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 .
Election/Restrictions
Newly submitted claims 13-20 are directed to an invention that is independent or distinct from the invention originally claimed for the following reasons:
Restriction to one of the following inventions is required under 35 U.S.C. 121:
I. Claims 1-12, drawn to a method and system for maintaining a purchase history of purchased items and predicting when an order should be placed for a particular item including analyzing purchase history to identify time gaps between purchases, classified in G06Q 30/0631.
II. Claims 13-20, drawn to a method for analyzing transaction records for transactions by identifying time gaps to identify consumption patterns and frequencies of consumption for items purchased by the customer, classified in G06Q 30/0635.
The inventions are distinct, each from the other because of the following reasons:
Inventions I and II are related as subcombinations disclosed as usable together in a single combination. The subcombinations are distinct if they do not overlap in scope and are not obvious variants, and if it is shown that at least one subcombination is separately usable. In the instant case, subcombination I includes identifying time gaps based on a time gap, frequency and pattern analysis and further involves a Virtual Reality (VR) communication channel, whereas subcombination II includes identifying time gaps based on consumption patterns and frequencies of consumption and further involves the use of an Application Programming Interface (API). See MPEP § 806.05(d).
Restriction for examination purposes as indicated is proper because all these inventions listed in this action are independent or distinct for the reasons given above and there would be a serious search and/or examination burden if restriction were not required because one or more of the following reasons apply:
(a) the inventions have acquired a separate status in the art in view of their different classification;
(b) the inventions have acquired a separate status in the art due to their recognized divergent subject matter;
(c) the inventions require a different field of search (for example, searching different classes/subclasses or electronic resources, or employing different search queries);
(d) the prior art applicable to one invention would not likely be applicable to another invention.
Since applicant has received an action on the merits for the originally presented invention, this invention has been constructively elected by original presentation for prosecution on the merits. Accordingly, claims 13-20 are withdrawn from consideration as being directed to a non-elected invention. See 37 CFR 1.142(b) and MPEP § 821.03.
Claims 1, 5-9 and 11-12 have been amended.
Claims 1-12 are pending and rejected.
Response to Arguments
35 USC 101 rejection
Applicant's arguments with respect to the rejection of claims 1-12 under 35 USC 101, as being directed to a judicial exception, have been fully considered but are not persuasive, in view of the accompanying amendments and in view of MPEP 2106. The rejection under 35 USC 101 is explained in further detail below.
Examiner appreciates Applicant’s inclusion of additional limitations, however, the Examiner asserts that even with the inclusion of additional features, the claims remain abstract and directed to a judicial exception. As written, the claims merely set forth a process for facilitating an order, which is considered to be an abstract idea as it relates to ‘certain methods of organizing human activity,’ namely marketing or sales activities or behaviors.
Although the claim may use technology to perform steps related to solving a problem, the claim does not amount to a ‘technical improvement’ as the technology (i.e. a computer or other machinery) is merely used in its ordinary capacity for economic or other tasks (e.g., to receive and transmit data). Furthermore, claim 4 merely discloses usage of a recurrent neural network algorithm in a new environment. This new environment is product purchase history analysis and the reorder of previous items. The claimed methods are not rendered patent eligible by the fact that (using existing technology) they perform a task previously undertaken by humans with greater speed and efficiency than could previously be achieved.
Examiner reiterates that although claims 1 and 11 may use technology (i.e. a processor, a storage medium, etc.) to perform steps related to solving a problem, the claim does not amount to a ‘technical improvement’ as the technology (i.e. a computer or other machinery) is merely used in its ordinary capacity for economic or other tasks (e.g., to store, receive and transmit data). Further clarification with regards to the structures performing each function, specific improvements achieved by the recurrent neural network and time gap analysis may assist with the analysis of these claims. “After the examiner has consulted the specification and determined that the disclosed invention improves technology, the claim must be evaluated to ensure the claim itself reflects the disclosed improvement in technology” (see MPEP 2106.05(A)). Also, “if the specification sets forth an improvement in technology, the claim must be evaluated to ensure that the claim itself reflects the disclosed improvement. That is, the claim includes the components or steps of the invention that provide the improvement described in the specification” (see MPEP 2106.04 (d)(1)).
The additional elements are merely recited at a high level of generality and amount to little more than the mere instructions to implement an abstract idea on a computer or similar hardware. Further, these elements represent little more than a general link to a technological environment (i.e. a mere attempt to restrict use of the idea to a technical environment such as the Internet or computer networks – see Ultramercial, Inc. v. Hulu, LLC) as currently written. In each case, the courts have found such limitations insufficient to qualify as “significantly more” when recited in a claim with a judicial exception (see MPEP 2106.05(A)).
As written, the claims fail to be significantly more than the abstract idea because the claims use a computer or other machinery in its ordinary capacity for economic or other tasks or simply add a general purpose computer or computer components (i.e. machine learning model) after the fact to an abstract idea. Therefore, the claims continue to be viewed as examples of an abstract idea without significantly more and thus lack subject matter eligibility.
Additionally, where certain dependent claims rely upon similar additional elements as recited in claims 1 and 11 these do not result in significantly more than the abstract idea itself. The additional elements of the dependent claims are treated at least similarly as those discussed above with respect to claims 1 and 11.
Even when viewed as an ordered combination, the dependent claims simply convey the abstract idea itself applied on a generic computer and are held to be ineligible under Steps 2A/2B of the Mayo framework at least similar rationale as discussed above regarding claims 1 and 11.
In view of the above, the Examiner concludes that there are no meaningful limitations in the claim that transform the judicial exception into a patent eligible application such that the claim amounts to significantly more than the judicial exception itself.
The analysis above applies to all statutory categories of invention. For at least these reasons above, the rejections under 35 USC 101 have been maintained and are explained in further detail below.
35 USC 103 rejection
Applicant’s arguments with respect to the rejection of claims 1-12 under 35 USC 103 have been fully considered but are not persuasive, in view of the accompanying amendments.
Applicant's amended claim 1 now requires “analyzing transaction records from the purchase history to identify time gaps between purchases and a non-linear sequence of the time gaps” and “proactively initiating an interactive dialogue with the customer through a networked communication channel”. This added language had not been previously recited and changes the scope of the claimed invention.
Applicant’s amendments have necessitated the new grounds of rejection presented below. The claims remain rejected as ineligible for patenting under the current 35 USC 103 rejection, explained in detail below.
Applicant is invited to schedule an interview with the Examiner to discuss the pending rejections.
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 1-12 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Step 1:
Claims 1-10 are directed to a method, which is a process. Claims 11-12 are directed to a system, which is a machine. Therefore, claims 1-12 are directed to one of the four statutory categories of invention.
Step 2A (Prong 1):
Representative claim 1 sets forth the following limitations which recite the abstract idea of facilitating an order:
maintaining a purchase history of purchased items that are purchased by a customer;
predicting when an order should be placed for a particular purchased item by analyzing transaction records from the purchase history to identify time gaps between purchases and a non-linear sequence of the time gaps based on a time gap, frequency and pattern analysis of the purchased items from the purchase history;
proactively initiating an interactive dialogue with the customer to obtain authorization or modifications to the order; and
placing the order for the particular purchased item with an order system based on the interactive dialogue.
The recited limitations above set forth steps to facilitate an order. These limitations amount to certain methods of organizing human activity, including commercial or legal interactions (e.g. advertising, marketing or sales activities or behaviors).
Such concepts have been identified by the courts as abstract ideas (see: MPEP 2106).
Step 2A (Prong 2):
Examiner notes that representative claim 1 fails to recite any additional elements such as a computer, etc. However, even if claim were to recite additional features such as these, the claims would fail to integrate the recited judicial exception into a practical application of the exception. The claims would merely include instruction to implement an abstract idea on a computer, or to merely use a computer as a tool to perform an abstract idea, while the additional elements would do no more than generally link the use of a judicial exception to a particular field of technological environment or field of use.
Furthermore, this is also because the claim fails to (i) reflect an improvement in the functioning of a computer, or an improvement to other technology or technical field, (ii) implement a judicial exception with a particular machine, (iii) effect a transformation or reduction of a particular article to a different state or thing, or (iv) apply the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment.
In view of the above, under Step 2A (Prong 2), claim 1 does not integrate the recited exception into a practical application (see again: MPEP 2106).
Step 2B:
When taken individually or as a whole, the lack of additional elements of claim 1 do not provide an inventive concept (i.e. whether the additional elements amount to significantly more than the exception itself). As discussed above with respect to integration of the abstract idea into a practical application, even including an additional element of a processor or computer to perform the steps would amount to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. Certain additional elements also recite well-understood, routine, and conventional activity (See MPEP 2106.05(d)).
Even if considered as an ordered combination, any additional elements of claim 1 would not add anything further than when they are considered individually.
In view of the above, claim 1 does not provide an inventive concept under step 2B, and is ineligible for patenting.
Dependent claims 2-10 recite further complexity to the judicial exception (abstract idea) of claim 1, such as by further defining the steps for facilitating an order. Thus, each of claims 2-10 are held to recite a judicial exception under Step 2A (Prong 1) for at least similar reasons as discussed above.
Therefore, dependent claims 2-10 do not add “significantly more” to the abstract idea. The dependent claims recite additional functions that describe the abstract idea and only generally link the abstract idea to a particularly technological environment, and applied on a generic computer. Further, the additional limitations fail to provide an improvement to the functioning of the computer, another technology, or a technical field.
Even when viewed as an ordered combination, the dependent claims simply convey the abstract idea itself applied on a generic computer and are held to be ineligible under Steps 2A/2B for at least similar rationale as discussed above regarding claim 1.
The analysis above applies to all statutory categories of invention. Regarding independent claim 11 (system), the claim recites substantially similar limitations as set forth in claim 1. As such, claim 11 and its dependent claim 12 are rejected for at least similar rationale as discussed above.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-10 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor, or for pre-AIA the applicant regards as the invention.
Claim 1 contains the limitations “analyzing transaction records from the purchase history to identify time gaps between purchases and a non-linear sequence of the time gaps based on a time gap, frequency and pattern analysis of the purchased items from the purchase history”. It is unclear the distinction between the identification of ‘time gaps’ and ‘a time gap’ upon which the identification is based on. Are these time gaps the same or different? Further, if these are indeed the same time gaps, as it appears that they are, it is unclear how the identification of time gaps could be based on the time gaps themselves. Additionally, how is the time gap determined or calculated? What is the algorithm/structure to perform this acquisition of data?
Claims 2-10 depend from claim 1 and inherit all the deficiencies of the claim from which they depend and are therefore rejected under the same basis.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102 of this title, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-12 are rejected under 35 U.S.C. 103 as being unpatentable over Szabo et al. (U.S. Patent No. 6,963,851) (“Szabo”), in view of Bifolco et al. (U.S. Pre-Grant Publication No. 2021/0004881) (“Bifolco).
Regarding claim 1, Szabo teaches a method, comprising:
maintaining a purchase history of purchased items that are purchased by a customer (col 2, ln 20-22, storing the purchase history of the items purchased by one or more consumers at the e-commerce site);
predicting when an order should be placed for a particular purchased item based on a time gap, frequency and pattern analysis of the purchased items from the purchase history (col 2, ln 30-32, reviewing the purchase history for the at least one consumer; col 5, ln 47-50, Data mining may include the classification, or the recognition of patterns and a resulting new organization of data (for example, profiles of customers who make purchases).; col 11, ln 45-50, numerous other examples of analyzing consumers' past shopping patterns are contemplated under this invention. The data mining logic 418, uses a combination of the information stored in the consumer profile 402, the stored inventory 408 and purchase history database 406 to predict useful life of a consumable item; col 11, ln 33-39, if the consumer purchases toner every three months, a rate of 3,333 sheets a month can be calculated by taking the (manufacturer's expectancy)/(frequency of purchase from purchase history database). The data mining logic 418 is able to remind the consumer to purchase additional toner when the replenishment list 506 is generated; col 11, ln 45-50, numerous other examples of analyzing consumers' past shopping patterns are contemplated under this invention. The data mining logic 418, uses a combination of the information stored in the consumer profile 402, the stored inventory 408 and purchase history database 406 to predict useful life of a consumable item.);
Although Szabo teaches a maintaining a purchase history for the consumer (col 5, ln 11-15, Consumer profile 402 stores consumer preferences such as brand names, model, types, size and other information related to the purchase of goods including, but not limited to, historical purchase data, personal information, charge authorization data and the like.), Szabo does not explicitly teach analyzing transaction records from the purchase history to identify time gaps between purchases and a non-linear sequence of the time gaps and proactively interacting in an interactive dialogue with the customer through a networked communication channel to obtain authorization or modifications to the order; and placing the order for the particular purchased item with an order system based on the interactive dialogue.
In a similar field of endeavor, Bifolco teaches:
wherein predicting includes: analyzing transaction records from the purchase history to identify time gaps between purchases and a non-linear sequence of the time gaps (Fig. 12; para [105], default frequency suggestion adapter 1214, which, in turn, includes a frequency calculator 1216, a frequency optimizer 1218, and an item characteristic correlator; para [0107], item characteristic correlator 1217 maybe configured to detect patterns or classifications among datasets and other data (e.g., data 681 to data 687) through the use of Bayesian networks, clustering analysis, as well as other known machine learning techniques or deep-learning techniques (e.g., including any known artificial intelligence techniques, or any of k-NN algorithms, linear support vector machine (“SVM”) algorithm, regression and variants thereof (e.g., linear regression, non-linear regression, etc.))
proactively interacting in an interactive dialogue with the customer through a networked communication channel to obtain authorization or modifications to the order (Fig. 1A; para [0041], integrated message 141 may be implemented as an electronic mail (e.g., email) message in which a portion 168 c includes an offer to either ordering item or reorder an item by, for example, activating a link 175.); and
placing the order for the particular purchased item with an order system based on the interactive dialogue (Fig. 1A; para [0041], An example of link 175 may be a hyperlink to either default frequency suggestion adapter 114 a or merchant computing system 130 a, which, when activated, may cause the item to ship in accordance with the default frequency and/or predicted date of delivery).
Since each individual element and its function are shown in the prior art, albeit shown in separate references, the difference between the claimed subject matter and the prior art rests not on any individual element or function but in the very combination itself. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include the noted limitations as taught by Bifolco in the method of Szabo, since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable. Namely, an improvement over conventional metrics which are not well-suited to correlate accurately how a product or service is consumed or depleted so as to accurately facilitate techniques to adaptively schedule items for automatic distribution of items (See Bifolco: para [0007]-[0008]).
Regarding claim 2, Szabo and Bifolco teach the above method of claim 1. Szabo also teaches wherein maintaining further includes dynamically collecting real-time transaction records for transactions of the customer and updating the purchase history with the real-time transaction records (col 5, ln 47-50, Data mining may include the classification, or the recognition of patterns and a resulting new organization of data (for example, profiles of customers who make purchases).; col 11, ln 45-50, numerous other examples of analyzing consumers' past shopping patterns are contemplated under this invention. The data mining logic 418, uses a combination of the information stored in the consumer profile 402, the stored inventory 408 and purchase history database 406 to predict useful life of a consumable item.).
Regarding claim 3, Szabo and Bifolco teach the above method of claim 1. Szabo also teaches wherein maintaining further includes periodically, at predefined intervals of time, collecting transaction records for transactions of the customer and updating the purchase history with the transaction records (col 5, ln 47-50, Data mining may include the classification, or the recognition of patterns and a resulting new organization of data (for example, profiles of customers who make purchases).; col 11, ln 45-50, numerous other examples of analyzing consumers' past shopping patterns are contemplated under this invention. The data mining logic 418, uses a combination of the information stored in the consumer profile 402, the stored inventory 408 and purchase history database 406 to predict useful life of a consumable item.).
Regarding claim 4, Szabo and Bifolco teach the above method of claim 1. Bifolco also teaches wherein predicting further includes processing a recurrent neural network algorithm to perform the time gap, frequency, and pattern analysis on the purchased items from the purchase history (para [0107], item characteristic correlator 1217 maybe configured to detect patterns or classifications among datasets and other data (e.g., data 681 to data 687) through the use of Bayesian networks, clustering analysis, as well as other known machine learning techniques or deep-learning techniques (e.g., including any known artificial intelligence techniques, or any of k-NN algorithms, linear support vector machine (“SVM”) algorithm, regression and variants thereof (e.g., linear regression, non-linear regression, etc.), Bayesian inferences and the like, including classification algorithms, such as Naïve Bayes classifiers, or any other statistical or empirical technique)).
Regarding claim 5, Szabo and Bifolco teach the above method of claim 1. Bifolco also teaches wherein proactively initiating further includes engaging the customer in the interactive dialogue through a messaging channel through text (para [0042], integrated message 141 may be implemented as any electronic message, including text messages or any other electronic format).
Regarding claim 6, Szabo and Bifolco teach the above method of claim 1. Bifolco also teaches wherein proactively initiating further includes engaging the customer in the interactive dialogue through a networked voice and speaker enabled device through voice (para [0042], integrated message 141 may be an automated telephone call including an audio portion that includes instructions to ask the callee whether to reorder a product by, for example, pressing a number on a keypad of a phone).
Regarding claim 7, Szabo and Bifolco teach the above method of claim 1. Szabo also teaches wherein proactively initiating further includes engaging the customer in the interactive dialogue through a Virtual Reality (VR) communication channel (col 5, ln 55-59, The e-commerce server 102 provides a virtual retail store that permits shoppers using a browser 112 to purchase goods to be delivered to a consumer. The goods in the virtual retail store are browsed and selected in a way similar to a shopper searching through a brick and mortar store.).
Regarding claim 8, Szabo and Bifolco teach the above method of claim 1. Szabo also teaches wherein proactively initiating further includes initiating the interactive dialogue with the customer on a specific date that precedes a predicted date that the customer is likely to purchase the particular purchased item (col 5, ln 47-50, Data mining may include the classification, or the recognition of patterns and a resulting new organization of data (for example, profiles of customers who make purchases).; col 11, ln 45-50, numerous other examples of analyzing consumers' past shopping patterns are contemplated under this invention. The data mining logic 418, uses a combination of the information stored in the consumer profile 402, the stored inventory 408 and purchase history database 406 to predict useful life of a consumable item.).
Regarding claim 9, Szabo and Bifolco teach the above method of claim 1. Bifolco also teaches wherein proactively initiating further includes initiating the interactive dialogue with the customer a predefined number of days that precedes a predicted date that the customer is likely to purchase the particular purchased item (para [0041], when activated, may cause the item to ship in accordance with the default frequency and/or predicted date of delivery).
Regarding claim 10, Szabo and Bifolco teach the above method of claim 1. Szabo also teaches further comprising: providing the customer with a specific date and location that the particular purchased item can be picked up by the customer or providing the customer with the specific date and a specific delivery address that the particular purchased item will be delivered to the customer (col 6, ln 24-27, After purchase, arrangements can be made for delivery of the items, either to a specific site or address, or to a pickup facility in a warehouse.).
Regarding claim 11, Szabo teaches a system, comprising:
a server comprising a processor and a non-transitory computer- readable storage medium (Fig. 1; col 3, ln 39-55);
the non-transitory computer-readable storage medium comprises executable instructions (Fig. 1; col 3, ln 39-55);
the executable instructions when executed by the processor from the non-transitory computer-readable storage medium cause the processor (Fig. 1; col 3, ln 39-55) to perform operations comprising:
predicting at least one item that is likely to be purchased by a customer within a predefined number of days of a predicted purchase date based on a time, frequency, and pattern analysis of transaction records for a transaction history of the customer (col 2, ln 30-32, reviewing the purchase history for the at least one consumer; col 5, ln 47-50, Data mining may include the classification, or the recognition of patterns and a resulting new organization of data (for example, profiles of customers who make purchases).; col 11, ln 45-50, numerous other examples of analyzing consumers' past shopping patterns are contemplated under this invention. The data mining logic 418, uses a combination of the information stored in the consumer profile 402, the stored inventory 408 and purchase history database 406 to predict useful life of a consumable item; col 11, ln 33-39, if the consumer purchases toner every three months, a rate of 3,333 sheets a month can be calculated by taking the (manufacturer's expectancy)/(frequency of purchase from purchase history database). The data mining logic 418 is able to remind the consumer to purchase additional toner when the replenishment list 506 is generated; col 11, ln 45-50, numerous other examples of analyzing consumers' past shopping patterns are contemplated under this invention. The data mining logic 418, uses a combination of the information stored in the consumer profile 402, the stored inventory 408 and purchase history database 406 to predict useful life of a consumable item.);
However, Szabo does not explicitly teach analyzing transaction records from the purchase history to identify time gaps between purchases and a non-linear sequence of the time gaps and proactively interacting in an interactive dialogue with the customer through a networked communication channel to confirm an order for the at least one item at a predefined number of days before the predicted purchase date; and automatically placing the order for the particular purchased item with an order system based on the interactive dialogue.
In a similar field of endeavor, Bifolco teaches:
wherein predicting includes: analyzing transaction records from the purchase history to identify time gaps between purchases and a non-linear sequence of the time gaps (Fig. 12; para [105], default frequency suggestion adapter 1214, which, in turn, includes a frequency calculator 1216, a frequency optimizer 1218, and an item characteristic correlator; para [0107], item characteristic correlator 1217 maybe configured to detect patterns or classifications among datasets and other data (e.g., data 681 to data 687) through the use of Bayesian networks, clustering analysis, as well as other known machine learning techniques or deep-learning techniques (e.g., including any known artificial intelligence techniques, or any of k-NN algorithms, linear support vector machine (“SVM”) algorithm, regression and variants thereof (e.g., linear regression, non-linear regression, etc.))
proactively interacting in an interactive dialogue with the customer through a networked communication channel to confirm an order for the at least one item at a predefined number of days before the predicted purchase date (Fig. 1A; para [0041], integrated message 141 may be implemented as an electronic mail (e.g., email) message in which a portion 168 c includes an offer to either ordering item or reorder an item by, for example, activating a link 175. An example of link 175 may be a hyperlink to either default frequency suggestion adapter 114 a or merchant computing system 130 a, which, when activated, may cause the item to ship in accordance with the default frequency and/or predicted date of delivery); and
automatically placing the order for the particular purchased item with an order system based on the interactive dialogue (Fig. 1A; para [0041], An example of link 175 may be a hyperlink to either default frequency suggestion adapter 114 a or merchant computing system 130 a, which, when activated, may cause the item to ship in accordance with the default frequency and/or predicted date of delivery).
Since each individual element and its function are shown in the prior art, albeit shown in separate references, the difference between the claimed subject matter and the prior art rests not on any individual element or function but in the very combination itself. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include the noted limitations as taught by Bifolco in the method of Szabo, since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable. Namely, an improvement over conventional metrics which are not well-suited to correlate accurately how a product or service is consumed or depleted so as to accurately facilitate techniques to adaptively schedule items for automatic distribution of items (See Bifolco: para [0007]-[0008]).
Regarding claim 12, Szabo and Bifolco teach the above system of claim 11. Bifolco also teaches wherein the executable instructions associated with the proactively initiating further cause the processor to perform additional operations comprising: establishing the interactive dialogue as a voice dialogue or a messaging dialogue (para [0042], integrated message 141 may be implemented as any electronic message, including text messages or any other electronic format. In some cases, integrated message 141 may be an automated telephone call including an audio portion that includes instructions to ask the callee whether to reorder a product).
Conclusion
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any extension fee pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ANAND LOHARIKAR whose telephone number is 571-272-8756. The examiner can normally be reached Monday-Friday, 9am-5pm.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Marissa Thein can be reached at 571-272-6764. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/ANAND LOHARIKAR/Primary Examiner, Art Unit 3689
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