DETAILED ACTION
This action is in reply to the Amendments filed on 05/11/2026.
Claims 10 and 19 are cancelled.
Claims 1-9, 11-18, and 20 are rejected.
Claims 1-9, 11-18, and 20 are currently pending and have been examined.
Response to Amendment
Applicant’s amendment, filed 05/11/2026, has been entered. Claims 1, 11, and 20 have been amended.
Interview Letter
Examiner acknowledges receipt of the interview letter submitted by Applicant, however, in accordance with MPEP 713.09, no interview will be granted prior to the submission of this non-final Office Action. Applicant is encourages to submit an interview request following the receipt of this non-final Office Action.
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 07/01/2026 has been entered.
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-9, 11-18, and 20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Under Step 1 of the Subject Matter Eligibility Test for Products and Processes, the claims must be directed to one of the four statutory categories. All the claims are directed to one of the four statutory categories (YES).
Under Step 2A in MPEP 2106.04, it is determined whether the claims are directed to a judicially recognized exception. Step 2A is a two-prong inquiry.
Under Prong 1, it is determined whether the claim recites a judicial exception (YES). Taking Claim 20 as representative, the claim recites limitations that fall within the certain methods of organizing human activity groupings of abstract ideas, including:
-a memory storing instructions; and
-at least one processor configured to execute the instructions to perform operations comprising:
-retrieving, from one or more data structures: a product search query by a user comprising at least an alphanumeric product model number, a text string, or any combination thereof, at least one data set comprising at least a catalogue of product model numbers collected over a predefined time frame, and a set of experimental data comprising at least aggregated customer data from all customers or a subset of all customers;
-determining, using at least one machine-learning algorithm: a search type, and a plurality of attributes associated with the product query comprising at least a product model number, a product name, or product description;
-performing an iterative match between the product search query and a product index data set using iterative string matching and a set of key features;
-upon performing the iterative match, determining no matches between the product search query and the product index data set;
-based on the determination of no matches: using a machine learning algorithm to determine a product category and key features associated with the second product, and determining at least one top alternative product based on the determined product category and key features associated with the second product which has a highest search frequency by one or more users within a time period immediately prior to the product search query;
-transmitting the at least one top alternative product for display to the user on an interactive web page of a user device, the [display] web page including interactive user elements regarding the top alternative product, including at least one selectable element, a picture of the top alternative product, and an ordered list of sellers associated with the top alternative product; and
-transmitting a selection from the user on an interface of the web page, to the system, wherein the selection includes a new order
The above limitations recite the concept determining and providing an alternative product for purchase. The above limitations fall within the “Certain Methods of Organizing Human Activity” groupings of abstract ideas, enumerated in MPEP 2106.04(a).
Certain methods of organizing human activity include:
fundamental economic principles or practices (including hedging, insurance, and mitigating risk)
commercial or legal interactions (including agreements in the form of contracts; legal obligations; advertising, marketing or sales activities or behaviors; and business relations)
managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions)
The limitations of performing an iterative match between the product search query and a product index data set using iterative string matching and a set of key features; and upon performing the iterative match, determining no matches between the product search query and the product index data set are processes that, under their broadest reasonable interpretation, cover a commercial interaction. For example, “performing” and “determining” in the context of this claim encompass advertising, and marketing or sales activities.
Similarly, the limitations of at least one processor configured to execute the instructions to perform operations comprising: retrieving, from one or more data structures: a product search query by a user comprising at least an alphanumeric product model number, a text string, or any combination thereof, at least one data set comprising at least a catalogue of product model numbers collected over a predefined time frame, and a set of experimental data comprising at least aggregated customer data from all customers or a subset of all customers; determining, using at least one machine-learning algorithm: a search type, and a plurality of attributes associated with the product query comprising at least a product model number, a product name, or product description; based on the determination of no matches: using a machine learning algorithm to determine a product category and key features associated with the second product, and determining at least one top alternative product based on the determined product category and key features associated with the second product which has a highest search frequency by one or more users within a time period immediately prior to the product search query; transmitting the at least one top alternative product for display to the user on an interactive web page of a user device, the [display] web page including interactive user elements regarding the top alternative product, including at least one selectable element, a picture of the top alternative product, and an ordered list of sellers associated with the top alternative product; and transmitting a selection from the user on an interface of the web page, to the system, wherein the selection includes a new order are processes that, under their broadest reasonable interpretation, cover a commercial interaction. That is, other than reciting that the operations are performed by at least one processor configured to execute the instructions, that the retrieving is from one or more data structures, that the determining is using at least one machine-learning algorithm, that the determining of at least one top alternative product is using a machine-learning algorithm, that the display is on an interactive web page of a user device, that the displaying is on a web page, that the user elements are interactive user elements, that the selection from the user is on an interface of the web page, and that the transmitting of the selection is to the system, nothing in the claim element precludes the step from practically being performed by people. For example, but for the “at least one processor configured to execute the instructions,” “one or more data structures,” “at least one machine-learning algorithm,” “a machine learning algorithm,” “an interactive web page,” “a user device,” “interactive user elements,” “an interface of the web page,” and “the system” language, “retrieving,” “determining,” “determine,” “transmitting,” and “transmitting” in the context of this claim encompasses advertising, and marketing or sales activities.
Under Prong 2, it is determined whether the claim recites additional elements that integrate the exception into a practical application of the exception. This judicial exception is not integrated into a practical application (NO).
-a memory storing instructions; and
-at least one processor configured to execute the instructions to perform operations comprising:
-retrieving, from one or more data structures: a product search query by a user comprising at least an alphanumeric product model number, a text string, or any combination thereof, at least one data set comprising at least a catalogue of product model numbers collected over a predefined time frame, and a set of experimental data comprising at least aggregated customer data from all customers or a subset of all customers;
-determining, using at least one machine-learning algorithm: a search type, and a plurality of attributes associated with the product query comprising at least a product model number, a product name, or product description;
-performing an iterative match between the product search query and a product index data set using iterative string matching and a set of key features;
-upon performing the iterative match, determining no matches between the product search query and the product index data set;
-based on the determination of no matches: using a machine learning algorithm to determine a product category and key features associated with the second product, and determining at least one top alternative product based on the determined product category and key features associated with the second product which has a highest search frequency by one or more users within a time period immediately prior to the product search query;
-transmitting the at least one top alternative product for display to the user on an interactive web page of a user device, the web page including interactive user elements regarding the top alternative product, including at least one selectable element, a picture of the top alternative product, and an ordered list of sellers associated with the top alternative product; and
-transmitting a selection from the user on an interface of the web page, to the system, wherein the selection includes a new order
The additional elements of claim 20 are recited at a high level of generality (i.e. as generic computing hardware) such that they amount to nothing more than mere instructions to implement or apply the abstract idea on a generic computing hardware (or, merely use a computer as a tool to perform an abstract idea) as supported by paragraph [0090] of Applicant’s specification – “Various programs or program modules can be created using any of the techniques known to one skilled in the art or can be designed in connection with existing software.” Specifically, the additional elements of a computer-implemented system, a memory storing instructions, at least one processor configured to execute the instructions, one or more data structures, at least one machine-learning algorithm, a machine learning algorithm, an interactive web page, a user device, interactive user elements, an interface of the web page, and the system are recited at a high-level of generality (i.e. as a generic processor performing the generic computer functions of retrieving data, determining data, performing a match, and transmitting data) such that they amount do no more than mere instructions to apply the exception using generic computer components. Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea. Further, the additional elements do no more than generally link the use of the judicial exception to a particular technological environment or field of use (such as computers or computing networks). Employing well-known computer functions to execute an abstract idea, even when limiting the use of the idea to one particular environment, does not integrate the exception into a practical application.
Additionally, the additional elements are insufficient to integrate the abstract idea into a practical application because the claim fails to i) reflect an improvement in the functioning of a computer or an improvement to another technology or technical field, ii) apply the judicial exception with, or use the judicial exception in conjunction with, a particular machine or manufacture that is integral to the claim, iii) effect a transformation or reduction of a particular article to a different state or thing, or iv) apply or use the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment.
Accordingly, the judicial exception is not integrated into a practical application.
Under Step 2B, it is determined whether the claims recite additional elements that amount to significantly more than the judicial exception. The claims of the present application do not include additional elements that are sufficient to amount to significantly more than the judicial exception (NO).
In the case of claim 20, taken individually or as a whole, the additional elements of claim 21 do not provide an inventive concept. As discussed above under step 2A (prong 2) with respect to the integration of the abstract idea into a practical application, the additional elements used to perform the claimed functions amount to no more than a general link to a technological environment.
Even considered as an ordered combination (as a whole), the additional elements do not add anything significantly more than when considered individually.
Claim 1 is a computer-implemented system reciting similar functions as claim 20. Examiner notes that claim 1 recites the additional elements of a computer-implemented system, a memory storing instructions, at least one processor configured to execute the instructions, one or more data structures, at least one machine-learning algorithm, a machine learning algorithm, an interactive web page, a user device, interactive user elements, an interface of the web page, and the system, claim 1 does not qualify as eligible subject matter for similar reasons as claim 20 indicated above.
Claim 11 is a computer-implemented method reciting similar functions as claim 20. Examiner notes that claim 1 recites the additional elements of a computer-implemented method, one or more data structures, at least one machine-learning algorithm, a machine learning algorithm, an interactive web page, a user device, interactive user elements, an interface of the web page, and the system, however, claim 1 does not qualify as eligible subject matter for similar reasons as claim 20 indicated above.
Therefore, claims 1 and 11 do not provide an inventive concept and do not qualify as eligible subject matter.
Dependent claims 2-9, and 12-18, when analyzed as a whole, are held to be patent ineligible under 35 U.S.C. § 101 because they do not add “significantly more” to the abstract idea. More specifically, dependent claims 2-9, and 12-18 further fall within the “Certain Methods of Organizing Human Activity” grouping of abstract ideas in that they recite commercial interactions. Dependent claims 2-4, 6-9, 12-13, and 15-18, do not recite any farther additional elements, and as such are not indicative of integration into a practical application for at least similar reasons discussed above. Dependent claims 5 and 14 recite the additional elements of the data structures, linear data structures, and non-linear data structures, but similar to the analysis under prong two of Step 2A these additional elements are used as a tool to perform the abstract idea. As such, under prong two of Step 2A, claims 2-9, and 12-18 are not indicative of integration into a practical application for at least similar reasons as discussed above. Thus, dependent claims 2-9, and 12-18 are “directed to” an abstract idea. Next, under Step 2B, similar to the analysis of claims 1, 11 and 20, dependent claims 2-9, and 12-18 when analyzed individually and as an ordered combination, merely further define the commonplace business method (i.e. determining and providing an alternative product for purchase) being applied on a general-purpose computer and, therefore, do not amount to significantly more than the abstract idea itself. Accordingly, the Examiner concludes that there are no meaningful limitations in the claims that transform the judicial exception into a patent eligible application such that the claims amount to significantly more than the judicial exception itself. The analysis above applies to all statutory categories of invention.
Subject Matter Allowable Over the Art
In the present application, claims 1-9, 11-18, and 20 would be allowable if rewritten or amended to overcome the rejections under 35 USC § 101 set forth in this Office action. The following is the Examiner's statement of reasons of allowance:
Regarding 35 U.S.C. §103, upon review of the evidence at hand, it is hereby concluded that the totality of the evidence, alone or in combination, neither anticipates, reasonably teaches, nor renders obvious the below noted features of the applicant’s invention. Claims 1-9, 11-18, and 20 are allowable over the prior art as follows:
Claims 1-9, 11-18, and 20 are allowable over 35 U.S.C. §103 as follows:
The most relevant prior art made of record includes previously cited Zhang et al. (US 2021/0358007 A1), previously cited Sun et al. (US 2011/0078157 A1), previously cited Garg et al. (US 9,830,392 B1), and previously cited Sullivan et al. (US 11,361,331 B2). Zhang teaches a memory storing instructions (Zhang, see at least: [0047]); and at least one processor configured to execute the instructions to perform operations (Zhang, see at least: [0061]) comprising: retrieving, from one or more data structures (Zhang, see at least: [0048]): a product search query by a user (Zhang, see at least: [0048] and [0018]), at least one data set (Zhang, see at least: [0048] and [0016]), and a set of experimental data (Zhang, see at least: [0048], [0046], and [0008]); determining, using at least one machine-learning algorithm (Zhang, see at least: [0024] and [0025]): a search type (Zhang, see at least: [0013], [0024], and [0025]), and a plurality of attributes associated with the product search query (Zhang, see at least: [0017], [0018], [0013], [0024], and [0025]); using a machine learning algorithm to determine at least one top alternative product (Zhang, see at least: [0042], [0017], and [0018]); and transmitting the at least one top alternative product for display to the user on an interactive web page of a user device (Zhang, see at least: [0043]); and transmitting a selection from the user on an interface of the web page, to the system, wherein the selection includes a new order (Zhang, see at least: [0033]).
Zhang is deficient in a number of ways. As written, the claims require performing an iterative match between the product search query and a product index data set using iterative string matching and a set of key features; upon performing the iterative match, determining no matches between the product search query and the product index data set; based on the determination of no matches: using a machine learning algorithm to determine a product category and key features associated with the second product, and determining at least one top alternative product based on the determined product category and key features associated with the second product which has a highest search frequency by one or more users within a time period immediately prior to the product search query; the web page including interactive user elements regarding the top alternative product, including at least one selectable element, a picture of the top alternative product, and an ordered list of sellers associated with the top alternative product.
Regarding Sun, Sun teaches performing an iterative match between the product search query and a product index data set using iterative string matching and a set of key features (Sun, see at least: [0026] and [0025]).
Though disclosing these features, Sun does not disclose or render obvious the features discussed above.
Regarding Garg, Garg teaches upon performing the iterative match, determining no matches between the product search query and the product index data set (Garg, see at least: Col. 11 Ln. 45-52 and Col. 11 Ln. 53-65); using a machine learning algorithm to determine a product category and key features associated with the second product, and determining at least one top alternative product based on the determined product category and key features associated with the second product (Garg, see at least: Col. 11 Ln. 45-52, Col. 11 Ln. 53-65 and Col. 6 Ln. 37-42); the web page including interactive user elements regarding the top alternative product, including at least one selectable element, a picture of the top alternative product, and an ordered list of sellers associated with the top alternative product (Garg, see at least: Col. 21 Ln. 57-64, Col. 18 Ln. 31-37, and Col. 12 Ln. 33-43).
Though disclosing these features, Garg does not disclose or render obvious the features discussed above.
Regarding Sullivan, Sullivan teaches a second product which has a highest search frequency by one or more users within a time period immediately prior to the product search query (Sullivan, see at least: Col. 2 Ln. 28-45).
Though disclosing these features, Sullivan does not disclose or render obvious the features discussed above.
Ultimately, the particular combination of limitations as claimed, is not anticipated nor rendered obvious in view of Zhang, Sun, Garg, and Sullivan, and the totality of the prior art. While certain references may disclose more general concepts and parts of the claim, the prior art available does not specifically disclose the particular combination of these limitations.
Zhang, Sun, Garg, and Sullivan, however, do not teach or suggest, alone or in combination the claimed invention. Examiner emphasizes that the prior art/additional art would only be combined and deemed obvious based on knowledge gleaned from the applicant’s disclosure. Such a reconstruction is improper (i.e. hindsight reasoning). See In re McLaughlin, 443 F.2d 1392, 170 USPQ 209 (CCPA 1971).
Cited NPL Doring (reference U cited 01/29/2026 and 07/09/2026 in PTO-892) teaches optimizing a catalog search, but does not teach or suggest the recited claims.
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. The combination of features as claimed would not be obvious to one of ordinary skill in the art as combining various references from the totality of evidence to reach the combination of features as claimed would be a substantial reconstruction of Applicant’s claimed invention relying on improper hindsight bias.
It is thereby asserted by Examiner that, in light of the above and further deliberation over all of the evidence at hand, that the claims are allowable 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.
Response to Arguments
Rejections under 35 U.S.C. §101
Applicant argues that Applicant's claims are not directed to the abstract idea of "certain methods of organizing human activity." Office Action at 5. Applicant respectfully traverses the rejection, for at least the reason that the amened claims are "not directed to an abstract idea" but instead are directed to a specific data-processing technique implemented in a computer system. Such processing cannot be practically performed in the human mind and does not merely recite a commercial interaction and therefore fall outside the methods of "organizing human activity". For example, "based on the determination of no matches: identifying, from the experimental data, a second product having a highest search frequency by one or more users_within a time period immediately prior to the product search query", recites a specific data-driven determination based on temporal user interaction data, which requires analysis of structured datasets and cannot be performed as a mental process or a fundamental economic practice. Thus, the claimed subject matter relies on identifying patterns in historical search behavior across one or more of users and within a defined temporal window, which is inherently a computer based process (Remarks, pages 12-13).
Examiner respectfully disagrees. The amended claims recite the concept of determining and providing an alternative product for purchase, which falls within the “Certain Methods of Organizing Human Activity” groupings of abstract ideas, enumerated in MPEP 2106.04(a), as the recited abstract idea covers a commercial interaction in that it encompasses advertising, and marketing or sales activities. The limitation of "based on the determination of no matches: identifying, from the experimental data, a second product having a highest search frequency by one or more users_within a time period immediately prior to the product search query" is a advertising, and marketing or sales activity as this limitation is using user data to recommend a product. Additionally, Examiner has not stated that the claims fall under the mental processes or a fundamental economic practices groupings; the amended claims fall under within the “Certain Methods of Organizing Human Activity” groupings of abstract ideas as they encompass commercial interactions. Furthermore, identifying patterns in historical search behavior across one or more of users and within a defined temporal window is not an inherently computer based process. Accordingly, the claims are directed to an abstract idea.
Applicant further argues that the claims further recite "using a machine learning algorithm to determine a product category and key features associated with the second product, and determining at least one top alternative product based on the determined product category and the key features associated with the second product which has a highest search frequency by one or more users within a time period immediately prior to the product search query." These limitations define a particular computational technique that applies machine learning to derive features and generate alternative results based on prior interaction data. Such operations go beyond merely presenting or organizing information and instead recite a specific technological process for transforming historical input data based on the frequency into a structured output (Remarks, pages 13-14).
Examiner respectfully disagrees. Determining a product category and key features associated with the second product, and determining at least one top alternative product based on the determined product category and the key features associated with the second product which has a highest search frequency by one or more users within a time period immediately prior to the product search query covers a commercial interaction in that it encompasses advertising, and marketing or sales activities. Merely utilizing a machine learning algorithm to output data based on input data amount to nothing more than mere instructions to implement or apply the abstract idea on a generic computing hardware (or, merely use a computer as a tool to perform an abstract idea) and do no more than generally link the use of the judicial exception to a particular technological environment or field of use. Accordingly, the claims are directed to an abstract idea.
Applicant further argues that even assuming arguendo that the claims recite an abstract idea, the amended claims-and the dependent claims-integrate any such alleged abstract idea into a practical application. The Office Action asserts that dependent claims 2-10 and 12-19 merely recite elements falling within "certain methods of organizing human activity" and do not add meaningful limitations. Applicant respectfully disagrees. The dependent claims recite specific technical limitations directed to structured data, data processing and system implementation that include: Claim 2: data collected over a predefined time frame, Claim 3: data structure representation of a product search query, Claim 4: data collected from plurality of users, Claim 5: implementation of both linear and non-linear data, Claim 6: data structure attributes associated with a product search query, Claim 7: feature determination based on data processing, Claim 8 and 9: specific computational techniques for data processing and its implementation (Remarks, page 14).
Examiner respectfully disagrees. Data collected over a predefined time frame, data structure representation of a product search query, data collected from plurality of users, implementation of both linear and non-linear data, data structure attributes associated with a product search query, feature determination based on data processing, and specific computational techniques for data processing and its implementation are not technical limitations. Merely describing and processing data are not additional elements and, in the context of the claims, encompasses advertising, and marketing or sales activities. Accordingly, the claims are not integrated into a practical application.
Applicant further argues that the claims do not merely recite generic data storage, but instead specify the use of particular data structures and datasets as part of a defined processing pipeline. In particular, the recited data structures provide the product search query, product index data, and experimental data that are operated on by the machine learning algorithm. The machine learning algorithm does not merely access these datasets but rather analyzes the structured data to determine a product category and key features associated with the second product. The outcomes are then used to identify a top alternative product having the highest recent search frequency. As a whole, such operations define how the computer system processes historical interaction data to handle a search query that fails to match indexed data. Thus, the amended claims are directed to a specific manner of using recited data structures and machine learning algorithm to process product search query in a computer-based search system, rather than to an abstract idea (Remarks, page 15).
Examiner respectfully disagrees. Reciting the use of generic ‘data structures’ are not specific data structures and merely storing data in data structures does not improve technology or a technological field. Additionally, specifying a type of data is still merely data and merely utilizing a machine learning algorithm and generic computer components does not improve machine learning technology or the computer technology; no technology or technological field is improved. Furthermore, using data analysis to determine a product category and key features associated with the second product encompasses advertising, and marketing or sales activities and identifying a top alternative product is not a technical improvement. Accordingly, the claims are not integrated into a practical application.
Applicant further argues that, when considered as an ordered combination, the amended claims and dependent claims define a non-conventional and non-generic data processing technique, and cannot be reduced to mere routine or conventional computer functions. The Office Action does not adequately address these limitations in combination, nor explain how such claims would be routine or conventional (Remarks, page 15).
Examiner respectfully disagrees. The additional elements are insufficient to integrate the abstract idea into a practical application because the additional elements amount to nothing more than mere instructions to implement or apply the abstract idea on a generic computing hardware (or, merely use a computer as a tool to perform an abstract idea) and do no more than generally link the use of the judicial exception to a particular technological environment or field of use (such as computers or computing networks). Even considered as an ordered combination (as a whole), the additional elements do not add anything significantly more than when considered individually. Additionally, novelty is not the test for eligibility.
Furthermore, as is described in the MPEP 2106.05(II) (i.e. “Thus, in Step 2B, examiners should: … Re-evaluate any additional element or combination of elements that was considered to be insignificant extra-solution activity per MPEP § 2106.05(g), because if such re-evaluation finds that the element is unconventional or otherwise more than what is well-understood, routine, conventional activity in the field, this finding may indicate that the additional element is no longer considered to be insignificant”), step 2B considers whether additional elements concluded to be insignificant extra-solution activity in Step 2A are more than well-understood, routine, conventional activity in the field. Examiner did not identify any of the additional elements as insignificant extra-solution activity in Step 2A so there weren’t elements to be evaluated in terms of whether they are more than well-understood, routine, conventional activity in the field. Accordingly, the claims are ineligible.
Applicant further argues that Applicant does not concede that the claims lack significantly more. Even if, arguendo, the claims are found to be directed to an abstract idea, Applicant submits that, under Step 2B, the claims, as a whole, recite "significantly more" than the alleged abstract idea. Applicant's amended claim 1 recites significantly more than the alleged abstract idea. For example, the claims recite, "using a machine learning algorithm to determine a product category and key features associated with the second product, and determining at least one top alternative product based on the product category and the key features associated with the second product which has a highest search frequency by one or more users within a time period immediately prior to the product search query." At least this claim element addresses the identified limitation of existing systems by requiring, after no match is determined, use of a machine learning algorithm to determine the product category and key features associated with the second product, and then determine the top alternative product based on those determinations and the highest search frequency within the recited time period. As described in paragraphs [0070] and [0077] of the Specification, the machine learning algorithm operates on structured historical interaction data to generate product query attributes and patterns, validate those outputs using custom knowledge, and utilize the resulting features to identify and generate alternative products. The Specification further describes that the system "update[s] the relevant entries within the experimental data set 409 and the product index data set within database 407 with the set of attributes and at least one pattern determined by machine learning." See [0082]. These operations are performed with the recited data structures and are not merely generic data storage. Rather, the data structures and machine learning algorithm are used together in the claimed ordered combination to determine a top alternative product (Remarks, pages 16-17).
Examiner respectfully disagrees. Utilizing a machine learning algorithm to determine, after no match is determined, the product category and key features associated with the second product, and then determine the top alternative product based on those determinations and the highest search frequency within the recited time period merely utilizes a machine learning algorithm to amount to nothing more than mere instructions to implement or apply the abstract idea on a generic computing hardware (or, merely use a computer as a tool to perform an abstract idea). The utilization of the data structures and machine learning algorithm, even when considered as a whole, are insufficient to integrate the abstract idea into a practical application because the claim fails to i) reflect an improvement in the functioning of a computer or an improvement to another technology or technical field, ii) apply the judicial exception with, or use the judicial exception in conjunction with, a particular machine or manufacture that is integral to the claim, iii) effect a transformation or reduction of a particular article to a different state or thing, or iv) apply or use the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment. Accordingly, the claims do not amount to significantly more than the abstract idea and are ineligible.
Applicant further argues that the Office Action indicated that dependent claim 10 and 19 would be allowable if rewritten or amended to overcome the subject matter eligibility, and further recognized that the particular combination of limitations recited therein is not taught, suggested, or rendered obvious by the cited prior art. Office Action page. 52. Accordingly, amended claim 1 recites significantly more than the alleged abstract idea. Therefore, even under Step 2B, the claims are patient eligible, thus Applicant respectfully requests the withdrawal 35 U.S.C. § 101 (Remarks, page 17).
Examiner respectfully disagrees. Novelty is not the test for eligibility (see MPEP 2106.03-2106.05). Accordingly, as detailed above, the claims are ineligible.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
-Ramer et al. (US 2018/0025010 A1) teaches suggesting content based on the frequency of query activity.
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/ARIELLE E WEINER/ Primary Examiner, Art Unit 3689
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