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 .
Response to Arguments
Regarding objections to claims for minor informalities, amendments to claims 2 and 17 have overcome the objections to those claims, which are withdrawn. Amendments to claim 10 have not overcome all objections to that claim, which are given below.
Regarding the rejection of claims as judicial exceptions to 35 U.S.C. 101, Applicant repeats the argument that the claims do not recite any mental processes or mathematical concepts, but provides no reasoning in support. Examiner notes that, for example, the limitation (bold only) “utilizing, by the at least one processor, an entity classification model engine to predict at least one entity-type classification classifying at least one first entity of the plurality of entities as a first entity type,” in claim 1, recites a mental process. A person could review transaction records to classify an entity as a certain type.
Applicant further submits that any abstract idea is integrated into a practical application because the claimed invention improves a technology, quoting paragraphs 0022, 0030-0031, and 0045 from the specification, adding:
“Thus, the Application as filed identifies a particular technical problem in database management where entity resolution is impeded by lack of sufficient data to reliably identify common entities. The Application as filed identifies a particular solution of machine learning-based prediction of entity type, which is used to modify the entity profile such that duplicate entity profiles and activity records associated with entity profiles can be more accurately and reliably identified, for more efficient use of data storage resources.”
Applicant the submits that the improvement is reflected in amended claim 1.
Examiner respectfully disagrees. The method steps cited by the Applicant as implementations of the improvement are abstract ideas under MPEP 2106.
The first cited step is (bold only) “utilizing, by the at least one processor, an entity classification model engine to predict at least one entity-type classification classifying at least one first entity of the plurality of entities as a first entity type.” A person could use transaction records to “predict at least one entity-type classification classifying at least one first entity of the plurality of entities as a first entity type” as a mental process using observation and judgement. The remainder of the limitation, “utilizing, by the at least one processor, an entity classification model engine to,” merely recites the use of a computer as a tool to perform the abstract idea, and is equivalent to adding the words “apply it” or the equivalent to the judicial exception (MPEP 2106.05(f)).
The second cited step is (bold only) “generating, by the at least one processor, at least one first entity record associated with the at least one first entity by linking a first plurality of transaction records based at least in part on the transaction data and the at least one entity-type classification of the at least one first entity.” This limitation, in its broadest reasonable interpretation, includes merely associating an entity-type classification (e.g., “sporting goods merchant”) with a plurality of transaction records (e.g., records of sales of sporting goods), and considering this association to be an “entity record.” This can be performed as a mental process using observation and judgement. The remainder of the limitation, “by the at least one processor,” merely recites the use of a computer as a tool to perform the abstract idea, and is equivalent to adding the words “apply it” or the equivalent to the judicial exception (MPEP 2106.05(f)).
The third cited step is (bold only) “extracting, by the at least one processor, a first plurality of entity-related transaction characteristics associated with the at least one first entity from the transaction data of the first plurality of transaction records linked to the at least one first entity record.” A person could extract “a first plurality of entity-related transaction characteristics associated with the at least one first entity from the transaction data of the first plurality of transaction records linked to the at least one first entity record” as a mental process using observation and judgement. The remainder of the limitation, “by the at least one processor,” merely recites the use of a computer as a tool to perform the abstract idea, and is equivalent to adding the words “apply it” or the equivalent to the judicial exception (MPEP 2106.05(f)).
In summary, the purported improvement can be performed as a mental process. As stated in MPEP 2016.05(a), “It is important to note, the judicial exception alone cannot provide the improvement. The improvement can be provided by one or more additional elements.” The claim does not provide a particular way of implementing the abstract idea, but merely recites its implementation on a generic computer.
Applicant further submits that the claim is eligible under step 2B for reciting non-conventional, non-generic steps, but does not provide any support for this argument.
The argument is therefore found unpersuasive.
Regarding the rejection of claims under 35 U.S.C. 103, Applicant submits that the previously recited combination of references does not teach the limitations of claim 1, in particular, the amended limitation “generating, by the at least one processor, at least one first entity record associated with the at least one first entity by linking a first plurality of transaction records based at least in part on the transaction data and the at least one entity-type classification of the at least one first entity.”
Examiner respectfully disagrees. The claim does not describe a form or structure for the “entity record” and the specification provides no explicit definition. The nearest reference is to “entity records 104,” which are used to build entity profiles and classify the entities, not to link the entity-type classification to transaction data after the classification has been made. Examiner finds no support in the specification for an explicit “entity record” that would store, for example, a table that linked transaction records and an entity-type classification. The claim is therefore interpreted such that the action of “linking a first plurality of transaction records based at least in part on the transaction data and the at least one entity-type classification of the at least one first entity” necessarily generates an entity record. That is, a “first entity record” is a label applied to the linking of “a first plurality of transaction records based at least in part on the transaction data” and “the at least one entity-type classification of the at least one first entity.”
Pandya generates such a linking in paragraph 0070: “The peer group module 220 generates peer group data 208 based on the transaction patterns 204 [peer group membership is an entity-type classification, hence, this step links transaction records based at least in part on the transaction data and entity-type classification of the at least one first entity, thus generating, by the at least one processor, at least one first entity record] and entity data 206. The peer group data 208 classifies entities within the entity data 206 within specified peer groups based on shared attributes. The peer group data 208 can identify entities that are assigned to each peer group [hence, associates each entity with an entity-type classification], and a set of attributes that are shared amongst the entities of the same peer group. For example, the peer group data 208 can identify a peer group that includes money service businesses and another peer group that includes banks.”
The argument is therefore found unpersuasive.
Claim Objections
Claim 10 objected to because of the following informalities. In the limitation, ”wherein the at least one entity rating prediction comprises at least one predicted level performance of the at least one capability of the at least one first entity,” Examiner respectfully suggests the intended wording is “wherein the at least one entity rating prediction comprises at least one predicted level of the at least one capability of the at least one first entity.” Further, in the limitation “utilizing, by the at least one processor, an entity classification model engine to predict at least one entity-type classification classifying the at least one first entity of the plurality of entities as a entity type,” the phrase “a entity type” should be “an entity type.”
In further examination below, the claim will be interpreted as though modified according to Examiner’s suggestions.
Appropriate correction is required.
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 1-20 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:
Claim 1 recites the limitations “generating, by the at least one processor, at least one first entity record associated with the at least one first entity by linking a first plurality of transaction records based at least in part on the transaction data and the at least one entity-type classification of the at least one first entity” and “extracting, by the at least one processor, a first plurality of entity-related transaction characteristics associated with the at least one first entity from the transaction data of the first plurality of transaction records linked to the at least one first entity record.” Examiner finds no support in the specification for an explicit “entity record” that would store, for example, a table that linked transaction records and an entity-type classification. The nearest reference is to “entity records 104,” which are used to build entity profiles and classify the entities, not to link the entity-type classification to transaction data after the classification has been made. Therefore the specification does not convey to a person skilled in the relevant art that the inventors had possession of the claimed invention at the time of application filing.
In further examination below, the claim is interpreted such that the action of “linking a first plurality of transaction records based at least in part on the transaction data and the at least one entity-type classification of the at least one first entity” necessarily generates an entity record. That is, a “first entity record” is a label applied to the linking of “a first plurality of transaction records based at least in part on the transaction data” and “the at least one entity-type classification of the at least one first entity.”
Regarding claim 10 and claim 19:
These claims recite language analogous to that of claim 1 and are rejected by the same arguments.
Regarding claims 2-9, 11-18, and 20:
These claims are dependent on claims 1, 10, or 19, and therefore recite the language of one of those claims and are rejected by the same arguments.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. Analysis is provided for the claims under the guidelines of MPEP 2106.
Regarding claim 1:
Analysis is provided for the claim under the guidelines of MPEP 2106.
Step 1:
The claim recites “a method, comprising” the steps that follow. Thus, the claim is to a process, which is a statutory category of invention.
Step 2A prong 1:
The limitation (bold only) “utilizing, by the at least one processor, an entity classification model engine to predict at least one entity-type classification classifying at least one first entity of the plurality of entities as a first entity type” recites a mental process. The claim states that entities are associated with transaction records. A person could review transaction records to classify an entity as a certain type; for example, an entity associated with selling tennis and golf equipment could be classified as a sporting goods store. This can be performed by a person using observation and judgement.
The limitation “wherein the at least one entity-type classification is indicative of at least one capability associated with the at least one first entity” further limits the mental process of predicting an entity type, but it remains a mental process. “Capability” can be interpreted to include, for example, the ability to make online sales, which a person could predict from a review of transaction records, using observation and judgement.
The limitation (bold only) “generating, by the at least one processor, at least one first entity record associated with the at least one first entity by linking a first plurality of transaction records based at least in part on the transaction data and the at least one entity-type classification of the at least one first entity” recites a mental process. This limitation, in its broadest reasonable interpretation, includes merely associating an entity-type classification (e.g., “sporting goods merchant”) with a plurality of transaction records (e.g., records of sales of sporting goods), and considering this association to be an “entity record.” This can be performed as a mental process using observation and judgement.
The limitation (bold only) “extracting, by the at least one processor, a first plurality of entity-related transaction characteristics associated with the at least one first entity from the transaction data of the first plurality of transaction records linked to the at least one first entity record, wherein the first plurality of entity-related transaction characteristics represents a first entity-related transaction pattern of transactions across the transaction data“ recites a mental process. A person could review transaction data to extract characteristics representative of a pattern (e.g., transaction volume over a time period, or that certain transactions repeat for certain entities). This could be performed by a person using observation, judgment, and evaluation.
The limitation (bold only) “utilizing, by the at least one processor, an entity rating model engine, comprising an entity rating model, to predict at least one entity rating prediction for the at least one entity based at least in part on the entity-related transaction pattern” recites a mental process. A person could review transaction data and observe patterns and use judgment predict ratings for entities based on those patterns. For example, an entity could be observed to have a transaction volume that has increased month-to-month, and could predict a higher future sales volume (i.e., a rating) on that basis.
The limitation “wherein the at least one entity rating prediction comprises at least one predicted level performance of that least one capability of the at least one first entity” further limits the mental process of predicting an entity rating, but it remains a mental process. For example, a person could review transaction data to predict an entity’s performance in the capability of online sales. This could be performed by a person using observation, judgment, and evaluation.
Step 2A prong 2:
The limitation “receiving, by at least one processor, transaction records associated with a plurality of entities; wherein the transaction records comprise transaction data for electronic transactions with the plurality of entities“ recites data gathering at a high level of generality, which is insignificant extra-solution activity (MPEP 2106.05(g)).
The element “utilizing, by the at least one processor, an entity classification model engine” recites a classification model at a high level of generality. No details of the model are given. The element merely recites the use of a computer as a tool to perform the abstract idea, and is equivalent to adding the words “apply it” or the equivalent to the judicial exception (MPEP 2106.05(f)).
The limitation “wherein the entity classification model engine comprises an entity classification model that comprises a plurality of classification parameters trained based on a plurality of annotated training transaction records “ recites model training at a high level of generality. No method of training is provided. The limitation thus merely recites the use of a computer as a tool to perform the abstract idea, and is equivalent to adding the words “apply it” or the equivalent to the judicial exception (MPEP 2106.05(f)).
The element “by the at least one processor” in the limitations “generating, by the at least one processor, at least one first entity record associated with the at least one first entity by linking a first plurality of transaction records based at least in part on the transaction data and the at least one entity-type classification of the at least one first entity” and “extracting, by the at least one processor, a first plurality of entity-related transaction characteristics associated with the at least one first entity from the transaction data of the first plurality of transaction records linked to the at least one first entity record” merely applies the abstract idea to the use of a generic computer. The elements thus merely recite the use of a computer as a tool to perform the abstract idea, and are equivalent to adding the words “apply it” or the equivalent to the judicial exception (MPEP 2106.05(f)).
The element “utilizing, by the at least one processor, an entity rating model engine, comprising an entity rating model“ recites a rating model at a high level of generality. No details of the method are given. The limitation thus merely recites the use of a computer as a tool to perform the abstract idea, and is equivalent to adding the words “apply it” or the equivalent to the judicial exception (MPEP 2106.05(f)).
The limitation “wherein the entity rating model engine comprises a plurality of trained rating parameters trained based on at least one historical entity rating prediction for the plurality of entities“ recites model training at a high level of generality. No method of training is provided. The limitation thus merely recites the use of a computer as a tool to perform the abstract idea, and is equivalent to adding the words “apply it” or the equivalent to the judicial exception (MPEP 2106.05(f)).
The following limitations describe a user interface for displaying ordered data at a high level of generality:
“generating, by the at least one processor, an entity rating interface comprising at least one entity rating prediction interface element for the at least one entity; wherein the entity rating interface comprises:”
“at least one results interface presenting a plurality of entities ordered according to a respective level of performance of the at least one capability of each respective entity, wherein the plurality of entities comprises the at least one first entity;”
“at least one second interface programmed element associated with each entity in the at least one results interface, the at least one second interface programmed element displaying the at least one updated entity rating prediction”
“causing to display, by the at least one processor, the entity rating interface on a display of at least one computing device associated with at least one user”
Displaying ordered data in a user interface is insignificant extra-solution activity that does not integrate the abstract idea into a practical application (MPEP 2106.05(g)).
The limitation “at least one first interface programmed element associated with each entity in the at least one results interface, the at least one first interface programmed element enabling a user to define at least one entity rating prediction modification so as cause the at least one processor to modify the at least one entity rating prediction to obtain at least one updated entity rating prediction” describes a method of acquiring input from the user at a high level of generality. This is mere data gathering, which is insignificant extra-solution activity that does not integrate the abstract idea into a practical application (MPEP 2106.05(g)).
Thus, the additional elements merely recite the use of a computer as a tool to perform the abstract idea or recite insignificant extra-solution activity. Taken alone, the additional elements do not integrate the abstract idea into a practical application. Considering the elements together as an ordered combination adds nothing that is not present from examining the elements individually; the additional elements merely represent the steps necessary to implement the mental processes on a computer. The elements, individually or together, do not describe an improvement in the functioning of technology.
Step 2B:
The claim as a whole does not amount to significantly more than the recited judicial exception.
These additional claim elements merely recite the use of a computer as a tool to perform the abstract idea:
“utilizing, by the at least one processor, an entity classification model engine”
“wherein the entity classification model engine comprises an entity classification model that comprises a plurality of classification parameters trained based on a plurality of annotated training transaction records “
“by the at least one processor” in the limitations “generating, by the at least one processor, at least one first entity record associated with the at least one first entity by linking a first plurality of transaction records based at least in part on the transaction data and the at least one entity-type classification of the at least one first entity” “extracting, by the at least one processor, a first plurality of entity-related transaction characteristics associated with the at least one first entity from the transaction data”
“utilizing, by the at least one processor, an entity rating model engine, comprising an entity rating model“
“wherein the entity rating model engine comprises a plurality of trained rating parameters trained based on at least one historical entity rating prediction for the plurality of entities“
The following limitations describe a user interface for displaying ordered data at a high level of generality:
“generating, by the at least one processor, an entity rating interface comprising at least one entity rating prediction interface element for the at least one entity; wherein the entity rating interface comprises:”
“at least one results interface presenting a plurality of entities ordered according to a respective level of performance of the at least one capability of each respective entity, wherein the plurality of entities comprises the at least one first entity;”
“at least one second interface programmed element associated with each entity in the at least one results interface, the at least one second interface programmed element displaying the at least one updated entity rating prediction”
“causing to display, by the at least one processor, the entity rating interface on a display of at least one computing device associated with at least one user”
Displaying ordered data in a user interface is well-understood, routine, and conventional activity in the art. For reference, see Tidwell et al., Designing Interfaces, 3rd Edition, 2019, Chapter 7, “Back To Information Architecture,” in which the common choices to be considered in designing an interface are discussed (“We discussed information architecture in Chapter 2—organizing information, independent of its visual representation. Let’s return to it for a minute. If you have a list of things to show on a screen, what are the salient nonvisual characteristics of that list? … Does the list have a natural order, such as alphabetical or by time? (See Chapter 2 for a more in-depth discussion of ways to organize data and content.) Would it make sense for a user to change the sorting order of the list? If so, what would the user sort on? If you choose to put a list into an order, would it actually make more sense as a grouping scheme, or vice versa?”).
The element “at least one first interface programmed element associated with each entity in the at least one results interface, the at least one first interface programmed element enabling a user to define at least one entity rating prediction modification so as cause the at least one processor to modify the at least one entity rating prediction to obtain at least one updated entity rating prediction” recites mere data gathering, which is recognized as well-understood, routine, and conventional activity in the art (see MPEP § 2106.05(d)(II)(i)).
Even when considered in combination, the additional elements merely add to the abstract idea or represent mere instructions to apply the abstract idea to a computer or represent insignificant extra-solution activity, which do not provide an inventive concept. The claim is not eligible under 35 U.S.C. 101.
Regarding claim 2:
For step 2A prong 1, claim 2 further limits the abstract idea in claim 1 and the same elements in claim 2 could still be performed as a mental process.
The element “wherein the entity-related transaction characteristics comprise the entity-related transaction pattern of activities associated with the transaction data and the enhanced transaction data“ merely couples the data from two sources for use in the previously described mental process of extracting the entity-related transaction characteristics. The use of enhanced transaction data does not prevent the limitation from being performed as a mental process.
For step 2A prong 2:
The element “receiving, by the at least one processor, enhanced transaction data associated with the transaction data of the transaction records; and wherein the enhanced transaction data is provided by a transaction data enrichment service” recites data gathering at a high level of generality, which is insignificant extra-solution activity that fails to integrate the abstract idea into a practical application.
Thus, the additional elements merely add to the abstract idea or recite insignificant extra-solution activity. Taken alone, the additional elements do not integrate the abstract idea into a practical application. Considering the elements together as an ordered combination adds nothing that is not present from examining the elements individually. The elements, individually or together, do not describe an improvement in the functioning of technology.
For step 2B:
The claim as a whole does not amount to significantly more than the recited judicial exception.
The element “receiving, by the at least one processor, enhanced transaction data associated with the transaction data of the transaction records; and wherein the enhanced transaction data is provided by a transaction data enrichment service” recites mere data gathering, which is recognized as well-understood, routine, and conventional activity in the art (see MPEP § 2106.05(d)(II)(i)).
Even when considered in combination, the additional merely add to the abstract idea or represent insignificant extra-solution activity, which do not provide an inventive concept. The claim is not eligible under 35 U.S.C. 101.
Regarding claim 3:
For step 2A prong 1, claim 3 further limits the abstract idea in claim 1 and the same elements in claim 3 could still be performed as a mental process.
The element “wherein the transaction records comprise transaction authorization request messages” merely limits the transaction records used in the mental process to a particular kind. The use of authorization request messages for transaction data does not prevent the limitation from being performed as a mental process.
For step 2A prong 2, and step 2B, no further elements remain to be considered. The claim as a whole does not amount to significantly more than the recited judicial exception and is ineligible under 35 U.S.C. 101.
Regarding claim 4:
For step 2A prong 1, claim 4 further limits the abstract idea in claim 3 and the same elements in claim 4 could still be performed as a mental process.
The element “wherein the first entity type comprises a physical goods supplier” merely provides a possible classification result for the mental process, but does not prevent the method from being performed as a mental process.
For step 2A prong 2, and step 2B, no further elements remain to be considered. The claim as a whole does not amount to significantly more than the recited judicial exception and is ineligible under 35 U.S.C. 101.
Regarding claim 5:
For step 2A prong 1, claim 5 further limits the abstract idea in claim 4 and the same elements in claim 4 could still be performed as a mental process.
The elements “wherein the entity-related activity pattern of activities of the activity data associated with the physical goods supplier comprises: i) a repurchase rate by each second entity of a plurality of second entities that purchase from the physical goods supplier, ii) a seasonality of purchases by each second entity of the plurality of second entities that purchase from the physical goods supplier, iii) a transaction volume by each second entity of the plurality of second entities that purchase from the physical goods supplier, and iv) a type of goods by each second entity of the plurality of second entities that purchase from the physical goods supplier” recites a list of possible patterns that can be discovered by a previously identified mental process, but does not prevent the method from being performed as a mental process.
For step 2A prong 2, and step 2B, no further elements remain to be considered. The claim as a whole does not amount to significantly more than the recited judicial exception and is ineligible under 35 U.S.C. 101.
Regarding claim 6:
For step 2A prong 1, claim 6 further limits the abstract idea in claim 1 and the same elements in claim 6 could still be performed as a mental process.
The element (bold only) “determining, by the at least one processor, a category code associated with the at least one entity” can be performed as a mental process. A person could assign a category code to an entity using observation of transaction records and judgement. For example, given transaction records, a seller could be given a code based on the kinds of goods or services sold.
The element (bold only) “and utilizing, by the at least one processor, the entity rating model engine to predict the at least one entity rating prediction for the at least one entity based at least in part on the entity-related transaction pattern and the category code” can be performed as a mental process. A person could assign an entity rating prediction using observation of activity records and category codes and judgement. For example, noting that a seller of a particular type of goods (i.e., category code) tended to ship items soon after ordering, a person could predict a high customer satisfaction (i.e., an entity rating).
Thus, the elements recite mental processes and are part of the abstract idea.
For step 2A prong 2:
The elements “by the at least one processor” and “utilizing, by the at least one processor, the entity rating model engine” in the limitations above merely apply the abstract idea to the use of a generic computer, and fail to integrate the abstract idea into a practical application.
For step 2B:
The claim as a whole does not amount to significantly more than the recited judicial exception. Even when considered in combination, the additional elements represent mere instructions to apply the abstract idea to a computer or represent insignificant extra-solution activity, which do not provide an inventive concept. The claim is not eligible under 35 U.S.C. 101.
Regarding claim 7:
For step 2A prong 1, claim 7 further limits the abstract idea in claim 1 and the same elements in claim 7 could still be performed as a mental process.
For step 2A prong 2:
The element “receiving, by the at least one processor, a user selection of the at least one entity rating prediction modification for the at least one entity to obtain the at least one updated entity rating prediction“ recites mere data gathering, which is insignificant extra-solution activity (MPEP 2106.05(g)).
The element “and training, by the at least one processor, the plurality of trained rating parameters of the entity rating model engine based on a difference between the at least one entity rating prediction and the at least one updated entity rating prediction” recites model training at a high level of generality. No particular model or method of training is described. The element merely recites the use of a computer as a tool to perform the abstract idea, and is equivalent to adding the words “apply it” or the equivalent to the judicial exception (MPEP 2106.05(f)).
For step 2B:
The claim as a whole does not amount to significantly more than the recited judicial exception.
The element “receiving, by the at least one processor, a user selection of the at least one entity rating prediction modification for the at least one entity to obtain the at least one updated entity rating prediction” recites mere data gathering, which is recognized as well-understood, routine, and conventional activity in the art (see MPEP § 2106.05(d)(II)(i)).
The limitation “and training, by the at least one processor, the plurality of trained rating parameters of the entity rating model engine based on a difference between the at least one entity rating prediction and the at least one updated entity rating prediction” merely recites the use of a computer as a tool to perform the abstract idea.
Even when considered in combination, the additional elements represent mere instructions to apply the abstract idea to a computer or represent insignificant extra-solution activity, which do not provide an inventive concept. The claim is not eligible under 35 U.S.C. 101.
Regarding claim 8:
For step 2A prong 1, claim 8 further limits the abstract idea in claim 7 and the same elements in claim 8 could still be performed as a mental process.
For step 2A prong 2:
The element “further comprising updating, by the at least one processor, the entity rating interface to represent the at least one updated entity rating prediction” recites updating data from user input at a high level of generality, and thus recites mere data gathering, which is insignificant extra-solution activity (MPEP 2106.05(g)).
For step 2B:
The element “further comprising updating, by the at least one processor, the entity rating interface to represent the at least one updated entity rating prediction” recites mere data gathering, which is recognized as well-understood, routine, and conventional activity in the art (see MPEP § 2106.05(d)(II)(i)).
Even when considered in combination, the additional elements represent mere instructions to apply the abstract idea to a computer or represent insignificant extra-solution activity, which do not provide an inventive concept. The claim is not eligible under 35 U.S.C. 101.
Regarding claim 9:
For step 2A prong 1, claim 9 further limits the abstract idea in claim 1 and the same elements in claim 9 could still be performed as a mental process.
The element (bold only) “utilizing, by the at least one processor, the entity rating model engine to predict, for the second entity, the at least one entity rating prediction of the at least one entity based at least in part on the entity-related transaction pattern and the category code so as to customize the at least one entity rating prediction of the at least one entity for the category code of the second entity” can be performed as a mental process. A person could assign an entity rating prediction using observation of activity records and category codes and judgement, and as this prediction would be for a particular entity, it would be customized for that entity. For example, noting that a seller of a particular type of goods (i.e., category code) tended to ship items soon after ordering, a person could predict a high customer satisfaction (i.e., an entity rating).
Thus, the elements recite mental processes and are part of the abstract idea.
For step 2A prong 2:
The element “receiving, by the at least one processor, a category code associated with a second entity” recites mere data gathering, which is insignificant extra-solution activity (MPEP 2106.05(g)).
The element “utilizing, by the at least one processor, the entity rating model engine” in the limitation above merely apply the abstract idea to the use of a generic computer. The element merely recites the use of a computer as a tool to perform the abstract idea, and is equivalent to adding the words “apply it” or the equivalent to the judicial exception (MPEP 2106.05(f)).
For step 2B:
The claim as a whole does not amount to significantly more than the recited judicial exception.
The element “receiving, by the at least one processor, a category code associated with a second entity” recites mere data gathering, which is recognized as well-understood, routine, and conventional activity in the art (see MPEP § 2106.05(d)(II)(i)).
The limitation “utilizing, by the at least one processor, the entity rating model engine” merely recites the use of a computer as a tool to perform the abstract idea.
Even when considered in combination, the additional elements represent mere instructions to apply the abstract idea to a computer or represent insignificant extra-solution activity, which do not provide an inventive concept. The claim is not eligible under 35 U.S.C. 101.
Regarding claim 10:
Step 1:
The claim recites “a method,” which is a series of steps or an algorithm. Thus, the claim is to a process, which is a statutory category of invention.
Step 2A prong 1:
The limitation (bold only) “utilizing, by the at least one processor, an entity classification model engine to predict at least one entity-type classification classifying the at least one first entity of the plurality of entities as a entity type” recites a mental process. The claim states that entities are associated with transaction records. A person could review transaction records to classify an entity as a certain type; for example, an entity associated with selling tennis and golf equipment could be classified as a sporting goods store. This can be performed by a person using observation and judgement.
The limitation “wherein the at least one entity-type classification is indicative of at least one capability associated with the at least one entity” further limits the mental process of predicting an entity type, but it remains a mental process. “Capability” can be interpreted to include, for example, the ability to make online sales, which a person could predict from a review of transaction records, using observation and judgement.
The limitation (bold only) “generating, by the at least one processor, at least one entity record associated with the at least one entity by linking a plurality of transaction records based at least in part on transaction data and the at least one entity-type classification of the at least one entity” recites a mental process. This limitation, in its broadest reasonable interpretation, includes merely associating an entity-type classification (e.g., “sporting goods merchant”) with a plurality of transaction records (e.g., records of sales of sporting goods), and considering this association to be an “entity record.” This can be performed as a mental process using observation and judgement.
The limitation (bold only) “extracting, by the at least one processor, entity-related transaction characteristics associated with the at least one entity record from transaction records associated with the at least one entity; wherein the transaction records comprise “ recites a mental process. A person could review transaction data to extract entity-related characteristics comprising a pattern of activities (e.g., transaction volume over a time period, or that certain transactions repeat for certain entities). This could be performed by a person using observation, judgment, and evaluation.
The limitation (bold only) “utilizing, by the at least one processor, an entity rating model engine to predict at least one entity rating prediction for the at least one entity based at least in part on the entity-related” recites a mental process. A person could review transaction data and observe patterns and use judgment predict ratings for entities based on those patterns. For example, an entity could be observed to have a transaction volume that has increased month-to-month, and could predict a higher future sales volume (i.e., a rating) on that basis.
The limitation “wherein the at least one entity rating prediction comprises at least one predicted level performance of that least one capability of the at least one first entity” further limits the mental process of predicting an entity rating, but it remains a mental process. For example, a person could review transaction data to predict an entity’s performance in the capability of online sales. This could be performed by a person using observation, judgment, and evaluation.
Step 2A prong 2:
The element “by the at least one processor” in the limitations “utilizing, by the at least one processor, an entity classification model engine to predict at least one entity-type classification classifying the at least one first entity of the plurality of entities as a entity type,” “generating, by the at least one processor, at least one entity record associated with the at least one entity by linking a plurality of transaction records based at least in part on transaction data and the at least one entity-type classification of the at least one entity,” and “extracting, by the at least one processor, entity-related transaction characteristics associated with the at least one entity record from transaction records associated with the at least one entity” merely applies the abstract idea to the use of a generic computer. The elements thus merely recite the use of a computer as a tool to perform the abstract idea, and are equivalent to adding the words “apply it” or the equivalent to the judicial exception (MPEP 2106.05(f)).
The following limitations describe a user interface for displaying ordered data at a high level of generality:
“receiving, by at least one processor via an entity rating interface, a user selection of an entity rating prediction modification for an entity rating prediction interface element of at least one entity”
“wherein the entity rating interface comprises: at least one results interface presenting a plurality of entities ordered according to a respective level of performance of at least one capability of each respective entity, wherein the plurality of entities comprises at least one first entity”
“at least one second interface programmed element associated with each entity in the at least one results interface, the at least one second interface programmed element displaying the at least one updated entity rating prediction”
“updating, by the at least one processor, the entity rating interface comprising the at least one entity rating prediction for the at least one entity”
“and causing to display, by the at least one processor, the entity rating interface on a display of at least one computing device associated with at least one user”
Displaying ordered data in a user interface is insignificant extra-solution activity that does not integrate the abstract idea into a practical application (MPEP 2106.05(g)).
The limitation “at least one first interface programmed element associated with each entity in the at least one results interface, the at least one first interface programmed element enabling a user to define the at least one entity rating prediction modification so as cause the at least one processor to modify at least one entity rating prediction to obtain at least one updated entity rating prediction” describes a method of acquiring input from the user at a high level of generality. This is mere data gathering, which is insignificant extra-solution activity that does not integrate the abstract idea into a practical application (MPEP 2106.05(g)).
The limitation “wherein the entity classification model engine comprises an entity classification model that comprises a plurality of classification parameters trained based on a plurality of annotated training transaction records“ recites model training at a high level of generality. No method of training is provided. The limitation thus merely recites the use of a computer as a tool to perform the abstract idea, and is equivalent to adding the words “apply it” or the equivalent to the judicial exception (MPEP 2106.05(f)).
The limitation “training, by the at least one processor, plurality of trained rating parameters of an entity rating model engine based on a difference between the entity-related transaction pattern and the at least one entity rating prediction modification” recites model training at a high level of generality. No method of training is provided. The limitation thus merely recites the use of a computer as a tool to perform the abstract idea, and is equivalent to adding the words “apply it” or the equivalent to the judicial exception (MPEP 2106.05(f)).
The limitation (bold only) “utilizing, by the at least one processor, an entity rating model engine to predict at least one entity rating prediction for the at least one entity based at least in part on the entity-related
The limitation “wherein the entity rating model engine comprises plurality of trained rating parameters trained based on historical entity rating predictions for a plurality of entities” recites model training at a high level of generality. No method of training is provided. The limitation thus merely recites the use of a computer as a tool to perform the abstract idea, and is equivalent to adding the words “apply it” or the equivalent to the judicial exception (MPEP 2106.05(f)).
Step 2B:
The claim as a whole does not amount to significantly more than the recited judicial exception.
The element “at least one first interface programmed element associated with each entity in the at least one results interface, the at least one first interface programmed element enabling a user to define the at least one entity rating prediction modification so as cause the at least one processor to modify at least one entity rating prediction to obtain at least one updated entity rating prediction” recites mere data gathering, which is recognized as well-understood, routine, and conventional activity in the art (see MPEP § 2106.05(d)(II)(i)).
These additional claim elements merely recite the use of a computer as a tool to perform the abstract idea:
“by the at least one processor” in the limitations “utilizing, by the at least one processor, an entity classification model engine to predict at least one entity-type classification classifying the at least one first entity of the plurality of entities as a entity type,” “generating, by the at least one processor, at least one entity record associated with the at least one entity by linking a plurality of transaction records based at least in part on transaction data and the at least one entity-type classification of the at least one entity,” and “extracting, by the at least one processor, entity-related transaction characteristics associated with the at least one entity record from transaction records associated with the at least one entity”
“wherein the entity classification model engine comprises an entity classification model that comprises a plurality of classification parameters trained based on a plurality of annotated training transaction records“
“training, by the at least one processor, plurality of trained rating parameters of an entity rating model engine based on a difference between the entity-related transaction pattern and the at least one entity rating prediction modification”
(bold only) “utilizing, by the at least one processor, an entity rating model engine to predict at least one entity rating prediction for the at least one entity based at least in part on the entity-related
“wherein the entity rating model engine comprises plurality of trained rating parameters trained based on historical entity rating predictions for a plurality of entities”
The following limitations describe a user interface for displaying ordered data at a high level of generality: “receiving, by at least one processor via an entity rating interface, a user selection of an entity rating prediction modification for an entity rating prediction interface element of at least one entity”
“wherein the entity rating interface comprises: at least one results interface presenting a plurality of entities ordered according to a respective level of performance of at least one capability of each respective entity, wherein the plurality of entities comprises at least one first entity”
“at least one second interface programmed element associated with each entity in the at least one results interface, the at least one second interface programmed element displaying the at least one updated entity rating prediction”
“updating, by the at least one processor, the entity rating interface comprising the at least one entity rating prediction for the at least one entity”
“and causing to display, by the at least one processor, the entity rating interface on a display of at least one computing device associated with at least one user”
Displaying ordered data in a user interface is well-understood, routine, and conventional activity in the art. For reference, see Tidwell et al., Designing Interfaces, 3rd Edition, 2019, Chapter 7, “Back To Information Architecture,” in which the common choices to be considered in designing an interface are discussed (“We discussed information architecture in Chapter 2—organizing information, independent of its visual representation. Let’s return to it for a minute. If you have a list of things to show on a screen, what are the salient nonvisual characteristics of that list? … Does the list have a natural order, such as alphabetical or by time? (See Chapter 2 for a more in-depth discussion of ways to organize data and content.) Would it make sense for a user to change the sorting order of the list? If so, what would the user sort on? If you choose to put a list into an order, would it actually make more sense as a grouping scheme, or vice versa?”).
Even when considered in combination, the additional elements merely add to the abstract idea or represent mere instructions to apply the abstract idea to a computer or represent insignificant extra-solution activity, which do not provide an inventive concept. The claim is not eligible under 35 U.S.C. 101.
Regarding claim 11:
For step 2A prong 1, claim 11 further limits the abstract idea in claim 10 and the same elements in claim 11 could still be performed as a mental process.
The element “wherein the entity-related transaction characteristics comprise the entity-related transaction pattern of activities associated with the transaction data and the enhanced transaction data“ merely couples the data from two sources for use in the previously described mental process of extracting the entity-related transaction characteristics. The use of enhanced transaction data does not prevent the limitation from being performed as a mental process.
For step 2A prong 2:
The element “receiving, by the at least one processor, enhanced transaction data associated with the transaction data of the transaction records; and wherein the enhanced transaction data is provided by an transaction data enrichment service” recites data gathering at a high level of generality, which is insignificant extra-solution activity that fails to integrate the abstract idea into a practical application.
Thus, the additional elements merely add to the abstract idea or recite insignificant extra-solution activity. Taken alone, the additional elements do not integrate the abstract idea into a practical application. Considering the elements together as an ordered combination adds nothing that is not present from examining the elements individually. The elements, individually or together, do not describe an improvement in the functioning of technology.
For step 2B:
The claim as a whole does not amount to significantly more than the recited judicial exception.
The element “receiving, by the at least one processor, enhanced transaction data associated with the transaction data of the transaction records; and wherein the enhanced transaction data is provided by an transaction data enrichment service” recites mere data gathering, which is recognized as well-understood, routine, and conventional activity in the art (see MPEP § 2106.05(d)(II)(i)).
Even when considered in combination, the additional merely add to the abstract idea or represent insignificant extra-solution activity, which do not provide an inventive concept. The claim is not eligible under 35 U.S.C. 101.
Regarding claim 12:
For step 2A prong 1, claim 12 further limits the abstract idea in claim 10 and the same elements in claim 12 could still be performed as a mental process.
The element “wherein the transaction records comprise transaction authorization request messages” merely limits the transaction records used in the mental process to a particular kind. The use of authorization request messages for transaction data does not prevent the limitation from being performed as a mental process.
For step 2A prong 2, and step 2B, no further elements remain to be considered. The claim as a whole does not amount to significantly more than the recited judicial exception and is ineligible under 35 U.S.C. 101.
Regarding claim 13:
For step 2A prong 1, claim 13 further limits the abstract idea in claim 12 and the same elements in claim 13 could still be performed as a mental process.
The element “wherein the first entity type comprises a physical goods supplier” merely provides a possible classification result for the mental process, but does not prevent the method from being performed as a mental process.
For step 2A prong 2, and step 2B, no further elements remain to be considered. The claim as a whole does not amount to significantly more than the recited judicial exception and is ineligible under 35 U.S.C. 101.
Regarding claim 14:
For step 2A prong 1, claim 14 further limits the abstract idea in claim 13 and the same elements in claim 14 could still be performed as a mental process.
The elements “wherein the entity-related activity pattern of activities of the activity data associated with the physical goods supplier comprises: i) a repurchase rate by each second entity of a plurality of second entities that purchase from the physical goods supplier, ii) a seasonality of purchases by each second entity of the plurality of second entities that purchase from the physical goods supplier, iii) a transaction volume by each second entity of the plurality of second entities that purchase from the physical goods supplier, and iv) a type of goods by each second entity of the plurality of second entities that purchase from the physical goods supplier” recites a list of possible patterns that can be discovered by a previously identified mental process, but does not prevent the method from being performed as a mental process.
For step 2A prong 2, and step 2B, no further elements remain to be considered. The claim as a whole does not amount to significantly more than the recited judicial exception and is ineligible under 35 U.S.C. 101.
Regarding claim 15:
For step 2A prong 1, claim 15 further limits the abstract idea in claim 10 and the same elements in claim 15 could still be performed as a mental process.
The element (bold only) “determining, by the at least one processor, a category code associated with the at least one entity” can be performed as a mental process. A person could assign a category code to an entity using observation of transaction records and judgement. For example, given transaction records, a seller could be given a code based on the kinds of goods or services sold.
The element (bold only) “and utilizing, by the at least one processor, the entity rating model engine to predict the at least one entity rating prediction for the at least one entity based at least in part on the entity-related transaction pattern and the category code” can be performed as a mental process. A person could assign an entity rating prediction using observation of activity records and category codes and judgement. For example, noting that a seller of a particular type of goods (i.e., category code) tended to ship items soon after ordering, a person could predict a high customer satisfaction (i.e., an entity rating).
Thus, the elements recite mental processes and are part of the abstract idea.
For step 2A prong 2:
The elements “by the at least one processor” and “utilizing, by the at least one processor, the entity rating model engine” in the limitations above merely apply the abstract idea to the use of a generic computer, and fail to integrate the abstract idea into a practical application.
For step 2B:
The claim as a whole does not amount to significantly more than the recited judicial exception. Even when considered in combination, the additional elements represent mere instructions to apply the abstract idea to a computer or represent insignificant extra-solution activity, which do not provide an inventive concept. The claim is not eligible under 35 U.S.C. 101.
Regarding claim 16:
For step 2A prong 1, claim 16 further limits the abstract idea in claim 10 and the same elements in claim 16 could still be performed as a mental process.
The limitation (bold only) “utilizing, by the at least one processor, an entity classification model engine to predict at least one entity-type classification classifying at least one first entity of the plurality of entities as a first entity type” recites a mental process. The claim states that entities are associated with transaction records. A person could review transaction records to classify an entity as a certain type; for example, an entity associated with selling tennis and golf equipment could be classified as a sporting goods store. This can be performed by a person using observation and judgement.
Step 2A prong 2:
The limitation “receiving, by the at least one processor, the transaction records associated with the at least one entity” recites data gathering at a high level of generality, which is insignificant extra-solution activity (MPEP 2106.05(g)).
The limitation (bold only) “utilizing, by the at least one processor, an entity classification model engine to predict at least one entity-type classification classifying the at least one entity as a first entity type” recites a predictive model at a high level of generality. No particular model is provided. The limitation thus merely recites the use of a computer as a tool to perform the abstract idea, and is equivalent to adding the words “apply it” or the equivalent to the judicial exception (MPEP 2106.05(f)).
The limitation “wherein the entity classification model engine comprises a plurality of classification parameters trained based on annotated training transaction data” recites model training at a high level of generality. No method of training is provided. The limitation thus merely recites the use of a computer as a tool to perform the abstract idea, and is equivalent to adding the words “apply it” or the equivalent to the judicial exception (MPEP 2106.05(f)).
Step 2B:
The claim as a whole does not amount to significantly more than the recited judicial exception.
The element “receiving, by the at least one processor, the transaction records associated with the at least one entity” recites mere data gathering, which is recognized as well-understood, routine, and conventional activity in the art (see MPEP § 2106.05(d)(II)(i)).
These additional claim elements merely recite the use of a computer as a tool to perform the abstract idea:
(bold only) “utilizing, by the at least one processor, an entity classification model engine to predict at least one entity-type classification classifying the at least one entity as a first entity type”
“wherein the entity classification model engine comprises a plurality of classification parameters trained based on annotated training transaction data”
The claim as a whole does not amount to significantly more than the recited judicial exception. Even when considered in combination, the additional elements represent mere instructions to apply the abstract idea to a computer or represent insignificant extra-solution activity, which do not provide an inventive concept. The claim is not eligible under 35 U.S.C. 101.
Regarding claim 17:
For step 2A prong 1, claim 17 further limits the abstract idea in claim 16 and the same elements in claim 17 could still be performed as a mental process.
For step 2A prong 2:
The element “further comprising updating, by the at least one processor, the entity rating interface to represent the at least one updated entity rating prediction” recites updating data from user input at a high level of generality, and thus recites mere data gathering, which is insignificant extra-solution activity (MPEP 2106.05(g)).
For step 2B:
The element “further comprising updating, by the at least one processor, the entity rating interface to represent the at least one updated entity rating prediction” recites mere data gathering, which is recognized as well-understood, routine, and conventional activity in the art (see MPEP § 2106.05(d)(II)(i)).
Even when considered in combination, the additional elements represent mere instructions to apply the abstract idea to a computer or represent insignificant extra-solution activity, which do not provide an inventive concept. The claim is not eligible under 35 U.S.C. 101.
Regarding claim 18:
For step 2A prong 1, claim 18 further limits the abstract idea in claim 10 and the same elements in claim 18 could still be performed as a mental process.
The element (bold only) “utilizing, by the at least one processor, the entity rating model engine to predict, for the second entity, the at least one entity rating prediction of the at least one entity based at least in part on the entity-related transaction pattern and the category code so as to customize the at least one entity rating prediction of the at least one entity for the category code of the second entity” can be performed as a mental process. A person could assign an entity rating prediction using observation of activity records and category codes and judgement, and as this prediction would be for a particular entity, it would be customized for that entity. For example, noting that a seller of a particular type of goods (i.e., category code) tended to ship items soon after ordering, a person could predict a high customer satisfaction (i.e., an entity rating).
Thus, the elements recite mental processes and are part of the abstract idea.
For step 2A prong 2:
The element “receiving, by the at least one processor, a category code associated with a second entity associated with the transaction records” recites mere data gathering, which is insignificant extra-solution activity (MPEP 2106.05(g)).
The element “utilizing, by the at least one processor, the entity rating model engine” in the limitation above merely apply the abstract idea to the use of a generic computer. The element merely recites the use of a computer as a tool to perform the abstract idea, and is equivalent to adding the words “apply it” or the equivalent to the judicial exception (MPEP 2106.05(f)).
For step 2B:
The claim as a whole does not amount to significantly more than the recited judicial exception.
The element “receiving, by the at least one processor, a category code associated with a second entity associated with the transaction records” recites mere data gathering, which is recognized as well-understood, routine, and conventional activity in the art (see MPEP § 2106.05(d)(II)(i)).
The limitation “utilizing, by the at least one processor, the entity rating model engine” merely recites the use of a computer as a tool to perform the abstract idea.
Even when considered in combination, the additional elements represent mere instructions to apply the abstract idea to a computer or represent insignificant extra-solution activity, which do not provide an inventive concept. The claim is not eligible under 35 U.S.C. 101.
Regarding claims 19 and 20:
These claims recite a “A system comprising: at least one processor in communication with at least one non-transitory computer readable medium comprising software instructions that, when executed, cause the at least one processor to perform steps to” which falls under the statutory category of a machine for Step 1. The steps performed are analogous to claims 1 and 7, respectively, and the claims are rejected by the same arguments.
Claim Rejections - 35 USC § 103
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 1-4, 6-13, and 14-20 rejected under 35 U.S.C. 103 over Pandya et al., US Pre-Grant Publication No. 2020/0184487 (hereafter Pandya) in view of Cohen et al., Us Pre-Grant Publication No. 2020/0151824 (hereafter Cohen) and Liden, US Pre-Grant Publication No. 2016/0267086 (hereafter Liden).
Regarding claim 1 and analogous claim 19:
Pandya teaches:
“A method comprising”: Pandya, paragraph 0035, “FIG. 6 is a diagram that illustrates an example of a computer system that may be applied to any of the computer-implemented methods [method] and other techniques described herein”; Pandya, paragraph 0104, “The system 600 includes a processor 610, a memory 620, a storage device 630, and an input/output device 640.”
“receiving, by at least one processor, transaction records associated with a plurality of entities; wherein the transaction records comprise transaction data for electronic transactions with the plurality of entities”: Pandya, paragraph 0042, “Transactions, as described herein, can include electronic transactions [electronic transactions], e g., transactions recorded on or using a computing device, or paper transactions, e.g., transactions recorded on paper without the use of a computer”; Pandya, paragraph 0075, “The peer group module 310 also receives entity transaction data 304 for the entities 302A-C [receiving, by at least one processor, transaction records associated with a plurality of entities; wherein the transaction records comprise transaction data]. In some instances, the entity transaction data is obtained from a data upload provided by the regulator, e.g., using batch data upload through the batch management module 132 of the client portal 130. In other instances, the entity transaction data 304 is extracted from an associated database, such as a database associated with the computing device 120 that stores transaction data for entities that are monitored by the end-user 104. In some other instances, the entity transaction data 304 is obtained from an external data source, i.e., a data source that is external from, and independently managed by, a third-party data provider that is independent and distinct from the institution associated with the end-user 104.”
“utilizing, by the at least one processor, an entity classification model engine to predict at least one entity-type classification classifying at least one first entity of the plurality of entities as a first entity type”: Pandya, paragraph 0070, “The peer group module 220 generates peer group data 208 based on the transaction patterns 204 and entity data 206. The peer group data 208 classifies entities within the entity data 206 within specified peer groups based on shared attributes. The peer group data 208 can identify entities that are assigned to each peer group, and a set of attributes that are shared amongst the entities of the same peer group. For example, the peer group data 208 can identify a peer group that includes money service businesses and another peer group that includes banks [utilizing, by the at least one processor, an entity classification model engine to predict at least one entity-type classification classifying at least one first entity of the plurality of entities as a first entity type].”
“wherein the entity classification model engine comprises an entity classification model that comprises a plurality of classification parameters trained based on a plurality of annotated training transaction records”: Pandya, paragraph 0062, “As described throughout, once deployed, the system applies a set of trained transaction models, peer group classifications and associated attributes, to predict potentially anomalous activity in production transaction data with minimal or no human input [trained based on a plurality of annotated training transaction records].”
“wherein the at least one entity-type classification is indicative of at least one capability associated with the at least one first entity”: Pandya, paragraph 0040, “In addition, a ‘peer group’ refers to a classification for a group of entities based on a set of shared attributes. For example, a peer group can include money service businesses that conduct currency exchange transactions [indicative of at least one capability associated with the at least one first entity] in Washington D.C. In this example, the entities assigned the peer group share the attributes of business classification, i.e., money service businesses, and geographic location, i.e., Washington D.C.”
“generating, by the at least one processor, at least one first entity record associated with the at least one first entity by linking a first plurality of transaction records based at least in part on the transaction data and the at least one entity-type classification of the at least one first entity”: Pandya, paragraph 0070, “The peer group module 220 generates peer group data 208 based on the transaction patterns 204 [peer group membership is an entity-type classification, hence, this step links transaction records based at least in part on the transaction data and entity-type classification of the at least one first entity, thus generating, by the at least one processor, at least one first entity record] and entity data 206. The peer group data 208 classifies entities within the entity data 206 within specified peer groups based on shared attributes. The peer group data 208 can identify entities that are assigned to each peer group [hence, associates each entity with an entity-type classification], and a set of attributes that are shared amongst the entities of the same peer group. For example, the peer group data 208 can identify a peer group that includes money service businesses and another peer group that includes banks.”
“extracting, by the at least one processor, a first plurality of entity-related transaction characteristics associated with the at least one first entity from the transaction data of the first plurality of transaction records linked to the at least one first entity record”: Pandya, paragraph 0047, “The transaction processing device 110 includes software modules that perform various processes relating to training, generating, and applying transaction models to identify potentially anomalous activity. The feature generation module can be used to identify attributes associated with peer group classifications for entities [a first plurality of entity-related transaction characteristics associated with the at least one first entity from the transaction data of the first plurality of transaction records linked to the at least one first entity record]”; Pandya, paragraph 0070, “The peer group module 220 generates peer group data 208 based on the transaction patterns 204 and entity data 206 [the first plurality of transaction records linked to the at least one first entity record]. The peer group data 208 classifies entities within the entity data 206 within specified peer groups based on shared attributes. The peer group data 208 can identify entities that are assigned to each peer group, and a set of attributes that are shared amongst the entities of the same peer group. For example, the peer group data 208 can identify a peer group that includes money service businesses and another peer group that includes banks.”
”wherein the first plurality of entity-related transaction characteristics represents a first entity-related transaction pattern of transactions across the transaction data”: Pandya, paragraph 0041, “As described herein, ‘attribute’ refers to an individual measurable property or characteristic of a phenomenon being observed using machine learning or pattern recognition. Attributes, as described here in, can be numeric, e.g., average transaction value, or structural, e.g., strings and graphs used for syntactic pattern recognition [represents a first entity-related transaction pattern of transactions across the transaction data].”
Pandya does not explicitly teach:
“utilizing, by the at least one processor, an entity rating model engine, comprising an entity rating model, to predict at least one entity rating prediction for the at least one entity based at least in part on the entity-related transaction pattern; wherein the entity rating model engine comprises a plurality of trained rating parameters trained based on at least one historical entity rating prediction for the plurality of entities”
“wherein the at least one entity rating prediction comprises at least one predicted level performance of that least one capability of the at least one first entity”
“generating, by the at least one processor, an entity rating interface comprising
“wherein the plurality of entities comprises the at least one first entity”
“at least one first interface programmed element associated with each entity in the at least one results interface, the at least one first interface programmed element enabling a user to define at least one entity rating prediction modification so as cause the at least one processor to modify the at least one entity rating prediction to obtain at least one updated entity rating prediction”
“and at least one second interface programmed element associated with each entity in the at least one results interface, the at least one second interface programmed element displaying the at least one updated entity rating prediction; and”
“causing to display, by the at least one processor, the entity rating interface on a display of at least one computing device associated with at least one user”
Cohen teaches:
“utilizing, by the at least one processor, an entity rating model engine, comprising an entity rating model, to predict at least one entity rating prediction for the at least one entity based at least in part on the entity-related transaction pattern; wherein the entity rating model engine comprises a plurality of trained rating parameters trained based on at least one historical entity rating prediction for the plurality of entities”: Cohen, paragraph 0047, “A computer can begin performance of the process 200 by obtaining a first data structure that includes fields structuring data that represents a transaction (210) [an entity rating model engine, comprising an entity rating model, to predict at least one entity rating prediction for the at least one entity based at least in part on the entity-related transaction pattern]. In some implementations, the transaction can include a financial transaction such as a bank deposit, debit or credit transaction, or the like. In some implementations, data representing the transaction can be obtained from a financial institutions database. In other implementations, data representing the transaction can be obtained from one or more merchant computers 103”; Cohen, paragraph 0033, “In some implementations, if the current transaction is determined to be related to a previous transaction, it can provide a useful signal to the similarity engine regarding classification of the current transaction. For example, if the current transaction is determined to be similar to a previous transaction, then the current transaction may have the same taxonomy classification as the related previous transaction. In other implementations, if the current transaction is determined to not be related to a previous transaction, it can similarly provide a useful signal to the similarity engine regarding classification of the current transaction [at least one historical entity rating prediction for the plurality of entities]”; Cohen, paragraph 0039, “The predictive algorithm 170 can be trained to predict a particular entity outcome based on the predictive algorithm's 170 processing of the input data structure 165 [the entity rating model engine comprises a plurality of trained rating parameters trained].”
“wherein the at least one entity rating prediction comprises at least one predicted level performance of that least one capability of the at least one first entity”: Cohen, paragraph 0039, “In some implementations, the system 100 can include only a single predictive model 170 is used to predict output data 175 that is indicative of a likelihood of a particular entity outcome such as probability of default, probability that an entity is likely to become delinquent on a future obligation, a probability that an entity that becomes delinquent will subsequently be able to satisfy the delinquent obligation, a probability that an entity will purchase a product or service, a probability that an entity will achieve a financial goal or objective, probability that a point-of-sale debit or credit authorization request is fraudulent, or the like [wherein the at least one entity rating prediction comprises at least one predicted level performance of that least one capability of the at least one first entity].”
Cohen and Pandya are both related to the same field of endeavor (machine language predictions from transactional data). Pandya teaches a method for classifying an entity from transaction data. Cohen teaches for predicting future entity performance from classifications of transaction data. It would have been obvious to a person having ordinary skill in the art prior to the effective filing date of the claimed invention to have combined the entity performance prediction of Cohen to the teachings of Pandya to arrive at the present invention, in order to users to make informed financial decisions related to entities, as stated in Cohen, paragraphs 0008-0010, “In some implementations, the value that represents a likely outcome for the entity includes a probability of default by the entity. In some implementations, the value that represents a likely outcome for the entity includes a probability that the entity will become delinquent on a future financial obligation. In some implementations, the value that represents a likely outcome for the entity includes a probability that and entity that becomes delinquent will subsequently be able to satisfy the obligation.”
Liden teaches:
“generating, by the at least one processor, an entity rating interface comprising at least one entity rating prediction interface element for the at least one entity; wherein the entity rating interface comprises: at least one results interface presenting a plurality of entities ordered according to a respective level of performance of the at least one capability of each respective entity”: Liden, paragraph 0049, “The user may also choose the facet that is used to order the content items 140. In the example shown, the content items 140 are ordered by ‘Score’ [presenting a plurality of entities ordered according to a respective level of performance of the at least one capability of each respective entity] in the display region 320. However, the user may change the ordering by selecting the facet ‘Author’ or the facet ‘Time’. Ordering by other facets may be supported by the user interface.”
“wherein the plurality of entities comprises the at least one first entity”: Liden, paragraph 0051, “FIG. 4 is an illustration of a user interface 405 that is display on the client device after the user selected the ‘score’ hyperlink from the user interface 305, or automatically after the user finished viewing the content item 140. As shown, some or all of the facets and facet values associated with content item are displayed to the user in a display area 410 [wherein the plurality of entities comprises the at least one first entity]. The particular scores shown for the facet values may have been generated by the user on a previous occasion, or may be predictions that are based on the scores provided by the user for other content items 140.”
“at least one first interface programmed element associated with each entity in the at least one results interface, the at least one first interface programmed element enabling a user to define at least one entity rating prediction modification so as cause the at least one processor to modify the at least one entity rating prediction to obtain at least one updated entity rating prediction”: Liden, paragraph 0052, “The user may change the scores for some or all of the facet values in the user interface elements of the display area 410 that correspond to the facet values. After changing the scores, the user may submit the changes by selecting a user interface element 415 labeled ‘Update Scores’. The content item provider 160 may update the facet value scores for the content item 140 and the user in the content item score data 165 [enabling a user to define at least one entity rating prediction modification so as cause the at least one processor to modify the at least one entity rating prediction to obtain at least one updated entity rating prediction]. In addition, if the changes to the facet values scores affect the overall content item score for the content item 140, the content item provider 160 may update the content item feed 140 to reflect the changes.”
“and at least one second interface programmed element associated with each entity in the at least one results interface, the at least one second interface programmed element displaying the at least one updated entity rating prediction; and”: Liden, paragraph 0052, “The user may change the scores for some or all of the facet values in the user interface elements of the display area 410 that correspond to the facet values. After changing the scores, the user may submit the changes by selecting a user interface element 415 labeled ‘Update Scores’. The content item provider 160 may update the facet value scores for the content item 140 and the user in the content item score data 165. In addition, if the changes to the facet values scores affect the overall content item score for the content item 140, the content item provider 160 may update the content item feed 140 to reflect the changes [the at least one second interface programmed element displaying the at least one updated entity rating prediction].”
“causing to display, by the at least one processor, the entity rating interface on a display of at least one computing device associated with at least one user”: Liden, paragraph 0011, “FIG. 3 is an illustration of an example client device showing an example user interface.”
Liden and Pandya as modified by Cohen are both related to the same field of endeavor (machine learning entity rating). Pandya as modified by Cohen teaches a method of determining scores or ratings for entities. The teachings of Liden provide a user interface for the ordered display and modification of ratings. It would have been obvious to a person having ordinary skill in the art prior to the effective filing date of the claimed invention to have combined the user interface of Liden to the teachings of Pandya as modified by Cohen to arrive at the present invention, in order to provide a mechanism for users to review and possibly correct performance score predictions.
Regarding claim 2:
Pandya as modified by Cohen and Liden teaches the method of claim 1.
Cohen further teaches:
“receiving, by the at least one processor, enhanced transaction data associated with the transaction data of the transaction records; and wherein the enhanced transaction data is provided by a transaction data enrichment service”: Cohen, paragraph 0048, “The computer can continue performance of the process 200 by determining a level of similarity of the transaction represented by the first data structure to each of a plurality of predetermined categories such as transaction classifications of a taxonomy classifier (220) [wherein the enhanced transaction data is provided by an transaction data enrichment service]”; The computer can continue performance of the process 200 by generating an input data structure that includes fields structuring data representing (I) at least a portion of the data representing the transaction that is structured by the first data structure and (II) data describing the determined category (240) [enhanced transaction data associated with the transaction data of the transaction records].”
“wherein the entity-related transaction characteristics comprise the entity-related transaction pattern of activities associated with the transaction data and the enhanced transaction data”: Cohen, paragraph 0050, “The computer can continue performance of process 200 by providing the input data structure generated at stage 240 as an input to a predictive algorithm that has been trained to determine a value that represents a likely outcome for an entity that initiated a transaction (250) [wherein the entity-related transaction characteristics comprise the entity-related transaction pattern of activities associated with the transaction data and the enhanced transaction data].”
Cohen and Pandya are combinable for the rationale given under claim 1.
Regarding claim 3:
Pandya as modified by Cohen and Liden teaches the method of claim 1.
Cohen further teaches “wherein the transaction records comprise transaction authorization request messages”: Cohen, paragraph 0026, “In manner similar to the analysis, by the system, of bank transaction records, a single electronic point-of-sale transaction record can be evaluated to predict a likelihood of fraud. In such implementations, a financial transaction record that is submitted with request for point-of-sale authorization can be processed by the system in the same manner described with respect to the bank transaction records above [wherein the transaction records comprise transaction authorization request messages].”
Cohen and Pandya are combinable for the rationale given under claim 1.
Regarding claim 4:
Pandya as modified by Cohen and Liden teaches the method of claim 3.
Pandya further teaches “wherein the first entity type comprises a physical goods supplier”: Pandya, paragraph 0040, “For example, an entity that is a restaurant can be assigned to a food service peer group [wherein the first entity type comprises a physical goods supplier] as well as a peer group for entities having a gross revenue exceeding a threshold value.”
Regarding claim 6:
Pandya as modified by Cohen and Liden teaches the method of claim 1.
Pandya further teaches “determining, by the at least one processor, a category code associated with the at least one entity”: Pandya, paragraph 0076, “The natural language processor 312 processes the entity transaction data 304 in a similar manner as discussed above with respect to the natural language processor 212. For example, the natural language processor 312 can classify transactions in the entity transaction data 304 as belonging to certain transaction categories [a category code associated with the at least one entity], segment transactions that are associated with the same entity, among others. The transaction information is then provided to the transaction aggregator 314.”
Cohen further teaches “and utilizing, by the at least one processor, the entity rating model engine to predict the at least one entity rating prediction for the at least one entity based at least in part on the entity-related transaction pattern and the category code”: Cohen, paragraph 0039, “The data structure 165 having (i) fields representing information 165a that includes information that represents at least a portion of the transaction information of the current transaction 105 and (ii) fields structuring information that represents a taxonomy classification can be provided as an input to a predictive algorithm 170 [the at least one entity rating prediction for the at least one entity based at least in part on the entity-related transaction pattern and the category code].”
Cohen and Pandya are combinable for the rationale given under claim 1.
Regarding claim 7 and analogous claim 20:
Pandya as modified by Cohen and Liden teaches the method of claim 1.
Pandya further teaches “and training, by the at least one processor, the plurality of trained rating parameters of the entity rating model engine based on a difference between the at least one entity rating prediction and the at least one updated entity rating prediction”: Pandya, paragraph 0060, “The system can use different types of training techniques to train the transaction models to predict potentially anomalous transaction activity. For example, the system can use a supervised learning technique where a target or outcome variable is predicted from a given set of predictors, and used to generate function that maps inputs to desired outputs [training, by the at least one processor, the plurality of trained rating parameters of the entity rating model engine based on a difference between the at least one entity rating prediction and the at least one updated entity rating prediction]. In this example, the training process is iterated until the model achieves a desired level of accuracy on training data, e.g., 85% percent accuracy in identifying potentially anomalous activity in training transaction data.”
Liden teaches “receiving, by the at least one processor, a user selection of the at least one entity rating prediction modification for the at least one entity to obtain the at least one updated entity rating prediction”: Liden, paragraph 0052, “The user may change the scores for some or all of the facet values in the user interface elements of the display area 410 that correspond to the facet values. After changing the scores, the user may submit the changes by selecting a user interface element 415 labeled ‘Update Scores’ [receiving, by the at least one processor, a user selection of the at least one entity rating prediction modification for the at least one entity to obtain the at least one updated entity rating prediction].”
Liden and Pandya are combinable for the rationale given under claim 1.
Regarding claim 8:
Pandya as modified by Cohen and Liden teaches the method of claim 7.
Liden further teaches “further comprising updating, by the at least one processor, the entity rating interface to represent the at least one updated entity rating prediction”: Liden, paragraph 0052, “The user may change the scores for some or all of the facet values in the user interface elements of the display area 410 that correspond to the facet values. After changing the scores, the user may submit the changes by selecting a user interface element 415 labeled ‘Update Scores’. The content item provider 160 may update the facet value scores for the content item 140 and the user in the content item score data 165. In addition, if the changes to the facet values scores affect the overall content item score for the content item 140, the content item provider 160 may update the content item feed 140 to reflect the changes [further comprising updating, by the at least one processor, the entity rating interface to represent the at least one updated entity rating prediction].”
Liden and Pandya are combinable for the rationale given under claim 1.
Regarding claim 9:
Pandya as modified by Cohen and Liden teaches the method of claim 1.
Pandya further teaches “receiving, by the at least one processor, a category code associated with a second entity”: Pandya, paragraph 0076, “The natural language processor 312 processes the entity transaction data 304 in a similar manner as discussed above with respect to the natural language processor 212. For example, the natural language processor 312 can classify transactions in the entity transaction data 304 as belonging to certain transaction categories [a category code associated with a second entity], segment transactions that are associated with the same entity, among others. The transaction information is then provided to the transaction aggregator 314.”
Cohen further teaches “and utilizing, by the at least one processor, the entity rating model engine to predict, for the second entity, the at least one entity rating prediction of the at least one entity based at least in part on the entity-related transaction pattern and the category code so as to customize the at least one entity rating prediction of the at least one entity for the category code of the second entity”: Cohen, paragraph 0039, “The data structure 165 having (i) fields representing information 165a that includes information that represents at least a portion of the transaction information of the current transaction 105 and (ii) fields structuring information that represents a taxonomy classification can be provided as an input [the entity-related transaction pattern and the category code] to a predictive algorithm 170. The predictive algorithm 170 can be trained to predict a particular entity outcome based on the predictive algorithm's 170 processing of the input data structure 165 [predict, for the second entity, the at least one entity rating prediction].”
Cohen and Pandya are combinable for the rationale given under claim 1.
Regarding claim 10:
Pandya teaches:
“A method comprising”: Pandya, paragraph 0035, “FIG. 6 is a diagram that illustrates an example of a computer system that may be applied to any of the computer-implemented methods [method] and other techniques described herein”; Pandya, paragraph 0104, “The system 600 includes a processor 610, a memory 620, a storage device 630, and an input/output device 640.”
“utilizing, by the at least one processor, an entity classification model engine to predict at least one entity-type classification classifying the at least one first entity of the plurality of entities as a entity type”: Pandya, paragraph 0070, “The peer group module 220 generates peer group data 208 based on the transaction patterns 204 and entity data 206. The peer group data 208 classifies entities within the entity data 206 within specified peer groups based on shared attributes. The peer group data 208 can identify entities that are assigned to each peer group, and a set of attributes that are shared amongst the entities of the same peer group. For example, the peer group data 208 can identify a peer group that includes money service businesses and another peer group that includes banks [utilizing, by the at least one processor, an entity classification model engine to predict at least one entity-type classification classifying the at least one first entity of the plurality of entities as a entity type].”
“wherein the entity classification model engine comprises an entity classification model that comprises a plurality of classification parameters trained based on a plurality of annotated training transaction records”: Pandya, paragraph 0062, “As described throughout, once deployed, the system applies a set of trained transaction models, peer group classifications and associated attributes, to predict potentially anomalous activity in production transaction data with minimal or no human input [trained based on a plurality of annotated training transaction records].”
“wherein the at least one entity-type classification is indicative of at least one capability associated with the at least one entity”: Pandya, paragraph 0040, “In addition, a ‘peer group’ refers to a classification for a group of entities based on a set of shared attributes. For example, a peer group can include money service businesses that conduct currency exchange transactions [indicative of at least one capability associated with the at least one first entity] in Washington D.C. In this example, the entities assigned the peer group share the attributes of business classification, i.e., money service businesses, and geographic location, i.e., Washington D.C.” “generating, by the at least one processor, at least one entity record associated with the at least one entity by linking a plurality of transaction records based at least in part on transaction data and the at least one entity-type classification of the at least one entity”: Pandya, paragraph 0070, “The peer group module 220 generates peer group data 208 based on the transaction patterns 204 [peer group membership is an entity-type classification, hence, this step links a plurality of transaction records based at least in part on transaction data and the at least one entity-type classification of the at least one entity, thus generating, by the at least one processor, at least one entity record] and entity data 206. The peer group data 208 classifies entities within the entity data 206 within specified peer groups based on shared attributes. The peer group data 208 can identify entities that are assigned to each peer group [hence, associates each entity with an entity-type classification], and a set of attributes that are shared amongst the entities of the same peer group. For example, the peer group data 208 can identify a peer group that includes money service businesses and another peer group that includes banks.”
“training, by the at least one processor, plurality of trained rating parameters of an entity rating model engine based on a difference between the entity-related transaction pattern and the at least one entity rating prediction modification“: Pandya, paragraph 0060, “The system can use different types of training techniques to train the transaction models to predict potentially anomalous transaction activity. For example, the system can use a supervised learning technique where a target or outcome variable is predicted from a given set of predictors, and used to generate function that maps inputs to desired outputs [training, by the at least one processor, plurality of trained rating parameters of an entity rating model engine based on a difference between the entity-related transaction pattern and the at least one entity rating prediction modification]. In this example, the training process is iterated until the model achieves a desired level of accuracy on training data, e.g., 85% percent accuracy in identifying potentially anomalous activity in training transaction data.”
“extracting, by the at least one processor, entity-related transaction characteristics associated with the at least one entity record from transaction records associated with the at least one entity”: Pandya, paragraph 0047, “The transaction processing device 110 includes software modules that perform various processes relating to training, generating, and applying transaction models to identify potentially anomalous activity. The feature generation module can be used to identify attributes associated with peer group classifications for entities [extracting, by the at least one processor, entity-related transaction characteristics associated with the at least one entity record from transaction records associated with the at least one entity].”
“wherein the transaction records comprise transaction data for electronic activities associated with the at least one entity”: Pandya, paragraph 0042, “Transactions, as described herein, can include electronic transactions [wherein the transaction records comprise transaction data for electronic activities], e g., transactions recorded on or using a computing device, or paper transactions, e.g., transactions recorded on paper without the use of a computer”;
“wherein the entity-related transaction characteristics comprise entity-related transaction pattern of activities across the transaction data”: Pandya, paragraph 0041, “As described herein, ‘attribute’ refers to an individual measurable property or characteristic of a phenomenon being observed using machine learning or pattern recognition. Attributes, as described here in, can be numeric, e.g., average transaction value, or structural, e.g., strings and graphs used for syntactic pattern recognition [the entity-related transaction characteristics comprise entity-related transaction pattern of activities across the transaction data].”
Pandya does not explicitly teach:
“receiving, by at least one processor via an entity rating interface, a user selection of an entity rating prediction modification for an entity rating prediction interface element of at least one entity”
“wherein the entity rating interface comprises: at least one results interface presenting a plurality of entities ordered according to a respective level of performance of at least one capability of each respective entity, wherein the plurality of entities comprises at least one first entity“
“at least one first interface programmed element associated with each entity in the at least one results interface, the at least one first interface programmed element enabling a user to define the at least one entity rating prediction modification so as cause the at least one processor to modify at least one entity rating prediction to obtain at least one updated entity rating prediction, and at least one second interface programmed element associated with each entity in the at least one results interface, the at least one second interface programmed element displaying the at least one updated entity rating prediction”
“updating, by the at least one processor, the entity rating interface comprising the at least one entity rating prediction for the at least one entity“
“and causing to display, by the at least one processor, the entity rating interface on a display of at least one computing device associated with at least one user”
“utilizing, by the at least one processor, an entity rating model engine to predict at least one entity rating prediction for the at least one entity based at least in part on the entity-related pattern; wherein the entity rating model engine comprises plurality of trained rating parameters trained based on historical entity rating predictions for a plurality of entities”
“wherein the at least one entity rating prediction comprises at least one predicted level performance of that least one capability of the at least one first entity”
Cohen teaches:
“utilizing, by the at least one processor, an entity rating model engine to predict at least one entity rating prediction for the at least one entity based at least in part on the entity-related pattern; wherein the entity rating model engine comprises plurality of trained rating parameters trained based on historical entity rating predictions for a plurality of entities”: Cohen, paragraph 0047, “A computer can begin performance of the process 200 by obtaining a first data structure that includes fields structuring data that represents a transaction (210) [an entity rating model engine to predict at least one entity rating prediction for the at least one entity based at least in part on the entity-related]. In some implementations, the transaction can include a financial transaction such as a bank deposit, debit or credit transaction, or the like. In some implementations, data representing the transaction can be obtained from a financial institutions database. In other implementations, data representing the transaction can be obtained from one or more merchant computers 103”; Cohen, paragraph 0033, “In some implementations, if the current transaction is determined to be related to a previous transaction, it can provide a useful signal to the similarity engine regarding classification of the current transaction. For example, if the current transaction is determined to be similar to a previous transaction, then the current transaction may have the same taxonomy classification as the related previous transaction. In other implementations, if the current transaction is determined to not be related to a previous transaction, it can similarly provide a useful signal to the similarity engine regarding classification of the current transaction [based on historical entity rating predictions for a plurality of entities]”; Cohen, paragraph 0039, “The predictive algorithm 170 can be trained to predict a particular entity outcome based on the predictive algorithm's 170 processing of the input data structure 165 [the entity rating model engine comprises a plurality of trained rating parameters].”
“wherein the at least one entity rating prediction comprises at least one predicted level performance of that least one capability of the at least one first entity”: Cohen, paragraph 0039, “In some implementations, the system 100 can include only a single predictive model 170 is used to predict output data 175 that is indicative of a likelihood of a particular entity outcome such as probability of default, probability that an entity is likely to become delinquent on a future obligation, a probability that an entity that becomes delinquent will subsequently be able to satisfy the delinquent obligation, a probability that an entity will purchase a product or service, a probability that an entity will achieve a financial goal or objective, probability that a point-of-sale debit or credit authorization request is fraudulent, or the like [at least one predicted level performance of that least one capability of the at least one first entity].”
Cohen and Pandya are both related to the same field of endeavor (machine language predictions from transactional data). Pandya teaches a method for classifying an entity from transaction data. Cohen teaches for predicting future entity performance from classifications of transaction data. It would have been obvious to a person having ordinary skill in the art prior to the effective filing date of the claimed invention to have combined the entity performance prediction of Cohen to the teachings of Pandya to arrive at the present invention, in order to users to make informed financial decisions related to entities, as stated in Cohen, paragraphs 0008-0010, “In some implementations, the value that represents a likely outcome for the entity includes a probability of default by the entity. In some implementations, the value that represents a likely outcome for the entity includes a probability that the entity will become delinquent on a future financial obligation. In some implementations, the value that represents a likely outcome for the entity includes a probability that and entity that becomes delinquent will subsequently be able to satisfy the obligation.”
Liden teaches:
“receiving, by at least one processor via an entity rating interface, a user selection of an entity rating prediction modification for an entity rating prediction interface element of at least one entity”: Liden, paragraph 0052, “The user may change the scores for some or all of the facet values in the user interface elements of the display area 410 that correspond to the facet values. After changing the scores, the user may submit the changes by selecting a user interface element 415 labeled ‘Update Scores’ [receiving, by at least one processor via an entity rating interface, a user selection of an entity rating prediction modification for an entity rating prediction interface element of at least one entity].”
“wherein the entity rating interface comprises: at least one results interface presenting a plurality of entities ordered according to a respective level of performance of at least one capability of each respective entity, wherein the plurality of entities comprises at least one first entity“: Liden, paragraph 0049, “The user may also choose the facet that is used to order the content items 140. In the example shown, the content items 140 are ordered by ‘Score’ [presenting a plurality of entities ordered according to a respective level of performance of at least one capability of each respective entity, wherein the plurality of entities comprises at least one first entity] in the display region 320. However, the user may change the ordering by selecting the facet ‘Author’ or the facet ‘Time’. Ordering by other facets may be supported by the user interface.”
“at least one first interface programmed element associated with each entity in the at least one results interface, the at least one first interface programmed element enabling a user to define the at least one entity rating prediction modification so as cause the at least one processor to modify at least one entity rating prediction to obtain at least one updated entity rating prediction, and at least one second interface programmed element associated with each entity in the at least one results interface, the at least one second interface programmed element displaying the at least one updated entity rating prediction”: Liden, paragraph 0052, “The user may change the scores for some or all of the facet values in the user interface elements of the display area 410 that correspond to the facet values. After changing the scores, the user may submit the changes by selecting a user interface element 415 labeled ‘Update Scores [enabling a user to define the at least one entity rating prediction modification so as cause the at least one processor to modify at least one entity rating prediction to obtain at least one updated entity rating prediction]’. The content item provider 160 may update the facet value scores for the content item 140 and the user in the content item score data 165. In addition, if the changes to the facet values scores affect the overall content item score for the content item 140, the content item provider 160 may update the content item feed 140 to reflect the changes [displaying the at least one updated entity rating prediction].”
“updating, by the at least one processor, the entity rating interface comprising the at least one entity rating prediction for the at least one entity“: Liden, paragraph 0052, “The user may change the scores for some or all of the facet values in the user interface elements of the display area 410 that correspond to the facet values. After changing the scores, the user may submit the changes by selecting a user interface element 415 labeled ‘Update Scores’. The content item provider 160 may update the facet value scores for the content item 140 and the user in the content item score data 165. In addition, if the changes to the facet values scores affect the overall content item score for the content item 140, the content item provider 160 may update the content item feed 140 to reflect the changes [updating, by the at least one processor, the entity rating interface comprising the at least one entity rating prediction for the at least one entity].”
“and causing to display, by the at least one processor, the entity rating interface on a display of at least one computing device associated with at least one user”: Liden, paragraph 0011, “FIG. 3 is an illustration of an example client device showing an example user interface.”
Liden and Pandya as modified by Cohen are both related to the same field of endeavor (machine learning entity rating). Pandya as modified by Cohen teaches a method of determining scores or ratings for entities. The teachings of Liden provide a user interface for the ordered display and modification of ratings. It would have been obvious to a person having ordinary skill in the art prior to the effective filing date of the claimed invention to have combined the user interface of Liden to the teachings of Pandya as modified by Cohen to arrive at the present invention, in order to provide a mechanism for users to review and possibly correct performance score predictions.
Regarding claim 11:
Pandya as modified by Cohen and Liden teaches the method of claim 10.
Cohen further teaches:
“receiving, by the at least one processor, enhanced transaction data associated with the transaction data of the transaction records; and wherein the enhanced transaction data is provided by an transaction data enrichment service”: Cohen, paragraph 0048, “The computer can continue performance of the process 200 by determining a level of similarity of the transaction represented by the first data structure to each of a plurality of predetermined categories such as transaction classifications of a taxonomy classifier (220) [wherein the enhanced transaction data is provided by an transaction data enrichment service]”; The computer can continue performance of the process 200 by generating an input data structure that includes fields structuring data representing (I) at least a portion of the data representing the transaction that is structured by the first data structure and (II) data describing the determined category (240) [enhanced transaction data associated with the transaction data of the transaction records].”
“wherein the entity-related transaction characteristics comprise the entity-related transaction pattern of activities associated with the transaction data and the enhanced transaction data”: Cohen, paragraph 0050, “The computer can continue performance of process 200 by providing the input data structure generated at stage 240 as an input to a predictive algorithm that has been trained to determine a value that represents a likely outcome for an entity that initiated a transaction (250) [wherein the entity-related transaction characteristics comprise the entity-related transaction pattern of activities associated with the transaction data and the enhanced transaction data].”
Cohen and Pandya are combinable for the rationale given under claim 10.
Regarding claim 12:
Pandya as modified by Cohen and Liden teaches the method of claim 10.
Cohen further teaches “wherein the transaction records comprise transaction authorization request messages”: Cohen, paragraph 0026, “In manner similar to the analysis, by the system, of bank transaction records, a single electronic point-of-sale transaction record can be evaluated to predict a likelihood of fraud. In such implementations, a financial transaction record that is submitted with request for point-of-sale authorization can be processed by the system in the same manner described with respect to the bank transaction records above [wherein the transaction records comprise transaction authorization request messages].”
Cohen and Pandya are combinable for the rationale given under claim 10.
Regarding claim 13:
Pandya as modified by Cohen and Liden teaches the method of claim 12.
Pandya further teaches “wherein the at least one entity comprises an entity type comprising a physical goods supplier”: Pandya, paragraph 0040, “For example, an entity that is a restaurant can be assigned to a food service peer group [wherein the first entity type comprises a physical goods supplier] as well as a peer group for entities having a gross revenue exceeding a threshold value.”
Regarding claim 15:
Pandya as modified by Cohen and Liden teaches the method of claim 10.
Pandya further teaches “determining, by the at least one processor, a category code associated with the at least one entity”: Pandya, paragraph 0076, “The natural language processor 312 processes the entity transaction data 304 in a similar manner as discussed above with respect to the natural language processor 212. For example, the natural language processor 312 can classify transactions in the entity transaction data 304 as belonging to certain transaction categories [a category code associated with the at least one entity], segment transactions that are associated with the same entity, among others. The transaction information is then provided to the transaction aggregator 314.”
Regarding claim 16:
Pandya as modified by Cohen and Liden teaches the method of claim 10.
Pandya further teaches:
“receiving, by the at least one processor, the transaction records associated with the at least one entity”: Pandya, paragraph 0075, “The peer group module 310 also receives entity transaction data 304 for the entities 302A-C [receiving, by the at least one processor, the transaction records associated with the at least one entity]. In some instances, the entity transaction data is obtained from a data upload provided by the regulator, e.g., using batch data upload through the batch management module 132 of the client portal 130. In other instances, the entity transaction data 304 is extracted from an associated database, such as a database associated with the computing device 120 that stores transaction data for entities that are monitored by the end-user 104. In some other instances, the entity transaction data 304 is obtained from an external data source, i.e., a data source that is external from, and independently managed by, a third-party data provider that is independent and distinct from the institution associated with the end-user 104.”
“and utilizing, by the at least one processor, an entity classification model engine to predict at least one entity-type classification classifying the at least one entity as a first entity type”: Pandya, paragraph 0070, “The peer group module 220 generates peer group data 208 based on the transaction patterns 204 and entity data 206. The peer group data 208 classifies entities within the entity data 206 within specified peer groups based on shared attributes. The peer group data 208 can identify entities that are assigned to each peer group, and a set of attributes that are shared amongst the entities of the same peer group. For example, the peer group data 208 can identify a peer group that includes money service businesses and another peer group that includes banks [utilizing, by the at least one processor, an entity classification model engine to predict at least one entity-type classification classifying at least one first entity of the plurality of entities as a first entity type].”
“wherein the entity classification model engine comprises a plurality of classification parameters trained based on annotated training transaction data”: Pandya, paragraph 0062, “As described throughout, once deployed, the system applies a set of trained transaction models, peer group classifications and associated attributes, to predict potentially anomalous activity in production transaction data with minimal or no human input [trained based on a plurality of annotated training transaction records].”
Regarding claim 17:
Pandya as modified by Cohen and Liden teaches the method of claim 16.
Liden further teaches “further comprising updating, by the at least one processor, the entity rating interface to represent the at least one entity rating prediction modification”: Liden, paragraph 0052, “The user may change the scores for some or all of the facet values in the user interface elements of the display area 410 that correspond to the facet values. After changing the scores, the user may submit the changes by selecting a user interface element 415 labeled ‘Update Scores’. The content item provider 160 may update the facet value scores for the content item 140 and the user in the content item score data 165. In addition, if the changes to the facet values scores affect the overall content item score for the content item 140, the content item provider 160 may update the content item feed 140 to reflect the changes [further comprising updating, by the at least one processor, the entity rating interface to represent the at least one entity rating prediction modification].”
Liden and Pandya and modified by Cohen are combinable for the rationale given under claim 10.
Regarding claim 18:
Pandya as modified by Cohen and Liden teaches the method of claim 10.
Pandya further teaches “receiving, by the at least one processor, a category code associated with a second entity associated with the transaction records”: Pandya, paragraph 0076, “The natural language processor 312 processes the entity transaction data 304 in a similar manner as discussed above with respect to the natural language processor 212. For example, the natural language processor 312 can classify transactions in the entity transaction data 304 as belonging to certain transaction categories [a category code associated with a second entity], segment transactions that are associated with the same entity, among others. The transaction information is then provided to the transaction aggregator 314.”
Cohen further teaches “and utilizing, by the at least one processor, the entity rating model engine to predict, for the second entity, the at least one entity rating prediction of the at least one entity based at least in part on the entity-related transaction pattern and the category code so as to customize the at least one entity rating prediction of the at least one entity for the category code of the second entity”: Cohen, paragraph 0039, “The data structure 165 having (i) fields representing information 165a that includes information that represents at least a portion of the transaction information of the current transaction 105 and (ii) fields structuring information that represents a taxonomy classification can be provided as an input [the entity-related transaction pattern and the category code] to a predictive algorithm 170. The predictive algorithm 170 can be trained to predict a particular entity outcome based on the predictive algorithm's 170 processing of the input data structure 165 [predict, for the second entity, the at least one entity rating prediction].”
Cohen and Pandya are combinable for the rationale given under claim 10.
Claims 5 and 14 rejected as unpatentable under 35 U.S.C. 103 Pandya as modified by Cohen and Liden in view of Boal, US Pre-Grant Publication No. 2014/0180793 (hereafter Boal).
Regarding claim 5:
Pandya as modified by Cohen and Liden teaches the method of claim 4.
Pandya further teaches:
“wherein the entity-related transaction pattern of activities of the transaction data associated with the physical goods supplier comprises: i) a repurchase rate by each second entity of a plurality of second entities that purchase from the physical goods supplier”: Pandya, paragraph 0043, “For example, a transaction of an entity is deemed likely to be ‘anomalous’ if the transaction deviates from historical transactions of the entity or if the transaction deviates from historical transactions of other entities that are classified to the same peer group as the entity [a repurchase rate by each second entity of a plurality of second entities that purchase from the physical goods supplier].”
“iii) a transaction volume by each second entity of the plurality of second entities that purchase from the physical goods supplier, and“: Pandya, paragraph 0057, “The transaction patterns can represent different scenarios in which anomalous activity can be identified. For example, transaction patterns for money laundering can be associated with a set of evidence factors, such as a large number of currency transactions below a reporting requirement, transactions with a large number of foreign accounts, or a larger volume of transactions compared to other entities in a peer group [a transaction volume by each second entity of the plurality of second entities that purchase from the physical goods supplier].”
Pandya as modified by Cohen and Liden does not explicitly teach:
“ii) a seasonality of purchases by each second entity of the plurality of second entities that purchase from the physical goods supplier“
“iv) a type of goods by each second entity of the plurality of second entities that purchase from the physical goods supplier”
Boal teaches:
“ii) a seasonality of purchases by each second entity of the plurality of second entities that purchase from the physical goods supplier“: Boal, paragraph 0060, “In an embodiment, the selection of the recommended offer includes at least one of: customer clustering and macro-personalization or recommendations based on customer demographics and transaction data, tailoring recommendations to users having similar behavior using collaborative filtering techniques, weighing recommendations based on seasonality [a seasonality of purchases] or recency, or predictive filtering to anticipate a future customer purchase based on a historical purchase. In an embodiment, the selection of the recommended offer includes determining a list of offer recommendations on a periodic basis.“
“iv) a type of goods by each second entity of the plurality of second entities that purchase from the physical goods supplier”: Boal, paragraph 0077, “Product data store 1354 may include information about a plurality of different items, including both goods and services, that may be found in receipts or that may be the subject of an offer. The information may include identifiers such as Universal Product Codes (UPCs) and/or stock-keeping unit (SKU) numbers, taxonomical categorizations [a type of goods], names, descriptions, nutritional information, product specifications, price histories, inventory levels, and/or any other suitable metadata.”
Boal and Pandya are both related to the same field of endeavor (transaction analysis). It would have been obvious to a person having ordinary skill in the art prior to the effective filing date of the claimed invention to have combined the transaction details of Boal to the teachings of Pandya to arrive at the present invention, in order to produce results better tailored to each entity, as stated in Boal, paragraph 0014, “[…] for improving the ability of offer providers, offer distributors and retailers to customize digital offer selection, delivery, utilization, management, monetization and redemption analysis, and for otherwise increasing the efficiency and financial return of the digital offer industry and market.”
Regarding claim 14:
Pandya as modified by Cohen and Liden teaches the method of claim 13.
Pandya further teaches:
“wherein the entity-related transaction pattern of activities of the transaction data associated with the physical goods supplier comprises: i) a repurchase rate by each second entity of a plurality of second entities that purchase from the physical goods supplier”: Pandya, paragraph 0043, “For example, a transaction of an entity is deemed likely to be ‘anomalous’ if the transaction deviates from historical transactions of the entity or if the transaction deviates from historical transactions of other entities that are classified to the same peer group as the entity [a repurchase rate by each second entity of a plurality of second entities that purchase from the physical goods supplier].”
“iii) a transaction volume by each second entity of the plurality of second entities that purchase from the physical goods supplier, and“: Pandya, paragraph 0057, “The transaction patterns can represent different scenarios in which anomalous activity can be identified. For example, transaction patterns for money laundering can be associated with a set of evidence factors, such as a large number of currency transactions below a reporting requirement, transactions with a large number of foreign accounts, or a larger volume of transactions compared to other entities in a peer group [a transaction volume by each second entity of the plurality of second entities that purchase from the physical goods supplier].”
Pandya as modified by Cohen and Liden does not explicitly teach:
“ii) a seasonality of purchases by each second entity of the plurality of second entities that purchase from the physical goods supplier“
“iv) a type of goods by each second entity of the plurality of second entities that purchase from the physical goods supplier”
Boal teaches:
“ii) a seasonality of purchases by each second entity of the plurality of second entities that purchase from the physical goods supplier“: Boal, paragraph 0060, “In an embodiment, the selection of the recommended offer includes at least one of: customer clustering and macro-personalization or recommendations based on customer demographics and transaction data, tailoring recommendations to users having similar behavior using collaborative filtering techniques, weighing recommendations based on seasonality [a seasonality of purchases] or recency, or predictive filtering to anticipate a future customer purchase based on a historical purchase. In an embodiment, the selection of the recommended offer includes determining a list of offer recommendations on a periodic basis.“
“iv) a type of goods by each second entity of the plurality of second entities that purchase from the physical goods supplier”: Boal, paragraph 0077, “Product data store 1354 may include information about a plurality of different items, including both goods and services, that may be found in receipts or that may be the subject of an offer. The information may include identifiers such as Universal Product Codes (UPCs) and/or stock-keeping unit (SKU) numbers, taxonomical categorizations [a type of goods], names, descriptions, nutritional information, product specifications, price histories, inventory levels, and/or any other suitable metadata.”
Boal and Pandya are both related to the same field of endeavor (transaction analysis). It would have been obvious to a person having ordinary skill in the art prior to the effective filing date of the claimed invention to have combined the transaction details of Boal to the teachings of Pandya to arrive at the present invention, in order to produce results better tailored to each entity, as stated in Boal, paragraph 0014, “[…] for improving the ability of offer providers, offer distributors and retailers to customize digital offer selection, delivery, utilization, management, monetization and redemption analysis, and for otherwise increasing the efficiency and financial return of the digital offer industry and market.”
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Erenrich et al., US Pre-Grant Publication No. 2018/0292959, discloses a computer-based method for analyzing the performance of entities, and predicting the future performance of entities, using transaction data.
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.
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/VAS/ Examiner, Art Unit 2129
/MICHAEL J HUNTLEY/Supervisory Patent Examiner, Art Unit 2129
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