expressly disclose a second value indicative of the number of listed interests in a user’s profile.
Krishnamoorthy in view of De further does not expressly disclose:
implementing the suggestion to change the piece of content, in response to determining that the discrepancy exists regarding the piece of content to be changed, and receiving instructions from the target user to correct the discrepancy.
However, Fredinburg addresses this by teaching:
implementing the suggestion to change the piece of content, in response to determining that the discrepancy exists regarding the piece of content to be changed, (Fredinburg col. 14, line 41-col. 15, line 21: the evaluation module 210 compares the profile impression data for a professional profile impression to the user's source data and determines the difference. The evaluation module 210 determines that work related content (e.g., photos or videos including users wearing suites, posts including work discussion, etc.) ranks higher in the profile impression data than in the user's source data ... the evaluation module 210 generates a summary including difference between the user's source data and the profile impression data and instructs the GUI module 212 to generate a user interface displaying the summary in the user device 115; Fredinburg FIG. 3, col. 16, lines 1-11: The method 300 can include determining 308 user profile impression data based at least in part on the source data and the user review data. For example, the determination module 206 modifies the user's 125 source data based at least in part on the user review data and determines profile impression data including modified source data (e.g., modified posts, photos, videos, etc.))
and receiving instructions from the target user to correct the discrepancy. (Fredinburg FIGs. 4, col. 16, lines 19-46: generating 406 profile preview data. In some implementations, the preview module 204 generates profile preview data by modifying the user's 125 source data based at least in part on the selection of the profile impression; Fredinburg FIGs. 6-7, col. 17-col. 18, line 8, see primarily Fredinburg FIG. 6A, col. 17, lines 3-30: The popup dialog box 606 includes a recommended profile photo 608 for a professional profile impression. The popup dialog box 606 also includes a “Yes” button 610 and a “No” button 612 which are clickable by the user to indicate if the user agrees to change to the recommended profile photo 608; see then Fredinburg FIG. 6B, col. 17, lines 31-41: FIG. 6B depicts a graphic representation of an example user interface 650 for displaying modified user content for a professional profile impression ... The user icon 608 displays the profile photo for the user that is recommended in the profile impression review interface 600)
It would have been obvious to one of ordinary skill in the art at the time the invention was made to combine the user profile comparison and suggestions of Krishnamoorthy as modified with the user profile comparison and suggestions of Fredinburg.
In addition, both of the references (Krishnamoorthy as modified and Fredinburg) disclose features that are directed to analogous art, and they are directed to the same field of endeavor, such as user data comparison and suggestions.
Motivation to do so would be to improve the functioning of Krishnamoorthy as modified as modified performing a comparison of user-provided data objects with the ability in similar reference Fredinburg to perform comparison of user data but with the added functionality of different profile impressions to apply to an existing user profile.
Krishnamoorthy in view of De and Fredinburg does not expressly disclose the following:
Krishnamoorthy in view of De and Fredinburg does not expressly disclose criteria that include: … a defined number of photos in a user’s profile;
Krishnamoorthy in view of De and Fredinburg does not expressly disclose a value indicative of the number of listed interests in a user’s profile.
However, Gobeyn addresses this by teaching the following:
Gobeyn teaches criteria that include … a defined number of photos in a user’s profile; (Gobeyn FIGs. 7-8, ¶ 0060-0063: The chart 800 represents how the digital image records in this subset are separated by the individual rock stars (i.e., instances) they have been deemed to have imaged [shows association with a user's profile] ... The X-axis 804 of the chart 800 represents the individual rock starts (i.e., instances) that have been imaged. The threshold 806 indicates the minimum number of digital image records (zero in this example) needed for a rock star (i.e., an instance) to be considered in the level-of-interest calculation [shows defined number of photos])
Gobeyn teaches, “criteria that include: … a number of listed interests in a user’s profile” and a value indicative of the number of listed interests in a user’s profile. (Gobeyn ¶ 0046: The user subject interest or interests that are identified in step 14 can be any of a number of items that are associated with the trends identified in step 12 … in a simple case, a user subject interest may be deemed "fireworks in summer." This user subject interest may be included in the user profile 20 at step 16)
It would have been obvious to one of ordinary skill in the art at the time the invention was made to combine the user metadata and scoring as in Krishnamoorthy as modified with the user metadata and level of interest calculations of Gobeyn.
In addition, both of the references (Krishnamoorthy as modified and Gobeyn) disclose features that are directed to analogous art, and they are directed to the same field of endeavor, such as user-related data and scoring management.
Motivation to do so would be to improve the functioning of Krishnamoorthy as modified performing a comparison and scoring of user-provided data objects with the ability in similar reference Gobeyn to perform score comparisons of user data but to do so against thresholds.
Claim 4 is rejected under pre-AIA 35 U.S.C. 103(a) as being unpatentable over Krishnamoorthy in view of De and Fredinburg and Gobeyn in further view of De Cristofaro et al., U.S. Patent Application Publication No. 2014/0156750 (filed December 5, 2012, prior to the instant application date of March 13, 2013; hereinafter De Cristofaro).
Regarding claim 4, Krishnamoorthy in view of De and Fredinburg and Gobeyn teaches all the features with respect to claim 1 above including correcting the discrepancy… (Fredinburg col. 14, line 41-col. 15, line 21: the evaluation module 210 compares the profile impression data for a professional profile impression to the user's source data and determines the difference. The evaluation module 210 determines that work related content (e.g., photos or videos including users wearing suites, posts including work discussion, etc.) ranks higher in the profile impression data than in the user's source data ... the evaluation module 210 generates a summary including difference between the user's source data and the profile impression data and instructs the GUI module 212 to generate a user interface displaying the summary in the user device 115)
De further teaches the discrepancy between the user's messaging activities compared to the information on the user's profile… (De FIG. 3B, col. 15, lines 41-60: At step 364, activity on the social network for the alleged impersonator's profile can be compared against activity related to the alleged victim's profile to identify indicators of impersonation and/or indicators contrary to impersonation. Activity on a social network associated with a user's profile can be gauged from a variety of sources, such as a number of messages sent and/or received on the social network ... The strength of such an indicator can depend on the disparity in activity between the alleged victim and the alleged impersonator--a greater disparity can provide a stronger indication of impersonation while a small disparity can provide little, if any, indication of impersonation)
Krishnamoorthy in view of De and Fredinburg and Gobeyn does not expressly disclose the discrepancy … further comprises when the user's stated relationship intent in the user's profile does not match the user's own actions or activities.
However, De Cristofaro addresses this by teaching the discrepancy … further comprises when the user's stated relationship intent in the user's profile does not match the user's own actions or activities. (De Cristofaro ¶ 0022: The profile can include fields of information about the participant, including, inter alia, age, relationship status; De Cristofaro FIG. 3, ¶ 0027-0028: Information from a social network page can be used to certify one or more information fields of an online dating profile by comparing and matching the information or by uploading information from the social network page to the online dating profile ... the types of information to be compared can include all textual information [relevant to messaging activities]; see also De Cristofaro FIG. 4, ¶ 0032: The list of information fields 52 can include one or more fields of information about the participant that is listed on the social network page, online dating profile, or both the social network page and online dating profile. The information fields can include age, relationship status; see also De Cristofaro ¶ 0040-0042: The undetermined status can be assigned when the data for an information field does not match ... An undermined status may be assigned when, for instance, a participant's social network profile lists the relationship status as "its complicated," while the online dating profile lists the participant's status as single)
It would have been obvious to one of ordinary skill in the art at the time the invention was made to combine the user profile comparison and suggestions of Krishnamoorthy with the user profile comparison and notifications of De Cristofaro.
In addition, both of the references (Krishnamoorthy and De Cristofaro) disclose features that are directed to analogous art, and they are directed to the same field of endeavor, such as user data comparison and suggestions.
Motivation to do so would be the teaching, suggestion, or motivation for one of ordinary skill in the art to automatically verify online dating profiles to ensure the veracity of the information provided on the profile and to prevent misleading other users as seen in De Cristofaro ¶ 0002-0006.
Claims 5-6 are rejected under pre-AIA 35 U.S.C. 103(a) as being unpatentable over Krishnamoorthy in view of De and Fredinburg and Gobeyn in further view of Chen et al., U.S. Patent Application Publication No. 2013/0325948 (filed June 1, 2012, prior to the instant application date of March 13, 2013; provided in the IDS as a result of inclusion in a parent application; hereinafter "Chen").
Regarding claim 5, Krishnamoorthy in view of De and Fredinburg and Gobeyn teaches all the features with respect to claim 1 above but does not expressly disclose:
wherein the user profile is compared to the set of defined evaluation criteria automatically on a periodic basis, independent of any requests received from the target user.
However, Chen teaches:
wherein the user profile is compared to the set of defined evaluation criteria automatically on a periodic basis, independent of any requests received from the target user. (Chen ¶ 0008: the engagement level of the user may be continuously [automatically/periodic] monitored to determine whether the engagement level is less than the threshold [shows a comparison]; ¶ 0053: The low engagement module 150 may measure and monitor the level of activity of a user of the social networking system 130; see also FIG. 8A, step 802)
It would have been obvious to one of ordinary skill in the art at the time the invention was made to combine the user metadata and parameters as in Krishnamoorthy as modified with the user profiles of Chen.
In addition, both of the references (Krishnamoorthy as modified and Chen) disclose features that are directed to analogous art, and they are directed to the same field of endeavor, such as tracking scores associated with user profiles.
Motivation to do so would be to improve the functioning of Krishnamoorthy as modified performing a scoring of user-provided data objects with the ability in similar reference Chen to perform scoring of user profiles but with frequencies and thresholds.
Regarding claim 6, Krishnamoorthy in view of De and Fredinburg and Gobeyn teaches all the features with respect to claim 1 above but does not expressly disclose:
further comprising: determining how recently the target user profile was updated;
comparing a recentness of the update of the target user profile to a defined evaluation recentness; and
identifying a discrepancy between the recentness of the update of the target user profile and the defined evaluation recentness.
However, Chen teaches:
further comprising: determining how recently the target user profile was updated; (Chen ¶ 0059: the low engagement ID module 224 can identify the user as a low feedback user if the user posts little content; In an embodiment, the low engagement ID module 224 may also identify the user as a low feedback user if the amount of content posts (or shares) of the user in a first time period falls below a threshold; The predetermined feedback frequency can be, for example, 1 feedback entry per day, 2 feedback entries per week, 2 feedback entries every two weeks, 8 feedback entries per month, or any desired frequency [Chen teaches examining a time period for a threshold of content posts; if the threshold over a period such as a day or a week is one post, and the user does not meet this threshold, that means that their profile was not updated recently; Chen must then contemplate determining the amount of content posts in a first time period if Chen is to determine whether it falls below a threshold])
comparing a recentness of the update of the target user profile to a defined evaluation recentness; and (Chen ¶ 0059: the low engagement ID module 224 can identify the user as a low feedback user if the user posts little content; In an embodiment, the low engagement ID module 224 may also identify the user as a low feedback user if the amount of content posts (or shares) of the user in a first time period falls below a threshold; the low engagement ID module 224 may identify a user as a low feedback user if the amount of content posts (or shares) of the user in first time period falls below a first threshold [in this paragraph, Chen also discusses a predetermined feedback frequency])
identifying a discrepancy between the recentness of the update of the target user profile and the defined evaluation recentness. (Chen ¶ 0059: the low engagement ID module 224 can identify the user as a low feedback user if the user posts little content; the low engagement ID module 224 may also identify the user as a low feedback user if the amount of content posts (or shares) of the user in a first time period falls below a threshold; [as in if there are no posts in a period, the latest update is longer than the predetermined threshold] ¶ 0066: Once the user is identified as a low engagement user, the low engagement module 150 may provide a communication to the user to encourage increased activity on the social networking system 130)
It would have been obvious to one of ordinary skill in the art at the time the invention was made to combine the user metadata and parameters as in Krishnamoorthy as modified with the user profiles of Chen.
In addition, both of the references (Krishnamoorthy as modified and Chen) disclose features that are directed to analogous art, and they are directed to the same field of endeavor, such as tracking scores associated with user profiles.
Motivation to do so would be to improve the functioning of Krishnamoorthy as modified performing a scoring of user-provided data objects with the ability in similar reference Chen to perform scoring of user profiles but with frequencies and thresholds.
Claims 11-14 and 16-18 are rejected under pre-AIA 35 U.S.C. 103(a) as being unpatentable over Krishnamoorthy in view of De and Fredinburg in further view of Chen.
Regarding claim 11, Krishnamoorthy teaches:
A processor-based hosted services system, (Krishnamoorthy ¶ 0032: the user may provide the information in a web form hosted on application server 110; ¶ 0087: FIG. 5 illustrates an example hardware system 500 to implement score enhancing system 112 according to one embodiment. Hardware system 500 includes at least one processor 502)
wherein a subset of users from a plurality of users are identified, based at least in part on one or more defined success-evaluation criteria that include at least a defined number… (Krishnamoorthy ¶ 0021: enhancing module 112 may compare the information and/or requirements included in one or more data objects of the user with that of competing users. The competing users are selected based at least in part on the similarity of requirements included in the data objects of the user and the data objects of the one or more competing users; see this in light of ¶ 0047: Thus, relative score associated with the user's data object is indicative of the likelihood of achieving the requirement included in the data object of the user. For example, if the user's data object has a score of 75, while that of his competitor is 80, then the user has lesser chances of fulfilling his requirement as compared to his competitor)
and wherein a discrepancy from desired behavior is determined between the components of a user's profile and a set of defined evaluation criteria, wherein the discrepancy is a piece of content to be changed, (Krishnamoorthy ¶ 0045-0048: comparison module 208 may compare the one or more data objects [shows claimed 'piece of content'] of the user with that of the competing users for the same requirements … the relative score associated with the data objects is calculated based on the internal parameters, external parameters, and the comparison data received from comparison module 208. Thus, the relative score of data objects may provide the standing of the data objects submitted by the user to that of other data objects submitted by competing users that belong to the same class as that of the user's data object. The relative score may thus be used to compare the user's data object with that of competing user's data objects. Thus, relative score associated with the user's data object is indicative of the likelihood of achieving the requirement included in the data object of the user. For example, if the user's data object has a score of 75, while that of his competitor is 80, then the user has lesser chances of fulfilling his requirement as compared to his competitor; see also FIG. 4, steps 406-408, ¶ 0079-0082: At step 406, comparison module 208 may compare the one or more data objects of the user with that of the competing users for the same requirements ... the absolute score of the data object may provide the measure of how closely the user's requirements may be matched to the corresponding complementary requirements of another user (who provide the service and/or product to match the user requirement). In other words, the absolute score provides an indication of a likelihood of how the user fares against the complimentary requirements)
the system comprising: communications ports that provide communications with a plurality of end user devices, the end user devices logically associable with a plurality of user client accounts of the processor-based hosted services system, the user client accounts logically associable with user clients of the processor-based hosted services system; (Krishnamoorthy FIG. 1, ¶ 0015-0019: Network 108 generally represents one or more interconnected networks, over which network-based service provider 102, other data sources 104, and client nodes 106 can communicate with each other ... Client nodes 106 are communicatively coupled to network 108 via network-based service provider 102 or any other suitable methods known in the art; Client nodes 106 are computing devices [shows 'end user devices'] from which a user [shows 'user clients'] accesses the services provided by network-based service provider 102; the user may be required to have an account with network-based service provider 102 in order to submit the data objects [shows 'user client accounts']; FIG. 5, ¶ 0087-0088: A network/communication interface 510 provides communication between hardware system 500 and any of a wide range of networks, such as an Ethernet (e.g., IEEE 802.3) network, etc.)
a nontransitory processor-readable medium that stores processor executable instructions; and at a processor communicatively coupled to the communications ports and the processor-readable medium, wherein the processor executes the processor-executable instructions and causes the processor to: (Krishnamoorthy FIG. 5, ¶ 0087-0088: Hardware system 500 includes at least one processor 502, a system memory 504, and mass storage 506 ... A network/communication interface 510 provides communication between hardware system 500 and any of a wide range of networks, such as an Ethernet (e.g., IEEE 802.3) network, etc. ... These software routines comprise a plurality or series of instructions to be executed by a processor in a hardware system, such as processor 502. Initially, the series of instructions are stored on a storage device, such as mass storage 506. However, the series of instructions can be stored on any suitable storage medium)
providing a suggestion to correct the discrepancy from desired behavior by changing the piece of content and modifying the user's … behavior if the discrepancy from desired behavior is sufficiently large; (Krishnamoorthy ¶ 0045-0048: Thus, relative score associated with the user's data object is indicative of the likelihood of achieving the requirement included in the data object of the user. For example, if the user's data object has a score of 75, while that of his competitor is 80, then the user has lesser chances of fulfilling his requirement as compared to his competitor; ¶ 0055-0058 describe the claimed 'sufficiently large': the user may be informed that on completing a particular course of study, his relative score may increase from 75 to 82, thereby increasing his chances of acquiring a suitable job, as compared to that of the competing user's data object which may have a relative score of 80 ... improving the absolute score of the user desiring a particular job position from 60 to 80 will increase his chances of meeting his needs based on the available jobs pertaining to his requirements ... feedback generator 212 may also suggest the best match to the user's requirement, by judging the minimum modifications that need to be made in order to achieve the best match judged by a high absolute score, made to compare one or more distinctly comparable data objects that lie in the same domain; see also FIG. 4, steps 408-410, ¶ 0082-0084: the absolute score provides an indication of a likelihood of how the user fares against the complimentary requirements ... At step 410, feedback generator 212 may analyze the one or more scores computed by computing module 210 and provides a listing of the internal parameters, external parameters, optionally including historical parameters, recommendations, or all, that need to be modified, improved, added or removed, in order to enhance the one or more scores associated with the data object)
receive a first value indicative of … a user's profile; (Krishnamoorthy FIG. 3C-3D, ¶ 0066-0068: comparison module 208 may compare data object 300 of the user with that of the competing users' based on the one or more extracted parameters … The relative score may be based on one or more extracted parameters associated with data object 300 and the comparison data received from comparison module 208; FIG. 4, steps 406-408, ¶ 0079-0085: At step 406, comparison module 208 may compare the one or more data objects of the user with that of the competing users for the same requirements ... At step 408, computing module 210 may compute the one or more scores based at least on one of the comparison data received from comparison module 208 and/or from the information extracted from the one or more internal parameters, external parameters, optionally including historical parameters and/or recommendations)
receive a second value indicative of … a user's profile following a change in the piece of content in the user's profile, (Krishnamoorthy ¶ 0062: feedback generator 212 may receive the modified data objects from the user and re-compute the score associated with the data objects based on one or more changes in the internal parameters, or change of value assigned to the parameters. The change of the parameter may be due to user amendment, change in the external parameters; see also FIG. 3D, ¶ 0068: the feedback may also include suggestions such as, but not limited to, that if the user changes his residence to Los Angeles, his chances of acquiring his desired job will increase by 5% ... if the user performs one or more changes in data object 300 as per the suggestions provided by feedback generator 212, then computing module 210 may re-compute the new relative and absolute score for the user; ¶ 0085: if the user modifies the one or more data objects, based on the suggestions from feedback generator 212, then the computing module 210 may re-compute the one or more scores associated with the one or more data objects)
wherein a difference between the first value and the second value indicates an effectiveness of the change in the piece of content in attracting attention of the another user; (Krishnamoorthy ¶ 0053: the feedback generator may inform the user that on acquiring a particular skill set, the user may improve the chance of acquiring his desired post; ¶ 0055: the user may be informed that on completing a particular course of study, his relative score may increase from 75 to 82, thereby increasing his chances of acquiring a suitable job, as compared to that of the competing user's data object which may have a relative score of 80 ... improving the absolute score of the user desiring a particular job position from 60 to 80 will increase his chances of meeting his needs based on the available jobs pertaining to his requirements FIG. 3D, ¶ 0068: the feedback may also include suggestions such as, but not limited to, that if the user changes his residence to Los Angeles, his chances of acquiring his desired job will increase by 5% ... if the user performs one or more changes in data object 300 as per the suggestions provided by feedback generator 212, then computing module 210 may re-compute the new relative and absolute score for the user)
Krishnamoorthy does not expressly disclose the following:
Krishnamoorthy does not expressly disclose criteria that include a defined number or rate of positive interactions with other users from the plurality of users, and a defined number of photos in a user’s profile,
Krishnamoorthy does not expressly disclose the user’s messaging behavior.
Krishnamoorthy does not expressly disclose a value indicative of the recency of the photos in a user’s profile.
Krishnamoorthy does not expressly disclose:
implement the suggestion to change the piece of content when the discrepancy exists regarding the piece of content to be changed and instructions are received from target user to correct the discrepancy from desired behavior to improve success in finding potential candidates,
and wherein the discrepancy from desired behavior to be corrected is between the user’s messaging activities compared to the information on the user’s profile.
However, De addresses at least a portion of these limitations by teaching the following:
De teaches criteria that include a defined number or rate of positive interactions with other users, (De FIG. 3B, col. 15, lines 41-60: Activity on a social network associated with a user's profile can be gauged from a variety of sources, such as a number of messages sent and/or received on the social network, a number of fans on the social network, a number of flags from other users, a number of friends on the social network, frequency of profile updates and page views, etc. If the alleged victim is determined to have a greater level of activity than the alleged impersonator, then an indicator of impersonation can be identified. The strength of such an indicator can depend on the disparity in activity between the alleged victim and the alleged impersonator--a greater disparity can provide a stronger indication of impersonation while a small disparity can provide little, if any, indication of impersonation)
De also teaches the user’s messaging behavior. (De FIG. 3B, col. 15, lines 41-60: Activity on a social network associated with a user's profile can be gauged from a variety of sources, such as a number of messages sent and/or received on the social network)
De also teaches “a first value of … the photos in a user’s profile” and “a second value indicative of … the photos in a user’s profile.” (De col. 14, lines 15-35: At step 356, photos and/or images contained on the profile page of the alleged victim and the profile page of the alleged impersonator are compared to identify indicators of impersonation and/or indicators contrary to impersonation ... Although subject to the same strength of impersonation factors (e.g., degree of similarity, timing, commonality on the social network, field type, etc.) discussed above, a match of a photo and/or an image can indicate impersonation more strongly than some field matches, such as non-sensitive fields)
De also teaches:
and wherein the discrepancy from desired behavior to be corrected is between the user's messaging activities compared to the information on the user's profile. (De FIG. 3B, col. 15, lines 41-60: At step 364, activity on the social network for the alleged impersonator's profile can be compared against activity related to the alleged victim's profile to identify indicators of impersonation and/or indicators contrary to impersonation. Activity on a social network associated with a user's profile can be gauged from a variety of sources, such as a number of messages sent and/or received on the social network ... The strength of such an indicator can depend on the disparity in activity between the alleged victim and the alleged impersonator--a greater disparity can provide a stronger indication of impersonation while a small disparity can provide little, if any, indication of impersonation)
It would have been obvious to one of ordinary skill in the art at the time the invention was made to combine the user profile comparison and suggestions of Krishnamoorthy with the user profile comparison and notifications of De.
In addition, both of the references (Krishnamoorthy and De) disclose features that are directed to analogous art, and they are directed to the same field of endeavor, such as user data comparison and suggestions.
Motivation to do so would be to improve the functioning of Krishnamoorthy as modified performing a comparison and scoring of user-provided data objects with the ability in similar reference De to perform comparison and scoring of user data but with the added functionality of various comparison techniques present in De (see De col. 14, lines 15-35).
Krishnamoorthy in view of De does not expressly disclose criteria that include … a defined number of photos in a user’s profile;
Krishnamoorthy in view of De does not expressly disclose a value indicative of the recency of the photos in a user’s profile.
Krishnamoorthy in view of De further does not expressly disclose:
implement the suggestion to change the piece of content when the discrepancy exists regarding the piece of content to be changed and instructions are received from target user to correct the discrepancy from desired behavior to improve success in finding potential candidates,
However, Fredinburg addresses this by teaching:
implement the suggestion to change the piece of content when the discrepancy exists regarding the piece of content to be changed (Fredinburg col. 14, line 41-col. 15, line 21: the evaluation module 210 compares the profile impression data for a professional profile impression to the user's source data and determines the difference. The evaluation module 210 determines that work related content (e.g., photos or videos including users wearing suites, posts including work discussion, etc.) ranks higher in the profile impression data than in the user's source data ... the evaluation module 210 generates a summary including difference between the user's source data and the profile impression data and instructs the GUI module 212 to generate a user interface displaying the summary in the user device 115; Fredinburg FIG. 3, col. 16, lines 1-11: The method 300 can include determining 308 user profile impression data based at least in part on the source data and the user review data. For example, the determination module 206 modifies the user's 125 source data based at least in part on the user review data and determines profile impression data including modified source data (e.g., modified posts, photos, videos, etc.))
and instructions are received from target user to correct the discrepancy from desired behavior to improve success in finding potential candidates, (Fredinburg FIGs. 4, col. 16, lines 19-46: generating 406 profile preview data. In some implementations, the preview module 204 generates profile preview data by modifying the user's 125 source data based at least in part on the selection of the profile impression; Fredinburg FIGs. 6-7, col. 17-col. 18, line 8, see primarily Fredinburg FIG. 6A, col. 17, lines 3-30: The popup dialog box 606 includes a recommended profile photo 608 for a professional profile impression. The popup dialog box 606 also includes a “Yes” button 610 and a “No” button 612 which are clickable by the user to indicate if the user agrees to change to the recommended profile photo 608; see then Fredinburg FIG. 6B, col. 17, lines 31-41: FIG. 6B depicts a graphic representation of an example user interface 650 for displaying modified user content for a professional profile impression ... The user icon 608 displays the profile photo for the user that is recommended in the profile impression review interface 600; Fredinburg addresses the claimed improving success in finding potential candidates in col. 5, line 57-col. 6, line 63: profile impression is a configuration of a user profile with the social network application 109 that is designed to make a first user 125 appear a certain way to other second users 125 who view the user profile ... the makeover module 103 then modifies the user profile for the first user 125 so that it leaves other second users 125 with the impression that the first user 125 is professional. Accordingly, the profile impression is a configuration of a user profile for a first user 125 that is designed to cause other second users 125 to perceive the first user 125 in a predetermined way)
It would have been obvious to one of ordinary skill in the art at the time the invention was made to combine the user profile comparison and suggestions of Krishnamoorthy as modified with the user profile comparison and suggestions of Fredinburg.
In addition, both of the references (Krishnamoorthy as modified and Fredinburg) disclose features that are directed to analogous art, and they are directed to the same field of endeavor, such as user data comparison and suggestions.
Motivation to do so would be to improve the functioning of Krishnamoorthy as modified as modified performing a comparison of user-provided data objects with the ability in similar reference Fredinburg to perform comparison of user data but with the added functionality of different profile impressions to apply to an existing user profile.
Krishnamoorthy in view of De and Fredinburg does not expressly disclose criteria that include … a defined number of photos in a user’s profile;
Krishnamoorthy in view of De and Fredinburg does not expressly disclose a first value indicative of the recency of the photos in a user’s profile.
However, Chen addresses this by teaching the following:
Chen teaches criteria that include … a defined number of photos in a user’s profile. (Chen ¶ 0058: Content can include, but is not limited to status updates, photos, videos, articles, links, announcements, events, "likes", comments, and the like. The low engagement ID module 224 can monitor the amount of content or the number of content items posted by a user; see then Chen ¶ 0059: the low engagement ID module 224 may also identify the user as a low feedback user if the amount of content posts (or shares) of the user in a first time period falls below a threshold … the threshold relating to the number of posts and the threshold relating to the number of feedback may be less than or equal to eight posts per month and two feedback on posts within the last 14 days, respectively)
Chen also teaches a value indicative of the recency of the photos in a user’s profile. (Chen ¶ 0034: posts may include data such as images such as photos; Chen ¶ 0059 contemplates the claimed 'recency of photos': the low engagement ID module 224 can identify the user as a low feedback user if the user posts little content [or] if the amount of content posts (or shares) of the user in a first time period falls below a threshold; The predetermined feedback frequency can be... any desired frequency; the low engagement ID module 224 may identify a user as a low feedback user if the amount of content posts (or shares) of the user in first time period falls below a first threshold)
It would have been obvious to one of ordinary skill in the art at the time the invention was made to combine the user metadata and parameters as in Krishnamoorthy as modified with the user profiles of Chen.
In addition, both of the references (Krishnamoorthy as modified and Chen) disclose features that are directed to analogous art, and they are directed to the same field of endeavor, such as tracking scores associated with user profiles.
Motivation to do so would be to improve the functioning of Krishnamoorthy as modified performing a scoring of user-provided data objects with the ability in similar reference Chen to perform scoring of user profiles but with frequencies and thresholds.
Regarding claim 12, Krishnamoorthy in view of De and Fredinburg and Chen teaches all the features with respect to claim 11 above including:
determines a value that represents an action taken by the target user; compares the determined value to an evaluation-criteria value for the successful users; (Krishnamoorthy ¶ 0045-0048: comparison module 208 may compare the one or more data objects of the user with that of the competing users for the same requirements … the relative score associated with the data objects is calculated based on the internal parameters, external parameters, and the comparison data received from comparison module 208. Thus, the relative score of data objects may provide the standing of the data objects submitted by the user to that of other data objects submitted by competing users that belong to the same class as that of the user's data object. The relative score may thus be used to compare the user's data object with that of competing user's data objects. Thus, relative score associated with the user's data object is indicative of the likelihood of achieving the requirement included in the data object of the user. For example, if the user's data object has a score of 75, while that of his competitor is 80, then the user has lesser chances of fulfilling his requirement as compared to his competitor; see also FIG. 4, steps 406-408, ¶ 0079-0082: At step 406, comparison module 208 may compare the one or more data objects of the user with that of the competing users for the same requirements ... the absolute score of the data object may provide the measure of how closely the user's requirements may be matched to the corresponding complementary requirements of another user (who provide the service and/or product to match the user requirement). In other words, the absolute score provides an indication of a likelihood of how the user fares against the complimentary requirements)
determines whether a discrepancy exists between the determined value and the defined evaluation-criteria value for the successful users; and in response to determining that the discrepancy exists, prompts the user of the existence of the discrepancy. (Krishnamoorthy ¶ 0055-0058 shows prompting: the user may be informed that on completing a particular course of study, his relative score may increase from 75 to 82, thereby increasing his chances of acquiring a suitable job, as compared to that of the competing user's data object which may have a relative score of 80 ... feedback generator 212 may also suggest the best match to the user's requirement, by judging the minimum modifications that need to be made in order to achieve the best match judged by a high absolute score, made to compare one or more distinctly comparable data objects that lie in the same domain; see also FIG. 4, steps 408-410, ¶ 0082-0084 also shows prompting: the absolute score of the data object may provide the measure of how closely the user's requirements may be matched to the corresponding complementary requirements of another user (who provide the service and/or product to match the user requirement). In other words, the absolute score provides an indication of a likelihood of how the user fares against the complimentary requirements ... At step 410, feedback generator 212 may analyze the one or more scores computed by computing module 210 and provides a listing of the internal parameters, external parameters, optionally including historical parameters, recommendations, or all, that need to be modified, improved, added or removed, in order to enhance the one or more scores associated with the data object)
Regarding claim 13, Krishnamoorthy in view of De and Fredinburg and Chen teaches all the features with respect to claim 11 above including:
wherein the processor compares the number of components in response to a request received from the target user. (Krishnamoorthy FIG. 4, ¶ 0071: At step 402, receiving module 202 may receive the one or more data objects from application server 110; see this occurring before step 406 of ¶ 0079: At step 406, comparison module 208 may compare the one or more data objects of the user with that of the competing users for the same requirements)
Regarding claim 14, Krishnamoorthy in view of De and Fredinburg and Chen teaches all the features with respect to claim 11 above including:
wherein the processor automatically compares the number of components in response to an updating of the user profile by the target user. (Krishnamoorthy FIG. 3B, ¶ 0065 describes the claimed 'user profile' in more detail: FIG. 3B illustrates a table 302 that includes the extracted parameters associated with data object 300. Extraction module 204 may extract the one or more internal parameters from data object 300 submitted by the user; see that this corresponds to steps 402-406 of FIG. 4, ¶ 0071-0072: At step 402, receiving module 202 may receive the one or more data objects from application server 110 [shows an updating] ... At step 404, extraction module 204 extracts the information included in the data objects; see this occurring before step 406 of FIG. 4, ¶ 0079: At step 406, comparison module 208 may compare the one or more data objects of the user with that of the competing users for the same requirements)
Regarding claim 16, Krishnamoorthy in view of De and Fredinburg and Chen teaches all the features with respect to claim 11 above including:
determines how recently the target user profile was updated; (Chen ¶ 0059: the low engagement ID module 224 can identify the user as a low feedback user if the user posts little content; In an embodiment, the low engagement ID module 224 may also identify the user as a low feedback user if the amount of content posts (or shares) of the user in a first time period falls below a threshold; The predetermined feedback frequency can be, for example, 1 feedback entry per day, 2 feedback entries per week, 2 feedback entries every two weeks, 8 feedback entries per month, or any desired frequency [Chen teaches examining a time period for a threshold of content posts; if the threshold over a period such as a day or a week is one post, and the user does not meet this threshold, that means that their profile was not updated recently; Chen must then contemplate determining the amount of content posts in a first time period if Chen is to determine whether it falls below a threshold])
compares a recentness of the update of the target user profile to a defined evaluation recentness; and (Chen ¶ 0059: the low engagement ID module 224 can identify the user as a low feedback user if the user posts little content; In an embodiment, the low engagement ID module 224 may also identify the user as a low feedback user if the amount of content posts (or shares) of the user in a first time period falls below a threshold; the low engagement ID module 224 may identify a user as a low feedback user if the amount of content posts (or shares) of the user in first time period falls below a first threshold [in this paragraph, Chen also discusses a predetermined feedback frequency])
identifies a discrepancy between the recentness of the update of the target user profile and the defined evaluation recentness. (Chen ¶ 0059: the low engagement ID module 224 can identify the user as a low feedback user if the user posts little content; the low engagement ID module 224 may also identify the user as a low feedback user if the amount of content posts (or shares) of the user in a first time period falls below a threshold; [as in if there are no posts in a period, the latest update is longer than the predetermined threshold] ¶ 0066: Once the user is identified as a low engagement user, the low engagement module 150 may provide a communication to the user to encourage increased activity on the social networking system 130)
It would have been obvious to one of ordinary skill in the art at the time the invention was made to combine the user metadata and parameters as in Krishnamoorthy as modified with the user profiles of Chen.
Motivation to do so would be to improve the functioning of Krishnamoorthy as modified performing a scoring of user-provided data objects with the ability in similar reference Chen to perform scoring of user profiles but with frequencies and thresholds.
Regarding claim 17, Krishnamoorthy in view of De and Fredinburg and Chen teaches all the features with respect to claim 11 above including:
determines how frequently the target user profile is updated; and (Chen ¶ 0059: the low engagement ID module 224 can identify the user as a low feedback user if the user posts little content; In an embodiment, the low engagement ID module 224 may also identify the user as a low feedback user if the amount of content posts (or shares) of the user in a first time period falls below a threshold; The predetermined feedback frequency can be, for example, 1 feedback entry per day, 2 feedback entries per week, 2 feedback entries every two weeks, 8 feedback entries per month, or any desired frequency)
compares the determined frequency of the updating of the target user profile to a defined evaluation frequency; and (Chen ¶ 0059: the low engagement ID module 224 can identify the user as a low feedback user if the user posts little content; In an embodiment, the low engagement ID module 224 may also identify the user as a low feedback user if the amount of content posts (or shares) of the user in a first time period falls below a threshold; the low engagement ID module 224 may identify a user as a low feedback user if the amount of content posts (or shares) of the user in first time period falls below a first threshold [checking if an amount is below a threshold is a comparing against the threshold, the threshold is a defined frequency])
identifies a discrepancy between the frequency of the updating of the target user profile and the defined evaluation frequency. (Chen ¶ 0059: the low engagement ID module 224 can identify the user as a low feedback user if the user posts little content; the low engagement ID module 224 may also identify the user as a low feedback user if the amount of content posts (or shares) of the user in a first time period falls below a threshold; ¶ 0066: Once the user is identified as a low engagement user, the low engagement module 150 may provide a communication to the user to encourage increased activity on the social networking system 130)
It would have been obvious to one of ordinary skill in the art at the time the invention was made to combine the user metadata and parameters as in Krishnamoorthy as modified with the user profiles of Chen.
Motivation to do so would be to improve the functioning of Krishnamoorthy as modified performing a scoring of user-provided data objects with the ability in similar reference Chen to perform scoring of user profiles but with frequencies and thresholds.
Regarding claim 18, Krishnamoorthy in view of De and Fredinburg and Chen teaches all the features with respect to claim 11 above including:
determines how recently an image of the user was updated on the target user profile; and (Chen ¶ 0059: the low engagement ID module 224 can identify the user as a low feedback user if the user posts little content [or] if the amount of content posts (or shares) of the user in a first time period falls below a threshold; The predetermined feedback frequency can be... any desired frequency [Chen teaches examining a time period for a threshold of content posts; if the threshold over a period such as a day or a week is one post, and the user does not meet this threshold, that means that their profile was not updated recently; Chen must then contemplate determining the amount of content posts in a first time period if Chen is to determine whether it falls below a threshold] // Chen ¶ 0034: posts may include data such as images such as photos; ¶ 0058: the low engagement ID module 224 may identify users who have posted little content to the social networking system 130 as low feedback users; content can include photos)
compares a recentness of the image update with a defined evaluation recentness; (Chen ¶ 0059: the low engagement ID module 224 can identify the user as a low feedback user if the user posts little content; In an embodiment, the low engagement ID module 224 may also identify the user as a low feedback user if the amount of content posts (or shares) of the user in a first time period falls below a threshold; the low engagement ID module 224 may identify a user as a low feedback user if the amount of content posts (or shares) of the user in first time period falls below a first threshold [in this paragraph, Chen also discusses a predetermined feedback frequency])
identifies a discrepancy between the recentness of the image update and the defined evaluation recentness. (Chen ¶ 0059: the low engagement ID module 224 can identify the user as a low feedback user if the user posts little content; the low engagement ID module 224 may also identify the user as a low feedback user if the amount of content posts (or shares) of the user in a first time period falls below a threshold; [as in if there are no posts in a period, the latest update is longer than the predetermined threshold] ¶ 0066: Once the user is identified as a low engagement user, the low engagement module 150 may provide a communication to the user to encourage increased activity on the social networking system 130)
It would have been obvious to one of ordinary skill in the art at the time the invention was made to combine the user metadata and parameters as in Krishnamoorthy as modified with the user profiles of Chen.
Motivation to do so would be to improve the functioning of Krishnamoorthy as modified performing a scoring of user-provided data objects with the ability in similar reference Chen to perform scoring of user profiles but with frequencies and thresholds.
Claim 15 is rejected under pre-AIA 35 U.S.C. 103(a) as being unpatentable over Krishnamoorthy in view of De and Fredinburg and Chen in further view of De Cristofaro et al., U.S. Patent Application Publication No. 2014/0156750 (filed December 5, 2012, prior to the instant application date of March 13, 2013; hereinafter De Cristofaro).
Regarding claim 15, Krishnamoorthy in view of De and Fredinburg and Chen teaches all the features with respect to claim 11 above including correcting the discrepancy… (Fredinburg col. 14, line 41-col. 15, line 21: the evaluation module 210 compares the profile impression data for a professional profile impression to the user's source data and determines the difference. The evaluation module 210 determines that work related content (e.g., photos or videos including users wearing suites, posts including work discussion, etc.) ranks higher in the profile impression data than in the user's source data ... the evaluation module 210 generates a summary including difference between the user's source data and the profile impression data and instructs the GUI module 212 to generate a user interface displaying the summary in the user device 115)
De further teaches the discrepancy from desired behavior between the user's messaging activities compared to the information on the user's profile… (De FIG. 3B, col. 15, lines 41-60: At step 364, activity on the social network for the alleged impersonator's profile can be compared against activity related to the alleged victim's profile to identify indicators of impersonation and/or indicators contrary to impersonation. Activity on a social network associated with a user's profile can be gauged from a variety of sources, such as a number of messages sent and/or received on the social network ... The strength of such an indicator can depend on the disparity in activity between the alleged victim and the alleged impersonator--a greater disparity can provide a stronger indication of impersonation while a small disparity can provide little, if any, indication of impersonation)
Krishnamoorthy in view of De and Fredinburg and Gobeyn does not expressly disclose the discrepancy … further comprises when the user's stated relationship intent in the user's profile does not match the user's own actions or activities.
However, De Cristofaro addresses this by teaching the discrepancy … further comprises when the user's stated relationship intent in the user's profile does not match the user's own actions or activities. (De Cristofaro ¶ 0022: The profile can include fields of information about the participant, including, inter alia, age, relationship status; De Cristofaro FIG. 3, ¶ 0027-0028: Information from a social network page can be used to certify one or more information fields of an online dating profile by comparing and matching the information or by uploading information from the social network page to the online dating profile ... the types of information to be compared can include all textual information [relevant to messaging activities]; see also De Cristofaro FIG. 4, ¶ 0032: The list of information fields 52 can include one or more fields of information about the participant that is listed on the social network page, online dating profile, or both the social network page and online dating profile. The information fields can include age, relationship status; see also De Cristofaro ¶ 0040-0042: The undetermined status can be assigned when the data for an information field does not match ... An undermined status may be assigned when, for instance, a participant's social network profile lists the relationship status as "its complicated," while the online dating profile lists the participant's status as single)
It would have been obvious to one of ordinary skill in the art at the time the invention was made to combine the user profile comparison and suggestions of Krishnamoorthy with the user profile comparison and notifications of De Cristofaro.
In addition, both of the references (Krishnamoorthy and De Cristofaro) disclose features that are directed to analogous art, and they are directed to the same field of endeavor, such as user data comparison and suggestions.
Motivation to do so would be the teaching, suggestion, or motivation for one of ordinary skill in the art to automatically verify online dating profiles to ensure the veracity of the information provided on the profile and to prevent misleading other users as seen in De Cristofaro ¶ 0002-0006.
Claim 19 is rejected under pre-AIA 35 U.S.C. 103(a) as being unpatentable over Krishnamoorthy in view of De and Fredinburg in further view of Chen in further view of Haynes et al., U.S. Patent Application Publication No. 2002/0156632 (published October 24, 2002, at least one year prior to the instant application date of March 13, 2013; provided in the IDS as a result of inclusion in a parent application; hereinafter "Haynes").
Regarding claim 19, Krishnamoorthy in view of De and Fredinburg and Chen teaches all the features with respect to claim 11 above but does not expressly disclose:
determines a length of a profile description, and identifies a discrepancy between the determined length of the profile description and a defined evaluation profile description length.
However, Haynes teaches:
determines a length of a profile description, and identifies a discrepancy between the determined length of the profile description and a defined evaluation profile description length. (Haynes ¶ 0040: validity check module can operate in various ways to identify suspect summaries in response to one or more detected parameters, such as word usage, word count, i.e, where the summary is too short or too long to be a valid summary, and/or other parameters or peculiarities)
It would have been obvious to one of ordinary skill in the art at the time the invention was made to combine the user element comparison and evaluation as in Krishnamoorthy as modified with the suspect summary identification of Haynes.
In addition, both of the references (Krishnamoorthy as modified and Haynes) disclose features that are directed to analogous art, and they are directed to the same field of endeavor, such as using detected discrepancies to improve user-submitted information.
Motivation to do so would be to improve the functioning of Krishnamoorthy as modified performing an analysis of user-provided parameters with the ability in similar reference Haynes to perform a detection of parameters but with the ability to flag suspect data for future evaluation.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Pocklington et al., U.S. Patent Application Publication No. 2009/0228583; see Pocklington FIG. 2A, ¶ 0041-0045, "Once a relationship classification has been established then it can be used to analyze messages to determine if a discrepancy exists between a participant's intent and the likely meaning or predicted effect of a message to be sent. For example, in FIG. 2A, there is evidence of a friendly relationship between Richard and Charlie since at line 110 the greeting is the more informal "Hey, Richard," instead of simply "Richard," or even "Hi, Richard," ... the Richard/Charlie friendship attribute can be defined by a parameter associated with Richard's profile or intent data … if Charlie is sending Richard an email but it is detected that Richard's profile has a Richard/Charlie friendship value of 3 then a more formal salutation may be suggested than what Charlie had initially used"; relevant to at least the independent claim limitations involving a discrepancy being between a user's messaging activities compared to the information on the user's profile.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JEDIDIAH P FERRER whose telephone number is (571)270-7695. The examiner can normally be reached Monday-Friday 12:00pm-8:00pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Kavita Stanley can be reached at (571)272-8352. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/J.P.F/Examiner, Art Unit 2153 March 11, 2026
/KAVITA STANLEY/Supervisory Patent Examiner, Art Unit 2153