DETAILED ACTION
1. 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 .
2. Status of Application and Claims
Claims 1 and 3-20 are pending.
Claims 1, 3, 7 and 10-12 were amended and/or newly added in the Applicant’s filing(s) on 12/19/2025.
This office action is being issued in response to the Applicant's filing(s) on 12/19/2025.
3. Claim Objections
Claim 12 is objected to because of the following informalities: typographical error.
Claim 12 recites a system, wherein the instructions which, when executed by the at least one processor, cause the processor to perform operations comprising:
training the machine learning model using weakly supervised learned.
Appropriate correction is requested.
4. Claim Interpretation
The subject matter of a properly construed claim is defined by the terms that limit its scope when given their broadest reasonable interpretation. see MPEP §2013(I)(C). Specifically, the “broadest reasonable construction ‘in light of the specification as it would be interpreted by one of ordinary skill in the art.’” See MPEP §2111, citing Phillips v. AWH Corp., 75 USPQ2d 1321, 1329 (Fed. Cir. 2005). However, “[t]hough understanding the claim language may be aided by explanations contained in the written description, it is important not to import into claim limitations that are not part of the claim.” See MPEP §2111.01, citing Superguide Corp. v. DirecTV Enterprises, Inc., 69 USPQ2d 1865, 1868 (Fed. Cir. 2004). Construing claims broadly during prosecution is not unfair to the applicant, because the applicant has the opportunity to amend the claims to obtain more precise claim coverage. See MPEP §2111, citing In re Yamamoto, 222 USPQ 934, 936 (Fed. Cir. 1984).
As a general matter, grammar and the plain meaning of terms as understood by one having ordinary skill in the art used in a claim will dictate whether, and to what extent, the language limits the claim scope. See MPEP §2013(I)(C). Language that suggests or makes a feature or step optional but does not require that feature or step does not limit the scope of a claim under the broadest reasonable claim interpretation. See MPEP §2013(I)(C).
As such, claim limitations that contain statement(s) such as “if,” “may,” “might,” “can,” and “could” are treated as containing optional language. See MPEP §2013(I)(C). As matter of linguistic precision, optional claim elements do not narrow claim limitations, since they can always be omitted. See MPEP §2013(I)(C).
Similarly, a method step exercised or triggered upon the satisfaction of a condition, where there remains the possibility that the condition was not satisfied under the broadest reasonable interpretation, is an optional claim limitation. See MPEP §2111.04(II). As the Applicant does not address what happens should the optional claim limitations fail, Examiner assumes that nothing happens (i.e., the method stops). An alternate interpretation is that merely the claim limitations based upon the condition are not triggered or performed.
In addition, when a claim requires selection of an element from a list of alternatives, the prior art teaches the element if one of the alternatives is taught by the prior art. See MPEP §2143.03, citing Fresenius USA, Inc. v. Baxter Int’l, Inc., 582 F.3d 1288, 1298 (Fed. Cir. 2009);
Language in a method or system claim that states only the intended use or intended result, but does not result in a manipulative difference in the steps of the method claim nor a structural difference between the system claim and the prior art, fails to distinguish the claims from the prior art.
The following types of claim language may raise a question as to its limiting effect (this list is not exhaustive):
Statements of intended use or field of use, including statements of purpose or intended use in the preamble. See MPEP §2111.02;
Clauses such as “adapted to”, “adapted for”, “wherein”, and “whereby.” See MPEP §2111.04;
Contingent limitations. See MPEP §2111.04(II);
Printed matter. See MPEP §2111.05; and
Functional language associated with a claim term. See MPEP §2181.
As such, while all claim limitations have been considered and all words in the claims have been considered in judging the patentability of the claimed invention, the following italicized, underlined and/or boldened language is interpreted as not further limiting the scope of the claimed invention.
Additionally, the following italicized, underlined and emboldened language is not necessarily an exhaustive list of claim language that is interpreted as not further limiting the scope of the claimed invention. Applicant should review all claims for additional claim interpretation issues.
Claim 1 recites a system performing an operation comprising:
inputting a first type of data indicating an index corresponding to the relationship between the target person and the reference person into a machine learning model and obtaining an output from the machine learning model; and
…
wherein an input to the learning model to determine the closeness score includes at least one of a usage history of an electronic commerce system, or data from another information source.
Method claims are defined by the method steps being actively performed. Similarly, operations performed by a system are defined by the operations performed.
Claim 1 recites inputting a first type of data indicating an index into the machine learning model. While Claim 1 recites another input (i.e., a usage history or data from another information source) intended to be inputted into the machine learning model, Claim 1 does not recite inputting another input into the machine learning model nor the machine learning model utilizing said input.
Claim elements pertain to nonfunctional descriptive material and are not functionally involved in the steps recited. Thus, this descriptive material will not distinguish the claimed invention from the prior art in terms of patentability. See MPEP §2111.05 (III).
Claims 10, 11 and 20 have similar issues.
Claim 12 recites a system, wherein the instructions which, when executed by the at least one processor, cause the processor to perform operations comprising:
training the machine learning model using weakly supervised learn[ing];
wherein the weakly supervised learning comprises:
training the machine learning model with learning indicating the index corresponding to the relationship between the target person and the reference person;
comparing the output data from the machine learning model to teaching data in response to the learning data being input into the machine learning model;
wherein the teaching data includes data indicating a value of the reasonable closeness scores in the learning data and a probability that the value is reasonable.
Under the broadest reasonable interpretation, the learning data and the teaching data is compared (i.e., the similarity and/or dissimilarity between the data is noted). The result of the comparison has no impact on the operations performed by the system.
Additionally, as the learning data and the teaching data is only compared (i.e., the similarity and/or dissimilarity between the data is noted), the component data elements of the component data elements of the teaching data or what said data elements are supposed to communicate are not functionally involved in the steps recited. The data is just compared.
5. 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 and 3-20 are rejected under 35 U.S.C. § 101 because the claimed invention is directed to an abstract idea without significantly more.
STEP 1
The claimed invention falls within one of the four statutory categories of invention (i.e., process, machine, manufacture and composition of matter). See MPEP §2106.03.
STEP 2A – PRONG ONE
The claim(s) recite(s) a method, a system to perform a method and/or computer-readable medium containing instructions, when executed, causes a computer to perform a method comprising:
specifying a relationship between the target person and a reference person;
determining a closeness score indicating closeness between the target person and the reference person;
determining credit of the target person based on information about the at least one reference person and the closeness score determined for the reference person;
inputting a first type of data indicating an index corresponding to the relationship between the target person and the reference person into a … model and obtaining an output from the … model; and
wherein the closeness score is determined based upon the output obtained from the … model;
wherein specifying the relationship comprises specifying the relationship between the target person and the reference person based on at least one of a surname, an IP address, an address, a credit card number, an age difference, or a gender;
wherein an input to the … model to determine the closeness score includes at least one of a usage history of an electronic commerce system, or data from another information source.
These limitations, as drafted, under its broadest reasonable interpretation, covers a series of steps instructing how to determining a credit score and/or limit for a person which is a fundamental economic practice, a sub-category of certain method(s) of organizing human activity, an enumerated grouping of abstract ideas. See MPEP §2106.04(a)(2)(II)(A).
Examiner notes that determining a credit score and/or limit for a person is mitigation of financial risk and that the mitigation of financial risk is a court-provided example of a fundamental economic practice. See MPEP §2106.04(a)(2)(II)(A), citing Alice Corp. v. CLS Bank. (2014).
Additionally, these limitations, as drafted, under its broadest interpretation, covers a series of steps that can be practically performed in the human mind (e.g., observations, evaluations, judgments and opinions) which are mental process, a second enumerated grouping of abstract ideas. See MPEP §2106.04(a)(2)(III).
Examiner notes that “’collecting information, analyzing it, and displaying certain results of the collection and analysis,’ where the data analysis steps are recited at a high level of generality such that they could practically be performed in the human mind” is a court-provided example of a mental process. See MPEP §2106.04(a)(2)(III)(A) citing Electric Power Group v. Alstom, SA. (Fed. Cir. 2016).
Accordingly, the claimed invention recites an abstract idea.
STEP 2A – PRONG TWO
The claimed invention recites additional elements (i.e., computer elements) of a processor (Claim(s) 1), a memory (Claim(s) 1), a machine-learning model (Claim(s) 1, 10 and 11) and a computer-readable readable information storage medium (Claim(s) 11).
The claimed invention does not include additional elements that integrate the judicial exception into a practical application of the exception because the claims do not provide improvements to another technology or technical field; improvements to the functioning of the computer itself; are not applying or using a judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition; are not applying the judicial exception with or by use of a particular machine; are not effecting a transformation or reduction of a particular article to a different state or thing; and are not applying the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment. See MPEP §2106.04(d).
The additional elements are recited at a high-level of generality such that it amounts no more than mere instructions to apply the exception using a generic computer component. See MPEP §2106.05(f). Alternately, the additional elements amount to no more than generally linking the exception to a particular technological environment or field of use. See MPEP §2106.05(h). Accordingly, these additional element(s), when considered separately and as an ordered combination, do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
Accordingly, the claimed invention is directed to an abstract idea without a practical application.
STEP 2B
Upon reconsideration of the indicia noted under Step 2A in concert with the Step 2B considerations, the additional claim element(s) amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer. See MPEP §2106.07(a)(II). The same analysis applies in Step 2B, i.e., mere instructions to apply an exception using a generic computer component cannot integrate a judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B. The claim does not provide an inventive concept significantly more than the abstract idea.
Accordingly, these additional elements, when considered separately and as an ordered combination, do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea.
DEPENDENT CLAIMS
Dependent Claim(s) 3-9, 12 and 13 recite claim limitations that further define the abstract idea recited in respective independent Claim(s) 1. As such, the dependent claims are also grouped an abstract idea utilizing the same rationale as previously asserted against the independent claims.
No additional computer components other than those found in the respective independent claims is recited, thus it is presumed that the claim is further utilizing the same generically recited computer.
As such, the dependent claims do not include any additional elements that integrate the abstract idea into a practical application of the judicial exception or are sufficient to amount to significantly more than the judicial exception when considered both individually and as an ordered combination.
Accordingly, the dependent claim(s) are also not patent eligible.
Appropriate correction is requested.
6. 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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claim(s) 1, 4-11, 13, 15-17 and 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Chen (US PG Pub. 2018/0276748) in view of Gordon (US PG Pub. 2014/0045530).
Regarding Claim 10, Chen discloses a method comprising:
specifying a relationship (social-network relationship) between the target person (target user) and a reference person (social-network user). (see para. 65);
determining a closeness score (closeness degree) indicating closeness between the target person and the reference person. (see para. 69);
determining credit of the target person (target user) based on information (credit score) about the at least one reference person (social-network user) and the closeness score determined for the reference person. (see para. 70);
inputting (obtaining) a first type of data (personal information) indicating an index corresponding to the relationship between the target person (target user) and the reference person (social-network user) into a model (specific predictive model) and obtaining output (weight value or closeness degree) from the model. (see para. 24, 25, 30 and 85); and
wherein the closeness score (weight value or closeness degree) is determined based on the output obtained from the model. (see para. 25, 30 and 69); and
wherein specifying the relationship comprises specifying the relationship between the target person and the reference person based on at least one of a surname, an IP address, an address (geographic location), a credit card number, an age difference, or a gender. (see para. 66).
Chen does not explicitly teach a method comprising specifying the relationship between the target person and the reference person based on at least one of a surname, an IP address, an address, a credit card number, an age difference, or a gender, although Chen discloses specifying the relationship between a target person and a reference person based on a geographic location. (see para. 66).
Chen does not explicitly teach a method wherein an input to the machine learning model to determine the closeness score includes at least a usage history of an electronic commerce system, or data obtained from another information source, although if a machine learning model receives input it inherently has to be obtained from another information source (i.e., an information source outside the machine learning model).
Chen does not teach a method wherein the model is a machine learning model.
Gordon discloses a method:
specifying a relationship (relationship) between the target person (first user) and a reference person (second user). (see abstract);
determining a closeness score (measure of preciseness) indicating closeness between the target person and the reference person. (see para. 134-144);
inputting a first type of data (proper attributes or relevant considerations) indicating an index corresponding to the relationship between the target person and the reference person into a machine learning model and obtaining output (geo-temporal patterns) from the machine learning model. (see para. 133); and
wherein the closeness score (measure of preciseness) is determined based on the output obtained from the machine learning model. (see para. 133-144);
wherein specifying the relationship comprises specifying the relationship between the target person and the reference person based on at least one of a surname, an IP address (IP address), an address (street address), a credit card number (credit card account), an age difference, or a gender. (see abstract; para. 12, 41 and 167-170); and
wherein an input to the learning model to determine the closeness score includes at least one of a usage history (credit card transactions, bank account transactions, debit card transactions) of an electronic commerce system (financial system), or data obtained from another source (telecommunications network, global positioning system, surveillance system). (see para. 105-108).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to have modified Chen to incorporate a machine learning model, as disclosed by Gordon, thereby capturing the benefits of machine learning such as accuracy and speed.
Regarding Claims 1 and 11, such claims recite substantially similar limitations as claimed in previously rejected claims and, therefore, would have been anticipated based upon previously rejected claims.
Regarding Claim 3, Chen does not teach a system wherein the operations comprise retrieving account data of the target person from a first computer system and account data of the reference person from a second computer system; or specifying the relationship between the target person and the reference person based on the retrieved account data of the target person and the retrieved account data of the reference person.
Gordon discloses a system wherein the operations comprise:
retrieving account data of the target person from a first computer system (first candidate wireless terminal) and account data of the reference person registered from a second computer system (second candidate wireless terminal). (see para. 195); and
specifying the relationship between the target person and the reference person based on the retrieved account data of the target person and the retrieved account data of the reference person. (see para. 195).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to have modified Chen and Gordon to incorporate the retrieval of external information, as disclosed by Gordon, as a computer system retrieving external information as input is standard and conventional for a computer processing data.
Regarding Claim 4, Chen discloses a system wherein the operations further comprise:
specifying a pair of persons (target user and social-network user) related to each other based on respective attributes (e.g., number of common social friends, number of social-network groups in which they jointly participate, frequency of joint social events participation) of a plurality of persons (target user and social-network user). (see para. 65 and 69); and
specifying, as the reference person (social-network user), a person who is specified as related to the target person (via number of common social friends, number of social-network groups in which they jointly participate, frequency of joint social events participation) of a plurality of persons (target user and social-network user) and a person who is related to a predetermined number or more of persons related to the target person (common social friends of the two users). (see para. 69);
wherein the predetermined number (e.g., 3) is a positive integer. (see para. 4).
Regarding Claim 5, Chen discloses a system wherein the operations comprise the specifying the relationship between the target person and the reference person as a family (relative). (see para. 86).
Regarding Claim 6, Chen discloses a system the operations comprise determining the credit comprises determining a credit score of the target person. (see para. 70).
Regarding Claim 7, Chen discloses a system comprising a plurality of machine learning models (prediction algorithms of machine learning). (see para. 4).
wherein the operations further comprise clustering (into user sets and associations) based on a value (e.g., interests or geographic locations) associated with a relationship between persons. (see fig. 2; para. 26 and 33);
wherein the specifying the relationship comprises specifying the relation between the target person and the reference person based upon the result of the clustering (e.g., based on the same interests or geographic locations). (see para. 26 and 33);
wherein there are two or more clusters (social-network user sets or associations). (see para. 33).
Chen does not explicitly teach a system wherein each cluster has a unique corresponding machine learning model trained for a specific corresponding cluster.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to have modified Chen and Gordon to assign to each of the plurality of clusters disclosed by Chen one of the plurality of machine learning models disclosed by Chen thereby associating a specific cluster to a specific machine learning model and thereby establishing the relationship between system components.
Regarding Claim 8, Chen discloses a system wherein the operations comprise:
clustering (into user sets or associations) based on a value (e.g., geographic locations) associated with a relationship between persons. (see fig. 2; para. 26 and 33); and
specifying the relationship between the target person and the reference person based on a result of clustering (into user sets or associations). (see fig. 2; para. 26 and 33).
Regarding Claim 9, Chen discloses a system wherein the operations comprise:
executing a first determination that determines credit (credit score) of the target person (target user) based on information about the target user (based upon their relationship to social-network user). (see para. 69-70); and
executing a determination that determines the credit (credit score) of the target person (target user) based on information about the at least one reference person (social-network user) and the closeness score determined for the reference person. (see para. 69-70).
Chen does not explicitly teach a system wherein multiple iterations of the method are performed.
However, it would have been obvious to one having ordinary skill in the art before the effective filing date of the invention to have modified Chen by duplicating claim elements contained in Chen (e.g., the first determination) to create additional claim elements (e.g., a second determination) wherein each additional claim element would serve the same function as the original claim element. In the combination each element, original element and additional element, would merely have performed the same function as it did previously, and one of ordinary skill in the art at the effective filing date of the invention would have recognized that the results of the combination were predictable. see MPEP §2144.04 (VI)(B).
Regarding Claim 13, Chen discloses a system the operations comprising:
specifying the relationship by performing clustering (into user sets or associations) on attribute value data (specific attributes) associated with the target person and the reference person. (see para. 26 and 87);
wherein the clustering includes classifying the relationship into a plurality of clusters (user sets). (see para. 26 and 87);
wherein each cluster in the plurality of clusters is associated with a type of relationship (interests or geographic location). (see para. 26 and 26).
Chen does not recite a system wherein there is a number of machine learning models equal to a number of clusters in the plurality of clusters.
It would have been obvious to one having ordinary skill in the art before the effective filing date of the invention to have modified Chen and Gordon by duplicating claim elements contained in Chen (e.g., the first model) to create additional claim elements (e.g., a second model) wherein each additional claim element would serve the same function as the original claim element. In the combination each element, original element and additional element, would merely have performed the same function as it did previously, and one of ordinary skill in the art at the effective filing date of the invention would have recognized that the results of the combination were predictable. see MPEP §2144.04 (VI)(B).
Regarding Claim 15, Chen does not teach a system wherein the operations comprise specifying the relationship comprises specifying the relationship between the target person and the reference person based on the IP address.
Gordon discloses a system wherein the operations comprise specifying the relationship comprises specifying the relationship between the target person and the reference person based on the IP address. (see para. 167).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to have modified Chen and Gordon to incorporate a specification of an IP address, as disclosed by Gordon, as a common IP address would be an obvious parameter by which to define a relationship between two parties.
Regarding Claim 16, Chen does not explicitly teach a system wherein the operations comprise specifying the relationship comprises specifying the relationship between the target person and the reference person based on the address, although Chen does disclose a system wherein the operations comprise specifying the relationship comprises specifying the relationship between the target person and the reference person based on the geographic location. (see para. 66).
Gordon discloses a system wherein the operations comprise specifying the relationship comprises specifying the relationship between the target person and the reference person based on the address (IP address, street or street address). (see para. 45, 127 and 167).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to have modified Chen and Gordon to incorporate a specification of an address, as disclosed by Gordon, as a common address would be an obvious parameter by which to define a relationship between two parties.
Regarding Claim 17, Chen does not teach a system wherein the operations comprise specifying the relationship comprises specifying the relationship between the target person and the reference person based on the credit card number.
Gordon discloses a system wherein the operations comprise specifying the relationship comprises specifying the relationship between the target person and the reference person based on the credit card number. (see para. 108 and 167).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to have modified Chen and Gordon to incorporate a specification of a credit card number, as disclosed by Gordon, as a common credit card number would be an obvious parameter by which to define a relationship between two parties.
Regarding Claim 20, Chen does not teach a system wherein an input to the learning model to determine the close score is the usage history of an electronic commerce system.
Gordon discloses a system wherein an input to the learning model to determine the close score is the usage history (a purchase, bank account transactions, credit card transactions, debit card transactions) of an electronic commerce system (financial system). (see para. 108 and 167).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to have modified Chen and Gordon to incorporate usage history, as disclosed by Gordon, as usage history provide numerous data points by which to define a relationship between two parties.
Claim(s) 12 is/are rejected under 35 U.S.C. 103 as being unpatentable over Chen and Gordon, as in Claim 1, and in further view of Hwang (US PG Pub. 2018/0060722).
Regarding Claim 12, Chen does not teach a system wherein the instructions which, when executed by the at least one processor, cause the processor to perform operations comprising: training the machine learning model using weakly supervised learning; wherein the weakly supervised learning comprises: training the machine learning model with learning data that is a same type as the data indicating the index corresponding to the relationship between the target person and the reference person; comparing the output data from the machine learning model to teaching data in response to the learning data being input into the machine learning model; wherein the teaching data includes data indicating a value of the reasonable closeness scores in the learning data and a probability that the value is reasonable.
Hwang discloses a system wherein the instructions which, when executed by the at least one processor, cause the processor to perform operations comprising:
training the machine learning model using weakly supervised learning. (see abstract).
wherein the weakly supervised learning comprises:
training the machine learning model with learning data (some information or a first type of information) that is a same type as the data indicating the index corresponding to the relationship between the target person and the reference person. (see para. 4 and 104).
comparing the output data from the machine learning model to teaching data (preset standard) in response to the learning data being input into the machine learning model. (see para. 105-106).
wherein the teaching data includes data indicating a value (preset standard) of the reasonable closeness scores in the learning data and a probability that the value is reasonable. (see para. 105-106).
It would have been obv1ious to one of ordinary skill in the art before the effective filing date of the invention to have modified Chen and Gordon to weakly supervised learning to develop and/or refine the machine learning models, as disclosed by Hwang, as weakly supervised learning is a standard and conventional method to develop and/or refine machine learning models.
Claim(s) 14, 18 and 19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Chen and Gordon, as in Claim 1, and in further view of Hulet (US PG Pub. 2014/0082568).
Regarding Claims 14, 18 and 19, Chen does not teach a system wherein the operations comprise specifying the relationship between the target person and the reference person based on a surname, an age difference or a gender.
Hulet discloses a system the operations comprise specifying the relationship between the target person and the reference person based on a surname, an age difference or a gender. (see para. 27; Claim 2).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to have modified Chen and Gordon to incorporate a specification of a surname, an age difference and a gender, as disclosed by Hulet, as a surname, an age difference or a gender are obvious parameters to use to define a relationship between two parties.
7. Response to Arguments
Applicant's arguments filed 12/19/2025 have been fully considered but they are not persuasive.
§101 Rejection
Applicant argues that the claimed invention recites a practical application, specifically “an improvement in the functioning of a computer, or an improvement to other technology or technical field,” and, as such, satisfies Step 2A Prong Two of the §101 Guidelines. See Arguments, pp. 11-12.
Specifically, Applicant argues:
Applicant submits that the present claims reflect an improvement to other technology or technical field consistent with the August 4, 2025 USPTO "Reminder Memo" which addresses technical improvements. Specifically, the technical problem solved is to determine credit from a user of an online platform using an improvement to the technical fields of (1) clustering and (2) graph-based analysis together with (3) machine learning tailored to different types of relationships based on the clustering.
As described in paragraphs [0004] and [0005] of the published application, "it is not possible to accurately determine the credit of the person without knowing the credit information of the person". The claimed invention solves this problem, using the three technical improvements listed above. See Arguments, p. 11.
The Examiner respectfully disagrees.
In DDR Holdings, LLC v. Hotels.com, the U.S. Court of Appeals stated:
As an initial matter, it is true that the claims here are similar to the claims in the cases discussed above in the sense that the claims involve both a computer and the Internet. But these claims stand apart because they do not merely recite the performance of some business practice known from the pre-Internet world along with the requirement to perform it on the Internet. Instead, the claimed solution is necessarily rooted in computer technology in order to overcome a problem specifically arising in the realm of computer networks. See DDR Holdings, LLC v. Hotels.com, 113 USPQ2d 1097, 1106 (Fed. Cir. 2014) – emphasis added.
In the instant case, the problem that the claimed invention is designed to overcome, “to accurately determine the credit of the person without knowing the credit information of the person,” is not a problem specifically arising from the realm of computers. This problem is a standard business problem that exists outside the realm of computers and existed before the age of computers.
Additionally, the USPTO “Reminder Memo” issued on August 4, 2025 recites:
For example, the examiner should consider whether the technological limitations are being used as a tool to improve the recited judicial exception (e.g., automating a manual business process) or whether the claim as a whole provides an improvement to technology or a technical field. Claims that are determined to improve computer capabilities or improve technology or a technical field support a finding that the claim integrates the judicial exception into a practical application or amounts to significantly more than the judicial exception itself. See USPTO Memo, p. 5, footnotes omitted – emphasis added.
While the claimed invention may be using clustering techniques, graph-based analysis and machine-learning models to “to accurately determine the credit of the person without knowing the credit information of the person,” those elements are being used as tools to improve the recited judicial exception not to improve the technology or a technical field.
For example, while this methodology may more accurately determine the credit of the person without knowing the credit information of the person, thereby improving the abstract idea, neither the technology (e.g., the underlying computer technology) nor a technical field (e.g., the field of artificial intelligence/machine learning) has been improved.
MPEP §2106.05(a) recites:
If it is asserted that the invention improves upon conventional functioning of a computer, or upon conventional technology or technological processes, a technical explanation as to how to implement the invention should be present in the specification. That is, the disclosure must provide sufficient details such that one of ordinary skill in the art would recognize the claimed invention as providing an improvement. The specification need not explicitly set forth the improvement, but it must describe the invention such that the improvement would be apparent to one of ordinary skill in the art. Conversely, if the specification explicitly sets forth an improvement but in a conclusory manner (i.e., a bare assertion of an improvement without the detail necessary to be apparent to a person of ordinary skill in the art), the examiner should not determine the claim improves technology. – emphasis added.
The specification does not provide any evidence that the claimed invention results in an improvement to the functioning of a computer, an improvement to conventional technology or technological processes, or is addressing a technology-based problem.
Additionally, the specification does not provide any evidence that there is even a technology-based problem to be solved.
Additionally, MPEP §2106.05(f)(1) recites:
Whether the claim recites only the idea of a solution or outcome i.e., the claim fails to recite details of how a solution to a problem is accomplished. The recitation of claim limitations that attempt to cover any solution to an identified problem with no restriction on how the result is accomplished and no description of the mechanism for accomplishing the result, does not integrate a judicial exception into a practical application or provide significantly more because this type of recitation is equivalent to the words “apply it”. See Electric Power Group, LLC v. Alstom, S.A., 830 F.3d 1350, 1356, 119 USPQ2d 1739, 1743-44 (Fed. Cir. 2016); Intellectual Ventures I v. Symantec, 838 F.3d 1307, 1327, 120 USPQ2d 1353, 1366 (Fed. Cir. 2016); Internet Patents Corp. v. Active Network, Inc., 790 F.3d 1343, 1348, 115 USPQ2d 1414, 1417 (Fed. Cir. 2015). In contrast, claiming a particular solution to a problem or a particular way to achieve a desired outcome may integrate the judicial exception into a practical application or provide significantly more. See Electric Power, 830 F.3d at 1356, 119 USPQ2d at 1743 – emphasis added.
Even assuming there was a technology-based problem, the claims, as written, fail to recite the details of how a technology-based solution to the technology-based problem was accomplished.
Examiner notes that clustering is only recited in dependent Claims 7 and 13 which are further defining the system claim of Claim 1. If the Applicant believes that these claim limitations enable the claimed invention to overcome the previously asserted §101 Guidelines, those claim limitations should be rolled up into the independent claim, Claim 1.
The method claims (Claim 10) and the computer-readable medium claims (Claim 11) do not recite any claim elements pertaining to clustering.
Examiner notes that none of the claims recite any claim elements pertaining to graph-based analysis.
Examiner asserts that the claimed invention is analogous to the invention in Electric Power Group LLC v. Alstom SA (Fed. Cir. 2016) stated:
The claims here are unlike the claims in Enfish. There, we relied on the distinction made in Alice between, on one hand, computer-functionality improvements and, on the other, uses of existing computers as tools in aid of processes focused on “abstract ideas” (in Alice, as in so many other § 101 cases, the abstract ideas being the creation and manipulation of legal obligations such as contracts involved in fundamental economic practices). Enfish, 822 F.3d at 1335-36; see Alice, 134 S. Ct. at 2358-59. That distinction, the Supreme Court recognized, has common-sense force even if it may present line-drawing challenges because of the programmable nature of ordinary existing computers. In Enfish, we applied the distinction to reject the § 101 challenge at stage one because the claims at issue focused not on asserted advances in uses to which existing computer capabilities could be put, but on a specific improvement—a particular database technique—in how computers could carry out one of their basic functions of storage and retrieval of data. Enfish, 822 F.3d at 1335-36; see Bascom, 2016 U.S. App. LEXIS 11687, 2016 WL 3514158, at *5; cf. Alice, 134 S. Ct. at 2360 (noting basic storage function of generic computer). The present case is different: the focus of the claims is not on such an improvement in computers as tools, but on certain independently abstract ideas that use computers as tools. see Electric Power Group LLC v. Alstom SA, 119 USPQ2d 1739, 1742 (Fed. Cir. 2016) – emphasis added.
The claimed invention is not an improvement to computer technology or computer functionality. Rather, the claimed invention is applying a computer’s existing capabilities to implement a particular abstract idea. As in Electric Power Group, the focus of the claimed invention is not on an improvement in computers as tools but on improving an abstract idea (i.e., “to accurately determine the credit of the person without knowing the credit information of the person”) that uses computers as tools.
Applicant further argues:
Applicant also submits that claim 7 as amended is patent eligible. Claim 7 clarifies that the credit determining system comprises a plurality of machine learning models wherein each cluster has a unique corresponding and specifically training machine learning model. Applicant submits that this furthers the arguments discussed with regard to claim 1. See Arguments, pp. 11-12.
The Examiner respectfully disagrees.
Examiner is uncertain what the Applicant envisions as the technological improvement. Clustering (i.e., grouping of similar data points) is an established machine learning technique. Generating one machine learning model of a plurality of machine learning models based upon one cluster of a plurality of clusters is an established machine learning technique.
Based upon the specification and the claims, the claimed invention does not result in an improvement to the functioning of a computer, an improvement to conventional technology or technological processes, and is not addressing a technology-based problem. Rather, the focus of the claimed invention is on an improvement to the economic or other tasks to which to computer is tasked in in its ordinary capacity (i.e., clustering data and utilizing multiple models). See Enfish, LLC v. Microsoft Corp., 118 USPQ2d 1684, 1689 (Fed. Cir. 2016).
Applicant further argues:
Moreover, applicant submits that these features could not be performed mentally and therefore are directed to patent eligible subject matter. See Arguments, p. 12.
The Examiner respectfully disagrees.
Examiner is uncertain what is meant by “these features.”
The claimed invention is rejected as being a fundamental economic practice, a sub-category of certain method(s) of organizing human activity. A fundamental economic practice does not require that the fundamental economic practice be performed mentally.
Additionally, mental processes include processes that would require “the use of a physical aid, such as pen and paper.” See MPEP §2106.04(a)(2)(III)(B). And processes that utilize a computer. See MPEP §2106.04(a)(2)(III)(C).
§103 Rejection
Applicant argues that the previously asserted prior art (Chen and Gordon) do not teach or suggest “specifying the relationship between the target person and the reference person based on at least one of a surname, an IP address, an address, a credit card number, an age difference, or a gender.” See Arguments, pp. 13-14.
Specifically, Applicant argues:
Applicant respectfully submits that these features are not disclosed in the cited references. For example, cited paragraph [0167] of Gordon describes specific examples of telecommunications-event records that report on various telecommunications events experienced by a wireless terminal. Here, Gordon discloses receiving reports which include IP addresses and credit card transactions. (Street addresses, age differences, and genders, however, do not appear to be disclosed.)
Receiving telecommunication events, however, does not disclose "specifying the relationship between the target person and the reference person", even if the reports disclose IP address and credit card transactions. See Arguments, pp. 13-14.
The Examiner respectfully disagrees.
Examiner notes that the claim, as written, recites “specifying the relationship between the target person and the reference person based on at least one of a surname, an IP address, an address, a credit card number, an age difference, or a gender.”
Examiner also notes that an IP address is an address.
Under the broadest reasonable interpretation, the asserted prior art only needs to disclose specifying the relationship between the target person and the reference person based on at least one of the six listed alternatives. Not all the alternatives.
Gordon recites:
An illustrative geo-temporal analysis system analyzes telecommunications-event records and other records associated with wireless terminals to infer a collaborative relationship between users who do not telecommunicate with each other, based on how precisely a first geo-temporal pattern matches a second geo-temporal pattern. When a collaborative relationship is inferred, the system transmits an indication thereof and a request for an estimated location of the respective wireless terminals. See abstract – emphasis added.
The present inventors recognized that the traditional approach of investigating people according to calls they made and received presupposes that suspects telecommunicate directly with each other, be it by voice, text, email, instant messaging, other data communications, etc. When a person does not directly telecommunicate with a known suspect to avoid detection, there are no records of direct telecommunications between them, so it would be useful to infer a relationship based on the actors' geographic and temporal behaviors instead. The present inventors recognized that location data in telecommunications-event records and in other records can be exploited to glean users' geo-temporal patterns (i.e., patterns of location and/or movement over time), even when the users are not actively using their wireless terminals and even when there is no direct telecommunications between them. In this way, location data in the records is exploited independently of the telecommunications events occurring at the wireless terminal. See para. 12 – emphasis added.
The term "location" is defined as any one of a zero-dimensional point, a one-dimensional line, a two-dimensional area, or a three-dimensional volume. Thus, a location can be described, for example, by a street address, geographic coordinates, a perimeter, a geofence, a cell ID, or an enhanced cell ID. See para. 41 – emphasis added.
At operation 503, geo-temporal analysis system 213 receives records that report on other events that are not telecommunications events that occurred at the wireless terminal, wherein each record comprises geo-temporal data associated with the respective event, illustratively a geo-temporal datum documenting the location and point in time that the event is estimated to have occurred. Illustrative examples of other-event records that report on other events that are not telecommunications events but which events are associated with a wireless terminal include without limitation:
…
a record at a twelfth location L.sub.12 at time T.sub.12 wherein the wireless terminal accessed an online portal/destination that is of interest, e.g., a bank, a bank account, a credit card account, a debit card account, an Internet domain, a Universal Resource Locator ("URL"), etc.,
a record at a thirteenth location L.sub.13 at time T.sub.13 wherein the wireless terminal received data from a source that is of interest, e.g., a bank, a financial institution, a person, or another entity, an IP address, an Internet domain, an Internet address, etc., See para. 167-170 – emphasis added.
Gordon discloses specifying a relationship (a collaborative relationship) between a target person and a reference person (users). See abstract. This specification of a relationship is based upon their location, such as a street address. See para. 12 and 41. This specification of a relationship can also be based upon records pertaining to a credit card number (credit card account) and an IP address. See para. 167-170.
Chen and Gordon disclose a method comprising specifying the relationship between the target person and the reference person based on at least one of a surname, an IP address, an address, a credit card number, an age difference, or a gender.
Applicant further argues:
Moreover, applicant submits that (1) there is no reason to combine the references; and (2) even if the references were combined, there is no rationale in either of Chen or Gordon to modify Chen to use a telecommunication report indicating an IP address or credit card transaction to determine a relationship. Chen uses social networks to determine an initial credit score, while Gordon uses telecommunication records to infer collaborative relationships. Neither reference suggests combination so that Chen can infer a collaborative relationship and then use this relationship to determine an initial credit score. Any modification of Chen using these features would appear to be improper hindsight bias. See Arguments, p. 14.
In response to the Applicant's argument concerning impermissible hindsight, Examiner asserts that "[a]ny judgment on obviousness is in a sense necessarily a reconstruction based upon hindsight reasoning, but so long as it takes into account only knowledge which was within the level of ordinary skill at the time claimed invention was made and does not include knowledge gleaned only from applicant's disclosure, reconstruction is proper." See MPEP §2145(X)(A), citing In re McLaughlin, 170 USPQ 209, 212 (CCPA 1971).
Chen recites:
An optimization method for obtaining a user credit score is provided. The method includes obtaining initial credit scores of users in multiple social-network user sets; obtaining initial credit scores of the social-network user sets according to the initial credit scores of the users; and determining a social-network relationship between each two social-network user sets according to a social-network relationship between the users in each two social-network user sets. The method also includes, according to a credit score of at least one social-network user set having a social-network relationship with a target social-network user set and the social-network relationship between the at least one social-network user set and the target social-network user set, optimizing and adjusting a credit score of the target social-network user set; and correcting credit scores of users in the target social-network user set according to the optimized and adjusted credit score of the target social-network user set. See abstract.
Gordon recites:
An illustrative geo-temporal analysis system analyzes telecommunications-event records and other records associated with wireless terminals to infer a collaborative relationship between users who do not telecommunicate with each other, based on how precisely a first geo-temporal pattern matches a second geo-temporal pattern. When a collaborative relationship is inferred, the system transmits an indication thereof and a request for an estimated location of the respective wireless terminals. See abstract.
Chen discloses determining an initial credit score based upon the specification of a relationship between two individuals, albeit a relationship within a social-network. See abstract. Gordon discloses additional means by which to a relationship between two individuals could be determined and specified. See abstract.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Chen to incorporate analysis of IP addresses, addresses and credit card numbers, as disclosed by Gordon, thereby providing additional parameters by which to establish a relationship between two people.
Applicant argues that the previously asserted prior art (Chen and Gordon) does not teach or suggest “machine learning [is] used for determining a closeness score [or] is … used with respect to each individual cluster to determine a closeness score.” See Arguments, p. 15.
The Examiner disagrees.
Chen recites:
In the existing technology, generally, to obtain credit assessment of a user, personal information of the user is collected, and then a default risk of the user is predicted by using a statistical model or some prediction algorithms of machine learning, for example, a frequently-used FICO credit score system and a Zestfinace credit rating system. However, in an existing credit score mechanism, only information of the user's own dimension is used. If the personal information of the user is collected incompletely or mistakenly, it is very difficult to implement accurate credit rating for the user. See para. 4 – emphasis added.
In an embodiment, if an initial credit score of a user is missing, an average score or a weighted average score of credit scores of other users who are social-network friends, colleagues, and relatives may be used as the initial credit score of the user. The weight value may be determined according to a closeness degree between the user and the other users or according to a frequency of social events occurring between the user and the other users. See para. 25 – emphasis added.
Chen discloses a method wherein the credit assessment is performed utilizing machine learning (prediction algorithms of machine learning). See para. 4. Chen also discloses a method wherein the calculation of a credit score includes determination of a closeness score. See para. 25.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Chen and Gordon to incorporate machine learning to calculate the closeness score, as the closeness score is a necessary component for the existing calculations performed by machine learning, thereby utilizing machine learning efficiencies to complete all calculations necessary to determine a credit score.
Chen recites:
In the existing technology, generally, to obtain credit assessment of a user, personal information of the user is collected, and then a default risk of the user is predicted by using a statistical model or some prediction algorithms of machine learning, for example, a frequently-used FICO credit score system and a Zestfinace credit rating system. However, in an existing credit score mechanism, only information of the user's own dimension is used. If the personal information of the user is collected incompletely or mistakenly, it is very difficult to implement accurate credit rating for the user. See para. 4 – emphasis added.
The multiple social-network user sets may be sets or collections of users participating in different social-network groups. Users participating in a same social-network group belong to a social-network user set corresponding to the social-network group. Alternatively, the multiple social-network user sets may be obtained by performing division according to specific attributes of the users, for example, interests or geographical locations of the users. In one embodiment, in the social-network user sets, a same user does not exist in more than one set. That is, one user belongs to only one social-network user set. See para. 26.
Chen discloses a method wherein there a plurality of machine learning models (prediction algorithms of machine learning). See para. 6. Chen discloses a method wherein there are a plurality of clusters (sets or collections of users). See para. 26.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Chen and Gordon to incorporate multiple machine learning models, as disclosed by Chen, and multiple clusters, as disclosed by Chen, and linking a specific machine learning model to a specific cluster, thereby ensuring that the machine learning model is associated with the current cluster of data for processing.
8. Conclusion
THIS ACTION IS MADE FINAL. 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 JASON M. BORLINGHAUS whose telephone number is (571)272-6924. The examiner can normally be reached M-F 9-5.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, RYAN D. DONLON can be reached on (571)270-3602. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/Jason M. Borlinghaus/Primary Examiner, Art Unit 3692 May 10, 2026
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