DETAILED ACTION
This action is in reply to the original application filed on 03/21/2025.
Claims 1-17 are rejected.
Claims 1-17 are currently pending and have been examined.
Information Disclosure Statement
Information Disclosure Statement received 04/21/2025 and 10/28/2025 has been reviewed and considered.
Priority
This patent Application claims priority from Foreign Application No. CN202410330697.2 filed 03/21/2024. This benefit has been received and acknowledged and therefore, the instant claims receive the effective filing date of 03/21/2024.
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 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 1-17 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 9 as representative, the claim recites limitations that fall within the certain methods of organizing human activity groupings of abstract ideas, including:
-A non-transitory computer-readable medium having a computer program stored thereon, wherein when the program is executed by a processing apparatus, a service determination method is implemented, wherein the service determination method comprises:
-acquiring service description information corresponding to a target service, wherein the service description information comprises a service type, key field information, and matching field information of the target service;
-determining, from a service database, a similar service corresponding to the target service based on the service type and the key field information;
-determining a ranking corresponding to the similar service based on the matching field information and service description information corresponding to the similar service; and
-outputting the similar service based on the ranking corresponding to the similar service
The above limitations recite the concept of determining, ranking, and providing similar services based on data corresponding to a target service. 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 acquiring service description information corresponding to a target service, wherein the service description information comprises a service type, key field information, and matching field information of the target service; determining a ranking corresponding to the similar service based on the matching field information and service description information corresponding to the similar service; and outputting the similar service based on the ranking corresponding to the similar service are processes that, under their broadest reasonable interpretation, cover a commercial interaction. For example, “acquiring,” “determining,” and “outputting” in the context of this claim encompass advertising, and marketing or sales activities.
Similarly, the limitations of a non-transitory computer-readable medium having a computer program stored thereon, wherein when the program is executed by a processing apparatus, a service determination method is implemented, wherein the service determination method comprises: determining, from a service database, a similar service corresponding to the target service based on the service type and the key field information are processes that, under their broadest reasonable interpretation, cover a commercial interaction. That is, other than reciting that the method is implemented by a processing apparatus executing a computer program stored on a non-transitory computer-readable medium and that the determining is from a service database, nothing in the claim element precludes the step from practically being performed by people. For example, but for the “a non-transitory computer-readable medium,” “a computer program,” “a processing apparatus,” and “a service database” language, “executed” and “determining” 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).
-A non-transitory computer-readable medium having a computer program stored thereon, wherein when the program is executed by a processing apparatus, a service determination method is implemented, wherein the service determination method comprises:
-acquiring service description information corresponding to a target service, wherein the service description information comprises a service type, key field information, and matching field information of the target service;
-determining, from a service database, a similar service corresponding to the target service based on the service type and the key field information;
-determining a ranking corresponding to the similar service based on the matching field information and service description information corresponding to the similar service; and
-outputting the similar service based on the ranking corresponding to the similar service
These limitations are not indicative of integration into a practical application because:
The additional elements of claim 9 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 [0124] of Applicant’s specification – “a client and a server may communicate with each other by using any currently known or future developed network protocol.” Specifically, the additional elements of a non-transitory computer-readable medium, a computer program, a processing apparatus, and a service database are recited at a high-level of generality (i.e. as a generic processor performing the generic computer functions of acquiring data, determining data, and outputting 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 9, 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 9. Examiner notes that claim 1 recites the additional element of a service database, however, claim 1 does not qualify as eligible subject matter for similar reasons as claim 9 indicated above.
Claim 17 is an electronic device reciting similar functions as claim 9. Examiner notes that claim 17 recites the additional elements of an electronic device, a storage apparatus, a computer program, a processing apparatus, and a service database, however, claim 17 does not qualify as eligible subject matter for similar reasons as claim 9 indicated above.
Therefore, claims 1, 9, and 17 do not provide an inventive concept and do not qualify as eligible subject matter.
Dependent claims 2-8 and 10-16, 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 2-8 and 10-16 further fall within the “Certain Methods of Organizing Human Activity” grouping of abstract ideas in that they recite commercial interactions. Dependent claims 5-8 and 13-16 do 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 2-4 and 10-12 recite the additional elements of a service configuration interface, a service information input interface, and the service database, 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 2-8 and 10-16 are not indicative of integration into a practical application for at least similar reasons as discussed above. Thus, dependent claims 2-8 and 10-16 are “directed to” an abstract idea. Next, under Step 2B, similar to the analysis of claims 1, 9, and 17, dependent claims 2-8 and 10-16 when analyzed individually and as an ordered combination, merely further define the commonplace business method (i.e. determining, ranking, and providing similar services based on data corresponding to a target service) 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.
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, 5-6, 9-11, 13-14, and 17 are rejected under 35 U.S.C. 103 as being unpatentable over Jedrzejowicz et al. (US 9,342,559 B1), hereinafter Jedrzejowicz, in view of Pocard et al. (US 2021/0390596 A1), hereinafter Pocard.
Regarding claim 1, Jedrzejowicz discloses a service determination method, comprising:
-acquiring service description information corresponding to a target service, wherein the service description information comprises matching field information of the target service (Jedrzejowicz, see at least: “process 400 may include receiving a search query from a user device (block 405). In some implementations, a user, of a user device 230, may use a browser to navigate to a web page or portal associated with interactive session system 240. The web page or portal may provide a user interface with which the user may interact to find a service in which the user is interested. For example, the web page or portal may provide a search box via which the user may enter one or more search terms, of a search query, that describe the service in which the user is interested [i.e. acquiring service description information corresponding to a target service]” Col. 13 Ln. 57-66 and “interactive session system 240 may match one or more search terms of the search query to keywords that have been previously associated with organizations. As one example, assume that the search query includes the search terms “planting flowers.” In this case, interactive session system 240 may match the search term “planting” to the keyword “plant,” and/or the search term “flowers” to the keyword “flower,” either or both of which have been previously associated with particular organizations that provide gardening services [i.e. wherein the service description information comprises matching field information of the target service]” Col. 14 Ln. 21-30);
-determining, from a service database, a similar service corresponding to the target service based on the service type and the key field information (Jedrzejowicz, see at least: “Process 400 may include generating scores for the organizations using a first scoring function (block 415). For example, interactive session system 240 may generate a score for each (or a subset) of the organizations identified in block 410 [i.e. determining a similar service corresponding to the target service based on the service type]. Interactive session system 240 may use a particular set of signals (referred to as the “first scoring signals”) [i.e. based on the service type and the key field information] to generate a score for each particular organization” Col. 14 Ln. 31-37 and “For example, interactive session system 240 may store the service provider information, received from organization systems 204, in one or more data structures, such as one or more indexes. The service provider information, in the one or more data structures, may be searched [i.e. from a service database], by interactive session system 240, to identify organizations and/or service providers relevant to a search query from a user device 230 and/or to score an organization and/or service provider based on the search query [i.e. determining a similar service corresponding to the target service based on the service type]. Any or all of the service provider information may be searchable and/or otherwise usable by interactive session system 240, and/or may be presented to a user of a user device 230” Col. 11 Ln. 62-67 & Col. 12 Ln. 1-6 and “the first scoring signals may include information regarding a cost charged for services by the particular organization [i.e. based on the key field information]” Col. 16 Ln. 41-43 and “process 300 may include receiving service provider information (block 302). For example, an interactive session system 240 may receive service provider information from organization systems 204. Examples of service provider information, which may be provided by a particular organization system 204, include: an identifier for the organization associated with the particular organization system 204 … a description of the services offered by the organization associated with the particular organization system 204, such as identifiers of the services, keywords associated with the services, costs for obtaining the services, or the like [i.e. the key field information]” Col. 11 Ln. 27-44);
-determining a ranking corresponding to the similar service based on the matching field information and service description information corresponding to the similar service (Jedrzejowicz, see at least: “Process 400 may include forming a ranked list that includes information identifying service providers based on the scores (block 445). For example, interactive session system 240 may sort information identifying the service providers based on the scores for the service providers [i.e. determining a ranking corresponding to the similar service]. Interactive session system 240 may form a ranked list that includes information identifying the service providers based on sorting the information identifying the service providers based on the scores” Col. 19 Ln. 41-48 and “interactive session system 240 may match one or more search terms of the search query to keywords that have been previously associated with organizations. As one example, assume that the search query includes the search terms “planting flowers.” In this case, interactive session system 240 may match the search term “planting” to the keyword “plant,” and/or the search term “flowers” to the keyword “flower,” either or both of which have been previously associated with particular organizations that provide gardening services [i.e. based on service description information corresponding to the similar service]” Col. 14 Ln. 21-30 and “the first scoring signals may include information regarding one or more geographic areas with which the particular organization, or a service provider of the particular organization, is associated. In some situations, a user may seek a service to be provided in a certain geographic area [i.e. based on the matching field information]” Col. 16 Ln. 55-60 and “the first scoring signals may include information regarding areas and/or levels of expertise of service providers associated with the particular organization. Certain service providers may be identified as having a particular level of expertise, such as expert or general knowledge, in particular areas. A higher level of expertise, in an area in which the user is seeking a service, may mean that the user is more likely to be satisfied with the session than a lower level of expertise in the area. Thus, areas and/or levels of expertise, of service providers associated with the particular organization, may be used to generate a score for the particular organization [i.e. based on the matching field information]” Col. 15 Ln. 60-67 & Col. 16 Ln. 1-4); and
-outputting the similar service based on the ranking corresponding to the similar service (Jedrzejowicz, see at least: “Process 400 may include presenting the ranked list (block 425) [i.e. outputting the similar service based on the ranking corresponding to the similar service]. Interactive session system 240 may generate a document, such as a web page, that includes the ranked list” Col. 17 Ln. 33-35 and “Interactive session system 240 may form a ranked list with information identifying the organizations based on sorting the information identifying the organizations based on the scores. In some implementations, an organization with a higher score may be included higher in the ranked list than an organization with a lower score. In some implementations, the ranked list may include information regarding fewer than all of the identified organizations, such as information regarding the top scoring Y (Y>1) organizations and/or information regarding organizations with scores that satisfy a threshold” Col. 17 Ln. 22-32).
Jedrzejowicz does not explicitly disclose the service description information comprises a service type, key field information, and matching field information of the target service.
Pocard, however, teaches matching a customer with a service provider (i.e. abstract), including the known technique of the service description information comprises a service type, key field information, and matching field information of the target service (Pocard, see at least: “the request build engine 134 [i.e. the service description information comprises] is configured to receive certain categorical values and discrete values from the user-customer. The request values are used in combination with the profiles to recommend a user-hair stylist [i.e. of the target service] to the user-customer. For example, a request build engine 134 can obtain user-customer categorical values of: present location, maximum distance willing to travel [i.e. key field information], hair styling service needed [i.e. a service type], preferred salon ambiance [i.e. and matching field information], the subjective importance to the user-customer of certain of the hair stylist's criteria, such as the hair stylist's hair styling skills, the hair stylist's personality, and the hair stylist's favorite brands. The values for all of these categories may be entered by the user-customer through the display 118 on the mobile device 108 to fill out the request or through analysis of the user-generated content, such as from social media” [0072]). This known technique is applicable to the method of Jedrzejowicz as they both share characteristics and capabilities, namely, they are directed to matching a customer with a service provider.
It would have been recognized that applying the known technique of the service description information comprises a service type, key field information, and matching field information of the target service, as taught by Pocard, to the teachings of Jedrzejowicz 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 service description information comprises a service type, key field information, and matching field information of the target service, as taught by Pocard, into the method of Jedrzejowicz would have been recognized by those of ordinary skill in the art as resulting in an improved method that would save a user time finding a qualified and compatible service provider (Pocard, [0056]).
Regarding claim 2, Jedrzejowicz in view of Pocard teaches the method of claim 1. Jedrzejowicz further discloses:
-in response to receiving input information in the service information input interface, generating the service description information corresponding to the service based on the input information and the service type of the service (Jedrzejowicz, see at least: “process 310 may include receiving service provider information (block 312). For example, interactive session system 240 may receive service provider information from organization systems 204 [i.e. in response to receiving input information in the service information input interface]” Col. 12 Ln. 25-28 and “Process 310 may include mapping organizations to topics (block 314). For example, interactive session system 240 may map organizations, associated with organization systems 204, to topics in a topical directory based on the service provider information, information provided by the organizations, information about the organizations, or the like [i.e. based on the input information and the service type of the service]” Col. 12 Ln. 32-37 and “Process 310 may include populating a directory with the service provider information and based on the mapping of the organizations to topics (block 316). For example, interactive session system 240 may create the topical directory by storing information, from the service provider information, relative to the topics in the topical directory [i.e. generating the service description information corresponding to the service based on the input information and the service type of the service]. For each topic, interactive session system 240 may store an identifier for the organization, information regarding the organization, a description of the services offered by the organization [i.e. based on the input information], information associated with service providers employed by or associated with the organization, or the like” Col. 12 Ln. 43-53).
Jedrzejowicz does not explicitly disclose the service description information corresponding to each of the target service and the similar service is determined by: displaying a service configuration interface, wherein the service configuration interface carries a plurality of candidate service types; in response to receiving a selection operation for a candidate service type, determining the candidate service type selected by the selection operation as the service type of the service; and displaying a service information input interface corresponding to the service type of the service.
Pocard, however, teaches matching a customer with a service provider (i.e. abstract), including the known technique of the service description information corresponding to each of the target service and the similar service is determined (Pocard, see at least: “FIG. 7D is one illustrative example of a GUI screen of a mobile device 108 for creating a salon page of the salon database [i.e. wherein the service description information corresponding to each of the target service and the similar service is determined by:]. Field 708 is for displaying the salon location from the previous screen. Field 710 includes one or more modes of contact, including, for example, phone, website, and social media accounts. Field 712 is for selecting a discrete value of the number of seatings available at the salon. Field 714 is for selecting the categorical value of the ambiance of the salon from a drop-down menu, for example” [0095]) by:
the known technique of displaying a service configuration interface, wherein the service configuration interface carries a plurality of candidate service types (Pocard, see at least: “FIG. 7A is one illustrative example of a GUI screen of a mobile device 108 for creating a user-hair stylist profile [i.e. displaying a service configuration interface]. Input field 702 includes three buttons that allows the user-hair stylist to identify the categorical value of the location type [i.e. wherein the service configuration interface carries a plurality of candidate service types]. Further screens may prompt the user-hair stylist to enter additional categorical values depending on the location type. For a physical salon location, further screens may require input of categorical values for the address, the number of total seatings in the salon, the salon ambiance from a drop down menu. For a mobile location, screens may require a discrete value for a maximum distance willing to travel to a customer. For both location types, input may be required of categorical values for contact information, such as phone, email, website, social medium accounts, an appointment calendar showing open times, prices for each hair styling skill, or combinations. Next, input field 704 can contain any number, six, for example, of drop-down menus for identifying the categorical values of the hair stylist's favorite product brands” [0092]);
the known technique of, in response to receiving a selection operation for a candidate service type, determining the candidate service type selected by the selection operation as the service type of the service (Pocard, see at least: “FIG. 7A is one illustrative example of a GUI screen of a mobile device 108 for creating a user-hair stylist profile. Input field 702 includes three buttons that allows the user-hair stylist to identify the categorical value of the location type. Further screens may prompt the user-hair stylist to enter additional categorical values depending on the location type [i.e. in response to receiving a selection operation for a candidate service type, determining the candidate service type selected by the selection operation as the service type of the service]. For a physical salon location, further screens may require input of categorical values for the address, the number of total seatings in the salon, the salon ambiance from a drop down menu. For a mobile location, screens may require a discrete value for a maximum distance willing to travel to a customer. For both location types, input may be required of categorical values for contact information, such as phone, email, website, social medium accounts, an appointment calendar showing open times, prices for each hair styling skill, or combinations. Next, input field 704 can contain any number, six, for example, of drop-down menus for identifying the categorical values of the hair stylist's favorite product brands” [0092]);
the known technique of displaying a service information input interface corresponding to the service type of the service (Pocard, see at least: “FIG. 7A is one illustrative example of a GUI screen of a mobile device 108 for creating a user-hair stylist profile. Input field 702 includes three buttons that allows the user-hair stylist to identify the categorical value of the location type. Further screens may prompt the user-hair stylist to enter additional categorical values depending on the location type [i.e. displaying a service information input interface corresponding to the service type of the service]. For a physical salon location, further screens may require input of categorical values for the address, the number of total seatings in the salon, the salon ambiance from a drop down menu. For a mobile location, screens may require a discrete value for a maximum distance willing to travel to a customer. For both location types, input may be required of categorical values for contact information, such as phone, email, website, social medium accounts, an appointment calendar showing open times, prices for each hair styling skill, or combinations. Next, input field 704 can contain any number, six, for example, of drop-down menus for identifying the categorical values of the hair stylist's favorite product brands” [0092]); and
the known technique of, in response to receiving input information in the service information input interface, generating the service description information corresponding to the service based on the input information and the service type of the service (Pocard, see at least: “FIG. 7A is one illustrative example of a GUI screen of a mobile device 108 for creating a user-hair stylist profile [i.e. in response to receiving input information in the service information input interface, generating the service description information corresponding to the service]. Input field 702 includes three buttons that allows the user-hair stylist to identify the categorical value of the location type [i.e. based on the input information and the service type of the service]. Further screens may prompt the user-hair stylist to enter additional categorical values depending on the location type. For a physical salon location, further screens may require input of categorical values for the address, the number of total seatings in the salon, the salon ambiance from a drop down menu. For a mobile location, screens may require a discrete value for a maximum distance willing to travel to a customer. For both location types, input may be required of categorical values for contact information, such as phone, email, website, social medium accounts, an appointment calendar showing open times, prices for each hair styling skill, or combinations. Next, input field 704 can contain any number, six, for example, of drop-down menus for identifying the categorical values of the hair stylist's favorite product brands” [0092]). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Jedrzejowicz with Pocard for the reasons identified above with respect to claim 1.
Regarding claim 3, Jedrzejowicz in view of Pocard teaches the method of claim 1. Jedrzejowicz further discloses:
-wherein the determining, from the service database, the similar service corresponding to the target service based on the service type and the key field information (Jedrzejowicz, see at least: “Process 400 may include generating scores for the organizations using a first scoring function (block 415). For example, interactive session system 240 may generate a score for each (or a subset) of the organizations identified in block 410 [i.e. determining a similar service corresponding to the target service based on the service type]. Interactive session system 240 may use a particular set of signals (referred to as the “first scoring signals”) [i.e. based on the service type and the key field information] to generate a score for each particular organization” Col. 14 Ln. 31-37 and “For example, interactive session system 240 may store the service provider information, received from organization systems 204, in one or more data structures, such as one or more indexes. The service provider information, in the one or more data structures, may be searched [i.e. from a service database], by interactive session system 240, to identify organizations and/or service providers relevant to a search query from a user device 230 and/or to score an organization and/or service provider based on the search query [i.e. determining a similar service corresponding to the target service based on the service type]. Any or all of the service provider information may be searchable and/or otherwise usable by interactive session system 240, and/or may be presented to a user of a user device 230” Col. 11 Ln. 62-67 & Col. 12 Ln. 1-6 and “the first scoring signals may include information regarding a cost charged for services by the particular organization [i.e. based on the key field information]” Col. 16 Ln. 41-43 and “process 300 may include receiving service provider information (block 302). For example, an interactive session system 240 may receive service provider information from organization systems 204. Examples of service provider information, which may be provided by a particular organization system 204, include: an identifier for the organization associated with the particular organization system 204 … a description of the services offered by the organization associated with the particular organization system 204, such as identifiers of the services, keywords associated with the services, costs for obtaining the services, or the like [i.e. the key field information]” Col. 11 Ln. 27-44) comprises:
-matching the service type of the target service with a service in the service database to determine a candidate service (Jedrzejowicz, see at least: “interactive session system 240 may match one or more search terms of the search query to keywords that have been previously associated with organizations. As one example, assume that the search query includes the search terms “planting flowers.” In this case, interactive session system 240 may match the search term “planting” to the keyword “plant,” and/or the search term “flowers” to the keyword “flower,” either or both of which have been previously associated with particular organizations that provide gardening services [i.e. matching the service type of the target service with a service to determine a candidate service]” Col. 14 Ln. 21-30 and “For example, interactive session system 240 may store the service provider information, received from organization systems 204, in one or more data structures, such as one or more indexes. The service provider information, in the one or more data structures, may be searched [i.e. a service in the service database], by interactive session system 240, to identify organizations and/or service providers relevant to a search query from a user device 230 and/or to score an organization and/or service provider based on the search query. Any or all of the service provider information may be searchable and/or otherwise usable by interactive session system 240, and/or may be presented to a user of a user device 230” Col. 11 Ln. 62-67 & Col. 12 Ln. 1-6);
-acquiring key field information corresponding to the candidate service (Jedrzejowicz, see at least: “interactive session system 240 may match one or more search terms of the search query to keywords that have been previously associated with organizations. As one example, assume that the search query includes the search terms “planting flowers.” In this case, interactive session system 240 may match the search term “planting” to the keyword “plant,” and/or the search term “flowers” to the keyword “flower,” either or both of which have been previously associated with particular organizations that provide gardening services [i.e. acquiring key field information corresponding to the candidate service]” Col. 14 Ln. 21-30); and
-in a case where the key field information corresponding to the candidate service is the same as the key field information corresponding to the target service, determining the candidate service as the similar service (Jedrzejowicz, see at least: “Process 400 may include identifying organizations based on the search query (block 410). For example, interactive session system 240 may perform a search of the service provider information to identify organizations that are relevant to the one or more search terms of the search query [i.e. determining the candidate service as the similar service]” Col. 14 Ln. 3-7 and “interactive session system 240 may match one or more search terms of the search query to keywords that have been previously associated with organizations. As one example, assume that the search query includes the search terms “planting flowers.” In this case, interactive session system 240 may match the search term “planting” to the keyword “plant,” and/or the search term “flowers” to the keyword “flower,” either or both of which have been previously associated with particular organizations that provide gardening services [i.e. in a case where the key field information corresponding to the candidate service is the same as the key field information corresponding to the target service, determining the candidate service as the similar service]” Col. 14 Ln. 21-30).
Regarding claim 5, Jedrzejowicz in view of Pocard teaches the method of claim 1. Jedrzejowicz further discloses:
-wherein the determining the ranking corresponding to the similar service based on the matching field information and the service description information corresponding to the similar service (Jedrzejowicz, see at least: “Process 400 may include forming a ranked list that includes information identifying service providers based on the scores (block 445). For example, interactive session system 240 may sort information identifying the service providers based on the scores for the service providers [i.e. determining a ranking corresponding to the similar service]. Interactive session system 240 may form a ranked list that includes information identifying the service providers based on sorting the information identifying the service providers based on the scores” Col. 19 Ln. 41-48 and “interactive session system 240 may match one or more search terms of the search query to keywords that have been previously associated with organizations. As one example, assume that the search query includes the search terms “planting flowers.” In this case, interactive session system 240 may match the search term “planting” to the keyword “plant,” and/or the search term “flowers” to the keyword “flower,” either or both of which have been previously associated with particular organizations that provide gardening services [i.e. based on service description information corresponding to the similar service]” Col. 14 Ln. 21-30 and “the first scoring signals may include information regarding areas and/or levels of expertise of service providers associated with the particular organization. Certain service providers may be identified as having a particular level of expertise, such as expert or general knowledge, in particular areas. A higher level of expertise, in an area in which the user is seeking a service, may mean that the user is more likely to be satisfied with the session than a lower level of expertise in the area. Thus, areas and/or levels of expertise, of service providers associated with the particular organization, may be used to generate a score for the particular organization [i.e. based on the matching field information]” Col. 15 Ln. 60-67 & Col. 16 Ln. 1-4) comprises:
-determining a similarity between the target service and the similar service based on the matching field information and the service description information corresponding to the similar service (Jedrzejowicz, see at least: “Process 400 may include forming a ranked list that includes information identifying service providers based on the scores (block 445). For example, interactive session system 240 may sort information identifying the service providers based on the scores for the service providers [i.e. determining a similarity between the target service and the similar service]. Interactive session system 240 may form a ranked list that includes information identifying the service providers based on sorting the information identifying the service providers based on the scores” Col. 19 Ln. 41-48 and “the first scoring signals may include information regarding one or more geographic areas with which the particular organization, or a service provider of the particular organization, is associated. In some situations, a user may seek a service to be provided in a certain geographic area [i.e. based on the matching field information]” Col. 16 Ln. 55-60 and “the first scoring signals may include information regarding areas and/or levels of expertise of service providers associated with the particular organization. Certain service providers may be identified as having a particular level of expertise, such as expert or general knowledge, in particular areas. A higher level of expertise, in an area in which the user is seeking a service, may mean that the user is more likely to be satisfied with the session than a lower level of expertise in the area. Thus, areas and/or levels of expertise, of service providers associated with the particular organization, may be used to generate a score for the particular organization [i.e. based on the matching field information]” Col. 15 Ln. 60-67 & Col. 16 Ln. 1-4); and
-ranking the respective similar services in an order of the similarity from high to low (Jedrzejowicz, see at least: “Interactive session system 240 may form a ranked list with information identifying the organizations based on sorting the information identifying the organizations based on the scores. In some implementations, an organization with a higher score may be included higher in the ranked list than an organization with a lower score [i.e. ranking the respective similar services in an order of the similarity from high to low]. In some implementations, the ranked list may include information regarding fewer than all of the identified organizations, such as information regarding the top scoring Y (Y>1) organizations and/or information regarding organizations with scores that satisfy a threshold” Col. 17 Ln. 22-32).
Regarding claim 6, Jedrzejowicz in view of Pocard teaches the method of claim 5.
Jedrzejowicz does not explicitly disclose the matching field information comprises field values of a plurality of matching fields; and the determining the similarity between the target service and the similar service based on the matching field information and the service description information corresponding to the similar service comprises: for each matching field, determining a similarity between the target service and the similar service under the matching field based on a field value of the matching field corresponding to the target service and a field value of the matching field in the service description information corresponding to the similar service; and performing a weighting process on the similarity under the matching field based on a weight corresponding to each matching field to obtain the similarity between the target service and the similar service.
Pocard, however, teaches matching a customer with a service provider (i.e. abstract), including the known technique of the matching field information comprising field values of a plurality of matching fields (Pocard, see at least: “FIG. 7D is one illustrative example of a GUI screen of a mobile device 108 for creating a salon page of the salon database. Field 708 is for displaying the salon location from the previous screen. Field 710 includes one or more modes of contact, including, for example, phone, website, and social media accounts. Field 712 is for selecting a discrete value of the number of seatings available at the salon. Field 714 is for selecting the categorical value of the ambiance of the salon from a drop-down menu, for example [i.e. wherein the matching field information comprises field values of a plurality of matching fields]” [0095] and “the request build engine 134 is configured to receive certain categorical values and discrete values from the user-customer. The request values are used in combination with the profiles to recommend a user-hair stylist to the user-customer. For example, a request build engine 134 can obtain user-customer categorical values of: present location, maximum distance willing to travel, hair styling service needed, preferred salon ambiance, the subjective importance to the user-customer of certain of the hair stylist's criteria, such as the hair stylist's hair styling skills, the hair stylist's personality, and the hair stylist's favorite brands [i.e. wherein the matching field information comprises field values of a plurality of matching fields]. The values for all of these categories may be entered by the user-customer through the display 118 on the mobile device 108 to fill out the request or through analysis of the user-generated content, such as from social media” [0072]); and
the known technique of the determining the similarity between the target service and the similar service based on the matching field information and the service description information corresponding to the similar service (Pocard, see at least: “the method 400 applies weighting formulas on the filtered set of hair stylists resulting from block 412. For example, the matching engine 136 will next select the categorical values rated highest in importance by the user-customer. The matching engine can set coefficients to apply a weighting algorithm to the categorical values as indicated in the request. A high importance, for example, can indicate that 75% to 100% of the hair stylist categorical values are included in the categorical values requested by the user-customer. However, not all categorical values can be weighted the same when they are designated at the same importance level. For example, high importance for matching the hair stylists whose hair styling skills include each of the hair styling services from input field 1006 requested by the user-customer can mean a 100% match [i.e. the determining the similarity between the target service and the similar service based on the matching field information and the service description information corresponding to the similar service comprises:]. High importance for matching personality categorical values to hair stylists' personality categorical values can mean a 50% to 75% match. The matching engine subsequently applies weighting formulas to the medium importance categorical values, and last, the low importance categorical values” [0115]) comprises:
the known technique of, for each matching field, determining a similarity between the target service and the similar service under the matching field based on a field value of the matching field corresponding to the target service and a field value of the matching field in the service description information corresponding to the similar service (Pocard, see at least: “the method 400 applies weighting formulas on the filtered set of hair stylists resulting from block 412. For example, the matching engine 136 will next select the categorical values rated highest in importance by the user-customer. The matching engine can set coefficients to apply a weighting algorithm to the categorical values as indicated in the request. A high importance, for example, can indicate that 75% to 100% of the hair stylist categorical values are included in the categorical values requested by the user-customer. However, not all categorical values can be weighted the same when they are designated at the same importance level. For example, high importance for matching the hair stylists whose hair styling skills include each of the hair styling services from input field 1006 requested by the user-customer can mean a 100% match. High importance for matching personality categorical values to hair stylists' personality categorical values can mean a 50% to 75% match [i.e. determining a similarity between the target service and the similar service under the matching field based on a field value of the matching field corresponding to the target service and a field value of the matching field in the service description information corresponding to the similar service]. The matching engine subsequently applies weighting formulas to the medium importance categorical values, and last, the low importance categorical values [i.e. for each matching field]” [0115]); and
the known technique of performing a weighting process on the similarity under the matching field based on a weight corresponding to each matching field to obtain the similarity between the target service and the similar service (Pocard, see at least: “the method 400 applies weighting formulas on the filtered set of hair stylists resulting from block 412. For example, the matching engine 136 will next select the categorical values rated highest in importance by the user-customer. The matching engine can set coefficients to apply a weighting algorithm to the categorical values as indicated in the request. A high importance, for example, can indicate that 75% to 100% of the hair stylist categorical values are included in the categorical values requested by the user-customer. However, not all categorical values can be weighted the same when they are designated at the same importance level. For example, high importance for matching the hair stylists whose hair styling skills include each of the hair styling services from input field 1006 requested by the user-customer can mean a 100% match. High importance for matching personality categorical values to hair stylists' personality categorical values can mean a 50% to 75% match. The matching engine subsequently applies weighting formulas to the medium importance categorical values, and last, the low importance categorical values [i.e. performing a weighting process on the similarity under the matching field based on a weight corresponding to each matching field]. After each requested categorical value, the matching engine can filter out the user-hair stylists that do not meet the weighting criteria for importance. The matching engine 136 tabulates the final matches for hair stylists as percentages. From block 414, the method 400 enters block 416 [i.e. to obtain the similarity between the target service and the similar service]” [0115]). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Jedrzejowicz with Pocard for the reasons identified above with respect to claim 1.
Claims 9-11 and 13-14 recite limitations directed towards a non-transitory computer-readable medium having a computer program stored thereon, wherein when the program is executed by a processing apparatus, a service determination method is implemented (Jedrzejowicz, see at least: “The memory 704 may also be another form of computer-readable medium, such as a magnetic or optical disk. A computer-readable medium may refer to a non-transitory memory device” Col. 24 Ln. 44-47). The limitations recited in claims 9-11 and 13-14 are parallel in nature to those addressed above for claims 1-3 and 5-6, respectively, and are therefore rejected for those same reasons set forth above in claims 1-3 and 5-6, respectively.
Claim 17 recites limitations directed towards an electronic device, comprising: a storage apparatus, having a computer program stored thereon; and a processing apparatus, configured to execute the computer program in the storage apparatus, so as to implement a service determination method (Jedrzejowicz, see at least: “The computer program product may also contain instructions that, when executed, perform one or more methods, such as those described herein. The information carrier is a computer or machine-readable medium, such as memory 704, storage device 706, or memory on processor 702” Col. 24 Ln. 58-63). The limitations recited in claim 17 are parallel in nature to those addressed above for claim 1, and are therefore rejected for those same reasons set forth above in claim 1.
Claims 4 and 12 are rejected under 35 U.S.C. 103 as being unpatentable over Jedrzejowicz, in view of Pocard, in further view of Majumder et al. (US 10,497,012 B1), hereinafter Majumder.
Regarding claim 4, Jedrzejowicz in view of Pocard teaches the method of claim 3.
Jedrzejowicz in view of Pocard does not explicitly teach the service type comprises a multi-level type label, and the matching the service type of the target service with the service in the service database to determine the candidate service comprises: determining a detection type corresponding to the target service based on the service type of the target service, wherein the detection type is initially set as the service type; determining a detection service under the detection type in the service database; and in a case where a number of the detection service is less than a preset threshold, taking other level type labels except for a bottom-level type label in the detection type as a new detection type, and returning to the step of determining the detection service under the detection type in the service database, until a sum of the number of detection services under the respective detection types is greater than or equal to the preset threshold, wherein the candidate service comprises the detection services under the respective detection types.
Majumder, however, teaches providing a set of recommendations (i.e. Col. 3 Ln. 40-47), including the known technique of the service type comprising a multi-level type label, and the matching the service type of the target service with the service in the service database to determine the candidate service (Majumder, see at least: “the offers can be part of an offering hierarchy. For products offered through an electronic marketplace, this can include products of certain brand that are part of a general or global classification, such as apparel or electronics, as well as part of a category such as casual attire and a sub-category such as t-shirts, among various other such options. While there may not be enough data for accurate bias determinations for a specific t-shirt, for example, going up one level in the offering hierarchy [i.e. wherein the service type comprises a multi-level type label] to all t-shirts will generally provide more data, as will going up to the category level for casual attire and the global classification for attire” Col. 3 Ln. 5-16 and “As known for such displays, a search query can be received to a search field 102 that can cause related items to be located and displayed as a list of search results 104 that are typically ranked by relevance. Similar displays can be obtained through other mechanisms as well, such as by receiving a selection to browse a page associated with a category of items. The display shows an example listing of a relevant portion 106 of an offering hierarchy [i.e. and the matching the service type of the target service with the service in the service database to determine the candidate service], where the content displayed can be updated by going “up” the hierarchy to a parent category or drilling “down” into the hierarchy by selecting a sub-category. This display also includes a set of recommendations 108 that may or may not be relevant to the particular search query” Col. 3 Ln. 30-42) comprises:
the known technique of determining a detection type corresponding to the target service based on the service type of the target service, wherein the detection type is initially set as the service type (Majumder, see at least: “As known for such displays, a search query can be received to a search field 102 that can cause related items to be located and displayed as a list of search results 104 that are typically ranked by relevance [i.e. determining a detection type corresponding to the target service based on the service type of the target service]. Similar displays can be obtained through other mechanisms as well, such as by receiving a selection to browse a page associated with a category of items. The display shows an example listing of a relevant portion 106 of an offering hierarchy, where the content displayed can be updated by going “up” the hierarchy to a parent category or drilling “down” into the hierarchy by selecting a sub-category [i.e. wherein the detection type is initially set as the service type]. This display also includes a set of recommendations 108 that may or may not be relevant to the particular search query” Col. 3 Ln. 30-42);
the known technique of determining a detection service under the detection type in the service database (Majumder, see at least: “As known for such displays, a search query can be received to a search field 102 that can cause related items to be located and displayed as a list of search results 104 that are typically ranked by relevance. Similar displays can be obtained through other mechanisms as well, such as by receiving a selection to browse a page associated with a category of items. The display shows an example listing of a relevant portion 106 of an offering hierarchy, where the content displayed can be updated by going “up” the hierarchy to a parent category or drilling “down” into the hierarchy by selecting a sub-category [i.e. determining a detection service under the detection type]. This display also includes a set of recommendations 108 that may or may not be relevant to the particular search query” Col. 3 Ln. 30-42 and “For content, such as offers, where it may be desirable to prioritize by performance, it can be desirable to train a bias model as discussed herein. A data store 424 can store the relevant action and position data, which can be analyzed by a training component 420 that can use the data to train, test, and/or refine the model over time. This can include using the data for various levels of the offering hierarchy [i.e. in the service database] to increase the size of the data pool as discussed and suggested elsewhere herein” Col. 11 Ln. 54-61); and
the known technique of, in a case where a number of the detection service is less than a preset threshold, taking other level type labels except for a bottom-level type label in the detection type as a new detection type, and returning to the step of determining the detection service under the detection type in the service database, until a sum of the number of detection services under the respective detection types is greater than or equal to the preset threshold, wherein the candidate service comprises the detection services under the respective detection types (Majumder, see at least: “the data for particular content can be analyzed and the relevant nodes of the content hierarchy determined 608. As mentioned, this can include determining the relevant sub-category, category, and global classification [i.e. and returning to the step of determining the detection service under the detection type in the service database] in one example, although the various categories and classifications can depend on the organization of the hierarchy. Further, in some embodiments the analysis does not need to include data for every level of the hierarchy, but can include a fixed number of levels, nodes up to a certain level [i.e. taking other level type labels except for a bottom-level type label in the detection type as a new detection type], nodes until a minimum amount of data is available, etc. [i.e. in a case where a number of the detection service is less than a preset threshold … until a sum of the number of detection services under the respective detection types is greater than or equal to the preset threshold, wherein the candidate service comprises the detection services under the respective detection types] Once the appropriate nodes are determined, the action and position data can be aggregated 610, or rolled up from child nodes, for each of those nodes determined nodes. The bias function or model can then be trained 612 using the data for each of the levels. As discussed, this can include determining the relevance and bias values for each level of the hierarchy, then using the relative weightings from the model to determine the appropriate position bias factor for a specific instance of content” Col. 13 Ln. 28-45 and “While there may not be enough data for accurate bias determinations for a specific t-shirt, for example, going up one level in the offering hierarchy to all t-shirts will generally provide more data [i.e. taking other level type labels except for a bottom-level type label in the detection type as a new detection type], as will going up to the category level for casual attire and the global classification for attire” Col. 3 Ln. 11-16). These known techniques are applicable to the method of Jedrzejowicz in view of Pocard as they both share characteristics and capabilities, namely, they are directed to providing a set of recommendations.
It would have been recognized that applying the known techniques of the service type comprising a multi-level type label, and the matching the service type of the target service with the service in the service database to determine the candidate service comprises: determining a detection type corresponding to the target service based on the service type of the target service, wherein the detection type is initially set as the service type; determining a detection service under the detection type in the service database; and in a case where a number of the detection service is less than a preset threshold, taking other level type labels except for a bottom-level type label in the detection type as a new detection type, and returning to the step of determining the detection service under the detection type in the service database, until a sum of the number of detection services under the respective detection types is greater than or equal to the preset threshold, wherein the candidate service comprises the detection services under the respective detection types, as taught by Majumder, to the teachings of Jedrzejowicz in view of Pocard 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 the service type comprising a multi-level type label, and the matching the service type of the target service with the service in the service database to determine the candidate service comprises: determining a detection type corresponding to the target service based on the service type of the target service, wherein the detection type is initially set as the service type; determining a detection service under the detection type in the service database; and in a case where a number of the detection service is less than a preset threshold, taking other level type labels except for a bottom-level type label in the detection type as a new detection type, and returning to the step of determining the detection service under the detection type in the service database, until a sum of the number of detection services under the respective detection types is greater than or equal to the preset threshold, wherein the candidate service comprises the detection services under the respective detection types, as taught by Majumder, into the method of Jedrzejowicz in view of Pocard would have been recognized by those of ordinary skill in the art as resulting in an improved method that would provide relevant recommendations to a user (Majumder, Col. 3 Ln. 40-47).
Claim 12 recites limitations directed towards a non-transitory computer-readable medium. The limitations recited in claims 12 are parallel in nature to those addressed above for claim 4, and are therefore rejected for those same reasons set forth above in claim 4.
Claims 7 and 15 are rejected under 35 U.S.C. 103 as being unpatentable over Jedrzejowicz, in view of Pocard, in further view of Maya et al. (US 12,164,591 B1), hereinafter Maya.
Regarding claim 7, Jedrzejowicz in view of Pocard teaches the method of claim 1. Jedrzejowicz further discloses:
-wherein the similar service is determined by:
-acquiring browsing information of a historical service within a historical period of time and a user feature that has been authorized by a user (Jedrzejowicz, see at least: “the first scoring signals may include information regarding prior sessions between the user and the particular organization [i.e. acquiring browsing information of a historical service within a historical period of time]. Previous favorable (or unfavorable) interactions between the user and the particular organization may be indicative of future favorable (or unfavorable) interactions between the user and the particular organization. For example, if the user has had prior sessions with service providers, of the particular organization, that have been favorable—which may be determined via favorable user ratings, favorable user reviews, [i.e. and a user feature that has been authorized by a user] a quantity of sessions between the user and the particular organization (e.g., a quantity that satisfies a threshold), a frequency of sessions between the user and the particular organization (e.g., a frequency that increases over time), and/or a length of sessions between the user and the particular organization (e.g., the sessions increase in length over time)—this may be indicative that the particular organization will continue to provide favorable services to the user via future sessions” Col. 15 Ln. 39-56 and “Process 400 may include generating scores for the organizations using a first scoring function (block 415). For example, interactive session system 240 may generate a score for each (or a subset) of the organizations identified in block 410. Interactive session system 240 may use a particular set of signals (referred to as the “first scoring signals”) to generate a score for each particular organization” Col. 14 Ln. 31-37);
-determining a matching degree between the user and the historical service based on the browsing information of the historical service and the user feature (Jedrzejowicz, see at least: “the first scoring signals may include information regarding prior sessions between the user and the particular organization. Previous favorable (or unfavorable) interactions between the user and the particular organization may be indicative of future favorable (or unfavorable) interactions between the user and the particular organization [i.e. determining a matching degree between the user and the historical service based on the browsing information of the historical service]. For example, if the user has had prior sessions with service providers, of the particular organization, that have been favorable—which may be determined via favorable user ratings, favorable user reviews, [i.e. and the user feature] a quantity of sessions between the user and the particular organization (e.g., a quantity that satisfies a threshold), a frequency of sessions between the user and the particular organization (e.g., a frequency that increases over time), and/or a length of sessions between the user and the particular organization (e.g., the sessions increase in length over time)—this may be indicative that the particular organization will continue to provide favorable services to the user via future sessions. Thus, information regarding prior sessions between the user and the particular organization may be may be used to generate a score for the particular organization for sessions with the user” Col. 15 Ln. 39-59 and “Interactive session system 240 may generate a score for the particular organization based on a first scoring function that uses one or more of the first scoring signals [i.e. determining a matching degree]” Col. 17 Ln. 6-11); and
-determining the similar service from the historical service based on the matching degree corresponding to the historical service (Jedrzejowicz, see at least: “the first scoring signals may include information regarding prior sessions between the user and the particular organization [i.e. determining the similar service from the historical service]. Previous favorable (or unfavorable) interactions between the user and the particular organization may be indicative of future favorable (or unfavorable) interactions between the user and the particular organization [i.e. corresponding to the historical service]. For example, if the user has had prior sessions with service providers, of the particular organization, that have been favorable—which may be determined via favorable user ratings, favorable user reviews, a quantity of sessions between the user and the particular organization (e.g., a quantity that satisfies a threshold), a frequency of sessions between the user and the particular organization (e.g., a frequency that increases over time), and/or a length of sessions between the user and the particular organization (e.g., the sessions increase in length over time)—this may be indicative that the particular organization will continue to provide favorable services to the user via future sessions. Thus, information regarding prior sessions between the user and the particular organization may be may be used to generate a score for the particular organization for sessions with the user” Col. 15 Ln. 39-59 and “Interactive session system 240 may generate a score for the particular organization based on a first scoring function that uses one or more of the first scoring signals [i.e. determining the similar service from the historical service based on the matching degree]” Col. 17 Ln. 6-11).
Jedrzejowicz in view of Pocard does not explicitly teach the target service being determined by: acquiring browsing information of a historical service within a historical period of time and a user feature that has been authorized by a user; determining a matching degree between the user and the historical service based on the browsing information of the historical service and the user feature; and determining the target service from the historical service based on the matching degree corresponding to the historical service.
Maya, however, teaches ranking relevant content (i.e. Col. 22 Ln. 35-37), including the known technique of the target service being determined by: acquiring browsing information of a historical service within a historical period of time and a user feature that has been authorized by a user (Maya, see at least: “other query parameters [i.e. wherein the target service is determined by:], such as the registered user's profile location [i.e. and a user feature that has been authorized by a user], real-time location as indicated by a global positioning system (GPS) other sensor and/or the like, preference data including explicitly selected or entered user data, and/or implicit data such as browser history [i.e. acquiring browsing information of a historical service within a historical period of time], purchase history, device context, consumer context, and/or the like, may be utilized to query the repository 140 for active data objects” Col. 20 Ln. 48-55);
the known technique of the target service being determined by: determining a matching degree between the user and the historical service based on the browsing information of the historical service and the user feature (Maya, see at least: “The optional browsing for toggle 403 enables a user to switch modes between a self-shopping user mode and a child-related user mode. Any number of child-related predefined persona data objects may be displayed in the browsing for toggle 403 along with an option such as ‘me’ representing the registered user. If the user selects ‘me,’ sessions applicant variants according to their associated registered user data object [i.e. wherein the target service is determined by: determining a matching degree between the user and the historical service] are populated in the user in scrollable exploration carousel 404 as described in further detail below” Col. 19 Ln. 59-67 & Col. 20 Ln. 1 and “other query parameters [i.e. wherein the target service is determined by:], such as the registered user's profile location [i.e. and the user feature], real-time location as indicated by a global positioning system (GPS) other sensor and/or the like, preference data including explicitly selected or entered user data, and/or implicit data such as browser history [i.e. based on the browsing information of the historical service], purchase history, device context, consumer context, and/or the like, may be utilized to query the repository 140 for active data objects” Col. 20 Ln. 48-55); and
the known technique of the target service being determined by: determining the target service from the historical service based on the matching degree corresponding to the historical service (Maya, see at least: “The optional browsing for toggle 403 enables a user to switch modes between a self-shopping user mode and a child-related user mode. Any number of child-related predefined persona data objects may be displayed in the browsing for toggle 403 along with an option such as ‘me’ representing the registered user. If the user selects ‘me,’ relevant applicant variants according to their associated registered user data object [i.e. based on the matching degree corresponding to the historical service] are populated in the user in scrollable exploration carousel 404 as described in further detail below” Col. 19 Ln. 59-67 & Col. 20 Ln. 1 “The scrollable exploration carousel 404 includes a plurality of selectable active application variant components 406, such as those labeled as ‘food and drink,’ ‘health and fitness,’ and ‘things to do,’ which are each associated with the active application variants of the registered and logged on user [i.e. determining the target service from the historical service]” Col. 20 Ln. 14-19 and “other query parameters [i.e. wherein the target service is determined by:], such as the registered user's profile location, real-time location as indicated by a global positioning system (GPS) other sensor and/or the like, preference data including explicitly selected or entered user data, and/or implicit data such as browser history [i.e. determining the target service from the historical service], purchase history, device context, consumer context, and/or the like, may be utilized to query the repository 140 for active data objects” Col. 20 Ln. 48-55 and scrollable exploration carousel 404 in Fig. 4). These known techniques are applicable to the method of Jedrzejowicz in view of Pocard as they both share characteristics and capabilities, namely, they are directed to ranking relevant content.
It would have been recognized that applying the known techniques of the target service being determined by: acquiring browsing information of a historical service within a historical period of time and a user feature that has been authorized by a user; determining a matching degree between the user and the historical service based on the browsing information of the historical service and the user feature; and determining the target service from the historical service based on the matching degree corresponding to the historical service, as taught by Maya, to the teachings of Jedrzejowicz in view of Pocard 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 the target service being determined by: acquiring browsing information of a historical service within a historical period of time and a user feature that has been authorized by a user; determining a matching degree between the user and the historical service based on the browsing information of the historical service and the user feature; and determining the target service from the historical service based on the matching degree corresponding to the historical service, as taught by Maya, into the method of Jedrzejowicz in view of Pocard would have been recognized by those of ordinary skill in the art as resulting in an improved method that would display relevant content to a user (Maya, Col. 20 Ln. 8-13).
Claim 15 recites limitations directed towards a non-transitory computer-readable medium. The limitations recited in claims 15 are parallel in nature to those addressed above for claim 7, and are therefore rejected for those same reasons set forth above in claim 7.
Claims 8 and 16 are rejected under 35 U.S.C. 103 as being unpatentable over Jedrzejowicz, in view of Pocard, in further view of Hawkins et al. (US 2025/0201028 A1), hereinafter Hawkins.
Regarding claim 8, Jedrzejowicz in view of Pocard teaches the method of claim 1. Jedrzejowicz further discloses:
-wherein top N similar services are selected and displayed according to the ranking, (Jedrzejowicz, see at least: “Interactive session system 240 may form a ranked list with information identifying the organizations based on sorting the information identifying the organizations based on the scores. In some implementations, an organization with a higher score may be included higher in the ranked list than an organization with a lower score. In some implementations, the ranked list may include information regarding fewer than all of the identified organizations, such as information regarding the top scoring Y (Y>1) organizations and/or information regarding organizations with scores that satisfy a threshold [i.e. wherein top N similar services are selected and displayed according to the ranking]” Col. 17 Ln. 22-32).
Jedrzejowicz does not explicitly disclose each of the top N similar services being displayed through a service card.
Pocard, however, teaches matching a customer with a service provider (i.e. abstract), including the known technique of each of the top N similar services being displayed through a service card (Pocard, see at least: “FIG. 11A is one illustrative example of a GUI screen of a mobile device 108 for displaying the highest ranked hair stylists to a user-customer. Display field 1102 can identify the user-customer, such as by a picture or name. Display field 1104 can identify any number, three, for example, of the highest ranked hair stylists matching the request of the user-customer. Identification can be by picture or name or both [i.e. wherein, each of the top N similar services is displayed through a service card]. In one embodiment, clicking on the hair stylist's picture brings up a new GUI screen as shown in FIG. 11B. A result display field 1106 below the hair stylist identification field shows pictograms of the location type and the matching percent. In one embodiment, clicking on the salon pictogram brings up a new GUI screen as shown in FIG. 11C. A second results display field 1108 can show the matching percent of a second location type” [0117] and Fig. 11A). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Jedrzejowicz with Pocard for the reasons identified above with respect to claim 1.
Jedrzejowicz in view of Pocard does not explicitly teach the remaining similar services being displayed through an aggregate card.
Hawkins, however, teaches identifying a list of recommendations (i.e. abstract), including the known technique of the remaining items are displayed through an aggregate card (Hawkins, see at least: “the search engine 108 receives information corresponding to a vehicle input from the computing device 104 (or another computing device not depicted), from sensors of the vehicle 116 via an application programming interface, and/or from an On-Board Diagnostic II scanner via an application programming interface. In aspects, the information comprises a year, make, model, and/or engine type of a vehicle. The information may also comprise mileage, type of miles driven, climate, or geographical location corresponding to the vehicle. In response to receiving the information, the search engine 108 utilizes a generative AI model to generate a maintenance schedule and a list of recommended products to perform tasks described by the maintenance schedule” [0026] and “The search engine 108 or the generative module 114 also ranks the search results. In some aspects, information learned from historical search sessions or user feedback is utilized to optimize the ranking of the search results. For example, selections made by other users submitting similar information may be leveraged to increase or decrease the ranking of individual items within the search results” [0039] and Fig. 2 displays an aggregated card indicating that there are three types of brake pads the user should consider [i.e. the remaining services are displayed through an aggregate card]). This known technique is applicable to the method of Jedrzejowicz in view of Pocard as they both share characteristics and capabilities, namely, they are directed to identifying a list of recommendations.
It would have been recognized that applying the known technique of the remaining items are displayed through an aggregate card, as taught by Hawkins, to the teachings of Jedrzejowicz in view of Pocard 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 remaining items are displayed through an aggregate card, as taught by Hawkins, into the method of Jedrzejowicz in view of Pocard would have been recognized by those of ordinary skill in the art as resulting in an improved method that would optimize the ranking of the search results (Hawkins, [0039]).
Claim 16 recites limitations directed towards a non-transitory computer-readable medium. The limitations recited in claims 16 are parallel in nature to those addressed above for claim 8, and are therefore rejected for those same reasons set forth above in claim 8.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
-Davar et al. (US 2016/0335603 A1) teaches identifying, scoring, and matching users with regard to providing professional services.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ARIELLE E WEINER whose telephone number is (571)272-9007. The examiner can normally be reached M-F 8:30-5:00.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Maria-Teresa (Marissa) Thein can be reached at 571-272-6764. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/ARIELLE E WEINER/ Primary Examiner, Art Unit 3689
Prosecution Insights