DETAILED ACTION
Status of Claims
Claims 1-3, 5, 9-10, 16-18, and 20 are currently amended.
Claims 1-20 are currently pending and have been examined.
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 .
Response to Arguments
35 USC 103
Applicant's arguments filed 06/02/2026 with respect to the 35 USC 103 rejection have been fully considered but they are not persuasive. Applicant argues that neither Suprasadachandran Pillai nor Cavanagh disclose the newly amended limitations of claims 1 and 16. Specifically, the use of user preferences and an individualized profile. Examiner respectfully disagrees. Suprsadachandran Pillai discloses not just a simple search query type system, bur rather includes the use of data form user preferences and a user profile that is updated in order to help provide relevant attributes/products. See Suprsadachandran Pillai at col 5, lns 20-31, col 6, lns 15-40, col 8, lns 5-27, and Claim 4.
35 USC 101
Applicant's arguments filed 06/02/2026 with respect to the 35 USC 101 rejection have been fully considered but they are not persuasive. Applicant argues that the claim recites features that integrate the receiving, storing, replacing, and use of the product attributes and personalized product value into a practical application. Applicant argues that the claimed method of local generation of an individualized profile and storage and dynamic replacement of the product attribute values in the individualized profile is a technical solution to a technical problem of personalized product recommendations. Examiner respectfully disagrees. There is no improvement to the “computer” and/or any other technical elements. Rather the improvement is in the product recommendations. An individualized profile and replaced product attribute values stored in the individualized profile is an improvement to the abstract idea of personalized product rankings, again, not to any technology or technical field. There is nothing regarding the local storage nor the “dynamic replacement” that suggests some sort of technical solution in how the additional elements accomplish this. Applicant argues improved user experience due to faster processing and reduced reliance on a server. However, the claim limitations do not recite what the currently claimed additional elements nor any others are actually doing in order to accomplish faster processing. Additionally, the only mention of the server is a data server from which product data/attributes are received. There is no discussion as to reduced reliance on a server. The claimed invention is not commensurate in scope with the alleged improvements in the remarks. The claims merely describe details to what data is received and then subsequently ranked with respect to product attributes and does not rise to a technical solution to a technical problem alleged in the remarks. Examiner recommends amending the clams to reflect the alleged improvements to be commensurate in scope and as supported with the disclosure. However, it is noted without specific amendments to consider, the examiner does not concede this alone may overcome the rejection under 35 USC 101.
Applicant argues that claim 1 recites additional elements which do not merely amount to using a generic computer to perform any abstract idea. Examiner respectfully disagrees. The claimed invention, in light of the disclosure looks to merely implement the business practice of product recommendation via displayed rankings/attribute considerations through the use of generic additional elements. For example, paragraph [0030] and [0124] discloses that the claimed invention is implemented using general purpose computers as well as many different types of well-known devices. The computer may be a device such as a desktop, laptop, PC, MacBook, etc. any suitable electronic device having a suitable display and data communications interface to network. See at least paragraph [0052]. Further, the additional elements are also merely linking the abstract idea to a particular environment (that of computers/user interfaces) – See MPEP 2106.05(h). The claim limitations are being performed in various manners that are well-known and generic to the operation of data retrieval, ranking, and display operations.
For at least these reasons, Examiner maintains the previous 35 USC 101 rejection.
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 without significantly more. The claims recite an abstract idea. This judicial exception is not integrated into a practical application. The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Under Step 1 of the eligibility analysis the claims are directed to statutory categories. MPEP 2106.03. Specifically, the method, as claimed in claims 1-15, is directed to the process. Additionally, the system, as claimed in claims 16-20, is directed to a machine.
While the claims fall within statutory categories, under Step 2A, Prong 1 of the eligibility analysis (MPEP 2106.04), the claimed invention recites the abstract idea of providing product information to a user. Specifically, representative claim 1 recites the abstract idea of:
Receiving product data comprising a plurality of product descriptions and a plurality of product attributes, wherein each of the product attributes is associated with one of the product descriptions and has an attribute type of a plurality of attribute types and an attribute value, and the attribute type describes a feature of the product described by the one of the product descriptions and the attribute value describes a value of the feature of the product;
Receiving user preference parameters wherein each of the user preference parameters corresponds to one of the attribute types and has a preference value;
Generating an initial product ranking of one or more of the product descriptions from the product data based on the product attributes of the product data and the user preference parameters;
Generating a display comprising the initial product ranking and an ordered attribute type list of a least two of the plurality of attribute types;
Displaying information;
Receiving an attribute ranking by the user visually reordering at least two of the attribute types in the ordered attribute type list;
Receiving a personalized product attribute, wherein the personalized product attribute is associated with a one of the product descriptions and has an attribute value and an attribute type equal to an attribute type of a one of the product attributes also associated with the one of the product descriptions;
Generating an individualized profile of personalized product data by replacing the attribute value of the one of the product attributes with the attribute value of the personalized product attribute;
Generating a personalized product ranking of one or more of the product descriptions from the individualized profile based on the product attributes of the personalized product data of the individualized profile having the personalized product attribute, the user preference parameters, and the attribute ranking;
Generating display of the personalized product ranking and a personalized ordered attribute tyle list of at least two of the plurality of attribute types; and
Displaying the display.
Under Step 2A, Prong 1 of the eligibility analysis, it is necessary to evaluate whether the claim recites a judicial exception by referring to subject matter groupings enumerated in MPEP 2106.04(a). The abstract idea identified above is considered to be a certain method of organizing human activity. In this case, the abstract idea recited in representative claim 1 is a certain method of organizing human activity because receiving product data, receiving search parameters, generating an initial product ranking, receiving an attribute ranking, receiving a personalized product attribute, replacing the attribute value of the one of the product attributes with the personalized product attribute value, generating a personalized product ranking, and displaying the personalized product ranking is a sales activity and/or relates to business relations. Thus, representative claim 1 recites an abstract idea.
Under Step 2A, Prong 2 of the eligibility analysis, if it is determined that the claims recite a judicial exception, it is then necessary to evaluate whether the claims recite additional elements that integrate the judicial exception into a practical application of that exception. MPEP 2106.04(d). The courts have identified limitations that did not integrate a judicial exception into a practical application include limitations merely reciting the words “apply it” (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea, as discussed in MPEP 2106.05(f). MPEP 2106.04(d). In this case, representative claim 1 includes additional elements such as a dynamically customized user interface, a product data server, a computing device, an interface, an initial user interface, and a personalized user interface. Although reciting such additional elements, the additional elements do not integrate the abstract idea into a practical application because they merely amount to no more than an instruction to apply the abstract idea using a generic computer or merely use a computer as a tool to perform the abstract idea. These additional elements are described at a high level in Applicant's specification without any meaningful detail about their structure or configuration. Similar to the limitations of Alice, representative claim 1 merely recites a commonplace business method (i.e., providing product rankings) being applied on a general-purpose computer. See MPEP 2106.05(f). Thus, the claimed additional elements are merely generic elements and the implementation of the elements merely amounts to no more than an instruction to apply the abstract idea using a generic computer. Since the additional elements merely include instructions to implement the abstract idea on a generic computer or merely use a generic computer as a tool to perform an abstract idea, the abstract idea has not been integrated into a practical application.
Under Step 2B of the eligibility analysis, if it is determined that the claims recite a judicial exception that is not integrated into a practical application of that exception, it is then necessary to evaluate the additional elements individually and in combination to determine whether they provide an inventive concept (i.e., whether the additional elements amount to significantly more than the exception itself). MPEP 2106.05. In this case, as noted above, the additional elements recited in independent claim 1 are recited and described in a generic manner merely amount to no more than an instruction to apply the abstract idea using a generic computer or merely use a generic computer as a tool to perform an abstract idea.
Even when considered as an ordered combination, the additional elements of representative claim 1 do not add anything that is not already present when they are considered individually. In Alice, the court considered the additional elements “as an ordered combination,” and determined that “the computer components...‘ad[d] nothing. ..that is not already present when the steps are considered separately’... [and] [v]iewed as a whole...[the] claims simply recite intermediated settlement as performed by a generic computer.” Alice Corp. Pty. Ltd. v. CLS Bank Int'l, 573 U.S. 208, 217, (2014) (citing Mayo, 566 U.S. at 79, 101 USPQ2d at 1972). Similarly, when viewed as a whole, representative claim 1 simply conveys the abstract idea itself facilitated by generic computing components. Therefore, under Step 2B of the Alice/Mayo test, there are no meaningful limitations in representative claim 1 that transforms the judicial exception into a patent eligible application such that the claims amount to significantly more than the judicial exception itself.
As such, representative claim 1 is ineligible.
Dependent Claims 2-15 do not aid in the eligibility of independent claim 1. For example, claims 2-15 merely further define the abstract limitations of claim 1.
Furthermore, it is noted that certain dependent claims include additional elements supplemental to those recited in independent claim 1: a mobile device (claims 6, 13, 14), a location module of the mobile device (claims 6, 13), a smartphone and/or wearable device (claim 14). However, these additional elements do not integrate the abstract idea into a practical application because they merely amount to no more than an instruction to apply the abstract idea using a generic computer. These additional elements are merely generic elements and are likewise described in a generic manner in Applicant’s specification. Additionally, the additional elements do not amount to significantly more because they merely amount to no more than an instruction to apply the abstract idea using a generic computer or merely use a generic computer as a tool to perform an abstract idea.
Dependent claims 2-5, 7-12, and 15 further define the same abstract idea noted in claim 1 and do not recite any additional elements other than what is disclosed in claim 1. Therefore, they are considered patent ineligible for the same reasons give above.
Thus, dependent claims 2-15 are also ineligible.
Independent claim 16 recites the same abstract idea represented in representative claim 1. In addition to reciting the additional elements already disclosed in claim 1, Independent Claim 16 recites the additional elements of a system. The additional elements in Independent claim 16 do not integrate the abstract idea into a practical application and do not amount to significantly more than the abstract idea for the reasons described above with respect to claim 1.
Similarly, the dependent claims 17-20 do not recite additional elements supplemental those recited in claims 2-15. Therefore, the additional elements to not integrate the abstract idea into a practical application and do not amount to significantly more than the abstract idea for the reasons described above with respect to claims 2-15, respectively.
Thus, dependent claims 17-20 are also ineligible.
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.
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.
Claim(s) 1, 4, 6, 7, 10-16, and 19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Suprasadachandran Pillai et al. (US 11,488,223) in view of Cavanagh et al. (US 2015/0127637).
Regarding Claims 1 and 16, Suprasadachandran Pillai discloses A method of providing an individually adaptive user interface for listing a plurality of products on a computing device, the method comprising: (See at least Abstract, col 4, lns 37-60)
receiving product data comprising a plurality of product descriptions and a plurality of product attributes from a product data server and storing the product data on the computing device, wherein each of the product attributes is associated with one of the product descriptions and has an attribute type of a plurality of attribute types and an attribute value, and the attribute type describes a feature of the product described by the one of the product descriptions and the attribute value describes a value of the feature of the product; (See at least col 2, lns 47-50 disclosing item information provided by merchant such as item images, item title/name, item attributes, etc., col 3, lns 4-15 disclosing item fields such as name, item rating, selected item details such as price, availability, etc., col 4, lns 16-22 disclosing allow client device to interact with one or more server computers, col 7, lns 1-25 disclosing attributes and values, Fig. 4 and col 3, lns 4-15, col 3, lns 17-35, col 6 lns 35-40, col 8, lns 35-50, and col 11, lns 15-444 all disclosing attribute types such “size” and values such as “size 8”)
receiving user preference parameters via an interface of the computing device, wherein each of the user preference parameters corresponds one of the attribute types and has a preference value; (See at least col 1, lns 50-56 disclosing provide search query, col 2, lns 36-40 disclosing receive query, col 2, lns 50-55 disclosing receive search query, col 3, lns 4-15 disclosing search queries, col 5, lns 20-31, col 6, lns 15-40, col 8, lns 5-27, and Claim 4)
generating an initial product ranking of one or more of the product descriptions from the product data stored on the computing device based on the product attributes of the product data on the computing device and the user preference parameters; (See at least col 2, lns 30-45 disclosing search query results, col 3, ln 17-35, col 11 lns 5-14 disclosing search query for coffee beans and ranking relevant attributes on user interface, col 5, lns 20-31, col 6, lns 15-40, col 8, lns 5-27, and Claim 4)
generating an initial user interface on the computing device comprising the initial product ranking; (See at least col 2, lns 30-45 disclosing search results, col 6, lns 55-67 disclosing base ranking, col 11 lns 5-14 disclosing search query for coffee beans and ranking relevant attributes on user interface)
displaying the initial user interface on the computing device; (See at least col 2, lns 55-60 disclosing search results page displaying search results, col 3, lns 17-35, col 6, lns 55-67 disclosing base ranking, col 11 lns 5-14 disclosing search query for coffee beans and ranking relevant attributes on user interface)
receiving an attribute ranking via the interface of the computing device, wherein the attribute ranking comprises a ranked list of at least two of the plurality of attribute types; (See at least col 3, lns 36-50 disclosing reordering and/or highlighting of elements on search results based on identified relevant attributes, col 6, lns 40-55 disclosing relevant attributes ranked related to search query, col 7 lns 27-55 disclosing manual curation of reviewing relevant attributes to rerank base rankings, col 8, lns 15-20, col 11 lns 15-44)
receiving a personalized product attribute via the interface of the computing device, wherein the personalized product attribute is associated with a one of the product descriptions and has an attribute value and an attribute type equal to an attribute type of a one of the product attributes also associated with the one of the product descriptions; (See at least col 3, lns 4-15 disclosing dynamically modified attribute fields including relevant attributes and expanded relevant attributes ranked based on search query and current user context, col 3, lns 17-35, col 6 lns 35-40 disclosing user provides explicit preferences such as certain size, col 8, lns 35-50 disclosing rerank identified attributes to optimize the rankings for the given user base don user’s persona and intent and reranking results or image to see most important attributes, col 11, lns 15-44)
generating an individualized profile of personalized product data on the computing device by replacing the attribute value of the one of the product attributes stored on the computing device with the attribute value of the personalized product attribute; (See col 2, lns 1-10 disclosing replacing details based on search query, col 8, lns 35-50 disclosing rerank identified attributes to optimize the rankings for the given user base don user’s persona and intent and reranking results or image to see most important attributes, col 5, lns 20-31, col 6, lns 15-40, col 8, lns 5-27, and Claim 4)
generating a personalized product ranking of one or more of the product descriptions from the individualized profile stored on the computing device and based on the product attributes of the personalized product data of the individualized profile having the attribute value of the personalized product attribute, the user preference parameters, and the attribute ranking; (See at least col 2, lns 1-20, col 8, lns 35-50 disclosing rerank identified attributes to optimize the rankings for the given user based on user’s persona and intent and reranking results or image to see most important attributes, col 12, lns 25-30, Fig. 4, 5B, col 5, lns 20-31, col 6, lns 15-40, col 8, lns 5-27, and Claim 4 )
generating a personalized user interface on the computing device comprising the personalized product ranking; and (See at least col 2, lns 1-20, col 8, lns 35-50 disclosing rerank identified attributes to optimize the rankings for the given user based on user’s persona and intent and reranking results or image to see most important attributes)
displaying the personalized user interface on the computing device. (See at least col 2, lns 1-20, col 8, lns 35-50 disclosing rerank identified attributes to optimize the rankings for the given user base don user’s persona and intent and reranking results or image to see most important attributes, col 10, lns 50-55 enabling attributes of importance to the user to be extracted and/or copied from other areas and brought toward top of product overview page for ease of identification of attributes)
Suprasadachandran Pillai does not expressly provide for generating an ordered attribute type list of at least two of the plurality of attribute types; receiving an attribute ranking by the user visually reordering at least of the attribute types in the ordered attribute type list; and generating a personalized ordered attribute type list of at least two of the plurality of attribute types.
However, Cavanagh discloses generating an ordered attribute type list of at least two of the plurality of attribute types; receiving an attribute ranking by the user visually reordering at least of the attribute types in the ordered attribute type list; and generating a personalized ordered attribute type list of at least two of the plurality of attribute types (See at least Fig. 1 disclosing initial search term or attribute (e.g., attribute type) input by user and then system uses that to determine search result attributes/rankings then user is able to do a tuning input on the specific search attributes in order to update and rerank, [0002] disclosing tuning input indicates that search result ranking model is to be adjusted for various specified search result attributes and then determine how each search result is ranked, then dynamically updating the displayed search results as the search result ranking model is tuned for the specified search result attributes, [0032] disclosing dials, sliders, etc. for adjusting attribute parameters and receiving real time results based on those adjustments, [0038] disclosing adjusting list of attributes by increasing/decreasing importance of certain term, type, or factor, etc., [0040], [0041], [0053]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have included the ordered attributes that may be reordered by the user as taught by Cavanagh in the attribute ranking system of Suprasadachandran Pillai because it would help a user better fine tune search results not just by sorting the ranked results by the search engine, but actually dynamically updating information to reflect real changed search results/rankings. See Cavanagh paragraph [0001]-[0003].
Regarding Claims 4 and 19, Suprasadachandran Pillai and Cavanagh teach or suggest all of the limitations of claims 1 and 16. Additionally, Suprasadachandran Pillai discloses wherein each of the attribute types is one of: a binary attribute, and the associated attribute value is either "true" or "false"; a range attribute, and the associated attribute value is any value within a range; an integer attribute, and the associated attribute value is any integer between a minimum value and a maximum value; a single-member attribute, and the associated attribute value is one of a set of two or more members; and a multi-member attribute, and the associated attribute value is one or more of a set of two or more members. (See at least Fig. 4 disclosing single-member attributes such as choosing whether an attribute value is a certain brand or not, range including price from $10-$20, and integer attributes where they are between minimum and maximum value such as selection of star ratings being at least a minimum up to the maximum of 5 stars).
Regarding Claim 6, Suprasadachandran Pillai and Cavanagh teach or suggest all of the limitations of claim 1. Additionally, Suprasadachandran Pillai discloses wherein the computing device comprises a mobile device, and the method further comprises receiving user location data via a location module of the mobile device, and generating personalized product ranking comprises generating the personalized product ranking based on the user location data (See at least col 4, lines 16-27 disclosing client device such as a mobile phone or smartphone, col 5, lines 20-30 disclosing user module determines data associated with a user such as location, col 14, lines 14-27 disclosing ranking and reranking of information based on user information such as location).
Regarding Claim 7, Suprasadachandran Pillai and Cavanagh teach or suggest all of the limitations of claim 1. Additionally, Suprasadachandran Pillai discloses receiving a personalized product attribute via the interface of the computing device, wherein the personalized product attribute is associated with a one of the product descriptions; and wherein the product attributes comprise customer review data, and generating the personalized product ranking comprises generating the personalized product ranking based on the personalized product attribute and the customer review data (See at least col 2, lns 20-30 disclosing modifying identified and ranked attributes including reordering customer reviews, col 2, lns 4-15 disclosing item rating data as part of search query and dynamically modified attribute fields “relevant attributes” and “expanded relevant attributes” and ranking based on search query and current user context, col 3, lns 16-59 disclosing generating rankings using personalized relevant attributes/values regarding user as well as certain user reviews).
Regarding Claim 10, Suprasadachandran Pillai and Cavanagh teach or suggest all of the limitations of claim 1. Additionally, Suprasadachandran Pillai discloses receiving a series of changes to the user preference parameters via the interface of the computing device; and in response to each of the series of changes to the user preference parameters, updating the personalized product ranking, generating the personalized user interface, and displaying the personalized user interface on the computing device. (See at least col 2, lns 30-46 disclosing search query engine configured to receive search queries and process the search queries to determine results based on one or more search query processing algorithms, col 3, lns 4-15 disclosing static fields for different search queries and dynamically modified attribute fields that are dynamically identified and ranked based on the search query and a user context, col 11 lns 66-67 & col 10 lns 1-5 disclosing the number, content, and/or ordering of the content in each of the dynamically modifiable portions of the user interface is adjusted based on the user providing the search query, col 5, lns 20-31, col 6, lns 15-40, col 8, lns 5-27, and Claim 4).
Regarding Claim 11, Suprasadachandran Pillai and Cavanagh teach or suggest all of the limitations of claim 1. Additionally, Suprasadachandran Pillai discloses receiving a series of changes to the attribute ranking via the interface of the computing device; and in response to each of the series of changes to the attribute ranking, updating the personalized product ranking, generating the personalized user interface, and displaying the personalized user interface on the computing device (See at least col 11, lns 15-44 disclosing user interface to allow for reordering of relevant attributes and determine level of relevancy of attributes to the first user by indicating an importance over a certain type of attribute over other attributes for that user).
Regarding Claim 12, Suprasadachandran Pillai and Cavanagh teach or suggest all of the limitations of claim 1. Additionally, Suprasadachandran Pillai discloses receiving a series of personalized product attributes via the interface of the computing device; and in response to each of the series of personalized product attributes, updating the personalized product ranking, generating the personalized user interface, and displaying the personalized user interface on the computing device (See at least col 2 lns 1-30 disclosing continuously updated clustering information related to products corresponding to the search query and/or user persona and/or intent (e.g., historical information related to demographics, shopping habits, intent or stage of shopping mission etc., col 3 lns 17-35 disclosing relevant attribute identification based on user intent/behaviors or user reviews, etc., col 6, lns 25-39 disclosing use of click patterns of user on attributes such as a certain size or user provide explicit preferences such as a certain size (e.g., personalized product attribute is size and associated with product description and value/type (Size 8) to replace other size values that are not size 8 for personalization, col 8, lns 15-27 disclosing suer preferences are explicit preferences that are selected by user to indicate attributes of interest to the user which helps identify and rank relevant attributes for the user, col 8, lns 28-41).
Regarding Claim 13, Suprasadachandran Pillai and Cavanagh teach or suggest all of the limitations of claim 1. Additionally, Suprasadachandran Pillai discloses wherein the computing device comprises a mobile device, and the method further comprises: receiving a series of user location data via a location module of the mobile device; and in response to receiving each of the series of user location data, updating the personalized product ranking based on the received user location data, generating the personalized user interface, and displaying the personalized user interface on the mobile device (See at least col 4, lines 16-27 disclosing client device such as a mobile phone or smartphone, col 5, lines 20-30 disclosing user module determines data associated with a user such as location, col 14, lines 14-27 disclosing ranking and reranking of information based on user information such as location).
Regarding Claim 14, Suprasadachandran Pillai and Cavanagh teach or suggest all of the limitations of claim 1. Additionally, Suprasadachandran Pillai discloses wherein the computing device comprises one of a smartphone and a wearable device (See at least col 4, lns 16-27 disclosing smartphone).
Regarding Claim 15, Suprasadachandran Pillai and Cavanagh teach or suggest all of the limitations of claim 1. Additionally, Suprasadachandran Pillai discloses wherein the product description comprise product descriptions for consumable bioactive products (See at least Fig. 4 & 5A-5B disclosing coffee product (e.g., bioactive product), col 9, lns 1-10 disclosing search for products with certain ingredients, col 11, lns 1-44 disclosing coffee product information such as caffeine content, roast level, etc.).
Claim(s) 2-3, 5, 8-9, 17-18, and 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Suprasadachandran Pillai et al. (US 11,488,223) in view of Cavanagh et al. (US 2015/0127637), and further in view of Kellogg et al. (US 2013/0211927).
Regarding Claims 2 and 17, Suprasadachandran Pillai and Cavanagh teach or suggest all of the limitations of claims 1 and 16. While Suprasadachandran does disclose a user preference parameter and value (col 5, lns 20-31, col 6, lns 15-40, col 8, lns 5-27, and Claim 4), Suprasadachandran Pillai does not expressly provide for wherein: the product descriptions comprise a first product description and a second product description; the product attributes comprise a first set of product attributes associated with the first product description, and a second set of product attributes associated with the second product description, wherein the first set of product attributes and the second set of product attributes each comprise a plurality of common product attributes with corresponding attribute types; the user preference parameters correspond to the attribute types of the common product attributes; the attribute ranking comprises a ranking of the common product attributes; and generating the personalized product ranking on the computing device comprises generating a product- attribute score for each of the common product attributes for each of the first and second product descriptions, scaling each product-attribute score by the rank of the associated common product attribute within the attribute ranking, and generating the personalized product ranking based on each scaled product-attribute score.
However, Kellogg discloses wherein: the product descriptions comprise a first product description and a second product description; the product attributes comprise a first set of product attributes associated with the first product description, and a second set of product attributes associated with the second product description, wherein the first set of product attributes and the second set of product attributes each comprise a plurality of common product attributes with corresponding attribute types; the user preference parameters correspond to the attribute types of the common product attributes; the attribute ranking comprises a ranking of the common product attributes; and generating the personalized product ranking on the computing device comprises generating a product- attribute score for each of the common product attributes for each of the first and second product descriptions, scaling each product-attribute score by the rank of the associated common product attribute within the attribute ranking, and generating the personalized product ranking based on each scaled product-attribute score (See at least paragraph [0031] disclosing scaling transform using min and max values to generate normalized values for each of the plurality of factors, [0095] disclosing factor score, factor value, and percent scale that may be selected and searched for, [0114] disclosing presence and absence of factors as well as scoring and weighting of different factors, [0211] disclosing applying scaling transformation to each of the factors, Fig. 3 & 4 disclosing different attributes with scalable weights, Fig. 7 disclosing results for each decision/possibility based on importance, Fig. 19, Fig. 26).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have included plurality of descriptions and attributes and ranking/scoring common attributes as taught by Kellogg in the attribute ranking system of Suprasadachandran Pillai/Cavanagh because it would help customers better find and focus on what specific category or products they wish to learn about. See at least Kellogg paragraph [0006]-[0007].
Regarding Claims 3 and 18, Suprasadachandran Pillai and Cavanagh teach or suggest all of the limitations of claims 1 and 16. While Suprasadachandran does disclose a user preference parameter and value (col 5, lns 20-31, col 6, lns 15-40, col 8, lns 5-27, and Claim 4), Suprasadachandran Pillai does not expressly provide for wherein the attribute types comprise a mandatory attribute type and the user preference parameters comprise a mandatory user preference parameter corresponding to the mandatory attribute type, and the personalized product ranking comprises only product descriptions with an associated product attribute having an attribute type equal to the mandatory attribute type and an attribute value meeting the preference value of the mandatory preference parameter.
However, Kellogg discloses wherein the attribute types comprise a mandatory attribute type and the user preference parameters comprise a mandatory user preference parameter corresponding to the mandatory attribute type, and the personalized product ranking comprises only product descriptions with an associated product attribute having an attribute type equal to the mandatory attribute type and an attribute value meeting the preference value of the mandatory preference parameter (See at least paragraph [0142] disclosing required vs. optional factors as part of the decision related data).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have included required factors as taught by Kellogg in the attribute ranking system of Suprasadachandran Pillai/Cavanagh because it would help customers better find and focus on what specific category or products they wish to learn about. See at least Kellogg paragraph [0006]-[0007].
Regarding Claims 5 and 20, Suprasadachandran Pillai and Cavanagh teach or suggest all of the limitations of claims 4 and 19. While Suprasadachandran does disclose a user preference parameter and value (col 5, lns 20-31, col 6, lns 15-40, col 8, lns 5-27, and Claim 4), Suprasadachandran Pillai does not expressly provide for wherein generating the personalized product ranking comprises one or more of: for a product attribute with the range attribute type, scaling a first product- attribute score higher than a second product-attribute score, wherein a first distance between the first attribute value and the preference value is less than a second distance between the second attribute value and the preference value; for a product attribute with the integer attribute type, scaling a first product- attribute score higher than a second product-attribute score, wherein a first distance between the first attribute value and the preference value is less than a second distance between the second attribute value and the preference value; and for a product attribute with the multi-member attribute type, scaling a first product-attribute score higher than a second product-attribute score, wherein the first attribute value has more members in common with the preference value than the second attribute value.
However, Kellogg discloses wherein generating the personalized product ranking comprises one or more of: for a product attribute with the range attribute type, scaling a first product- attribute score higher than a second product-attribute score, wherein a first distance between the first attribute value and the preference value is less than a second distance between the second attribute value and the preference value; for a product attribute with the integer attribute type, scaling a first product- attribute score higher than a second product-attribute score, wherein a first distance between the first attribute value and the preference value is less than a second distance between the second attribute value and the preference value; and for a product attribute with the multi-member attribute type, scaling a first product-attribute score higher than a second product-attribute score, wherein the first attribute value has more members in common with the preference value than the second attribute value (See at least paragraph [0031] disclosing applying a scaling transform using the determined minimum and maximum values of the factors, [0095] disclosing factor scores, [0117] disclosing multiple ranges or multiple choice values for defining factors, [0144], [0205], [0211]-[0212] disclosing analyzing value ranges for each factor and identify minimum and max value of each factor than create subjective score generated from customer reviews, applied by the lossless query user or an administrator, reviews including rating int eh range of one or five stars).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have included scaling scores higher based on their range/integer attribute type as taught by Kellogg in the attribute ranking system of Suprasadachandran Pillai/Cavanagh because it would help customers better find and focus on what specific category or products they wish to learn about. See at least Kellogg paragraph [0006]-[0007].
Regarding Claim 8, Suprasadachandran Pillai and Cavanagh teach or suggest all of the limitations of claim 7. Suprasadachandran Pillai does not expressly provide for wherein generating the personalized product ranking based on the personalized product attribute and the customer review data comprises excluding the one of the product descriptions from the personalized product ranking.
However, Kellogg discloses wherein generating the personalized product ranking based on the personalized product attribute and the customer review data comprises excluding the one of the product descriptions from the personalized product ranking (See at least paragraph [0092] disclosing excluding associated data form analysis for rankings of items in search results, [01552] disclosing when removing a factor a new or varied list of choices are based on the altered factors (e.g., excluding one of product descriptions), Fig. 26 and paragraph [0204] disclosing user preference indicators and being able to remove subset of factors from the consideration).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have included excluding product descriptions as taught by Kellogg in the attribute ranking system of Suprasadachandran Pillai/Cavanagh because it would help customers better find and focus on what specific category or products they wish to learn about. See at least Kellogg paragraph [0006]-[0007].
Regarding Claim 9, Suprasadachandran Pillai and Cavanagh teach or suggest all of the limitations of claim 7. Suprasadachandran Pillai does not expressly provide for wherein generating the personalized product ranking based on the personalized product attribute and the customer review data comprises excluding a second of the product descriptions from the personalized product ranking, wherein a product attribute associated with the second of the product descriptions has an attribute type equal to that of the personalized product attribute, and an attribute value similar to that of the personalized product attribute.
However, Kellogg discloses wherein generating the personalized product ranking based on the personalized product attribute and the customer review data comprises excluding a second of the product descriptions from the personalized product ranking, wherein a product attribute associated with the second of the product descriptions has an attribute type equal to that of the personalized product attribute, and an attribute value similar to that of the personalized product attribute (See at least paragraph [0092] disclosing excluding associated data form analysis for rankings of items in search results, [01552] disclosing when removing a factor a new or varied list of choices are based on the altered factors (e.g., excluding one of product descriptions), Fig. 26 and paragraph [0204] disclosing user preference indicators and being able to remove subset of factors from the consideration).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have included excluding product descriptions as taught by Kellogg in the attribute ranking system of Suprasadachandran Pillai/Cavanagh because it would help customers better find and focus on what specific category or products they wish to learn about. See at least Kellogg paragraph [0006]-[0007].
Conclusion
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
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/B.E.B/Examiner, Art Unit 3688
/KELLY S. CAMPEN/Primary Examiner, Art Unit 3691
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