Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
DETAILED ACTION
The instant application having Application No. 18064657 has a total of 26 claims pending in the application, of which claims 3-4, 9-10 and 19-20 have been cancelled.
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/apply/applying-online/eterminal-disclaimer.
Claims 1-20 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-7 of U.S. Patent No.11526773 B1. Although the claims at issue are not identical, they are not patentably distinct from each other because each of the limitations can be met as shown below.
As per claim 1,
Instant Application
11526773 B1
Examiners comment
A system comprising: one or more computers and one or more storage devices storing instructions that are operable, when executed by the one or more computers, to cause the one or more computers to perform operations comprising:
Claim 1: A system comprising: one or more computers and one or more storage devices storing instructions that are operable, when executed by the one or more computers, to cause the one or more computers to perform operations comprising
maintaining, by the system, a knowledge base accessible by multiple users, wherein the knowledge base comprises one or more attribute-value pairs for a plurality of entities;
Claim 1: maintaining, by a web search engine of the system, a knowledge base accessible by multiple users, wherein the knowledge base comprises one or more attribute-value pairs for a particular entity
obtaining, from a user having user profile data stored in the system, a value for an attribute of an entity of the plurality of entities, wherein the user profile data is stored independent of the knowledge base;
Claim 1: receiving, by the web search engine, a search query submitted by a user having user profile data stored in the system;… wherein the user profile data is not stored in the knowledge base
obtaining the user profile data for the user, the user profile data including information representing other subsystems of the system accessed by the user;
Claim 4: the operations further comprising: obtaining information representing other subsystems accessed by the user, the other subsystems including an email system, a social network system, a blogging system, or a shopping system; and computing the likelihood using the information representing the other subsystems and the information from the user profile data as the input to the trained user reliability model.
Computing, using a user reliability model with the information representing the other subsystems as input, a likelihood that the value is accurate, the user reliability model configured to weigh users who access more subsystems of the system as more reliable than users who access fewer subsystems of the system;
Claim 2: wherein the likelihood is higher when the user has accessed more subsystems of the system because the user reliability model is trained to compute a higher likelihood for users who access more subsystems than for users who access fewer subsystems of the system.
and updating the knowledge base with the value in response to determining that the likelihood satisfies a threshold.
Claim 1: updating the knowledge base with the new value received from the user in response to determining that the likelihood satisfies a threshold;
As can be seen, each of the limitations of the independent claims can be met by claims 1-7 of U.S. Patent No.11526773 B1 and therefore are rejected under Obvious-type double patenting.
As per claims 2-20, these claims can be similarly rejected over claims 1-7 of U.S. Patent No.11526773 B1.
Claims 1-20 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-10 of U.S. Patent No.10223637 B1. Although the claims at issue are not identical, they are not patentably distinct from each other because each of the limitations can be met as shown below.
As per claim 1,
Instant Application
10223637 B1
Examiners comment
A system comprising: one or more computers and one or more storage devices storing instructions that are operable, when executed by the one or more computers, to cause the one or more computers to perform operations comprising:
Claim 1: A computer-implemented method comprising
maintaining, by the system, a knowledge base accessible by multiple users, wherein the knowledge base comprises one or more attribute-value pairs for a plurality of entities;
Claim 1: maintaining a knowledge base accessible by multiple users, wherein the knowledge base comprises information about entities, the information about each entity being represented as one or more attribute-value pairs
obtaining, from a user having user profile data stored in the system, a value for an attribute of an entity of the plurality of entities, wherein the user profile data is stored independent of the knowledge base;
Claim 1: receiving, by a search system from a user having user profile data relating specifically to the user, a search request related to a topic, wherein the user profile data is not stored in the knowledge base
obtaining the user profile data for the user, the user profile data including information representing other subsystems of the system accessed by the user;
Claim 6: wherein the information from the user profile includes information about subsystems of the search system accessed by the user, wherein the search system considers users who access more subsystems of the search system to be more reliable than users who access fewer subsystems of the search system.
Computing, using a user reliability model with the information representing the other subsystems as input, a likelihood that the value is accurate, the user reliability model configured to weigh users who access more subsystems of the system as more reliable than users who access fewer subsystems of the system;
Claim 2: computing, using the user profile data as input to a user model, a likelihood that an update from the user to an entity related to the topic is accurate; and determining that the computed likelihood satisfies a threshold.
and updating the knowledge base with the value in response to determining that the likelihood satisfies a threshold.
Claim 1: updating the knowledge base with the updated value received from the user for the knowledge base attribute selected by the search system for the entity maintained in the knowledge base and related to the topic of the search request received from the user.
As can be seen, each of the limitations of the independent claims can be met by claims 1-10 of U.S. Patent No. 10223637 B1and therefore are rejected under Obvious-type double patenting.
As per claims 2-20, these claims can be similarly rejected over claims 1-10 of U.S. Patent No. 10223637 B1.
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 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1: Claim 1 is a machine type claim. Claim 8 is a process type claim. Claim 18 is a manufacture type claim. Therefore, claims 1-20 are directed to either a process, machine, manufacture or composition of matter.
As per claim 1,
2A Prong 1:
“Maintaining … a knowledge base accessible by multiple users, wherein the knowledge base comprises one or more attribute-value pairs for a plurality of entities;” EN: A user mentally or with pencil and paper keeps track of data in a knowledge base.
“computing, using a user reliability model with the information representing the other subsystems as input, a likelihood that the value is accurate, the user reliability model configured to weigh users who access more subsystems of the system as more reliable than users who access fewer subsystems of the system;” The user mentally or with pencil and paper evaluates the likelihood of the information being accurate based on information representing other subsystems.
“updating the knowledge base with the value in response to determining that the likelihood satisfies a threshold” The user mentally or with pencil and paper adds the information if it qualifies as accurate.
2A Prong 2: This judicial exception is not integrated into a practical application.
Additional elements:
One or more computers, one or more storage devices (mere instructions to apply the exception using a generic computer component);
“obtaining, from a user having user profile data stored in the system, a value for an attribute of an entity of the plurality of entities, wherein the user profile data is stored independent of the knowledge base;”, “obtaining the user profile data for the user, the user profile data including information representing other subsystems of the system accessed by the user” (Adding insignificant extra-solution activity to the judicial exception - see MPEP 2106.05(g)).
2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Additional elements:
One or more computers, one or more storage devices (mere instructions to apply the exception using a generic computer component)
“obtaining, from a user having user profile data stored in the system, a value for an attribute of an entity of the plurality of entities, wherein the user profile data is stored independent of the knowledge base;”, “obtaining the user profile data for the user, the user profile data including information representing other subsystems of the system accessed by the user” (MPEP 2106.05(d)(II) indicate that merely “transmitting and receiving data” is a well‐understood, routine, conventional function when it is claimed in a merely generic manner (as it is in the present claim). Thereby, a conclusion that the claimed obtaining steps are well-understood, routine, conventional activity is supported under Berkheimer).
As per claims 4-7, these claims denote additional mental steps similar to claim 1, and is rejected for similar reasons to claim 1.
As per claim 21-22, these claims denote similar mental steps to claim 1, and are rejected for similar reasons to claim 1.
As per claim 8,
2A Prong 1:
“Maintaining … a knowledge base accessible by multiple users, wherein the knowledge base comprises information about entities, the information about each entity of the entities being represented as one or more attribute-value pairs” EN: A user mentally or with pencil and paper keeps track of data in a knowledge base.
“computing, using a user reliability model with the information representing the other subsystems as input, a likelihood that the updated value is accurate, the user reliability model configured to consider users who access more subsystems as more reliable than users who access fewer subsystems…;” The user mentally or with pencil and paper evaluates the likelihood of the information being accurate based on information representing other subsystems.
“updating the knowledge base with the updated value in response to determining that the likelihood satisfies a threshold” The user mentally or with pencil and paper adds the information if it qualifies as accurate.
2A Prong 2: This judicial exception is not integrated into a practical application.
Additional elements:
A search system, One or more computers (mere instructions to apply the exception using a generic computer component);
“receiving, by the search system from a user having user profile data stored in the system, an updated value of an attribute of an entity in the knowledge base, wherein the user profile data is stored independent of the knowledge base;”, “obtaining the user profile data for the user, the user profile data including information representing other subsystems of the search system accessed by the user” (Adding insignificant extra-solution activity to the judicial exception - see MPEP 2106.05(g)).
2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Additional elements:
A search system, One or more computers (mere instructions to apply the exception using a generic computer component)
“receiving, by the search system from a user having user profile data stored in the system, an updated value of an attribute of an entity in the knowledge base, wherein the user profile data is stored independent of the knowledge base;”, “obtaining the user profile data for the user, the user profile data including information representing other subsystems of the search system accessed by the user” (MPEP 2106.05(d)(II) indicate that merely “transmitting and receiving data” is a well‐understood, routine, conventional function when it is claimed in a merely generic manner (as it is in the present claim). Thereby, a conclusion that the claimed obtaining steps are well-understood, routine, conventional activity is supported under Berkheimer).
As per claims 11-14, these claims denote additional mental steps similar to claim 8, and is rejected for similar reasons to claim 8.
As per claim 23-24, these claims denote similar mental steps to claim 8, and are rejected for similar reasons to claim 8.
As per claim 18,
2A Prong 1:
“Maintaining … a knowledge base accessible by multiple users, wherein the knowledge base comprises information about entities, the information about each entity of the entities being represented as one or more attribute-value pairs” EN: A user mentally or with pencil and paper keeps track of data in a knowledge base.
“computing, using a user reliability model with the information representing the other subsystems as input, a likelihood that the value is accurate, the user reliability model configured to consider users who access more subsystems to be more reliable than users who access fewer subsystems;” The user mentally or with pencil and paper evaluates the likelihood of the information being accurate based on information representing other subsystems.
“updating the knowledge base with the value in response to determining that the likelihood satisfies a threshold” The user mentally or with pencil and paper adds the information if it qualifies as accurate.
2A Prong 2: This judicial exception is not integrated into a practical application.
Additional elements:
One or more non-transitory computer storage media, One or more computers, A search system, (mere instructions to apply the exception using a generic computer component);
“obtaining, from a user having user profile data stored in the system, a value for an attribute of an entity of the plurality of entities, wherein the user profile data is stored independent of the knowledge base;”, “obtaining the user profile data for the user, the user profile data including information representing other subsystems of the system accessed by the user, wherein the other subsystems include an image search system, a map system, an email system, a social network system, a blogging system, or a shopping system” (Adding insignificant extra-solution activity to the judicial exception - see MPEP 2106.05(g)).
2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Additional elements:
One or more non-transitory computer storage media, One or more computers, A search system, (mere instructions to apply the exception using a generic computer component)
“obtaining, from a user having user profile data stored in the system, a value for an attribute of an entity of the plurality of entities, wherein the user profile data is stored independent of the knowledge base;”, “obtaining the user profile data for the user, the user profile data including information representing other subsystems of the system accessed by the user, wherein the other subsystems include an image search system, a map system, an email system, a social network system, a blogging system, or a shopping system” (MPEP 2106.05(d)(II) indicate that merely “transmitting and receiving data” is a well‐understood, routine, conventional function when it is claimed in a merely generic manner (as it is in the present claim). Thereby, a conclusion that the claimed obtaining steps are well-understood, routine, conventional activity is supported under Berkheimer).
As per claim 25-26, these claims denote similar mental steps to claim 18, and are rejected for similar reasons to claim 18.
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 1, 4-8, 11-18, and 21-26 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention.
As per claims 1, 8, and 18, these claims each call for “wherein the user profile data is stored independent of the knowledge base.” However, this limitation is not supported by the specification. The term “Independent” is not used at all in the specification, let alone in relation to how data is stored. Applicant cites figure 2 as support for this amendment, but it merely shows a User Database (presumably where the user profile data is stored) and the knowledge base. While these are separate things, they are both part of the search system 230, and data is used regularly between the two systems (see paragraph 0036 of the instant specification). Merely because data is shown to be stored in a separate spot does not make it stored “independent” of other information. This causes the limitation to be new matter, and therefore rejected under U.S.C. 112(a).
As per claims 4-7, 11-17, and 21-26, these claims are rejected as being dependent on a claim rejected under U.S.C. 112(a) for new matter.
As per claims 6 and 13, these claims call for “wherein the other subsystems of the system are independent of the knowledge base and any search engine function.” This is not supported by the specification. As discussed above, the term “independent” is never used in the specification. Applicant cites figure 2 for these aspects, but figure 2 does not even show any of the other subsystems. Further as stated above, the user profile which is stored in the user database regularly interacts with both the search engine and the other subsystems, as this is where data about the users interactions with these subsystems are stored, along with the fact that the specification explicitly states: “A user profile for registered or unregistered users may include user interactions with subsystems of the search system, e.g. web search system, an image search system, a map system, an email system, a social network system, a blogging system, a shopping system, just to name a few…” (Instant specification, paragraph 0036). Since the specification explicitly states that these are subsystems of the search system, they are clearly not “independent of the knowledge base and any search engine function” and therefore this limitation is new matter, and rejected under U.S.C. 112(a).
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-2, 4, 7-9, 11, and 14-17are rejected under 35 U.S.C. 103 as being unpatentable over Jain et al (US 20090055384 A1) in view of Skillen (US 6098065 A), Kaplan et al (US 20070078675 A1) and Levin (US 20120095977 A1).
As per claim 1, Jain discloses, “a system comprising: one or more computers” (pg.1, particularly paragraph 0010; EN: this denotes the system running on a computer system). “one or more storage devices storing instructions that are operable” (pg.2, particularly paragraph 0020; EN: this denotes the use of memory). “to cause the one or more computers to perform operations comprising:” (pg.1, particularly paragraph 0010; EN: this denotes the system running on a computer system).
“maintaining, by the system, a knowledge base accessible by multiple users” (Pg.1, particularly paragraph 0011; EN: this denotes the system being accessible by multiple users). “wherein the knowledge base comprises one or more attribute-value pairs for a plurality of entities” (Pg.2, particularly paragraphs 0013-0014; EN: this denotes experts in particular topics (attributes) with the associated search terms (values)).
“obtaining, from a user having user profile data stored in the system, a value for an attribute of an entity of the plurality of entities” (pg.1, particularly paragraph 0013-0014; EN: this denotes experts submitting search queries and getting search results and the system keeping track of feedback on the expert and their previous searches).
“obtaining the user profile data for the user” (Pg.2, particularly paragraphs 0013-0014; EN: this denotes experts in particular topics (attributes) with the associated search terms (values)). “The user profile data including information representing other subsystems of the system accessed by the user” (Pg.1, particularly paragraph 0015-0016; EN: this denotes considering the Experts actions such as topics of expertise, with the search terms and search results of each particular topic denoting different subsystems of the search system).
“Computing, using a user reliability model” (Pg.1, particularly paragraph 0013; EN: this denotes the system making use of the feedback/reputation information for the experts based on feedback in order to make sure the experts responses are useful/accurate). “with the information representing the other subsystems as input” (Pg.1, particularly paragraph 0013; EN: this denotes the system making use of the feedback/reputation information for the experts based on feedback in order to make sure the experts responses are useful/accurate). “… that the value is accurate …” (Pg.1, particularly paragraph 0013; EN: this denotes the system making use of the feedback/reputation information for the experts based on feedback in order to make sure the experts responses are useful/accurate).
“Updating the knowledge base with the value…” (Pg.1, particularly paragraph 0014. Pg.2, paragraph 0017; EN: this denotes information being collected from the expert on search terms, search results they got in regard to their query and other aspects of the search. In this case, the ranking of the search results are the attribute and the update is the expert’s feedback on refining the search results. This update is allowed for experts, when combined with the Kaplan reference, this denotes making sure of the user’s expertise before allowing their actions to be stored and used by others as an expert).
However, Jain fails to explicitly disclose, “wherein the user profile data is stored independent of the knowledge base”, “computing … a likelihood that the value is accurate, the user reliability model configured to weigh users who access more subsystems of the system as more reliable than users who access fewer subsystems of the system”, and “…in response to determining that the likelihood satisfies a threshold”
Skillen discloses, “wherein the user profile data is stored independent of the knowledge base” (C5, particularly L39-57; EN: this denotes a separate use profile database to store user profiles in relation to a search engine).
Kaplan discloses, “computing … a likelihood that the value is accurate”, and “…in response to determining that the likelihood satisfies a threshold” (pg.2, particularly paragraph 0023-0024; EN: this denotes monitoring the user’s previous estimations in order to give him a reputation value and using thresholds to determine when the expert is adequate in the subject matter).
Levin discloses, “the user reliability model configured to weigh users who access more subsystems of the system as more reliable than users who access fewer subsystems of the system” (pg.12, particularly paragraph 0151-0156; EN: this denotes experts having multiple expertises, and their reliability for a topic being based upon having more matching expertises than other experts in order to be higher ranked. When combined with Jain, this denotes ranking experts with more relevant topic expertise (i.e. subsystems) to the search at hand higher than those with less relevant topic expertises).
Jain and Skillen are analogous art because both involve search engines.
Before the effective filing date it would have been obvious to one skilled in the art of search engines to combine the work of Jain and Skillen in order to allow user profiles to be stored in a separate database.
The motivation for doing so would be use the database to include “ a comprehensive user profile database … about the end users preferences and previous search arguments which may be used to augment the individual search augment received with the search request” (Skillen, C5, L39-57) or in the case of Jain, allow the expert profiles to be stored in a separate place to keep track of the feedback and other data related to the profiles and their searches.
Therefore before the effective filing date it would have been obvious to one skilled in the art of search engines to combine the work of Jain and Skillen in order to allow user profiles to be stored in a separate database.
Jain and Kaplan are analogous art because both involve user reputation.
At the time of invention it would have been obvious to one skilled in the art of user reputation to combine the work of Jain and Kaplan in order to determine the reliability of a user/expert.
The motivation for doing so would be to allow searchers to be “automatically filtered based on Joe’s objective reputation, a reputation that can be specific to each message board” (Kaplan, Pg.2, paragraph 0024) or in the case of Jain, allow the system to automatically select experts based on reputations related to particular topics.
Therefore at the time of invention it would have been obvious to one skilled in the art of user reputation to combine the work of Jain and Kaplan in order to determine the reliability of a user/expert.
Jain and Levin are analogous art because both involve expert identification.
Before the effective filing date it would have been obvious to one skilled in the art of expert identification to combine the work of Jain and Levin in order to include relevant expertise when identifying the reliability of an expert.
The motivation for doing so would be to identify “the highest priority match is listed first and the remainder of the matches is listened in an order consistent with their decreasing priority” (Levin, Pg.12, paragraph 0156) or in the case of Jain, allow the system to make sure that an Expert has sufficient matching expertise to the topic at hand in order to select them as an expert in the particular search subject being considered.
Therefore before the effective filing date it would have been obvious to one skilled in the art of expert identification to combine the work of Jain and Levin in order to include relevant expertise when identifying the reliability of an expert.
As per claims 4 and 11, Jain discloses, “wherein the instructions further comprise: training the user reliability model using training examples comprising respective subsystems of the system accessed by each user of a plurality of users and a measure of accuracy of previously submitted updates to the knowledge base by each user” (pg.1, particularly paragraph 0013-0014; EN: this denotes experts submitting search queries and getting search results and the system keeping track of feedback on the expert and their previous searches).
As per claims 7 and 14, Jain discloses, “Wherein the =instructions further comprise: updating of the knowledge base with the value without intervention or inspection by a knowledge base administrator” (Pg.1, particularly paragraph 0014. Pg.2, paragraph 0017; EN: this denotes information being collected from the expert on search terms, search results they got in regard to their query and other aspects of the search. As there is no discussion of receiving confirmation from administrators, no such confirmation is received).
As per claim 8, Jain discloses, “A method performed by a search system” (Pg.1, particularly paragraph 0005; EN: this denotes the system being used for search). “Comprising one or more computers, the method comprising” (pg.1, particularly paragraph 0010; EN: this denotes the system running on a computer system).
“maintaining, by search the system, a knowledge base accessible by multiple users” (Pg.1, particularly paragraph 0011; EN: this denotes the system being accessible by multiple users). “wherein the knowledge base comprises information about entities, the information about each entity of the entities being represented as one or more attribute-value pairs” (Pg.2, particularly paragraphs 0013-0014; EN: this denotes experts in particular topics (attributes) with the associated search terms (values)).
“receiving, by the search system, from a user having user profile data stored in the search system, an updated value of an attribute of an entity in the knowledge base” (pg.1, particularly paragraph 0013-0014; EN: this denotes experts submitting search queries and getting search results and the system keeping track of feedback on the expert and their previous searches).
“obtaining the user profile data for the user” (Pg.2, particularly paragraphs 0013-0014; EN: this denotes experts in particular topics (attributes) with the associated search terms (values)). “The user profile data including information representing other subsystems of the search system accessed by the user” (Pg.1, particularly paragraph 0015-0016; EN: this denotes considering the Experts actions such as topics of expertise, with the search terms and search results of each particular topic denoting different subsystems of the search system).
“Computing, using a user reliability model” (Pg.1, particularly paragraph 0013; EN: this denotes the system making use of the feedback/reputation information for the experts based on feedback in order to make sure the experts responses are useful/accurate). “with the information representing the other subsystems as input” ” (Pg.1, particularly paragraph 0013; EN: this denotes the system making use of the feedback/reputation information for the experts based on feedback in order to make sure the experts responses are useful/accurate). “… that the updated value is accurate…of the search system” (Pg.1, particularly paragraph 0013; EN: this denotes the system making use of the feedback/reputation information for the experts based on feedback in order to make sure the experts responses are useful/accurate). “
“Updating the knowledge base with the updated value…” (Pg.1, particularly paragraph 0014. Pg.2, paragraph 0017; EN: this denotes information being collected from the expert on search terms, search results they got in regard to their query and other aspects of the search. In this case, the ranking of the search results are the attribute and the update is the expert’s feedback on refining the search results. This update is allowed for experts, when combined with the Kaplan reference, this denotes making sure of the user’s expertise before allowing their actions to be stored and used by others as an expert).
However, Jain fails to explicitly disclose, “wherein the user profile data is stored independent of the knowledge base”, “computing …a likelihood that the updated value is accurate, the user reliability model configured to consider users who access more subsystems as more reliable than users who access fewer subsystems…”, and “…in response to determining that the likelihood satisfies a threshold”
Skillen discloses, “wherein the user profile data is stored independent of the knowledge base” (C5, particularly L39-57; EN: this denotes a separate use profile database to store user profiles in relation to a search engine).
Kaplan discloses, “computing …a likelihood that the updated value is accurate” and “…in response to determining that the likelihood satisfies a threshold” (pg.2, particularly paragraph 0023-0024; EN: this denotes monitoring the user’s previous estimations in order to give him a reputation value and using thresholds to determine when the expert is adequate in the subject matter).
Levin discloses, “, the user reliability model configured to consider users who access more subsystems as more reliable than users who access fewer subsystems” (pg.12, particularly paragraph 0151-0156; EN: this denotes experts having multiple expertises, and their reliability for a topic being based upon having more matching expertises than other experts in order to be higher ranked. When combined with Jain, this denotes ranking experts with more relevant topic expertise (i.e. subsystems) to the search at hand higher than those with less relevant topic expertises).
Jain and Skillen are analogous art because both involve search engines.
Before the effective filing date it would have been obvious to one skilled in the art of search engines to combine the work of Jain and Skillen in order to allow user profiles to be stored in a separate database.
The motivation for doing so would be use the database to include “ a comprehensive user profile database … about the end users preferences and previous search arguments which may be used to augment the individual search augment received with the search request” (Skillen, C5, L39-57) or in the case of Jain, allow the expert profiles to be stored in a separate place to keep track of the feedback and other data related to the profiles and their searches.
Therefore before the effective filing date it would have been obvious to one skilled in the art of search engines to combine the work of Jain and Skillen in order to allow user profiles to be stored in a separate database.
Jain and Kaplan are analogous art because both involve user reputation.
At the time of invention it would have been obvious to one skilled in the art of user reputation to combine the work of Jain and Kaplan in order to determine the reliability of a user/expert.
The motivation for doing so would be to allow searchers to be “automatically filtered based on Joe’s objective reputation, a reputation that can be specific to each message board” (Kaplan, Pg.2, paragraph 0024) or in the case of Jain, allow the system to automatically select experts based on reputations related to particular topics.
Therefore at the time of invention it would have been obvious to one skilled in the art of user reputation to combine the work of Jain and Kaplan in order to determine the reliability of a user/expert.
Jain and Levin are analogous art because both involve expert identification.
Before the effective filing date it would have been obvious to one skilled in the art of expert identification to combine the work of Jain and Levin in order to include relevant expertise when identifying the reliability of an expert.
The motivation for doing so would be to identify “the highest priority match is listed first and the remainder of the matches is listened in an order consistent with their decreasing priority” (Levin, Pg.12, paragraph 0156) or in the case of Jain, allow the system to make sure that an Expert has sufficient matching expertise to the topic at hand in order to select them as an expert in the particular search subject being considered.
Therefore before the effective filing date it would have been obvious to one skilled in the art of expert identification to combine the work of Jain and Levin in order to include relevant expertise when identifying the reliability of an expert.
As per claim 15, Jain discloses, “receiving the updated value in the knowledge base by a web search engine of the search system” (Pg.1, particularly paragraph 0004; EN: this denotes the search engine being used for web sites).
As per claim 16, Jain discloses, “wherein receiving the updated value further comprises receiving the updated value through a knowledge panel user interface provided by the web search engine in response to the user submitting a search query” (Pg.1, particularly paragraph 0014. Pg.2, paragraph 0017; EN: this denotes information being collected from the expert on search terms, search results they got in regard to their query and other aspects of the search. This is all done through the search engine system).
As per claim 17, Jain discloses, “Wherein the knowledge panel user interface presents one or more items of information about the entity in the knowledge base” (Pg.1, particularly paragraph 0014. Pg.2, paragraph 0017; EN: this denotes information being collected from the expert on search terms, search results they got in regard to their query and other aspects of the search. Search results and other data are represented about the associated topic from the system).
As per claims 21 and 23, Jain discloses, “wherein the plurality of entities represent persistent unique identifiers” (Pg.1, particularly paragraph 0013; EN: this denotes the experts (i.e. entities) being people, who have their own accounts and are specific individuals, which will have unique identifiers to label them as a particular expert based upon their personal and unique actions/experiences).
Claim Rejections - 35 USC § 103
Claims 5-6 and 12-13 are rejected under 35 U.S.C. 103 as being unpatentable over Jain et al (US 20090055384 A1) in view of Skillen (US 6098065 A) and Kaplan et al (US 20070078675 A1), Levin (US 20120095977 A1) and further in view of Chen et al (US 20090164929 A1).
As per claims 5 and 12, Jain fails to explicitly disclose, “wherein the other subsystems include an image search system, a map system, an email system, a social network system, a blogging system or a shopping system.”
Chen discloses, “wherein the other subsystems include an image search system, a map system, an email system, a social network system, a blogging system or a shopping system” (Pg.6, particularly paragraph 0075; EN: this denotes using social media connections to increase reputation for recommendations on a topic Pg.3-4, particularly paragraphs 0046-0048; EN: this denotes topics such as restaurants).
Jain and Chen are analogous art because both involve search engines.
Before the effective filing date it would have been obvious to one skilled in the art of search engines to combine the work of Jain and Chen in order to use social media to affect reputation on a topic.
The motivation for doing so would be to “include an incentive/social-reputation engine to acknowledger users contributions to their friends (e.g., thanks Bob for suggesting this site!”)” (Chan, Pg.6, paragraph 0075) or in the case of Jain, allow the system to gather reputation from a social media site in order to confirm expertise/reputation on a topic.
Therefore before the effective filing date it would have been obvious to one skilled in the art of search engines to combine the work of Jain and Chen in order to use social media to affect reputation on a topic.
As per claims 6 and 13, Jain fails to explicitly disclose, “wherein the other subsystems of the system are independent of the knowledge base and any search engine function.”
However, Chen discloses, “wherein the other subsystems of the system are independent of the knowledge base and any search engine function” (Pg.6, particularly paragraph 0075; EN: this denotes using social media connections to increase reputation for recommendations on a topic with no shown relationship to a knowledge base or search engine functions).
Jain and Chen are analogous art because both involve search engines.
Before the effective filing date it would have been obvious to one skilled in the art of search engines to combine the work of Jain and Chen in order to use social media to affect reputation on a topic.
The motivation for doing so would be to “include an incentive/social-reputation engine to acknowledger users contributions to their friends (e.g., thanks Bob for suggesting this site!”)” (Chan, Pg.6, paragraph 0075) or in the case of Jain, allow the system to gather reputation from a social media site in order to confirm expertise/reputation on a topic.
Therefore before the effective filing date it would have been obvious to one skilled in the art of search engines to combine the work of Jain and Chen in order to use social media to affect reputation on a topic.
Claim Rejections - 35 USC § 103
Claims 18 are rejected under 35 U.S.C. 103 as being unpatentable over Jain et al (US 20090055384 A1) in view of Skillen (US 6098065 A), Kaplan et al (US 20070078675 A1), Levin (US 20120095977 A1), and Chen et al (US 20090164929 A1).
As per claim 18, Jain discloses, “One or more non-transitory computer storage media encoded with computer program instructions” (pg.2, particularly paragraph 0020; EN: this denotes the use of memory). “That when executed by one or more computers” (pg.1, particularly paragraph 0010; EN: this denotes the system running on a computer system). “of a search system” (Pg.1, particularly paragraph 0005; EN: this denotes the system being used for search). “cause the one or more computers to perform operations comprising” (pg.1, particularly paragraph 0010; EN: this denotes the system running on a computer system).
“maintaining, by search the system, a knowledge base accessible by multiple users” (Pg.1, particularly paragraph 0011; EN: this denotes the system being accessible by multiple users). “wherein the knowledge base comprises information about entities, the information about each entity of the entities being represented as one or more attribute-value pairs” (Pg.2, particularly paragraphs 0013-0014; EN: this denotes experts in particular topics (attributes) with the associated search terms (values)).
“receiving, by the search system, from a user having user profile data stored in the search system, a value of an attribute of an entity in the knowledge base” (pg.1, particularly paragraph 0013-0014; EN: this denotes experts submitting search queries and getting search results and the system keeping track of feedback on the expert and their previous searches).
“obtaining the user profile data for the user” (Pg.2, particularly paragraphs 0013-0014; EN: this denotes experts in particular topics (attributes) with the associated search terms (values)). “The user profile data including information representing other subsystems of the search system accessed by the user” (Pg.1, particularly paragraph 0015-0016; EN: this denotes considering the Experts actions such as topics of expertise, with the search terms and search results of each particular topic denoting different subsystems of the search system).
“Computing, using a user reliability model” (Pg.1, particularly paragraph 0013; EN: this denotes the system making use of the feedback/reputation information for the experts based on feedback in order to make sure the experts responses are useful/accurate). “with the information representing the other subsystems as input” (Pg.1, particularly paragraph 0013; EN: this denotes the system making use of the feedback/reputation information for the experts based on feedback in order to make sure the experts responses are useful/accurate).
… that the value is accurate …” (Pg.1, particularly paragraph 0013; EN: this denotes the system making use of the feedback/reputation information for the experts based on feedback in order to make sure the experts responses are useful/accurate).
“Updating the knowledge base with the updated value received from the user…” (Pg.1, particularly paragraph 0014. Pg.2, paragraph 0017; EN: this denotes information being collected from the expert on search terms, search results they got in regard to their query and other aspects of the search. In this case, the ranking of the search results are the attribute and the update is the expert’s feedback on refining the search results. This update is allowed for experts, when combined with the Kaplan reference, this denotes making sure of the user’s expertise before allowing their actions to be stored and used by others as an expert).
However, Jain fails to explicitly disclose, “wherein the user profile data is stored independent of the knowledge base”, “Wherein the other subsystems include an image search system, a map system, an email system, a social network system, a blogging system, or a shopping system” , “computing… a likelihood that the value is accurate, the user reliability model configured to consider users who access more subsystems to be more reliable than users who access fewer subsystems”, and “…in response to determining that the likelihood satisfies a threshold”
Skillen discloses, “wherein the user profile data is stored independent of the knowledge base” (C5, particularly L39-57; EN: this denotes a separate use profile database to store user profiles in relation to a search engine).
Chen discloses, “Wherein the other subsystems include an image search system, a map system, an email system, a social network system, a blogging system, or a shopping system” (Pg.6, particularly paragraph 0075; EN: this denotes using social media connections to increase reputation for recommendations on a topic Pg.3-4, particularly paragraphs 0046-0048; EN: this denotes topics such as restaurants).
Kaplan discloses, “computing… a likelihood that the value is accurate”, and “…in response to determining that the likelihood satisfies a threshold” (pg.2, particularly paragraph 0023-0024; EN: this denotes monitoring the user’s previous estimations in order to give him a reputation value and using thresholds to determine when the expert is adequate in the subject matter).
Levin discloses, “computing… a likelihood that the value is accurate, the user reliability model configured to consider users who access more subsystems to be more reliable than users who access fewer subsystems” (pg.12, particularly paragraph 0151-0156; EN: this denotes experts having multiple expertises, and their reliability for a topic being based upon having more matching expertises than other experts in order to be higher ranked. When combined with Jain, this denotes ranking experts with more relevant topic expertise (i.e. subsystems) to the search at hand higher than those with less relevant topic expertises).
Jain and Skillen are analogous art because both involve search engines.
Before the effective filing date it would have been obvious to one skilled in the art of search engines to combine the work of Jain and Skillen in order to allow user profiles to be stored in a separate database.
The motivation for doing so would be use the database to include “ a comprehensive user profile database … about the end users preferences and previous search arguments which may be used to augment the individual search augment received with the search request” (Skillen, C5, L39-57) or in the case of Jain, allow the expert profiles to be stored in a separate place to keep track of the feedback and other data related to the profiles and their searches.
Therefore before the effective filing date it would have been obvious to one skilled in the art of search engines to combine the work of Jain and Skillen in order to allow user profiles to be stored in a separate database.
Jain and Chen are analogous art because both involve search engines.
Before the effective filing date it would have been obvious to one skilled in the art of search engines to combine the work of Jain and Chen in order to use social media to affect reputation on a topic.
The motivation for doing so would be to “include an incentive/social-reputation engine to acknowledger users contributions to their friends (e.g., thanks Bob for suggesting this site!”)” (Chan, Pg.6, paragraph 0075) or in the case of Jain, allow the system to gather reputation from a social media site in order to confirm expertise/reputation on a topic.
Therefore before the effective filing date it would have been obvious to one skilled in the art of search engines to combine the work of Jain and Chen in order to use social media to affect reputation on a topic.
Jain and Kaplan are analogous art because both involve user reputation.
At the time of invention it would have been obvious to one skilled in the art of user reputation to combine the work of Jain and Kaplan in order to determine the reliability of a user/expert.
The motivation for doing so would be to allow searchers to be “automatically filtered based on Joe’s objective reputation, a reputation that can be specific to each message board” (Kaplan, Pg.2, paragraph 0024) or in the case of Jain, allow the system to automatically select experts based on reputations related to particular topics.
Therefore at the time of invention it would have been obvious to one skilled in the art of user reputation to combine the work of Jain and Kaplan in order to determine the reliability of a user/expert.
Jain and Levin are analogous art because both involve expert identification.
Before the effective filing date it would have been obvious to one skilled in the art of expert identification to combine the work of Jain and Levin in order to include relevant expertise when identifying the reliability of an expert.
The motivation for doing so would be to identify “the highest priority match is listed first and the remainder of the matches is listened in an order consistent with their decreasing priority” (Levin, Pg.12, paragraph 0156) or in the case of Jain, allow the system to make sure that an Expert has sufficient matching expertise to the topic at hand in order to select them as an expert in the particular search subject being considered.
Therefore before the effective filing date it would have been obvious to one skilled in the art of expert identification to combine the work of Jain and Levin in order to include relevant expertise when identifying the reliability of an expert.
As per claim 25, Jain discloses, “wherein the entities represent persistent unique identifiers” (Pg.1, particularly paragraph 0013; EN: this denotes the experts (i.e. entities) being people, who have their own accounts and are specific individuals, which will have unique identifiers to label them as a particular expert based upon their personal and unique actions/experiences).
Claim Rejections - 35 USC § 103
Claims 22 and 24 are rejected under 35 U.S.C. 103 as being unpatentable over Jain et al (US 20090055384 A1) in view of Skillen (US 6098065 A), Kaplan et al (US 20070078675 A1) and Levin (US 20120095977 A1) and further in view of Bertino et al (“The Challenge of Assuring Data Trustworthiness”).
As per claims 22 and 24, Jain discloses, “determining that the attribute is a new attribute for the entity” (Pg.1, particularly paragraph 0013; EN: this denotes experts being created based on feedback. Searches for new topics would be new and would not have associated feedback for that topic yet).
However, Jain fails to explicitly disclose, “in response to determining that the attribute is a new attribute, set the threshold to a new attribute threshold that is higher than an update attribute threshold.”
Bertino discloses, “in response to determining that the attribute is a new attribute, set the threshold to a new attribute threshold that is higher than an update attribute threshold” (Pg.26, particularly “A Data Provenance Trust Model” section; EN: this denotes that data that has been seen multiple times and from multiple sources is considered more reliable than data that has only one source (i.e. is new)).
Jain and Bertino are analogous art because both involve data reliability.
Before the effective filing date it would have been obvious to one skilled in the art of data reliability to combine the work of Jain and Bertino in order to have lower confidence in reliable data for new data than data that has been seen before.
The motivation for doing so would be because “if several independent sources provide the same data, such data is most likely to be true” (Bertino, Pg.26, “A Data Provenance Trust model” section) or in the case of Jain, allow the system to give more reputation to someone with repeated performance in a particular topic/attribute higher reliability than a new topic.
Therefore before the effective filing date it would have been obvious to one skilled in the art of data reliability to combine the work of Jain and Bertino in order to have lower confidence in reliable data for new data than data that has been seen before.
Claim Rejections - 35 USC § 103
Claim 26 is rejected under 35 U.S.C. 103 as being unpatentable over Jain et al (US 20090055384 A1) in view of Skillen (US 6098065 A), Kaplan et al (US 20070078675 A1), Levin (US 20120095977 A1), and Chen et al (US 20090164929 A1) and further in view of Bertino et al (“The Challenge of Assuring Data Trustworthiness”).
As per claims 26, Jain discloses, “determine that the attribute is a new attribute for the entity” (Pg.1, particularly paragraph 0013; EN: this denotes experts being created based on feedback. Searches for new topics would be new and would not have associated feedback for that topic yet).
However, Jain fails to explicitly disclose, “in response to determining that the attribute is a new attribute, set the threshold to a new attribute threshold that is higher than an update attribute threshold.”
Bertino discloses, “in response to determining that the attribute is a new attribute, set the threshold to a new attribute threshold that is higher than an update attribute threshold” (Pg.26, particularly “A Data Provenance Trust Model” section; EN: this denotes that data that has been seen multiple times and from multiple sources is considered more reliable than data that has only one source (i.e. is new)).
Jain and Bertino are analogous art because both involve data reliability.
Before the effective filing date it would have been obvious to one skilled in the art of data reliability to combine the work of Jain and Bertino in order to have lower confidence in reliable data for new data than data that has been seen before.
The motivation for doing so would be because “if several independent sources provide the same data, such data is most likely to be true” (Bertino, Pg.26, “A Data Provenance Trust model” section) or in the case of Jain, allow the system to give more reputation to someone with repeated performance in a particular topic/attribute higher reliability than a new topic.
Therefore before the effective filing date it would have been obvious to one skilled in the art of data reliability to combine the work of Jain and Bertino in order to have lower confidence in reliable data for new data than data that has been seen before.
Response to Arguments
In pg.10, Applicant argues in regards to the rejection under U.S.C. 103 of the independent claims,
Combining Jain's description of a user conducting a search query who may receive results "based at least in part upon previous actions taken by the expert relevant to the search query" and rank" the quality of [the expert's] previous searches" with Kaplan's description of "a poster or contributor" that can gain a "strong reputation" on the IBM stock message board because he "correctly predicted the movement of IBM stock 80% of the time" on the IBM stock message board and a "relatively poor" reputation on a Wal-Mart stock message board because he, "only correctly predicted the movement of Wal-Mart stock 50% of the time" cannot be combined to disclose "computing a likelihood that the value is accurate using the information representing the other subsystems," however, as recited by amended claim 1. For at least these reasons, amended claim 1 is allowable over the references.
In response, the Examiner maintains the rejection as shown above. Applicants arguments are conclusory. Applicant merely copies and pastes various portions of the references then says they cannot be combined, without giving any actual arguments or explanations as to why they cannot be. Therefore the rejection is maintained as shown above.
In pg.11, Applicant further argues in regards to the rejection of the independent claims under U.S.C. 103,
It is unclear how Jain can disclose that a value is accurate if it also "fails to explicitly disclose 'computing a likelihood that the value is accurate. "During patent examination and reexamination, the concept of prima facie obviousness establishes the framework for the obviousness determination and the burdens the parties face. Under this framework, the patent examiner must first set forth a prima facie case, supported by evidence, showing why the claims at issue would have been obvious in light of the prior art." M.P.E.P. § 2142 (emphasis added). In this case, the Office has not met its burden, as the Office has not shown how Jain can fail to disclose "computing a likelihood that the value is accurate" but then disclose a particular way of determining that the value is accurate. Such a conflict is not supported by evidence. As such, claim 2, and therefore currently amended claim 1, is also allowable over the references.
In response, the Examiner maintains the rejection as shown above. It appears Applicant is arguing that any system which can determine whether data is accurate would be able to determine a likelihood that the data is accurate. However, these two things are not the same thing. The Jain reference labels people as experts based upon feedback and their previous search queries. The system therefore labels their actions as “accurate” based on this feedback/systems. However, the Jain reference fails to explicitly disclose any sort of “likelihood” or other probability that the data will be accurate. The Jain reference merely states that it is or it is not, there is no likelihood involved. Thus, the Kaplan reference was brought in to show that one of ordinary skill in the art at the time of inception would have known that, based on reputation, probabilities or weights could be given to determine the likelihood that data coming from a source is accurate. Since the combined references meet the claimed limitation, the rejection is maintained as shown above.
In pg.12, Applicant further argues in regards to the rejection of the independent claims under U.S.C. 103,
First, the expertise clouds described by Levin are not the same as the subsystems described by the claims. Paragraph [0015] of Levin describes that, "the expertise clouds may be stored in a database" and paragraph [0012] explains that, "An expertise cloud may be generated for each expert of the plurality of experts using the received information" and includes "the expert's name, geographic location and contact information" and "one or more areas of expertise." The expertise clouds described are representations relating to one or more experts in a database, and may easily be distinguished from the subsystems of claim 1. As described in paragraph [0036] of the present application, subsystems of the search system may include, for example, "a web search system, an image search system, a map system, an email system, a social network system, a blogging system, a shopping system, just to name a few, topics of interest, and an indication of a level of expertise of the user for each of the topics of interest, e.g., novice or expert." The Office has provided no evidence that those of ordinary skill in the art would consider subsystems similar to those described by paragraph 36 as data stored in a database. Thus, the Office has failed to meet its evidentiary burden.
In response, the Examiner maintains the rejection as shown above. First, the discussion of the various subsystems are not included in independent claims 1 or 8, they are included in respective dependent claims 6 and 13. Claim 18 however, does include these limitations. Further, it is important to note that the claim does not require ALL of these subsystems, only a single one out of the set. The Levin reference is used to show that having expertise in multiple topics (i.e., multiple subsystems of an expertise based system) can lead to greater reliability on a particular topic. (See Levin, Pg.12, paragraph 0156). When combined with the Chen reference, which denotes the use of social media connections to increase reputation for recommendations on a topic (See Chen, paragraph 0075), as well as topics such as restaurants (shopping, see Chen, paragraphs 0046-0048) both of these are topics (i.e. subsystems) which could be accessed to provide further improvement to the Experts reputation when it comes to searching these topics in the Jain reference. Therefore the rejection is maintained as shown above.
In pg. 12, Applicant further argues in regards to the rejection of the independent claims under U.S.C. 103,
Second, Levin fails to describe rating users who access more subsystems as more reliable than those who access fewer subsystems. Applicant disagrees that the expertise clouds of Levin are equivalent to the subsystems of claim 1. In addition, nowhere does Levin describe considering users who access more "expert clouds" (which the Office alleges to constitute subsystems) to be more reliable than users who access fewer "expert clouds", as recited by amended claim 1. Levin discloses matching expertise clouds to question clouds. The Office does not explain how matching question clouds to expertise clouds relates to users. Because question clouds are not users, the rejection lacks articulated reasoning and the Office has failed to make a prima facie case of obviousness.
In response, the Examiner maintains the rejection as shown above. Applicants appear to be arguing that users are not rated based upon accessing the different subsystems. However, the combined references teach these limitations. First, the primary reference, Jain, discloses that the Experts are experts in particular topics, the experts are the users. The Levin reference is brought in to show that having multiple matching expertises for a subject will have higher credibility/ratings than those with less matching expertises. The various topics of expertise are the subsystems in the broadest reasonable interpretation of the claim. By searching/accessing/being involved in their various expertises, these subsystems are “accessed” by the experts via their search results and other interactions with the system that allow them to receive the feedback and reputation their expertise deserves. Since the Jain reference shows the Experts continually accessing their areas of expertise in order to receive feedback, and the Levin reference shows that having multiple matching expertise have higher credibility than those with less (i.e. accessing more subsystems), this meets the broadest reasonable interpretation, and therefore the rejection is maintained as shown above.
In pg.13, Applicant argues in regards to the rejection under U.S.C. 101 of the independent claims,
The claims, as presently amended, address the technical problem of data integrity in a large-scale knowledge base where data is updated by a high volume of decentralized users, some of whom have little or no known relationship to the system. Maintaining data accuracy when so many users are interacting with the data presents a significant technical challenge because users can intentionally or unintentionally enter incorrect data. See, e.g., Specification, paragraph [0021]. Data accuracy is a technical field, recognized by the ISO. See, e.g., ISO 8000 on data quality, https://www.iso.org/obp/ui/#iso:std:iso:8000:-1:ed-1:vl:en A technical solution recited by the independent claims improves data quality by "automatically determine[ing] whether a submission from a user is likely to be accurate," thus "reducing the amount of erroneous or spam inputs to the knowledge base." Specification, paragraph [0012]. Applicant respectfully submits that the specification thus reflects an improvement to the technical field of data quality.
In response, the Examiner maintains the rejection as shown above. Determining whether Data is accurate or not is a mental process, and not a technology. A person could, mentally or with pencil and paper, look at submissions and consider the source to determine whether an update or addition to a knowledge base should be included. Merely including generic computer equipment and sending/receiving of data is not enough to cause the claims to be more than the abstract idea, and therefore the rejection is maintained as shown above.
In pg.14, the Applicant argues in regards to the rejection under U.S.C. 101,
Applicant submits that the claims reflect this improvement. In particular, independent claims 1, 8, and 18 use information about a user's access of other subsystems across multiple subsystems as a signal to determine the likelihood that the user has provided an accurate value. The claims recite a method with a multi-signal reliability framework that uses a specialized model that is configured to consider users who access more subsystems to be more reliable than users who access fewer subsystems. By using user profile data from other subsystems outside the knowledge base, the system can compute a reliability likelihood that is intrinsically tied to the user's technical footprint, thereby quantifying user credibility based on their interaction depth across disparate system architectures to protect the data integrity in a knowledge tree with decentralized user access.
In response, the Examiner maintains the rejection as shown above. First, the “Specialized model” is never defined in the claims. Merely stating that a model is used is no different than stating a person considers things mentally or with pencil and paper. Tying data to “subsystems” also fails to define this “technical footprint” in any meaningful way. As stated above, determining whether data is reliable or not is a mental process as discussed above, and attaching it to generic hardware and undefined or generic “subsystems” does not cause it to be significantly more than the abstract idea, and therefore the rejection is maintained as shown above.
In pg.14, the Applicant further argues in regards to the rejection under U.S.C. 101,
Additionally, Applicant notes that MPEP 2106.05(a) was amended to indicate "xiv. Improvements to computer component or system performance based upon adjustments to parameters of a machine learning model associated with tasks or workstreams" as an example showing improvement in computer functionality. Memo, p. 4. Claims 1, 8, and 18 have been amended to recite "the user reliability model configured to weigh users who access more subsystems of the system as more reliable than users who access fewer subsystems of the system." Applicant submits that this weighing by the model implements the improvement to the technology area of data quality, and not a mental step. Accordingly, Applicant submits that the amended claims recite patentable subject matter and requests withdrawal of the Section 101 rejection of the claims.
First, the Claims at no time describe a machine learning model. The claims describe a “user reliability model” but provide no details as to the model beyond that it is used and the data going into it and coming out of it. The closest the claims have to anything relating machine learning models would be claim 4, which denotes “training” the model, but provides no details of the model itself or the training process beyond what data is used for it. Even if these limitations did explicitly disclose a machine learning model, the use of a generic model with no additional limitations beyond that the model is used and the data going into/coming out of the model is not enough to be significantly more than the abstract idea, and therefore the rejection is maintained as shown above.
Applicant's arguments with respect to claims 1, 4-8, 11-18, and 21-26 have been considered but are either moot in view of the new ground(s) of rejection or are repetitions of the above arguments and maintained for similar reasons given above.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to BEN M RIFKIN whose telephone number is (571)272-9768. The examiner can normally be reached Monday-Friday 9 am - 5 pm.
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/BEN M RIFKIN/Primary Examiner, Art Unit 2123