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 .
This communication is in response to application 17/928,606 filed 11-29/2022. No claims are amended. No claims are allowed.
Response to Arguments
Applicant's arguments filed 11/11/2025 have been fully considered but they are not persuasive.
Regarding 35 USC 112(a)
The applicant submits the “proximity score” is supported by the claims and specification. The applicant states the use of a machine learning model to calculate the score as well as the configuration of data used in the training are disclosed. This still does not disclose how the proximity score is calculated. There is a black box machine learning model that is trained on inputs and receives inputs to compute a proximity score. There is no clarity as to how the model arrives at the score aside from ingesting data. Simply stating a proximity score is calculated by a machine model trained on specific types of data does not reveal how the model goes from data to score. Therefore, the examiner respectfully disagrees and the rejection is maintained.
Regarding 35 USC 101:
The applicant argues there is a technical solution to a technical problem because it solves the problem of determining when a user needs to update information. Specifically, the applicant cites, “a sophisticated use of machine learning and specific data that is correlated to determining when it is likely that a user’s personal information has changed” However, this outlines an abstract idea being performed by machine learning. The use of specific data to make a decision is part of the abstract idea of a mental process, and the machine learning model is recited at a high level of generality as it makes its determination based on the specific data. Therefore, the examiner disagrees and the rejection is maintained.
The applicant submits there is an improvement to computer functionality by improving the efficiency of computer systems. Specifically, by reducing unnecessary update prompts and reducing erroneous services. However, this does not improve the functionality of the computer technology itself. A computer’s computing components do not become more efficient because there is a reduction of prompts or erroneous services. Rather, the computing components still operate the same (e.g. a processor executing instructions runs at the same efficiency despite if a package is sent to address 1 vs address 2). Therefore, there is no improvement to the computers themselves and the rejection is maintained.
Further, the applicant submits the use of the machine learning is non-generic. However, as previously discussed, the machine model is a black box with no algorithm as to how specific types of data result in a proximity score. The applicant is attempting to define the machine model by the data it uses, not the algorithm or steps it takes and is considered a generic model that falls under an apply it consideration.
Regarding 35 USC 103:
The applicant attempts to define the invention by the data it uses. The applicant submits the data is functional because the data is used to train a machine learning model. According to the applicant, the exact type of data is not interchangeable, and the specific data makes a difference in how the AI processes inputs to determine outputs. However, the machine learning model used to make the proximity score determination is claimed broadly. The functional steps that the model takes to process the data are not described. The claims describe a computer implemented method to perform steps of identifying, determining, estimating, and generating. Hence, a processor executes a set of instructions which allow the computer to perform those steps, and the type of data that is used does not functionally impact those steps. The computer can still perform those steps with different types of data, and the method would be executed identically. The only difference would be in the intended results, not the claimed invention itself. Therefore, the type of data is nonfunctional and the rejection is maintained.
Regarding paragraphs 0082 and 0053 (applicant correctly identified mislabeled paragraph with the quotation provided), the claim language was explained in the rejection to be nonfunctional descriptive material that does not carry patentable weight; therefore, no art was needed. However, the examiner showed some example relevant citations in the cited prior art with similar data types, simply as a courtesy to the applicant. The rejection does not rely on these citations. The applicant argues against the use of Resheff, because the data is used for a relationship graph rather than training a machine learning model. However, the applicant is describing data rather than defining the machine model. Therefore, the passage is relevant as it shows examples of nonfunctional descriptive data elements. Again, with emphasis added, the rejection does not rely on paragraphs 0082 and 0056 because the rejection states, “the limitations recite nonfunctional descriptive material that does not carry patentable weight in the claim”. Therefore, the rejection is maintained and the examiner respectfully disagrees.
Further the applicant argues Rosenbaum does not teach estimating when it is necessary to update a user’s personal information. Rather it discloses a periodic updating of data in a relational system relating to inferred data. However, estimating when it is necessary to update information is a very broad claim limitation. The art shows a narrower example of estimating when it is necessary to update data. See previously cited Rosenbaum (Column 4, lines 26-45): “When a change in public data is extracted from a sufficient number of data sources, the public data is updated if the change is considered "correct" as described further below”. Therefore, the examiner respectfully disagrees and the rejection is maintained.
Further the applicant argues the references do not teach an active step of “switching” machine learning models based on relationships. Although, there is a determination step in the claims taught by prior art, there is no positively recited “switching” step in the claims. In response to applicant's argument that the references fail to show certain features of the invention, it is noted that the features upon which applicant relies (i.e., switching) are not recited in the rejected claim(s). Although the claims are interpreted in light of the specification, limitations from the specification are not read into the claims. See In re Van Geuns, 988 F.2d 1181, 26 USPQ2d 1057 (Fed. Cir. 1993). Therefore, the examiner respectfully disagrees and the rejection is maintained.
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
Claims 1-15 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention.
Regarding Claim 1, the claimed subject matter of determining a “proximity score” is not properly described in the application. The broadest reasonable interpretation in light of the specification indicates a calculation is performed with a numerical value output, but the specification does not describe the necessary algorithms and or structure to perform the calculations. The same analysis applies to Claims 5, 8 and 9
Furthermore, claims 2-7 and 10-15 are also rejected due to the inadequacies of the claims on which they depend.
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 1-7 and 9-15 are systems claims, and Claim 8 is a method. Thus, each claim on its face is directed to one of the statutory categories of 35 USC 101. However, claims, 1-15 are rejected under 35 USC 101 because the claimed invention is directed to an abstract idea without significantly more.
The independent claims (1,8, and 9) recite systems and a method for using relationship graph information to estimate the need to update personal information. These claim elements are being interpreted as concepts performed in the human mind (including observation, evaluation, judgement, and opinion). Using relationship graph information to find potential information changes, can equivalently be achieved by human observation and evaluation of the different parts of the relationship graphs. Therefore, these steps fall under the abstract idea of mental processes or concepts performed in the human mind. Additionally, the independent claims (1,8, and 9) recite the determination of a “proximity score”. These claim elements are being interpreted as mathematical calculations because of the indication of a score associated with a numerical value output. Therefore, these steps fall under the abstract idea of mathematical concepts.
The independent claims recite the additional elements: “at least one processor; and at least one memory device that stores a plurality of instructions which, when executed by the at least one processor, causes the at least one processor to”, “at least one processor operating with a memory device in a system”, “a machine learning model” and “A non-transitory computer readable storage medium storing a plurality of instructions, where in when executed by at least one processor, the plurality of instructions cause the at least one processor to”. These claim elements are recited at a high level of generality such that it amounts to no more than mere instructions to apply the exceptions using a general computer environment. The machines merely act as a modality to implement the abstract ideas, and are not indicative of integration into a practical application (i.e. the additional elements are simply used as a tool to perform the abstract idea), see MPEP 2106.05(f).
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed in Step 2A Prong Two, the additional elements in the claims amount to no more than mere instructions to apply the exception using generic computer components. The same analysis applies here in 2B and does not provide an inventive concept.
In regards to the dependent claims:
Claims 2-7 and 10-15 do not introduce additional abstract ideas or additional elements and do not impact the analysis under 35 USC 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.
Claims 1-15 are rejected under 35 U.S.C. 103 as being unpatentable over Resheff (US 20210065245 A1), in view of Rosenbaum (US 7373389 B2) in further view of Agrawal (US 20210097339 A1).
Regarding Claim 1, 8 and 9, (substantially similar in scope and language) Resheff teaches:
An information processing system, comprising: at least one processor; and at least one memory device that stores a plurality of instructions which, when executed by the at least one processor, causes the at least one processor to: [(Para 0091-0095)]
identify a type of a relation between a person of interest and a reference person; [(Figure 1), (Para 0026) “each edge is a relationship between at least two entities”]
determine in accordance with a determination criterion corresponding to the type of the relation between the person of interest and the reference person, [(Figure 1), (Para 0002) “an edge in the plurality of edges represents a relationship type between two nodes”, (Para 0086) “a particular relationship label can be applied to an edge above a specified probability threshold.”; The limitations recite a determination criterion, referenced in the specification as a “threshold value”.] a proximity score indicating a proximity between the person of interest and the reference person [Figure 9] based on an index indicating a strength of a relationship between the person of interest and the reference person [(Para 0032)]
wherein a first machine learning model among the plurality of machine learning models is used for determining the proximity score for a first type of relation; [(Figure 9), (Para 0002) “Labeling is performed by receiving, as input to a machine learning model, a vector comprising attributes representing the plurality of clusters, the plurality of nodes, and the plurality of edges. Labeling is also performed by outputting, from the machine learning model, a plurality of probabilities. Each of the plurality of probabilities corresponds to a corresponding probability that an edge in the plurality of edges represents a relationship type between two nodes in the plurality of nodes”]
wherein a plurality of machine learning models are used to determine proximity scores; [(Para 0083) “In this example, the nodes, edges, and corresponding attributes are converted into a vector suitable for use as input to a random forest unsupervised machine learning model, though note that many different machine learning models could have been used”
wherein each machine learning model among the plurality of machine learning models corresponds to a type of the relation; [(Para 0002) “Labeling is performed by receiving, as input to a machine learning model, a vector comprising attributes representing the plurality of clusters, the plurality of nodes, and the plurality of edges. Labeling is also performed by outputting, from the machine learning model, a plurality of probabilities. Each of the plurality of probabilities corresponds to a corresponding probability that an edge in the plurality of edges represents a relationship type between two nodes in the plurality of nodes”]
determine the machine learning model to be used for determining the proximity score based on the type of the relation between the person of interest and the reference person; [(Para 0072) “The machine learning model may be programmed to be an unsupervised, supervised, or semi-supervised machine learning algorithm. An unsupervised algorithm is useful to detect common patterns, such as star-like relationship within the relationship graph”, (Para 0073) “A supervised machine learning model may be built using input from subject matter experts to label a relationship graph having a known set of data as having edges corresponding to specific labels… Once the supervised learning model is trained, the supervised learning model may then be applied to unknown data in a newly constructed relationship graph to predict relationship labels for the edges within a calculated degree of confidence”]
generate the input data; [(Para 0062) “Thus, clustering groups of nodes at Step 206 generates useful information which can be included in a vector to be fed as input to a machine learning model.”]
wherein a training data used to train each of the plurality of machine learning models is a same type as the input data [(Para 0074) “The machine learning model may be trained as follows. First, a relationship graph is accessed or provided for which the relationships between nodes are known. Next, the known data is converted into a vector format suitable for use by the machine learning model”] and wherein the training data comprises an attribute pair data, a data indicating a usage history of an electronic commerce transaction system, and a data acquired from a second service comprising at least one of a data indicating a number of phone calls or a number of messages exchanged per unit period between a first user and a second user, a number of gifts sent between the first user and the second user, and a number of common friends between the first user and the second user [The limitations recite non-functional descriptive material that does not carry patentable weight in the claim. However, art is still provided; (Para 0082) “Node 2 (502) has the attributes ‘User Tom.’”, (Para 0056) “Alternatively, or in some cases in addition to receiving data from the FMA (142), data may be received from or sent by electronic credit card accounts, electronic bank accounts, or other kinds of electronic accounts”]
and determine a value of the proximity score associated with the pair of the person of interest and the reference person [(Figure 9)] based on the output data output from the machine learning model in response to the input data. [(Para 0062) “the data vector is fed as input to a machine learning model that outputs a prediction which corresponds to the edge labeling”]
wherein the proximity score is determined [(Figure 9)] based on at least part of whether the person of interest and the reference person have a same street address, whether the person of interest and the reference person have a same street address, whether the person of interest and the reference person share a credit card, a number of friends in common between the person of interest and the reference person, a frequency of phone calls between the person of interest and the reference person, and a frequency of sending gifts between the person of interest and the reference person. [The limitations recite nonfunctional descriptive material that does not carry patentable weight in the claim. However, art is still provided; (Figure 5), (Para 0053) “The one or more embodiments contemplate that the financial transactions may be retrieved from or sent by a financial management application (FMA), such as financial management application (144) of FIG. 1. Alternatively, or in some cases in addition to receiving data from the FMA (142), data may be received from or sent by electronic credit card accounts, electronic bank accounts, or other kinds of electronic accounts.”]
While Resheff teaches identifying a relation between different nodes of a relationship graph, it does not explicitly teach:
and estimate an update necessity of personal information on the person of interest based on input data including an attribute of the person of interest, an attribute of the reference person, a change status of personal information on the reference person, and the proximity score and the type of the relation for both the person of interest and the reference person
wherein a second machine learning model among the plurality of machine learning models is used for determining the proximity score for a second type of relation;
wherein each of the plurality of machine learning models is trained for a single type of the relation
However, Rosenbaum teaches:
and estimate an update necessity of personal information on the person of interest based on input data [(Column 4, Lines 26-45)] including an attribute of the person of interest, an attribute of the reference person, a change status of personal information on the reference person, and the proximity score and the type of the relation for both the person of interest and the reference person [The limitations recite non-functional descriptive material that does not carry patentable weight in the claim]
Therefore, it would be reasonable for one of ordinary skill in the art before the effective filing date to use the relationship graph and inferred data in the system taught Resheff, to determine the need for an update of information taught by Rosenbaum. Furthermore, both references are in the same field of endeavor, and the claimed invention is merely a combination of old elements. In the combination each element merely would have performed the same function as it did separately, yielding predictable results.
While Resheff in view of Rosenbaum teach identifying relationships and update necessities, it does not explicitly teach multiple models each for individual relations:
wherein a second machine learning model among the plurality of machine learning models is used for determining the proximity score for a second type of relation;
wherein each of the plurality of machine learning models is trained for a single type of the relation
However, Agrawal teaches
wherein a second machine learning model among the plurality of machine learning models is used for determining the proximity score for a second type of relation; [(Para 0065) “Multiple versions of machine learning model 208 may be adapted to different types of nodes 216, edges 218, and/or labels 220, or the same machine learning model 208 may be used to generate edge scores 228 and/or probabilities 232 for all nodes 216 and/or edges”]
wherein each of the plurality of machine learning models is trained for a single type of the relation [(Para 0065) “Multiple versions of machine learning model 208 may be adapted to different types of nodes 216, edges 218, and/or labels 220, or the same machine learning model 208 may be used to generate edge scores 228 and/or probabilities 232 for all nodes 216 and/or edges”]
Therefore, it would have been obvious to one of ordinary skill before the effective filing date of the claimed invention to combine the singular machine model (Resheff in view of Rosenbaum) with multiple machine models (Agrawal). The model taught by Resheff in view of Rosenbaum labels different types of relationships. The model taught by Agrwal also labels different relationships, but can use multiple models according to each individual label type. The simple substitution of one machine learning model labeling multiple relationships, with multiple machine learning models each labeling a single relationship would yield predictable results.
Regarding Claim 2, Resheff in view of Rosenbaum in further view of Agrawal teach the limitations set forth above:
Resheff further teaches
wherein the instructions cause the at least one processor to [(Para 0091-0095)
train an update necessity machine learning estimation model using training data [(Para 0074) “The machine learning model may be trained as follows. First, a relationship graph is accessed or provided for which the relationships between nodes are known. Next, the known data is converted into a vector format suitable for use by the machine learning model”] including an attribute of a first person, an attribute of a second person, the type of the relation and the proximity score for both the first person and the second person, a change status of personal information on the second person, and ground truth data indicating whether personal information on the first person has been changed; [The limitations recite non-functional descriptive data that does not carry patentable weight in the claim]
While Resheff teaches machine learning models in relationship graphs it does not explicitly teach:
and estimate the update necessity by inputting the input data to the update necessity machine learning estimation model,
However, Rosenbaum teaches:
and estimate the update necessity by inputting the input data to the update necessity machine learning estimation model [(Column 4, Lines 26-45)]
Therefore, it would have been reasonable for one of ordinary skill in the art before the effective filing date to replace the model that determines update necessity taught by Rosenbaum with a machine learning model taught by Resheff. Furthermore, both references are in the same field of endeavor, and the substitution with a machine learning model would yield predictable results.
Regarding Claim 4, Resheff in view of Rosenbaum teach the limitations set forth above
Resheff further teaches
The information processing system according to claim 1, wherein the instructions cause the at least one processor to [(Para 0091-0095)] identify the type of the relation between the person of interest and the reference person based on at least part of whether a surname is the same, whether an IP address is the same, a similarity in street addresses, an age difference, and whether gender is the same. [The limitations recite determining a relationship based on input data; (Para 0002) “receiving a data structure comprising data… [c]onstructing a relationship graph from the data in the data structure”; Furthermore, the data type (name, address, etc.) describes an intended result and is nonfunctional descriptive material]
Regarding Claim 5, Resheff in view of Rosenbaum in further view of Agrawal teach the limitations set forth above
Resheff further teaches
The information processing system according to claim 1, wherein the instructions cause the at least one processor to [(Para 0091-0095)] determine the proximity score indicating the proximity between the person of interest and the reference person [(Figure 9)] based on an output of a proximity score determination model, which is a machine learning model corresponding to the type of the relation between the person of interest and the reference person, the output obtained when the index indicating the strength of the relationship between the person of interest and the reference person is input to the proximity score determination model. [The limitations recite using a machine learning model to determine a proximity score (output), based on strength of relationship data (input). Additionally, the machine learning model corresponds to relationship type; (Para 0085), “The labels indicate that the random forest machine learning model applied to the graph of FIG. 5 calculated an 80% probability that Edge 2-1 can be labeled with the label ‘Family Member’ and a 40% probability that Edge 2-1 can be labeled with the label ‘Son.’”]
Regarding Claim 6, Resheff in view of Rosenbaum in further view of Agrawal teach the limitations set forth above
Resheff further teaches
The information processing system according to claim 1, wherein the index indicating the strength of the relationship between the person of interest and the reference person [(Para 0032)] includes at least part of whether the person of interest and the reference person have the same street address, whether the person of interest and the reference person share the credit card, the number of friends in common between the person of interest and the reference person, the frequency of phone calls between the person of interest and the reference person, and the frequency of sending gifts between the person of interest and the reference person. [The limitations non-functional descriptive data that does not carry patentable weight in the claim]
Regarding Claim 7, Resheff in view of Rosenbaum in further view of Agrawal teach the limitations set forth above
Resheff further teaches
The information processing system according to claim 1, wherein the instructions cause the at least one processor to [(Para 0091-0095)] identify the type of the relation between the person of interest and the reference person based on attribute data of the person of interest registered in a first computer system and attribute data of the reference person registered in a second computer system. [(Para 0022)]
Regarding Claim 10, Resheff in view of Rosenbaum in further view of Agrawal teach the limitations set forth above, Resheff further teaches:
wherein the instructions cause the at least one processor to generate the input data from a plurality of systems comprising at least an electronic commerce transaction system and a second system. [(Para 0083) “In this example, the nodes, edges, and corresponding attributes are converted into a vector suitable for use as input to a random forest unsupervised machine learning model”, (Para 0100) “An established connection informs the client process that communications may commence. In response, the client process may generate a data request specifying the data that the client process wishes to obtain.”; The electronic commerce transaction system and second system are not positively recited components to the system]
Regarding Claim 11, Resheff in view of Rosenbaum in further view of Agrawal teach the limitations set forth above, Resheff further teaches:
wherein the input data comprises a frequency of interactions between the person of interest and the reference person. [The limitations describe nonfunctional descriptive data that does not carry patentable weight, however art is still provided; (Para 0025) “Examples of entities include electronic user account identifiers, usernames, and others. In one or more embodiments, nodes have a number of attributes. Examples of node attributes include the number or frequency of transactions, the number of other entities with which the node interacts, user identifiers, user data, time stamps, data creation dates, and possibly many other types of information”]
Regarding Claim 12, Resheff in view of Rosenbaum in further view of Agrawal teach the limitations set forth above
While Resheff teaches identifying relationships, it does not explicitly teach:
wherein the update necessity indicates whether or not the personal information of the person of interest is required to be updated.
However, Rosenbaum teaches:
wherein the update necessity indicates whether or not the personal information of the person of interest is required to be updated. [(Column 4, Lines 37-42) “Thus, A's private data may reflect a change in the mobile telephone number for B while C continues to see only the old number. Inferred data is information developed by the system based on interactions among the subscribers. Thus, in the above example, the system may infer that B has changed jobs based on A's private data”, (Column 5, Lines 34-44) “In particular, the applications 211 may ask subscribers to contribute pieces of data for existing entities of the relationship graph and then update the contents of the relationship graph data store 201 based on the data provided by the subscribers. The data may be desired to cover gaps in information about existing entities and their relationships, to validate or confirm information about existing entities and their relationships, to resolve conflicting information about existing entities and their relationships, to find alternate sources of information, to eliminate or consolidate duplicate entities, etc.”]
Therefore, it would have been obvious to one of ordinary skill in the art to combine the method of identifying relationships taught by Resheff, with the method of estimating update necessity of personal information taught by Rosenbaum. Both references are in the same field of endeavor, and the claimed invention is merely a combination of old elements. In the combination each element merely would have performed the same function as it did separately, yielding predictable results.
Regarding Claim 13, Resheff in view of Rosenbaum in further view of Agrawal teach the limitations set forth above, Resheff further teaches:
wherein the instructions cause the at least one processor to train each of the machine learning models using learning input data and teacher data; wherein the learning input data comprises training input data related to the type of relation corresponding to the machine learning model; [(Para 0074) “First, a relationship graph is accessed or provided for which the relationships between nodes are known. Next, the known data is converted into a vector format suitable for use by the machine learning model. Part of the data in that relationship graph is held back. The remaining portion of the data is provided to the machine learning model, and the machine learning model is instructed on what relationship labels apply to given nodes”]
wherein the teacher data comprises a proximity score value appropriate for the corresponding learning input data and data indicating a probability that the proximity score value is appropriate; [(0040) “For example, label A for edge A (134) could be either Relationship Type A (124) or Relationship Type B (126), or both, and may be associated with any of the edges in the relationship graph (108), such as Edge X (116). The probability, in turn, represents the machine-learning predicted probability that the associated label is correct. This process is described further with respect to FIG. 2B, and a specific example of this process is described with respect to FIG. 8 and FIG. 9.”]
and update the training of the machine learning models based on a comparison of the output of the machine leaning models and the teacher data. [(Para0074) “First, a relationship graph is accessed or provided for which the relationships between nodes are known. Next, the known data is converted into a vector format suitable for use by the machine learning model. Part of the data in that relationship graph is held back. The remaining portion of the data is provided to the machine learning model, and the machine learning model is instructed on what relationship labels apply to given nodes”]
Regarding Claim 14, Resheff in view of Rosenbaum in further view or Agrawal teach the limitations set forth above
While Resheff teaches a service being digital transactions data, it does not explicitly teach:
wherein the second service is a social networking service.
However, Rosenbaum teaches:
wherein the second service is a social networking service [(Column 2, Lines 62-64) “The system utilizes social network models to build graphs that represent relationships among entities.”]
Therefore, it would have been obvious to one of ordinary skill in the art to combine the method of identifying relationships with transactions data taught by Resheff, with the method of identifying relationships with social networking services taught by Rosenbaum. Simply substituting transactions data with social networking service information to determine relationships would have yielded predictable results.
Regarding Claim 15, Resheff in view of Rosenbaum in further view of Agrawal teach the limitations set forth above
While Resheff in view of Rosenbaum teach identifying relationships and update necessities, it does not explicitly teach multiple models each for trained on individual relations:
wherein each of the plurality of machine learning models is trained with a different type of training data.
However, Agrawal teaches:
wherein each of the plurality of machine learning models is trained with a different type of training data. [(Para 0030) “A model-creation apparatus 210 trains a machine learning model 208 to predict labels 220 based on a set of features. As shown in FIG. 2, the features include attributes 224 associated with node pairs 212 connected by edges 218 to which labels 220 are assigned.” (Para 0065) “Multiple versions of machine learning model 208 may be adapted to different types of nodes 216, edges 218, and/or labels 220, or the same machine learning model 208 may be used to generate edge scores 228 and/or probabilities 232 for all nodes 216 and/or edges”]
Therefore, it would have been obvious to one of ordinary skill before the effective filing date of the claimed invention to combine the method of determining relationships (Resheff in view of Rosenbaum) with the method of training individual models with different data (Agrawal). Training the individual models with their corresponding data types would have been obvious. The simple substitution of one model trained by a conglomerate of data, with multiple models trained with different label types, would yield predictable results.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Examiner Benjamin Truong, whose telephone number is 703-756-5883. The examiner can normally be reached on Monday-Friday from 9 am to 5 pm (EST).
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Nathan Uber SPE can be reached on 571-270-3923. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300 Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/B.L.T. /Examiner, Art Unit 3687
/NATHAN C UBER/Supervisory Patent Examiner, Art Unit 3626