DETAILED ACTION
Claims 1-20 are pending. Claims 1-20 are rejected.
The instant application has PRO 63/707,092 filed on 10/14/2024.
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 .
Information Disclosure Statement
The information disclosure statement (IDS) was submitted on 08/04/2026. The submission is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Claim Objections
Claim 18 is objected to because of the following informalities: There is a misplaced “T” at the start of the claim. Appropriate correction is required.
Claim 20 is objected to because of the following informalities: the claim is dependent upon claim 10 but refers to the system, the Examiner will treat this claim as depending from the system of claim 19. Appropriate correction is required.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
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 6, 8, 15, and 17 are rejected under 35 U.S.C. 112(b) or 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.
Claims 6, 8, 15, and 17 recite the limitation "the first machine learning model". There is insufficient antecedent basis for this limitation in the claim. Claims 6 and 8 depend on claim 4 while claims 15 and 17 depend on claim 13. None of these claims contain “a first machine learning model”. The first machine learning model appears in claims 2 and 11. It is unclear if claims 4 and 13 should depend on these other claims, if this should be the second machine learning model, or if this is a third machine learning model. The Examiner will treat this first machine learning model as a separate one from the second machine learning model.
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 (All Claims)
According to the first part of the analysis, in the instant case, claims 1-9 are directed to a method, claims 10-18 are directed to a computer-readable non-transitory recording medium, and claims 19-20 are directed to a system comprising at least one processor and memory. Thus, each of the claims falls within one of the four statutory categories (i.e. process, machine, manufacture, or composition of matter).
Step 2A, Prong 1 (Claims 1, 10, and 19)
Regarding claim 1, the following limitations are abstract ideas:
A method for editing description of a place of interest by crowdsourcing, comprising: is a step that can be performed as a mental process, with the aid of pen and paper.
accepting, based on verification of authenticity of the piece of proposal, the piece of proposal; is a step that can be performed as a mental process, with the aid of pen and paper.
updating the description of the place of interest according to the accepted piece of proposal. is a step that can be performed as a mental process, with the aid of pen and paper.
Regarding claim 19, the following limitations are abstract ideas:
to automatically generate a proposed place edit to at least a part of a description of a place of interest; is a step that can be performed as a mental process, with the aid of pen and paper.
wherein the voting agent automatically marks acceptance of the proposed place edit created by the instantiated reporting agent. is a step that can be performed as a mental process, with the aid of pen and paper.
The above analysis for claim 1 applies to claim 10 as they contain similar limitations.
Step 2A, Prong 2 (Claims 1, 10, and 19)
Regarding claim 1, the following limitations are additional elements:
receiving a piece of proposal for editing description of the place of interest; is directed to the insignificant extra-solution activity of mere data gathering and/or selecting a particular data source or type of data to be manipulated as identified in MPEP 2106.05(g).
Regarding claim 10, the following limitations are additional elements:
A computer-readable non-transitory recording medium storing a computer- executable program instructions that when executed by a processor cause a computer system to execute operations comprising: is a high-level recitation of a generic computer component and represents mere instructions to apply on a computer as in MPEP 2106.05(f), which does not provide integration into a practical application.
Regarding claim 19, the following limitations are additional elements:
at least one processor; is a high-level recitation of a generic computer component and represents mere instructions to apply on a computer as in MPEP 2106.05(f), which does not provide integration into a practical application.
memory encoding computer executable instructions that, when executed by the at least one processor, perform operations comprising: is a high-level recitation of a generic computer component and represents mere instructions to apply on a computer as in MPEP 2106.05(f), which does not provide integration into a practical application.
instantiating a reporting agent is a high-level recitation of a generic computer component and represents mere instructions to apply on a computer as in MPEP 2106.05(f), which does not provide integration into a practical application.
instantiating a voting agent, is a high-level recitation of a generic computer component and represents mere instructions to apply on a computer as in MPEP 2106.05(f), which does not provide integration into a practical application.
The above analysis for claim 1 applies to claim 10 as they contain similar limitations.
Step 2B (Claims 1, 10, and 19)
Regarding claim 1, the following limitations are additional elements:
receiving a piece of proposal for editing description of the place of interest; when re-evaluated under step 2B is further directed to the well-understood, routine, and conventional activity of receiving or transmitting data as identified in MPEP 2106.05(d)II “i. Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016) (using a telephone for image transmission); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network); but see DDR Holdings, LLC v. Hotels.com, L.P., 773 F.3d 1245, 1258, 113 USPQ2d 1097, 1106 (Fed. Cir. 2014) ("Unlike the claims in Ultramercial, the claims at issue here specify how interactions with the Internet are manipulated to yield a desired result‐‐a result that overrides the routine and conventional sequence of events ordinarily triggered by the click of a hyperlink." (emphasis added));”
Regarding claim 10, the following limitations are additional elements:
A computer-readable non-transitory recording medium storing a computer- executable program instructions that when executed by a processor cause a computer system to execute operations comprising: ((i.e., generic computer components performing generic computer functions) such that they amount to no more than components comprising mere instructions to apply the exception. Accordingly, these additional elements do not integrate the abstract idea(s) into a practical application because they do not impose any meaningful limits on practicing the abstract idea(s))
Regarding claim 19, the following limitations are additional elements:
at least one processor; ((i.e., generic computer components performing generic computer functions) such that they amount to no more than components comprising mere instructions to apply the exception. Accordingly, these additional elements do not integrate the abstract idea(s) into a practical application because they do not impose any meaningful limits on practicing the abstract idea(s))
memory encoding computer executable instructions that, when executed by the at least one processor, perform operations comprising: ((i.e., generic computer components performing generic computer functions) such that they amount to no more than components comprising mere instructions to apply the exception. Accordingly, these additional elements do not integrate the abstract idea(s) into a practical application because they do not impose any meaningful limits on practicing the abstract idea(s))
instantiating a reporting agent is a high-level recitation of a generic computer component and represents mere instructions to apply on a computer as in MPEP 2106.05(f), which does not provide integration into a practical application.
instantiating a voting agent, is a high-level recitation of a generic computer component and represents mere instructions to apply on a computer as in MPEP 2106.05(f), which does not provide integration into a practical application.
The above analysis for claim 1 applies to claim 10 as they contain similar limitations.
The dependent claims are directed to the same abstract ideas as their parent claims. The dependent claims add further limitations of accepting the proposal or adding machine learning models and agents that perform certain functions. Accepting the proposal is a further mental process similar to the above identified mental processes. The machine learning models and agents are similar to the above identified additional elements identified for claim 19 and are merely performing further additional elements or mental processes. Therefore, the claims are still rejected under 35 U.S.C. 101.
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.
Claim(s) 1, 3, 10, and 12 is/are rejected under 35 U.S.C. 103 as being unpatentable over Appadurai et al., Patent Application Publication No. 2021/0398144 (hereinafter Appadurai) in view of Gu et al., Patent Application Publication No. 2013/0332273 (hereinafter Gu).
Regarding claim 1, Appadurai teaches:
A method for editing description of a place of interest by crowdsourcing (Appadurai Paragraph [0089], one aspect of the present technology is the gathering and use of data available from various sources to provide crowd-sourced traffic information, point of interest ratings), comprising:
accepting, based on verification of authenticity of the piece of proposal, the piece of proposal (Appadurai Paragraph [0069], whether a POI is still operating or not, the POI hours of operations, or other data related to the POI, if verifiable. When the result of the user votes can be confirmed based on verification data from the verification source, the user credibility scores for each voting user can be updated to reflect which users provided reliable user submissions); and
Appadurai does not expressly disclose:
receiving a piece of proposal for editing description of the place of interest;
updating the description of the place of interest according to the accepted piece of proposal.
However, Gu teaches:
receiving a piece of proposal for editing description of the place of interest (Gu Paragraph [0044], The user 505 may then optionally edit the name and point of interest (POI) description of the location (block 320, step 555). Once complete, the user 505 selects save 450 (step 560), and the new location is saved (block 325));
updating the description of the place of interest according to the accepted piece of proposal (Gu Paragraph [0044], The user 505 may then optionally edit the name and point of interest (POI) description of the location (block 320, step 555). Once complete, the user 505 selects save 450 (step 560), and the new location is saved (block 325)).
The claimed invention and Gu are from the analogous art of point of interest systems. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention having the teachings of Appadurai in view of Gu to have combined Appadurai in view of Gu. Gu teaches improvements directed to receiving an indication of a desired product from a portable device, determining a location of the product, and calculating and providing a route (Paragraph 9).
Regarding claim 3, Appadurai in view of Gu further teaches:
The method according to claim 1, wherein the accepting the piece of proposal further comprises interactively accepting the piece of proposal through an interactive operations by a superuser with an elevated privilege to vote on the piece of proposal for acceptance (Appadurai Paragraph [0032], The credibility score can, for example, range from zero (e.g., no credibility) to 1 (e.g., highly credible). New users may start at a credibility score of one half (e.g., 0.5) indicating that the credibility of the user is unknown).
Claims 10 and 12 are rejected in the same manner as claims 1 and 3 but are merely directed to a different statutory category (method and computer-readable non-transitory recording medium). Appadurai further teaches one or more data processors and a memory interface (Paragraph 95).
Claim(s) 2, 4-9, 11, and 13-18 is/are rejected under 35 U.S.C. 103 as being unpatentable over Appadurai in view of Gu and Najmabadi et al., Patent Application Publication No. 2026/0023795 (hereinafter Najmabadi).
Regarding claim 2, Appadurai in view of Gu teaches parent claim 1.
Appadurai in view of Gu does not expressly disclose:
wherein the received piece of proposal is automatically created by a reporting agent by executing a first machine learning model, and the first machine learning model retrieves information about the place of interest over a network and creates the piece of proposal.
However, Najmabadi teaches:
wherein the received piece of proposal is automatically created by a reporting agent by executing a first machine learning model (Najmabadi Paragraph [0057], the AI agents 205A, 205B, and 205C calibrate the confidence scores using a variety of other machine learning techniques), and the first machine learning model retrieves information about the place of interest over a network and creates the piece of proposal (Najmabadi Paragraph [0037], the machine learning model 135 may use various training inputs (e.g., images, captions, metadata, traveler data, property data, etc.) to determine and, particularly, predict various outputs (e.g., travel recommendations, search suggestions, etc.) (Najmabadi teaches travel recommendations which is considered a place of interest)).
The claimed invention and Najmabadi are from the analogous art of machine learning systems. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention having the teachings of Appadurai in view of Gu and Najmabadi to have combined Appadurai in view of Gu and Najmabadi. Najmabadi teaches technical improvements to conventional travel data content acquisition system including acquiring and accessing travel more quickly and accurately such that high confidence travel can be integrated into output in a more efficient manner (Paragraph 16).
Regarding claim 4, Appadurai in view of Gu teaches parent claim 1.
Appadurai in view of Gu does not expressly disclose:
automatically accepting, by a voting agent through executing a second machine learning model, the piece of proposal, wherein the voting agent comprises a website search agent and a verification agent, wherein the website search agent automatically performs searching and identifying, based on the piece of proposal, a website to retrieve content, and wherein the verification agent performs, based on the retrieved content from the website, automatic verification of the piece of proposal as ground truth data.
However, Najmabadi teaches:
automatically accepting, by a voting agent through executing a second machine learning model (Najmabadi Paragraph [0057], the AI agents 205A, 205B, and 205C calibrate the confidence scores using a variety of other machine learning techniques, Paragraph [0078], master agent 216 may employ majority voting across the outputs of different source/model pairs to determine final output classes), the piece of proposal, wherein the voting agent comprises a website search agent and a verification agent (Najmabadi Paragraph [0062], the agents may also be utilized to validate the content that is surfaced in the search as will be explained in more detail below, Paragraph [0056], The confidence score 209 may be updated during validation, in which the output of each agent is compared with a ground truth), wherein the website search agent automatically performs searching and identifying, based on the piece of proposal (Najmabadi Paragraph [0069], e search augmented LLM system 210 utilizes generative AI techniques to develop an output to a travel query based on the travel content gathered from a web search), a website to retrieve content (Najmabadi Paragraph [0069], e search augmented LLM system 210 utilizes generative AI techniques to develop an output to a travel query based on the travel content gathered from a web search), and wherein the verification agent performs (Najmabadi Paragraph [0056], The confidence score 209 may be updated during validation, in which the output of each agent is compared with a ground truth), based on the retrieved content from the website (Najmabadi Paragraph [0069], e search augmented LLM system 210 utilizes generative AI techniques to develop an output to a travel query based on the travel content gathered from a web search), automatic verification of the piece of proposal as ground truth data (Najmabadi Paragraph [0056], The confidence score 209 may be updated during validation, in which the output of each agent is compared with a ground truth (Appadurai and Gu teach the claimed proposals while Najmabadi teaches the machine learning model)).
The claimed invention and Najmabadi are from the analogous art of machine learning systems. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention having the teachings of Appadurai in view of Gu and Najmabadi to have combined Appadurai in view of Gu and Najmabadi. Najmabadi teaches technical improvements to conventional travel data content acquisition system including acquiring and accessing travel more quickly and accurately such that high confidence travel can be integrated into output in a more efficient manner (Paragraph 16).
Regarding claim 5, Appadurai in view of Gu and Najmabadi further teaches:
The method according to claim 4, further comprising: calibrating, based on an automatic execution of the accepting the piece of proposal by the voting agent (Najmabadi Paragraph [0057], In some embodiments, the AI agents 205A, 205B, and 205C are configured to calibrate the confidence scores 209 based on the reliability of the output travel data items 207 and a sufficiency of the travel data items 207 satisfying the model query), one or more parameters of the second machine learning model through an interactive user interface to train the voting agent (Najmabadi Paragraph [0080], the master agent 216 only considers the outputs generated by the source/model pairs that have total scores (e.g., calibrated score multiplied by credibility weight) greater than a certain predetermined threshold. In some embodiments, the predetermined threshold may be determined by using validation data and can be dynamically adjusted based on the continuous feedback received from users over time), thereby enabling the voting agent to accept the piece of proposal with accuracy (Najmabadi Paragraph [0057], In some embodiments, the AI agents 205A, 205B, and 205C are configured to calibrate the confidence scores 209 based on the reliability of the output travel data items 207 and a sufficiency of the travel data items 207 satisfying the model query).
Regarding claim 6, Appadurai in view of Gu and Najmabadi further teaches:
The method according to claim 4, further comprising: automatically creating, the piece of proposal for editing the description of the place of interest (Gu Paragraph [0044], The user 505 may then optionally edit the name and point of interest (POI) description of the location (block 320, step 555). Once complete, the user 505 selects save 450 (step 560), and the new location is saved (block 325));
by a reporting agent through executing the first machine learning model (Najmabadi Paragraph [0055], the AI agents 205A, 205B, and 205C may be a retrieval augmented LLM, a search augmented LLM, or any other type of LLM or other AI agent/model used to process data),
updating, based on a result of validating accuracy of the automatically created piece of proposal (Najmabadi Paragraph [0015], the system may automatically take action on the content based on a confidence in the accuracy of the content, such as by automatically approving the content (e.g., for inclusion in information presented to travelers)), one or more parameters of the first machine learning model (Najmabadi Paragraph [0044], a model updater may configure (e.g., trains, updates, modifies, fine-tunes, etc.) the first machine learning model 135 to determine the one or more second machine learning model 135. In some implementations, the second machine learning model 135 can be used to provide application-specific outputs, such as outputs having greater precision, accuracy), thereby training the first machine learning model to enable the reporting agent to perform creating another piece of proposal for editing the description of the place of interest with accuracy (Najmabadi Paragraph [0044], a model updater may configure (e.g., trains, updates, modifies, fine-tunes, etc.) the first machine learning model 135 to determine the one or more second machine learning model 135. In some implementations, the second machine learning model 135 can be used to provide application-specific outputs, such as outputs having greater precision, accuracy (Appadurai and Gu teaches the proposal while Najmabadi teaches the machine learning models)).
Regarding claim 7, Appadurai in view of Gu and Najmabadi further teaches:
The method according to claim 4, further comprising: updating, based on a result of the automatically accepting the piece of proposal by the voting agent (Najmabadi Paragraph [0078], where a definitive judgment is not possible due to an equal number of votes for two different categories related to the target task), the master agent 216 calculates the average (e.g., calibrated score multiplied by credibility weight) score for the outputs under each category and select the one with the higher average to be the final output), one or more parameters of the second machine learning model to calibrate the voting agent (Najmabadi Paragraph [0078], where a definitive judgment is not possible due to an equal number of votes for two different categories related to the target task), the master agent 216 calculates the average (e.g., calibrated score multiplied by credibility weight) score for the outputs under each category and select the one with the higher average to be the final output), thereby causing the voting agent to perform a subsequent automatic acceptance of another proposal for editing the description of the place of interest with accuracy (Najmabadi Paragraph [0078], where a definitive judgment is not possible due to an equal number of votes for two different categories related to the target task), the master agent 216 calculates the average (e.g., calibrated score multiplied by credibility weight) score for the outputs under each category and select the one with the higher average to be the final output).
Regarding claim 8, Appadurai in view of Gu and Najmabadi further teaches:
The method according to claim 4, wherein the first machine learning model automatically generates (Najmabadi Paragraph [0037], the machine learning model 135 may use various training inputs (e.g., images, captions, metadata, traveler data, property data, etc.) to determine and, particularly, predict various outputs (e.g., travel recommendations, search suggestions, etc.)), based on received information about the place of interest as input (Najmabadi Paragraph [0037], the machine learning model 135 may use various training inputs (e.g., images, captions, metadata, traveler data, property data, etc.) to determine and, particularly, predict various outputs (e.g., travel recommendations, search suggestions, etc.) (Najmabadi teaches travel recommendations which is considered a place of interest)), the piece of proposal for editing the description of the place of interest (Najmabadi Paragraph [0020], The predetermined training data 130 may include one or more of image data, other forms of non-text data, text data (e.g., destination descriptions, travel policies, caption data, travel metadata, chat history data, user account data, etc.), or any other form of data).
Regarding claim 9, Appadurai in view of Gu and Najmabadi further teaches:
The method according to claim 4, wherein the second machine learning model automatically marks, based on verifying content of the website describing the place of interest (Najmabadi Paragraph [0062], the agents may also be utilized to validate the content that is surfaced in the search as will be explained in more detail below, Paragraph [0056], The confidence score 209 may be updated during validation, in which the output of each agent is compared with a ground truth), acceptance of the piece of proposal to edit the description of the place of interest (Najmabadi Paragraph [0078], where a definitive judgment is not possible due to an equal number of votes for two different categories related to the target task), the master agent 216 calculates the average (e.g., calibrated score multiplied by credibility weight) score for the outputs under each category and select the one with the higher average to be the final output).
Claims 11 and 13-18 are rejected in the same manner as claims 2 and 4-9 but are merely directed to a different statutory category (method and computer-readable non-transitory recording medium).
Claim(s) 19-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Appadurai in view of Najmabadi.
Regarding claim 19, Appadurai teaches:
A system comprising: at least one processor (Appadurai Paragraph [0095], The computing device 700 can include a memory interface 702, one or more data processors); and
memory encoding computer executable instructions that, when executed by the at least one processor, perform operations comprising (Appadurai Paragraph [0095], The computing device 700 can include a memory interface 702, one or more data processors):
to automatically generate a proposed place edit to at least a part of a description of a place of interest (Appadurai Paragraph [0089], one aspect of the present technology is the gathering and use of data available from various sources to provide crowd-sourced traffic information, point of interest ratings); and
wherein the voting agent automatically marks acceptance of the proposed place edit created by the instantiated reporting agent (Appadurai Paragraph [0069], whether a POI is still operating or not, the POI hours of operations, or other data related to the POI, if verifiable. When the result of the user votes can be confirmed based on verification data from the verification source, the user credibility scores for each voting user can be updated to reflect which users provided reliable user submissions (Najmabadi teaches the agents)).
Appadurai does not expressly disclose:
instantiating a reporting agent
instantiating a voting agent,
However, Najmabadi teaches:
instantiating a reporting agent (Najmabadi Paragraph [0055], the AI agents 205A, 205B, and 205C may be a retrieval augmented LLM, a search augmented LLM, or any other type of LLM or other AI agent/model used to process data)
instantiating a voting agent (Najmabadi Paragraph [0057], the AI agents 205A, 205B, and 205C calibrate the confidence scores using a variety of other machine learning techniques, Paragraph [0078], master agent 216 may employ majority voting across the outputs of different source/model pairs to determine final output classes),
The claimed invention and Najmabadi are from the analogous art of machine learning systems. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention having the teachings of Appadurai in view of Najmabadi to have combined Appadurai in view of Najmabadi. Najmabadi teaches technical improvements to conventional travel data content acquisition system including acquiring and accessing travel more quickly and accurately such that high confidence travel can be integrated into output in a more efficient manner (Paragraph 16).
Regarding claim 20, Appadurai in view of Najmabadi further teaches:
The system of claim 10, wherein the proposed place edit is generated using a first machine learning model and wherein the acceptance of the proposed edit is marked using a second machine learning model (Najmabadi Paragraph [0078], where a definitive judgment is not possible due to an equal number of votes for two different categories related to the target task), the master agent 216 calculates the average (e.g., calibrated score multiplied by credibility weight) score for the outputs under each category and select the one with the higher average to be the final output, Paragraph [0057], In some embodiments, the AI agents 205A, 205B, and 205C are configured to calibrate the confidence scores 209 based on the reliability of the output travel data items 207 and a sufficiency of the travel data items 207 satisfying the model query).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Rolf et al., Patent Application Publication No. 2020/0386556 (hereinafter Rolf). Rolf relates to systems using crowd-sourced feature and point-of-interest information (Paragraph 3). Rolf further teaches verification of information (Paragraph 41). This shows that the claimed invention and Rolf are analogous art as both systems use crowd-sourced and point-of-interest information.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to DUSTIN D EYERS whose telephone number is (408)918-7562. The examiner can normally be reached Monday-Thursday 9:00am-7:00pm ET.
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/DUSTIN D EYERS/ Examiner, Art Unit 2164
/AMY NG/ Supervisory Patent Examiner, Art Unit 2164
