DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application is being examined under the pre-AIA first to invent provisions.
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 11/05/2025 has been entered.
Response to Arguments
Double Patenting
With respect to Applicant’s argument that “Applicant submits that Applicant does not necessarily agree with the rejections. Applicant respectfully submits that the double patenting rejections are moot in light of the amendments above and requests withdrawal of the rejections”, Examiner respectfully disagrees.
Examiner cites that the amendments do not overcome the double patenting rejection. Detailed explanation is discussed in the double patenting section below.
112th rejection:
112th rejection cited on final office action sent on 08/05/2025 has been withdrawn based on the amendments filed on 11/05/2025. However, new 112th rejections have been cited for new amendments filed on 11/05/2025.
101 Rejections
With respect to Applicant’s argument that “At Step 2A, Prong One of the Alice/Mayo test, the Applicant respectfully submits that the claims should be found eligible at least because they do not recite a judicial exception as enumerated in the 2019 Revised Patent Subject Matter Eligibility Guidance,….The 2019 Revised Patent Subject Matter Eligibility Guidance is careful in listing only certain methods, and not every method, of organizing human activities when defining the enumerated groupings of abstract ideas. See also MPEP § 2106.: …..Further, the Applicant's amended claims do not recite a fundamental economic practice because they are not similar to any of the examples provided in the MPEP. For example, the Applicant's claims do not relate to: "mitigating settlement risk," rules for conducting a wagering game, "financial instruments that are designed to protect against the risk of investing in financial instruments," offer-based price optimization….processing of payments for remotely purchased goods," using a marking affixed to the outside of a mail object to communicate information about the mail object," or "placing an order based on displayed market information.". For at least the foregoing reasons, the Applicant submits that the claims as amended should be found eligible at Step 2A, Prong One. Accordingly, the Applicant respectfully requests withdrawal of the Section 101 rejections”, Examiner respectfully disagrees.
Examiner cites that claim 1 recites abstract idea as described below:
-“analyzing, process because human mind can analyze the information associated with historical data i.e. conversion activity and plurality of attributes associated with it by evaluation/observation and judgment of data.
-“generating, based on the analyzing, a regression formula capable of outputting predicted conversion factors for different unique items having a same category but a different combination of attribute values than the at least a portion of the plurality of unique items for which historical conversion activity information is stored” recites a mental process because human mind can generate a regression formula which will output (produce results) predicted conversion factors for different items for different combinations by observation/evaluation and judgment and/or by mathematical calculation. For example, a human can analyze historical sales data and observes that items with certain attributes (e.g. lower price, higher ratings) tend to have higher conversion rates. Based on these observations, the human forms a relationship between attributes and likelihood of conversion and applies that relationship to estimate conversion likelihood for new items having different attribute combinations by evaluation/observation and judgment of data. Moreover, “regression formula” constitutes a mathematical model expressing relationships between variables and generating predicted outputs, therefore, also mathematical calculation.
-“generating, plurality of unique items” recites a mental process because human mind can generate a predicted conversion factors by inputting metadata into the plurality of attributes in the regression formula by evaluation/observation and judgment of data and/or by mathematical calculation. For example, a human can look at an item’s attributes (i.e. price, rating, etc.) applies a mental rule and estimate/predict a sale by observation and evaluation and judgment of data. Moreover, the limitation is a mathematical calculation because it involves a mathematical calculation/relationship to input data to compute output values.
-automatically updating, at least based on the received feedback on the interactive result set, the predicted conversion factors associated with the plurality of unique items” recites a mental process because human mind can automatically update based on the received feedback, the predicted conversion factors for items by evaluation and judgement of data. For example, a human can review feedback from users regarding displayed items, evaluates that feedback to determine which items are more or less likely to convert, and updates their internal estimates of conversion likelihood for those items accordingly.
Therefore, the claims recite abstract ideas.
With respect to Applicant’s arguments that “Even if the claims were found to recite a judicial exception, they are patent-eligible under Step 2A, Prong Two, because they integrate any such exception into a practical application. ….. The system is explicitly designed to operate at scale, providing recommendations to thousands of users simultaneously and adapting to user interactions in real time. This technical improvement is analogous to the patent-eligible claims in McRO, Inc. v. Bandai Namco Games America Inc., 837 F.3d 1299 (Fed. Cir. 2016), where the court found claims eligible because they recited a specific way to achieve a technical result. Moreover, the claims are similar to those found eligible in USPTO Example 37, where a user interface is improved by automatically updating displayed content based on user interactions. Here, the claims recite not only the display of search results, but also the technical process of updating the underlying predictive model and reprioritizing the results in response to user feedback, as described in the specification. For at least these reasons, the claims as amended integrate any alleged judicial exception into a practical application and are patent-eligible under Step 2A, Prong Two”, Examiner respectfully disagrees.
Examiner cites that the additional elements recited in the claims are the following:
-"one or more electronic data stores", “non-transitory storage medium”, "one or more processors", “a user interface” which are all a high-level recitation of a generic computer components and represent mere instructions to apply the judicial exception on a computer as in MPEP 2106.05(f), which does not provide integration into a practical application.
-“a computer-implemented method of generating and presenting interactive search results for unique items”, “a computer readable, non-transitory storage medium having a computer program stored thereon for causing a computer system to process by one or more processors computer-program code by performing a method of generating and presenting interactive search results for unique items when the computer program is executed on the computer system” is generally linking the use of the judicial exception to a particular technological environment or field of use by limiting it to a particular data source or type. See MPEP § 2106.05(h) and Electric Power, 830 F.3d at 1354, 119 USPQ2d at 1742 (limiting application of abstract idea to power grid data).
-“providing, by a computer system, a user interface that comprises functionality that enables a user to search and interact with a plurality of unique items” is insignificant extra-solution activity as mere data outputting for data gathering purposes.
-"maintaining, by a computer system, one or more electronic data stores that store information relating to a plurality of unique items, each unique item comprising a plurality of attributes and a category, wherein each unique item of the plurality of unique items corresponds to a specific unique item currently or previously offered for sale” is insignificant extra-solution activity as mere data gathering such as 'obtaining information'. See MPEP 2106.05(g).
-“the stored information comprising at least: predicted conversion factors associated with the plurality of unique items, each of the predicted conversion factors being representative of a predicted likelihood of conversion of one of the plurality of unique items; information associated with the plurality of attributes, wherein each unique item of the plurality of unique items comprises a unique combination of attribute values; and information associated with historical conversion activity of at least a portion of the plurality of unique items" is generally linking the use of the judicial exception to a particular technological environment or field of use by limiting it to a particular data source or type. See MPEP § 2106.05(h) and Electric Power, 830 F.3d at 1354, 119 USPQ2d at 1742 (limiting application of abstract idea to power grid data).
-“wherein the regression formula is computer-generated, and wherein the regression formula is automatically and dynamically updated in real-time” is generally linking the use of the judicial exception to a particular technological environment or field of use by limiting it to a particular data source or type. See MPEP § 2106.05(h) and Electric Power, 830 F.3d at 1354, 119 USPQ2d at 1742 (limiting application of abstract idea to power grid data).
-“receiving, by the computer system, a user search request generated via a user interface, the user search request comprising an item search criteria” is insignificant extra-solution activity as mere data gathering such as 'obtaining information'. See MPEP 2106.05(g).
-“searching, by the computer system, the one or more electronic data stores for a plurality of unique items relating to the item search criteria” is insignificant extra-solution activity as mere data gathering such as 'obtaining information'. See MPEP 2106.05(g).
-“causing display via the user interface an interactive result set based on results of the searching, the interactive result set being prioritized based at least in part on the predicted conversion factors” is insignificant extra-solution activity as mere data outputting. See MPEP 2106.05(g).
-“receiving, via the user interface, a user input comprising feedback on the interactive result set” is insignificant extra-solution activity as mere data gathering such as 'obtaining information'. See MPEP 2106.05(g)
-“causing display via the user interface an updated interactive result set, the updated interactive result set being prioritized based at least in part on the updated predicted conversion factors” is insignificant extra-solution activity as mere data outputting. See MPEP 2106.05(g).
Viewing the additional limitations together and the claim as a whole, nothing provides integration into a practical application.
Moreover, the cited court case and the present invention are not the same, therefore, the arguments regarding the court case are not persuasive.
Additionally, Example 37 and the present invention are not the same, therefore, the arguments regarding Example 37 are not persuasive.
With respect to Applicant’s argument that “Without conceding that the Applicant's claims are ineligible at Step 2A, the Applicant further submits that the claims should be found eligible at Step 2B. At Step 2B of the Alice/Mayo test…….These features are not well-understood, routine, or conventional, as evidenced by the detailed technical disclosure in the specification. The claims do not merely recite generic computer functions, but instead require a specific, non-conventional sequence of operations that improves the functioning of the computer system and the user experience. Accordingly, the claims as amended are patent-eligible under the Alice/Mayo test, and Applicant respectfully requests withdrawal of the Section 101 rejections”, Examiner respectfully disagrees.
Examiner cites that the following additional elements are well-understood, routine and conventional activities.
-“providing, by a computer system, a user interface that comprises functionality that enables a user to search and interact with a plurality of unique items” is WURC as evidence by the court cases cited in MPEP 2106.05(d)(II) “iv. Presenting offers and gathering statistics, OIP Techs., 788 F.3d at 1362-63, 115 USPQ2d at 1092-9".
-“maintaining, by a computer system, one or more electronic data stores that store information relating to a plurality of unique items, each unique item comprising a plurality of attributes and a category, wherein each unique item of the plurality of unique items corresponds to a specific unique item currently or previously offered for sale” is WURC as evidence by the court cases cited in MPEP 2106.05(d)(II) "iv. Storing and retrieving information in memory, Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, … OIP Techs., 788 F.3d at 1363."
- “searching, by the computer system, the one or more electronic data stores for a plurality of unique items relating to the item search criteria” is WURC as evidence by the court cases cited in MPEP 2106.05(d)(II) "i. Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, … buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network)"
- “causing display via the user interface an interactive result set based on results of the searching, the interactive result set being prioritized based at least in part on the predicted conversion factors” is WURC as evidence by the court cases cited in MPEP 2106.05(d)(II) “iv. Presenting offers and gathering statistics, OIP Techs., 788 F.3d at 1362-63, 115 USPQ2d at 1092-9".
-“receiving, via the user interface, a user input comprising feedback on the interactive result set” is WURC as evidence by the court cases cited in MPEP 2106.05(d)(II) "i. Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, … buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network)".
-“causing display via the user interface an updated interactive result set, the updated interactive result set being prioritized based at least in part on the updated predicted conversion factors” is WURC as evidence by the court cases cited in MPEP 2106.05(d)(II) “iv. Presenting offers and gathering statistics, OIP Techs., 788 F.3d at 1362-63, 115 USPQ2d at 1092-9".
Accordingly, at step 2B, these additional elements, both individually and in combination, do not amount to significantly more than the judicial exception. See MPEP § 2106.05. Therefore, the claim is not eligible subject matter under 35 U.S.C. 101.
Detailed explanation is discussed in the 101 section below.
103 Rejections:
The new amendments are rejected based on the new prior art Brukman et al. (US 8,620,915).
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/apply/applying-online/eterminal-disclaimer.
Claims 12-31 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-12 of U.S. Patent No. US 11,270,252 (hereinafter ‘252) and in view of Wright et al. (US 2007/0156621) and in view of Brukman et al. (US 8,620,915) and in view of Tung (US 2007/0124110).
Although the claims at issue are not identical, they are not patentably distinct from each other because of the following reasons:
Instant application
US Pat. No. 11,270,252
Claim 12: A computer-implemented method of generating and presenting interactive search results for unique items, the computer-implemented method comprising:
Claim 1: A computer-implemented method of generating and presenting interactive search results for unique items, the method comprising:
providing, by a computer system, a user interface that comprises functionality that enables a user to search and interact with a plurality of unique items;
providing, by a computer system, a user interface that comprises functionality that enables a user to search for and interact with unique items offered for sale;
maintaining, by the computer system, one or more electronic data stores that store information relating to a plurality of unique items, each unique item comprising a plurality of attributes and a category, wherein each unique item of the plurality of unique items corresponds to a specific unique item currently or previously offered for sale, the stored information comprising at least:
maintaining, by the computer system, one or more electronic data stores that store information relating to a plurality of unique items each comprising a plurality of attributes and a category, wherein each unique item of the plurality of unique items corresponds to a specific unique item currently or previously offered for sale, the stored information comprising at least:
predicted conversion factors associated with the plurality of unique items, each of the predicted conversion factors being representative of a predicted likelihood of conversion of one of the plurality of unique items; information associated with the plurality of attributes, wherein each unique item of the plurality of unique items comprises a unique combination of attribute values; and information associated with historical conversion activity of at least a portion of the plurality of unique items;
predicted conversion factors associated with the plurality of unique items, each of the predicted conversion factors being representative of a predicted likelihood of conversion of one of the plurality of unique items; information associated with the plurality of attributes, wherein each unique item of the plurality of unique items comprises a unique combination of attribute values; and information associated with historical conversion activity of at least a portion of the plurality of unique items;
analyzing, by the computer system, the information associated with the historical conversion activity and the information associated with the plurality of attributes for the at least a portion of the plurality of unique items
analyzing, by the computer system, the information associated with the historical conversion activity and the information associated with the plurality of attributes for the at least a portion of the plurality of unique items,
to determine statistically how attributes affected conversion activity associated with the at least a portion of the plurality of unique items;
generating, based on the analyzing, a regression formula capable of outputting predicted conversion factors for different unique items having a same category but a different combination of attribute values than the at least a portion of the plurality of unique items for which historical conversion activity information is stored;
wherein the regression formula is computer-generated, and wherein the regression formula is automatically and dynamically updated in real-time;
generating, based on the analysis, a regression formula capable of outputting predicted conversion factors for different unique items having a same category but a different combination of attribute values than the at least a portion of the plurality of unique items for which historical conversion activity information is stored;
generating, by the computer system, at least a portion of the predicted conversion factors by inputting metadata related to the plurality of attributes of the plurality of unique items into the regression formula, wherein the metadata comprises stated metadata and derived metadata, wherein the metadata is unique to each of the plurality of unique items;
generating, by the computer system, at least a portion of the predicted conversion factors by inputting metadata related to the plurality of attributes of the plurality of unique items into the regression formula,
wherein the metadata comprises stated metadata and derived metadata, the stated metadata comprising attributes obtained from a source or promoter of at least a portion of the plurality of unique items, the derived metadata comprising attributes obtained from a third party source different than the source or promoter of the at least a portion of the plurality of unique items, wherein the metadata is unique to each of the plurality of unique items;
receiving, by the computer system, the user search request generated via a user interface, the user search request comprising an item search criteria;
receiving, by the computer system, a user search request generated via the user interface, the user search request comprising an item search criteria;
searching, by the computer system, the one or more electronic data stores for a plurality of unique items relating to the item search criteria;
searching, by the computer system, the one or more electronic data stores for a plurality of unique items relating to the item search criteria;
and causing display via the user interface an interactive result set based on results of the searching, the interactive result set being prioritized based at least in part on the predicted conversion factors.
receiving, via the user interface, a user input comprising feedback on the interactive result set;
automatically updating, at least based on the received feedback on the interactive result set, the predicted conversion factors associated with the plurality of unique items;
causing display via the user interface an updated interactive result set, the updated interactive result set being prioritized based at least in part on the updated predicted conversion factors.
and causing display via the user interface an interactive result set based on results of the searching, the interactive result set being prioritized based at least in part on the predicted conversion factors, wherein the computer system comprises one or more physical servers.
Claim 1 of ‘252 fails to teach “wherein the regression formula is computer-generated, and wherein the regression formula is automatically and dynamically updated in real-time and receiving, via the user interface, a user input comprising feedback on the interactive result set; automatically updating, at least based on the received feedback on the interactive result set, the predicted conversion factors associated with the plurality of unique items; causing display via the user interface an updated interactive result set, the updated interactive result set being prioritized based at least in part on the updated predicted conversion factors”.
However, Wright teaches wherein the regression formula is computer-generated ([0111, Logistic regression produces a formula that predicts the probability of the occurrence as a function of the independent predictor variables], [0116, Boosted stumps have been shown to approximate additive logistic regression models whereby each feature makes an additive nonlinear contribution (on the logistic scale) to the fitted model]; examiner’s note: the regression formula is computer generated).
One of ordinary skill in art would recognize that incorporating generating computer-generated regression formula of Wright to be incorporated with US Patent ‘252 to further improve the system to have computer generated regression formula.
Therefore, it would have been obvious to one of the ordinary skill in the art before the effective filling date of the invention to incorporate functionalities of Wright into the US Patent ‘252 to have and efficient system. The motivation would be to have a system will have computer generated regression formula to make the system faster (Wright, [0113, As one skilled in the art will recognize, "boosting" is a machine learning technique for building a statistical model by successively improving an otherwise weak statistical model]).
US Patent ‘252, Wright do not explicitly teach the regression formula is automatically and dynamically updated in real-time and receiving, via the user interface, a user input comprising feedback on the interactive result set; automatically updating, at least based on the received feedback on the interactive result set, the predicted conversion factors associated with the plurality of unique items; causing display via the user interface an updated interactive result set, the updated interactive result set being prioritized based at least in part on the updated predicted conversion factors.
However, Tung teaches wherein the regression formula is automatically and dynamically updated in real-time ([0026, the statistical model may be updated real-time. For example, when a user purchases a product as a result of the search, a set of features may be extracted from the primary product object and these features may be tabulated along with product-related information to update the model. An exemplary statistical model trained as a Bayesian network for identifying primary product images is described below]; examiner’s note: the statistical model (regression formula) is automatically and dynamically updated in real time; regression formula is also taught by Wright in paragraph [0096, 0102]).
One of ordinary skill in art would recognize that incorporating updating regression formula in real time of Tung to be incorporated with US Patent ‘252 and Wright to further improve the system to automatically and dynamically updated in real-time.
Therefore, it would have been obvious to one of the ordinary skill in the art before the effective filling date of the invention to incorporate functionalities of Tung into the US Patent ‘252 and Wright to have an updated model. The motivation would be to have a system which will be accurate and responsive and adaptive to changing conditions to predict better recommendations of items and improve the system to be consistent with change (Tung, [0007, the invention is to make online shopping more time-efficient and convenient]).
US Patent ‘252, Wright, Tung do not explicitly teach receiving, via the user interface, a user input comprising feedback on the interactive result set; automatically updating, at least based on the received feedback on the interactive result set, the predicted conversion factors associated with the plurality of unique items; causing display via the user interface an updated interactive result set, the updated interactive result set being prioritized based at least in part on the updated predicted conversion factors.
However, Brunkman teaches receiving, via the user interface, a user input comprising feedback on the interactive result set ([col. 13, lines 57-65, “the user search history record 200 includes information tracking the user's actions on the user-preferred search results 250. From the information, the user profiler 129 can determine one or more performance metrics for each user-preferred search result, such as its actual long click-through rate and its average click-through position in the list of search results. The fact that a particular search result has a high long click-through rate and a high average click-through position indicates that the search result indeed matches the user's search interests, which serves as a confirmation that the search result should stay within the user-preferred search results 250”], the users selection of users selection (feedback) from the search result set (interactive search results set));
automatically updating, at least based on the received feedback on the interactive result set, the predicted conversion factors associated with the plurality of unique items ([col. 12, lines 37-46, “the popularity metric of a user-preferred search result is a prediction of the search result's long click-through rate, which indicates the likelihood of a user selection of the search result being a long click-through. For example, a search result having a 7% long click-through rate means that, statistically, for each 100 impressions of the search result, a long click-through will result seven times], [fig. 6A, 6B], [col. 15, lines 15-20, “the user profiler 129 selects one of the user search history records (610) and identifies multiple user-selected search results in the user search history record (615). For each identified user-selected search result, the user profiler 129 determines a set of property values (620) and uses the set of property values to populate a multiple users' search behaviors table (625)], determining a popularity metric for search results, where the popularity metric is a predicted long-click through rate (predicted conversion factors), the popularity metric is adjusted which corresponds to automatically updating the predicted conversion factors based on the selected search results);
causing display via the user interface an updated interactive result set, the updated interactive result set being prioritized based at least in part on the updated predicted conversion factors (fig. 3, [col. 5. Lines 25-30, “Assuming that at least one search result changes its position, the search result ranker 126 then returns the reordered list of search results to the front end sever 120. The front end server 120 then provides the reordered list of search results to the requesting user at the client 103”], [col. 12, lines 10-15, “the search result ranker 126 moves each of the identified search results from its current position determined by its generic ranking score by an offset, based on a presumption that a user-preferred search result near the top of the list is likely to receive more attention from the user while a user-disfavored search result near the bottom of the list is likely to receive less attention from the same user”], the results the reordered (prioritized) based on the users predicted click through rate).
One of ordinary skill in art would recognize that incorporating updating conversion factors and prioritizing results based on the updated conversion factors and presenting results of Brunkman to be incorporated with US Patent ‘252 and Wright/Tung to further improve the system to have updated conversion factors for each unique items.
Therefore, it would have been obvious to one of the ordinary skill in the art before the effective filling date of the invention to incorporate functionalities of Brunkman into the US Patent ‘252 and Wright/Tung to have an efficient model. The motivation would be to have a system which will make more informed, performance-driven predictions to make the system more efficient (Brunkman, [col. 2, lines 3-5, “the information server reorders the list of search results by moving each of the identified search results from its initial position by an offset and then provides the reordered list of search results to the user.]).
Claim 13 of the instant application corresponds to claim 2 of ‘252 Patent.
Claim 14 of the instant application corresponds to claim 3 of ‘252 Patent.
Claim 15 of the instant application corresponds to claim 4 of ‘252 Patent.
Claim 16 of the instant application corresponds to claim 5 of ‘252 Patent.
Claim 17 of the instant application corresponds to claim 6 of ‘252 Patent.
Claim 18 of the instant application corresponds to claim 7 of ‘252 Patent.
Claim 19 of the instant application corresponds to claim 8 of ‘252 Patent.
Claim 20 of the instant application corresponds to claim 9 of ‘252 Patent.
Claim 21 of the instant application corresponds to claim 10 of ‘252 Patent.
Claim 22 of the instant application corresponds to claim 11 of ‘252 Patent.
Claim 23 of the instant application corresponds to claim 12 of ‘252 Patent.
Claim 24 corresponds to claim 13 of ‘252 with differences being similar to the differences in claim 12 above, the differences are oblivious for the same rational discussed above.
Claim 25 of the instant application corresponds to claim 14 of ‘252 Patent.
Claim 26 of the instant application corresponds to claim 16 of ‘252 Patent.
Claim 27 of the instant application corresponds to claim 17 of ‘252 Patent.
Claim 28 of the instant application corresponds to claim 18 of ‘252 Patent.
Claim 29 of the instant application corresponds to claim 19 of ‘252 Patent.
Claim 30 of the instant application corresponds to claim 21 of ‘252 Patent.
Claim 31 of the instant application corresponds to claim 22 of ‘252 Patent.
Claims 12-31 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-20 of U.S. Patent No. 11,755,598 (hereinafter ‘598) and in view of Wright et al. (US 2007/0156621) a and in view of Brukman et al. (US 8,620,915) and in view of Tung (US 2007/0124110).
Although the claims at issue are not identical, they are not patentably distinct from each other because of the following reasons:
Instant Application
U.S. Patent No. 11,755,598
Claim 12: A computer-implemented method of generating and presenting interactive search results for unique items, the computer-implemented method comprising:
proving, by the computer system, a user that comprises functionality that enables a user to search and interact and interact with a plurality of unique items;
Claim 1: A computer-implemented method of generating and presenting interactive search results for unique items, the method comprising:
providing, by a computer system, a user interface that comprises functionality that enables a user to search for and interact with unique items offered for sale;
maintaining, by a computer system, one or more electronic data stores that store information relating to a plurality of unique items, each unique item comprising a plurality of attributes and a category, wherein each unique item of the plurality of unique items corresponds to a specific unique item currently or previously offered for sale, the stored information comprising at least:
maintaining, by the computer system, one or more electronic data stores that store information relating to a plurality of unique items each comprising a plurality of attributes and a category, wherein each unique item of the plurality of unique items corresponds to a specific unique item currently or previously offered for sale, the stored information comprising at least:
predicted conversion factors associated with the plurality of unique items, each of the predicted conversion factors being representative of a predicted likelihood of conversion of one of the plurality of unique items; information associated with the plurality of attributes, wherein each unique item of the plurality of unique items comprises a unique combination of attribute values; and information associated with historical conversion activity of at least a portion of the plurality of unique items;
predicted conversion factors associated with the plurality of unique items, each of the predicted conversion factors being representative of a predicted likelihood of conversion of one of the plurality of unique items; information associated with the plurality of attributes, wherein each unique item of the plurality of unique items comprises a unique combination of attribute values; and information associated with historical conversion activity of at least a portion of the plurality of unique items;
analyzing, by the computer system, the information associated with the historical conversion activity and the information associated with the plurality of attributes for the at least a portion of the plurality of unique items
analyzing, by the computer system, the information associated with the historical conversion activity and the information associated with the plurality of attributes for the at least a portion of the plurality of unique items, determining statistically how attributes affected conversion activity associated with the at least a portion of the plurality of unique items;
generating, based on the analyzing, a regression formula capable of outputting predicted conversion factors for different unique items having a same category but a different combination of attribute values than the at least a portion of the plurality of unique items for which historical conversion activity information is stored,
wherein the regression formula is computer-generated, and wherein the regression formula is an automatically and dynamically updated in real-time;
generating, based on the analysis, a regression formula capable of outputting the predicted conversion factors for the plurality of unique items having a same category but a different combination of attribute values than the at least a portion of the plurality of unique items for which the historical conversion activity information is stored;
generating, by the computer system, at least a portion of the predicted conversion factors by inputting metadata related to the plurality of attributes of the plurality of unique items into the regression formula,
wherein the metadata comprises stated metadata and derived metadata, wherein the metadata is unique to each of the plurality of unique items;
generating, by the computer system, at least a portion of the predicted conversion factors by inputting metadata related to the plurality of attributes of the plurality of unique items into the regression formula, wherein the metadata is unique to each of the plurality of unique items;
receiving, by the computer system, a user search request generated via a user interface, the user search request comprising an item search criteria;
receiving, by the computer system, a user search request generated via the user interface, the user search request comprising an item search criteria;
searching, by the computer system, the one or more electronic data stores for a plurality of unique items relating to the item search criteria;
searching, by the computer system, the one or more electronic data stores for a plurality of unique items relating to the item search criteria;
and causing display via the user interface an interactive result set based on results of the searching, the interactive result set being prioritized based at least in part on the predicted conversion factors;
and causing display via the user interface an interactive result set based on results of the searching, the interactive result set being prioritized based at least in part on the predicted conversion factors, wherein the computer system comprises one or more physical servers.
Claim 1 of ‘598 fails to teach “wherein the regression formula is computer-generated, and wherein the regression formula is automatically and dynamically updated in real-time and receiving, via the user interface, a user input comprising feedback on the interactive result set; automatically updating, at least based on the received feedback on the interactive result set, the predicted conversion factors associated with the plurality of unique items; causing display via the user interface an updated interactive result set, the updated interactive result set being prioritized based at least in part on the updated predicted conversion factors”.
However, Wright teaches wherein the regression formula is computer-generated ([0111, Logistic regression produces a formula that predicts the probability of the occurrence as a function of the independent predictor variables], [0116, Boosted stumps have been shown to approximate additive logistic regression models whereby each feature makes an additive nonlinear contribution (on the logistic scale) to the fitted model]; examiner’s note: the regression formula is computer generated).
One of ordinary skill in art would recognize that incorporating generating computer-generated regression formula of Wright to be incorporated with US Patent ‘598 to further improve the system to have computer generated regression formula.
Therefore, it would have been obvious to one of the ordinary skill in the art before the effective filling date of the invention to incorporate functionalities of Wright into the US Patent ‘598 to have and efficient system. The motivation would be to have a system will have computer generated regression formula to make the system faster (Wright, [0113, As one skilled in the art will recognize, "boosting" is a machine learning technique for building a statistical model by successively improving an otherwise weak statistical model]).
US Patent ‘598, Wright do not explicitly teach the regression formula is automatically and dynamically updated in real-time and wherein the metadata inputted to the regression formula is weighted based on conversion rates of each of the plurality of unique item.
However, Tung teaches wherein the regression formula is automatically and dynamically updated in real-time ([0026, the statistical model may be updated real-time. For example, when a user purchases a product as a result of the search, a set of features may be extracted from the primary product object and these features may be tabulated along with product-related information to update the model. An exemplary statistical model trained as a Bayesian network for identifying primary product images is described below]; examiner’s note: the statistical model (regression formula) is automatically and dynamically updated in real time; regression formula is also taught by Wright in paragraph [0096, 0102]).
One of ordinary skill in art would recognize that incorporating updating regression formula in real time of Tung to be incorporated with US Patent ‘598 and Wright to further improve the system to automatically and dynamically updated in real-time.
Therefore, it would have been obvious to one of the ordinary skill in the art before the effective filling date of the invention to incorporate functionalities of Tung into the US Patent ‘598 and Wright to have an updated model. The motivation would be to have a system which will be accurate and responsive and adaptive to changing conditions to predict better recommendations of items and improve the system to be consistent with change (Tung, [0007, the invention is to make online shopping more time-efficient and convenient]).
US Patent, Wright, Tung do not explicitly teach receiving, via the user interface, a user input comprising feedback on the interactive result set; automatically updating, at least based on the received feedback on the interactive result set, the predicted conversion factors associated with the plurality of unique items; causing display via the user interface an updated interactive result set, the updated interactive result set being prioritized based at least in part on the updated predicted conversion factors.
However, Brunkman teaches receiving, via the user interface, a user input comprising feedback on the interactive result set ([col. 13, lines 57-65, “ the user search history record 200 includes information tracking the user's actions on the user-preferred search results 250. From the information, the user profiler 129 can determine one or more performance metrics for each user-preferred search result, such as its actual long click-through rate and its average click-through position in the list of search results. The fact that a particular search result has a high long click-through rate and a high average click-through position indicates that the search result indeed matches the user's search interests, which serves as a confirmation that the search result should stay within the user-preferred search results 250”], the users selection of users selection (feedback) from the search result set (interactive search results set));
automatically updating, at least based on the received feedback on the interactive result set, the predicted conversion factors associated with the plurality of unique items ([col. 12, lines 37-46, “the popularity metric of a user-preferred search result is a prediction of the search result's long click-through rate, which indicates the likelihood of a user selection of the search result being a long click-through. For example, a search result having a 7% long click-through rate means that, statistically, for each 100 impressions of the search result, a long click-through will result seven times], [fig. 6A, 6B], [col. 15, lines 15-20, “the user profiler 129 selects one of the user search history records (610) and identifies multiple user-selected search results in the user search history record (615). For each identified user-selected search result, the user profiler 129 determines a set of property values (620) and uses the set of property values to populate a multiple users' search behaviors table (625)], determining a popularity metric for search results, where the popularity metric is a predicted long-click through rate (predicted conversion factors), the popularity metric is adjusted which corresponds to automatically updating the predicted conversion factors based on the selected search results);
causing display via the user interface an updated interactive result set, the updated interactive result set being prioritized based at least in part on the updated predicted conversion factors (fig. 3, [col. 5. Lines 25-30, “Assuming that at least one search result changes its position, the search result ranker 126 then returns the reordered list of search results to the front end sever 120. The front end server 120 then provides the reordered list of search results to the requesting user at the client 103”], [col. 12, lines 10-15, “the search result ranker 126 moves each of the identified search results from its current position determined by its generic ranking score by an offset, based on a presumption that a user-preferred search result near the top of the list is likely to receive more attention from the user while a user-disfavored search result near the bottom of the list is likely to receive less attention from the same user”], the results the reordered (prioritized) based on the users predicted click through rate).
One of ordinary skill in art would recognize that incorporating metadata inputted into the regression formula is weighted based on conversion rates of each of the plurality of unique items of Brunkman to be incorporated with US Patent ‘598 and Wright/Tung to further improve the system to have weighted metadata based on conversion rates for each unique items.
Therefore, it would have been obvious to one of the ordinary skill in the art before the effective filling date of the invention to incorporate functionalities of Brunkman into the US Patent ‘598 and Wright/Tung to have an efficient model. The motivation would be to have a system which will make more informed, performance-driven predictions to make the system more efficient (Brunkman, [col. 2, lines 3-5, “the information server reorders the list of search results by moving each of the identified search results from its initial position by an offset and then provides the reordered list of search results to the user.]).
Claim 13 of the instant application corresponds to claim 2 of ‘598 Patent.
Claim 14 of the instant application corresponds to claim 3 of ‘598 Patent.
Claim 15 of the instant application corresponds to claim 4 of ‘598 Patent.
Claim 16 of the instant application corresponds to claim 5 of ‘598 Patent.
Claim 17 of the instant application corresponds to claim 6 of ‘598 Patent.
Claim 18 of the instant application corresponds to claim 7 of ‘598 Patent.
Claim 19 of the instant application corresponds to claim 8 of ‘598 Patent.
Claim 20 of the instant application corresponds to claim 9 of ‘598 Patent.
Claim 21 of the instant application corresponds to claim 10 of ‘598 Patent.
Claim 22 of the instant application corresponds to claim 11 of ‘598 Patent.
Claim 23 of the instant application corresponds to claim 12 of ‘598 Patent.
Claim 24 corresponds to claim 13 of ‘598 with differences being similar to the differences in claim 12 above, the differences are oblivious for the same rational discussed above.
Claim 25 of the instant application corresponds to claim 14 of ‘598 Patent.
Claim 26 of the instant application corresponds to claim 15 of ‘598 Patent.
Claim 27 of the instant application corresponds to claim 16 of ‘598 Patent.
Claim 28 of the instant application corresponds to claim 17 of ‘598 Patent.
Claim 29 of the instant application corresponds to claim 18 of ‘598 Patent.
Claim 30 of the instant application corresponds to claim 19 of ‘598 Patent.
Claim 31 is rejected on the same basis of rejection of claim 20 of ‘598 Patent.
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 12-31 are rejected under 35 U.S.C. 101 because of the following reasons:
Claims 12, 24:
At Step 1:
The claims are directed to a “method”, "system" and thus directed to a statutory category.
At Step 2A, Prong One:
The claim recites the following limitations directed to an abstract idea:
-“analyzing,
-“generating, based on the analyzing, a regression formula capable of outputting predicted conversion factors for different unique items having a same category but a different combination of attribute values than the at least a portion of the plurality of unique items for which historical conversion activity information is stored” recites a mental process because human mind can generate a regression formula which will output (produce results) predicted conversion factors for different items for different combinations by observation/evaluation and judgment and/or by mathematical calculation. For example, a human can analyze historical sales data and observes that items with certain attributes (e.g. lower price, higher ratings) tend to have higher conversion rates. Based on these observations, the human forms a relationship between attributes and likelihood of conversion and applies that relationship to estimate conversion likelihood for new items having different attribute combinations by evaluation/observation and judgment of data. Moreover, “regression formula” constitutes a mathematical model expressing relationships between variables and generating predicted outputs, therefore, also mathematical calculation.
-“generating,because it involves a mathematical calculation/relationship to input data to compute output values.
-automatically updating, at least based on the received feedback on the interactive result set, the predicted conversion factors associated with the plurality of unique items” recites a mental process because human mind can automatically update based on the received feedback, the predicted conversion factors for items by evaluation and judgement of data. For example, a human can review feedback from users regarding displayed items, evaluates that feedback to determine which items are more or less likely to convert, and updates their internal estimates of conversion likelihood for those items accordingly.
At Step 2A, Prong Two:
The claim recites the following additional elements:
-"one or more electronic data stores", “non-transitory storage medium”, "one or more processors", “a user interface” which are all a high-level recitation of a generic computer components and represent mere instructions to apply the judicial exception on a computer as in MPEP 2106.05(f), which does not provide integration into a practical application.
-“a computer-implemented method of generating and presenting interactive search results for unique items”, “a computer readable, non-transitory storage medium having a computer program stored thereon for causing a computer system to process by one or more processors computer-program code by performing a method of generating and presenting interactive search results for unique items when the computer program is executed on the computer system” is generally linking the use of the judicial exception to a particular technological environment or field of use by limiting it to a particular data source or type. See MPEP § 2106.05(h) and Electric Power, 830 F.3d at 1354, 119 USPQ2d at 1742 (limiting application of abstract idea to power grid data).
-“providing, by a computer system, a user interface that comprises functionality that enables a user to search and interact with a plurality of unique items” is insignificant extra-solution activity as mere data outputting for data gathering purposes.
-"maintaining, by a computer system, one or more electronic data stores that store information relating to a plurality of unique items, each unique item comprising a plurality of attributes and a category, wherein each unique item of the plurality of unique items corresponds to a specific unique item currently or previously offered for sale” is insignificant extra-solution activity as mere data gathering such as 'obtaining information'. See MPEP 2106.05(g).
-“the stored information comprising at least: predicted conversion factors associated with the plurality of unique items, each of the predicted conversion factors being representative of a predicted likelihood of conversion of one of the plurality of unique items; information associated with the plurality of attributes, wherein each unique item of the plurality of unique items comprises a unique combination of attribute values; and information associated with historical conversion activity of at least a portion of the plurality of unique items" is generally linking the use of the judicial exception to a particular technological environment or field of use by limiting it to a particular data source or type. See MPEP § 2106.05(h) and Electric Power, 830 F.3d at 1354, 119 USPQ2d at 1742 (limiting application of abstract idea to power grid data).
-“wherein the regression formula is computer-generated, and wherein the regression formula is automatically and dynamically updated in real-time” is generally linking the use of the judicial exception to a particular technological environment or field of use by limiting it to a particular data source or type. See MPEP § 2106.05(h) and Electric Power, 830 F.3d at 1354, 119 USPQ2d at 1742 (limiting application of abstract idea to power grid data).
-“receiving, by the computer system, a user search request generated via a user interface, the user search request comprising an item search criteria” is insignificant extra-solution activity as mere data gathering such as 'obtaining information'. See MPEP 2106.05(g).
-“searching, by the computer system, the one or more electronic data stores for a plurality of unique items relating to the item search criteria” is insignificant extra-solution activity as mere data gathering such as 'obtaining information'. See MPEP 2106.05(g).
-“causing display via the user interface an interactive result set based on results of the searching, the interactive result set being prioritized based at least in part on the predicted conversion factors” is insignificant extra-solution activity as mere data outputting. See MPEP 2106.05(g).
-“receiving, via the user interface, a user input comprising feedback on the interactive result set” is insignificant extra-solution activity as mere data gathering such as 'obtaining information'. See MPEP 2106.05(g)
-“causing display via the user interface an updated interactive result set, the updated interactive result set being prioritized based at least in part on the updated predicted conversion factors” is insignificant extra-solution activity as mere data outputting. See MPEP 2106.05(g).
Viewing the additional limitations together and the claim as a whole, nothing provides integration into a practical application.
At Step 2B:
The conclusions for the mere implementation using a computer are carried over and does not provide significantly more.
-“providing, by a computer system, a user interface that comprises functionality that enables a user to search and interact with a plurality of unique items” is WURC as evidence by the court cases cited in MPEP 2106.05(d)(II) “iv. Presenting offers and gathering statistics, OIP Techs., 788 F.3d at 1362-63, 115 USPQ2d at 1092-9".
-“maintaining, by a computer system, one or more electronic data stores that store information relating to a plurality of unique items, each unique item comprising a plurality of attributes and a category, wherein each unique item of the plurality of unique items corresponds to a specific unique item currently or previously offered for sale” is WURC as evidence by the court cases cited in MPEP 2106.05(d)(II) "iv. Storing and retrieving information in memory, Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, … OIP Techs., 788 F.3d at 1363."
- “searching, by the computer system, the one or more electronic data stores for a plurality of unique items relating to the item search criteria” is WURC as evidence by the court cases cited in MPEP 2106.05(d)(II) "i. Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, … buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network)"
- “causing display via the user interface an interactive result set based on results of the searching, the interactive result set being prioritized based at least in part on the predicted conversion factors” is WURC as evidence by the court cases cited in MPEP 2106.05(d)(II) “iv. Presenting offers and gathering statistics, OIP Techs., 788 F.3d at 1362-63, 115 USPQ2d at 1092-9" for Berkheimer support of being well-understood, routine, and conventional computer outputting of data.
-“receiving, via the user interface, a user input comprising feedback on the interactive result set” is WURC as evidence by the court cases cited in MPEP 2106.05(d)(II) "i. Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, … buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network)".
-“causing display via the user interface an updated interactive result set, the updated interactive result set being prioritized based at least in part on the updated predicted conversion factors” is WURC as evidence by the court cases cited in MPEP 2106.05(d)(II) “iv. Presenting offers and gathering statistics, OIP Techs., 788 F.3d at 1362-63, 115 USPQ2d at 1092-9".
Accordingly, at step 2B, these additional elements, both individually and in combination, do not amount to significantly more than the judicial exception. See MPEP § 2106.05. Therefore, the claim is not eligible subject matter under 35 U.S.C. 101.
Claims 13, 25:
At Step 2A, Prong Two:
The claims recite the following additional elements:
-“wherein the plurality of unique items comprises vehicles” is generally linking the use of the judicial exception to a particular technological environment or field of use by limiting it to a particular data source or type. See MPEP § 2106.05(h) and Electric Power, 830 F.3d at 1354, 119 USPQ2d at 1742 (limiting application of abstract idea to power grid data).
Claim 14:
At Step 2A, Prong Two:
The claims recite the following additional elements:
-“wherein the plurality of unique items comprises real estate” is generally linking the use of the judicial exception to a particular technological environment or field of use by limiting it to a particular data source or type. See MPEP § 2106.05(h) and Electric Power, 830 F.3d at 1354, 119 USPQ2d at 1742 (limiting application of abstract idea to power grid data).
Claim 15:
At Step 2A, Prong Two:
The claims recite the following additional elements:
- “wherein the plurality of unique items is associated with sales listings currently or previously offered by one or more third party systems separate from the computer system” is generally linking the use of the judicial exception to a particular technological environment or field of use by limiting it to a particular data source or type. See MPEP § 2106.05(h) and Electric Power, 830 F.3d at 1354, 119 USPQ2d at 1742 (limiting application of abstract idea to power grid data).
Claims 16, 26:
At Step 2A, Prong Two:
The claims recite the following additional elements:
- “wherein maintaining the one or more electronic data stores comprises updating the regression formula on a periodic basis, and updating the predicted conversion factors” is generally linking the use of the judicial exception to a particular technological environment or field of use by limiting it to a particular data source or type. See MPEP § 2106.05(h) and Electric Power, 830 F.3d at 1354, 119 USPQ2d at 1742 (limiting application of abstract idea to power grid data).
Claims 17, 27:
At Step 2A, Prong Two:
The claims recite the following additional elements:
- “wherein maintaining the one or more electronic data stores comprises updating the regression formula on a real-time basis, and updating the predicted conversion factors” is generally linking the use of the judicial exception to a particular technological environment or field of use by limiting it to a particular data source or type. See MPEP § 2106.05(h) and Electric Power, 830 F.3d at 1354, 119 USPQ2d at 1742 (limiting application of abstract idea to power grid data).
Claims 18, 28:
At Step 2A, Prong Two:
The claims recite the following additional elements:
- “wherein maintaining the one or more electronic data stores comprises receiving, via a network accessible feed, data related to one or more new unique items offered for sale” is insignificant extra-solution activity as mere data gathering such as 'obtaining information'. See MPEP 2106.05(g).
-“storing information relating to the one or more new unique items in the one or more electronic data stores” is insignificant extra-solution activity as mere data gathering such as 'obtaining information'. See MPEP 2106.05(g).
Viewing the additional limitations together and the claim as a whole, nothing provides integration into a practical application.
At Step 2B:
The conclusions for the mere implementation using a computer are carried over and does not provide significantly more.
- "wherein maintaining the one or more electronic data stores comprises receiving, via a network accessible feed, data related to one or more new unique items offered for sale" is WURC as evidenced by the court cases cited in MPEP 2106.05(d)(II) by at least "i. Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, … buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network)"
-"storing information relating to the one or more new unique items in the one or more electronic data stores" is WURC as evidenced by the court cases cited in MPEP 2106.05(d)(II) by at least "iv. Storing and retrieving information in memory, Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, … OIP Techs., 788 F.3d at 1363."
Accordingly, at step 2B, these additional elements, both individually and in combination, do not amount to significantly more than the judicial exception. See MPEP § 2106.05. Therefore, the claim is not eligible subject matter under 35 U.S.C. 101.
Claims 19, 29:
At Step 2A, Prong One:
The claim recites the following limitations directed to an abstract idea:
-“adapting the regression formula based on the user response data” Which is a mental process because human mind can adapt the regression formula based on the user response data by evaluation and judgment.
At Step 2A, Prong Two:
The claims recite the following additional element:
-“receiving, by the computer system, user response data indicative of a user interaction with the interactive result set” is insignificant extra-solution activity as mere data gathering such as 'obtaining information'. See MPEP 2106.05(g).
At Step 2B:
The conclusions for the mere implementation using a computer are carried over and does not provide significantly more.
-“receiving, by the computer system, user response data indicative of a user interaction with the interactive result set” is WURC as evidenced by the court cases cited in MPEP 2106.05(d)(II) by at least "i. Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, … buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network)".
Accordingly, at step 2B, these additional elements, both individually and in combination, do not amount to significantly more than the judicial exception. See MPEP § 2106.05. Therefore, the claim is not eligible subject matter under 35 U.S.C. 101.
Claim 20:
At Step 2A, Prong Two:
The claims recite the following additional elements:
- “wherein the user interaction comprises a selection of an item represented in the interactive result set” is generally linking the use of the judicial exception to a particular technological environment or field of use by limiting it to a particular data source or type. See MPEP § 2106.05(h) and Electric Power, 830 F.3d at 1354, 119 USPQ2d at 1742 (limiting application of abstract idea to power grid data).
Claims 21, 30:
At Step 2A, Prong Two:
The claims recite the following additional elements:
- “wherein the regression formula is adapted and the predicted conversion factors are updated on a real-time basis” is generally linking the use of the judicial exception to a particular technological environment or field of use by limiting it to a particular data source or type. See MPEP § 2106.05(h) and Electric Power, 830 F.3d at 1354, 119 USPQ2d at 1742 (limiting application of abstract idea to power grid data).
Claims 22 and 31:
At Step 2A, Prong Two:
The claims recite the following additional elements:
-“wherein the item search criteria is associated with at least one of the plurality of attributes of the plurality of unique items” is generally linking the use of the judicial exception to a particular technological environment or field of use by limiting it to a particular data source or type. See MPEP § 2106.05(h) and Electric Power, 830 F.3d at 1354, 119 USPQ2d at 1742 (limiting application of abstract idea to power grid data).
Claim 23:
At Step 2A, Prong Two:
The claims recite the following additional elements:
-“wherein the same category of the different unique items comprises a vehicle category” is generally linking the use of the judicial exception to a particular technological environment or field of use by limiting it to a particular data source or type. See MPEP § 2106.05(h) and Electric Power, 830 F.3d at 1354, 119 USPQ2d at 1742 (limiting application of abstract idea to power grid data).
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 12-31 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.
Independent claims 12 and 24 recite the limitation “automatically updating, at least based on the received feedback on the interactive result set, the predicted conversion factors associated with the plurality of unique items” is not supported by the specification. Specification paragraphs [0032, the generated logistic regression formula can be dynamically updated and/or generated on a real-time and/or periodic basis], [0045, Inputs by user searchers are feedback to refine, tune and update predictive capabilities, as well as system 200 features and usability], [0044, For example, a ranked listing can be generated by the prioritizer module 212. The ranked listing from the prioritizer module 212 can be made accessible to the searching user, for view, display, and use in making transaction choices (for example, purchase decisions).]. The instant specification paragraphs do not describe that the predicted conversion factors are automatically updated based on users received feedback for each item. Therefore, the limitations lack support.
The limitation “causing display via the user interface an updated interactive result set, the updated interactive result set being prioritized based at least in part on the updated predicted conversion factors” is not supported by the specification.
Specification paragraphs [0044, For example, a ranked listing can be generated by the prioritizer module 212. The ranked listing from the prioritizer module 212 can be made accessible to the searching user, for view, display, and use in making transaction choices (for example, purchase decisions).], [0051, The prioritizing block 312 allows search user access to lists, by rank, according to rules of in block 312. Predictive or estimated values and assessments, such as price estimation and sale conversion rates, are important to the rank generated by the block 312.], [0059, The ranking and prioritizing for customers can alternatively or additionally be derived by revenue interests of the operator/provider. In order to maximize revenue generation for the operator/provider, select revenue values are attributable to certain sale items versus other items. Other variations of revenue models can be implemented, as well. If operator/provider revenue is important, then ranking and prioritizing may include assessments based on market time prior to sale completion in each instance, pricing and favorability for sale close, features of respective items and impact to sale consummation, and similar matters]. The instant specification paragraphs describe that the results are prioritized and displayed based on the predicted conversion factors. The specification does not describe that an updated interactive results sets are prioritized and displayed based on the updated predicted conversion factors. Therefore, the limitation lacks support.
The dependent claims depend from the independent claims and they are likewise rejected.
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 12-31 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.
The limitation “automatically updating, at least based on the received feedback on the interactive result set, the predicted conversion factors associated with the plurality of unique items” lacks support from the specification and therefore, it is not clear what is meant by the limitation. For the purpose of the examination, it is interpreted that the conversion factors are updated automatically for each item based on the user’s feedback.
The limitation “causing display via the user interface an updated interactive result set, the updated interactive result set being prioritized based at least in part on the updated predicted conversion factors” lacks support from the specification, therefore, it is not clear what is meant the by the limitation. For the purpose of the examination, it is interpreted that the updated prioritized result set is displayed based on the updated predicted conversion factors.
The dependent claims are dependent from the independent claims and they are likewise rejected.
Claim Rejections - 35 USC § 103
The following is a quotation of pre-AIA 35 U.S.C. 103(a) which forms the basis for all obviousness rejections set forth in this Office action:
(a) A patent may not be obtained though the invention is not identically disclosed or described as set forth in section 102, if the differences between the subject matter sought to be patented and the prior art are such that the subject matter as a whole would have been obvious at the time the invention was made to a person having ordinary skill in the art to which said subject matter pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 12, 15, 17, 19, 20, 22, 24, 27, 29, 31 is/are rejected under pre-AIA 35 U.S.C. 103(a) as being unpatentable over Landau et al. (US 2008/0270398) and in view of Wright et al. (US 2007/0156621) and in view of Tung (US 2007/0124110) and in view of Brukman et al. (US 8,620,915).
With respect to claim 12, Landau teaches a computer-implemented method of generating and presenting interactive search results for unique items, the computer-implemented method comprising (([0029, The system can be provided as part of a system for conducting purchase and sale of products for value]; [0030]; examiner's note: the online buy/sale website enables users to buy and sell products (unique item)):
providing, by a computer system, a user interface that comprises functionality that enables a user to search and interact with a plurality of unique items ([0013, receiving information from said customer through a customer interface]; [0068, if a search for a type of product returns more than one such product (e.g., a customer searched for "microwave ovens")]; examiner’s note: user interface can provide the user with functionalities to search for items);
maintaining, by a computer system, one or more electronic data stores that store information relating to a plurality of unique items (([0031, clients sold by a number of vendors, as well as operating systems and servers and database access tools available from a number of sources]; examiner's note: the databases stores information related to products (unique item))), each unique item comprising a plurality of attributes and a category (fig. 4; [0041, The products 410 have been determined to fall into several categories, types or segments]; [0069, Attributes are more general than the brand, and include color, country of origin, construction material, and other attributes of a product]; examiner's note: products fall into multiple categories and it has multiple attributes), wherein each unique item of the plurality of unique items corresponds to a specific unique item currently or previously offered for sale, the stored information comprising at least (([0034, 0041]; [0068, recommendations for products that fall within a range (e.g., 20%) of the average or mean or typically priced product the customer has historically investigated or purchased]; examiner's note: the products are for sale or previously sold products history are stored in a database):
predicted conversion factors associated with the plurality of unique items ([0065, rank the affinities of various pairings to predict the likelihood of conversion of browsing to buying]; examiner's note: products are associated with conversion), each of the predicted conversion factors being representative of a predicted likelihood of conversion of one of the plurality of unique items (([0065, rank the affinities of various pairings to predict the likelihood of conversion of browsing to buying]; [0071, The "conversion rate" of a product is a measure of how well the product is converted from a product recommendation to a purchased product]; examiner's note: products are associated with conversion rate which is likelihood of the conversion of plurality of products);
information associated with the plurality of attributes, wherein each unique item of the plurality of unique items comprises a unique combination of attribute values ([0069, Another type of affinity is "brand affinity" (which can be generalized to other attributes of a product--or "attribute affinity"). Attributes are more general than the brand, and include color, country of origin, construction material, and other attributes of a product]; examiner's note: each product is associated with multiple attributes); and information associated with historical conversion activity of at least a portion of the plurality of unique items ([0068, make recommendations for products that fall within a range (e.g., 20%) of the average or mean or typically priced product the customer has historically investigated or purchased]; [0071, Affinity calculations may take into account the conversion rate of a product as a measure of its desirability or affinity in some related context. Therefore, conversion rate is another datum that can be tracked and stored in the database used by the affinity engine]; examiner's note: the conversion rate (conversion activity) is stored in the database);
analyzing, by the computer system, the information associated with the historical conversion activity and the information associated with the plurality of attributes for the at least a portion of the plurality of unique items ([0071, 0081]; examiner's note: the conversion rate of a product is stored in the database to be analyzed for predicting future conversion);
wherein the metadata comprises stated metadata and derived metadata wherein the metadata is unique to each of the plurality of unique items (based on further review Landu teaches the limitation in [0066, store and analyze purchase behavior that includes a product SKU number and SKU price, a maximum product item price, its average selling price, and a price standard deviation]; examiner’s note: the SKU is the stated metadata and the average selling price is derived metadata, each metadata is associated with products (plurality of unique items));
receiving, by the computer system, a user search request generated via a user interface, the user search request comprising an item search criteria ([0042, if the shopper selects a link to (or enters a search for) plumbing supplies, then the server serving the customer will present a plumbing-specific storefront to the customer; examiner's note: the user is searching for an item i.e. plumbing supplies is item search criteria);
searching, by the computer system, the one or more electronic data stores for a plurality of unique items relating to the item search criteria ([0042]); examiner’s note: the system searches for the results in multiple databases); and
causing display via the user interface an interactive result set based on results of the searching (fig.8; [0075, These pre-lookup items may be based on highest product acceleration and most popular selections overall customer persona]; [0077, The affinity engine attempts to determine a product-to-product affinity score at step 904 based on the customer's inquiry. If a satisfactory or acceptable affinity is found, the results are provided to the customer at step 914 in the form of a recommendation]; examiner's note: displaying the results of the search in the graphical user interface), the interactive result set being prioritized based at least in part on the predicted conversion factors (([0071, a merchant may place cross-sell recommendations on a shopping checkout page based on how high the products’ conversion rates have been, for example, placing the products with the highest conversion rates], [0077]; [0078]; [0120]; examiner's note: the result set contains results with the most scores (prioritized) and the user can select a product which makes the interface interactive).).
Landau does not explicitly teach generating, based on the analyzing, a regression formula capable of outputting predicted conversion factors for different unique items having a same category but a different combination of attribute values than the at least a portion of the plurality of unique items for which historical conversion activity information is stored; generating, by the computer system, at least a portion of the predicted conversion factors by inputting metadata related to the plurality of attributes of the plurality of unique items into the regression formula and wherein the metadata comprises stated metadata and derived metadata, wherein the metadata is unique to each of the plurality of unique items; and wherein the regression formula is automatically and dynamically updated in real-time; receiving, via the user interface, a user input comprising feedback on the interactive result set; automatically updating, at least based on the received feedback on the interactive result set, the predicted conversion factors associated with the plurality of unique items; causing display via the user interface an updated interactive result set, the updated interactive result set being prioritized based at least in part on the updated predicted conversion factors.
Landau teaches identifying predictions of a product sale with calculation of an affinity score using a formula (0092, 0115), but it does not explicitly teach how different attributes affects conversion activity and generating a regression formula to calculate the prediction conversion rate and regression formula is automatically and dynamically updated in real-time and the regression formula is weighted based on conversion rates of each of the plurality of unique items.
However, Wright teaches generating, based on the analyzing, a regression formula capable of outputting predicted conversion factors for different unique items having a same category but a different combination of attribute values than the at least a portion of the plurality of unique items for which historical conversion activity information is stored ([0096-0101]; [0102, an indication of what country the user is located in. Different cultures might lead to users reacting differently to the same ad or having different cultural reactions or staying on sites differently], [0104], [0113, 114, 0118], [0123, For each obtained ad/query feature (i.e., obtained in block 1420 above), the determined predictive values may be summed with stored values that correspond to the ad/query feature (block 1425). The determined predictive values may be summed with values stored in a data structure, such as, for example, data structure 1600 shown in FIG. 1]; examiner's note: the regression formula is used io calculate the predicted values and the ad is the same category but it has multiple different attributes such as different cost price and CTR; predicted value is detected of an ad based on the click through rate and cost using a regression formula);
wherein the regression formula is computer generated ([0111, Logistic regression produces a formula that predicts the probability of the occurrence as a function of the independent predictor variables. Logistic regression fits a special s-shaped curve by taking the linear regression (Eqn. (1) above),]; examiner’s note: the regression formula is computer generated), generating, by the computer system, at least a portion of the predicted conversion factors ([0102-0110], [0111, Logistic regression produces a formula that predicts the probability of the occurrence as a function of the independent predictor variables]; examiner's note: the predicted quality of an ad selection) by inputting metadata related to the plurality of attributes of the plurality of unique items into the regression formula ([0102, 0104, 0105], [0108, 43) effective CPC*predicted CTR; or [0109] 44) bid CPC*predicted CTR], [0110, Regression involves finding a function that relates an outcome variable (dependent variable y) to one or more predictors (independent variables x.sub.1, x.sub.2, etc.). ], [0136, FIG. 18 is a flow diagram illustrating one exemplary implementation of blocks 1710-1720 of FIG. 17. Initially, a confidence interval relating to the odds of a good ad or bad ad may be determined (act 1800). Using a confidence interval technique enables more accurate and stable estimates when ad/query features k having lesser amounts of historical data are used]; examiner’s note: the conversion of an ad selection is generated based on the value of attributes (metadata) i.e. cost/CTR into the regression formula determining a predicted factor).
One of ordinary skill in art would recognize that generating a regression formula for different attributes to output predicted conversion factors by inputting metadata into the formula and generating regression formula by computer of Wright to be incorporated with predicting a sale of a product of Landau to further improve the system to perform better predictions.
Therefore, it would have been obvious to one of the ordinary skill in the art before the effective filling date of the invention to incorporate functionalities of Wright into the buying/selling products of Landau to have an efficient prediction system. The motivation would be to have a system which will generate a regression formula to change a conversion factor and predict conversion rates in different scenarios with different attributes to find the best predicted conversion rates using regression formula to sell a product faster and efficiently (Wright, [0113, As one skilled in the art will recognize, "boosting" is a machine learning technique for building a statistical model by successively improving an otherwise weak statistical model]).
Landau and Wright in combination do not explicitly teach and wherein the regression formula is automatically and dynamically updated in real-time; receiving, via the user interface, a user input comprising feedback on the interactive result set; automatically updating, at least based on the received feedback on the interactive result set, the predicted conversion factors associated with the plurality of unique items; causing display via the user interface an updated interactive result set, the updated interactive result set being prioritized based at least in part on the updated predicted conversion factors.
However, Tung teaches and wherein the regression formula is automatically and dynamically updated in real-time ([0026, the statistical model may be updated real-time. For example, when a user purchases a product as a result of the search, a set of features may be extracted from the primary product object and these features may be tabulated along with product-related information to update the model. An exemplary statistical model trained as a Bayesian network for identifying primary product images is described below]; examiner’s note: the statistical model (regression formula) is automatically and dynamically updated in real time; regression formula is also taught by Wright in paragraph [0096, 0102]).
One of ordinary skill in art would recognize that incorporating updating regression formula in real time of Tung to be incorporated with predicting a sale of a product of Landau/Wright to further improve the system to automatically and dynamically updated in real-time.
Therefore, it would have been obvious to one of the ordinary skill in the art before the effective filling date of the invention to incorporate functionalities of Tung into the buying/selling products of Landau/Wright to have and updated model. The motivation would be to have a system which will be accurate and responsive and adaptive to changing conditions to predict better recommendations of items and improve the system to be consistent with change (Tung, [0007, the invention is to make online shopping more time-efficient and convenient]).
Landau, Wright and Tung do not in combination teach receiving, via the user interface, a user input comprising feedback on the interactive result set; automatically updating, at least based on the received feedback on the interactive result set, the predicted conversion factors associated with the plurality of unique items; causing display via the user interface an updated interactive result set, the updated interactive result set being prioritized based at least in part on the updated predicted conversion factors.
However, Brunkman teaches receiving, via the user interface, a user input comprising feedback on the interactive result set ([col. 13, lines 57-65, “the user search history record 200 includes information tracking the user's actions on the user-preferred search results 250. From the information, the user profiler 129 can determine one or more performance metrics for each user-preferred search result, such as its actual long click-through rate and its average click-through position in the list of search results. The fact that a particular search result has a high long click-through rate and a high average click-through position indicates that the search result indeed matches the user's search interests, which serves as a confirmation that the search result should stay within the user-preferred search results 250”], the users selection of users selection (feedback) from the search result set (interactive search results set));
automatically updating, at least based on the received feedback on the interactive result set, the predicted conversion factors associated with the plurality of unique items ([col. 12, lines 37-46, “the popularity metric of a user-preferred search result is a prediction of the search result's long click-through rate, which indicates the likelihood of a user selection of the search result being a long click-through. For example, a search result having a 7% long click-through rate means that, statistically, for each 100 impressions of the search result, a long click-through will result seven times], [fig. 6A, 6B], [col. 15, lines 15-20, “the user profiler 129 selects one of the user search history records (610) and identifies multiple user-selected search results in the user search history record (615). For each identified user-selected search result, the user profiler 129 determines a set of property values (620) and uses the set of property values to populate a multiple users' search behaviors table (625).], [col. 13, lines 50-55, “Necessary operations include adding new search results that become the user's latest preferences, evicting old search results that are no longer the user's preference, and adjusting the existing search results' popularity metrics to reflect the variation of the user's current search interests in the user-preferred search results”], [col. 16, lines 44-50, “the popularity metric is a predicted long click-through rate for the search result in the future. The user profiler 129 repeats the application process until the last candidate search result is processed (660, yes). As noted above in connection with FIG. 4, the top P search results having the highest predicted long click-through rates are selected as the user-preferred search results”], determining a popularity metric for search results, where the popularity metric is a predicted long-click through rate (predicted conversion factors), the popularity metric is adjusted which corresponds to automatically updating the predicted conversion factors based on the selected search results);
causing display via the user interface an updated interactive result set, the updated interactive result set being prioritized based at least in part on the updated predicted conversion factors (fig. 3, [col. 5. Lines 25-30, “Assuming that at least one search result changes its position, the search result ranker 126 then returns the reordered list of search results to the front end sever 120. The front end server 120 then provides the reordered list of search results to the requesting user at the client 103”], [col. 12, lines 10-15, “the search result ranker 126 moves each of the identified search results from its current position determined by its generic ranking score by an offset, based on a presumption that a user-preferred search result near the top of the list is likely to receive more attention from the user while a user-disfavored search result near the bottom of the list is likely to receive less attention from the same user”], the results the reordered (prioritized) based on the users predicted click through rate).
One of ordinary skill in art would recognize that incorporating selecting users feedback from the result set, updating the predicted conversion factors and updating the search result list based on the updated predicted conversion factors of Brunkman to be incorporated with predicting a sale of a product of Landau/Wright/Tung to further improve the system to updated prioritized search result list.
Therefore, it would have been obvious to one of the ordinary skill in the art before the effective filling date of the invention to incorporate functionalities of Brunkman into the buying/selling products of Landau/Wright/Tung to have a system which will make more informed, performance-driven predictions to make the system more efficient (Brunkman, [col. 2, lines 3-5, “the information server reorders the list of search results by moving each of the identified search results from its initial position by an offset and then provides the reordered list of search results to the user.]).
With respect to claim 15, Landau, Wright, Tung, Brunkman in combination teach the computer-implemented method of Claim 12, Landau further teaches wherein the plurality of unique items are associated with sales listings currently or previously offered by one or more third party systems separate from the computer system ([0064, select certain top products for presentation to a merchandiser or a custom]; examiner's note: items listing by merchandiser (third party)).
With respect to claim 17, Landau, Wright, Tung, Brunkman in combination teach the computer-implemented method of claim 12, Landau further teaches wherein maintaining the one or more electronic data stores (fig. 1) and predicted conversion factors ([0064, select certain top products for presentation to a merchandiser or a custom]), Wright teaches regression formula ([0104]) but do not explicitly teach updating in a real time basis.
However, Tung teaches updating data in real-time basis ([0026, the statistical model may be updated real-time. For example, when a user purchases a product as a result of the search, a set of features may be extracted from the primary product object and these features may be tabulated along with product-related information to update the model. An exemplary statistical model trained as a Bayesian network for identifying primary product images is described below]; examiner’s note: the statistical model (regression formula) is updated in real time; regression formula is also taught by Wright in paragraph [0096, 0102]).
One of ordinary skill in art would recognize that incorporating updating in real time regression formula of Tung to be incorporated with predicting a sale of a product of Landau/Wright/Sharma to further improve the system to automatically and dynamically updated in real-time.
Therefore, it would have been obvious to one of the ordinary skill in the art before the effective filling date of the invention to incorporate functionalities of Tung into the buying/selling products of Landau/Wright/ Brunkman to have an updated model. The motivation would be to have a system which will be accurate and responsive and adaptive to changing conditions to predict better recommendations of items and improve the system to be consistent with change (Tung,[0007, the invention is to make online shopping more time-efficient and convenient]).
With respect to claim 19, Landau, Wright, Tung, Brunkman in combination teach the computer-implemented method of Claim 12, Landau further comprising: receiving, by the computer system, user response data indicative of a user interaction with the interactive result set ([0064, the affinity engine can generate an output and rank the affinities of various pairings to predict the likelihood of conversion of browsing to buying]; examiner's note: from the results set the user can select an item to buy); but does not explicitly teach adapting the formula based on the user response data.
However, Wright teaches adapting the formula based on the user response data ([0112, A fit of the statistical model may be tested to determine which session features are correlated with good or bad quality advertisements. If a logistic regression technique is used to determine the statistical model, the goal of logistic regression is to correctly predict the outcome for individual cases using the most parsimonious model], [0121, The statistical model, derived in block 520 above, and the obtained session features may be used to determine predictive values 1530 that the ad is a good ad and/or a bad ad (block 1415). The predictive values may include a probability value (e.g., derived using Eqn. (3) or (5) above) that indicate the probability of a good ad given session features associated with user selection of that ad], examiner’s note: the regression formula is changed (adapted) based on users sessions data (users response data)). One of ordinary skill in art would recognize that adapting the regression formula based on users response data of Wright could be incorporated with predicting a sale of a product of Landau/Wright/ Brunkman to further improve the system to perform better predictions.
Therefore, it would have been obvious to one of the ordinary skill in the art before the effective filling date of the invention to incorporate features of Wright into the buying/selling products of Landau/Brunkman to have an efficient prediction system. The motivation would be to have a system which will generate a regression formula to change a conversion and predict conversion rates in different scenarios with different attributes to find the best predicted conversion rates using regression formula to sell a product faster and efficiently (Wright, [0113, As one skilled in the art will recognize, "boosting" is a machine learning technique for building a statistical model by successively improving an otherwise weak statistical model]).
With respect to claim 20, Landau, Wright, Tung, Brunkman in combination teach the computer-implemented method of Claim 12, Landau further teaches wherein the user interaction comprises a selection of an item represented in the interactive result set ([0044], [0065, A user interface 510 available to customer 502 presents customer 502 with an ordered list of products determined to most suit the needs or interests of customer 502. The ordering of Product R, Product A, Product D, then Product L, etc. can be arranged visually on a Web page of the user interface 510 or client application. The same system can be used to collect the decision (click, purchase, etc.) of customer 502 for inclusion into database 530.], users selecting a data from the results).
With respect to claim 22, Landau, Wright, Tung, Brunkman in combination teach the computer-implemented method of Claim 12, Landau further teaches wherein the item search criteria is associated with at least one of the plurality of attributes of the plurality of unique items ([0068, if a search for a type of product returns more than one such product (e.g., a customer searched for "microwave ovens")], the search criteria i.e. name of a product).
Claim 24 encompasses the same scope of limitation of claim 12, in additions of a computer readable non-transitory storage medium, processor, memory ([0012, 0037]). Therefore, claim 24 is rejected on the same basis of rejection of claim 12.
Claim 27 is rejected on the same basis of rejection of claim 17.
Claim 29 is rejected on the same basis of rejection of claim 19.
Claim 31 is rejected on the same basis of rejection of claim 22.
Claims 13, 23, 25 is/are rejected under pre-AIA 35 U.S.C. 103(a) as being unpatentable over Landau et al. (US 2008/0270398) and in view of Wright et al. (US 2007/0156621) and in view of Tung (US 2007/0124110) and in view of Brukman et al. (US 8,620,915) and in view of Micaelian (US 8,620,717).
With respect to claim 13, Landau, Wright, Tung, Brunkman in combination teach the method of claim 12, but do not explicitly teach wherein the plurality of unique items comprises vehicles.
However, Micaelian further teaches wherein the plurality of unique items comprises vehicles (col. 3, lines 55-60, “the Ford Focus ZX3 coupe had the best overall score and was ranked #1 based upon the weighted preferences that were input in the weight adjustment section of screen shot 90. This was followed by the Ford Escape TWD Sport Utility 4D, which was ranked #2, the Ford Taurus SE V6 Wagon 4D, ranked at #3, etc”; examiner’s note: car (vehicles)). One of ordinary skill in art would recognize that the process of selling a car item on the website of Micaelian to be incorporated with predicting a sale of a product of Landau/Wright/Tung/ Brukman to further improve the system to sale other products.
Therefore, it would have been obvious to one of the ordinary skill in the art before the effective filling date of the invention to incorporate selling cars as item of Micaelian into the buying/selling products of Landau/Wright/Tung/Brukman to have an efficient system which will sell multiple items. The motivation would be to have a system in car selling market to predict which car will sell faster for the business owners to recommendation to select an appropriate car to sell faster (Micaelian; col. 2, lines 33-35; “accepts parametric values from the singular tradeoff engine and operative to develop a new value to the singular tradeoff engine’).
With respect to claim 23, Landau, Wright, Tung and Brukman in combination teach the method of claim 12, but do not explicitly teach wherein the same category of the different unique items comprises vehicle category.
However, Micaelian further teaches wherein the same category of the different unique items comprises vehicle category (col. 3, lines 55-60, “the Ford Focus ZX3 coupe had the best overall score and was ranked #1 based upon the weighted preferences that were input in the weight adjustment section of screen shot 90. This was followed by the Ford Escape TWD Sport Utility 4D, which was ranked #2, the Ford Taurus SE V6 Wagon 4D, ranked at #3, etc”; examiner’s note: car (vehicles) the cars category are for sale). One of ordinary skill in art would recognize that the process of selling a car item on the website of Micaelian could be incorporated with predicting a sale of a product of Landau/Wright to further improve the system to sale other products.
Therefore, it would have been obvious to one of the ordinary skill in the art before the effective filling date of the invention to incorporate selling cars as item of Micaelian into the buying/selling products of Landau/Wright/ Brukman to have an efficient system which will sell multiple items. The motivation would be to have a system in car selling market to predict which car will sell faster for the business owners to recommendation to select an appropriate car to sell faster (Micaelian; col. 2, lines 33-35; “accepts parametric values from the singular tradeoff engine and operative to develop a new value to the singular tradeoff engine’).
Claim 25 is rejected on the same basis of rejection of claim 13.
Claims 14 is/are rejected under pre-AlA 35 U.S.C. 103(a) as being unpatentable over Landau et al. (US 2008/0270398) and in view of Wright et al. (US 2007/0156621) and in view of Tung (US 2007/0124110) and in view of Brukman et al. (US 8,620,915) and in view of Sealand et al. (US 2003/0014402).
With respect to claim 14, Landau, Wright, Tung, Brukman in combination teach the computer implemented method of claim 19, but do not explicitly teach wherein the plurality of unique items comprise real estate offered for sale. Landau teaches plurality of unique items for sale (Landau [0021]) but do not in combination teaches real estate items for sale.
However, Sealand teaches wherein the plurality of unique items comprise real estate ([0012, transacting retrieval of real estate property listings using a remote client interfaced over an information network]; [0013, interactive access to a real estate information database]; examiner's note: the real estate property listing). One of ordinary skill in art would recognize that the process of selling a real estate item on the website of Sealand could be incorporated with predicting a sale of a product of Landau/Wright/Tung/ Brukman to further improve the system to sale other products.
Therefore, it would have been obvious to one of the ordinary skill in the art before the effective filling date of the invention to incorporate selling real estate item of Sealand into the buying/selling products of Landau/Wright/Tung/Brukman to have an efficient system which will sell multiple items. The motivation would be to have a system in real estate market to predict real estate sell for users and also to give users recommendation to select an appropriate real estate faster (Sealand, [0055, This approach provides the practical benefits of dramatically increased battery life, low weight, and minimal form factor]).
Claims 16, 26 is/are rejected under pre-AlA 35 U.S.C. 103(a) as being unpatentable over Landau et al. (US 2008/0270398) and in view of in view Wright et al. (US 2007/0156621) and in view of Tung (US 2007/0124110) and in view of Brukman et al. (US 8,620,915) and in view of Lam et. al. (US 2005/0234574).
With respect to claim 16, Landau, Wright, Tung, Brukman in combination teach the computer implemented method of claim 12, Wright further teaches and updating the predicted conversion factors ([0047, sorting, analyzing and updating future affinity engine decisions], examiner’s note: updating the affinities (predicted factors)) but do not explicitly teach wherein maintaining the one or more electronic data stores comprises updating the regression formula on a periodic basis.
Landau teaches predicted conversion factors ([0047]) and Wright teaches regression formula ([0104]) but do not explicitly teach updating on a periodic basis.
However, Lam teaches updating on a periodic basis ([0092, The average error can be further improved by implementing periodic updates of the process performance prediction model 110]; examiners note: the prediction model (regression formula) is updated based on a periodic basis). One of ordinary skill in art would recognize that the process of updating data in a periodic basis of Lam could be incorporated with predicting a sale of a product of Landau/Wright/Tung/Brukman to further improve the system to update the prediction on a periodic basis.
Therefore, it would have been obvious to one of the ordinary skill in the art before the effective filling date of the invention to incorporate updating data periodically of Lam into the buying/selling products of Landau/Wright/Tung/ Brukman to have an efficient system to have most updated data. The motivation would be to have a system to update the predicated values periodically to have the most updated data (Lam, [0092, The average error can be further improved by implementing periodic updates of the process performance prediction model 11]).
Claim 26 is rejected on the same basis of rejection of claim 16.
Claims 18, 28 is/are rejected under pre-AIA 35 U.S.C. 103(a) as being unpatentable over Landau et al. (US 2008/0270398) and in view of Wright et al. (US 2007/0156621) and in view of Tung (US 2007/0124110) and in view of Brukman et al. (US 8,620,915) and in view of Moore (US 2008/0040151).
With respect to claim 18, Landau, Wright, Tung, Brukman in combination teach the computer-implemented method of Claim 12, Landau teaches wherein maintaining the one or more electronic data stores comprises receiving, via a network, data related to one or more new unique items offered for sale; and storing information relating to the one or more new unique items in the one or more electronic data stores ([0006, determination of the identity of an individual visitor to an electronic store], [0075, with pre-lookup information and recommendations by way of the site's electronic storefront], storing data into electronic data stores) but do not explicitly teach accessible feed.
However, Moore teaches accessible feed ([0082-0084], the accessible feed). One of ordinary skill in art would recognize that accessible feed of Moore could be incorporated with predicting a sale of a product of Landau/Wright/Tung/ Brukman to further improve the system to have an accessible feed.
Therefore, it would have been obvious to one of the ordinary skill in the art before the effective filling date of the invention to incorporate features of Moore into the buying/selling products of Landau/Wright/Tung/ Brukman to have an efficient system. The motivation would be to have a system to have accessible feed to display data faster and in an organized way ([0128, In the systems described herein, these and other techniques generally may be employed to improve performance of an RSS or other data feed network]).
Claim 28 is rejected on the same basis of rejection of claim 18.
Claims 21, 30 is/are rejected under pre-AlA 35 U.S.C. 103(a) as being unpatentable over Landau et al. (US 2008/0270398) and in view of in view Wright et al. (US 2007/0156621) and in view of Tung (US 2007/0124110) and in view of Brukman et al. (US 8,620,915) and in view of Nasle (US 2007/0239373).
With respect to claim 21, Landau, Wright, Tung, Brukman teaches in combination the computer-implemented method of claim 19, Landau teaches wherein the regression formula is adapted ([0105, 0112]) but do not explicitly teach and the predicted conversion factors are updated on a real time basis.
However, Nasle teaches the predicted conversion factors are updated on a real time basis ([0064, the virtual system model is periodically calibrated and synchronized with "real-time" sensor data outputs so that the virtual system model provides data output values that are consistent with the actual "real-time" values received from the sensor output signals], [0096, the predicted system output value for the virtual system model is updated with a real-time output value for the monitored system]: examiners note: the predicted values are updated on a real time basis). One of ordinary skill in art would recognize that the process of updating data in a real time basis of Nasle could be incorporated with predicting a sale of a product of Landau/Wright/Tung/ Brukman to further improve the system to update the prediction on a real-time basis. Therefore, it would have been obvious to one of the ordinary skill before the effective filling date of the invention to incorporate updating data in real time of Nasle into the buying/selling products of Landau/Wright/Tung/ Brukman to have an efficient system to have most updated data. The motivation would be to have a system to update the predicated values in real time to have the most updated data ([0006, With such improved techniques, operational costs could be greatly reduced]).
Claim 30 is rejected on the same basis of rejection of claim 21.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to FATIMA P MINA whose telephone number is (571)270-3556. The examiner can normally be reached Monday - Friday 9:00 am - 5:00 pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Ann Lo can be reached on 571-272-9767. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/FATIMA P MINA/ Examiner, Art Unit 2159
/ANN J LO/ Supervisory Patent Examiner, Art Unit 2159