DETAILED ACTION
This action is in reply to the original application filed on 10/17/2022.
Claims 1-20 are rejected.
Claims 1-20 are currently pending and have been examined.
Information Disclosure Statement
Information Disclosure Statement received 10/17/2022 has been reviewed and considered.
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Objections
Claims 6-7, 13-14, and 19-20 objected to because of the following informalities:
-Claims 6, 13, and 19 read “the selecting an electronic commerce website” but should likely read “the selecting the electronic commerce website”
-Claims 7, 14, and 20 read “the selecting an electronic commerce website” but should likely read “the selecting the electronic commerce website”
-Claim 13 reads “herein” but should likely read “wherein”
-Claim 19 reads “herein” but should likely read “wherein”
Appropriate correction is required.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Under Step 1 of the Subject Matter Eligibility Test for Products and Processes, the claims must be directed to one of the four statutory categories (see MPEP 2106.03). All the claims are directed to one of the four statutory categories (YES).
Under Step 2A of the Subject Matter Eligibility Test, it is determined whether the claims are directed to a judicially recognized exception (see MPEP 2106.04). Step 2A is a two-prong inquiry.
Under Prong 1, it is determined whether the claim recites a judicial exception (YES). Taking Claim 8 as representative, the claim recites limitations that fall within the certain methods of organizing human activity groupings of abstract ideas, including:
-one or more computer processors configured to execute operations including:
-based on user-specified input, determining an item of interest to a user and a geographic region of the user;
-aggregating, within a ledger of a blockchain, the item of interest as available from a plurality of electronic commerce [stores] websites of geographic regions that are different from the geographic region of the user;
-retrieving social networking data for the user and processing the social networking data using natural language processing to determine contacts of the user located in the geographic regions of the plurality of electronic commerce [stores] websites;
-determining a likelihood of each contact traveling to the geographic region of the user based on the social networking data;
-selecting an electronic commerce [store] website from the plurality of electronic commerce [stores] websites that offers the item of interest or a similar item and that corresponds to a geographic region of a selected contact, wherein the selected contact of the user has at least a minimum likelihood of traveling to the geographic region of the user; and
-presenting the selected electronic commerce [store] website to the user as an option for obtaining the item or the similar item
The above limitations recite the concept of determining a likelihood of customer contacts to travel to the geographic location of the customer and selecting a location where a desired item is available and a contact is located to provide as an option to the customer. The above limitations fall within the “Certain Methods of Organizing Human Activity” groupings of abstract ideas, enumerated in MPEP 2106.04(a).
Certain methods of organizing human activity include:
fundamental economic principles or practices (including hedging, insurance, and mitigating risk)
commercial or legal interactions (including agreements in the form of contracts; legal obligations; advertising, marketing or sales activities or behaviors; and business relations)
managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions)
The limitations of based on user-specified input, determining an item of interest to a user and a geographic region of the user; and determining a likelihood of each contact traveling to the geographic region of the user based on the social networking data are processes that, under their broadest reasonable interpretation, cover a commercial interaction. For example, “determining” and “determining” in the context of this claim encompass advertising, and marketing or sales activities.
Similarly, the limitations of aggregating, within a ledger of a blockchain, the item of interest as available from a plurality of electronic commerce [stores] websites of geographic regions that are different from the geographic region of the user; retrieving social networking data for the user and processing the social networking data using natural language processing to determine contacts of the user located in the geographic regions of the plurality of electronic commerce [stores] websites; selecting an electronic commerce [store] website from the plurality of electronic commerce [stores] websites that offers the item of interest or a similar item and that corresponds to a geographic region of a selected contact, wherein the selected contact of the user has at least a minimum likelihood of traveling to the geographic region of the user; and presenting the selected electronic commerce [store] website to the user as an option for obtaining the item or the similar item are processes that, under their broadest reasonable interpretation, cover a commercial interaction. That is, other than reciting that the ledger is of a blockchain, that the plurality of electronic commerce stores are a plurality of electronic commerce websites, that the social networking data is processed using natural language processing, and that the electronic commerce store is an electronic commerce website, nothing in the claim element precludes the step from practically being performed by people. For example, but for the “blockchain,” “plurality of electronic commerce websites,” “natural language processing,” and “electronic commerce website” language, “aggregating,” “retrieving,” “selecting,” and “presenting” in the context of this claim encompasses advertising, and marketing or sales activities.
Under Prong 2, it is determined whether the claim recites additional elements that integrate the exception into a practical application of the exception. This judicial exception is not integrated into a practical application (NO).
-one or more computer processors configured to execute operations including:
-based on user-specified input, determining an item of interest to a user and a geographic region of the user;
-aggregating, within a ledger of a blockchain, the item of interest as available from a plurality of electronic commerce websites of geographic regions that are different from the geographic region of the user;
-retrieving social networking data for the user and processing the social networking data using natural language processing to determine contacts of the user located in the geographic regions of the plurality of electronic commerce websites;
-determining a likelihood of each contact traveling to the geographic region of the user based on the social networking data;
-selecting an electronic commerce website from the plurality of electronic commerce websites that offers the item of interest or a similar item and that corresponds to a geographic region of a selected contact, wherein the selected contact of the user has at least a minimum likelihood of traveling to the geographic region of the user; and
-presenting the selected electronic commerce website to the user as an option for obtaining the item or the similar item
The additional elements of claim 8 are recited at a high level of generality (i.e. as generic computing hardware) such that they amount to nothing more than mere instructions to implement or apply the abstract idea on a generic computing hardware (or, merely use a computer as a tool to perform an abstract idea) as supported by paragraph [0026] of Applicant’s specification – “Computer 101 may take the form of a desktop computer, laptop computer, tablet computer, smart phone, smart watch or other wearable computer, mainframe computer, quantum computer or any other form of computer or mobile device now known or to be developed in the future that is capable of running a program, accessing a network or querying a database, such as remote database 130.” Specifically, the additional elements of one or more computer processors configured to execute operations, a blockchain, a plurality of electronic commerce websites, natural language processing, and an electronic commerce website are recited at a high-level of generality (i.e. as a generic processor performing the generic computer functions of determining data, aggregating data, receiving data, selecting data, and presenting data) such that they amount do no more than mere instructions to apply the exception using generic computer components. Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea. Further, the additional elements do no more than generally link the use of the judicial exception to a particular technological environment or field of use (such as computers or computing networks). Employing well-known computer functions to execute an abstract idea, even when limiting the use of the idea to one particular environment, does not integrate the exception into a practical application.
Additionally, the additional elements are insufficient to integrate the abstract idea into a practical application because the claim fails to i) reflect an improvement in the functioning of a computer or an improvement to another technology or technical field, ii) apply the judicial exception with, or use the judicial exception in conjunction with, a particular machine or manufacture that is integral to the claim, iii) effect a transformation or reduction of a particular article to a different state or thing, or iv) apply or use the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment.
Accordingly, the judicial exception is not integrated into a practical application.
Under Step 2B, it is determined whether the claims recite additional elements that amount to significantly more than the judicial exception. The claims of the present application do not include additional elements that are sufficient to amount to significantly more than the judicial exception (NO).
In the case of claim 8, taken individually or as a whole, the additional elements of claim 9 do not provide an inventive concept. As discussed above under step 2A (prong 2) with respect to the integration of the abstract idea into a practical application, the additional elements used to perform the claimed functions amount to no more than a general link to a technological environment.
Even considered as an ordered combination (as a whole), the additional elements do not add anything significantly more than when considered individually.
Claim 1 is a method reciting similar functions as claim 8. Examiner notes that claim 1 recites the additional elements of a computer-implemented method, a blockchain, a plurality of electronic commerce websites, natural language processing, and an electronic commerce website, however, claim 1 does not qualify as eligible subject matter for similar reasons as claim 8 indicated above.
Claim 15 is a computer program product reciting similar functions as claim 8. Examiner notes that claim 15 recites the additional elements of a computer program product, one or more computer readable storage media having program instructions embodied therewith, one or more computer processors, a blockchain, a plurality of electronic commerce websites, natural language processing, and an electronic commerce website however, claim 15 does not qualify as eligible subject matter for similar reasons as claim 8 indicated above.
Therefore, claims 1, 8, and 15 do not provide an inventive concept and do not qualify as eligible subject matter.
Dependent claims 2-7, 9-14, and 16-20 when analyzed as a whole, are held to be patent ineligible under 35 U.S.C. § 101 because they do not add “significantly more” to the abstract idea. More specifically, dependent claims 2-7, 9-14, and 16-20 further fall within the “Certain Methods of Organizing Human Activity” grouping of abstract ideas in that they recite commercial interactions. Dependent claim 2 does not recite any farther additional elements, and as such are not indicative of integration into a practical application for at least similar reasons discussed above. Dependent claims 3-7, 9-14, and 16-20 recite the additional elements of the natural language processing, one or more electronic commerce websites, an electronic commerce website, the plurality of electronic commerce websites, the one or more computer processors, and the program instructions executable by the one or more computer processors, but similar to the analysis under prong two of Step 2A these additional elements are used as a tool to perform the abstract idea. As such, under prong two of Step 2A, claims 2-7, 9-14, and 16-20 are not indicative of integration into a practical application for at least similar reasons as discussed above. Thus, dependent claims 2-7, 9-14, and 16-20 are “directed to” an abstract idea. Next, under Step 2B, similar to the analysis of claims 1, 8, and 15, dependent claims 2-7, 9-14, and 16-20 when analyzed individually and as an ordered combination, merely further define the commonplace business method (i.e. determining a likelihood of customer contacts to travel to the geographic location of the customer and selecting a location where a desired item is available and a contact is located to provide as an option to the customer) being applied on a general-purpose computer and, therefore, do not amount to significantly more than the abstract idea itself. Accordingly, the Examiner concludes that there are no meaningful limitations in the claims that transform the judicial exception into a patent eligible application such that the claims amount to significantly more than the judicial exception itself. The analysis above applies to all statutory categories of invention.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1, 3-4, 6-8, 10-11, 13-15, and 17-20 are rejected under 35 U.S.C. 103 as being unpatentable over Kim et al. (US 11,288,669 B1), hereinafter Kim, in view of Gopinath et al. (US 2014/0279187 A1), hereinafter Gopinath, in further view of Schiff et al. (US 2013/0024465 A1), hereinafter Schiff.
Regarding claim 1, Kim discloses a computer-implemented method of providing recommendations, the method comprising:
-based on user-specified input, determining an item of interest to a user and a geographic region of the user (Kim, see at least: “In 1202, transaction data may be received [i.e. based on user-specified input]. Transaction data may indicate the details of a transaction, such as the identities of the consumer and the merchant, the items purchased [i.e. determining an item of interest to a user], the pick-up and drop-off addresses [i.e. determining a geographic region of the user], the amount of the transaction, the currency of the transaction, and the request for relationship based fulfillment” Col. 36 Ln. 39-44 and “a first user conducts a transaction. The transaction may include purchasing of one or more items from one or more brick and mortar shops from the platform. The transaction may include a request for relationship based fulfillment of the order. In certain embodiments, the order and request for relationship based fulfillment may be included within the same request, but in other embodiments the order and the request may be different requests and may, in fact, be provided to different entities” Col. 21 Ln. 4-12);
-aggregating, within a ledger of a blockchain, the item of interest as available from a plurality of stores of geographic regions that are different from the geographic region of the user (Kim, see at least: “Retail manager 160 may configure a portal for user device 130 to search for localized product inventory [i.e. from a plurality of stores of geographic regions] and obtain product data as well as be configured to provide a seller management tool (i.e., inventory updates [i.e. aggregating, within a ledger of a blockchain, the item of interest as available] and marketing efforts) for retailer 150” Col. 8 Ln. 24-28 and “platform 102, platform 102 may include one or more databases. Such databases may include, for example, user database 112, group database 114, location database 116, association database 118, and/or wallet module 126, which may include one or more databases such as a ledger database” Col. 8 Ln. 52-57 and “Platform 302 may include a ledger for all allocations and/or transactions of the coin/cryptocurrency on platform 302 (e.g., to users, between users, and/or between the users and merchants). In certain embodiments, the ledger may include interface with the blockchain and provide handshake transactions. The ledger may, in certain embodiments, receive data from the records of the blockchain [i.e. within a ledger of a blockchain]” Col. 17 Ln. 51-57 and “The pick-up location may be determined in 604 by, for example, determining the location of the store from the order, by having the user specify a pick-up location, and/or through consulting of one or more databases described herein to determine the location of the store [i.e. that are different from the geographic region of the user]. In certain embodiments, the transaction may include orders from a plurality of locations of a retailer and/or from a plurality of different retailers” Col. 21 Ln. 15-24 and “Application 136 may be, for example, an application configured to allow a user to shop from various retailers (e.g., from brick and mortar retailers)” Col. 6 Ln. 22-24 Examiner notes that the locations of the stores require pickup so the user is not at the store locations associated with the webpages and so the geographic regions associated with the websites are not the same geographic region as the user location [i.e. that are different from the geographic region of the user]);
-retrieving social networking data for the user and processing the social networking data to determine contacts of the user located in the geographic region of the store (Kim, see at least: “User database 112 may be structured to associate social contacts with a specific user. As such, user database 112 may include, for example, phone, social, and/or e-mail contacts of the user (e.g., from user device 130). Such contacts may be contacts that the user has a pre-existing relationship with. For example, such contacts may be contacts that the user has had previously documented contact with (e.g., through social media [i.e. retrieving social networking data for the user and processing the social networking data to determine contacts of the user], phone calls, or message)” Col. 9 Ln. 3-14 and “the platform may limit the amount of possible candidates determined by, for example, limiting the pool of potential candidates to one associated within a specific geographical area. Thus, if a pick-up location for relationship based fulfillment is in a first area (e.g., city district, city, or county), a drop-off location is in a second area, and third and fourth areas separate the first and second areas, only users that are associated with one or more of the areas (e.g., users whose user data indicates that they live, work, visit, or travel through one or more of the areas) may be potential candidates for providing relationship based fulfillment [i.e. located in the geographic region of the store]. Additionally or alternatively, only users that have a relationship with the requesting user may be a potential candidate for providing relationship based fulfillment” Col. 28 Ln. 29-43 and “a requesting user may have purchased an item from retailer 202 and requested relationship based fulfillment of the purchased item. That, is, the requesting user may have purchased or may be about to purchase an item from retailer 202 and may be looking for fulfillment from another” Col. 11 Ln. 43-48);
-determining a likelihood of each contact traveling to the geographic region of the user based on the social networking data (Kim, see at least: “groups 822, 824, and 826 may be candidate groups determined according to the techniques described herein. In certain embodiments, group 822 may include higher quality (e.g., higher likelihood of acceptable) [i.e. determining a likelihood of each contact traveling to the geographic region of the user] candidates than group 824” Col. 29 Ln. 24-28 and “Machine learning model 712 may utilize data 702B to 710 as inputs and may determine parameters 714 and determine groups 716 from the inputs. Determining parameters 714 may include determining the factors that would be utilized in determining the recommendation groups in 716. Such groups may be groups of candidate users for providing relationship based fulfillment for one or more orders 702A. Thus, for example, the parameters of 714 may include … relationship parameters (e.g., how important it is for a requesting user to have a specific personal relationship to the user providing relationship based fulfillment) [i.e. based on the social networking data]” Col. 27 Ln. 5-20);
-selecting a store that offers the item of interest or a similar item and that corresponds to a geographic region of a selected contact, wherein the selected contact of the user has at least a minimum likelihood of traveling to the geographic region of the user (Kim, see at least: “The pick-up location may be determined in 604 by, for example, determining the location of the store from the order, by having the user specify a pick-up location, and/or through consulting of one or more databases described herein to determine the location of the store [i.e. selecting a store that offers the item of interest or a similar item]. In certain embodiments, the transaction may include orders from a plurality of locations of a retailer and/or from a plurality of different retailers” Col. 21 Ln. 15-24 and “Each of the users within groups 822, 824, and 826 may be determined based on various factors described herein. In certain embodiments, the platform may limit the amount of possible candidates determined by, for example, limiting the pool of potential candidates to one associated within a specific geographical area [i.e. and that corresponds to a geographic region of a selected contact]. Thus, if a pick-up location for relationship based fulfillment is in a first area (e.g., city district, city, or county), a drop-off location is in a second area, and third and fourth areas separate the first and second areas, only users that are associated with one or more of the areas (e.g., users whose user data indicates that they live, work, visit, or travel through one or more of the areas) [i.e. wherein the selected contact of the user has at least a minimum likelihood of traveling to the geographic region of the user] may be potential candidates for providing relationship based fulfillment” Col. 28 Ln. 27-43).
Kim does not disclose the plurality of stores being a plurality of electronic commerce websites; the geographic region of the store being geographic regions of the plurality of electronic commerce websites; selecting an electronic commerce website from the plurality of electronic commerce websites; and presenting the selected electronic commerce website to the user as an option for obtaining the item or the similar item.
Gopinath, however, teaches integrating social network data into a web site offering goods or services for sale (i.e. abstract), including the known technique of aggregating the item of interest as available from a plurality of electronic commerce websites of geographic regions that are different from the geographic region of the user (Gopinath, see at least: “at 114 narrows down a list of online retailers offering the item [i.e. aggregating the item of interest as available from a plurality of electronic commerce websites] to a subset of the online retailers that each also provide delivery service to the geographic location of the current determined second user current residence [i.e. hat are different from the geographic region of the user]” [0019] and “The websites of a plurality of different online retailers 210 (including local e-commerce retailers and group discount web sites, etc. as well as those in other countries) [i.e. a plurality of electronic commerce websites of geographic regions that are different from the geographic region of the user] are communicated with via a social network/online retailer website integration Application Programming Interface (API) 212” [0023]);
the known technique of contacts of the user located in the geographic regions of the plurality of electronic commerce websites (Gopinath, see at least: “in response to the first user viewing a $50 coupon for a certain restaurant with a certain style of cuisine that is on sale for a limited time for $25, the gifting application pops-up a list of friends who have each indicated a like or preference for the restaurant or the cuisine in (or determined from) their individual social network profile data. The list may include all the social network contacts (friends, family, associates, professional links, etc.) [i.e. contacts of the user] who meet these parameters, or only a subset of those who have higher relative preferences” [0033] and “at 114 narrows down a list of online retailers offering the item to a subset of the online retailers that each also provide delivery service to the geographic location of the current determined second user current residence [i.e. located in the geographic regions of the plurality of electronic commerce websites]” [0019]);
the known technique of selecting an electronic commerce website from the plurality of electronic commerce websites (Gopinath, see at least: “at 114 narrows down a list of online retailers offering the item to a subset of the online retailers that each also provide delivery service to the geographic location of the current determined second user current residence; and at 116 selects one of the subset of the online retailers [i.e. selecting an electronic commerce website from the plurality of electronic commerce websites] for execution of the additional gift purchase of the item for the second user as the recipient” [0019]); and
the known technique of presenting the selected electronic commerce website to the user as an option for obtaining the item or the similar item (Gopinath, see at least: “at 114 narrows down a list of online retailers offering the item to a subset of the online retailers that each also provide delivery service to the geographic location of the current determined second user current residence; and at 116 selects one of the subset of the online retailers for execution of the additional gift purchase of the item for the second user as the recipient. In some aspects, the selection at 116 is automatic and based on rules, for example selecting one of the subset of the online retailers that has one or more of the lowest transaction cost, the highest customer satisfaction ratings, the shortest estimated shipping time, etc., or a combination of best attributes of a plurality of ranking criteria. In some aspects, the selection at 116 may include presenting a list of the subset of the online retailers and allowing the first user to manually select one [i.e. presenting the selected electronic commerce website to the user as an option for obtaining the item or the similar item]” [0019]). These known techniques are applicable to the method of Kim as they both share characteristics and capabilities, namely, they are directed to integrating social network data into a web site offering goods or services for sale.
It would have been recognized that applying the known techniques of aggregating the item of interest as available from a plurality of electronic commerce websites of geographic regions that are different from the geographic region of the user; contacts of the user located in the geographic regions of the plurality of electronic commerce websites; selecting an electronic commerce website from the plurality of electronic commerce websites; and presenting the selected electronic commerce website to the user as an option for obtaining the item or the similar item, as taught by Gopinath, to the teachings of Kim would have yielded predictable results because the level of ordinary skill in the art demonstrated by the references applied shows the ability to incorporate such references into similar methods. Further, adding the modifications of aggregating the item of interest as available from a plurality of electronic commerce websites of geographic regions that are different from the geographic region of the user; contacts of the user located in the geographic regions of the plurality of electronic commerce websites; selecting an electronic commerce website from the plurality of electronic commerce websites; and presenting the selected electronic commerce website to the user as an option for obtaining the item or the similar item, as taught by Gopinath, into the method of Kim would have been recognized by those of ordinary skill in the art as resulting in an improved method that would automatically transfer all needed relevant purchasing data about the end user and his friend acquired from the social networking site to the e-commerce site (Gopinath, [0031]).
Kim in view of Gopinath does not teach processing the social networking data using natural language processing.
Schiff, however, teaches evaluating entities for a target user (i.e. abstract), including the known technique of processing the social networking data using natural language processing (Schiff, see at least: “The entity evaluation system according to embodiments of the invention may use natural language processing (NPL) [i.e. processing the social networking data using natural language processing], and optionally sentiment analysis, on user generated messages, such as those on Facebook, and Twitter, to understand the content of messages”[0044]). This known technique is applicable to the method of Kim in view of Gopinath as they both share characteristics and capabilities, namely, they are directed to evaluating entities for a target user.
It would have been recognized that applying the known technique of processing the social networking data using natural language processing, as taught by Schiff, to the teachings of Kim in view of Gopinath would have yielded predictable results because the level of ordinary skill in the art demonstrated by the references applied shows the ability to incorporate such references into similar methods. Further, adding the modification of processing the social networking data using natural language processing, as taught by Schiff, into the method of Kim in view of Gopinath would have been recognized by those of ordinary skill in the art as resulting in an improved method that would provide personalized recommendations (Schiff, [0010]).
Regarding claim 3, the combination of Kim/Gopinath/Schiff teaches the method of claim 1. Kim further discloses:
-wherein the likelihood of traveling to the geographic region of the user is determined based on travel history data extracted from the social networking data (Kim, see at least: “groups 822, 824, and 826 may be candidate groups determined according to the techniques described herein. In certain embodiments, group 822 may include higher quality (e.g., higher likelihood of acceptable) [i.e. wherein the likelihood of traveling to the geographic region of the user] candidates than group 824” Col. 29 Ln. 24-28 and “Group database 114 may be a database configured to store groups associated with the user. Such groups may be, for example, interest groups of the user or groups that the user may be interested in (e.g., based on social history of the user, such as locations that the user frequents) [i.e. is determined based on travel history data]. In various embodiments, the data of group database 114 may be at least partially obtained from various social media platforms [i.e. extracted from the social networking data] that the user is a member of” Col. 9 Ln. 57-64).
Kim in view of Gopinath does not teach the travel history data being extracted from the social networking data using the natural language processing.
Schiff, however, teaches evaluating entities for a target user (i.e. abstract), including the known technique of data extracted from the social networking data using the natural language processing (Schiff, see at least: “The entity evaluation system according to embodiments of the invention may use natural language processing (NLP) [i.e. using the natural language processing], and optionally sentiment analysis, on user generated messages, such as those on Facebook, and Twitter, to understand the content of messages [i.e. data extracted from the social networking data]”[0044]). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Kim in view of Gopinath with Schiff for the reasons identified above with respect to claim 1.
Regarding claim 4, the combination of Kim/Gopinath/Schiff teaches the method of claim 1. Kim further discloses:
-wherein the likelihood of traveling to the geographic region of the user is determined based on travel itinerary data extracted from the social networking data (Kim, see at least: “groups 822, 824, and 826 may be candidate groups determined according to the techniques described herein. In certain embodiments, group 822 may include higher quality (e.g., higher likelihood of acceptable) [i.e. wherein the likelihood of traveling to the geographic region of the user] candidates than group 824” Col. 29 Ln. 24-28 and “Group database 114 may be a database configured to store groups associated with the user. Such groups may be, for example, interest groups of the user or groups that the user may be interested in (e.g., based on social history of the user, such as locations that the user frequents) [i.e. is determined based on travel itinerary data]. In various embodiments, the data of group database 114 may be at least partially obtained from various social media platforms [i.e. extracted from the social networking data] that the user is a member of” Col. 9 Ln. 57-64 and “Potential delivering users may be determined based on their relationship to the requesting user, their distance to retailer 202, their distance from the requesting user's delivery address, and/or other such factors. In various embodiments, a platform such as platform 102 may presort potential delivering users based on one or more factors. Such factors may include, for example, the potential delivering user' daily schedule [i.e. is determined based on travel itinerary data], ending location (e.g., place of residence), their current or planned activities, their mode of movement (e.g., driving, biking, walking, ride sharing, or another way they are moving), and/or their current or predicted future location” Col. 11 Ln. 53-64).
Kim in view of Gopinath does not teach the travel itinerary data being extracted from the social networking data using the natural language processing.
Schiff, however, teaches evaluating entities for a target user (i.e. abstract), including the known technique of data extracted from the social networking data using the natural language processing (Schiff, see at least: “The entity evaluation system according to embodiments of the invention may use natural language processing (NPL) [i.e. using the natural language processing], and optionally sentiment analysis, on user generated messages, such as those on Facebook, and Twitter, to understand the content of messages [i.e. data extracted from the social networking data]”[0044]). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Kim in view of Gopinath with Schiff for the reasons identified above with respect to claim 1.
Regarding claim 6, the combination of Kim/Gopinath/Schiff teaches the method of claim 1.
Kim in view of Gopinath does not teach the selecting an electronic commerce website from the plurality of electronic commerce websites including choosing the selected electronic commerce website based at least in part on a purchase history of the user and a quality of the item.
Schiff, however, teaches evaluating entities for a target user (i.e. abstract), including the known technique of the selecting an electronic commerce website from the plurality of electronic commerce websites including choosing the selected electronic commerce website based at least in part on a purchase history of the user and a quality of the item (Schiff, see at least: “such inputs may be used as evaluation data by an entity evaluation system to generate evaluations and aid a personalized recommendations system to provide recommendations [i.e. wherein the selecting an electronic commerce website from the plurality of electronic commerce websites includes] and/or recommendation explanations to users. The entity evaluation system may be operated by the entity evaluation systems, or they may be operating separately or in conjunction with one another. Exemplary evaluation data used by the entity evaluation system may include ratings on a discrete star scale for the quality of a restaurant for a restaurant discovery website/product or answers to personality questions for a match making website/service [i.e. choosing the selected electronic commerce website based at least in part on a quality of the item]” [0038] and “The entity evaluation system according to embodiments of the invention may have a variety of options for the initial ordering of entity evaluations presented to the user including a manually curated category ordering, orderings computed based on the context of the user and their evaluations, optionally including their previous evaluation history [i.e. choosing the selected electronic commerce website based at least in part on a quality of the item], and popularity” [0042]). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Kim in view of Gopinath with Schiff for the reasons identified above with respect to claim 1.
Regarding claim 7, the combination of Kim/Gopinath/Schiff teaches the method of claim 1.
Kim does not disclose the selecting an electronic commerce website from the plurality of electronic commerce websites including choosing the selected electronic commerce website based at least in part on pricing information for the item or the similar item.
Gopinath, however, teaches integrating social network data into a web site offering goods or services for sale (i.e. abstract), including the known technique of the selecting an electronic commerce website from the plurality of electronic commerce websites including choosing the selected electronic commerce website based at least in part on pricing information for the item or the similar item (Gopinath, see at least: “at 114 narrows down a list of online retailers offering the item to a subset of the online retailers that each also provide delivery service to the geographic location of the current determined second user current residence; and at 116 selects one of the subset of the online retailers [i.e. wherein the selecting an electronic commerce website from the plurality of electronic commerce websites includes] for execution of the additional gift purchase of the item for the second user as the recipient. In some aspects, the selection at 116 is automatic and based on rules, for example selecting one of the subset of the online retailers that has one or more of the lowest transaction cost, [i.e. choosing the selected electronic commerce website based at least in part on pricing information for the item or the similar item] the highest customer satisfaction ratings, the shortest estimated shipping time, etc., or a combination of best attributes of a plurality of ranking criteria” [0019]). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Kim with Gopinath for the reasons identified above with respect to claim 1.
Claims 8, 10-11, and 13-14 recite limitations directed towards a system, comprising: one or more computer processors configured to execute operations (Kim, see at least: “Some or all of the various components described herein may be implemented with a combination of processors, memories, and APIs” Col. 5 Ln. 50-52). The rest of the limitations recited in claims 8, 10-11, and 13-14 are parallel in nature to those addressed above for claims 1, 3-4, and 6-7, respectively, and are therefore rejected for those same reasons set forth above in claims 1, 3-4, and 6-7, respectively.
Claims 15, 17-18, 19 and 20 recite limitations directed towards a computer program product comprising one or more computer readable storage media having program instructions embodied therewith, the program instructions executable by one or more computer processors to cause the one or more computer processors to execute operations (Kim, see at least: “The processor 2002 may perform operations such as those described herein. Instructions for performing such operations may be embodied in the memory 2004, on one or more non-transitory computer readable media, or on some other storage device” Col. 43 Ln. 60-64). The rest of the limitations recited in claims 15, 17-18, 19 and 20 are parallel in nature to those addressed above for claims 1, 3-4, 6, and 7, respectively, and are therefore rejected for those same reasons set forth above in claims 1, 3-4, 6, and 7, respectively.
Claims 2, 9, and 16 are rejected under 35 U.S.C. 103 as being unpatentable over Kim, in view of Gopinath, in further view of Schiff, in further view of Henderson et al. (US 2019/0205832 A1), hereinafter Henderson.
Regarding claim 2, the combination of Kim/Gopinath/Schiff teaches the method of claim 1.
The combination of Kim/Gopinath/Schiff does not explicitly teach presenting the selected contact to the user as a shipping option for delivery of the item or the similar item.
Henderson, however, teaches ensuring delivery of packages (i.e. abstract), including the known technique of presenting the selected contact to the user as a shipping option for delivery of the item or the similar item (Henderson, see at least: “FIG. 5 depicts a shipping portal within the checkout module 118. The shipping portal 126 may include a shipping options drop down bar 128. The shipping options drop down bar 128 may include at least three categories, however other categories with fewer options are entirely possible. Particularly, the shipping options drop down bar 128 may include an option to insert a shipping address 130, select a saved address 132, or ship the package 114 of item 120 to a ship safe neighbor 134 (i.e., the designated consignee 108) [i.e. presenting the selected contact to the user as a shipping option for delivery of the item or the similar item]” [0048] and Fig. 5). This known technique is applicable to the method of the combination of Kim/Gopinath/Schiff as they both share characteristics and capabilities, namely, they are directed to ensuring delivery of packages.
It would have been recognized that applying the known technique of presenting the selected contact to the user as a shipping option for delivery of the item or the similar item, as taught by Henderson, to the teachings of the combination of Kim/Gopinath/Schiff would have yielded predictable results because the level of ordinary skill in the art demonstrated by the references applied shows the ability to incorporate such references into similar methods. Further, adding the modification of presenting the selected contact to the user as a shipping option for delivery of the item or the similar item, as taught by Henderson, into the method of the combination of Kim/Gopinath/Schiff would have been recognized by those of ordinary skill in the art as resulting in an improved method that would greatly reducing shipping costs (Henderson, abstract).
Claim 9 recites limitations directed towards a system. The limitations recited in claim 9 are parallel in nature to those addressed above for claim 2, and are therefore rejected for those same reasons set forth above in claim 2.
Claim 16 recites limitations directed towards a computer program product. The limitations recited in claim 16 are parallel in nature to those addressed above for claim 2, and are therefore rejected for those same reasons set forth above in claim 2.
Claims 5 and 12 are rejected under 35 U.S.C. 103 as being unpatentable over Kim, in view of Gopinath, in further view of Schiff, in further view of Haimovitch et al. (US 11,568,459 B2), hereinafter Haimovitch.
Regarding claim 5, the combination of Kim/Gopinath/Schiff teaches the method of claim 1.
The combination of Kim/Gopinath/Schiff does not explicitly teach the item of interest being specified in a wish-list of the user maintained in one or more electronic commerce websites or was previously viewed in one or more electronic commerce websites.
Haimovitch, however, teaches an e-commerce system having social networking aspects (i.e. abstract) including the known technique of the item of interest is specified in a wish-list of the user maintained in one or more electronic commerce websites or was previously viewed in one or more electronic commerce websites (Haimovitch, see at least: “By activating the “Like It” control 361, the member may cause the e-commerce system 30 to add the product to a “Things I Like” or “Like It” personal catalog 333 which maintains a listing of products which the member likes. Similarly, by activating the “Want It” control 363, the member may cause the e-commerce system 30 to add the product to a “Wishlist” or “Want It” personal catalog 335 which maintains a listing of products which the member wants to own” Col. 4 Ln. 50-58). This known technique is applicable to the method of the combination of Kim/Gopinath/Schiff as they both share characteristics and capabilities, namely, they are directed to an e-commerce system having social networking aspects.
It would have been recognized that applying the known technique of the item of interest is specified in a wish-list of the user maintained in one or more electronic commerce websites or was previously viewed in one or more electronic commerce websites, as taught by Haimovitch, to the teachings of the combination of Kim/Gopinath/Schiff would have yielded predictable results because the level of ordinary skill in the art demonstrated by the references applied shows the ability to incorporate such references into similar methods. Further, adding the modification of the item of interest is specified in a wish-list of the user maintained in one or more electronic commerce websites or was previously viewed in one or more electronic commerce websites, as taught by Haimovitch, into the method of the combination of Kim/Gopinath/Schiff would have been recognized by those of ordinary skill in the art as resulting in an improved method that would leverage social networks to promote products (Haimovitch, Col. 2 Ln. 22-23).
Claim 12 recites limitations directed towards a system. The limitations recited in claim 12 are parallel in nature to those addressed above for claim 5, and are therefore rejected for those same reasons set forth above in claim 5.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
-Gillen et al. (US 9,916,557 B1) teaches delivering items in connection with social networks.
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/ARIELLE E WEINER/ Primary Examiner, Art Unit 3689