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 RCE filed on 10/17/2025.
Claims 1 and 4 have been amended and are hereby entered.
Claims 1-6 are currently pending and have been examined.
Request for Continued Examination
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 10/17/2025 has been entered.
Domestic Benefit
The ADS filed 12/01/2022 claims the benefit of US Applications 16999306 (filed 8/21/2020) and 15789622 (filed 10/20/2017) as a continuation thereof. Multiple aspects of Claim 1 are not supported by the original disclosure (see 112(a) rejections below) shared by the present application and parent applications. As such, all claims as presently drafted are granted an effective filing date of 12/01/2022.
Response to Applicant’s Arguments
Preliminary Matters
The present amendments to the claims are non-compliant with the requirements of 37 CFR 1.121, specifically in that they contain significant newly presented language, both in terms of minor adjustments and large swathes of language representing entirely new functions, which is not properly annotated (ie: underlined). As a courtesy and in the interests of compact prosecution, Examiner treats the claims as if they were properly compliant with 37 CFR 1.121. In the future, similar non-compliant amendments will receive a Notice of Non-Compliant Amendment (see, e.g., MPEP 714). To avoid this, Applicant is cautioned to take greater care when submitting amendments.
Domestic Benefit
The present amendments to Claim 4 significantly modify what is additionally claimed therein, and the newly claimed functionality is not unsupported in the same way as the previous claim language. While the additional language of Claim 4 is now considered as supported by the original disclosure, Claim 4 remains unsupported based on its dependence upon Claim 1 (see 112(a) new matter rejections below).
Claim Interpretation
Applicant asserts that Claim 1 is amended in part to render Examiner’s previous claim interpretation moot, citing to Paragraphs 0018 and 0020-0022 as allegedly supporting this amendment. In light of the limitations specifically mentioned in the previous Claim Interpretation and the content of said Claim Interpretation, Examiner considers this argument in relation to the following claim language: “determining an initial preferred price range for the user based on the user's location as a search criteria.” Applicant is mistaken regarding the content of their own application, both in the cited paragraphs and elsewhere. As a result, the previous Claim Interpretation is modified in view of this amended language, and new 112(a) new matter rejections are made.
Paragraph 0018 discloses a search module configured to search (ie: filter) properly listings based on search criteria, and Paragraphs 0020-0022 disclose the respective searching based on search criteria (which explicitly might include a user-defined price range in Paragraph 0020) and the subsequent sorting of the resulting property listings by way of preference parameter(s); however, in both this argument and the claim amendments, Applicant improperly conflates a “price range” defined by a searching user (ie: a search criterion) with a “preferred price range” used as a preference parameter (or “attribute preference data” as claimed) to sort (rather than filter) derived search results. While no special definitions are provided for these terms in the original disclosure, all references to a “preferred price range” in the specification are described as preference parameters rather than search criteria (see, e.g., Paragraphs 0035, 0039, and 0045-0053). Further, the only price range disclosed as being “based on a user’s location” correlates to this preference parameter rather than a search criterion (see Paragraphs 0038-0039 and 0052-0053). In other words, while what Applicant claims as an “initial preferred price parameter” could reasonably be interpreted as supported as a search criterion (e.g. Paragraph 0020) or based on a location (e.g., Paragraphs 0038-0039 and 0052-0053, wherein such a price range is a preference parameter rather than a search criterion), it is not supported as both as presently claimed. See 112(a) rejections below for more information.
Claim Rejections – 35 USC § 101
Applicant’s arguments regarding the 101 analysis have been considered and are unpersuasive.
Regarding Prong One, Applicant intermingles arguments regarding different types of abstract idea (relating to different standards associated therewith) in arguing that the machine learning-based limitations of Claim 1 do not recite abstract ideas. Examiner does his best to parse these improperly intermingled arguments presently in order to clearly address the distinct standards thereof.
Regarding mental processes, Applicant points to the 101 Memorandum of August 4, 2025 as indicating that “the mental process grouping is not without limit” and further that “claim limitations that encompass AI in a way that cannot practically be performed in the human mind do not fall within the mental process grouping.” While these statements are generally true, Applicant drops all discussion of mental processes at this point, never explaining how Applicant believes the present claim language purportedly would “encompass AI in a way that cannot practically be performed in the human mind” or even mentioning mental processes again. Generally, Examiner notes that this topic has been addressed ad nauseam at this point, with numerous previous Office Actions explaining that the claimed use of AI in the present invention (ie: applying machine learning to detect patterns in search interaction data and search results) certainly recites mental processes in that the claimed high-level functionality (and even the greater detail in which such functionality is described in the original disclosure, e.g., in Paragraphs 0025, 0029, 0032) represent basic mentally performable pattern recognition/statistical analysis which is easily within the capabilities of the human mind or with the aid of pen and paper. See said previous Office Actions for a multitude of explanations on this topic in relation to previous arguments to the same effect.
Regarding the separate category of mathematical concepts, Applicant points to the Memorandum’s comparison of AI-training claim limitations of Examples 39 and 47, concluding that “[s]imilar to Example 39, the pending claims recite training a machine learning algorithm using the search interaction data to recognize common relationships in data patterns and detecting, using the machine learning algorithm to analyze 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 that does not includes any mathematical relationships, calculations, formulas, or equations using words or mathematical symbols. Thus, claims are eligible because they do not recite a judicial exception.” As a preliminary point, and as will be discussed in greater detail below regarding similar language of the Remarks, Applicant’s conclusion that “claims are eligible” because Applicant believes a single limitation of Claim 1 does not recite a judicial exception is incorrect, as even if Applicant’s contention that this limitation did not recite a judicial exception were true, numerous other claim limitations which Applicant makes no attempt to argue against do recite judicial exceptions.
Regarding the substance of this argument, Applicant is in part correct and in part incorrect. Specifically, the notion that “to recognize common relationships in data patterns and detecting, using the machine learning algorithm to analyze 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” doesn’t recite mathematical relationships (e.g., between the search interaction data and the search results) is clearly untrue, as explained in numerous previous Office Actions as well as Applicant’s own specification. However, this functionality is related to the purpose for which the machine learning algorithm is used, not for the separate functionality of training the machine learning algorithm.
Regarding this separate training functionality, while an analogy of this training step to that of Example 39 is of extremely limited value, given the vast distinction between training an AI based on images versus abstract commercial data, the comparison between Example 39 and Example 47 (as well as the analysis set forth in Example 47 itself) makes clear that the training of the AI element in Example 47 recited an abstract idea because of the detail of this training step provided in the claim itself (ie: the mathematical concepts of backpropagation and gradient descent). Unlike the training step language of Example 47, the present application does not recite any such mathematical concept, merely specifying that the training is performed “using the search interaction data.” While, as noted above, the function of the machine learning algorithm (ie: “to recognize common relationships in data patterns”) is abstract, the claims do not recite sufficient details of this training step such that this training functionality would similarly recite an abstract idea (nor would Applicant be able to do so, given the lack of any more detailed explanation in the original disclosure as to how this training would actually occur). As such, as claimed, the training of the machine learning algorithm does not recite an abstract idea (and thus constitutes an additional element), though the manner in which the machine learning algorithm is used continues to recite abstract ideas.
Regarding recitation of judicial exceptions generally, Applicant also states that “[t]he memorandum further explains that claims reciting an exception should be distinguished from claims that merely involve an exception, which are eligible and do not require further eligibility analysis.” While this statement generally is accurate, the implied analogy to the present claims is unpersuasive. Specifically, Applicant argues against recitation of judicial exceptions in a bare fraction of the functionality drafted in Claim 1, the vast majority of which has been found to recite judicial exceptions at every stage of prosecution thus far. Even to the limited extent the substantive arguments addressed above are persuasive, the vast majority of the content of Claim 1 continues to recite judicial exceptions in the form of various abstract ideas. As thoroughly explained in the myriad Office Actions previously provided in relation to the prosecution of this application and both of its parent applications, nearly the entirety of the functionality claimed in the present invention is abstract. As such, even though some few limitations as presently drafted recite additional elements (e.g., as discussed above, the vaguely recited training of the machine learning algorithm is not presently claimed at a level of specificity which would directly recite an abstract idea in relation to such training), this in no way negates the wealth of other limitations which do recite judicial exceptions, and as such the present claims do not “merely involve an exception” as seemingly implied. Given the core abstract nature of the present invention, Examiner cannot fathom a scenario where the claiming of this invention does not recite any abstract ideas at all and thus would not require further eligibility analysis (ie: under Steps 2A, Prong Two and 2B), nor do the present arguments even attempt to illustrate this.
Applicant next presents arguments related to Step 2A, Prong Two, reiterating and slightly modifying previously advanced and refuted arguments that the claims are integrated into a practical application embodying an improvement to a technology. Particularly here, Applicant argues that the limitations of “’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 a subset of an ordered list of subsequent 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 device of the user displays subsequent search results in the subset of the ordered list,’ and ‘tracking, via a streaming platform, the search interaction data in real-time as events’ reflect technical improvement in the technical field of machine learning based reservation system.” Examiner disagrees for multiple reasons.
Examiner firstly notes that Applicant yet again misapprehends the distinction between abstract ideas and additional elements, only the latter of which may show integration into a practical application (see, e.g., MPEP 2106.04(d)). Indeed, with the exception of the streaming platform and the machine learning algorithm itself, this list of limitations entirely represents abstract ideas. As has been repeatedly explained previously, Abstract ideas may not integrate themselves into a practical application.
Secondly, nothing in these limitations “reflect[s] technical improvement in the technical field of machine learning based reservation system,” not least of all because these limitations have nothing to do with the functioning of the machine learning algorithm in the present invention, instead all representing actions taken subsequent to the use of the machine learning algorithm to detect a relationship between attribute parameters and search results, which occurs in a separate limitation occurring prior to all of the presently argued limitations. Indeed, the provided list of limitations only mentions a result of this machine learning functionality, and solely in relation to the separate function of calculating preference scores based on the results of the machine learning functionality. It 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.
Even considering the actual use of the machine learning algorithm as claimed, there is no technological improvement here. As also repeatedly explained in previous Office Actions, the claimed pattern recognition might be performed in the same way manually to achieve the same results (and indeed, was repeatedly claimed thusly in the parent applications of the present application). As such, it is abundantly clear that, if anything, this functionality represents an improvement to an abstract idea rather than a technology (e.g., from MPEP 2106.05(a): “it is important to keep in mind that an improvement in the abstract idea itself (e.g. a recited fundamental economic concept) is not an improvement in technology”). See said previous Office Actions for more information.
Applicant further cites to various passages of the original disclosure as purportedly supporting findings of separate improvements to technology other than the above-asserted and rejected improvement to machine learning. Regarding the assertion of Paragraphs 0022-0030 as purportedly showing that “the method can reduce the amount of data processed and minimize rendering requirements for displaying the search results at a user device by computing preference scores, sorting search results, configuring a subset of the ordered list and transmitting the user device information regarding the subset of the ordered list at server side,” Examiner disagrees.
Firstly, Applicant either misunderstands or misrepresents their own original disclosure in this argument, as nothing in Paragraphs 0022-0030 or elsewhere therein supports the configuring of a subset of the ordered list for presentation. As explained in greater detail below in the 112(a) new matter rejections, Paragraph 0022 instead describes that “each displayed property listing 77 may include only a subset of overall information of the property listing 77 stored in memory 61,” which is entirely different from what Applicant presently claims and argues. Even ignoring this, providing a smaller number of results to present in no way represents an improvement to a technology, and describing this as “minimiz[ing] rendering requirements for displaying search results at a user device” (even if the asserted paragraphs indeed described this, which they do not) merely cloaks a broader, purely abstract concept in technological language, similar to multiple arguments addressed in previous Office Actions.
Further, the computing of preference scores, sorting of search results, configuring of a subset of search results to present, and transmitting of said subset of search results for presentation are entirely abstract concepts, and merely claiming them as occurring by way of a server and user device does nothing to either make this otherwise or integrate these abstract concepts into a practical application (see, e.g., Interval Licensing LLC, v. AOL, Inc., 896 F.3d 1335, 127 USPQ2d 1553 (Fed. Cir. 2018); Electric Power Group v. Alstom, S.A., 830 F.3d 1350, 1353-54, 119 USPQ2d 1739, 1741-42 (Fed. Cir. 2016); and the seminal Alice). While the server and user device themselves are non-abstract, in the context of the claim as a whole, they represent no more than mere instructions to apply a judicial exception (ie: use of a computer as a tool to perform an abstract idea). Additionally, the notions of reducing the amount of data processed and minimizing rendering requirements are nowhere to be found in the asserted Paragraphs 0022-0030 or elsewhere. Rather, this appears to be no more than mere attorney conjecture going well beyond the actual content of the original disclosure, which cannot take the place of evidence in the record (see e.g., Cf In re Geisler, 116 F.3d 1465, 1470 (Fed. Cir. 1997)).
Regarding the assertion of Paragraphs 0024 and 0030 as supporting the argument that “the method sorts the filtered search results before display at the server, it reduces complexity of user interface and enhance the user interface display performance at the user device,” Examiner disagrees, finding this to be entirely untrue. As claimed, the user interface of the user device functions in the standard manner of any such interface: it receives an instruction to display something, and it does so. As relates to this functionality, the present claims merely specify the data to display. That this data takes the form of sorted search results does not make this otherwise, and further this functionality is abstract. Displaying abstract search results of an abstract search for commercial properties which are further sorted in an abstract manner such that they are placed in an order of interest to a user does not constitute an improvement to a user interface or any other technology, not least of all because they do not solve a problem specifically arising from the interface itself. Indeed, as illustrated in at least Examples 23 and 37, improvements to an interface are not merely based on what an interface is commanded to present, but rather how the interface does so (ie: such that some additional functionality or technological benefit of the interface itself is achieved). See also Customedia Technologies v. Dish Network, 951 F.3d 1359, 1365 (Fed. Cir. 2020) and Trading Techs. Int'l, Inc. v. IBG LLC, 921 F.3d 1084, 1092-93 (Fed. Cir. 2019).
Lastly on the topic of Step 2A, Prong Two, regarding the assertion of Paragraphs 0028-0035 as supporting the argument that “the method provides real-time tracking the search interaction data that dynamically trains a machine learning algorithm in real time and increases learning efficiency,” Examiner again disagrees. Regarding the real time tracking of data, this continues to represent an abstract idea and further this argument has been addressed multiple times previously (see, e.g., the Interview of 1/14/2025 and the Final Rejection of 6/24/2025), which remains applicable to the nearly identical claim language related to this functionality. Further, the notion that this somehow “increases learning efficiency” appears to be another instance of mere attorney conjecture, as nothing in Paragraphs 0028-0035 or elsewhere discuss learning efficiency or efficiency of any aspect of the present invention at all. As previously noted, such attorney argument cannot take the place of evidence in the record. Indeed, 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.
Regarding Step 2B, Applicant provides nothing beyond a bare conclusory assertion that particular functions (most of which continue to recite abstract ideas as discussed above, and thus may not evidence an inventive concept as per at least MPEP 2106.05) both alone and in combination provide significantly more than the abstract idea, providing no substantive explanation or support for this conclusion, giving Examiner no understanding of this argument and nothing particular to which he may respond. Generally, Examiner disagrees that these functions individually represent improvements to a technology as addressed above in relation to Step 2A, Prong Two, and the consideration of these elements (at least those which actually represent additional elements) in combination adds nothing to the claim which would constitute an inventive concept. Further, the assertion that particular features is “not taught in the prior art” is a consideration of 102 and 103, and has nothing to do with subject matter eligibility under 101.
Claim Interpretation
Claim 1 discloses the following limitations: “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” and “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 per the original disclosure (e.g., Paragraphs 0029-0030, 0034-0045, 0049-0053), the updated preferred price range is considered an embodiment of the “attribute preference data” as claimed. The claims are interpreted based on this understanding.
Claim Rejections – 35 USC § 112
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 1-6 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.
Claim 1 contains the following limitations: “configuring a subset of an ordered list of subsequent search results, sorted based on the preference score” and “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.” These limitations are not supported by the original disclosure, and thus constitute new matter. The concept of defining and displaying a subset of a list of search results (ie: a further filtering or otherwise limiting of a list of search results, resulting in a smaller set of search results) is nowhere to be found in the original disclosure. The term “subset” only appears in Paragraphs 0022 and 0024, which uses this term to describe the display of some but not all data related to particular properly listings, which is not what is claimed. Many passages disclose the subsequent sorting of a set of search results based on preference parameters (see, e.g., Paragraphs 0029-0030, 0041, 0043), but never discloses a further filtering of the search results based on these preference parameters. Nor, so far as Examiner can see, does the original disclosure ever describe the limiting of search results to a certain amount in any other way, e.g., selecting and displaying only the best sorted results. Rather, it appears that the filtered list of search results is always displayed to a user in its entirety, though as just noted, this list is displayed in a particular order based on preference scores of the properties thereof. The only disclosure related at all to a particular number of results in found in Paragraph 0022’s description of the exemplary list of results in Fig. 3 as having nine results arranged in three rows and three columns; however, nowhere is it disclosed or even implied that the displayed results were intentionally capped at nine, instead seeming to disclose a scenario where the filtering based on search criteria generated nine results. As such, these limitations recite new matter. Claims 2-6 are rejected due to their dependence upon Claim 1.
Claim 1 contains the following limitation: “determining an initial preferred price range for the user based on the user's location as a search criteria.” This limitation is not supported by the original disclosure, and thus constitutes new matter. As noted in the Response to Applicant Arguments section above (see Interpretation Note subsection), the original disclosure does not support an “initial preferred price range” being both a search criterion (e.g., as in Paragraph 0020) and simultaneously “based on the user’s location” (such price ranges being solely described as preference parameters in the original disclosure rather than search criteria; see, e.g., Paragraphs 0038-0039 and 0052-0053). These are two different concepts as described in the original disclosure, with the former being used to filter a pool of potential properly listings into a list of search results, and the latter being used to sort said derived list of search results into an order deemed to be beneficial to a user). The present conflation of these distinct concepts in the claim language is not supported in the original disclosure. Claims 2-6 are rejected due to their dependence upon Claim 1.
Claim 1 contains the following limitation: “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.” This limitation is not supported by the original disclosure, and thus constitutes new matter. This language represents new matter for a number of reasons. Firstly, this language claims the results of a subsequent search request as being derived from the results of earlier search requests in a manner different from what is described in the original disclosure. Particularly, the “subsequent search results” thereof, which given the distinction in language, must be different from “a plurality of search results displayable” determined in response to the initial search requests claimed earlier in Claim 1, must be interpreted as a different term (e.g., the seeming intent of these “subsequent search results” appears to be for them to be identified in response to the received “subsequent search request” claimed in the limitation immediately above this one. Additionally, the term “the plurality of search results displayable” used twice in this limitation clearly relates back to the aforementioned “a plurality of search results displayable,” which as noted above are identified in response to the separate “initial search requests.” Nowhere in the original disclosure is it described that the results of a subsequent search request are derived from (or “filtered from”) the results of an earlier (or “initial”) search request or requests, nor would it make logical sense to do so as these disparate requests might use entirely different search criteria. If Applicant intended to draft this limitation to say that subsequent search results are identified in response to the subsequent search request, said subsequent search request including a price range as a criterion thereof, said subsequent search results also being filtered based on preference scores derived from attribute preference data detected in relation to the plurality of search results provided in response to the initial search requests (which Examiner notes would be properly supported), Applicant has failed to do so as this is not what is claimed by the present claim language. Secondly, nowhere in the original disclosure is any “filtering” described as taking place based on a price range “based on the user’s location” (see additional 112(a) rejection above), nor indeed is any set of search results described as being further filtered at all. Rather, the only filtering described in the original disclosure is a filtering of a pool of property listings by search criteria, said filtering resulting in a list of search results (see, e.g., Paragraphs 0021 and 0028-0029). No such search results (such as the claimed “the plurality of search results displayable”) ever filtered again by any metric. Thirdly, nowhere in the original disclosure is the concept of one set of search results being greater (presumably in number) than another set of search results. Claims 2-6 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; 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 initial preferred price range for the user based on the user's location as a search criteria; 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 a subset of an ordered list of subsequent 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; and processing streams of real-time user interaction events to update the preference data, 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; 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 initial preferred price range for the user based on the user's location as a search criteria; 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 a subset of an ordered list of subsequent 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; and processing streams of real-time user interaction events to update the preference data, 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 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; and 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, 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, 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 using the search interaction data, and a streaming platform.
A server, a search user interface, training a machine learning algorithm using the search interaction data, 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 2-6, 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 4 discloses wherein the subsequent search request comprises a user specific search criteria (further defining the abstract idea already set forth in Claim 1), 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
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/MARK C CLARE/Examiner, Art Unit 3628
/MICHAEL P HARRINGTON/Primary Examiner, Art Unit 3628