DETAILED ACTION
Status of Claims
This action is in reply to the response received on 31 August 2025.
Claims 1-3, 11-13, and 20 have been amended in the response.
Claims 1-20 are pending and have been examined.
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 .
Allowable Subject Matter
Claims 1-20 now recite allowable subject matter and would be allowable of the claims were amended or re-written to overcome the current 112 and 101 rejections indicated in the Office Action below.
Claim Rejections - 35 USC § 112(a)
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 1-20 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention.
Regarding claim 1, the claim recites:
omitting for further processing of one or more candidate alternatives whose composite replacement scores are below a threshold to reduce computation resources spent on selecting alternatives
Regarding claims 11 and 20, the claims recite
omit for further processing of one or more candidate alternatives whose composite replacement scores are below a threshold to reduce computation resources spent on selecting alternatives;
The disclosure is silent regarding claims 1, 11, and 20 and the amended features of omitting for further processing of one or more candidate alternatives whose composite replacement scores are below a threshold to reduce computation resources spent on selecting alternatives. Specifically, the disclosure does not discuss or explain any type of reduction in computation resources that are spent on selecting alternatives. The specification does discuss that if a degree of association is lower than a threshold, the online concierge system may select another item alternatively with a different brand or a suitable sponsorship opportunity (see paragraph [0093] of the Applicant’s specification). However, there is no mention of a composite replacement score that would be below a threshold in order to reduce computation resources spent on selecting alternatives. Therefore, since the disclosure lacks explanation of these features, the current claims are considered to be new matter.
Claims 2-10 and 12-19 depending from claims 1 and 11 respectively, inherit the deficiencies noted above and are rejected for the same reasoning.
Claim Rejections - 35 USC § 112(b)
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 1-20 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 applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Regarding claim 1, the claim recites:
omitting for further processing of one or more candidate alternatives whose composite replacement scores are below a threshold to reduce computation resources spent on selecting alternatives
Regarding claims 11 and 20, the claims recite
omit for further processing of one or more candidate alternatives whose composite replacement scores are below a threshold to reduce computation resources spent on selecting alternatives;
The limitations in this case, render the claims unclear as to how there would be reduced computation resources spent on selecting alternatives based on replacement scores that are below a threshold. Specifically, it is not clear how there would be a reduction in computational resources from simply omitting candidate alternatives that have composite replacement scores below a threshold. How would this cause any change in computational resources in any manner? The disclosure does not explain any type of impact that the omitting of the candidate alternatives would have on the actual computer or computing components, or how the specific computation resources would be reduced. The specification describes how that if a degree of association is lower than a threshold, the online concierge system may select another item alternatively with a different brand or a suitable sponsorship opportunity (see paragraph [0093] of the Applicant’s specification). However, the specification does not mention any type of change or reduction in the computation resources from omitting candidate alternatives. As such, the metes and the bounds of the claim are unclear inasmuch as one of the ordinary skill in the art cannot determine how to avoid infringement of the claims because they are not apprised of how any type of reduction of computation resources spent on selecting alternatives would be occurring.
Claims 2-10 and 12-19 depending from claims 1 and 11 respectively, inherit the deficiencies noted above and are rejected for the same reasoning.
For examination purposes, the Examiner will interpret the claim limitation as best as can be understood.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 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.
Under step 1, it is determined whether the claims are directed to a statutory category of invention (see MPEP 2106.03(II)). In the instant case, claims 1-10 are directed to a method, claims 11-19 are directed to a product of manufacture, and claim 20 is directed to a system.
While the claims fall within statutory categories, under revised Step 2A, Prong 1 of the eligibility analysis (MPEP 2106.04), the claimed invention recites an abstract idea of presenting alternative items to a customer. Specifically, representative claim 1 recites the abstract idea of:
receiving, from a customer, a selection of an item that is associated with a first brand;
extracting a first set of features associated with the customer and a second set of features associated with the item;
inputting the first set of features and the second set of features to predict a degree of association between the customer and the first brand associated with the item, the degree of association being specific to the customer, wherein using a training set that comprises a past replacement record of a user relacing the item with another item associated with another brand;
identifying a plurality of candidate alternatives for replacing the item, the plurality of candidate alternatives comprising a first alternative that is associated with the first brand and a second alternative that is associated with a second brand different from the first brand;
predicting, to output an availability score for an item at a physical store location, a plurality of availability scores that respectively indicates a likelihood that a candidate alternative in the plurality of candidate alternatives is in stock at the physical store location;
computing composite replacement scores that are functions of the availability scores predicted by the degree of association predicted;
omitting for further processing of one or more candidate alternatives whose composite replacement scores are below a threshold to reduce resources spent on selecting alternatives;
selecting, based on the degree of association between the customer and the first brand, one or more candidate alternatives to be presented to the customer to replace the item.
Under revised Step 2A, Prong 1 of the eligibility analysis, it is necessary to evaluate whether the claim recites a judicial exception by referring to subject matter groupings articulated in 2106.04(a) of the MPEP. Even in consideration of the analysis, the claims recite an abstract idea. Representative claim 1 recites the abstract idea of presenting alternative items to a customer, as noted above. This concept is considered to be a method of organizing human activity. Certain methods of organizing human activity include “fundamental economic principles or practices (including hedging, insurance, mitigating risk); commercial or legal interactions (including agreements in the form of contracts; legal obligations; advertising, marketing or sales activities or behaviors; business relations); managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions).” MPEP 2106.04(a)(2)(II). In this case, the abstract idea recited in representative claim 1 is a certain method of organizing human activity because it relates to sale activities since the claims recite the activities of receiving a selection of item from a customer that is associated with a brand, extracting a first set of features associated with the customer and a second set of features associated with the item, inputting the first and second set of features to predict an association between the customer and the brand of the item, where the sets of data comprise a replacement record of a user replacing the item with another item associated with another brand, identifying a plurality of candidate alternatives for replacing the item comprising a first and second alternative, predicting, to output an availability score for an item at a physical store location, a plurality of availability scores that respectively indicates a likelihood that a candidate alternative in the plurality of candidate alternatives is in stock at the physical store location, computing composite replacement scores that are functions of the availability scores predicted by the degree of association predicted, omitting for further processing of one or more candidate alternatives whose composite replacement scores are below a threshold to reduce resources spent on selecting alternatives, and selecting based on the degree of association between the customer and the brand of the item one or more candidate alternatives to be presented to the customer to replace the item with, thereby making this a sales activity or behavior.
Thus, representative claim 1 recites an abstract idea.
The Examiner additionally notes that that the step of identifying a plurality of candidate alternatives for replacing the item, would fall into the enumerated grouping of mental processes. A mental process is defined as and includes “concepts performed in the human mind (including an observation, evaluation, judgement, and opinion)” (see MPEP 2106.04(a)(2)(III)). In this case, the step of identifying candidate alternatives for replacing the item comprising a first alternatives that is associated with the first brand and a second alternative that is associated with a second brand that is different than the first, would be considered a concepts performed in the human mind, such as an observation and an evaluation. Thus, representative claim 1 recites an abstract idea that also falls into the grouping of mental processes.
Further, the step of computing composite replacement scores that are functions of the availability scores predicted and the degree of association predicted, would be considered a mathematical concept. The MPEP defines “mathematical concepts” as mathematical relationships, mathematical formulas or equations, and mathematical calculations (MPEP 2106.04(a)(2)). The claim recites computing a composite replacement score using the availability scores, which would fall within the “mathematical concepts” grouping and directed to a mathematical relationships and a mathematical calculation. Thus, representative claim 1 recites an abstract idea directed to a mathematical concept.
Under Step 2A, Prong 2 of the eligibility analysis, if it is determined that the claims recite a judicial exception, it is then necessary to evaluate whether the claims recite additional elements that integrate the judicial exception into a practical application of that exception. MPEP 2106.04(d). The courts have identified limitations that did not integrate a judicial exception into a practical application include limitations merely reciting the words “apply it” (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea, as discussed in MPEP 2106.05(f). MPEP 2106.04(d). In this case, representative claim 1 includes additional elements: a computer system, a processor and a computer-readable medium, a brand-association machine learning model that is trained, the brand-association machine learning model is trained, an availability machine-learning model that is trained, the availability machine learning model, the brand-association machine learning model, and computation.
Although reciting such additional elements, the additional elements do not integrate the abstract idea into a practical application because they merely amount to no more than an instruction to apply the abstract idea using a generic computer or merely use a computer as a tool to perform the abstract idea. These additional elements are described at a high level in Applicant’s specification without any meaningful detail about their structure or configuration. Similar to the limitations of Alice, representative claim 1 merely recites a commonplace business method (i.e., presenting alternative items to a customer) being applied on a general-purpose computer using general purpose computer technology. MPEP 2106.05(f). While the claim recites training a machine learning model, the recitations are results based in nature and do not include details as to how the machine learning is actually functioning beyond known functions. Thus, the claimed additional elements are merely generic elements and the implementation of the elements merely amounts to no more than an instruction to apply the abstract idea using a generic computer. Since the additional elements merely include instructions to implement the abstract idea on a generic computer or merely use a generic computer as a tool to perform an abstract idea, the abstract idea has not been integrated into a practical application.
Under Step 2B of the eligibility analysis, if it is determined that the claims recite a judicial exception that is not integrated into a practical application of that exception, it is then necessary to evaluate the additional elements individually and in combination to determine whether they provide an inventive concept (i.e., whether the additional elements amount to significantly more than the exception itself). MPEP 2106.05. In this case, as noted above, the additional elements recited in independent claim 1 of a computer system, a processor and a computer-readable medium, a brand-association machine learning model that is trained, the brand-association machine learning model is trained, an availability machine-learning model that is trained, the availability machine learning model, the brand-association machine learning model, and computation, are recited and described in a generic manner merely amount to no more than an instruction to apply the abstract idea using a generic computer or merely use a generic computer as a tool to perform an abstract idea.
Even when considered as an ordered combination, the additional elements of representative claim 1 do not add anything that is not already present when they considered individually. In Alice, the court considered the additional elements “as an ordered combination,” and determined that “the computer components…‘ad[d] nothing…that is not already present when the steps are considered separately’… [and] [v]iewed as a whole…[the] claims simply recite intermediated settlement as performed by a generic computer.” Alice Corp. Pty. Ltd. v. CLS Bank Int'l, 573 U.S. 208, 217, (2014) (citing Mayo, 566 U.S. at 79, 101 USPQ2d at 1972). Similarly, when viewed as a whole, representative claim 1 simply conveys the abstract idea itself facilitated by generic computing components. Therefore, under Step 2B of the Alice/Mayo test, there are no meaningful limitations in representative claim 1 that transforms the judicial exception into a patent eligible application such that the claims amount to significantly more than the judicial exception itself.
As such, representative claim 1 is ineligible.
Independent claims 11 and 20 are similar in nature to representative claim 1, and Step 2A, Prong 1 analysis is the same as above for representative claim 1. It is noted that in independent claim 11 includes the additional elements of a non-transitory computer-readable medium configured to store code comprising instructions, the instructions, when executed by a processor, cause the processor, and independent claim 20 includes the additional elements of a processor and; memory configured to store code comprising instructions, wherein the instructions, when executed by the processor, cause the processor. The Applicant’s specification does not provide any discussion or description of the claimed additional elements recited in claims 11 and 20, as being anything other than generic elements. Thus, the claimed additional elements of claims 11 and 20 are merely generic elements and the implementation of the elements merely amounts to no more than an instruction to apply the abstract idea using a generic computer. As such, the additional elements of claims 11 and 20 do not integrate the judicial exception into a practical application of the abstract idea. Additionally, the additional elements of claim [35] and [36], considered individually and in combination, do not provide an inventive concept because they merely amount to no more than an instruction to apply the abstract idea using a generic computer.
As such, claims 11 and 20 are ineligible.
Dependent claims 2-10, and 12-19, depending from claims 1 and 11 respectively, do not aid in the eligibility of the independent claim 1. The claims of 2-10 and 12-19 merely act to provide further limitations of the abstract idea and are ineligible subject matter.
It is noted that dependent claims include the additional elements of a forward propagation, the machine learning model, backpropagation, and the machine learning model (claims 3 & 13). Applicant’s specification does not provide any discussion or description of the additional elements as being anything other than a generic element. The claimed additional elements, individually and in combination do not integrate into a practical application and do not provide an inventive concept because they are merely being used to apply the abstract idea using a generic computer (see MPEP 2106.05(f)). Accordingly, claims 3 and 13 are directed towards an abstract idea. Additionally, the additional elements of claims 3 and 13, considered individually and in combination, do not provide an inventive concept because they merely amount to no more than an instruction to apply the abstract idea using a generic computer. It is further noted that the remaining dependent claims 2, 4-12, and 14-19 do not recite any further additional elements to consider in the analysis, and therefore would not provide additional elements that would integrate the abstract idea into a practical application and would not provide an inventive concept.
As such, dependent claims 2-10 and 12-19 are ineligible.
Reasons for Allowable Subject Matter
Prior Art Considerations:
Upon review of the evidence at hand, it is concluded that the totality of evidence in combination, neither anticipates, reasonably teaches, nor renders obvious the below noted features of the Applicant’s invention.
Regarding the independent claims, the features are as follows: compute composite replacement scores that are functions of the availability scores predicted by the availability machine learning model and the degree of association predicted by the brand-association machine learning model
The most apposite prior art of record includes Liu, C., et al. (US Patent 10,861,077 B1), in view of Cai, L., et al. (PGP No. US 2022/0237681 A1), and Saad, M., et al. (PGP No. US 2022/0366265 A1), to teach a system for selecting alternative products.
The reference of Liu discloses that a customer views a content product page of a website, accessing information about a specific product, where the information contains brand information, where once the user has viewed this brand, there is a score generated for that specific brand (Liu, Col. 2, ln. 62-64 and Col. 11, ln. 3-5, 6-8). According to the system of Liu, using machine learning in order to discover weighted factors that might influence a user’s purchase decisions, where the machine learning methods include analyzing user purchase history and the item attribute information for each purchased (Liu, Col. 2, ln. 50-54). The system of Liu generates scores representing levels of affinity or relationships between the customer reviews for items and a brand score generator calculates scores representing a level of relationship between the brands of items (Liu, see: Col. 2, ln. 25-25, 50-54, Col. 8, ln. 23-24 and 50-52). Based on these calculations of Liu, the scores are used to identify other or alternative items, such as substitute items for that specific original item (Liu, Col. 1, 31-32). The machine learning methods of Liu confirm or determine whether or not the item pairs are purchasable by the user, or if the items are even available for purchase (Liu, see: Col. 6, ln. 4-6; Col. 16, ln. 47-50).
The reference of Cai is relied upon to teach that a user’s history of purchases are considered and alternative brands could be suggested and displayed to the user based on similar cost to the previously purchased items, where omitting alternative items can indicate that there are no recommendations even available for the user (Cai, paragraphs [0091]-[0092]).
Next, the reference of Saad is relied upon to demonstrate that recommendations may be offered to a user that contains alternative products, such as different models or brands of a specific product (Saad, paragraphs [0005] and [0022]). Saad also describes that user behavior data is used in machine learning models that concludes specific patterns of the user, such as searching a website, browsing products, and purchase the products (Saad, paragraph [0042]), where the models can use propagation techniques of the set of behavior data through a plurality of layers of the network (Saad, paragraph [0066]). The system of Saad goes further into explaining that feature embeddings are used for comparisons amongst groups of users that purchase or browse similar products (Saad, paragraphs [0039] and [0052]), and the propagation can then be adjusted based on these comparisons of the feature embeddings of different user groups (Saad, paragraphs [0050] and [0052]).
Although the references in combination, teach these features, the references of Liu, Cai, and Saad do not teach or suggest the allowable features indicated above of compute composite replacement scores that are functions of the availability scores predicted by the availability machine learning model and the degree of association predicted by the brand-association machine learning model.
The Examiner further emphasizes the claims as a whole and hereby asserts that the totality of the evidence fails to set forth, either explicitly or implicitly, an appropriate rationale for further modification of the evidence at hand to arrive at the claimed invention. Moreover, the combination of features of independent claims, would not have been obvious to one of ordinary skill in the art because any combination of evidence at hand to reach the combination of features as claimed would require substantial reconstruction of Applicant’s claimed invention relying on improper hindsight bias and resulting in an inappropriate combination.
It is hereby asserted by the Examiner, that in light of the above and in further deliberation over all of the evidence at hand, that the claims recite allowable subject matter, as the evidence at hand does not anticipate the claims and does not render obvious any further modification of the references to a person of ordinary skill in the art.
Examiner’s Comment
The Examiner notes that the non-patent literature (NPL) document, titled Machine learning accurately predicts food exchange list and the exchangeable portion, published in Frontiers in 2023, documented on PTO-892 form as reference U, and hereinafter referred to as ‘Machine learning’, describes how machine learning methods and data mining techniques are used to analyze patterns of user information such as patterns, resulting in food lists of exchangeable or alternative items. Although ‘Machine learning’ describes such features, the NPL does not disclose or teach the allowable features that are stated above, and does not remedy the deficiencies of the noted prior art.
Response to Arguments
With respect to the rejections made under 35 USC § 101, the Applicant’s arguments filed on 31 August 2025, have been fully considered but are not considered persuasive.
In response to the Applicant’s arguments found on pages 11-12 of the remarks stating that “The claims recite a specific improvement to computer technology through the fusion of multiple machine learning models in order to generate composite replacement scores that balance brand association with real-time availability at a physical store” and “the claims require integrating outputs of two different trained models” and further improve both accuracy and computation performance by combining predictions from different models in a way that neither model alone could achieve,” and further “therefore go beyond organizing human activity” and “are rooted in computer technology and provide a specific technical solution to the problem of efficiently generating personalized and available replacement recommendations,” the Examiner respectfully disagrees. Under Step 2A, Prong 1 of the eligibility analysis, the claims are directed to the abstract idea of presenting alternative items to a customer. The abstract idea in this case, falls into the grouping of a certain method of organizing human activity, as the claims are specifically reciting the steps of receiving a selection of item from a customer that is associated with a brand, extracting a first set of features associated with the customer and a second set of features associated with the item, inputting the first and second set of features to predict an association between the customer and the brand of the item, where the sets of data comprise a replacement record of a user replacing the item with another item associated with another brand, identifying a plurality of candidate alternatives for replacing the item comprising a first and second alternative, predicting, to output an availability score for an item at a physical store location, a plurality of availability scores that respectively indicates a likelihood that a candidate alternative in the plurality of candidate alternatives is in stock at the physical store location, computing composite replacement scores that are functions of the availability scores predicted by the degree of association predicted, omitting for further processing of one or more candidate alternatives whose composite replacement scores are below a threshold to reduce resources spent on selecting alternatives, and selecting based on the degree of association between the customer and the brand of the item one or more candidate alternatives to be presented to the customer to replace the item with, which are related to sales activities or behaviors.
Next, under Step 2A, Prong 2 of the eligibility analysis, the amended claims do not integrate the abstract idea into a practical application. The claimed additional elements, when considered individually and in combination, are still recited in a generic manner and are being used to apply the abstract idea with generic computing components. And even though the claims now recite the computing of composite replacement scores, the availability machine learning model and the brand-association machine learning model, these features are still recited at a high-level, and are not explained or described in enough specificity that would be sufficient to integrate the abstract idea into a practical application. Even though the models are used to generate this information and used to ultimately determine alternative items to be presented, the models are using existing data, and the outcome is directed to the abstract idea which is related to sales activities or behaviors. Further, the claims are not demonstrating any type of technical solution or improvement to the technology itself. Although the claims recite reduce computation resources spent on selecting alternatives, the feature is not described in the specification, and appears to be a conclusory type of limitation recited in the claims. The MPEP (2106.05(a)), however, provides further guidance on how to evaluate whether claims recite an improvement in the functioning of a computer or an improvement to other technology or technical field. For example, as indicated in 2106.05(d)(1) of the MPEP “the specification should be evaluated to determine if the disclosure provides sufficient details such that one of ordinary skill in the art would recognize the claimed invention as providing an improvement,” and that “[t]he specification need not explicitly set forth the improvement, but it must describe the invention such that the improvement would be apparent to one of ordinary skill in the art.” Looking to the specification is a standard that the courts have employed when analyzing claims as it relates to improvements in technology. For example, in Enfish, the specification provided teaching that the claimed invention achieves benefits over conventional databases, such as increased flexibility, faster search times, and smaller memory requirements. Enfish LLC v. Microsoft Corp., 822 F.3d 1327, 1335-36 (Fed. Cir. 2016). Additionally, in Core Wireless the specification noted deficiencies in prior art interfaces relating to efficient functioning of the computer. Core Wireless Licensing v. LG Elecs. Inc., 880 F.3d 1356 (Fed Cir. 2018). With respect to McRO, the claimed improvement, as confirmed by the originally filed specification, was “…allowing computers to produce ‘accurate and realistic lip synchronization and facial expressions in animated characters…’” and it was “…the incorporation of the claimed rules, not the use of the computer, that “improved [the] existing technological process” by allowing the automation of further tasks”. McRO, Inc. v. Bandai Namco Games America Inc., 837 F.3d 1299, (Fed. Cir. 2016).
In this case, Applicant’s specification provides no explanation of an improvement to the functioning of a computer or other technology. Rather, the claims focus “on a process that qualifies as an ‘abstract idea’ for which computers are invoked merely as a tool”. Id citing Enfish at 1327, 1336. Although the claims include computer technology such as a computer system, a processor and a computer-readable medium, a brand-association machine learning model that is trained, the brand-association machine learning model is trained, an availability machine-learning model that is trained, the availability machine learning model, the brand-association machine learning model, and computation, such elements are merely peripherally incorporated in order to implement the abstract idea. This is unlike the improvements recognized by the courts in cases such as Enfish, Core Wireless, and McRO. Unlike precedential cases, neither the specification nor the claims of the instant invention identify such a specific improvement to computer capabilities. The instant claims are not directed to improving the existing technological process but are directed to improving the commercial task of presenting alternative items to a user. The claimed process, while arguably resulting in improved alternative items presented, is not providing any improvement to another technology or technical field as the claimed process is not, for example, improving the processor and/or computer components that operate the system. Rather, the claimed process is utilizing different data while still employing the same processor and/or computer components used in conventional systems to improve presenting alternative items to a user, e.g. commercial process. As such, the claims do not recite specific technological improvements.
Further, the claimed invention is not rooted in computer technology and does not provide specific technical solution, but rather, finds its roots in a process that could exist outside of computing networks. To this accord, the Examiner notes the decision in DDR Holdings in which the court found that the “claimed solution [was] necessarily rooted in computer technology to overcome a problem specifically arising in the realm of computer networks.” DDR Holdings, LLC v. Hotels.com, L.P., 773 F.3d 1245 (Fed. Cir. 2014). In arriving at this conclusion, the court noted that the problem addressed by the claimed invention was a problem specific to the Internet and/or computer networks –that is the problem of retaining website visitors that, if adhering to the routine and convention functioning of Internet link protocol, would instantly be transported away from a host's website after “clicking” on an advertisement and activating a hyperlink. Id. The court noted that the DDR Holdings claims did not broadly and generically claim "use of the Internet" to perform an abstract business practice and, unlike Ultramercial, the DDR Holdings claims “specif[ied] how interactions with the Internet are manipulated to yield a desired result—a result that overrides the routine and conventional sequence of events ordinarily triggered by the click of a hyperlink.” Id. Furthermore, the court noted that the claims in DDR Holdings did not “recite a commonplace business method aimed at processing business information, applying a known business process to the particular technological environment of the Internet, or creating or altering contractual relations using generic computer functions and conventional network operations.” Id.
In contrast to the claims in DDR Holdings, the claims at issue are not “necessarily rooted in computer technology to overcome a problem specifically arising in the realm of computer networks.” Unlike the claims in DDR Holdings, the present claims recite functions that could be performed outside of the Internet or computing networks. The present claims merely employ computing elements to facilitate the implementation of the abstract idea. As such, unlike DDR Holdings, the claims do “recite a commonplace business method aimed at processing business information, applying a known business process to the particular technological environment of the Internet, or creating or altering contractual relations using generic computer functions and conventional network operations.” Therefore, the claims are not directed to patent-eligible subject matter, and thus, the Examiner maintains the 101 rejection.
With respect to the rejections made under 35 USC § 103, and in light of the Applicant’s amendments to the claims, the claims now recite allowable subject matter, and therefore the 103 rejection has been withdrawn.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/ASHLEY D PRESTON/Primary Examiner, Art Unit 3688