DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Priority
Acknowledgment is made of applicant’s claim for foreign priority under 35 U.S.C. 119 (a)-(d) to JP2023-145167 filed 9/7/2023. The certified copy was filed in current application on 12/21/2022.
Title Objection
The title of the invention is not descriptive. A new title is required that is clearly indicative of the invention to which the claims are directed. The following title is suggested:
“System, method and recording medium for extraction of similar products”
Claim Objection – Minor Informalities
Claim 20 recites the following:
A non-transiently recording medium that records an extraction program for causing a computer to execute:
The term “non-transiently” is an adverb used in conjunction with the noun “recording medium”. This is grammatically incorrect. The more proper wording utilizes the adjective form of the word: “A non-transient[[ly]] recording medium that records an extraction program for causing a computer to execute:”.
For examination purposes, the claim will be interpreted as using the adjective form as above. Appropriate correction is required.
Claim Interpretation – Claim term
The below listed terms have been given the following interpretations in view of their meaning in the art:
Nan-transient: synonymous with “non-transitory” (e.g., not lasting, enduring, or permanent; transitory - see https://www.dictionary.com/browse/transient, https://www.merriam-webster.com/dictionary/transient)
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 (abstract idea) without significantly more.
Regarding claims 1-20, under Step 2A claims 1-20 recite a judicial exception (abstract idea) that is not integrated into a practical application and does not provide significantly more.
Under Step 2A (prong 1), and taking claim 1 as representative, claim 1 recites
output a feature amount used for similarity determination by an extraction model that extracts a product similar to a target product, a set value of an importance level of the feature amount, and changing the set value of the importance level of the feature amount;
acquire a change value for changing the set value of the importance level of the feature amount input to the change field; and
update a set value of an importance level of the feature amount in the extraction model based on the change value.
These limitations recite ‘certain methods of organizing human activity’, such as by performing commercial interactions (see: MPEP 2106.04(a)(2)(II)). This is because claim 1 sets forth or describes the process for extracting products similar to a target product. This represents the performance of a marketing or sales activities or behaviors, which is a commercial interaction and falls under organizing human activity. Accordingly, under step 2A (prong 1) claim 1 recites an abstract idea because claim 1 recites limitations that fall within the “Certain methods of organizing human activity” grouping of abstract ideas.
Under Step 2A (prong 2), the abstract idea is not integrated into a practical application. The Examiner acknowledges that representative claim 1 does recite additional elements, including
an extraction system,
at least one memory storing instructions,
at least one processor configured to access the at least one memory and execute the instructions, and,
a screen and a change field.
Although reciting these additional elements, taken alone or in combination these elements are not sufficient to integrate the abstract idea into a practical application. This is because the additional elements of claim 1 are recited at a high level of generality (i.e. as generic computing hardware) such that they amount to nothing more than the mere instructions to implement or apply the abstract idea on generic computing hardware (or, merely uses a computer as a tool to perform an abstract idea). Further, the additional elements do no more than generally link the use of a judicial exception to a particular technological environment or field of use (such as the Internet or computing networks).
Secondly, 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 other technology or technical field, (ii) implement 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) applies or uses the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment.
In view of the above, under Step 2A (prong 2), claim 1 does not integrate the recited exception into a practical application.
Under Step 2B, examiners should evaluate 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). In this case, the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Returning to representative claim 1, taken individually or as a whole the additional elements of claim 1 do not provide an inventive concept (i.e. they do not amount to “significantly more” than the exception itself). As discussed above with respect to the integration of the abstract idea into a practical application, the additional elements used to perform the claimed process amount to no more than the mere instructions to apply the exception using a generic computer and/or no more than a general link to a technological environment. Even considered as an ordered combination (as a whole), the additional elements of claim 1 do not add anything further than when they are considered individually.
In view of the above, representative claim 1 does not provide an inventive concept (“significantly more”) under Step 2B, and is therefore ineligible for patenting.
Regarding dependent claims 2-12, dependent claims 2-12 recite more complexities descriptive of the abstract idea itself, and at least inherit the abstract idea of claim 1. As such, claims 2-12 are understood to recite an abstract idea under step 2A (prong 1) for at least similar reasons as discussed above.
Under prong 2 of step 2A, the additional elements of dependent claims 2-12 also do not integrate the abstract idea into a practical application, considered both individually or as a whole. More specifically, claims 2-12 rely on at least similar additional elements as addressed for claim 1. Further additional elements training or machine learning (e.g., claims 4-5) are also recited only at a high level of generality (i.e. as generic computing hardware) such that they amount to nothing more than the mere instructions to implement or apply the abstract idea on generic computing hardware (or, merely uses a computer as a tool to perform an abstract idea). Further, the additional elements do no more than generally link the use of a judicial exception to a particular technological environment or field of use (such as the Internet or computing networks).
Lastly, under step 2B, claims 2-12 also fail to result in “significantly more” than the abstract idea under step 2B. This is again because the claims merely apply the exception on generic computing hardware, and generally link the exception to a technological environment. Even when viewed as an ordered combination (as a whole), the additional elements of the dependent claims do not add anything further than when they are considered individually.
In view of the above, claims 2-12 do not provide an inventive concept (“significantly more”) under Step 2B, and are therefore ineligible for patenting.
Regarding claims 13-19, claims 13-19 recite at least substantially similar concepts and elements as recited in claims 1-12 such that similar analysis of the claims would be readily apparent to one of ordinary skill in the art. As such, claims 13-19 are rejected under at least similar rationale.
Regarding claim 20, claim 20 recites at least substantially similar concepts and elements as recited in claim 1 such that similar analysis of the claims would be readily apparent to one of ordinary skill in the art. As such, claim 20 are rejected under at least similar rationale.
Claim Rejections - 35 USC § 103
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) 1-3, 6, 11, 13-15, 18 and 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Prendki (US 2018/0218431) in view of Bazzani (US 11,829,445).
Regarding claim 1, Prendki discloses an extraction system comprising:
at least one memory storing instructions (see: 0043-0044, Fig. 2); and
at least one processor configured to access the at least one memory and execute the instructions (see: 0043-0044, Fig. 2) to:
output a screen displaying a feature amount used for similarity determination by an extraction model that extracts a product [ ], a set value of an importance level of the feature amount (e.g., weight), and a change field (e.g., numerical input, sliders) for changing the set value of the importance level of the feature amount (see: 0128, 0137-0138, Fig. 19, Fig. 20 (2010, 2031));
acquire a change value for changing the set value of the importance level of the feature amount input to the change field of the output screen (see: Fig. 20 (2011-2018) [Wingdings font/0xE0]Fig. 21 (2111-2113), 0131 (intent weights received are explicitly provided by the first user), 0137 (allow the user to update the intent weights for the features), 0141 (adjusting the intent weight sliders for an input element (e.g., 2011-2018))); and
update a set value of an importance level of the feature amount in the extraction model based on the change value (see: 0131 (updating a weight vector for the first user based on the intent weights), 0140 (the weighting vector can be updated based on the update to the intent weights), Fig. 18 (1820); see also: 0108).
Though disclosing all of the above, Prendki does not expressly disclose the feature amount to be used for similarity determination by an extraction model that extracts a product similar to a target product. While this feature is arguably intended use, it is also implied by at least Fig. 14 and 0105 of Prendki (where alternative items are recommended and are similar to the target product 1416), as well as the ability of the user of Prendki to click on a specific item from a list of results extracted based on the feature criteria and weighting (e.g., Fig. 20 (2021-2023), 0139).
To this accord, Bazzani discloses an extraction system that provides an output a screen displaying a feature amount (e.g., attribute) used for similarity determination by an extraction model that extracts a product similar to a target product (e.g., Fig. 1 (134)), a set value of an importance level of the feature amount (e.g., weight), and a change field (e.g., “sliders”) for changing the set value of the importance level of the feature amount (see: Fig. 7 (710, 720), col. 4 lines 23-37, Fig. 1 (134, 136, 170)). That is, a user selects a particular feature (e.g., toe shape) of target products to perform a search (“conditional similarity retrieval”) for similar products based on selected attributes (feature amounts). The attributes are also associated with weights, which may be modified.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to have modified the invention of Prendki to have utilized the known technique for extracting products similar to a target product as taught by Bazzani in order to have enabled users to more readily search for content of interest on the basis of aesthetic features by maintaining an overall similarity with currently-displayed content but which differs with respect to one or more attributes of the currently-displayed content (see: Bazzani: col. 2 lines 1-10).
2. The extraction system according to claim 1, wherein
the at least one processor is further configured to execute the instructions to:
extract a product similar to the target product using the extraction model (see: Fig. 7, col. 4 lines 23-37, col. 15 lines 15-34; Prandki: Fig. 14-16, 0105); and
output, together with a result of the extraction, a feature amount used by the extraction model for the similarity determination, a set value of an importance level of the feature amount, and a change field for changing the set value (see: Prandki: Fig. 20 (2011-2018) [Wingdings font/0xE0]Fig. 21 (2111-2113), 0131 (intent weights received are explicitly provided by the first user), 0137 (allow the user to update the intent weights for the features), 0141).
3. The extraction system according to claim 2, wherein the at least one processor is further configured to execute the instructions to:
re-extract a product similar to the target product using the extraction model, based on the set value of the importance level of the feature amount after the updating (see: Prandki: 0024, 0026, 0147, Fig. 21 (2121-2123), Fig. 23 (2321-2323); Bazzani: col. 4 lines 23-37, Fig. 2 (210, 212)).
6. The extraction system according to claim 2, wherein the at least one processor is further configured to execute the instructions to:
output a screen that further displays a feature amount that has contributed to similarity determination on similarity with the target product, when outputting a screen displaying a product extracted by the extraction model (see: Prendki: Fig. 20 (2041-2043), 0138-0139).
11. The extraction system according to claim 1, wherein the at least one processor is further configured to execute the instructions to:
output a button for increasing or decreasing a set value by an operation on a screen as a change field for changing the set value of the importance level of the feature amount (see: Prendki: Fig. 20 (2011-2018), 0137).
Regarding claims 13-15 and 18, claims 13-15 and 18 recite at least substantially similar concepts and elements as recited in claims 1-3 such that similar analysis of the claims would be readily apparent to one of ordinary skill in the art. As such, claims 13-15 and 18 are rejected under at least similar rationale.
Regarding claim 20, claim 20 recites at least substantially similar concepts and elements as recited in claim 1 such that similar analysis of the claims would be readily apparent to one of ordinary skill in the art. As such, claim 20 are rejected under at least similar rationale.
Claim(s) 4 and 16 is/are rejected under 35 U.S.C. 103 as being unpatentable over Prendki in view of Bazzani as applied to claims 1-2 and 13-14 above, and further in view of Palumbo (US 2024/0281445).
Regarding claim 4 and parallel claim 16, Prendki in view of Bazzani teaches all of the above as noted including supervised training (e.g., Bazzani: col. 5 lines 52-53) but does not disclose the extraction system according to claim 2, wherein the at least one processor is further configured to execute the instructions to:
output a screen for selecting a product to be used as training data from the products extracted by the extraction model;
acquire a selection result of a product to be used as training data, the product being selected on the output screen; and
update the extraction model by executing machine learning using a product selected in the selection result as training data.
To this accord and in the field of item searching, Palumbo teaches a system configured to:
output a screen for selecting a product to be used as training data from the products extracted by the extraction model (see: 0059, 0078, Fig. 4A-4B (406) );
acquire a selection result of a product to be used as training data, the product being selected on the output screen (see: 0066 (item that the user selected), 0078 (item selected by the fist user), Fig. 7 (706));
update the extraction model by executing machine learning using a product selected in the selection result as training data (see: Fig. 7 (716), 0084, 0005 (training the classifier, using the generated training data)).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to have modified the invention of Prendki in view of Bazzani to have utilized the known technique for generating training data sets as taught by Palumbo in order to have enabled the system to have generated training data by comparing search queries provided by users to metadata associated with the respective content items selected by the respective users from the search queries, thereby facilitating personalized recommendations (see: Palumbo: 0002, 0004).
Claim(s) 5 and 17 is/are rejected under 35 U.S.C. 103 as being unpatentable over Prendki in view of Bazzani as applied to claims 1-2 and 13-14 above, and further in view of Konik (US 2016/0342288).
Regarding claim 5 and parallel claim 17, Prendki in view of Bazzani teaches all of the above including wherein the at least one processor is further configured to execute the instructions to:
output a screen for selecting a feature amount (see: Prendki: Fig. 20 (2012), 0137, 0146), and,
acquire a selection result of a feature amount (see: Prendki: Fig. 21 (2112), 0146, 0154)
Though disclosing the above including training (e.g., Bazzani: col. 5 lines 52-53), the combination does not teach that the feature amount is to be used also as training data from feature amounts used for training of the extraction model and update the extraction model by executing machine learning using a feature amount selected in the selection result as training data is further provided.
To this accord, Konik teaches a system configured to display item listing results having features that are to be used also as training data from feature amounts used for training of the extraction model and update the extraction model by executing machine learning using a feature amount selected in the selection result as training data is further provided (see: 0029 (update model), 0058 (model update module), 0080, Fig. 6 (Feature 1-N), Fig. 8 (824), Fig. 10 (1012-1042), Fig. 12).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to have modified the invention of Prendki in view of Bazzani to have utilized the known technique for generating training data sets as taught by Palumbo in order to have enabled determination of features most relevant to a user for a particular stage of the user's online experience so as to improve the retrieval of relevant search results (see: Konik: 0070, 0048).
Claim(s) 7 and 19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Prendki in view of Bazzani as applied to claim 1 and 13 above, and further in view of Biswas (US 11,403,006).
Regarding claim 7 and parallel claim 19, Prendki in view of Bazzani teaches all of the above including output a screen displaying a feature amount in the extraction model selected in the selection result, a set value of an importance level of the feature amount, and a change field for changing the set value of the importance level of the feature amount (see: Prendki: 0128, 0137-0138, Fig. 19, Fig. 20-21). The combination, however, does not teach:
output a screen for selecting any extraction model among a plurality of extraction models;
acquire a selection result of an extraction model selected on the output screen; or, the feature amount in the selected extraction model.
To this accord, teaches output a screen for selecting any extraction model among a plurality of extraction models such that the feature amount is in the selected extraction model (see: Fig. 7, col. 23 lines 3-21 & 52-64; see also: Fig. 9 (902-908)), and,
acquire a selection result of an extraction model selected on the output screen (see: Fig. 9 (912-914), col. 26 lines 51-62, Fig. 4 (410), col. 17 lines 37-45, col. 14 lines 8-10).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to have modified the invention of Prendki in view of Bazzani to have utilized the known technique for enabling selection of a particular extraction model as taught by Biswas in order to have provided a system which simplified the use of machine learning systems such that users of various backgrounds can use the machine learning systems (see: Biswas: col. 2 lines 39-41).
Claim(s) 8 is/are rejected under 35 U.S.C. 103 as being unpatentable over Prendki in view of Bazzani as applied to claim 1 and 13 above, and further in view of Biswas and Burke (US 2015/0134694).
Regarding claim 8, Prendki in view of Bazzani teaches all of the above including a feature amount used in search by an extraction model and an importance level (see again: Prendki: 0128, 0137-0138, Fig. 19, Fig. 20 (2010, 2031)), as well as a training the extraction model (see: Bazzani: col. 2 line 66-col. 3 line 3, col. 5 lines51-62, col. 6 lines 54-64). The combination, however, does not teach that the system is configured to generate a name of the extraction model, based on at least one of a feature amount used as training data of the extraction model and an importance level of the feature amount.
To this accord, Burke teaches a search system configured to generate a name of the extraction model, based on at least one of a feature amount used as training data of the extraction model and an importance level of the feature amount (e.g., (see: Fig. 50 (Saved Query), 0503, Fig. 95, 1033).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to have modified the invention of Prendki in view of Bazzani to have utilized the known technique as taught by Burk in order to have enabled users to readily access previously saved results (see: Burke: 0293, 0503).
Claim(s) 9 is/are rejected under 35 U.S.C. 103 as being unpatentable over Prendki in view of Bazzani as applied to claim 1 and 13 above, and further in view of Cohen (US 2008/0104542).
Regarding claim 9, Prendki in view of Bazzani teaches an output a screen including a button for switching between a screen for displaying a product extracted by the extraction model (see: Prendki: Fig. 14-15, 0105) as well as a screen for displaying a change field for changing the set value of the importance level of the feature amount in the extraction model (see: Prendki: Fig. 20-21, 0137-0138, 0141). The combination, however, does not teach wherein the at least one processor is further configured to execute the instructions to output a screen including a button for switching between the screens.
To this accord, Cohen teaches a search system configured to provide a screen including a button for switching between the screens (see: 0064-0065, 0132).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to have modified the invention of Prendki in view of Bazzani to have utilized the known technique for enabling switching between screens as taught by Cohen in order to have enabled a user to readily switch between views by clicking on a control to toggle to a desired view (see: Cohen: 0065, 0171).
Claim(s) 10 is/are rejected under 35 U.S.C. 103 as being unpatentable over Prendki in view of Bazzani as applied to claim 1 and 13 above, and further in view of Shioda (US 2025/0342172).
Regarding claim 10, Prendki in view of Bazzani teaches all of the above including
wherein the at least one processor is further configured to execute the instructions to:
output a screen that displays a feature amount that has contributed to the similarity determination by the extraction model (see above: claim 6). The combination, however, does not teach presenting the feature amounts in descending order of contribution to the similarity determination.
Initially, the Examiner asserts that the presentation of the data as claimed is little more than a mere design choice and further represents the rearrangement of parts (e.g., the data presented by Prendki in view of Bazzani). See MPEP 2144.04(I) and MPEP 2144.04(VI)(C).
Even presuming this were not true, such reordering was well-known and would have been obvious to one of ordinary skill in the art. For example, Shioda teaches enabling visualization of feature amounts in descending order of contribution (see: 0076, 0065).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to have modified the invention of Prendki in view of Bazzani to have utilized the known technique for enabling switching between screens as taught by Cohen in order to have enabled users to understand the importance of features when they do not have domain knowledge or knowledge of data analysis (see: Shioda: 0004, 0072).
Claim(s) 12 is/are rejected under 35 U.S.C. 103 as being unpatentable over Prendki in view of Bazzani as applied to claim 1 and 13 above, and further in view of Franke (US 2020/0293580).
Regarding claim 12, Prendki in view of Bazzani teaches all of the above including wherein the at least one processor is further configured to execute the instructions to output a standard feature amount (see: 0128 (e.g., neutral value), Fig. 19) but does not teach and an importance level of the feature amount in an industry in which the target product is distributed.
To this accord, Franke teaches outputting a standard feature amount (see: Fig. 1 (102)) and an importance level of the feature amount in an industry in which the target product is distributed (see: Fig. 1 (106-108 show impact on resale value), 0022, 0036, Fig. 7 (706-718), 0068, Fig. 8 (836-838), 0077).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to have modified the invention of Prendki in view of Bazzani to have utilized the known technique as taught by Franke in order to have provided a system that analyzed unique items to determine the influence that different significant attributes or features have to the pricing and/or demand of that item (see: Franke: 0026).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Ouimet (US 20130325653) discloses a shopping search interface for selecting types of products and assignment weight (see: Fig. 19-21, 0091-0094)
Gokturk (US 20080082426) discloses search enabled to select a particular visual attribute from within a product image (see: Fig. 15B, 0236, Fig. 17 (1710), 0241)
Agrawal (US 20170124624) discloses presenting visual product representations and monitoring interactions with tagged regions to modify sets of recommended products (see: Fig. 4-5, 0050)
Cottingham (US 20110302177) discloses a toggle button for switching between search and sort functions (see: Fig. 1 (32), 0026, 0028, Fig. 2 (52))
Any inquiry concerning this communication or earlier communications from the examiner should be directed to WILLIAM J ALLEN whose telephone number is (571)272-1443. The examiner can normally be reached Monday-Friday, 8:00-4:00.
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, Anita Coupe can be reached at 571-270-3614. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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WILLIAM J. ALLEN
Primary Examiner
Art Unit 3625
/WILLIAM J ALLEN/ Primary Examiner, Art Unit 3619