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 .
Status of Claims
This action is in reply to the amendment filed on 5/07/2026.
Claim 1 has been amended and are hereby entered.
Claim 4 has been canceled.
Claims 1-3 and 5-6 are currently pending and have been examined.
This action is made FINAL.
Response to Applicant’s Arguments
Claim Rejections – 35 USC § 112
The present amendments to the claims obviate the previous 112(a) rejections thereto; therefore, these rejections are withdrawn.
Claim Rejections – 35 USC § 101
Applicant’s arguments regarding the 101 analysis have been considered and are unpersuasive.
Applicant solely presents arguments related to Step 2A, Prong Two, particularly asserting an improvement to machine learning functionality. As a preliminary matter, while Applicant’s statement that “there may be sufficient evidence of patentable subject matter when the technical improvement is in the manner the model operates or learns” is generally correct in relation to Ex parte Desjardins and the Prong Two analysis more generally, Applicant presents no such evidence presently, nor does Applicant make a cogent case for such an improvement to “the manner the model operates or learns” as relates to the present invention.
Regarding Applicant’s assertion that “[t]he claim amendments explicitly dictates [sic] an implementation of a neural network in the system,” wherein the machine learning algorithm is claimed as being trained on the neural network comprising an embedded vector. Applicant’s only support for this is to paraphrase a selection of passages from the claims as presently amended, ignoring that a large portion of these passages recite abstract ideas rather than additional elements. Applicant fails to provide any explanation as to how or why this would constitute an improvement to machine learning models in any way, much less an improvement to the manner in which such models operate or learn. Given the existence of such neural networks well-prior to Applicant’s effective filing date, as well as their customary relation to machine learning, it is unclear to examiner how the neural network as presently claimed effectuates any technical improvement.
This is further borne out in the original disclosure, wherein neural networks are solely mentioned in Paragraph 0044, and wherein a “deep learning neural network” is described as a black box absent any technical details as to how it might function. Rather, Paragraph 0044 merely describes input data fed into said deep learning neural network (e.g., “trained based on guest interactions”), outputs of such a neural network (e.g., “to learn the preferences of the guest, indicated by an embedded vector within the deep learning neural network”), and provide an abstract example of what might subsequently be done with such outputs once determined by the neural network (e.g., “then sort the property listings 77 such that they are listed in order according to the extent to which the listings 77 match the guest's preferences, as described above”). Particularly in light of the remainder of the original disclosure and the claim language (including the use of machine learning/neural network for mentally performable pattern recognition and discernment of common relationships), the inputs and outputs listed in Paragraph 0044 (and as claimed) are part of the abstract idea recited by Claim 1, and as has been explained many times in the prosecution of this application and its parent applications, abstract ideas may not integrate themselves into a practical application (either by way of embodying an improvement to a technology or otherwise). This extremely vague and high level description of the neural network and the functioning thereof does no more than describe the abstract, commercial purpose for which it is used, and provides no explanation of the technical functioning of how the model might operate or learn.
There is nothing in relation to the neural network here, nor any disclosure in relation to the machine learning functionality more broadly anywhere in the original disclosure (see, in addition to the aforementioned Paragraph 0044, the Abstract and Paragraphs 0036 and 0049), which provides such an explanation of the technical functionings thereof at all, much less in a manner which could be recognized by one of ordinary skill in the art as an improvement over previous model operation and learning methods. As stated in the Non-Final Rejection of 11/12/2025 regarding similar arguments of an improvement to machine learning, “the discussion of machine learning in the original disclosure is so sparse and high level that Examiner sees no reasonable way for an improvement to machine learning to be argued.” Applicant may wish to review Examples 47-49 of the July 2024 PEG Update for more information on the application of subject matter eligibility standards to AI-based functions, said Examples in relevant part making clear that merely providing high level details in relation thereto (e.g., the context in which such models are used, as in the present claims) is insufficient to either prevent the recitation of abstract ideas under Prong One or show integration into a practical application under Prong Two, even in instances where such Examples provide significantly more than black box-type claiming and disclosure of how such models operate.
Further in relation to the content of Paragraph 0044, the assertion of the claimed sorting functionality as providing an improvement to machine learning has been previously advanced and refuted, with the previously provided explanation given in response thereto being equally sufficient to refute the present reiteration of this argument. Summarily, the sorting of property listings based on the model outputs is a subsequent step performed after the model has run, and has nothing to do with how the model operates or learns. As stated in the previous Office Action, “[i]t does not logically follow that such subsequent actions unconnected to the functioning of the machine learning algorithm would somehow represent an improvement to machine learning.” See the Non-Final Rejection of 11/12/2025 for more information.
Claim Objections
Claim 1 is objected to because of the following informalities:
“training a machine learning algorithm on the neural network comprising an embedded vector uses the embedded vector to search interaction data to recognize common relationships in data patterns” should read “training a machine learning algorithm on the neural network comprising an embedded vector, wherein the machine learning algorithm uses the embedded vector to search interaction data to recognize common relationships in data patterns” or similar.
“an preferred price range” should read “a preferred price range.”
“transmitting to the user the ordered list of subsequent search results” should read “transmitting to the user the ordered list of search results” to maintain antecedent basis and consistency with the present amendments.
Appropriate correction is required.
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 1-3 and 5-6 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.
Claim 1 discloses the language: “wherein the search interaction data comprises: … user interaction data indicating user interest with respect to interaction with the search result by the user as event records generated by the search results user interface and ingested by the streaming platform.” Within this language, the term “the streaming platform” lacks antecedent basis, as no streaming platform is disclosed prior to this term. Relatedly, Claim 1 subsequently uses the terms “a streaming platform” and “the streaming platform,” which compound this indefiniteness issue as (1) it is unclear as drafted if “a streaming platform” is intended to relate back to “the streaming platform” of the above-quoted language or indicate an additional streaming platform, (2) it is unclear to which of the preceding streaming platforms (if, indeed, they are different) the subsequent instance of “the streaming platform” is intended to relate back, and (3) it is unclear whether the user interaction data ingested by the streaming platform in the above-quoted language is the same or a different function from that disclosed in the subsequent limitation “tracking, via a streaming platform, the search interaction data in real-time as events” (particularly as “user interaction data” is explicitly disclosed as falling within the larger umbrella of “search interaction data”). The original disclosure and its singular mention of streaming platforms (see Paragraph 0023) and their use in the claimed invention’s search method would appear to indicate that the most accurate interpretation of the streaming platform-related language here would be a superfluous reiteration of the same functionality already captured in the limitation “tracking, via a streaming platform, the search interaction data in real-time as events.” In view of this, for the purposes of this examination, the above-quoted language will be interpreted as “wherein the search interaction data comprises: … user interaction data indicating user interest with respect to interaction with the search result by the user as event records generated by the search results user interface.” Claims 2-3 and 5-6 are rejected due to their dependence upon Claim 1.
Claim 1 contains the following terms: “the plurality of subsequent search results,” “the subsequent search results,” and “the identified plurality of subsequent search results.” These terms lack antecedent basis, particularly in light of the present claim amendments. It is additionally unclear as drafted whether these disparate terms are intended to indicate the same or different search results. For the purposes of this examination, the first instance of these terms will be interpreted as “subsequent search results,” and all other instances of these terms are interpreted as “the subsequent search results.” Claims 2-3 and 5-6 are rejected due to their dependence upon Claim 1.
Claim 1 contains the following limitation: “configuring an ordered list of search results, sorted based on the preference score.” As drafted, it is unclear whether this ordered list of search results relates to “the search results,” “the subsequent search results” (and variations thereof), or some other search results. It is further unclear whether and how the functionality of this limitation is distinct from that of the preceding limitation “sorting the identified plurality of subsequent search results into an order based on the preference score for each of the plurality of subsequent search results.” As the original disclosure fails to provide support for multiple instances of sorting for the same set of search results (see previous 112(a) rejections), the most proper interpretation of the above-quoted limitation appears to be as a superfluous reiteration of the same functionality of said preceding limitation. As such, for the purposes of this examination, the limitation “configuring an ordered list of search results, sorted based on the preference score” is interpreted as if it were deleted. Claims 2-3 and 5-6 are rejected due to their dependence upon Claim 1.
Claim 1 contains the following limitation: “transmitting to the user the ordered list of subsequent search results, sorted based on the preference score and information regarding the subset of the ordered list such that a user device of the user displays subsequent search results in the subset of the ordered list.” The term “the subset of the ordered list,” occurring twice in this limitation, lacks antecedent basis as no subset of an ordered list is previously claimed (particularly based on present claim amendments). For the purposes of this examination, and in light of both the present claim amendments and the previous 112(a) rejections, this limitation will be interpreted as “transmitting to the user the ordered list of subsequent search results, sorted based on the preference score, such that a user device of the user displays the ordered list of subsequent search results.” Claims 2-3 and 5-6 are rejected due to their dependence upon Claim 1.
Claim 1 contains the following language: “…to update ordering of subsequently transmitted search results.” It is unclear as drafted whether the term “subsequently transmitted search results” relates back to any presently claimed language variation of “the subsequent search results” (as per the 112(b) rejection above, all such variations are treated as indicating the same thing) or some further set of search results not previously disclosed in Claim 1. While the use of the language “update ordering” would appear to indicate the former interpretation (as no ordering of a third set of search results is previously claimed such that it may be updated), the most proper interpretation of this term appears to be a new step related to a thus far unclaimed third set of search results as the original disclosure does not disclose multiple instances of sorting the same set of search results (see previous 112(a) rejections). For the purposes of this examination, this limitation will be interpreted as “…to sort a third set of search results provided in response to a third search request.” Claims 2-3 and 5-6 are rejected due to their dependence upon Claim 1.
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 1-6 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Regarding Claim 1, the limitations of during an initial phase: receiving a plurality of initial search requests submitted by the user; identifying the user and associating the user with the plurality of initial search requests; for each initial search request, providing a plurality of search results displayable on a search results user interface in response to the respective initial search requests, wherein each of the plurality of search results comprises a plurality of attribute types indicative of a common relationship between the respective search result and other search results of the plurality of search results, each of the plurality of search results having an attribute parameter for each of the plurality of attribute types; collecting search interaction data indicative of interactions, on the search results user interface, by the user with at least some of the plurality of search results; user interaction data indicating user interest with respect to interaction with the search result by the user as event records generated by the search results user interface and ingested by the streaming platform; using the embedded vector to search interaction data to recognize common relationships in data patterns; recognizing common relationships and patterns; detecting, by analyzing the search interaction data, a common relationship between the respective attribute parameters for one of the plurality of attribute types of the search results for which the user interest data indicates interest of the user; upon detection of the common relationship for the respective attribute parameters for one of the plurality of attribute types, storing: attribute preference data relating to the attribute type, and the respective attribute parameter data for which the commonality was detected; determining an preferred price range for the user based on the user's location; updating the preferred price range for the user based on the search interaction data, including weighting recent interactions more heavily than older interactions, and the detected common relationships; during an implementation phase: receiving a subsequent search request after the plurality of initial search requests, from the user; identifying a plurality of subsequent search results filtered from the plurality of search results displayable based on the initial preferred price range, wherein the plurality of search results displayable is greater than the plurality of subsequent search results; for each of the plurality of subsequent search results, computing a preference score based on the attribute preference data detected using the machine learning algorithm, the updated preferred price range, and the attribute parameter for the attribute type of the subsequent search results; sorting the identified plurality of subsequent search results into an order based on the preference score for each of the plurality of subsequent search results; configuring an ordered list of search results, sorted based on the preference score; transmitting to the user the ordered list of subsequent search results, sorted based on the preference score and information regarding the subset of the ordered list such that a user device of the user displays subsequent search results in the subset of the ordered list; tracking the search interaction data in real-time as events; processing streams of real-time user interaction events to update the preference data stored for the user; and updating ordering of subsequently transmitted search results, as drafted, are processes that, under their broadest reasonable interpretations, cover certain methods of organizing human activity. For example, these limitations fall at least within the enumerated categories of commercial or legal interactions and/or managing personal behavior or relationships or interactions between people (see MPEP 2106.04(a)(2)(II)).
Additionally, the limitations of during an initial phase: receiving a plurality of initial search requests submitted by the user; identifying the user and associating the user with the plurality of initial search requests; for each initial search request, providing a plurality of search results displayable on a search results user interface in response to the respective initial search requests, wherein each of the plurality of search results comprises a plurality of attribute types indicative of a common relationship between the respective search result and other search results of the plurality of search results, each of the plurality of search results having an attribute parameter for each of the plurality of attribute types; collecting search interaction data indicative of interactions, on the search results user interface, by the user with at least some of the plurality of search results; user interaction data indicating user interest with respect to interaction with the search result by the user as event records generated by the search results user interface and ingested by the streaming platform; using the embedded vector to search interaction data to recognize common relationships in data patterns; recognizing common relationships and patterns; detecting, by analyzing the search interaction data, a common relationship between the respective attribute parameters for one of the plurality of attribute types of the search results for which the user interest data indicates interest of the user; upon detection of the common relationship for the respective attribute parameters for one of the plurality of attribute types, storing: attribute preference data relating to the attribute type, and the respective attribute parameter data for which the commonality was detected; determining an preferred price range for the user based on the user's location; updating the preferred price range for the user based on the search interaction data, including weighting recent interactions more heavily than older interactions, and the detected common relationships; during an implementation phase: receiving a subsequent search request after the plurality of initial search requests, from the user; identifying a plurality of subsequent search results filtered from the plurality of search results displayable based on the initial preferred price range, wherein the plurality of search results displayable is greater than the plurality of subsequent search results; for each of the plurality of subsequent search results, computing a preference score based on the attribute preference data detected using the machine learning algorithm, the updated preferred price range, and the attribute parameter for the attribute type of the subsequent search results; sorting the identified plurality of subsequent search results into an order based on the preference score for each of the plurality of subsequent search results; configuring an ordered list of search results, sorted based on the preference score; transmitting to the user the ordered list of subsequent search results, sorted based on the preference score and information regarding the subset of the ordered list such that a user device of the user displays subsequent search results in the subset of the ordered list; tracking the search interaction data in real-time as events; processing streams of real-time user interaction events to update the preference data stored for the user; and updating ordering of subsequently transmitted search results, as drafted, are processes that, under their broadest reasonable interpretations, cover mental processes. For example, these limitations recite activity comprising observations, evaluations, judgments, and opinions (see MPEP 2106.04(a)(2)(III)).
Additionally, the limitations of using the embedded vector to search interaction data to recognize common relationships in data patterns; detecting, by analyzing the search interaction data, a common relationship between the respective attribute parameters for one of the plurality of attribute types of the search results for which the user interest data indicates interest of the user; updating the preferred price range for the user based on the search interaction data, including weighting recent interactions more heavily than older interactions, and the detected common relationships; identifying a plurality of subsequent search results filtered from the plurality of search results displayable based on the initial preferred price range, wherein the plurality of search results displayable is greater than the plurality of subsequent search results; for each of the plurality of subsequent search results, computing a preference score based on the attribute preference data detected using the machine learning algorithm, the updated preferred price range, and the attribute parameter for the attribute type of the subsequent search results, as drafted; and updating ordering of subsequently transmitted search results, are processes that, under their broadest reasonable interpretations, cover mathematical concepts. For example, these limitations recite mathematical relationships and/or calculations (see MPEP 2106.04(a)(2)(I)).
If a claim limitation, under its broadest reasonable interpretation, covers fundamental economic principles or practices, commercial or legal interactions, managing personal behavior or relationships, or managing interactions between people, it falls within the “Certain Methods of Organizing Human Activity” grouping of abstract ideas. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind or with the aid of pen and paper but for recitation of generic computer components, it falls within the “Mental Processes” grouping of abstract ideas. If a claim limitation, under its broadest reasonable interpretation, covers mathematical relationships, mathematical formulae or equations, or mathematical calculations, it falls within the “Mathematical Concepts” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
The judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements of a server, implementing a neural network, a search user interface, attribute parameter data relating to attribute parameter for each attribute type of each of the at least some of the plurality of search results with which the user has interacted, training a machine learning algorithm on the neural network comprising an embedded vector, and a streaming platform. A server, implementing a neural network, a search user interface, training a machine learning algorithm on the neural network comprising an embedded vector, and a streaming platform, in the context of the claim as a whole, amount to no more than mere instructions to apply a judicial exception (see MPEP 2106.05(f)). Attribute parameter data relating to attribute parameter for each attribute type of each of the at least some of the plurality of search results with which the user has interacted, in the context of the claim as a whole, amounts to no more than generally linking the use of a judicial exception to a particular technological environment or field of use (see MPEP 2106.05(h)). Accordingly, these additional elements do not integrate the abstract ideas into a practical application because they do not, individually or in combination, impose any meaningful limits on practicing the abstract ideas. The claim is therefore directed to an abstract idea.
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the judicial exception into a practical application, the additional elements amount to no more than mere instructions to apply a judicial exception, and generally linking the use of a judicial exception to a particular technological environment or field of use for the same reasons as discussed above in relation to integration into a practical application. These cannot provide an inventive concept. Therefore, when considering the additional elements alone and in combination, there is no inventive concept in the claim, and thus the claim is not patent eligible.
Claims 2-3 and 5-6, describing various additional limitations to the method of Claim 1, amount to substantially the same unintegrated abstract idea as Claim 1 (upon which these claims depend, directly or indirectly) and are rejected for substantially the same reasons.
Claim 2 discloses wherein the user interaction data indicating interest of the user with respect to the interaction of the user with the search result comprises data relating to (i) a number of times the user has interacted with the search result, and (ii) a duration of time the user has spent viewing and interacting with the search result (generally linking the use of a judicial exception to a particular technological environment or field of use), which does not integrate the claim into a practical application.
Claim 3 discloses wherein the user interaction data indicating interest of the user with respect to the interaction of the user with the search result comprises data relating to (iii) a number of requests the user has submitted for additional information regarding the search result, and (iv) a number of transactions the user has initiated with respect to the search result (generally linking the use of a judicial exception to a particular technological environment or field of use), which does not integrate the claim into a practical application.
Claim 5 discloses wherein updating the preferred price range comprises decreasing a weight of the user's location and increasing a weight of the user interaction data over time (an abstract idea in the form of a certain method of organizing human activity, a mental process, and a mathematical concept), which does not integrate the claim into a practical application.
Claim 6 discloses wherein the initial phase establishes baseline preferences using initial search interactions, and wherein the implementation phase applies the established preferences to subsequent searches (an abstract idea in the form of a certain method of organizing human activity and a mental process), which does not integrate the claim into a practical application.
Discussion of Prior Art Cited but Not Applied
For additional information on the state of the art regarding the claims of the present application, please see the following documents not applied in this Office Action (all of which are prior art to the present application):
PGPub 20150324434 – “User-Trained Searching Application System and Method,” Greenwood et al, disclosing a system for suggesting websites determined to be relevant based on the user’s browsing history, past search results, and interactions therewith
PGPub 20040267731 – “Method and System to Facilitate Building and Using a Search Database,” Gino Monier et al, disclosing a search engine system which determines preference parameters based on various factors
US Patent 7,949,659 – “Recommendation System with Multiple Integrated Recommenders,” Chakrabarti et al, describing a search engine system which determines preference data for each user and generates candidate recommendations based thereon
US Patent 8,527,361 – “Service for Adding In-Application Shopping Functionality to Applications,” Paleja et al, disclosing a search engine system which determines preference data for each user and generates candidate recommendations based thereon
PGPub 20200067789, claiming the benefit of US Application 15631685 – “Systems and Methods for Distributed Systemic Anticipatory Industrial Asset Intelligence,” Khuti et al, disclosing a system for training a machine learning model to make recommendations based in part on the receipt, storage, and processing of streams of data via the Apache Kafka streaming platform
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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MARK C CLARE whose telephone number is (571)272-8748. The examiner can normally be reached Monday-Friday 6:30am-2:30pm EST.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Jeffrey Zimmerman can be reached at (571) 272-4602. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/MARK C CLARE/Examiner, Art Unit 3628
/MICHAEL P HARRINGTON/Primary Examiner, Art Unit 3628
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