DETAILED ACTION
Claims 1-11, 13-14, and 16-22 are presented for examination.
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Rejections - 35 USC § 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 1-11, 13-14, and 16-22 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter.
Claims 1-11, 13-14, and 16-22 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claimed invention is directed to generating prediction information related to demand (abstract) without significantly more.
Step
Analysis
1: Statutory Category?
Yes – The claims fall within at least one of the four categories of patent eligible subject matter. Process (claims 1-11), Apparatus (claims 13, 16-22), Article of Manufacture (claim 14)
Independent claims:
Step
Analysis
2A – Prong 1: Judicial Exception Recited?
Yes – Aside from the additional elements identified in Step 2A – Prong 2 below, the claims recite:
[Claims 1, 13, 14] A method of generating a prediction information, comprising:
acquiring feature data corresponding to a target object for a plurality of object demand influence features;
determining an object category corresponding to the target object according to the feature data;
determining at least one information to be predicted for the target object according to the object category;
generating at least one first feature demand prediction information for a target time according to at least one first feature demand prediction model and the feature data, wherein the at least one first feature demand prediction model is in one-to-one correspondence with the at least one information to be predicted, and the first feature demand prediction model is an interpretable model; and
inputting the at least one first feature demand prediction information and the feature data into a pre-trained second feature demand prediction model, so as to generate at least one second feature demand prediction information and a total demand prediction information for the target time, wherein the second feature demand prediction model is an uninterpretable model.
It is noted that, even though all of the claims recite various operations of inputting data into a pre-trained model, there is no actual training of a model or use of machine learning (for example) explicitly required and performed within the scope of the claims. Even though paragraph 64 of Applicant’s Specification states, “On this basis, the problem of uninterpretable prediction process caused by using only deep learning neural network may be avoided” and paragraph 80 states that “the second feature demand prediction model may be a deep learning neural network model,” the details of an “uninterpretable model” in the claims do not necessarily limit the “uninterpretable model” to a deep learning neural network and the claims also do not preclude the “uninterpretable model” from being any model for which at least some details of the model are not fully known or made available to a particular entity, for example.
Aside from the additional elements, the aforementioned claim details exemplify the abstract idea(s) of a mental process (since the details include concepts performed in the human mind, including an observation, evaluation, judgment, and/or opinion). As explained in MPEP § 2106(a)(2)(C)(III), “The courts consider a mental process (thinking) that ‘can be performed in the human mind, or by a human using a pen and paper’ to be an abstract idea. CyberSource Corp. v. Retail Decisions, Inc., 654 F.3d 1366, 1372, 99 USPQ2d 1690, 1695 (Fed. Cir. 2011). As the Federal Circuit explained, ‘methods which can be performed mentally, or which are the equivalent of human mental work, are unpatentable abstract ideas the ‘basic tools of scientific and technological work’ that are open to all.’’ 654 F.3d at 1371, 99 USPQ2d at 1694 (citing Gottschalk v. Benson, 409 U.S. 63, 175 USPQ 673 (1972)).” The limitations reproduced above, as drafted, are a process that, under its broadest reasonable interpretation, covers performance of the limitations in the mind but for the recitation of generic computer components. That is, other than reciting the additional elements identified in Step 2A – Prong 2 below, nothing in the claim elements precludes the steps from practically being performed in the mind and/or by a human using a pen and paper. For example, but for the recitations of generic computer and other processing components (identified in Step 2A – Prong 2 below), the respectively recited steps/functions of the claims, as drafted and set forth above, are a process that, under its broadest reasonable interpretation, covers performance of the limitations in the mind and/or with the use of pen and paper. A human user can perform all of the operations of the claims cited above. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind (and/or with pen and paper) but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claims recite an abstract idea.
Aside from the additional elements, the aforementioned claim details exemplify a method of organizing human activity (since the details include examples of commercial or legal interactions, including advertising, marketing or sales activities or behaviors, and/or business relations and managing personal behavior or relationships or interactions between people, including social activities, teaching, and following rules or instructions). More specifically, the evaluated process is related to generating prediction information related to demand (abstract), which (under its broadest reasonable interpretation) is an example of marketing and sales activities related to demand (i.e., organizing human activity); therefore, aside from the recitations of generic computer and other processing components (identified in Step 2A – Prong 2 below), the limitations identified in the more detailed claim listing above encompass the abstract idea of organizing human activity.
2A – Prong 2: Integrated into a Practical Application?
No – The judicial exception(s) is/are not integrated into a practical application.
Claim 1 does not incorporate any additional elements and, thus, is directed to the abstract ideas per se.
Claim 13 includes an electronic device, comprising: one or more processors; and a storage device configured to store one or more programs, wherein the one or more programs, when executed by the one or more processors, cause the one or more processors to at least perform the recited operations.
Claim 14 includes a non-transitory computer readable medium storing a computer program, wherein the computer program, when executed by a processor, at least performs the recited operations.
It is noted that, even though all of the claims recite various operations of inputting data into a pre-trained model, there is no actual training of a model or use of machine learning (for example) explicitly required and performed within the scope of the claims. Even though paragraph 64 of Applicant’s Specification states, “On this basis, the problem of uninterpretable prediction process caused by using only deep learning neural network may be avoided” and paragraph 80 states that “the second feature demand prediction model may be a deep learning neural network model,” the details of an “uninterpretable model” in the claims do not necessarily limit the “uninterpretable model” to a deep learning neural network and the claims also do not preclude the “uninterpretable model” from being any model for which at least some details of the model are not fully known or made available to a particular entity, for example. At best, as currently claimed, the use of pre-trained models might present a general link to technology.
The claims as a whole merely describe how to generally “apply” the abstract idea(s) in a computer environment. The claimed processing elements are recited at a high level of generality and are merely invoked as a tool to perform the abstract idea(s). Simply implementing the abstract idea(s) on a general-purpose processor is not a practical application of the abstract idea(s); Applicant’s specification discloses that the invention may be implemented using general-purpose processing elements and other generic components (Spec: ¶¶ 31-33).
The use of a processor/processing elements (e.g., as recited in all of the claims) facilitates generic processor operations. The use of a memory or machine-readable media with executable instructions facilitates generic processor operations.
The additional elements are recited at a high-level of generality (i.e., as generic processing elements performing generic computer functions) such that the incorporation of the additional processing elements amounts to no more than mere instructions to apply the judicial exception(s) using generic computer components. There is no indication in the Specification that the steps/functions of the claims require any inventive programming or necessitate any specialized or other inventive computer components (i.e., the steps/functions of the claims may be implemented using capabilities of general-purpose computer components). Accordingly, the additional elements do not integrate the abstract ideas into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claims are directed to an abstract idea(s).
The processing components presented in the claims simply utilize the capabilities of a general-purpose computer and are, thus, merely tools to implement the abstract idea(s). As seen in MPEP § 2106.05(a)(I) and § 2106.05(f)(2), the court found that accelerating a process when the increased speed solely comes from the capabilities of a general-purpose computer is not sufficient to show an improvement in computer-functionality and it amounts to a mere invocation of computers or machinery as a tool to perform an existing process (see FairWarning IP, LLC v. Iatric Sys., 839 F.3d 1089, 1095, 120 USPQ2d 1293, 1296 (Fed. Cir. 2016)).
There is no transformation or reduction of a particular article to a different state or thing recited in the claims.
Additionally, even when considering the operations of the additional elements as an ordered combination, the ordered combination does not amount to significantly more than what is present in the claims when each operation is considered separately.
2B: Claim(s) Provide(s) an Inventive Concept?
No – The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception(s). As discussed above with respect to integration of the abstract idea(s) into a practical application, the use of the additional elements to perform the steps identified in Step 2A – Prong 1 above amounts to no more than mere instructions to apply the exceptions using a generic computer component(s). Mere instructions to apply an exception using a generic computer component(s) cannot provide an inventive concept. The claims are not patent eligible.
Dependent claims:
Step
Analysis
2A – Prong 1: Judicial Exception Recited?
Yes – Aside from the additional elements identified in Step 2A – Prong 2 below, the claims recite:
[Claims 2, 16] wherein determining at least one information to be predicted for the target object according to the object category comprises:
determining, in response to determining that the object category is a long-tail object category, a demand trend feature information as an information to be predicted.
[Claims 3, 17] wherein determining at least one information to be predicted for the target object according to the object category comprises:
determining, in response to determining that the object category is a first object category, a similar object demand prediction information as an information to be predicted, wherein an object corresponding to the first object category has no value transfer data.
[Claims 4, 18] wherein determining at least one information to be predicted for the target object according to the object category comprises:
determining, in response to determining that the object category is a second object category, a sensitive information corresponding to the target object, wherein value transfer data of an object corresponding to the second object category meets a preset transfer condition; and
determining, in response to determining that the sensitive information indicates that the target object is an object of which a value attribute transformation meets a preset transformation condition, a first value related feature influence information and a demand trend feature information as an information to be predicted respectively.
[Claim 5] wherein after determining, in response to determining that the sensitive information indicates that the target object is an object of which a value attribute transformation meets a preset transformation condition, a first value related feature influence information and a demand trend feature information as an information to be predicted respectively, the method further comprises:
determining, in response to determining that the sensitive information indicates that an association degree between value transfer data corresponding to the target object and an object flow information meets a preset association condition, a second value related feature influence information and the demand trend feature information as an information to be predicted respectively.
[Claim 19] determining, in response to determining that the sensitive information indicates that an association degree between value transfer data corresponding to the target object and an object flow information meets a preset association condition, a second value related feature influence information and the demand trend feature information as an information to be predicted respectively.
[Claims 6, 20] wherein determining at least one information to be predicted for the target object according to the object category comprises:
determining, in response to determining that the object category is a seasonal object category, a sensitive information corresponding to the target object; and
determining, in response to determining that the sensitive information indicates that the target object is an object of which a value attribute transformation meets a preset transformation condition, a first value related feature influence information, a demand trend feature information and a seasonal feature influence information as an information to be predicted respectively.
[Claims 7, 21] wherein generating at least one first feature demand prediction information for a target time according to at least one first feature demand prediction model and the feature data comprises:
for each information to be predicted among the at least one information to be predicted, performing a first input step, comprising:
determining, in response to determining that the information to be predicted is a demand trend feature information, demand trend feature data corresponding to the demand trend feature information among the feature data;
determining a first feature demand prediction model corresponding to the demand trend feature information as a demand trend information prediction model; and
inputting the demand trend feature data into the demand trend information prediction model pre-trained, so as to output a demand trend prediction information as a first feature demand prediction information for the target time.
[Claims 8, 22] for each information to be predicted among the at least one information to be predicted, performing a second input step, comprising:
determining, in response to determining that the information to be predicted is a first value related feature influence information, first value related feature data corresponding to a first value related feature among the feature data;
determining a first feature demand prediction model corresponding to the first value related feature influence information as a first demand information prediction model; and
inputting the demand trend prediction information and the first value related feature data into the first demand information prediction model pre-trained, so as to output a first demand prediction information under influence of the first value related feature as a first feature demand prediction information for the target time.
[Claim 9] for each information to be predicted among the at least one information to be predicted, performing a second input step, comprising:
determining, in response to determining that the information to be predicted is a second value related feature influence information, second value related feature data corresponding to a second value related feature among the feature data;
determining a first feature demand prediction model corresponding to the second value related feature influence information as a second demand information prediction model; and
inputting the demand trend prediction information and the second value related feature data into the second demand information prediction model pre-trained, so as to output a second demand prediction information under influence of the second value related feature as a first feature demand prediction information for the target time.
[Claim 10] for each information to be predicted among the at least one information to be predicted, performing a third input step, comprising:
determining, in response to determining that the information to be predicted is a seasonal feature influence information, seasonal feature data corresponding to a seasonal feature among the feature data;
determining a first feature demand prediction model corresponding to the seasonal feature influence information as a third demand information prediction model; and
inputting the seasonal feature data and the demand trend prediction information into the third demand information prediction model pre-trained, so as to output a third demand prediction information under influence of the seasonal feature as a first feature demand prediction information for the target time.
[Claim 11] performing replenishment processing on the target object according to the at least one second feature demand prediction information and the total demand prediction information.
The dependent claims further present details of the abstract ideas identified in regard to the independent claims.
It is noted that, even though all of the claims recite various operations of inputting data into a pre-trained model, there is no actual training of a model or use of machine learning (for example) explicitly required and performed within the scope of the claims. Even though paragraph 64 of Applicant’s Specification states, “On this basis, the problem of uninterpretable prediction process caused by using only deep learning neural network may be avoided” and paragraph 80 states that “the second feature demand prediction model may be a deep learning neural network model,” the details of an “uninterpretable model” in the claims do not necessarily limit the “uninterpretable model” to a deep learning neural network and the claims also do not preclude the “uninterpretable model” from being any model for which at least some details of the model are not fully known or made available to a particular entity, for example.
Aside from the additional elements, the aforementioned claim details exemplify the abstract idea(s) of a mental process (since the details include concepts performed in the human mind, including an observation, evaluation, judgment, and/or opinion). As explained in MPEP § 2106(a)(2)(C)(III), “The courts consider a mental process (thinking) that ‘can be performed in the human mind, or by a human using a pen and paper’ to be an abstract idea. CyberSource Corp. v. Retail Decisions, Inc., 654 F.3d 1366, 1372, 99 USPQ2d 1690, 1695 (Fed. Cir. 2011). As the Federal Circuit explained, ‘methods which can be performed mentally, or which are the equivalent of human mental work, are unpatentable abstract ideas the ‘basic tools of scientific and technological work’ that are open to all.’’ 654 F.3d at 1371, 99 USPQ2d at 1694 (citing Gottschalk v. Benson, 409 U.S. 63, 175 USPQ 673 (1972)).” The limitations reproduced above, as drafted, are a process that, under its broadest reasonable interpretation, covers performance of the limitations in the mind but for the recitation of generic computer components. That is, other than reciting the additional elements identified in Step 2A – Prong 2 below, nothing in the claim elements precludes the steps from practically being performed in the mind and/or by a human using a pen and paper. For example, but for the recitations of generic computer and other processing components (identified in Step 2A – Prong 2 below), the respectively recited steps/functions of the claims, as drafted and set forth above, are a process that, under its broadest reasonable interpretation, covers performance of the limitations in the mind and/or with the use of pen and paper. A human user can perform all of the operations of the claims cited above. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind (and/or with pen –––and paper) but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claims recite an abstract idea.
Aside from the additional elements, the aforementioned claim details exemplify a method of organizing human activity (since the details include examples of commercial or legal interactions, including advertising, marketing or sales activities or behaviors, and/or business relations and managing personal behavior or relationships or interactions between people, including social activities, teaching, and following rules or instructions). More specifically, the evaluated process is related to generating prediction information related to demand (abstract), which (under its broadest reasonable interpretation) is an example of marketing and sales activities related to demand (i.e., organizing human activity); therefore, aside from the recitations of generic computer and other processing components (identified in Step 2A – Prong 2 below), the limitations identified in the more detailed claim listing above encompass the abstract idea of organizing human activity.
2A – Prong 2: Integrated into a Practical Application?
No – The judicial exception(s) is/are not integrated into a practical application.
The dependent claims include the additional elements of their independent claims.
Claims 1-11 do not incorporate any additional elements and, thus, are directed to the abstract ideas per se.
Claims 13 and 16-22 include an electronic device, comprising: one or more processors; and a storage device configured to store one or more programs, wherein the one or more programs, when executed by the one or more processors, cause the one or more processors to at least perform the recited operations.
Claim 14 includes a non-transitory computer readable medium storing a computer program, wherein the computer program, when executed by a processor, at least performs the recited operations.
It is noted that, even though all of the claims recite various operations of inputting data into a pre-trained model, there is no actual training of a model or use of machine learning (for example) explicitly required and performed within the scope of the claims. Even though paragraph 64 of Applicant’s Specification states, “On this basis, the problem of uninterpretable prediction process caused by using only deep learning neural network may be avoided” and paragraph 80 states that “the second feature demand prediction model may be a deep learning neural network model,” the details of an “uninterpretable model” in the claims do not necessarily limit the “uninterpretable model” to a deep learning neural network and the claims also do not preclude the “uninterpretable model” from being any model for which at least some details of the model are not fully known or made available to a particular entity, for example. At best, as currently claimed, the use of pre-trained models might present a general link to technology.
The claims as a whole merely describe how to generally “apply” the abstract idea(s) in a computer environment. The claimed processing elements are recited at a high level of generality and are merely invoked as a tool to perform the abstract idea(s). Simply implementing the abstract idea(s) on a general-purpose processor is not a practical application of the abstract idea(s); Applicant’s specification discloses that the invention may be implemented using general-purpose processing elements and other generic components (Spec: ¶¶ 31-33).
The use of a processor/processing elements (e.g., as recited in all of the claims) facilitates generic processor operations. The use of a memory or machine-readable media with executable instructions facilitates generic processor operations.
The additional elements are recited at a high-level of generality (i.e., as generic processing elements performing generic computer functions) such that the incorporation of the additional processing elements amounts to no more than mere instructions to apply the judicial exception(s) using generic computer components. There is no indication in the Specification that the steps/functions of the claims require any inventive programming or necessitate any specialized or other inventive computer components (i.e., the steps/functions of the claims may be implemented using capabilities of general-purpose computer components). Accordingly, the additional elements do not integrate the abstract ideas into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claims are directed to an abstract idea(s).
The processing components presented in the claims simply utilize the capabilities of a general-purpose computer and are, thus, merely tools to implement the abstract idea(s). As seen in MPEP § 2106.05(a)(I) and § 2106.05(f)(2), the court found that accelerating a process when the increased speed solely comes from the capabilities of a general-purpose computer is not sufficient to show an improvement in computer-functionality and it amounts to a mere invocation of computers or machinery as a tool to perform an existing process (see FairWarning IP, LLC v. Iatric Sys., 839 F.3d 1089, 1095, 120 USPQ2d 1293, 1296 (Fed. Cir. 2016)).
There is no transformation or reduction of a particular article to a different state or thing recited in the claims.
Additionally, even when considering the operations of the additional elements as an ordered combination, the ordered combination does not amount to significantly more than what is present in the claims when each operation is considered separately.
2B: Claim(s) Provide(s) an Inventive Concept?
No – The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception(s). As discussed above with respect to integration of the abstract idea(s) into a practical application, the use of the additional elements to perform the steps identified in Step 2A – Prong 1 above amounts to no more than mere instructions to apply the exceptions using a generic computer component(s). Mere instructions to apply an exception using a generic computer component(s) cannot provide an inventive concept. The claims are not patent eligible.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1, 3-10, 13-14, and 17-22 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Cohen et al. (US 2024/0249299).
[Claim 1] Cohen discloses a method of generating a prediction information (abstract – “…applying an explainability model to the decision model to generate one or more predictors or drivers of the output of the decision model, wherein the one or more predictors or drivers (1) are features of the channel affinity sub-model and/or the content affinity sub-model and (2) provide an explanation of an effect of the action on the target variable.”), comprising:
acquiring feature data corresponding to a target object for a plurality of object demand influence features (¶ 21 – “Another aspect provides a system for enhancing explainability of one or more models that are useable to increase sales of one or more products. The system may comprise: one or more processors; and a memory storing instructions that, when executed by the one or more processors, cause the one or more processors to perform operations comprising: generating one or more predictive models based at least in part on (i) a set of target variables, (ii) a set of features, and (iii) a set of decision variables, wherein the features are predictive of and have an influence on the target variables…”; ¶¶ 44. 48. 61. 66, 88, 90, 93, 93, 113 – Learning and training are performed on various sets of data, including specific features (¶¶ 48, 61, 66).);
determining an object category corresponding to the target object according to the feature data (¶¶ 21, 60, 63, 88 – Any defining aspect of the analyzed product/object, such as a time period, a facility, constraints, conditions, a model used, etc. can be interpreted as an example of an object category.);
determining at least one information to be predicted for the target object according to the object category (abstract – “An example method may comprise (a) using a decision model to predict an action that a sales representative should take to maximize a target variable…”; ¶ 59 – “X may be features that are predictive or believed to be predictive of the target variable Y. With continued reference to a predictive model for a sales organization, X may include demographic information of about a customer (e.g., age, gender, educational background, and the like). The demographic profile of a customer may, for example, be predictive of the type of communication that the customer prefers to receive (e.g., a phone call rather than an email). X may also include data about the customer's business. For example, if the sales organization is a pharmaceutical sales organization and the customer is a health care provider (“HCP”), X may include data about the HCP's patient population. X may also include a history of previous contacts with the customer, including the substance, dates and times, and outcomes of in-person visits to the customer, emails sent to the customer, documents provided to the customer, webinars and conferences attended by the customer, and the like. X may be configured in multiple ways, depending on whether the prediction model is time-dependent or not.”; ¶ 87 – “The decision model is based on a predictive model f(x,d) that maps features, including facility visits, to sales. D*(x) may represent the constrained decision model.”; ¶ 107 – “The bar charts in FIG. 10A show the coefficient values for the sampled instances. Positive values are interpreted as increases in the predictor driving increases in the number of visits that optimize sales.”; ¶ 112 – “The recommendations may be actions that are predicted to maximize a target variable (e.g., likelihood of making a sale, or sales amount).”);
generating at least one first feature demand prediction information for a target time according to at least one first feature demand prediction model and the feature data, wherein the at least one first feature demand prediction model is in one-to-one correspondence with the at least one information to be predicted, and the first feature demand prediction model is an interpretable model (¶ 47 – “Explainability models may be models that are inherently interpretable or models that explain other uninterpretable models. Explainability models may include deep explanation models, interpretable models, and models of models (“model induction”). Deep explanation models are neural networks in which nodes are identified as features so that the weights of the various layers illuminate the drivers of the neural network. Interpretable models are models that are inherently interpretable, including linear models, parametric models, tree models, Bayesian models, and the like. And model induction is a technique whereby a more interpretable model is built on top of an underlying model. Examples of models that may be used in model induction are local interpretable model-agnostic explanations (LIME), Shapley additive explanations (SHAP), counterfactual local explanations via regression (CLEAR), Anchors, and leave one covariate out (LOCO).”; ¶ 87 – “A pharmaceutical company wants to determine the quantity of quarterly visits to each facility that the company serves (e.g., doctor's offices, clinics, and hospitals) that maximizes the sale of each of two therapeutic products. The company is motivated to reduce costly individual visits, potentially replacing them with group conferences or emails and freeing up resources so that more facilities can be served with the same resource overhead. However, in-person visits may result in more sales. The company builds a decision model that determines the number of visits to each facility that maximizes the sale of the two therapeutics, considering historical data. The decision model is based on a predictive model f(x,d) that maps features, including facility visits, to sales. D*(x) may represent the constrained decision model.”; ¶ 59 – “X may be features that are predictive or believed to be predictive of the target variable Y. With continued reference to a predictive model for a sales organization, X may include demographic information of about a customer (e.g., age, gender, educational background, and the like). The demographic profile of a customer may, for example, be predictive of the type of communication that the customer prefers to receive (e.g., a phone call rather than an email). X may also include data about the customer's business. For example, if the sales organization is a pharmaceutical sales organization and the customer is a health care provider (“HCP”), X may include data about the HCP's patient population. X may also include a history of previous contacts with the customer, including the substance, dates and times, and outcomes of in-person visits to the customer, emails sent to the customer, documents provided to the customer, webinars and conferences attended by the customer, and the like. X may be configured in multiple ways, depending on whether the prediction model is time-dependent or not.”; ¶ 63 – “The system 200 can also include a decision model generator 210. The decision model generator 210 can generate a decision model from the predictive model. The decision model can predict the values of decision variables D that maximize the target variable Y, where decision variables D are a subset of features X. Decision variables may be variables over which a person or entity has some control. For example, a sales representative can control the content and timing of emails, topics of discussion on a phone call, and the like. The predictive problem may therefore be recharacterized as f(X,D)=Y. The goal of finding f( ) may be to use the information contained therein to make decisions about what values of D maximize Y. This may be expressed as the unconstrained decision model:”; ¶ 75 – “The set of features may include features that are or are believed to be predictive of the target variable. The set of features may include decision variables. Decision variables may be actions that are under the control of and executed by the person or entity that implements or uses the predictive model (e.g., a sales representative). In other words, decision variables may be variables that can be deliberately controlled. The set of features may also include variables that cannot be controlled directly that are also predictive of the target variable. For example, a company's pre-existing market share, which the company may not be able to control directly, may be predictive of sales.”; ¶ 76 – “In the case of a pharmaceutical company, the set of features may include demographic data associated with an HCP. The demographic data may be predictive, for example, of whether the HCP will respond to a particular mode of contact but not another (e.g., a phone call, but not an email). The demographic data may include age, gender, education background, and the segment membership of the HCP. Additionally or alternatively, the set of features may include data that is indicative of the HCP's patient population (e.g., the percentage of the HCP's patient population that has a particular disease). Additionally or alternatively, the set of features may include a contact history associated with the HCP and sales representatives of the pharmaceutical company. The contact history may include one or more of the following: (1) a number of visits by the one or more sales representatives to the HCP, (2) topics of conversations during the visits, (3) a number of email correspondences sent by the one or more sales representatives to the HCP, (4) topics of the email correspondences sent, (5) documents relating to the pharmaceutical product provided by the one or more sales representatives to the HCP, (6) webinars attended by the one or more sales representatives and the HCP, and (7) conferences attended by the one or more sales representatives and the HCP. Such contact history and corresponding sales data may indicate which types of contact are most valuable to the pharmaceutical company.”); and
inputting the at least one first feature demand prediction information and the feature data into a pre-trained second feature demand prediction model, so as to generate at least one second feature demand prediction information and a total demand prediction information for the target time, wherein the second feature demand prediction model is an uninterpretable mode (fig. 3 –
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; ¶¶ 61-63 – “[0061] The predictive model generator 205 can train the predictive model using a supervised, semi-supervised, or unsupervised learning process, for example. A supervised predictive model can be trained using labeled training inputs, i.e., features X and corresponding target variables Y. Features X can be provided to an untrained or partially trained version of the predictive model to generate a predicted output. The predicted output can be compared to the known target variable Y for that set of features X, and if there is a difference, the parameters of the predictive model can be updated. A semi-supervised predictive model can be trained using a large number of unlabeled features X and a small number of labeled features X. An unsupervised predictive model, e.g., a clustering or dimensionality reduction model, can find previously unknown patterns in features X. [0062] The predictive model generated by the predictive model generator 205 may be a neural network (e.g., a feedforward neural network, a convolutional neural network (CNN), a recurrent neural network (RNN), a long short-term memory network (LSTM), etc.), an autoencoder, a regression model, a decision tree, a random forest model, a support vector machines, a Bayesian network, a clustering model, a reinforcement learning algorithm, or the like. [0063] The system 200 can also include a decision model generator 210. The decision model generator 210 can generate a decision model from the predictive model. The decision model can predict the values of decision variables D that maximize the target variable Y, where decision variables D are a subset of features X. Decision variables may be variables over which a person or entity has some control. For example, a sales representative can control the content and timing of emails, topics of discussion on a phone call, and the like. The predictive problem may therefore be recharacterized as f(X,D)=Y. The goal of finding f( ) may be to use the information contained therein to make decisions about what values of D maximize Y. This may be expressed as the unconstrained decision model:”; ¶ 44 – “The present disclosure provides methods for explaining models that drive decision-making processes for businesses. Such models may be referred to as “decision models” in this disclosure. A decision model may include a predictive model, e.g., a machine learning (ML) model, that is trained on historical data and limited by one or more constraints and identifies decisions that optimize some business financial objective. The constraints may be operational constraints imposed on the business that limit the range of practical outputs that the predictive model can generate. Additionally or alternatively, the constraints may be rules set by the business that align with the goals of the business that likewise limit the range of decision outputs that the predictive model can generate and which optimizes the business objective. The trained decision model can determine one or more optimal actions for maximizing one or more target variables. The target variables may be business metrics, e.g., sales metrics. The methods described herein can comprise generating an explanation model from the decision model. The explanation model may be useable to gain insight into the structure and function of the model.”; ¶ 84 – “In a retail example, the set of features may include demographic and purchasing history associated with a particular customer. The features may be predictive as to whether the customer at a particular store, when the customer may make a purchase, what types of items the customer may purchase, or other target variables. The decision variables in such a scenario may be features under which retail companies or individual retail employees have some control, such as distribution of coupons and employee interactions with the customer. Thus, the decision model may determine the relative importances of the decision variables to a target outcome, while an explanation model may provide insight into how the decision variable features interact with one another.”; ¶ 93 – “FIG. 5 is a scatter plot of the predictive model's predicted values against the actual target values for each of the therapeutics. The plots show a strong diagonal pattern which confirms that the model fit is good. As described above, the approaches to building an explanation model evaluate the predictive model either on a sample of the data set used to train the model or on a set of counterfactuals. In this case, counterfactuals were used to generate observations that cover the complete space of the predictors. The system may use these data to build the decision model.”; ¶ 110 – “The system may build a linear model with the weighted hypercube values as predictors. To determine contributions of predictor variables (predictor), the system may test different model targets that are particular percentage deviations from an optimum value. The plot of FIG. 10C shows values of coefficients for two estimated LIME explanation models: for a constrained model and for an unconstrained model. The predictors are the horizontal axis and the values of their coefficients are on the vertical access. The table gives the exact values of the coefficients. The value of r2 for the unconstrained model is 0.97 and for the constrained model is 0.98, meaning that the model is an effective predictive tool. The plot shows that, for each model, the variables “appointment”, “conference”, and “visit” were highly determinative of the prediction. Although these results match those for the recursive model, the LIME model may not determine the multivariate impact of the predictors as explainers in the decision model.”; fig. 5, ¶ 94 – “The surface defined by the predictions of f( ) is an 8-dimensional surface. Since the observations that comprise the surface are from the predictions of a random forest model and not a parametric model, there are discontinuities in the surface, as the plots of FIG. 6 show. FIG. 6 shows the surface across 4-dimensions for two quarters. The surface varies across the quarters, across the facilities, and across the products. The first row in each plot shows data for product 1 and the second is for product 2; as the plots move from left to right the facility decile increases. Some of the variance and fluctuations in the plots is caused by the discontinuities of the random forest model and some is caused by the hidden variables that are not shown in the plots. FIG. 6 illustrates more detail on this prediction surface and provides insight into the decision model. The plot on the right has blue and red lines—these are the maximum and the 95% quantile for the prediction in each of the identified dimensions, respectively. The value of visit where the maximum intersects is the value for d*(x) for that set of predictors. Since there is variance associated with the predictive model method, the average number of visits where the prediction for those values is above the 95% quantile within that bin of predictors is used as the value for d*(x).”; ¶ 88 – “The company trained the predictive model on historical sales data of the two products to different medical facilities. The historical sales data included quarterly sales data for each facility for each of the two products. A particular data record contained an indication of the product (product), quarter (qtr), and facility of the data record; a code indicating the decile of the sales of the facility (facility); the number of scheduled visits sales representatives made to an HCP in the facility (appointment); the number of conferences that HCPs within the facility attended (conference): the number of group meetings that HCPs within the facility attended (group); the number of emails sent to HCPs within the facility (email); and the number of unscheduled visits to HCPs within the facility (visit).”; ¶ 47 – “Explainability models may be models that are inherently interpretable or models that explain other uninterpretable models. Explainability models may include deep explanation models, interpretable models, and models of models (“model induction”). Deep explanation models are neural networks in which nodes are identified as features so that the weights of the various layers illuminate the drivers of the neural network. Interpretable models are models that are inherently interpretable, including linear models, parametric models, tree models, Bayesian models, and the like. And model induction is a technique whereby a more interpretable model is built on top of an underlying model. Examples of models that may be used in model induction are local interpretable model-agnostic explanations (LIME), Shapley additive explanations (SHAP), counterfactual local explanations via regression (CLEAR), Anchors, and leave one covariate out (LOCO).”; ¶ 54 – “One framework for SHAP is additive feature attribution methods, which provides a representation of relative feature importances within a prediction model. Additive feature attribution may estimate an underlying prediction model as a sum of transformed, weighted feature terms. The method may determine the weights by minimizing a loss function. Features which are more heavily weighted may be thus inferred to be more important to the prediction.”).
[Claim 3] Cohen discloses wherein determining at least one information to be predicted for the target object according to the object category comprises:
determining, in response to determining that the object category is a first object category, a similar object demand prediction information as an information to be predicted, wherein an object corresponding to the first object category has no value transfer data (¶ 17 – “In some embodiments, the explanation model may comprise a global explanation model. Alternatively, the global explanation model may comprise an unconstrained global decision tree. In some embodiments, the global explanation model may comprise a constrained global decision tree.”; ¶ 82 – “In some cases, the system may apply the explainability modeling (e.g., recursive partitioning) over the entire set of features used to train the decision model, resulting in a global explanation model (e.g., global decision tree). The global explanation model may be a constrained global explanation model in that it considers constraints applied to the decision model, or it may be an unconstrained global explanation model. In other cases, however, the system may apply explainability modeling over only a subset of the features used to train the decision model, e.g., a margin of the space instead of the entire space, resulting in a local explanation model. In the case of recursive partitioning, for example, this may result in a local decision tree.”; ¶ 44 – “The present disclosure provides methods for explaining models that drive decision-making processes for businesses. Such models may be referred to as “decision models” in this disclosure. A decision model may include a predictive model, e.g., a machine learning (ML) model, that is trained on historical data and limited by one or more constraints and identifies decisions that optimize some business financial objective. The constraints may be operational constraints imposed on the business that limit the range of practical outputs that the predictive model can generate. Additionally or alternatively, the constraints may be rules set by the business that align with the goals of the business that likewise limit the range of decision outputs that the predictive model can generate and which optimizes the business objective. The trained decision model can determine one or more optimal actions for maximizing one or more target variables. The target variables may be business metrics, e.g., sales metrics. The methods described herein can comprise generating an explanation model from the decision model. The explanation model may be useable to gain insight into the structure and function of the model.”; ¶ 60 – “These decision variables may be variables on which humans may have control, and thus may allow humans to calibrate or optimize their actions (e.g., contacts from pharma reps to HCPs) to achieve desired results (e.g., increased sales or prescriptions filled). The values of decision variables that achieve desired results may not be feasible in the real world. Further, entities (e.g., businesses or regulators) may bar persons from taking actions represented by decision variables. In these situations, the system may add constraints to the decision model to better simulate real-world conditions or reflect real-world needs.” Having “no value transfer data” may mean that there are no constraints, for example. Additionally, having “no value transfer data” could mean that an object is new (like a new product), which Cohen addresses in ¶ 65 – “In practice, brand management and sales operations teams may also specify certain rules. Such rules may result from various plans and goals that may not be captured in the relationship between (X,D) and Y. For example, a brand team may want to prioritize the sale of a new product on the marketplace. Additionally or alternatively, the brand team may specify rules for interacting with uncontrolled publications, rules that require visits when commercial metrics change in statistically relevant ways, rules for timing interactions with seasonal commercial drivers, rules for coordinating messaging across products brands, and the like. Let R denote the set of rules and D denote the union of constraints and rules, namely D=C U R…”).
[Claim 4] Cohen discloses wherein determining at least one information to be predicted for the target object according to the object category comprises:
determining, in response to determining that the object category is a second object category, a sensitive information corresponding to the target object, wherein value transfer data of an object corresponding to the second object category meets a preset transfer condition (¶ 17 – “In some embodiments, the explanation model may comprise a global explanation model. Alternatively, the global explanation model may comprise an unconstrained global decision tree. In some embodiments, the global explanation model may comprise a constrained global decision tree.”; ¶ 82 – “In some cases, the system may apply the explainability modeling (e.g., recursive partitioning) over the entire set of features used to train the decision model, resulting in a global explanation model (e.g., global decision tree). The global explanation model may be a constrained global explanation model in that it considers constraints applied to the decision model, or it may be an unconstrained global explanation model. In other cases, however, the system may apply explainability modeling over only a subset of the features used to train the decision model, e.g., a margin of the space instead of the entire space, resulting in a local explanation model. In the case of recursive partitioning, for example, this may result in a local decision tree.”; ¶ 44 – “The present disclosure provides methods for explaining models that drive decision-making processes for businesses. Such models may be referred to as “decision models” in this disclosure. A decision model may include a predictive model, e.g., a machine learning (ML) model, that is trained on historical data and limited by one or more constraints and identifies decisions that optimize some business financial objective. The constraints may be operational constraints imposed on the business that limit the range of practical outputs that the predictive model can generate. Additionally or alternatively, the constraints may be rules set by the business that align with the goals of the business that likewise limit the range of decision outputs that the predictive model can generate and which optimizes the business objective. The trained decision model can determine one or more optimal actions for maximizing one or more target variables. The target variables may be business metrics, e.g., sales metrics. The methods described herein can comprise generating an explanation model from the decision model. The explanation model may be useable to gain insight into the structure and function of the model.”; ¶ 60 – “These decision variables may be variables on which humans may have control, and thus may allow humans to calibrate or optimize their actions (e.g., contacts from pharma reps to HCPs) to achieve desired results (e.g., increased sales or prescriptions filled). The values of decision variables that achieve desired results may not be feasible in the real world. Further, entities (e.g., businesses or regulators) may bar persons from taking actions represented by decision variables. In these situations, the system may add constraints to the decision model to better simulate real-world conditions or reflect real-world needs.”; ¶ 65 – “In practice, brand management and sales operations teams may also specify certain rules. Such rules may result from various plans and goals that may not be captured in the relationship between (X,D) and Y. For example, a brand team may want to prioritize the sale of a new product on the marketplace. Additionally or alternatively, the brand team may specify rules for interacting with uncontrolled publications, rules that require visits when commercial metrics change in statistically relevant ways, rules for timing interactions with seasonal commercial drivers, rules for coordinating messaging across products brands, and the like. Let R denote the set of rules and D denote the union of constraints and rules, namely D=C U R…” Having a “present transformation condition” may refer to conditions and constraints that must be met and/or are at least evaluated.); and
determining, in response to determining that the sensitive information indicates that the target object is an object of which a value attribute transformation meets a preset transformation condition, a first value related feature influence information and a demand trend feature information as an information to be predicted respectively (¶ 17 – “In some embodiments, the explanation model may comprise a global explanation model. Alternatively, the global explanation model may comprise an unconstrained global decision tree. In some embodiments, the global explanation model may comprise a constrained global decision tree.”; ¶ 82 – “In some cases, the system may apply the explainability modeling (e.g., recursive partitioning) over the entire set of features used to train the decision model, resulting in a global explanation model (e.g., global decision tree). The global explanation model may be a constrained global explanation model in that it considers constraints applied to the decision model, or it may be an unconstrained global explanation model. In other cases, however, the system may apply explainability modeling over only a subset of the features used to train the decision model, e.g., a margin of the space instead of the entire space, resulting in a local explanation model. In the case of recursive partitioning, for example, this may result in a local decision tree.”; ¶ 44 – “The present disclosure provides methods for explaining models that drive decision-making processes for businesses. Such models may be referred to as “decision models” in this disclosure. A decision model may include a predictive model, e.g., a machine learning (ML) model, that is trained on historical data and limited by one or more constraints and identifies decisions that optimize some business financial objective. The constraints may be operational constraints imposed on the business that limit the range of practical outputs that the predictive model can generate. Additionally or alternatively, the constraints may be rules set by the business that align with the goals of the business that likewise limit the range of decision outputs that the predictive model can generate and which optimizes the business objective. The trained decision model can determine one or more optimal actions for maximizing one or more target variables. The target variables may be business metrics, e.g., sales metrics. The methods described herein can comprise generating an explanation model from the decision model. The explanation model may be useable to gain insight into the structure and function of the model.”; ¶ 60 – “These decision variables may be variables on which humans may have control, and thus may allow humans to calibrate or optimize their actions (e.g., contacts from pharma reps to HCPs) to achieve desired results (e.g., increased sales or prescriptions filled). The values of decision variables that achieve desired results may not be feasible in the real world. Further, entities (e.g., businesses or regulators) may bar persons from taking actions represented by decision variables. In these situations, the system may add constraints to the decision model to better simulate real-world conditions or reflect real-world needs.”; ¶ 65 – “In practice, brand management and sales operations teams may also specify certain rules. Such rules may result from various plans and goals that may not be captured in the relationship between (X,D) and Y. For example, a brand team may want to prioritize the sale of a new product on the marketplace. Additionally or alternatively, the brand team may specify rules for interacting with uncontrolled publications, rules that require visits when commercial metrics change in statistically relevant ways, rules for timing interactions with seasonal commercial drivers, rules for coordinating messaging across products brands, and the like. Let R denote the set of rules and D denote the union of constraints and rules, namely D=C U R…” Having a “present transformation condition” may refer to conditions and constraints that must be met and/or are at least evaluated.; ¶ 75 – “The set of features may include features that are or are believed to be predictive of the target variable. The set of features may include decision variables. Decision variables may be actions that are under the control of and executed by the person or entity that implements or uses the predictive model (e.g., a sales representative). In other words, decision variables may be variables that can be deliberately controlled. The set of features may also include variables that cannot be controlled directly that are also predictive of the target variable. For example, a company's pre-existing market share, which the company may not be able to control directly, may be predictive of sales.”; ¶ 76 – “In the case of a pharmaceutical company, the set of features may include demographic data associated with an HCP. The demographic data may be predictive, for example, of whether the HCP will respond to a particular mode of contact but not another (e.g., a phone call, but not an email). The demographic data may include age, gender, education background, and the segment membership of the HCP. Additionally or alternatively, the set of features may include data that is indicative of the HCP's patient population (e.g., the percentage of the HCP's patient population that has a particular disease). Additionally or alternatively, the set of features may include a contact history associated with the HCP and sales representatives of the pharmaceutical company. The contact history may include one or more of the following: (1) a number of visits by the one or more sales representatives to the HCP, (2) topics of conversations during the visits, (3) a number of email correspondences sent by the one or more sales representatives to the HCP, (4) topics of the email correspondences sent, (5) documents relating to the pharmaceutical product provided by the one or more sales representatives to the HCP, (6) webinars attended by the one or more sales representatives and the HCP, and (7) conferences attended by the one or more sales representatives and the HCP. Such contact history and corresponding sales data may indicate which types of contact are most valuable to the pharmaceutical company.”).
[Claim 5] Cohen discloses wherein after determining, in response to determining that the sensitive information indicates that the target object is an object of which a value attribute transformation meets a preset transformation condition, a first value related feature influence information and a demand trend feature information as an information to be predicted respectively, the method further comprises:
determining, in response to determining that the sensitive information indicates that an association degree between value transfer data corresponding to the target object and an object flow information meets a preset association condition, a second value related feature influence information and the demand trend feature information as an information to be predicted respectively (¶ 17 – “In some embodiments, the explanation model may comprise a global explanation model. Alternatively, the global explanation model may comprise an unconstrained global decision tree. In some embodiments, the global explanation model may comprise a constrained global decision tree.”; ¶ 82 – “In some cases, the system may apply the explainability modeling (e.g., recursive partitioning) over the entire set of features used to train the decision model, resulting in a global explanation model (e.g., global decision tree). The global explanation model may be a constrained global explanation model in that it considers constraints applied to the decision model, or it may be an unconstrained global explanation model. In other cases, however, the system may apply explainability modeling over only a subset of the features used to train the decision model, e.g., a margin of the space instead of the entire space, resulting in a local explanation model. In the case of recursive partitioning, for example, this may result in a local decision tree.”; ¶ 44 – “The present disclosure provides methods for explaining models that drive decision-making processes for businesses. Such models may be referred to as “decision models” in this disclosure. A decision model may include a predictive model, e.g., a machine learning (ML) model, that is trained on historical data and limited by one or more constraints and identifies decisions that optimize some business financial objective. The constraints may be operational constraints imposed on the business that limit the range of practical outputs that the predictive model can generate. Additionally or alternatively, the constraints may be rules set by the business that align with the goals of the business that likewise limit the range of decision outputs that the predictive model can generate and which optimizes the business objective. The trained decision model can determine one or more optimal actions for maximizing one or more target variables. The target variables may be business metrics, e.g., sales metrics. The methods described herein can comprise generating an explanation model from the decision model. The explanation model may be useable to gain insight into the structure and function of the model.”; ¶ 60 – “These decision variables may be variables on which humans may have control, and thus may allow humans to calibrate or optimize their actions (e.g., contacts from pharma reps to HCPs) to achieve desired results (e.g., increased sales or prescriptions filled). The values of decision variables that achieve desired results may not be feasible in the real world. Further, entities (e.g., businesses or regulators) may bar persons from taking actions represented by decision variables. In these situations, the system may add constraints to the decision model to better simulate real-world conditions or reflect real-world needs.”; ¶ 65 – “In practice, brand management and sales operations teams may also specify certain rules. Such rules may result from various plans and goals that may not be captured in the relationship between (X,D) and Y. For example, a brand team may want to prioritize the sale of a new product on the marketplace. Additionally or alternatively, the brand team may specify rules for interacting with uncontrolled publications, rules that require visits when commercial metrics change in statistically relevant ways, rules for timing interactions with seasonal commercial drivers, rules for coordinating messaging across products brands, and the like. Let R denote the set of rules and D denote the union of constraints and rules, namely D=C U R…” Having a “present transformation condition” may refer to conditions and constraints that must be met and/or are at least evaluated.; ¶ 75 – “The set of features may include features that are or are believed to be predictive of the target variable. The set of features may include decision variables. Decision variables may be actions that are under the control of and executed by the person or entity that implements or uses the predictive model (e.g., a sales representative). In other words, decision variables may be variables that can be deliberately controlled. The set of features may also include variables that cannot be controlled directly that are also predictive of the target variable. For example, a company's pre-existing market share, which the company may not be able to control directly, may be predictive of sales.”; ¶ 76 – “In the case of a pharmaceutical company, the set of features may include demographic data associated with an HCP. The demographic data may be predictive, for example, of whether the HCP will respond to a particular mode of contact but not another (e.g., a phone call, but not an email). The demographic data may include age, gender, education background, and the segment membership of the HCP. Additionally or alternatively, the set of features may include data that is indicative of the HCP's patient population (e.g., the percentage of the HCP's patient population that has a particular disease). Additionally or alternatively, the set of features may include a contact history associated with the HCP and sales representatives of the pharmaceutical company. The contact history may include one or more of the following: (1) a number of visits by the one or more sales representatives to the HCP, (2) topics of conversations during the visits, (3) a number of email correspondences sent by the one or more sales representatives to the HCP, (4) topics of the email correspondences sent, (5) documents relating to the pharmaceutical product provided by the one or more sales representatives to the HCP, (6) webinars attended by the one or more sales representatives and the HCP, and (7) conferences attended by the one or more sales representatives and the HCP. Such contact history and corresponding sales data may indicate which types of contact are most valuable to the pharmaceutical company.”).
[Claim 6] Cohen discloses wherein determining at least one information to be predicted for the target object according to the object category comprises:
determining, in response to determining that the object category is a seasonal object category, a sensitive information corresponding to the target object (¶ 17 – “In some embodiments, the explanation model may comprise a global explanation model. Alternatively, the global explanation model may comprise an unconstrained global decision tree. In some embodiments, the global explanation model may comprise a constrained global decision tree.”; ¶ 82 – “In some cases, the system may apply the explainability modeling (e.g., recursive partitioning) over the entire set of features used to train the decision model, resulting in a global explanation model (e.g., global decision tree). The global explanation model may be a constrained global explanation model in that it considers constraints applied to the decision model, or it may be an unconstrained global explanation model. In other cases, however, the system may apply explainability modeling over only a subset of the features used to train the decision model, e.g., a margin of the space instead of the entire space, resulting in a local explanation model. In the case of recursive partitioning, for example, this may result in a local decision tree.”; ¶ 44 – “The present disclosure provides methods for explaining models that drive decision-making processes for businesses. Such models may be referred to as “decision models” in this disclosure. A decision model may include a predictive model, e.g., a machine learning (ML) model, that is trained on historical data and limited by one or more constraints and identifies decisions that optimize some business financial objective. The constraints may be operational constraints imposed on the business that limit the range of practical outputs that the predictive model can generate. Additionally or alternatively, the constraints may be rules set by the business that align with the goals of the business that likewise limit the range of decision outputs that the predictive model can generate and which optimizes the business objective. The trained decision model can determine one or more optimal actions for maximizing one or more target variables. The target variables may be business metrics, e.g., sales metrics. The methods described herein can comprise generating an explanation model from the decision model. The explanation model may be useable to gain insight into the structure and function of the model.”; ¶ 60 – “These decision variables may be variables on which humans may have control, and thus may allow humans to calibrate or optimize their actions (e.g., contacts from pharma reps to HCPs) to achieve desired results (e.g., increased sales or prescriptions filled). The values of decision variables that achieve desired results may not be feasible in the real world. Further, entities (e.g., businesses or regulators) may bar persons from taking actions represented by decision variables. In these situations, the system may add constraints to the decision model to better simulate real-world conditions or reflect real-world needs.”; ¶ 65 – “In practice, brand management and sales operations teams may also specify certain rules. Such rules may result from various plans and goals that may not be captured in the relationship between (X,D) and Y. For example, a brand team may want to prioritize the sale of a new product on the marketplace. Additionally or alternatively, the brand team may specify rules for interacting with uncontrolled publications, rules that require visits when commercial metrics change in statistically relevant ways, rules for timing interactions with seasonal commercial drivers, rules for coordinating messaging across products brands, and the like. Let R denote the set of rules and D denote the union of constraints and rules, namely D=C U R…” Having a “present transformation condition” may refer to conditions and constraints that must be met and/or are at least evaluated.; ¶ 75 – “The set of features may include features that are or are believed to be predictive of the target variable. The set of features may include decision variables. Decision variables may be actions that are under the control of and executed by the person or entity that implements or uses the predictive model (e.g., a sales representative). In other words, decision variables may be variables that can be deliberately controlled. The set of features may also include variables that cannot be controlled directly that are also predictive of the target variable. For example, a company's pre-existing market share, which the company may not be able to control directly, may be predictive of sales.”; ¶ 76 – “In the case of a pharmaceutical company, the set of features may include demographic data associated with an HCP. The demographic data may be predictive, for example, of whether the HCP will respond to a particular mode of contact but not another (e.g., a phone call, but not an email). The demographic data may include age, gender, education background, and the segment membership of the HCP. Additionally or alternatively, the set of features may include data that is indicative of the HCP's patient population (e.g., the percentage of the HCP's patient population that has a particular disease). Additionally or alternatively, the set of features may include a contact history associated with the HCP and sales representatives of the pharmaceutical company. The contact history may include one or more of the following: (1) a number of visits by the one or more sales representatives to the HCP, (2) topics of conversations during the visits, (3) a number of email correspondences sent by the one or more sales representatives to the HCP, (4) topics of the email correspondences sent, (5) documents relating to the pharmaceutical product provided by the one or more sales representatives to the HCP, (6) webinars attended by the one or more sales representatives and the HCP, and (7) conferences attended by the one or more sales representatives and the HCP. Such contact history and corresponding sales data may indicate which types of contact are most valuable to the pharmaceutical company.”; ¶¶ 65, 78 – Seasonality is taken into account.); and
determining, in response to determining that the sensitive information indicates that the target object is an object of which a value attribute transformation meets a preset transformation condition, a first value related feature influence information, a demand trend feature information and a seasonal feature influence information as an information to be predicted respectively (¶ 17 – “In some embodiments, the explanation model may comprise a global explanation model. Alternatively, the global explanation model may comprise an unconstrained global decision tree. In some embodiments, the global explanation model may comprise a constrained global decision tree.”; ¶ 82 – “In some cases, the system may apply the explainability modeling (e.g., recursive partitioning) over the entire set of features used to train the decision model, resulting in a global explanation model (e.g., global decision tree). The global explanation model may be a constrained global explanation model in that it considers constraints applied to the decision model, or it may be an unconstrained global explanation model. In other cases, however, the system may apply explainability modeling over only a subset of the features used to train the decision model, e.g., a margin of the space instead of the entire space, resulting in a local explanation model. In the case of recursive partitioning, for example, this may result in a local decision tree.”; ¶ 44 – “The present disclosure provides methods for explaining models that drive decision-making processes for businesses. Such models may be referred to as “decision models” in this disclosure. A decision model may include a predictive model, e.g., a machine learning (ML) model, that is trained on historical data and limited by one or more constraints and identifies decisions that optimize some business financial objective. The constraints may be operational constraints imposed on the business that limit the range of practical outputs that the predictive model can generate. Additionally or alternatively, the constraints may be rules set by the business that align with the goals of the business that likewise limit the range of decision outputs that the predictive model can generate and which optimizes the business objective. The trained decision model can determine one or more optimal actions for maximizing one or more target variables. The target variables may be business metrics, e.g., sales metrics. The methods described herein can comprise generating an explanation model from the decision model. The explanation model may be useable to gain insight into the structure and function of the model.”; ¶ 60 – “These decision variables may be variables on which humans may have control, and thus may allow humans to calibrate or optimize their actions (e.g., contacts from pharma reps to HCPs) to achieve desired results (e.g., increased sales or prescriptions filled). The values of decision variables that achieve desired results may not be feasible in the real world. Further, entities (e.g., businesses or regulators) may bar persons from taking actions represented by decision variables. In these situations, the system may add constraints to the decision model to better simulate real-world conditions or reflect real-world needs.”; ¶ 65 – “In practice, brand management and sales operations teams may also specify certain rules. Such rules may result from various plans and goals that may not be captured in the relationship between (X,D) and Y. For example, a brand team may want to prioritize the sale of a new product on the marketplace. Additionally or alternatively, the brand team may specify rules for interacting with uncontrolled publications, rules that require visits when commercial metrics change in statistically relevant ways, rules for timing interactions with seasonal commercial drivers, rules for coordinating messaging across products brands, and the like. Let R denote the set of rules and D denote the union of constraints and rules, namely D=C U R…” Having a “present transformation condition” may refer to conditions and constraints that must be met and/or are at least evaluated.; ¶ 75 – “The set of features may include features that are or are believed to be predictive of the target variable. The set of features may include decision variables. Decision variables may be actions that are under the control of and executed by the person or entity that implements or uses the predictive model (e.g., a sales representative). In other words, decision variables may be variables that can be deliberately controlled. The set of features may also include variables that cannot be controlled directly that are also predictive of the target variable. For example, a company's pre-existing market share, which the company may not be able to control directly, may be predictive of sales.”; ¶ 76 – “In the case of a pharmaceutical company, the set of features may include demographic data associated with an HCP. The demographic data may be predictive, for example, of whether the HCP will respond to a particular mode of contact but not another (e.g., a phone call, but not an email). The demographic data may include age, gender, education background, and the segment membership of the HCP. Additionally or alternatively, the set of features may include data that is indicative of the HCP's patient population (e.g., the percentage of the HCP's patient population that has a particular disease). Additionally or alternatively, the set of features may include a contact history associated with the HCP and sales representatives of the pharmaceutical company. The contact history may include one or more of the following: (1) a number of visits by the one or more sales representatives to the HCP, (2) topics of conversations during the visits, (3) a number of email correspondences sent by the one or more sales representatives to the HCP, (4) topics of the email correspondences sent, (5) documents relating to the pharmaceutical product provided by the one or more sales representatives to the HCP, (6) webinars attended by the one or more sales representatives and the HCP, and (7) conferences attended by the one or more sales representatives and the HCP. Such contact history and corresponding sales data may indicate which types of contact are most valuable to the pharmaceutical company.”; ¶¶ 65, 78 – Seasonality is taken into account.).
[Claim 7] Cohen discloses wherein generating at least one first feature demand prediction information for a target time according to at least one first feature demand prediction model and the feature data comprises:
for each information to be predicted among the at least one information to be predicted, performing a first input step (Fig. 3; ¶¶ 17, 44, 47, 54, 60-63, 65, 75-76, 78, 82, 84, 88, 93-94, 110), comprising:
determining, in response to determining that the information to be predicted is a demand trend feature information, demand trend feature data corresponding to the demand trend feature information among the feature data (Fig. 3; ¶¶ 17, 44, 47, 54, 60-63, 65, 75-76, 78, 82, 84, 88, 93-94, 110; ¶ 96 – “In the plots on the left, the estimate lines show the number of visits that maximize sales as a function of facility sales size, number of emails sent, and number of appointments. Appointments increase for the plots further to the right and emails sent increase for the plots further toward the top. The plots show that the value of visits increases with facility size when there are fewer appointments (estimate lines with positive slopes on the left), but that trend inverts as appointments grow (kernel estimator lines with negative slopes on the right). One might expect that appointments are more important as facilities grow. The plots on the left also show that the impact of the number of emails sent is more subtle (only a small variation in the slope of the estimate lines in the same column).”; ¶¶ 65, 78 – Seasonality is taken into account.; ¶ 107 – “The bar charts in FIG. 10A show the coefficient values for the sampled instances. Positive values are interpreted as increases in the predictor driving increases in the number of visits that optimize sales. Notice that in the observations, increasing the quarter is associated with increasing visits in the sales-optimizing scenarios. This is consistent with the observations from the recursive partitioning explanation models as shown, for example, in FIGS. 8 and 9.”; ¶ 108 – “The plot shows the strong influence of the quarter on the optimal number of visits for maximizing sales. What cannot be seen in this plot is the detail that the recursive partitioning reveals in, for example, FIG. 9, where for smaller facilities it is favorable to have fewer appointments in the second half of the year in comparison to larger facilities, where it is favorable to have more appointments in the first half of the year.”);
determining a first feature demand prediction model corresponding to the demand trend feature information as a demand trend information prediction model (Fig. 3; ¶¶ 17, 44, 47, 54, 60-63, 65, 75-76, 78, 82, 84, 88, 93-94, 110; ¶ 96 – “In the plots on the left, the estimate lines show the number of visits that maximize sales as a function of facility sales size, number of emails sent, and number of appointments. Appointments increase for the plots further to the right and emails sent increase for the plots further toward the top. The plots show that the value of visits increases with facility size when there are fewer appointments (estimate lines with positive slopes on the left), but that trend inverts as appointments grow (kernel estimator lines with negative slopes on the right). One might expect that appointments are more important as facilities grow. The plots on the left also show that the impact of the number of emails sent is more subtle (only a small variation in the slope of the estimate lines in the same column).”; ¶¶ 65, 78 – Seasonality is taken into account.; ¶ 107 – “The bar charts in FIG. 10A show the coefficient values for the sampled instances. Positive values are interpreted as increases in the predictor driving increases in the number of visits that optimize sales. Notice that in the observations, increasing the quarter is associated with increasing visits in the sales-optimizing scenarios. This is consistent with the observations from the recursive partitioning explanation models as shown, for example, in FIGS. 8 and 9.”; ¶ 108 – “The plot shows the strong influence of the quarter on the optimal number of visits for maximizing sales. What cannot be seen in this plot is the detail that the recursive partitioning reveals in, for example, FIG. 9, where for smaller facilities it is favorable to have fewer appointments in the second half of the year in comparison to larger facilities, where it is favorable to have more appointments in the first half of the year.”); and
inputting the demand trend feature data into the demand trend information prediction model pre-trained, so as to output a demand trend prediction information as a first feature demand prediction information for the target time (Fig. 3; ¶¶ 17, 44, 47, 54, 60-63, 65, 75-76, 78, 82, 84, 88, 93-94, 110; ¶ 96 – “In the plots on the left, the estimate lines show the number of visits that maximize sales as a function of facility sales size, number of emails sent, and number of appointments. Appointments increase for the plots further to the right and emails sent increase for the plots further toward the top. The plots show that the value of visits increases with facility size when there are fewer appointments (estimate lines with positive slopes on the left), but that trend inverts as appointments grow (kernel estimator lines with negative slopes on the right). One might expect that appointments are more important as facilities grow. The plots on the left also show that the impact of the number of emails sent is more subtle (only a small variation in the slope of the estimate lines in the same column).”; ¶¶ 65, 78 – Seasonality is taken into account.; ¶ 107 – “The bar charts in FIG. 10A show the coefficient values for the sampled instances. Positive values are interpreted as increases in the predictor driving increases in the number of visits that optimize sales. Notice that in the observations, increasing the quarter is associated with increasing visits in the sales-optimizing scenarios. This is consistent with the observations from the recursive partitioning explanation models as shown, for example, in FIGS. 8 and 9.”; ¶ 108 – “The plot shows the strong influence of the quarter on the optimal number of visits for maximizing sales. What cannot be seen in this plot is the detail that the recursive partitioning reveals in, for example, FIG. 9, where for smaller facilities it is favorable to have fewer appointments in the second half of the year in comparison to larger facilities, where it is favorable to have more appointments in the first half of the year.”).
[Claim 8] Cohen discloses for each information to be predicted among the at least one information to be predicted, performing a second input step, comprising:
determining, in response to determining that the information to be predicted is a first value related feature influence information, first value related feature data corresponding to a first value related feature among the feature data (Fig. 3; ¶¶ 17, 44, 47, 54, 60-63, 65, 75-76, 78, 82, 84, 88, 93-94, 110; ¶ 96 – “In the plots on the left, the estimate lines show the number of visits that maximize sales as a function of facility sales size, number of emails sent, and number of appointments. Appointments increase for the plots further to the right and emails sent increase for the plots further toward the top. The plots show that the value of visits increases with facility size when there are fewer appointments (estimate lines with positive slopes on the left), but that trend inverts as appointments grow (kernel estimator lines with negative slopes on the right). One might expect that appointments are more important as facilities grow. The plots on the left also show that the impact of the number of emails sent is more subtle (only a small variation in the slope of the estimate lines in the same column).”; ¶¶ 65, 78 – Seasonality is taken into account.; ¶ 107 – “The bar charts in FIG. 10A show the coefficient values for the sampled instances. Positive values are interpreted as increases in the predictor driving increases in the number of visits that optimize sales. Notice that in the observations, increasing the quarter is associated with increasing visits in the sales-optimizing scenarios. This is consistent with the observations from the recursive partitioning explanation models as shown, for example, in FIGS. 8 and 9.”; ¶ 108 – “The plot shows the strong influence of the quarter on the optimal number of visits for maximizing sales. What cannot be seen in this plot is the detail that the recursive partitioning reveals in, for example, FIG. 9, where for smaller facilities it is favorable to have fewer appointments in the second half of the year in comparison to larger facilities, where it is favorable to have more appointments in the first half of the year.”);
determining a first feature demand prediction model corresponding to the first value related feature influence information as a first demand information prediction model (Fig. 3; ¶¶ 17, 44, 47, 54, 60-63, 65, 75-76, 78, 82, 84, 88, 93-94, 110; ¶ 96 – “In the plots on the left, the estimate lines show the number of visits that maximize sales as a function of facility sales size, number of emails sent, and number of appointments. Appointments increase for the plots further to the right and emails sent increase for the plots further toward the top. The plots show that the value of visits increases with facility size when there are fewer appointments (estimate lines with positive slopes on the left), but that trend inverts as appointments grow (kernel estimator lines with negative slopes on the right). One might expect that appointments are more important as facilities grow. The plots on the left also show that the impact of the number of emails sent is more subtle (only a small variation in the slope of the estimate lines in the same column).”; ¶¶ 65, 78 – Seasonality is taken into account.; ¶ 107 – “The bar charts in FIG. 10A show the coefficient values for the sampled instances. Positive values are interpreted as increases in the predictor driving increases in the number of visits that optimize sales. Notice that in the observations, increasing the quarter is associated with increasing visits in the sales-optimizing scenarios. This is consistent with the observations from the recursive partitioning explanation models as shown, for example, in FIGS. 8 and 9.”; ¶ 108 – “The plot shows the strong influence of the quarter on the optimal number of visits for maximizing sales. What cannot be seen in this plot is the detail that the recursive partitioning reveals in, for example, FIG. 9, where for smaller facilities it is favorable to have fewer appointments in the second half of the year in comparison to larger facilities, where it is favorable to have more appointments in the first half of the year.”); and
inputting the demand trend prediction information and the first value related feature data into the first demand information prediction model pre-trained, so as to output a first demand prediction information under influence of the first value related feature as a first feature demand prediction information for the target time (Figs. 3, 7-10C; ¶¶ 17, 44, 47, 54, 60-63, 65, 75-76, 78, 82, 84, 88, 93-94, 110; ¶ 96 – “In the plots on the left, the estimate lines show the number of visits that maximize sales as a function of facility sales size, number of emails sent, and number of appointments. Appointments increase for the plots further to the right and emails sent increase for the plots further toward the top. The plots show that the value of visits increases with facility size when there are fewer appointments (estimate lines with positive slopes on the left), but that trend inverts as appointments grow (kernel estimator lines with negative slopes on the right). One might expect that appointments are more important as facilities grow. The plots on the left also show that the impact of the number of emails sent is more subtle (only a small variation in the slope of the estimate lines in the same column).”; ¶¶ 65, 78 – Seasonality is taken into account.; ¶ 107 – “The bar charts in FIG. 10A show the coefficient values for the sampled instances. Positive values are interpreted as increases in the predictor driving increases in the number of visits that optimize sales. Notice that in the observations, increasing the quarter is associated with increasing visits in the sales-optimizing scenarios. This is consistent with the observations from the recursive partitioning explanation models as shown, for example, in FIGS. 8 and 9.”; ¶ 108 – “The plot shows the strong influence of the quarter on the optimal number of visits for maximizing sales. What cannot be seen in this plot is the detail that the recursive partitioning reveals in, for example, FIG. 9, where for smaller facilities it is favorable to have fewer appointments in the second half of the year in comparison to larger facilities, where it is favorable to have more appointments in the first half of the year.”).
[Claim 9] Cohen discloses for each information to be predicted among the at least one information to be predicted, performing a second input step, comprising:
determining, in response to determining that the information to be predicted is a second value related feature influence information, second value related feature data corresponding to a second value related feature among the feature data (Fig. 3; ¶¶ 17, 44, 47, 54, 60-63, 65, 75-76, 78, 82, 84, 88, 93-94, 110; ¶ 96 – “In the plots on the left, the estimate lines show the number of visits that maximize sales as a function of facility sales size, number of emails sent, and number of appointments. Appointments increase for the plots further to the right and emails sent increase for the plots further toward the top. The plots show that the value of visits increases with facility size when there are fewer appointments (estimate lines with positive slopes on the left), but that trend inverts as appointments grow (kernel estimator lines with negative slopes on the right). One might expect that appointments are more important as facilities grow. The plots on the left also show that the impact of the number of emails sent is more subtle (only a small variation in the slope of the estimate lines in the same column).”; ¶¶ 65, 78 – Seasonality is taken into account.; ¶ 107 – “The bar charts in FIG. 10A show the coefficient values for the sampled instances. Positive values are interpreted as increases in the predictor driving increases in the number of visits that optimize sales. Notice that in the observations, increasing the quarter is associated with increasing visits in the sales-optimizing scenarios. This is consistent with the observations from the recursive partitioning explanation models as shown, for example, in FIGS. 8 and 9.”; ¶ 108 – “The plot shows the strong influence of the quarter on the optimal number of visits for maximizing sales. What cannot be seen in this plot is the detail that the recursive partitioning reveals in, for example, FIG. 9, where for smaller facilities it is favorable to have fewer appointments in the second half of the year in comparison to larger facilities, where it is favorable to have more appointments in the first half of the year.”);
determining a first feature demand prediction model corresponding to the second value related feature influence information as a second demand information prediction model (Fig. 3; ¶¶ 17, 44, 47, 54, 60-63, 65, 75-76, 78, 82, 84, 88, 93-94, 110; ¶ 96 – “In the plots on the left, the estimate lines show the number of visits that maximize sales as a function of facility sales size, number of emails sent, and number of appointments. Appointments increase for the plots further to the right and emails sent increase for the plots further toward the top. The plots show that the value of visits increases with facility size when there are fewer appointments (estimate lines with positive slopes on the left), but that trend inverts as appointments grow (kernel estimator lines with negative slopes on the right). One might expect that appointments are more important as facilities grow. The plots on the left also show that the impact of the number of emails sent is more subtle (only a small variation in the slope of the estimate lines in the same column).”; ¶¶ 65, 78 – Seasonality is taken into account.; ¶ 107 – “The bar charts in FIG. 10A show the coefficient values for the sampled instances. Positive values are interpreted as increases in the predictor driving increases in the number of visits that optimize sales. Notice that in the observations, increasing the quarter is associated with increasing visits in the sales-optimizing scenarios. This is consistent with the observations from the recursive partitioning explanation models as shown, for example, in FIGS. 8 and 9.”; ¶ 108 – “The plot shows the strong influence of the quarter on the optimal number of visits for maximizing sales. What cannot be seen in this plot is the detail that the recursive partitioning reveals in, for example, FIG. 9, where for smaller facilities it is favorable to have fewer appointments in the second half of the year in comparison to larger facilities, where it is favorable to have more appointments in the first half of the year.”); and
inputting the demand trend prediction information and the second value related feature data into the second demand information prediction model pre-trained, so as to output a second demand prediction information under influence of the second value related feature as a first feature demand prediction information for the target time (Figs. 3, 7-10C; ¶¶ 17, 44, 47, 54, 60-63, 65, 75-76, 78, 82, 84, 88, 93-94, 110; ¶ 96 – “In the plots on the left, the estimate lines show the number of visits that maximize sales as a function of facility sales size, number of emails sent, and number of appointments. Appointments increase for the plots further to the right and emails sent increase for the plots further toward the top. The plots show that the value of visits increases with facility size when there are fewer appointments (estimate lines with positive slopes on the left), but that trend inverts as appointments grow (kernel estimator lines with negative slopes on the right). One might expect that appointments are more important as facilities grow. The plots on the left also show that the impact of the number of emails sent is more subtle (only a small variation in the slope of the estimate lines in the same column).”; ¶¶ 65, 78 – Seasonality is taken into account.; ¶ 107 – “The bar charts in FIG. 10A show the coefficient values for the sampled instances. Positive values are interpreted as increases in the predictor driving increases in the number of visits that optimize sales. Notice that in the observations, increasing the quarter is associated with increasing visits in the sales-optimizing scenarios. This is consistent with the observations from the recursive partitioning explanation models as shown, for example, in FIGS. 8 and 9.”; ¶ 108 – “The plot shows the strong influence of the quarter on the optimal number of visits for maximizing sales. What cannot be seen in this plot is the detail that the recursive partitioning reveals in, for example, FIG. 9, where for smaller facilities it is favorable to have fewer appointments in the second half of the year in comparison to larger facilities, where it is favorable to have more appointments in the first half of the year.”).
[Claim 10] Cohen discloses for each information to be predicted among the at least one information to be predicted, performing a third input step, comprising:
determining, in response to determining that the information to be predicted is a seasonal feature influence information, seasonal feature data corresponding to a seasonal feature among the feature data (Fig. 3; ¶¶ 17, 44, 47, 54, 60-63, 65, 75-76, 78, 82, 84, 88, 93-94, 110; ¶ 96 – “In the plots on the left, the estimate lines show the number of visits that maximize sales as a function of facility sales size, number of emails sent, and number of appointments. Appointments increase for the plots further to the right and emails sent increase for the plots further toward the top. The plots show that the value of visits increases with facility size when there are fewer appointments (estimate lines with positive slopes on the left), but that trend inverts as appointments grow (kernel estimator lines with negative slopes on the right). One might expect that appointments are more important as facilities grow. The plots on the left also show that the impact of the number of emails sent is more subtle (only a small variation in the slope of the estimate lines in the same column).”; ¶¶ 65, 78 – Seasonality is taken into account.; ¶ 107 – “The bar charts in FIG. 10A show the coefficient values for the sampled instances. Positive values are interpreted as increases in the predictor driving increases in the number of visits that optimize sales. Notice that in the observations, increasing the quarter is associated with increasing visits in the sales-optimizing scenarios. This is consistent with the observations from the recursive partitioning explanation models as shown, for example, in FIGS. 8 and 9.”; ¶ 108 – “The plot shows the strong influence of the quarter on the optimal number of visits for maximizing sales. What cannot be seen in this plot is the detail that the recursive partitioning reveals in, for example, FIG. 9, where for smaller facilities it is favorable to have fewer appointments in the second half of the year in comparison to larger facilities, where it is favorable to have more appointments in the first half of the year.”);
determining a first feature demand prediction model corresponding to the seasonal feature influence information as a third demand information prediction model (Fig. 3; ¶¶ 17, 44, 47, 54, 60-63, 65, 75-76, 78, 82, 84, 88, 93-94, 110; ¶ 96 – “In the plots on the left, the estimate lines show the number of visits that maximize sales as a function of facility sales size, number of emails sent, and number of appointments. Appointments increase for the plots further to the right and emails sent increase for the plots further toward the top. The plots show that the value of visits increases with facility size when there are fewer appointments (estimate lines with positive slopes on the left), but that trend inverts as appointments grow (kernel estimator lines with negative slopes on the right). One might expect that appointments are more important as facilities grow. The plots on the left also show that the impact of the number of emails sent is more subtle (only a small variation in the slope of the estimate lines in the same column).”; ¶¶ 65, 78 – Seasonality is taken into account.; ¶ 107 – “The bar charts in FIG. 10A show the coefficient values for the sampled instances. Positive values are interpreted as increases in the predictor driving increases in the number of visits that optimize sales. Notice that in the observations, increasing the quarter is associated with increasing visits in the sales-optimizing scenarios. This is consistent with the observations from the recursive partitioning explanation models as shown, for example, in FIGS. 8 and 9.”; ¶ 108 – “The plot shows the strong influence of the quarter on the optimal number of visits for maximizing sales. What cannot be seen in this plot is the detail that the recursive partitioning reveals in, for example, FIG. 9, where for smaller facilities it is favorable to have fewer appointments in the second half of the year in comparison to larger facilities, where it is favorable to have more appointments in the first half of the year.”); and
inputting the seasonal feature data and the demand trend prediction information into the third demand information prediction model pre-trained, so as to output a third demand prediction information under influence of the seasonal feature as a first feature demand prediction information for the target time (Figs. 3, 7-10C; ¶¶ 17, 44, 47, 54, 60-63, 65, 75-76, 78, 82, 84, 88, 93-94, 110; ¶ 96 – “In the plots on the left, the estimate lines show the number of visits that maximize sales as a function of facility sales size, number of emails sent, and number of appointments. Appointments increase for the plots further to the right and emails sent increase for the plots further toward the top. The plots show that the value of visits increases with facility size when there are fewer appointments (estimate lines with positive slopes on the left), but that trend inverts as appointments grow (kernel estimator lines with negative slopes on the right). One might expect that appointments are more important as facilities grow. The plots on the left also show that the impact of the number of emails sent is more subtle (only a small variation in the slope of the estimate lines in the same column).”; ¶¶ 65, 78 – Seasonality is taken into account.; ¶ 107 – “The bar charts in FIG. 10A show the coefficient values for the sampled instances. Positive values are interpreted as increases in the predictor driving increases in the number of visits that optimize sales. Notice that in the observations, increasing the quarter is associated with increasing visits in the sales-optimizing scenarios. This is consistent with the observations from the recursive partitioning explanation models as shown, for example, in FIGS. 8 and 9.”; ¶ 108 – “The plot shows the strong influence of the quarter on the optimal number of visits for maximizing sales. What cannot be seen in this plot is the detail that the recursive partitioning reveals in, for example, FIG. 9, where for smaller facilities it is favorable to have fewer appointments in the second half of the year in comparison to larger facilities, where it is favorable to have more appointments in the first half of the year.”).
[Claims 13, 17-22] Claims 13 and 17-22 recite limitations already addressed by the rejections of claims 1 and 3-8 above; therefore, the same rejections apply.
Furthermore, Cohen discloses an electronic device, comprising: one or more processors; and a storage device configured to store one or more programs, wherein the one or more programs, when executed by the one or more processors, cause the one or more processors to at least perform the disclosed operations (Cohen: ¶¶ 21-24).
[Claim 14] Claim 14 recites limitations already addressed by the rejection of claim 1 above; therefore, the same rejection applies.
Furthermore, Cohen discloses a non-transitory computer readable medium storing a computer program, wherein the computer program, when executed by a processor, at least performs the disclosed operations (Cohen: ¶¶ 21-24).
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 2, 11, and 16 are rejected under 35 U.S.C. 103 as being unpatentable over Cohen et al. (US 2024/0249299), as applied to claims 1 and 13 above, in view of Yuan et al. (CN-112926615-A, referencing the English Translation).
[Claim 2] Cohen does not explicitly disclose wherein determining at least one information to be predicted for the target object according to the object category comprises:
determining, in response to determining that the object category is a long-tail object category, a demand trend feature information as an information to be predicted.
Yuan uses long-tail data for an article of interest to predict future sales of the article (Yuan: pp. 9-10 – “Specifically, after determining the sale of the article, can be based on the sale of each article, responding to the service request of the main server, so that the main server receives the response of each slave server, integrating, obtaining the sale of all product terminal request, feeding back to the terminal; so as to make the related personnel to prepare goods according to the sale of the product.
wherein, if the original service data comprises the business data of the product with high sales volume, the main server can predict the future sales according to the existing prediction mode, also can be distributed to each slave server for prediction, specifically can be set according to the actual requirement, the invention is not limited.
The processing method of the service provided by this embodiment, after receiving the service request, according to the service request comprises the service data to determine the target long-tail data, and according to the target long-tail data and pre-obtained diagonal matrix to determine the target long-tail data corresponding to the correlation matrix; so as to decompose the correlation matrix based on the characteristic decomposition, obtaining the normalized characteristic vector corresponding to the correlation matrix, and determining the correlation random number corresponding to the target long-tail data based on the pre-obtained diagonal matrix and the normalized feature vector, based on the correlation random data; determining the sale of each article included in the target long-tail data; because of non-zero filling the zero data in the long-tail data, for predicting the future sale of the article, increasing the information amount contained in the long-tail data, improving the accuracy of the product future sales prediction, so as to reduce the storage cost.”).
Additionally, Yuan explains that the prediction of future sales volume for a product is for product restocking (Yuan: p. 6 – “The processing method of service provided by the embodiment of the invention is suitable for but not limited to the application scene for determining future sales volume for product restocking.”).
The Examiner submits that it would have been obvious to one of ordinary skill in the art before the effective filing date of Applicant’s invention to modify Cohen wherein determining at least one information to be predicted for the target object according to the object category comprises:
determining, in response to determining that the object category is a long-tail object category, a demand trend feature information as an information to be predicted
in order to “increas[e] the information amount contained in the long-tail data, improving the accuracy of the product future sales prediction, so as to reduce the storage cost” (as suggested on p. 10 of Yuan), which would specifically be useful in more efficient and accurate replenishment planning for products that often take longer to sell relative to other products.
[Claim 16] Claim 16 recites limitations already addressed by the rejection of claim 2 above; therefore, the same rejection applies.
[Claim 11] Cohen does not explicitly disclose performing replenishment processing on the target object according to the at least one second feature demand prediction information and the total demand prediction information.
Yuan uses long-tail data for an article of interest to predict future sales of the article (Yuan: pp. 9-10 – “Specifically, after determining the sale of the article, can be based on the sale of each article, responding to the service request of the main server, so that the main server receives the response of each slave server, integrating, obtaining the sale of all product terminal request, feeding back to the terminal; so as to make the related personnel to prepare goods according to the sale of the product.
wherein, if the original service data comprises the business data of the product with high sales volume, the main server can predict the future sales according to the existing prediction mode, also can be distributed to each slave server for prediction, specifically can be set according to the actual requirement, the invention is not limited.
The processing method of the service provided by this embodiment, after receiving the service request, according to the service request comprises the service data to determine the target long-tail data, and according to the target long-tail data and pre-obtained diagonal matrix to determine the target long-tail data corresponding to the correlation matrix; so as to decompose the correlation matrix based on the characteristic decomposition, obtaining the normalized characteristic vector corresponding to the correlation matrix, and determining the correlation random number corresponding to the target long-tail data based on the pre-obtained diagonal matrix and the normalized feature vector, based on the correlation random data; determining the sale of each article included in the target long-tail data; because of non-zero filling the zero data in the long-tail data, for predicting the future sale of the article, increasing the information amount contained in the long-tail data, improving the accuracy of the product future sales prediction, so as to reduce the storage cost.”).
Additionally, Yuan explains that the prediction of future sales volume for a product is for product restocking (Yuan: p. 6 – “The processing method of service provided by the embodiment of the invention is suitable for but not limited to the application scene for determining future sales volume for product restocking.”).
The Examiner submits that it would have been obvious to one of ordinary skill in the art before the effective filing date of Applicant’s invention to modify Cohen to incorporate the step of performing replenishment processing on the target object according to the at least one second feature demand prediction information and the total demand prediction information in order to “increas[e] the information amount contained in the long-tail data, improving the accuracy of the product future sales prediction, so as to reduce the storage cost” (as suggested on p. 10 of Yuan), which would specifically be useful in more efficient and accurate replenishment planning for products that often take longer to sell relative to other products.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
De et al. (US 2021/0142169) – Evaluates surrogate models as they relate to different feature variables, including for sales forecasting (¶¶ 15, 60).
Kühn et al. (US 2023/0244837) – Includes a model explain-ability approach to evaluate suitability of a model (¶ 54).
Khasanova et al. (US 2023/0334343) – Evaluates model-agnostic explanations for machine learning models (¶ 34).
Yu et al. (CN-112529491-A) – Associates products with long-tail sales prediction models and replenishes the product accordingly.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to SUSANNA M DIAZ whose telephone number is (571)272-6733. The examiner can normally be reached M-F, 8 am-4:30 pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Brian Epstein can be reached at (571) 270-5389. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/SUSANNA M. DIAZ/
Primary Examiner
Art Unit 3625A
Prosecution Insights