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 .
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 3-4 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ),
second paragraph.
The term “specific” in claim 3 is a relative term which renders the claim indefinite. The term “specific” is not defined by the claim, the specification does not provide a standard for ascertaining the requisite degree, and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention. Dependent claim 4 inherits the deficiency and are rejected for the same rationale. Appropriate action is required.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-8 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1: The claims recite a device and non-transitory machine-readable medium,
each of which are one of the four categories of eligible subject matter.
Claims 1, 7, and 8
Step 2A Prong 1: The claims recite the following limitations:
generating first reliability information that indicates reliability of the first property information, based on a reliability list in which a generator reliable for the recipient among generators of the first property information is registered (Mental Process).
Under the broadest reasonable interpretation of the claim language, generating reliability information indicating reliability of property information is a mental process because a human mind can practically perform the process with the aid of a pencil, paper, and data. Accordingly, the claims recite an abstract idea.
Step 2A Prong 2: The judicial exceptions are not integrated into a practical application. The claims recite the following additional elements:
wherein the computer includes a trained first machine learning model that receives, as an input, property information which indicates a probability of data and is assigned reliability information which indicates reliability of the property information, and outputs credibility of the data, and the process comprises: acquiring first property information that indicates a probability of predetermined data received by a recipient;… and inputting, to the first machine learning model, the first property information that is assigned the first reliability information, determining credibility of the predetermined data, and presenting a result.
The processors, memory, and non-transitory computer-readable recording medium storing an information processing program are generic computing components recited at a high level as a means to apply the judicial exception, as discussed in MPEP 2106.05(f). Inputting data to a machine learning model to determine credibility and present a result is generally linking the abstract ideas to the technological environment of machine learning, as discussed in MPEP 2106.05(h). Acquiring property information is mere data gathering, which is an insignificant extra-solution activity as discussed in MPEP 2106.05(g). The claims are directed towards an abstract idea.
Step 2B: The claims do not include additional elements that are sufficient to
amount to significantly more than the judicial exception. The processors, memory, and non-transitory computer-readable recording medium storing an information processing program are generic computing components recited at a high level as a means to apply the judicial exception, as discussed in MPEP 2106.05(f). Inputting data to a machine learning model to determine credibility and present a result is generally linking the abstract ideas to the technological environment of machine learning, as discussed in MPEP 2106.05(h). Acquiring property information is mere data gathering, which is an insignificant extra-solution activity as discussed in MPEP 2106.05(g). The claims are not patent eligible.
Dependent Claims:
Claims 3: These claims recite further abstract ideas (mental processes) and thus
are ineligible.
Claims 2-6: These claims recite further mere data gathering and generally linking the abstract ideas to the technological environment of machine learning and as explained above these do not provide a practical application or inventive concept and thus are ineligible.
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 1-8 are rejected under 35 U.S.C. 103 as being unpatentable over Trusted Internet Architecture Lab (TIAL): "Trustable Internet: towards a trustable Internet in which people can use information securely", White paper, October 13, 2022, hereafter TIAL in view of Biswas et al (Pub. No.: US 20230385607 A1), hereafter Biswas.
Regarding claims 1, 7, and 8, TIAL teaches acquiring first property information that indicates a probability of predetermined data received by a recipient (probability of a flood is received by people in a neighborhood through the internet, page 4-5, section 1.2, figure 1); generating first reliability information that indicates reliability of the first property information, based on a reliability list in which a generator reliable for the recipient among generators of the first property information is registered (endorsement layer and endorsement graph improve credibility of data acquired from the internet based on target data linked to endorsement data, with the target data being data sent to the viewers or recipients, page 7, section 2.1, figures 2-3); and inputting, to the … model, the first property information that is assigned the first reliability information, determining credibility of the predetermined data, and presenting a result (data models receive data from a sender before verifying the data should be added to an endorsement graph to be presented to the viewers, pages 9-11, figures 4-6).
TIAL does not appear to explicitly teach a machine learning model.
Biswas teaches a method, non-transitory computer-readable recording medium, method, and apparatus (“Various embodiments of the disclosure may provide a non-transitory computer-readable medium and/or storage medium having stored thereon, computer-executable instructions executable by a machine and/or a computer to operate an electronic device”, P0119. “The present disclosure may also be positioned in a computer program product, which comprises all the features that enable the implementation of the methods described herein, and which when loaded in a computer system is able to carry out these methods.”, P0129)… wherein the computer includes a trained first machine learning model that receives, as an input, property information which indicates a probability of data and is assigned reliability information which indicates reliability of the property information, and outputs credibility of the data (For example, a trained GNN model such as, the GNN model 114 may recognize different nodes in the input graph data, and edges between each node in the input graph data. Graph neural networks analyze input graph data to provide reliable and accurate information associated with the particular node, P0032).
Accordingly, it would have been obvious to a person having ordinary skill in the
art before the effective filing date of the claimed invention, having the teachings of
TIAL and Biswas before them, to include Biswas’s specific teaching of a graph neural network analyzing input graph data in TIAL’s method of Trustable Internet. One
would have been motivated to make such a combination of a graph neural network analyzing input graph data (see Biswas P0032) and using models to generate endorsement data for viewers verifying target data (see TIAL page 11, figure 6) for more accurate user content recommendations (see Biswas P0003).
Regarding claim 2, TIAL in view of Biswas teaches the limitations of claim 1 as outlined above. TIAL further teaches wherein the acquiring of the first property information is performed by acquiring graph information that represents a relationship among the predetermined data, the first property information, and the generator (endorsement graphs outline the relationship between target information, the sender of information, and existing information provided by a local government and/or sensor device, page 10, section 3.2, figure 5). Biswas further teaches the first machine learning model is trained by using, as input data, peculiar graph information obtained by adding the first reliability information to the graph information (during model training, GNN models are trained to take input graph data, identify relationships between input data, and output graph representations, P0033-P0034).
Regarding claim 3, TIAL in view of Biswas teaches the limitations of claim 2 as outlined above. Biswas further teaches wherein the computer further includes a second machine learning model… inputting, to the second machine learning model (multiple machine learning models may be trained to identify a relationship between inputs, P0034).
TIAL further teaches …that is trained based on a past determination result of reliability for second property information by the recipient, and that receives, as inputs, specific property information and information on a generator of the specific property information, and outputs reliability information of the specific property information (when viewers are not satisfied with information presented, they may request more endorsement data in order to judge credibility of target data, with the additional data being more specific than initial endorsement data, page 12, section 3.4, figure 7), and in the generating of the first reliability information, the first reliability information is generated by…third property information associated with a generator that is not registered in the reliability list among the generators of the first property information and information of a generator of the third property information (separate senders such as neighbors or expert such as a local government disaster prevention manager may submit additional endorsement data via the internet and/or sensors regarding target data, page 11, section 3.3, subsection ‘Adding to endorsement graph’, figure 6).
Regarding claim 4, TIAL in view of Biswas teaches the limitations of claim 3 as outlined above. TIAL further teaches receiving a determination result of the reliability for the first property information by the recipient (after receiving initial information, viewers may request additional endorsement data if not satisfied or able to judge previously presented endorsement graphs, page 12, section 3.4, figure 7). Biswas further teaches updating the second machine learning model (machine learning models may have parameters and weights updated in between training epochs based on output recommendations, P0034).
Regarding claim 5, TIAL in view of Biswas teaches the limitations of claim 1 as outlined above. TIAL further teaches receiving a determination result of the credibility for the predetermined data by the recipient (when viewers are not satisfied with information presented, they may request more endorsement data in order to judge credibility of target data, page 12, section 3.4, figure 7). Biswas further teaches updating the first machine learning model based on the determination result of the credibility (machine learning models may have parameters and weights updated in between training epochs based on output recommendations, P0034).
Regarding claim 6, TIAL in view of Biswas teaches the limitations of claim 5 as outlined above. TIAL further teaches updating the first machine learning model by using virtual property information for which reliability is determined to be high by the recipient in addition to the determination result of the credibility (in view of P0069 and P0075 of the specification of the instant application, virtual property information may include property information that is not currently added to the acquired peculiar graph information but with which the recipient determines that the credibility of the determination target is high if it is added to the peculiar graph information. Figure 8 on page 12-13 of TIAL recites the use of external and internal criteria for viewers to further aid viewers in judging endorsement data that was not presented in the original endorsement graph).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
US 20190146636 A1 (Kremer-Davidson et al) teaches a system including using machine learning to analyze user behavior in social networks.
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/I.M./ Examiner, Art Unit 2141
/MATTHEW ELL/ Supervisory Patent Examiner, Art Unit 2141
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