DETAILED ACTION
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 . In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. The applicant's submission, the Amendment filed on 13 April 2026, has been entered.
Status of the Claims
The pending claims in the present application are claims 1-5, 7-14, and 16-21 of the Amendment.
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 1-5, 7-14, and 16-21 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The paragraphs below provide rationales for the rejection. The rationales are based on the multi-step subject matter eligibility test outlined in MPEP 2106.
Step 1 of the eligibility analysis involves determining whether a claim falls within one of the four enumerated categories of patentable subject matter recited in 35 USC 101. (See MPEP 2106.03(I).) That is, Step 1 asks whether a claim is to a process, machine, manufacture, or composition of matter. (See MPEP 2106.03(II).) Referring to the pending claims, the “system” of claims 1-5 and 7-11 constitutes a machine under 35 USC 101, the “system” of claims 12-14 and 16-19 also constitutes a machine under the statute, the “process” of claim 20 constitutes a process under the statute, and the “method” of claim 21 also constitutes a process under the statute. Accordingly, claims 1-5, 7-14, and 16-21 meet the criteria of Step 1 of the eligibility analysis. The claims, however, fail to meet the criteria of subsequent steps of the eligibility analysis, as explained in the paragraphs below.
The next step of the eligibility analysis, Step 2A, involves determining whether a claim is directed to a judicial exception. (See MPEP 2106.04(II).) This step asks whether a claim is directed to a law of nature, a natural phenomenon (product of nature) or an abstract idea. (See id.) Step 2A is a two-prong inquiry. (See MPEP 2106.04(II)(A).) Prong One and Prong Two are addressed below.
In the context of Step 2A of the eligibility analysis, Prong One asks whether a claim recites an abstract idea, law of nature, or natural phenomenon. (See MPEP 2106.04(II)(A)(1).) Using claim 1 as an example, the claim recites the following abstract idea limitations:
“... predicting the probability that an entity will purchase a product within a future time period, comprising: ...” - See below regarding MPEP 2106.04(a), certain methods of organizing human activity, and mental processes
“... receive input data in the form of entries, each entry comprising, ...” - See below regarding MPEP 2106.04(a), certain methods of organizing human activity, and mental processes
“... an entity identifier that identifies an entity that is a potential purchaser of a product, ...” - See below regarding MPEP 2106.04(a), certain methods of organizing human activity, and mental processes
“... a product identifier that identifies a product that the entity associated with the entity identifier might purchase based on an interest event that is indicative of the product being relevant to the entity, ...” - See below regarding MPEP 2106.04(a), certain methods of organizing human activity, and mental processes
“... a time period identifier that specifies a past time period measured backward from a prescribed date of interest to an interest event date corresponding to the date the interest event associated with the entry occurred, and ...” - See below regarding MPEP 2106.04(a), certain methods of organizing human activity, and mental processes
“... an intensity value indicative of the degree to which the product associated with the product identifier is deemed relevant to the entity associated with the entity identifier, ...” - See below regarding MPEP 2106.04(a), certain methods of organizing human activity, and mental processes
“... generate a matrix from a portion of the input data entries, said matrix generation comprising assigning an entity identifier and product identifier pair associated with each interest event to a different location in the matrix, along with a time identifier indicative of how far back in time the interest event associated with each entity-product identifier pair occurred from the prescribed date of interest, ...” - See below regarding MPEP 2106.04(a), certain methods of organizing human activity, and mental processes
“... employ a ... technique to create a separate initial prediction model for each product of interest in the input data that estimates the probability that an entity in the input data will purchase the product using the matrix as input and validate each initial prediction model by comparing predicted purchases for the product of interest associated with the initial prediction model under consideration derived using the matrix against actual purchases found in the portion of the input data entries not employed to generate the matrix, and iteratively modifying one or more control parameters until the accuracy of the predicted purchases to the actual purchases is maximized, ...” - See below regarding MPEP 2106.04(a), certain methods of organizing human activity, and mental processes
“... generate a final matrix only using the input data entries, said final matrix generation comprising assigning an entity identifier and product identifier pair associated with each interest event to a different location in the matrix, along with a time identifier indicative of how far back in time the interest event associated with each entity-product identifier pair occurred from the prescribed date of interest, ...” - See below regarding MPEP 2106.04(a), certain methods of organizing human activity, and mental processes
“... employ the ... technique to create a separate final prediction model for each product of interest in the input data that estimates the probability that an entity in the input data will purchase the product within the future time period using the final matrix, and using the last-modified control parameters established in creating the initial prediction model for each product as input or if no modification was made to a control parameter in creating the initial prediction model for a product employing the initially-used control parameter as the input for that control parameter, and forgoing validation; ...” - See below regarding MPEP 2106.04(a), certain methods of organizing human activity, and mental processes
“... for each product, using the input data, apply the finalized prediction model associated with that product to estimate the probability that an entity will purchase the product within the future time period; and ...” - See below regarding MPEP 2106.04(a), certain methods of organizing human activity, and mental processes
“... establish a list of entities, the products they are predicted to purchase and the probability of the purchases.” - See below regarding MPEP 2106.04(a), certain methods of organizing human activity, and mental processes
The above-listed limitations of claim 1, when applying their broadest reasonable interpretations in light of their context in the claim as a whole, fall under enumerated groupings of abstract ideas outlined in MPEP 2106.04(a). For example, limitations of the claim can be characterized as: fundamental economic principles or practices, including market research; and commercial interactions, including advertising, marketing, or sales activities or behaviors (associated with, among other things, predicting product purchases), which fall under the certain methods of organizing human activity grouping of abstract ideas (see MPEP 2106.04(a)). Limitations of the claim also can be characterized as: concepts performed in the human mind, including observation (e.g., the recited “receive” step), and evaluation, judgment, and/or opinion (e.g., the recited “generate,” “employ,” “apply,” and “establish” steps), which fall under the mental processes grouping of abstract ideas (see MPEP 2106.04(a)). Accordingly, for at least these reasons, claim 1 fails to meet the criteria of Step 2A, Prong One of the eligibility analysis.
In the context of Step 2A of the eligibility analysis, Prong Two asks if the claim recites additional elements that integrate the judicial exception into a practical application. (See MPEP 2106.04(II)(A)(2).) Continuing to use claim 1 as an example, the claim recites the following additional element limitations:
The claimed “predicting” is performed by a “system” - See below regarding MPEP 2106.05(a)-(c) and (f)-(h)
“... a purchase probability predictor comprising one or more computing devices, and a purchase probability prediction computer program having a plurality of sub-programs executable by said computing device or devices, wherein the sub-programs configure said computing device or devices to ...” - See below regarding MPEP 2106.05(a)-(c) and (f)-(h)
The claimed “technique” includes “supervised machine learning” - See below regarding MPEP 2106.05(a)-(c) and (f)-(h)
The above-listed additional element limitations of claim 1, when applying their broadest reasonable interpretations in light of their context in the claim as a whole, are analogous to: accelerating a process of analyzing audit log data when the increased speed comes solely from the capabilities of a general-purpose computer, and mere automation of manual processes, which courts have indicated may not be sufficient to show an improvement in computer-functionality (see MPEP 2106.05(a)(I)); a commonplace business method being applied on a general purpose computer, gathering and analyzing information using conventional techniques and displaying the result, and selecting a particular generic function for computer hardware to perform from within a range of fundamental or commonplace functions performed by the hardware, which courts have indicated may not be sufficient to show an improvement to technology (see MPEP 2106.05(a)(II)); a general purpose computer that applies a judicial exception, such as an abstract idea, by use of conventional computer functions, and merely adding a generic computer, generic computer components, or a programmed computer to perform generic computer functions, which do not qualify as a particular machine or use thereof (see MPEP 2106.05(b)(I)); a machine that is merely an object on which the method operates, which does not integrate the exception into a practical application (see MPEP 2106.05(b)(II)); use of a machine that contributes only nominally or insignificantly to the execution of the claimed method, which does not integrate a judicial exception (see MPEP 2106.05(b)(III)); transformation of an intangible concept such as a contractual obligation or mental judgment, which is not likely to provide significantly more (see MPEP 2106.05(c)); use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea, a commonplace business method or mathematical algorithm being applied on a general purpose computer, and requiring the use of software to tailor information and provide it to the user on a generic computer, which courts have found to be mere instructions to apply an exception, because they do no more than merely invoke computers or machinery as a tool to perform an existing process (see MPEP 2106.05(f)); mere data gathering in the form of obtaining information about transactions using the Internet to verify transactions and consulting and updating an activity log, which courts have found to be insignificant extra-solution activity (see MPEP 2106.05(g)); and specifying that the abstract idea of monitoring audit log data relates to transactions or activities that are executed in a computer environment, because this requirement merely limits the claims to the computer field, i.e., to execution on a generic computer, which courts have described as merely indicating a field of use or technological environment in which to apply a judicial exception (see MPEP 2106.05(h)). For at least these reasons, claim 1 fails to meet the criteria of Step 2A, Prong Two of the eligibility analysis.
The next step of the eligibility analysis, Step 2B, asks whether a claim recites additional elements that amount to significantly more than the judicial exception. (See MPEP 2106.05(II).) The step involves identifying whether there are any additional elements in the claim beyond the judicial exceptions, and evaluating those additional elements individually and in combination to determine whether they contribute an inventive concept. (See id.) The ineligibility rationales applied at Step 2A, Prong Two, also apply to Step 2B. (See id.) For all of the reasons covered in the analysis performed at Step 2A, Prong Two, claim 1 fails to meet the criteria of Step 2B. As a result, claim 1 is rejected under 35 USC 101 as ineligible for patenting.
Regarding claims 2-5 and 7-11, the claims depend from claim 1, and expand upon limitations introduced by claim 1. The dependent claims are rejected at least for the same reasons as claim 1. For example, the dependent claims recite abstract idea elements similar to the abstract idea elements of claim 1, that fall under the same abstract idea groupings as the abstract idea elements of claim 1 (e.g., the “wherein said interest event that is indicative of the product being relevant to the entity comprises one of the entity expressing an interest in the product in a communication or a third party mentioning the entity and the product in a communication” of claim 2, the “wherein the intensity value indicative of the degree to which the product associated with the product identifier is deemed relevant to the entity associated with the entity identifier in an entry comprises the number of times an interest event in the input data is associated with the same entity and product” of claim 3, the “wherein ... generating a matrix from a portion of the input data entries comprises: mapping each input data entry onto a timeline based on the entry’s time period identifier; splitting the timeline so that a prescribed percentage of the entries closest to the prescribed date of interest are designated as test entries and the remaining entries are designated as training entries; and for the portion of the timeline comprising training entries, stepping a time window of a prescribed size over the timeline starting at the time corresponding to the mapped entry having largest time period identifier and moving forward in time a prescribed stride amount with each successive step, and at each step of the time window, creating an entity identifier and product identifier pair for each entry mapped onto the timeline that falls within the current time window step, and assigning each created pair to a different location in the matrix and associate a time window identifier assigned to the current time window step with the created pair whenever an entity identifier and product identifier pair corresponding to the same interest event as the created pair is not already assigned to a location in the matrix, and whenever an entity identifier and product identifier pair corresponding to the same interest event as the created pair is already assigned to a location in the matrix, associating a time window identifier assigned to the current time window step with the entity identifier and product identifier pair corresponding to the same interest event as the created pair” of claim 4, the “wherein ... generating a matrix from a portion of the input data entries, further comprises splitting the timeline so that 30% of the entries closest to the prescribed date of interest are designated as test entries and the remaining 70% of the entries are designated as training entries” of claim 5, the “further comprising ... eliminating, during the creation of the initial prediction models, probability estimates for entities that are known to already have the product or a product from a same category of products as the product” of claim 7, the “wherein ... generating a final matrix from the input data entries comprises: mapping each input data entry onto a timeline based on the entry’s time period identifier; stepping a time window of a prescribed size over the timeline starting at the time corresponding to the mapped entry having largest time period identifier and moving forward in time a prescribed stride amount with each successive step, and at each step of the time window, creating an entity identifier and product identifier pair for each entry mapped onto the timeline that falls within the current time window step, and assigning each created pair to a different location in the final matrix and associate a time window identifier assigned to the current time window step with the created pair whenever an entity identifier and product identifier pair corresponding to the same interest event as the created pair is not already assigned to a location in the final matrix, and whenever an entity identifier and product identifier pair corresponding to the same interest event as the created pair is already assigned to a location in the final matrix, associating a time window identifier assigned to the current time window step with the entity identifier and product identifier pair corresponding to the same interest event as the created pair” of claim 8, the “further comprising ... eliminating the input data entries deemed likely to be inaccurate, prior to ... generating the matrix, said eliminating comprising: identifying outlier entries in the input data using a seasonal ESD test on the time period identifiers and intensity values of the input data; and eliminating identified outlier entries from the input data up to a prescribed percentage of the entries” of claim 9, the “further comprising ... eliminating, after ... applying the finalized prediction model associated with each product to the input data to estimate the probability that an entity will purchase the product within the future time period, probability estimates for entities that are known to already have the product, or a product from a same category of products as the product, to establish a revised list of entities, the products they are predicted to purchase and the probability of that purchase” of claim 10, and the “wherein the future time period is 200 days starting from the prescribed date of interest” of claim 11). The dependent claims recite further additional elements that are similar to the additional elements of claim 1, that fail to warrant eligibility for the same reasons as the additional elements of claim 1 (e.g., the “system” of claims 2-5 and 7-11, the “sub-program” of claims 4, 5, and 7-10, and the “executing the sub-program for” of claims 9 and 10). Accordingly, claims 2-5 and 7-11 also are rejected as ineligible under 35 USC 101.
Regarding claims 12-14 and 16-19, while the claims are of different scope relative to claims 1-5 and 7-10, the claims recite limitations similar to the limitations of claims 1-5 and 7-10. As such, the rejection rationales applied to reject claims 1-5 and 7-10 also apply for purposes of rejecting claims 12-14 and 16-19. Claims 12-14 and 16-19 are, therefore, also rejected as ineligible under 35 USC 101.
Regarding claim 20, while the claim is of different scope relative to independent claims 1 and 12, the claim recites limitations similar to the limitations of claims 1 and 12. As such, the rejection rationales applied to reject claims 1 and 12 also apply for purposes of rejecting claim 20. Claim 20 is, therefore, also rejected as ineligible under 35 USC 101.
Regarding claim 21, the claim recites abstract idea elements similar to those recited by claims 1, 12, and 20, including:
“A ... method ... for predicting the probability that an entity will purchase a product within a future time period, comprising: ...” - See above regarding MPEP 2106.04(a), certain methods of organizing human activity, and mental processes
“... collecting input data in the form of entries ..., each entry comprising, ...” - See above regarding MPEP 2106.04(a), certain methods of organizing human activity, and mental processes
“... an entity identifier that identifies an entity that is a potential purchaser of a product, ...” - See above regarding MPEP 2106.04(a), certain methods of organizing human activity, and mental processes
“... a product identifier that identifies a product that the entity associated with the entity identifier might purchase based on an interest event that is indicative of the product being relevant to the entity, ...” - See above regarding MPEP 2106.04(a), certain methods of organizing human activity, and mental processes
“... a time period identifier that specifies a past time period back from a prescribed date of interest to an interest event date corresponding to the date the interest event associated with the entry occurred, and ...” - See above regarding MPEP 2106.04(a), certain methods of organizing human activity, and mental processes
“... an intensity value indicative of the degree to which the product associated with the product identifier is deemed relevant to the entity associated with the entity identifier, ...” - See above regarding MPEP 2106.04(a), certain methods of organizing human activity, and mental processes
“... creating an initial training set from a portion of the input data entries, said initial training set comprising an initial matrix assigning an entity identifier and product identifier pair associated with each interest event to a different location in the matrix, along with a time identifier indicative of how far back in time the interest event associated with each entity-product identifier pair occurred from the prescribed date of interest, ...” - See above regarding MPEP 2106.04(a), certain methods of organizing human activity, and mental processes
“... in an initial stage using the initial matrix to create a separate initial prediction model for each product of interest in the input data that estimates the probability that an entity in the input data will purchase the product and validate each initial prediction model by comparing predicted purchases for the product of interest associated with the initial prediction model under consideration derived using the matrix against actual purchases found in the portion of the input data entries not employed to generate the matrix, and iteratively employing one or more modified control parameters ... until the accuracy of the predicted purchases to the actual purchases is maximized, ...” - See above regarding MPEP 2106.04(a), certain methods of organizing human activity, and mental processes
“... creating a final training set for a final stage of training that is made up of the input data entries, and only the input data entries, said final training set comprising a final matrix assigning an entity identifier and product identifier pair associated with each interest event to a different location in the matrix, along with a time identifier indicative of how far back in time the interest event associated with each entity-product identifier pair occurred from the prescribed date of interest, and ...” - See above regarding MPEP 2106.04(a), certain methods of organizing human activity, and mental processes
“... in the final stage using the final matrix to create a separate final prediction model for each product of interest in the input data that estimates the probability that an entity in the input data will purchase the product within the future time period, ... using the last-modified control parameters established in creating the initial prediction model for each product as input or if no modification was made to a control parameter in creating the initial prediction model for a product employing the initially-used control parameter as the input for that control parameter, and forgoing validation.” - See above regarding MPEP 2106.04(a), certain methods of organizing human activity, and mental processes
Still regarding claim 21, the claim recites additional elements that fail to warrant eligibility under one or more of the rationales outlined in MPEP 2106.05(a)-(c) and (f)-(h), for similar reasons as the additional elements of claims 1, 12, and 20. The additional elements of claim 21 include:
The claimed “method” is “computer-implemented” and is “of training a supervised machine learning technique” - See above regarding MPEP 2106.05(a)-(c) and (f)-(h)
The claimed “collecting” is “from a database” - See above regarding MPEP 2106.05(a)-(c) and (f)-(h)
The claimed “initial stage” is when “training a supervised machine learning technique” occurs - See above regarding MPEP 2106.05(a)-(c) and (f)-(h)
The claimed “control parameters” are “of the supervised machine learning technique” - See above regarding MPEP 2106.05(a)-(c) and (f)-(h)
The claimed “final stage” is when “training the supervised machine learning technique” occurs - See above regarding MPEP 2106.05(a)-(c) and (f)-(h)
The claimed “last-modified control parameters” are used for “said final stage training” - See above regarding MPEP 2106.05(a)-(c) and (f)-(h)
For at least the reasons outlined above, claim 21 also is rejected as ineligible under 35 USC 101.
Examiner Comments
While claims 1-5, 7-14, and 16-21 are rejected as ineligible under 35 USC 101, no prior art rejections are being asserted against the claims. This is because the claims appear to distinguish over the prior art of record. The closest prior art of record is U.S. Pat. App. Pub. No. 2010/0049538 A1 to Frazer et al. (hereinafter referred to as “Frazer”). Frazer discloses, “The method 100 also includes predicting the likelihood of the occurrence of a future event 112. In retail situations, the future event many times is the purchase of another product” (para. [0044]), “In addition to predicting that an event will occur, in some embodiments of the invention, a time frame in which the event will occur is also predicted. In one embodiment, predicting the likelihood of the occurrence of a future event 112 is generally done as a risk factor over a number of selected times. This is referred to as predicting the time to the event. The risk factor is set for the various time frames. The time frames can be as short or as long as desired. For example, the time frame may be a second, or it may be several days. The risk factor is based on the risk that the action takes place over the time frame” (para. [0045]), “For each time frame, a propensity matrix including one or more customers and at least one product is formed. Several propensity matrices will be produced for each of the future time slots. This data is input to the selection module 440. The selection module 440 selects from among the best times to make a recommendation to the consumer. The selection module 440 can also be thought of as an optimization module for timing recommendations that will be the most effective in causing the future event” (para. [0052]), and “Traditional modeling frameworks in statistical pattern recognition and machine learning, such as classification and regression, seek optimal causal or correlation based mapping from a set of input features to one or more target values” (para. [0076]). Generally speaking, these disclosures read on elements of the pending claims, including the recited “purchase probability prediction,” “time period,” “matrix,” and “machine learning” limitations of independent claim 1. Frazer does not, however, disclose or suggest, the matrices receiving past time periods (in that Frazer appears to associate future time slots with matrices, not past ones), or the use of machine learning in conjunction with such matrices. As such, the disclosures of Frazer do not read on the recited “generate a matrix from a portion of the input data entries, said matrix generation comprising assigning an entity identifier and product identifier pair associated with each interest event to a different location in the matrix, along with a time identifier indicative of how far back in time the interest event associated with each entity-product identifier pair occurred from the prescribed date of interest” and “employ a supervised machine learning technique comprising logistic regression with elastic net regularization to create a separate initial prediction model for each product of interest in the input data that estimates the probability that an entity in the input data will purchase the product using the matrix as input and validate each initial prediction model by comparing predicted purchases for the product of interest associated with the initial prediction model under consideration derived using the matrix against actual purchases found in the portion of the input data entries not employed to generate the matrix, and iteratively modifying one or more control parameters until the accuracy of the predicted purchases to the actual purchases is maximized” limitations of claim 1. Claim 1, therefore, distinguishes over Frazer.
Searching also turned up CA Pat. App. Pub. No. 2 619 667 A1 to Lazarus et al. (hereinafter referred to as “Lazarus”). While Lazarus discloses, “Predictive modeling of consumer financial behavior” (Abstract), “For each merchant segment a predictive model is trained using consumer transaction data in selected past time periods to predict spending in subsequent time periods” (Abstract), and various matrices (see Tables 6, 8, and 9), the matrices relate to co-occurrences as opposed to times, and their use with machine learning is not disclosed or suggested. As such, the disclosures of Lazarus do not read on the recited “generate a matrix from a portion of the input data entries, said matrix generation comprising assigning an entity identifier and product identifier pair associated with each interest event to a different location in the matrix, along with a time identifier indicative of how far back in time the interest event associated with each entity-product identifier pair occurred from the prescribed date of interest” and “employ a supervised machine learning technique comprising logistic regression with elastic net regularization to create a separate initial prediction model for each product of interest in the input data that estimates the probability that an entity in the input data will purchase the product using the matrix as input and validate each initial prediction model by comparing predicted purchases for the product of interest associated with the initial prediction model under consideration derived using the matrix against actual purchases found in the portion of the input data entries not employed to generate the matrix, and iteratively modifying one or more control parameters until the accuracy of the predicted purchases to the actual purchases is maximized” limitations of claim 1. Claim 1, therefore, distinguishes over Lazarus.
Searching also turned up Vieira, Armando. “Predicting online user behaviour using deep learning algorithms.” arXiv prepring arXiv:1511.06247 (2015) (hereinafter referred to as “Vieira”). While Vieira is similar to the claimed invention in that it discloses predicting online user behaviour using deep learning algorithms (see Abstract), and involves the use of various matrices (see, e.g., pp. 5, 6, and 10-14), Vieira does not appear to disclose or suggest the specific steps associated with the matrix, machine learning, and related inputs, of the claimed invention (e.g., the recited “generate a matrix from a portion of the input data entries, said matrix generation comprising assigning an entity identifier and product identifier pair associated with each interest event to a different location in the matrix, along with a time identifier indicative of how far back in time the interest event associated with each entity-product identifier pair occurred from the prescribed date of interest” and “employ a supervised machine learning technique comprising logistic regression with elastic net regularization to create a separate initial prediction model for each product of interest in the input data that estimates the probability that an entity in the input data will purchase the product using the matrix as input and validate each initial prediction model by comparing predicted purchases for the product of interest associated with the initial prediction model under consideration derived using the matrix against actual purchases found in the portion of the input data entries not employed to generate the matrix, and iteratively modifying one or more control parameters until the accuracy of the predicted purchases to the actual purchases is maximized” limitations of claim 1). Claim 1, therefore, distinguishes over Vieira.
The statements above apply also to claims 12, 20, and 21, which recite limitations similar to those of claim 1. Claims 2-5 and 7-11, 13, 14, and 16-19 also distinguish over the closest prior art of record at least due to their dependency from one of claims 1 and 12.
Response to Arguments
On pp. 14-24 of the Amendment, the applicant requests reconsideration and withdrawal of any rejection under 35 USC 101 of claim 21. The applicant argues that claim 21 is patterned after the eligible claim of Example 39. (Amendment, p. 14.) With respect to Step 2A, Prong One of the eligibility analysis, the applicant argues that claim 21 is similar to Example 39 in that both merely involve abstract ideas, but do not recite them. (Amendment, p. 16.) The applicant then links limitations of claim 21 to allegedly similar or analogous limitations of Example 39, and that: the limitations of claim 21, like the limitations of Example 39, do not recite a mental process because the steps are not practically performed in the human mind; predicting product sales is not a commercial interaction and is not advertising, marketing or sales activities or behaviors; and that the training of Example 39 is like the training recited by claim 21. (Amendment, pp. 17-24.) The examiner finds the arguments unpersuasive. Example 39 is geared toward facial detection. Facial detection is a computer technology for identifying human faces in digital images. Such aspects do not recite any abstract ideas from the enumerated groupings set forth in MPEP 2106.04(a). The applicant’s claim 21, however, is explicitly for predicting the probability that an entity will purchase a product. This falls squarely within multiple enumerated groupings. One of the groupings is certain methods of organizing human activity in the form of advertising, marketing or sales activities or behaviors. (MPEP 2106.04(a).) The connection between claim 21 and the grouping is clearly specified in at least para. [002] of the applicant’s specification. Further, it is the digital nature of the images in Example 39 that takes the claim out of the realm of mental processes. The applicant’s claim 21 has no analogous limitation. Claim 21 involves types of business information that can be mentally stored and processed (with or without pen and paper, and matrices that can be mentally stored and processed (with or without pen and paper). For at least these reasons, the eligibility rationales from Example 39 are inapplicable to claim 21.
On pp. 24-40 of the Amendment, the applicant requests reconsideration and withdrawal of the rejection of claims 1-5, 7-14, and 16-20 under 35 USC 101. With respect to Step 2A, Prong Two of the eligibility analysis, the applicant argues that the additional elements integrate any abstract idea into a practical application because they effect a transformation of a particular article to a different state or thing in accordance with MPEP 2106.05(c), and the applicant addresses the five factors outlined in the MPEP. (Amendment, pp. 29-32.) The examiner finds the arguments unpersuasive. While MPEP 2106.05(c) indicates that the five factors are relevant to analysis, the MPEP makes statement that appear to supersede the merely relevant factors. For example, “Purely mental processes in which thoughts or human based actions are "changed" are not considered an eligible transformation. For data, mere "manipulation of basic mathematical constructs [i.e.,] the paradigmatic ‘abstract idea,’" has not been deemed a transformation. CyberSource v. Retail Decisions, 654 F.3d 1366, 1372 n.2, 99 USPQ2d 1690, 1695 n.2 (Fed. Cir. 2011) (quoting In re Warmerdam, 33 F.3d 1354, 1355, 1360, 31 USPQ2d 1754, 1755, 1759 (Fed. Cir. 1994)).” The applicant’s claimed processing of various types of data involves mental processes, and manipulation of data that is like mere manipulation of mathematical constructs. Further, MPEP 2106.05(c) makes clear that “An "article" includes a physical object or substance,” and for factor no. 4, “Transformation of a physical or tangible object or substance is more likely to provide significantly more (or integrate a judicial exception into a practical application) than the transformation of an intangible concept such as a contractual obligation or mental judgment.” At least factor nos. 1, 3, and 5 appear to be directed primarily to transformation of physical objects or substances, not data. For at least these reasons, the examiner does not view the applicant’s claims as warranting eligibility under the particular transformation rationale.
With respect to Step 2B of the eligibility analysis, the applicant makes similar arguments as those from Step 2A, Prong Two. (Amendment, pp. 33-40.) The examiner finds the arguments about particular transformations unpersuasive for the same (or similar) reasons as those provided in the discussion of Step 2A, Prong Two of the eligibility analysis.
Conclusion
The prior art made of record and not relied upon is considered pertinent to the applicant's disclosure. Such prior art includes the following:
U.S. Pat. Pub. No. 2013/0346150 A1 to Beddo et al. discloses, “determining forecasting data relating to a product using a neural network and accessing that forecasting data. In some embodiments, a system is provided that includes (a) forecasting apparatus, which stores product information, a data matrix, and a neural network; and (b) a computing system that access the forecasting apparatus via a web portal and transmits some or all of the product information to the forecasting apparatus. In some embodiments, the forecasting apparatus is configured to determine a sales forecast using the product information, data matrix, and neural network and present the sales forecast” (Abstract.)
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/THOMAS YIH HO/Primary Examiner, Art Unit 3624
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